Monitoring system based on multi-channel strain

Abstract

The invention provides a monitoring system based on multi-channel strain, and the system comprises a strain collection module which comprises a plurality of independent strain sensor bridge circuits and is used for outputting differential analog signals of corresponding measurement points; the signal processing module is used for carrying out signal conditioning and analog-to-digital conversion on the differential analog signal gated by the multiplexer and outputting a digital signal; the main control module is used for controlling the multiplexer to carry out channel switching, receiving the digital signal, converting the digital signal into strain data and carrying out abnormity monitoring based on the strain data; and the communication module is used for transmitting the strain data and the abnormity monitoring result to a cloud platform and transmitting a remote control instruction generated by the cloud platform to the main control module. And the automation level, the intelligence level and the remote collaborative management capability of strain monitoring are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of monitoring and data processing technology, and in particular to a monitoring system based on multi-channel strain. Background Technology

[0002] Currently, in the field of structural health monitoring for bridges, buildings, and large machinery, multi-point, long-term, and continuous strain monitoring is required. Traditional wired strain gauge acquisition systems are complex, costly, and inflexible, making them difficult to deploy in remote or mobile environments. Existing wireless strain gauge acquisition modules often suffer from problems such as a limited number of channels, measurement accuracy being susceptible to environmental interference (e.g., temperature drift, common-mode noise), high power consumption, and a lack of intelligent data management and remote maintenance capabilities (e.g., OTA upgrades). In the existing technology, the most similar implementation is a multi-channel data acquisition instrument based on wireless technologies such as Zigbee or LoRa. This type of solution typically uses a standard strain gauge Wheatstone bridge circuit, switches channels through a multiplexer, amplifies the signal through an instrumentation amplifier, converts it into a digital signal by an ADC (analog-to-digital converter), and finally transmits it to a nearby gateway or concentrator through a wireless module. Although this type of solution achieves wireless connectivity, it still has limitations: 1) Limited transmission distance (such as Zigbee) or low bandwidth (such as LoRa), making it unsuitable for remote monitoring that requires high-frequency data upload; 2) Limited common-mode noise suppression capability of the front-end circuit, resulting in unstable measurement accuracy in complex industrial environments; 3) Fixed gain, unable to adapt to strain measurements of different ranges; 4) Fixed functionality, making it difficult to update its data processing logic or fix software defects after field deployment. Therefore, in order to overcome the above-mentioned defects, the present invention provides a multi-channel strain monitoring system. Summary of the Invention

[0003] This invention provides a multi-channel strain monitoring system that achieves efficient synchronous monitoring of multiple measuring points through a multi-channel data acquisition architecture, improving overall monitoring efficiency. It employs a combination of graded amplification and automatic gain control to ensure accurate capture and conversion of a wide range of analog signals, from weak to strong, into high-quality digital signals. The core processing unit not only performs data conversion and encapsulation but also conducts real-time intelligent analysis to identify abnormal states, enhancing the system's early warning capabilities. Furthermore, the built-in communication mechanism enables the reporting of monitoring data and early warning information and can receive and execute remote commands, thus forming a closed-loop intelligent monitoring system from on-site perception and intelligent processing to cloud interaction. This significantly improves the automation, intelligence, and remote collaborative management capabilities of strain monitoring.

[0004] This invention provides a multi-channel strain monitoring system, comprising: The strain acquisition module includes multiple independent strain sensor bridges, and each strain sensor bridge is composed of strain gauges, used to output differential analog signals for the corresponding measurement points. The signal processing module has its input terminal connected to the output terminals of each strain sensor bridge via a multiplexer. It is used to perform signal conditioning and analog-to-digital conversion on the differential analog signals selected by the multiplexer and output digital signals. The main control module is connected to the output of the signal processing module and the control terminal of the multiplexer. It is used to control the multiplexer to switch channels, receive the digital signal and convert it into strain data, and perform anomaly monitoring based on the strain data. The communication module is bidirectionally connected to the main control module and is used to transmit strain data and anomaly monitoring results to the cloud platform, and to transmit remote control commands generated by the cloud platform to the main control module.

[0005] Preferably, in a multi-channel strain monitoring system, the communication module includes a remote control command firmware upgrade package, and the main control module further includes an OTA upgrade unit for: Receive the firmware upgrade package; Perform integrity verification on the firmware upgrade package; After verification, the new firmware will be written to the designated backup storage area; When the system restarts, the new firmware is loaded and run from the backup storage area via the Bootloader program.

[0006] Preferably, a multi-channel strain monitoring system includes a signal processing module comprising: The signal amplification unit is used to input the differential analog signal selected by the multiplexer as the initial differential analog signal to the instrumentation amplifier for fixed-gain first-stage amplification to obtain the target differential analog signal. The digital-to-analog conversion unit is used to convert the target differential analog signal into an analog-to-digital signal using an analog-to-digital converter to obtain the initial digital signal. The signal amplification and determination unit is used to acquire the signal strength of the initial digital signal and analyze the signal strength based on the main control MCU to determine whether to perform signal conditioning on the initial digital signal. The signal conditioning unit is used to convert the initial digital signal into a digital-to-analog signal using a digital-to-analog converter when signal conditioning is required, thereby obtaining the target analog signal. At the same time, it generates control signals based on the main control MCU. The control signal is input to the programmable gain amplifier to dynamically adjust the target gain factor that needs to be conditioned, and the target analog signal is conditioned based on the target gain factor. The conditioned target analog signal is then converted from analog to digital by an analog-to-digital converter to obtain the target digital signal.

