Ecological water quality intelligent evaluation system for microbial fuel cell sensor network

Abstract

The present application provides a kind of ecological water quality intelligent evaluation system for microbial fuel cell sensing network, it is related to ecological environment monitoring and water quality intelligent evaluation technical field.The system accesses the biological electricity output data of sensing node, with the stable output state of sensing node itself as reference, continues to obtain the biological electricity output after deviation after output deviation, and according to its regression and whether to reach deviation again, distinguish recoverable response and sustained stress response.System generates water quality detection result according to recoverable response, generates ecological water quality early warning result according to sustained stress response;When multiple sensing nodes with water body connection relationship appear sustained stress response, combine occurrence time and water body connection direction to generate propagation type ecological water quality early warning result.

Description

Technical Field

[0001] This invention relates to the field of ecological environment monitoring and intelligent water quality assessment technology, and more specifically, to an intelligent ecological water quality assessment system for microbial fuel cell sensor networks. Background Technology

[0002] Ecological environment monitoring and intelligent water quality assessment technologies are typically used to continuously monitor water quality changes in water bodies such as rivers and pipe networks, and generate water quality detection results or early warning results based on electrical signals, physical parameters, or chemical parameters collected by sensors. Existing water quality monitoring systems mostly determine the state of water bodies based on whether the collected values ​​exceed preset ranges, whether the anomalies are continuous, and whether multiple monitoring points are simultaneously abnormal.

[0003] The existing technology has the following shortcomings: In microbial fuel cell sensing networks, the bioelectric output of sensing nodes is affected by the electron transport state of electroactive biomembranes. When the water body is subjected to short-term disturbances, the bioelectric output may initially deviate, then return to a stable output state as water conditions recover. When the water body is subjected to continuous stress, the bioelectric output may deviate for a long period or deviate again during the recovery process. If it is still treated as a threshold anomaly of ordinary sensors, it is easy to misjudge recoverable disturbances as persistent ecological risks, and it is also easy to miss the persistent stress state reflected by repeated deviations.

[0004] When multiple sensor nodes are deployed along the water body connectivity path, the sustained stress response may occur sequentially among upstream, midstream, and downstream nodes. This type of response does not necessarily manifest as multiple nodes being abnormal simultaneously. Spatial fusion based solely on the number of abnormal nodes, the consistency of readings, or the size of the abnormal range can easily break down ecological risks developing along the water body connectivity direction into isolated anomalies, making it difficult to form a timely propagation-based ecological water quality early warning judgment. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an intelligent ecological water quality assessment system for microbial fuel cell sensor networks, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: An intelligent ecological water quality assessment system for microbial fuel cell sensor networks includes: The data access module is used to access the bioelectric output data of the sensing node; The response processing module is used to take the stable output state of the sensing node itself as a reference for the electron transfer state of the electroactive biomembrane. After the bioelectric output deviates from the condition of continuous deviation, it continues to acquire the deviated bioelectric output and determines whether it is a recoverable response or a continuous stress response based on the return to the allowable fluctuation range corresponding to the stable output state during the recovery observation period and the situation of reaching the condition of continuous deviation again. The evaluation output module is used to generate water quality detection results based on recoverable responses, generate ecological water quality early warning results based on continuous stress responses, and generate propagation-type ecological water quality early warning results based on the occurrence time and water body connection direction when at least two sensor nodes with water body connectivity form continuous stress responses.

[0007] In a preferred embodiment, the response processing module is further configured to determine a stable output state from the continuous bioelectric output of the sensing node before the current deviation occurs; wherein the continuous bioelectric output is not marked as non-water quality response interference data and its output variation amplitude meets the stability maintenance condition, so that the stable output state characterizes the stable electron transfer output of the electroactive biofilm in the sensing node under the current water body background.

[0008] In a preferred embodiment, the response processing module is further configured to determine the allowable fluctuation range corresponding to the stable output state based on the output fluctuation level of the continuous bioelectric output, and synchronously adjust the allowable fluctuation range when the stable output state is redefined, so that the allowable fluctuation range is adapted to the natural output fluctuation of the electroactive biomembrane in the sensing node.

[0009] In a preferred embodiment, the response processing module is further configured to determine that the bioelectric output has deviated from the allowable fluctuation range and reaches the deviation condition when the bioelectric output continuously deviates from the allowable fluctuation range and reaches the deviation condition, so as to distinguish the continuous output change caused by external water quality disturbance from the instantaneous output fluctuation of the sensing node.

[0010] In a preferred embodiment, the response processing module is further configured to determine the recovery observation period based on the existing recovery time of the sensing node and the flow characteristics of the water body, and to take the sampling stage after the current deviation reaches the deviation persistence condition as the starting point of the recovery observation period; wherein, the existing recovery time is determined based on the time required for the previously recoverable response of the sensing node to meet the recovery condition from the occurrence of the deviation.

[0011] In a preferred embodiment, the response processing module is further configured to determine the residual deviation of the bioelectrical output after deviation from the allowable fluctuation range during the recovery observation period; when the residual deviation decreases as the recovery observation progresses and the deviation persistence condition used to determine the deviation is not met again during the recovery observation period, the bioelectrical output after deviation is determined to meet the recovery condition, and the corresponding deviation response process is determined to be a recoverable response; when the bioelectrical output after deviation still does not meet the recovery condition by the end of the recovery observation period, or when the deviation persistence condition is met again during the recovery observation period, the corresponding deviation response process is determined to be a persistent stress response.

[0012] In a preferred embodiment, the response processing module is further configured to determine the degree of persistent stress response based on the residual deviation at the end of the recovery observation period and the duration of the current deviation after the deviation is determined to be a persistent stress response; the evaluation output module is further configured to generate an ecological water quality early warning result based on the degree of persistent stress; wherein, the degree of persistent stress is used to characterize the degree of persistent influence of external water quality disturbance on the electron transport state of the electroactive biofilm within the sensing node.

