Ecological carrying capacity overload risk real-time monitoring method based on big data analysis

Abstract

This invention relates to the field of ecological environment monitoring and intelligent early warning control technology, specifically to a real-time monitoring method for ecological carrying capacity overload risk based on big data analysis, comprising: multimodal monitoring data including first modality spatial matrix data, second modality temporal sensing data, and third modality text statistical data; generating a modality confidence imbalance index to characterize the degree of distortion in the multimodal data input distribution; determining whether the modality confidence imbalance index reaches a preset trust threshold; if the modality confidence imbalance index is lower than the trust threshold, generating a first overload risk probability and a first intervention command; if the modality confidence imbalance index is greater than or equal to the trust threshold, generating a second overload risk probability and a second intervention command; and executing feedback control and human-machine node scheduling for the target ecosystem. This invention improves the reliability of information source assessment under multi-source sensing.

Description

Technical Field

[0001] This invention relates to the field of ecological environment monitoring and intelligent early warning control technology, specifically to a method for real-time monitoring of ecological carrying capacity overload risk based on big data analysis. Background Technology

[0002] In the current ecosystem management environment based on big data analysis and feature retrieval, various physical nodes and external systems within the region continuously generate massive amounts of multimodal monitoring data. These data cover spatial remote sensing matrices, time-series sensing waveforms, and text statistical logs, exhibiting significant spatiotemporal scale heterogeneity differences. Furthermore, they are often accompanied by asynchronous communication interruptions and partial sampling gaps due to extreme operating conditions or equipment failures.

[0003] To analyze the regional ecological carrying capacity, existing solutions generally adopt a static high-dimensional fusion architecture, which directly splices multi-source features and inputs them into a single model to perform judgment calculations. Although this solution has a certain situational inference capability in the ideal scenario of lossless full-modality operation, it is highly dependent on the complete input of all data and lacks dynamic confidence quantification and controlled dimensionality reduction mechanisms for heterogeneous channel quality. Once it encounters local sensor disconnection or distribution distortion caused by environmental occlusion, it is very easy to cause frequent oscillations of algorithm channels and over-inference of local features, which in turn leads to secondary allocation contradictions and makes it difficult to maintain the robust and continuous operation of the feedback control loop in the disaster recovery state.

[0004] Therefore, how to improve the dynamic assessment capability of channel confidence under multi-source heterogeneous sensing conditions, and ensure the robust output of overload risk warning and intervention control when the system faces sensing distortion, has become an urgent technical problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a real-time monitoring method for the risk of ecological carrying capacity overload based on big data analysis, and to solve the following technical problems:

[0006] To avoid losing physical takeover and protection control during critical disaster recovery periods due to over-reliance on the resilience of complex models, and to adopt a forced low-frequency degradation stable operation rule architecture when encountering channel crisis environments such as perceived distortion, thereby avoiding the risk of secondary allocation contradictions caused by frequent oscillations of algorithm channels, and to achieve continuous monitoring and executable control under disaster recovery conditions such as highly incomplete input.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A real-time monitoring method for the risk of ecological carrying capacity overload based on big data analysis includes the following steps:

[0009] S1. Collect multimodal monitoring data of the target ecosystem's operational status through the physical acquisition interface of the environmental sensor network. The multimodal monitoring data includes first-modal spatial matrix data, second-modal temporal sensor data, and third-modal textual statistical data. Store the multimodal monitoring data as an ecological big data set in a pre-set distributed database, and pre-configure generative multimodal models and principal component analysis or clustering statistical dimensionality reduction models in the big data analysis system for data retrieval and feature extraction.

[0010] S2. Extract the communication drop rate matrix representing the physical connection integrity of each modality data, the generation weight ratio parameter representing the current distribution state of each modality data, and the trust destruction cost factor generated by the historical data anomaly tracing model corresponding to the physical acquisition interface. Use a preset weighted distribution coefficient to perform a weighted summation calculation on the aggregated dimensionality reduction value of the communication drop rate matrix, the generation weight ratio parameter, and the trust destruction cost factor to generate a modality confidence imbalance index used to characterize the degree of distortion in the multimodal data input distribution.

[0011] S3. Determine whether the modal confidence imbalance index has reached a preset trust threshold.

[0012] S4. If the modal confidence imbalance index is lower than the trust threshold, the multimodal monitoring data is input into the generative multimodal model for feature extraction and vector splicing fusion to generate a first reconstructed tensor. The first reconstructed tensor is then substituted into a preset ultimate bearing pressure function, wherein the ultimate bearing pressure function is configured as a network mapping function that extracts pollutant input, heat load index and flow fluctuation amplitude from the first reconstructed tensor and performs multidimensional weighted calculation, and numerical solutions are obtained to generate a first overload risk probability and a first intervention instruction.

[0013] S5. If the modal confidence imbalance index is greater than or equal to the trust threshold, the system resource degradation mechanism is triggered, the generative multimodal model is suspended, and a preset principal component analysis or clustering statistical dimensionality reduction model is called to perform trend fitting on the non-missing data modal that does not contain null value fields in the multimodal monitoring data and extract key feature quantities to generate a second reconstruction tensor. The second reconstruction tensor is then substituted into a preset ultimate bearing pressure function for numerical solution to generate a second overload risk probability and a second intervention instruction.

[0014] S6. Based on the aforementioned judgment and calculation, the first overload risk probability and the first intervention command, or the second overload risk probability and the second intervention command, send a matching electrical control or mechanical braking action command data packet to the automatic control equipment in the target ecosystem, and simultaneously send the corresponding scheduling event data packet to the human-machine interaction node to perform feedback control and human-machine node scheduling of the target ecosystem.

[0015] Furthermore, S1 also includes a preprocessing sub-step:

[0016] The spatial and temporal resolution of the first modal spatial matrix data and the second modal temporal sensing data are downsampled using a downsampling algorithm, and the time zone and spatial reference system coordinate data are unified. The downsampled data and the third modal text statistical data are then converted to generate a fused network graph structure data with the same timestamp and spatial coordinate reference attributes. This data is stored as a big data graph in a preset monitoring data stream queue and a distributed graph database for rapid retrieval and retrieval by the big data analysis platform.

[0017] Furthermore, the specific computational characteristics of S2 include:

[0018] The communication drop rate matrix is ​​generated by calculating and assigning values ​​to the packet loss rate by traversing the heartbeat packet reception status of each of the physical acquisition interfaces.

[0019] The trust breach cost factor is calculated based on the historical data anomaly tracing model to assess the degree of loss when each of the physical acquisition interfaces fails in the past. The trust breach cost factor increases positively with the increase of the loss rate value of the corresponding interface in the communication drop rate matrix.

[0020] Furthermore, generating the first reconstructed tensor in S4 includes the following sub-steps:

[0021] S41. Activate the generative multimodal model that is in the running state, and read the pre-stored associated feature prior knowledge base and the weight array of the temporal long short-term memory network nodes;

[0022] S42. In response to detecting missing transition points that are determined to be null values ​​in the first modality spatial matrix data and the second modality temporal sensing data, the semantic association feature vector of the corresponding time segment is extracted using the third modality text statistical data. The distribution data in the prior knowledge base of the association features is combined to perform adjacent matrix pixel-level interpolation or temporal mean filling of the missing data blank area. The first reconstructed tensor composed of high-dimensional vectors is generated based on the filling value and the observed value.

[0023] Furthermore, generating the second reconstructed tensor and the instructions based thereon in S5 includes the following sub-steps:

[0024] S51. Interrupt the link process of inputting observations to the generative multimodal model and release the related computing memory resources;

[0025] S52. Filter the high-dimensional sensing point parameters in the multimodal monitoring data that contain missing discontinuous feature fields in the statistical dimension, and filter and retain low-dimensional benchmark observation station data that only have time-continuous measured feature values ​​and preset fixed data packet return interval frequency.

[0026] S53. Call the principal component analysis or clustering statistical dimensionality reduction model to extract the maximum and minimum extreme value coordinates of the low-dimensional benchmark observation station data and the pre-set system safety bearing threshold boundary distribution feature array, and concatenate them into the second reconstructed tensor;

[0027] S54. Calculate the linear geometric approximation distance between the maximum and minimum extreme value coordinates and the safety boundary distribution feature item of the preset bearing limit of the system safety bearing threshold boundary distribution feature array, substitute the absolute value of the distance into the linear classification mapping to generate the second overload risk probability as a numerical result, and the second intervention instruction to be issued directly to indicate the adjustment of the downgraded operation status of the called equipment.

[0028] Furthermore, S3 includes a system boundary anti-oscillation mechanism, specifically including:

[0029] Using a preset fixed system clock cycle as the step size, the generated values ​​of the modal confidence imbalance index are periodically extracted and recorded and stored in a sliding time observation window queue of fixed length.

[0030] The probability distribution information of all modal confidence imbalance index values ​​in the sliding time observation window queue is statistically analyzed in real time. If the ratio of the number of samples with the modal confidence imbalance index greater than or equal to the trust threshold to the total number of samples in the sliding time observation window queue is greater than or equal to a preset coverage ratio safety limit, the condition for exceeding the limit is confirmed and S5 is triggered. If the ratio is less than the coverage ratio safety limit, the original state of the channel is maintained and S4 is executed.

[0031] Furthermore, the first intervention instruction corresponds to a first intervention level control instruction data packet, configured to trigger the cutting off of the power supply to the corresponding automatic control device;

[0032] The second intervention instruction corresponds to the setting of a second interference level redundancy adjustment control instruction data packet, configured to trigger an increase in the internal load overload circuit breakage operation threshold of the corresponding automatic control equipment; and the second interference level redundancy adjustment control instruction data packet does not contain an action instruction to directly cut off the power supply of the automatic control equipment.

[0033] Furthermore, S6 includes a threshold-level loop tuning step:

[0034] Receive secondary environmental sensor feedback log data containing physical status or early warning records returned by the automatic control device after responding to and executing the corresponding intervention command action;

[0035] Based on the secondary environmental sensor feedback log data, the sensor array unit damage frequency value and the information entropy value offset loss increment of the text warning source relative to the reference steady state are extracted and recorded statistically.