[0007] Preferably, a signal amplification and determination unit in a multi-channel strain monitoring system includes: Comparison subunit, used for: The main control MCU reads the signal strength of the initial digital signal and compares the signal strength of the initial digital signal with the first preset signal strength threshold and the second preset signal strength threshold, respectively; Wherein, if the first preset signal strength threshold is greater than the second preset signal strength threshold, it is determined whether to perform signal conditioning on the initial digital signal; The decision result output sub-unit is used for: When the signal strength of the initial digital signal is greater than the first preset signal strength threshold, it is determined that the output digital signal needs to be reduced. When the signal strength of the initial digital signal is less than the second preset signal threshold strength, it is determined that the output digital signal needs to be amplified in a second stage. Otherwise, it is determined that no signal conditioning will be performed on the initial digital signal.

[0008] Preferably, in a multi-channel strain monitoring system, the main control module is further configured to encapsulate strain data, along with corresponding channel identifiers, timestamps, and gain status, into packets according to a compact protocol to obtain packet data; the communication module transmits the packet data to the cloud platform based on MQTT or TCP / IP protocols.

[0009] Preferably, a multi-channel strain monitoring system includes a main control module comprising: The channel switching unit is used to output a channel selection signal to the control terminal of the multiplexer, and connect the target strain sensor bridge corresponding to the selected channel to the multiplexer based on the channel selection signal, and acquire the differential analog signal of the corresponding channel based on the target strain sensor bridge. The signal receiving unit is used to receive the digital signal of the differential analog signal corresponding to the target channel after the channel is connected. The data conversion unit is used to convert digital signals into strain data corresponding to the current channel; The anomaly monitoring unit is used to analyze strain data and determine whether there are any anomalies at the measurement points based on the analysis results.

[0010] Preferably, an anomaly monitoring unit in a multi-channel strain monitoring system includes: The data aggregation sub-unit is used for: The analysis results of strain data from different channels are summarized, and historical monitoring results are obtained based on the summarized results; The parameter determination subunit is used to determine the trend of strain data changes and the frequency of abnormal events based on historical monitoring results; The strategy generation subunit is used to generate adaptive monitoring strategies for different channels when the trend of change exceeds a preset stability threshold or the frequency of occurrence exceeds a preset frequency threshold. The adaptive monitoring strategy includes at least one of the following: adjusting the channel scanning order of the multiplexer, adjusting the sampling frequency of the corresponding channel, and adjusting the gain control parameter of the corresponding channel. The update subunit is used to send the adaptive monitoring strategy to the channel switching unit and the signal processing module, and update the data acquisition and processing behavior based on the sending result; The verification subunit is used to acquire new strain data based on the updated data acquisition and processing behavior, and input the new strain data into the anomaly monitoring unit for verification. Based on the verification results, the parameters in the adaptive monitoring strategy are iteratively optimized, and the optimized strategy parameters are stored in the configuration file of the main control module.

[0011] Preferably, a multi-channel strain monitoring system includes a data conversion unit comprising: The first intermediate value determines the sub-unit, used for: The dynamic reference parameter set uniquely bound to the current gating channel is invoked based on the identifier of the current gating channel, wherein the dynamic reference parameter set includes: adaptive zero-point reference value; The digital signal is initially compensated based on the adaptive zero-point reference value to obtain the first intermediate value; Retrieve historical strain data and perform trend analysis on the historical strain data to obtain the gain correction coefficient of the current gating channel; The second intermediate value determination subunit is used to combine the first intermediate value with the gain correction coefficient, and calculate the second intermediate value based on the combination result and the pre-stored unit strain range coefficient. Comparison subunit, used for: Compare the second intermediate value with the preset physical range threshold; When the second intermediate value is less than or equal to the preset physical range threshold, it is determined to be valid strain data and output. When the second intermediate value is greater than the preset physical range threshold, the adaptive zero-point reference value is recalibrated until the second intermediate value is less than or equal to the preset physical range threshold.

[0012] Preferably, a multi-channel strain monitoring system includes a main control module comprising: An environmental data acquisition unit is used to acquire current environmental data, including at least temperature and humidity, from a preset environmental sensor connected to the main control module. An abnormal fluctuation pattern acquisition unit is used to acquire abnormal fluctuation patterns in historical strain data from at least two channels. Analysis unit, used for: The current environmental data and abnormal fluctuation patterns are input into a pre-trained correlation model for analysis, and the correlation between the abnormal fluctuation patterns and the current environmental data is determined based on the analysis results. Determine whether the abnormal fluctuation pattern is caused by environmental disturbance; When it is determined that the abnormal fluctuation pattern is caused by the current environmental data, a collaborative calibration instruction is generated based on the preset environmental adaptive collaborative calibration mechanism. The collaborative calibration instruction includes at least an environmental compensation coefficient for the programmable gain amplifier and a correction offset for the dynamic reference parameter set. The gain control parameters of the programmable gain amplifier are synchronously adjusted with the adaptive zero-point reference value in the dynamic reference parameter set based on the collaborative calibration command. Based on the adjustment results, verification strain data of all channels are collected according to a preset period, and the parameters of the pre-trained correlation model are iteratively optimized based on the verification strain data.

[0013] Preferably, a multi-channel strain monitoring system includes a communication module comprising: The data acquisition unit is used to acquire strain data and anomaly monitoring results, and to initiate the data upload process based on the acquired results; The link construction unit is used to obtain the preset communication protocol and network parameters between the main control module and the cloud platform based on the data upload process startup result, and to construct a remote communication link between the main control module and the cloud platform based on the preset communication protocol and network parameters. Data transmission unit, used for: Based on communication requirements, the remote communication link is split into a parallel dual communication link, which includes a first communication link from the main control module to the cloud platform and a second communication link from the cloud platform to the main control module. The strain data and anomaly monitoring results are encapsulated and transmitted to the cloud platform via the first communication link. Meanwhile, the working status of the cloud platform is monitored in real time, and when the cloud platform generates a remote control command data packet, the remote control command data packet is transmitted to the main control module based on the second communication link.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: The multi-channel data acquisition architecture enables efficient synchronous monitoring of multiple measurement points, improving overall monitoring efficiency. The combination of hierarchical amplification and automatic gain control ensures that a wide range of analog signals, from weak to strong, can be accurately captured and converted into high-quality digital signals. The core processing unit not only completes data conversion and encapsulation but also performs real-time intelligent analysis to identify abnormal states, enhancing the system's early warning capabilities. In addition, the built-in communication mechanism enables the reporting of monitoring data and early warning information and can receive and execute remote commands, thus forming a closed-loop intelligent monitoring system from on-site perception and intelligent processing to cloud interaction, significantly improving the automation, intelligence, and remote collaborative management capabilities of strain monitoring.

