Alarm layout optimization method based on digital twinning

Abstract

The invention relates to the technical field of alarms, in particular to an alarm layout optimization method based on digital twinning. The method comprises the following steps: acquiring operation log data of an alarm, acquiring operation building data of the alarm, and constructing a digital twinborn model of the alarm based on the data; based on the digital twinborn model, alarm monitoring coverage efficiency analysis is carried out to obtain monitoring coverage efficiency data, and the abnormal state of the alarm is identified through the monitoring environment data; the method comprises the steps of analyzing alarm layout defects by combining abnormal state data and monitoring efficiency, identifying redundancy or insufficiency in layout, and finally performing adjustment through a layout optimization algorithm to ensure rationality of alarm distribution and maximization of a coverage range; according to the invention, the layout of the alarm is optimized, so that the alarm recognition of the alarm is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of alarms, and particularly to an alarm layout optimization method based on digital twin. Background Art

[0002] As a core device in the security monitoring system, the rationality of the alarm layout directly affects the effectiveness of security protection. However, traditional alarm layout design methods rely on manual experience and simple geometric planning, and it is difficult to cope with complex building structures, dynamic operating environments, and multi-device collaboration requirements, which easily lead to problems such as monitoring blind spots, too high picture overlap rate, and equipment performance attenuation. These problems not only reduce the reliability of the alarm system, but also cause resource waste and potential safety hazards. The rise of digital twin technology provides a new solution idea for alarm layout optimization. Digital twin realizes real-time mapping and interaction between the physical space and the virtual space by constructing a virtual model of the physical system. However, there are problems in the traditional alarm layout optimization method based on digital twin that the recognition of alarm layout defects is inaccurate and the calculation of alarm monitoring coverage efficiency is inaccurate. Summary of the Invention

[0003] Based on this, it is necessary to provide an alarm layout optimization method based on digital twin to solve at least one of the above technical problems.

[0004] To achieve the above object, an alarm layout optimization method based on digital twin includes the following steps:

[0005] Step S1: Obtain alarm operation log data; collect alarm operation buildings based on the alarm operation log data to obtain alarm operation building data; construct an alarm digital twin model based on the alarm operation log data and the alarm operation building data to obtain an alarm digital twin model;

[0006] Step S2: Analyze the alarm monitoring coverage efficiency of the alarm operation building data according to the alarm digital twin model to obtain alarm monitoring coverage efficiency data;

[0007] Step S3: Collect alarm operation environment data according to the alarm operation log data; identify alarm abnormal states for the alarm operation environment data according to the alarm digital twin model to obtain alarm monitoring abnormal state data;

[0008] Step S4: Analyze alarm layout defects based on the alarm abnormal state data and the alarm monitoring coverage efficiency data to obtain alarm layout defect data; optimize the alarm layout according to the alarm layout defect data to obtain alarm layout optimization data.

[0009] The present invention realizes the accurate modeling of the building characteristics of the alarm by comprehensively obtaining the operation log data of the alarm and combining with the building information collection, laying a solid foundation for the construction of the digital twin model. By using digital twin technology, it effectively integrates the building geometric structure, the installation position of the alarm, and the function data to form an accurate virtual mapping model, greatly improving the comprehensive perception ability of the operation characteristics of the alarm. Based on the digital twin model for monitoring coverage efficiency analysis, it can intuitively identify the coverage of the monitoring area and the existing blind spots, so as to provide accurate coverage efficiency data and provide a scientific basis for optimizing the monitoring layout. Through further mining of the operation log data, it captures the changes in the environment where the alarm is located, and combines with the digital twin model to realize the in-depth coupling analysis of the alarm operation environment and the equipment state. This method can efficiently identify the abnormal state of the equipment and timely discover the problems of monitoring performance attenuation caused by environmental changes or equipment aging. By comprehensively analyzing the coverage efficiency data and abnormal state data of the alarm, it can not only comprehensively reveal the layout defects, optimize the allocation of monitoring resources for different scenarios, so as to avoid the situation of resource redundancy or insufficiency. The layout optimization link can intelligently generate an optimization plan according to the data analysis results, effectively improving the comprehensiveness and reliability of the monitoring coverage, and significantly reducing the existence of blind spots and the waste of overlapping monitoring pictures. Therefore, the present invention is an optimization process for the traditional digital twin-based alarm layout optimization method, solving the problems of inaccurate identification of alarm layout defects and inaccurate calculation of alarm monitoring coverage efficiency in the traditional digital twin-based alarm layout optimization method. It improves the accuracy of alarm layout defect identification and the accuracy of alarm monitoring coverage efficiency calculation.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain the operation log data of the alarm;

[0012] Step S12: Conduct building collection for the alarm operation based on the operation log data of the alarm to obtain the alarm operation building data;

[0013] Step S13: Obtain the alarm usage function according to the operation log data of the alarm to obtain the alarm usage function data;

[0014] Step S14: Build an alarm digital twin model based on the alarm usage function data, the operation log data of the alarm, and the alarm operation building data to obtain the alarm digital twin model.

[0015] By obtaining the operation log data of the alarm, the running status, historical records, and fault conditions of the device are comprehensively grasped, which provides a basic guarantee for subsequent data analysis. At the same time, by combining the operation log data with the collection of building information, the physical installation environment and operation environment of the alarm are comprehensively characterized, laying a solid foundation for the accuracy and scenario adaptability of the model. By further extracting the usage function data of the alarm, the functional characteristics and operation modes of the device are accurately understood, providing key parameters for introducing functional constraints and optimization rules in the modeling process. By integrating the usage function data, building information, and log data to construct a digital twin model, the accurate mapping of the alarm system in the virtual space is realized. This digital twin model not only has high-fidelity building geometry and functional characteristics but also can dynamically reflect the running status and usage requirements of the device, providing a scientific basis for subsequent monitoring coverage optimization, abnormal status analysis, and layout adjustment. The overall method effectively improves the integrity and relevance of the data, provides a new technical path for the intelligent optimization and dynamic management of the alarm, and significantly enhances the operation efficiency and reliability of the system.

[0016] Preferably, step S14 includes the following steps:

[0017] Step S141: Collect the building geometric structure of the alarm according to the building data of the alarm operation to obtain the building geometric structure data of the alarm;

[0018] Step S142: Measure the installation coordinate position of the alarm according to the operation log data of the alarm to obtain the installation coordinate position data of the alarm;

[0019] Step S143: Construct the space rectangular coordinate system of the alarm according to the installation coordinate position data of the alarm and the building geometric structure data of the alarm to obtain the space rectangular coordinate system data of the alarm;

[0020] Step S144: Measure the monitoring field of view range of the alarm according to the usage function data of the alarm to obtain the monitoring field of view range data of the alarm;

[0021] Step S145: Analyze the image coverage range of the alarm according to the space rectangular coordinate system data of the alarm and the monitoring field of view range data of the alarm to obtain the image coverage range data of the alarm;

[0022] Step S146: Construct the digital twin model of the alarm based on the image coverage range data of the alarm and the space rectangular coordinate system data of the alarm to obtain the digital twin model of the alarm.

[0023] Through the implementation of the above steps, the present invention can significantly improve the scientificity and accuracy of the alarm layout optimization. Based on the collection of the building's geometric structure, the spatial layout and complexity of the building where the alarm is located are completely restored, providing basic spatial geometric support for subsequent analysis and model construction. The measurement of the installation coordinate position further enhances the spatial positioning accuracy of the actual installation position of the alarm. After combining with the geometric structure data, a high-resolution spatial rectangular coordinate system is constructed, enabling the position and monitoring range of the alarm to be accurately characterized in the unified coordinate system. By measuring the monitoring visual field range of the alarm, the monitoring coverage area and boundary characteristics of the device can be comprehensively grasped, laying a data foundation for analyzing the integrity of the monitoring range and the distribution of blind areas. Combining the rectangular coordinate system and the monitoring visual field data for image coverage range analysis helps to deeply understand the interaction relationship between the monitoring areas of each alarm, especially the overlapping and blank area distribution of the coverage range. These data not only provide higher-precision input for the construction of the digital twin model but also improve the model's ability to restore the actual environment and predict the monitoring effect. Through the high-fidelity digital twin model, the virtual mapping and real-time monitoring of the alarm operation scenario can be realized, providing a basis for dynamic adjustment and efficient management of the layout optimization. The overall method enhances the spatial accuracy and coverage efficiency of the alarm layout optimization, significantly improving the adaptability and operation stability of the system in complex building environments.

[0024] Preferably, step S2 includes the following steps:

[0025] Step S21: Perform alarm monitoring operation identification simulation according to the alarm digital twin model to obtain alarm monitoring operation identification simulation data;

[0026] Step S22: Collect alarm monitoring images according to the alarm monitoring operation identification simulation data to obtain alarm monitoring image data;

[0027] Step S23: Detect the alarm coverage blind area for the alarm monitoring image data according to the building data where the alarm operates to obtain alarm monitoring coverage blind area data;

[0028] Step S24: Calculate the overlapping rate of the alarm monitoring video images according to the alarm monitoring image data to obtain alarm video monitoring image overlapping rate data;

[0029] Step S25: Analyze the alarm monitoring coverage efficiency based on the alarm monitoring coverage blind area data and the alarm video monitoring image overlapping rate data to obtain alarm monitoring coverage efficiency data.