[0036] Based on the positive and negative signs and magnitude of the incremental information entropy value offset loss, the corresponding step distance is mapped, and the specific offset adjustment correction of the baseline limit cursor of the trust critical threshold is performed by increasing or decreasing upward or decreasing downward. If the incremental information entropy value offset loss is zero, the original baseline limit cursor is kept unchanged, and the original value is replaced and saved after correction.

[0037] Furthermore, the first modality spatial matrix data is defined as data generated from a network of grayscale and color values ​​of satellite remote sensing images covering a large area, captured by an optical sensor array.

[0038] The second modal time-series sensing data is defined as data consisting of single-node sampled continuous time-series voltage or flow velocity hydrological waveform values ​​generated by IoT nodes deployed in the target water system or soil monitoring area.

[0039] Furthermore, the built-in monitoring algorithm module performs state determination actions on the configuration generated by the multimodal monitoring data to identify whether data is missing.

[0040] The established communication heartbeat detection packet loss log identifier is used to monitor the response replies corresponding to the channel requests of each of the aforementioned physical acquisition interfaces.

[0041] When a communication protocol heartbeat response timeout is detected and a physical disconnection event or failure and pressure loss are determined, a combination of feature vectors with no response and a Boolean false value is written to the corresponding data stream node log tag to perform Boolean feature recording to complete packet loss archiving.

[0042] The beneficial effects of this invention are:

[0043] 1. This invention dynamically calculates the modal confidence imbalance index by extracting the communication drop rate matrix, generating weight ratio parameters and trust breach cost factors. This mechanism effectively solves the problem of lack of dynamic channel quality identification in existing technologies, quantifies communication interruption and sampling distortion into structured evaluation indicators, avoids incomplete or distorted data from directly entering high-dimensional models and causing excessive inference of local features, and improves the reliability of source assessment under multi-source sensing.

[0044] 2. This invention introduces a resource degradation and transfer mechanism based on critical thresholds. When sensing distortion or disconnection, it actively suspends complex generation models and calls dimensionality reduction models to extract key features of non-missing modalities for robust inference. This mechanism breaks the strong dependence of traditional static fusion on full lossless data and can automatically switch to low-order redundant adjustment and control commands under extreme conditions, effectively ensuring the robust and continuous transfer of the ecological management and control feedback intervention loop in disaster recovery mode.

[0045] 3. This invention constructs a system boundary anti-oscillation processing mechanism that includes a sliding time observation window. It confirms the channel switching conditions by statistically analyzing the coverage ratio of samples exceeding the imbalance index limit within a continuous period. This design effectively overcomes the problem of frequent algorithm oscillation caused by short-term local occlusion or transient network jitter in existing systems, provides a stable criterion in the time dimension for model degradation or recovery, and reduces the risk of high-frequency oscillation in the intervention channel.

[0046] 4. This invention designs a threshold-based bottom-level cyclic optimization step based on feedback logs. It uses the sensor array damage frequency and text information entropy offset loss increment after the control action is executed to adaptively correct the baseline limit of the trust critical threshold. This mechanism enables the system's judgment benchmark to no longer be limited to fixed human experience, but to be continuously closed-loop corrected according to the actual collaborative state on site, realizing adaptive evolution and optimization in the long-term operation scenario of the control system.

[0047] 5. This invention constructs a fusion network graph structure by downsampling a unified time zone and spatial reference system, and performs matrix interpolation or temporal filling on local missing data using prior knowledge base and semantic association features under high confidence. This step eliminates the natural spatial fragmentation differences of multimodal data, and can still constrain and maintain the continuous expression of high-dimensional features when there is a small local perceptual loss, avoiding a sharp drop in the overall early warning and judgment capability of the system due to a small number of blank blind spots. Attached Figure Description

[0048] The invention will now be further described with reference to the accompanying drawings.

[0049] Figure 1 This is a flowchart illustrating the real-time monitoring method for ecological carrying capacity overload risk based on big data analysis provided in this embodiment of the invention. Detailed Implementation

[0050] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Please see Figure 1 A real-time monitoring method for ecological carrying capacity overload risk based on big data analysis includes the following steps: S1. Collecting multimodal monitoring data of the target ecosystem under its operating state through the physical acquisition interface of an environmental sensor network, wherein the multimodal monitoring data includes first-modal spatial matrix data, second-modal temporal sensor data, and third-modal textual statistical data; storing the multimodal monitoring data as an ecological big data set in a preset distributed database, and pre-configuring generative multimodal models and principal component analysis or clustering statistical dimensionality reduction models in the big data analysis system for data retrieval and feature extraction;

[0052] S2. Extract the communication drop rate matrix representing the physical connection integrity of each modality data, the generation weight ratio parameter representing the current distribution state of each modality data, and the trust destruction cost factor generated by the historical data anomaly tracing model corresponding to the physical acquisition interface. Use a preset weighted distribution coefficient to perform a weighted summation calculation on the aggregated dimensionality reduction value of the communication drop rate matrix, the generation weight ratio parameter, and the trust destruction cost factor to generate a modality confidence imbalance index used to characterize the degree of distortion in the multimodal data input distribution.

[0053] S3. Determine whether the modal confidence imbalance index has reached a preset trust threshold.

[0054] S4. If the modal confidence imbalance index is lower than the trust threshold, the multimodal monitoring data is input into the generative multimodal model for feature extraction and vector splicing fusion to generate a first reconstructed tensor. The first reconstructed tensor is then substituted into a preset ultimate bearing pressure function, wherein the ultimate bearing pressure function is configured as a network mapping function that extracts pollutant input, heat load index and flow fluctuation amplitude from the first reconstructed tensor and performs multidimensional weighted calculation, and numerical solutions are obtained to generate a first overload risk probability and a first intervention instruction.

[0055] S5. If the modal confidence imbalance index is greater than or equal to the trust threshold, the system resource degradation mechanism is triggered, the generative multimodal model is suspended, and a preset principal component analysis or clustering statistical dimensionality reduction model is called to perform trend fitting on the non-missing data modal that does not contain null value fields in the multimodal monitoring data and extract key feature quantities to generate a second reconstruction tensor. The second reconstruction tensor is then substituted into the ultimate bearing pressure function for numerical solution to generate a second overload risk probability and a second intervention instruction.

[0056] S6. Based on the aforementioned judgment and calculation, the first overload risk probability and the first intervention command, or the second overload risk probability and the second intervention command, send a matching electrical control or mechanical braking action command data packet to the automatic control equipment in the target ecosystem, and simultaneously send the corresponding scheduling event data packet to the human-machine interaction node to perform feedback control and human-machine node scheduling of the target ecosystem.

[0057] This embodiment provides a real-time monitoring mechanism for ecological carrying capacity overload risk based on multimodal fusion; specifically, the main scenario is set as a composite ecological zone in a coastal new area, which simultaneously includes a chemical industrial park, a river channel flowing into the sea, tidal flat wetlands, and an agricultural buffer zone;

[0058] The goal of regional monitoring and control is not simply to detect pollution, but to assess in real time whether the physical carrying capacity of the ecosystem is approaching the threshold limit under the normal operation of high-load equipment, and to output actionable intervention and regulation actions between maintaining functional operation and ensuring the safety of the ecological baseline.

[0059] Specifically, the system accesses three types of data from different but complementary sources on the acquisition side; the first type is spatial matrix data, preferably remote sensing imaging results from the area above, which is used to reflect spatial distribution characteristics such as large-scale water surface color changes, vegetation degradation zones, and the expansion boundaries of bare land;

[0060] The second category is time-series sensing data, preferably from IoT nodes deployed in river cross-sections, around outlets, wetland soil layers, and inside gate equipment, used to continuously record observations of continuous changes in dissolved oxygen, conductivity, flow velocity, liquid level, soil moisture content, etc.

[0061] The third category is textual statistical data, which is preferably derived from enterprise daily reports, sewage discharge declarations, manual inspection records, early warning logs, and emergency duty summaries, and is used to supplement the structured operation and maintenance parameters that are inconvenient to be directly represented in pure physical sensing.

[0062] The three types of data, in terms of monitoring logic, respectively map the macroscopic spatial distribution morphology, continuous time series sensor observation, and structured manual operation and maintenance background records. They are input synchronously through a three-dimensional channel, making the final tensor more closely approximate the real ecological physical carrying capacity boundary.

[0063] Furthermore, the system does not directly regard all collected data as equally reliable. Instead, it first generates a modal confidence imbalance index to characterize the reliability of the input. The logic of this index has a three-layer structure: the first is the communication drop rate matrix, which is used to reflect the online stability characteristics of each acquisition interface.

[0064] Secondly, it generates weight ratio parameters to map whether a specific single modality in the current judgment and inference occupies a dominant power shift that exceeds the preset distribution deviation threshold; thirdly, it generates a trust destruction cost factor to quantify the negative impact on the reliability of subsequent control and scheduling when a certain interface experiences sampling distortion; after configuring the corresponding weights and superimposing the above three information dimensions, the system outputs a comprehensive quantitative evaluation index of the current feature network model fusion capability.

[0065] The system uses a big data analytics engine to retrieve and call ecological big data datasets from a distributed database in real time within the corresponding time window. The calculation execution process for a specific physical node is defined as follows: The corresponding communication dropout rate matrix item is set to a fixed value. The currently allocated generation weight ratio parameter value is set to The trust breach cost factor, pre-allocated through historical cost mapping, is defined as... The system uses three pre-set and stored independent non-negative weighted distribution coefficients. , , and limited Through weighted polynomials:

[0066]

[0067] Output modal confidence imbalance index This operational logic, through an explicit normalized weighted allocation method, structurally aggregates the discrete states of three multi-source dimensions into a unified indexed inference result, eliminating the implicit constraint that multimodal trust evaluation in complex systems is in an unobservable state.

