Safety engineering management and control system integrating real-time monitoring and intelligent decision

Abstract

The invention discloses a security engineering management and control system integrating real-time monitoring and intelligent decision, and relates to the technical field of intelligent security management and control. Generating a risk preliminary screening result with confidence; intelligent decision-making of safety engineering management and control is completed; and the information is distributed to a remote control platform to complete safety engineering management and control. According to the method, full-process intelligent management from data perception to closed-loop control is realized, the method has remarkable beneficial effects, an information island is effectively broken, and the data availability is improved; the false alarm rate and the response delay are obviously reduced; passive alarm is changed into active prevention and control, the real-time performance, the accuracy and the intelligent level of large-scale engineering safety control are greatly improved, and the method has prominent practical value and popularization prospect.

Description

Technical Field

[0001] This invention relates to the field of intelligent safety management and control technology, and in particular to a safety engineering management and control system that integrates real-time monitoring and intelligent decision-making. Background Technology

[0002] With the rapid development of modern industrial technology and the continuous acceleration of urbanization, various large-scale engineering construction projects are increasing, and the scale of these projects is becoming more complex and integrated. As a result, the requirements for safety management and control are becoming increasingly stringent. At the same time, the widespread application of new-generation information technologies such as the Internet of Things, big data, and artificial intelligence has led to the gradual evolution of on-site monitoring methods towards digitalization and intelligence.

[0003] Current safety engineering management systems generally suffer from a technical deficiency of prioritizing monitoring over decision-making. The core problem lies in the fact that most systems only achieve data collection and visualization, lacking the ability to deeply mine and intelligently analyze massive amounts of monitoring data. This results in the system's inability to autonomously identify potential risks and generate effective management decisions. Specifically, when sensors collect abnormal data, the system often only issues simple alarms, failing to combine the engineering context, historical data, and environmental factors for comprehensive judgment, and thus unable to propose targeted handling suggestions or automatically trigger control measures. This disconnect between monitoring and decision-making seriously affects the response efficiency and scientific nature of safety management, and can easily delay the opportunity to handle risks. Summary of the Invention

[0004] In view of the problems existing in the existing safety engineering management and control system that integrates real-time monitoring and intelligent decision-making, this invention is proposed.

[0005] Therefore, the problem to be solved by this invention is: how to overcome the defect of monitoring and decision-making disconnect in the existing safety engineering management system, and realize the transformation from data acquisition and alarm to closed-loop management and control that integrates engineering context for real-time risk identification and intelligent autonomous decision-making.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a safety engineering management and control system integrating real-time monitoring and intelligent decision-making, comprising: a multi-source heterogeneous data fusion module for preprocessing multi-dimensional heterogeneous data; a dynamic risk perception module for modeling an engineering safety situation data pool using a lightweight anomaly detection algorithm, identifying risk levels in engineering operations, and generating risk screening results with confidence; an intelligent decision engine module for combining the acquired multi-dimensional heterogeneous data and risk levels in engineering operations to reason and analyze the risk screening results and complete intelligent decision-making for safety engineering management and control; and a closed-loop linkage execution feedback module for distributing the risk screening results reasoned by the intelligent decision engine module to a remote control platform to complete safety engineering management and control.

[0008] As a preferred embodiment of the safety engineering management and control system integrating real-time monitoring and intelligent decision-making described in this invention, the acquisition of multi-dimensional heterogeneous data includes acquiring multi-dimensional heterogeneous data using IoT sensors, video surveillance, environmental parameters, and construction progress management systems. The preprocessing includes unified access, cleaning, time-series alignment, and feature extraction of the acquired multi-dimensional heterogeneous data to form an engineering safety situation data pool.

[0009] The reasoning analysis of the initial risk screening results refers to the process of determining whether the multi-dimensional heterogeneous data reaches the risk level in the engineering operation after generating the initial risk screening results with confidence.

[0010] If this is achieved, the initial risk screening is successful;

[0011] Otherwise, the initial risk screening will fail.

[0012] As a preferred embodiment of the safety engineering management system integrating real-time monitoring and intelligent decision-making described in this invention, the multi-source heterogeneous data fusion module includes a protocol adaptive access submodule, a spatiotemporal alignment and cleaning submodule, and a semantic feature extraction submodule.

