Slope state comprehensive prediction and early warning method and device based on multi-modal data fusion

Abstract

According to the slope state comprehensive prediction and early warning method and device based on multi-modal data fusion, a multi-source monitoring data processing mechanism is innovatively constructed, displacement, crack and pressure sensing data are integrated, and accurate monitoring of the slope state is achieved. And designing a feature extraction optimization model based on complexity evaluation, and establishing a model adaptive cutting strategy for calculation optimization in combination with a lightweight convolutional network and an attention mechanism. An edge cloud collaborative early warning mechanism is introduced, and dynamic allocation and multi-level early warning of prediction tasks are realized through task affinity evaluation and model thermal migration technologies. According to the method, the defects of the traditional technology in the aspects of data processing, feature extraction, early warning analysis and the like are effectively overcome, and the accuracy and reliability of slope state prediction and early warning are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, in particular to a slope state comprehensive prediction and early warning method and device based on multi-modal data fusion. BACKGROUND

[0002] The existing slope state prediction and early warning method has obvious deficiencies. The traditional system lacks systematicness in multi-modal data processing, and it is difficult to effectively integrate displacement, crack and pressure sensor data, affecting the accuracy of monitoring.

[0003] In addition, the existing technology has bottlenecks in slope feature extraction. Most systems do not fully consider the limitations of computing resources, lack a model optimization mechanism based on complexity evaluation, resulting in less than ideal prediction efficiency.

[0004] The existing system has technical shortcomings in early warning analysis. Lack of dynamic scheduling ability for prediction tasks, difficult to achieve accurate determination of early warning through edge-cloud collaborative strategy, affecting service reliability. The solution to these problems is of great significance to improve the level of slope monitoring. SUMMARY

[0005] In view of the problems in the prior art, the present application provides a slope state comprehensive prediction and early warning method and device based on multi-modal data fusion, which can effectively solve the deficiencies of traditional technology in data processing, feature extraction and early warning analysis, and significantly improve the accuracy and reliability of slope state prediction and early warning.

[0006] In order to solve at least one of the above problems, the present application provides the following technical scheme:

[0007] In a first aspect, the present application provides a slope state comprehensive prediction and early warning method based on multi-modal data fusion, comprising:

[0008] Collecting multi-modal slope monitoring data, collecting displacement sensor, crack sensor and pressure sensor monitoring data through an open protocol interface, converting the monitoring data into a standard signal format, performing channel allocation and signal conditioning on the standard signal, performing noise reduction and de-redundancy on signal data based on a sliding window, aligning the time stamps of multi-source data using a data synchronization alignment algorithm, constructing a unified data fusion format, standardizing the data after fusion according to metadata specifications, and generating a structured slope state data stream;

[0009] The slope state data stream is subjected to feature extraction and calculation optimization, time series model is used for segmented modeling of the data, light weight convolution network is used for extracting slope deformation features, long-range dependence is captured through attention mechanism, model complexity evaluator is constructed, the model complexity evaluator generates complexity score based on computing resource occupancy and memory usage, the model structure is adaptively pruned according to the complexity score, quantization compression technology is used for bit width compression of model parameters, and the optimized features are input into the slope state prediction model;

[0010] Warning information is generated based on the slope state prediction model, the slope state prediction model constructs a prediction task scheduling pool through container virtualization technology, a resource monitoring module is used to collect processor occupancy and network bandwidth state, a task affinity score is calculated according to the processor occupancy and network bandwidth state, high-load prediction tasks are migrated to the cloud for processing, slope state data is uploaded in an incremental transmission mode, prediction task dynamic switching is realized through model hot migration technology, prediction results are subjected to hierarchical warning judgment, multi-level warning rules are constructed to evaluate the slope instability risk, and a slope warning analysis report is generated.

[0011] Further, an open protocol interface is constructed, the open protocol interface includes a physical interface layer, a signal conditioning layer, a protocol analysis layer and a data fusion layer, the physical interface layer is configured with RS485, analog current, analog voltage and frequency signal interface circuit, the signal conditioning layer is configured with an analog-to-digital converter and a timing counter, the protocol analysis layer is configured with Modbus RTU and MQTT communication protocol stack, and the data fusion layer is configured with a unified data frame format; monitoring data of displacement sensors, crack sensors and pressure sensors is collected based on the open protocol interface.

[0012] The monitoring data is subjected to signal processing, signal conditioning circuit is used for amplifying and filtering analog signals, 24-bit analog-to-digital converter is used for digitizing and sampling the conditioned signals, timer counting circuit is used for measuring the period of frequency signals, the digitized monitoring data is distributed according to channel numbers, sensor calibration parameters are read based on the channel numbers, the monitoring data is subjected to linear mapping and unit conversion, and monitoring data in standard signal format is generated.

[0013] Further, a signal denoising module is constructed, a signal smoother is constructed based on a sliding window, the window length of the signal smoother is adapted to the signal sampling frequency, the data in the window is subjected to median filtering to remove sudden noise, wavelet transform is used for multi-scale decomposition of the filtered signal, high-frequency noise coefficients are set to zero and the signal is reconstructed, data redundancy is calculated based on signal variance, data segments with redundancy higher than a threshold are down-sampled and compressed, and denoised and de-redundant monitoring signals are generated.

[0014] The data fusion module is constructed, a time sequence index table is established based on timestamp information of the monitoring data, a linear interpolation algorithm is used to resample data of different sampling periods, the resampled data is aligned according to a unified time reference, a data fusion format template is constructed, the data fusion format template includes sensor identification, physical quantity type, numerical range and unit information, the aligned data is standardized and mapped according to the data fusion format template, and a structured slope state data stream is generated.

[0015] Further, it further includes: constructing a time sequence segmentation model, sliding segmenting the slope state data stream, determining the segment point position based on data variance and trend change rate, inputting the segmented data into a long short-term memory network, the long short-term memory network includes a forget gate and an input gate structure, the forget gate filters historical state information, the input gate fuses current input features, the time sequence features are modeled based on a recurrent connection, a residual structure is used to retain short-term detailed features, and the modeling result is input into a feature extraction module;

[0016] The feature extraction module is constructed, the feature extraction module uses a lightweight convolutional network structure, reduces the parameter size through a depth separable convolution, sets a multi-scale convolution kernel to extract slope deformation features, uses a self-attention mechanism to calculate feature map correlation, combines the attention weight and the convolution feature map, collects processor and memory occupation data based on a resource monitoring interface, calculates a model calculation complexity score, prunes the network structure according to the complexity score, and generates optimized slope state features.

[0017] Further, it further includes: constructing a model pruning module, constructing an importance score matrix based on the complexity score, importance sorting the convolution layer channels, pruning the channels with importance scores lower than a threshold, migrating the original model knowledge to the pruned model using a distillation learning method, fine-tuning the pruned model based on a verification data set, and inputting the pruned model into a compression module;

[0018] The compression optimization module is constructed, a mixed precision quantization strategy is used to compress the bit width of the model parameters, a symmetric quantization mapping is performed on the weight tensor, floating point data is converted into fixed point integer representation, the parameter sparsity degree is measured based on information entropy, the parameter matrix is decomposed into sparse, non-zero parameters are rearranged to construct a sparse storage structure, and sparse matrix multiplication is used to accelerate inference calculation, and the compressed and optimized model is deployed to the slope state prediction module.

[0019] Further, it also comprises: constructing a prediction task scheduling module, creating a prediction task instance based on container virtualization technology, deploying the prediction task instance to a task scheduling pool, the task scheduling pool including a resource allocation unit and a load balancing unit, the resource allocation unit isolating and configuring processor cores and memory space, the load balancing unit distributing tasks based on resource monitoring data, collecting processor occupancy and network bandwidth data using a resource monitoring interface, and inputting the monitoring data into a task scoring unit;

[0020] Constructing a task migration module, constructing a task affinity score model based on processor occupancy and network bandwidth status, the task affinity score model performing weighted calculation on computing load and network transmission overhead, generating a task migration decision score, prioritizing high-load prediction tasks, matching the priority sorting result with a cloud resource pool, transmitting computing data using an incremental synchronization method, and deploying the prediction task to a cloud node based on model live migration technology.

[0021] Further, it also comprises: constructing a data transmission module, performing incremental calculation on slope state data, identifying new data blocks based on data timestamp, encoding the new data using a data compression algorithm, establishing a data transmission channel, the data transmission channel including a breakpoint resume and data verification mechanism, transmitting compressed data fragments to a cloud storage, constructing a model cache pool based on a model version management strategy, the model cache pool version identifying and state maintaining the prediction model, switching tasks using model live migration technology, and applying the switched model to slope state prediction;

[0022] Constructing a warning analysis module, constructing a warning level evaluator based on the prediction results, the warning level evaluator including deformation rate, cumulative displacement, and pressure change rate thresholds, performing hierarchical warning judgment on monitoring data, establishing multi-level warning rules using a fuzzy comprehensive judgment method, associating and mapping the warning level with the risk assessment result, generating an analysis report based on a warning information template, the analysis report including the warning level, the risk assessment result, and disposal suggestions.

[0023] In a second aspect, the application provides a slope state comprehensive prediction and warning device based on multi-modal data fusion, comprising:

[0024] A data monitoring module is configured to collect multi-modal slope monitoring data, collect monitoring data of displacement sensors, crack sensors, and pressure sensors through an open protocol interface, convert the monitoring data into a standard signal format, perform channel allocation and signal conditioning on the standard signal, perform noise reduction and de-redundancy on signal data based on a sliding window, perform timestamp alignment on multi-source data using a data synchronization alignment algorithm, construct a unified data fusion format, standardize the fused data according to metadata specifications, and generate structured slope state data streams.

[0025] a feature processing module configured to perform feature extraction and calculation optimization on the slope state data stream, perform segmented modeling on the data based on a time series model, extract slope deformation features using a lightweight convolutional network, capture long-range dependencies through an attention mechanism, construct a model complexity evaluator configured to generate a complexity score based on a computing resource occupancy rate and a memory usage rate, perform adaptive pruning on a model structure according to the complexity score, perform bit width compression on model parameters using a quantization compression technique, reduce calculation complexity through sparsification processing, and input the optimized features into a slope state prediction model;

[0026] a slope early warning module configured to generate early warning information based on the slope state prediction model, construct a prediction task scheduling pool through container virtualization technology, collect a processor occupancy rate and a network bandwidth state using a resource monitoring module, calculate a task affinity score according to the processor occupancy rate and the network bandwidth state, migrate high-load prediction tasks to a cloud for processing, upload slope state data using an incremental transmission method, implement dynamic switching of prediction tasks through model hot migration technology, perform hierarchical early warning determination on prediction results, construct multi-level early warning rules to evaluate slope instability risks, and generate a slope early warning analysis report.

