Ecological environment monitoring data processing method

Abstract

The invention relates to the field of data processing, in particular to an ecological environment monitoring data processing method. The method comprises the steps of collecting ecological environment monitoring data in real time, performing fusion processing, and purifying a fused signal through convolution operation to obtain a purified signal; performing signal reconstruction based on the purified signal; dynamically adjusting the adaptability weight of the sensor based on the ecological environment monitoring data and the reconstruction signal; and based on the adaptive weight and the reconstruction signal of the sensor, constructing a space-time model to obtain a space-time modeling result, and predicting a future ecological environment state. The problems that a traditional ecological environment monitoring data processing method lacks effective purification processing on signals, and environment monitoring data is obviously interfered by the outside are solved; the adaptability of the sensor is neglected, so that errors of some sensors are excessively amplified or reduced; the problems of low prediction precision, slow response speed and incapability of effectively coping with complex environmental changes due to the fact that the comprehensive influence of spatial and temporal changes is not considered in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method for processing ecological environment monitoring data. Background Art

[0002] With the increasing severity of global climate change and ecological and environmental problems, ecological and environmental monitoring has become an important means to protect natural resources and achieve sustainable development. In order to timely understand and predict changes in the ecological environment, many fields rely on sensor networks to monitor multiple environmental factors such as air quality, water quality, soil moisture, temperature, and light in real time. These sensors can provide a large amount of high-frequency data to help researchers, governments, and environmental organizations understand the current environmental conditions and make decisions. However, due to the complexity and multidimensionality of environmental changes, and the differences in measurement accuracy, response time, and sensitivity between different sensors, traditional monitoring methods often find it difficult to effectively integrate and process data from different sensors, making environmental monitoring data often affected by noise, outliers, and synergy between different sensors.

[0003] Therefore, how to effectively integrate environmental monitoring data from multi-source sensors, eliminate noise interference, and improve data reliability and accuracy has become a key issue in the current ecological environment monitoring system. In addition, with the surge in the amount of environmental monitoring data, how to effectively mine the potential laws in the data through intelligent data processing methods, predict future environmental conditions, and provide timely feedback and decision support has become an urgent need to improve the efficiency of environmental monitoring.

[0004] The above-mentioned technology has the following technical problems: the lack of effective purification processing of sensor signals leads to obvious external interference of environmental monitoring data, affecting the reliability of subsequent analysis; the adaptability of sensors is ignored, and the monitoring capability of each sensor at a specific moment is not fully utilized, which may cause the errors of some sensors to be excessively amplified or reduced; the comprehensive impact of temporal and spatial changes is not considered, and the complex relationship and dynamic changes between environmental variables cannot be accurately captured, resulting in low prediction accuracy, slow response speed, and inability to effectively cope with complex environmental changes. Summary of the invention

[0005] The present invention provides an ecological environment monitoring data processing method to solve the problems that traditional ecological environment monitoring data processing methods lack effective signal purification processing, resulting in obvious external interference of environmental monitoring data, affecting the reliability of subsequent analysis; ignoring the adaptability of sensors, failing to give full play to the monitoring capability of each sensor at a specific moment, which may cause the errors of some sensors to be excessively amplified or reduced; not considering the comprehensive impact of temporal and spatial changes, failing to accurately capture the complex relationships and dynamic changes between environmental variables, resulting in low prediction accuracy, slow response speed, and failure to effectively cope with complex environmental changes.

[0006] The present invention provides an ecological environment monitoring data processing method, which specifically includes the following technical solutions: A method for processing ecological environment monitoring data comprises the following steps: S1. Collect ecological environment monitoring data in real time and perform fusion processing to obtain fused signals; purify the fused signals through convolution operations to obtain purified signals; reconstruct signals based on the purified signals to generate reconstructed signals; S2. Based on the ecological environment monitoring data and the reconstructed signal, dynamically adjust the adaptive weight of the sensor; based on the adaptive weight and the reconstructed signal of the sensor, construct a spatiotemporal model to obtain the spatiotemporal modeling results; based on the spatiotemporal modeling results, predict the future ecological environment status and obtain the environmental status prediction results at future moments.

