Multi-sensor fusion multi-modal hydrological data synchronous acquisition method

Abstract

The application discloses a multi-sensor fusion multi-modal hydrological data synchronous acquisition method, relates to the technical field of data acquisition, and comprises the following steps: connecting a plurality of sensors arranged in a hydrological region to an intelligent PGA module to form a synchronous acquisition topology, issuing an acquisition instruction package through a central main controller, performing hardware synchronization of an absolute timestamp of the intelligent PGA module, generating a sampling edge marker, performing marker alignment and channel autonomous sampling switching of a plurality of sensing channels, and performing relative independent acquisition of sensing channels of the plurality of sensors under the adaptive gain regulation of the intelligent PGA module, so as to solve the technical problem of how to ensure high-precision synchronization of the plurality of sensors while giving consideration to channel adaptive regulation and data fusion quality in the prior art, effectively improve complete synchronization and data quality of data acquisition, and improve the reliability and data accuracy of long-term monitoring.

Description

Technical Field

[0001] This invention relates to the field of data acquisition technology, specifically to a method for synchronous acquisition of multimodal hydrological data through multi-sensor fusion. Background Technology

[0002] In the field of hydrological monitoring, the deployment and data acquisition of multiple sensors have become important means of obtaining hydrological environmental information. Existing technologies typically employ distributed sensor nodes for data acquisition, with each node acquiring data through independent acquisition circuits or synchronous trigger signals.

[0003] However, when deploying sensors on a large scale, this method often suffers from clock drift, bus delay, and differences in trigger response, leading to discrepancies in sampling time between different sensor channels and making it difficult to guarantee global data synchronization consistency. Furthermore, due to the large dynamic range of signal amplitude in hydrological environments, the output voltage of different sensors fluctuates significantly, easily causing signal saturation or low signal-to-noise ratios, thus affecting the accuracy and reliability of subsequent data.

[0004] Some existing solutions attempt to perform time alignment and gain compensation on the data through software, but due to limitations in hardware latency and sampling accuracy, their synchronization and real-time performance are still difficult to meet the needs of high-precision hydrological monitoring. In particular, in multimodal data fusion scenarios, the accumulation of timing errors and signal distortion will further reduce the effectiveness of fusion analysis.

[0005] In summary, how to ensure high-precision synchronization of multiple sensors while taking into account both channel adaptive control and data fusion quality remains a problem that urgently needs to be solved in existing technologies. Summary of the Invention

[0006] This application provides a method for synchronous acquisition of multimodal hydrological data through multi-sensor fusion, which addresses the technical problem in existing technologies of how to ensure high-precision synchronization of multiple sensors while simultaneously considering adaptive channel control and data fusion quality.

[0007] In view of the above problems, this application provides a method for synchronous acquisition of multimodal hydrological data through multi-sensor fusion.

[0008] This application provides a method for synchronous acquisition of multimodal hydrological data using multi-sensor fusion. The method includes: connecting multiple sensors deployed in a hydrological area to a smart PGA module to form a synchronous acquisition topology, wherein the sensors and the smart PGA module are connected in a one-to-one correspondence, and the synchronous acquisition topology is a star-bus hybrid topology; based on the synchronous acquisition topology, issuing acquisition command packets through a central main controller to perform hardware synchronization of the smart PGA module under absolute timestamps, generating sampling edge markers; based on the sampling edge markers, performing marker alignment and channel autonomous sampling switching of multiple sensor channels, and under the adaptive gain control based on the smart PGA module, performing relatively independent acquisition of the sensor channels of multiple sensors to obtain multi-channel sensor flow data.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] The multi-sensor fusion multimodal hydrological data synchronous acquisition method provided in this application addresses the issue of multiple sensors deployed in a hydrological area being connected to an intelligent PGA module to form a synchronous acquisition topology. A central main controller issues acquisition command packets to execute hardware synchronization under the absolute timestamp of the intelligent PGA module, generating sampling edge markers. Based on these sampling edge markers, multi-sensor channel alignment and autonomous channel sampling switching are performed. Under the adaptive gain control of the intelligent PGA module, relatively independent acquisition of multiple sensor channels is performed to obtain multi-channel sensor stream data. This method solves the technical problem in existing technologies of how to ensure high-precision synchronization of multiple sensors while simultaneously considering adaptive channel control and data fusion quality. Through hardware-level high-precision synchronization, physical synchronization markers are generated using the synchronous gain switching action, achieving accuracy far exceeding that of software timestamps. Front-end data quality assurance and adaptive gain control ensure that the acquired signal is always the best, undistorted signal, improving data validity. This effectively improves the complete synchronization and data quality of data acquisition, enhancing the reliability and accuracy of long-term monitoring. Attached Figure Description

[0011] Figure 1 This application provides a schematic flowchart of a method for synchronous acquisition of multimodal hydrological data through multi-sensor fusion.

[0012] Figure 2 This application provides a schematic diagram of the synchronous acquisition topology in the multi-sensor fusion multi-modal hydrological data synchronous acquisition method. Detailed Implementation

[0013] This application provides a method for synchronous acquisition of multimodal hydrological data through multi-sensor fusion, which addresses the technical problem in the prior art of how to ensure high-precision synchronization of multiple sensors while simultaneously considering channel adaptive control and data fusion quality.