[0013] The dynamic risk perception module includes a lightweight learning submodule, a multi-scale anomaly scoring submodule, and a confidence quantification output submodule.

[0014] The intelligent decision engine module includes a scenario modeling submodule and a coupled risk reasoning submodule;

[0015] The closed-loop linkage execution feedback module includes an instruction intelligent routing submodule, an execution status tracking submodule, and a feedback closed-loop calibration submodule.

[0016] As a preferred embodiment of the integrated real-time monitoring and intelligent decision-making safety engineering management system of the present invention, the protocol adaptive access submodule is used to preprocess multi-dimensional heterogeneous data.

[0017] The heterogeneous devices used are connected in a plug-and-play unified manner;

[0018] The spatiotemporal alignment and cleaning submodule is used to eliminate temporal misalignment, remove noise and outliers from the acquired multi-dimensional heterogeneous data, and ensure the temporal consistency of the multi-dimensional heterogeneous data.

[0019] The semantic feature extraction submodule is used to extract statistical features, frequency domain features and time series trend features from multi-dimensional heterogeneous data based on knowledge base annotation, and to construct a structured engineering security situation data pool.

[0020] As a preferred embodiment of the safety engineering management and control system integrating real-time monitoring and intelligent decision-making described in this invention, the lightweight learning submodule is used to deploy a compressed LSTM-AE neural network model to complete the streaming data modeling of the engineering safety situation data pool at the edge computing node.

[0021] The multi-scale anomaly scoring submodule is used to output anomaly scores from both temporal and spatial dimensions, and supports the setting of differentiated risk thresholds.

[0022] The confidence quantification output submodule is used to dynamically calculate the reliability probability of the initial risk screening results, avoiding false alarms that could disrupt intelligent decision-making.

[0023] As a preferred embodiment of the safety engineering management system integrating real-time monitoring and intelligent decision-making described in this invention, the scenario modeling submodule is used to integrate the current process plan and meteorological early warning information to construct a dynamic engineering scenario simulation.

[0024] The coupled risk reasoning submodule is used to determine the risk level in the engineering operation based on the engineering operation deduced from the dynamic engineering scenario. At this time, the risk level in the engineering operation is determined when the multi-dimensional heterogeneous data reaches the risk level in the engineering operation.

[0025] The intelligent routing submodule is used to select the execution channel to be distributed to the remote control platform based on the risk level of the engineering operation after assessment.

[0026] The execution status tracking submodule is used to verify the validity of instruction execution and prevent orders from being ignored. The process of judging orders not being executed includes collecting the response status of the controlled device to verify the instruction. The response status of the controlled device includes power outage confirmation or personnel evacuation check-in.

[0027] The feedback closed-loop calibration submodule is used to transmit the execution results to the safety engineering management system, establish a self-evolution mechanism, and complete safety engineering management. The self-evolution mechanism includes a decision-making mechanism, an execution mechanism, and a learning mechanism.

[0028] As a preferred embodiment of the safety engineering management system integrating real-time monitoring and intelligent decision-making described in this invention, the calculation formula of the lightweight anomaly detection algorithm is:

[0029]

[0030] in, This indicates the abnormal score of t. This represents the actual input value of the i-th sensor or feature channel at time t. This represents the predicted value reconstructed from the compressed LSTM-AE model. This indicates the number of feature dimensions in the current input vector. This indicates the adjustment weighting coefficient. This represents the standard deviation of the input data within the sliding window;

[0031] The formula for calculating the reliability probability of the dynamic risk screening results is as follows:

[0032]

[0033] in, This indicates the reliability probability of the initial risk screening results. This represents the model reconstruction error for the current sample. This indicates the most recent historical reconstruction error for the device or area. Indicates the historical false alarm rate. This indicates a scale anomaly score.

[0034] Secondly, embodiments of the present invention provide a safety engineering management and control system integrating real-time monitoring and intelligent decision-making, comprising: preprocessing multi-dimensional heterogeneous data; modeling an engineering safety situation data pool using a lightweight anomaly detection algorithm, identifying risk levels in engineering operations, and generating preliminary risk screening results with confidence; combining the acquired multi-dimensional heterogeneous data and risk levels in engineering operations, reasoning and analyzing the preliminary risk screening results, and completing intelligent decision-making for safety engineering management and control; and distributing the preliminary risk screening results reasoned by the intelligent decision engine module to a remote control platform to complete safety engineering management and control.

