A smart supervision method and system based on big data

Abstract

The present invention relates to the field of data management technology, and specifically to a smart supervision method and system based on big data, comprising the following steps: S1, multi-source data acquisition: real-time acquisition of multi-source data; S2, data preprocessing and spatiotemporal feature extraction: preprocessing the acquired multi-source data, and extracting spatiotemporal features from the preprocessed multi-source data; S3, multi-dimensional risk assessment based on spatiotemporal features: applying a multi-dimensional risk assessment algorithm to conduct risk assessment on the behavior and status of the supervised object; S4, dynamic early warning: combining preset early warning rules, automatically triggering dynamic early warnings, and providing corresponding risk response suggestions; S5, supervision strategy optimization: based on risk assessment results and dynamic early warning feedback, optimizing and updating supervision strategies through a self-learning mechanism. The present invention improves the accuracy and response speed of risk identification, making supervision more flexible and efficient.

Description

Technical Field

[0001] The present invention relates to the field of data management technology, and in particular to a smart supervision method and system based on big data. Background Art

[0002] With the rapid development of big data, the Internet of Things, and artificial intelligence technologies, regulatory systems in various industries have gradually introduced these advanced technologies to improve the intelligence level of monitoring and management. Through the real-time collection and analysis of multi-source data, regulatory systems can better cope with complex and changing environments and ensure the security and stability of business processes, equipment status, and environmental conditions. Against this background, smart regulatory systems have begun to be applied to various high-risk industries, such as production and manufacturing, energy management, transportation and logistics, to improve the efficiency of risk identification, early warning, and response.

[0003] Although existing smart regulatory systems have made progress in multi-source data collection and monitoring, existing technologies still have several shortcomings. First, the accuracy and real-time performance of data processing and analysis are relatively weak, making it difficult for the system to quickly respond to potential risks in complex scenarios. Second, existing systems generally lack in-depth analysis of spatiotemporal characteristics and cannot fully utilize the time and space dimensions in the data to make accurate risk predictions. In addition, the formulation of regulatory strategies often relies on preset rules and lacks adaptive capabilities, making it difficult to adjust strategies in a timely manner according to the dynamically changing risk environment, resulting in limited flexibility and self-optimization capabilities of the system in long-term operation.

[0004] In response to the shortcomings of existing technologies, the present invention proposes a smart supervision method and system based on big data to improve the system's risk identification accuracy and response speed, enhance the system's adaptability and continuous optimization capabilities in complex and dynamic environments, and realize intelligent and efficient risk management and decision support. Summary of the Invention

[0005] The present invention provides a big data-based intelligent supervision method and system.

[0006] A smart supervision method based on big data, comprising the following steps:

[0007] S1, Multi-source data collection: Through sensors and data collection devices deployed at multiple monitoring points, multi-source data, including environmental data, business process data, and equipment status data, is collected in real time;

[0008] S2, data preprocessing and spatiotemporal feature extraction: preprocess the collected multi-source data, including data denoising, missing value filling and outlier identification, and extract spatiotemporal features from the preprocessed multi-source data;

[0009] S3, Multidimensional Risk Assessment Based on Spatiotemporal Features: Using the extracted spatiotemporal features, a multidimensional risk assessment algorithm is applied to conduct risk assessments on the behavior and status of the regulated entity. Risks are graded based on the risk assessment results, and corresponding regulatory strategies are generated for risk response. Specifically, the following are included:

[0010] S31, Spatiotemporal Feature Analysis and Modeling: By analyzing the extracted spatiotemporal features, we can identify the changing trends of multi-source data in different time and space dimensions and build spatiotemporal models to capture the dynamic changing patterns of the behavior and status of the regulated objects;

[0011] S32, Risk Assessment: Based on the spatiotemporal model, a multi-dimensional risk assessment algorithm is applied to conduct risk assessment on the behavior and status of the regulated object;

[0012] S33, Risk Grading and Strategy Generation: Based on the calculation results of the multi-dimensional risk assessment algorithm, risks are divided into different levels and corresponding regulatory strategies are generated according to the risk level;

[0013] S4, Dynamic Alert: Based on the graded risk assessment results and generated regulatory strategies, combined with pre-set alert rules, dynamic alerts are automatically triggered and corresponding risk response recommendations are provided;

[0014] S5, Supervision Strategy Optimization: Based on risk assessment results and dynamic early warning feedback, the supervision strategy is optimized and updated through a self-learning mechanism.

[0015] Optionally, the multi-source data acquisition in S1 includes:

[0016] S11, Sensor Deployment: Deploy different types of sensors and data acquisition equipment at multiple monitoring points, including environmental sensors, process monitoring equipment, and equipment status monitoring devices;

[0017] S12, real-time data monitoring: Real-time monitoring and collection of multi-source data through various sensors, including environmental data (such as temperature, humidity, and air quality), business process data (such as production process operation information), and equipment status data (such as equipment operating status and fault information).