[0013] In a preferred embodiment, the evaluation output module is further configured to determine the degree of recoverable influence of external water quality disturbance on the electron transport state of the electroactive biofilm within the sensing node based on the deviation intensity and recovery speed corresponding to the recoverable response, and generate water quality detection results characterizing the recoverable water quality disturbance effect based on the degree of recoverable influence.

[0014] In a preferred embodiment, the evaluation output module is further configured to determine the water connectivity relationship and water connectivity direction between the sensor nodes based on the deployment topology information of the sensor nodes and the water flow direction information, and use the water connectivity direction as the basis for judging whether the continuous stress response occurs sequentially along the water connectivity path.

[0015] In a preferred embodiment, the evaluation output module is further configured to determine, after the first sensing node forms a continuous stress response, whether the second sensing node located at a subsequent position in the water body connection direction forms a continuous stress response within the acceptance time range; wherein, the acceptance time range is determined based on the occurrence time of the continuous stress response of the first sensing node and the water body connection path from the first sensing node to the second sensing node; when the second sensing node forms a continuous stress response within the acceptance time range, and the order of occurrence of the continuous stress responses of the first sensing node and the second sensing node is consistent with the water body connection direction, the corresponding continuous stress response is determined to occur sequentially along the water body connection path, and a propagation-type ecological water quality early warning result is generated.

[0016] The effects and advantages of the ecological water quality intelligent assessment system for microbial fuel cell sensor networks of this invention are as follows: This invention uses the stable output state of the sensing node itself as a reference for judging the bioelectrical response, and continues to observe its regression after deviations in the bioelectrical output. This transforms the judgment of single-node response from deviation (abnormality) to recovery judgment after deviation, thereby reducing the direct classification of short-term water quality disturbances into ecological water quality warnings. Furthermore, this invention determines whether to generate a propagating ecological water quality warning result based solely on the formation of a continuous stress response by the sensing node, combined with water body connectivity, occurrence time, and water body connectivity direction. This ensures that propagating ecological water quality warning results no longer rely solely on the number of abnormal nodes or spatial consistency, avoiding the treatment of ecological risk processes with water body connectivity characteristics as multiple isolated anomalies, and improving the consistency between the warning result and the evolution of water body disturbances. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the processing flow of the ecological water quality intelligent assessment system of the present invention; Figure 2 This is a schematic diagram illustrating the recoverable response determination of the midstream node in this invention; Figure 3 This is a schematic diagram of the continuous stress response judgment of the midstream node in this invention; Figure 4 This is a schematic diagram illustrating the early warning judgment of the continuous stress response of the water body connectivity path according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The described embodiments are used to illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention. Other implementation methods obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention.

[0019] This invention provides an intelligent ecological water quality assessment system for microbial fuel cell sensor networks, suitable for scenarios that utilize the bioelectrical output of multiple sensor nodes to identify water quality disturbances, continuous stress responses, and propagation-based ecological water quality early warning results. Conventional water quality monitoring typically generates detection or early warning results based on whether sensor outputs exceed normal ranges, whether the deviation is persistent, or whether multiple nodes deviate simultaneously. The default premise is that deviations in sensor outputs can directly reflect the degree of water quality change.

[0020] However, in the sensing nodes of microbial fuel cells, the bioelectric output is not an instantaneous reading from a regular physical probe, but rather a response influenced by the electron transport state of the electroactive biofilm. The same output deviation may correspond to a recoverable response after a short-term water quality disturbance, or a sustained stress response caused by changes in water composition, toxic stress, or hypoxia. If ecological water quality early warning results are generated solely based on the magnitude or duration of the deviation, recoverable responses are easily misjudged as sustained stress responses. Conversely, if the judgment is based solely on the number of deviations from multiple nodes, sustained stress responses with water connectivity and temporal relationships are easily treated as isolated responses.

[0021] Based on the above scenario, this invention uses the stable output state of the sensing node itself as a reference for the electron transfer state of the electroactive biomembrane. After the bioelectric output deviates from the stable output state, the deviated bioelectric output is continuously acquired. Based on whether the deviated output returns to the allowable fluctuation range and whether the deviation continues to occur again, the corresponding response is classified into recoverable response and continuous stress response. Furthermore, when a continuous stress response occurs in at least two sensing nodes with water body connectivity, the occurrence time of the continuous stress response and the direction of water body connectivity between the sensing nodes are combined to determine whether to generate a propagation-type ecological water quality early warning result.

[0022] Based on the above design, this invention constructs an intelligent ecological water quality assessment system for microbial fuel cell sensor networks. (Refer to...) Figure 1 The system includes a data access module, a response processing module, and an evaluation output module. The data access module is used to access the bioelectric output data of the sensing nodes in the microbial fuel cell sensor network; the response processing module is used to judge the recovery status and continuous stress status after the bioelectric output deviates, using the stable output state of the sensing node itself as a reference; the evaluation output module is used to generate water quality detection results, ecological water quality early warning results, or propagation-type ecological water quality early warning results based on the judgment results of the response processing module.

[0023] The bioelectric output data received by the data access module represents the electrical response of the electroactive biofilm within the microbial fuel cell sensing nodes under the influence of the aquatic environment. Since this bioelectric output is related to the electron transport state of the electroactive biofilm, the same current output value or the same deviation does not necessarily correspond to the same ecological water quality meaning. Therefore, the system does not directly generate water quality change conclusions based on a uniform fixed threshold shared by all sensing nodes. Instead, the response processing module determines the stable output state of each sensing node.