[0068] The trust threshold has a preset initial benchmark value, which is a fixed constant set based on the maximum envelope boundary value of the modal confidence imbalance index calculated during the historical fault-free steady-state operation cycle of the target ecosystem.

[0069] In the judgment process, if the modal confidence imbalance index is lower than the trust threshold, it indicates that although there is a lack of local input, the overall topology of the multi-type data remains usable, and the system allows the high-precision feature extraction channel to be opened. At this time, the generative multimodal model extracts features from three directions: space, time, and text, and splices the features from the same time period, the same region, and the same event chain into a unified representation, thereby forming the first reconstruction tensor.

[0070] Preferably, the generative multimodal model includes three parallel feature extraction branches: a convolutional neural network branch for processing the spatial matrix data, a long short-term memory network branch for processing the temporal sensing data, and a transformer semantic extraction branch for processing the text statistical data; the low-dimensional feature vectors extracted by each branch are dimensionally aligned and fused through a fully connected layer to generate a unified high-dimensional feature map set.

[0071] At the system architecture level, this tensor is represented as a multidimensional operational status feature atlas of the current ecosystem, including regional water surface dynamic evolution parameters, physical cross-sectional temporal waveform variations, and structured patrol event semantics.

[0072] Specifically, the first reconstruction tensor is a three-dimensional data structure containing the time step dimension, spatial node coordinate dimension, and feature channel dimension, which is used to provide a standardized matrix input with a unified spatiotemporal reference for subsequent numerical solutions; the corresponding second reconstruction tensor is a two-dimensional feature matrix after dimensionality reduction.

[0073] The system inputs the feature map into the ultimate bearing pressure function and outputs the first overload risk probability and the first intervention command;

[0074] The ultimate carrying capacity pressure function here is used to characterize the structured correlation mechanism between regional ecosystems and pollutant input, heat load, flow fluctuations and buffer recovery capacity. Its numerical results are used not only for determining the over-limit state, but also for quantifying the safety margin characteristic value of the current state to the irreversible instability boundary.

[0075] Specifically, the ultimate bearing pressure function is configured as a pre-trained multivariate nonlinear regression model or a deep neural network mapping function. Its input is the pollutant input amount, heat load index and flow fluctuation amplitude extracted from the first reconstructed tensor or the second reconstructed tensor. It performs multi-dimensional weighted calculation through internally set weight coefficients, and its output is an overload risk probability value normalized to the interval of 0 to 1.

[0076] If the modal confidence imbalance index reaches or exceeds the confidence threshold, it indicates that the current data sample set has developed severe bias characteristics, and continuing to call the generative multimodal high parameter model is likely to trigger a chain of errors of over-inference of local characteristics.

[0077] At this point, the system triggers an active control resource degradation and transfer mechanism, suspending the generative multimodal model to stop the delivery of interrupted observation parameters, and transferring the computational load to the statistical dimensionality reduction model for takeover and analysis;

[0078] The degradation channel extracts and retains only the benchmark constraint acquisition unit source with good continuity, such as parameters that have not had missing breakpoint records, such as flow rate, liquid level, and dissolved oxygen. Then, it extracts the component data reflecting the boundary safety threshold through principal component analysis or clustering to generate the second reconstruction tensor.

[0079] The second overload risk probability derived from this channel obtains the execution control logic with enhanced interpretability by reducing the assessment resolution, which is beneficial for maintaining the robustness of the control loop and the continuous flow of management execution efficiency under large-scale incomplete operating conditions.

[0080] During the control execution phase, the system determines the execution level based on the different response states of the current evaluation channel. The automatic control equipment covers the discharge electric valve, lift pump, gate actuator, aeration equipment, and power relay terminal node.

[0081] For the high-reliability monitoring channel output mechanism, aggressive isolation actions that target physical barriers are adopted to prevent the spread of abnormal pollution. In the degraded mechanism state, the secondary level redundant safety boundary protection instructions are automatically switched to be executed, without involving a forced power outage of all equipment with rated power greater than the set limit, in order to reduce equipment shutdown chain failures caused by accidental valve closure, and to assist in issuing manual inspection pending dispatch event orders.

[0082] As a disaster recovery protection mechanism, when all system sensing modes have serious fault holes, or when a large number of data collection nodes go offline, fine-grained interval intervention and iteration will no longer be intervened. Instead, the system will be forced to switch to sending global area protection limit defense command packets and reduce the peak load level of all terminals. At the same time, the missing nodes will be listed as the target group of emergency operation and maintenance work orders.

[0083] If the log text is outdated and has low timeliness, then remove the text from the system status assessment. If the spatial matrix is ​​obscured or blocked by strong cloud interference, then restrict the start of independent blocks that are not physically contaminated and add them to the calculation and prediction scope.

[0084] For example, during a sudden heavy rainfall following a period of continuous high temperatures in the coastal new area, thick clouds caused blurry and unreadable blind spots in the remote sensing image in the estuary area, with the area ratio exceeding a preset threshold. In addition, the weak network caused some terminals to lose connection and disconnect from the network. The system monitoring and detection found that the packet drop rate of some master nodes increased, and the passive expansion of the text feature dimension caused an overload, which made the confidence imbalance index reach the extreme alarm threshold range.

[0085] If a high-dimensional synthetic architecture is forcibly invoked, it may misjudge and amplify the impact of conventional police report records, causing the real underground abnormal dynamic characteristics information to fall into the deduction deviation.

[0086] After the system switches to the standby reduced-order protection loop, it selects and isolates cross-sectional feedback sampling sets with sufficient reliability and conforming to the correlation continuity law, detects that the river self-cleaning oxygen replenishment system is becoming passive, and sends constraint flow adjustment commands to the corresponding control valves and related node unit clusters and issues inspection frequency increase request work orders;

[0087] This action mechanism ensures that even if the global mapping conditions of the microenvironment are lost, the non-boundary control strategy for the phenomenon of extreme deterioration of water quality and the spread of large-scale pollution sources can still be maintained.

[0088] The purpose of this mechanism is to build a large-scale and complex simulation and early warning system under the panoramic conditions of rich sensor resources. At the same time, when encountering channel crisis environments such as sensor distortion, a forced low-frequency degradation stable operation rule architecture is adopted to avoid the risk of secondary allocation contradictions caused by frequent oscillations of algorithm channels, and to avoid losing physical takeover protection and control authority during critical disaster recovery periods due to the deficiencies in the resilience of complex models.

[0089] In a preferred embodiment of the present invention, S1 further includes a preprocessing sub-step: the first modal spatial matrix data and the second modal temporal sensing data are subjected to spatial and temporal resolution downsampling processing using a downsampling algorithm, and the time zone and spatial reference system coordinate data are unified. The downsized data and the third modal text statistical data are converted to generate a fusion network graph structure data with the same timestamp and spatial coordinate reference attributes, and stored as a big data graph in a preset monitoring data stream queue and a distributed graph database for rapid retrieval and retrieval by the big data analysis platform.

[0090] This embodiment provides a preprocessing mechanism for multimodal input alignment; specifically, in the aforementioned coastal new area scenario, if remote sensing images, second-level sensing curves, and daily report texts are directly sent into the same monitoring process, misalignment problems such as timestamp deviation and spatial coordinate system misalignment will occur.

[0091] Remote sensing images cover a large area but refresh slowly, sensor nodes refresh quickly but are scattered, and text statistics are usually reported by shift or by day. If these three are not aligned first, any subsequent fusion may easily mistake information from different times and locations for the same event.

[0092] Specifically, the system first downgrades the spatial matrix data and temporal sensing data; for remote sensing images, the finest-grained pixels are no longer retained, but are re-aggregated according to watershed zoning, wetland units or outlet influence buffer zones, so that they are transformed from pixel representation to management unit representation.

[0093] For sensing curves at the second or minute level, they are converged into a unified observation rhythm according to a preset window, for example, a representative value is output once at a fixed sampling period. The engineering significance of doing this is to compress the original observation frequency, which is higher than the preset threshold and inconsistent, to a level that can logically correspond to the management action, so as to prevent the analysis link from being interfered with by local irregular high-frequency noise fluctuations and causing redundant over-response.

[0094] Furthermore, the system unifies the time zone and spatial reference benchmark for the three types of data; the estuaries, sluice gates, wetland boundaries, and enterprise discharge outlets in the spatial matrix need to be mapped to the same coordinate system;

[0095] Geographical descriptions in the text, such as the East Embankment Pumping Station, No. 2 Discharge Outlet, and downstream aquaculture area, are mapped to standard coordinate nodes through a pre-established location dictionary. In terms of time, descriptions such as night shift, morning, and previous inspection cycle in the daily report are converted into standard timestamp intervals. After completing this process, the system converts the three types of data into fused network graph structure data.

[0096] The nodes in the diagram can be regional units, monitoring points, or event records, while the edges represent spatial adjacency, upstream and downstream transmission, association within the same time period, or textual reference relationships.

[0097] The data flow verification mechanism can be illustrated by taking a topological association scenario with local key node features as an example. Suppose that at a certain moment there are only three regional nodes, namely upstream industrial section A, midstream wetland section B, and estuary section Z; at the same time there are two sensor nodes P1 and P2 and a text record T1.

[0098] If P1 is located between A and B, and P2 is located between B and Z, and T1 records that the load of the No. 2 discharge outlet increases at night, then in the fusion graph, the system can set A, B, and Z as the main region nodes, regard P1 and P2 as the auxiliary observation nodes, and attach T1 to the corresponding No. 2 discharge outlet node, so that the subsequent algorithm link can obtain the feature association network structure expression based on the unified spatiotemporal benchmark, forming a fusion tensor support foundation with physical and temporal logical relationships;

[0099] As a disaster recovery protection mechanism, if a text record cannot be reliably mapped to a spatial node, for example, if there is an anomalous description of a certain area but the location is not specified, the system will only mark it as weakly associated background information and will not bind it to a specific area unit; if the sampling frequency of some sensor data in this cycle is greater than the preset sampling threshold but the difference in change between adjacent times is lower than the stable feature threshold, it is allowed to be directly merged into a stable segment to avoid repeated writing to the data queue.