[0018] Optionally, the data preprocessing and spatiotemporal feature extraction in S2 include:

[0019] S21, data denoising: denoising the collected multi-source data using Gaussian filtering algorithm;

[0020] S22, missing value filling: missing values in multi-source data are filled by linear interpolation;

[0021] S23, outlier identification: using the Z-score algorithm anomaly detection algorithm to identify outliers;

[0022] S24, spatiotemporal feature extraction: extract spatiotemporal features from the preprocessed multi-source data, including spatial features, temporal features, spatiotemporal interaction features, motion features, and environmental state features.

[0023] Optionally, the spatiotemporal feature extraction in S24 includes:

[0024] S241, Spatial Feature Extraction: Extract spatial features from pre-processed multi-source data through spatial distance calculation to analyze the changes of regulatory objects in different geographical locations;

[0025] S242, Time Feature Extraction: Capturing time series trends by analyzing the dynamic changes of data at different time points;

[0026] S243, spatiotemporal interaction feature extraction: Spatiotemporal interaction features combine the dynamic changes of space and time to analyze the interactive changes of data under different spatiotemporal conditions;

[0027] S244, motion feature extraction: Motion features describe the behavior changes of objects by extracting their movement trajectory and speed;

[0028] S245, environmental state feature extraction: Environmental state features are extracted by averaging the changes in environmental data (such as temperature, humidity, air quality, etc.).

[0029] Optionally, the spatiotemporal feature analysis and modeling in S31 includes:

[0030] S311, Dynamic Change Trend Identification: Based on the change patterns of multi-source data under different temporal and spatial conditions, the dynamic change trends are identified using the K-Means spatiotemporal clustering algorithm;

[0031] S312, spatiotemporal model construction: Based on the dynamic change trend identification results of spatiotemporal characteristics, a spatiotemporal regression model is constructed to capture the behavior and state change patterns of the regulated objects in different time and space dimensions.

[0032] Optionally, the risk assessment in S32 includes:

[0033] S321, Risk assessment based on spatiotemporal model: Based on the constructed spatiotemporal regression model, calculate the state value of each dimension feature prediction ;

[0034] S322, Application of multi-dimensional risk assessment algorithm: Calculate the comprehensive risk value of characteristic data in multiple dimensions (such as time, space, state, and behavior) through weighted multi-dimensional risk algorithm , in order to assess the risk level of the regulated entity.

[0035] Optionally, the risk grading and strategy generation in S33 includes:

[0036] S331, Risk classification: Based on the calculation results of the multi-dimensional risk assessment algorithm , the comprehensive risk value is compared with the preset lower limit threshold and upper threshold Make comparisons and classify risks into different levels, including low risk, medium risk, and high risk;

[0037] S332, Strategy Generation: Automatically generate corresponding regulatory strategies based on different risk levels. For low-risk levels, only information is recorded. For medium-risk levels, an early warning is issued and the regulatory strategy is activated. For high-risk levels, an emergency response procedure is triggered and the regulatory intensity is adjusted. The regulatory strategy includes adjusting the monitoring frequency, reallocating resources, and triggering risk response actions.

[0038] Optionally, the dynamic warning in S4 includes:

[0039] S41, Dynamic Alert Triggering: Based on the graded risk assessment results and the generated regulatory strategy, combined with the preset alert rules, it is automatically determined whether a dynamic alert needs to be triggered. The alert rules include response thresholds and strategies for different risk levels, specifically including:

[0040] Low-risk warning rules:

[0041] Trigger condition: When the risk value Less than the lower threshold When triggered;

[0042] Early warning mechanism: Generate low-priority early warning notifications through background logging, low-frequency email alerts, or mark the monitoring device as under observation;

[0043] Medium risk warning rules:

[0044] Trigger condition: When the risk value Between lower threshold and upper threshold If a risk is detected, a medium-risk warning will be triggered;

[0045] Early warning mechanism: When a medium-risk warning is triggered, a medium-priority warning notification will be generated, and a reminder will be sent to the relevant responsible person via email or instant messaging tools, and the risk will be marked as alert on the monitoring platform;

[0046] High-risk warning rules:

[0047] Trigger condition: When the risk value Above the upper threshold Triggered when a serious risk is detected;

[0048] Early warning mechanism: When a high-risk warning is triggered, a high-priority warning will be generated, and an emergency alert will be sent to relevant personnel through multiple channels (SMS, phone, email, etc.). At the same time, a red alert will be issued on the monitoring platform, requiring immediate intervention;