[0030] Through the implementation of these steps, the present invention can effectively improve the accuracy and monitoring efficiency of the alarm layout optimization. By running and identifying the simulation of the alarm digital twin model, the operation behavior and monitoring coverage characteristics of the alarm can be comprehensively predicted in a virtual environment, thereby providing high-fidelity simulation data for the actual operation scenario. These data are further transformed into specific monitoring screen information through image acquisition, revealing the monitoring range and image boundary characteristics during the actual operation of the alarm. By combining the building data to detect the coverage blind spots of the monitoring image, the monitoring dead spots existing in the alarm layout can be accurately located, thereby revealing potential problems in the layout. Further, by calculating the picture overlap rate of the monitoring image, the overlapping degree of the monitoring areas between different alarms can be identified, which can not only discover the problem of resource waste but also provide key data support for optimizing the layout. Through the comprehensive analysis of the blind spot data and the overlap rate data, the monitoring coverage efficiency index of the alarm is obtained, which not only provides a quantitative basis for evaluating the performance of the current layout but also provides a clear improvement direction for subsequent layout optimization. The overall method effectively solves the problem of insufficient recognition of blind spots, overlapping areas, and monitoring efficiency in the traditional layout method through high-precision modeling, comprehensive data collection, and in-depth analysis, improving the overall performance of the alarm system and the resource utilization efficiency.

[0031] Preferably, step S23 includes the following steps:

[0032] Step S231: Calculate the building connectivity based on the building data of the alarm operation, so as to obtain the building connectivity data;

[0033] Step S232: Calculate the building wall surface based on the building data of the alarm operation, so as to obtain the building wall surface data;

[0034] Step S233: Collect the building corner walls according to the building wall surface data and the building data of the alarm operation to obtain the building corner wall data;

[0035] Step S234: Analyze the complexity of the building wall surface based on the building corner wall data and the building connectivity data to obtain the building wall surface complexity data;

[0036] Step S235: Detect the alarm coverage blind spots of the alarm monitoring image data according to the building wall surface complexity data to obtain the alarm monitoring coverage blind spot data.

[0037] Through a comprehensive analysis of building characteristics, the present invention ensures a high degree of fit between the monitoring layout and the building structure. The building connectivity calculation can effectively characterize the coherence degree of the internal space of the building, laying a foundation for identifying the emerging monitoring blind spots. Further, the physical boundary characteristics of the building are obtained through wall calculation, providing the necessary data support for identifying the physical factors that block the monitoring line of sight. The collection of corner walls focuses on the key areas in the building that are prone to form line-of-sight dead ends, providing an accurate spatial reference for subsequent complexity analysis. The wall complexity analysis based on corner wall and connectivity data comprehensively reveals the geometric complexity of the building space, providing a scientific basis for determining the potential problem areas in the monitoring coverage. Through the analysis of these complexity data, the alarm system can more accurately detect the coverage blind spots in the monitoring images, effectively identify the potential defects in the layout, especially in the environment with complex building structures or a large number of corner areas. This process enables the alarm system to achieve more efficient monitoring coverage in complex scenarios through multi-dimensional analysis of building characteristics, while avoiding monitoring blind spots or resource waste problems caused by ignoring building characteristics, significantly improving the overall performance and intelligent level of the alarm system.

[0038] Preferably, step S24 includes the following steps:

[0039] Step S241: Identify the boundary area of the alarm monitoring image based on the alarm monitoring image data to obtain the alarm monitoring area boundary data;

[0040] Step S242: Analyze the geometric shape of the alarm monitoring area based on the alarm monitoring area boundary data to obtain the alarm monitoring area geometric shape data;

[0041] Step S243: Calculate the intersection of the alarm areas based on the alarm monitoring area geometric shape data to obtain the alarm monitoring area intersection data;

[0042] Step S244: Calculate the area of the intersection area of the alarm based on the alarm monitoring area intersection data to obtain the alarm intersection area area data;

[0043] Step S245: Calculate the total area of the alarm monitoring area based on the alarm monitoring area boundary data and the alarm monitoring area geometric shape data to obtain the alarm monitoring area total area data;

[0044] Step S246: Calculate the overlapping rate of the alarm monitoring video screen for the total area data of the alarm monitoring area using the alarm intersection area area data to obtain the alarm video monitoring screen overlapping rate data.

[0045] Through the precise analysis and calculation of the alarm monitoring image data, the present invention effectively improves the management and analysis capabilities of the monitoring area, identifies the boundary area of the alarm monitoring image, helps to accurately determine the scope of the monitoring area, and provides basic data for subsequent geometric shape analysis and intersection calculation. This step ensures the accurate definition of the boundary of the entire monitoring area and guarantees the effectiveness of subsequent calculations. Then, the analysis of the geometric shape of the alarm monitoring area helps to better understand the physical characteristics of the monitoring area and provides a more refined analysis framework for complex monitoring environments. Through geometric shape data, different types of monitoring areas can be accurately identified and distinguished, improving the refinement of data analysis. By further calculating the intersection data of the alarm monitoring areas, the overlapping situation between multiple monitoring areas can be judged, thereby effectively identifying the blank or overlapping areas covered by the monitoring images. The calculated intersection area provides a quantitative basis for the overlapping situation of the monitoring images, enabling further optimization of the monitoring area layout and avoiding unnecessary resource waste. The calculation of the intersection area ensures the integrity of the monitoring images and the reasonable coverage of the areas, improving the monitoring efficiency. The calculation of the total area of the monitoring area helps to further understand the distribution of monitoring resources. Combining the total area with the intersection area can more accurately evaluate the coverage range of the monitoring video images. This is crucial for optimizing the performance of the overall monitoring system and improving the efficiency of regional monitoring. Through the calculated overlapping rate data, the overlapping rate of the existing monitoring areas is optimized, the layout of the monitoring images is adjusted, thereby enhancing the monitoring effect, reducing redundant and overlapping parts, and improving the utilization rate of the monitoring areas. Generally speaking, the implementation of the above steps can provide refined data support for the monitoring system, thereby reasonably optimizing the layout and configuration of the monitoring areas in different scenarios and improving the efficiency and effect of the monitoring work.

[0046] Preferably, step S3 includes the following steps:

[0047] Step S31: Collect the alarm operating environment according to the alarm operation log data to obtain alarm operating environment data;

[0048] Step S32: Estimate the alarm performance attenuation based on the alarm digital twin model for the alarm operating environment data to obtain alarm performance attenuation data;

[0049] Step S33: Analyze the attenuation of the alarm monitoring and recognition ability according to the alarm performance attenuation data to obtain alarm monitoring and recognition ability attenuation data;

[0050] Step S34: Identify the abnormal state of the alarm based on the alarm monitoring and recognition ability attenuation data and the alarm performance attenuation data to obtain alarm monitoring abnormal state data.

[0051] Through the collection and analysis of the operation log data of the alarm, the operation environment of the alarm is comprehensively understood, thereby providing accurate data support for performance evaluation and subsequent maintenance. These environmental data can help identify the external conditions faced by the alarm during actual use, providing a basis for the prediction and management of performance degradation. Further, through the digital twin model, the performance degradation of the alarm operation environment is estimated, and the degradation trend of the alarm under different environmental conditions is simulated, providing an early warning mechanism for the maintenance and upgrade of the actual monitoring system. This process not only improves the understanding of the device performance but also helps to identify potential failures or efficiency drops in advance. Through the analysis of the performance degradation data, the degradation of the monitoring and recognition ability of the alarm during long-term use can be quantitatively evaluated, thereby providing guidance for timely repair and adjustment to ensure the efficient operation of the monitoring system. By combining the monitoring and recognition ability degradation data with the alarm performance degradation data, the abnormal state of the alarm can be accurately identified, which helps to detect the functional decline or failure of the alarm early and prevent system failure. This series of steps can significantly improve the maintenance efficiency of the alarm system, extend the service life of the device, and ensure its continuous and reliable monitoring ability.

[0052] Preferably, step S32 includes the following steps:

[0053] Step S321: Measure the temperature of the alarm operation environment according to the alarm operation environment data to obtain the alarm operation environment temperature data;

[0054] Step S322: Collect the light of the alarm operation environment according to the alarm operation environment data to obtain the alarm operation environment light data;

[0055] Step S323: Estimate the aging trend of the alarm device based on the alarm operation environment light data and the alarm operation environment temperature data to obtain the alarm aging trend data;

[0056] Step S324: Estimate the performance degradation of the alarm according to the alarm aging trend data to obtain the alarm performance degradation data.

[0057] By measuring the operating environment temperature and light data of the alarm, the working state of the alarm under different environmental conditions is accurately monitored to ensure that the impact of environmental factors on the device performance is effectively evaluated. Temperature and light are key factors affecting the aging of electronic devices. Precise collection of these data helps to comprehensively understand the working environment of the alarm. Combining these environmental data to estimate the device aging trend, predicting the performance decline of the alarm in advance, and then providing an early warning mechanism to help maintenance personnel take corresponding measures before the device performance deteriorates. Through the aging trend data, the performance attenuation of the alarm can be further calculated, providing strong support for repairing, replacing or optimizing the device. Overall, this process effectively extends the service life of the device, reduces the failure rate, and ensures the continuous reliability and efficiency of the monitoring system by quantifying the environmental impact and aging trend.

