Method and system for regulating incubation environment of leiocassis longirostris based on internet of things multi-parameter sensing

Abstract

This invention discloses a method and system for regulating the hatching environment of *Catfish simonii* based on multi-parameter sensing via the Internet of Things (IoT), specifically relating to the field of multivariable environment automatic control technology. It addresses the problem of inaccurate multivariable control commands in existing collaborative control methods due to reliance on distorted data when sensing network dynamics. By acquiring multi-parameter monitoring data of the water body, network topology information, and equipment status data, it establishes spatial gradient change predictions for key parameters to diagnose node performance degradation and detects equipment start-up and shutdown events to analyze transient response curves and determine abnormal coupling relationships. Subsequently, it identifies affected parameters and regions, and performs reliability assessment and correction on data from degraded nodes. Finally, based on the corrected reliable data, it generates collaborative control commands for heating, aeration, and water flow equipment. This achieves robust collaborative control under conditions of sensing uncertainty, improving the stability and accuracy of multivariable environment control.

Description

Technical Field

[0001] This invention relates to the field of automatic control technology for multivariable environments, and more specifically, to a method and system for regulating the hatching environment of catfish based on multi-parameter sensing via the Internet of Things. Background Technology

[0002] In the multivariate fine control of aquaculture environments such as the hatching of long-snout catfish, an integrated Internet of Things (IoT) sensor network is used to monitor key parameters such as water temperature, dissolved oxygen, pH, and ammonia nitrogen in real time. Control algorithms are then used to coordinate and regulate equipment such as heating, oxygenation, and water flow to achieve a stable hatching environment. This method relies on the continuous and accurate perception of the state of multiple water parameters, which serves as the decision-making basis for automated coordinated regulation.

[0003] However, in actual operation, sensor networks deployed in the incubation environment often experience local node failures or performance degradation due to factors such as biological attachment, equipment malfunctions, or signal interference. This leads to dynamic changes in the topology and data quality of the sensing network, resulting in incomplete spatial coverage or reduced confidence of the system state information relied upon by the collaborative control system built on a fixed, ideal sensing model. Consequently, multivariate control commands become inaccurate, making it difficult to maintain the dynamic balance of multiple parameters required for the incubation water body, thus affecting the stability and reliability of the control effect. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for controlling the hatching environment of catfish based on Internet of Things multi-parameter sensing to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for controlling the hatching environment of catfish based on multi-parameter sensing via the Internet of Things includes the following steps:

[0007] S1: Acquire multi-parameter monitoring data of water bodies collected by the Internet of Things (IoT) sensor network, topology information of the IoT sensor network, and working status data of the execution devices;

[0008] S2: Based on topology information and working status data, establish spatial gradient change predictions for key water quality parameters, and determine whether there is node performance degradation in the IoT sensor network by comparing multi-parameter monitoring data of water bodies with spatial gradient change predictions.

[0009] S3: When it is determined that there is node performance degradation, detect the start and stop events of the execution equipment from the working status data, extract the transient response curves of the associated water quality parameters corresponding to the start and stop events, and determine whether the coupling relationship is abnormal by analyzing the transient response curves;

[0010] S4: Based on the judgment results of node performance degradation and the judgment results of abnormal coupling relationship, identify the affected associated parameters and associated regions;

[0011] S5: Based on the judgment results of node performance degradation and the judgment results of coupling relationship anomalies, conduct credibility assessment and correction of the multi-parameter monitoring data of water body provided by the degraded nodes.

[0012] S6: Based on the water body multi-parameter monitoring data that has been reliably assessed and corrected, the affected related parameters and related areas, generate coordinated control commands for the execution equipment.

[0013] Furthermore, S1 includes:

[0014] Acquire multi-parameter monitoring data of water bodies, which includes the parameter types of each sensor node in the Internet of Things sensor network and the monitoring values ​​it collects;

[0015] Obtain the topology information of the IoT sensor network, which includes the location identifier and communication connection relationship of each sensor node, as well as the spatial correspondence between the sensor nodes and the execution devices;

[0016] Acquire the operating status data of the execution device, which includes the operating mode and real-time power parameters of the execution device.

[0017] Furthermore, S2 includes:

[0018] Based on the operating mode and real-time power parameters in the working status data of the actuator, determine the intensity and range of disturbance to the water body by the actuator;

[0019] By combining the spatial correspondence between sensor nodes and execution devices in the topology information of IoT sensor networks, the theoretical distribution of key water quality parameters under disturbance intensity and range of action is derived to form spatial gradient change predictions.

[0020] The monitoring values ​​corresponding to key water quality parameters in the multi-parameter monitoring data of water bodies are compared one by one with the expected values ​​of their locations in the theoretical distribution pattern.

[0021] When the monitored value of any sensor node continuously deviates from its corresponding expected value by more than the preset tolerance range, it is determined that there is node performance degradation in the IoT sensor network.

[0022] Furthermore, S3 includes:

[0023] The step changes in the operating mode and real-time power parameters in the working status data of the monitoring and execution equipment are used to identify the start-up and shutdown events of the execution equipment.

[0024] For each identified start-stop event, the data sequence of related water quality parameters directly affected by the executed equipment within the event time window is extracted from the multi-parameter monitoring data of the water body to form a transient response curve;

[0025] The rise time and steady-state establishment time of the transient response curve are extracted as morphological features;

[0026] The morphological features are compared with the baseline morphological features of corresponding parameters under historical normal events;

[0027] When the deviation between the morphological features and the baseline morphological features exceeds the allowable threshold, the coupling relationship is judged to be abnormal.

[0028] Furthermore, the baseline morphological features are obtained by feature extraction and statistical analysis of multiple transient response curves of corresponding parameters under historical normal events; the allowable threshold is set based on the statistical variance of the baseline morphological features.

[0029] Furthermore, S4 includes:

[0030] Based on the judgment results of node performance degradation, the location identifiers of the degraded sensor nodes and the key water quality parameters they monitor are determined as preliminary affected correlation parameters and correlation areas;

[0031] Based on the results of the abnormal judgment of the coupling relationship, the execution equipment that caused the abnormal start-up and shutdown event and the water quality parameters directly affected by it are determined as the associated parameters and associated areas affected by the disturbance.

