Parameter monitoring method, device, apparatus and storage medium

Abstract

This invention relates to the field of parameter monitoring technology and discloses a parameter monitoring method, device, equipment, and storage medium. The method includes: driving a MEMS triaxial digital accelerometer and a wire displacement meter in a driving detector to synchronously acquire data, obtaining timed acquisition data frames; waking up the main control unit and calculating the trigger acquisition data frame; storing the timed acquisition data frames in a local circular queue buffer; when the accumulated number of frames reaches a preset batch number, uploading them in batches to the cloud platform via a 4G module; uploading the trigger acquisition data frame to the cloud platform in real time via a 4G module and simultaneously broadcasting an early warning message to the local monitoring network via a LoRa module. This method utilizes the built-in hardware threshold comparator of the MEMS chip to achieve real-time monitoring of microampere-level power consumption, breaking through the power consumption bottleneck of the traditional software polling method, and still being able to respond to sudden deformation events of structures at the microsecond level in a low-power sleep state.

Description

Technical Field

[0001] This invention relates to the field of parameter monitoring technology, and in particular to a parameter monitoring method, apparatus, equipment and storage medium. Background Technology

[0002] Traditional monitoring systems employ fixed-period data acquisition. This single-mode approach exhibits significant response delays when sudden structural deformation occurs, leading to the failure to promptly detect abnormal events and miss optimal early warning opportunities. Shortening the acquisition cycle to improve response speed increases equipment power consumption, making long-term monitoring difficult in environments without power. Current technologies rely on independent equipment and data acquisition systems for monitoring multi-dimensional parameters such as tilt angle, acceleration, and cracks. The lack of collaborative mechanisms between devices hinders simultaneous acquisition and correlation analysis of multiple parameters, resulting in an incomplete assessment of the overall structural deformation state. Furthermore, existing monitoring systems generally lack the ability to dynamically adjust acquisition strategies based on real-time conditions. This leads to energy waste during stable structural periods and an inability to automatically increase monitoring frequency during abnormal conditions, making it difficult to simultaneously meet the dual requirements of long-term endurance and emergency response. Summary of the Invention

[0003] This invention provides a parameter monitoring method, device, equipment, and storage medium. Through a dual-mode collaborative monitoring mechanism, this invention ensures data continuity by periodically collecting data at set intervals, and utilizes a hardware threshold comparator built into a MEMS chip to achieve real-time monitoring of microampere-level power consumption. This breaks through the power consumption bottleneck of traditional software polling methods and can still provide microsecond-level rapid response to sudden deformation events of structures even in low-power sleep mode.

[0004] In a first aspect, the present invention provides a parameter monitoring method, the parameter monitoring method comprising: Based on the first acquisition particle size time interval, the MEMS triaxial digital accelerometer and wire displacement meter in the detection instrument are driven to perform synchronous acquisition to obtain timed acquisition data frames; The upper and lower limits of the acceleration threshold are written into the threshold register of the MEMS chip, and the main control unit is woken up based on the timed acquisition data frame. The acceleration waveform sampling data in the FIFO buffer of the MEMS triaxial digital accelerometer is read to calculate the trigger acquisition data frame. The timed data frames are stored in a local circular queue buffer. When the cumulative number of frames reaches the preset batch number, they are uploaded to the cloud platform in batches via the 4G module. The triggered data frames are uploaded to the cloud platform in real time via the 4G module and a warning message is broadcast to the local monitoring network simultaneously via the LoRa module.

[0005] In conjunction with the first aspect, in the first implementation of the first aspect of the present invention, before the MEMS triaxial digital accelerometer and the wire displacement meter in the detector are synchronously acquired based on the first acquisition granularity time interval to obtain the timed acquisition data frame, the parameter monitoring method further includes: The first acquisition granularity time interval, acceleration threshold upper and lower limits, crack displacement threshold upper and lower limits, tilt angle threshold upper and lower limits, cloud platform server address and port number, and LoRa networking parameters are persistently stored through the RS485 configuration interface. The system reads the reference zero-point state value of the MEMS triaxial digital accelerometer, the initial wire length value of the wire displacement meter, and the triaxial initial angle value of the tilt sensor. It then activates the GPS positioning module to obtain the latitude and longitude coordinates of the device installation location and uploads them to the cloud platform via the 4G module to complete the binding and association.

[0006] In conjunction with the first aspect, in a second implementation of the first aspect of the present invention, the MEMS triaxial digital accelerometer and the wire displacement meter in the detector are driven to perform synchronous acquisition based on the first acquisition granularity time interval to obtain a timed acquisition data frame, including: When the timer count reaches the first acquisition granularity time interval, the raw digital acceleration value of the MEMS triaxial digital accelerometer is read, and the raw digital acceleration value is transmitted to the main control unit via the I2C bus and converted into a triaxial acceleration value according to the sensor range and accuracy calibration parameters. Calculate the triaxial tilt angle data based on the triaxial acceleration values; The sampled voltage value of the wire displacement gauge is read, the sampled voltage value is converted into crack displacement, and the triaxial tilt data, the triaxial acceleration value and the crack displacement are encapsulated to obtain a timed acquisition data frame.

[0007] In conjunction with the first aspect, in the third implementation of the first aspect of the present invention, the upper and lower limits of the acceleration threshold are written into the threshold register of the MEMS chip, and the main control unit is woken up based on the timed data acquisition frame, and the acceleration waveform sampling data in the FIFO buffer of the MEMS triaxial digital accelerometer is read to calculate the trigger acquisition data frame, including: Before the main control unit enters a low-power sleep state, the upper and lower limits of the acceleration threshold are written into the threshold register of the MEMS chip, and the MEMS triaxial digital accelerometer is configured to continuously sample real-time acceleration values ​​based on the timed data acquisition frame. An interrupt signal is generated by driving the interrupt pin level to jump based on the real-time acceleration value and the upper and lower limits of the acceleration threshold using a built-in digital comparator in the MEMS chip. After the interrupt controller of the main control unit detects the rising edge of the interrupt signal, it triggers the interrupt service routine, reads the interrupt status register to obtain the trigger axis and over-threshold direction information, and sets the system status flag to obtain the threshold trigger event. Based on the threshold trigger event, the acceleration waveform sampling data in the FIFO buffer of the MEMS triaxial digital accelerometer is read to calculate the trigger acquisition data frame.

