An electroactive microbial toxicity sensing data processing method and system

Abstract

This invention discloses a method and system for processing electroactive microbial toxicity sensing data, relating to the field of electroactive microbial toxicity sensing data processing, used to improve the accuracy and efficiency of toxicity monitoring. The method includes: acquiring bioelectrical signals from multiple monitoring nodes; calculating a reference level for the bioelectrical signals and correcting the bioelectrical signals based on the reference level to obtain corrected bioelectrical signals; decomposing the corrected bioelectrical signals to identify and remove signal components with specific frequency and time distributions to obtain purified bioelectrical signals; quantifying bioelectrochemical indicators reflecting toxicity effects from the purified bioelectrical signals; and transmitting the bioelectrochemical indicators to a data processing platform to achieve data storage, management, and parallel analysis of data from multiple monitoring nodes.

Description

Technical Field

[0001] This invention relates to the field of electroactive microbial toxicity sensing data processing, and more particularly to a method and system for electroactive microbial toxicity sensing data processing. Background Technology

[0002] In the daily operation of modern wastewater treatment plants, ensuring the stable operation of biochemical treatment processes and preventing the microbial community from being harmed by toxic substances is a crucial task. Therefore, real-time and accurate biotoxicity monitoring of water entering the wastewater treatment plant is particularly important. In recent years, with the rapid development of the Internet of Things and sensor technology, a biosensor utilizing electroactive microorganisms as its core component has shown great application potential in the field of environmental toxicity monitoring due to its significant advantages such as fast response speed and high sensitivity. It is considered a key technological equipment for achieving early warning of water quality safety.

[0003] However, when these advanced biosensors are practically applied to wastewater environments with complex and variable compositions, their data processing systems face a series of severe challenges. On the one hand, the raw response signals generated by the sensors are easily interfered with by factors such as fluctuations in water quality background and changes in electrode surface conditions, resulting in drift and cluttered signals at the basic signal level, leading to difficulties in feature extraction and low efficiency. On the other hand, existing systems are mostly designed for single devices or specific platforms, with poor compatibility of data processing methods, making it difficult to efficiently integrate and parallelize multiple widely distributed monitoring nodes, thus hindering the establishment and application effectiveness of large-scale monitoring networks. Therefore, the current technical challenge is how to design a data processing method and system that can effectively overcome the above challenges and ensure efficient and accurate processing and analysis of electroactive microbial toxicity sensor data in complex and variable real-world wastewater environments. Summary of the Invention

[0004] This application discloses a method and system for processing electroactive microbial toxicity sensing data, aiming to solve the technical problems faced by existing electroactive microbial toxicity sensing data processing systems in complex and variable wastewater environments, such as the susceptibility of raw signals to interference, horizontal drift of basic signals, numerous cluttered signals, poor compatibility of data processing methods, and difficulty in efficiently integrating and parallel analyzing multiple monitoring nodes.

[0005] In a first aspect, this application discloses a method for processing electroactive microbial toxicity sensing data, comprising the following steps: Bioelectric signals from multiple monitoring nodes are acquired; a reference level for the bioelectric signals is calculated, and the bioelectric signals are corrected based on the reference level to obtain corrected bioelectric signals; the corrected bioelectric signals are decomposed to identify and remove signal components with specific frequencies and time distributions to obtain purified bioelectric signals; bioelectrochemical indicators reflecting toxic effects are quantified from the purified bioelectric signals; and the bioelectrochemical indicators are transmitted to a data processing platform to achieve data storage, management, and parallel analysis of multiple monitoring nodes.

[0006] Optionally, bioelectrical signals from multiple monitoring nodes can be acquired, including: Acquire raw bioelectrical signals from multiple monitoring nodes; The raw bioelectric signals are processed in a unified format and synchronized in time to obtain bioelectric signals from multiple monitoring nodes.

[0007] Optionally, a reference level for calculating the bioelectrical signal may be included, including: Calculate the exponentially weighted moving average (EWMA) of the bioelectrical signal; The exponentially weighted moving average (EWMA) was used as a reference level for the bioelectrical signal.

[0008] Optionally, the corrected bioelectric signal is decomposed to identify and remove signal components with specific frequencies and time distributions, resulting in a purified bioelectric signal, including: Discrete wavelet transform is used to decompose the corrected bioelectrical signal in order to identify and remove signal components with specific frequency and time distributions. The purified bioelectric signal is obtained by reconstructing the corrected bioelectric signal using inverse wavelet transform (IDWT).

[0009] Optionally, bioelectrochemical indicators reflecting the effects of toxicity may include the peak current of the bioelectric signal, the response time, and the integral charge of the change in the bioelectric signal over the duration of toxic exposure; the response time refers to the time required for the bioelectric signal to reach its peak or minimum value from the time of toxic exposure; toxic exposure refers to the bioelectric signal being outside a preset threshold range.

[0010] Optionally, the integrated charge satisfies the following relationship: Q = ∫|I(t) - I_baseline| dt; Where Q is the integrated charge, I(t) is the corrected bioelectrical signal, I_baseline is the mean of the bioelectrical signal before toxic exposure, and the integration interval is the duration of toxic exposure.

[0011] Optionally, the method also includes: Establish the first mapping relationship; the first mapping relationship includes the mapping relationship between different environmental parameter data and the thresholds of different bioelectrical signals; Obtain current environmental parameter data; Based on the current environmental parameter data, the threshold range of bioelectrical signals that have a mapping relationship with the current environmental parameter data is determined from the first mapping relationship and is set as the preset threshold range.

[0012] Optionally, after transmitting the bioelectrochemical indicators to the data processing platform, the method further includes: Continuously monitor the data inflow rate from multiple monitoring nodes and the current queue length of tasks to be processed on the data processing platform; Adjust the number of computing instances on the data processing platform based on the data inflow rate and task queue length; Based on the adjusted number of computational instances, allocate high-priority computational resource channels for toxicity early warning tasks; toxicity early warning tasks are generated when bioelectrochemical indicators meet preset conditions; preset conditions are that the peak current decreases by more than a preset decrease within a preset time period, or the response time is less than a time threshold, or the integrated charge is greater than a preset integrated charge.