[0007] Preferably, the S1 specifically includes: Based on the sensitivity and reliability of environmental changes, the contribution coefficient of sensor data in the fusion process is introduced, and the ecological environment monitoring data is weightedly fused to obtain the fused signal.

[0008] Preferably, the S1 specifically includes: The fused signal is processed by sliding window through convolution operation to remove high-frequency noise in the fused signal and obtain a purified signal.

[0009] Preferably, the S1 specifically includes: The purified signal is combined with the normal signal, and the contribution of the purified signal and the normal signal is adjusted through a weighted method to generate a reconstructed signal.

[0010] Preferably, the S2 specifically includes: Based on the error between the ecological environment monitoring data and the reconstructed signal, as well as the signal change trend, the adaptive weight of the sensor is dynamically adjusted. The specific formula is: , in, For the The adaptability weight of each sensor; is the adjustment factor used to control the sensitivity of the error; It is Ecological environment monitoring data collected by sensors; is the reconstruction signal; It is The square error between the ecological environment monitoring data collected by the sensor and the reconstructed signal; is the number of sensors; is the reconstructed signal and The influence relationship between the ecological environment monitoring data collected by the sensors is used to reflect the signal change trend; is the correlation coefficient.

[0011] Preferably, the S2 specifically includes: The ecological environment monitoring data collected by the sensor at the same time and the sensor's adaptive weight are integrated, combined with the reconstructed signal, and the time change factor is introduced to build a spatiotemporal model to obtain the spatiotemporal modeling results.

[0012] Preferably, the S2 specifically includes: Based on the results of spatiotemporal modeling, combined with ecological environmental monitoring data and reconstructed signals, future ecological environmental changes are predicted.

[0013] Preferably, the S2 specifically includes: In the process of predicting future ecological environment changes, the spatiotemporal feedback factor and the cumulative factor are introduced, and the spatiotemporal feedback is combined with the spatiotemporal integration to obtain the environmental state prediction results at future moments. The prediction formula is: , in, Indicates future time The environmental status prediction results; It is A weighting coefficient is used to adjust the contribution of each sensor to the prediction result of the environmental state at the future moment; For the The sensors in Ecological environment monitoring data collected at all times; is the spatiotemporal feedback factor; yes The prediction result of the environmental status at the moment; is the accumulation factor; is the time-integrated variable; yes Reconstruction signal at the moment; is a weighting coefficient used to adjust the impact of spatiotemporal modeling results on the environmental state prediction results at future moments; yes The spatiotemporal modeling results at the moment.

[0014] The beneficial effects of the technical solution of the present invention are: 1. Through the weighted fusion of multiple sensor data, the ecological environment monitoring data collected by different sensors under different environmental conditions can be effectively integrated to ensure that the contribution of each sensor in the data processing process is reasonably reflected; the convolution operation is used to purify the fused signal, which effectively removes high-frequency noise while retaining the low-frequency components in the fused signal, making the purified signal more representative; the introduction of the convolution operation can not only improve the smoothness of the purified signal, but also ensure the accuracy of subsequent signal reconstruction by eliminating interference, providing high-quality input for subsequent data processing steps.

[0015] 2. By weighting the purified signal and the normal signal to reconstruct the signal, it is possible to remove abnormal fluctuations in the data while maintaining the trend of environmental changes, making the reconstructed signal closer to the actual environmental state, thereby improving the responsiveness of the ecological environment monitoring system. Especially in the face of complex environmental changes, it can effectively restore the true characteristics of the ecological environment monitoring data, providing an accurate foundation for subsequent spatiotemporal modeling and prediction.

[0016] 3. The present invention introduces the concept of adaptive weights, which dynamically adjusts the contribution of sensors according to the error between the ecological environment monitoring data collected by the sensor and the reconstructed signal, thereby optimizing the role of each sensor in the monitoring process. The application of adaptive weights enables the ecological environment monitoring system to more accurately reflect the actual state of the ecological environment according to changes in different time points and environmental conditions, thereby avoiding possible deviations or distortions of a single sensor.

