Iron tower basic environment data fusion control method and system for low-temperature environment

Abstract

The invention provides an iron tower basic environment data fusion control method and system for a low-temperature environment, and relates to the technical field of data fusion, and the method comprises the steps: configuring a basic environment sensing network of an iron tower; performing preliminary anomaly perception on the plurality of environment perception data sets; inputting the plurality of environment anomaly perception data sets into a low-temperature acquisition frequency adjustment module for low-temperature environment anomaly filtering, generating an adaptive acquisition frequency according to the plurality of filtered environment anomaly perception data sets, and obtaining a plurality of updated environment perception data sets; whether a mode switching instruction is triggered or not is judged, and if the mode switching instruction is triggered, fusion processing is conducted on the multiple updated environment sensing data sets through a central data fusion mode. The technical problem that the iron tower basic environment data fusion control precision is low in the prior art can be solved, and the technical effect of improving the iron tower basic environment data fusion control precision is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data fusion, and particularly relates to a tower foundation environment data fusion control method and system for a low-temperature environment. BACKGROUND

[0002] With the continuous expansion of the power transmission network scale and the increasing complexity of the tower operation environment, the environment monitoring and safety evaluation technology for the tower structure gradually becomes a key means to ensure the stability of the line. In the alpine region, strong wind area or mountainous area, the temperature, humidity, wind load and vibration load of the environment where the tower is located often show significant spatio-temporal fluctuation characteristics, and environmental mutation events may occur in a very short time, so it is necessary to rely on large-scale distributed sensors to realize continuous and high-precision environment perception. However, due to the difference of the monitoring scene in space and time, the effectiveness and importance of different monitoring points also show dynamic changes, and the traditional monitoring system often uses fixed acquisition frequency and fixed data processing mode, which is difficult to adapt to the monitoring demand of multi-scene, multi-load and strong fluctuation.

[0003] In summary, in the prior art, due to the fixed acquisition frequency, the inflexible abnormality identification, the rough damage analysis model and the missing mode switching, the environmental abnormality detection precision is insufficient, the structure state evaluation is not comprehensive, the data processing efficiency is low, which further affects the tower operation safety, the monitoring real-time and the reliability of large-scale monitoring deployment. SUMMARY

[0004] The purpose of the present application is to provide a tower foundation environment data fusion control method and system for a low-temperature environment, which solves the technical problem in the prior art that due to the fixed acquisition frequency, the inflexible abnormality identification, the rough damage analysis model and the missing mode switching, the environmental abnormality detection precision is insufficient, the structure state evaluation is not comprehensive, the data processing efficiency is low, which further affects the tower operation safety, the monitoring real-time and the reliability of large-scale monitoring deployment.

[0005] In view of the above problems, the present application provides a tower foundation environment data fusion control method and system for a low-temperature environment.

[0006] In a first aspect, the application provides a tower foundation environment data fusion control method for low-temperature environment, which is implemented by a tower foundation environment data fusion control system for low-temperature environment, and includes the following steps: configuring a tower foundation environment sensing network, wherein the tower foundation environment sensing network includes a plurality of environment sensors, each of which is independently connected through a low-temperature collection frequency adjustment module; acquiring a plurality of environment perception data sets of the plurality of environment sensors according to the tower foundation environment sensing network, performing preliminary anomaly perception on the plurality of environment perception data sets, and acquiring a plurality of environment anomaly perception data sets; inputting the plurality of environment anomaly perception data sets into the low-temperature collection frequency adjustment module for low-temperature environment anomaly filtering, generating an adaptive collection frequency according to the filtered plurality of environment anomaly perception data sets, and acquiring a plurality of updated environment perception data sets through the adaptive collection frequency; determining whether a mode switching instruction is triggered according to the plurality of updated environment perception data sets, and if the mode switching instruction is triggered, performing fusion processing on the plurality of updated environment perception data sets through a central data fusion mode.

[0007] Preferably, the tower foundation environment data fusion control method for low-temperature environment further includes configuring an adaptive collection frequency threshold, comparing a plurality of adaptive collection frequencies corresponding to the plurality of updated environment perception data sets with the adaptive collection frequency threshold, and acquiring a quantity proportion greater than the adaptive collection frequency threshold; if the quantity proportion is greater than a preset proportion threshold, triggering a mode switching instruction, and the mode switching instruction is used to switch an edge data fusion mode to a central data fusion mode.

[0008] Preferably, the tower foundation environment data fusion control method for low-temperature environment further includes collecting an edge computing unit in an edge data fusion mode, analyzing configuration parameters of the edge computing unit to acquire an edge computing resource index and an edge computing power scaling index, and performing adaptive collection frequency threshold optimization according to the edge computing resource index and the edge computing power scaling index to obtain an adaptive collection frequency threshold with a risk false negative probability within an expected probability.

[0009] Preferably, the tower foundation environment data fusion control method for low-temperature environment further includes constructing a historical environment health perception data sample based on the plurality of environment sensors; training a plurality of environment anomaly recognition models with the historical environment health perception data sample, performing preliminary anomaly perception on the plurality of environment perception data sets based on the plurality of environment anomaly recognition models, and acquiring a plurality of environment anomaly perception data sets.

[0010] Preferably, the tower foundation environment data fusion control method for low temperature environment further comprises: acquiring a low temperature environment perception data fingerprint library, the low temperature environment perception data fingerprint library comprising a first type of low temperature event, a second type of low temperature event and a third type of low temperature event, the first type of low temperature event being a sudden low temperature abnormal event, the second type of low temperature event being a continuous low temperature abnormal event, and the third type of low temperature event being a drift low temperature abnormal event; the low temperature collection frequency adjustment module performs data abnormality fingerprint matching on the multiple environment abnormality perception data sets by calling the low temperature environment perception data fingerprint library to obtain low temperature environment abnormality perception data sets; and the filtered multiple environment abnormality perception data sets are obtained according to the low temperature environment abnormality perception data sets.