[0013] Optionally, continuously monitor the data inflow rate from multiple monitoring nodes and the current queue length of pending tasks, including: Deploy a data acquisition unit at each monitoring node; The data acquisition unit captures the generation rate of local bioelectrical signals and the number of data packets to be sent; The data acquisition unit locally aggregates the captured information to generate a monitoring report containing timestamps and aggregated data. The data acquisition unit pushes the monitoring report to the data processing platform at a preset reporting cycle; After receiving the monitoring report, the data processing platform verifies the timestamp in the monitoring report; Based on the verification results, the data processing platform updates the data inflow rate and task queue length records of the corresponding monitoring nodes.

[0014] Secondly, this application also discloses an electroactive microbial toxicity sensing data processing system, the system comprising: The signal acquisition module is used to acquire bioelectrical signals from multiple monitoring nodes; The reference level correction module is used to calculate the reference level of the bioelectric signal and correct the bioelectric signal according to the reference level to obtain the corrected bioelectric signal. The signal purification module is used to decompose the corrected bioelectric signal to identify and remove signal components with specific frequencies and time distributions from the corrected bioelectric signal, thereby obtaining the purified bioelectric signal. The index quantification module is used to quantify bioelectrochemical indicators reflecting the toxic effects from the purified bioelectric signals. The data transmission and management module is used to transmit bioelectrochemical indicators to the data processing platform to realize the data storage, management and parallel analysis of multiple monitoring nodes.

[0015] Beneficial effects This application discloses a method for processing electroactive microbial toxicity sensing data, which aims to solve the technical problems faced by existing electroactive microbial toxicity sensing data processing systems, such as the susceptibility of raw signals to interference, horizontal drift of basic signals, numerous cluttered signals, poor compatibility of data processing methods, and difficulty in efficiently integrating and parallel analyzing multiple monitoring nodes.

[0016] This application effectively solves the aforementioned problems through the following technical solutions: First, bioelectrical signals from multiple monitoring nodes are acquired and processed in a unified format and synchronized in time, ensuring data standardization and consistency. Second, the exponentially weighted moving average (EWMA) of the bioelectrical signals is calculated as a reference level, and the bioelectrical signals are corrected based on this reference level, effectively eliminating signal drift caused by background fluctuations and electrode surface changes. Third, discrete wavelet transform is used to decompose the corrected bioelectrical signals, identifying and removing noise components with specific frequencies and time distributions, and reconstructing the purified bioelectrical signals through inverse wavelet transform, greatly improving signal purity. Subsequently, bioelectrochemical indicators reflecting toxic effects are quantified from the purified bioelectrical signals, including peak current, response time, and integrated charge. These multi-dimensional indicators can comprehensively and accurately assess toxic effects. Finally, these bioelectrochemical indicators are transmitted to a data processing platform to realize data storage, management, and parallel analysis of multiple monitoring nodes, and can dynamically adjust computing resources according to the data inflow rate and task queue length, allocating high-priority channels for toxicity early warning tasks.

[0017] Through the above technical solution, this application overcomes the limitations of existing technologies, such as low data processing efficiency, insufficient accuracy, and difficulty in integrating multi-node data. This application can effectively remove noise and drift from signals, improving the accuracy of toxicity feature extraction. Simultaneously, through unified data processing and parallel analysis capabilities, it achieves effective management of large-scale monitoring networks, significantly improving the real-time performance, accuracy, and reliability of biotoxicity monitoring in wastewater treatment plants, and providing strong technical support for early warning of water quality safety. Attached Figure Description

[0018] Figure 1 This is a schematic flowchart of a method for processing electroactive microbial toxicity sensing data provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of another electroactive microbial toxicity sensing data processing method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electroactive microbial toxicity sensing data processing system provided in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] To better understand the electroactive microbial toxicity sensing data processing method proposed in this application, the following will elaborate on some key terms and implementation environments involved.

[0022] The "bioelectric signals" mentioned in this application refer to electrical signals generated by electroactive microorganisms under specific environmental conditions due to their metabolic activities or responses to external stimuli (such as toxic substances), typically manifested as changes in current or voltage. These signals reflect the physiological state and activity of microorganisms and are important indicators for assessing water toxicity. "Monitoring nodes" refer to sensor units deployed at different locations for real-time acquisition of bioelectric signals; each node typically contains one or more electroactive microbial biosensors. The "data processing platform" is a centralized computing and storage system responsible for receiving, processing, analyzing, and managing data from all monitoring nodes, and providing data visualization, early warning, and other functions.

[0023] The following specific embodiments will provide a detailed description and explanation of the electroactive microbial toxicity sensing data processing method provided in this application.

[0024] Reference Figure 1 This invention provides a method for processing electroactive microbial toxicity sensing data, comprising the following steps: S1, acquire bioelectrical signals from multiple monitoring nodes.

[0025] For example, each monitoring node can be configured with a data acquisition module that connects directly to an electroactive microbial sensor and acquires raw bioelectrical signals at a preset sampling frequency and precision. The acquired signals can be temporarily stored in a local cache, awaiting further processing or transmission. Alternatively, the monitoring node can employ a wireless communication module to transmit the acquired raw bioelectrical signals in real time via a wireless network (such as Wi-Fi, LoRa, NB-IoT, etc.) to a central data aggregation point, which then sends the data to a unified data processing platform.

[0026] S2. Calculate the reference level of the bioelectric signal and correct the bioelectric signal according to the reference level to obtain the corrected bioelectric signal.

[0027] Specifically, the reference level can be calculated using various statistical methods. For example, the average value of the bioelectrical signal over a period of time can be simply calculated as the reference level. Alternatively, a moving average method can be used, which calculates the average value of the bioelectrical signal within a fixed time window and continuously updates the reference level as the time window slides. After obtaining the reference level, the correction process can be achieved by subtracting the reference level from the original bioelectrical signal, thereby eliminating baseline drift of the signal.

[0028] S3. Decompose the corrected bioelectric signal to identify and remove signal components with specific frequencies and time distributions from the corrected bioelectric signal to obtain the purified bioelectric signal.

[0029] Signal decomposition methods can employ Fourier transform to convert the signal from the time domain to the frequency domain, thereby identifying and removing noise components within a specific frequency range. For example, a low-pass filter can be used to remove high-frequency noise, or a band-stop filter can be used to remove periodic interference at a specific frequency.

[0030] Specifically, these signal components mainly refer to various interferences present in bioelectrical signals. They do not reflect the true response of electroactive microorganisms to toxic substances, but are caused by environmental factors, sensor characteristics, or system noise. These interference signals are described as having "specific frequency and time distributions" because they exhibit identifiable patterns in the signal's spectrum (frequency) and time domain (time).