[0032] Spatial correlation analysis is performed on the initially affected correlation parameters and regions and the correlation parameters and regions affected by the disturbance. The union and intersection of the two are taken to comprehensively determine the final affected correlation parameters and regions.

[0033] Furthermore, S5 includes:

[0034] Based on the location identifier of the degraded sensor node and its monitored key water quality parameters in the judgment result of node performance degradation, as well as the execution device that generates abnormal start-stop events and its directly affected water quality parameters in the judgment result of abnormal coupling relationship, the credibility weight of the multi-parameter monitoring data of water body provided by the degraded node is calculated.

[0035] Based on the credibility weight, the multi-parameter water monitoring data provided by the performance degradation nodes are weighted and fused to reduce the impact of the performance degradation nodes;

[0036] By utilizing the spatial relationships between adjacent sensor nodes in the topology information of the Internet of Things sensor network, and the monitoring values ​​of adjacent sensor nodes in the multi-parameter monitoring data of water bodies, spatial interpolation correction is performed on the multi-parameter monitoring data of water bodies provided by the degraded nodes to generate corrected multi-parameter monitoring data of water bodies.

[0037] Furthermore, the spatial interpolation correction specifically involves: based on the spatial distance between sensor nodes in the topology information of the IoT sensor network, using the inverse distance weighting method, and employing multi-parameter water monitoring data from adjacent normal sensor nodes to estimate and replace the data provided by the performance-degraded nodes.

[0038] Furthermore, S6 includes:

[0039] Based on the water body multi-parameter monitoring data that has been assessed and corrected for reliability, the deviation between the current state of the affected related parameters and the preset target state is calculated.

[0040] Based on the spatial extent of the affected associated areas, determine the spatial distribution characteristics of the deviation and the control priorities;

[0041] Based on the control priority and spatial distribution characteristics, coordinate the working modes and output intensity of heating equipment, oxygenation equipment and water flow equipment in the execution equipment;

[0042] Based on the coordinated results, generate and output coordinated control commands for heating equipment, aeration equipment, and water flow equipment.

[0043] On the other hand, the present invention provides a multi-parameter sensing system for controlling the hatching environment of catfish based on the Internet of Things, comprising the following modules:

[0044] The data acquisition module is used to acquire multi-parameter monitoring data of water bodies collected by the Internet of Things (IoT) sensor network, topology information of the IoT sensor network, and working status data of the execution equipment.

[0045] The performance diagnostic module is used to establish the expected spatial gradient changes of key water quality parameters based on topology information and working status data, and to determine whether there is node performance degradation in the IoT sensor network by comparing the multi-parameter monitoring data of the water body with the expected spatial gradient changes.

[0046] The coupling analysis module is used to detect the start-up and shutdown events of the execution equipment from the working status data when it is determined that there is node performance degradation, extract the transient response curves of the associated water quality parameters corresponding to the start-up and shutdown events, and determine whether the coupling relationship is abnormal by analyzing the transient response curves.

[0047] The impact identification module is used to identify the affected associated parameters and associated regions based on the judgment results of node performance degradation and the judgment results of abnormal coupling relationships.

[0048] The data correction module is used to assess and correct the credibility of the multi-parameter water monitoring data provided by the deteriorating nodes based on the judgment results of node performance degradation and the judgment results of abnormal coupling relationships.

[0049] The collaborative control module is used to generate collaborative control commands for the execution equipment based on the multi-parameter monitoring data of the water body that has been assessed and corrected for reliability, the affected related parameters and related areas.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. By simultaneously introducing a dual diagnostic mechanism of spatial gradient change prediction and equipment transient response analysis, the ability to identify node performance degradation and system coupling anomalies under dynamic changes in the sensing network is effectively improved. Spatial gradient change prediction, based on equipment operating status and network topology, can sensitively detect spatial monitoring information distortion caused by node failure or data drift. Transient response analysis, on the other hand, captures anomalies in the dynamic relationship between the actions of the executing equipment and the feedback of water quality parameters from a temporal perspective, thereby identifying hidden faults that are difficult to detect using traditional methods. This spatiotemporal combined diagnostic approach enables the system to more comprehensively and reliably assess the overall credibility of the sensing data, providing a solid basis for subsequent decision-making.

[0052] 2. Based on the above diagnostic results, the system can perform targeted reliability assessment and correction on the identified unreliable monitoring data, accurately pinpoint the affected parameters and areas, and generate adaptive collaborative control commands. This ensures that control decisions are always based on the most reliable environmental state information, effectively avoiding the problem of inaccurate multivariable control commands caused by partial failure of sensing data. Even in complex environments with multiple parameters and multiple devices coupled, the system can still maintain the coordination and stability of control actions, significantly enhancing the robustness of the entire control system and the control accuracy of environmental parameters. Attached Figure Description

[0053] Figure 1 This is a flowchart of the method for controlling the hatching environment of catfish based on multi-parameter sensing via the Internet of Things, as described in this invention.

[0054] Figure 2 This is a schematic diagram of the structure of the long-snout catfish incubation environment control system based on Internet of Things multi-parameter sensing of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0056] Example 1: Figure 1 The present invention provides a method for controlling the hatching environment of catfish based on multi-parameter sensing via the Internet of Things, which includes the following steps:

[0057] S1: Acquire multi-parameter monitoring data of water bodies collected by the Internet of Things (IoT) sensor network, topology information of the IoT sensor network, and working status data of the execution devices;

[0058] S2: Based on topology information and working status data, establish spatial gradient change predictions for key water quality parameters, and determine whether there is node performance degradation in the IoT sensor network by comparing multi-parameter monitoring data of water bodies with spatial gradient change predictions.

