Substation ground deformation sensor monitoring signal acquisition and monitoring method, system, equipment and medium

Abstract

The invention relates to the technical field of wireless sensor networks, and discloses a transformer substation ground deformation sensor monitoring signal collecting and monitoring method, system and device and a medium, and the method comprises the steps: dividing monitoring areas and selecting corresponding types of sensors according to ground deformation risks of different functional areas of a transformer substation; constructing a multi-channel synchronous signal acquisition system, synchronously acquiring sensor signals through a clock synchronization technology, and dynamically adjusting the sampling frequency; a wireless communication network is adopted to transmit the collected data, and data transmission is carried out; processing and analyzing the data transmitted to the monitoring center, and establishing a ground deformation prediction system comprising a finite element model, a neural network model and a time sequence model; and setting an early warning threshold based on the real-time analysis result, triggering a corresponding early warning signal, and displaying the monitoring data and the early warning state through a visual interface. According to the method, a data transmission and processing mechanism is improved, rapid and stable data transmission is ensured, the ground deformation trend is analyzed in time through an efficient algorithm, a reliable basis is provided for safe operation of a transformer substation, and potential risks are early warned in advance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless sensor networks, and in particular to a substation ground deformation sensor monitoring signal collection and monitoring method, system, device and medium. BACKGROUND

[0002] In the existing substation ground deformation monitoring technology, the commonly used monitoring methods have many shortcomings. Some technologies use artificial periodic inspection, which is inefficient and cannot realize real-time monitoring, making it difficult to discover small ground deformation in time. Once the deformation develops to the extent that affects the safe operation of substation equipment, it will be detected, which may lead to serious safety accidents.

[0003] Some automatic monitoring technologies also have unreasonable sensor layout and low signal collection accuracy. For example, some sensors are greatly affected by environmental interference. In the strong electromagnetic interference environment of the substation, the collected signals are prone to errors, affecting the accurate judgment of the ground deformation. Moreover, the existing technology also has defects in data transmission and processing. Data transmission may have delay and packet loss, and the data processing algorithm is not efficient enough to quickly analyze the trend and potential risks of ground deformation. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a substation ground deformation sensor monitoring signal collection and monitoring method, which can realize real-time and accurate monitoring of substation ground deformation, overcome the problems of low efficiency and timeliness of artificial inspection, and ensure that the ground deformation can be discovered in the first time. The sensor layout and signal collection method are optimized to reduce the influence of environmental interference on signal collection accuracy and improve the accuracy of monitoring data.

[0006] To solve the above technical problems, the present application provides the following technical scheme. A substation ground deformation sensor monitoring signal collection and monitoring method comprises: dividing the monitoring area and selecting the corresponding type of sensor according to the ground deformation risk of different functional areas of the substation; constructing a multi-channel synchronous signal collection system, realizing synchronous collection of signals of each sensor through clock synchronization technology, and dynamically adjusting the sampling frequency according to the ground deformation state; transmitting the collected data through a wireless communication network, and transmitting the data based on data retransmission, error correction and encryption mechanism; processing and analyzing the data transmitted to the monitoring center, establishing a ground deformation prediction system including a finite element model, a neural network model and a time series model; setting an early warning threshold based on real-time analysis results, triggering corresponding early warning signals and displaying monitoring data and early warning state through a visual interface.

[0007] As a preferred scheme of the substation ground deformation sensor monitoring signal acquisition monitoring method, the sensor comprises a three-dimensional displacement sensor, a strain sensor, an inclination sensor, a distributed optical fiber strain sensor, and a static level instrument.

[0008] Three-dimensional displacement sensors are arranged at the base quadrangles and the center point of the main transformer area, and auxiliary monitoring points are additionally arranged along the periphery.

[0009] Strain sensors are installed at the base support points of the high-voltage switchgear area, and inclination sensors are installed at the top and bottom.

[0010] Distributed optical fiber strain sensors are arranged at a preset interval along the entire length of the cable trench area, and the density is increased at key nodes.

[0011] Static level instruments are arranged around the building foundation area to form a closed monitoring loop. As a preferred scheme of the substation ground deformation sensor monitoring signal acquisition monitoring method, the sensor comprises.