[0035] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any step of the above-mentioned integrated real-time monitoring and intelligent decision-making security engineering management system.

[0036] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the aforementioned integrated real-time monitoring and intelligent decision-making security engineering control system.

[0037] The beneficial effects of this invention are as follows: This invention realizes intelligent management of the entire process from data perception to closed-loop control, with significant benefits. First, through a multi-source heterogeneous data fusion module, it achieves unified access and semantic processing of multi-dimensional data such as IoT sensors, video surveillance, environmental parameters, and construction progress, effectively breaking down information silos and improving data availability. Second, the dynamic risk perception module adopts a lightweight anomaly detection algorithm combined with a confidence quantification mechanism, enabling real-time modeling at the edge and outputting credible initial risk screening results, significantly reducing false alarm rates and response delays. Third, the intelligent decision engine module integrates BIM models, process plans, and meteorological information to construct a scenario knowledge graph, and combines coupled risk reasoning to achieve context-based intelligent judgment. Finally, the closed-loop linkage execution feedback module forms a complete closed loop of perception-decision-execution-evolution through intelligent instruction routing, execution tracking, and feedback calibration, ensuring not only the accurate implementation of control measures but also supporting system self-learning optimization. Overall, this invention achieves a leap from passive alarm to proactive prevention and control, significantly improving the real-time performance, accuracy, and intelligence level of safety management in large-scale projects, and has outstanding practical value and promotion prospects. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0039] Figure 1 This is a schematic diagram of a safety engineering management system that integrates real-time monitoring and intelligent decision-making, provided as an embodiment of the present invention.

[0040] Figure 2 A flowchart illustrating a method for a safety engineering management system integrating real-time monitoring and intelligent decision-making, provided as an embodiment of the present invention.

[0041] Figure 3 This is a schematic diagram of the structure of a medium for a safety engineering management system that integrates real-time monitoring and intelligent decision-making, as provided in an embodiment of the present invention.

[0042] Figure 4 This is a schematic diagram of the structure of a computing device for a safety engineering management and control system that integrates real-time monitoring and intelligent decision-making, as provided in an embodiment of the present invention. Detailed Implementation

[0043] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0044] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0045] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0046] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0047] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0048] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0049] Example

[0050] Reference Figures 1-4This is the first embodiment of the present invention, which provides a safety engineering management and control system integrating real-time monitoring and intelligent decision-making, including:

[0051] S1: Multi-source heterogeneous data fusion module, used for preprocessing multi-dimensional heterogeneous data;

[0052] S2: Dynamic Risk Perception Module, used to model the engineering safety situation data pool using a lightweight anomaly detection algorithm, identify the risk level in engineering operations, and generate risk screening results with confidence.

[0053] S3: Intelligent Decision Engine Module, which combines the acquired multi-dimensional heterogeneous data and the risk level in the engineering operation to reason and analyze the initial risk screening results and complete intelligent decision-making for safety engineering management;

[0054] S4: Closed-loop linkage execution feedback module, used to distribute the initial risk screening results inferred by the intelligent decision engine module to the remote control platform to complete safety engineering management.

[0055] The acquisition of multi-dimensional heterogeneous data includes using IoT sensors, video surveillance, environmental parameters and construction progress management systems to acquire multi-dimensional heterogeneous data. Preprocessing includes unified access, cleaning, time-series alignment and feature extraction of the acquired multi-dimensional heterogeneous data to form an engineering safety situation data pool.

[0056] The reasoning analysis of the initial risk screening results refers to the process of determining whether the multi-dimensional heterogeneous data reaches the risk level in the engineering operation after generating the initial risk screening results with confidence.

[0057] If this is achieved, the initial risk screening is successful;

[0058] Otherwise, the initial risk screening will fail.

[0059] Furthermore, the multi-source heterogeneous data fusion module includes a protocol adaptive access submodule, a spatiotemporal alignment and cleaning submodule, and a semantic feature extraction submodule;

[0060] The dynamic risk perception module includes a lightweight learning submodule, a multi-scale anomaly scoring submodule, and a confidence quantification output submodule.