[0027] In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the slope state comprehensive prediction and early warning method based on multi-modal data fusion when executing the program.

[0028] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement the steps of the slope state comprehensive prediction and early warning method based on multi-modal data fusion.

[0029] In a fifth aspect, the present application provides a computer program product comprising computer programs / instructions executable by a processor to implement the steps of the slope state comprehensive prediction and early warning method based on multi-modal data fusion.

[0030] As described above, this application provides a method and device for comprehensive prediction and early warning of slope conditions based on multimodal data fusion. It innovatively constructs a multi-source monitoring data processing mechanism, integrating displacement, crack, and pressure sensor data to achieve accurate monitoring of slope conditions. A feature extraction optimization model based on complexity assessment is designed, combining lightweight convolutional networks and attention mechanisms to establish an adaptive model pruning strategy for computational optimization. An edge-cloud collaborative early warning mechanism is introduced, using task affinity assessment and model hot migration technology to achieve dynamic allocation of prediction tasks and multi-level early warning. This method effectively addresses the shortcomings of traditional techniques in data processing, feature extraction, and early warning analysis, significantly improving the accuracy and reliability of slope condition prediction and early warning. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart illustrating the comprehensive prediction and early warning method for slope condition based on multimodal data fusion in the embodiments of this application.

[0033] Figure 2 This is a structural diagram of the slope condition comprehensive prediction and early warning device based on multimodal data fusion in the embodiments of this application;

[0034] Figure 3 This is a schematic diagram of the structure of the electronic device in the embodiments of this application.

[0035] Figure label:

[0036] Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver storage unit 9144, antenna 9111, speaker 9131, microphone 9132. Detailed Implementation

[0037] To make the purposes, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0038] The acquisition, storage, use, processing, and the like of data in the technical solutions of the present application comply with relevant provisions of national laws and regulations.

[0039] In view of the problems in the prior art, the present application provides a slope state comprehensive prediction and early warning method and device based on multi-modal data fusion, which innovatively constructs a multi-source monitoring data processing mechanism, integrates displacement, crack, and pressure sensor data, and realizes accurate monitoring of the slope state. A feature extraction optimization model based on complexity evaluation is designed, combined with a lightweight convolutional network and an attention mechanism, to establish a model self-adaptive pruning strategy for calculation optimization. An edge-cloud collaborative early warning mechanism is introduced, and through task affinity evaluation and model hot migration technology, dynamic allocation and multi-level early warning of the prediction task are realized. This method effectively solves the deficiencies of traditional technology in data processing, feature extraction, and early warning analysis, and significantly improves the accuracy and reliability of slope state prediction and early warning.

[0040] In order to effectively solve the deficiencies of traditional technology in data processing, feature extraction, and early warning analysis, and significantly improve the accuracy and reliability of slope state prediction and early warning, the present application provides an embodiment of a slope state comprehensive prediction and early warning method based on multi-modal data fusion, as shown in Figure 1 , which specifically includes the following contents:

[0041] Step S101: Collect multi-modal slope monitoring data, collect monitoring data of displacement sensors, crack sensors, and pressure sensors through an open protocol interface, convert the monitoring data into a standard signal format, perform channel allocation and signal conditioning on the standard signal, perform noise reduction and de-redundancy on the signal data based on a sliding window, perform timestamp alignment on multi-source data using a data synchronization alignment algorithm, construct a unified data fusion format, standardize the data after fusion according to metadata specifications, and generate structured slope state data flow;

[0042] Optionally, the embodiment is directed to the problems of multi-source sensor data collection in slope monitoring systems, such as asynchronous data collection, unstable signal quality, and non-uniform data format. A multi-modal data collection and processing scheme is designed. The embodiment first constructs an open protocol interface to collect data from displacement sensors, crack sensors, and pressure sensors through the ModBus RTU protocol. During the collection process, the system designs a signal conversion mechanism: Signal_Value = k x Raw_Data + b, where Raw_Data is the original data value, k is the proportional coefficient, and b is the zero-point offset value. These parameters are obtained through sensor calibration. In slope monitoring applications, this standardized data collection method can effectively solve the interconnection problem of sensors from different manufacturers.

[0043] The embodiment optimizes the signal conditioning strategy. Differentiated signal processing schemes are designed for different types of monitoring data, such as slope displacement, crack, and pressure. For analog signals, the system uses a high-precision 24-bit AD converter for digital sampling. For frequency signals, a timer counting circuit is used for period measurement. The system uses optical isolation technology to achieve signal isolation and improves anti-interference ability through differential amplification circuit. For example, when collecting deep displacement meter data, the system automatically adjusts the signal conditioning parameters according to the output characteristics of the sensor to ensure the accuracy of the collected data. This intelligent signal processing method significantly improves the reliability of monitoring data.

[0044] The embodiment innovatively realizes signal noise reduction and redundancy removal mechanism. To solve the problems of electromagnetic interference and mechanical vibration in the slope monitoring environment, the system constructs a signal processing framework based on sliding window. By setting appropriate window length, the original signal is processed in segments. The system uses wavelet transform for multi-scale analysis and eliminates high-frequency noise through threshold processing method. At the same time, by calculating the redundancy of data, repeated data segments are identified and merged, effectively reducing data storage and transmission overhead. This multi-level signal processing mechanism can significantly improve the quality of monitoring data.

[0045] The embodiment optimizes the data synchronization alignment strategy. The system designs a timestamp-based data synchronization mechanism, which adds precise time labels to each data packet through a global clock source. The system uses linear interpolation algorithm to realize data resampling to ensure the alignment of multi-source data in time dimension. For example, when collecting surface displacement and deep displacement data simultaneously, the system sets a synchronization reference according to the highest sampling rate, and supplements the data points with lower sampling rate through interpolation calculation to ensure the time sequence consistency of monitoring data.

[0046] The embodiment realizes unified management of multi-modal data through a data fusion module. The system constructs a standardized data format definition, including sensor identification, physical quantity type, numerical value range, and other key fields. Special attention is paid to the semantic expression of data, and the field mapping rules are defined through metadata specification to ensure that monitoring data from different sources can be uniformly stored and processed. This standardized data management method lays a foundation for subsequent feature extraction and early warning analysis.

[0047] The innovative design of the embodiment not only solves the data acquisition problem in traditional methods, but also establishes a sustainable optimization data processing framework. Through multi-level signal processing and data fusion, the system can obtain high-quality slope state data from complex monitoring environments. This standardized processing-based acquisition mechanism ensures that the system always maintains efficient acquisition capability and reliable processing effect when facing different types of monitoring equipment. In slope monitoring applications, this intelligent data acquisition scheme significantly improves the integrity and accuracy of monitoring data.

[0048] The embodiment realizes intelligent upgrading for slope monitoring by establishing a complete data acquisition and processing link. The system can dynamically adjust the processing strategy based on real-time state, avoiding the limitations of traditional fixed parameter schemes. Through multi-dimensional signal processing and data fusion, the accuracy and real-time performance of monitoring data are significantly improved, providing high-quality data support for subsequent slope state early warning. This intelligent acquisition mechanism exhibits strong adaptability and processing effect in slope monitoring scenarios.

[0049] The embodiment not only improves the reliability of data acquisition, but also establishes a continuously evolving monitoring system through continuous optimization strategies and data management. This real-time feedback-based optimization mechanism ensures that the system can continuously improve as monitoring needs change, providing increasingly efficient data services for slope safety monitoring. In practical applications, this self-optimization mechanism significantly improves the long-term service quality and processing effect of the system, providing a reliable data foundation for slope monitoring and early warning.

[0050] Step S102: feature extraction and calculation optimization of the slope state data stream, segment modeling of the data based on a time series model, extraction of slope deformation features using a lightweight convolutional network, capture of long-range dependencies through an attention mechanism, construction of a model complexity evaluator that generates a complexity score based on computational resource occupancy and memory usage, adaptive pruning of the model structure according to the complexity score, bit width compression of model parameters using quantization compression technology, reduction of computational complexity through sparsification, and input of the optimized features into a slope state prediction model;

[0051] Optionally, the embodiment is directed to the problem of low efficiency in feature extraction of multi-source data and limited computing resources in slope monitoring. A feature extraction and model optimization scheme is innovatively designed. The embodiment first constructs a time series data modeling framework, and segments the slope state data stream through a sliding time window. An autoregressive modeling mechanism is designed: AR(p) = Σ(φi × X(t-i)) + ε(t), where φi is the autoregressive coefficient, X(t-i) is the historical observation value, and ε(t) is the random error term. In the application of slope monitoring, this time series modeling method can effectively capture the dynamic change law of monitoring data such as displacement, crack width and pore water pressure.

[0052] The embodiment deeply optimizes the slope deformation feature extraction strategy. To solve the problem of limited computing power of edge devices, the system uses a depth separable convolution structure instead of a standard convolution layer. Special attention is paid to the balance between computing efficiency and feature expression ability. Through the combination of pointwise convolution and depth convolution, the parameter quantity and computational complexity are significantly reduced. For example, when processing displacement sensor data, the system first uses depth convolution to capture local deformation features, and then uses pointwise convolution to realize the fusion of multi-sensor information. This lightweight network structure can extract effective slope deformation features under limited computing resources.

[0053] The embodiment innovatively realizes the attention mechanism. To solve the problem of long-term dependence in slope deformation, the system designs a multi-head self-attention module. Through parallel attention calculation heads, the system captures the correlation patterns of data sequences from different representation spaces. Special attention is paid to the importance weight distribution of features. The system dynamically adjusts the attention weight according to the physical meaning and correlation of different monitoring parameters. For example, when detecting rapid growth of crack width, the system automatically increases the weight of related displacement data to enhance the recognition ability of potential instability risk.

[0054] The embodiment deeply optimizes the model complexity evaluation strategy. The system constructs an evaluation framework based on resource monitoring, and collects hardware indicators such as processor usage and memory occupation in real time. Special attention is paid to the actual running overhead of the model. A complexity score function is established: Complexity_Score = w1CPU_Usage + w2Memory_Usage + w3 × Parameter_Count, where w1, w2, w3 are weight coefficients of different resource indicators. This quantitative evaluation method provides an objective basis for model optimization.

[0055] The embodiment realizes the dynamic optimization of the model structure through the adaptive pruning technology. The system sorts the network layers based on the complexity score, identifies the redundant and inefficient network structure by calculating the contribution of each layer to the slope state prediction performance, and pays special attention to the balance of model performance. In the pruning process, the knowledge distillation technology is used to migrate the knowledge of the original model to the simplified network. This progressive optimization method can significantly reduce the computational complexity while maintaining the prediction accuracy of the model.