[0012] As a preferred scheme of the substation ground deformation sensor monitoring signal acquisition monitoring method, the multi-channel synchronous signal acquisition system comprises a multi-channel high-speed data acquisition card and supports dynamic adjustment of the sampling frequency within a set range.

[0013] Time synchronization of each channel is achieved through an external high-precision clock source. In the signal preprocessing stage, a composite filtering algorithm combining low-pass filtering, median filtering, and wavelet filtering is used, and the signal is amplified and calibrated.

[0014] As a preferred scheme of the substation ground deformation sensor monitoring signal acquisition monitoring method, the use of wireless communication network to transmit collected data comprises using ZigBee to cover the near-end area, LoRa or 5G to cover the far-end area, according to different transmission distance and rate requirements.

[0015] Data retransmission and error correction are achieved through CRC check and adaptive ARQ protocol, and the number of retransmissions and interval time are dynamically adjusted.

[0016] The data is encrypted using the AES-256 encryption algorithm, and the communication link is protected based on the WPA3 protocol.

[0017] As a preferred scheme of the substation ground deformation sensor monitoring signal acquisition monitoring method, the processing and analysis of data transmitted to the monitoring center comprises using cubic spline interpolation to fill in missing data, with an interpolation error of ≤1%.

[0018] The normalized data is input into a finite element model, an LSTM neural network and a time series model to predict the ground deformation trend;

[0019] The finite element model simulates deformation based on geological parameters and load distribution, and the LSTM neural network predicts the deformation amount in the next 24 hours based on historical data.

[0020] As a preferred scheme of the substation ground deformation sensor monitoring signal acquisition and monitoring method, wherein: the pre-warning threshold is set based on real-time analysis results, including triggering yellow pre-warning when the deformation amount is greater than or equal to 5mm or the deformation rate is greater than or equal to 1mm / day;

[0021] When the deformation amount is greater than or equal to 10mm or the deformation rate is greater than or equal to 2mm / day, orange pre-warning is triggered;

[0022] When the deformation amount is greater than or equal to 20mm or the foundation inclination is greater than or equal to 0.5°, red pre-warning is triggered, and an emergency response mechanism is started.

[0023] As a preferred scheme of the substation ground deformation sensor monitoring signal acquisition and monitoring method, wherein: the visualization interface includes generating a three-dimensional heat map and a historical curve based on WebGL technology, displaying real-time deformation data and pre-warning status of each region, and positioning the collapse point coordinates through GPS.

[0024] Another object of the present application is to provide a substation ground deformation sensor monitoring signal acquisition and monitoring method system.

[0025] As a preferred scheme of the substation ground deformation sensor monitoring signal acquisition and monitoring method system, wherein: it includes a sensor deployment module, a signal acquisition and processing module, a data transmission and security module, a data analysis and modeling module, and a pre-warning and visualization module.

[0026] The sensor deployment module is arranged according to the risk of each region to form a monitoring network covering the whole station.

[0027] The signal acquisition and processing module synchronously acquires sensor signals, dynamically adjusts the sampling frequency, and filters out interference and calibrates data.

[0028] The data transmission and security module transmits data through a hybrid wireless network to ensure transmission reliability and communication link security.

[0029] The data analysis and modeling module cleanses data, fills in missing values, and predicts ground deformation trend through multiple models.

[0030] The pre-warning and visualization module triggers hierarchical pre-warning, displays deformation heat map and historical data in real time, and assists emergency response.

[0031] The application provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the processor realizes the steps of the substation ground deformation sensor monitoring signal acquisition monitoring method when executing the computer program.

[0032] The application provides a computer readable storage medium, which stores a computer program, and the computer program realizes the steps of the substation ground deformation sensor monitoring signal acquisition monitoring method when being executed by a processor.