[0100] If the time difference between the remote sensing data refresh cycle and the ground sensing cycle is greater than the preset synchronization cycle, the spatial boundary information of the previous remote sensing image will be retained before a new image is obtained, but its timeliness label will be reduced to avoid the old image from excessively affecting the current judgment.

[0101] For example, before the aforementioned heavy rainfall event, the system simultaneously received a remote sensing image of the estuary at 10:00 AM, a set of flow velocity curves returned every 30 seconds, and a daily report summary submitted by the park's duty center at 11:00 AM. After preprocessing, the remote sensing image was converted into the spatial status of three regional units: the industrial shoreline section, the wetland transition section, and the estuary buffer section. The flow velocity curves were aggregated into key representative values ​​for the same hour, and the high-load operation of the No. 2 discharge outlet during the night shift in the daily report was mapped to the discharge outlet node corresponding to the industrial shoreline section.

[0102] All three enter the monitoring data stream queue with the same time scale, providing a unified input that can be directly called upon for subsequent confidence assessment and risk calculation;

[0103] The purpose of this step mechanism is to first eliminate the natural spatial fragmentation and isolated coordinate offset between heterogeneous data, and then carry out subsequent risk analysis matching and feeding action processing, so as to achieve physical model support for time series on the same ecological event evolution path that can correspond to and have consistent normalized execution parameter expression conditions.

[0104] In a preferred embodiment of the present invention, the specific calculation features of S2 include: the communication drop rate matrix is ​​generated by calculating and assigning values ​​to the packet loss rate by traversing the heartbeat packet reception status of each of the physical acquisition interfaces; the trust breach cost factor is calculated based on the loss degree assessment value of each of the physical acquisition interfaces when historical failures occur according to the historical data anomaly tracing model, and the trust breach cost factor increases positively correlated with the increase of the loss rate value of the corresponding interface in the communication drop rate matrix.

[0105] This embodiment provides a refined generation mechanism for the modal confidence imbalance index; specifically, knowing only that some data is missing is not enough, and the negative convergence degree of the system's early warning function varies significantly after communication is interrupted at heterogeneous monitoring interfaces; the lack of upstream water inflow sections may directly affect the support for determining the direction of pollution diffusion.

[0106] The impact of deviations and disruptions from external auxiliary meteorological nodes on the main early warning judgment is relatively reduced; therefore, it is necessary to quantify and map the frequency of drops and the corresponding interference costs simultaneously.

[0107] Specifically, the system maintains the heartbeat status for each physical acquisition interface; wherein, the heartbeat is configured as a connection keep-alive data message sent by the terminal node to the centralized control communication module at a preset clock frequency; the system iterates through the heartbeat response status of each interface at a preset period. If an interface fails to receive a response for multiple response periods, it is recorded and marked as an offline status feature vector, and a communication drop rate matrix is ​​generated by cumulative calculation.

[0108] This matrix is ​​configured to quantitatively characterize the integrity decay distribution characteristics of each heterogeneous mode, topology network location, and communication backbone link connection status within the current data aggregation cycle. For example, within multiple interface groups belonging to the same water quality mode, if a group communication disconnection occurs in the cross section area near the estuary with high concentration influence zone while other branches are not abnormal, the corresponding matrix output will focus on presenting the regional dense pressure loss high-risk characteristic cross section.

[0109] Furthermore, the system introduces a historical data anomaly tracing model to generate a trust breach cost factor;

[0110] The historical data anomaly tracing model is configured as a decision tree classifier or random forest model trained based on historical equipment failure ledgers and corresponding environmental deterioration accident records. Its input parameters include the equipment type of the physical acquisition interface where communication failure occurred, the duration of disconnection and the location of the network topology. The output is a quantized penalty weight coefficient mapped to the [0,1] interval.

[0111] The model algorithm matches based on the feature experience distribution statistical function of the call anomaly knowledge base, and reviews the negative lag difference impact event caused by the execution of management actions when the target interface failed, and provides a statistical feedback report.

[0112] Its core logic is: for data distortion and link interruption evolution events of target interfaces, quantify the constraint effect of the secondary consequences on the current regional ecological linkage intervention measures; as the probability density of offline jumper failure of the same communication backbone increases, the model will accelerate the increase of its weighted allocation of the proportion of the destructive impact factor, so that the evolution of the overall system judgment logic presents a conservative retreat mechanism to prevent high-risk vulnerabilities from continuing to spread.

[0113] To demonstrate the specific execution process of this damage cost mapping strategy, a local interface communication failure environment is constructed for supplementary explanation; it is set that there are three important interface terminals I1, I2 and I3 in the current observation sandbox, which respectively manage the total flow sensor of the main industrial discharge outlet, the nearshore ecological dissolved oxygen measurement station and the peripheral auxiliary weather terminal;

[0114] If it is found that the I1 feedback channel has experienced a delay jump in the past period, which indirectly caused the downstream over-processing and slow response accident, I2 caused the reoxygenation strategy to be executed intermittently, and I3 itself did not trigger the failure of strongly related business connection, then due to the inconsistent characteristics of the damage caused by each source, the system engine will prioritize to forcibly increase the upper limit of the penalty factor calculated by the product of I1 communication loss in the tier sequence and moderately maintain the influence of I2, so that the seemingly conventional node disconnection alarm has a differentiated weight recognition under the background of physical risk business constraints;

[0115] As a disaster recovery protection mechanism, if an interface is a newly accessed network and there is no previous operation log support library, the average value of the initial allocation factor of the system defaults to the normal parameter base of the service loop type where the interface is located;

[0116] If some fixed network links are in stable transmission for a long time, but occasionally encounter a brief transient gateway impact that causes an over-limit alarm, the system sets a damping delay to prevent the severity from being immediately increased, thereby shielding the model reliability from the misleading impact caused by jitter.

[0117] When a partial aggregation access switching layer experiences a power supply failure across the entire area and triggers a group jump-off disconnection event, the qualitative level of the affiliation is raised, triggering a network-wide communication operation and maintenance notification. This is not included in the isolated calculation penalty process for specific measurement nodes.

[0118] For example, during the aforementioned stages before and after the rainstorm in the new district, the platform's monitoring network aggregation center detected abnormal heartbeats at the dissolved oxygen station in the water system and a sudden loss of pressure at key gate equipment, while the external meteorological feedback mechanism showed no significant deviation from the normal range.

[0119] By cross-checking historical source logs, the system identifies that if the dissolved oxygen parameter monitoring equipment experiences a gap in its lineage, it typically causes widespread water quality alarm response delays, reaching high-risk thresholds and resulting in cascading damage. Instead of downgrading such connection failures to parallel equal-weighted network failures and ignoring them, the system simultaneously increases the parameter penalty impact according to a preset gain ratio, exacerbating the destructive derivative component when the equipment participates in index evaluation. This strengthens the defense control threshold limit and prevents the risk of blind spots in observation.

[0120] The purpose of this mechanism is to endow the discrete failure state characteristics of the monitoring node equipment with logical causal business attributes and to complete the measurement structure transformation and calibration capabilities. It can detect and identify the distribution of potential core execution link collapse and fault crisis points in advance, so as to achieve more stable control and assessment of the underlying information source.

[0121] In a preferred embodiment of the present invention, generating the first reconstructed tensor in S4 includes the following sub-steps: S41. Activate the generative multimodal model in the running state, and read the pre-stored associated feature prior knowledge base and the weight array of temporal long short-term memory network nodes;

[0122] S42. In response to detecting missing transition points that are determined to be null values ​​in the first modality spatial matrix data and the second modality temporal sensing data, the semantic association feature vector of the corresponding time segment is extracted using the third modality text statistical data. The distribution data in the prior knowledge base of the association features is combined to perform adjacent matrix pixel-level interpolation or temporal mean filling of the missing data blank area. The first reconstructed tensor composed of high-dimensional vectors is generated based on the filling value and the observed value.

[0123] This embodiment provides a reconstruction and fusion mechanism under a high-confidence state. Specifically, the aforementioned basic scheme can enter higher-order analysis when the overall modality is reliable. However, in real-world conditions, much data is not completely missing, but rather exhibits local jumps, short-term frame breaks, or small-scale blank areas.

[0124] If high-order fusion is completely abandoned due to local defects, a large amount of effective information will be lost; however, if it is filled directly without discrimination, occasional anomalies may be mistaken for normal fluctuations. Therefore, this embodiment introduces a prior knowledge base of associated features and a temporal memory structure to reconstruct local defects in a bounded manner.

[0125] Specifically, when the system enters the higher-order channel, it first activates the generative multimodal model and retrieves the pre-stored prior knowledge base. This knowledge base does not record abstract mathematical relationships, but rather stable correlations in ecological processes. For example, during periods of high temperature and low flow rate, the dissolved oxygen in the downstream area usually decreases faster than that in the upstream area.

[0126] After continuous rainfall, the soil moisture content rises before the peak of surface runoff occurs; when the night shift load of enterprises increases, the flow of some discharge outlets shows a regular jump before and after the shift handover; at the same time, the temporal memory structure is used to retain the continuity of the state of several sampling periods before and after, so that the system knows whether the current observation is a link in the long-term trend or short-term noise.

[0127] When the system detects blank blocks in the spatial matrix data or null value jump points in the time series sensing data, it will call the text statistics information of the corresponding time period to extract semantic association features.