[0014] like Figure 1 As shown, this application provides a method for synchronous acquisition of multimodal hydrological data through multi-sensor fusion, the method comprising:

[0015] S1: Multiple sensors deployed in the hydrological area are connected to the intelligent PGA module to form a synchronous acquisition topology, wherein the sensors and the intelligent PGA module are connected in a one-to-one correspondence, and the synchronous acquisition topology is a star-bus hybrid topology.

[0016] In this embodiment, multiple sensors are deployed for a hydrological region, which is a target environment with representative hydrological characteristics, such as a river cross-section, lake area, or groundwater observation point. This enables comprehensive dynamic monitoring of the region by deploying various types of sensors at different spatial locations, including but not limited to water level sensors, flow velocity sensors, water quality sensors, and rainfall sensors, to obtain raw hydrological signals with broad coverage and diverse data types.

[0017] In this application, all the aforementioned sensors need to be connected to a smart PGA module. The smart PGA module is constructed from a smart programmable gain amplifier. In one specific embodiment, the smart PGA module integrates a dedicated hardware device with an adjustable gain amplifier circuit and a local logic processing unit. Its key feature is the ability to automatically adjust the gain according to the input signal voltage range to ensure the output signal is within the optimal dynamic range, avoiding oversaturation or a low signal-to-noise ratio. Each sensor is equipped with an independent smart PGA module, i.e., the dedicated connection method in this application, ensuring a one-to-one correspondence in the signal processing link, thereby avoiding signal interference between multiple sensors and enhancing the stability and independence of the data acquisition path.

[0018] Based on this, multiple intelligent PGA modules are configured into a synchronous acquisition topology according to a specific connection method, achieving a unified clock reference and sampling boundary consistency for multi-channel signal acquisition. In this application, the synchronous acquisition topology adopts a star-bus hybrid form.

[0019] The sensor and the intelligent PGA module have a one-to-one relationship, meaning each sensor corresponds to only one PGA module, which is simple and easy to expand. The bus structure is reflected in the fact that multiple PGA modules are connected to the central main controller through a unified data bus. That is, the central controller is the central node and multiple PGA modules are multiple parallel nodes in a radial connection relationship. For example, the data bus can use the industrial CAN bus or RS-485 bus as the implementation method, thereby ensuring the high efficiency of data communication and synchronous command issuance between multiple modules.

[0020] In summary, the star-bus hybrid topology not only enables parallel access and centralized control of multiple sensors, but also balances scalability and control centralization in terms of physical structure.

[0021] Furthermore, such as Figure 2 As shown, a synchronous acquisition topology is formed. Step S1 of this application includes:

[0022] Multiple intelligent PGA modules connected to the multi-sensor are acquired, and the multiple intelligent PGA modules are connected to the central main controller via a data bus. The intelligent PGA modules are constructed using programmable gain amplifiers. A connection is established between the central main controller and a reference clock source. The synchronous acquisition topology is constituted by the connection architecture of the multi-sensor, the multiple intelligent PGA modules, the central main controller, and the reference clock source.

[0023] In this embodiment of the application, multiple intelligent PGA modules connected to the multi-sensor are obtained. In the implementation provided by this application, the intelligent PGA module is an intelligent programmable gain amplifier module. Its core function is to adjust the gain of the electrical signal output by the sensor in real time through the internal programmable amplifier circuit so as to ensure that the signal amplitude is within the dynamic range that the sampling device can recognize.

[0024] In practical implementation, since different types of hydrological sensors output signal amplitudes vary, for example, water level sensors may output weak electrical signals at the millivolt level, while flow velocity sensors may output voltage signals at the volt level, equipping each sensor with an independent intelligent PGA module can avoid the problem of uniform amplification failure caused by inconsistent signal strength, thereby achieving differentiated processing and high-fidelity acquisition.

[0025] Furthermore, the multiple intelligent PGA modules are connected to the central main controller via a data bus. Here, the data bus refers to the hardware link used to enable multiple devices to share a communication channel, such as an I²C bus, SPI bus, or industrial-grade CAN bus. Through the data bus, multiple intelligent PGA modules can simultaneously access the central main controller, which can issue commands to each module and receive collected data via broadcast or addressing.

[0026] For example, in a typical application, the central main controller can send a synchronous start command to all intelligent PGA modules at once to ensure the time consistency of multi-channel sampling actions.

[0027] The intelligent PGA module is built using a programmable gain amplifier, which enables flexible and precise control of gain adjustment at the hardware level through a programmable gain amplifier chip. Specifically, the programmable gain amplifier means that its gain is no longer fixed, but can switch between multiple gain levels according to the instructions input at the control port, for example, it can be programmably adjusted between 1x, 10x, and 100x.

[0028] In conjunction with the application scenarios of this invention, the intelligent PGA module also adds a local logic unit, which is used to dynamically determine whether the gain needs to be adjusted based on the voltage status during the acquisition process. This not only improves adaptability but also ensures that data acquisition remains within a high accuracy range even when hydrological environmental signals fluctuate significantly.