[0033] The application has the following beneficial effects: compared with manual inspection, the method realizes real-time monitoring of the ground deformation of a substation, can discover slight deformation in time, greatly improves the timeliness of monitoring, and effectively avoids safety accidents caused by untimely monitoring. Reasonable sensor layout and advanced signal acquisition and processing technology reduce the influence of environmental interference on monitoring data, improve the signal acquisition accuracy, and can more accurately reflect the ground deformation of the substation. Efficient data transmission and processing mechanism, combined with big data analysis and machine learning algorithm, can quickly analyze the ground deformation trend, issue an alarm in advance, provide sufficient time for operation and maintenance personnel to take measures, and ensure the safe and stable operation of the substation. The automatic monitoring mode reduces the labor cost of manual inspection, at the same time, the ground deformation problem can be discovered and handled in time, and great economic losses caused by equipment damage are avoided, so that the method has good cost-effectiveness. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0035] Figure 1 The substation ground deformation sensor monitoring signal acquisition monitoring method flowchart provided by the embodiment of the application is shown. DETAILED DESCRIPTION

[0036] In order to make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the specific embodiments of the application will be described in detail in combination with the drawings of the specification. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor should be within the protection scope of the application.

[0037] Embodiment 1, refer to Figure 1 For the first embodiment of the application, the embodiment provides a substation ground deformation sensor monitoring signal acquisition monitoring method, comprising:

[0038] S1: According to the ground deformation risk of different functional areas of the substation, the monitoring area is divided and the corresponding type of sensor is selected.

[0039] Furthermore, in the substation, the ground bearing pressure and deformation risk of different areas are quite different. First, according to the distribution of equipment in the substation, importance and geological conditions, the entire substation ground is divided into multiple functional areas, such as the main transformer area, high-voltage switchgear area, cable trench area, building foundation area, etc.

[0040] For the main transformer area, since the transformer is heavy and sensitive to ground settlement and horizontal displacement, a high-precision three-dimensional displacement sensor is selected, such as a displacement sensor based on the laser interference principle, which can achieve a measurement accuracy of millimeters or even sub-millimeters. This sensor can simultaneously measure the displacement changes in X, Y and Z directions, fully reflecting the deformation of the transformer foundation.

[0041] In the high-voltage switchgear area, the stress change and slight tilt of the equipment foundation are mainly concerned, and strain sensors and tilt sensors are used. Strain sensors can monitor the stress state of the foundation structure in real time, and when the stress exceeds a certain threshold, it may indicate that the foundation has damage or deformation; tilt sensors can accurately measure the tilt angle of the equipment foundation and timely discover potential safety hazards.

[0042] In the cable trench area, considering that the laying and operation of cables are sensitive to ground deformation, distributed fiber optic strain sensors are selected. This sensor can be distributed along the entire length of the cable trench, achieving continuous monitoring of the ground deformation around the trench, and can resist electromagnetic interference and adapt to complex substation environments.

[0043] In the building foundation area, static water level meters are used to monitor the settlement of the foundation. Static water level meters measure the relative elevation change between each measuring point through the communication tube principle, with high measurement accuracy and good stability.

[0044] It should be noted that three-dimensional displacement sensors are installed at the four corner points and the center of the main transformer foundation, forming a complete monitoring network that can accurately capture the overall settlement and uneven settlement of the transformer foundation. At the same time, auxiliary monitoring points are set within a certain range around the foundation to further expand the monitoring range and improve the reliability of the monitoring.

[0045] Strain sensors are installed near each support point of the high-voltage switchgear foundation to monitor the stress changes of the foundation during equipment operation in real time. Tilt sensors are installed at the top and bottom of the foundation to measure the top and bottom tilt of the foundation, respectively, to comprehensively evaluate the stability of the foundation.

[0046] A distributed optical fiber strain sensor is installed at regular intervals (e.g., 5-10 meters) along the cable trench. The sensor is tightly coupled to the trench wall through a special fixing method, ensuring accurate sensing of the strain changes in the optical fiber caused by ground deformation. At the same time, the density of sensor placement is increased at key locations such as bends and branches of the cable trench, improving the monitoring capability of local deformation.

[0047] A static level gauge is installed at regular intervals (e.g., 3-5 meters) around the building foundation, forming a closed monitoring loop. By monitoring the water level difference between each measuring point in real time, the settlement and settlement rate of the foundation can be calculated.

[0048] S2: Construct a multi-channel synchronous signal acquisition system to realize synchronous acquisition of sensor signals through clock synchronization technology, and dynamically adjust the sampling frequency according to the ground deformation state.

[0049] Furthermore, in order to realize the simultaneous acquisition of multiple sensor signals, a multi-channel synchronous acquisition system is constructed. This system uses high-performance data acquisition cards with multiple independent acquisition channels, each of which can connect a sensor. The data acquisition card has high-speed sampling capability, and the sampling frequency can be flexibly adjusted according to actual needs, with a maximum sampling frequency of more than 1000Hz, ensuring that the rapidly changing deformation signals can be captured.