[0049] S42, early warning rules combined with strategies: Early warning rules not only consider the risk level, but also combine the generated supervision strategy to determine the form and intensity of the warning. Based on the generated supervision strategy, the early warning rules dynamically adjust the alarm frequency, notification channels and response level, including:

[0050] Low risk warning: Low risk warning is only monitored regularly. When the risk assessment value detected is lower than the lower threshold When the system is in operation, it only indicates the existence of potential risks;

[0051] Medium risk warning: Regularly monitor the risk source for medium risk warning, and increase the intensity of monitoring as the risk persists or increases;

[0052] High-risk warning: Real-time monitoring of high-risk warnings and continuous assessment of the status of risk sources until the risk is resolved;

[0053] S43, risk response advice provision: If a dynamic warning is triggered, corresponding risk response advice will be automatically generated and provided based on the risk level and the generated regulatory strategy, including strengthening monitoring, adjusting resource scheduling, and implementing emergency measures.

[0054] Optionally, the supervision policy optimization in S5 includes:

[0055] S51, Strategy Optimization Trigger: Based on risk assessment results and dynamic early warning feedback, the effectiveness of the current regulatory strategy is analyzed in real time. When it is found that the current regulatory strategy is unable to cope with the risk, the regulatory strategy optimization process will be automatically triggered;

[0056] S52, application of self-learning mechanism: Through the self-learning mechanism, the Q-learning algorithm is used to continuously optimize and update the supervision strategy.

[0057] A big data-based smart supervision system, used to implement the above-mentioned big data-based smart supervision method, includes the following modules:

[0058] Multi-source data acquisition module: Through sensors and data acquisition devices deployed at multiple monitoring points, multi-source data is collected in real time, including environmental data, business process data, and equipment status data;

[0059] Data preprocessing and spatiotemporal feature extraction module: preprocesses the collected multi-source data, including data denoising, missing value filling, and outlier identification, and extracts spatiotemporal features from the preprocessed multi-source data;

[0060] Multidimensional risk assessment module: Based on the extracted spatiotemporal features, a multidimensional risk assessment algorithm is applied to evaluate the behavior and status of the regulated object, and the risk is graded according to the assessment results, and corresponding regulatory strategies are generated;

[0061] Dynamic early warning module: Automatically triggers dynamic early warnings based on the graded risk assessment results and generated supervision strategies, combined with preset early warning rules;

[0062] Regulatory strategy optimization module: Based on risk assessment results and dynamic early warning feedback, the regulatory strategy is optimized and updated through a self-learning mechanism.

[0063] Beneficial effects of the present invention:

[0064] This invention comprehensively captures the dynamic changes of regulatory objects in multiple dimensions such as space, time, and environment through multi-source data collection, data preprocessing, and spatiotemporal feature extraction. The system uses high-precision sensors to monitor multi-source data such as environment, business processes, and equipment status in real time, ensuring comprehensive coverage and accurate acquisition of information, providing a solid data foundation for subsequent risk assessment and regulatory strategies.

[0065] The present invention can effectively identify the potential risks of regulatory objects through multi-dimensional risk assessment and dynamic early warning functions based on spatiotemporal characteristics, and ensure the system's rapid response capabilities to different risk levels through risk grading and automated strategy generation. The system greatly improves the accuracy and response speed of risk identification by combining multi-dimensional characteristics, automatically triggering early warning mechanisms and providing response suggestions, making regulatory work more flexible and efficient.

[0066] The present invention, through its self-learning mechanism and supervisory strategy optimization function, can continuously adjust and optimize supervisory strategies based on risk assessment results and dynamic early warning feedback, ensuring that the system maintains efficient adaptability and accuracy in complex and changing environments. Through continuous iterative learning of the Q-learning algorithm, the intelligence level of the system is enhanced, thereby having higher decision-making capabilities and the ability to flexibly respond to different risk scenarios in long-term operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art.

[0068] Figure 1 A flowchart of a supervision method according to an embodiment of the present invention is shown;

[0069] Figure 2 Schematic diagram of system function modules according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0071] It should be noted that references in the specification to "one embodiment," "an embodiment," "exemplary embodiments," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment will include such specific features, structures, or characteristics. Furthermore, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0072] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0073] like Figure 1 As shown, a smart supervision method based on big data includes the following steps:

[0074] S1, Multi-source data collection: Through sensors and data collection devices deployed at multiple monitoring points, multi-source data, including environmental data, business process data, and equipment status data, is collected in real time;

[0075] S2, data preprocessing and spatiotemporal feature extraction: preprocess the collected multi-source data, including data denoising, missing value filling and outlier identification, and extract spatiotemporal features from the preprocessed multi-source data;