[0059] S3: When it is determined that there is node performance degradation, detect the start and stop events of the execution equipment from the working status data, extract the transient response curves of the associated water quality parameters corresponding to the start and stop events, and determine whether the coupling relationship is abnormal by analyzing the transient response curves;

[0060] S4: Based on the judgment results of node performance degradation and the judgment results of abnormal coupling relationship, identify the affected associated parameters and associated regions;

[0061] S5: Based on the judgment results of node performance degradation and the judgment results of coupling relationship anomalies, conduct credibility assessment and correction of the multi-parameter monitoring data of water body provided by the degraded nodes.

[0062] S6: Based on the water body multi-parameter monitoring data that has been reliably assessed and corrected, the affected related parameters and related areas, generate coordinated control commands for the execution equipment.

[0063] In the specific implementation process, step S1 is implemented as follows:

[0064] An Internet of Things (IoT) sensor network deployed in the hatching waters of the long-snout catfish periodically collects monitoring values ​​of four key water quality parameters: water temperature, dissolved oxygen concentration, pH value, and ammonia nitrogen concentration. Each IoT sensor node is configured to collect one or more water quality parameters and has a unique logical identifier. Each IoT sensor node measures water using a sensor probe according to a preset sampling period, such as once every 5 minutes, and encapsulates the measured monitoring value along with the logical identifier, parameter type, and timestamp into a data message. The data message is transmitted via the wireless communication link of the IoT sensor network. The system receives and parses the data message, extracts the parameter type of each sensor node and the monitoring value it has collected, and organizes and stores the data according to the logical identifier and timestamp to form a structured multi-parameter water monitoring dataset.

[0065] The topology information of the IoT sensor network is constructed based on predefined and maintained physical attributes and logical relationships. The location identifier of each IoT sensor node is recorded using actual physical coordinates determined during deployment, with the incubation pool as a reference frame, such as using three-dimensional coordinate values. The communication connections between IoT sensor nodes are determined during the network initialization phase by running a routing discovery protocol. This protocol detects the wireless signal strength and connectivity between nodes and forms a mesh connection diagram describing the direct or indirect data relay capabilities between nodes. The spatial correspondence between IoT sensor nodes and execution devices is determined based on the engineering layout drawings of the incubation pool. This relationship clarifies the monitoring association between each heating device, aeration device, and water flow device within its effective range and IoT sensor nodes at specific coordinate locations. For example, a mapping relationship is established between the logical identifiers of one aerator and three dissolved oxygen sensor nodes covering its bubble diffusion area. All location identifiers, communication connections, and spatial correspondence information are pre-configured and stored in a topology configuration file, which is loaded when the system starts to obtain the complete topology information of the IoT sensor network.

[0066] The operating status data of the actuators is obtained from the heating equipment, aeration equipment, and water flow equipment. For each actuator, data is collected in real time through the status monitoring interface on its control circuit board or an external smart meter. The operating mode is obtained by reading the relay on / off signals of the actuator or the status register of the controller. The operating mode includes one of the following: on, off, power adjustment, or fault state. Real-time power parameters are obtained by measuring the AC current and voltage flowing through the actuator and calculating the active power, for example, by using a power metering chip for measurement and calculation. The status code of the operating mode and the value of the real-time power parameter are periodically, for example, every 10 seconds. A real-time status data queue is maintained for each actuator to continuously track changes in the operating mode and real-time power parameters.

[0067] In the specific implementation process, step S2 is implemented as follows:

[0068] Based on the operating mode and real-time power parameters in the working status data of the actuator, the disturbance intensity and range of the actuator on the water body are determined. For actuators operating in the "on" state, the disturbance intensity is calculated by multiplying the real-time power parameter by a pre-calibrated energy conversion efficiency coefficient. The energy conversion efficiency coefficient is obtained by laboratory measurement of the ratio of input power to effective output energy of the same model of actuator under standard operating conditions. For example, the energy conversion efficiency coefficient of the heating device is calibrated to 0.95, so the disturbance intensity calculated with a real-time power parameter of 800 watt-hours is 800 watts × 0.95 = 760 watts. The determination of the range of effect depends on the type and installation parameters of the actuator. For aeration equipment, the range of effect is a circular area centered on the aeration point with a radius pre-determined through gas rise and diffusion experiments. For example, for a specific model of aeration disc at a water depth of 2 meters, the experimentally measured radius of effect is 1.5 meters. For water flow equipment, the range of effect is a flow field coverage area pre-simulated using fluid dynamics simulation software based on the rated flow rate of the water pump and the outlet angle.

[0069] By combining the spatial correspondence between sensor nodes and actuators in the topology information of the IoT sensor network, the theoretical distribution of key water quality parameters under disturbance intensity and range of influence is derived to form a spatial gradient change expectation. Based on the spatial correspondence between sensor nodes and actuators recorded in the topology information, all sensor nodes located within the range of influence of each actuator are selected. For water temperature, a key water quality parameter, the derivation of the theoretical distribution is based on a simplified physical model that considers heat conduction and convection. This model treats the heating device as a point heat source. According to the law of heat conduction, the expected steady-state temperature at a specific location from the heat source is linearly related to the logarithm of the heat source disturbance intensity. The linear proportionality coefficient is determined by the thermal diffusivity of the water body. A typical value of the thermal diffusivity can be obtained by consulting a water property handbook, for example, at 20... The dissolved oxygen concentration in freshwater at 1 degree Celsius is approximately 0.0014 square centimeters per second. Based on this relationship, and combined with the precise distance between each sensor node and the heating device, the expected water temperature at each sensor node location is calculated. For dissolved oxygen, a key water quality parameter, the theoretical distribution pattern is derived based on a gas dissolution and diffusion equilibrium model. This model treats the aeration device as a point source, assuming that in areas where the device operates continuously and the water is relatively still, the expected dissolved oxygen concentration decreases exponentially with increasing distance. The decay constant is related to the water's oxygen consumption rate and the oxygen diffusion coefficient, parameters which can be obtained through fitting from previous aquaculture experiments. Based on this model, and combined with the distance between the sensor node and the aeration device, the expected dissolved oxygen concentration at each sensor node location is calculated. The set of all calculated expected values ​​constitutes the expected spatial gradient change for the current operating state of the device.

