An environmental analysis and early warning method and system assisted by an intelligent vest

Abstract

The present invention discloses an environmental analysis and early warning method and system assisted by an intelligent vest, which relates to the technical field of environmental monitoring, including collecting multi-source environmental data and performing preprocessing, extracting features from the preprocessed environmental data and performing fusion; constructing a deep neural network DNN model to evaluate the risk value of environmental data, and real-time monitoring the risk value and early warning of environmental data; constructing a visual interface to display the risk value and early warning of environmental data in real time, and storing the environmental data generated by collection and analysis. By collecting multi-source environmental data and performing preprocessing, extracting features from the preprocessed environmental data and performing fusion; constructing a deep neural network DNN model to evaluate the risk value of environmental data, and real-time monitoring the risk value and early warning of environmental data, the present invention improves the accuracy and effectiveness of environmental data analysis, and enhances the flexibility and security of environmental risk early warning.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and particularly to an environmental analysis and early warning method and system assisted by an intelligent vest. Background Art

[0002] With the acceleration of the modern urbanization process, environmental pollution problems have become increasingly serious. Especially in some industrial cities, the monitoring and analysis of environmental factors such as air quality, temperature, humidity, and harmful gases have become crucial. Existing environmental monitoring technologies mainly rely on fixed monitoring stations, which have limited coverage and significant deficiencies in real-time performance and personalization. To make up for this defect, in recent years, wearable devices such as intelligent vests have gradually been applied to the field of environmental monitoring. These devices can collect surrounding environmental data in real-time while the user is moving by integrating multiple sensors, and combine big data analysis and artificial intelligence technologies to achieve dynamic environmental early warning. However, there is still a large room for optimization in the multi-source data fusion, feature extraction, and the accuracy and efficiency of intelligent early warning in existing technologies.

[0003] The deficiencies of existing technologies in environmental monitoring and early warning are mainly manifested in the following aspects. Traditional environmental monitoring methods usually rely on a single type of data source, ignoring the diversity of environmental data and the importance of multi-source data fusion. This single data processing method makes it impossible to comprehensively and accurately evaluate the risks in complex environments. Most existing environmental early warning models are based on static threshold settings and cannot flexibly respond to environmental changes at different times and conditions, easily resulting in false alarms or missed alarms. Summary of the Invention

[0004] In view of the problems existing in the above-mentioned existing environmental analysis and early warning methods assisted by intelligent vests, the present invention is proposed.

[0005] Therefore, the problems to be solved by the present invention are that traditional environmental monitoring methods usually rely on a single type of data source, ignoring the diversity of environmental data and the importance of multi-source data fusion. This single data processing method makes it impossible to comprehensively and accurately evaluate the risks in complex environments. Most existing environmental early warning models are based on static threshold settings and cannot flexibly respond to environmental changes at different times and conditions, easily resulting in false alarms or missed alarms.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: an environmental analysis and early warning method assisted by an intelligent vest, which includes collecting multi-source environmental data and performing preprocessing, extracting features from the preprocessed environmental data and performing fusion; constructing a deep neural network DNN model to evaluate the risk value of environmental data, and real-time monitoring the risk value and early warning of environmental data; constructing a visualization interface to display the risk value and early warning of environmental data in real-time, and storing the environmental data generated by collection and analysis.

[0007] As a preferred solution of the environmental analysis and early warning method assisted by the intelligent vest of the present invention, wherein: the collection of multi-source environmental data and preprocessing means installing a temperature sensor in the chest area of the intelligent vest, a humidity sensor in the waist area of the intelligent vest, an air quality sensor at the central position of the back of the intelligent vest, a harmful gas sensor near the breathing area of the chest of the intelligent vest, and a radiation sensor in the shoulder area of the intelligent vest, and collecting multi-source environmental data by the sensors at a unified collection frequency;

[0008] The multi-source environmental data includes temperature, humidity, air quality, harmful gas concentration, and radiation intensity data;

[0009] Use the interquartile range method IQR to perform anomaly detection on the collected multi-source environmental data and delete the outliers, use the weighted moving average filtering algorithm to denoise the collected multi-source environmental data, and perform standardization processing on the denoised multi-source environmental data.