[0061] The intelligent decision engine module includes a scenario modeling submodule and a coupled risk reasoning submodule;

[0062] The closed-loop linkage execution feedback module includes an instruction intelligent routing submodule, an execution status tracking submodule, and a feedback closed-loop calibration submodule.

[0063] The protocol adaptive access submodule is used to preprocess multi-dimensional heterogeneous data.

[0064] The heterogeneous devices used are connected in a plug-and-play unified manner;

[0065] The spatiotemporal alignment and cleaning submodule is used to eliminate temporal misalignment, remove noise and outliers from the acquired multi-dimensional heterogeneous data, and ensure the temporal consistency of multi-dimensional heterogeneous data.

[0066] The semantic feature extraction submodule is used to extract statistical features, frequency domain features and time series trend features from multi-dimensional heterogeneous data based on knowledge base annotation, and to build a structured engineering security situation data pool.

[0067] The lightweight learning submodule is used to deploy compressed LSTM-AE neural network models and complete streaming data modeling of the engineering security situation data pool at edge computing nodes;

[0068] The multi-scale anomaly scoring submodule is used to output anomaly scores from both temporal and spatial dimensions, and supports the setting of differentiated risk thresholds;

[0069] The confidence quantification output submodule is used to dynamically calculate the confidence probability of the initial risk screening results, avoiding false alarms that could disrupt intelligent decision-making.

[0070] Preferably, the adaptive parameter system is dynamically adjusted according to the engineering scenario. The threshold settings at the time granularity are set at the second level (1~5 seconds) for high-frequency equipment state change detection (such as triggering an early warning if the vibration acceleration exceeds 3 times the standard deviation), and at the minute level (1~10 minutes) for trend anomaly judgment (such as the continuous temperature rise rate greater than 0.5°C / min and the cumulative increase exceeding 8°C). In the spatial dimension, different anomaly scoring thresholds are set for different areas or equipment types. For example, the personnel boundary risk threshold in the high-altitude operation area is set at 0.7 (scoring range 0~1), while the tilt angle anomaly threshold in the large machinery operation area is set at 0.6. The gas concentration change threshold in the underground construction area is set as 80%~90% of the PPM value as the early warning line according to the type of harmful gas. The feedback closed-loop calibration module continuously optimizes the system during operation to ensure that it can not only sensitively capture real risks, but also effectively avoid misjudgments caused by environmental interference.

[0071] The scenario modeling submodule is used to integrate current process plans and weather warning information to construct dynamic engineering scenario simulations;

[0072] The Coupled Risk Reasoning submodule is used to determine the risk level in engineering operations based on the engineering operations deduced from the dynamic engineering scenario. The risk level determined in the engineering operations is based on the judgment that the multi-dimensional heterogeneous data has reached the risk level in the engineering operations.

[0073] The intelligent routing submodule is used to select the execution channel to be distributed to the remote control platform based on the risk level of the engineering operation after assessment.

[0074] The execution status tracking submodule is used to verify the validity of instruction execution and prevent orders from being ignored. The process of judging orders not being executed includes collecting the response status of the controlled equipment to verify the instructions. The response status of the controlled equipment includes power outage confirmation or personnel evacuation check-in.

[0075] The feedback closed-loop calibration submodule is used to transmit the execution results to the safety engineering management system, establish a self-evolution mechanism, and complete safety engineering management. The self-evolution mechanism includes a decision-making mechanism, an execution mechanism, and a learning mechanism.