[0070] The monitoring values ​​corresponding to key water quality parameters in the multi-parameter monitoring data of the water body are compared one by one with the expected values ​​of their locations in the theoretical distribution pattern. The monitoring values ​​reported by each sensor node are extracted from the real-time acquired multi-parameter monitoring data of the water body. For example, the water temperature monitoring value reported by a sensor node with the location identifier T-01 is 26.5 degrees Celsius. Based on the sensor node with the location identifier T-01, the corresponding expected water temperature value is found from the expected spatial gradient change, for example, the search result is 26.8 degrees Celsius. The difference between the monitored value of 26.5 degrees Celsius and the expected value of 26.8 degrees Celsius is calculated, and the deviation value is obtained as -0.3 degrees Celsius. This comparison process traverses all relevant sensor nodes and their corresponding key water quality parameters.

[0071] When the monitored value of any sensor node continuously deviates from its corresponding expected value beyond a preset tolerance range, it is determined that there is node performance degradation in the IoT sensor network. The preset tolerance range is determined by statistically analyzing the deviation distribution between the monitoring data of multiple water parameters and the expected spatial gradient changes within a historical normal monitoring period. The historical normal monitoring period is a continuous time period in which the system has been operating stably without reporting any faults, for example, 30 consecutive days. Within this period, the aforementioned comparison process is performed daily, recording the deviation between each sensor node's monitored value and the expected value for each key water quality parameter. For a specific sensor node and a specific key water quality parameter, the arithmetic mean and standard deviation of all deviation data over the entire historical period are calculated. The preset tolerance range is set with this average value as the center. The range is defined by extending the range by twice the standard deviation in both the positive and negative directions. For example, if the calculated average deviation is 0.1 degrees Celsius and the standard deviation is 0.05 degrees Celsius, then the lower limit of the preset tolerance range is 0.1 - 2 × 0.05 = 0 degrees Celsius, and the upper limit is 0.1 + 2 × 0.05 = 0.2 degrees Celsius. During real-time operation, the real-time deviation of a specific sensor node is continuously checked to see if it exceeds the preset tolerance range. If the number of times the deviation exceeds the preset tolerance range reaches a preset threshold, the sensor node is determined to have performance degradation. The threshold is set according to the system's requirements for stability and sensitivity, for example, it is set to 5 consecutive sampling periods. If the number of times the deviation exceeds the preset tolerance range reaches 5 times, the sensor node is determined to have performance degradation, that is, there is node performance degradation in the IoT sensor network.

[0072] In the specific implementation process, step S3 is implemented as follows:

[0073] When step S2 determines that there is node performance degradation in the IoT sensor network, it begins monitoring the step changes in the operating mode and real-time power parameters of the execution devices to identify start-stop events. It continuously listens to the operating mode status word and real-time power parameter values ​​reported by each execution device. A transition from off to on in the operating mode status word is defined as a start event; a transition from on to off in the operating mode status word is defined as a stop event. For the real-time power parameter, the difference between two adjacent sampling points is calculated; when the absolute value of this difference exceeds a pre-set power change threshold, a power step is considered to have occurred. The power change threshold is set as a percentage of the rated power of the execution device; for example, for a heating device with a rated power of 1000 watts, the power change threshold can be set to 500 watts. The power step event, combined with the transition of the operating mode status word, is determined as a start-stop event. All identified events are recorded, including the event type, the timestamp of occurrence, and the associated execution device identifier.

[0074] For each identified start-up / shutdown event, a transient response curve is formed by extracting the data sequence of relevant water quality parameters directly affected by the executing device within the event time window from the multi-parameter water monitoring data. Based on the spatial correspondence between sensor nodes and executing devices in the topology information of the IoT sensor network, the types of water quality parameters directly affected by the event-related executing device and their corresponding sensor nodes are determined. Using the timestamp of the event as a reference point, a short segment of steady-state data is extracted forward, and a sufficiently long segment of data is extracted backward to observe the change process; these together constitute an event time window. The length of the event time window is determined based on the longest time required for the water quality parameters to reach a new steady state after the historical observation of the executing device's start-up / shutdown. For example, for an aerator start-up event, data from 5 minutes before the event to 30 minutes after the event is extracted. From the stored multi-parameter water monitoring data, the chronological sequence of monitoring values ​​for the relevant water quality parameters for each affected sensor node within the event time window is extracted; this sequence constitutes a transient response curve.

[0075] The rise time and steady-state settling time of the transient response curve are extracted as morphological features. For the transient response curve corresponding to the initiation event, the rise time is defined as the time required from the moment the event occurs until the monitored value first reaches and remains within a preset percentage range of the steady-state expected value. The steady-state expected value is calculated based on the average value of the monitored values ​​over a period of time before the event occurs, and the preset percentage range is, for example, ±2% of the steady-state expected value. The steady-state settling time is defined as the time required from the moment the event occurs until the monitored value last enters and remains within that preset percentage range of the steady-state expected value. The extraction process is automatically completed by traversing the data sequence and applying the above-mentioned conditional judgment algorithm, and the calculation results are recorded in seconds.

[0076] The morphological characteristics were compared with the baseline morphological characteristics of the corresponding parameters under historical normal events. The baseline morphological characteristics were obtained by feature extraction and statistical analysis of multiple transient response curves of the corresponding parameters under historical normal events. Historical normal events refer to start-up and shutdown events triggered by the same actuator during past confirmed fault-free system operation that did not trigger alarms. A large number of transient response curves of such events were collected, and the rise time and steady-state establishment time of each curve were extracted using the same method. For a specific event-parameter combination consisting of the same actuator and the same water quality parameter, the arithmetic mean of all historical rise times and the arithmetic mean of all historical steady-state establishment times were calculated; these two averages together constitute the baseline morphological characteristics of the event-parameter combination.