[0056] The innovative design of the embodiment not only solves the problem of computational efficiency in traditional methods, but also establishes a sustainable optimization feature extraction framework. Through model quantization and sparsification processing, the system can deploy the optimized feature extraction model to resource-constrained edge devices. This hardware-aware optimization mechanism ensures that the system always maintains efficient feature extraction capability and reliable processing effect when facing different monitoring scenarios. In the slope monitoring application, this intelligent feature extraction scheme significantly improves the real-time performance of edge computing.

[0057] The embodiment realizes the intelligent upgrade of early warning for slope monitoring by establishing a complete feature extraction and model optimization link. The system can dynamically adjust the processing strategy based on the hardware state, avoiding the limitations of traditional fixed models. Through multi-dimensional feature extraction and model optimization, the efficiency and adaptability of edge computing are significantly improved, providing reliable technical support for slope instability risk warning. This intelligent feature extraction mechanism has strong practical value and optimization effect in the slope monitoring scenario.

[0058] The embodiment not only improves the efficiency of feature extraction, but also establishes an evolving edge computing system through continuous model optimization and resource management. This resource-aware optimization mechanism ensures that the system can continuously improve as the monitoring needs change, providing more and more efficient data processing services for slope monitoring. In practical applications, this self-optimization mechanism significantly improves the long-term service quality and processing effect of the system, providing strong technical support for slope state warning.

[0059] Step S103: generating early warning information based on the slope state prediction model, the slope state prediction model constructs a prediction task scheduling pool through container virtualization technology, uses a resource monitoring module to collect processor occupancy and network bandwidth state, calculates task affinity score based on the processor occupancy and network bandwidth state, migrates high-load prediction tasks to the cloud for processing, uses incremental transmission method to upload slope state data, realizes dynamic switching of prediction tasks through model live migration technology, performs hierarchical early warning judgment on prediction results, constructs multi-level early warning rules to evaluate slope instability risk, and generates slope early warning analysis report.

[0060] Optionally, the embodiment is directed to the problems of large early warning calculation load and unbalanced resource allocation in slope monitoring, and innovatively designs a set of edge-cloud collaborative early warning analysis scheme. The embodiment first constructs a prediction task scheduling framework based on Docker container technology, and realizes the encapsulation and isolation of calculation tasks through container orchestration. The system designs a task affinity calculation mechanism: Affinity_Score=w1CPU_Load+w2Memory_Usage+w3×Network_BW, wherein CPU_Load is the processor load, Memory_Usage is the memory occupation, Network_BW is the bandwidth usage rate, and w1, w2 and w3 are resource weight coefficients. In the application of slope monitoring, this resource-aware scheduling method can effectively balance the calculation load.

[0061] The embodiment deeply optimizes the resource monitoring strategy. A lightweight monitoring module is designed for the characteristics of limited resources of edge devices. The system obtains the processor usage, memory occupation and network bandwidth and other key indicators by periodic sampling, and constructs a real-time resource state portrait. Special attention is paid to the dynamic changes of resource usage, and when a bottleneck of a certain resource is detected, the system will automatically adjust the task allocation strategy. For example, when processing multiple monitoring point early warning analysis tasks, if it is found that the local processor load continuously exceeds the threshold, the system will start the task migration mechanism in time to ensure the real-time performance of early warning analysis.

[0062] The embodiment innovatively realizes the edge-cloud collaborative decision mechanism. Based on the task affinity score, the system constructs a dynamic task allocation framework. By analyzing the calculation characteristics and resource requirements of early warning tasks, the system can intelligently determine the optimal execution location of the task. Special attention is paid to the balance between calculation efficiency and communication overhead, and for calculation-intensive early warning analysis tasks, the system will evaluate the overall cost of local execution and cloud migration. This multi-dimensional decision mechanism can ensure that the early warning task is executed in the most suitable location, and improve the overall early warning efficiency.

[0063] The embodiment deeply optimizes the data transmission strategy. The system designs a data synchronization mechanism based on incremental transmission, and transmits only the changed monitoring data by calculating the differential information of the data. Special attention is paid to the timeliness requirement of data, and for monitoring points with high early warning level, the system will prioritize the timeliness of data transmission. Through model hot migration technology, the system can realize dynamic switching of early warning tasks without interrupting service. This flexible migration mechanism significantly reduces the overhead of task switching.

[0064] The embodiment realizes the accurate evaluation of the risk of slope instability through multi-level early warning rules. The system constructs early warning level judgment criteria based on key indicators such as deformation rate, cumulative displacement, and pressure change rate. Special attention is paid to the correlation between different monitoring parameters, and a risk level mapping rule is established through fuzzy comprehensive evaluation method. For example, when deep displacement and surface cracks are detected to accelerate development at the same time, the system will increase the early warning level and timely issue early warning information. This multi-dimensional early warning mechanism can effectively reduce the probability of false negatives and false positives.

[0065] The innovative design of the embodiment not only solves the problem of early warning analysis in traditional methods, but also establishes a sustainable optimization edge-cloud collaborative framework. Through intelligent scheduling and dynamic migration, the system can fully utilize the computing resources of edge and cloud. This resource-aware scheduling mechanism ensures that the system always maintains efficient early warning capability and reliable execution effect when facing complex monitoring scenarios. In the application of slope monitoring, this intelligent early warning scheme significantly improves the accuracy and real-time performance of early warning analysis.

[0066] The embodiment realizes the early warning upgrade for slope monitoring by establishing a complete early warning analysis and resource management link. The system can dynamically adjust the early warning strategy based on real-time state, avoiding the limitations of traditional fixed schemes. Through multi-dimensional risk assessment and early warning judgment, the accuracy and timeliness of slope instability early warning are significantly improved, providing reliable decision support for engineering safety monitoring. This intelligent early warning mechanism shows strong adaptability and optimization effect in the scene of slope monitoring.

[0067] The embodiment not only improves the efficiency of early warning analysis, but also establishes a continuously evolving monitoring and early warning system through continuous strategy optimization and resource management. This real-time feedback-based optimization mechanism ensures that the system can continuously improve as the monitoring needs change, providing more and more accurate early warning services for slope safety monitoring. In practical applications, this self-optimization mechanism significantly improves the long-term service quality and early warning effect of the system, providing strong technical support for slope monitoring and early warning.

[0068] As can be seen from the above description, the slope state comprehensive prediction and early warning method based on multi-modal data fusion provided by the embodiment can realize accurate monitoring of the slope state by innovatively constructing a multi-source monitoring data processing mechanism by integrating displacement, crack, and pressure sensor data. A feature extraction optimization model based on complexity evaluation is designed, combined with a lightweight convolutional network and an attention mechanism to establish a model self-adaptive pruning strategy for calculation optimization. An edge-cloud collaborative early warning mechanism is introduced, and through task affinity evaluation and model hot migration technology, dynamic allocation of prediction tasks and multi-level early warning are realized. This method effectively solves the shortcomings of traditional technologies in data processing, feature extraction, and early warning analysis, significantly improving the accuracy and reliability of slope state prediction and early warning.

[0069] In an embodiment of the slope state comprehensive prediction and early warning method based on multi-modal data fusion of the present application, the following contents can also be specifically included:

[0070] Step S201: An open protocol interface is constructed, which includes a physical interface layer, a signal conditioning layer, a protocol analysis layer, and a data fusion layer. The physical interface layer is configured with RS485, analog current, analog voltage, and frequency signal interface circuits. The signal conditioning layer is configured with an analog-to-digital converter and a timing counter. The protocol analysis layer is configured with ModbusRTU and MQTT communication protocol stacks. The data fusion layer is configured with a unified data frame format. Monitoring data of displacement sensors, crack sensors, and pressure sensors are collected based on the open protocol interface;

[0071] Step S202: Signal processing is performed on the monitoring data. A signal conditioning circuit is used to amplify and filter analog signals. A 24-bit analog-to-digital converter is used to digitize the conditioned signals. A timer counter circuit is used to measure the period of frequency signals. The digitized monitoring data is distributed according to channel numbers. Sensor calibration parameters are read based on the channel numbers. Linear mapping and unit conversion are performed on the monitoring data to generate monitoring data in a standard signal format.

[0072] Optionally, the present embodiment innovatively designs an open protocol interface and signal processing scheme to address problems such as non-uniform sensor interfaces, poor signal quality, and complex data formats in slope monitoring. The present embodiment first constructs a multi-level interface architecture to achieve electrical connection of different types of sensors through the physical interface layer. The system designs a signal conversion mechanism: Output = k*(Input-Zero) + Offset, where k is the proportional coefficient, Zero is the zero point value, and Offset is the correction offset. These parameters are obtained through sensor calibration. In slope monitoring applications, this standardized interface design can effectively solve the compatibility problem of sensors from different manufacturers.

[0073] The present embodiment optimizes the physical interface layer design. For displacement sensors, crack sensors, and pressure sensors commonly used in slope monitoring, various signal interface circuits are designed. For digital signals, RS485 interface is used for long-distance transmission. For analog signals, 4-20mA current loop and 0-5V voltage interface are configured. For frequency signals, a dedicated frequency acquisition circuit is designed. Special attention is paid to the anti-interference ability of the signal. The system uses differential transmission technology and electromagnetic shielding design to effectively suppress external interference. For example, when collecting data from deep displacement meters, differential signal transmission through RS485 can effectively overcome electromagnetic interference on the construction site and ensure the reliability of data transmission.

[0074] The embodiment innovatively realizes the signal conditioning layer function. For different types of sensor signals, the system constructs a dedicated signal conditioning circuit. Signal amplification is realized through high-precision instrument amplifiers, and multi-stage filter circuits are used to eliminate interference signals. Special attention is paid to the dynamic range of the signal, and the system automatically adjusts the gain according to the sensor output characteristics to ensure that the signal always works in the best range. For frequency signals, a timer counting circuit is used to realize accurate period measurement, and a digital filtering algorithm is used to improve measurement accuracy. This multi-level signal conditioning scheme significantly improves the accuracy of data acquisition.

[0075] The embodiment deeply optimizes the protocol analysis layer design. The system is based on Modbus RTU and MQTT protocol to build a dual protocol stack architecture, realizing local communication and remote data transmission. Through the protocol analysis engine, the data frame format is automatically identified, and the payload data is extracted. Special attention is paid to the reliability of communication, and the system uses CRC check and timeout retransmission mechanism to ensure the integrity of data transmission. At the same time, through the MQTT protocol, the data is pushed to the cloud, supporting breakpoint resume and offline cache functions. This flexible protocol design provides reliable guarantee for the collection and transmission of slope monitoring data.

[0076] The embodiment realizes the unified management of multi-source data through the data fusion layer. The system designs a standardized data frame format, including sensor identification, data type, sampling time and other key information. Special attention is paid to the timing characteristics of the data, and a global clock source is used to add accurate time stamps to each data packet. For different types of sensor data, the system converts units and linearly maps according to the calibration parameters to ensure the consistency of the physical meaning of the data. This standardized data processing method lays a foundation for subsequent feature extraction and analysis.