[0029] Subsequently, a connection is established between the central main controller and the reference clock source to provide a unified time reference for the entire synchronous acquisition process. In one feasible implementation, the reference clock source can be a high-precision crystal oscillator, a GPS timing module, or an atomic clock module, which provides an absolute timestamp and a stable clock pulse. By connecting to the reference clock source, the central main controller can attach a unified absolute time identifier when issuing commands, ensuring time consistency among different PGA modules when executing commands.

[0030] For example, in a multi-channel sampling task, the central main controller generates a unified trigger timestamp based on the reference clock and broadcasts it to all PGA modules. After local clock calibration, each module will strictly follow the timestamp to perform gain switching and sampling start, thereby avoiding error accumulation caused by local crystal oscillator drift.

[0031] The synchronous acquisition topology comprises a connection architecture between the multiple sensors, multiple intelligent PGA modules, a central main controller, and the reference clock source. Its core lies in multi-level division of labor and cooperation: the sensors act as signal sources providing raw physical quantities; the intelligent PGA modules, as front-end processing units, perform signal conditioning and local synchronization responses; the central main controller, as the global scheduling core, is responsible for issuing commands and integrating data; and the reference clock source serves as a global time anchor point, ensuring the accuracy of synchronization.

[0032] In summary, the synchronous acquisition topology constructed using this architecture not only achieves time alignment of multi-channel data but also provides excellent scalability and maintainability in its structure. For example, when a new water quality sensor needs to be added, simply adding a smart PGA module and connecting it to the bus allows for seamless integration into the overall synchronous architecture.

[0033] S2: Based on the synchronous acquisition topology, the central main controller issues acquisition instruction packets to perform hardware synchronization under the absolute timestamp of the intelligent PGA module and generate sampling edge markers.

[0034] In this embodiment of the application, based on the synchronous acquisition topology, the central main controller can act as the global scheduling core and issue acquisition instruction packets with a unified time identifier.

[0035] Specifically, the acquisition instruction package refers to a set of control information generated by the central main controller and broadcast to each intelligent PGA module via the data bus. It includes at least an instruction code to trigger sampling, an absolute timestamp to calibrate the unified time, and target parameters related to gain. Through the unified distribution of the instruction package, it is ensured that all intelligent PGA modules receive the same synchronization instruction, establishing initial consistency of acquisition at the hardware level.

[0036] Subsequently, hardware synchronization is performed using the absolute timestamp of the intelligent PGA module. Specifically, upon receiving the acquisition command packet, each intelligent PGA module does not immediately trigger the acquisition action. Instead, it matches the absolute timestamp attached to the command packet with its own local countdown timer or internal clock. When the local time and the absolute timestamp match, the module will automatically trigger the corresponding operation, such as gain switching or sampling initiation.

[0037] The absolute timestamp here comes from the connection between the central main controller and the reference clock source, and has a unified and high-precision timing characteristic, thereby avoiding trigger deviations caused by local clock differences and realizing true hardware-level global synchronization.

[0038] For example, within a single sampling period, the triggering error of all PGA modules can be controlled within the nanosecond range, ensuring strict consistency of the multi-channel sampling boundaries.

[0039] Synchronously, a sampling edge marker is generated. That is, when the absolute timestamp triggers the gain switching or sampling action, a transient characteristic voltage with a sharp change in amplitude will appear at the output of the intelligent PGA module. This characteristic change is defined as the sampling edge marker.

[0040] Among them, the sampling edge marker, as a hardware-level time reference symbol, can be used to identify the timing of sampling actions in each channel, thereby enabling channel alignment and fusion in subsequent data processing stages.

[0041] Furthermore, by issuing acquisition command packets through the central main controller, hardware synchronization under the absolute timestamp of the intelligent PGA module is executed. Step S2 of this application includes:

[0042] The central main controller generates an acquisition instruction packet, which consists of at least an instruction code, an absolute timestamp, and a target gain value. The instruction code is a gain synchronization sequence instruction, and the absolute timestamp is generated based on a reference clock source. The central main controller broadcasts the acquisition instruction packet to the multiple intelligent PGA modules via the data bus. Each intelligent PGA module starts a countdown timer on its internal clock until its local countdown timer matches the absolute timestamp, triggering a gain switch and updating the gain register within the intelligent PGA module to the target gain value.

[0043] In this embodiment of the application, after the entire topology is established and each intelligent PGA module is confirmed to be in a controllable state, the central main controller generates a data acquisition instruction package according to the data acquisition needs. That is, the data instruction set constructed by the central main controller at a specific time according to the sampling needs consists of at least an instruction code, an absolute timestamp, and a target gain value.

[0044] In this invention, the instruction code is specifically defined as a gain synchronization sequence instruction, which is a command sequence used to coordinate the gain adjustment of each intelligent PGA module, ensuring consistency among multiple channels during gain switching. The absolute timestamp is generated based on the reference clock source connected to the central main controller. This timestamp possesses global uniformity and high precision, serving as the core basis for hardware synchronization. The target gain value is a preset, uniform low gain value, for example, G=1, i.e., unity gain.

[0045] Subsequently, the central main controller broadcasts the acquisition instruction packet to the multiple intelligent PGA modules through the data bus. That is, at the physical connection level, the instruction is distributed to all the mounted intelligent PGA modules at once using the bus broadcast mechanism, so that the same instruction content can be received within the same time window, avoiding the difference in the order of instruction transmission caused by communication delay.