[0011] Preferably, the tower foundation environment data fusion control method for low temperature environment further comprises: performing compensation collection frequency optimization according to the low temperature environment abnormality perception data sets to obtain a low temperature compensation collection frequency under which the low temperature data restoration quality is greater than a preset quality threshold; and performing constraint update on the adaptive collection frequency according to the low temperature compensation collection frequency to obtain an updated adaptive collection frequency.

[0012] Preferably, the tower foundation environment data fusion control method for low temperature environment further comprises: identifying abnormal time sequence distribution characteristics of the filtered multiple environment abnormality perception data sets; performing abnormality perception confidence calculation on the current collection frequency according to the abnormal time sequence distribution characteristics to obtain a confidence index; and performing response with the current collection frequency and the corresponding confidence index as input variables to obtain an adaptive collection frequency, with the preset confidence index as an adaptive target.

[0013] Preferably, the tower foundation environment data fusion control method for low temperature environment further comprises: acquiring monitoring item weights of the multiple environment sensors, generating an adjustment frequency weight according to the monitoring item weights; and optimizing the generated adaptive collection frequency with the adjustment frequency weight to obtain an optimized adaptive collection frequency.

[0014] Preferably, the tower foundation environment data fusion control method for low temperature environment further comprises: performing feature extraction on the multiple updated environment perception data sets to obtain multiple damage feature data sets; constructing a damage weight network according to sensor positions of the multiple updated environment perception data sets and a position of the tower; and performing fusion processing on the multiple damage feature data sets according to the damage weight network to obtain a damage fusion index.

[0015] Preferably, the tower foundation environment data fusion control method for low temperature environment further comprises: if the filtered multiple environment abnormality perception data sets return empty, triggering a mode return instruction, the mode return instruction being used for processing the multiple updated environment perception data sets through an edge data fusion mode.

[0016] In a second aspect, the application also provides a tower foundation environment data fusion control system for a low-temperature environment, configured to implement the tower foundation environment data fusion control method for a low-temperature environment as described in the first aspect, comprising: a network configuration module configured to configure a tower foundation environment sensor network of a tower, the tower foundation environment sensor network comprising a plurality of environment sensors, each environment sensor being independently connected through a low-temperature collection frequency adjustment module; a data set acquisition module configured to acquire a plurality of environment perception data sets of the plurality of environment sensors according to the tower foundation environment sensor network, to perform preliminary abnormal perception on the plurality of environment perception data sets, and to acquire a plurality of environment abnormal perception data sets; a frequency generation module configured to input the plurality of environment abnormal perception data sets into the low-temperature collection frequency adjustment module to perform low-temperature environment abnormal filtering, to generate an adaptive collection frequency according to the filtered plurality of environment abnormal perception data sets, and to acquire a plurality of updated environment perception data sets through the adaptive collection frequency; and a fusion processing module configured to determine whether a mode switching instruction is triggered according to the plurality of updated environment perception data sets, and to perform fusion processing on the plurality of updated environment perception data sets through a central data fusion mode if the mode switching instruction is triggered.

[0017] In a third aspect, the application also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the tower foundation environment data fusion control method for a low-temperature environment as described in any one of the first aspect.

[0018] In a fourth aspect, a computer-readable storage medium stores a computer program, and the computer program, when executed, implements the steps of the tower foundation environment data fusion control method for a low-temperature environment as described in any one of the first aspect.

[0019] The technical solutions provided in the application have at least the following technical effects or advantages: by achieving the technical goal of adaptively, finely, and scenario-based collection and regulation of tower foundation environment data in a low-temperature environment, and constructing a dynamic data fusion system that integrates edge computing capability and central computing capability, the technical effect of improving the effectiveness of monitoring data, enhancing the structural damage perception capability of multi-source data fusion, reducing the probability of abnormal omission, and improving the overall monitoring real-time response capability in the context of environmental abnormality diversification is achieved.

[0020] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, characteristics and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and other drawings can be obtained by those skilled in the art without creating labor on the basis of the provided drawings.

[0022] Figure 1 The flowchart of the low-temperature environment tower foundation environment data fusion control method of the present application.

[0023] Figure 2 The structure diagram of the low-temperature environment tower foundation environment data fusion control system of the present application.

[0024] Figure 3 The structure diagram of the exemplary electronic device of the present application.

[0025] Explanation of reference numerals: network configuration module 1, data set acquisition module 2, frequency generation module 3, fusion processing module 4, bus 300, receiver 301, processor 302, transmitter 303, memory 304, bus interface 305. DETAILED DESCRIPTION

[0026] The present application provides a low-temperature environment tower foundation environment data fusion control method and system, which solves the technical problems in the prior art that due to fixed acquisition frequency, inflexible abnormality identification, rough damage analysis model and missing mode switching, the environmental abnormality detection precision is insufficient, the structure state evaluation is not comprehensive, the data processing efficiency is low, which further affects the tower operation safety, the monitoring real-time and the reliability of large-scale monitoring deployment. The technical goal of realizing adaptive, fine and scenario-based acquisition regulation of tower foundation environment data in low-temperature environment and constructing a dynamic data fusion system with fusion edge computing capability and central computing capability is achieved, and the technical effects of improving monitoring data effectiveness, enhancing multi-source data fusion structure damage perception capability, reducing abnormality missing report probability and improving overall monitoring real-time response capability in the background of environmental abnormality diversification are achieved.

[0027] Below, the technical solutions in the present application will be described clearly and completely with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application. In addition, it should be noted that, for the convenience of description, only parts related to the present application are shown in the drawings, not all.