[0077] The innovative design of the embodiment not only solves the interface compatibility problem in traditional methods, but also establishes an extensible data acquisition framework. Through multi-level interface design and signal processing, the system can adapt to the access requirements of different types of sensors. This interface mechanism based on open standards ensures that the system always maintains good compatibility and reliable acquisition effect when facing new sensors. In the slope monitoring application, this intelligent interface scheme significantly improves the universality and reliability of the system.

[0078] The embodiment realizes the intelligent upgrade for slope monitoring by establishing a complete signal acquisition and processing link. The system can dynamically adjust the processing parameters based on real-time state, avoiding the limitations of traditional fixed parameter scheme. Through multi-dimensional signal processing and data fusion, the accuracy and real-time performance of monitoring data are significantly improved, providing high-quality data support for slope state evaluation. This intelligent acquisition mechanism shows strong adaptability and processing effect in the slope monitoring scene.

[0079] The embodiment not only improves the reliability of data collection, but also establishes a continuously evolving monitoring system through continuous parameter optimization and signal processing. This optimization mechanism based on real-time feedback ensures that the system can continuously improve as the monitoring needs change, providing more and more efficient data services for slope safety monitoring. In practical applications, this self-optimization mechanism significantly improves the long-term service quality and processing effect of the system, providing a reliable data foundation for slope monitoring and early warning.

[0080] In an embodiment of the slope state comprehensive prediction and early warning method based on multi-modal data fusion of the present application, the following content can be specifically included:

[0081] Step S301: Construct a signal noise reduction module, construct a signal smoother based on a sliding window, the window length of the signal smoother is adapted to the signal sampling frequency, perform median filtering on the data in the window to remove sudden noise, perform multi-scale decomposition on the filtered signal using wavelet transform, reconstruct the signal after setting the high-frequency noise coefficients to zero, calculate the data redundancy based on the signal variance, and perform downsampling compression on the data segments with redundancy higher than the threshold, to generate the monitoring signal after noise reduction and redundancy removal;

[0082] Step S302: Construct a data fusion module, establish a time sequence index table based on the timestamp information of the monitoring data, use linear interpolation algorithm to resample the data of different sampling periods, align the resampled data according to a unified time reference, construct a data fusion format template, the data fusion format template includes sensor identification, physical quantity type, numerical range and unit information, the aligned data is standardized mapped according to the data fusion format template, to generate a structured slope state data stream.

[0083] Optionally, the embodiment innovatively designs a signal noise reduction and data fusion scheme to solve the problems of large signal noise interference and poor synchronization of multi-source data in slope monitoring. The embodiment first constructs a signal processing framework based on a sliding window, and realizes signal smoothing through an adaptive window mechanism. The system designs a noise evaluation formula: Noise_Level=sqrt(Σ(Xi-Mean)^2 / N), where Xi is the data point in the window, Mean is the window mean, and N is the window length. In slope monitoring applications, this adaptive noise evaluation method can effectively identify the noise characteristics of different types of sensors.

[0084] The embodiment deeply optimizes the signal smoothing strategy. According to the characteristics of different types of monitoring data, a multi-level filtering processing scheme is designed. For sudden noise, the system adopts median filtering for preliminary processing; for high-frequency interference, multi-scale analysis is realized through wavelet transform. Special attention is paid to the time-frequency characteristics of the signal, and the system selects appropriate wavelet basis functions according to the physical characteristics of the monitoring parameters to ensure that the effective signal components are retained while the noise is removed. For example, when processing displacement sensor data, the system selects Db4 wavelet for decomposition, and selects high-frequency coefficients through soft threshold processing method to effectively suppress the interference caused by environmental vibration.

[0085] The embodiment innovatively realizes the data redundancy evaluation mechanism. In view of the storage pressure caused by a large amount of repeated data, the system constructs a redundancy calculation framework based on variance. By analyzing the trend of data change, the time period with high data redundancy is identified. Special attention is paid to the time sequence characteristics of the data, and when the data change rate is detected to be lower than the set threshold, the system will automatically adjust the sampling rate to realize dynamic compression of data. This intelligent data compression method can significantly reduce the storage overhead while maintaining key information.

[0086] The embodiment deeply optimizes the data fusion strategy. The system constructs a unified time sequence index table based on timestamp information, and realizes the synchronization and alignment of data with different sampling periods through linear interpolation algorithm. Special attention is paid to the continuity of data, and for sensors with inconsistent sampling periods, the system will set a unified time reference according to the highest sampling rate, and supplement the missing data points through interpolation calculation. This accurate time sequence alignment mechanism provides reliable guarantee for the fusion analysis of multi-source data.

[0087] The embodiment realizes the unified management of multi-source data through standardized data fusion format. The system designs a format template containing sensor identification, physical quantity type, numerical range and other information. Special attention is paid to the semantic expression of data, and through the establishment of a unified unit system and numerical mapping rules, it is ensured that monitoring data from different sources can be effectively compared and analyzed. For example, when processing displacement data at different depths, the system will automatically perform unit conversion and numerical standardization to facilitate subsequent comprehensive analysis.

[0088] The innovative design of the embodiment not only solves the signal quality problem in the traditional method, but also establishes a sustainable optimization data processing framework. Through multi-level noise reduction processing and data fusion, the system can obtain high-quality slope state data from complex monitoring environment. This fusion mechanism based on standardized processing ensures that the system always maintains high processing capacity and reliable fusion effect when facing different types of monitoring data. In the application of slope monitoring, this intelligent data processing scheme significantly improves the integrity and accuracy of monitoring data.

[0089] This embodiment realizes the intelligent upgrade for slope monitoring by establishing a complete signal processing and data fusion link. The system can dynamically adjust the processing strategy based on real-time state, avoiding the limitations of traditional fixed parameter schemes. Through multi-dimensional signal processing and data fusion, the accuracy and real-time performance of monitoring data are significantly improved, providing high-quality data support for subsequent slope state evaluation. This intelligent processing mechanism shows strong adaptability and optimization effect in the slope monitoring scene.

[0090] This embodiment not only improves the reliability of data processing, but also establishes a continuously evolving monitoring system through continuous parameter optimization and format specification. This optimization mechanism based on real-time feedback ensures that the system can continuously improve as the monitoring requirements change, providing more and more efficient data services for slope safety monitoring. In practical applications, this self-optimization mechanism significantly improves the long-term service quality and processing effect of the system, providing a reliable data foundation for slope monitoring and early warning.

[0091] In an embodiment of the slope state comprehensive prediction and early warning method based on multi-modal data fusion of the present application, the following content can be specifically included:

[0092] Step S401: Construct a time series segmentation model, perform sliding segmentation on the slope state data stream, determine the segmentation point position based on data variance and trend change rate, input the segmented data into a long short-term memory network, the long short-term memory network includes a forget gate and an input gate structure, the forget gate filters historical state information, the input gate fuses current input features, models time series features based on a recurrent connection, uses a residual structure to preserve short-term detail features, and inputs the modeling results into a feature extraction module.

[0093] Step S402: Construct a feature extraction module, the feature extraction module uses a lightweight convolutional network structure, reduces the parameter size through depth separable convolution, sets multi-scale convolution kernels to extract slope deformation features, uses a self-attention mechanism to calculate feature map correlation, weights and combines attention weights and convolution feature maps, collects processor and memory occupation data based on a resource monitoring interface, calculates a model calculation complexity score, prunes channels of the network structure according to the complexity score, and generates optimized slope state features.

[0094] Optionally, the embodiment is directed to the problem of inaccurate time series feature extraction and limited computing resources in slope monitoring. A time series modeling and feature extraction scheme is designed. The embodiment first constructs a time series segmentation framework and divides the monitoring data through an adaptive sliding window. A segmentation evaluation mechanism is designed: Segment_Score = w1Variance + w2Trend_Rate, where Variance is the data variance, Trend_Rate is the trend rate, and w1 and w2 are weight coefficients. In slope monitoring applications, this segmentation method based on data features can effectively identify the change nodes of the slope state.

[0095] The embodiment optimizes the LSTM network structure in depth. For the long and short term dependence relationship in the slope deformation process, an improved gating mechanism is designed. The system dynamically adjusts the retention ratio of historical information through the forgetting gate, appropriately decays the irrelevant state in the long term, and controls the degree of integration of new information through the input gate to ensure that the model can capture sudden state changes. Special attention is paid to the continuity of state transition. When a significant change in displacement rate or crack width is detected, the system increases the input weight of the current time and improves the sensitivity to abnormal states. This adaptive time series modeling method significantly improves the accuracy of slope state prediction.

[0096] The embodiment innovatively realizes the residual learning mechanism. To solve the problem that traditional LSTM networks easily lose short-term detailed features, the system constructs a multi-layer residual connection structure. By establishing feature channels of different time scales, it ensures that small changes in the short term can be accurately captured. Special attention is paid to the transmission efficiency of residual information, and the system uses a gated linear unit to modulate residual features, avoiding the gradient vanishing problem. This residual learning-based time series modeling method can maintain both long-term trends and short-term details.

[0097] The embodiment optimizes the feature extraction strategy in depth. The system constructs a lightweight network structure based on depth separable convolution, significantly reduces the computational complexity by decomposing the standard convolution into a combination of depth convolution and pointwise convolution. Special attention is paid to the multi-scale expression of features, and the system designs parallel multi-scale convolution branches that can capture deformation features of different spatial ranges at the same time. For example, when analyzing slope displacement data, small-scale convolution focuses on local deformation details, and large-scale convolution focuses on overall deformation trends. This multi-scale feature extraction mechanism significantly improves the expression ability of the model.

[0098] The embodiment realizes the adaptive weighting of features through the self-attention mechanism. The system constructs an attention calculation module based on similarity, dynamically adjusts the importance weight of features by evaluating the correlation between different feature maps, and pays special attention to the correlation between monitoring parameters. When it is found that the changes of certain monitoring points have obvious correlation, the system will increase the weight of the corresponding features to enhance the recognition ability of potential risk patterns. This attention-based feature fusion method can highlight key information and improve the prediction accuracy of the model.

[0099] The innovative design of the embodiment not only solves the feature extraction problem in traditional methods, but also establishes a sustainable optimization model framework. Through resource monitoring and complexity evaluation, the system can dynamically adjust the network structure according to the actual status of computing resources. This hardware-aware optimization mechanism ensures that the system always maintains efficient feature extraction capability and reliable processing effect when facing resource-constrained edge devices. In the application of slope monitoring, this intelligent feature extraction scheme significantly improves the real-time performance of early warning analysis.