[0010] As a preferred solution of the environmental analysis and early warning method assisted by the intelligent vest of the present invention, wherein: the extraction of features from the preprocessed environmental data and fusion means using principal component analysis PCA to extract the main features from the multi-source environmental data ;

[0011] Use the logarithmic function to perform logarithmic transformation on the extracted main features to obtain the logarithmically transformed eigenvalue ;

[0012] Use the Gaussian kernel function to calculate the similarity weight of the i-th type of environmental data at time t ;

[0013] Use the time decay function to calculate the time decay weight of the i-th type of environmental data at time t ;

[0014] Use the non-linear scoring mechanism to perform non-linear processing on the environmental data and calculate the score of the environmental data at time t , the formula is: , where m is the number of categories of environmental data, is the standardized value of the j-th type of environmental data at time t;

[0015] Based on the logarithmically transformed eigenvalue , calculate the similarity weight of the i-th type of environmental data at time t , the time decay weight of the i-th type of environmental data at time t and the score of the environmental data at time t , construct a feature fusion formula to fuse the extracted main features, and the formula is: ,

[0016] where is the feature fusion result at time t, T and 0 are the upper and lower limits of integration respectively, k is the number of main features extracted by principal component analysis (PCA), is the i-th regularization parameter.

[0017] As a preferred solution of the environmental analysis and early warning method assisted by the intelligent vest described in the present invention, wherein: constructing the deep neural network (DNN) model to evaluate the risk value of environmental data means collecting environmental training data from the development data platform, preprocessing, feature extraction and fusion to generate a training set;

[0018] Construct a deep neural network (DNN) model, including an input layer, a hidden layer and an output layer;

[0019] Set the format of the input layer as the feature fusion result ;

[0020] Use the training set to train the deep neural network (DNN) model, and use the loss function and Adam optimizer to iteratively optimize the model parameters;

[0021] Bring the feature fusion result into the trained deep neural network (DNN) model to obtain the risk value of environmental data at time t .

[0022] As a preferred solution of the environmental analysis and early warning method assisted by the intelligent vest described in the present invention, wherein: the real-time monitoring of the risk value of environmental data and early warning means constructing an adaptive dynamic threshold and setting an early warning threshold, and the formula is: where is the early warning threshold at time t, u is the adjustment coefficient, is the standard deviation of the risk value;

[0023] Compare the risk value of environmental data at time t with the early warning threshold at time t. If , it is determined as a dangerous area, and the intelligent vest emits an audible and visual alarm, and adjusts the frequency of collecting multi-source environmental data according to the ratio of the current risk value and the early warning threshold to obtain the adjusted frequency of collecting multi-source environmental data ;

[0024] If If so, it is determined as a normal area, and the intelligent vest continuously monitors the multi-source environmental data at the initial frequency of collecting multi-source environmental data.

[0025] As a preferred solution of the environmental analysis and early warning method assisted by the intelligent vest of the present invention, wherein: the constructed visual interface is used to display the risk value and early warning of environmental data in real time. The D3.js front-end data visualization framework is used to develop the visual interface, and a data visualization tool is used to display the risk value of environmental data, and the access operation is performed on the user request that has passed the verification.

[0026] As a preferred solution of the environmental analysis and early warning method assisted by the intelligent vest of the present invention, wherein: the storage of the environmental data generated by collection and analysis means storing the multi-source environmental data collected and the risk value of the environmental data generated by analysis into the database. The database is sorted in chronological order and marked with corresponding tags. At the same time, the multi-source environmental data collected and the environmental data generated by analysis are backed up to the cloud, and the integrity of the backup data is detected regularly.

[0027] Another object of the present invention is to provide an environmental analysis and early warning system assisted by an intelligent vest, which includes,

[0028] A collection and fusion module, configured to collect multi-source environmental data and perform preprocessing, extract features from the preprocessed environmental data and perform fusion;

[0029] A construction and early warning module, configured to construct a deep neural network DNN model, evaluate the risk value of environmental data, and monitor the risk value and early warning of environmental data in real time;

[0030] A visualization and storage module, configured to construct a visual interface to display the risk value and early warning of environmental data in real time, and store the environmental data generated by collection and analysis.