[0031] The specific signal components include, but are not limited to, the following: 1. Power frequency interference: This type of interference typically manifests as a periodic sine wave with a fixed frequency (such as 50 Hz or 60 Hz) and its harmonic components. It appears as sharp spectral lines in the frequency domain and persists continuously in the time domain.

[0032] It mainly originates from the coupling of AC power lines, electrical equipment, and surrounding electromagnetic fields.

[0033] For example, superimposing a continuous 50 Hz or 60 Hz waveform onto a bioelectric signal may result in a relatively stable amplitude, but it will mask the actual changes in the bioelectric signal.

[0034] 2. High-frequency random noise: This type of noise typically has a wide frequency distribution and exhibits random, irregular fluctuations in the time domain. Its energy is mainly concentrated in the high-frequency region.

[0035] It may be caused by thermal noise from the electronic components inside the sensor, high-frequency electromagnetic radiation in the environment, or inherent noise from the data acquisition circuit.

[0036] For example, tiny, rapidly fluctuating spikes appear in bioelectrical signals. These spikes do not have obvious periodicity, but they can make the signal appear "rough".

[0037] 3. Baseline drift: It manifests as slow, low-frequency fluctuations in the baseline of the bioelectrical signal. In the frequency domain, it is mainly concentrated in the extremely low frequency region, while in the time domain, it shows a slow change in the overall signal level.

[0038] This may be caused by slow changes in the electrode-electrolyte interface, slow fluctuations in environmental parameters such as temperature or pH, or slow, non-toxic adjustments in the physiological state of the microorganisms themselves.

[0039] For example, the overall level of bioelectrical signals rises or falls slowly over minutes or hours, rather than remaining near a stable zero point.

[0040] 4. Transient spike interference: This type of interference manifests as short-duration, high-amplitude pulses or spikes. It typically has a wide frequency spectrum (due to its dramatic time-domain variations), but it is localized in the time domain, meaning it only occurs at specific moments.

[0041] It may be caused by external transient electromagnetic pulses, transient poor contact of sensor connection lines, or mechanical disturbances such as the formation and rupture of bubbles in the water.

[0042] For example, a short-duration but large-amplitude positive or negative pulse may suddenly appear in a bioelectric signal, after which the signal quickly returns to normal.

[0043] The identification process typically includes the following steps: 1. Discrete wavelet decomposition: Principle: The corrected bioelectric signal is decomposed step-by-step using a series of high-pass and low-pass filters. In each decomposition stage, the signal is divided into two parts: approximation coefficients (A) and detail coefficients (D). The approximation coefficients represent the low-frequency components of the signal, while the detail coefficients represent the high-frequency components. Through multi-stage decomposition, the components of the signal at different frequency scales can be obtained. For example, after N-stage decomposition, the original signal S can be expressed as: S = A_N + D_N + D_{N-1} + ... + D_1. Where A_N is the lowest frequency approximation component, and D_i is the high-frequency detail component of the i-th stage.

[0044] Identification Mechanism: Through discrete wavelet decomposition, different noise and interference components will be concentrated in specific wavelet coefficient sub-bands. For example, high-frequency random noise is usually concentrated in the detail coefficients of lower decomposition levels (such as D1, D2); low-frequency interference such as baseline drift is mainly reflected in the approximate coefficients of higher decomposition levels (such as A_N); power frequency interference will appear in a certain detail coefficient sub-band corresponding to its frequency; and transient spike interference will simultaneously produce large local coefficients in multiple detail coefficient sub-bands.

[0045] 2. Wavelet coefficient analysis and thresholding: Principle: After discrete wavelet decomposition, noise components can be identified by analyzing the amplitude, distribution, and temporal characteristics of wavelet coefficients at different levels. Typically, noise wavelet coefficients have relatively small amplitudes and random distributions, while effective signals (such as toxic responses) have larger amplitudes and specific patterns. After identification, thresholding methods (such as hard or soft thresholding) can be used to remove or suppress noise. Wavelet coefficients below a certain threshold are considered noise and are set to zero or shrunk, while coefficients above the threshold are retained or slightly adjusted.

[0046] Identification mechanism: Power frequency interference identification: Through discrete wavelet decomposition, the energy of power frequency interference is concentrated in a specific detail coefficient subband corresponding to the 50 / 60 Hz frequency. It can be identified by analyzing the energy or periodicity characteristics of the coefficients in this subband. For example, if the energy of the D3 subband (assuming the corresponding frequency range includes 50 / 60 Hz) is abnormally high and exhibits periodicity, it can be determined to be power frequency interference.

[0047] High-frequency random noise identification: High-frequency random noise is mainly distributed in the detail coefficients at lower decomposition levels such as D1 and D2. These coefficients typically have small amplitudes and are irregular. High-frequency noise can be removed by setting a global or adaptive threshold to zero for these low-amplitude, high-frequency random coefficients.

[0048] Baseline drift identification: Baseline drift, as a slow-varying component of the signal, is mainly reflected in the approximation coefficients of the highest decomposition level (such as A_N). By analyzing the trends of these low-frequency approximation coefficients, slow baseline drift can be identified and removed. For example, A_N can be further smoothed or trend-fitted, and then the trend can be subtracted from the original signal.

[0049] Transient spike interference identification: In the DWT domain, transient spike interference manifests as a simultaneous occurrence of large amplitudes in coefficients at multiple detail levels (D1, D2, D3, etc.) at a specific time point. Transient spikes can be identified by detecting wavelet coefficients that are highly localized in time and significant across multiple frequency scales. For example, a threshold based on local energy or amplitude can be set; when wavelet coefficients at multiple levels simultaneously exceed this threshold at a certain time point, a spike is considered to exist.

[0050] 3. Wavelet Transform: Principle: After identifying, removing or suppressing the wavelet coefficients corresponding to noise, the remaining processed wavelet coefficients are recombined through inverse wavelet transform (IDWT) to reconstruct the purified bioelectric signal.

[0051] Objective: To ensure that while removing interference, the true information reflecting the toxic effects in the bioelectrical signal is preserved to the maximum extent, thereby obtaining a clearer and more accurate signal and providing high-quality data for subsequent quantification of bioelectrochemical indicators.