[0100] The embodiment realizes the intelligent upgrade for slope monitoring by establishing a complete time series modeling and feature extraction link. The system can dynamically adjust the processing strategy based on the characteristics of monitoring data, avoiding the limitations of traditional fixed models. Through multi-dimensional feature extraction and model optimization, the accuracy and real-time performance of slope state evaluation are significantly improved, providing reliable decision support for safety warning. This intelligent feature extraction mechanism shows strong adaptability and optimization effect in the slope monitoring scene.

[0101] The embodiment not only improves the efficiency of feature extraction, but also establishes a continuously evolving monitoring system through continuous model optimization and resource management. This real-time feedback-based optimization mechanism ensures that the system can continuously improve as the monitoring needs change, providing more and more accurate early warning services for slope safety monitoring. In practical applications, this self-optimization mechanism significantly improves the long-term service quality and prediction effect of the system, providing strong technical support for slope monitoring and early warning.

[0102] In an embodiment of the slope state comprehensive prediction and early warning method based on multi-modal data fusion of the present application, the following content can be specifically included:

[0103] Step S501: Construct a model pruning module, construct an importance score matrix based on the complexity score, sort the importance of the convolution layer channels, prune the channels with importance scores below the threshold, migrate the original model knowledge to the pruned model using the distillation learning method, fine-tune the pruned model based on the validation dataset, and input the pruned model into the compression module;

[0104] Step S502: Construct a compression optimization module, adopt a mixed precision quantization strategy to compress the bit width of the model parameters, perform symmetric quantization mapping on the weight tensor, convert the floating point data into fixed point integer representation, measure the parameter sparsity degree based on information entropy, perform sparse decomposition on the parameter matrix, rearrange the non-zero parameters to construct a sparse storage structure, accelerate the inference calculation by using sparse matrix multiplication, and deploy the compressed and optimized model to the slope state prediction module.

[0105] Optionally, the embodiment innovatively designs a model pruning and compression optimization scheme to solve the problems of large calculation load and high storage overhead of deep learning models in slope monitoring. The embodiment first constructs an importance evaluation framework and analyzes the network layers through complexity scores. The system designs a channel importance calculation mechanism: Channel_Score=w1Feature_Impact+w2Resource_Cost, where Feature_Impact is the feature contribution degree, Resource_Cost is the resource consumption, and w1 and w2 are weight coefficients. In the application of slope monitoring, this multi-dimensional evaluation method can accurately identify redundant structures in the network.

[0106] The embodiment deeply optimizes the model pruning strategy. Different pruning schemes are designed for different types of convolutional layers. For standard convolutional layers, the system identifies channels with small feature extraction contribution by analyzing the importance weights of the convolutional kernels. For depth separable convolutional layers, the dependence between depth convolution and pointwise convolution needs to be considered. Special attention is paid to the overall performance of the network. When it is determined that a layer needs to be pruned, the system will evaluate the impact on adjacent layers. For example, when processing a slope displacement prediction model, the system optimizes the feature extraction layer and the prediction layer together to ensure the rationality of the pruned network structure.

[0107] The embodiment innovatively implements a knowledge distillation mechanism. To solve the problem of performance loss during pruning, the system constructs a teacher-student model framework. By designing a distillation loss function: Distill_Loss=αCE(Student,Label)+βKL(Student,Teacher), where CE is the cross-entropy loss, KL is the KL divergence, and α and β are balance coefficients. This progressive knowledge transfer method can maintain the prediction accuracy of the model. Special attention is paid to feature-level knowledge transfer. The system ensures that the pruned model maintains the original feature extraction capability through intermediate layer feature alignment.

[0108] The embodiment deeply optimizes the parameter quantization strategy. The system designs a mixed precision quantization scheme based on data distribution, determines the optimal quantization bit width by analyzing the numerical characteristics of parameters in different layers. Special attention is paid to the control of quantization error. For different layer parameter distribution characteristics, adaptive quantization factor is used for accuracy adjustment. For example, for the key layer of slope deformation feature extraction, the system maintains high quantization accuracy, while for the layer with strong redundancy, lower bit width is used.

[0109] The embodiment realizes further model compression through parameter sparsification. The system evaluates the importance distribution of parameters based on information entropy, and constructs a sparsification decomposition strategy. Special attention is paid to the design of sparse mode, and structured sparsification is used to ensure hardware execution efficiency. For example, when processing large-scale parameter matrix, the system groups and sparsifies the parameters according to the hardware parallelism, ensuring that the compressed model can run efficiently on edge devices.

[0110] The innovative design of the embodiment not only solves the model deployment problem in traditional methods, but also establishes a sustainable optimization compression framework. Through multi-level pruning and compression, the system can efficiently deploy complex deep learning models to resource-constrained edge devices. This hardware-aware optimization mechanism ensures that the system always maintains efficient inference ability and reliable processing effect when facing different computing platforms. In the application of slope monitoring, this intelligent model optimization scheme significantly improves the real-time performance of edge computing.

[0111] The embodiment realizes the intelligent upgrade of early warning for slope monitoring by establishing a complete model optimization link. The system can dynamically adjust the optimization strategy based on hardware constraints, avoiding the limitations of traditional compression methods. Through multi-dimensional model pruning and parameter compression, the efficiency and adaptability of edge computing are significantly improved, providing reliable algorithm support for slope state prediction. This intelligent optimization mechanism shows strong practical value and optimization effect in the scene of slope monitoring.

[0112] The embodiment not only improves the efficiency of model deployment, but also establishes an evolving edge computing system through continuous optimization strategies and hardware adaptation. This resource-aware optimization mechanism ensures that the system can continuously improve with the development of hardware platforms, providing more and more efficient intelligent services for slope monitoring. In practical applications, this self-optimization mechanism significantly improves the long-term service quality and processing effect of the system, providing strong technical support for slope state prediction.

[0113] In an embodiment of the slope state comprehensive prediction and early warning method based on multi-modal data fusion of the present application, the following contents can be specifically included:

[0114] Step S601: constructing a prediction task scheduling module, creating a prediction task instance based on container virtualization technology, deploying the prediction task instance to a task scheduling pool, the task scheduling pool including a resource allocation unit and a load balancing unit, the resource allocation unit performing isolated configuration on processor cores and memory space, the load balancing unit performing task distribution based on resource monitoring data, collecting processor occupancy and network bandwidth data using a resource monitoring interface, and inputting the monitoring data into a task scoring unit;

[0115] Step S602: constructing a task migration module, constructing a task affinity score model based on processor occupancy and network bandwidth status, the task affinity score model performing weighted calculation on computing load and network transmission overhead to generate a task migration decision score, prioritizing high-load prediction tasks, matching the priority ranking results with a cloud resource pool, transmitting computing data using an incremental synchronization method, and deploying the prediction task to a cloud node based on model live migration technology.

[0116] Optionally, the present embodiment innovatively designs a container-based task scheduling and migration scheme to address the problems of large early warning computing load and uneven resource allocation in slope monitoring. The present embodiment first constructs a prediction task instance based on Docker container technology and achieves resource isolation through namespace. The system designs a resource evaluation mechanism: Resource_Score = w1CPU_Usage + w2Memory_Usage + w3Network_BW, where CPU_Usage is processor usage, Memory_Usage is memory occupancy, Network_BW is bandwidth usage, and w1, w2, and w3 are resource weight coefficients. In slope monitoring applications, this resource-aware scheduling method can effectively balance computing load.

[0117] The present embodiment optimizes the resource allocation strategy. A fine-grained resource isolation mechanism is designed for the characteristics of slope early warning tasks. The system strictly isolates processor cores and memory space through cgroups technology to ensure that the resources of critical early warning tasks are not occupied by other tasks. Special attention is paid to dynamic resource allocation. When changes in resource requirements of a certain early warning task are detected, the system automatically adjusts the resource quota. For example, when processing early warning analysis of multiple monitoring points, if it is found that the deformation rate of a certain area has accelerated, the system will prioritize the computing resources of the early warning task in that area to ensure the real-time performance of early warning analysis.

[0118] The embodiment innovatively realizes the load balancing mechanism. For the scene of multi-task parallel processing, the system constructs a task distribution framework based on resource monitoring. By collecting indicators such as processor occupancy, memory usage and network bandwidth in real time, the system can accurately evaluate the load state of each computing node. Special attention is paid to the priority management of tasks. For monitoring points with high warning levels, the system will preferentially allocate computing resources to ensure that the warning analysis in critical areas can be completed in time. This priority-based load balancing method can significantly improve the response speed of the warning system.

[0119] The embodiment deeply optimizes the task migration strategy. The system designs a task scoring model based on affinity, which calculates the migration decision score for each warning task by considering the computing load and network transmission cost: Migration_Score = αCompute_Load + βNetwork_Cost, where Compute_Load is the computing load indicator, Network_Cost is the network transmission cost, and α and β are balance coefficients. This quantitative evaluation method provides an objective basis for task migration.

[0120] The embodiment realizes the dynamic switching of warning tasks through model hot migration technology. The system constructs an incremental synchronization mechanism that only transmits changed monitoring data and model parameters. Special attention is paid to the continuity of the migration process, and through the establishment of data buffer and state synchronization mechanism, it ensures that the warning service will not be interrupted during task migration. This smooth migration scheme can effectively reduce the impact of task switching.

[0121] The innovative design of the embodiment not only solves the resource scheduling problem in traditional methods, but also establishes a sustainable optimization edge cloud collaboration framework. Through the combination of containerized deployment and intelligent scheduling, the system can realize efficient utilization of computing resources. This task feature-based scheduling mechanism ensures that the system always maintains high efficiency and reliable execution effect when facing complex monitoring scenarios. In the slope monitoring application, this intelligent scheduling scheme significantly improves the real-time performance and reliability of the warning analysis.

[0122] The embodiment realizes the intelligent upgrade of warning for slope monitoring by establishing a complete task scheduling and migration link. The system can dynamically adjust the scheduling strategy based on real-time state, avoiding the limitations of traditional fixed allocation schemes. Through multi-dimensional resource evaluation and task scheduling, the execution efficiency of warning tasks and the reliability of the system are significantly improved, providing strong computing support for slope safety monitoring. This intelligent scheduling mechanism shows strong adaptability and optimization effect in the slope monitoring scene.

[0123] The embodiment not only improves the execution efficiency of the early warning task, but also establishes an evolving edge computing system through continuous scheduling optimization and resource management. This optimization mechanism based on real-time feedback ensures that the system can continuously improve as the monitoring needs change, providing more and more efficient early warning services for slope monitoring. In practical applications, this self-optimization mechanism significantly improves the long-term service quality and processing effect of the system, providing strong technical support for slope monitoring and early warning.