[0008] In conjunction with the first aspect, in a fourth implementation of the first aspect of the present invention, based on the threshold trigger event, calculating the trigger acquisition data frame by reading the acceleration waveform sampling data in the FIFO buffer of the MEMS triaxial digital accelerometer includes: Based on the threshold trigger event, acceleration waveform sampling data is read from the FIFO buffer of the MEMS triaxial digital accelerometer, and the acceleration waveform sampling data is encapsulated with the trigger timestamp, trigger axis identifier and trigger threshold direction identifier to obtain trigger event metadata; The MEMS triaxial digital accelerometer and wire displacement meter are simultaneously activated to collect triaxial tilt data, triaxial acceleration data and crack displacement data a set number of times, and then the moving average filtering is performed to obtain the filtered triaxial tilt data, filtered triaxial acceleration data and filtered crack displacement data. The changes in tilt angle, peak acceleration, and crack displacement are calculated based on the filtered triaxial tilt angle data, the filtered triaxial acceleration data, and the filtered crack displacement data. The trigger event metadata, the filtered triaxial tilt data, the filtered triaxial acceleration data, the filtered crack displacement data, the tilt change, the peak acceleration change, and the crack displacement change are associated, encapsulated, and a trigger type identifier is added to obtain a trigger acquisition data frame.

[0009] In conjunction with the first aspect, in the fifth implementation of the first aspect of the present invention, the timed data frames are stored in a local circular queue buffer. When the accumulated number of frames reaches a preset batch number, they are uploaded to the cloud platform in batches via a 4G module. The triggered data frames are uploaded to the cloud platform in real time via a 4G module and a warning message is simultaneously broadcast to the local monitoring network via a LoRa module, including: The timed data frames are stored in a local circular queue buffer. When the number of accumulated frames reaches the preset batch number, multiple timed data frames are encapsulated into a batch transmission packet. The 4G module is activated to establish a TCP connection with the cloud platform server and upload the batch transmission packets to the cloud platform. For the triggered data collection frame, the 4G module is activated to establish a TCP connection and add a trigger type identifier before uploading it to the cloud platform in real time. The LoRa wireless module is activated in parallel, encapsulating the device ID, trigger timestamp, trigger axis identifier, and acceleration peak value into an early warning message and broadcasting it to other devices within the local monitoring network.

[0010] In conjunction with the first aspect, in the sixth implementation of the first aspect of the present invention, after the triggered data frame is uploaded to the cloud platform in real time via the 4G module and a warning message is simultaneously broadcast to the local monitoring network via the LoRa module, the parameter monitoring method further includes: The cloud platform parses the triggered acquisition data frame and calculates the time derivative of the tilt angle change, peak acceleration change, and crack displacement change in the triggered acquisition data frame to obtain the tilt angle change rate, acceleration change rate, and crack displacement change rate. The rate of change of the tilt angle, the rate of change of the acceleration, and the rate of change of the crack displacement are compared with the corresponding rate of change thresholds. When any rate of change exceeds the threshold, it is marked as a level three warning. When the warning level is Level III, the first data collection granularity time interval parameter is modified to one-quarter of its original value to obtain the second data collection granularity time interval, and the second data collection granularity time interval is sent to the monitoring instrument.

[0011] Secondly, the present invention provides a parameter monitoring device, the parameter monitoring device comprising: The synchronous acquisition module is used to drive the MEMS triaxial digital accelerometer and wire displacement meter in the detector to perform synchronous acquisition based on the first acquisition granularity time interval, so as to obtain timed acquisition data frames; The calculation module is used to write the upper and lower limits of the acceleration threshold into the threshold register of the MEMS chip, wake up the main control unit based on the timed acquisition data frame, and read the acceleration waveform sampling data in the FIFO buffer of the MEMS triaxial digital accelerometer to calculate the trigger acquisition data frame. The upload module is used to store the timed data frames into a local circular queue buffer. When the accumulated number of frames reaches the preset batch number, it uploads them to the cloud platform in batches through the 4G module. The triggered data frames are uploaded to the cloud platform in real time through the 4G module and a warning message is broadcast to the local monitoring network simultaneously through the LoRa module.

[0012] A third aspect of the present invention provides a parameter monitoring device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the parameter monitoring device to perform the parameter monitoring method described above.

[0013] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the parameter monitoring method described above.

[0014] The technical solution provided by this invention employs a dual-mode collaborative monitoring mechanism. While ensuring data continuity through periodic data acquisition, it utilizes a hardware threshold comparator built into the MEMS chip to achieve real-time monitoring of microampere-level power consumption. This overcomes the power consumption bottleneck of traditional software polling methods, enabling microsecond-level rapid response to sudden deformation events of structures even in low-power sleep mode. The hardware comparator continuously judges the triaxial acceleration values; once the threshold is exceeded, an interrupt signal immediately wakes up the main control unit to initiate emergency full acquisition. This ensures real-time capture of abnormal events while avoiding energy consumption caused by continuous high-frequency acquisition, effectively resolving the contradiction between power consumption and response speed. A differentiated transmission strategy batches and uploads periodically acquired data, significantly reducing the number of communication module startups and transmission energy consumption. Triggered acquisition data is uploaded to the cloud platform in real-time via the 4G network and simultaneously broadcast to the local monitoring network via the LoRa module, achieving dual protection for remote monitoring and regional linkage. The cloud platform has built a three-level progressive early warning system, which consists of a first-level threshold judgment at the device end, a second-level threshold comparison in the cloud, and a third-level assessment of the rate of change. This forms a comprehensive early warning mechanism from the edge to the cloud. The system automatically adjusts the collection granularity and time interval according to the early warning level. When the highest early warning level is reached, the collection cycle is shortened to one-quarter of the original value. This achieves an intelligent balance between monitoring density and power consumption, and improves the intelligence level of structural health monitoring and emergency response capabilities.