[0031] A computer device, including: a memory and a processor; the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned environmental analysis and early warning method assisted by the intelligent vest are implemented.

[0032] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned environmental analysis and early warning method assisted by the intelligent vest are implemented.

[0033] The beneficial effects of the present invention are as follows: By collecting multi-source environmental data and performing preprocessing, extracting features from the preprocessed environmental data and performing fusion; constructing a deep neural network DNN model, evaluating the risk value of environmental data, and monitoring the risk value and early warning of environmental data in real time, the accuracy and effectiveness of environmental data analysis are improved, and the flexibility and security of environmental risk early warning are enhanced. Description of the Drawings

[0034] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0035] Figure 1 It is a schematic flow chart of an environment analysis and early warning method assisted by an intelligent vest.

[0036] Figure 2 It is a schematic structural diagram of an environment analysis and early warning system assisted by an intelligent vest. Specific embodiments

[0037] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the accompanying drawings of the specification.

[0038] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0039] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is separate or selectively mutually exclusive with other embodiments.

[0040] Embodiment 1, referring to Figure 1 , which is the first embodiment of the present invention. This embodiment provides an environment analysis and early warning method assisted by an intelligent vest. The environment analysis and early warning method assisted by an intelligent vest includes:

[0041] S1. Collect multi-source environmental data and perform preprocessing, extract features from the preprocessed environmental data and perform fusion;

[0042] Specifically, collecting multi-source environmental data and performing preprocessing means installing a temperature sensor in the chest area of the intelligent vest, a humidity sensor in the waist area of the intelligent vest, an air quality sensor at the central position of the back of the intelligent vest, a harmful gas sensor near the breathing area of the chest of the intelligent vest, and a radiation sensor in the shoulder area of the intelligent vest, and collecting multi-source environmental data by the sensors at a unified collection frequency.

[0043] The multi-source environmental data includes temperature, humidity, air quality, harmful gas concentration, and radiation intensity data;

[0044] The interquartile range method IQR is used to detect anomalies in the collected multi-source environmental data and remove the outliers. The weighted moving average filtering algorithm is used to denoise the collected multi-source environmental data, and the denoised multi-source environmental data is normalized.

[0045] The temperature, humidity, air quality, harmful gas concentration, and radiation intensity data are independent and complementary to each other. Only by collecting these multi-source environmental data simultaneously can the intelligent vest provide a comprehensive and accurate environmental risk assessment. Each data type plays a unique role in specific health risk assessments. By using the IQR method, abnormal data can be detected more robustly, reducing misjudgments caused by environmental fluctuations, thereby improving the reliability of the data, enhancing the accuracy of the multi-source environmental data, and laying a solid foundation for subsequent data processing and analysis. The multi-dimensional characteristics of environmental data make the denoising process complex, but the weighted moving average filtering can handle the noise of multiple data sources simultaneously, retain the real and effective environmental change information, enhance the system's real-time response ability to environmental changes, and improve the accuracy of environmental risk assessment. Through the normalization process, all data is transformed into the same scale, which can fairly reflect the importance of each type of data in subsequent data fusion and analysis, ensure a more balanced data fusion process for different environmental data, avoid data imbalance problems, and improve the overall accuracy of environmental analysis and early warning.

[0046] Furthermore, feature extraction and fusion of the preprocessed environmental data refer to using the principal component analysis PCA to extract the main features from the multi-source environmental data ;

[0047] Using the logarithmic function to perform a logarithmic transformation on the extracted main features to obtain the logarithmic transformation feature values , and the formula is: , where is the i-th extracted main feature at time t, is a very small positive number;

[0048] Using the Gaussian kernel function to calculate the similarity weight of the i-th type of environmental data at time t , and the formula is: , where is the normalized value of the i-th type of environmental data at time t, is the bandwidth parameter of the kernel function, which is used to control the range of similarity;

[0049] Using the time decay function to calculate the time decay weight of the i-th type of environmental data at time t , the formula is: , where is the time decay rate parameter, used to control the rate at which each main feature decays over time;