[0058] Preferably, step S322 includes the following steps:

[0059] Perform thermal overload detection of the operating environment based on the operating environment temperature data of the alarm to obtain thermal overload data of the operating environment;

[0060] Estimate the failure of the alarm image sensor based on the thermal overload data of the operating environment to obtain failure data of the alarm image sensor;

[0061] Perform ultraviolet intensity detection of the operating environment light based on the operating environment light data of the alarm to obtain ultraviolet intensity data of the operating environment light;

[0062] Estimate the deterioration trend of the coating layer on the surface of the monitoring lens based on the ultraviolet intensity data of the operating environment light to obtain deterioration trend data of the coating layer on the surface of the lens;

[0063] Perform attenuation detection of the anti-reflection ability of the alarm lens on the deterioration trend data of the coating layer on the surface of the lens to obtain attenuation data of the anti-reflection ability of the alarm lens;

[0064] Estimate the aging trend of the alarm device based on the failure data of the alarm image sensor and the attenuation data of the anti-reflection ability of the alarm lens to obtain aging trend data of the alarm.

[0065] The present invention conducts thermal overload detection on the temperature data of the operating environment of the alarm, effectively identifying the overload situation of the device in a high-temperature environment, and providing a basis for the early warning of potential device failures. The thermal overload data helps to promptly detect the damage caused by excessive temperature to the device, especially the impact on the image sensor, and then estimate the failure of the image sensor. At the same time, detecting the ultraviolet intensity in the ambient light helps to evaluate the impact of the lighting conditions on the device, especially the long-term effect on the coating layer on the surface of the monitoring lens, thereby predicting the deterioration trend of the coating layer. This process can identify potential lens damage in advance, preventing the decline in image quality or device failure caused by the deterioration of the coating layer. Combining the deterioration data of the coating layer on the lens surface for anti-reflection ability attenuation detection, accurately evaluating the image transmission performance of the lens, ensuring that the lens maintains good optical effects. Based on the image sensor failure data and the lens anti-reflection ability attenuation data, it is possible to comprehensively predict the aging trend of the alarm device, providing precise guidance for subsequent device maintenance and replacement. The implementation of these steps can effectively extend the service life of the alarm, improve the reliability of the device, avoid sudden failures caused by environmental factors, and thus ensure the continuous and efficient operation of the monitoring system.

[0066] Preferably, step S4 includes the following steps:

[0067] Step S41: Calculate the alarm response duration based on the alarm abnormal state data to obtain the alarm response duration data;

[0068] Step S42: Analyze the alarm layout defects based on the alarm abnormal state data, the alarm response duration data, and the alarm monitoring coverage efficiency data to obtain the alarm layout defect data;

[0069] Step S43: Detect the alarm resource redundancy based on the alarm layout defect data to obtain the alarm resource redundancy data;

[0070] Step S44: Optimize the alarm layout based on the alarm resource redundancy data and the alarm layout defect data to obtain the alarm layout optimization data.

[0071] The present invention calculates the response duration of the abnormal state data of the alarm, accurately evaluates the reaction speed of the alarm when problems occur, and provides real-time data support for system optimization and fault troubleshooting. The response duration data can help analyze the efficiency of the alarm in handling problems, thereby timely discovering and solving potential performance bottlenecks to ensure the rapid response of the system. Combining the abnormal state data with the monitoring coverage efficiency data for layout defect analysis can reveal problems of insufficient coverage or unreasonable distribution existing in the existing alarm layout. This analysis provides a specific direction for improving the system layout and avoids the influence of monitoring blind spots or overlapping areas. Further, by performing resource redundancy detection on the layout defect data, over-configuration or resource waste is identified, and the resource usage efficiency is optimized. This process can ensure the reasonable deployment of the alarm, enabling each device to maximize its role and avoiding unnecessary redundant investment. Optimizing the alarm layout based on the resource redundancy data and the layout defect data helps to precisely adjust the device distribution, improve the uniformity and efficiency of monitoring coverage, and thus enhance the working efficiency of the entire alarm system. Through this series of steps, the comprehensive performance of the alarm system can be effectively improved, ensuring its stability, response speed, and rationality of resource usage.

[0072] The present invention lies in that by comprehensively acquiring the operation log data of the alarm and combining it with the building information collection, an accurate modeling of the building characteristics of the alarm operation is realized, laying a solid foundation for the construction of the digital twin model. Using digital twin technology, the building geometric structure, the installation position of the alarm, and the function data are effectively integrated to form an accurate virtual mapping model, greatly improving the comprehensive perception ability of the operation characteristics of the alarm. Based on the digital twin model, the monitoring coverage efficiency analysis can intuitively identify the coverage of the monitoring area and the existing blind areas, so as to provide accurate coverage efficiency data and provide a scientific basis for optimizing the monitoring layout. Through further mining of the operation log data, the changes in the environment where the alarm is located are captured, and the in-depth coupling analysis of the alarm operation environment and the device state is realized in combination with the digital twin model. This method can efficiently identify the abnormal state of the device and timely discover the problem of monitoring performance attenuation caused by environmental changes or device aging. By comprehensively analyzing the coverage efficiency data and abnormal state data of the alarm, not only can the layout defects be comprehensively revealed, but the allocation of monitoring resources can be optimized for different scenarios, thus avoiding the situation of resource redundancy or insufficiency. The layout optimization link can intelligently generate an optimization plan according to the data analysis results, effectively improving the comprehensiveness and reliability of the monitoring coverage, and significantly reducing the existence of blind areas and the waste of overlapping monitoring screens. Therefore, the present invention is an optimization process for the traditional digital twin-based alarm layout optimization method, solving the problems of inaccurate identification of alarm layout defects and inaccurate calculation of alarm monitoring coverage efficiency in the traditional digital twin-based alarm layout optimization method. It improves the accuracy of alarm layout defect identification and the accuracy of alarm monitoring coverage efficiency calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a schematic diagram of the step flow of a digital twin-based alarm layout optimization method;

[0074] Figure 2 For Figure 1 it is a schematic diagram of the detailed implementation step flow of step S2 in

[0075] Figure 3 For Figure 1 it is a schematic diagram of the detailed implementation step flow of step S3 in

[0076] The realization, functional characteristics, and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0077] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0078] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0079] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0080] To achieve the above object, please refer to Figures 1 to 3 , an alarm layout optimization method based on digital twin, comprising the following steps:

[0081] Step S1: Obtain the alarm operation log data; perform alarm operation building collection based on the alarm operation log data to obtain the alarm operation building data; construct an alarm digital twin model based on the alarm operation log data and the alarm operation building data to obtain the alarm digital twin model;

[0082] Step S2: Analyze the alarm monitoring coverage efficiency of the alarm operation building data according to the alarm digital twin model to obtain the alarm monitoring coverage efficiency data;

[0083] Step S3: Collect the alarm operation environment based on the alarm operation log data to obtain the alarm operation environment data; identify the abnormal state of the alarm according to the alarm digital twin model for the alarm operation environment data to obtain the alarm monitoring abnormal state data;

[0084] Step S4: Analyze the layout defects of the alarm devices based on the abnormal status data and the monitoring coverage efficiency data of the alarm devices to obtain the alarm device layout defect data; optimize the alarm device layout according to the alarm device layout defect data to obtain the alarm device layout optimization data.

[0085] In the embodiment of the present invention, with reference to Figure 1 As shown, in this example, the method for optimizing the layout of alarm devices based on digital twin includes the following steps:

[0086] Step S1: Obtain the operation log data of the alarm devices; collect the buildings where the alarm devices are operating based on the operation log data of the alarm devices to obtain the alarm device operation building data; construct the digital twin model of the alarm devices based on the operation log data of the alarm devices and the alarm device operation building data to obtain the digital twin model of the alarm devices.

[0087] In the embodiment of the present invention, the operation log data of the alarm devices is obtained. These logs contain the operation records and status information of the devices, such as the startup of the alarm devices, operation time, detected faults or abnormalities, changes in the operating environment, etc. By extracting this data from the system, the actual usage of the alarm devices is obtained. Then, based on the extracted operation log data of the alarm devices, the collection of the buildings where the alarm devices are operating is carried out. This process will combine the physical structure information of the buildings, such as the size, number of floors, and locations of each area of the building, to determine the installation locations and operating environments of the alarm devices within the buildings. In this stage, through the comparison of sensor data collection and the building design drawings, the installation points of each alarm device and their surrounding environmental conditions are detailedly recorded. According to the collected data, the digital twin model of the alarm devices is further constructed. This model not only considers the performance characteristics of the devices themselves but also combines the geometric structure and environmental factors of the buildings to accurately simulate the operating states of the alarm devices in different scenarios.

[0088] Step S2: Analyze the monitoring coverage efficiency of the alarm device operation building data according to the digital twin model of the alarm devices to obtain the monitoring coverage efficiency data of the alarm devices.

[0089] In the embodiment of the present invention, according to the constructed digital twin model, the monitoring coverage efficiency analysis of the operation data of the buildings where the alarm devices are located is carried out. This analysis evaluates whether the existing layout meets the monitoring requirements by comparing the installation locations of the alarm devices with the coverage ranges of the building areas. Specifically, it analyzes and calculates whether there are blind spots, overlapping areas, or monitoring dead corners in the areas that each alarm device can monitor. Using the spatial data of the digital model, by comparing the field of view range of the alarm devices in the model with the actual monitoring requirements, the monitoring coverage efficiency of each alarm device is calculated and the monitoring coverage efficiency data is formed.