[0015] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of one embodiment of the parameter monitoring method in this invention; Figure 2 This is a schematic diagram of one embodiment of the parameter monitoring device in this invention; Figure 3 This is a schematic diagram of one embodiment of the parameter monitoring device in this invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The terms "comprising" and "having," and any variations thereof, used in the embodiments of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0020] To facilitate understanding of this embodiment, a parameter monitoring method disclosed in this invention will first be described in detail. For example... Figure 1 As shown, this method includes the following steps: 101. Based on the first acquisition granularity time interval, the MEMS triaxial digital accelerometer and wire displacement meter in the driving detector are synchronously acquired to obtain timed acquisition data frames; Specifically, the acquisition period parameter T1 is set as the upper limit of the timer count. After the system enters the running state, the low-power timer continuously accumulates the count. When the timer reaches the set first acquisition granularity time interval T1, the system main control unit automatically wakes up and issues an acquisition command to activate the MEMS triaxial digital accelerometer module. The module integrates a temperature compensation unit and a 16-bit high-precision analog-to-digital converter. Through the internal ADC, it completes the digital processing of the acceleration analog signals in the X, Y, and Z axes to form the corresponding raw digital acceleration quantity A. x原始 A y原始 and A z原始 Through I 2 The C bus sequentially transmits data from the three channels to the main control unit's memory buffer. Based on the accelerometer's calibrated range of ±2g and accuracy of ±0.005mg, the main control unit maps the raw digital values ​​to the measured acceleration value A in physical quantity units. x A y and A z The values ​​are then uniformly represented as mg. The main control unit calls the attitude calculation submodule to perform inversion calculations on the triaxial acceleration data based on the principle of gravity vector decomposition, and obtains the X-axis tilt angle θ. x Y-axis tilt angle θ y and Z-axis tilt angle θ z The calculation model is θ x = arcsin(Ax / g), θ y =arcsin(A y / g), θ z = arccos(A z / g), where g is the gravitational acceleration constant, and the calculation result is converted by trigonometric functions to obtain the tilt angle data. The main control unit starts the excitation circuit of the wire displacement meter and reads the corresponding analog voltage signal V. 输出 After the analog voltage signal is converted into a digital voltage value by the ADC module built into the main control unit, it is calculated according to the calibration proportional coefficient K=200mm / V of the displacement gauge. 参考 Performing linear calculations yields the actual displacement D corresponding to the structural cracks, D=(V 输出 / V 参考 The measurement dimensions are 200mm x 100mm, with a measurement accuracy of ±0.5mm. The above three types of measurement results constitute the triaxial tilt data (θ). x , θ y , θ z ), acceleration triaxial data (A x A y A z The crack displacement data D is uniformly encapsulated into a timed acquisition data frame, and the current timestamp and GPS positioning information are attached and stored in the local buffer.

[0021] 102. Write the upper and lower limits of the acceleration threshold into the threshold register of the MEMS chip, wake up the main control unit based on the timed acquisition data frame, and read the acceleration waveform sampling data in the FIFO buffer of the MEMS triaxial digital accelerometer to calculate the trigger acquisition data frame; Specifically, after generating a timed data acquisition frame and confirming that the system has entered the stable monitoring phase, the main control unit writes preset acceleration threshold upper and lower limits to the MEMS chip via the configuration bus. The thresholds are then stored in their corresponding internal threshold registers. Simultaneously, the accelerometer's operating mode is set to continuous sampling mode. Based on the structural operating status reflected in the most recent timed data acquisition frame, the MEMS triaxial digital accelerometer is configured to continuously sample real-time acceleration values ​​along the X, Y, and Z axes in a low-power manner, maintaining real-time perception of the structure's dynamic behavior without waking the main control unit. The digital comparator module integrated within the MEMS chip compares the continuously sampled real-time acceleration values ​​with the acceleration threshold upper and lower limits axis by axis. When the real-time acceleration value in any axis exceeds the corresponding threshold range, the digital comparator generates a comparison result and drives the MEMS chip's interrupt pin to transition from low to high, generating a valid interrupt signal output. The main control unit is in a low-power sleep state, but its interrupt controller constantly monitors the external interrupt pin. When a rising edge transition is detected on the interrupt pin, the interrupt controller triggers a pre-registered interrupt service routine, switching the main control unit from sleep to working state and entering the interrupt response process. In the interrupt service routine, the main control unit reads the interrupt status register of the MEMS chip, parsing the specific axial information that triggered the interrupt and the corresponding over-threshold direction information to determine which axis's acceleration exceeded the limit in the positive or negative direction. It then sets the corresponding status flag internally to indicate that a valid threshold trigger event has occurred. Based on the threshold trigger event, the main control unit executes an emergency acquisition process. By accessing the FIFO buffer of the MEMS triaxial digital accelerometer, it sequentially reads the pre-stored acceleration waveform sampling data in the buffer. The waveform data covers continuous sampling results within a certain time window before and after the threshold trigger, reflecting the dynamic response characteristics of the structure at the moment of triggering. After completing the data reading, the main control unit processes and filters the acceleration waveform sampling data, and combines it with the currently re-acquired acceleration data and system time information to uniformly encapsulate and generate a trigger acquisition data frame.

[0022] 103. Store the timed data frames in a local circular queue buffer. When the accumulated number of frames reaches the preset batch number, upload them to the cloud platform in batches via the 4G module. Simultaneously upload the triggered data frames to the cloud platform in real time via the 4G module and broadcast the warning message to the local monitoring network via the LoRa module.