[0028] Embodiment one, please refer to the attached Figure 1 The present application provides a tower foundation environment data fusion control method for low temperature environment, which is applied to a tower foundation environment data fusion control system for low temperature environment, and specifically includes the following steps: S1: configuring a tower foundation environment sensing network, the tower foundation environment sensing network comprising a plurality of environment sensors, each environment sensor being independently connected through a low temperature collection frequency adjustment module.

[0029] Specifically, configuring a tower foundation environment sensing network means that sensing units for sensing environment states are pre-disposed in the tower foundation area, and through point planning, layout density and installation method, the sensing units can cover the key stress areas of the tower and the parts susceptible to low temperature, for continuously collecting basic environment parameters such as temperature, humidity, frost heave displacement and foundation water content, thereby forming the bottom structure of environment data collection.

[0030] The tower foundation environment sensing network comprises a plurality of environment sensors, and different types, different ranges and different response speeds of sensing devices are deployed in the same tower foundation area, including temperature sensors, pressure sensors, humidity sensors and micro-shaking sensors, etc., for obtaining multi-dimensional sensing quantities reflecting the change characteristics of the foundation environment. The plurality of sensors are topologically connected through wired or wireless data transmission, and work cooperatively in a distributed manner, thereby forming a redundant detection link.

[0031] Each environment sensor is independently connected through a low temperature collection frequency adjustment module, and the data collection rhythm of each sensor is no longer operated according to a fixed frequency, but is dynamically adjusted by the low temperature collection frequency adjustment module according to the degree of environmental change, the risk level of low temperature events and the energy consumption constraint of the sensor itself, so that each sensor has a differentiated sampling strategy.

[0032] S2: acquiring a plurality of environment sensing data sets of the plurality of environment sensors according to the tower foundation environment sensing network, preliminarily sensing the environment for the plurality of environment sensing data sets, and acquiring a plurality of environment anomaly sensing data sets.

[0033] Further, the application further comprises: constructing a historical environment health perception data sample based on the plurality of environment sensors; training a plurality of environment anomaly recognition models based on the historical environment health perception data sample, performing preliminary anomaly perception on the plurality of environment perception data sets based on the plurality of environment anomaly recognition models, and obtaining a plurality of environment anomaly perception data sets.

[0034] Specifically, the environment sensors arranged in the tower foundation area continuously collect environmental parameters such as temperature, humidity, water content of foundation, frost heave displacement or micro vibration through their respective collection mechanisms, and form a plurality of data sets according to time sequence or spatial distribution. Relying on the topology structure of the basic environment sensor network, the real-time data of different sensor nodes are converged to the edge node or the center server for subsequent analysis.

[0035] The historical environment health perception data sample based on the plurality of environment sensors is constructed, and the stable, normal and non-low-temperature interference environment data accumulated during long-time operation are screened, labeled and structured to form a multi-dimensional environment feature sample set that can reflect the health state of the tower foundation, including typical data under different time conditions, weather conditions and foundation operation conditions.

[0036] The plurality of environment anomaly recognition models are trained based on the historical environment health perception data sample, so that the environment anomaly recognition model can learn the correlation between the time sequence pattern and the variable of the environment data in the healthy state, and thus has the ability to identify abnormal deviation. The environment anomaly recognition model includes a time series prediction model based on a long short-term memory network, a probability model based on a Gaussian distribution, and an anomaly detection model based on an isolated forest, etc., which uses different mechanisms to determine whether the environment parameter has abnormal change.

[0037] Based on the plurality of environment anomaly recognition models, the preliminary anomaly perception is performed on the plurality of environment perception data sets, the real-time collected environment data are detected by using the trained anomaly recognition models, it is judged whether there is a parameter value or change trend that deviates significantly from the health sample, such as sudden temperature drop, sudden displacement or abnormal humidity rise, and thus the plurality of environment anomaly perception data sets are obtained. The anomaly result is output according to the prediction deviation, anomaly score or confidence index of the plurality of environment anomaly recognition models.

[0038] S3: inputting the plurality of environment anomaly perception data sets into the low-temperature collection frequency adjustment module to perform low-temperature environment anomaly filtering, generating an adaptive collection frequency according to the filtered plurality of environment anomaly perception data sets, and obtaining a plurality of updated environment perception data sets through the adaptive collection frequency.

[0039] Further, the application further comprises: acquiring a low-temperature environment perception data fingerprint library, the low-temperature environment perception data fingerprint library comprising a first type of low-temperature event, a second type of low-temperature event, and a third type of low-temperature event, the first type of low-temperature event being a sudden low-temperature abnormal event, the second type of low-temperature event being a sustained low-temperature abnormal event, and the third type of low-temperature event being a drifting low-temperature abnormal event; the low-temperature collection frequency adjustment module performs data abnormality fingerprint matching on the plurality of environment abnormality perception data sets by calling the low-temperature environment perception data fingerprint library, to obtain a low-temperature environment abnormality perception data set; and the plurality of environment abnormality perception data sets after filtering are obtained according to the low-temperature environment abnormality perception data set.

[0040] Further, the application further comprises: performing compensation collection frequency optimization according to the low-temperature environment abnormality perception data set, to obtain a low-temperature compensation collection frequency under which a low-temperature data restoration quality is greater than a preset quality threshold; and performing constraint updating on the adaptive collection frequency according to the low-temperature compensation collection frequency, to obtain an updated adaptive collection frequency.

[0041] Further, the application further comprises: identifying abnormal time sequence distribution characteristics of the plurality of environment abnormality perception data sets after filtering; performing abnormality perception confidence calculation on a current collection frequency according to the abnormal time sequence distribution characteristics, to obtain a confidence index; and performing a response with the current collection frequency and the corresponding confidence index as input variables, to obtain an adaptive collection frequency, with a preset confidence index as an adaptive target.