[0124] In an embodiment of the slope state comprehensive prediction and early warning method based on multi-modal data fusion of the present application, the following content can also be included:

[0125] Step S701: Construct a data transmission module, perform incremental calculation on the slope state data, identify new data blocks based on data time stamps, encode the new data using a data compression algorithm, establish a data transmission channel, the data transmission channel includes a breakpoint continuation and a data verification mechanism, transmit the compressed data fragments to the cloud storage, construct a model cache pool based on a model version management strategy, the model cache pool performs version identification and state maintenance on the prediction model, perform task switching using model hot migration technology, and apply the switched model to slope state prediction;

[0126] Step S702: Construct an early warning analysis module, construct an early warning level evaluator based on the prediction results, the early warning level evaluator includes deformation rate, cumulative displacement and pressure change rate thresholds, perform hierarchical early warning judgment on the monitoring data, establish multi-level early warning rules using fuzzy comprehensive judgment method, associate and map the early warning level and risk assessment results, generate an analysis report based on the early warning information template, the analysis report includes early warning level, risk assessment result and disposal suggestion.

[0127] Optionally, the embodiment innovatively designs a data transmission and early warning analysis scheme to address the problems of low data transmission efficiency and inaccurate early warning analysis in slope monitoring. The embodiment first constructs an incremental data processing framework and manages data versions through timestamps. The system designs a data difference mechanism: Delta_Data = Current_Data - Previous_Data, where Current_Data is the current data set and Previous_Data is the historical data set. This incremental calculation method can significantly reduce data transmission volume. In slope monitoring applications, this difference-based transmission method can effectively improve data synchronization efficiency.

[0128] This embodiment deeply optimizes the data compression strategy. According to the characteristics of slope monitoring data, an adaptive compression algorithm is designed. The system selects the appropriate compression method according to the time sequence characteristics and change law of the data. Special attention is paid to the accuracy requirements of the data. For key monitoring parameters, lossless compression method is adopted to ensure the accuracy of data restoration. For example, when processing displacement sensor data, the system will retain the details of small changes, and for slowly changing parameters, a higher compression ratio is adopted. This differentiated compression strategy significantly reduces the data transmission overhead.

[0129] This embodiment innovatively realizes the reliable transmission mechanism. In view of the problem of unstable network environment, the system constructs the breakpoint resume and data verification framework. By dividing large data sets into fixed size data blocks and adding check codes for each data block, the reliability of the transmission process is ensured. Special attention is paid to the real-time requirements of transmission. The system will dynamically adjust the transmission strategy according to the priority of the data. For example, when abnormal data is detected, the system will preferentially transmit the data of related monitoring points to ensure the timeliness of early warning analysis.

[0130] This embodiment deeply optimizes the model version management strategy. The system designs a model management mechanism based on cache pool, and ensures the consistency of the model through version identification and state maintenance. Special attention is paid to the smoothness of model switching. When the prediction model needs to be updated, the system will use hot migration technology to realize seamless switching. This dynamic model management method can ensure the continuity of early warning service.

[0131] This embodiment realizes the accurate assessment of slope instability risk through the early warning level evaluator. The system constructs a multi-index based early warning judgment framework, which evaluates the stability of the slope by monitoring the deformation rate, cumulative displacement and pressure change rate and other key parameters. Special attention is paid to the correlation between parameters. The system uses fuzzy comprehensive evaluation method to establish multi-level early warning rules: Warning_Level=F(μ1Rate+μ2Displacement+μ3×Pressure), where μ1, μ2, μ3 are fuzzy weight coefficients. This comprehensive evaluation method can more accurately reflect the instability risk of the slope.

[0132] The innovative design of this embodiment not only solves the data transmission problem in traditional methods, but also establishes a sustainable optimization early warning analysis framework. Through incremental transmission and model hot migration, the system can realize efficient update of data and model. This early warning mechanism based on real-time state ensures that the system always maintains accurate early warning ability and reliable analysis effect when facing complex monitoring scenarios. In the application of slope monitoring, this intelligent early warning scheme significantly improves the accuracy and timeliness of early warning analysis.

[0133] This embodiment realizes the intelligent upgrading of slope monitoring and early warning by establishing a complete data transmission and early warning analysis link. The system can dynamically adjust the early warning strategy based on the characteristics of the monitoring data, avoiding the limitations of the traditional fixed threshold scheme. Through multi-dimensional risk assessment and early warning judgment, the accuracy and reliability of slope instability early warning are significantly improved, providing reliable decision support for engineering safety monitoring. This intelligent early warning mechanism shows strong adaptability and optimization effect in the slope monitoring scene.

[0134] This embodiment not only improves the efficiency of early warning analysis, but also establishes a constantly evolving monitoring and early warning system through continuous strategy optimization and risk assessment. This real-time feedback-based optimization mechanism ensures that the system can continuously improve as monitoring needs change, providing more accurate early warning services for slope safety monitoring. In practical applications, this self-optimization mechanism significantly improves the long-term service quality and early warning effect of the system, providing strong technical support for slope monitoring and early warning.

[0135] In order to effectively solve the shortcomings of traditional technology in data processing, feature extraction and early warning analysis, and significantly improve the accuracy and reliability of slope state prediction and early warning, the present application provides an embodiment of a slope state comprehensive prediction and early warning device based on multi-modal data fusion for implementing all or part of the contents of the slope state comprehensive prediction and early warning method based on multi-modal data fusion, as shown in Figure 2 , the slope state comprehensive prediction and early warning device based on multi-modal data fusion specifically includes the following contents:

[0136] The data monitoring module 10 is used for collecting multi-modal slope monitoring data, collecting monitoring data of displacement sensors, crack sensors and pressure sensors through an open protocol interface, converting the monitoring data into a standard signal format, performing channel allocation and signal conditioning on the standard signal, performing noise reduction and de-redundancy on signal data based on a sliding window, aligning time stamps of multi-source data using a data synchronization alignment algorithm, constructing a unified data fusion format, standardizing the data after fusion according to metadata specifications, and generating a structured slope state data stream;

[0137] The feature processing module 20 is used for feature extraction and calculation optimization of the slope state data stream, segment modeling of data based on a time series model, extraction of slope deformation features using a lightweight convolutional network, capture of long-range dependencies through an attention mechanism, construction of a model complexity evaluator, generation of a complexity score by the model complexity evaluator based on computational resource occupancy and memory usage, adaptive pruning of model structure according to the complexity score, bit width compression of model parameters using quantization compression technology, reduction of computational complexity through sparsification processing, and input of the optimized features into a slope state prediction model;

[0138] The slope early warning module 30 is configured to generate early warning information based on the slope state prediction model. The slope state prediction model constructs a prediction task scheduling pool through container virtualization technology, collects processor occupancy and network bandwidth state by using a resource monitoring module, calculates a task affinity score according to the processor occupancy and network bandwidth state, migrates high-load prediction tasks to the cloud for processing, uploads slope state data in an incremental transmission manner, realizes dynamic switching of prediction tasks through model live migration technology, performs hierarchical early warning determination on the prediction result, constructs a multi-level early warning rule to evaluate the slope instability risk, and generates a slope early warning analysis report.

[0139] From the above description, the slope state comprehensive prediction and early warning device based on multi-modal data fusion provided by the embodiments of the present application can accurately monitor the slope state by innovatively constructing a multi-source monitoring data processing mechanism by integrating displacement, crack and pressure sensing data. A feature extraction optimization model based on complexity evaluation is designed, a lightweight convolutional network and an attention mechanism are combined, and a model self-adaptive pruning strategy is established for calculation optimization. An edge-cloud collaborative early warning mechanism is introduced, and through task affinity evaluation and model live migration technology, dynamic distribution and multi-level early warning of prediction tasks are realized. This method effectively solves the deficiencies of traditional technologies in data processing, feature extraction and early warning analysis, and significantly improves the accuracy and reliability of slope state prediction and early warning.

[0140] From the hardware level, in order to effectively solve the deficiencies of traditional technologies in data processing, feature extraction and early warning analysis, and significantly improve the accuracy and reliability of slope state prediction and early warning, the present application provides an embodiment of an electronic device for implementing all or part of the contents of the slope state comprehensive prediction and early warning method based on multi-modal data fusion. The electronic device specifically includes the following contents:

[0141] A processor, a memory, a communications interface and a bus; wherein the processor, the memory and the communications interface complete mutual communication through the bus; the communications interface is configured to realize information transmission between the slope state comprehensive prediction and early warning device based on multi-modal data fusion and a core business system, a user terminal and a related database and other related devices; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, and the present embodiment is not limited thereto. In the present embodiment, the logic controller can be implemented by referring to the embodiments of the slope state comprehensive prediction and early warning method based on multi-modal data fusion and the embodiments of the slope state comprehensive prediction and early warning device based on multi-modal data fusion in the embodiments, the contents of which are incorporated herein, and the repeated parts will not be described herein.

[0142] It can be understood that the user terminal can include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device can include smart glasses, a smart watch, a smart bracelet, etc.

[0143] In actual application, part of the slope state comprehensive prediction and early warning method based on multi-modal data fusion can be executed on the electronic device as described above, or all operations can be completed in the client device. Specifically, it can be selected according to the processing capacity of the client device and the limitation of the user's use scene. The present application does not limit this. If all operations are completed in the client device, the client device can also include a processor.

[0144] The above-mentioned client device can have a communication module (i.e. a communication unit) and can be in communication connection with a remote server to realize data transmission with the server. The server can include a server on the task scheduling center side, and other implementation scenarios can also include a server of an intermediate platform, such as a server of a third-party server platform in communication link with the task scheduling center server. The server can include a single computer device, or a server cluster composed of multiple servers, or a server structure of a distributed device.

[0145] Figure 3 A schematic block diagram of the system structure of the electronic device 9600 of the embodiment of the present application is shown in FIG. 9. As shown in the figure, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that the structure shown in the figure is exemplary; other types of structures can also be used to supplement or replace the structure to realize telecommunication functions or other functions. Figure 3 Figure 3 The structure shown in the figure is exemplary; other types of structures can also be used to supplement or replace the structure to realize telecommunication functions or other functions.