[0052] Through the decomposition, coefficient analysis and reconstruction process of the discrete wavelet transform described above, this application can effectively identify and remove noise and interference components with specific frequency and time distribution in bioelectric signals, thereby obtaining a highly purified bioelectric signal, which significantly improves the accuracy and reliability of toxicity detection.

[0053] S4. Quantify the bioelectrochemical indicators reflecting the toxic effects from the purified bioelectric signals.

[0054] Quantification of bioelectrochemical indicators can include features such as peak current and response time of the extracted signal. For example, a threshold range can be set, and the maximum and minimum values ​​of the signal can be recorded when the purified bioelectrochemical signal is outside this preset threshold range. Response time can be defined as the time required from the start of toxic exposure to the signal reaching its peak or minimum value.

[0055] In one example, the bioelectrochemical indicators reflecting the toxic effects include the peak current of the bioelectric signal, the response time, and the integral charge of the change in the bioelectric signal over the duration of toxic exposure; the response time refers to the time required for the bioelectric signal to reach its peak or minimum value from the time of toxic exposure; toxic exposure refers to the bioelectric signal being outside a preset threshold range.

[0056] The peak current of the bioelectrical signal refers to the maximum current intensity that the bioelectrical signal produced by electroactive microorganisms can reach during toxic exposure, directly reflecting the immediate intensity of the microorganism's response to the toxic substance. Response time refers to the time required from the onset of microbial exposure to the toxic substance until its bioelectrical signal reaches its peak or minimum value; this indicator measures the speed at which the microorganism responds to the toxic stimulus. Integrated charge is the cumulative change in the bioelectrical signal relative to the baseline level over the entire duration of toxic exposure, providing a comprehensive indicator to assess the long-term or cumulative effects of toxicity on microbial metabolic activity. Specifically, toxic exposure is defined as the bioelectrical signal being outside a preset threshold range.

[0057] The integrated charge satisfies the following relationship: Q = ∫|I(t) - I_baseline| dt; Where Q is the integrated charge, I(t) is the corrected bioelectrical signal, I_baseline is the mean of the bioelectrical signal before toxic exposure, and the integration interval is the duration of toxic exposure.

[0058] Specifically, Q refers to the cumulative change in the bioelectrical signal relative to the baseline level during the duration of toxic exposure, typically measured in coulombs (C). I(t) is the instantaneous current value of the bioelectrical signal after reference level correction, reflecting the electroactive state of the microorganism at a specific moment. I_baseline is the average value of the bioelectrical signal before toxic exposure, serving as a benchmark for measuring the normal state of the bioelectrical signal. The integration interval is the duration of toxic exposure, meaning that integration is performed only within the timeframe during which the bioelectrical signal deviates from the normal baseline due to toxic effects, thus accurately capturing the total charge change under toxic influence.

[0059] Thus, by clearly defining these bioelectrochemical indicators, a clear and operational standard is established for quantifying toxic effects from purified bioelectrical signals. Peak current, response time, and integrated charge characterize the microbial response to toxic substances from different dimensions. For example, peak current reflects the intensity of the reaction, response time reflects the rate of the reaction, and integrated charge reflects the cumulative effect of toxicity. By correlating toxic exposure with preset threshold ranges, the occurrence and duration of toxic events can be accurately identified, providing a reliable basis for subsequent toxicity assessment and early warning.

[0060] S5. Transmit bioelectrochemical indicators to the data processing platform to realize data storage, management and parallel analysis of multiple monitoring nodes.

[0061] For example, monitoring nodes can connect to a local area network (LAN) via Ethernet, and then connect to a data processing platform via the LAN. Alternatively, monitoring nodes can upload data to a cloud-based data processing platform via cellular networks (such as 4G / 5G). After receiving the metrics, the data processing platform can store them in a database and provide API interfaces for other applications to access and analyze them.

[0062] The electroactive microbial toxicity sensing data processing method of this application effectively solves the problems of signal drift, noise interference, and low data processing efficiency in traditional biotoxicity monitoring through a systematic data processing workflow. First, by acquiring bioelectrical signals from multiple monitoring nodes, distributed real-time monitoring of water quality over a wide area is achieved. Second, calculating and correcting the reference level of the bioelectrical signals effectively eliminates the influence of environmental background fluctuations and sensor drift, ensuring the accuracy of subsequent analysis. Next, the corrected bioelectrical signals are decomposed and purified, identifying and removing interfering signal components with specific frequencies and temporal distributions, greatly improving the signal-to-noise ratio and making the toxicity-related bioelectrochemical characteristics more prominent. Subsequently, bioelectrochemical indicators reflecting toxicity effects are quantified from the purified bioelectrical signals, providing a quantitative basis for toxicity assessment. Finally, these indicators are transmitted to a data processing platform, enabling centralized storage, efficient management, and parallel analysis of multi-node data, thereby enabling rapid response to potential toxicity events and providing timely early warnings for water quality safety. The entire method is interconnected, with each step working closely together to form an efficient, accurate, and reliable electroactive microbial toxicity sensing data processing system.

[0063] Compared to existing technologies, the electroactive microbial toxicity sensing data processing method of this application has significant advantages and innovations. Traditional methods often lack refined correction and purification mechanisms in the signal preprocessing stage, making it difficult to effectively remove baseline drift and noise interference in the original bioelectrical signals, thus affecting the accuracy and reliability of subsequent toxicity index extraction. For example, some existing systems may only use simple filtering, which cannot effectively cope with multi-source heterogeneous interference signals in complex aquatic environments. This application greatly improves the purity of the signal by introducing the calculation and correction of the bioelectrical signal reference level, and decomposing the corrected signal to identify and remove signal components with specific frequencies and time distributions, enabling the toxicity response characteristics to be captured more clearly. In addition, existing systems have poor compatibility in multi-node data processing, making it difficult to achieve efficient integration and parallel analysis, which limits their application in large-scale monitoring networks. This application achieves centralized storage, management and parallel analysis of data by uniformly transmitting the quantified bioelectrochemical indicators to the data processing platform, significantly improving the efficiency of data processing and the scalability of the system. This integrated data processing architecture not only simplifies the management complexity of multi-node data but also enables toxicity early warning and source tracing based on big data analytics. Therefore, this application demonstrates significant improvements and innovations over existing technologies in terms of signal processing precision and the efficiency and compatibility of multi-node data processing.