[0076] The risk level adopts a four-level classification system, which is dynamically divided according to the severity of the risk, the possibility of spread, and the harmful consequences. Specifically, it includes: Level 1 (low risk), Level 2 (medium risk), Level 3 (high risk), and Level 4 (emergency risk). Level 1 risk refers to abnormal states with minor potential hazards that are unlikely to cause accidents in the short term, such as slight fluctuations in individual sensor data or temperature and humidity exceeding limits in non-critical areas. The system only records these events and prompts inspections for attention. Level 2 risk refers to clear violations or equipment parameters deviating from normal ranges that may develop into accidents, such as working at heights without wearing safety belts or tower cranes with loads approaching 90% of their rated value. The system automatically pushes warnings to on-site management personnel and initiates video verification. Level 3 risk refers to abnormal coupling of multi-source data or key parameters exceeding limits, with a high probability of accidents, such as the displacement rate of deep foundation pit support structures exceeding 5 mm / h accompanied by rainfall warnings. The system immediately triggers a regional work stoppage command and uses the intelligent routing submodule to link the PLC control system to reduce load. Level 4 risk is an emergency state, referring to critical situations requiring immediate intervention, such as equipment instability, gas leaks, or sudden structural deformation. The system automatically executes strong control measures such as power outages, blockades, and evacuation broadcasts within 0.5 seconds and simultaneously reports to the emergency command center. Risk assessment at all levels not only relies on threshold comparisons, but also incorporates a comprehensive weighted evaluation of process stages, job types, and external environmental factors output by the scenario modeling submodule to ensure that the risk classification is scientific and accurate, supporting subsequent differentiated decision-making and execution.

[0077] In a preferred embodiment, a safety engineering management method integrating real-time monitoring and intelligent decision-making includes: preprocessing multi-dimensional heterogeneous data; modeling an engineering safety situation data pool using a lightweight anomaly detection algorithm to identify risk levels in engineering operations and generate preliminary risk screening results with confidence; combining the acquired multi-dimensional heterogeneous data and risk levels in engineering operations to infer and analyze the preliminary risk screening results and complete intelligent decision-making for safety engineering management; and distributing the preliminary risk screening results inferred by the intelligent decision engine module to a remote control platform to complete safety engineering management.

[0078] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or independent of it, or they can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0079] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface of the computer device is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen of the computer device may be an LCD screen or an e-ink display screen. The input device of the computer device may be a touch layer covering the display screen, or buttons, a trackball, or a touchpad located on the casing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0080] In summary, this invention achieves intelligent management of the entire process from data perception to closed-loop control, with significant beneficial effects. First, the multi-source heterogeneous data fusion module enables unified access and semantic processing of multi-dimensional data from IoT sensors, video surveillance, environmental parameters, and construction progress, effectively breaking down information silos and improving data availability. Second, the dynamic risk perception module employs a lightweight anomaly detection algorithm combined with a confidence quantification mechanism, enabling real-time modeling at the edge and outputting credible initial risk screening results, significantly reducing false alarm rates and response delays. Third, the intelligent decision engine module integrates BIM models, process plans, and meteorological information to construct a scenario knowledge graph, achieving context-based intelligent judgment through coupled risk reasoning. Finally, the closed-loop linkage execution feedback module forms a complete closed loop of perception-decision-execution-evolution through intelligent instruction routing, execution tracking, and feedback calibration, ensuring not only the accurate implementation of control measures but also supporting system self-learning optimization. Overall, this invention achieves a leap from passive alarm to proactive prevention and control, significantly improving the real-time performance, accuracy, and intelligence level of safety management in large-scale projects, demonstrating outstanding practical value and promising prospects for widespread application.

[0081] After introducing the method and system of exemplary embodiments of the present invention, the following references are made. Figure 3 A computer-readable storage medium according to exemplary embodiments of the present invention will be described, please refer to... Figure 3The computer-readable storage medium shown is an optical disc 30, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it implements the steps described in the above method implementation. For example, a multi-source heterogeneous data fusion module is used to preprocess multi-dimensional heterogeneous data; a dynamic risk perception module is used to model the engineering safety situation data pool using a lightweight anomaly detection algorithm, identify the risk level in engineering operations, and generate a risk screening result with confidence; an intelligent decision engine module is used to combine the acquired multi-dimensional heterogeneous data and the risk level in engineering operations to reason and analyze the risk screening result and complete the intelligent decision-making for safety engineering management; and a closed-loop linkage execution feedback module is used to distribute the risk screening result reasoned by the intelligent decision engine module to a remote control platform to complete safety engineering management. The specific implementation methods of each step will not be repeated here.

[0082] It should be noted that examples of computer-readable storage media may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0083] After introducing the methods and media of exemplary embodiments of the present invention, the following references are made. Figure 4 A computational device for adaptive recovery of low-voltage power grid self-healing control according to an exemplary embodiment of the present invention.