[0023] Specifically, the timed data frames are written into a locally configured circular queue buffer in chronological order. The buffer adopts a FIFO structure to ensure data timeliness and spatial reusability. When the number of data frames accumulated in the buffer reaches the preset batch quantity threshold N1, the main control unit triggers the batch encapsulation logic. The N1 timed data frames are sequentially concatenated and encapsulated into a batch transmission packet according to the data frame format. The batch packet contains fields such as device number, timestamp sequence, tilt triaxial data, acceleration triaxial data, crack displacement, geographic coordinate information, and data check code. A type identifier field is added to the packet header to indicate that the data belongs to the timed acquisition class. After batch packaging is completed, the 4G Cat.1 wireless communication module is activated, a TCP session is initialized, and a connection is established to the preset cloud platform address and port. Immediately after the TCP handshake, the batch transmission packets are pushed to the cloud and await ACK confirmation. Upon confirmation, the local cache is cleared to release resources. When the system generates trigger acquisition data frames in trigger acquisition mode, due to the bursty and highly responsive nature of this data, the system does not enter a buffering process. Instead, it activates the 4G module to independently establish a TCP connection, uploads the trigger acquisition data frames to the cloud platform, and adds a trigger type identifier field to the data frames for differentiated processing. Simultaneously, the main control unit activates the LoRa wireless communication module in parallel, using a high spreading factor and low code rate to quickly broadcast the core information of the current trigger event to all node devices in the local monitoring network. The broadcast content includes the device ID, trigger timestamp, over-limit axial identification, and acceleration peak field. The broadcast message structure is optimized and compressed to be within 12 bytes in length.

[0024] In one specific embodiment, before the MEMS triaxial digital accelerometer and wire displacement meter in the detector are synchronously acquired based on the first acquisition granularity time interval to obtain the timed acquisition data frame, the parameter monitoring method further includes: The first acquisition granularity time interval, acceleration threshold upper and lower limits, crack displacement threshold upper and lower limits, tilt angle threshold upper and lower limits, cloud platform server address and port number, and LoRa networking parameters are persistently stored through the RS485 configuration interface. The system reads the reference zero-point state value of the MEMS triaxial digital accelerometer, the initial wire length value of the wire displacement meter, and the triaxial initial angle value of the tilt sensor. It then activates the GPS positioning module to obtain the latitude and longitude coordinates of the device installation location and uploads them to the cloud platform via the 4G module to complete the binding and association.

[0025] Specifically, a connection is established with the host computer management software via a physical RS485 communication interface, and parameters are entered or selected for setting in the configuration interface. After receiving the configuration command, the main control unit performs integrity and boundary validity checks on each parameter value, ensuring that the granularity time interval is within the range of 1 to 24 hours, the acceleration threshold is within ±2g of the range, the crack displacement threshold does not exceed ±200mm, and the tilt angle threshold is within the effective range of ±90°. It also verifies that the cloud platform address is in a valid IPv4 or URL format, the port number is within the range of open ports, and that LoRa parameters, including channel frequency, spreading factor, transmit power, and device ID, all meet protocol specifications. After all checks pass, the parameters are written to the internal non-volatile storage area in key-value pairs, completing the persistent writing process, and a "CFG: OK" confirmation response is returned, ensuring that the parameters remain valid even after the device is powered off. Based on a MEMS triaxial digital accelerometer, a local sampling is performed to read the initial digital acceleration values ​​under the current environmental conditions from its three channels. These values ​​are then converted into measured acceleration values ​​in the three axes according to the sensor temperature compensation matrix and calibration model. These measured values ​​are then stored in the system's internal state register, approximating the initial zero-point reference values ​​in a static state. Simultaneously, the current voltage output value of the cable displacement meter circuit is read and converted into the initial cable length. The initial tilt angle is processed by the main control unit, which reads the acceleration components and calculates the initial tilt angle values ​​θ for the X, Y, and Z axes. x0 θ y0 θ z0 The sensor ID and sensor number are encapsulated into the initialization data structure. The GPS positioning module is activated to capture the current geographic latitude and longitude coordinates. The latitude, longitude, elevation, and UTC time fields are parsed using the NMEA protocol and encapsulated along with the device's unique ID and the initial values ​​of the three parameters into a registration and reporting message. The 4G Cat.1 module is activated to establish a TCP connection, sending a binding registration request data frame to the preset cloud platform address and port number, and waiting for a successful binding confirmation from the cloud. After this process, the device completes activation, binding, and measurement point identification association operations on the platform.

[0026] In one specific embodiment, the MEMS triaxial digital accelerometer and wire displacement meter in the detector are driven to perform synchronous acquisition based on the first acquisition granularity time interval, resulting in a timed acquisition data frame, including: When the timer count reaches the first acquisition granularity time interval, the raw digital acceleration value of the MEMS triaxial digital accelerometer is read, and the raw digital acceleration value is transmitted to the main control unit via the I2C bus and converted into a triaxial acceleration value according to the sensor range and accuracy calibration parameters. Calculate the triaxial tilt angle data based on the triaxial acceleration values; The sampled voltage value of the wire displacement gauge is read, the sampled voltage value is converted into crack displacement, and the triaxial tilt data, the triaxial acceleration value and the crack displacement are encapsulated to obtain a timed acquisition data frame.

[0027] Specifically, a low-power timer runs continuously and accumulates the count. When the count value equals or exceeds the preset first acquisition granularity time interval T1, the main control unit is awakened from sleep mode and starts the data acquisition task. The main control unit sends a start command to the MEMS triaxial digital accelerometer. The accelerometer integrates a high-precision analog-to-digital conversion circuit and a temperature compensation mechanism. It acquires the analog acceleration signals in the X, Y, and Z directions of the current environment at a stable sampling frequency and converts them into digital quantities, denoted as A. x原始 A y原始 and A z原始 The original digital quantity of acceleration is transmitted through I. 2 The C-bus synchronously transmits data to the register buffer of the main control unit under standard communication timing. The main control unit scales and linearly maps the raw digital value according to the MEMS chip's range parameters (e.g., ±2g) and internal calibration coefficient table, converting it into a measured triaxial acceleration value A with units. x A y A z The unit is uniformly mg. The attitude calculation module is invoked, and based on the standard gravity vector decomposition principle, the tilt angles of the device along the three spatial axes are calculated using triaxial acceleration values. The calculation model is θ. x =arcsin(A x / g), θ y =arcsin(A y / g), θ z =arccos(A z / g), where g is the standard gravitational acceleration constant 9800mg. This calculation is performed in the MCU using floating-point arithmetic, yielding the tilt angle value θ. x θ y θ z Within an effective range of ±90°, with an accuracy of ±0.005°, the main control unit activates the analog acquisition channel and enables the drive circuit of the wire displacement meter. After the excitation voltage stabilizes, it samples the output voltage value V. 输出 The output voltage value corresponds to a linear proportional relationship with the wire displacement. The main control unit uses a built-in ADC module to convert V... 输出 Converted to a digital voltage value, and calculated based on the displacement gauge's range and calibration ratio K=200mm / V, the absolute displacement D of the current structural crack is obtained. 输出 / V 参考(×200mm) Ax, Ay, Az, θx, θy, θz, and D, along with metadata such as the current real-time timestamp, device ID, and GPS positioning coordinates, are encapsulated into a standard timed acquisition data frame and written to a local buffer or circular queue.