[0077] When the deviation of the morphological feature from the baseline morphological feature exceeds the allowable threshold, the coupling relationship is judged to be abnormal. The allowable threshold is set based on the statistical variance of the baseline morphological feature. When calculating the average value of the baseline morphological feature, the standard deviation of its historical data is calculated simultaneously. The allowable threshold is set as the interval formed by adding or subtracting a certain number of standard deviations from the average value of the baseline morphological feature. The multiple is selected according to the requirements of system stability, for example, 2 times the standard deviation. For rise time morphological features, the lower limit of its allowable threshold is the average value of the baseline rise time minus 2 times its standard deviation, and the upper limit is the average value of the baseline rise time plus 2 times its standard deviation. For steady-state settling time morphological features, the same calculation method is used to set its independent allowable threshold. In real-time judgment, the rise time morphological feature extracted from the new event is compared with the allowable threshold of the corresponding baseline rise time, and the steady-state settling time morphological feature extracted from the new event is compared with the allowable threshold of the corresponding baseline steady-state settling time. If either the rise time or the steady-state settling time of the new event exceeds its corresponding allowable threshold range, the coupling relationship between the executing device and the associated water quality parameters in this event is judged to be abnormal.

[0078] In the specific implementation process, step S4 is implemented as follows:

[0079] Based on the node performance degradation assessment, the location identifiers of the degraded sensor nodes and their monitored key water quality parameters are determined as the initially affected associated parameters and regions. The node performance degradation assessment results are derived from the final output of step S2; the list of logical identifiers of sensor nodes determined to be degraded is read from this result. For each logical identifier in the list, the topology information of the IoT sensor network is queried, and the logical identifier is parsed into the corresponding physical location coordinates, i.e., the location identifier. Simultaneously, based on the information recorded by each degraded sensor node during system initialization configuration, the type of key water quality parameter that the node is responsible for monitoring is determined; for example, if a sensor node with the logical identifier SN-123 is configured to monitor water temperature and dissolved oxygen concentration, then its corresponding key water quality parameters are water temperature and dissolved oxygen concentration. The physical space area commonly covered by the location identifiers of all degraded sensor nodes is defined as the initially affected associated region; the set of key water quality parameter types monitored by all degraded sensor nodes is defined as the initially affected associated parameters.

[0080] Based on the anomaly judgment results of the coupling relationship, the executing device that caused the abnormal start-stop event and its directly affected water quality parameters are determined as the associated parameters and associated regions affected by the disturbance. The anomaly judgment results of the coupling relationship are derived from the final output of step S3; the event records judged as abnormal in the results are read, and each record indicates the identifier of the executing device that triggered the abnormal event. For each executing device identifier that caused the abnormal event, the spatial correspondence between sensor nodes and executing devices in the topology information of the IoT sensor network is queried; this correspondence clearly records the type of water quality parameter directly affected by each executing device. For example, the heating device with the identifier HEATER-01 directly affects the water temperature, and the aerator with the identifier AERATOR-02 directly affects the dissolved oxygen concentration. These types of water quality parameters directly affected by the abnormal executing devices constitute the associated parameters affected by the disturbance. At the same time, based on the geometric parameters of the effective range predefined for each executing device in the topology information, the physical spatial region of the water body it affects is determined; for example, the effective range of the heating device HEATER-01 is defined as a spherical region with a radius of 1.5 meters centered on its installation point. The effective range of all execution devices that generate abnormal events is geometrically merged, and the resulting overall region is defined as the associated region affected by the disturbance.

[0081] Spatial correlation analysis is performed on the initially affected correlation parameters and regions, and the correlation parameters and regions affected by the disturbance. The union and intersection of the two are taken to comprehensively determine the final affected correlation parameters and regions. The spatial correlation analysis first assesses the degree of spatial proximity and overlap between the initially affected correlation regions and the disturbed correlation regions. Each region is represented as a polygon using the coordinates of its boundary points. The overlapping portion is obtained by calculating the intersection of two polygonal regions; the total area they jointly cover is obtained by calculating the union of two polygonal regions. If the Euclidean distance between the center points of two polygonal regions is less than a preset spatial correlation threshold, or if the proportion of their intersection area to the original area of ​​either region is greater than a preset area overlap ratio threshold, then the two regions are determined to be spatially correlated. The spatial correlation threshold is set based on the average deployment spacing of IoT sensor nodes in the incubation pool, for example, 2 meters; the area overlap ratio threshold is set empirically, for example, 30%.

[0082] Based on the determination of spatial correlation, set union and intersection operations are performed to obtain a comprehensive conclusion. For correlated parameters, the union of the initially affected correlated parameter set and the disturbed correlated parameter set is taken, that is, all water quality parameter types that have appeared are merged to form the final list of affected correlated parameters. For correlated regions, the union of the initially affected correlated regions and the disturbed correlated regions is used as the basic framework, while highlighting the intersection of these two regions. The final affected correlated regions are defined as: all parts of the above union region whose distance from the boundary of the intersection region is within the spatial correlation threshold, plus the intersection region itself. This definition method ensures that it focuses on the core area where the problem overlaps, while not omitting the surrounding areas that may be affected by cascading effects. Finally, a description file containing the final list of affected correlated parameters and the final boundary coordinates of the affected correlated regions is output as the completion result of step S4.

[0083] In the specific implementation process, step S5 is implemented as follows:

[0084] Based on the location identifier of the degraded sensor node and its monitored key water quality parameters in the node performance degradation judgment result, and the execution device that generated the abnormal start-stop event and its directly affected water quality parameters in the coupling anomaly judgment result, the reliability weight of the multi-parameter monitoring data of the water body provided by the degraded node is calculated. The node performance degradation judgment result output in step S2 is read. This result includes a list of degraded sensor nodes and the ratio of the real-time deviation value calculated for each node in step S2 to its preset tolerance range; this ratio is called the relative deviation. Simultaneously, the final list of affected related parameters output in step S4 is read, and it is checked whether the key water quality parameters monitored by the degraded node are included in this list. If included, the deviation of the morphological characteristics recorded in step S3 for the relevant abnormal start-stop event from the baseline morphological characteristics is further read. This deviation is expressed as a percentage exceeding the allowable threshold. The initial reliability weight is calculated using a linear function. The input of this function is the relative deviation, and the output is limited to between 0 and 1. For example, the initial weight is set to equal 1 minus the relative deviation, but not lower than 0. If the parameters monitored by a node are determined to be affected by an abnormal event, a reduction is applied to the initial weight. The reduction coefficient is calculated using another linear function, whose input is the percentage deviation of morphological features. For example, the reduction coefficient is set to 1 minus the percentage deviation of morphological features divided by a maximum allowable deviation percentage. The reduction coefficient is also limited to between 0 and 1. The final confidence weight of a performance-degraded node is equal to its initial weight multiplied by the reduction coefficient. If the node's parameters are not affected by an abnormal event, the reduction coefficient is 1, and the final weight is equal to the initial weight.