[0042] Further, the application further comprises: acquiring monitoring item weights of the plurality of environment sensors, and generating an adjustment frequency weight according to the monitoring item weights; and optimizing the generated adaptive collection frequency with the adjustment frequency weight, to obtain an optimized adaptive collection frequency.

[0043] Specifically, acquiring a low-temperature environment perception data fingerprint library means extracting representative low-temperature event modes from long-term monitoring and multi-region collected low-temperature environment data, and forming a standardized feature set according to event types, change rates, duration, and characteristic parameters, for subsequent abnormality identification comparison. The first type of low-temperature event contained in the low-temperature environment perception data fingerprint library refers to a temperature sudden drop behavior occurring in a very short time, which is characterized by a temperature drop of more than a certain threshold within a few minutes, for example, a drop of 3 degrees Celsius within 5 consecutive minutes; the second type of low-temperature event refers to a temperature that remains below the normal range for a long period of time, for example, a temperature maintained below zero for 12 consecutive hours; and the third type of low-temperature event refers to a phenomenon that the temperature or related environmental parameters presents a slow drift, which has no significant change in a short period of time, but gradually deviates from the healthy baseline in several days.

[0044] The low-temperature collection frequency adjustment module matches the multiple environmental abnormality perception data sets by calling a low-temperature environment perception data fingerprint library, judges which category of low-temperature event or whether it belongs to a composite abnormal event the abnormal data belongs to by comparing the similarity between the data change amplitude, change rate and fluctuation mode and the existing event templates in the fingerprint library, and outputs the classified low-temperature environmental abnormality perception data sets. The matching process adopts distance measurement, similarity score, template alignment and other algorithm methods, and determines the abnormal level according to the matching degree.

[0045] The filtered multiple environmental abnormality perception data sets obtained according to the low-temperature environmental abnormality perception data set refer to removing the non-low-temperature factors in the original abnormal data according to the identified low-temperature event category and event level, and only retaining the abnormal information sensitive to low temperature or triggered by low temperature, so as to remove noise events or false data related to other environmental factors. The filtering process can reduce the complexity of subsequent analysis and also improve the purity of the abnormality recognition result.

[0046] According to the low-temperature environmental abnormality perception data set, the compensation collection frequency optimization is performed, the data missing amount or data sparsity degree that may occur under the current environmental condition is calculated according to the category, intensity and change rate of the abnormality, and the optimal sampling frequency that can restore the real change curve of the low-temperature event is found through an optimization algorithm. Based on the data restoration model or the time series reconstruction model, the evaluation is performed to compensate for the loss of time sequence information caused by insufficient sampling. The compensation collection frequency optimization takes the low-temperature data restoration quality greater than a preset quality threshold as the target, for example, the quality threshold is set to 0.85, and finally the low-temperature compensation collection frequency that meets the condition is output, which is used to ensure that the key low-temperature event can be completely presented on the data.

[0047] According to the low-temperature compensation collection frequency, the adaptive collection frequency is updated, after the compensation collection frequency that can improve the data restoration quality is obtained, the original adaptive collection frequency strategy is corrected, so that it can consider the special needs of general environmental changes and low-temperature events in the subsequent collection process. The constraint update mechanism is implemented based on upper bound constraint, lower bound constraint or weight correction method, so that the adaptive collection frequency remains energy-saving in the normal state, and enhances the agility in the low-temperature state.

[0048] The abnormal time sequence distribution characteristics of the filtered multiple environmental abnormality perception data sets are identified, after obtaining the environmental abnormality data that has excluded the non-low-temperature interference factors, the distribution law in the time dimension is analyzed, and the characteristic parameters such as the density, duration, change rate and periodicity of the abnormality are extracted. The abnormal time sequence distribution characteristics include the instantaneous jump characteristics of the burst-type abnormality, the long-period smooth offset characteristics of the sustained-type abnormality, and the slow trend change characteristics of the drift-type abnormality.

[0049] Anomaly perception confidence is calculated based on the anomaly time series distribution characteristics of the current sampling frequency to obtain a confidence index. The suitability of the current sampling frequency for capturing anomaly events is calculated based on the importance, frequency of occurrence, and trend complexity of the anomaly time series characteristics, and a quantified confidence index is output. The confidence index indicates whether the current sampling frequency is sufficient to accurately record the change process of anomaly events. As the complexity of anomaly time series characteristics increases from a single fluctuation pattern to a multi-segment composite curve, the feature dimensions involved in the confidence calculation also increase accordingly, making the confidence results more refined.

[0050] Using a pre-set confidence index as the adaptive target, and the current acquisition frequency and the corresponding confidence index as input variables, the adaptive acquisition frequency is obtained. That is, based on the minimum confidence requirement and the correspondence between the current acquisition frequency and the confidence, the frequency is adjusted by adjusting the model, and the acquisition frequency that can meet or exceed the target confidence is output. The response process uses function fitting, interpolation optimization or adaptive control algorithm to adjust the acquisition frequency up or down.

[0051] The weights of monitoring items from multiple environmental sensors are obtained. This involves calculating the importance of each monitoring item within the overall monitoring system based on the impact, sensitivity, and risk level of the parameters monitored by different types of sensors in the tower foundation environmental monitoring. This importance is then expressed as a weight value. Monitoring item weights can be obtained based on historical event contribution, expert rules, risk sensitivity curves, or data-driven models. Frequency adjustment weights are generated based on these weights, mapping the importance of monitoring items to reference factors for adjusting the acquisition frequency. This ensures that sensors with higher risk levels correspond to higher frequency weights, and sensors with lower risk levels correspond to lower frequency weights. This guides the adjustment of the acquisition frequency, ensuring that the acquisition strategy reflects the distribution of monitoring priorities.