[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 may be realized and obtained by means of the structures particularly pointed out in this application.

[0016] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram illustrating the connection relationship between different modules in a multi-channel strain monitoring system according to an embodiment of the present invention. Figure 2 This is a structural diagram of a signal processing module in a multi-channel strain monitoring system according to an embodiment of the present invention; Figure 3 This is a structural diagram of the main control module in a multi-channel strain monitoring system according to an embodiment of the present invention. Detailed Implementation

[0018] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0019] Example 1: This example provides a multi-channel strain monitoring system, such as... Figure 1 As shown, it includes: The strain acquisition module includes multiple independent strain sensor bridges, and each strain sensor bridge is composed of strain gauges, used to output differential analog signals for the corresponding measurement points. The signal processing module has its input terminal connected to the output terminals of each strain sensor bridge via a multiplexer. It is used to perform signal conditioning and analog-to-digital conversion on the differential analog signals selected by the multiplexer and output digital signals. The main control module is connected to the output of the signal processing module and the control terminal of the multiplexer. It is used to control the multiplexer to switch channels, receive the digital signal and convert it into strain data, and perform anomaly monitoring based on the strain data. The communication module is bidirectionally connected to the main control module and is used to transmit strain data and anomaly monitoring results to the cloud platform, and to transmit remote control commands generated by the cloud platform to the main control module.

[0020] In this embodiment, the differential analog signal refers to a pair of differential electrical signals output by the strain sensor bridge circuit, whose voltage value changes with the resistance of the strain gauge. It has strong anti-interference ability and is used to accurately characterize the degree of deformation at the measurement point.

[0021] In this embodiment, channel switching refers to the process by which the main control module sends control signals to the multiplexer to connect different strain sensor bridges sequentially or as needed, so as to realize time-division multiplexing of the back-end signal processing circuit.

[0022] In this embodiment, signal conditioning refers to the process by which the signal processing module dynamically adjusts the amplification factor according to the magnitude of the input signal, aiming to ensure that the analog-to-digital converter always operates within its optimal input range in order to optimize conversion accuracy.

[0023] In this embodiment, anomaly monitoring refers to the process by which the main control module compares and analyzes the strain data calculated in real time with the preset safety threshold or historical data model to determine whether the structure has damage, overload or abnormal deformation.

[0024] In this embodiment, the multi-channel strain monitoring system can be a 4G multi-channel strain monitoring system.

[0025] The beneficial effects of the above technical solution are as follows: the multi-channel data acquisition architecture enables efficient synchronous monitoring of multiple measuring points, improving overall monitoring efficiency; the combination of hierarchical amplification and automatic gain control ensures that a wide range of analog signals, from weak to strong, can be accurately captured and converted into high-quality digital signals; the core processing unit can not only complete data conversion and encapsulation, but also perform real-time intelligent analysis to identify abnormal states, enhancing the system's early warning capability; in addition, the built-in communication mechanism enables the reporting of monitoring data and early warning information, and can receive and execute remote commands, thus forming a closed-loop intelligent monitoring system from on-site perception and intelligent processing to cloud interaction, significantly improving the automation, intelligence level and remote collaborative management capability of strain monitoring.

[0026] Example 2: Based on Example 1, this example provides a multi-channel strain monitoring system. In the communication module, the remote control command firmware upgrade package, and the main control module further include an OTA upgrade unit, used for: Receive the firmware upgrade package; Perform integrity verification on the firmware upgrade package; After verification, the new firmware will be written to the designated backup storage area; When the system restarts, the new firmware is loaded and run from the backup storage area via the Bootloader program.

[0027] In this embodiment, the system supports remote maintenance via a cloud platform: the communication module can receive remote control commands from the cloud platform. Specifically, when the remote control command is a firmware upgrade package, the OTA management unit built into the main control module will initiate the upgrade process: first, the received firmware upgrade package is verified (e.g., MD5 checksum) to ensure its integrity and correctness; after verification, the new firmware is written to the backup storage area in the system flash memory; when the system restarts next time, the bootloader program will detect the valid backup firmware and guide the system to start from that area, thereby completing the remote firmware update and realizing remote upgrade of system functions and online repair of software defects.

[0028] Example 3: Based on Example 1, this example provides a multi-channel strain monitoring system, such as... Figure 2 As shown, the signal processing module includes: The signal amplification unit is used to input the differential analog signal selected by the multiplexer as the initial differential analog signal to the instrumentation amplifier for fixed-gain first-stage amplification to obtain the target differential analog signal. The digital-to-analog conversion unit is used to convert the target differential analog signal into an analog-to-digital signal using an analog-to-digital converter to obtain the initial digital signal. The signal amplification and determination unit is used to acquire the signal strength of the initial digital signal and analyze the signal strength based on the main control MCU to determine whether to perform signal conditioning on the initial digital signal. The signal conditioning unit is used to convert the initial digital signal into a digital-to-analog signal using a digital-to-analog converter when signal conditioning is required, thereby obtaining the target analog signal. At the same time, it generates control signals based on the main control MCU. The control signal is input to the programmable gain amplifier to dynamically adjust the target gain factor that needs to be conditioned, and the target analog signal is conditioned based on the target gain factor. The conditioned target analog signal is then converted from analog to digital by an analog-to-digital converter to obtain the target digital signal.