[0064] like Figure 2 As shown, the acquisition of bioelectrical signals from multiple monitoring nodes specifically includes: S101. Acquire raw bioelectric signals from multiple monitoring nodes.

[0065] Specifically, acquiring raw bioelectrical signals from multiple monitoring nodes refers to directly collecting electrical signals without any preprocessing from bioelectrochemical sensors or monitoring devices deployed in different locations. These raw signals may contain noise and interference, and their data formats may be inconsistent due to differences in sensor models, sampling rates, or transmission protocols.

[0066] S102. Perform unified format processing and time synchronization on the raw bioelectric signals to obtain bioelectric signals from multiple monitoring nodes.

[0067] The process of standardizing the raw bioelectrical signals can be understood as converting the raw bioelectrical signals from different monitoring nodes into a predefined, standardized data format. For example, all signals can be standardized to a specific sampling frequency, data type (such as floating-point numbers), and data structure (such as time-series arrays). The aim is to eliminate processing barriers caused by the heterogeneity of data sources and ensure that subsequent algorithms can seamlessly process data from all nodes.

[0068] In practical applications, time synchronization specifically refers to adjusting or calibrating the timestamps of bioelectrical signals from different monitoring nodes to align them on a unified time reference. For example, this can be achieved through a global clock synchronization protocol (such as NTP) or by attaching a precise timestamp to each data point during data acquisition and then calibrating it on the data processing platform. The goal is to ensure that signals acquired by different nodes at the same time can be accurately correlated and compared, which is crucial for analyzing the spatial and temporal propagation patterns of toxic events.

[0069] This application's solution addresses the issues of heterogeneity and temporal inconsistency in multi-node data by first acquiring the raw bioelectrical signals, then processing them in a unified format and synchronizing them with time. The unified format processing ensures structural compatibility across all data, enabling subsequent steps such as reference level calculation, signal correction, decomposition, and index quantification to be performed based on consistent data standards. Time synchronization guarantees the temporal accuracy of data from different nodes, allowing for precise identification and comparison of bioelectrical responses from different nodes within the same time period during parallel analysis or event correlation. This, in turn, enables a more accurate assessment of the scope and dynamic changes of toxic effects.

[0070] As a specific implementation, assume there are three monitoring nodes A, B, and C, deployed in different water areas, for real-time monitoring of the bioelectrical signals of electroactive microorganisms. Node A may use a sensor with a sampling rate of 100Hz, outputting CSV format data; Node B may use a sensor with a sampling rate of 50Hz, outputting JSON format data; and Node C may use a sensor with a sampling rate of 200Hz, outputting binary format data. Furthermore, due to network latency or local clock drift, the data from these nodes may have time deviations on the order of microseconds or even milliseconds.

[0071] According to this implementation, the data processing platform first acquires raw bioelectrical signals from these three nodes. Then, these raw signals undergo uniform format processing. For example, all data is converted to a uniform 100Hz sampling rate and stored as a standardized time-series array. For data with different sampling rates, interpolation or downsampling techniques can be used for adjustment. Simultaneously, the timestamps of each data point are calibrated by comparing them with the global clock of the data processing platform, ensuring that the data from all nodes are precisely aligned on the timeline. For example, if the data from node B is 50ms behind the global clock, all its timestamps will be adjusted forward by 50ms. Through this processing, even with diverse raw data sources, a uniformly formatted and time-synchronized bioelectrical signal can be obtained, laying a solid foundation for subsequent toxicity analysis.

[0072] Specifically, in the above-mentioned method for processing electroactive microbial toxicity sensing data, the step of calculating the reference level of the bioelectrical signal can be implemented as follows: S201. Calculate the exponentially weighted moving average (EWMA) of the bioelectric signal.

[0073] Exponentially weighted moving average (EWMA) is a statistical method that calculates an average by assigning higher weights to recent data, thus more sensitively reflecting the latest trends in the data sequence. In the processing of electroactive microbial toxicity sensing data, EWMA is used to dynamically estimate the baseline or reference level of bioelectrical signals. By calculating the EWMA of the bioelectrical signal, a smooth reference value that can adapt to slow changes in the signal can be obtained. Using this EWMA value as the reference level of the bioelectrical signal aims to provide a dynamic benchmark for subsequent signal correction.

[0074] S202. Use the exponentially weighted moving average (EWMA) as the reference level for bioelectrical signals.

[0075] The proposed method employs an exponentially weighted moving average (EWMA) to calculate a reference level for bioelectrical signals, effectively capturing the long-term trends and slow drift of these signals. Traditional simple moving averages may not be sensitive enough to recent changes, while EWMA, by assigning higher weights to recent data, allows the calculated reference level to reflect the current state of the bioelectrical signals more promptly, thus providing a more accurate and dynamic benchmark for subsequent signal correction. This ensures that abnormal changes in bioelectrical signals can be more precisely identified and quantified during toxic exposure.

[0076] In some embodiments of this application described above, the corrected bioelectric signal is decomposed to identify and remove signal components with specific frequencies and time distributions, thereby obtaining a purified bioelectric signal. Specifically, the step of decomposing the corrected bioelectric signal to identify and remove signal components with specific frequencies and time distributions, thereby obtaining a purified bioelectric signal, can be implemented in the following manner.

[0077] The corrected bioelectric signal is decomposed to identify and remove signal components with specific frequencies and time distributions, resulting in a purified bioelectric signal, including: S301. Discrete wavelet transform is used to decompose the corrected bioelectric signal to identify and remove signal components with specific frequency and time distribution in the corrected bioelectric signal.

[0078] Discrete Wavelet Transform (DWT) is a multi-resolution analysis tool that decomposes a signal into wavelet coefficients of different frequency subbands, enabling simultaneous localized analysis of the signal in both the time and frequency domains. By selecting appropriate wavelet basis functions and decomposition levels, the corrected bioelectrical signal can be effectively decomposed into a series of components with different frequencies and time scales. Specifically, during the decomposition process, noise or interference components unrelated to the toxic signal can be identified based on preset frequency and time distribution characteristics. These components include power line interference, baseline drift, or specific frequency noise generated by the sensor itself. The frequency and time distributions of these identified signal components differ significantly from the target toxic signal, allowing for precise localization.

[0079] S302. The corrected bioelectric signal is reconstructed by inverse wavelet transform IDWT to obtain the purified bioelectric signal.