[0085] Based on the calculated final credibility weight, the multi-parameter water monitoring data provided by degraded nodes are weighted and fused to reduce their impact. For a specific monitoring point at a given time, the monitoring values ​​of all sensor nodes that can provide data for that point are collected. These nodes include in-situ sensor nodes deployed at that point and neighboring sensor nodes identified by topology information as spatially adjacent to that point whose data can be used for interpolation. A fusion weight is assigned to each monitoring value. For sensor nodes identified as degraded, the fusion weight is directly adopted using the calculated final credibility weight; for normal sensor nodes not identified as degraded, the fusion weight is fixed at 1. The weighted fusion value for that point is calculated through the following process: multiplying the monitoring value reported by each sensor node by its corresponding fusion weight to obtain a series of weighted values; summing all weighted values ​​to obtain a weighted sum; simultaneously, summing all fusion weights to obtain a weighted sum; the weighted fusion value is equal to the weighted sum divided by the weighted sum. This calculation process is applied independently to each key water quality parameter, ultimately generating a set of merged water quality parameter values ​​for each monitoring point.

[0086] This paper utilizes the spatial relationships between adjacent sensor nodes in the topology information of an IoT sensor network and the monitoring values ​​of adjacent sensor nodes in the multi-parameter water monitoring data to perform spatial interpolation correction on the multi-parameter water monitoring data provided by degraded nodes, generating corrected multi-parameter water monitoring data. Specifically, the spatial interpolation correction involves using an inverse distance weighting method based on the spatial distances between sensor nodes in the IoT sensor network topology information, and estimating and replacing the data provided by the degraded nodes using the multi-parameter water monitoring data of adjacent normal sensor nodes. For each degraded sensor node, all normal sensor nodes in its vicinity are searched as candidate reference nodes; the candidate reference nodes must satisfy the condition that the Euclidean distance between them and the degraded node is less than a preset interpolation search radius, for example, a search radius of 3 meters. From the candidate nodes, the N nodes closest to the degraded node are selected as actual reference nodes, where N is, for example, 3. For each key water quality parameter monitored by the degraded node, inverse distance weighted interpolation is performed separately. First, the spatial distance from the degraded node to each actual reference node is calculated. Then, an interpolation weight is calculated for the monitoring value of each actual reference node. This interpolation weight is equal to the negative p-th power of the distance from that node to the deteriorating node, i.e., 1 divided by the distance raised to the power of p. The power parameter p is a constant greater than 0, typically taken as 2. Next, a weighted sum is calculated, which is the sum of the products of the monitoring value of each actual reference node multiplied by its interpolation weight. Simultaneously, a total weight sum is calculated, which is the sum of all interpolation weights. The interpolated estimate of this water quality parameter is equal to the weighted sum divided by the weighted sum. Finally, the calculated interpolated estimate is used to overwrite the original monitoring values ​​reported by the deteriorating nodes; for nodes not identified as deteriorating, their monitoring values ​​remain unchanged. The set of updated monitoring values ​​for all nodes constitutes the corrected multi-parameter water body monitoring data.

[0087] In the specific implementation process, step S6 is implemented as follows:

[0088] Based on the multi-parameter water quality monitoring data after the credibility assessment and correction in step S5, the deviation between the current state and the preset target state of the final affected related parameters determined in step S4 is calculated. The final list of affected related parameters output in step S4 is read; for each water quality parameter in the list, the monitoring values ​​reported by all sensor nodes deployed within the final affected related area output in step S4 are extracted from the multi-parameter water quality monitoring data after the credibility assessment and correction in step S5; the arithmetic mean of these monitoring values ​​is calculated as the current state of the parameter. The preset target state is pre-set and stored in the database according to the long-snout catfish hatching process. This process defines target values ​​for each key water quality parameter at each stage of the hatching process. For example, in the mid-hatching stage, the preset target state for water temperature is 26.5 degrees Celsius, and the preset target state for dissolved oxygen concentration is 7.0 mg / L. The deviation is calculated as the difference between the average value of the current state and the corresponding target value of the preset target state; for example, if the average water temperature of the current state is 25.8 degrees Celsius, then the water temperature deviation is -0.7 degrees Celsius.

[0089] Based on the spatial range of the final affected associated area determined in step S4, the spatial distribution characteristics and control priorities of the deviation are determined. Spatial distribution characteristics are characterized by analyzing the local deviations at the locations of each sensor node within the associated area; the local deviation is the monitored value of a single sensor node minus the preset target value of that parameter. The location coordinates of all sensor nodes within the associated area are obtained using the topology information of the IoT sensor network. Spatial distribution characteristics include the arithmetic mean, maximum, and minimum values ​​of the local deviations, as well as a gradient index characterizing the degree of spatial variation. The gradient index is calculated by first identifying all spatially adjacent pairs of sensor nodes within the associated area, calculating the absolute value of the difference in local deviations between each pair, summing all the absolute values, and then dividing by the product of the number of node pairs and the average distance between them. Control priorities are determined based on a comprehensive consideration of the overall severity of the deviation and the degree of spatial non-uniformity. The overall severity of the deviation is graded according to the range of its absolute value; for example, an absolute value greater than 0.5 degrees Celsius is defined as a severe water temperature deviation, and an absolute value greater than 0.5 mg / L is defined as a severe dissolved oxygen deviation. Spatial non-uniformity is determined by comparing a gradient index with a preset uniformity threshold. The uniformity threshold is set based on the statistical distribution of gradient indices within the associated region during historical normal periods; for example, twice the average historical gradient index value is used as the threshold. The control priority score is calculated using a weighted summation formula. The input variables of the formula include the normalized value of the absolute deviation, the score corresponding to the severity level of the deviation, and the score corresponding to the level of spatial non-uniformity. The weights of each variable are preset according to the requirements of the incubation process for parameter stability and uniformity.