[0076] S3, Multidimensional Risk Assessment Based on Spatiotemporal Features: Using the extracted spatiotemporal features, a multidimensional risk assessment algorithm is applied to conduct risk assessments on the behavior and status of the regulated entity. Risks are graded based on the risk assessment results, and corresponding regulatory strategies are generated for risk response. Specifically, the following are included:

[0077] S31, Spatiotemporal Feature Analysis and Modeling: By analyzing the extracted spatiotemporal features, we can identify the changing trends of multi-source data in different time and space dimensions and build spatiotemporal models to capture the dynamic changing patterns of the behavior and status of the regulated objects;

[0078] S32, Risk Assessment: Based on the spatiotemporal model, a multi-dimensional risk assessment algorithm is applied to conduct risk assessment on the behavior and status of the regulated object;

[0079] S33, Risk Grading and Strategy Generation: Based on the calculation results of the multi-dimensional risk assessment algorithm, risks are divided into different levels and corresponding regulatory strategies are generated according to the risk level;

[0080] S4, Dynamic Alert: Based on the graded risk assessment results and generated regulatory strategies, combined with pre-set alert rules, dynamic alerts are automatically triggered and corresponding risk response recommendations are provided;

[0081] S5, Supervision Strategy Optimization: Based on risk assessment results and dynamic early warning feedback, the supervision strategy is optimized and updated through a self-learning mechanism;

[0082] Through the above steps, risk assessment is carried out in combination with spatiotemporal characteristics, and the regulatory strategy is continuously optimized through the self-learning mechanism, which can respond to complex and changing regulatory environments in real time, improve the accuracy of risk identification and the speed of regulatory response, and at the same time enhance the system's adaptability and continuous optimization capabilities.

[0083] Multi-source data acquisition in S1 includes:

[0084] S11, Sensor Deployment: Deploy different types of sensors and data acquisition equipment at multiple monitoring points, including environmental sensors, process monitoring equipment, and equipment status monitoring devices;

[0085] S12, real-time data monitoring: Various sensors are used to monitor and collect multi-source data in real time, including environmental data (such as temperature, humidity, and air quality), business process data (such as production process operation information), and equipment status data (such as equipment operating status and fault information);

[0086] Through the above steps, different types of sensors and data acquisition equipment are deployed at multiple monitoring points. The system can monitor multi-dimensional data such as environment, business processes and equipment status in real time, ensuring comprehensive coverage and high-precision collection of information.

[0087] Data preprocessing and spatiotemporal feature extraction in S2 include:

[0088] S21, data denoising: Use Gaussian filtering algorithm to denoise the collected multi-source data, expressed as:

[0089] ;

[0090] in, is the denoised data value, is the original data value, is the data center point, is the standard deviation of the Gaussian distribution;

[0091] S22, missing value filling: For missing values in multi-source data, linear interpolation is used to fill them, which is expressed as:

[0092] ;

[0093] in, are known data points, is the corresponding known data value, is the estimated value for the missing data points;

[0094] S23, outlier identification: using the Z-score algorithm anomaly detection algorithm to identify outliers;

[0095] S24, spatiotemporal feature extraction: extract spatiotemporal features from the preprocessed multi-source data, including spatial features, temporal features, spatiotemporal interaction features, motion features, and environmental state features;

[0096] Through the above content, data quality can be effectively improved, the integrity and consistency of multi-source data can be ensured, and the dynamic changes of regulatory objects can be deeply analyzed from multiple dimensions such as space, time, spatiotemporal interaction, movement and environmental status, providing rich and accurate basic data for subsequent multi-dimensional risk assessment, greatly enhancing the flexibility and adaptability of the system in complex scenarios, and improving the accuracy and response speed of risk identification.

[0097] The spatiotemporal feature extraction in S24 includes:

[0098] S241, Spatial Feature Extraction: Extract spatial features from pre-processed multi-source data through spatial distance calculation, and analyze the changes of regulatory objects in different geographical locations, expressed as:

[0099] ;

[0100] in, and are the spatial coordinates of the two monitoring points, is the spatial distance between two points;

[0101] S242, Time Feature Extraction: Capture time series trends by analyzing the dynamic changes of data at different time points, expressed as:

[0102] ;

[0103] in, and At different time points, Indicates time gap;

[0104] S243, spatiotemporal interaction feature extraction: Spatiotemporal interaction features combine the dynamic changes of space and time to analyze the interactive changes of data under different spatiotemporal conditions, expressed as:

[0105] ;

[0106] S244, motion feature extraction: Motion features describe the behavior changes of objects by extracting their movement trajectory and speed, which can be expressed as:

[0107] ;

[0108] in, is the object's speed, and are the coordinates of an object at different points in time and space;

[0109] S245, Environmental Status Feature Extraction: Environmental status features are extracted by averaging the changes in environmental data (such as temperature, humidity, air quality, etc.), expressed as:

[0110] ;

[0111] in, It is Environmental data values at a time point, is the number of time points monitored, is the average value of the environmental state;

[0112] Through the above content, it is possible to comprehensively capture the dynamic changes of the regulated objects in the time and space dimensions, especially showing excellent adaptability in complex scenarios of time and space interaction, movement behavior and environmental status, providing rich feature data for multi-dimensional risk assessment, enabling the system to more accurately identify potential risks and make timely responses to abnormal situations under different time and space conditions, greatly improving the system's risk warning capabilities and regulatory efficiency.