[0080] The Inverse Discrete Wavelet Transform (IDWT) is a process of recombining processed wavelet coefficients to reconstruct the original signal. After removing specific signal components, the IDWT reconstructs the remaining wavelet coefficients, which do not contain interference components, to obtain a purified bioelectrical signal. Its purpose is to preserve key information reflecting toxic effects in the bioelectrical signal while minimizing background noise and interference, thereby improving the accuracy and reliability of subsequent toxicity quantification.

[0081] This application's solution achieves refined processing of the corrected bioelectrical signal by introducing discrete wavelet transform and inverse wavelet transform. Discrete wavelet transform can decompose complex bioelectrical signals at different scales, allowing various components in the signal, including toxic response signals, noise, and other interferences, to be separated in the wavelet domain. It is precisely because of the superior time-frequency localization of wavelet transform that the system can accurately identify non-toxic related signal components with specific frequency and time distributions. Once these interfering components are identified, their corresponding wavelet coefficients can be selectively removed or suppressed. Subsequently, through inverse wavelet transform, the remaining, purified wavelet coefficients are reconstructed back into the time domain signal, thereby obtaining a highly purified bioelectrical signal. This processing method avoids signal distortion or loss of key information that may be caused by traditional filtering methods, ensuring the integrity of the toxic-related bioelectrical signal.

[0082] In some embodiments of this application, toxic exposure is defined as the bioelectrical signal falling outside a preset threshold range, thereby quantifying the bioelectrochemical indicators reflecting the toxic effects. However, in practical applications, changes in environmental parameters (such as temperature, pH, background interference, etc.) can significantly affect the baseline level of the bioelectrical signal and its response characteristics to toxicity. If the preset threshold range remains fixed, it may lead to deviations in the judgment of toxic exposure under different environmental conditions, such as false alarms or missed alarms in certain environments, thus affecting the accuracy and reliability of toxicity sensing. Therefore, this application further proposes a method for dynamically adjusting the preset threshold range to improve the accuracy and environmental adaptability of toxic exposure determination.

[0083] The above methods also include: S401. Establish the first mapping relationship.

[0084] The first mapping relationship includes the mapping relationship between different environmental parameter data and the thresholds of different bioelectrical signals.

[0085] Specifically, the first mapping relationship can be understood as a storage or computational model that establishes a correlation between environmental parameter data and bioelectrical signal thresholds. This mapping relationship can be obtained through pre-conducted calibration experiments, historical data analysis, or machine learning training. Here, different environmental parameter data refer to the measured values ​​of various environmental factors that affect the physiological state and bioelectrical signal response of electroactive microorganisms, such as temperature, pH, dissolved oxygen concentration, conductivity, and background noise level. The threshold values ​​for different bioelectrical signals refer to the critical values ​​used to determine whether a bioelectrical signal has reached a toxic exposure level under specific environmental parameter conditions.

[0086] S402. Obtain current environmental parameter data.

[0087] Acquiring current environmental parameter data refers to collecting current environmental information such as temperature and pH value in real time through environmental sensors deployed at the monitoring site.

[0088] S403. Based on the current environmental parameter data, the threshold range of the bioelectrical signal that has a mapping relationship with the current environmental parameter data is determined from the first mapping relationship and set as the preset threshold range.

[0089] This step refers to the system using real-time acquired environmental parameters to dynamically determine the most suitable bioelectrical signal threshold by querying a preset mapping table and executing a preset algorithm or model calculation, and using it as the preset threshold range for subsequent toxicity exposure determination.

[0090] This application's solution establishes a primary mapping relationship between environmental parameter data and bioelectrical signal thresholds, and dynamically adjusts the preset threshold range based on real-time acquired environmental parameter data. This addresses the potential bias in judging toxic exposure using fixed thresholds under different environmental conditions. Specifically, when environmental parameters change, the baseline and response characteristics of the bioelectrical signal also change. Through this mapping relationship, the system can automatically select or calculate the most suitable preset threshold range based on current environmental conditions, making the determination of toxic exposure more consistent with reality. For example, when temperature rises or pH changes, the metabolic activity of organisms may increase or decrease, causing changes in the normal fluctuation range of bioelectrical signals. If a fixed threshold is used, it may be impossible to accurately capture abnormal signals caused by toxicity. This solution, by dynamically adjusting the threshold, can effectively filter out normal fluctuations caused by environmental factors, thereby more accurately identifying abnormal bioelectrical signals caused by toxic substances and improving the sensitivity and specificity of toxicity warnings.

[0091] In some embodiments described above, this application proposes methods for processing bioelectrical signals, quantifying bioelectrochemical indicators, and transmitting them to a data processing platform for data storage, management, and parallel analysis. However, in practical multi-node monitoring applications, the data inflow rate from different monitoring nodes may fluctuate significantly, and the data processing platform may face dynamically changing computational loads. Without an effective mechanism to dynamically manage computing resources and prioritize urgent tasks, the system may experience processing delays, resource underutilization or overutilization, or even be unable to respond promptly to sudden toxicity warning events, thus affecting the real-time performance and reliability of the entire system.

[0092] In response, this application further proposes a method for processing electroactive microbial toxicity sensing data. After transmitting the aforementioned bioelectrochemical indicators to the data processing platform, the method further includes: S501: Continuously monitor the data inflow rate from multiple monitoring nodes and the current queue length of tasks to be processed on the data processing platform.

[0093] Specifically, continuously monitoring the data inflow rate from multiple monitoring nodes and the current task queue length on the data processing platform refers to the system acquiring and tracking in real time the speed at which each monitoring node sends data to the data processing platform, as well as the number of tasks waiting to be processed within the platform. The data inflow rate can be understood as the number of data packets or the amount of data received per unit time, while the task queue length reflects the current system load. This monitoring data can be collected and updated periodically to provide an up-to-date view of the system's operational status.

[0094] S502. Adjust the number of computing instances on the data processing platform based on the data inflow rate and task queue length.

[0095] In practical applications, this refers to the system dynamically increasing or decreasing computing resources used for data processing based on real-time load monitoring. A computing instance can be understood as a virtual server, container, or processing unit that provides computing power to perform data processing tasks. When the data inflow rate is high or the task queue length increases, the system can automatically expand the number of computing instances to handle the higher load; conversely, when the load decreases, the number of instances can be reduced to conserve resources. This adjustment can be based on preset threshold rules or predictive adjustments based on machine learning models.