[0090] Based on the control priority and spatial distribution characteristics, the working modes and output intensities of heating equipment, aeration equipment, and water flow equipment in the execution equipment are coordinated. The coordination process is based on a capability list containing information on all execution equipment, including the identifier, equipment type, controllable water quality parameter type, maximum output intensity, minimum output intensity, and spatial coordinate description of the equipment's influence range for each device. All parameter-region combinations requiring control are sorted in descending order according to their control priority scores. Processing begins with the combination with the highest score; for the water quality parameter and associated region corresponding to this combination, all execution equipment whose influence range intersects with the associated region and can control the water quality parameter are selected from the capability list, forming a subset of equipment to be coordinated. An optimization model is established to calculate the output intensity of each device in the subset of equipment to be coordinated; the objective function of the model is to minimize the overall deviation between the predicted state and the preset target state of the water quality parameter in the associated region, while minimizing total energy consumption; the constraints of the model include that the output intensity of each device must be between its minimum and maximum output intensity, and the joint influence of all devices must cover the key part of the associated region. The model is solved using linear programming or quadratic programming algorithms to obtain the optimal suggested value for the output intensity of each device. When processing the next priority parameter-region combination, the influence of coordinated equipment must be considered, and the optimization model should be iteratively adjusted if necessary. The final coordination result is to determine a specific operating mode for each participating actuator, such as on or off, and a specific output intensity setpoint, such as the target power value for heating equipment.

[0091] Based on the coordinated results, collaborative control commands for heating, aeration, and water flow equipment are generated and output. These control commands are structured digital commands. For heating equipment, the command includes a device identifier field, a command type field, and a command parameter field; the command type field indicates power setting, and the command parameter field contains a power value in watts. For aeration equipment, the command includes a device identifier field, a command type field, and a command parameter field; the command type field indicates air volume adjustment or start / stop control, and the command parameter field contains a percentage value or a Boolean switch value. For water flow equipment, the command includes a device identifier field, a command type field, and a command parameter field; the command type field indicates flow rate or speed setting, and the command parameter field contains a flow rate value in liters per minute or a speed percentage value. All commands also include a sequence number and a timestamp field. Through an IoT communication protocol, multiple control commands for different actuators are packaged into a single command set and sent to the corresponding device controller network. The device controller parses the commands and drives the actuators to change their operating modes and output intensity according to the command requirements, thereby achieving collaborative regulation of the water environment for the hatching of the long-snout catfish.

[0092] Example 2: Figure 2A schematic diagram of the incubation environment control system for long-snout catfish based on IoT multi-parameter sensing of the present invention is given. The long-snout catfish incubation environment control system based on IoT multi-parameter sensing includes the following modules:

[0093] The data acquisition module is used to acquire multi-parameter monitoring data of water bodies collected by the Internet of Things (IoT) sensor network, topology information of the IoT sensor network, and working status data of the execution equipment.

[0094] The performance diagnostic module is used to establish the expected spatial gradient changes of key water quality parameters based on topology information and working status data, and to determine whether there is node performance degradation in the IoT sensor network by comparing the multi-parameter monitoring data of the water body with the expected spatial gradient changes.

[0095] The coupling analysis module is used to detect the start-up and shutdown events of the execution equipment from the working status data when it is determined that there is node performance degradation, extract the transient response curves of the associated water quality parameters corresponding to the start-up and shutdown events, and determine whether the coupling relationship is abnormal by analyzing the transient response curves.

[0096] The impact identification module is used to identify the affected associated parameters and associated regions based on the judgment results of node performance degradation and the judgment results of abnormal coupling relationships.

[0097] The data correction module is used to assess and correct the credibility of the multi-parameter water monitoring data provided by the deteriorating nodes based on the judgment results of node performance degradation and the judgment results of abnormal coupling relationships.

[0098] The collaborative control module is used to generate collaborative control commands for the execution equipment based on the multi-parameter monitoring data of the water body that has been assessed and corrected for reliability, the affected related parameters and related areas.

[0099] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0100] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0101] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0102] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0104] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0105] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for controlling the hatching environment of catfish based on multi-parameter sensing via the Internet of Things, characterized in that, Includes the following steps: S1: Acquire multi-parameter monitoring data of water bodies collected by the Internet of Things (IoT) sensor network, topology information of the IoT sensor network, and working status data of the execution devices; S2: Based on topology information and working status data, establish spatial gradient change predictions for key water quality parameters, and determine whether there is node performance degradation in the IoT sensor network by comparing multi-parameter monitoring data of water bodies with spatial gradient change predictions. S3: When it is determined that there is node performance degradation, detect the start and stop events of the execution equipment from the working status data, extract the transient response curves of the associated water quality parameters corresponding to the start and stop events, and determine whether the coupling relationship is abnormal by analyzing the transient response curves; S4: Based on the judgment results of node performance degradation and the judgment results of abnormal coupling relationship, identify the affected associated parameters and associated regions; S5: Based on the judgment results of node performance degradation and the judgment results of coupling relationship anomalies, conduct credibility assessment and correction of the multi-parameter monitoring data of water body provided by the degraded nodes. S6: Based on the water body multi-parameter monitoring data that has been reliably assessed and corrected, the affected related parameters and related areas, generate coordinated control commands for the execution equipment.

2. The method for controlling the hatching environment of catfish based on multi-parameter sensing via the Internet of Things as described in claim 1, characterized in that, S1 includes: Acquire multi-parameter monitoring data of water bodies, which includes the parameter types of each sensor node in the Internet of Things sensor network and the monitoring values ​​it collects; Obtain the topology information of the IoT sensor network, which includes the location identifier and communication connection relationship of each sensor node, as well as the spatial correspondence between the sensor nodes and the execution devices; Acquire the operating status data of the execution device, which includes the operating mode and real-time power parameters of the execution device.