[0050] is the i-th extracted main feature at time t extracted from multi-source environmental data through principal component analysis (PCA). These environmental data are dynamic and time-related, and air quality, temperature, etc. are not constant and will change over time. A non-linear scoring mechanism is used to non-linearly process the environmental data, and the score of the environmental data at time t is calculated , the formula is: , where m is the number of categories of environmental data, is the standardized value of the j-th category of environmental data at time t;

[0051] Environmental data may change drastically in some cases, such as a sudden increase in temperature or harmful gas concentration. These changes usually mean potential risks. To ensure that the system is more sensitive to these abnormal data, these outliers are amplified non-linearly, enabling the system to capture potential environmental risks faster. If linear weighting or mean calculation is used, the system's response to these sudden changes will be relatively slow, possibly resulting in missing important risk signals. In environmental data, many times small fluctuations with little change, such as slight changes in humidity, should not be overreacted to. The logarithmic square term smooths out these slight fluctuations, preventing the system from being overly sensitive to these small fluctuations, thereby reducing false alarms or overreactions. If a linear method is used to process these slight fluctuations, the system will frequently respond to these unnecessary changes, resulting in an increase in false alarms. Environmental data is multi-dimensional, and the data collected by each sensor may affect the overall risk assessment. It is necessary to uniformly score these multi-dimensional data for further fusion and processing. If the multi-dimensional data is not scored but solely relies on a single sensor or data source, the system's response will be limited to a single dimension and unable to accurately assess the overall risk. Directly processing single-dimensional data or using linear weighting will ignore the interaction of multiple dimensions.

[0052] Based on the logarithm-transformed eigenvalue calculate the similarity weight of the i-th category of environmental data at time t the time decay weight of the i-th category of environmental data at time t and the score of the environmental data at time t , construct a feature fusion formula to fuse the extracted main features. The formula is: , where is the feature fusion result at time t, T and 0 are the upper and lower limits of the integral respectively, k is the number of main features extracted through principal component analysis (PCA), is the i-th main feature, is the standardized value of the i-th type of environmental data at time t, is the bandwidth parameter of the kernel function, is the i-th main feature which is the square of the Euclidean distance between the i-th main feature and the standardized value of the i-th type of environmental data at time t, is the i-th regularization parameter, is the time decay rate parameter, where m is the number of types of environmental data, is the standardized value of the j-th type of environmental data at time t.

[0053] The relationships between environmental data are usually non-linear, and traditional weighted average methods cannot effectively capture these non-linear relationships. By introducing the RBF kernel function, the non-linear similarity between the principal components and the real-time data can be quantified. This method is necessary for feature fusion. The kernel function can automatically adjust the contribution of features according to the similarity of data, and is an effective tool for capturing the features of complex multi-dimensional data. Existing technologies mostly use linear weighting methods for data fusion, which easily ignore the non-linear correlations between data and cannot accurately evaluate the interactions between multi-dimensional data. The fixed weight method is difficult to adapt to the changing environmental data. The logarithmic function is used to perform non-linear processing on the feature values to ensure that the feature values do not produce negative values during the fusion process, and it amplifies the smaller features and suppresses the larger feature values. For the features in environmental data, the change ranges of some data are large. Directly using these features will lead to imbalance. The logarithmic function processing is necessary because it can adjust the distribution of the feature values, making the contributions of all features to the fusion process more balanced. In particular, small values will not be ignored, and large values will not dominate the entire process. Traditional evaluation methods directly use the original feature values for linear weighting, resulting in the features with larger feature values dominating the results, while the smaller features have insufficient influence. Through the logarithmic function, the smaller feature values are amplified and the larger feature values are compressed to ensure that each feature can play a reasonable role during fusion. The Gaussian kernel function captures the complex relationships between data in a non-linear manner, making the feature fusion process more flexible and able to adaptively adjust, thus improving the accuracy of feature fusion. Integrating the data over the entire time period can capture the long-term trends of environmental data, rather than just evaluating the risks at a single moment. Dynamic time integration can provide a more comprehensive risk assessment result and is therefore irreplaceable. Existing environmental risk assessment models are mostly static assessments, only considering the data at the current moment and ignoring the dynamic impact of historical data. By introducing the time decay function and time integration, the model can automatically adjust according to the influence of historical data over time, thus more flexibly evaluating risks and enhancing the dynamic response ability of the model.