[0113] The spatiotemporal feature analysis and modeling in S31 include:

[0114] S311, dynamic change trend identification: Based on the change patterns of multi-source data under different time and space conditions, the dynamic change trends are identified using the K-Means spatiotemporal clustering algorithm, which is expressed as:

[0115] ;

[0116] in, is the number of clusters, For the clusters, is the center point of the cluster, is a data point;

[0117] S312, Spatiotemporal Model Construction: Based on the dynamic trend identification results of spatiotemporal characteristics, a spatiotemporal regression model is constructed to capture the behavior and state change patterns of the regulated object in different time and space dimensions. The spatiotemporal regression model is expressed as:

[0118] ;

[0119] in, is the predicted state value, is the spatiotemporal characteristic variable, is the regression coefficient;

[0120] Through the above content, it is possible to effectively identify the dynamic change trends of regulatory objects in different time and space dimensions, and build a spatiotemporal regression model to accurately predict future behaviors and states, thereby improving the system's adaptability in complex environments, capturing subtle changes in multiple dimensions, and providing more accurate data support for real-time monitoring and risk warnings, while improving the scientific nature and efficiency of regulatory decisions.

[0121] The risk assessment in S32 includes:

[0122] S321, Risk assessment based on spatiotemporal model: Based on the constructed spatiotemporal regression model, calculate the state value of each dimension feature prediction ;

[0123] S322, Application of multi-dimensional risk assessment algorithm: Calculate the comprehensive risk value of characteristic data in multiple dimensions (such as time, space, state, and behavior) through weighted multi-dimensional risk algorithm , to assess the risk level of the regulatory object, expressed as:

[0124] ;

[0125] in, is the comprehensive risk value, For the The weight of the feature dimension, for The state value predicted by the feature dimension, is the number of feature dimensions;

[0126] Through the above content, it is possible to accurately integrate data on time, space and other characteristic dimensions to conduct comprehensive risk predictions. Using spatiotemporal regression models and weighted risk assessment algorithms, the behavior and status of regulatory objects can be dynamically analyzed.

[0127] Risk grading and strategy generation in S33 include:

[0128] S331, Risk classification: Based on the calculation results of the multi-dimensional risk assessment algorithm , the comprehensive risk value is compared with the preset lower limit threshold and upper threshold For comparison, risks are divided into different levels, including low risk, medium risk, and high risk, which are expressed as:

[0129] ;

[0130] in, for grade;

[0131] Lower threshold and upper threshold The statistical analysis method based on historical data is set, including:

[0132] Data collection and collation: Collect a large amount of historical regulatory data, including past risk assessment results, incident records, and regulatory measures taken;

[0133] Calculate the distribution of risk value: Statistically analyze historical risk assessment values to find the comprehensive risk value The distribution of risk values is fitted using the normal distribution model, and the risk values of historical data are set to According to the normal distribution, the average value is calculated and standard deviation , expressed as:

[0134] ;

[0135] Setting thresholds: Analyze the distribution of risk values and use statistical quantiles to set thresholds and threshold , in order to classify the risk level, we choose to define the risk value below the 25th percentile as low risk, between the 25th and 75th percentiles as medium risk, and above the 75th percentile as high risk. represents the 25th percentile in the risk distribution, that is, , represents the 75th percentile in the risk distribution, that is, ;

[0136] Adjustment and verification: In setting and After the threshold is set, we can test and verify known events in historical data to ensure that the set threshold can reasonably divide the risk level. If the risk classification effect in the actual scenario is not ideal, we can adjust the average value based on the new data. and standard deviation Re-estimate and dynamically adjust the threshold;

[0137] S332, Strategy Generation: Automatically generate corresponding regulatory strategies based on different risk levels. For low-risk levels, only information is recorded. For medium-risk levels, an early warning is issued and the regulatory strategy is activated. For high-risk levels, emergency response procedures are triggered and regulatory intensity is adjusted. The regulatory strategy includes adjusting monitoring frequency, reallocating resources, and triggering risk response actions.