[0028] In one specific embodiment, the upper and lower limits of the acceleration threshold are written into the threshold register of the MEMS chip, and the main control unit is woken up based on the timed data acquisition frame. The acceleration waveform sampling data in the FIFO buffer of the MEMS triaxial digital accelerometer is then read to calculate the trigger data acquisition frame, including: Before the main control unit enters a low-power sleep state, the upper and lower limits of the acceleration threshold are written into the threshold register of the MEMS chip, and the MEMS triaxial digital accelerometer is configured to continuously sample real-time acceleration values ​​based on the timed data acquisition frame. An interrupt signal is generated by driving the interrupt pin level to jump based on the real-time acceleration value and the upper and lower limits of the acceleration threshold using a built-in digital comparator in the MEMS chip. After the interrupt controller of the main control unit detects the rising edge of the interrupt signal, it triggers the interrupt service routine, reads the interrupt status register to obtain the trigger axis and over-threshold direction information, and sets the system status flag to obtain the threshold trigger event. Based on the threshold trigger event, the acceleration waveform sampling data in the FIFO buffer of the MEMS triaxial digital accelerometer is read to calculate the trigger acquisition data frame.

[0029] Specifically, before the main control unit enters a low-power sleep state, preset upper and lower limits of acceleration thresholds are written into the threshold registers inside the MEMS triaxial digital accelerometer. The positive and negative thresholds are written into dedicated THRESH upper and lower limit registers within the chip, respectively. Specific values, such as ±800mg, can be adjusted according to the application scenario. This, combined with enabling relevant function bits in the interrupt control register, ensures the accelerometer maintains independent operation capability during the main control unit's sleep state. Based on the static acceleration reference value from the previous timed data acquisition frame, the main control unit configures the accelerometer's real-time operating state, sets the sampling frequency to 100Hz, and enables continuous sampling mode. The digital comparator module inside the MEMS chip starts operating automatically without the main controller's involvement. It compares the current triaxial acceleration samples with the set upper and lower limits of the acceleration threshold in real time. When the absolute value of the acceleration in any direction exceeds its corresponding threshold range, the digital comparator triggers an interrupt request logic, causing the accelerometer's interrupt output pin INT to transition from low to high, forming a rising edge signal. This signal is directly transmitted to the main control unit's external interrupt receiver pin EXT_INT via hardware connections. When the main control unit is in low-power mode, its interrupt controller constantly monitors the EXT_INT pin's state. If a rising edge signal is detected, it wakes up the system core, skipping the regular wake-up process and directly entering the interrupt service routine execution path with the highest priority (ISR) to ensure a response time in the microsecond range. The interrupt service routine accesses the interrupt status register INT_SOURCE in the MEMS chip, reads the acceleration direction information corresponding to the current trigger event, including whether it is the X, Y, or Z axis, and whether the over-limit direction is positive or negative. This information, along with a timestamp, is written to the system status variable. Simultaneously, the system status flag FLAG_TRIGGER is set to 1, indicating that the current state is a threshold trigger response state. The main control unit sends instructions to the MEMS chip to read the historical acceleration sampling data buffered in its FIFO buffer. The read data range covers waveforms 0.5 seconds before and after the interrupt point, for example, 100 data points. This data reflects the dynamic evolution process before and after the trigger event. After reading, the main control unit encapsulates the acceleration waveform sequence, trigger time, trigger axis identifier, trigger direction, trigger acceleration peak value, and the currently remeasured triaxial acceleration value, triaxial tilt value, and crack displacement together to form a trigger acquisition data frame, adding a type identifier field to the frame header.

[0030] In one specific embodiment, based on the threshold trigger event, calculating the trigger acquisition data frame by reading the acceleration waveform sampling data in the FIFO buffer of the MEMS triaxial digital accelerometer includes: Based on the threshold trigger event, acceleration waveform sampling data is read from the FIFO buffer of the MEMS triaxial digital accelerometer, and the acceleration waveform sampling data is encapsulated with the trigger timestamp, trigger axis identifier and trigger threshold direction identifier to obtain trigger event metadata; The MEMS triaxial digital accelerometer and wire displacement meter are simultaneously activated to collect triaxial tilt data, triaxial acceleration data and crack displacement data a set number of times, and then the moving average filtering is performed to obtain the filtered triaxial tilt data, filtered triaxial acceleration data and filtered crack displacement data. The changes in tilt angle, peak acceleration, and crack displacement are calculated based on the filtered triaxial tilt angle data, the filtered triaxial acceleration data, and the filtered crack displacement data. The trigger event metadata, the filtered triaxial tilt data, the filtered triaxial acceleration data, the filtered crack displacement data, the tilt change, the peak acceleration change, and the crack displacement change are associated, encapsulated, and a trigger type identifier is added to obtain a trigger acquisition data frame.