[0046] Subsequently, each intelligent PGA module starts a countdown timer on its internal clock. That is, upon receiving an acquisition command packet containing an absolute timestamp, each intelligent PGA module does not immediately perform gain adjustment, but instead uses its internal clock circuit to set a countdown process. The purpose of the countdown timer is to delay the local operation until the target time specified by the absolute timestamp, thereby achieving alignment with global time.

[0047] Furthermore, gain switching is triggered when the local countdown timer matches the absolute timestamp. Specifically, when the internal countdown ends and perfectly matches the absolute timestamp, each intelligent PGA module immediately performs a gain switching operation. This involves the programmable gain amplifier within the module changing its amplification factor; specifically, the gain register within the intelligent PGA module is updated to the target gain value.

[0048] This means that at the instant the trigger action occurs, the target gain value is written to the gain register. The gain register, as a key storage unit controlling the amplification factor, immediately drives the amplifier circuit to complete the physical gain adjustment. This achieves synchronous gain adjustment of multiple channels under globally unified time, ensuring the time consistency of multimodal hydrological data acquisition.

[0049] Furthermore, after triggering the gain switch, step S2 of this application includes:

[0050] Upon triggering the gain switching, sampling edge markers are generated. As the gain switching is triggered, the output voltage of each intelligent PGA module undergoes a sharp change in amplitude, forming sampling edge markers on the time axis. The sampling edge markers are hardware synchronization action markers corresponding to the multi-sensing channels of multiple sensors.

[0051] In this embodiment, the generation of sampling edge markers upon triggering the gain switching refers to the specific physical change that occurs in the output signal at the instant the acquisition command packet issued by the central main controller is executed by each intelligent PGA module and the gain register is updated. This change is defined as the sampling edge marker. Here, the edge is not a virtual identifier in a logical sense, but rather an actual electrical signal characteristic, dependent on the abrupt change in the voltage waveform, used to identify the sampling start point.

[0052] Specifically, as the gain switching is triggered, the output voltage of each intelligent PGA module changes abruptly. That is, when the internal amplification factor switches under the trigger of the target timestamp, the input signal will show a sharp change in voltage amplitude at the output of the amplification channel.

[0053] For example, when the output voltage of a water level sensor is 0.1V, and the target gain value switches from 1x to 10x, the voltage at the module's output terminal will instantaneously jump to 1.0V. This voltage change appears as a distinct steep edge on the time axis. In this application, since this switch is a response based on an absolute timestamp, it can serve as a synchronization trigger point for hardware sampling.

[0054] In this application, the aforementioned voltage surge events are abstracted into a unified time reference symbol, serving as sampling edge markers on the time axis. Each intelligent PGA module generates similar voltage edges when performing gain switching, thus forming a series of consistent marker points on the time axis. These marker points possess global synchronization properties because they are all triggered by the same absolute timestamp and implemented at the hardware circuit level, thus enabling timing alignment across multiple channels. That is, they serve as a shared synchronization reference for the entire multi-sensor system.

[0055] In other words, the data acquired by any sensor channel can be aligned with the data from other channels using the corresponding sampling edge markers.

[0056] For example, in river monitoring scenarios, when flow velocity sensors and water quality sensors simultaneously trigger gain switching at different locations, sampling edge markers will be generated in their output waveforms. These markers, as a manifestation of global hardware synchronization, enable data from different channels to achieve strict time uniformity in the subsequent fusion processing stage, thereby ensuring high-precision synchronous acquisition of multimodal hydrological data.

[0057] S3: Based on the sampling edge markers, perform marker alignment and channel autonomous sampling switching for multiple sensing channels. Under the adaptive gain control based on the intelligent PGA module, perform relatively independent acquisition of sensing channels of multiple sensors to obtain multi-channel sensing flow data.

[0058] In this application, after all intelligent PGA modules perform gain switching and generate a uniform voltage change at the output, the change point is used as a time reference, and the synchronization of the data streams from each sensor is used to perform timing calibration.

[0059] Specifically, the mark alignment means identifying the sampling edge marks that appear in the waveform of each channel and using them as the sampling start point, thereby eliminating sampling misalignment caused by differences in sensor characteristics, wiring delays, or local clock deviations.

[0060] For example, in river cross-section monitoring, flow velocity sensors and water level sensors may be located in different positions, but by aligning edge markers, it can be ensured that their data streams are accurately compared and fused at the same time.

[0061] Correspondingly, after completing the global alignment based on the sampling edge markers, the central main controller switches each intelligent PGA module from a unified synchronous mode to an independent autonomous sampling mode.

[0062] Specifically, each intelligent PGA module no longer relies on instructions from the central controller, but instead schedules its data acquisition rhythm independently based on its own sampling status and the judgments of its local logic unit. It can flexibly adjust the sampling rate or acquisition strategy according to real-time changes in channel signals, thereby avoiding the problems of low resource utilization or high data redundancy in global synchronization mode.