[0054] By extracting the main features through PCA, it can effectively reduce the dimension of the data, retain the most representative information in the data, thereby improving the calculation efficiency and reducing redundancy. The use of PCA not only simplifies the data processing process but also solves the problem of how to extract useful information from multi-source data, enabling the system to respond to environmental changes faster, effectively improving the data processing efficiency, and enhancing the real-time performance and stability of the system. The Gaussian kernel function calculates the distance between data points and assigns higher weights to similar data, thereby improving the accuracy of feature fusion. Compared with traditional linear weight calculation methods, it solves the problem that it is difficult to accurately quantify data association in a non-linear environment, making data fusion more accurate, especially when dealing with multi-source data with complex internal relationships, the effect is particularly significant. Traditional data processing methods usually ignore the impact of time on data, resulting in the system being overly dependent on historical data and being slow to respond. The introduction of the time decay function solves this problem, enabling the system to quickly respond based on the latest data, greatly improving the accuracy and timeliness of real-time warning. By comprehensively considering the timeliness, similarity, and normalized scores of the data, various types of data are effectively fused, which not only solves the imbalance problem in the process of multi-source data fusion but also provides high-quality feature data for subsequent risk assessment and warning, thereby improving the accuracy and robustness of the entire environmental analysis system.

[0055] S2. Construct a deep neural network DNN model to evaluate the risk value of environmental data and real-time monitor the risk value and warning of environmental data;

[0056] Specifically, constructing a deep neural network DNN model to evaluate the risk value of environmental data means collecting environmental training data from the development data platform and performing preprocessing, feature extraction, and fusion to generate a training set;

[0057] Construct a deep neural network DNN model, including an input layer, a hidden layer, and an output layer;

[0058] Set the format of the input layer as the feature fusion result ;

[0059] Use the training set to train the deep neural network DNN model, and use the loss function and Adam optimizer to iteratively optimize the model parameters;

[0060] Bring the feature fusion result into the trained deep neural network DNN model to obtain the risk value of environmental data at time t .

[0061] Different from traditional environmental monitoring methods, the preprocessing step of the present invention not only improves the quality of data, but also solves the problem of difficult integration of multi-dimensional data through multi-source data fusion, ensuring high-quality input of data, laying a solid foundation for subsequent DNN model training, and at the same time improving the accuracy and robustness of risk assessment. By introducing the DNN model, the system can better capture the complex relationships in environmental data and make more accurate predictions of environmental risks. Through the multi-layer network structure, the system can extract deep feature information from complex environmental data, significantly improving the accuracy and sensitivity of risk assessment. The Adam optimizer can adaptively adjust the learning rate of the model, reducing the training time and increasing the convergence speed. By using the loss function, the model can continuously adjust the parameters in each iteration, making the prediction results gradually approach the true value. Compared with the traditional gradient descent algorithm, the Adam optimizer has better robustness, especially suitable for complex and noisy environmental data. While ensuring the accuracy of the model, it greatly improves the training efficiency, avoids the model falling into local optima, and enhances the system's adaptability to complex environmental data. Through dynamic data input and deep learning models, it can more accurately evaluate the potential risks of the environment. By introducing dynamic environmental data, the system can analyze the risk level of the current environment in real time, enhancing the accuracy and timeliness of environmental early warning.

[0062] Furthermore, an adaptive dynamic threshold is constructed by using the risk value of the environmental data monitored in real time and the warning index, and the warning threshold is set. The formula is: , where is the warning threshold at time t, u is the adjustment coefficient, is the standard deviation of the risk value;

[0063] The risk value of the environmental data at time t is compared with the warning threshold at time t. If , it is determined as a dangerous area, and the intelligent vest gives out a sound and light alarm. According to the ratio of the current risk value and the warning threshold , the frequency of collecting multi-source environmental data is adjusted to obtain the adjusted frequency of collecting multi-source environmental data. The formula is: , where is the initial frequency of collecting multi-source environmental data, is the frequency increase coefficient, which is used to control the sensitivity of the increase in the acquisition frequency;

[0064] If , it is determined as a normal area, and the intelligent vest continuously monitors the multi-source environmental data at the initial frequency of collecting multi-source environmental data.