[0031] Specifically, after the main control unit receives the rising edge transition signal generated by the interrupt pin of the MEMS triaxial digital accelerometer and completes the interrupt service routine startup, it executes the trigger data extraction process. It reads real-time acceleration waveform sampling data from the MEMS accelerometer's FIFO buffer for 0.5 seconds before and after the interruption. The data contains 100 consecutive sampling points, describing the dynamic response process of the structure before and after the threshold is exceeded. The acceleration waveform data, along with the timestamp recorded at the moment of the interruption, the trigger axis identifier (X, Y, or Z axis) parsed from the interrupt status register, and the trigger threshold direction identifier (positive or negative), are structurally encapsulated to form the first segment of trigger event metadata, used to characterize the event background and process. Simultaneously, the MEMS triaxial digital accelerometer and the wire displacement meter are started, and triaxial tilt data, triaxial acceleration data, and crack displacement data are collected according to the set sampling number (e.g., 10 times). To reduce measurement noise caused by transient disturbances or electromagnetic interference, the main control unit performs moving average filtering on each type of acquired data. The arithmetic mean of ten sets of data in each direction is taken as the filtering result, thus obtaining the filtered triaxial tilt angle data θ. x滤波 θ y滤波 θ z滤波 Filtered triaxial acceleration data A x滤波 A y滤波 A z滤波 and the filtered crack displacement data D 滤波 The main control unit calculates key changes based on the difference between the current filtered data and the most recent timed acquisition data, where the tilt angle change Δθ is the current θ.x滤波 θ y滤波 θ z滤波 Compared to the last θ x θ y θ z The difference; the change in peak acceleration ΔA is the current A x滤波 A y滤波 A z滤波 The difference between the maximum value and the previous peak value, the change in crack displacement ΔD is D. 滤波 The difference from the previous D value. The trigger event metadata, filtered triaxial tilt data, filtered triaxial acceleration data, filtered crack displacement data, as well as the changes in tilt, peak acceleration, and crack displacement are uniformly associated and encapsulated, and an identifier field is added to the data frame header to indicate that it is a "trigger acquisition type", thus obtaining the trigger acquisition data frame.

[0032] In one specific embodiment, the timed data frames are stored in a local circular queue buffer. When the accumulated number of frames reaches a preset batch size, they are uploaded to the cloud platform in batches via a 4G module. The triggered data frames are also uploaded to the cloud platform in real time via the 4G module and simultaneously broadcast as an early warning message to the local monitoring network via the LoRa module, including: The timed data frames are stored in a local circular queue buffer. When the number of accumulated frames reaches the preset batch number, multiple timed data frames are encapsulated into a batch transmission packet. The 4G module is activated to establish a TCP connection with the cloud platform server and upload the batch transmission packets to the cloud platform. For the triggered data collection frame, the 4G module is activated to establish a TCP connection and add a trigger type identifier before uploading it to the cloud platform in real time. The LoRa wireless module is activated in parallel, encapsulating the device ID, trigger timestamp, trigger axis identifier, and acceleration peak value into an early warning message and broadcasting it to other devices within the local monitoring network.

[0033] Specifically, the periodically acquired data frames are written to a circular queue buffer allocated in the local RAM. The buffer uses a FIFO structure to organize the data frames and sets a maximum frame capacity and a circular coverage rule. When the cumulative number of frames after the newly acquired data is written reaches the preset batch threshold N1 (e.g., 6 frames), the main control unit triggers a batch encapsulation process. All data frames to be sent in the current buffer are packaged in chronological order and a complete batch transmission packet is constructed. The structure of the transmission packet includes a header (such as device number, data packet type identifier 0x01, data frame quantity field, etc.), a data frame body (each frame contains complete acquisition content such as timestamp, three-axis tilt angle, three-axis acceleration, crack displacement, GPS position, etc.), and a CRC16 check field at the end. After the batch encapsulation is completed, the 4G Cat.1 wireless communication module is started, initialized and a TCP connection with the cloud platform server is established. After the channel is established through a three-way handshake, the batch transmission packet is sent as the TCP payload to the specified server address and port number. After the transmission is completed and an ACK response is received, the corresponding uploaded data frames in the local queue are cleared and the 4G module is turned off to ensure that communication power consumption is controlled. When a threshold-triggered event occurs and a trigger data frame is generated, due to the urgency and real-time requirements of this type of data, the 4G module is activated to independently establish a new TCP connection. The type identifier field in the header of the trigger data frame is set to 0x02 to distinguish its source, and the trigger data is directly uploaded to the cloud platform for rapid response and early warning analysis. Simultaneously with the real-time upload process, the main control unit concurrently calls the LoRa communication module to initiate a broadcast process. It reads the trigger event's field content from the device cache, including the device ID, event timestamp, the axis of acceleration exceeding the limit (e.g., X / Y / Z), and the maximum peak acceleration in that direction. This information is then encapsulated in a compressed format into a 12-byte broadcast data packet and sent via the LoRa module to all device nodes within the local monitoring network range using the currently set channel parameters. Upon receiving the broadcast warning message, each receiving node triggers a local response mechanism, such as rapid LED flashing, buzzer alarm, log recording, or improvement of the local subsystem sampling strategy.

[0034] In one specific embodiment, after the triggered data frame is uploaded to the cloud platform in real time via the 4G module and a warning message is simultaneously broadcast to the local monitoring network via the LoRa module, the parameter monitoring method further includes: The cloud platform parses the triggered acquisition data frame and calculates the time derivative of the tilt angle change, peak acceleration change, and crack displacement change in the triggered acquisition data frame to obtain the tilt angle change rate, acceleration change rate, and crack displacement change rate. The rate of change of the tilt angle, the rate of change of the acceleration, and the rate of change of the crack displacement are compared with the corresponding rate of change thresholds. When any rate of change exceeds the threshold, it is marked as a level three warning. When the warning level is Level III, the first data collection granularity time interval parameter is modified to one-quarter of its original value to obtain the second data collection granularity time interval, and the second data collection granularity time interval is sent to the monitoring instrument.