[0063] Furthermore, each intelligent PGA module, during independent operation, automatically determines whether to adjust the amplification factor by monitoring the output voltage in real time and combining it with the preset standard range, in order to ensure that the data is within the effective sampling range.

[0064] The adaptive gain control proposed in this application refers to the module automatically reducing the gain when the sampled signal is too strong and approaches saturation; automatically increasing the gain when the signal is too weak and affects the signal-to-noise ratio; and maintaining the existing gain value when the signal is within the normal range. This is to maintain the quality stability of the data in each channel.

[0065] In summary, through the combined use of the alignment and independent acquisition mechanisms described above, a continuous data stream acquired in parallel by multiple sensors is ultimately obtained. This data stream is not only strictly synchronized in the time dimension, but also undergoes gain optimization in its amplitude characteristics, resulting in high accuracy.

[0066] For example, when monitoring rainfall intensity, river level, and flow velocity simultaneously, it is possible to acquire aligned data streams of the three types of signals at the same time point and ensure that the amplitude of each signal is within the optimal sampling range, providing a solid foundation for subsequent multimodal hydrological data fusion and comprehensive analysis.

[0067] Furthermore, based on the sampling edge markers, multi-sensor channel marker alignment and channel autonomous sampling switching are performed. Step S3 of this application includes:

[0068] The multiple sensor channels are marked and aligned, with the sampling edge markings as the alignment basis. After the marking and alignment are completed, the central main controller generates a mode switching command and broadcasts it to the multiple intelligent PGA modules through the data bus. Each intelligent PGA module leaves the synchronization mode and switches to the autonomous sampling mode. The multiple sensors perform autonomous sampling management of the sensor channels.

[0069] In this embodiment of the application, during the multi-channel data acquisition process, the sampling edge markers generated in the previous step are first used as time anchors to unify the sampling start points or sampling sequences of different channels.

[0070] In actual sampling, due to differences in spatial distribution, line length, and local response speed among multiple sensors, time deviations are inevitable in the original state of the sampled signals. By identifying the sampling edge markers formed by voltage abrupt changes in the sampled waveforms of each channel, the unified starting point of sampling can be accurately located, thereby achieving synchronous correction between channels.

[0071] In other words, the marking alignment process establishes a shared time reference system for different sensing channels by using hardware edge marking, so that the data of each channel achieves a strict one-to-one correspondence in time sequence.

[0072] The use of the sampled edge markers as the alignment basis further defines the execution method of the alignment process, namely, it does not rely on software interpolation or time compensation, but directly uses the edge markers generated by the hardware as the sole alignment standard.

[0073] The method proposed in this application offers higher accuracy and lower latency compared to traditional software post-processing methods because the sampling edge markers originate from voltage abrupt changes under a uniform absolute timestamp, ensuring consistency on a nanosecond-level timescale. For example, in a data acquisition system with ten sensing channels, edge markers will appear in the output waveform of each channel at the same time point. By identifying these markers, the sampling streams of the ten channels can be uniformly aligned.

[0074] Subsequently, after the marker alignment is completed, the central main controller generates a mode switching instruction. This means that after the marker alignment stage is completed, the central main controller will generate a new control instruction to notify each intelligent PGA module to exit the current synchronous mode and enter a relatively independent operating mode to carry out the data acquisition process.

[0075] In a specific implementation, the data is broadcast to the multiple intelligent PGA modules via a data bus. Within the same time window, the switching command is received and executed. Each intelligent PGA module leaves the synchronous mode and switches to the autonomous sampling mode. That is, it turns off the dependence on the global synchronization clock and edge markers, has completed the synchronous acquisition alignment, and then enters its own independent sampling logic.

[0076] Specifically, in autonomous sampling mode, each intelligent PGA module autonomously determines the sampling frequency, triggering conditions, and gain adjustment strategy through internal algorithms and circuit mechanisms, thereby achieving localized data acquisition and management.

[0077] Each sensor operates independently and collects data under the control of its corresponding intelligent PGA module. The essence of management lies in the fact that the intelligent PGA module determines whether to adjust the gain or change the sampling parameters based on the real-time monitored signal status, thereby ensuring that the collected data is always within the optimal range.

[0078] Furthermore, switching to autonomous sampling mode, the multi-sensor performs autonomous sampling management of the sensing channels. Step S3 of this application includes:

[0079] According to the first intelligent PGA module, the data sampling and control of the sensing channel of the first sensor is performed. The first intelligent PGA module is any intelligent PGA module, and the first sensor is a sensor associated with the first intelligent PGA module. The sampling signal status of the sensing channel of the first sensor is determined by monitoring the output voltage, and the gain is adaptively adjusted.

[0080] In this embodiment, the first intelligent PGA module manages and controls the data sampling of the first sensor's sensing channel. Specifically, any connected intelligent PGA module is used as the execution object to perform local data sampling management on its associated sensors.

[0081] In this application, the first intelligent PGA module is not limited to a specific number, but is used to indicate that the mechanism applies to any module; similarly, the first sensor refers to any sensor corresponding to it. This ensures that the acquisition, amplification, and modulation of each sensor signal are completed in a dedicated hardware unit, avoiding interference or priority conflicts that may arise from multi-channel shared processing.