[0017] 4. Through spatiotemporal modeling technology, the present invention can accurately predict the environmental status at future moments according to the data of different sensors and the environmental change trend; spatiotemporal modeling not only takes into account the influence of historical environmental status prediction results, but also combines adaptive weights and time factors to improve the accuracy and timeliness of the prediction. The predicted environmental status at future moments can provide strong support for environmental monitoring and management, helping decision makers to better respond to environmental changes and formulate reasonable response measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of an ecological environment monitoring data processing method described in the present invention. DETAILED DESCRIPTION

[0019] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0021] The specific scheme of the ecological environment monitoring data processing method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0022] Refer to the attached Figure 1 , which shows a flow chart of an ecological environment monitoring data processing method provided by an embodiment of the present invention, the method comprising the following steps: S1. Collect ecological environment monitoring data in real time and perform fusion processing to obtain fused signals; purify the fused signals through convolution operations to obtain purified signals; reconstruct signals based on the purified signals to generate reconstructed signals; During the ecological environment monitoring process, ecological environment monitoring data is collected in real time through multiple sensors; the sensors include temperature sensors, humidity sensors, air pressure sensors, light sensors, etc., and the ecological environment monitoring data collected by each sensor has different characteristics under different environmental conditions.

[0023] Since the measurement accuracy and reliability of each sensor are different, based on the sensitivity and reliability of environmental changes, the ecological environment monitoring data collected by each sensor can be weighted fused to ensure that each sensor can reasonably reflect its role in the overall monitoring according to its actual contribution during the data fusion process, and effectively reduce the impact of a sensor's measurement error on the overall result, so as to ultimately generate a more stable and accurate comprehensive signal, that is, the fused signal, thereby providing more reliable monitoring data; the specific fusion formula is: , in, is The signal after moment fusion; For the The sensors in Ecological environment monitoring data collected at all times; is the number of sensors; is the collection cycle; It is The contribution coefficient of each sensor data in the fusion process determines the proportion of each sensor data in the fused signal and is obtained through experiments; It is a coefficient used to control the nonlinearity of each sensor data in the fusion process, which is obtained through experiments.

[0024] In the process of data processing, ecological environment monitoring data is often affected by noise, outliers and external disturbances. In order to eliminate noise, the input fused signal is processed by sliding window through convolution operation, and the high-frequency noise in the fused signal is gradually removed, and the important low-frequency components are retained to reduce the volatility of the data, thereby effectively suppressing noise interference, making the purified signal obtained after the convolution operation more representative, and providing clear and reliable input for the subsequent signal reconstruction step. The formula is expressed as follows: , in, Indicated in The purified signal obtained after the moment convolution operation; For the A convolution kernel is used to filter the fused signal during the convolution process; is the number of convolution kernels; Represents the convolution operation; is the residual term, which indicates the error or noise in the convolution process. The convolution kernel is dynamically adjusted according to the characteristics of the ecological environment and the change pattern of the fused signal to ensure that the signal in each time window can remove interference to the greatest extent and retain effective information.

[0025] After signal purification, the purified signal is combined with the normal signal, and the contribution of the purified signal and the normal signal is adjusted through a weighted method to generate a reconstructed signal that best conforms to the actual ecological environment changes; the normal signal represents the normal data state of the ecological environment, and the range of the normal signal is determined by comparing the ecological environment standards (such as regulations or benchmark values); the reconstructed signal can restore the trend of the normal signal while retaining the abnormal changes in the purified signal as much as possible to ensure the accuracy of the ecological environment monitoring data; in addition, the reconstructed signal will contain more ecological environment information, especially in the face of complex environmental changes, it can effectively improve the detection and prediction capabilities of the ecological environment monitoring system. The generation formula of the reconstructed signal is: , in, yes Reconstruction signal at the moment; is the weighting factor used to adjust At the moment, the contribution of the purified signal and the normal signal to the reconstructed signal is obtained through experiments; is The normal signal at the moment is used to represent the normal data state of the ecological environment, without noise or abnormalities. The range of the normal signal is determined by comparing it with ecological environment standards (such as regulations or benchmark values); is the weighting factor used to adjust The influence of normal signals at all times on the reconstructed signals is obtained through experiments. The combination of purified signals and normal signals can effectively remove irregularities in ecological environment monitoring data while maintaining the reasonable trend of ecological environment changes, thus making the reconstructed signals closer to the real environmental state.