[0128] For example, if the text records that the river gate was temporarily closed after a rainstorm, that the company started the backup treatment line, and that the wetland patrol found that the water color had darkened, these descriptions, although not directly giving numerical values, can provide directional background for local reconstruction; the system performs neighborhood interpolation on spatial blanks and performs mean or trend-preserving filling on temporal breakpoints, based on the prior knowledge base, thereby forming the first reconstruction tensor composed of the original observation value and the filling value;

[0129] The reconstruction here is not intended to replace the real data, but to maintain the continuity of the data structure as much as possible so that subsequent carrying capacity assessments can still reflect the complete ecological process;

[0130] A microscopic illustration can be used; assuming that within a certain time period, there are three regions R1, R2, and R3 in the spatial matrix, where R2 is missing due to cloud cover, while R1 and R3 are visible; at the same time, in the time series data, node Q2 has null values ​​at two sampling points, while Q1 and Q3 before and after are continuous and stable; the text record shows that the midstream gate restricts flow at night;

[0131] At this point, the system does not treat R2 and Q2 as completely unknown. Instead, it combines the states of adjacent regions, the continuous curves of upstream and downstream, and the flow-limiting background in the text to form a constrained estimate of R2 and smoothly fill in Q2. The resulting high-dimensional vector retains both the original data source label and the reconstructed source label so that the subsequent model can distinguish between the observed and supplementary quantities.

[0132] As a disaster recovery mechanism, if the missing area is too large and exceeds the range that prior knowledge can stably cover, the system will not perform fine-grained reconstruction, but will instead fall back to the coarser-grained regional summary features to avoid mistakenly reconstructing large unknown areas into fully detailed state diagrams.

[0133] If there is a significant conflict between textual statistics and physical observations, such as a textual description of stable emissions while multiple cross-sections simultaneously show abnormal conductivity spikes, the system reduces the impact of textual semantics on reconstruction and retains it only as event background. If the continuous time of a time-series breakpoint is too long, the system does not use the mean before and after to fill the gap, but directly marks the segment as a low-confidence segment to prevent the generation of false continuity.

[0134] For example, during the combined high temperature and rainstorm event in the new district, satellite images obscured the estuary wetland area, and some soil moisture nodes were briefly interrupted after the lightning strike. However, the duty log recorded information such as intermittent water release from the upstream gate and some enterprises switching to emergency pools. Based on this background, the system performed constrained supplementation of the spatial status of the wetland area and processed the short-term interrupted humidity curves to make them continuous.

[0135] After the first reconstructed tensor is fed into the bearing pressure function, it can more accurately determine whether the wetland buffering capacity is declining, thereby deciding whether new emissions should be restricted in advance.

[0136] The purpose of this mechanism is to preserve complementary information between multiple modalities as much as possible while the overall credibility remains within a safe range, so as to achieve a continuous expression of the ecological state in locally missing scenarios, rather than causing a sharp drop in the overall judgment capability due to a small number of gaps.

[0137] In a preferred embodiment of the present invention, generating the second reconstructed tensor and instructions based thereon in S5 includes the following sub-steps: S51. Interrupting the link process of inputting observations to the generative multimodal model and releasing the associated computing memory resources;

[0138] S52. Filter the high-dimensional sensing point parameters in the multimodal monitoring data that contain missing discontinuous feature fields in the statistical dimension, and filter and retain low-dimensional benchmark observation station data that only have time-continuous measured feature values ​​and preset fixed data packet return interval frequency.

[0139] S53. Call the principal component analysis or clustering statistical dimensionality reduction model to extract the maximum and minimum extreme value coordinates and threshold boundary distribution feature arrays of the low-dimensional benchmark observation station data, and concatenate them into the second reconstructed tensor;

[0140] S54. Calculate the linear geometric approximation distance between the maximum and minimum extreme value coordinates and the safety boundary distribution feature item of the preset bearing limit of the system safety bearing threshold boundary distribution feature array, substitute the absolute value of the distance into the linear classification mapping to generate the second overload risk probability as a numerical result, and the second intervention instruction to be issued directly to indicate the adjustment of the downgraded operation status of the called equipment.

[0141] This embodiment provides a degradation solution mechanism for high imbalance scenarios. Specifically, in the aforementioned high-order reconstruction path, although the system can fill in local missing parts, if the key modes are distorted over a large area, continuing to use a complex fusion model will not result in the inability to calculate the result, but rather in the fact that the calculated result, while seemingly detailed, lacks a stable basis.

[0142] Especially in areas where the ecological carrying capacity has been close to its limit for a long time, the decision-making chain needs to make a bottom-line judgment that is explainable, verifiable, and executable. Therefore, after the trust threshold is triggered, this embodiment no longer pursues the most detailed state restoration, but actively switches to a low-dimensional, transparent, and robust statistical path.

[0143] Specifically, the system interrupts the process of feeding observations into the higher-order model and releases the occupied GPU memory and computing resources. This action is not only a management of computing resources, but also a clear signal of switching decision logic, meaning that the system acknowledges that the current input conditions are no longer suitable for high-complexity inference.

[0144] The platform filters out high-dimensional sensing parameters that return discontinuous data, have gaps in fields, or have significant sampling time drift from all monitoring data, retaining only a batch of low-dimensional baseline observation station data.

[0145] The so-called benchmark site usually refers to the key monitoring point with stronger power supply and communication guarantee, higher maintenance level, and most sensitive to the regional carrying capacity boundary, such as national control section, core gate station, main discharge outlet total volume meter and wetland inlet level meter;

[0146] Furthermore, the system performs principal component analysis or clustering on these benchmark site data to extract core features representing the current safety boundary; the maximum and minimum extreme value coordinates here can be understood as: in the current observation set, which points are closest to the upper risk edge and which points are still at the lower safety edge;

[0147] The threshold boundary distribution features represent the location of the regional safety boundary in different dimensions, such as the flow velocity trough, the lower edge of dissolved oxygen, and the upper edge of liquid level rise. When these features are spliced ​​into the second reconstruction tensor, the system actually obtains a minimum safety profile that can still be confirmed at present.

[0148] Furthermore, the system maps the approximation relationship between these extreme points and the preset bearing limit to a second overload risk probability and generates a second intervention command; the geometric approximation distance here can be understood as how much buffer zone is left between the key observation point and the safety boundary;

[0149] In the actual process calculation decomposition, the system first extracts the same attribute scale variables from the constructed high-dimensional feature coordinates and the preset bearing limit multi-dimensional space boundary points, and uses the Euclidean distance or Manhattan distance rules to obtain the absolute mathematical difference results from the extreme value coordinates to each boundary distribution feature surface to characterize the geometric approximation distance.

[0150] Obtain the absolute distance and denote it as The system then substitutes it into a probability mapping line based on an inverse linear scaling ratio configuration, where the maximum safety reference distance matching the aforementioned load limit is denoted as... Based on the business rule that a smaller distance value means less remaining buffer safety margin and a higher probability of risk, the following mapping function is used:

[0151]

[0152] Perform numerical solutions, with the following constraints: If it exceeds the limit, it will be forcibly reset. For a very small probability to tend to Directly converted The characteristic percentage value within the interval, which is the second overload risk probability that is ultimately output as a numerical result. This decomposition completely avoids the impact of unstructured implicit non-transparent mapping calculations, and clarifies the entire process of generating coarse-grained probability confidence values ​​under the degradation mode using a spatial limit approximation distance formula with a unique mathematical constraint relationship.

[0153] If multiple key points approach the boundary at the same time, even if the full range of details cannot be restored, the system can confirm that the area is losing its buffer capacity and thus initiate conservative intervention, such as flow restriction, load reduction, improving the sensitivity of on-site protection actions, or switching to manual control.

[0154] A microscopic illustration can be used; assuming that after downgrading, only four benchmark points G1, G2, G3, and G4 are retained, corresponding to the total discharge from the main outlet, dissolved oxygen in the estuary, liquid level at the wetland inlet, and midstream flow velocity, respectively;

[0155] If current observations show that G1 and G2 are significantly close to the danger boundary, while G3 and G4 still have margin, the system will regard G1 and G2 as the extreme points of the dominant risk and generate a second reconstruction tensor that is locally overloaded and requires load limiting across the entire domain. At this time, even if the spatial image is completely missing and the text information is biased, the system can still form a degraded intervention that is manageable and operable.

[0156] As a disaster recovery protection mechanism, if the number of baseline sites retained after filtering is insufficient to support the minimum statistical requirements, such as only a single section is available, the system will not output the partitioned second overload risk probability, but will output a data shortage type backup alarm, requiring the current load limit to be maintained and manual inspection to be supplemented immediately.

[0157] If contradictory trends emerge between benchmark sites, such as a sudden increase in upstream flow velocity while the downstream liquid level remains stagnant for an extended period, the system will prioritize checking for equipment jamming, transmission delays, or localized blockages, and if necessary, freeze the participation weight of the abnormal point. If the statistical dimensionality reduction results are too dispersed and cannot form a stable boundary, the platform will adopt a conservative interpretation principle, that is, prioritize performing protective actions on the side closer to the boundary.

[0158] For example, when heavy rain in the new area caused remote sensing distortion and half of the shoreline nodes to go offline, the system shut down the high-order fusion link and only retained four types of baseline data: total discharge volume at the main discharge outlet, dissolved oxygen at the national control section, sluice gate liquid level, and wetland inlet flow velocity. Statistical results showed that the total discharge volume at the main discharge outlet continued to rise, while the dissolved oxygen in the estuary slowly declined, indicating that the safety margin of the buffer zone between the region's reoxygenation capacity and discharge pressure was decreasing at a rate greater than the preset rate.

[0159] Based on this, the system generates a second overload risk probability and issues a downgrade operation instruction: restrict high water consumption processes, keep the wetland water replenishment bypass constantly open, increase the warning frequency before the on-site circuit breaker protection threshold is triggered, and require on-duty personnel to conduct manual retesting of the missing areas;

[0160] The purpose of this mechanism is to proactively abandon fine-grained but unstable high-dimensional inference when the input is highly incomplete, and instead output a suboptimal safe solution that is more stable and acceptable to the device link and management link, thereby achieving continuous monitoring and executable control under disaster recovery conditions.