[0082] For example, although the water level sensor and the water quality sensor are connected to the system at the same time, their signal acquisition paths are completely independent in hardware and do not affect each other.

[0083] Specifically, by monitoring the output voltage, the sampling signal status of the sensing channel of the first sensor is determined. That is, the intelligent PGA module detects the voltage amplitude at its output terminal in real time during the acquisition process, and uses this as the basis for judging the signal quality and dynamic range.

[0084] Specifically, if the output voltage is close to full range, it indicates that the signal is too strong and there is a risk of saturation; if the output voltage is too low, it indicates that the signal is weak and the signal-to-noise ratio is poor; if the voltage is within the normal standard range, it indicates that the current gain setting is reasonable. Through this real-time monitoring, the intelligent PGA module can quickly and accurately obtain the channel sampling status and perform adaptive gain adjustment accordingly.

[0085] In summary, this ensures that the channel data of the first sensor is always in the optimal sampling range, thereby improving the stability and reliability of the overall acquisition system.

[0086] Furthermore, the sampling signal state of the sensing channel of the first sensor is determined, and adaptive gain adjustment is performed. Step S3 of this application includes:

[0087] If the output voltage detected by the first intelligent PGA module is close to full range, a first sampling signal state is generated. The first intelligent PGA module then lowers the gain and records the gain lowering event. Here, the first sampling signal state indicates that the sampling signal of the channel sensor data is too strong and there is a risk of distortion. If the output voltage detected by the first intelligent PGA module is low, a second sampling signal state is generated. The first intelligent PGA module then raises the gain and records the gain raising event. Here, the second sampling signal state indicates that the sampling signal of the channel sensor data is too weak and the signal-to-noise ratio is poor. If the output voltage detected by the first intelligent PGA module is within the standard range, the gain is maintained. Here, each intelligent PGA module corresponds to a standard range based on the sensor type.

[0088] In this embodiment, if the output voltage detected by the first intelligent PGA module is close to full range, that is, the voltage amplitude has reached the upper limit of the preset threshold, for example, when the full range is 5V, it enters the near-full range state when the voltage exceeds 4.5V. This state is determined as the first sampling signal state. This means that the signal is too strong and will soon be distorted. The first intelligent PGA module will automatically reduce the gain within microseconds to prevent saturation and record the gain reduction event and time.

[0089] Specifically, the gain of the internal programmable amplifier is immediately reduced to ensure the output signal returns to a safe range, thus avoiding overload distortion. Simultaneously, the timing of the gain adjustment, the magnitude of the reduction, and the corresponding sensor channel are recorded for subsequent data tracking and status analysis.

[0090] Synchronously, if the output voltage detected by the first intelligent PGA module is low, that is, when the output voltage value is continuously maintained in the lower limit region close to zero, specifically, when the voltage amplitude is lower than the set preset threshold, for example, when the full range is 5V, if the voltage is lower than 0.5V, it means that the amplified signal is insufficient to reflect the original data characteristics of the sensor, and a second sampling signal state is generated.

[0091] The problem with the second sampling signal state is that the input signal is too small, making it difficult for the sampling circuit to capture accurately and potentially causing it to be submerged in noise, resulting in a decrease in the effective data rate. Therefore, gain adjustment is a necessary compensation method.

[0092] Specifically, after determining that the signal is in the second sampling state, the gain is automatically increased to amplify the weak signal, increase its amplitude, and bring it into a reasonable dynamic range. This operation is also recorded as a gain increase event.

[0093] Synchronously, if the output voltage monitored by the first intelligent PGA module is within the standard operating range—that is, the ideal voltage range preset according to the characteristics and operating conditions of different sensors—then the standard operating range for a water level sensor might be 1.0V to 3.5V, while the standard operating range for a flow rate sensor might be set to 0.8V to 4.0V. When the output voltage stabilizes within the preset normal operating range, no adjustments are needed; instead, the existing gain value is maintained. For example, the midpoint between the full-scale and low-scale values ​​of each sensor is used as the corresponding standard operating range.

[0094] Furthermore, after performing adaptive gain control, step S3 of this application includes:

[0095] Based on the recorded gain downsampling events, a gain event sequence is generated, wherein each sensor sampling corresponds to a gain event sequence, and the gain event elements include at least time and gain amount; a gain change curve is generated for the gain event sequence; and hydrological event verification is performed based on the gain change curve.

[0096] In this embodiment of the application, during the aforementioned adaptive control process, each gain adjustment operation generates an event record. Whether the adjustment is triggered by excessively high output voltage or by excessively low voltage, it will be completely recorded by the system. Arranging the recorded discrete event points in chronological order constitutes the gain event sequence.

[0097] The gain event sequence reflects the dynamic response of a sensor channel to a signal within a specific sampling period, and therefore is essentially a gain adjustment trajectory in the time dimension.

[0098] Each intelligent PGA module independently monitors and regulates the sampling of its connected sensors, thus each channel generates an independent sequence of gain events.

[0099] For example, a water level sensor may frequently trigger a downward adjustment event during flood peaks, while a water quality sensor may trigger an upward adjustment event when there is strong background noise.