[0029] In this embodiment, in the signal conditioning unit, the signal strength of the target digital signal is within the range of being equal to or greater than a second preset signal strength threshold and less than or equal to a first preset signal strength threshold.

[0030] In this embodiment, the initial differential analog signal refers to the raw differential voltage signal directly output from the multiplexer and before any amplification.

[0031] In this embodiment, the target differential analog signal refers to the differential voltage signal that has been initially amplified after the initial differential analog signal has been amplified by the first stage of fixed gain amplification.

[0032] In this embodiment, the initial digital signal refers to the digital quantization result obtained after the first analog-to-digital conversion of the target differential analog signal, which has not yet undergone programmable gain processing.

[0033] In this embodiment, the target digital signal refers to the digital signal that meets the requirements of subsequent processing, which is obtained by analyzing and judging the initial digital signal and then performing programmable gain adjustment as needed.

[0034] The beneficial effects of the above technical solution are as follows: through the coordinated work of two-stage amplification and the intermediate intelligent judgment mechanism, the problem of accurate acquisition of strain signals with a wide dynamic range is effectively solved. The first-stage fixed amplification provides a stable signal foundation for subsequent processing, while the dynamic second-stage amplification based on real-time signal strength analysis ensures that the input of the analog-to-digital converter is always at the optimal range, thereby significantly improving the system's adaptability to weak and strong signals and the overall measurement accuracy.

[0035] Example 4: Based on Example 3, this example provides a multi-channel strain monitoring system, including a signal amplification and determination unit, comprising: Comparison subunit, used for: The main control MCU reads the signal strength of the initial digital signal and compares the signal strength of the initial digital signal with the first preset signal strength threshold and the second preset signal strength threshold, respectively; Wherein, if the first preset signal strength threshold is greater than the second preset signal strength threshold, it is determined whether to perform signal conditioning on the initial digital signal; The decision result output sub-unit is used for: When the signal strength of the initial digital signal is greater than the first preset signal strength threshold, it is determined that the output digital signal needs to be reduced. When the signal strength of the initial digital signal is less than the second preset signal threshold strength, it is determined that the output digital signal needs to be amplified in a second stage. Otherwise, it is determined that no signal conditioning will be performed on the initial digital signal.

[0036] In this embodiment, the signal strength of the initial digital signal refers to the digital value or amplitude corresponding to the initial digital signal after analog-to-digital conversion, which is used to characterize the strength of the original analog signal.

[0037] In this embodiment, the first preset signal strength threshold refers to an upper limit reference value set to prevent signal overload or distortion. When the signal strength exceeds this value, it indicates that the signal is too strong.

[0038] In this embodiment, the second preset signal strength threshold refers to a lower limit reference value set to ensure that the signal has a sufficient signal-to-noise ratio. When the signal strength is lower than this value, it indicates that the signal is too weak.

[0039] The beneficial effects of the above technical solution are as follows: by setting a dual threshold comparison mechanism, intelligent judgment of signal strength is realized. Secondary amplification adjustment is only initiated when the signal is too strong and may saturate or too weak and cause insufficient signal-to-noise ratio. This avoids unnecessary gain adjustment and potential noise introduction, which not only optimizes the efficiency of the amplification process and reduces system power consumption, but more importantly, ensures that the subsequent processing stage can always obtain a digital signal with appropriate amplitude and stable quality, thereby improving the adaptability and reliability of the system under different operating conditions.

[0040] Example 5: Based on Example 1, this example provides a multi-channel strain monitoring system. The main control module is also used to encapsulate the strain data and the corresponding channel identifier, timestamp, and gain status according to a compact protocol to obtain packet data. The communication module transmits the packet data to the cloud platform based on MQTT or TCP / IP protocol.

[0041] Example 6: Based on Example 1, this example provides a multi-channel strain monitoring system, such as... Figure 3 As shown, the main control module includes: The channel switching unit is used to output a channel selection signal to the control terminal of the multiplexer, and connect the target strain sensor bridge corresponding to the selected channel to the multiplexer based on the channel selection signal, and acquire the differential analog signal of the corresponding channel based on the target strain sensor bridge. The signal receiving unit is used to receive the digital signal of the differential analog signal corresponding to the target channel after the channel is connected. The data conversion unit is used to convert digital signals into strain data corresponding to the current channel; The anomaly monitoring unit is used to analyze strain data and determine whether there are any anomalies at the measurement points based on the analysis results.

[0042] In this embodiment, road switching refers to the process by which the main control module controls the multiplexer by outputting specific logic signals, causing its internal electronic switches to connect a specified physical line.

[0043] In this embodiment, the target strain sensor bridge circuit refers to the bridge circuit composed of strain gauges that is uniquely turned on among the many input channels of the multiplexer according to the channel selection signal.

[0044] In this embodiment, the current channel refers to the only physical path that is activated at a certain moment in the multiplexer and is currently undergoing data acquisition and processing.

[0045] In this embodiment, anomaly monitoring refers to the process by which the system performs real-time or near-real-time analysis on the calculated strain values ​​and compares them with preset thresholds or models to determine whether the structural health status deviates from the normal range. The specific process is as follows: Receives the digital signal from the current channel of the signal processing module and converts the digital signal into strain data with timing identifiers; Retrieve the historical strain data sequence bound to the current channel, and concatenate the current strain data with the historical strain data sequence to form an updated data sequence; Based on the updated data sequence, the statistical characteristic values ​​of the data sequence within a preset time window are calculated, where the statistical characteristic values ​​include the mean, standard deviation, and rate of change. The calculated rate of change is compared with the rate of change threshold pre-set for the current channel. When the rate of change exceeds the rate of change threshold, an abnormal event is triggered, and continuous tracking of statistical feature values ​​is initiated. During continuous tracking, if the standard deviation of the strain data exceeds the preset fluctuation threshold for a specified number of consecutive times, it is determined that there is a structural anomaly at the current measurement point.