[0052] The generated adaptive acquisition frequency is optimized by adjusting the frequency weights. After obtaining the initial adaptive acquisition frequency, the frequency weights of each monitoring item are incorporated into the frequency adjustment model, enabling globally differentiated configuration of the acquisition frequencies of different sensors, thereby obtaining an optimized acquisition frequency that better meets monitoring requirements. This optimization process can be based on weighted adjustment, linear combination, or multi-objective optimization models. By establishing a correlation between the acquisition frequency and the weights, the sensitivity of the acquisition frequency to key monitoring items is significantly improved.

[0053] Multiple updated environmental sensing datasets are acquired through adaptive acquisition frequency, enabling dynamic data acquisition from multiple environmental sensors deployed in the tower foundation environment. The adaptive acquisition frequency, a sampling rate that is dynamically adjusted based on the degree of environmental change, the importance of the monitored items, and historical data fluctuations, is used to control the number of times each environmental sensor acquires environmental parameters per unit time.

[0054] S4: determining whether to trigger a mode switching instruction according to the plurality of updated environment perception data sets, and if the mode switching instruction is triggered, performing fusion processing on the plurality of updated environment perception data sets by a central data fusion mode.

[0055] Further, the application further comprises: configuring an adaptive collection frequency threshold, comparing a plurality of adaptive collection frequencies corresponding to the plurality of updated environment perception data sets with the adaptive collection frequency threshold, and obtaining a quantity proportion greater than the adaptive collection frequency threshold; if the quantity proportion is greater than a preset proportion threshold, triggering a mode switching instruction, and the mode switching instruction is used to switch the edge data fusion mode to the central data fusion mode.

[0056] Further, the application further comprises: collecting an edge computing unit in an edge data fusion mode, analyzing configuration parameters of the edge computing unit to obtain an edge computing resource index and an edge computing power scaling index; performing adaptive collection frequency threshold optimization according to the edge computing resource index and the edge computing power scaling index to obtain an adaptive collection frequency threshold in which a risk missing probability is within an expected probability.

[0057] Further, the application further comprises: extracting features from the plurality of updated environment perception data sets to obtain a plurality of damage feature data sets; extracting sensor positions of the plurality of updated environment perception data sets and positions of the towers to construct a damage weight network; and performing fusion processing on the plurality of damage feature data sets according to the damage weight network to obtain a damage fusion index.

[0058] Further, the application further comprises: if the plurality of filtered environment anomaly perception data sets returns empty, triggering a mode return instruction, and the mode return instruction is used to process the plurality of updated environment perception data sets by an edge data fusion mode.

[0059] Specifically, the edge data fusion mode is adopted to collect the edge computing unit, and the computing node information in the edge data fusion mode is obtained, wherein the edge computing unit refers to a local computing device deployed near the sensor, capable of performing data preprocessing, preliminary fusion and rapid response computing tasks. The configuration parameters of the edge computing unit are analyzed to obtain edge computing resource indicators and edge computing power scaling indicators, and the configuration parameters include edge processor types, available memory capacity, cache structure, data bandwidth, real-time processing queue length and the like. The edge computing resource indicators are used to quantify the overall availability of hardware and running resources, such as combining available processing capacity, available memory and available communication bandwidth by weight to form a numerical indicator, and the edge computing power scaling indicators are used to describe the improvement ratio or decline ratio of computing performance under different task loads. Subsequently, the adaptive acquisition frequency threshold optimization is performed according to the edge computing resource indicators and the edge computing power scaling indicators, and the two indicators are used as constraint conditions to find an acquisition frequency threshold that balances resource consumption and task performance through an optimization strategy. The adaptive acquisition frequency threshold is used to represent the limit value at which the sensor sampling frequency reaches a certain upper limit and higher-level processing needs to be started. Finally, the adaptive acquisition frequency threshold with the risk miss probability within the expected probability indicates that the determined threshold needs to ensure the reliability of the risk identification ability under the condition of limited edge computing power, wherein the risk miss probability is used to represent the probability of not being able to identify or collect in time in the environmental abnormal event, and the expected probability is used to define the maximum miss level allowed by the system.

[0060] Subsequently, the plurality of adaptive acquisition frequencies corresponding to the plurality of updated environmental perception data sets are compared with the adaptive acquisition frequency threshold, and the number of times that the sensor acquisition frequency is in the interval higher than the normal working state in the current monitoring period is counted. The number ratio represents the proportion of the total number of times that the adaptive acquisition frequency exceeds the adaptive acquisition frequency threshold in all acquisition frequencies in all sensors or all time windows.

[0061] Then, if the number ratio is greater than a preset ratio threshold, a mode switching instruction is triggered, the preset ratio threshold is used to represent that the current environmental fluctuation has reached a degree that the data fusion accuracy must be improved, and the mode switching instruction is used to drive the data processing module to switch from the edge data fusion mode to the central data fusion mode. The edge data fusion mode is used for fast, low-cost and low-energy data preprocessing near the sensor node, and the central data fusion mode is used for uploading a large amount of raw data to the central computing node for high-precision fusion, deep correlation analysis and global situation identification.

[0062] If the mode switching instruction is triggered, feature extraction is performed on the plurality of updated environmental perception data sets, that is, structured processing is performed on the updated data from the plurality of environmental sensors. Feature extraction refers to identifying a representative set of parameters from continuous or discrete environmental monitoring data, such as temperature gradient change features, humidity mutation amplitude, wind load disturbance frequency, vibration energy distribution, or the slope of the low-temperature response curve, and quantifying the extracted features according to time, space, or physical dimensions to ultimately form a plurality of damage feature data sets, which are used as input basis in the subsequent judgment of the tower structure state.