[0090] Step S3: Collect the operating environment data of the alarm based on the alarm operation log data, and obtain the alarm operating environment data; identify the abnormal state of the alarm based on the alarm digital twin model for the alarm operating environment data, and obtain the alarm monitoring abnormal state data.

[0091] In the embodiment of the present invention, the operating environment data of the alarm is collected through the alarm operation log data. The environmental data includes environmental conditions closely related to the operating state of the device, such as temperature, humidity, light intensity, etc. The real-time data of the environment where the alarm is located is obtained through real-time monitoring by sensors or Internet of Things devices. Next, the digital twin model is combined to identify the abnormal state of the alarm operating environment data. By comparing the historical records of the environmental data with the current data, potential abnormal environmental situations are identified, such as too high temperature, abnormal humidity, etc., which cause alarm failures or performance degradation. Based on the model-based abnormal state identification function, the system can automatically detect and record the abnormal state of the alarm, and form the monitoring abnormal state data.

[0092] Step S4: Analyze the alarm layout defects based on the alarm abnormal state data and the alarm monitoring coverage efficiency data, and obtain the alarm layout defect data; optimize the alarm layout according to the alarm layout defect data, and obtain the alarm layout optimization data.

[0093] In the embodiment of the present invention, the alarm layout defects are analyzed based on the alarm abnormal state data and the monitoring coverage efficiency data. In this process, the abnormal state of the device is combined to analyze whether there is a situation where the monitoring coverage is incomplete or the device cannot work properly due to improper layout. By comparing the abnormal state and the monitoring efficiency data, it is evaluated which areas of the alarm fail to reach the best working state due to layout defects. According to these analysis results, the alarm layout defect data is generated to identify unreasonable layouts or areas that affect the device performance. Then, according to the analysis results, the alarm layout is optimized. The optimization process includes re-planning the installation location of the alarm, adjusting the field of view of the device, adding necessary redundant alarms, etc. By these measures, the overall coverage efficiency of the alarm system is improved, the blind area of the device is reduced, and each area can be effectively monitored. The layout design of the alarm is adjusted through the optimized data.

[0094] Preferably, step S1 includes the following steps:

[0095] Step S11: Obtain the alarm operation log data;

[0096] Step S12: Collect the alarm operating building based on the alarm operation log data, and obtain the alarm operating building data;

[0097] Step S13: Obtain the alarm usage function based on the alarm operation log data to get the alarm usage function data;

[0098] Step S14: Construct a digital twin model of the alarm based on the alarm usage function data, the alarm operation log data, and the building data where the alarm operates to obtain the digital twin model of the alarm.

[0099] In an embodiment of the present invention, the operation log data of the alarm device is obtained. The operation log data is automatically collected by a data recording device installed in the alarm system, and the log content includes the startup time, operation time, fault records, temperature and humidity changes, environmental monitoring, etc. of the device. These data are collected from the alarm device to the central control system directly through a serial communication interface, network protocol, or Internet of Things device. A dedicated monitoring software is used in this process to regularly download and store the data of the alarm device, providing a complete source of raw data for subsequent data processing and analysis. Based on the above-collected operation log data of the alarm device, the building where the alarm device operates is collected. The alarm device is installed in a building, and different building structures, environments, and layouts will affect the actual working effect of the alarm device. Therefore, by analyzing the building data in the operation log, the basic information of the building is extracted, such as the number of floors of the building, the area of each floor, the structural type, and the use of the building, etc. The data is integrated and interacted through tools such as architectural design drawings, Geographic Information System (GIS) data, and Building Information Modeling (BIM), and information such as the specific location of the alarm device, the surrounding environment, and the monitored area is extracted from them, providing accurate building data support for constructing a digital twin model. The usage function of the alarm device is obtained according to the operation log data of the alarm device. The usage function data of the alarm device refers to the monitoring tasks undertaken by the alarm device in the actual environment, including security monitoring of a specific area, anomaly detection, alarm response, etc. This information is extracted by analyzing the alarm trigger events and response records recorded in the operation log. For example, indicators such as the number of times the alarm device is triggered, the response situation of the alarm device, and the alarm time are recorded in the log. Combining the actual application situation of the alarm device, such as for fire detection, intrusion detection, etc., the system can extract various functional characteristics and application scenarios of the alarm device. Based on the usage function data of the alarm device, the operation log data of the alarm device, and the building data where the alarm device operates, a digital twin model of the alarm device is constructed. The building data collected in step S12 is combined with the alarm device function data extracted in step S13 to ensure that the digital twin model can reflect the layout, function, and performance of the alarm device in its actual working environment. The construction of the digital twin model requires the use of technologies such as 3D modeling tools, Computer-Aided Design (CAD) software, and BIM software. According to the spatial layout of the building and the installation position of the alarm device, a corresponding virtual model is established. In this model, each working state, sensor feedback, alarm record, etc. of the alarm device will be closely associated with the actual situation of the building. Through data interaction and synchronization, the model can update the working situation of the alarm device in real time, reflecting the performance and response ability of the alarm device under different environmental conditions.

[0100] Preferably, step S14 includes the following steps:

[0101] Step S141: Collect the building geometric structure of the alarm device according to the building data where the alarm device operates, and obtain the building geometric structure data of the alarm device;

[0102] Step S142: Measure the installation coordinate position of the alarm according to the alarm operation log data to obtain the alarm installation coordinate position data;

[0103] Step S143: Construct the spatial rectangular coordinate system of the alarm according to the alarm installation coordinate position data and the alarm building geometric structure data to obtain the alarm spatial rectangular coordinate system data;

[0104] Step S144: Measure the monitoring visual field range of the alarm according to the alarm usage function data to obtain the alarm monitoring visual field range data;

[0105] Step S145: Analyze the image coverage range of the alarm according to the alarm spatial rectangular coordinate system data and the alarm monitoring visual field range data to obtain the alarm image coverage range data;

[0106] Step S146: Construct the digital twin model of the alarm based on the alarm image coverage range data and the alarm spatial rectangular coordinate system data to obtain the digital twin model of the alarm.

[0107] In the embodiments of the present invention, the geometric structure of the alarm in the building is collected by analyzing the building data during the operation of the alarm. The key point of this step is to obtain the spatial structure information of the building, including geometric parameters such as walls, doors and windows, floor height, and floor distribution. The building geometric structure data is usually extracted through Building Information Modeling (BIM), or manually or automatically collected according to two-dimensional design drawings such as the floor plan, elevation view, and section view of the building. In specific operations, CAD tools or BIM software are used to extract relevant three-dimensional spatial data from the building design drawings to ensure that the spatial distribution of all walls, doors and windows, ceilings, and floors is accurately recorded. The installation coordinate position of the alarm is measured based on the alarm operation log data. This step mainly determines the installation position of the alarm by analyzing the installation records and operation data of the alarm. During the installation process, the specific coordinate position of the alarm in the building is obtained through a GPS positioning system or an internal building positioning system. For indoor environments without GPS signals, indoor positioning technologies (such as Bluetooth, Wi-Fi positioning systems, etc.) can be used to determine the precise position of the alarm. This data also includes information such as the installed floor, orientation, and relative position to facilities, generating the coordinate position data of the alarm. Based on the alarm installation coordinate position data and the alarm building geometric structure data, the spatial rectangular coordinate system of the alarm is constructed. In this step, the geometric data of the building is combined with the installation position data of the alarm, and the space of the building is converted into a rectangular coordinate system through spatial calculation tools (such as 3D modeling software, GIS tools, etc.). Each installation point of the alarm is assigned a corresponding three-dimensional coordinate value, forming an accurate spatial layout diagram. The monitoring field of view range of the alarm is measured according to the alarm usage function data. This step mainly calculates the monitoring field of view range of the alarm by analyzing the functional parameters of the alarm, such as the field of view angle of the camera, lens focal length, installation height, etc. To accurately measure the monitoring field of view range, optical measurement tools or 3D simulation software can be used for simulation to calculate the effective coverage area of the camera. In actual operations, perspective models or ray tracing algorithms are used, taking into account different floors, obstacles, and interference factors, to comprehensively evaluate the monitoring field of view range. Based on the alarm spatial rectangular coordinate system data and the alarm monitoring field of view range data, the alarm image coverage range analysis is carried out. In this step, the monitoring field of view range data is combined with the spatial coordinate data, and by calculating the field of view coverage area of each alarm, the overlap and blind areas between different alarms are analyzed. Computer-Aided Design (CAD) software, BIM systems, or spatial analysis tools are used to draw the alarm image coverage area diagram. Through this graphical analysis, monitoring blind areas and overlapping areas can be identified, thereby optimizing the layout of the alarms. Based on the alarm image coverage range data and the alarm spatial rectangular coordinate system data, the digital twin model of the alarm is constructed.In this process, all geometric data, coordinate data, field of view data, and monitoring coverage data will be integrated into a unified digital twin platform to construct a real-time updated virtual model of the alarm. Using BIM modeling tools or 3D modeling software, the physical structure of the building and the monitoring information of the alarm are virtualized for further layout optimization and performance simulation on the digital platform. By integrating information such as sensor data, operating status, and historical records, the constructed digital twin model can reflect the operating status and monitoring effect of the alarm in the actual environment in real time.