[0046] The beneficial effects of the above technical solution are: through orderly channel management and a complete data processing flow, fully automated monitoring from signal selection and accurate reception to intelligent analysis is realized. This not only ensures the reliability and timeliness of multi-point data acquisition, but also converts raw data into monitoring results with physical meaning in real time and automatically identifies anomalies, thereby significantly improving the overall intelligence level and real-time response capability of the system.

[0047] Example 7: Based on Example 6, this example provides a multi-channel strain monitoring system, including an anomaly monitoring unit: The data aggregation subunit is used to aggregate the analysis results of strain data from different channels and obtain historical monitoring results based on the aggregated results; The parameter determination subunit is used to determine the trend of strain data changes and the frequency of abnormal events based on historical monitoring results; The strategy generation subunit is used to generate adaptive monitoring strategies for different channels when the trend of change exceeds a preset stability threshold or the frequency of occurrence exceeds a preset frequency threshold. The adaptive monitoring strategy includes at least one of the following: adjusting the channel scanning order of the multiplexer, adjusting the sampling frequency of the corresponding channel, and adjusting the gain control parameter of the corresponding channel. The update subunit is used to send the adaptive monitoring strategy to the channel switching unit and the signal processing module, and update the data acquisition and processing behavior based on the sending result; Verification subunit, used for: New strain data is obtained based on the updated data acquisition and processing behavior, and the new strain data is input into the anomaly monitoring unit for verification. Based on the verification results, the parameters in the adaptive monitoring strategy are iteratively optimized, and the optimized strategy parameters are stored in the configuration file of the main control module.

[0048] In this embodiment, historical monitoring results refer to the comprehensive results obtained by summarizing and analyzing the strain data of each channel over a period of time, including data statistical characteristics and identified abnormal records.

[0049] In this embodiment, the trend of change refers to the overall direction and regularity of change of strain data over time, such as continuous growth, decay or periodic fluctuation.

[0050] In this embodiment, the frequency of occurrence of abnormal events refers to the number of times or the occurrence rate per unit time of strain data events that are judged to be abnormal within a specific statistical period.

[0051] In this embodiment, the preset stability threshold refers to a pre-set quantitative limit value used to determine whether the fluctuation of the data change trend is within an acceptable stable range; if it exceeds this range, it is considered that the trend is unstable.

[0052] In this embodiment, the preset frequency threshold refers to a pre-set critical value for the number of abnormal events, used to determine whether the abnormality is too frequent, and if it is exceeded, the system needs to be adjusted.

[0053] In this embodiment, the adaptive monitoring strategy refers to a set of instructions dynamically generated by the system based on real-time analysis results to adjust subsequent monitoring behavior. Specifically, it may include modifying the channel access order, data acquisition rate, and signal amplification factor.

[0054] In this embodiment, the channel scanning order refers to the order in which the main control module controls the multiplexer to sequentially connect each strain sensor bridge for data acquisition.

[0055] In this embodiment, the sampling frequency refers to the number of times data is collected per unit time when data is collected from a selected channel.

[0056] In this embodiment, the gain control parameter refers to the amplification factor set by the programmable gain amplifier in the signal processing module, which is used to adjust the amplitude of the input signal.

[0057] In this embodiment, verification refers to the process of evaluating and confirming the actual effect of the adaptive monitoring strategy using strain data collected according to the new strategy.

[0058] In this embodiment, iterative optimization refers to the process of repeatedly and iteratively adjusting and improving the parameters in the adaptive monitoring strategy based on the verification results, in order to gradually approach the optimal configuration.

[0059] In this embodiment, the strategy parameters refer to the specific configurable values ​​that constitute the adaptive monitoring strategy, such as the adjusted channel scanning sequence, the specific sampling frequency value, or the gain setting value.

[0060] The beneficial effects of the above technical solution are as follows: by intelligently analyzing historical monitoring data, dynamically identifying strain trends and anomaly frequencies, and automatically generating and implementing adaptive monitoring strategies when data fluctuations or anomalies exceed preset thresholds, the system flexibly adjusts key parameters such as channel scanning, sampling rate, and gain. Through new data verification and continuous iterative optimization of strategy parameters, the system can autonomously focus on key monitoring points or abnormal states, thereby significantly improving the targeting, real-time performance, and resource utilization efficiency of monitoring, and realizing an upgrade from static monitoring to dynamic intelligent response.

[0061] Example 8: Based on Example 6, this example provides a multi-channel strain monitoring system, including a data conversion unit: The first intermediate value determines the sub-unit, used for: The dynamic reference parameter set uniquely bound to the current gating channel is invoked based on the identifier of the current gating channel, wherein the dynamic reference parameter set includes: adaptive zero-point reference value; The digital signal is initially compensated based on the adaptive zero-point reference value to obtain the first intermediate value; Retrieve historical strain data and perform trend analysis on the historical strain data to obtain the gain correction coefficient of the current gating channel; The second intermediate value determines the sub-unit, used for The first intermediate value is combined with the gain correction coefficient, and the second intermediate value is calculated based on the combination result and the pre-stored unit strain range coefficient. Comparison subunit, used for: Compare the second intermediate value with the preset physical range threshold; When the second intermediate value is less than or equal to the preset physical range threshold, it is determined to be valid strain data and output. When the second intermediate value is greater than the preset physical range threshold, the adaptive zero-point reference value is recalibrated until the second intermediate value is less than or equal to the preset physical range threshold.

[0062] In this embodiment, the dynamic reference parameter set refers to a set of parameters uniquely associated with each measurement channel and adjustable over time and in response to the environment, used to provide personalized compensation and correction for the original signal of that channel.

[0063] In this embodiment, the adaptive zero-point reference value refers to a key parameter in the dynamic reference parameter set. It represents the output reference of the channel under strain-free conditions and is used to compensate for the zero-point drift of the sensor.

[0064] In this embodiment, the first intermediate value refers to the intermediate calculation result obtained after subtracting the adaptive zero-point reference value from the original digital signal as a preliminary compensation.