[0050] In the acquisition system, clock synchronization technology is used to ensure that the sampling time of all acquisition channels is strictly consistent. A high-precision external clock source (such as a GPS clock) is used to provide a unified clock signal to the data acquisition card, so that each channel starts sampling at the same time, ensuring that the acquired data has time synchronization, facilitating subsequent data processing and analysis.

[0051] Furthermore, according to the different stages and actual situations of the substation ground deformation, the sampling frequency is dynamically adjusted. In the normal operation stage, the ground deformation is relatively slow, and the sampling frequency is set to a lower value (such as 10Hz-20Hz) to reduce the pressure of data storage and transmission. When abnormal changes in ground deformation are detected, such as sudden increase in deformation rate or close to the warning threshold, the sampling frequency is automatically increased (such as 50Hz-100Hz) to record the deformation process in more detail and timely grasp the deformation trend.

[0052] After acquiring the sensor signals, the signals are first filtered to remove noise interference. A combination of multiple filtering algorithms is used, such as low-pass filtering, median filtering, and wavelet filtering. Low-pass filtering is used to remove high-frequency noise and retain low-frequency deformation signals; median filtering can effectively remove impulse noise and improve signal stability; wavelet filtering can adaptively filter signals according to their characteristics, removing noise while preserving signal details.

[0053] The filtered signal is amplified and calibrated. Since the sensor output signal is usually very weak, it needs to be amplified to an appropriate amplitude by an amplifier for subsequent data acquisition and processing. At the same time, the signal is calibrated according to the calibration parameters of the sensor to ensure that the collected data is accurate and reliable.

[0054] S3: Use wireless communication network to transmit collected data, and perform data transmission based on data retransmission, error correction and encryption mechanism.

[0055] Furthermore, a combination of multiple wireless communication technologies is used to build a data transmission network to adapt to the complex environment of the substation and different transmission needs. For sensor nodes close to the monitoring center, ZigBee wireless communication technology is used. ZigBee has the characteristics of low power consumption and strong self-organizing network capability, which can quickly establish a communication link between the sensor nodes and the sink node. Multiple sensor nodes transmit the collected data to the sink node through the ZigBee network.

[0056] For areas far from the monitoring center or requiring high-speed data transmission, LoRa or 5G wireless communication technology is used. LoRa has the advantages of long distance and low power consumption, which is suitable for long distance data transmission; 5G has the characteristics of high speed and low delay, which can meet the needs of real-time transmission of a large amount of data. The sink node transmits the received data to the monitoring center through the LoRa or 5G network.

[0057] It should be noted that in order to ensure the reliability of data transmission, data retransmission and error correction mechanisms are set. During data transmission, the sender will attach a check code (such as CRC check code) when sending data. After receiving the data, the receiver checks the data according to the check code. If the check finds that the data has errors, the receiver sends a retransmission request to the sender, and the sender re-sends the data.

[0058] At the same time, the automatic repeat request (ARQ) protocol is used, and when the sender does not receive the confirmation information from the receiver within a certain time, the data is automatically retransmitted. In order to avoid the problem of too much retransmission caused by network congestion, an adaptive retransmission strategy is used to dynamically adjust the number of retransmissions and the retransmission interval time according to the network status.

[0059] Furthermore, in order to prevent data from being stolen or tampered with during transmission, the transmitted data is encrypted. Symmetric encryption algorithm (such as AES algorithm) is used to encrypt the data, and the same key is used for encryption and decryption operations at the sender and receiver respectively. The management of the key uses a secure key distribution mechanism to ensure the security of the key.

[0060] During wireless transmission, a secure communication protocol such as WPA3 is used to encrypt the communication link, preventing data from being illegally monitored and attacked. At the same time, the network of the monitoring center is protected by security devices such as firewalls and intrusion detection systems to ensure the security and reliability of data transmission.

[0061] S4: Process and analyze the data transmitted to the monitoring center, and establish a ground deformation prediction system containing finite element models, neural network models, and time series models.