[0065] Feature fusion result The result is a numerical value. The square root function is mathematically valid and is usually used in a physical sense to weaken the intensity of features or represent a change in a certain amplitude. By smoothing the feature fusion result, the excessive influence of high-risk data on the threshold can be reduced, preventing the system from frequently generating false alarms. It also reduces the excessive fluctuations in the threshold setting when the environmental data changes drastically, thereby enhancing the stability and robustness of the system. The existing threshold setting method based on the mean and standard deviation cannot comprehensively consider the complex multi-dimensional features and real-time changes of the environment, which leads to a lag in threshold adjustment and inability to quickly respond to environmental changes. The present invention introduces a non-linear adjustment mechanism to adjust the threshold to ensure that the system can smoothly handle drastic changes in complex data and avoid over-reaction. The threshold setting in the prior art usually only considers environmental data in a single dimension or fuses data through a simple weighting method, unable to comprehensively reflect the interaction in a multi-dimensional environment. By combining multiple environmental data dimensions through feature fusion, it is ensured that the system can comprehensively integrate multi-dimensional feature data and accurately reflect the overall change of the current environment. The existing dynamic threshold method based on the standard deviation completely relies on data volatility and ignores the multi-dimensionality and complex features of the data. Therefore, it is prone to a lag in threshold adjustment in a complex environment. By using the standard deviation as an auxiliary adjustment term and combining non-linear scoring and multi-dimensional feature fusion, it is ensured that when the system responds to environmental data fluctuations, it not only considers the data volatility but also can comprehensively consider multi-dimensional features and make flexible adjustments.

[0066] By introducing adaptive dynamic thresholds, the system can adjust the warning standards according to the fluctuations of environmental data. The threshold is dynamically adjusted as the environmental data changes, so that the system shows greater flexibility and accuracy when dealing with changing environments. Through the formula, the system can not only adjust the threshold according to the standard deviation, but also flexibly set warning strategies of different sensitivities through the adjustment coefficient u. Through dynamic adjustment, the warning system can quickly adapt to different environmental conditions, significantly reduce the false alarm rate, and improve the accuracy and effectiveness of the warning. The warning threshold is dynamically changing and is adjusted in real time according to data fluctuations and environmental conditions. When the environmental risk value exceeds the threshold, the system will automatically determine it as a dangerous area and issue an audible and visual alarm to remind the user. Compared with the existing system based on static thresholds, the present invention solves the problem of false alarms caused by environmental changes. By comparing the risk value and the warning threshold in real time, the system can make judgments in time and confirm It ensures a quick response when environmental risks increase, improving the accuracy and timeliness of early warnings. The introduction of the adjustment coefficient u effectively solves the problem that the early warning system cannot flexibly adapt to different scenarios. Through the adjustment of u, the system can strike a balance between sensitivity and stability, ensuring that the early warning system can work effectively in different scenarios. By adjusting the initial frequency and the frequency boost coefficient, the system can increase the frequency of data collection during high-risk periods, ensuring that the system can quickly collect more environmental data when the environment changes more drastically, thereby improving the ability to perceive risks. In low-risk situations, maintaining the initial frequency can avoid excessive consumption of resources. Under normal circumstances, the system still maintains basic monitoring of the environment to ensure that potential risks are not missed due to too low a frequency. Compared with the fixed frequency design of the prior art, the invention adjusts the frequency in combination with the risk assessment results, making the system's monitoring more intelligent.

[0067] S3, build a visual interface to display the risk value and warning of environmental data in real time, and store the environmental data collected and analyzed;

[0068] Specifically, building a visualization interface to display the risk value and warning of environmental data in real time means using the D3.js front-end data visualization framework to develop a visualization interface, using data visualization tools to display the risk value of environmental data, and performing review operations on verified user requests.