[0024] The response processing module uses the stable output state of the sensing node itself as a reference for the electron transport state of the electroactive biomembrane within that sensing node, and determines the allowable fluctuation range corresponding to the stable output state. The stable output state characterizes the stable electron transport output of the sensing node in the current water body context, and the allowable fluctuation range represents the natural output fluctuations allowed under this stable electron transport state. Therefore, subsequent judgments are based on the bioelectric response state of a single sensing node itself, rather than directly comparing the absolute output magnitudes between different sensing nodes.

[0025] When the bioelectric output deviates from the allowable fluctuation range, the response processing module determines whether the deviation meets the sustained deviation condition. The sustained deviation condition is used to distinguish continuous output changes from transient fluctuations, sampling spikes, or transient invalid data. Only when the bioelectric output continuously deviates from the allowable fluctuation range and meets the sustained deviation condition does the response processing module determine that the bioelectric output has deviated from the stable output state and continue to acquire the deviated bioelectric output.

[0026] Once a deviation is established, the response processing module determines during the recovery observation period whether the bioelectrical output after the deviation returns to the allowable fluctuation range corresponding to the stable output state, and whether the deviation persistence condition is met again during the recovery observation period. If the bioelectrical output after the deviation returns to the allowable fluctuation range, and the deviation persistence condition is not met again during the recovery observation period, the corresponding deviation response process is determined to be a recoverable response. A recoverable response indicates that a bioelectrical output deviation has occurred, but the corresponding water quality disturbance is recoverable.

[0027] If the bioelectric output after deviation fails to meet the recovery condition by the end of the recovery observation period, or if the deviation continues to occur again during the recovery observation period, the response processing module will determine the corresponding deviation response process as a continuous stress response. The continuous stress response indicates that external water quality disturbances may have a continuous impact on the electron transfer state of the electroactive biofilm within the sensing node, which is different from the deviation performance formed by only instantaneous output fluctuations.

[0028] The evaluation output module generates water quality detection results characterizing the recoverable impact of water quality disturbances based on recoverable responses, and generates ecological water quality early warning results based on persistent stress responses. Thus, the system distinguishes between recoverable bioelectrical output deviations that have already occurred and persistent stress states where recovery conditions are not met or where persistent deviation conditions are met again, enabling single-node evaluation results to reflect the differences in recovery capabilities of microbial fuel cell sensing nodes after deviations.

[0029] When outputting results, the evaluation output module records the response judgment result along with the corresponding sampling stage information, and organizes the output fields according to water quality detection results, ecological water quality early warning results, and propagation-type ecological water quality early warning results. For water quality detection results generated by recoverable responses, the output fields include sensor node number, deviation occurrence time, deviation intensity, recovery time, regression status during the recovery observation period, and recoverable impact degree. These fields characterize the process of the sensor node reverting to the allowable fluctuation range corresponding to the stable output state after deviation. For ecological water quality early warning results generated by persistent stress responses, the output fields include sensor node number, persistent stress response occurrence time, residual deviation degree at the end of the recovery observation period, deviation duration, and persistent stress process degree. These fields characterize the process of the sensor node failing to meet the recovery conditions by the end of the recovery observation period, or reaching the persistent deviation condition again during the recovery observation period. For propagation-type ecological water quality early warning results, the output fields include the sequence of sensor nodes forming the persistent stress response, the water body connectivity direction, the transition time range between adjacent sensor nodes, and whether a transition relationship has been formed. The above fields are used to characterize whether the continuous stress responses of multiple sensor nodes are sequentially formed along the water body connectivity direction. The above records can serve as the basis for subsequent early warning display, manual review, or historical response statistics.

[0030] When multiple sensor nodes form a sensor network, the evaluation output module determines the succession relationship between multiple persistent stress responses based on the water connectivity and direction of the water flow between the sensor nodes. When at least two sensor nodes with water connectivity generate persistent stress responses, and the occurrence time of each persistent stress response matches the direction of water flow, the evaluation output module determines that the persistent stress responses occur sequentially along the water flow path and generates a propagation-based ecological water quality early warning result. Therefore, the propagation-based ecological water quality early warning result is based on the temporal succession relationship of persistent stress responses along the water flow path, rather than a simple superposition of the deviations in the current bioelectrical output of multiple nodes.

[0031] In one specific implementation, before determining the current deviation, the response processing module first reviews the valid sampling points of the sensor node before the deviation occurred and selects a continuous sampling segment with relatively small output changes as the stability judgment object. When verifying this continuous sampling segment, the module can check the variation range between adjacent sample values ​​or the difference between the maximum and minimum output values ​​within the segment. If the variation range or difference does not exceed the stability holding threshold, the continuous sampling segment is determined as a stable output segment. The response processing module then determines the stable output state based on the average value, median value, or representative output interval within the stable output segment. The stability holding threshold can be set by considering the sensor node's historical output fluctuations, system configuration parameters, or monitoring scenario requirements.

[0032] When determining a stable output segment, the response processing module can exclude sampling phases marked as non-water quality response interference data. Non-water quality response interference data can include data during sensor node maintenance, data during communication interruptions or unstable communication, data flagged as faulty in the sampling circuit, or data during sensor node self-test failures; this data can be flagged based on node operation logs, communication status flags, or maintenance records. Sampling phases marked as non-water quality response interference data are not used as a basis for determining a stable output state, nor are they considered valid data for judging deviations from continuous conditions.

[0033] The permissible fluctuation range can be formed centered on the stable output state, with its upper and lower limits determined by the output fluctuation level of the stable output segment or a preset fluctuation margin. The preset fluctuation margin can be determined based on the maximum fluctuation amplitude, standard deviation, or system configuration ratio of the stable output segment. When the stable output state is redefined due to changes in the water background, changes in the operating status of the sensor node, or system recalibration, the response processing module synchronously adjusts the corresponding permissible fluctuation range to ensure that the permissible fluctuation range continues to match the natural output fluctuations of the electroactive biofilm within the sensor node.