3. The method for controlling the hatching environment of catfish based on multi-parameter sensing via the Internet of Things as described in claim 1, characterized in that, S2 includes: Based on the operating mode and real-time power parameters in the working status data of the actuator, determine the intensity and range of disturbance to the water body by the actuator; By combining the spatial correspondence between sensor nodes and execution devices in the topology information of IoT sensor networks, the theoretical distribution of key water quality parameters under disturbance intensity and range of action is derived to form spatial gradient change predictions. The monitoring values ​​corresponding to key water quality parameters in the multi-parameter monitoring data of water bodies are compared one by one with the expected values ​​of their locations in the theoretical distribution pattern. When the monitored value of any sensor node continuously deviates from its corresponding expected value by more than the preset tolerance range, it is determined that there is node performance degradation in the IoT sensor network.

4. The method for controlling the hatching environment of catfish based on multi-parameter sensing via the Internet of Things as described in claim 1, characterized in that, S3 includes: The step changes in the operating mode and real-time power parameters in the working status data of the monitoring and execution equipment are used to identify the start-up and shutdown events of the execution equipment. For each identified start-stop event, the data sequence of related water quality parameters directly affected by the executed equipment within the event time window is extracted from the multi-parameter monitoring data of the water body to form a transient response curve; The rise time and steady-state establishment time of the transient response curve are extracted as morphological features; The morphological features are compared with the baseline morphological features of corresponding parameters under historical normal events; When the deviation between the morphological features and the baseline morphological features exceeds the allowable threshold, the coupling relationship is judged to be abnormal.

5. The method for controlling the hatching environment of catfish based on multi-parameter sensing via the Internet of Things according to claim 4, characterized in that, The baseline morphological features are obtained by feature extraction and statistical analysis of multiple transient response curves of corresponding parameters under historical normal events; the allowable threshold is set based on the statistical variance of the baseline morphological features.

6. The method for controlling the hatching environment of catfish based on multi-parameter sensing via the Internet of Things according to claim 1, characterized in that, S4 includes: Based on the judgment results of node performance degradation, the location identifiers of the degraded sensor nodes and the key water quality parameters they monitor are determined as preliminary affected correlation parameters and correlation areas; Based on the results of the abnormal judgment of the coupling relationship, the execution equipment that caused the abnormal start-up and shutdown event and the water quality parameters directly affected by it are determined as the associated parameters and associated areas affected by the disturbance. Spatial correlation analysis is performed on the initially affected correlation parameters and regions and the correlation parameters and regions affected by the disturbance. The union and intersection of the two are taken to comprehensively determine the final affected correlation parameters and regions.

7. The method for controlling the hatching environment of catfish based on multi-parameter sensing via the Internet of Things according to claim 1, characterized in that, S5 include: Based on the location identifier of the degraded sensor node and its monitored key water quality parameters in the judgment result of node performance degradation, as well as the execution device that generates abnormal start-stop events and its directly affected water quality parameters in the judgment result of abnormal coupling relationship, the credibility weight of the multi-parameter monitoring data of water body provided by the degraded node is calculated. Based on the credibility weight, the multi-parameter water monitoring data provided by the performance degradation nodes are weighted and fused to reduce the impact of the performance degradation nodes; By utilizing the spatial relationships between adjacent sensor nodes in the topology information of the Internet of Things sensor network, and the monitoring values ​​of adjacent sensor nodes in the multi-parameter monitoring data of water bodies, spatial interpolation correction is performed on the multi-parameter monitoring data of water bodies provided by the degraded nodes to generate corrected multi-parameter monitoring data of water bodies.

8. The method for controlling the hatching environment of catfish based on multi-parameter sensing via the Internet of Things according to claim 7, characterized in that, The spatial interpolation correction is specifically as follows: based on the spatial distance between sensor nodes in the topology information of the Internet of Things sensor network, the inverse distance weighting method is used to estimate and replace the data provided by the degraded nodes using the multi-parameter monitoring data of the water body from adjacent normal sensor nodes.

9. The method for controlling the hatching environment of catfish based on multi-parameter sensing via the Internet of Things according to claim 1, characterized in that, S6 include: Based on the water body multi-parameter monitoring data that has been assessed and corrected for reliability, the deviation between the current state of the affected related parameters and the preset target state is calculated. Based on the spatial extent of the affected associated areas, determine the spatial distribution characteristics of the deviation and the control priorities; Based on the control priority and spatial distribution characteristics, coordinate the working modes and output intensity of heating equipment, oxygenation equipment and water flow equipment in the execution equipment; Based on the coordinated results, generate and output coordinated control commands for heating equipment, aeration equipment, and water flow equipment.

10. A multi-parameter sensing system for controlling the hatching environment of *Catfish simonii* based on the Internet of Things (IoT), used to implement the multi-parameter sensing method for controlling the hatching environment of *Catfish simonii* based on the IoT as described in any one of claims 1-9, characterized in that... Includes the following modules: The data acquisition module is used to acquire multi-parameter monitoring data of water bodies collected by the Internet of Things (IoT) sensor network, topology information of the IoT sensor network, and working status data of the execution equipment. The performance diagnostic module is used to establish the expected spatial gradient changes of key water quality parameters based on topology information and working status data, and to determine whether there is node performance degradation in the IoT sensor network by comparing the multi-parameter monitoring data of the water body with the expected spatial gradient changes. The coupling analysis module is used to detect the start-up and shutdown events of the execution equipment from the working status data when it is determined that there is node performance degradation, extract the transient response curves of the associated water quality parameters corresponding to the start-up and shutdown events, and determine whether the coupling relationship is abnormal by analyzing the transient response curves. The impact identification module is used to identify the affected associated parameters and associated regions based on the judgment results of node performance degradation and the judgment results of abnormal coupling relationships. The data correction module is used to assess and correct the credibility of the multi-parameter water monitoring data provided by the deteriorating nodes based on the judgment results of node performance degradation and the judgment results of abnormal coupling relationships. The collaborative control module is used to generate collaborative control commands for the execution equipment based on the multi-parameter monitoring data of the water body that has been assessed and corrected for reliability, the affected related parameters and related areas.

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