[0065] In this embodiment, the gain correction coefficient refers to a scaling factor obtained by analyzing the historical strain data trend of a specific channel, which is used to fine-tune the sensitivity of the channel to compensate for its long-term changes or nonlinearity.

[0066] In this embodiment, the unit strain coefficient refers to a pre-stored fixed scaling factor that converts the voltage signal into standard physical strain units (such as microstrain).

[0067] In this embodiment, the second intermediate value refers to the preliminary strain result with physical units obtained by combining the first intermediate value with the gain correction coefficient and converting it using the unit strain range coefficient.

[0068] In this embodiment, the preset physical range threshold refers to the maximum allowable value that the strain data should theoretically not exceed, set according to the physical characteristics and safety requirements of the monitored structure.

[0069] The beneficial effects of the above technical solution are as follows: by combining dynamic benchmark compensation with historical trend correction, high-precision conversion of strain data is achieved. This not only effectively eliminates the influence of channel differences and zero drift, but also adaptively optimizes the conversion parameters. Through the final physical range verification and automatic calibration triggering mechanism, the validity and reliability of the output data are ensured, and the accuracy and stability of the system's long-term monitoring are improved.

[0070] Example 9: Based on Example 1, this example provides a multi-channel strain monitoring system, including a main control module comprising: An environmental data acquisition unit is used to acquire current environmental data, including at least temperature and humidity, from a preset environmental sensor connected to the main control module. An abnormal fluctuation pattern acquisition unit is used to acquire abnormal fluctuation patterns in historical strain data from at least two channels. Analysis unit, used for: The current environmental data and abnormal fluctuation patterns are input into a pre-trained correlation model for analysis, and the correlation between the abnormal fluctuation patterns and the current environmental data is determined based on the analysis results. Determine whether the abnormal fluctuation pattern is caused by environmental disturbance; When it is determined that the abnormal fluctuation pattern is caused by the current environmental data, a collaborative calibration instruction is generated based on the preset environmental adaptive collaborative calibration mechanism. The collaborative calibration instruction includes at least an environmental compensation coefficient for the programmable gain amplifier and a correction offset for the dynamic reference parameter set. The gain control parameters of the programmable gain amplifier are synchronously adjusted with the adaptive zero-point reference value in the dynamic reference parameter set based on the collaborative calibration command. Based on the adjustment results, verification strain data of all channels are collected according to a preset period, and the parameters of the pre-trained correlation model are iteratively optimized based on the verification strain data.

[0071] In this embodiment, the current environmental data refers to physical quantity data that reflects the environmental status of the monitoring site and is collected in real time by external sensing devices such as temperature sensors and humidity sensors.

[0072] In this embodiment, the abnormal fluctuation pattern refers to a specific waveform, trend, or statistical feature extracted from historical strain data that deviates from the normal pattern of change.

[0073] In this embodiment, the correlation model refers to a pre-trained mathematical model used to analyze and quantify the potential causal relationship or statistical correlation between changes in environmental data and abnormal fluctuations in strain data.

[0074] In this embodiment, the preset environment adaptive collaborative calibration mechanism refers to a set of procedural rules and methods set within the system, which aims to automatically initiate the process of synchronously adjusting multiple related parameters when it is determined that the abnormality is caused by the environment.

[0075] In this embodiment, the collaborative calibration instruction refers to a control command generated by the mechanism that contains specific adjustment parameters, used to guide the subsequent signal amplification and reference compensation stages to make collaborative changes.

[0076] In this embodiment, the environmental compensation coefficient refers to a proportional factor included in the instruction specifically used to correct the impact of environmental factors on the gain of the signal amplifier.

[0077] In this embodiment, the correction offset refers to a value included in the instruction, which is used to directly adjust the dynamic reference parameter set (especially the adaptive zero-point reference value) by addition or subtraction to counteract environmental drift.

[0078] In this embodiment, the verification strain data refers to a new batch of strain data collected at a set period after the calibration and adjustment are completed, which is used to evaluate the effect of this calibration.

[0079] In this embodiment, the pre-trained association model, when performing business processing, has the following specific structure and execution process: The input features of the correlation model include the quantitative features of abnormal fluctuation patterns extracted from historical strain data from at least two channels, as well as the historical environmental data collected simultaneously. The quantitative features of abnormal fluctuation patterns are statistical quantities obtained through time domain or frequency domain analysis, including fluctuation amplitude, fluctuation duration and fluctuation energy ratio. The historical environmental data includes at least temperature and humidity data. The correlation model adopts a multilayer perceptron neural network structure containing an input layer, at least one hidden layer and an output layer. The number of neurons in the input layer corresponds to the dimension of the input features and is used to receive the quantitative features of abnormal fluctuation patterns and historical environmental data. The output layer outputs a scalar value representing the correlation strength between environmental factors and abnormal fluctuations in strain. The training data for the correlation model comes from the strain data sequences and corresponding environmental data sequences of multiple channels that are synchronously collected and stored during the initial deployment phase or historical monitoring period. The abnormal event is identified from the strain data sequence by the abnormal monitoring unit, and the quantitative features of the abnormal fluctuation pattern corresponding to the abnormal event are extracted. Together with the environmental data before and after the occurrence time, they form a training sample pair with correlation labels. Then, in the model training phase, using all the training sample pairs, based on the aforementioned neural network model, supervised training is performed using the error backpropagation algorithm. The training objective is to minimize the mean square error between the association strength estimate output by the model and the sample label, thereby obtaining the pre-trained association model. Subsequently, during system operation, the data conversion unit quantifies the characteristics of the real-time collected current environmental data and the real-time detected abnormal fluctuation patterns, and inputs them into the pre-trained correlation model for analysis. If the correlation strength of the model output exceeds the preset threshold, the current abnormal fluctuation pattern is determined to be caused by environmental disturbance, and the subsequent collaborative calibration process is triggered. Finally, the iterative optimization of the correlation model is implemented through the following algorithm: After each environmental adaptive collaborative calibration, the system collects verification strain data according to a preset period. Using the stability of the actual environmental data and the calibrated strain data within the calibration period, a verification error is calculated. This verification error is used as the loss. The stochastic gradient descent method is used to iteratively update the hidden layer weight parameters of the correlation model. The updated model parameters are then saved for subsequent analysis.