[0084] Figure 4 A block diagram is shown of an exemplary computing device 40 suitable for implementing embodiments of the present invention. The computing device 40 may be a computer system or a server. Figure 4 The computing device 40 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0085] like Figure 4 As shown, the components of computing device 40 may include, but are not limited to: one or more processors or processing units 401, system memory 402, and bus 403 connecting different system components (including system memory 402 and processing unit 401).

[0086] The computing device 40 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 40, including volatile and non-volatile media, and removable and non-removable media.

[0087] System memory 402 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 4021 and / or cache memory 4022. Computing device 40 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 4023 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 4 (Not shown in the image, usually referred to as "hard drive"). Although not shown in... Figure 4 The diagram illustrates that disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., CD-ROMs, DVD-ROMs, or other optical media) can be provided. In these cases, each drive can be connected to bus 403 via one or more data media interfaces. System memory 402 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0088] A program / utility 4025 having a set (at least one) of program modules 4024 may be stored, for example, in system memory 402, and such program modules 4024 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment. Program modules 4024 typically perform the functions and / or methods described in the embodiments of the present invention.

[0089] The computing device 40 can also communicate with one or more external devices 404 (such as a keyboard, pointing device, display, etc.). This communication can be performed via the input / output (I / O) interface 405. Furthermore, the computing device 40 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 406. Figure 4 As shown, network adapter 406 communicates with other modules of computing device 40 (such as processing unit 401) via bus 403. It should be understood that, although... Figure 4 As not shown, it can be used in conjunction with computing device 40 with other hardware and / or software modules.

[0090] The processing unit 401 executes various functional applications and data processing by running programs stored in the system memory 402. For example, a multi-source heterogeneous data fusion module is used to preprocess multi-dimensional heterogeneous data; a dynamic risk perception module is used to model the engineering safety situation data pool using a lightweight anomaly detection algorithm, identify the risk level in engineering operations, and generate risk screening results with confidence; an intelligent decision engine module is used to combine the acquired multi-dimensional heterogeneous data and the risk level in engineering operations to reason and analyze the risk screening results and complete intelligent decision-making for safety engineering management; and a closed-loop linkage execution feedback module is used to distribute the risk screening results reasoned by the intelligent decision engine module to the remote control platform to complete safety engineering management.

[0091] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0092] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0093] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0094] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0095] If the functionality is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0096] Finally, it should be noted that the above embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0097] Furthermore, although the operations of the method of the present invention are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0098] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A safety engineering control system integrating real-time monitoring and intelligent decision-making, characterized in that: include, The multi-source heterogeneous data fusion module is used for preprocessing multi-dimensional heterogeneous data; The dynamic risk perception module is used to model the engineering safety situation data pool using a lightweight anomaly detection algorithm, identify the risk level in engineering operations, and generate risk screening results with confidence. The intelligent decision engine module is used to combine the acquired multi-dimensional heterogeneous data and the risk level in the engineering operation to reason and analyze the initial risk screening results and complete the intelligent decision-making for safety engineering management. The closed-loop linkage execution feedback module is used to distribute the initial risk screening results inferred by the intelligent decision engine module to the remote control platform to complete the safety engineering management.

2. The safety engineering control system integrating real-time monitoring and intelligent decision-making as described in claim 1, characterized in that: The acquisition of multi-dimensional heterogeneous data includes using IoT sensors, video surveillance, environmental parameters and construction progress management systems to acquire multi-dimensional heterogeneous data. The preprocessing includes unified access, cleaning, time-series alignment and feature extraction of the acquired multi-dimensional heterogeneous data to form an engineering safety situation data pool. The reasoning analysis of the initial risk screening results refers to the process of determining whether the multi-dimensional heterogeneous data reaches the risk level in the engineering operation after generating the initial risk screening results with confidence. If this is achieved, the initial risk screening is successful; Otherwise, the initial risk screening will fail.

3. The safety engineering control system integrating real-time monitoring and intelligent decision-making as described in claim 2, characterized in that: The multi-source heterogeneous data fusion module includes a protocol adaptive access submodule, a spatiotemporal alignment and cleaning submodule, and a semantic feature extraction submodule. The dynamic risk perception module includes a lightweight learning submodule, a multi-scale anomaly scoring submodule, and a confidence quantification output submodule. The intelligent decision engine module includes a scenario modeling submodule and a coupled risk reasoning submodule; The closed-loop linkage execution feedback module includes an instruction intelligent routing submodule, an execution status tracking submodule, and a feedback closed-loop calibration submodule.