[0034] The deviation duration condition can be defined as multiple consecutive sampling points of bioelectric output deviating from the allowable fluctuation range, or the duration of deviation from the allowable fluctuation range reaching a preset duration. If a single sampling point deviates from the allowable fluctuation range but does not meet the continuity or duration requirements, the response processing module is unsure if the deviation is valid. Therefore, the deviation duration condition is used to distinguish between continuous output changes caused by external water quality disturbances and instantaneous output fluctuations, sampling spikes, or transient invalid data.

[0035] The recovery observation period begins with the sampling phase after the current deviation reaches the deviation persistence condition and ends with the expiration of the observation duration determined based on the existing recovery time, sampling cycle, and water flow characteristics. The existing recovery time can be determined based on the time required for the previously recoverable response of the sensor node to meet the recovery conditions from the occurrence of the deviation. When the sensor node does not have an existing recovery time, or the existing recovery time is insufficient to reflect the current water background, the recovery observation period can be determined by system configuration parameters. Water flow characteristics may include water flow direction, the length of the water connection path between nodes, the average water velocity, the type of water exchange path, or preset water arrival time parameters.

[0036] The residual deviation is used to represent the amount by which the current bioelectrical output exceeds the boundary of the allowable fluctuation range; when the bioelectrical output returns to the allowable fluctuation range, the residual deviation is zero. During the recovery observation period, if the deviated bioelectrical output returns to the allowable fluctuation range, or if the residual deviation decreases as the recovery observation progresses and does not reach the deviation persistence condition again, then the deviated bioelectrical output is determined to meet the recovery condition; if the above recovery condition is not met by the end of the recovery observation period, or if the deviation persistence condition is reached again during the recovery observation period, then the recovery condition is determined not to be met.

[0037] When determining whether the residual deviation decreases as recovery observation progresses, the response processing module does not necessarily require a monotonically decreasing trend at every sampling point. Instead, it considers the overall changes in the sampling phases before and after the recovery observation period. For example, the recovery observation period can be divided into a pre-period and a post-period. If the average residual deviation in the post-period sampling phase is lower than that in the pre-period sampling phase, or if the residual deviation at several consecutive valid sampling points is lower than that at the beginning of the recovery observation, it is determined that the bioelectrical output after deviation has a tendency to revert to the allowable fluctuation range. If the bioelectrical output has returned to the allowable fluctuation range, the response processing module can also require that it does not reach the deviation persistence condition again in one or more subsequent valid sampling points to avoid misjudging a brief decline as recovery.

[0038] The following specific embodiments illustrate the ecological water quality assessment process of this system in a microbial fuel cell sensor network. These embodiments explain the system's data access, stable output state determination, deviation assessment, recovery observation, recoverable response determination, continuous stress response determination, and propagation-based ecological water quality early warning result generation processes. The specific number of sensor nodes, node spacing, water body type, sampling cycle, bioelectric output type, stability maintenance conditions, allowable fluctuation range, deviation persistence conditions, recovery observation period, acceptance time range, and result output format can be configured according to actual water body monitoring requirements and the operating status of the microbial fuel cell sensor nodes.

[0039] In the following embodiments, a monitoring section of an ecological ditch with upstream to downstream water connectivity is used as an example. Sensor nodes A, B, and C are sequentially deployed along the water flow direction in this monitoring section. Sensor node A is located at the upstream inflow section, sensor node B is located at the section behind the midstream confluence, and sensor node C is located at the downstream outflow section. Sensor nodes A, B, and C are all microbial fuel cell sensor nodes. The bioelectric output data from each sensor node is used to characterize the electron transfer state of its internal electroactive biofilm at the corresponding water location. Based on the node deployment topology and water flow direction information, the system determines the water connectivity direction between the sensor nodes to be from sensor node A to sensor node B and then to sensor node C.

[0040] In Example 1, refer to Figure 2 , Figure 2 This is a schematic diagram for judging the recoverable response of a midstream node. Taking the sensor node B at the section behind the midstream confluence of the ecological ditch monitoring section as an example, which is affected by short-term changes in water composition, the process of determining the recoverable response in this invention is explained.

[0041] In this embodiment, sensor node B is located at the midstream confluence section of the ecological ditch monitoring segment, serving to contact the water flowing through this section and generate bioelectric output through its internal electroactive biofilm. The data access module accesses the bioelectric output data of sensor node B according to a preset sampling process, and the response processing module determines the stable output state of sensor node B and its corresponding allowable fluctuation range based on the continuous bioelectric output before the current disturbance, as described above.

[0042] In this scenario, a water mass with altered composition may briefly enter the vicinity of the midstream confluence. When this water mass passes the section where sensor node B is located, the bioelectrical output of sensor node B may continuously deviate from the permissible fluctuation range. If an ecological water quality warning result is directly generated solely based on the current deviation of sensor node B's bioelectrical output from the permissible fluctuation range, the potentially resilient water quality disturbance may be treated as a persistent stress response. Therefore, this invention does not directly generate an ecological water quality warning result when the bioelectrical output first deviates from the permissible fluctuation range. Instead, it first determines whether the deviation meets the criteria for persistent deviation, and then continues to observe whether the deviation response process is resilient after the deviation is confirmed.

[0043] Specifically, when the bioelectric output of sensor node B continuously deviates from the allowable fluctuation range and reaches the deviation persistence condition used to determine the existence of the deviation, the response processing module determines that the bioelectric output of sensor node B has deviated from its stable output state. The deviation persistence condition is used to exclude single sampling point spikes, instantaneous output fluctuations, or brief invalid data, so that subsequent recovery observation is performed on the deviation response process that has been established, rather than on occasional sampling fluctuations.