[0096] S503. Allocate high-priority computing resource channels for the toxicity warning task based on the adjusted number of computing instances.

[0097] Among them, the toxicity warning task is generated when the bioelectrochemical indicators meet the preset conditions; the preset conditions are that the peak current decreases by more than the preset decrease within a preset time period, or the response time is less than the time threshold, or the integrated charge is greater than the preset integrated charge.

[0098] A high-priority computing resource channel ensures that toxicity warning tasks can be rapidly scheduled and executed, avoiding delays caused by blockages from other routine tasks. Toxicity warning tasks are automatically generated when bioelectrochemical indicators meet specific preset conditions. These preset conditions include: a peak current decrease exceeding a preset decrease within a preset time period, indicating potentially severe inhibition of microbial activity and foreshadowing serious toxic effects; or a response time less than a time threshold, indicating rapid toxicity requiring immediate attention; or an integrated charge exceeding a preset integrated charge, reflecting the cumulative effect or intensity of toxicity, potentially indicating sustained or severe toxicity exposure. A toxicity warning task is triggered when any of these conditions are met.

[0099] This application's solution effectively addresses the issues of data processing efficiency and real-time response capability in multi-node toxicity sensing systems by introducing dynamic resource management and task priority scheduling mechanisms. Specifically, by continuously monitoring the data inflow rate and task queue length, the system can perceive its current operating status and load pressure in real time. Based on this monitoring data, the number of computing instances on the data processing platform is dynamically adjusted, ensuring that the system always has sufficient processing capacity when data volume fluctuates, avoiding performance bottlenecks caused by insufficient resources. More importantly, when bioelectrochemical indicators are detected to meet preset toxicity warning conditions, the system can immediately generate a toxicity warning task and allocate high-priority computing resource channels to it. This mechanism ensures that the most critical toxicity warning information is processed first, thereby significantly shortening the time from the occurrence of a toxic event to the system issuing a warning, and improving the timeliness and accuracy of the warning.

[0100] As a specific implementation, suppose a large-scale water quality monitoring network deploys multiple electroactive microbial toxicity sensing monitoring nodes. During normal operation, the data inflow rate of each node is low, and the data processing platform maintains a small number of computing instances to conserve resources. When a sudden toxic substance leak occurs near an industrial wastewater discharge outlet, the affected monitoring nodes will quickly detect abnormal changes in bioelectrical signals, such as a sharp drop in peak current within a preset time period, or a response time much shorter than a time threshold. These abnormal data cause a sudden increase in the data inflow rate, and the task queue length of the data processing platform also increases accordingly. At this time, the system will automatically expand the number of computing instances based on the detected high data inflow rate and task queue length to cope with the surge in data processing demands. Simultaneously, since the bioelectrochemical indicators (such as the magnitude of the peak current drop) meet the preset toxicity warning conditions, the system will immediately generate a toxicity warning task. This warning task will be allocated to a high-priority computing resource channel, ensuring that it can be processed before other routine data analysis tasks. For example, the system can quickly calculate the toxicity intensity and diffusion trend, and issue an alert to relevant management departments in a very short time, thereby gaining valuable time for emergency response measures and minimizing environmental harm.

[0101] In some embodiments described above in this application, the continuous monitoring of the data inflow rate from multiple monitoring nodes and the current queue length of pending tasks includes: S601. Deploy a data acquisition unit at each monitoring node.

[0102] Specifically, the data acquisition unit can be understood as a hardware or software module deployed on each monitoring node, whose main function is to collect and process local data in real time. This data acquisition unit is configured to capture the generation rate of local bioelectrical signals, i.e., the amount of bioelectrical signal data generated per unit time, and the number of data packets to be sent, reflecting the data transmission pressure of the current node. For example, the data acquisition unit can be an embedded system running dedicated data acquisition and preprocessing programs.

[0103] S602, The data acquisition unit captures the generation rate of local bioelectrical signals and the number of data packets to be sent.

[0104] S603 The data acquisition unit performs local aggregation on the captured information to form a monitoring report containing timestamps and aggregated data.

[0105] The data acquisition unit performs local aggregation of the captured information, which involves integrating information such as the generation rate of captured bioelectrical signals and the number of data packets to be sent, and adding a timestamp to form a structured monitoring report. This timestamp identifies the time the report was generated, ensuring the data processing platform can accurately determine the timeliness of the data. For example, the aggregated data may include statistical information such as the average generation rate, the maximum generation rate, and the current length of the queue to be sent.

[0106] S604 The data acquisition unit pushes the monitoring report to the data processing platform at a preset reporting cycle.

[0107] In practical applications, the data acquisition unit pushes monitoring reports to the data processing platform at preset reporting intervals, such as every 5 seconds, 10 seconds, or 1 minute. This periodic push mechanism ensures that the data processing platform can obtain the latest operating status of each monitoring node in a timely manner. The push method can use standard network protocols such as MQTT, HTTP, or TCP / IP.

[0108] S605. After receiving the monitoring report, the data processing platform verifies the timestamp in the monitoring report.

[0109] The purpose of verification is to confirm the timeliness and completeness of reports and prevent data distortion caused by network latency or system failures. For example, the platform can check whether the timestamp is within a reasonable time window or compare it with the platform's own system time.

[0110] S606. Based on the verification results, the data processing platform updates the data inflow rate and task queue length records of the corresponding monitoring nodes.

[0111] If the verification passes, the data in the report will be used to update the platform's internal status records; if the verification fails, an exception handling mechanism may be triggered, such as requesting a retransmission or marking the node's data as abnormal. These records form the basis for subsequent adjustments to the number of compute instances and resource allocation.

[0112] The proposed solution deploys data acquisition units at each monitoring node, enabling real-time and refined capture of the local bioelectric signal generation rate and the number of data packets to be sent. This local information is aggregated and timestamped to form monitoring reports, which are then pushed to the data processing platform at preset intervals. Upon receiving the reports, the data processing platform verifies the timestamps to ensure data validity and updates the data inflow rate and task queue length records for each monitoring node accordingly. It is precisely this distributed, periodic data acquisition and centralized verification and update mechanism that allows the data processing platform to accurately and promptly grasp the real-time load status and data transmission pressure of the entire monitoring network, providing reliable data support for subsequent dynamic adjustments to computing resources.