[0063] Subsequently, the sensor positions of the plurality of updated environmental perception data sets and the positions of the tower are extracted to construct a damage weight network. Based on the latitude and longitude coordinates of the sensor installation points, the relative height of the pile foundation to the tower body, and the overall spatial layout of the tower, a mapping relationship between each monitoring point and the real stress area of the tower is established. The damage weight network is used to assign weights to different monitoring points through spatial distance attenuation rules, node connectivity rules, or structure sensitivity rules, so as to reflect the law that the perception contribution decreases with the increase of distance.

[0064] Next, the plurality of damage feature data sets are fused according to the damage weight network to obtain a damage fusion index. Each sensor's contribution to the damage feature is combined according to the weight. The fusion processing includes steps such as weighted average, spatial correlation convolution, feature superposition, or multi-source consistency verification, and finally obtains the damage fusion index, which is used to represent the development trend of potential damage to the tower in the monitoring period.

[0065] If the plurality of filtered environmental abnormal perception data sets return empty, it means that after filtering the environmental perception data, it is found that there is no data sample that meets the abnormal judgment condition in the current monitoring period. Returning empty means that the result set processed by various abnormal identification models, fingerprint matching modules, or confidence filtering mechanisms does not contain any abnormal event data, that is, the current environment is in a normal and stable state.

[0066] Subsequently, a mode return instruction is triggered. When the abnormality is not sufficient to support the continued use of a data processing mode with high cost or high resource occupation, a mode regulation mechanism is started, and an instruction is sent to the upper layer control module to switch the current data fusion and processing mode back to the edge side execution. The mode return instruction is used to process the plurality of updated environmental perception data sets through the edge data fusion mode, and the edge computing unit is re-enabled to perform near-source processing on the updated data, including preliminary denoising, edge feature extraction, and low-delay fusion, so that the mode is switched to a low-resource occupation mode in the absence of abnormality.

[0067] In summary, the iron tower foundation environment data fusion control method for low temperature environment provided by the present application has the following technical effects: by achieving adaptive, fine and scenario-based collection and regulation of iron tower foundation environment data in a low temperature environment, and constructing a dynamic data fusion system integrating edge computing capability and central computing capability, the technical effects of improving the effectiveness of monitoring data, enhancing the structural damage perception capability of multi-source data fusion, reducing the probability of abnormal omission and improving the overall monitoring real-time response capability are achieved in the context of environmental abnormal diversification.

[0068] Embodiment two, based on the same inventive concept as the iron tower foundation environment data fusion control method for low temperature environment in the preceding embodiments, the present application also provides an iron tower foundation environment data fusion control system for low temperature environment, please refer to the attached Figure 2 , including: a network configuration module 1 for configuring the foundation environment sensor network of the iron tower, the foundation environment sensor network including a plurality of environment sensors, each environment sensor being independently connected through a low temperature collection frequency adjustment module; a data set acquisition module 2 for acquiring a plurality of environment perception data sets of the plurality of environment sensors according to the foundation environment sensor network, performing preliminary abnormal perception on the plurality of environment perception data sets, and acquiring a plurality of environment abnormal perception data sets; a frequency generation module 3 for inputting the plurality of environment abnormal perception data sets into the low temperature collection frequency adjustment module for low temperature environment abnormal filtering, generating an adaptive collection frequency according to the filtered plurality of environment abnormal perception data sets, and acquiring a plurality of updated environment perception data sets through the adaptive collection frequency; a fusion processing module 4 for determining whether a mode switching instruction is triggered according to the plurality of updated environment perception data sets, and performing fusion processing on the plurality of updated environment perception data sets through a central data fusion mode if the mode switching instruction is triggered.

[0069] Further, the iron tower foundation environment data fusion control system for low temperature environment is also used for: configuring an adaptive collection frequency threshold, comparing the plurality of adaptive collection frequencies corresponding to the plurality of updated environment perception data sets with the adaptive collection frequency threshold, and acquiring a quantity proportion greater than the adaptive collection frequency threshold; if the quantity proportion is greater than a preset proportion threshold, triggering a mode switching instruction, and the mode switching instruction is used to switch the edge data fusion mode to the central data fusion mode.

[0070] Further, the iron tower foundation environment data fusion control system for low temperature environment is also used for: collecting an edge computing unit in an edge data fusion mode, analyzing configuration parameters of the edge computing unit to acquire edge computing resource indicators and edge computing power scaling indicators; performing adaptive collection frequency threshold optimization according to the edge computing resource indicators and the edge computing power scaling indicators to obtain an adaptive collection frequency threshold with a risk omission probability within an expected probability.

[0071] Further, the tower foundation environment data fusion control system for low-temperature environment is also used for: constructing historical environment health perception data samples based on the plurality of environment sensors; training a plurality of environment anomaly recognition models with the historical environment health perception data samples, performing preliminary anomaly perception on the plurality of environment perception data sets based on the plurality of environment anomaly recognition models, and obtaining a plurality of environment anomaly perception data sets.

[0072] Further, the tower foundation environment data fusion control system for low-temperature environment is also used for: obtaining a low-temperature environment perception data fingerprint library, the low-temperature environment perception data fingerprint library including a first type of low-temperature event, a second type of low-temperature event, and a third type of low-temperature event, the first type of low-temperature event being a sudden low-temperature anomaly event, the second type of low-temperature event being a continuous low-temperature anomaly event, and the third type of low-temperature event being a drift low-temperature anomaly event; the low-temperature collection frequency adjustment module performs data anomaly fingerprint matching on the plurality of environment anomaly perception data sets by calling the low-temperature environment perception data fingerprint library, and obtains a low-temperature environment anomaly perception data set; and according to the low-temperature environment anomaly perception data set, a filtered plurality of environment anomaly perception data sets are obtained.