[0138] Through the above content, risks can be accurately divided into different levels according to actual conditions, and corresponding regulatory strategies can be generated, ensuring the scientific nature of risk assessment. Thresholds can be dynamically adjusted to adapt to changes in complex environments. Combined with an automated strategy generation mechanism, high-risk scenarios can be responded to in a timely manner, and appropriate response measures can be taken for different levels of risks, thereby improving the efficiency, accuracy and flexibility of regulatory work.

[0139] Dynamic warnings in S4 include:

[0140] S41, Dynamic Alert Triggering: Based on the graded risk assessment results and the generated regulatory strategy, combined with the preset alert rules, it is automatically determined whether a dynamic alert needs to be triggered. The alert rules include response thresholds and strategies for different risk levels, specifically including:

[0141] Low-risk warning rules:

[0142] Trigger condition: When the risk value Less than the lower threshold When triggered;

[0143] Early warning mechanism: Generate low-priority early warning notifications through background logging, low-frequency email alerts, or mark the monitoring device as under observation;

[0144] Medium risk warning rules:

[0145] Trigger condition: When the risk value Between lower threshold and upper threshold If a risk is detected, a medium-risk warning will be triggered;

[0146] Early warning mechanism: When a medium-risk warning is triggered, a medium-priority warning notification will be generated, and a reminder will be sent to the relevant responsible person via email or instant messaging tools, and the risk will be marked as alert on the monitoring platform;

[0147] High-risk warning rules:

[0148] Trigger condition: When the risk value Above the upper threshold Triggered when a serious risk is detected;

[0149] Early warning mechanism: When a high-risk warning is triggered, a high-priority warning will be generated, and an emergency alert will be sent to relevant personnel through multiple channels (SMS, phone, email, etc.). At the same time, a red alert will be issued on the monitoring platform, requiring immediate intervention;

[0150] S42, early warning rules combined with strategies: Early warning rules not only consider the risk level, but also combine the generated supervision strategy to determine the form and intensity of the warning. Based on the generated supervision strategy, the early warning rules dynamically adjust the alarm frequency, notification channels and response level, including:

[0151] Low risk warning: Low risk warning is only monitored regularly. When the risk assessment value detected is lower than the lower threshold When the system is in operation, it only indicates the existence of potential risks;

[0152] Medium risk warning: Regularly monitor the risk source for medium risk warning, and increase the intensity of monitoring as the risk persists or increases;

[0153] High-risk warning: Real-time monitoring of high-risk warnings and continuous assessment of the status of risk sources until the risk is resolved;

[0154] S43, Risk Response Recommendations: If a dynamic warning is triggered, corresponding risk response recommendations are automatically generated and provided based on the risk level and generated regulatory strategy. These recommendations include strengthening monitoring, adjusting resource scheduling, and implementing emergency measures to ensure that regulators can take effective response actions based on the warning information.

[0155] Through the above content, the warning frequency and notification method can be flexibly adjusted according to different risk levels, ensuring that the system only makes observation records when the risk is low, and promptly reminds relevant personnel to take preventive measures when the risk is medium, and ensures immediate response through high-frequency notifications through multiple channels when the risk is high. This mechanism effectively avoids the information overload caused by excessive warnings, while ensuring a rapid response to major risks, improving the flexibility and response efficiency of the system, and making supervision more targeted and accurate.

[0156] Supervision strategy optimizations in S5 include:

[0157] S51, Strategy Optimization Trigger: Based on risk assessment results and dynamic early warning feedback, the effectiveness of the current regulatory strategy is analyzed in real time. When it is found that the current regulatory strategy is unable to cope with the risk, the regulatory strategy optimization process will be automatically triggered;

[0158] S52, application of self-learning mechanism: Through the self-learning mechanism, the Q-learning algorithm is used to continuously optimize and update the supervision strategy, which is expressed as:

[0159] ;

[0160] in, Indicates that the status Take action The expected return, is the learning rate, is the immediate return of current feedback, is the discount factor, which is used to weight future returns, is the next state, It’s the next move;

[0161] Through the above content, it is possible to continuously optimize and update regulatory strategies based on real-time risk assessment results and dynamic early warning feedback, adapt to complex and changing environments, automatically learn and select the best strategies to improve the ability to deal with different risk scenarios. Through continuous iterative updates, the intelligence level of the system is improved, making regulatory decisions more accurate and efficient, and enhancing the adaptability and flexibility of the system in long-term operation.