[0069] For the risk assessment of environmental data, D3.js can not only present traditional chart forms, but also, through its rich interactive functions, help users better understand the changing trends behind the data. Compared with existing static visualization solutions, the use of D3.js significantly improves users' sensitivity and insight into data, realizes the real-time and dynamic display of environmental data, enhances the interactivity and visualization effect of the system, enables users to more intuitively grasp the environmental risk situation. When the environmental risk value exceeds the warning threshold, the system can prompt users of potential environmental crises through graphical highlighting or animations. Compared with traditional digital or tabular presentations, this display method helps users make quick decisions in complex data, improves the response efficiency of the system and the user experience. Through identity authentication, it not only ensures the security of data, but also ensures the compliance of the system, solving the problems of insufficient data security and access control in the prior art. By strengthening access control over data, it ensures the soundness of the system in protecting data privacy and security, and enhances the security of the system and users' trust.

[0070] Furthermore, storing the environmental data generated by collection and analysis means storing the multi-source environmental data collected and the risk values of the environmental data generated by analysis into a database. The database is sorted in chronological order and corresponding tags are marked. At the same time, the multi-source environmental data collected and the environmental data generated by analysis are backed up to the cloud, and the integrity of the backup data is detected regularly.

[0071] By sorting in chronological order, it can not only help users quickly locate environmental data for a specific time period, but also provide a structured data basis for subsequent trend analysis. This chronological sorting method helps the system conduct more efficient data management and query. By storing data in chronological order, it significantly improves the efficiency of data management and provides a solid foundation for subsequent data analysis and trend prediction. By synchronously backing up data to the cloud, it greatly enhances the reliability and security of the data. Even if local devices fail, the data will not be lost. Cloud backup enables data to be accessed at any time on multiple platforms, meeting the requirements of mobility and convenience. Through cloud backup, the redundancy of data is ensured, greatly reducing the risk of data loss and improving the accessibility of data. Through regular integrity detection, it ensures that all backup data remains intact. Through regular detection, it ensures the integrity of the backup data and enhances the stability and reliability of the system during long-term operation.

[0072] Example 2, referring to Figure 2 , is the second embodiment of the present invention. This embodiment is different from the previous one and provides an environmental analysis and early warning system assisted by an intelligent vest.

[0073] A collection and fusion module, which is used to collect multi-source environmental data and perform preprocessing, extract features from the preprocessed environmental data and perform fusion;

[0074] A warning building module, which is used to build a deep neural network (DNN) model, evaluate the risk value of environmental data, and monitor the risk value and warning of environmental data in real time;

[0075] A visualization and storage module, which is used to build a visualization interface to display the risk value and warning of environmental data in real time, and store the environmental data generated by collection and analysis.

[0076] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0077] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or used in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0078] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0079] It should be understood that the various parts of the present invention can be implemented with hardware, software, firmware, or a combination thereof. In the above-described embodiments, the multiple steps or methods can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented with hardware, as in another embodiment, any one or a combination of the following techniques known in the art can be used: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application specific integrated circuit having appropriate combinational logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. An intelligent vest-assisted environmental analysis and early warning method, characterized in that: include, Collect and preprocess multi-source environmental data, extract features from the preprocessed environmental data, and perform fusion; The multi-source environmental data includes temperature, humidity, air quality, harmful gas concentration and radiation intensity data; Build a deep neural network (DNN) model to evaluate the risk value of environmental data, and monitor the risk value and early warning of environmental data in real time; Build a visual interface to display the risk value and warning of environmental data in real time, and store, collect and analyze the generated environmental data; The feature extraction and fusion of the pre-processed environmental data refers to extracting the main features from the multi-source environmental data using principal component analysis PCA ; Use logarithmic function to extract main features Perform logarithmic transformation to obtain the eigenvalue after logarithmic transformation , the formula is: ,in Extract the main features for the i-th time at time t, is a very small positive number; Use the Gaussian kernel function to calculate the similarity weight of the i-th type of environmental data at time t , the formula is: ,in is the standardized value of the i-th type of environmental data at time t, is the bandwidth parameter of the kernel function, which is used to control the range of similarity; Use the time decay function to calculate the time decay weight of the i-th type of environmental data at time t , the formula is: ,in is the time decay rate parameter, which is used to control the decay rate of each main feature over time; Use a nonlinear scoring mechanism to process the environmental data nonlinearly and calculate the score of the environmental data at time t , the formula is: , Where m is the number of categories of environmental data, is the standardized value of the j-th type of environmental data at time t; Based on the eigenvalues ​​after logarithmic transformation , calculate the similarity weight of the i-th type of environmental data at time t , the time attenuation weight of the i-th type of environmental data at time t And the score of the environmental data at time t , construct the feature fusion formula, and fuse the extracted main features. The formula is: ， in is the feature fusion result at time t, T and 0 are the upper and lower limits of the integration, k is the number of main features extracted by principal component analysis PCA, is the i-th regularization parameter; The risk value and early warning of real-time monitoring of environmental data refer to building an adaptive dynamic threshold and setting an early warning threshold. The formula is: , in is the warning threshold at time t, u is the adjustment coefficient, is the standard deviation of the risk value; The risk value of the environmental data at time t The warning threshold at time t For comparison, if When the area is identified as a dangerous area, the smart vest will sound an audible and visual alarm and and warning thresholds The frequency of collecting multi-source environmental data is adjusted by the ratio of ; like When the area is detected, it is judged as a normal area, and the smart vest continues to monitor the multi-source environmental data at the frequency of initial collection of multi-source environmental data.