[0044] After a deviation is established, the response processing module uses the sampling phase after the deviation reaches the deviation persistence condition as the starting point of the recovery observation period and continues to acquire the bioelectric output of sensor node B after the deviation. In this embodiment, the recovery observation period is determined based on the existing recovery time required for sensor node B's previous recoverable response from the establishment of deviation to meeting the recovery condition, combined with the water flow characteristics of the section behind the midstream confluence. During the recovery observation period, if the influence of the water mass flow that caused the aforementioned short-term water composition change weakens after passing, the bioelectric output of sensor node B gradually returns to the allowable fluctuation range corresponding to the stable output state, the residual deviation decreases with the recovery observation process, and the deviation persistence condition used to determine the establishment of deviation is not reached again during the recovery observation period, then the response processing module determines the deviation response process corresponding to sensor node B as a recoverable response. In this judgment process, if the bioelectric output of sensor node B exhibits slight fluctuations, but the residual deviation degree in the later part of the recovery observation period is generally lower than that in the earlier part, and a continuous deviation state that meets the deviation persistence condition is not formed again, the response processing module can still treat it as a process of returning to the allowable fluctuation range.

[0045] In the above processing, the recoverable response of sensor node B does not indicate that the water body was not disturbed, but rather that the disturbance has caused a deviation in bioelectric output. However, the electron transport state of the corresponding electroactive biofilm can return to a stable output state during the recovery observation process. The evaluation output module generates water quality detection results based on this recoverable response. Specifically, the evaluation output module determines the degree of recoverable impact based on the intensity of the deviation relative to the allowable fluctuation range and the speed at which the bioelectric output returns to the allowable fluctuation range after the deviation, and outputs this degree of recoverable impact as part of the water quality detection results.

[0046] The intensity of deviation can be determined based on the maximum or cumulative deviation of bioelectrical output relative to the allowable fluctuation range during the period when the deviation occurred; the recovery speed can be determined based on the number of samples or time required for the residual deviation to decrease to the recovery condition after the deviation occurred. The greater the intensity of deviation, or the longer the number of samples or time required for recovery, the higher the recoverable impact. The water quality detection results can record sensor node B, the time period of the deviation, the intensity of deviation, the recovery time, the regression status after the deviation, and the recoverable impact. Since sensor node B did not reach the condition of continued deviation again during the recovery observation period, and the bioelectrical output returned to the allowable fluctuation range after the deviation, the evaluation output module does not use this deviation response process as a continuous stress response to generate an ecological water quality early warning result.

[0047] In another processing scenario, if the bioelectric output of sensor node B only briefly deviates from the allowable fluctuation range but does not reach the condition for sustained deviation, the response processing module will not determine the brief deviation as a valid deviation response process, nor will it initiate a recoverable response judgment based on the brief deviation. If the bioelectric output of sensor node B approaches the allowable fluctuation range during the recovery observation period but subsequently reaches the condition for sustained deviation again, it will not be determined as a recoverable response according to this embodiment, but should instead enter the judgment process for sustained stress response.

[0048] In this embodiment, after the bioelectric output of sensor node B deviates to meet the condition for continued deviation, the system uses the recovery observation period to determine whether the bioelectric output after the deviation has returned and whether the condition for continued deviation has been met again. This allows the water quality disturbance that has occurred but is recoverable to be output as the water quality detection result, avoiding the direct treatment of recoverable deviation response process as a continuous stress response.

[0049] In Example 2, refer to Figure 3 , Figure 3 The diagram illustrates the judgment path when a midstream node deviates and fails to meet the recovery condition or reaches the continued deviation condition again. Taking sensor node B at the section downstream of the midstream confluence of the ecological ditch monitoring segment as an example, the process of determining the continuous stress response in this invention is explained.

[0050] In this embodiment, the deployment location and data access method of sensor node B can be the same as in Embodiment 1. Before the occurrence of this water quality stress, sensor node B has already formed a continuous and stable bioelectric output. The response processing module uses this continuous and stable bioelectric output to determine the stable output state of sensor node B itself, and determines the allowable fluctuation range corresponding to this stable output state. Unlike Embodiment 1, in this embodiment, the midstream inlet continuously enters water that affects the electron transfer state of the electroactive biofilm within sensor node B, causing the bioelectric output of sensor node B to deviate and fail to return according to a recoverable response process, or to reach the continued deviation condition again during the return process.

[0051] In this scenario, the bioelectric output of sensor node B may deviate from the allowable fluctuation range at a certain stage and experience a local decline during the recovery observation period. If the system directly generates an ecological water quality early warning result based solely on the initial deviation from the allowable fluctuation range, it will confuse the continuous stress response with the instantaneous output fluctuation; if the system directly determines recovery based solely on the local decline, it may miss the continuous stress response corresponding to the re-reaching of the deviation from the continuous condition during the recovery process. Therefore, this invention continues to observe the deviation response process after the deviation is established, and uses whether the recovery condition is met at the end of the recovery observation period and whether the deviation from the continuous condition is reached again during the recovery observation period as the criteria for judging the continuous stress response.

[0052] Specifically, when persistent water quality stress enters the section where sensor node B is located, the bioelectrical output of sensor node B continuously deviates from the allowable fluctuation range, reaching the deviation persistence condition used to determine the existence of the deviation. Based on this, the response processing module determines that the bioelectrical output of sensor node B has deviated from its stable output state, and uses the sampling phase after this deviation reaches the deviation persistence condition as the starting point of the recovery observation period, continuing to acquire the bioelectrical output of sensor node B after the deviation. The recovery observation period can be determined based on the existing recovery time of sensor node B and the water flow characteristics of the section downstream of the midstream inlet, enabling the system to judge the recovery status after the deviation within an observation range suitable for the node's response characteristics. The existing recovery time of sensor node B can be determined by the time required for the previously recoverable response of the sensor node to go from the occurrence of the deviation to the fulfillment of the recovery condition.