[0108] Preferably, step S2 includes the following steps:

[0109] Step S21: Conduct alarm monitoring operation identification simulation based on the digital twin model of the alarm to obtain alarm monitoring operation identification simulation data;

[0110] Step S22: Collect alarm monitoring images based on the alarm monitoring operation identification simulation data to obtain alarm monitoring image data;

[0111] Step S23: Detect the blind area of alarm coverage for the alarm monitoring image data based on the building data of the alarm operation to obtain alarm monitoring coverage blind area data;

[0112] Step S24: Calculate the overlapping rate of the alarm monitoring video images based on the alarm monitoring image data to obtain alarm video monitoring image overlapping rate data;

[0113] Step S25: Analyze the alarm monitoring coverage efficiency based on the alarm monitoring coverage blind area data and the alarm video monitoring image overlapping rate data to obtain alarm monitoring coverage efficiency data.

[0114] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0115] Step S21: Conduct alarm monitoring operation identification simulation based on the digital twin model of the alarm to obtain alarm monitoring operation identification simulation data;

[0116] In the embodiments of the present invention, the digital twin model of the alarm is used to simulate the monitoring operation identification of the alarm to obtain the simulation data of the monitoring operation identification of the alarm. In this step, the existing building environment, the installation location of the alarm and its function data in the digital twin model are used to simulate to identify the monitoring status of the alarm. Environmental modeling is carried out on the digital twin platform, and the field of view range, installation angle and monitoring area of each alarm are loaded. Use simulation software or integrated simulation tools to simulate the operation of the alarm under different time and environmental conditions. This simulation involves functions such as sensor response, video capture and analysis. The actual monitoring effect of the alarm, including its coverage range, blind area and other information, is obtained through the virtualized environmental data, and the simulation data of the monitoring operation identification of the alarm is obtained.

[0117] Step S22: Collect the monitoring images of the alarm according to the simulation data of the monitoring operation identification of the alarm to obtain the monitoring image data of the alarm;

[0118] In the embodiments of the present invention, the monitoring images of the alarm are collected based on the simulation data of the monitoring operation identification of the alarm to obtain the monitoring image data of the alarm. According to the operation situation of the alarm simulated in the previous step, the digital twin platform is used to simulate the image data captured by each alarm during operation from the perspective of a virtual sensor. The specific operation is to generate the monitoring video image of the alarm in the simulation environment by combining parameters such as the viewing angle range, resolution and sampling rate of the sensor. This data can be processed by image processing software or video analysis tools to extract the original image of the alarm monitoring and store it in a video format. This image data reflects the monitoring quality of the alarm, and the data will be stored in the digital twin system in a time series.

[0119] Step S23: Detect the coverage blind area of the alarm for the monitoring image data of the alarm according to the building data of the alarm operation to obtain the coverage blind area data of the alarm monitoring;

[0120] In the embodiments of the present invention, the coverage blind area of the alarm is detected for the monitoring image data of the alarm according to the building data of the alarm operation. In this step, the monitoring image data of the alarm is combined with the geometric structure data of the building where the alarm is located to analyze the blind area in the image. In the operation, 3D modeling software or spatial analysis tools are used to accurately model the structures such as the walls, doors, windows and floors of the building to identify potential visual blind areas. Then, the monitoring image is combined with the spatial layout of the building, and computer vision techniques (such as edge detection algorithms, image segmentation, etc.) are used to analyze the image data to detect the areas that cannot be covered or are blocked. Through this analysis result, the coverage blind area data of the alarm is generated, and the areas and areas that each alarm fails to cover are recorded.

[0121] Step S24: Calculate the overlapping rate of the alarm monitor video images based on the alarm monitor image data to obtain the overlapping rate data of the alarm video monitor images;

[0122] In the embodiment of the present invention, the overlapping rate of the alarm monitor video images is calculated based on the alarm monitor image data. This step calculates the overlapping area between the alarm monitor images through image analysis technology. In specific operations, using image processing software, the overlapping area of the video images captured by each alarm is calculated, the boundary area of the images is identified, and then the geometric overlap calculation of the covered areas in each alarm monitor video is performed. Through geometric figure processing methods (such as the convex hull algorithm or the spatial intersection algorithm), the overlapping parts of different alarm monitoring areas are calculated, and the overlapping rate of each area is obtained. The calculation results are displayed through visualization tools and further optimized and analyzed to obtain the overlapping rate data of the alarm video monitor images.

[0123] Step S25: Analyze the alarm monitoring coverage efficiency based on the alarm monitoring coverage blind area data and the overlapping rate data of the alarm video monitor images to obtain the alarm monitoring coverage efficiency data.

[0124] In the embodiment of the present invention, based on the alarm monitoring coverage blind area data and the overlapping rate data of the alarm video monitor images, the alarm monitoring coverage efficiency is analyzed. The core of this step is to comprehensively evaluate the monitoring coverage efficiency of each alarm through the monitoring blind area data and the overlapping rate data obtained in the previous steps. By analyzing the blind area data, the areas with monitoring dead angles are identified, and the overlapping rate data can be used to evaluate the redundancy degree of the monitoring area. When calculating, the evaluation index of the coverage efficiency is used, considering factors such as the field of view range, overlapping area, and blind area of the alarm, and the overall coverage efficiency of the alarm is comprehensively obtained. This analysis is processed through data analysis software or customized algorithms to generate the alarm coverage efficiency data.

[0125] Preferably, step S23 includes the following steps:

[0126] Step S231: Calculate the building connectivity based on the building data where the alarm operates to obtain the building connectivity data;

[0127] Step S232: Calculate the building wall surface based on the building data where the alarm operates to obtain the building wall surface data;

[0128] Step S233: Collect the building corner walls based on the building wall surface data and the building data where the alarm operates to obtain the building corner wall data;

[0129] Step S234: Analyze the complexity of the building wall surface based on the building corner wall data and the building connectivity data to obtain the building wall surface complexity data;

[0130] Step S235: Detect the blind area covered by the alarm based on the building wall complexity data for the alarm monitoring image data, and obtain the alarm monitoring coverage blind area data.

[0131] In the embodiments of the present invention, building connectivity data is obtained by running building data through an alarm to calculate the building connectivity. In this step, it is necessary to collect structural information of all rooms, corridors, doors, windows, etc. in the building, as well as the installation locations and functional areas of the alarms. Using spatial analysis methods, such as the Dijkstra algorithm or the A* algorithm, calculate the connectivity between various parts of the building. These calculation methods determine which areas are directly connected through the existing structure according to the door, window, wall, and passage conditions of the building, and which areas require additional paths or channels. Through this algorithm, building connectivity data is obtained, which characterizes the accessibility and connection situation between various areas inside the building. Building wall data is obtained by running building data through an alarm to calculate the building walls. In this step, all external and internal walls are identified using the geometric structure data of the building. Through modeling tools, such as CAD or BIM software, the wall structure of the building is accurately extracted. Particular attention is paid to information such as the type, material, and thickness of the walls, and these information are used to further calculate data such as the area, angle, and curvature of the walls. Through the calculation and analysis of all walls, building wall data is obtained, including the position, shape of each wall, and its relationship with the building structure. Building corner wall data is collected according to the building wall data and running building data through an alarm. A corner wall refers to the corner part formed by the intersection of two or more walls in a building, and these areas often have an important impact on monitoring coverage and spatial connectivity. In this step, using the building wall data, all corner positions are identified, especially those corner areas that affect the monitoring effect of the alarm. Using spatial analysis tools, through geometric calculations of the wall coordinates, determine the angle and position of each corner wall. Through these calculations, detailed building corner wall data is obtained, including information such as the spatial coordinates, angle, and wall material of the corner. Based on the building corner wall data and the building connectivity data, building wall complexity analysis is performed to obtain building wall complexity data. In this step, by combining the corner wall data and the building connectivity data, analyze the complexity of the walls in the building. According to the number and position of the corner walls, determine which areas of the walls cause monitoring blind spots or signal blockages. Then, analyze factors such as the curvature, intersection degree, and area of the walls in the building to quantify the complexity of the walls. Using complexity analysis tools (such as algorithms based on grid division or topological analysis), according to the wall characteristics of each area, calculate the building wall complexity data. The obtained complexity data will indicate which areas of the walls affect the monitoring effect of the alarm or increase the difficulty of layout. Alarm monitoring coverage blind spot detection is performed on the alarm monitoring image data according to the building wall complexity data to obtain alarm monitoring coverage blind spot data. In this step, by using the wall complexity data obtained in the previous steps, combined with the alarm installation location and the monitoring visual range, blind spot detection is performed. Based on the building wall complexity data, determine which areas form visual blind spots due to the complexity of the walls.By calculating the intersection of the walls in each area of the building with the monitoring field of view of the alarm, image processing and spatial analysis algorithms (such as ray tracing method) are used to determine whether there are monitoring blind spots, and the obtained blind spot data will reflect which areas cannot be effectively monitored by the existing alarms.