[0035] Specifically, after the monitoring instrument uploads the triggered acquisition data frame to the cloud server, the cloud platform's data receiving service module classifies the data frame according to the type identifier field in the frame header. Once identified as a triggered data frame, it automatically calls the event parsing engine to extract its structured content item by item, including trigger event metadata, filtered triaxial tilt data, filtered triaxial acceleration data, filtered crack displacement data, and the tilt change Δθ, peak acceleration change ΔA, and crack displacement change ΔD calculated between the previous period's timed acquisition data and the current triggered data. The platform calculates the interval Δt between the two data points based on the timestamp field and performs time derivative calculations on the three types of changes based on the standard derivative definition, obtaining the tilt change rate dθ / dt = Δθ / Δt, the acceleration change rate dA / dt = ΔA / Δt, and the crack displacement change rate dD / dt = ΔD / Δt. These three rate values ​​are temporarily stored in the event analysis data buffer. The platform retrieves the configured three-level early warning judgment strategy parameters. The strategy sets three rate thresholds: tilt change rate threshold of 0.5° / min, acceleration change rate threshold of 300mg / s, and crack displacement change rate threshold of 2mm / min. It compares the real-time calculated rate values ​​with their respective thresholds. When any rate of change exceeds its corresponding preset threshold upper limit, the platform marks the current event as a level three early warning and writes the result to the event processing log table. Simultaneously, it triggers the strategy adjustment logic module to modify the acquisition cycle parameters. The adjustment method is as follows: based on the first acquisition granularity time interval T1 configured on the current monitor, its value is divided by 4 to obtain a new second acquisition granularity time interval T2 = T1 / 4, increasing the timed acquisition frequency to four times the original to improve data sampling density and the ability to track continuous abnormal states. The generated new granularity parameter T2 is encapsulated into a remote configuration instruction package and sent to the corresponding monitoring device through the platform's downlink control channel. After receiving the new sampling interval parameter, the device parses the instruction and updates the local timer configuration, thereby entering a high-density sampling state. After a certain number of cycles (e.g., 12 hours) without any new triggering events, it automatically returns to the original sampling frequency.

[0036] The parameter monitoring method in the embodiments of the present invention has been described above. The parameter monitoring device in the embodiments of the present invention will be described below. Please refer to [link / reference]. Figure 2 One embodiment of the parameter monitoring device in this invention includes: The synchronous acquisition module 201 is used to drive the MEMS triaxial digital accelerometer and wire displacement meter in the detector to perform synchronous acquisition based on the first acquisition granularity time interval, so as to obtain timed acquisition data frames. The calculation module 202 is used to write the upper and lower limits of the acceleration threshold into the threshold register of the MEMS chip, wake up the main control unit based on the timed acquisition data frame, and read the acceleration waveform sampling data in the FIFO buffer of the MEMS triaxial digital accelerometer to calculate the trigger acquisition data frame. The upload module 203 is used to store the timed data frames into a local circular queue buffer. When the accumulated number of frames reaches the preset batch number, it uploads them to the cloud platform in batches through the 4G module. The triggered data frames are uploaded to the cloud platform in real time through the 4G module and a warning message is broadcast to the local monitoring network simultaneously through the LoRa module.

[0037] above Figure 2 The parameter monitoring device in this embodiment of the invention will be described in detail from the perspective of modular functional entities. The parameter monitoring device in this embodiment of the invention will be described in detail from the perspective of hardware processing.

[0038] Figure 3 This is a schematic diagram of a parameter monitoring device 300 provided in an embodiment of the present invention. The parameter monitoring device 300 can vary significantly due to different configurations or performance characteristics. It may include one or more central processing units (CPUs) 310 (e.g., one or more processors) and a memory 320, and one or more storage media 330 (e.g., one or more mass storage devices) for storing application programs 333 or data 332. The memory 320 and storage media 330 can be temporary or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the parameter monitoring device 300. Furthermore, the processor 310 may be configured to communicate with the storage media 330 and execute the series of instruction operations in the storage media 330 on the parameter monitoring device 300 to implement the steps of the above-described parameter monitoring method.

[0039] The parameter monitoring device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 3The illustrated parameter monitoring device structure does not constitute a limitation on the parameter monitoring device provided by the present invention. It may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0040] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the parameter monitoring method.

[0041] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0042] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0043] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A parameter monitoring method, characterized in that, include: Based on the first acquisition particle size time interval, the MEMS triaxial digital accelerometer and wire displacement meter in the detection instrument are driven to perform synchronous acquisition to obtain timed acquisition data frames; The upper and lower limits of the acceleration threshold are written into the threshold register of the MEMS chip, and the main control unit is woken up based on the timed acquisition data frame. The acceleration waveform sampling data in the FIFO buffer of the MEMS triaxial digital accelerometer is read to calculate the trigger acquisition data frame. The timed data frames are stored in a local circular queue buffer. When the cumulative number of frames reaches the preset batch number, they are uploaded to the cloud platform in batches via the 4G module. The triggered data frames are uploaded to the cloud platform in real time via the 4G module and a warning message is broadcast to the local monitoring network simultaneously via the LoRa module.

2. The parameter monitoring method according to claim 1, characterized in that, Before synchronously acquiring data from the MEMS triaxial digital accelerometer and wire displacement meter in the detector based on the first acquisition granularity time interval, and obtaining the timed acquisition data frame, the parameter monitoring method further includes: The first acquisition granularity time interval, acceleration threshold upper and lower limits, crack displacement threshold upper and lower limits, tilt angle threshold upper and lower limits, cloud platform server address and port number, and LoRa networking parameters are persistently stored through the RS485 configuration interface. The system reads the reference zero-point state value of the MEMS triaxial digital accelerometer, the initial wire length value of the wire displacement meter, and the triaxial initial angle value of the tilt sensor. It then activates the GPS positioning module to obtain the latitude and longitude coordinates of the device installation location and uploads them to the cloud platform via the 4G module to complete the binding and association.