[0062] Furthermore, after receiving the transmitted data at the monitoring center, data cleaning and preprocessing are performed first. Check the integrity and accuracy of the data, remove invalid data and outliers. For missing data, interpolation methods such as linear interpolation and spline interpolation are used for filling.

[0063] The cleaned data is normalized to unify the data collected by different sensors to the same scale range, facilitating subsequent data analysis and comparison. At the same time, data smoothing is performed using moving average, exponential smoothing and other methods to reduce data fluctuations and highlight data trends.

[0064] Further, historical monitoring data and geological survey data are used to establish a mathematical model of substation ground deformation. A combination of multiple modeling methods is used, such as finite element models, neural network models, and time series models.

[0065] The finite element model simulates the deformation of the ground under different loads by modeling the geological structure and mechanical properties of the substation ground. This model can take into account factors such as geological conditions and foundation structure, and has high accuracy and reliability.

[0066] The neural network model learns and trains on a large amount of historical monitoring data to establish a nonlinear mapping relationship between input (sensor monitoring data) and output (ground deformation). This model has strong adaptive and generalization capabilities, and can accurately predict the trend of ground deformation.

[0067] The time series model is based on time series analysis methods to model and analyze the time series of monitoring data. By mining the time rules and trends in the data, it can predict the development of ground deformation in the future.

[0068] S5: Based on the real-time analysis results, set the warning threshold, trigger the corresponding warning signal and display the monitoring data and warning status through the visualization interface.

[0069] The established ground deformation model is used to analyze and process real-time monitoring data. The real-time monitoring data is input into the model to calculate the current ground deformation and future deformation trend. At the same time, multiple warning thresholds are set to determine whether to trigger a warning based on the deformation and trend.

[0070] When the monitoring data exceeds the first warning threshold, the system issues a yellow warning signal, prompting the operation and maintenance personnel to pay attention to the ground deformation and conduct further inspection and analysis. When the monitoring data exceeds the second warning threshold, the system issues an orange warning signal, notifying the operation and maintenance personnel to take immediate action to investigate and handle potential safety hazards. When the monitoring data exceeds the third warning threshold, the system issues a red warning signal, starting the emergency response mechanism to ensure the safety of substation equipment and personnel.

[0071] In addition, the analysis results are displayed in the form of intuitive charts, curves, and three-dimensional models using visualization technology, making it easy for operation and maintenance personnel to quickly understand the ground deformation and development trend and make accurate decisions.

[0072] Example 2, an embodiment of the present application, provides a substation ground deformation sensor monitoring signal acquisition and monitoring method. To verify the beneficial effects of the present application, scientific demonstration is carried out through experiments.

[0073] A 110kV substation (located in a soft soil area with high ground subsidence risk) is equipped with a deformation prevention monitoring system.

[0074] Five three-dimensional laser displacement sensors (model LDS-500) are installed in the main transformer area, arranged at the four corners and center of the transformer foundation, monitoring X / Y / Z axis displacement with an accuracy of ±0.1mm. Additional sensors are installed every 2 meters outside the foundation to form a dense monitoring network.

[0075] In the high-voltage switchgear area, four strain sensors (model STG-2000, range ±2000με) and two inclination sensors (model TILT-05, accuracy ±0.01°) are installed on each device foundation to monitor stress changes and inclination angles in real time.

[0076] In the cable trench area, distributed fiber optic strain sensors (model DFOS-100) are arranged every 8 meters along the entire length of the trench, with a total length of 300 meters, covering critical turning points and branch nodes, and an electromagnetic interference resistance level ≥60dB.

[0077] In the building foundation area, eight static leveling instruments (model SL-200) are used, with a spacing of 4 meters around the building foundation, with a measurement accuracy of ±0.05mm.

[0078] A 32-channel high-speed data acquisition card (Model DAQ-32H) was configured with a dynamic adjustable sampling frequency (10 Hz-1000 Hz). The time error of each channel was ≤1 μs through a GPS clock module (Model GPS-10).

[0079] The dynamic sampling frequency included a regular mode and a pre-warning mode. The regular mode had a sampling frequency of 20 Hz and a data storage interval of 5 minutes. When the deformation rate of a certain area was ≥0.5 mm / h, the pre-warning mode automatically switched to 100 Hz and the storage interval was shortened to 1 minute.