[0161] In a preferred embodiment of the present invention, S3 includes a system boundary anti-oscillation processing mechanism, specifically including: periodically extracting and recording the generated values ​​of the modal confidence imbalance index and storing them in a sliding time observation window queue of fixed length, with a preset fixed system clock cycle as the step size;

[0162] The probability distribution information of all modal confidence imbalance index values ​​in the sliding time observation window queue is statistically analyzed in real time. If the ratio of the number of samples whose modal confidence imbalance index is greater than or equal to the trust threshold to the total number of samples in the sliding time observation window queue is greater than or equal to the preset coverage ratio safety limit, the condition for exceeding the limit is confirmed and the execution of S5 is triggered.

[0163] This embodiment provides a boundary stabilization mechanism to prevent frequent jitter in the algorithm channel. Specifically, in the aforementioned process, the system needs to decide whether to use a higher-order fusion or a degraded statistical channel based on the modal confidence imbalance index. If the system switches immediately based on a single sampling result, it is easy for high-frequency oscillations to occur, where the system enters a higher-order state in the previous cycle and then reverts to a degraded state in the next cycle. Such oscillations not only consume computing power but also cause inconsistent instructions to be received by on-site equipment and management personnel, weakening their confidence in execution.

[0164] Specifically, the system extracts the modal confidence imbalance index at a fixed clock cycle and writes it into a sliding time observation window of fixed length. This window can be understood as a short-term state memory band, whose function is not to retain history indefinitely, but to observe whether the current instability is persistent.

[0165] The platform further analyzes the coverage of samples falling above the critical line within the window; when the samples exceeding the limit are only occasional and a minority, the system tends to consider this as a short-term deviation caused by network jitter, local occlusion, or single-point error, and does not immediately trigger a downgrade; only when the exceeding state occupies a sufficiently high proportion in the entire window is it confirmed that the current data credibility has indeed systematically decreased.

[0166] A simplified sandbox illustration can be used; assuming that five cycle states are recorded sequentially in a sliding window, denoted as W1 to W5; if only one cycle shows a high imbalance index, while the other four cycles are within the safe range, the system will maintain the original channel.

[0167] If four of the cycles continuously cross the critical line, it indicates that the imbalance is not transient, and the system is confirmed to have entered a degradation path; the core of this design is to look at the persistence rather than a single point spike.

[0168] As a disaster recovery protection mechanism, if the window is not yet full, such as when the system has just started up or power has just been restored, a start protection strategy can be adopted, allowing channel switching only after the minimum number of samples is reached; if the window shows consecutive identical values ​​and multiple interfaces are simultaneously silent, the system should also be wary of the acquisition link freezing, rather than simply treating it as a stable input.

[0169] If the proportion of samples exceeding the limit is near the critical point, the current channel can be maintained and a pre-switch prompt can be issued to allow on-site maintenance time for verification, rather than switching immediately;

[0170] For example, in the first ten minutes after the arrival of severe convective weather in the new area, cloud cover caused the spatial mode to deteriorate, but several core hydrological nodes remained online normally. After the system wrote the imbalance index of several consecutive cycles into the sliding window, it found that only the first two cycles were high, and the index dropped after communication was restored. Therefore, the high-order model was not immediately suspended.

[0171] After a period of time, multiple nodes on the shore went offline continuously due to water accumulation and pressure loss. The number of samples exceeding the limit within the window increased rapidly and reached the preset coverage standard. Only then did the system confirm that it had entered the downgrade channel. This avoided repeated switching caused by short-term weather disturbances.

[0172] The purpose of this mechanism is to add a time-dimensional stability criterion to channel switching, thereby distinguishing between short-term anomalies and persistent imbalances, and reducing secondary risks caused by system oscillations and repeated commands.

[0173] In a preferred embodiment of the present invention, the first intervention instruction corresponds to a first intervention level control instruction data packet, configured to trigger the cutting off of the power supply of the corresponding automatic control device; the second intervention instruction corresponds to a second intervention level redundancy adjustment control instruction data packet, configured to trigger the increase of the internal load overload circuit breaking operation threshold of the corresponding automatic control device; and the second intervention level redundancy adjustment control instruction data packet does not contain an action instruction to directly cut off the power supply of the automatic control device.

[0174] This embodiment provides a hierarchical intervention mechanism that matches different trusted channels; specifically, the aforementioned scheme has distinguished between high-trust status and degraded status, but if the control actions output by the two statuses are still exactly the same, it will still bring execution risks in practice;

[0175] Because the system has more complete information in the high-reliability channel, it is suitable to take more direct and forceful interventions; while in the degraded channel, the system has a baseline profile, which is more suitable to take less impactful and reversible safety adjustment actions; therefore, this embodiment distinguishes control messages by level.

[0176] Specifically, the first intervention instruction corresponds to the first intervention level control message; this message is used for strong intervention situations under high-confidence judgment, and can directly trigger the power cut-off or forced shutdown of the corresponding automatic control equipment, such as shutting down key discharge pump groups, cutting off the execution power of a high-load drainage branch, and stopping high-risk process conveying equipment, etc.; its applicable premise is: the multimodal input is generally stable, the system has reached the preset confidence threshold to confirm that the ecological carrying capacity of the area is about to exceed the limit, and continuing to wait will lead to irreversible consequences;

[0177] The second intervention command corresponds to the second intervention level redundancy adjustment control command data packet; this message is used for conservative intervention under the degradation channel, which does not directly cut off the equipment, but adjusts the internal load overload circuit breaking operation threshold, modifies the current limiting boundary, increases the warning trigger frequency, switches to the low load operation level, or opens the redundant protection circuit; its essence is to let the equipment enter a more cautious working zone, rather than immediately stopping all production activities.

[0178] Because this type of action has a relatively small impact on the continuity of on-site production, it is more suitable for execution in scenarios where data is incomplete but the bottom line of the ecosystem must be maintained immediately.

[0179] As a disaster recovery protection mechanism, if a device itself does not support threshold adjustment and only supports switching action, then a hard cut-off will not be performed in the degradation channel. Instead, a soft load limit request will be sent to the upper control system, and the indirect load reduction will be completed manually or by the upper scheduling program.

[0180] If the device is already in a fault-locked state, the cut-off message will no longer be sent repeatedly. Instead, a status confirmation and reset prohibition message will be sent. If network congestion at the site causes the strong intervention message to not receive a reply, the system should initiate a double insurance of retransmission and local interlocking, rather than assuming that the action has been successfully executed.

[0181] For example, during a rainstorm and high temperature event in the new district, when the high-reliability channel was still operational and identified an abnormal increase in the load at the main discharge outlet and a rapid decline in the downstream reoxygenation capacity, the platform sent a first-level interference message to the control cabinet of the discharge outlet booster pump, directly cutting off the power supply to the corresponding pump group to prevent high-concentration wastewater from continuing to enter the river.

[0182] After multiple nodes lost contact and the system entered a degradation channel, in the face of similar risks, the platform no longer shut down equipment on a large scale. Instead, it issued a second-level interference message to switch several auxiliary pumps and conveying equipment to low-load mode and increase the frequency of protection configuration related to internal overload circuit breaker thresholds, so that the equipment can be kept under control but no longer allowed to be subjected to impulsive load increases.

[0183] The purpose of this mechanism is to match the control intensity with the data credibility, thereby realizing a hierarchical handling logic that allows for decisive intervention when credibility is high and priority for steady-state load limiting when credibility is low.

[0184] In a preferred embodiment of the present invention, after S6, a threshold bottom-level loop optimization step is further included: receiving secondary environmental sensor feedback log data containing physical status or early warning records returned by the automatic control device after responding to and executing the corresponding intervention command action; extracting the sensor array unit damage frequency value recorded and statistically and the information entropy value offset loss increment magnitude of the text early warning source relative to the reference steady state based on the secondary environmental sensor feedback log data.

[0185] Based on the positive and negative signs and magnitude of the obtained information entropy numerical offset loss increment, the corresponding step distance is mapped, and the specific offset adjustment correction of the baseline limit cursor of the trust critical threshold is performed by increasing or decreasing upward or decreasing downward. If the information entropy numerical offset loss increment is zero, the original baseline limit cursor is kept unchanged, and the original value is replaced and saved after correction.

[0186] This embodiment provides a closed-loop tuning mechanism for a trust threshold. Specifically, although the aforementioned scheme can switch between high-order channels and degraded channels based on the current data status, if the threshold is fixed for a long time, the system will gradually expose two problems: first, the threshold is too sensitive, leading to frequent degrades, and the system is considered to be overly conservative on site.

[0187] Second, the threshold is too sluggish, causing it to remain in a high-order channel even when the data is severely damaged. To prevent the threshold from deviating from the real operating environment for a long time, this embodiment adds feedback correction after execution.

[0188] Specifically, after the automatic control equipment executes the control message, it will send back the secondary environmental sensor feedback log. This type of log includes the equipment's own status, such as whether the valve is in position, whether the pump current drops, and whether the gate is blocked, as well as the environmental feedback after the action, such as changes in liquid level, flow rate correction, and reduction or increase of local warnings.

[0189] The system extracts information in two directions from these feedbacks: one is the failure frequency of the sensor array unit, that is, whether the control action is accompanied by more equipment abnormalities, impact failures or communication losses; the other is the information entropy offset increment of the text warning source relative to the steady-state background, which can be understood as whether the on-site reports, inspection records and alarm descriptions become more chaotic and scattered after the action, or instead tend to be consistent.