[0053] In one scenario, the bioelectrical output of sensor node B fails to consistently return to the allowable fluctuation range during the recovery observation period, or fails to meet the recovery conditions by the end of the recovery observation period. In this case, the response processing module determines that the deviation response process has not formed the regression state required for a recoverable response and identifies the deviation response process as a persistent stress response.

[0054] In another scenario, the bioelectric output of sensor node B initially approached the allowable fluctuation range during the recovery observation period, but subsequently deviated from the allowable fluctuation range again, reaching the condition for continued deviation. This determination of reaching the continued deviation condition is not based on a rebound at a single sampling point, but rather on a continuous deviation from the allowable fluctuation range or a deviation duration reaching a preset duration. This avoids directly classifying small fluctuations during the recovery process as a continuous stress response. In this case, although the bioelectric output experienced a localized decline after the deviation, its re-reaching of the continued deviation condition during the recovery observation period indicates that the electron transport state of the electroactive biomembrane within sensor node B is still continuously affected. The response processing module also classifies this deviation response process as a continuous stress response.

[0055] After determining that sensor node B has formed a persistent stress response, the response processing module determines the degree of persistent stress based on the residual deviation at the end of the recovery observation period and the duration of the current deviation. The residual deviation indicates the extent of deviation of the bioelectric output from the allowable fluctuation range at the end of the recovery observation period, while the duration of the current deviation indicates the length of time the bioelectric output maintains the deviation. The larger the residual deviation or the longer the duration of the current deviation, the more significant the sustained impact of external water quality disturbance on the electron transport state of the electroactive biofilm within sensor node B, and the higher the corresponding degree of persistent stress. The system can classify the degree of persistent stress into different levels or output it as a continuous evaluation value in the ecological water quality early warning results; the specific level classification can be determined according to the system configuration parameters. In one output mode, the response processing module can determine the degree of persistent stress as low, medium, or high according to the levels corresponding to the residual deviation at the end of the recovery observation period and the duration of the current deviation, respectively; alternatively, it can output the residual deviation and the duration of the current deviation as continuous evaluation parameters to characterize the impact of the persistent stress response.

[0056] The evaluation output module generates an ecological water quality early warning result based on the continuous stress response generated by sensor node B. This early warning result records sensor node B, the time of occurrence of the continuous stress response, the degree of continuous stress, and the corresponding monitoring location. Compared to the water quality detection results in Example 1, the ecological water quality early warning result in this example is not based on the first deviation from the allowable fluctuation range, but rather on the failure to meet the recovery conditions after the deviation is established, or the re-reaching of the continuous deviation condition during the recovery observation period.

[0057] In another processing scenario, if the bioelectrical output of sensor node B deviates from the allowable fluctuation range but does not reach the condition for sustained deviation, the response processing module will not directly determine this brief deviation as a sustained stress response. If sensor node B returns to the allowable fluctuation range during the recovery observation period and does not reach the condition for sustained deviation again, it will be determined as a recoverable response according to Example 1, and no ecological water quality early warning result will be generated.

[0058] In this embodiment, the system establishes the sustained stress response based on the recovery observation results after the deviation is established, so that the ecological water quality early warning result corresponds to the deviation response process that fails to recover or reaches the sustained deviation condition again, rather than corresponding to the initial output deviation of sensor node B. Therefore, the system can distinguish between the restorative effects of water quality disturbances and water quality stresses that have a sustained impact on the electron transport state of electroactive biofilms.

[0059] In Example 3, refer to Figure 4 , Figure 4This diagram illustrates the early warning judgment based on the continuous stress response of a water body connectivity path. Taking sensor nodes A, B, and C, sequentially deployed along the water flow direction in an ecological ditch monitoring section, as an example, the generation process of the propagation-type ecological water quality early warning result in this invention is explained.

[0060] In this embodiment, sensor node A is located at the upstream inflow section, sensor node B is located at the midstream confluence section, and sensor node C is located at the downstream outflow section. The evaluation output module determines that there is a water connectivity relationship between sensor nodes A, B, and C based on the deployment topology information of sensor nodes A, B, and C, and the water flow direction information of the ecological ditch monitoring section. The module also determines that the water connectivity direction is from sensor node A to sensor node B and then to sensor node C. The deployment topology information may include sensor node numbers, the section where the node is located, the upstream and downstream order of the node, and the water connectivity path between adjacent nodes. The water flow direction information is used to determine the water connectivity direction between different sensor nodes. This water connectivity direction serves as the basis for subsequent judgment on whether the sustained stress response occurs sequentially along the water connectivity path.

[0061] In this scenario, multiple sensor nodes may individually exhibit deviations in their bioelectrical output. If a propagation-type ecological water quality warning is generated solely based on the fact that sensor nodes A, B, and C all exhibit current bioelectrical output deviations, or solely based on the fact that multiple nodes generate ecological water quality warnings, it is easy to misjudge independent local water quality stresses as water quality risks carried along the water body connectivity path. Therefore, this invention, when making propagation-type ecological water quality warning judgments, does not use the number of current bioelectrical output deviations of multiple nodes as a direct basis. Instead, it uses whether each sensor node generates a continuous stress response, whether the occurrence time of the continuous stress response meets the carrying time range, and whether the order of occurrence is consistent with the water body connectivity direction as the judgment criteria.