[0080] The beneficial effects of the above technical solution are as follows: by introducing an environmental perception and data correlation analysis mechanism, it is possible to effectively distinguish between signal fluctuations caused by environmental disturbances such as temperature and humidity and actual structural anomalies. Based on the analysis results, targeted collaborative calibration is automatically triggered, and signal amplification and reference compensation parameters are optimized simultaneously, thereby significantly reducing measurement errors and false alarms caused by environmental interference. By continuously collecting verification data and iteratively optimizing the analysis model, the system has the ability to continuously improve its environmental adaptability, thus enhancing the accuracy and reliability of long-term monitoring.

[0081] Example 10: Based on Example 1, this example provides a multi-channel strain monitoring system, including a communication module: The data acquisition unit is used to acquire strain data and anomaly monitoring results, and to initiate the data upload process based on the acquired results; The link construction unit is used to obtain the preset communication protocol and network parameters between the main control module and the cloud platform based on the data upload process startup result, and to construct a remote communication link between the main control module and the cloud platform based on the preset communication protocol and network parameters. Data transmission unit, used for: Based on communication requirements, the remote communication link is split into a parallel dual communication link, which includes a first communication link from the main control module to the cloud platform and a second communication link from the cloud platform to the main control module. The strain data and anomaly monitoring results are encapsulated and transmitted to the cloud platform via the first communication link. Meanwhile, the working status of the cloud platform is monitored in real time, and when the cloud platform generates a remote control command data packet, the remote control command data packet is transmitted to the main control module based on the second communication link.

[0082] In this embodiment, the remote communication link refers to a stable data connection channel established between the main control module of the monitoring system and the remote cloud platform based on specific network protocols (such as MQTT / TCP / IP) and parameters.

[0083] In this embodiment, parallel dual communication links refer to dividing the aforementioned single communication link logically or physically into two independent and simultaneously operating sub-channels, which are dedicated to uplink data transmission and downlink command transmission, respectively.

[0084] In this embodiment, encapsulation refers to the process of combining the strain data to be transmitted, the anomaly monitoring results, and related additional information (such as timestamps and channel numbers) into a complete data packet according to a predetermined data format protocol.

[0085] In this embodiment, real-time monitoring of the working status refers to the communication module continuously detecting and determining whether the remote cloud platform service is online, accessible, and responding normally.

[0086] In this embodiment, the remote control command data packet refers to a formatted data unit generated and issued by the cloud platform that contains commands for parameter configuration or operation control of the on-site monitoring equipment.

[0087] The beneficial effects of the above technical solution are as follows: by constructing a parallel and independent bidirectional communication link, the synchronous and efficient processing of monitoring data upload and remote command issuance is realized. This not only significantly improves data throughput efficiency and reduces mutual interference and delay in bidirectional communication, but also ensures that control commands can be received and responded to in real time and reliably, thereby effectively enhancing the remote interaction capability and collaborative management efficiency of the entire system.

[0088] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A multi-channel strain monitoring system, characterized in that, include: The strain acquisition module includes multiple independent strain sensor bridges, and each strain sensor bridge is composed of strain gauges, used to output differential analog signals for the corresponding measurement points. The signal processing module has its input terminal connected to the output terminals of each strain sensor bridge via a multiplexer. It is used to perform signal conditioning and analog-to-digital conversion on the differential analog signals selected by the multiplexer and output digital signals. The main control module is connected to the output of the signal processing module and the control terminal of the multiplexer. It is used to control the multiplexer to switch channels, receive the digital signal and convert it into strain data, and perform anomaly monitoring based on the strain data. The communication module is bidirectionally connected to the main control module and is used to transmit strain data and anomaly monitoring results to the cloud platform, and to transmit remote control commands generated by the cloud platform to the main control module.

2. The multi-channel strain monitoring system according to claim 1, characterized in that, In the communication module, the remote control command firmware upgrade package, and the main control module further includes an OTA upgrade unit, used for: Receive the firmware upgrade package; Perform integrity verification on the firmware upgrade package; After verification, the new firmware will be written to the designated backup storage area; When the system restarts, the new firmware is loaded and run from the backup storage area via the Bootloader program.

3. The multi-channel strain monitoring system according to claim 1, characterized in that, The signal processing module includes: The signal amplification unit is used to input the differential analog signal selected by the multiplexer as the initial differential analog signal to the instrumentation amplifier for fixed-gain first-stage amplification to obtain the target differential analog signal. The digital-to-analog conversion unit is used to convert the target differential analog signal into an analog-to-digital signal using an analog-to-digital converter to obtain the initial digital signal. The signal amplification and determination unit is used to acquire the signal strength of the initial digital signal and analyze the signal strength based on the main control MCU to determine whether to perform signal conditioning on the initial digital signal. The signal conditioning unit is used to convert the initial digital signal into a digital-to-analog signal using a digital-to-analog converter when signal conditioning is required, thereby obtaining the target analog signal. At the same time, it generates control signals based on the main control MCU. The control signal is input to the programmable gain amplifier to dynamically adjust the target gain factor that needs to be conditioned, and the target analog signal is conditioned based on the target gain factor. The conditioned target analog signal is then converted from analog to digital by an analog-to-digital converter to obtain the target digital signal.