[0190] When performing structured execution decomposition of the information entropy offset loss operation, the system periodically obtains the statistical probability distribution of the frequency of abnormal common words in the content extracted from the warning source within the total communication vocabulary, and calculates the current text information entropy, denoted as... ; directly subtract the historical background information entropy value stored as the reference baseline when the system was running in a confirmed alarm-free steady state, denoted as The deviation value obtained by subtracting the two values ​​can be calculated using the formula:

[0191]

[0192] The specified value is the increment magnitude of the information entropy numerical offset loss. When the increment magnitude When a positive sign greater than 0 is displayed, it indicates that the intervention action issued by the system caused the on-site recorded information to diverge and become chaotic as expected. In this case, its magnitude will be directly multiplied by a preset robust scaling pace constant. To export the configuration formula:

[0193]

[0194] in, Indicates multiplication operation. A dimensional scaling factor is used to convert the dimension of the information entropy loss increment into a scalar dimension that matches the vernier step size; this determines the step size distance. and with that step distance Decrease the baseline limit cursor of the trust threshold downwards; conversely, if the increment magnitude... Presenting less than The negative sign reflects that the entropy reduction of the system tends to be clear and stable after the system performs an action. The excitation step constant is set as... The system calculates using formulas. The corresponding step size distance is calculated, and the cursor configuration is increased or decreased upwards to expand the range of tolerance for higher-order fusion modes; this rule derivation process clarifies how to perform quantitative adjustment of the cursor based on the entropy change derived from specific word frequency statistics;

[0195] When feedback indicates that the field condition is more stable after control, the warning description tends to converge, and the equipment damage does not increase, it means that the current threshold setting is relatively safe, and the system can appropriately increase its tolerance for short-term imbalances to avoid excessive triggering of degradation.

[0196] Conversely, if more sensor anomalies, more text disputes, or more inconsistent human-machine feedback occur after each use of the higher-order channel, it indicates that the critical threshold setting is too high and should be appropriately lowered so that the system enters conservative mode earlier.

[0197] A simplified illustration can be used; suppose two rainstorm events occur within the same week; in the first one, the system downgrades later, and the logs afterwards show that the damage to multiple nodes has increased, and the patrol records contain a lot of text that is inconsistent with the on-site perception and the platform's judgment;

[0198] In the second instance, the system downgraded ahead of time, and the logs afterwards showed that the equipment was running smoothly and the warning records were consistent. Based on this, the platform will fine-tune the critical threshold to a more conservative side. Conversely, if the risk is proven to have not increased after multiple downgrades and the equipment is moving significantly more than necessary, the threshold can be adjusted back to a more lenient side.

[0199] As a disaster recovery mechanism, if the feedback log itself is severely missing, the threshold correction will not be performed in this round, and the baseline value saved last time will still be used; if the device action is not executed successfully, the subsequent environmental anomalies should not be simply attributed to the threshold setting problem, but the execution link failure should be ruled out first.

[0200] If the information entropy shift exhibits a bidirectional split, meaning that some information sources are highly consistent while others are extremely chaotic, the system should evaluate them separately according to the information source level to prevent a small number of low-quality texts from having an excessive impact on the threshold adjustment.

[0201] For example, after two consecutive flood seasons of operation in the new area, the platform reviewed the execution logs and found that when the system switched to the degradation path as soon as the spatial mode and hydrological mode showed a synchronous imbalance, the on-site discharge outlet load limit and wetland water replenishment actions were more readily accepted, and the subsequent text warning records were more focused on risk control.

[0202] In other incidents, the system failed to downgrade for a long time, and the platform's judgment was constantly questioned on-site, with an increase in sensor damage. Based on this, the platform appropriately lowered the trust threshold, enabling disaster recovery paths to be activated earlier in subsequent similar incidents.

[0203] The purpose of this mechanism is to ensure that the trust threshold is no longer a static value based on human experience, but is continuously adjusted based on equipment feedback and on-site collaboration, thereby achieving adaptive steady-state optimization in long-term operation.

[0204] In a preferred embodiment of the present invention, the first modal spatial matrix data is defined as data generated by a network of grayscale and color values ​​of satellite remote sensing images covering a large area, captured by an optical sensor array, and the second modal temporal sensing data is defined as data composed of single-node sampling continuous temporal voltage or flow velocity hydrological waveform values ​​generated by Internet of Things nodes deployed in the target water system or soil monitoring area.

[0205] This embodiment provides a specific constraint mechanism for two types of key physical modes. Specifically, in the aforementioned reconstruction path, if the source boundaries of spatial and temporal modes are unclear, the scheme is prone to overgeneralization, which is not conducive to engineering implementation. Therefore, this embodiment limits spatial matrix data to optical remote sensing imaging results and temporal sensing data to continuous sampling results of IoT nodes in water systems or soil areas, so that the overall scheme corresponds to the actual equipment system for ecological carrying capacity monitoring.

[0206] Specifically, spatial matrix data is preferably obtained by optical sensor arrays carried by satellites or high-altitude platforms. The resulting data mainly reflects grayscale, color, and the derived changes in water color, differences in land cover, vegetation status, and turbidity zone expansion characteristics. For large-scale areas such as coastal new areas, optical remote sensing is suitable for rapidly covering large-scale targets, and is especially suitable for identifying spatial phenomena such as estuarine plume morphology, wetland edge shrinkage, and anomalous color spot diffusion.

[0207] Temporal sensing data is preferably collected continuously by IoT nodes deployed around the target water system section, tidal ditch, discharge outlet or soil monitoring area. Typical data include voltage response, flow velocity waveform, liquid level change and soil moisture content time series. This type of data can capture dynamic processes that are difficult to reflect in spatial images, such as sudden discharge at night, short-term backflow, and rapid changes in groundwater content.

[0208] Furthermore, this limitation also contributes to the physical rationality of the aforementioned local reconstruction; when spatial modes are missing, it usually corresponds to cloud cover, rainfall interference, or deterioration of imaging conditions, which is suitable for supplementing using spatial neighborhood continuity; when temporal modes are discontinuous, it usually corresponds to loss of pressure, communication interruption, or single node failure, which is suitable for continuous processing by referring to the trends of previous and subsequent time frames and similar nodes; since the missing mechanisms of the two are different, the reconstruction methods should also be different.

[0209] As a disaster recovery mechanism, if optical remote sensing is unavailable for multiple consecutive cycles, it should no longer be forced to maintain its dominant position, and its generation weight should be reduced in the modal confidence assessment.

[0210] If some IoT nodes can only upload discrete alarms and cannot form continuous waveforms, these nodes can be used as auxiliary event sources, but are not included in the continuous time-series backbone described in this embodiment; if soil nodes are drifted for a long time due to salt spray corrosion, they need to be calibrated or removed before participating in the load-bearing assessment.

[0211] For example, in the monitoring of estuary wetlands in the new area, the system uses satellite optical images to identify the boundary of abnormal turbidity zones at the river mouth, and at the same time uses flow velocity, liquid level and voltage response nodes deployed in tidal channels and wetland soil layers to continuously track hydrodynamic changes.

[0212] When heavy rain causes partial obscuration of optical images by clouds, the system can still maintain the judgment of key sections by relying on continuous time-series nodes; when some shoreline nodes are destroyed by water accumulation, the abnormal diffusion range can still be observed through remote sensing color changes; the two types of physical modes complement each other and support the aforementioned reconstruction and degradation logic.

[0213] The purpose of this step mechanism is to clarify the core physical observation foundation upon which this solution relies, so that reconstruction, confidence assessment and disaster recovery are all based on a deployable and maintainable real data acquisition system.

[0214] In a preferred embodiment of the present invention, a built-in monitoring algorithm module performs a status determination action on the configuration of the multimodal monitoring data to identify whether data is missing. The established communication heartbeat detection packet loss log identifier monitors the response reply corresponding to the channel request of each physical acquisition interface. When a communication protocol heartbeat response timeout is detected and a physical disconnection event or failure and pressure loss are determined, a combination of feature vectors with no response and Boolean false values ​​is written to the log tag of the corresponding data stream node to perform Boolean feature recording to complete packet loss archiving.

[0215] This embodiment provides a monitoring mechanism for missing data identification and packet loss archiving; specifically, in the aforementioned scheme, the modal confidence imbalance index depends on the accurate identification of the interface online status and data missing status;

[0216] If the missing data identification itself is not standardized, such as mixing up data not being reported, data being zero, device power failure, and network congestion, the subsequent confidence calculation will be distorted; therefore, this embodiment sets up a built-in monitoring module and a heartbeat detection log recognizer to standardize the labeling of missing states.

[0217] Specifically, the monitoring module continuously observes the data configuration output of each physical acquisition interface to determine whether there are empty fields, abnormal jumps, long periods of silence, or update time drift within the current period; the heartbeat detection and recognition device, in conjunction with it, monitors the interface channel requests and response replies.

[0218] If no response is received within the agreed communication period, and this is accompanied by evidence such as abnormal power supply, broken link, or site power failure, the system will recognize the event as a physical disconnection or power failure, rather than a simple data delay.

[0219] Regarding the archiving method, the system will write a no-response tag to the corresponding data stream node and attach a Boolean false value feature. The Boolean record here is not to simplify the problem, but to enable the subsequent process to clearly distinguish three states: one is that there is a value and it is valid; two is that there is a value but it is abnormal; and three is that there is no value and there is no response. Only by distinguishing these three can the system know whether to use interpolation, weight reduction or direct downgrade switching.

[0220] This can be illustrated with a microscopic diagram: Suppose node N1 uploads normally, node N2 returns a value but it is seriously inconsistent with the upstream and downstream, and node N3 does not respond at all; at this time, N1 will be marked as valid, N2 will be marked as abnormal and pending verification, and N3 will be written with a boolean false label and recorded in the packet loss file.

[0221] In subsequent modal confidence assessment, N2 mainly affects the outlier weights, while N3 directly affects the connectivity integrity and trust breach cost; this prevents outlier measurements and physical disappearances from being treated as the same type of problem.

[0222] As a disaster recovery protection mechanism, if the interface times out briefly but complete data is retransmitted, the system can retain a momentary late tag instead of directly recording it in the long-term packet loss file; if the device returns a fixed constant value that remains unchanged for a long time, although it is not a null value in form, the monitoring module can still identify it as suspected frozen data and classify it separately.

[0223] If multiple nodes on the same branch simultaneously become unresponsive and are accompanied by abnormal power monitoring, it can be escalated to a station-level fault event. Priority should be given to notifying the operation and maintenance team to investigate the power supply and gateway, rather than dealing with each node individually.