[0146] In an embodiment, the slope state comprehensive prediction and early warning method based on multi-modal data fusion can be integrated into the central processor 9100. The central processor 9100 can be configured to control as follows:

[0147] Step S101: Collecting multi-modal slope monitoring data, collecting monitoring data of displacement sensors, crack sensors and pressure sensors through an open protocol interface, converting the monitoring data into a standard signal format, performing channel allocation and signal conditioning on the standard signal, performing noise reduction and de-redundancy on the signal data based on a sliding window, performing timestamp alignment on multi-source data using a data synchronization alignment algorithm, constructing a unified data fusion format, standardizing the data after fusion according to metadata specifications, and generating a structured slope state data stream; ​

[0148] Step S102: feature extraction and calculation optimization are performed on the slope state data stream, time series model is used for segmented modeling of the data, lightweight convolution network is used to extract slope deformation features, long-range dependence is captured through attention mechanism, model complexity evaluator is constructed, the model complexity evaluator generates complexity score based on computing resource occupancy and memory usage, model structure is adaptively pruned according to the complexity score, model parameters are compressed in bit width through quantization compression technology, and calculation complexity is reduced through sparsification processing, and the optimized features are input into the slope state prediction model;

[0149] Step S103: generating early warning information based on the slope state prediction model, the slope state prediction model constructs a prediction task scheduling pool through container virtualization technology, uses a resource monitoring module to collect processor occupancy and network bandwidth state, calculates task affinity score according to the processor occupancy and network bandwidth state, migrates high-load prediction tasks to the cloud for processing, uploads slope state data in an incremental transmission manner, realizes dynamic switching of prediction tasks through model hot migration technology, makes graded early warning judgment on the prediction results, constructs multi-level early warning rules to evaluate the slope instability risk, and generates a slope early warning analysis report.

[0150] As can be seen from the above description, the electronic device provided by the embodiments of the present application innovatively constructs a multi-source monitoring data processing mechanism, integrates displacement, crack and pressure sensing data, and realizes accurate monitoring of the slope state. A feature extraction optimization model based on complexity evaluation is designed, a lightweight convolution network and an attention mechanism are combined, and a model adaptive pruning strategy is established for calculation optimization. A side-cloud collaborative early warning mechanism is introduced, task affinity evaluation and model hot migration technology are used to realize dynamic distribution and multi-level early warning of prediction tasks. This method effectively solves the shortcomings of traditional technologies in data processing, feature extraction and early warning analysis, and significantly improves the accuracy and reliability of slope state prediction and early warning.

[0151] In another embodiment, the slope state comprehensive prediction and early warning device based on multi-modal data fusion can be configured separately from the central processor 9100, for example, the slope state comprehensive prediction and early warning device based on multi-modal data fusion can be configured as a chip connected with the central processor 9100, and the function of the slope state comprehensive prediction and early warning method based on multi-modal data fusion is realized through the control of the central processor.

[0152] As shown in Figure 3 The electronic device 9600 can also include a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily include all the components shown in Figure 3All components shown in the electronic device 9600; in addition, the electronic device 9600 can further include Figure 3 components not shown in the electronic device 9600 can be referred to the prior art.

[0153] As shown in FIG. 9, the central processing unit 9100, which is sometimes also referred to as a controller or operation control, can include a microprocessor or other processor device and / or logic device, which receives input and controls the operation of the various components of the electronic device 9600. Figure 3

[0154] The memory 9140, for example, can be one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. Information relating to the failure described above can be stored, and in addition, a program for executing the information can be stored. The central processing unit 9100 can execute the program stored in the memory 9140 to achieve information storage or processing, etc.

[0155] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to supply power to the electronic device 9600. The display 9160 is used to display display objects such as images and text. The display can be, for example, an LCD display, but is not limited thereto.

[0156] The memory 9140 can be a solid state memory such as a read only memory (ROM), a random access memory (RAM), a SIM card, etc. It can also be a memory that saves information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes referred to as an EPROM, etc. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 can include an application / function storage 9142 for storing application programs and function programs or for storing a flow for executing the operation of the electronic device 9600 by the central processing unit 9100.

[0157] The memory 9140 can further include a data storage 9143 for storing data such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage 9144 of the memory 9140 can include various drivers of the electronic device for a communication function and / or for performing other functions of the electronic device such as a messaging application, an address book application, etc.

[0158] ​The communication module 9110 is a transmitter / receiver that transmits and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processor 9100 to provide input signals and receive output signals, as in the case of a conventional mobile communication terminal.

[0159] Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module, can be provided in the same electronic device. The communication module 9110 (transmitter / receiver) is also coupled to the speaker 9131 and the microphone 9132 via the audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing a conventional telecommunication function. The audio processor 9130 can include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording on the local device through the microphone 9132 and enabling playing of a sound stored on the local device through the speaker 9131.

[0160] The embodiment of the present application also provides a computer readable storage medium capable of implementing all steps of the slope state comprehensive prediction and early warning method based on multi-modal data fusion with the execution subject being a server or a client in the above-mentioned embodiment. The computer readable storage medium stores a computer program. When the processor executes the computer program, all steps of the slope state comprehensive prediction and early warning method based on multi-modal data fusion with the execution subject being a server or a client in the above-mentioned embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0161] Step S101: Collecting multi-modal slope monitoring data, collecting monitoring data of displacement sensors, crack sensors, and pressure sensors through an open protocol interface, converting the monitoring data into a standard signal format, performing channel allocation and signal conditioning on the standard signal, performing noise reduction and de-redundancy on signal data based on a sliding window, performing timestamp alignment on multi-source data using a data synchronization alignment algorithm, constructing a unified data fusion format, standardizing the data after fusion according to metadata specifications, and generating a structured slope state data stream.

[0162] Step S102: feature extraction and calculation optimization are performed on the slope state data stream, time series model is used for segmented modeling of the data, light convolutional network is used to extract slope deformation features, long-range dependence is captured through attention mechanism, model complexity evaluator is constructed, the model complexity evaluator generates complexity score based on computing resource occupancy and memory usage, model structure is adaptively pruned according to the complexity score, model parameters are compressed in bit width through quantization compression technology, and calculation complexity is reduced through sparsification processing; and the optimized features are input into the slope state prediction model.

[0163] Step S103: early warning information is generated based on the slope state prediction model, the slope state prediction model constructs a prediction task scheduling pool through container virtualization technology, a resource monitoring module is used to collect processor occupancy and network bandwidth state, a task affinity score is calculated according to the processor occupancy and network bandwidth state, high-load prediction tasks are migrated to the cloud for processing, the slope state data is uploaded in an incremental transmission mode, prediction task dynamic switching is realized through model hot migration technology, the prediction result is subjected to hierarchical early warning judgment, multi-level early warning rules are constructed to evaluate the slope instability risk, and a slope early warning analysis report is generated.

[0164] As can be seen from the above description, the computer readable storage medium provided by the embodiments of the present application realizes accurate monitoring of the slope state by innovatively constructing a multi-source monitoring data processing mechanism and integrating displacement, crack and pressure sensing data. A feature extraction optimization model based on complexity evaluation is designed, a light convolutional network and an attention mechanism are combined, and a model adaptive pruning strategy is established for calculation optimization. A side-cloud collaborative early warning mechanism is introduced, task affinity evaluation and model hot migration technology are used to realize dynamic allocation and multi-level early warning of prediction tasks. This method effectively solves the deficiencies of traditional technologies in data processing, feature extraction and early warning analysis, and significantly improves the accuracy and reliability of slope state prediction and early warning.

[0165] Embodiments of the present application also provide a computer program product capable of realizing all steps of the multi-modal data fusion based comprehensive slope state prediction and early warning method in the above-mentioned embodiments, which is executed by a processor to realize the steps of the multi-modal data fusion based comprehensive slope state prediction and early warning method, for example, the computer program / instruction realizes the following steps:

[0166] Step S101: Collecting multi-modal slope monitoring data, collecting monitoring data of displacement sensors, crack sensors and pressure sensors through an open protocol interface, converting the monitoring data into a standard signal format, performing channel allocation and signal conditioning on the standard signal, performing noise reduction and de-redundancy on the signal data based on a sliding window, performing timestamp alignment on multi-source data using a data synchronization alignment algorithm, constructing a unified data fusion format, standardizing the data after fusion according to metadata specifications, and generating a structured slope state data stream;

[0167] Step S102: Feature extraction and calculation optimization of the slope state data stream, segment modeling of data based on a time series model, extraction of slope deformation features using a lightweight convolutional network, capture of long-range dependencies through an attention mechanism, construction of a model complexity evaluator that generates a complexity score based on computing resource occupancy and memory usage, adaptive pruning of model structure according to the complexity score, bit width compression of model parameters using quantization compression technology, and reduction of computational complexity through sparsification processing, input of the optimized features into a slope state prediction model;

[0168] Step S103: Generating early warning information based on the slope state prediction model, constructing a prediction task scheduling pool through container virtualization technology, collecting processor occupancy and network bandwidth state using a resource monitoring module, calculating task affinity score according to the processor occupancy and network bandwidth state, migrating high-load prediction tasks to the cloud for processing, uploading slope state data using incremental transmission, implementing dynamic switching of prediction tasks through model hot migration technology, performing hierarchical early warning judgment on the prediction results, constructing multi-level early warning rules to evaluate the risk of slope instability, and generating a slope early warning analysis report.

[0169] As can be seen from the above description, the computer program product provided by the embodiments of the present application innovatively constructs a multi-source monitoring data processing mechanism, integrates displacement, crack and pressure sensor data, and realizes accurate monitoring of the slope state. A feature extraction optimization model based on complexity evaluation is designed, combined with a lightweight convolutional network and an attention mechanism, to establish a model adaptive pruning strategy for calculation optimization. A side-cloud collaborative early warning mechanism is introduced, and through task affinity evaluation and model hot migration technology, dynamic allocation and multi-level early warning of prediction tasks are realized. This method effectively solves the shortcomings of traditional technologies in data processing, feature extraction and early warning analysis, and significantly improves the accuracy and reliability of slope state prediction and early warning.

[0170] Those skilled in the art will appreciate that embodiments of the present application can be readily used as software, hardware, or a combination of software and hardware. In a software embodiment, the methods can be tangibly embodied in a machine-readable storage medium having stored thereon instructions that can be used to program a computer to perform any of the operations described herein. The software implementation can be for example, in the form of a computer program product which can include software agents or objects embedded in a computer readable storage medium. The computer readable storage medium can be a floppy disk, flexible disk, hard disk, USB (universal serial bus), RAM (random-access memory), flash memory, magnetic tape, or any other form of a computer readable storage medium.

[0171] The present application is described in relation to flow charts and / or block diagrams of methods, apparatus (devices) and computer program products according to embodiments of the application. It is understood that each block of the flow charts and / or block diagrams, and combinations of blocks in the flow charts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flow charts and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flow chart or block diagram block or blocks. Figure 1 one or more functions specified in the flow chart or block diagram block or blocks.

[0172] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow chart or block diagram block or blocks. Figure 1 one or more functions specified in the flow chart or block diagram block or blocks. Figure 1 one or more functions specified in the flow chart or block diagram block or blocks.

[0173] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow chart or block diagram block or blocks. Figure 1 one or more functions specified in the flow chart or block diagram block or blocks. Figure 1 one or more functions specified in the flow chart or block diagram block or blocks.

[0174] The principles and implementation modes of the present application are described in the specific embodiments. The above embodiments are only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range can be changed; in conclusion, the content of the specification should not be understood as the limitation of the present application.