[0073] Further, the tower foundation environment data fusion control system for low-temperature environment is also used for: performing compensation collection frequency optimization according to the low-temperature environment anomaly perception data set, obtaining a low-temperature compensation collection frequency under which a low-temperature data restoration quality is greater than a preset quality threshold; and performing constraint update on the adaptive collection frequency according to the low-temperature compensation collection frequency, and obtaining an updated adaptive collection frequency.

[0074] Further, the tower foundation environment data fusion control system for low-temperature environment is also used for: identifying anomaly timing distribution characteristics of the filtered plurality of environment anomaly perception data sets; performing anomaly perception confidence calculation on a current collection frequency according to the anomaly timing distribution characteristics, obtaining a confidence index; and taking a preset confidence index as an adaptive target, and responding to the current collection frequency and the corresponding confidence index as input variables, obtaining an adaptive collection frequency.

[0075] Further, the tower foundation environment data fusion control system for low-temperature environment is also used for: obtaining monitoring item weights of the plurality of environment sensors, generating an adjustment frequency weight according to the monitoring item weights; and optimizing the generated adaptive collection frequency with the adjustment frequency weight, obtaining an optimized adaptive collection frequency.

[0076] Further, the tower foundation environment data fusion control system for low-temperature environment is further used for: performing feature extraction on the plurality of updated environment perception data sets to obtain a plurality of damage feature data sets; extracting sensor positions of the plurality of updated environment perception data sets and a position of the tower to construct a damage weight network; and performing fusion processing on the plurality of damage feature data sets according to the damage weight network to obtain a damage fusion index.

[0077] Further, the tower foundation environment data fusion control system for low-temperature environment is further used for: if the filtered plurality of environment anomaly perception data sets returns empty, triggering a mode return instruction, the mode return instruction being used for processing the plurality of updated environment perception data sets through an edge data fusion mode.

[0078] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The tower foundation environment data fusion control system for low-temperature environment in the foregoing embodiment one is also applicable to the tower foundation environment data fusion control system for low-temperature environment in the present embodiment. Through the foregoing detailed description of the tower foundation environment data fusion control method for low-temperature environment, those skilled in the art can clearly know the tower foundation environment data fusion control system for low-temperature environment in the present embodiment. Therefore, for the sake of brevity of the specification, the tower foundation environment data fusion control system for low-temperature environment in the present embodiment is not described in detail.

[0079] In the third embodiment, based on the inventive concept of the tower foundation environment data fusion control method for low-temperature environment in the foregoing embodiments, the present application further provides an electronic device, which comprises: at least one processor; a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the tower foundation environment data fusion control method for low-temperature environment in any one of the foregoing embodiment one.

[0080] The structure of the electronic device of the present application is shown in the accompanying drawings. Figure 3 The structure of the electronic device of the present application is shown in the accompanying drawings. Figure 3In particular embodiments, bus architecture 300 is represented as a bus 300, which can include any number of interconnecting buses and bridges, depending on the specific application of processor 302 and memory 304. Bus 300 can be used to connect various circuits of the device, such as peripheral devices, voltage regulators, and power management circuitry, as well as various processors and memories, which will be described in more detail below. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 can be the same device, i.e., a transceiver, providing a unit for communicating with various other apparatus over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data for use by processor 302 during execution of operations.

[0081] In the fourth embodiment, based on the tower foundation environment data fusion control method for low temperature environment in the foregoing embodiments, the same inventive concept is provided, and the application also provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program implements the steps of the tower foundation environment data fusion control method for low temperature environment in any one of the foregoing embodiments when executed.

[0082] The above description of disclosed embodiments allows one of ordinary skill in the art to make and use the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0083] Obviously, various modifications and changes can be made to the present application by those of ordinary skill in the art without departing from the spirit and scope of the application. Accordingly, it is intended that the present application embrace all such modifications and changes that fall within the scope of the application and its equivalents.

Claims

1. A data fusion control method for tower foundations in low-temperature environments, characterized in that, The method includes: Configure a basic environmental sensing network for the tower, which includes multiple environmental sensors, each of which is independently connected through a low-temperature acquisition frequency adjustment module; Based on the basic environmental sensing network, multiple environmental perception datasets from the multiple environmental sensors are obtained, and preliminary anomaly detection is performed on the multiple environmental perception datasets to obtain multiple environmental anomaly perception datasets. The multiple environmental anomaly sensing datasets are input into the low-temperature acquisition frequency adjustment module for low-temperature environmental anomaly filtering. An adaptive acquisition frequency is generated based on the filtered multiple environmental anomaly sensing datasets, and multiple updated environmental sensing datasets are obtained through the adaptive acquisition frequency. Based on the multiple updated environmental awareness datasets, determine whether a mode switching command is triggered. If a mode switching command is triggered, fuse the multiple updated environmental awareness datasets using the central data fusion mode.

2. The method for environmental data fusion control of iron tower foundations in low-temperature environments as described in claim 1, characterized in that, The method for determining whether to trigger a mode switching command based on the multiple updated environment-aware datasets further includes: Configure an adaptive acquisition frequency threshold, compare the multiple adaptive acquisition frequencies corresponding to the multiple updated environmental perception datasets with the adaptive acquisition frequency threshold, and obtain the percentage of the number of datasets with frequencies greater than the adaptive acquisition frequency threshold. If the proportion of the quantity is greater than a preset proportion threshold, a mode switching instruction is triggered. The mode switching instruction is used to switch the edge data fusion mode to the center data fusion mode.