4. The multi-channel strain monitoring system according to claim 3, characterized in that, The signal amplification and determination unit includes: Comparison subunit, used for: The main control MCU reads the signal strength of the initial digital signal and compares the signal strength of the initial digital signal with the first preset signal strength threshold and the second preset signal strength threshold, respectively; Wherein, if the first preset signal strength threshold is greater than the second preset signal strength threshold, it is determined whether to perform signal conditioning on the initial digital signal; The decision result output sub-unit is used for: When the signal strength of the initial digital signal is greater than the first preset signal strength threshold, it is determined that the output digital signal needs to be reduced. When the signal strength of the initial digital signal is less than the second preset signal threshold strength, it is determined that the output digital signal needs to be amplified in a second stage. Otherwise, it is determined that no signal conditioning will be performed on the initial digital signal.

5. A multi-channel strain monitoring system according to claim 1, characterized in that, The main control module is also used to encapsulate the strain data and the corresponding channel identifier, timestamp, and gain status into packets according to a compact protocol to obtain packet data; the communication module transmits the packet data to the cloud platform based on MQTT or TCP / IP protocol.

6. The multi-channel strain monitoring system according to claim 1, characterized in that, The main control module includes: The channel switching unit is used to output a channel selection signal to the control terminal of the multiplexer, and connect the target strain sensor bridge corresponding to the selected channel to the multiplexer based on the channel selection signal, and acquire the differential analog signal of the corresponding channel based on the target strain sensor bridge. The signal receiving unit is used to receive the digital signal of the differential analog signal corresponding to the target channel after the channel is connected. The data conversion unit is used to convert digital signals into strain data corresponding to the current channel; The anomaly monitoring unit is used to analyze strain data and determine whether there are any anomalies at the measurement points based on the analysis results.

7. A multi-channel strain monitoring system according to claim 6, characterized in that, The anomaly monitoring unit includes: The data aggregation subunit is used to aggregate the analysis results of strain data from different channels and obtain historical monitoring results based on the aggregated results; The parameter determination subunit is used to determine the trend of strain data changes and the frequency of abnormal events based on historical monitoring results; The strategy generation subunit is used to generate adaptive monitoring strategies for different channels when the trend of change exceeds a preset stability threshold or the frequency of occurrence exceeds a preset frequency threshold. The adaptive monitoring strategy includes at least one of the following: adjusting the channel scanning order of the multiplexer, adjusting the sampling frequency of the corresponding channel, and adjusting the gain control parameter of the corresponding channel. The update subunit is used to send the adaptive monitoring strategy to the channel switching unit and the signal processing module, and update the data acquisition and processing behavior based on the sending result; The verification subunit is used for: New strain data is obtained based on the updated data acquisition and processing behavior, and the new strain data is input into the anomaly monitoring unit for verification. Based on the verification results, the parameters in the adaptive monitoring strategy are iteratively optimized, and the optimized strategy parameters are stored in the configuration file of the main control module.

8. A multi-channel strain monitoring system according to claim 6, characterized in that, The data conversion unit includes: The first intermediate value determines the sub-unit, used for: The dynamic reference parameter set uniquely bound to the current gating channel is invoked based on the identifier of the current gating channel, wherein the dynamic reference parameter set includes: adaptive zero-point reference value; The digital signal is initially compensated based on the adaptive zero-point reference value to obtain the first intermediate value; Retrieve historical strain data and perform trend analysis on the historical strain data to obtain the gain correction coefficient of the current gating channel; The second intermediate value determination subunit is used to combine the first intermediate value with the gain correction coefficient, and calculate the second intermediate value based on the combination result and the pre-stored unit strain range coefficient. Comparison subunit, used for: Compare the second intermediate value with the preset physical range threshold; When the second intermediate value is less than or equal to the preset physical range threshold, it is determined to be valid strain data and output. When the second intermediate value is greater than the preset physical range threshold, the adaptive zero-point reference value is recalibrated until the second intermediate value is less than or equal to the preset physical range threshold.

9. A multi-channel strain monitoring system according to claim 1, characterized in that, The main control module includes: An environmental data acquisition unit is used to acquire current environmental data, including at least temperature and humidity, from a preset environmental sensor connected to the main control module. An abnormal fluctuation pattern acquisition unit is used to acquire abnormal fluctuation patterns in historical strain data from at least two channels. Analysis unit, used for: The current environmental data and abnormal fluctuation patterns are input into a pre-trained correlation model for analysis, and the correlation between the abnormal fluctuation patterns and the current environmental data is determined based on the analysis results. Determine whether the abnormal fluctuation pattern is caused by environmental disturbance; When it is determined that the abnormal fluctuation pattern is caused by the current environmental data, a collaborative calibration instruction is generated based on the preset environmental adaptive collaborative calibration mechanism. The collaborative calibration instruction includes at least an environmental compensation coefficient for the programmable gain amplifier and a correction offset for the dynamic reference parameter set. The gain control parameters of the programmable gain amplifier are synchronously adjusted with the adaptive zero-point reference value in the dynamic reference parameter set based on the collaborative calibration command. Based on the adjustment results, verification strain data of all channels are collected according to a preset period, and the parameters of the pre-trained correlation model are iteratively optimized based on the verification strain data.

10. A multi-channel strain monitoring system according to claim 1, characterized in that, The communication module includes: The data acquisition unit is used to acquire strain data and anomaly monitoring results, and to initiate the data upload process based on the acquired results; The link construction unit is used to obtain the preset communication protocol and network parameters between the main control module and the cloud platform based on the data upload process startup result, and to construct a remote communication link between the main control module and the cloud platform based on the preset communication protocol and network parameters. Data transmission unit, used for: Based on communication requirements, the remote communication link is split into a parallel dual communication link, which includes a first communication link from the main control module to the cloud platform and a second communication link from the cloud platform to the main control module. The strain data and anomaly monitoring results are encapsulated and transmitted to the cloud platform via the first communication link. Meanwhile, the working status of the cloud platform is monitored in real time, and when the cloud platform generates a remote control command data packet, the remote control command data packet is transmitted to the main control module based on the second communication link.

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