[0100] The gain event elements include, but are not limited to, time and gain amount. Time is used to identify the specific moment the event occurs, ensuring that the event sequence reflects a continuous dynamic process. The gain amount is used to characterize the specific adjustment magnitude, such as a 5dB decrease or a 10dB increase. This accurately describes the background and magnitude change of a gain adjustment action.

[0101] Subsequently, a gain change curve is generated for the gain event sequence. That is, a continuous curve trajectory is formed by mapping the time horizontal axis to the gain vertical axis. This visually displays the dynamic change pattern of the gain of a sensor channel throughout the entire sampling period, and also reflects the process of signal strength fluctuations. For example, when the water level rises sharply in a short period of time, the curve will show a continuous downward movement; when the signal environment tends to stabilize, the curve enters a relatively flat phase.

[0102] In a further optimization process, hydrological events are verified based on the gain change curve. That is, the dynamic characteristics of gain control are combined with actual hydrological events to cross-verify the occurrence and development of hydrological events.

[0103] Specifically, hydrological events, such as sudden rises or falls in floodwaters or sudden changes in water quality, are usually accompanied by dramatic fluctuations in signal amplitude. Gain change curves can serve as evidence of the authenticity and rationality of the event.

[0104] For example, if the raw data from the flow velocity sensor indicates an accelerated water flow, and the corresponding gain curve shows a continuous downward adjustment during the same period, it can be verified that the flow velocity data does indeed originate from a real hydrological phenomenon, rather than accidental noise or sensor failure.

[0105] For example, at a certain point in time, the gain begins to increase continuously to compensate for the weakening of the turbidity signal. This clearly indicates the start time of a high-turbidity sewage cloud. The magnitude and rate of gain increase can indirectly reflect the intensity and speed of the increase in turbidity of the sewage cloud. When the gain sequence returns to stability, it signifies the end of the sewage cloud's passage.

[0106] In summary, this technology not only collects data but also verifies the reliability of hydrological events based on hardware dynamic characteristics, thereby enhancing the credibility of the overall monitoring results.

[0107] Furthermore, after acquiring the multi-channel sensor stream data, the steps in this application also include:

[0108] The timing of multi-channel sensor stream data is aligned by marking the sampling edges of each channel to determine the synchronous stream data; the synchronous stream data is traversed, and the comprehensive hydrological status of the hydrological region is evaluated by multi-sensor fusion.

[0109] Furthermore, the steps of this application also include: generating directional feedback information based on the comprehensive hydrological status; and sending the directional feedback information to the central main controller for sensing and data acquisition constraints.

[0110] In this embodiment, after multi-sensor autonomous sampling is completed, the data streams generated by different channels need to be integrated using a unified time reference. Sampling edge markers, as voltage jump signals generated by the intelligent PGA module during gain switching, possess natural synchronization characteristics and can serve as the sole reference point for cross-channel data alignment. By identifying and matching the edge marker positions in each channel's data stream, all sensor signals can be rearranged onto a unified time axis, thereby achieving precise alignment.

[0111] In summary, a set of data streams that are completely consistent across time scales is obtained. This eliminates the deviations caused by hardware latency or asynchronous sampling.

[0112] Subsequently, the synchronous stream data is traversed, and the data from all channels are jointly processed and comprehensively analyzed. Specifically, different physical quantities, i.e., data from different sensors, such as water level, flow velocity, rainfall, and water quality indicators, are collaboratively calculated and feature extracted to compensate for the shortcomings of a single sensor in terms of spatial coverage, time response, or signal characteristics.

[0113] For example, the fusion method can include any of the following: weighted average, feature vector-based comprehensive discrimination, etc.

[0114] For example, in flood monitoring, relying solely on water level data may not accurately predict sudden rises in water levels, but combining flow velocity and rainfall data can significantly improve the accuracy of predictions.

[0115] Subsequently, the fused multimodal data allows for a scientific assessment of the overall hydrological conditions of the target area. This includes not only water level and flow velocity, but also rainfall intensity, water quality changes, and their inherent correlations. Through fusion analysis, a comprehensive assessment of the regional hydrological environment can be generated.

[0116] For example, it can be used to determine whether there is a risk of flooding, whether a sudden pollution incident has occurred, or whether the dry season has begun. Its advantage lies in the fact that hydrological monitoring goes beyond single-point data collection, enabling comprehensive and dynamic assessments across the entire region.

[0117] Further, based on the comprehensive hydrological conditions, the focus of data collection is determined. For example, in the event of a sudden pollution incident, water quality is chosen as the corresponding focus, resulting in the generated directional feedback information. Subsequently, the directional feedback information is sent to the central controller. The central controller, based on the directional feedback information, coordinates the interaction between multiple sensors and imposes sensing constraints to further ensure the effectiveness of data collection.

[0118] Through the foregoing detailed description of the method for synchronous acquisition of multimodal hydrological data fusion using multisensor fusion, those skilled in the art can clearly understand the method for synchronous acquisition of multimodal hydrological data fusion using multisensor fusion in this embodiment. As for the apparatus disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be referred to the method section description.