[0162] like Figure 2 As shown, a smart supervision system based on big data is used to implement the above-mentioned smart supervision method based on big data, including the following modules:

[0163] Multi-source data acquisition module: Through sensors and data acquisition devices deployed at multiple monitoring points, multi-source data is collected in real time, including environmental data, business process data, and equipment status data;

[0164] Data preprocessing and spatiotemporal feature extraction module: preprocesses the collected multi-source data, including data denoising, missing value filling, and outlier identification, and extracts spatiotemporal features from the preprocessed multi-source data;

[0165] Multidimensional risk assessment module: Based on the extracted spatiotemporal features, a multidimensional risk assessment algorithm is applied to evaluate the behavior and status of the regulated object, and the risk is graded according to the assessment results, and corresponding regulatory strategies are generated;

[0166] Dynamic early warning module: Automatically triggers dynamic early warnings based on the graded risk assessment results and generated supervision strategies, combined with preset early warning rules;

[0167] Regulatory strategy optimization module: Based on risk assessment results and dynamic early warning feedback, the regulatory strategy is optimized and updated through a self-learning mechanism.

[0168] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0169] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A smart supervision method based on big data, characterized in that: The following steps are involved: S1, Multi-source data collection: Through sensors and data collection devices deployed at multiple monitoring points, multi-source data, including environmental data, business process data, and equipment status data, is collected in real time; S2, data preprocessing and spatiotemporal feature extraction: preprocess the collected multi-source data, including data denoising, missing value filling and outlier identification, and extract spatiotemporal features from the preprocessed multi-source data; The spatiotemporal feature extraction includes spatial features, temporal features, spatiotemporal interaction features, motion features, and environmental state features: S241, Spatial Feature Extraction: Extract spatial features from pre-processed multi-source data through spatial distance calculation to analyze the changes of regulatory objects in different geographical locations; S242, Time Feature Extraction: Capturing time series trends by analyzing the dynamic changes of data at different time points; S243, spatiotemporal interaction feature extraction: Spatiotemporal interaction features combine the dynamic changes of space and time to analyze the interactive changes of data under different spatiotemporal conditions; S244, motion feature extraction: Motion features describe the behavior changes of objects by extracting their movement trajectory and speed; S245, environmental state feature extraction: environmental state features are extracted by averaging the changes in environmental data; S3, Multidimensional Risk Assessment Based on Spatiotemporal Features: Using the extracted spatiotemporal features, a multidimensional risk assessment algorithm is applied to conduct risk assessments on the behavior and status of the regulated entity. Risks are graded based on the risk assessment results, and corresponding regulatory strategies are generated for risk response. Specifically, the following are included: S31, Spatiotemporal Feature Analysis and Modeling: By analyzing the extracted spatiotemporal features, we identify the changing trends of multi-source data in different time and space dimensions and construct a spatiotemporal model to capture the dynamic changing patterns of the behavior and status of the regulated objects. This spatiotemporal feature analysis and modeling includes: S311, Dynamic Change Trend Identification: Based on the change patterns of multi-source data under different temporal and spatial conditions, the dynamic change trends are identified using the K-Means spatiotemporal clustering algorithm; S312, Spatiotemporal Model Construction: Based on the results of the dynamic change trend identification of spatiotemporal characteristics, a spatiotemporal regression model is constructed to capture the behavior and state change patterns of the regulated objects in different time and space dimensions; S32, Risk Assessment: Based on the spatiotemporal model, a multi-dimensional risk assessment algorithm is used to conduct a risk assessment of the behavior and status of the regulated entity. The risk assessment includes: S321, risk assessment based on spatiotemporal model: Based on the constructed spatiotemporal regression model, calculate the state value Y predicted by each dimension feature; S322, Application of Multidimensional Risk Assessment Algorithm: Calculate the comprehensive risk value R of the characteristic data from multiple dimensions through a weighted multidimensional risk algorithm to assess the risk level of the regulated entity; S33, Risk Grading and Strategy Generation: Based on the calculation results of the multi-dimensional risk assessment algorithm, risks are divided into different levels and corresponding regulatory strategies are generated according to the risk level; S4, Dynamic Alert: Based on the graded risk assessment results and generated regulatory strategies, combined with pre-set alert rules, dynamic alerts are automatically triggered and corresponding risk response recommendations are provided; S5, Supervision Strategy Optimization: Based on risk assessment results and dynamic early warning feedback, the supervision strategy is optimized and updated through a self-learning mechanism.

2. The intelligent supervision method based on big data according to claim 1, characterized in that: The multi-source data acquisition in S1 includes: S11, Sensor Deployment: Deploy different types of sensors and data acquisition equipment at multiple monitoring points, including environmental sensors, process monitoring equipment, and equipment status monitoring devices; S12, real-time data monitoring: Real-time monitoring and collection of multi-source data through various sensors, including environmental data, business process data, and equipment status data.