[0113] like Figure 3 As shown in the figure, this invention also provides an electroactive microbial toxicity sensing data processing system. The system includes: The signal acquisition module is used to acquire bioelectrical signals from multiple monitoring nodes; The reference level correction module is used to calculate the reference level of the bioelectric signal and correct the bioelectric signal according to the reference level to obtain the corrected bioelectric signal. The signal purification module is used to decompose the corrected bioelectric signal to identify and remove signal components with specific frequencies and time distributions from the corrected bioelectric signal, thereby obtaining the purified bioelectric signal. The index quantification module is used to quantify bioelectrochemical indicators reflecting the toxic effects from the purified bioelectric signals. The data transmission and management module is used to transmit bioelectrochemical indicators to the data processing platform to realize the data storage, management and parallel analysis of multiple monitoring nodes.

[0114] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by a computer program instructing related hardware. This program can be stored in the computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be an internal storage unit of the task execution device (including a data sending end and / or a data receiving end) of any of the foregoing embodiments, such as the hard disk or memory of the task execution device. The computer-readable storage medium can also be an external storage device of the terminal device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device. Further, the computer-readable storage medium can include both the internal storage unit of the task execution device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the task execution device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0115] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0116] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0117] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.

Claims

1. A method for processing electroactive microbial toxicity sensing data, characterized in that, include: Acquire bioelectrical signals from multiple monitoring nodes; Calculate the reference level of the bioelectric signal, and correct the bioelectric signal according to the reference level to obtain the corrected bioelectric signal; The corrected bioelectric signal is decomposed to identify and remove signal components with specific frequencies and time distributions, thereby obtaining a purified bioelectric signal. From the purified bioelectric signals, bioelectrochemical indicators reflecting the toxic effects are quantified; The bioelectrochemical indicators are transmitted to the data processing platform to realize the data storage, management and parallel analysis of the multiple monitoring nodes.

2. The method for processing electroactive microbial toxicity sensing data according to claim 1, characterized in that, The acquisition of bioelectrical signals from multiple monitoring nodes includes: Acquire raw bioelectrical signals from multiple monitoring nodes; The raw bioelectric signals are processed in a unified format and synchronized in time to obtain bioelectric signals from multiple monitoring nodes.

3. The method for processing electroactive microbial toxicity sensing data according to claim 1, characterized in that, The calculation of the reference level for the bioelectrical signal includes: Calculate the exponentially weighted moving average of the bioelectrical signal; The exponentially weighted moving average is used as a reference level for the bioelectrical signal.

4. The method for processing electroactive microbial toxicity sensing data according to claim 1, characterized in that, The step of decomposing the corrected bioelectric signal to identify and remove signal components with specific frequencies and time distributions to obtain a purified bioelectric signal includes: Discrete wavelet transform is used to decompose the corrected bioelectrical signal to identify and remove signal components with specific frequency and time distributions. The purified bioelectric signal is obtained by reconstructing the corrected bioelectric signal using inverse wavelet transform.

5. The method for processing electroactive microbial toxicity sensing data according to claim 1, characterized in that, The bioelectrochemical indicators reflecting the effects of toxicity include the peak current of the bioelectric signal, the response time, and the integral charge of the bioelectric signal changes over the duration of toxic exposure; the response time refers to the time required from toxic exposure to the bioelectric signal reaching its peak or minimum value; the toxic exposure refers to the bioelectric signal being outside a preset threshold range.

6. The method for processing electroactive microbial toxicity sensing data according to claim 5, characterized in that, The integral charge satisfies the following relationship: Q = ∫|I(t) - I_baseline| dt; Where Q is the integrated charge, I(t) is the corrected bioelectrical signal, I_baseline is the mean of the bioelectrical signal before toxic exposure, and the integration interval is the duration of toxic exposure.

7. The method for processing electroactive microbial toxicity sensing data according to claim 5, characterized in that, The method further includes: Establish a first mapping relationship; the first mapping relationship includes the mapping relationship between different environmental parameter data and the threshold ranges of different bioelectrical signals; Obtain current environmental parameter data; Based on the current environmental parameter data, the threshold range of bioelectrical signals that have a mapping relationship with the current environmental parameter data is determined from the first mapping relationship and is defined as the preset threshold range.

8. The method for processing electroactive microbial toxicity sensing data according to claim 5, characterized in that, After transmitting the bioelectrochemical indicators to the data processing platform, the method further includes: Continuously monitor the data inflow rate from the multiple monitoring nodes and the current queue length of the tasks to be processed on the data processing platform; The number of computing instances of the data processing platform is adjusted according to the data inflow rate and the task queue length. Based on the adjusted number of computational instances, a high-priority computational resource channel is allocated to the toxicity early warning task; the toxicity early warning task is generated when the bioelectrochemical indicator meets preset conditions; the preset conditions are that the decrease in peak current within a preset time period is greater than a preset decrease, or the response time is less than a time threshold, or the integrated charge is greater than a preset integrated charge.

9. The method for processing electroactive microbial toxicity sensing data according to claim 8, characterized in that, The continuous monitoring of the data inflow rate from the multiple monitoring nodes and the current queue length of the tasks to be processed on the data processing platform includes: Deploy a data acquisition unit at each monitoring node; The data acquisition unit captures the generation rate of local bioelectrical signals and the number of data packets to be sent; The data acquisition unit locally aggregates the captured information to generate a monitoring report containing timestamps and aggregated data. The data acquisition unit pushes the monitoring report to the data processing platform at a preset reporting cycle; After receiving the monitoring report, the data processing platform verifies the timestamp in the monitoring report; The data processing platform updates the data inflow rate and task queue length records of the corresponding monitoring nodes based on the verification results.

10. A data processing system for electroactive microbial toxicity sensing, characterized in that, The system includes: The signal acquisition module is used to acquire bioelectrical signals from multiple monitoring nodes; A reference level correction module is used to calculate the reference level of the bioelectric signal and correct the bioelectric signal according to the reference level to obtain the corrected bioelectric signal. The signal purification module is used to decompose the corrected bioelectric signal to identify and remove signal components with specific frequencies and time distributions from the corrected bioelectric signal, thereby obtaining a purified bioelectric signal. The index quantification module is used to quantify the bioelectrochemical indicators reflecting the toxic effects from the purified bioelectric signals. The data transmission and management module is used to transmit the bioelectrochemical indicators to the data processing platform to realize the data storage, management and parallel analysis of the multiple monitoring nodes.

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