[0062] Specifically, when persistent water quality stress occurs at the upstream inflow section, the bioelectric output of sensor node A deviates from its stable output state, satisfying the condition for continued deviation. Following the continuous stress response judgment method in Example 2, the response processing module continues to acquire the deviated bioelectric output after the deviation of sensor node A is established, and determines whether the recovery condition is met during the recovery observation period. If sensor node A still does not meet the recovery condition by the end of the recovery observation period, or if it reaches the condition for continued deviation again during the recovery observation period, the response processing module determines that sensor node A has formed a continuous stress response. The evaluation output module records the occurrence time of the continuous stress response of sensor node A and uses this occurrence time as the starting time for subsequent judgments.

[0063] Subsequently, sensor node B, located at a later position in the water body connectivity direction, also exhibits a deviation response. The response processing module still uses the stable output state of sensor node B itself as a reference to determine whether its bioelectrical output has deviated to meet the deviation persistence condition, and enters the recovery observation period after the deviation is confirmed. If sensor node B is determined to be a persistent stress response, the evaluation output module further determines whether the occurrence time of the persistent stress response of sensor node B falls within the acceptance time range corresponding to sensor node A to sensor node B. The occurrence time of the persistent stress response used for acceptance judgment is recorded as the time when the deviation of this sensor node reaches the deviation persistence condition; when the persistent stress response is determined to be a result of reaching the deviation persistence condition again during the recovery observation period, the time when the deviation persistence condition is reached again can be recorded separately as the persistent stress response confirmation time, but this confirmation time does not replace the occurrence time of the persistent stress response used for acceptance judgment.

[0064] In this embodiment, the acceptance time range corresponding to sensor node A to sensor node B is determined by taking the occurrence time of the continuous stress response of sensor node A as the starting reference, and combining the length of the water connection path, the path connection type, the average flow velocity range of the water body, or a preset water arrival time parameter between sensor node A and sensor node B. Specifically, the evaluation output module can determine the shortest and longest arrival times of the water disturbance from the section where sensor node A is located to the section where sensor node B is located based on the above parameters, and take the occurrence time of the continuous stress response of sensor node A plus the shortest arrival time as the starting point of the acceptance time range, and take the occurrence time of the continuous stress response of sensor node A plus the longest arrival time as the ending point of the acceptance time range. Sensor node B is only judged to have formed an acceptance with the continuous stress response of sensor node A if it forms a continuous stress response within this acceptance time range; if the occurrence time of the continuous stress response of sensor node B is earlier or later than this acceptance time range, it is uncertain whether it has formed an acceptance with the continuous stress response of sensor node A.

[0065] When sensor node B generates a sustained stress response within the time frame corresponding to the connection between sensor node A and sensor node B, the evaluation output module confirms that a sustained stress response connection has been formed between sensor node A and sensor node B. Subsequently, the system continues to assess the response status of downstream sensor node C. If sensor node C generates a sustained stress response with reference to its stable output state, and the occurrence time of sensor node C's sustained stress response falls within the time frame corresponding to the connection between sensor node B and sensor node C, then the evaluation output module confirms that a sustained stress response connection has been formed between sensor node B and sensor node C. For any two adjacent sensor nodes on the water connectivity path, the upstream sensor node that first generates a sustained stress response is designated as the first sensor node, and the sensor node located at a subsequent position in the water connectivity direction is designated as the second sensor node. The evaluation output module determines whether a sustained stress response connection has been formed between the two nodes according to the aforementioned time frame.

[0066] When sensor nodes A, B, and C each generate persistent stress responses, and the order in which these responses occur aligns with the water connectivity path from A to B and then to C, the evaluation output module identifies the corresponding persistent stress responses as occurring sequentially along the water connectivity path and generates a propagation-based ecological water quality early warning result. This propagation-based early warning result records the sequence of sensor nodes generating persistent stress responses, the water connectivity path, the occurrence time of each persistent stress response, and the succession relationship between nodes, thus characterizing the temporal succession of persistent stress responses along the water connectivity path.

[0067] In another scenario, if sensor node A only generates a recoverable response while sensor node B generates a persistent stress response, the evaluation output module will not generate a propagation-type ecological water quality early warning result based on sensor nodes A and B. If sensor node C generates a persistent stress response before sensor node A, or if sensor node B generates a persistent stress response but its occurrence time is earlier or later than the corresponding transition time range from sensor node A to sensor node B, the evaluation output module will not determine the corresponding persistent stress response as occurring sequentially along the direction from sensor node A to sensor node B to sensor node C, nor will it directly generate a propagation-type ecological water quality early warning result. In the above cases, the system can output single-node ecological water quality early warning results separately, or generate a prompt for review.

[0068] In this embodiment, the system establishes propagation-type ecological water quality early warning results based on the temporal continuity of continuous stress responses along the water body connectivity path. Responses from multiple sensor nodes do not automatically constitute a propagation-type ecological water quality early warning result. Only when a confirmed continuous stress response meets the temporal continuity range and its order of occurrence aligns with the direction of water body connectivity, is a propagation-type ecological water quality early warning result generated. Therefore, the system can distinguish water quality risks formed along the water body connectivity path from localized continuous stress responses occurring at multiple monitoring locations.

[0069] The intelligent ecological water quality assessment system in the above embodiments can be implemented through software, hardware, firmware, or a combination thereof. When implemented in software, the program instructions for implementing the above processing can be stored in a computer-readable storage medium and read and executed by a processor to complete processes such as bioelectrical output data access, stable output state determination, allowable fluctuation range determination, deviation from continuous conditions judgment, recovery observation, recoverable response determination, continuous stress response determination, and generation of propagation-type ecological water quality early warning results. In specific deployment, the data access module, response processing module, and assessment output module can be integrated into the same water quality monitoring terminal, or they can be set at the sensor node, edge computing terminal, or server terminal respectively; the modules can transmit bioelectrical output data, response judgment results, and early warning results according to the actual monitoring area, number of sensor nodes, sampling cycle, and communication conditions.