3. The method for environmental data fusion control of iron tower foundations in low-temperature environments as described in claim 2, characterized in that, Configuring an adaptive sampling frequency threshold can be achieved through the following methods: The edge computing unit in the edge data fusion mode is collected, and the configuration parameters of the edge computing unit are analyzed to obtain edge computing resource indicators and edge computing power scaling indicators. Based on the edge computing resource indicators and edge computing power scaling indicators, an adaptive sampling frequency threshold is optimized to obtain an adaptive sampling frequency threshold where the probability of missed detection is within the expected probability.

4. The method for environmental data fusion control of iron tower foundations in low-temperature environments as described in claim 1, characterized in that, The method for performing preliminary anomaly detection on the multiple environmental perception datasets to obtain multiple environmental anomaly perception datasets includes: Construct a historical environmental health perception data sample based on the aforementioned multiple environmental sensors; Multiple environmental anomaly identification models are trained using the historical environmental health perception data samples. Based on the multiple environmental anomaly identification models, preliminary anomaly perception is performed on the multiple environmental perception datasets to obtain multiple environmental anomaly perception datasets.

5. The method for environmental data fusion control of iron tower foundations in low-temperature environments as described in claim 1, characterized in that, The method involves inputting the multiple environmental anomaly perception datasets into the low-temperature acquisition frequency adjustment module for low-temperature environmental anomaly filtering, including: A low-temperature environment sensing data fingerprint database is obtained, which includes a first type of low-temperature event, a second type of low-temperature event, and a third type of low-temperature event. The first type of low-temperature event is a sudden low-temperature anomaly event, the second type of low-temperature event is a continuous low-temperature anomaly event, and the third type of low-temperature event is a drifting low-temperature anomaly event. The low-temperature acquisition frequency adjustment module obtains the low-temperature environment abnormality sensing dataset by calling the low-temperature environment sensing data fingerprint library and performing data abnormality fingerprint matching on the multiple environmental abnormality sensing datasets. Based on the aforementioned low-temperature environment anomaly perception dataset, multiple filtered environment anomaly perception datasets are obtained.

6. The method for environmental data fusion control of iron tower foundations in low-temperature environments as described in claim 5, characterized in that, After acquiring the low-temperature environment anomaly sensing dataset, the low-temperature acquisition frequency adjustment module further includes: Based on the low-temperature environment anomaly perception dataset, the compensation acquisition frequency is optimized to obtain the low-temperature compensation acquisition frequency where the low-temperature data restoration quality is greater than a preset quality threshold. The adaptive acquisition frequency is constrained and updated based on the low-temperature compensation acquisition frequency to obtain the updated adaptive acquisition frequency.

7. The method for environmental data fusion control of iron tower foundations in low-temperature environments as described in claim 1, characterized in that, An adaptive sampling frequency is generated based on multiple filtered environmental anomaly perception datasets. The method includes: Identify the anomalous temporal distribution characteristics of multiple filtered environmental anomaly perception datasets; Based on the aforementioned abnormal time-series distribution characteristics, the confidence level of the current acquisition frequency is calculated to obtain a confidence index. Using a preset confidence index as the adaptive target, and the current acquisition frequency and the corresponding confidence index as input variables, the adaptive acquisition frequency is obtained.

8. The method for environmental data fusion control of iron tower foundations in low-temperature environments as described in claim 1, characterized in that, The method generates an adaptive sampling frequency based on multiple filtered environmental anomaly perception datasets. The method also includes: Obtain the monitoring item weights of the multiple environmental sensors, and generate adjustment frequency weights based on the monitoring item weights; The generated adaptive acquisition frequency is optimized using the adjusted frequency weights to obtain the optimized adaptive acquisition frequency.

9. The method for environmental data fusion control of iron tower foundations in low-temperature environments as described in claim 1, characterized in that, The method involves fusing the multiple updated environmental awareness datasets using a central data fusion model, including: Feature extraction is performed on the multiple updated environmental awareness datasets to obtain multiple damage feature datasets; The sensor locations and the location of the tower are extracted from the multiple updated environmental perception datasets to construct a damage weighting network; The multiple damage feature datasets are fused according to the damage weight network to obtain a damage fusion index.

10. The method for environmental data fusion control of iron tower foundations in low-temperature environments as described in claim 1, characterized in that, After fusing the multiple updated environmental awareness datasets using a central data fusion mode, the method further includes: If the filtered multiple environmental anomaly perception datasets return empty, a mode return instruction is triggered. The mode return instruction is used to process the multiple updated environmental perception datasets through edge data fusion mode.

11. A data fusion control system for tower foundations in low-temperature environments, characterized in that, The steps for implementing the data fusion control method for tower foundations in low-temperature environments according to any one of claims 1 to 10 include: The network configuration module is used to configure the basic environmental sensing network of the tower. The basic environmental sensing network includes multiple environmental sensors, each of which is independently connected through a low-temperature acquisition frequency adjustment module. The dataset acquisition module is used to acquire multiple environmental perception datasets from the multiple environmental sensors based on the basic environmental sensing network, perform preliminary anomaly detection on the multiple environmental perception datasets, and acquire multiple environmental anomaly perception datasets. The frequency generation module is used to input the multiple environmental anomaly perception datasets into the low temperature acquisition frequency adjustment module for low temperature environmental anomaly filtering, generate an adaptive acquisition frequency based on the filtered multiple environmental anomaly perception datasets, and obtain multiple updated environmental perception datasets through the adaptive acquisition frequency. The fusion processing module is used to determine whether a mode switching command is triggered based on the multiple updated environmental perception datasets. If a mode switching command is triggered, the multiple updated environmental perception datasets are fused using the central data fusion mode.

12. An electronic device, characterized in that, include: At least one processor; A memory that is communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the steps of the data fusion control method for tower foundations in low-temperature environments as described in any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed, implements the steps of the data fusion control method for the environmental conditions of iron tower foundations in low-temperature environments as described in any one of claims 1 to 10.

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