4. The safety engineering control system integrating real-time monitoring and intelligent decision-making as described in claim 3, characterized in that: The protocol adaptive access submodule is used to preprocess multi-dimensional heterogeneous data. The heterogeneous devices used are connected in a plug-and-play unified manner; The spatiotemporal alignment and cleaning submodule is used to eliminate temporal misalignment, remove noise and outliers from the acquired multi-dimensional heterogeneous data, and ensure the temporal consistency of the multi-dimensional heterogeneous data. The semantic feature extraction submodule is used to extract statistical features, frequency domain features and time series trend features from multi-dimensional heterogeneous data based on knowledge base annotation, and to construct a structured engineering security situation data pool.

5. The safety engineering control system integrating real-time monitoring and intelligent decision-making as described in claim 4, characterized in that: The lightweight learning submodule is used to deploy a compressed LSTM-AE neural network model to complete the streaming data modeling of the engineering security situation data pool at the edge computing node; The multi-scale anomaly scoring submodule is used to output anomaly scores from both temporal and spatial dimensions, and supports the setting of differentiated risk thresholds. The confidence quantification output submodule is used to dynamically calculate the reliability probability of the initial risk screening results, avoiding false alarms that could disrupt intelligent decision-making.

6. The safety engineering management and control system integrating real-time monitoring and intelligent decision-making as described in claim 5, characterized in that: The scenario modeling submodule is used to integrate the current process plan and meteorological early warning information to construct a dynamic engineering scenario simulation. The coupled risk reasoning submodule is used to determine the risk level in the engineering operation based on the engineering operation deduced from the dynamic engineering scenario. At this time, the risk level in the engineering operation is determined when the multi-dimensional heterogeneous data reaches the risk level in the engineering operation. The intelligent routing submodule is used to select the execution channel to be distributed to the remote control platform based on the risk level of the engineering operation after assessment. The execution status tracking submodule is used to verify the validity of instruction execution and prevent orders from being ignored. The process of judging orders not being executed includes collecting the response status of the controlled device to verify the instruction. The response status of the controlled device includes power outage confirmation or personnel evacuation check-in. The feedback closed-loop calibration submodule is used to transmit the execution results to the safety engineering management system, establish a self-evolution mechanism, and complete safety engineering management. The self-evolution mechanism includes a decision-making mechanism, an execution mechanism, and a learning mechanism.

7. The safety engineering control system integrating real-time monitoring and intelligent decision-making as described in claim 6, characterized in that: The calculation formula for the lightweight anomaly detection algorithm is as follows: ； in, This indicates the abnormal score of t. This represents the actual input value of the i-th sensor or feature channel at time t. This represents the predicted value reconstructed from the compressed LSTM-AE model. This indicates the number of feature dimensions in the current input vector. This indicates the adjustment weighting coefficient. This represents the standard deviation of the input data within the sliding window; The formula for calculating the reliability probability of the dynamic risk screening results is as follows: ； in, This indicates the confidence probability of the initial risk screening results. This represents the model reconstruction error for the current sample. This indicates the most recent historical reconstruction error for a device or region. Indicates the historical false alarm rate. This indicates a scale anomaly score.

8. A safety engineering management and control method integrating real-time monitoring and intelligent decision-making, based on the safety engineering management and control system integrating real-time monitoring and intelligent decision-making as described in any one of claims 1 to 7, characterized in that: include, Preprocessing of multi-dimensional heterogeneous data; A lightweight anomaly detection algorithm is used to model the engineering safety situation data pool, identify the risk level in engineering operations, and generate preliminary risk screening results with confidence. By combining the acquired multi-dimensional heterogeneous data and the risk levels in engineering operations, the results of the initial risk screening are analyzed and reasoned to complete intelligent decision-making for safety engineering management; The initial risk screening results inferred by the intelligent decision engine module are distributed to the remote control platform to complete the safety engineering management.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the integrated real-time monitoring and intelligent decision-making safety engineering control system as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the integrated real-time monitoring and intelligent decision-making safety engineering control system as described in any one of claims 1 to 7.

Images