[0070] The above embodiments are used to illustrate the specific implementation process of the technical solution of this application. The scope of protection of this application is determined by the claims. Within the scope defined by the claims, conventional substitutions or adaptations made to the sensor node access method, computing device, storage medium, communication method, module deployment method and result output method are still within the scope of protection of this application.

Claims

1. An intelligent ecological water quality assessment system for microbial fuel cell sensor networks, characterized in that, include: The data access module is used to access the bioelectric output data of the sensing node; The response processing module is used to take the stable output state of the sensing node itself as a reference for the electron transfer state of the electroactive biomembrane. After the bioelectric output deviates from the condition of continuous deviation, it continues to acquire the deviated bioelectric output and determines whether it is a recoverable response or a continuous stress response based on the return to the allowable fluctuation range corresponding to the stable output state during the recovery observation period and the situation of reaching the condition of continuous deviation again. The response processing module is also used to determine the recovery observation period based on the existing recovery time of the sensor node and the flow characteristics of the water body, and to take the sampling stage after the current deviation reaches the deviation persistence condition as the starting point of the recovery observation period; wherein, the existing recovery time is determined based on the time required for the previous recoverable response of the sensor node to meet the recovery condition from the occurrence of the deviation. The response processing module is also used to determine the residual deviation of the bioelectrical output after deviation from the allowable fluctuation range during the recovery observation period; when the residual deviation decreases as the recovery observation progresses and the deviation persistence condition used to determine the existence of deviation is not reached again during the recovery observation period, the bioelectrical output after deviation is determined to meet the recovery condition, and the corresponding deviation response process is determined to be a recoverable response; when the bioelectrical output after deviation still does not meet the recovery condition when the recovery observation period ends, or the deviation persistence condition is reached again during the recovery observation period, the corresponding deviation response process is determined to be a persistent stress response; The evaluation output module is used to generate water quality detection results based on recoverable responses, generate ecological water quality early warning results based on continuous stress responses, and generate propagation-type ecological water quality early warning results based on the occurrence time and water body connection direction when at least two sensor nodes with water body connectivity form continuous stress responses.

2. The ecological water quality intelligent assessment system for microbial fuel cell sensor networks according to claim 1, characterized in that, The response processing module is also used to determine the stable output state from the continuous bioelectric output of the sensing node before the current deviation occurs; wherein the continuous bioelectric output is not marked as non-water quality response interference data, and its output change amplitude meets the stability maintenance condition, so that the stable output state characterizes the stable electron transfer output of the electroactive biofilm in the sensing node under the current water body background.

3. The ecological water quality intelligent assessment system for microbial fuel cell sensor networks according to claim 2, characterized in that, The response processing module is also used to determine the allowable fluctuation range corresponding to the stable output state based on the output fluctuation level of the continuous bioelectric output, and to synchronously adjust the allowable fluctuation range when the stable output state is redefined, so that the allowable fluctuation range is adapted to the natural output fluctuation of the electroactive biomembrane in the sensing node.

4. The ecological water quality intelligent assessment system for microbial fuel cell sensor networks according to claim 3, characterized in that, The response processing module is also used to determine that the bioelectric output has deviated from the allowable fluctuation range and reached the deviation condition when the bioelectric output continues to deviate from the allowable fluctuation range and reaches the deviation condition, so as to distinguish the continuous output change caused by external water quality disturbance from the instantaneous output fluctuation of the sensing node.

5. The ecological water quality intelligent assessment system for microbial fuel cell sensor networks according to claim 4, characterized in that, The response processing module is also used to determine the degree of persistent stress response after the deviation is identified as a persistent stress response, based on the residual deviation at the end of the recovery observation period and the duration of the deviation. The evaluation output module is also used to generate ecological water quality early warning results based on the degree of persistent stress. The degree of persistent stress is used to characterize the degree of persistent impact of external water quality disturbance on the electron transport state of electroactive biofilms within the sensing node.

6. The ecological water quality intelligent assessment system for microbial fuel cell sensor networks according to claim 1, characterized in that, The evaluation output module is also used to determine the degree of recoverable impact of external water quality disturbance on the electron transfer state of electroactive biofilm within the sensing node based on the deviation intensity and recovery speed corresponding to the recoverable response, and to generate water quality detection results characterizing the recoverable impact of water quality disturbance based on the degree of recoverable impact.

7. The intelligent ecological water quality assessment system for microbial fuel cell sensor networks according to claim 1, characterized in that, The evaluation output module is also used to determine the water connectivity relationship and water connectivity direction between sensor nodes based on the deployment topology information of the sensor nodes and the water flow direction information, and to use the water connectivity direction as the basis for judging whether the continuous stress response occurs sequentially along the water connectivity path.

8. The intelligent ecological water quality assessment system for microbial fuel cell sensor networks according to claim 7, characterized in that, The evaluation output module is also used to determine whether a second sensor node located at a subsequent position in the water body connection direction forms a continuous stress response within the acceptance time range after the first sensor node forms a continuous stress response. The acceptance time range is determined based on the occurrence time of the continuous stress response of the first sensor node and the water body connection path from the first sensor node to the second sensor node. When the second sensor node forms a continuous stress response within the acceptance time range, and the order of occurrence of the continuous stress responses of the first and second sensor nodes is consistent with the water body connection direction, the corresponding continuous stress response is determined to occur sequentially along the water body connection path, and a propagation-type ecological water quality early warning result is generated.

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