[0119] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for synchronous acquisition of multimodal hydrological data through multi-sensor fusion, characterized in that, The method includes: For multiple sensors deployed in the hydrological area, they are connected to the intelligent PGA module to form a synchronous acquisition topology, wherein the sensors and the intelligent PGA module are connected in a one-to-one correspondence, and the synchronous acquisition topology is a star-bus hybrid topology; Based on the aforementioned synchronous acquisition topology, the central main controller issues acquisition command packets to execute hardware synchronization under the absolute timestamp of the intelligent PGA module, generating sampling edge markers; Based on the sampling edge markers, the marker alignment and channel autonomous sampling switching of multiple sensing channels are performed. Under the adaptive gain control based on the intelligent PGA module, the sensing channels of multiple sensors are collected relatively independently to obtain multi-channel sensing flow data. This includes issuing acquisition command packets through the central main controller to perform hardware synchronization of the intelligent PGA module under the absolute timestamp, including: The central main controller generates an acquisition instruction package, which consists of at least an instruction code, an absolute timestamp, and a target gain value. The instruction code is a gain synchronization sequence instruction, and the absolute timestamp is generated based on a reference clock source. The central main controller broadcasts the acquisition instruction packet to multiple intelligent PGA modules via the data bus; Each intelligent PGA module starts its internal clock countdown timer until the local countdown timer matches the absolute timestamp, triggering gain switching and updating the gain register in the intelligent PGA module to the target gain value; After triggering the gain switch, the following are included: Upon triggering the gain switching, sampling edge markers are generated. As the gain switching is triggered, the output voltage of each intelligent PGA module undergoes a sharp change in amplitude, forming sampling edge markers on the time axis. The sampling edge marker is a hardware synchronization action marker corresponding to the multi-sensing channels of multiple sensors.

2. The method for synchronous acquisition of multi-modal hydrological data through multi-sensor fusion as described in claim 1, characterized in that, The synchronous acquisition topology consists of: Multiple intelligent PGA modules connected to the multi-sensor are acquired, and the multiple intelligent PGA modules are connected to the central main controller via a data bus. The intelligent PGA modules are constructed from programmable gain amplifiers. Establish the connection between the central main controller and the reference clock source; The synchronous acquisition topology is formed by the connection architecture of the multiple sensors, multiple intelligent PGA modules, the central main controller and the reference clock source.

3. The method for synchronous acquisition of multi-modal hydrological data through multi-sensor fusion as described in claim 2, characterized in that, Based on the sampling edge markers, perform marker alignment and autonomous channel sampling switching for multiple sensing channels, including: The multiple sensor channels are marked and aligned, wherein the sampling edge markings are used as the alignment basis; After the marker alignment is completed, the central main controller generates a mode switching command and broadcasts it to the multiple intelligent PGA modules via the data bus. Each intelligent PGA module then leaves the synchronization mode and switches to the autonomous sampling mode, and the multiple sensors perform autonomous sampling management of the sensing channels.

4. The method for synchronous acquisition of multi-modal hydrological data through multi-sensor fusion as described in claim 3, characterized in that, Switching to autonomous sampling mode, the multi-sensor performs autonomous sampling management of the sensing channels, including: According to the first intelligent PGA module, the data sampling and control of the sensing channel of the first sensor is performed. The first intelligent PGA module is any intelligent PGA module, and the first sensor is the sensor associated with the first intelligent PGA module. Specifically, by monitoring the output voltage, the sampling signal status of the sensing channel of the first sensor is determined, and the gain is adaptively adjusted.

5. The method for synchronous acquisition of multi-modal hydrological data through multi-sensor fusion as described in claim 4, characterized in that, Determine the sampling signal state of the sensing channel of the first sensor and perform adaptive gain adjustment, including: If the output voltage detected by the first intelligent PGA module is close to full range, a first sampling signal state is generated. The first intelligent PGA module performs gain reduction and records the gain reduction event. The first sampling signal state indicates that the sampling signal of the channel sensor data is too strong and there is a risk of distortion. If the output voltage detected by the first intelligent PGA module is low, a second sampling signal state is generated. The first intelligent PGA module increases the gain and records the gain increase event. The second sampling signal state indicates that the sampling signal of the channel sensing data is too weak and the signal-to-noise ratio is poor. If the output voltage detected by the first intelligent PGA module is within the standard range, gain maintenance is performed. Each intelligent PGA module corresponds to a standard range based on the sensor type.

6. The method for synchronous acquisition of multi-modal hydrological data through multi-sensor fusion as described in claim 5, characterized in that, After adaptive gain control, the following is included: Based on the recorded gain down-adjustment events, a gain event sequence is generated, wherein each sensor sampling corresponds to a gain event sequence, and the gain event elements include at least time and gain amount; For the aforementioned gain event sequence, a gain change curve is generated; Hydrological events are verified based on the gain change curve.

7. The method for synchronous acquisition of multi-modal hydrological data through multi-sensor fusion as described in claim 1, characterized in that, After acquiring multi-channel sensor stream data, the following is included: The timing of multi-channel sensor stream data is aligned by marking the sampling edges of each channel to determine the synchronous stream data; By traversing the synchronous stream data and performing multi-sensor fusion, the overall hydrological status of the hydrological region is evaluated.

8. The method as described in claim 7, characterized in that, Based on the comprehensive hydrological conditions, directional feedback information is generated; The directional feedback information is sent to the central main controller for sensing and data acquisition constraints.

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