[0026] S2. Based on the ecological environment monitoring data and the reconstructed signal, dynamically adjust the adaptive weight of the sensor; based on the adaptive weight and the reconstructed signal of the sensor, construct a spatiotemporal model to obtain the spatiotemporal modeling results; based on the spatiotemporal modeling results, predict the future ecological environment status and obtain the environmental status prediction results at future moments.

[0027] Since the accuracy and environmental adaptability of sensors may change over time, it is necessary to assign an adaptability weight to each sensor at each time point to correct the contribution of the ecological environment monitoring data collected by each sensor to the comprehensive environmental state; the adaptability weight of the sensor is dynamically adjusted according to the error between the ecological environment monitoring data collected by the sensor and the reconstructed signal and the signal change trend. The larger the error, the smaller its adaptability weight, so as to reduce the impact of the current sensor on the overall data processing results. The specific formula is: , in, For the The adaptability weight of each sensor; is the adjustment factor, used to control the sensitivity of the error, obtained through experiments; For the Ecological environment monitoring data collected by sensors; is the reconstruction signal; is the number of sensors; It is The square error between the ecological environment monitoring data collected by each sensor and the reconstructed signal is used to measure the size of the error; is the reconstructed signal and The influence relationship between the ecological environment monitoring data collected by the sensors is used to reflect the signal change trend; is the correlation coefficient, obtained through experiments.

[0028] The ecological environment monitoring data collected by the sensor at the same time and the sensor's adaptive weight are integrated to reflect the changes in the ecological environment state in the temporal and spatial dimensions, and combined with the reconstructed signal to more realistically reflect the trend of ecological environment changes. At the same time, time change factors (such as time interval) are introduced. ), construct the spatiotemporal model, and obtain the spatiotemporal modeling results; by adjusting the adaptive weights, it can more accurately reflect the response capabilities of each sensor to the changes in the ecological environment at different time points; the basic formula of the spatiotemporal model is as follows: , in, yes The spatiotemporal modeling results at time The comprehensive environmental status at the moment; for Moment The adaptability weight of each sensor; is a regulating factor used to control the influence of the reconstructed signal on the spatiotemporal modeling results, obtained through experiments; It is the time interval, which reflects the dynamic changes of time series data; For the The sensors in Ecological environment monitoring data collected at all times; yes Reconstruction signal at the moment.

[0029] Based on the ecological environment monitoring data and spatiotemporal modeling results, the future ecological environment changes are effectively predicted, and the environmental state prediction results at future moments are obtained to improve the intelligent level of ecological environment monitoring; the prediction formula is: , in, Indicates future time The environmental status prediction results; It is A weighting coefficient is used to adjust the contribution of each sensor to the prediction result of the environmental state at the future moment, which is obtained through experiments; For the The sensors in Ecological environment monitoring data collected at all times; It is a spatiotemporal feedback factor, which is used to express the correlation between the predicted results of the environmental state at the future moment and the predicted results of the historical environmental state, and is obtained through experiments; yes The environmental state prediction result at the moment, that is, the historical environmental state prediction result; It is a cumulative factor, reflecting the impact of environmental changes within a time interval, obtained through experiments; is the time-integrated variable; yes Reconstruction signal at the moment; is a weighting coefficient, which is used to adjust the impact of spatiotemporal modeling results on the prediction results of environmental states at future moments and is obtained through experiments; yes The spatiotemporal modeling results at time The comprehensive environmental state at each moment. Combining spatiotemporal feedback with spatiotemporal integration enables the spatiotemporal model to effectively capture the dynamic changes of long-term dependencies, thereby improving prediction accuracy.