[0080] Signal filtering and calibration were performed, including low-pass filtering (cutoff frequency 10 Hz), median filtering (window width 5), and wavelet filtering (db4 wavelet basis). The sensors were calibrated on site every quarter, and the calibration error was controlled within ±0.5%.

[0081] A wireless network architecture was constructed: ZigBee network coverage radius 50 meters area, transmission rate 250 kbps, connecting the main transformer area and high-voltage equipment area sensors; using LoRa+5G hybrid transmission, cable channel area using LoRa (transmission distance 1 km, power consumption 10 mW), building area through 5GCPE (Model HUAWEI5G-01) real-time data backhaul. CRC-32 check + adaptive ARQ protocol was used, and when the network was congested, the retransmission times were automatically reduced to 3 times, and the delay threshold was set to 200 ms.

[0082] AES-256 encryption was used in the data transmission layer, and the key was dynamically updated every 24 hours. The WPA3 protocol was enabled in the network layer, and the firewall was configured to filter abnormal IP access.

[0083] Three times spline interpolation was used to fill in missing data, with an interpolation error ≤1%. A finite element model was constructed based on geological exploration data (soil elastic modulus 50 MPa, Poisson's ratio 0.3) to simulate load distribution. LSTM neural network was used to input historical 30-day data (time step 1 hour) to predict the deformation trend in the next 24 hours, with an RMSE ≤0.2 mm.

[0084] A hierarchical warning mechanism was set up, including

[0085] Yellow warning: deformation ≥5 mm or deformation rate ≥1 mm / day, triggering manual re-inspection.

[0086] Orange warning: deformation ≥10 mm or deformation rate ≥2 mm / day, starting reinforcement plan.

[0087] Red warning: deformation ≥20 mm or foundation tilt ≥0.5°, immediately cutting off the power supply of the equipment and evacuating personnel.

[0088] The WebGL-based three-dimensional visualization interface displays the regional deformation thermal map, the historical curve and the early warning state in real time.

[0089] It should be noted that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

[0090] Embodiment 3 is the third embodiment of the present application, which is different from the first two embodiments:

[0091] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of software products, which are stored in a storage medium and include a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0092] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a list of executable instructions for implementing logical functions, which can be embodied in any computer readable medium for use by or in conjunction with an instruction execution system, device or apparatus, such as a computer-based system, a system including a processor or other system that can fetch and execute instructions from an instruction execution system, device or apparatus. For the purpose of this specification, "computer readable medium" can be any device that can contain, store, communicate, propagate or transport programs for use by or in conjunction with an instruction execution system, device or apparatus, or in conjunction with these instruction execution systems, devices or apparatus.

[0093] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer readable medium can be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via an optical scanner, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, to generate an electronically readable version of the program, which can then be stored in the computer memory.

[0094] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the embodiments described above, various steps or methods can be implemented, for example, by software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following technologies, known in the art, or a combination thereof, can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

[0095] Embodiment 4, as an embodiment of the present application, provides a substation ground deformation sensor monitoring signal acquisition monitoring method system, data analysis and modeling module, early warning and visualization module;

[0096] Sensor deployment module, according to the risk of region, layout corresponding sensor, form monitoring network covering the whole station;

[0097] Signal acquisition and processing module, synchronous acquisition of sensor signal, dynamic adjustment of sampling frequency, and filtering of interference and calibration data;

[0098] Data transmission and security module, transmit data through hybrid wireless network, guarantee transmission reliability and communication link security;

[0099] Data analysis and modeling module, clean data, fill in missing values, and predict ground deformation trend through multiple models;

[0100] Early warning and visualization module, trigger hierarchical early warning, real-time display of deformation heat map and historical data, and auxiliary emergency response.

[0101] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not to limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A method of monitoring the acquisition of monitoring signals from substation ground deformation sensors, characterized by: The application relates to a ground deformation monitoring system for substations. According to the ground deformation risk of different functional areas of the substation, the monitoring area is divided and the corresponding type of sensor is selected; A multi-channel synchronous signal acquisition system is constructed, and the synchronous acquisition of signals of various sensors is realized through clock synchronization technology, and the sampling frequency is dynamically adjusted according to the ground deformation state; Wireless communication network is used to transmit the collected data, and data transmission is carried out based on data retransmission, error correction and encryption mechanism; The data transmitted to the monitoring center are processed and analyzed, and a ground deformation prediction system including a finite element model, a neural network model and a time series model is established; Based on the real-time analysis result, the warning threshold is set, the corresponding warning signal is triggered, and the monitoring data and warning state are displayed through the visual interface.