2. The intelligent vest-assisted environmental analysis and early warning method according to claim 1, characterized in that: The collecting of multi-source environmental data and preprocessing thereof refers to installing an air temperature sensor on the chest area of ​​the smart vest, installing a humidity sensor on the waist area of ​​the smart vest, installing an air quality sensor on the central back of the smart vest, installing a harmful gas sensor on the chest of the smart vest near the breathing area, and installing a radiation sensor on the shoulder area of ​​the smart vest, and the sensors are used to collect multi-source environmental data at a unified collection frequency; The interquartile range method (IQR) is used to detect anomalies in the collected multi-source environmental data and delete outliers. The weighted moving average filtering algorithm is used to denoise the collected multi-source environmental data, and the denoised multi-source environmental data is standardized.

3. The intelligent vest-assisted environmental analysis and early warning method according to claim 2, characterized in that: The construction of the deep neural network DNN model to evaluate the risk value of environmental data refers to collecting environmental training data from the development data platform and performing preprocessing, feature extraction and fusion to generate a training set; Build a deep neural network DNN model, including input layer, hidden layer and output layer; Set the format of the input layer to feature fusion result ; Use the training set to train the deep neural network DNN model, and use the loss function and Adam optimizer to iteratively optimize the model parameters; The feature fusion results Bring it into the trained deep neural network DNN model to obtain the risk value of the environmental data at time t .

4. The smart vest-assisted environmental analysis and early warning method according to claim 3, characterized in that: The construction of a visualization interface to display the risk value and warning of environmental data in real time refers to developing a visualization interface using the D3.js front-end data visualization framework, using data visualization tools to display the risk value of environmental data, and performing a review operation on verified user requests.

5. The smart vest-assisted environmental analysis and early warning method according to claim 4, characterized in that: The storage of collected and analyzed environmental data refers to storing the risk values ​​of the collected multi-source environmental data and the environmental data generated by analysis in a database, sorting the database in chronological order and marking corresponding tags, synchronously backing up the collected multi-source environmental data and the environmental data generated by analysis to the cloud, and regularly performing integrity checks on the backup data.

6. An intelligent vest-assisted environmental analysis and early warning system based on the intelligent vest-assisted environmental analysis and early warning method according to any one of claims 1 to 5, characterized in that: include, The collection and fusion module is used to collect and preprocess multi-source environmental data, extract features from the preprocessed environmental data, and fuse them; Build an early warning module to build a deep neural network (DNN) model, evaluate the risk value of environmental data, and monitor the risk value and early warning of environmental data in real time; The visualization storage module is used to build a visualization interface to display the risk value and warning of environmental data in real time, and to store, collect and analyze the environmental data generated.

7. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that when the processor executes the computer program, the steps of the smart vest-assisted environmental analysis and early warning method described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the smart vest-assisted environmental analysis and early warning method described in any one of claims 1 to 5 are implemented.

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