[0132] Preferably, step S24 includes the following steps:

[0133] Step S241: Identify the boundary area of the alarm monitoring image based on the alarm monitoring image data to obtain the alarm monitoring area boundary data;

[0134] Step S242: Analyze the geometric shape of the alarm monitoring area based on the alarm monitoring area boundary data to obtain the alarm monitoring area geometric shape data;

[0135] Step S243: Calculate the intersection of the alarm areas based on the alarm monitoring area geometric shape data to obtain the alarm monitoring area intersection data;

[0136] Step S244: Calculate the area of the intersection area of the alarm based on the alarm monitoring area intersection data to obtain the alarm intersection area area data;

[0137] Step S245: Calculate the total area of the alarm monitoring area based on the alarm monitoring area boundary data and the alarm monitoring area geometric shape data to obtain the alarm monitoring area total area data;

[0138] Step S246: Calculate the overlapping rate of the alarm monitoring video screen for the total area data of the alarm monitoring area using the alarm intersection area area data to obtain the alarm video monitoring screen overlapping rate data.

[0139] In the embodiments of the present invention, the boundary region of the alarm monitor image data is identified to obtain the boundary data of the alarm monitor area. Through image processing technology, the boundary line of the field of view is extracted from the monitoring video or real-time image of the alarm. In this process, image preprocessing is performed on the monitoring image to remove the interference of background noise and light changes. Then, edge detection algorithms (such as the Canny algorithm) and contour extraction methods are applied to identify the boundary of the monitoring area. By analyzing different regions in the monitoring image, the boundary of the effective area that the alarm can monitor is obtained, and the spatial coordinates of the boundary line are recorded and output. These boundary data reflect the monitoring area of the alarm. Based on the boundary data of the alarm monitoring area, geometric shape analysis of the alarm monitoring area is performed to obtain geometric shape data of the alarm monitoring area. In this step, according to the previously identified monitoring area boundary, the geometric shape of the monitoring area is analyzed using computational geometry methods. Through geometric analysis tools, it is judged whether the monitoring area is a simple geometric shape (such as a rectangle, a circle, etc.), and its basic geometric characteristics such as area, perimeter, and angle are calculated. If the boundary of the monitoring area is a complex shape, polygon partitioning or curve fitting techniques are used for more accurate analysis. This process not only helps to identify the specific area form covered by the alarm, but also provides detailed geometric feature data for subsequent intersection calculation and overlap rate analysis. According to the geometric shape data of the alarm monitoring area, intersection calculation of the alarm areas is performed to obtain intersection data of the alarm monitoring areas. In this step, the monitoring area of each alarm is regarded as a geometric figure, and intersection calculation is performed using geometric operation methods. Specifically, spatial geometric algorithms such as Boolean operations and intersection detection algorithms are used to calculate whether there is an overlapping part between the monitoring areas of different alarms. If there is an overlapping area, the area or spatial characteristics of the overlapping part are calculated. Through intersection calculation, intersection data of the monitoring areas of every two or more alarms are obtained, which describes the overlapping situation of the monitoring areas of different alarms. According to the intersection data of the alarm monitoring areas, area calculation of the intersection areas of the alarms is performed to obtain area data of the intersection areas of the alarms. Through the aforementioned intersection calculation steps, the intersection areas between multiple alarm monitoring areas are identified. In this step, geometric calculation methods are used to accurately calculate the area of the intersection areas. Through numerical integration or spatial partitioning techniques, the areas of these intersection areas are accurately quantified, reflecting the size of the overlapping part. If the intersection area is a complex polygon or an irregular area, the polygon area calculation formula or the Monte Carlo method is used to solve the area, and the area data of each intersection area is obtained. According to the boundary data of the alarm monitoring area and the geometric shape data of the alarm monitoring area, the total area of the alarm monitoring area is calculated to obtain the total area data of the alarm monitoring area. According to the monitoring area boundary and geometric shape data of each alarm, the total area of each area is calculated using geometric principles.For simple geometric shapes (such as rectangles or circles), the area can be directly calculated using standard area formulas; for complex shapes, polygon area calculation methods are used. By summing the areas of all the monitoring areas of the alarms, the total area data is obtained, which reflects the total coverage area of all the monitoring areas of the alarms. These data are used for further calculation of the area overlap rate and analysis of the monitoring coverage efficiency. Using the area data of the intersection region of the alarms to calculate the overlap rate of the monitoring areas of the alarms for the alarm monitoring video images, the overlap rate data of the alarm monitoring video images is obtained. In this step, by combining the area data of the intersection region and the total area data of the monitoring region obtained in the previous steps, the overlap rate is calculated. The overlap rate can be calculated by the following formula: Overlap rate = Area of the intersection region / Total area of the monitoring region. This calculation process precisely quantifies the overlap degree between the monitoring areas of multiple alarms, indicating the overlap rate of each alarm monitoring image.

[0140] Preferably, step S3 includes the following steps:

[0141] Step S31: Collect the operating environment of the alarm according to the alarm operation log data to obtain the alarm operating environment data;

[0142] Step S32: Estimate the performance decay of the alarm based on the digital twin model of the alarm for the alarm operating environment data to obtain the alarm performance decay data;

[0143] Step S33: Analyze the decay of the alarm monitoring and recognition ability according to the alarm performance decay data to obtain the alarm monitoring and recognition ability decay data;

[0144] Step S34: Identify the abnormal state of the alarm based on the alarm monitoring and recognition ability decay data and the alarm performance decay data to obtain the alarm monitoring abnormal state data.

[0145] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0146] Step S31: Collect the operating environment of the alarm according to the alarm operation log data to obtain the alarm operating environment data;

[0147] In the embodiment of the present invention, the operating environment of the alarm is collected through the alarm operation log data to obtain the alarm operating environment data. The specific process is to extract the environmental data from the operation log of the alarm, including external environmental factors such as temperature, humidity, air quality, and vibration conditions where the alarm is located, which affect the performance of the alarm. Using the data obtained by the sensors or the historical records extracted from the system, the environmental changes during the operation of the alarm are further analyzed. During this process, all the environmental data related to the operating state of the alarm will be collected and organized into an analyzable format.

[0148] Step S32: Estimate the performance attenuation of the alarm based on the digital twin model of the alarm for the alarm operation environment data to obtain the alarm performance attenuation data;

[0149] In the embodiment of the present invention, the performance attenuation of the alarm is estimated based on the digital twin model of the alarm for the alarm operation environment data to obtain the alarm performance attenuation data. In this step, the operation environment data of the alarm is combined with its digital twin model to estimate the performance attenuation. Using the digital twin model of the alarm, the environmental factors are associated with the working characteristics of the alarm to simulate the performance attenuation process under different environmental conditions. According to historical data and experimental results, rules for the influence of environmental factors (such as temperature, humidity, dust, etc.) on the performance of the alarm are formulated. According to these rules and the collected operation environment data, the performance attenuation value of the alarm under the current environmental conditions is estimated by numerical calculation methods.

[0150] Step S33: Analyze the attenuation of the alarm monitoring and recognition ability based on the alarm performance attenuation data to obtain the alarm monitoring and recognition ability attenuation data;

[0151] In the embodiment of the present invention, the attenuation of the alarm monitoring and recognition ability is analyzed based on the alarm performance attenuation data to obtain the alarm monitoring and recognition ability attenuation data. According to the alarm performance attenuation data estimated in step S32, the attenuation of the monitoring and recognition ability is analyzed. Specifically, by analyzing the relationship between the performance attenuation and the alarm monitoring and recognition ability, the factors that cause the decline of the monitoring and recognition ability are identified. This analysis quantitatively estimates the attenuation degree of the monitoring and recognition ability by comparing the difference between the historical recognition data and the current device performance and using methods such as regression analysis and causal relationship analysis.

[0152] Step S34: Identify the abnormal state of the alarm based on the alarm monitoring and recognition ability attenuation data and the alarm performance attenuation data to obtain the alarm monitoring abnormal state data.

[0153] In an embodiment of the present invention, based on the alarm monitoring recognition ability attenuation data and the alarm performance attenuation data, the abnormal state of the alarm is recognized to obtain the alarm monitoring abnormal state data. This step utilizes the alarm monitoring recognition ability attenuation data and the alarm performance attenuation data, combines the operating conditions of the monitoring system, and conducts the abnormal state recognition of the alarm. During this process, the performance change law of the alarm over time is analyzed through the performance attenuation data, and then combined with the monitoring recognition ability attenuation data to analyze whether there is monitoring abnormality caused by aging or environmental factors. The abnormal state is determined by a set performance threshold. For example, when the attenuation of the recognition ability exceeds a certain percentage, the alarm is determined to enter the abnormal state. In addition, machine learning or data mining techniques are used to analyze historical data to identify potential trends of alarm failure or performance degradation.

[0154] Preferably, step S32 includes the following steps:

[0155] Step S321: Measure the operating environment temperature of the alarm according to the alarm operating environment data to obtain the alarm operating environment temperature data;

[0156] Step S322: Collect the operating environment light of the alarm according to the alarm operating environment data to obtain the alarm operating environment light data;

[0157] Step S323: Estimate the aging trend of the alarm device based on the alarm operating environment light data and the alarm operating environment temperature data to obtain the alarm aging trend data;

[0158] Step S324: Estimate the performance attenuation of the alarm according to the alarm aging trend data to obtain the alarm performance attenuation data.