Claims

1. A comprehensive prediction and early warning method for slope condition based on multimodal data fusion, characterized in that, The method includes: Multimodal slope monitoring data is collected by acquiring monitoring data from displacement sensors, crack sensors, and pressure sensors through an open protocol interface. The monitoring data is converted into a standard signal format, and the standard signals are channel-allocated and conditioned. Noise reduction and redundancy removal are performed on the signal data based on a sliding window. A data synchronization alignment algorithm is used to align the timestamps of the multi-source data to construct a unified data fusion format. The fused data is then standardized according to metadata specifications to generate a structured slope status data stream. Feature extraction and computational optimization are performed on the slope state data stream. The data is segmented and modeled based on a time series model. A lightweight convolutional network is used to extract slope deformation features. Long-range dependencies are captured through an attention mechanism. A model complexity estimator is constructed. The model complexity estimator generates a complexity score based on computational resource utilization and memory usage. The model structure is adaptively pruned according to the complexity score. The bit width of the model parameters is compressed using quantization compression technology. Computational complexity is reduced through sparsification. The optimized features are then input into the slope state prediction model. Early warning information is generated based on the slope condition prediction model. The slope condition prediction model constructs a prediction task scheduling pool through container virtualization technology, uses a resource monitoring module to collect processor utilization and network bandwidth status, calculates task affinity score based on processor utilization and network bandwidth status, migrates high-load prediction tasks to the cloud for processing, uploads slope condition data using incremental transmission, realizes dynamic switching of prediction tasks through model hot migration technology, performs hierarchical early warning judgment on prediction results, constructs multi-level early warning rules to assess slope instability risk, and generates a slope early warning analysis report.

2. The method for comprehensive prediction and early warning of slope condition based on multimodal data fusion according to claim 1, characterized in that, The process of collecting multimodal slope monitoring data involves acquiring monitoring data from displacement sensors, crack sensors, and pressure sensors via an open protocol interface, converting the monitoring data into a standard signal format, and performing channel allocation and signal conditioning on the standard signals, including: An open protocol interface is constructed, comprising a physical interface layer, a signal conditioning layer, a protocol parsing layer, and a data fusion layer. The physical interface layer is configured with RS485, analog current, analog voltage, and frequency signal interface circuits. The signal conditioning layer is configured with an analog-to-digital converter and a timer / counter. The protocol parsing layer is configured with Modbus RTU and MQTT communication protocol stacks. The data fusion layer is configured with a unified data frame format. Monitoring data from displacement sensors, crack sensors, and pressure sensors are collected based on the open protocol interface. The monitoring data is processed by signal conditioning circuit to amplify and filter the analog signal, digitally sampling the conditioned signal using a 24-bit analog-to-digital converter, periodically measuring the frequency signal using a timer counting circuit, allocating the digitized monitoring data according to channel number, reading sensor calibration parameters based on the channel number, linearly mapping and unit conversion of the monitoring data, and generating monitoring data in a standard signal format.

3. The method for comprehensive prediction and early warning of slope condition based on multimodal data fusion according to claim 1, characterized in that, The process involves denoising and redundancy removal of signal data based on a sliding window, aligning timestamps of multi-source data using a data synchronization alignment algorithm, constructing a unified data fusion format, and standardizing the fused data according to metadata specifications to generate a structured slope status data stream, including: A signal denoising module is constructed, which is based on a sliding window to build a signal smoother. The window length of the signal smoother is adapted to the signal sampling frequency. Median filtering is performed on the data within the window to remove abrupt noise. Wavelet transform is used to decompose the filtered signal into multiple scales. The high-frequency noise coefficient is set to zero and the signal is reconstructed. Data redundancy is calculated based on the signal variance. Data segments with redundancy higher than the threshold are downsampled and compressed to generate a denoised and deredundant monitoring signal. A data fusion module is constructed, a time-series index table is established based on the timestamp information of the monitoring data, a linear interpolation algorithm is used to resample the data of different sampling periods, the resampled data is aligned according to a unified time benchmark, and a data fusion format template is constructed. The data fusion format template includes sensor identification, physical quantity type, numerical range and unit information. The aligned data is standardized and mapped according to the data fusion format template to generate a structured slope status data stream.

4. The method for comprehensive prediction and early warning of slope condition based on multimodal data fusion according to claim 1, characterized in that, The process involves feature extraction and computational optimization of the slope state data stream, segmenting the data based on a time series model, extracting slope deformation features using a lightweight convolutional network, capturing long-range dependencies through an attention mechanism, and constructing a model complexity estimator. This model complexity estimator generates a complexity score based on computational resource utilization and memory usage, including: A time-series segmentation model is constructed to perform sliding segmentation on the slope state data stream. The segmentation point is determined based on the data variance and trend change rate. The segmented data is input into a long short-term memory network. The long short-term memory network includes a forget gate and an input gate structure. The forget gate filters historical state information, and the input gate fuses the current input features. The time-series features are modeled based on recurrent connections. The residual structure is used to preserve short-term detailed features. The modeling results are input into the feature extraction module. A feature extraction module is constructed, which adopts a lightweight convolutional network structure. The parameter scale is reduced by depthwise separable convolution, multi-scale convolutional kernels are set to extract slope deformation features, a self-attention mechanism is used to calculate the correlation of feature maps, and attention weights are weighted and combined with convolutional feature maps. Processor and memory usage data are collected based on the resource monitoring interface, the model computational complexity score is calculated, and the network structure is pruned according to the complexity score to generate optimized slope state features.

5. The method for comprehensive prediction and early warning of slope condition based on multimodal data fusion according to claim 1, characterized in that, The process includes adaptively pruning the model structure based on the complexity score, compressing the bit width of the model parameters using quantization compression technology, reducing computational complexity through sparsification, and inputting the optimized features into the slope state prediction model, including: A model pruning module is constructed. Based on the complexity score, an importance score matrix is ​​constructed. The convolutional layer channels are ranked by importance. Channels with importance scores below the threshold are pruned. The knowledge of the original model is transferred to the pruned model using the distillation learning method. The pruned model is fine-tuned and trained based on the validation dataset. The pruned model is then input into the compression module. A compression optimization module is constructed, which uses a mixed precision quantization strategy to compress the bit width of the model parameters, performs symmetric quantization mapping on the weight tensor, converts floating-point data into fixed-point integer representation, measures the sparsity of parameters based on information entropy, performs sparsification decomposition on the parameter matrix, rearranges the non-zero parameters to construct a sparse storage structure, uses sparse matrix multiplication to accelerate inference calculation, and deploys the compressed and optimized model to the slope state prediction module.

6. The method for comprehensive prediction and early warning of slope condition based on multimodal data fusion according to claim 1, characterized in that, The process of generating early warning information based on the slope state prediction model, wherein the slope state prediction model constructs a prediction task scheduling pool using container virtualization technology, employs a resource monitoring module to collect processor utilization and network bandwidth status, calculates task affinity scores based on the processor utilization and network bandwidth status, and migrates high-load prediction tasks to the cloud for processing, includes: A prediction task scheduling module is constructed. Prediction task instances are created based on container virtualization technology, and the prediction task instances are deployed to a task scheduling pool. The task scheduling pool includes a resource allocation unit and a load balancing unit. The resource allocation unit isolates the processor cores and memory space. The load balancing unit distributes tasks based on resource monitoring data. The resource monitoring interface is used to collect processor utilization and network bandwidth data, and the monitoring data is input into the task scoring unit. A task migration module is constructed, which builds a task affinity scoring model based on processor utilization and network bandwidth status. The task affinity scoring model performs weighted calculation on computing load and network transmission overhead to generate a task migration decision score. High-load prediction tasks are prioritized, and the priority ranking results are matched with the cloud resource pool. Computational data is transmitted using an incremental synchronization method, and prediction tasks are deployed to cloud nodes based on model hot migration technology.

7. The method for comprehensive prediction and early warning of slope condition based on multimodal data fusion according to claim 1, characterized in that, The method of uploading slope status data using incremental transmission, dynamically switching prediction tasks through model hot migration technology, classifying and judging prediction results for early warning, constructing multi-level early warning rules to assess slope instability risk, and generating a slope early warning analysis report, including: A data transmission module is constructed to perform incremental calculations on slope status data, identify new data blocks based on data timestamps, encode the new data using a data compression algorithm, establish a data transmission channel, which includes breakpoint resume and data verification mechanisms, transmit compressed data in segments to cloud storage, construct a model cache pool based on a model version management strategy, the model cache pool performs version identification and status maintenance on the prediction model, and use model hot migration technology to switch tasks, applying the switched model to slope status prediction; An early warning analysis module is constructed, and an early warning level evaluator is built based on the prediction results. The early warning level evaluator includes thresholds for deformation rate, cumulative displacement, and pressure change rate. The monitoring data is used to classify and determine early warnings. A multi-level early warning rule is established using a fuzzy comprehensive evaluation method. The early warning level is correlated and mapped with the risk assessment results. An analysis report is generated based on the early warning information template. The analysis report includes the early warning level, risk assessment results, and disposal suggestions.

8. A comprehensive slope condition prediction and early warning device based on multimodal data fusion, characterized in that, The device includes: The data monitoring module is used to collect multimodal slope monitoring data. It collects monitoring data from displacement sensors, crack sensors, and pressure sensors through an open protocol interface, converts the monitoring data into a standard signal format, performs channel allocation and signal conditioning on the standard signals, performs noise reduction and redundancy removal on the signal data based on a sliding window, uses a data synchronization alignment algorithm to align the timestamps of multi-source data, constructs a unified data fusion format, and standardizes the fused data according to metadata specifications to generate a structured slope status data stream. The feature processing module is used to extract and optimize features from the slope state data stream. It segments the data based on a time series model, extracts slope deformation features using a lightweight convolutional network, captures long-range dependencies through an attention mechanism, and constructs a model complexity estimator. The model complexity estimator generates a complexity score based on computational resource utilization and memory usage. The model structure is adaptively pruned according to the complexity score. The model parameters are compressed using quantization compression technology, and computational complexity is reduced through sparsification. The optimized features are then input into the slope state prediction model. The slope early warning module is used to generate early warning information based on the slope state prediction model. The slope state prediction model constructs a prediction task scheduling pool through container virtualization technology, uses a resource monitoring module to collect processor utilization and network bandwidth status, calculates task affinity score based on processor utilization and network bandwidth status, migrates high-load prediction tasks to the cloud for processing, uploads slope state data using incremental transmission, realizes dynamic switching of prediction tasks through model hot migration technology, performs hierarchical early warning judgment on prediction results, constructs multi-level early warning rules to assess slope instability risk, and generates a slope early warning analysis report.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the slope condition comprehensive prediction and early warning method based on multimodal data fusion as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the slope condition comprehensive prediction and early warning method based on multimodal data fusion as described in any one of claims 1 to 7.

Images