3. The parameter monitoring method according to claim 2, characterized in that, Based on the first acquisition granularity time interval, the MEMS triaxial digital accelerometer and wire displacement meter in the detector are driven to perform synchronous acquisition, obtaining timed acquisition data frames, including: When the timer count reaches the first acquisition granularity time interval, the raw digital acceleration value of the MEMS triaxial digital accelerometer is read, and the raw digital acceleration value is transmitted to the main control unit via the I2C bus and converted into a triaxial acceleration value according to the sensor range and accuracy calibration parameters. Calculate the triaxial tilt angle data based on the triaxial acceleration values; The sampled voltage value of the wire displacement gauge is read, the sampled voltage value is converted into crack displacement, and the triaxial tilt data, the triaxial acceleration value and the crack displacement are encapsulated to obtain a timed acquisition data frame.

4. The parameter monitoring method according to claim 3, characterized in that, The upper and lower limits of the acceleration threshold are written into the threshold register of the MEMS chip, and the main control unit is woken up based on the timed acquisition data frame. The acceleration waveform sampling data in the FIFO buffer of the MEMS triaxial digital accelerometer is read to calculate the trigger acquisition data frame, including: Before the main control unit enters a low-power sleep state, the upper and lower limits of the acceleration threshold are written into the threshold register of the MEMS chip, and the MEMS triaxial digital accelerometer is configured to continuously sample real-time acceleration values ​​based on the timed data acquisition frame. An interrupt signal is generated by driving the interrupt pin level to jump based on the real-time acceleration value and the upper and lower limits of the acceleration threshold using a built-in digital comparator in the MEMS chip. After the interrupt controller of the main control unit detects the rising edge of the interrupt signal, it triggers the interrupt service routine, reads the interrupt status register to obtain the trigger axis and over-threshold direction information, and sets the system status flag to obtain the threshold trigger event. Based on the threshold trigger event, the acceleration waveform sampling data in the FIFO buffer of the MEMS triaxial digital accelerometer is read to calculate the trigger acquisition data frame.

5. The parameter monitoring method according to claim 4, characterized in that, Based on the threshold trigger event, the acceleration waveform sampling data in the FIFO buffer of the MEMS triaxial digital accelerometer is read to calculate the trigger acquisition data frame, including: Based on the threshold trigger event, acceleration waveform sampling data is read from the FIFO buffer of the MEMS triaxial digital accelerometer, and the acceleration waveform sampling data is encapsulated with the trigger timestamp, trigger axis identifier and trigger threshold direction identifier to obtain trigger event metadata; The MEMS triaxial digital accelerometer and wire displacement meter are simultaneously activated to collect triaxial tilt data, triaxial acceleration data and crack displacement data a set number of times, and then the moving average filtering is performed to obtain the filtered triaxial tilt data, filtered triaxial acceleration data and filtered crack displacement data. The changes in tilt angle, peak acceleration, and crack displacement are calculated based on the filtered triaxial tilt angle data, the filtered triaxial acceleration data, and the filtered crack displacement data. The trigger event metadata, the filtered triaxial tilt data, the filtered triaxial acceleration data, the filtered crack displacement data, the tilt change, the peak acceleration change, and the crack displacement change are associated, encapsulated, and a trigger type identifier is added to obtain a trigger acquisition data frame.

6. The parameter monitoring method according to claim 1, characterized in that, The timed data frames are stored in a local circular queue buffer. When the accumulated number of frames reaches a preset batch size, they are uploaded to the cloud platform in batches via a 4G module. The triggered data frames are also uploaded to the cloud platform in real time via a 4G module and simultaneously broadcast to the local monitoring network via a LoRa module, including: The timed data frames are stored in a local circular queue buffer. When the cumulative number of frames reaches the preset batch number, multiple timed data frames are encapsulated into a batch transmission packet. The 4G module is activated to establish a TCP connection with the cloud platform server and upload the batch transmission packets to the cloud platform. For the triggered data collection frame, the 4G module is activated to establish a TCP connection and add a trigger type identifier before uploading it to the cloud platform in real time. The LoRa wireless module is activated in parallel, encapsulating the device ID, trigger timestamp, trigger axis identifier, and acceleration peak value into an early warning message and broadcasting it to other devices within the local monitoring network.

7. The parameter monitoring method according to claim 1, characterized in that, After the triggered data frame is uploaded to the cloud platform in real time via the 4G module and a warning message is simultaneously broadcast to the local monitoring network via the LoRa module, the parameter monitoring method further includes: The cloud platform parses the triggered acquisition data frame and calculates the time derivative of the tilt angle change, peak acceleration change, and crack displacement change in the triggered acquisition data frame to obtain the tilt angle change rate, acceleration change rate, and crack displacement change rate. The rate of change of the tilt angle, the rate of change of the acceleration, and the rate of change of the crack displacement are compared with the corresponding rate of change thresholds. When any rate of change exceeds the threshold, it is marked as a level three warning. When the warning level is Level III, the first data collection granularity time interval parameter is modified to one-quarter of its original value to obtain the second data collection granularity time interval, and the second data collection granularity time interval is sent to the monitoring instrument.

8. A parameter monitoring device, characterized in that, For performing the parameter monitoring method as described in any one of claims 1-7, the parameter monitoring device comprises: The synchronous acquisition module is used to drive the MEMS triaxial digital accelerometer and wire displacement meter in the detector to perform synchronous acquisition based on the first acquisition granularity time interval, so as to obtain timed acquisition data frames; The calculation module is used to write the upper and lower limits of the acceleration threshold into the threshold register of the MEMS chip, wake up the main control unit based on the timed acquisition data frame, and read the acceleration waveform sampling data in the FIFO buffer of the MEMS triaxial digital accelerometer to calculate the trigger acquisition data frame. The upload module is used to store the timed data frames into a local circular queue buffer. When the accumulated number of frames reaches the preset batch number, it uploads them to the cloud platform in batches through the 4G module. The triggered data frames are uploaded to the cloud platform in real time through the 4G module and a warning message is broadcast to the local monitoring network simultaneously through the LoRa module.

9. A parameter monitoring device, characterized in that, The parameter monitoring device includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor invokes the instructions in the memory to cause the parameter monitoring device to perform the parameter monitoring method as described in any one of claims 1-7.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instruction is executed by the processor, it implements the parameter monitoring method as described in any one of claims 1-7.

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