2. The substation ground deformation sensor monitoring signal acquisition monitoring method of claim 1, wherein: The sensor includes a three-dimensional displacement sensor, a strain sensor, an inclination sensor, a distributed optical fiber strain sensor and a static leveling instrument; Three-dimensional displacement sensors are arranged at the base quadrangles and the center point of the main transformer area, and auxiliary monitoring points are additionally arranged along the periphery; Strain sensors are installed at the base support points of the high-voltage switch device area, and inclination sensors are installed at the top and bottom; Distributed optical fiber strain sensors are arranged at a preset interval along the full length of the cable channel area, and the density is increased at key nodes; Static leveling instruments are arranged around the building foundation area to form a closed monitoring loop.

3. The substation ground deformation sensor monitoring signal acquisition monitoring method of claim 2, wherein: The multi-channel synchronous signal acquisition system includes a multi-channel high-speed data acquisition card, and supports dynamic adjustment of the sampling frequency within a certain range; Through an external high-precision clock source, the time synchronization of each channel is realized, in the signal preprocessing stage, a composite filtering algorithm combining low-pass filtering, median filtering and wavelet filtering is adopted, and the signal is amplified and calibrated.

4. The substation ground deformation sensor monitoring signal acquisition monitoring method of claim 3, wherein: The collected data are transmitted through a wireless communication network, ZigBee is used to cover the near-end area, LoRa or 5G is used to cover the far-end area according to different transmission distance and rate requirements; Through CRC check and adaptive ARQ protocol, data retransmission and error correction are realized, and the retransmission times and interval time are dynamically adjusted; The data are encrypted by using an AES-256 encryption algorithm, and the communication link is protected based on a WPA3 protocol.

5. The substation ground deformation sensor monitoring signal acquisition monitoring method of claim 4, wherein: The missing data are filled by using a cubic spline interpolation, and the interpolation error is less than or equal to 1%; The normalized data are input into a finite element model, an LSTM neural network and a time series model to predict the ground deformation trend. The finite element model simulates deformation based on geological parameters and load distribution, and the LSTM neural network predicts the deformation amount in the next 24 hours based on historical data.

6. The substation ground deformation sensor monitoring signal acquisition monitoring method of claim 5, wherein: When the deformation amount is greater than or equal to 5mm or the deformation rate is greater than or equal to 1mm / day, a yellow warning is triggered; When the deformation amount is greater than or equal to 10mm or the deformation rate is greater than or equal to 2mm / day, an orange warning is triggered; When the deformation amount is greater than or equal to 20mm or the foundation inclination is greater than or equal to 0.5°, a red warning is triggered, and an emergency response mechanism is started.

7. The substation ground deformation sensor monitoring signal acquisition monitoring method of claim 6, wherein: The visual interface includes a three-dimensional heat map and a historical curve generated based on WebGL technology, real-time display of deformation data and warning state of each area, and GPS positioning of the collapse point coordinates.

8. A system for monitoring signal collection of a substation ground deformation sensor using the method according to any one of claims 1 to 7, characterized in that: The substation ground deformation sensor monitoring system comprises a sensor deployment module, a signal acquisition and processing module, a data transmission and security module, a data analysis and modeling module, and an early warning and visualization module. The sensor deployment module is arranged according to the risk of each region to form a monitoring network covering the whole station. The signal acquisition and processing module synchronously acquires sensor signals, dynamically adjusts the sampling frequency, and filters out interference and calibrates data. The data transmission and security module transmits data through a hybrid wireless network to ensure transmission reliability and communication link security. The data analysis and modeling module cleanses data, fills in missing values, and predicts ground deformation trends through multiple models. The early warning and visualization module triggers graded early warning, displays real-time deformation heat maps and historical data, and assists emergency response. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to implement the steps of the substation ground deformation sensor monitoring signal acquisition and monitoring method according to any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the substation ground deformation sensor monitoring signal acquisition and monitoring method according to any one of claims 1 to 7.

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