[0224] For example, during a night of heavy rainfall in the new area, a group of water quality monitoring nodes along the shore successively stopped responding. The monitoring module found that these nodes not only did not upload sample values, but also did not receive a response to the communication heartbeat for several consecutive cycles. At the same time, the edge power cabinet transmitted a power outage alarm. Therefore, the system wrote a Boolean false label of no response to each of these nodes and archived the area as a physical disconnection cluster. In contrast, although another node still returned values, the readings remained unchanged for a long time and were obviously contradictory to the adjacent cross sections. The system classified it as suspected freezing rather than no response. This subdivision makes the subsequent imbalance judgment and degradation flow more accurate.

[0225] The purpose of this mechanism is to establish a unified and traceable underlying labeling rule for multimodal missing identification, thereby enabling fine differentiation of different fault modes such as packet loss, pressure loss, delay, and freezing, and providing reliable input for upper-level trust judgment.

[0226] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A real-time monitoring method for ecological carrying capacity overload risk based on big data analysis, characterized in that, Includes the following steps: S1. Collect multimodal monitoring data on the target ecosystem and its internal self-control devices and human-machine interaction nodes under operating conditions through the physical acquisition interface of the environmental sensor network. The multimodal monitoring data includes first-modal spatial matrix data, second-modal temporal sensor data, and third-modal text statistical data. Store the multimodal monitoring data as an ecological big data set in a preset distributed database, and pre-configure generative multimodal models and principal component analysis or clustering statistical dimensionality reduction models in the big data analysis system for data retrieval and feature extraction. S2. Extract the communication drop rate matrix representing the physical connection integrity of each modality data, the generation weight ratio parameter representing the current distribution state of each modality data, and the trust destruction cost factor generated by the historical data anomaly tracing model corresponding to the physical acquisition interface. Use a preset weighted distribution coefficient to perform a weighted summation calculation on the aggregated dimensionality reduction value of the communication drop rate matrix, the generation weight ratio parameter, and the trust destruction cost factor to generate a modality confidence imbalance index used to characterize the degree of distortion in the multimodal data input distribution. S3. Determine whether the modal confidence imbalance index has reached a preset trust threshold. S4. If the modal confidence imbalance index is lower than the trust threshold, the multimodal monitoring data is input into the generative multimodal model for feature extraction and vector splicing fusion to generate a first reconstructed tensor. The first reconstructed tensor is then substituted into a preset ultimate bearing pressure function, wherein the ultimate bearing pressure function is configured as a network mapping function that extracts pollutant input, heat load index and flow fluctuation amplitude from the first reconstructed tensor and performs multidimensional weighted calculation, and numerical solutions are obtained to generate a first overload risk probability and a first intervention instruction. S5. If the modal confidence imbalance index is greater than or equal to the trust threshold, the system resource degradation mechanism is triggered, the generative multimodal model is suspended, and a preset principal component analysis or clustering statistical dimensionality reduction model is called to perform trend fitting on the non-missing data modal that does not contain null value fields in the multimodal monitoring data and extract key feature quantities to generate a second reconstruction tensor. The second reconstruction tensor is then substituted into a preset ultimate bearing pressure function for numerical solution to generate a second overload risk probability and a second intervention instruction. S6. Based on the aforementioned judgment and calculation, the first overload risk probability and the first intervention command, or the second overload risk probability and the second intervention command, send a matching electrical control or mechanical braking action command data packet to the automatic control equipment in the target ecosystem, and simultaneously send the corresponding scheduling event data packet to the human-machine interaction node to perform feedback control and human-machine node scheduling of the target ecosystem.

2. The method for real-time monitoring of ecological carrying capacity overload risk based on big data analysis according to claim 1, characterized in that, S1 further includes a preprocessing sub-step: The spatial and temporal resolution of the first modal spatial matrix data and the second modal temporal sensing data are downsampled using a downsampling algorithm, and the time zone and spatial reference system coordinate data are unified. The downsampled data and the third modal text statistical data are then converted to generate a fused network graph structure data with the same timestamp and spatial coordinate reference attributes. This data is stored as a big data graph in a preset monitoring data stream queue and a distributed graph database for rapid retrieval and retrieval by the big data analysis platform.

3. The method for real-time monitoring of ecological carrying capacity overload risk based on big data analysis according to claim 1, characterized in that, The specific calculation features of S2 include: The communication drop rate matrix is ​​generated by calculating and assigning data loss rate scores by traversing the heartbeat packet reception status of each of the physical acquisition interfaces; The trust breach cost factor is calculated based on the historical data anomaly tracing model to assess the degree of loss when each of the physical acquisition interfaces fails in the past. The trust breach cost factor increases positively correlated with the increase of the data loss rate score of the corresponding interface in the communication drop rate matrix.

4. The method for real-time monitoring of ecological carrying capacity overload risk based on big data analysis according to claim 1, characterized in that, The generation of the first reconstructed tensor in step S4 includes the following sub-steps: S41. Activate the generative multimodal model that is in the running state, and read the pre-stored associated feature prior knowledge base and the weight array of the temporal long short-term memory network nodes; S42. In response to detecting missing transition points that are determined to be null values ​​in the first modality spatial matrix data and the second modality temporal sensing data, the semantic association feature vector of the corresponding time segment is extracted using the third modality text statistical data. The distribution data in the prior knowledge base of the association features is combined to perform adjacent matrix pixel-level interpolation or temporal mean filling of the missing data blank area. The first reconstructed tensor composed of high-dimensional vectors is generated based on the filling value and the observed value.

5. The method for real-time monitoring of ecological carrying capacity overload risk based on big data analysis according to claim 1, characterized in that, The generation of the second reconstructed tensor and the instructions based thereon in step S5 includes the following sub-steps: S51. Interrupt the link process of inputting observations to the generative multimodal model and release the related computing memory resources; S52. Filter the high-dimensional sensing point parameters in the multimodal monitoring data that contain missing discontinuous feature fields in the statistical dimension, and filter and retain low-dimensional benchmark observation station data that only have time-continuous measured feature values ​​and preset fixed data packet return interval frequency. S53. Call the principal component analysis or clustering statistical dimensionality reduction model to extract the maximum and minimum extreme value coordinates of the low-dimensional benchmark observation station data and the pre-set system safety bearing threshold boundary distribution feature array, and concatenate them into the second reconstructed tensor; S54. Calculate the linear geometric approximation distance between the maximum and minimum extreme value coordinates and the safety boundary distribution feature item of the preset bearing limit of the system safety bearing threshold boundary distribution feature array, substitute the absolute value of the distance into the linear classification mapping to generate the second overload risk probability as a numerical result, and the second intervention instruction to be issued directly to indicate the adjustment of the downgraded operation status of the called equipment.

6. The method for real-time monitoring of ecological carrying capacity overload risk based on big data analysis according to claim 1, characterized in that, The S3 includes a system boundary anti-oscillation processing mechanism, specifically including: Using a preset fixed system clock cycle as the step size, the generated values ​​of the modal confidence imbalance index are periodically extracted and recorded and stored in a sliding time observation window queue of fixed length. The probability distribution information of all modal confidence imbalance index values ​​in the sliding time observation window queue is statistically analyzed in real time. If the ratio of the number of samples with the modal confidence imbalance index greater than or equal to the trust threshold to the total number of samples in the sliding time observation window queue is greater than or equal to a preset coverage ratio safety limit, the condition for exceeding the limit is confirmed and S5 is triggered; if the ratio is less than the coverage ratio safety limit, the original state of the channel is maintained and S4 is executed.

7. The method for real-time monitoring of ecological carrying capacity overload risk based on big data analysis according to claim 1, characterized in that, The first intervention instruction corresponds to a first intervention level control instruction data packet, configured to trigger the cutting off of the power supply of the corresponding automatic control device; The second intervention instruction corresponds to the setting of a second interference level redundancy adjustment control instruction data packet, configured to trigger an increase in the internal load overload circuit breakage operation threshold of the corresponding automatic control equipment; and the second interference level redundancy adjustment control instruction data packet does not contain an action instruction to directly cut off the power supply of the automatic control equipment.

8. The method for real-time monitoring of ecological carrying capacity overload risk based on big data analysis according to claim 1, characterized in that, The step S6 is followed by a threshold low-level loop optimization step: Receive secondary environmental sensor feedback log data containing physical status or early warning records returned by the automatic control device after responding to and executing the corresponding intervention command action; Based on the secondary environmental sensor feedback log data, the sensor array unit damage frequency value and the information entropy value offset loss increment of the text warning source relative to the reference steady state are extracted and recorded statistically. Based on the positive and negative signs and magnitude of the incremental information entropy value offset loss, the corresponding step distance is mapped, and the specific offset adjustment correction of the baseline limit cursor of the trust critical threshold is performed by increasing or decreasing upward or decreasing downward. If the incremental information entropy value offset loss is zero, the original baseline limit cursor is kept unchanged, and the original value is replaced and saved after correction.

9. The method for real-time monitoring of ecological carrying capacity overload risk based on big data analysis according to claim 1, characterized in that, The first modal spatial matrix data is defined as data generated from grayscale and color numerical networks of satellite remote sensing images covering a large area, captured by an optical sensor array. The second modal time-series sensing data is defined as data consisting of single-node sampled continuous time-series voltage or flow velocity hydrological waveform values ​​generated by IoT nodes deployed in the target water system or soil monitoring area.

10. The method for real-time monitoring of ecological carrying capacity overload risk based on big data analysis according to claim 1, characterized in that, The built-in monitoring algorithm module generates a configuration for the multimodal monitoring data and performs a status determination action to identify whether data is missing. The established communication heartbeat detection packet loss log identifier is used to monitor the response replies corresponding to the channel requests of each of the aforementioned physical acquisition interfaces. When a communication protocol heartbeat response timeout is detected and a physical disconnection event or failure and pressure loss are determined, a combination of feature vectors with no response and a Boolean false value is written to the corresponding data stream node log tag to perform Boolean feature recording to complete packet loss archiving.

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