3. The intelligent supervision method based on big data according to claim 1, characterized in that: The data preprocessing and spatiotemporal feature extraction in S2 include: S21, data denoising: denoising the collected multi-source data using Gaussian filtering algorithm; S22, missing value filling: missing values in multi-source data are filled by linear interpolation; S23, Outlier Identification: Use the Z-score algorithm to identify outliers.

4. The intelligent supervision method based on big data according to claim 1, characterized in that: The risk classification and strategy generation in S33 include: S331, Risk classification: Based on the calculation result R of the multi-dimensional risk assessment algorithm, the comprehensive risk value is compared with the preset lower limit threshold. and upper threshold Compare and classify risks into different levels, including low risk, medium risk, and high risk; S332, Strategy Generation: Automatically generate corresponding regulatory strategies based on different risk levels. For low-risk levels, only information is recorded. For medium-risk levels, an early warning is issued and the regulatory strategy is activated. For high-risk levels, an emergency response procedure is triggered and the regulatory intensity is adjusted. The regulatory strategy includes adjusting the monitoring frequency, reallocating resources, and triggering risk response actions.

5. The intelligent supervision method based on big data according to claim 4 is characterized in that: The dynamic warning in S4 includes: S41, Dynamic Alert Triggering: Based on the graded risk assessment results and the generated regulatory strategy, combined with the preset alert rules, it is automatically determined whether a dynamic alert needs to be triggered. The alert rules include response thresholds and strategies for different risk levels, specifically including: Low-risk warning rules: Trigger condition: When the risk value R is less than the lower limit threshold When triggered; Early warning mechanism: Generate low-priority early warning notifications through background logging, low-frequency email alerts, or mark the monitoring device as under observation; Medium risk warning rules: Trigger condition: When the risk value R is between the lower limit threshold and upper threshold If a risk is detected, a medium-risk warning will be triggered; Early warning mechanism: When a medium-risk warning is triggered, a medium-priority warning notification will be generated, and a reminder will be sent to the relevant responsible person via email or instant messaging tools, and the risk will be marked as alert on the monitoring platform; High-risk warning rules: Trigger condition: When the risk value R is higher than the upper threshold Triggered when a serious risk is detected; Early warning mechanism: When a high-risk warning is triggered, a high-priority warning will be generated, and an emergency alert will be issued to relevant personnel through multiple channels. At the same time, a red alert will be issued on the monitoring platform, requiring immediate intervention; S42, early warning rules combined with strategies: Early warning rules not only consider the risk level, but also combine the generated supervision strategy to determine the form and intensity of the warning. Based on the generated supervision strategy, the early warning rules dynamically adjust the alarm frequency, notification channels and response level, including: Low risk warning: Low risk warning is only monitored regularly. When the risk assessment value detected is lower than the lower threshold When the system is in operation, it only indicates the existence of potential risks; Medium risk warning: Regularly monitor the risk source for medium risk warning, and increase the intensity of monitoring as the risk persists or increases; High-risk warning: Real-time monitoring of high-risk warnings and continuous assessment of the status of risk sources until the risk is resolved; S43, risk response advice provision: If a dynamic warning is triggered, corresponding risk response advice will be automatically generated and provided based on the risk level and the generated regulatory strategy, including strengthening monitoring, adjusting resource scheduling, and implementing emergency measures.

6. The intelligent supervision method based on big data according to claim 1, characterized in that: The supervision strategy optimization in S5 includes: S51, Strategy Optimization Trigger: Based on risk assessment results and dynamic early warning feedback, the effectiveness of the current regulatory strategy is analyzed in real time. When it is found that the current regulatory strategy is unable to cope with the risk, the regulatory strategy optimization process will be automatically triggered; S52, application of self-learning mechanism: Through the self-learning mechanism, the Q-learning algorithm is used to continuously optimize and update the supervision strategy.

7. A big data-based smart supervision system, used to implement a big data-based smart supervision method as described in any one of claims 1 to 6, characterized in that: Includes the following modules: Multi-source data acquisition module: Through sensors and data acquisition devices deployed at multiple monitoring points, multi-source data is collected in real time, including environmental data, business process data, and equipment status data; Data preprocessing and spatiotemporal feature extraction module: preprocesses the collected multi-source data, including data denoising, missing value filling, and outlier identification, and extracts spatiotemporal features from the preprocessed multi-source data; Multidimensional risk assessment module: Based on the extracted spatiotemporal features, a multidimensional risk assessment algorithm is applied to evaluate the behavior and status of the regulated object, and the risk is graded according to the assessment results, and corresponding regulatory strategies are generated; Dynamic early warning module: Automatically triggers dynamic early warnings based on the graded risk assessment results and generated supervision strategies, combined with preset early warning rules; Regulatory strategy optimization module: Based on risk assessment results and dynamic early warning feedback, the regulatory strategy is optimized and updated through a self-learning mechanism.

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