[0159] In the embodiments of the present invention, the temperature of the operating environment of the alarm is measured to obtain the temperature data of the operating environment of the alarm. This step directly collects the real-time temperature data of the environment where the alarm is located through a temperature sensor. The temperature change around the installation position of the alarm has a significant impact on the performance of the device. Therefore, the collection of temperature data is very crucial. The temperature sensor is usually installed near the alarm or directly obtains relevant data through an existing temperature control system. The collected temperature data is stored in a database, recording the change of temperature over time. By collecting the light in the operating environment data of the alarm, the light data of the operating environment of the alarm is obtained. This step measures the light intensity around the alarm through a light sensor. The light intensity has a certain impact on the working performance of the alarm and the internal components of the device (such as batteries, electronic components, etc.). Therefore, it is necessary to collect the light data in the actual operating environment of the alarm. The collection method is to install the light sensor near the alarm or directly integrate it into the alarm to measure and record the light intensity of the surrounding environment in real time. This light data will be dynamically recorded over time. Based on the light data and temperature data of the operating environment of the alarm, the aging trend of the device is estimated to obtain the aging trend data of the alarm. The temperature data and light data are correlated and analyzed to study the influence of temperature and light intensity on the alarm device. According to different combinations of temperature changes and light intensities, the aging of the alarm is predicted. The aging trend prediction is usually based on empirical models, regression analysis, or statistical modeling methods. By establishing a mathematical model between environmental factors (such as temperature, light intensity) and device aging, the aging process of the device under different environmental conditions is estimated. By calculating the damage caused to the device by factors such as temperature and light, the prediction results of device aging are obtained, including the estimation of the aging rate and the device life. According to the aging trend data of the alarm, the performance attenuation of the alarm is estimated to obtain the performance attenuation data of the alarm. This step uses the aging trend data obtained from step S323 to estimate the performance attenuation. By analyzing historical data, combining environmental factors (such as temperature and light) and the usage of the device, a quantitative relationship between device performance and aging is established. Through the numerical analysis of the device aging trend, a decay model is used to estimate the performance degradation of the alarm at a specific aging stage. This decay model includes a calculation formula or a regression algorithm based on machine learning. Using the device performance data collected in the early stage, the performance attenuation degree of the device at different aging stages is calculated to obtain the performance attenuation data of the alarm.

[0160] Preferably, step S322 includes the following steps:

[0161] Perform thermal overload detection on the operating environment according to the temperature data of the operating environment of the alarm, so as to obtain the thermal overload data of the operating environment;

[0162] Perform failure prediction on the image sensor of the alarm according to the thermal overload data of the operating environment to obtain the failure data of the image sensor of the alarm;

[0163] Detect the ultraviolet intensity of the operating environment light according to the light data of the operating environment of the alarm, and obtain the ultraviolet intensity data of the operating environment light;

[0164] Estimate the deterioration trend of the coating layer on the surface of the monitoring lens based on the ultraviolet intensity data of the operating environment light, and obtain the deterioration trend data of the coating layer on the surface of the lens;

[0165] Detect the attenuation of the anti-reflection ability of the alarm lens for the deterioration trend data of the coating layer on the surface of the lens, and obtain the attenuation data of the anti-reflection ability of the alarm lens;

[0166] Estimate the aging trend of the alarm device based on the failure data of the alarm image sensor and the attenuation data of the anti-reflection ability of the alarm lens, and obtain the aging trend data of the alarm.

[0167] In the embodiments of the present invention, thermal overload detection is performed on the temperature data of the operating environment of the alarm to obtain the thermal overload data of the operating environment. The alarm monitors the temperature change of the operating environment in real time through a temperature sensor. The temperature sensor is installed in the surrounding environment of the alarm and can collect temperature data at different time intervals. By setting a temperature threshold range, when the ambient temperature exceeds the set safe range, it is determined as a thermal overload phenomenon. The thermal overload detection performs data analysis based on whether the temperature value exceeds the normal operating range and combines the historical data obtained by the temperature sensor to detect the thermal overload problem existing in the operating environment of the alarm. The thermal overload data of the operating environment records the frequency and severity of the overload occurrence. Based on the thermal overload data of the operating environment, a failure prediction model of the alarm image sensor is established by predicting the failure of the alarm image sensor, and the failure data of the alarm image sensor is obtained. The performance of the image sensor is affected by excessive temperature, which may lead to the failure or performance degradation of the sensor. According to different degrees of thermal overload, the service life of the alarm image sensor is predicted. This prediction is based on the data obtained from the thermal overload detection, and the failure time of the image sensor is estimated through an attenuation model to obtain the time node when the image sensor fails, forming the failure data of the alarm image sensor. By detecting the ultraviolet intensity of the light data in the operating environment of the alarm, the ultraviolet intensity data of the light in the operating environment is obtained. The light sensor measures the ultraviolet intensity in the environment, and this intensity data helps to evaluate the potential impact of external light on the device. By installing an ultraviolet sensor near the alarm device, the ultraviolet intensity data of the ambient light is collected in real time. These data record the changes in the strength of ultraviolet rays in the environment and provide the necessary light intensity data for the subsequent analysis of the deterioration of the coating layer. These ultraviolet intensity data will reflect the damage risk caused by long-term high-intensity ultraviolet irradiation to the coating on the surface of the alarm lens. Based on the ultraviolet intensity data of the light in the operating environment, the deterioration trend of the coating layer on the surface of the monitoring lens is predicted to obtain the deterioration trend data of the coating layer on the surface of the lens. According to the ultraviolet intensity data obtained in the foregoing steps and combining the relationship between the characteristics of the coating material and the light intensity, a deterioration trend prediction model of the coating layer is established. This model correlates the ultraviolet intensity with the damage degree of the coating layer on the surface of the lens and can predict the deterioration speed and influence range of the coating layer on the surface of the lens according to the change of the ultraviolet intensity. Through the analysis of the data, the deterioration trend data of the coating layer is obtained to help judge the risk of the decrease in the ultraviolet resistance of the lens during long-term use. By detecting the attenuation of the antireflection ability based on the deterioration trend data of the coating layer on the surface of the lens, the antireflection ability attenuation data of the alarm lens is obtained. The coating layer on the surface of the lens has a significant impact on the reflectivity and imaging quality of the lens. The deterioration of the coating layer will lead to the attenuation of the reflection ability, thereby reducing the acquisition quality of the image sensor. By regularly detecting the antireflection ability of the lens and combining the deterioration trend data of the coating layer, the attenuation degree of the antireflection ability can be accurately estimated.The detection method includes measuring the change in the reflectivity of the lens surface through an optical test device, and evaluating the actual reflection ability of the lens and its attenuation speed by combining the influence of ambient light and ultraviolet intensity. Through the attenuation model, the anti-reflection ability attenuation data of the alarm lens is obtained. Based on the failure data of the alarm image sensor and the anti-reflection ability attenuation data of the alarm lens, the aging trend of the alarm device is estimated to obtain the alarm aging trend data. By combining the failure prediction data of the image sensor and the anti-reflection ability attenuation data of the lens, the overall aging trend of the alarm is evaluated. The image sensor and the lens are the core devices of the alarm, and the attenuation of their performance directly affects the function and working efficiency of the alarm. Through the comprehensive analysis of the failure data of the image sensor and the lens attenuation data, the aging prediction model is used to estimate the aging trend of the overall alarm device. This step combines the aging data of multiple devices through data fusion technology to comprehensively evaluate the performance attenuation trend of the alarm and obtain the alarm aging trend data.

[0168] Preferably, step S4 includes the following steps:

[0169] Step S41: Calculate the alarm response duration according to the alarm abnormal state data to obtain the alarm response duration data;

[0170] Step S42: Analyze the alarm layout defect based on the alarm abnormal state data, the alarm response duration data, and the alarm monitoring coverage efficiency data to obtain the alarm layout defect data;

[0171] Step S43: Detect the alarm resource redundancy according to the alarm layout defect data to obtain the alarm resource redundancy data;

[0172] Step S44: Optimize the alarm layout based on the alarm resource redundancy data and the alarm layout defect data to obtain the alarm layout optimization data.