[0030] Through spatiotemporal modeling and prediction of environmental change trends in the future, we can achieve comprehensive monitoring and prediction of the ecological environment status and help decision makers make effective responses. Ultimately, it not only improves the accuracy and sensitivity of the ecological environment monitoring process, but also provides more precise support and basis for environmental protection, resource management and policy making.

[0031] In summary, a method for processing ecological environment monitoring data has been completed.

[0032] The order of the embodiments of the invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0033] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0034] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for processing ecological environment monitoring data, characterized in that: The following steps are involved: S1. Collect ecological environment monitoring data in real time and perform fusion processing to obtain fused signals; purify the fused signals through convolution operations to obtain purified signals; reconstruct signals based on the purified signals to generate reconstructed signals; S2. Based on the ecological environment monitoring data and the reconstructed signal, dynamically adjust the adaptive weight of the sensor; based on the adaptive weight and the reconstructed signal of the sensor, construct a spatiotemporal model to obtain the spatiotemporal modeling results; based on the spatiotemporal modeling results, predict the future ecological environment status and obtain the environmental status prediction results at future moments.

2. The method for processing ecological environment monitoring data according to claim 1, characterized in that: The S1 specifically includes: Based on the sensitivity and reliability of environmental changes, the contribution coefficient of sensor data in the fusion process is introduced, and the ecological environment monitoring data is weightedly fused to obtain the fused signal.

3. The method for processing ecological environment monitoring data according to claim 2, characterized in that: The S1 specifically includes: The fused signal is processed by sliding window through convolution operation to remove high-frequency noise in the fused signal and obtain a purified signal.

4. The method for processing ecological environment monitoring data according to claim 3, characterized in that: The S1 specifically includes: The purified signal is combined with the normal signal, and the contribution of the purified signal and the normal signal is adjusted through a weighted method to generate a reconstructed signal.

5. The method for processing ecological environment monitoring data according to claim 1, characterized in that: The S2 specifically includes: Based on the error between the ecological environment monitoring data and the reconstructed signal, as well as the signal change trend, the adaptive weight of the sensor is dynamically adjusted. The specific formula is: ， in, For the The adaptability weight of each sensor; is the adjustment factor used to control the sensitivity of the error; It is Ecological environment monitoring data collected by sensors; is the reconstruction signal; It is The square error between the ecological environment monitoring data collected by the sensor and the reconstructed signal; is the number of sensors; is the reconstructed signal and The influence relationship between the ecological environment monitoring data collected by the sensors is used to reflect the signal change trend; is the correlation coefficient.

6. The method for processing ecological environment monitoring data according to claim 5, characterized in that: The S2 specifically includes: The ecological environment monitoring data collected by the sensor at the same time and the sensor's adaptive weight are integrated, combined with the reconstructed signal, and the time change factor is introduced to build a spatiotemporal model to obtain the spatiotemporal modeling results.

7. The method for processing ecological environment monitoring data according to claim 6, characterized in that: The S2 specifically includes: Based on the results of spatiotemporal modeling, combined with ecological environmental monitoring data and reconstructed signals, future ecological environmental changes are predicted.

8. The method for processing ecological environment monitoring data according to claim 7, characterized in that: The S2 specifically includes: In the process of predicting future ecological environment changes, the spatiotemporal feedback factor and the cumulative factor are introduced, and the spatiotemporal feedback is combined with the spatiotemporal integration to obtain the environmental state prediction results at future moments. The prediction formula is: ， in, Indicates future time The environmental status prediction results; It is A weighting coefficient is used to adjust the contribution of each sensor to the prediction result of the environmental state at the future moment; For the The sensors in Ecological environment monitoring data collected at all times; is the spatiotemporal feedback factor; yes The prediction result of the environmental status at the moment; is the accumulation factor; is the time-integrated variable; yes Reconstruction signal at the moment; is a weighting coefficient used to adjust the impact of spatiotemporal modeling results on the environmental state prediction results at future moments; yes The spatiotemporal modeling results at each moment.

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