[0173] In the embodiments of the present invention, the response duration of the alarm is calculated based on the abnormal state data of the alarm to obtain the alarm response duration data. The abnormal state data of the alarm is collected and recorded, and this data includes information such as the type of event triggered by the alarm, the occurrence time, and the response time of the alarm. To calculate the response duration of the alarm, it is necessary to record the time difference between when the alarm detects an abnormality and when the alarm makes a response. The calculation method of the response duration is to compare the time point when the alarm triggers an abnormality with the time point when the alarm starts to respond and calculate the time interval. This calculation process determines whether the response duration meets the predetermined standard by setting a time threshold, and at the same time calculates the response duration distribution of various abnormal events to obtain a set of alarm response duration data. The alarm layout defect analysis is performed through the alarm abnormal state data and the alarm monitoring coverage efficiency data to obtain the alarm layout defect data. This step relies on two types of data: the alarm abnormal state data and the monitoring coverage efficiency data. The alarm abnormal state data provides detailed information about the faults or failures to respond in a timely manner that occur during the actual operation of the alarm, while the alarm monitoring coverage efficiency data reflects the working efficiency of each alarm within its monitoring range. By analyzing these two types of data, it is possible to identify problems with the unreasonable layout of some alarms, such as overlapping monitoring ranges, blind spots, or response delays. The implementation method of the layout defect analysis includes calculating the relationship between the response time and the monitoring coverage efficiency of each alarm, and combining the distribution of alarms in the area to identify the areas with unreasonable layout to obtain the alarm layout defect data. The alarm resource redundancy detection is performed through the alarm layout defect data to obtain the alarm resource redundancy data. Using the alarm layout defect data, analyze whether there is duplicate or excessive resource allocation in the spatial layout between alarms. In the redundancy detection, mainly based on the monitoring range of the alarm and the area it covers, by checking the overlapping areas between each alarm, identify those alarms with highly overlapping monitoring areas, and these alarms have resource redundancy. The detection process involves comparing the monitoring range of the alarm with the overlapping area of the building floor plan, calculating the number and location of redundant alarms, and obtaining the resource redundancy data through the calculated results. The alarm resource redundancy detection is performed through the alarm layout defect data to obtain the alarm resource redundancy data. Using the alarm layout defect data, analyze whether there is duplicate or excessive resource allocation in the spatial layout between alarms. In the redundancy detection, mainly based on the monitoring range of the alarm and the area it covers, by checking the overlapping areas between each alarm, identify those alarms with highly overlapping monitoring areas, and these alarms have resource redundancy. The detection process involves comparing the monitoring range of the alarm with the overlapping area of the building floor plan, calculating the number and location of redundant alarms, and obtaining the resource redundancy data through the calculated results.

[0174] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A method for optimizing alarm layout based on digital twins, characterized in that: The following steps are involved: Step S1: Obtain alarm operation log data; Based on the alarm operation log data, the alarm operation building data is collected to obtain the alarm operation building data; The digital twin model of the alarm is constructed based on the alarm operation log data and the alarm operation building data to obtain the digital twin model of the alarm; Step S2: analyzing the alarm monitoring coverage efficiency of the alarm operation building data according to the alarm digital twin model to obtain alarm monitoring coverage efficiency data; Step S3: collecting the alarm operating environment according to the alarm operating log data to obtain the alarm operating environment data; identifying the abnormal state of the alarm according to the alarm digital twin model to obtain the abnormal state data of the alarm monitoring; Step S4: Perform alarm layout defect analysis based on the alarm abnormal state data and the alarm monitoring coverage efficiency data to obtain alarm layout defect data; optimize the alarm layout according to the alarm layout defect data to obtain alarm layout optimization data.

2. The alarm layout optimization method based on digital twin according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Obtain alarm operation log data; Step S12: Collect alarm operation building data based on the alarm operation log data to obtain alarm operation building data; Step S13: Acquire the alarm usage function according to the alarm operation log data to obtain the alarm usage function data; Step S14: Construct a digital twin model of the alarm based on the alarm usage function data, the alarm operation log data, and the alarm operation building data to obtain a digital twin model of the alarm.

3. The alarm layout optimization method based on digital twin according to claim 2 is characterized in that: Step S14 includes the following steps: Step S141: Collect the geometric structure of the alarm building according to the alarm operation building data to obtain the geometric structure data of the alarm building; Step S142: measuring the alarm installation coordinate position according to the alarm operation log data to obtain the alarm installation coordinate position data; Step S143: constructing a spatial rectangular coordinate system of the alarm according to the alarm installation coordinate position data and the alarm building geometric structure data to obtain the alarm spatial rectangular coordinate system data; Step S144: measuring the monitoring visual range of the alarm according to the alarm usage function data to obtain the monitoring visual range data of the alarm; Step S145: analyzing the image coverage of the alarm according to the spatial rectangular coordinate system data of the alarm and the monitoring field of view data of the alarm to obtain the image coverage data of the alarm; Step S146: Construct a digital twin model of the alarm based on the alarm image coverage data and the alarm spatial rectangular coordinate system data to obtain a digital twin model of the alarm.

4. The alarm layout optimization method based on digital twin according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: performing alarm monitoring operation identification simulation according to the alarm digital twin model to obtain alarm monitoring operation identification simulation data; Step S22: collecting alarm monitoring images according to the alarm monitoring operation recognition simulation data to obtain alarm monitoring image data; Step S23: performing alarm coverage blind area detection on the alarm monitoring image data according to the alarm operation building data to obtain alarm monitoring coverage blind area data; Step S24: Calculate the alarm monitoring video screen overlap rate according to the alarm monitoring image data to obtain the alarm video monitoring screen overlap rate data; Step S25: analyzing the alarm monitoring coverage efficiency based on the alarm monitoring coverage blind area data and the alarm video monitoring screen overlap rate data to obtain the alarm monitoring coverage efficiency data.

5. The alarm layout optimization method based on digital twin according to claim 4 is characterized in that: Step S23 includes the following steps: Step S231: Calculate building connectivity according to the alarm operation building data, thereby obtaining building connectivity data; Step S232: Calculate the building wall surface according to the alarm running building data, so as to obtain the building wall surface data; Step S233: collecting building corner wall data according to the building wall surface data and the alarm operation building data to obtain building corner wall data; Step S234: performing building wall complexity analysis based on the building corner wall data and the building connectivity data to obtain building wall complexity data; Step S235: Perform alarm coverage blind area detection on the alarm monitoring image data according to the building wall complexity data to obtain the alarm monitoring coverage blind area data.

6. The alarm layout optimization method based on digital twin according to claim 4 is characterized in that: Step S24 includes the following steps: Step S241: performing alarm monitoring image boundary area recognition according to the alarm monitoring image data to obtain alarm monitoring area boundary data; Step S242: performing geometric shape analysis of the alarm monitoring area according to the alarm monitoring area boundary data to obtain geometric shape data of the alarm monitoring area; Step S243: performing alarm area intersection calculation according to the alarm monitoring area geometric shape data to obtain alarm monitoring area intersection data; Step S244: Calculate the area of ​​the intersection area of ​​the alarm according to the intersection data of the alarm monitoring areas to obtain the area data of the intersection area of ​​the alarm; Step S245: Calculate the total area of ​​the alarm monitoring area according to the alarm monitoring area boundary data and the alarm monitoring area geometric shape data to obtain the total area data of the alarm monitoring area; Step S246: Calculate the alarm monitoring video screen overlap rate using the alarm intersection area area data for the alarm monitoring area total area data to obtain the alarm video monitoring screen overlap rate data.

7. The alarm layout optimization method based on digital twin according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: collecting the alarm operating environment according to the alarm operating log data to obtain the alarm operating environment data; Step S32: estimating the performance attenuation of the alarm based on the alarm operating environment data according to the alarm digital twin model to obtain the alarm performance attenuation data; Step S33: performing an alarm monitoring and recognition capability attenuation analysis according to the alarm performance attenuation data to obtain the alarm monitoring and recognition capability attenuation data; Step S34: Identify the abnormal state of the alarm based on the alarm monitoring recognition capability attenuation data and the alarm performance attenuation data to obtain the alarm monitoring abnormal state data.

8. The alarm layout optimization method based on digital twin according to claim 7 is characterized in that: Step S32 includes the following steps: Step S321: measuring the alarm operating environment temperature according to the alarm operating environment data to obtain the alarm operating environment temperature data; Step S322: collecting the alarm operation illumination data according to the alarm operation environment data to obtain the alarm operation environment illumination data; Step S323: estimating the aging trend of the alarm device based on the illumination data of the alarm operating environment and the temperature data of the alarm operating environment to obtain the aging trend data of the alarm; Step S324: Estimate the performance degradation of the alarm according to the alarm aging trend data to obtain the performance degradation data of the alarm.

9. The alarm layout optimization method based on digital twin according to claim 8 is characterized in that: Step S322 includes the following steps: Performing operating environment thermal overload detection according to the operating environment temperature data of the alarm, thereby obtaining operating environment thermal overload data; Failure prediction of the alarm image sensor is performed based on the thermal overload data of the operating environment to obtain failure data of the alarm image sensor; Performing ultraviolet intensity detection of operating environment lighting according to the operating environment lighting data of the alarm to obtain ultraviolet intensity data of the operating environment lighting; Based on the ultraviolet intensity data of the operating environment, the degradation trend of the coating layer on the surface of the monitoring lens is estimated to obtain the degradation trend data of the coating layer on the surface of the lens; Perform anti-reflection capability attenuation detection on the lens of the alarm device based on the lens surface coating layer degradation trend data to obtain anti-reflection capability attenuation data of the lens of the alarm device; Based on the alarm image sensor failure data and the alarm lens anti-reflection ability attenuation data, the alarm equipment aging trend is estimated to obtain the alarm aging trend data.

10. The alarm layout optimization method based on digital twin according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Calculate the alarm response time according to the alarm abnormal state data to obtain the alarm response time data; Step S42: performing alarm layout defect analysis based on the alarm abnormal state data, the alarm response time data, and the alarm monitoring coverage efficiency data to obtain alarm layout defect data; Step S43: performing alarm resource redundancy detection according to the alarm layout defect data to obtain alarm resource redundancy data; Step S44: Optimize the layout of the alarm based on the alarm resource redundancy data and the alarm layout defect data to obtain alarm layout optimization data.

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