Security monitoring system and method for constructional engineering management

Abstract

The invention relates to the technical field of security and protection monitoring, and discloses a security and protection monitoring system for constructional engineering management, and the system comprises an information collection module which obtains target information, and the target information comprises a target monitoring region and image data information; the image analysis module is used for analyzing whether the target monitoring area is abnormal or not according to the target information and forming an abnormal signal; the information processing module is used for forming a data exception analysis strategy according to the exception signal; the abnormity alarm module is used for giving an alarm according to the data after the data abnormity analysis strategy is executed; judging whether an abnormal signal occurs or not by comparing the features of the target image data with the size of the shadow region, and performing abnormality analysis according to the angle of the monitoring camera and the moving condition of the target shelter; intelligent identification and abnormity triggering of security and protection monitoring are achieved, it is ensured that response can be made in time when an abnormal condition occurs, and high intelligence and real-time performance are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of security monitoring, and in particular to a security monitoring system and method for construction project management. Background Art

[0002] IoT image recognition refers to the process of identifying and analyzing images using IoT technology and artificial intelligence algorithms. By connecting sensors, cameras, and other devices to the internet, image data can be transmitted in real time to cloud or edge servers for processing and analysis. IoT image recognition can be applied in many fields, such as security monitoring, intelligent transportation, industrial production, and agriculture. It uses image recognition technology to automatically identify and analyze objects, faces, and actions, enabling automated and intelligent application scenarios. In security monitoring, IoT image recognition can capture image data from surveillance cameras and use technologies such as facial recognition and behavioral analysis to determine whether there are any anomalies, enabling real-time response and early warning of security incidents. In intelligent transportation, IoT image recognition can be used for vehicle identification, traffic flow monitoring, and violation detection, improving the efficiency and safety of traffic management. In industrial production, IoT image recognition can be used to inspect and control product quality during the production process, improving production efficiency and quality.

[0003] In agriculture, IoT image intelligent recognition technology can be used to monitor crop growth and detect pests and diseases, helping farmers manage their fields scientifically and improve agricultural yields and quality. In short, IoT image intelligent recognition leverages the combination of the Internet of Things and artificial intelligence to provide us with more intelligent applications and solutions, bringing more efficient, safer, and smarter management and services to various fields. IoT image intelligent recognition relies on advanced image processing and analysis technologies, including the following key technologies: Image recognition: Leveraging deep learning and computer vision techniques, it automatically identifies and classifies objects, scenes, faces, and more in images. This includes techniques such as target detection, image segmentation, and feature extraction, and can be applied to a variety of fields.

[0004] Behavioral analysis: By analyzing the movements and behaviors in the image, different behavioral patterns and abnormal situations can be determined. For example, in security monitoring, abnormal behaviors such as theft and break-in can be identified. Sensor technology: Sensors are a core component of the Internet of Things, which can collect physical quantities and environmental information and convert them into digital signals. In intelligent image recognition of the Internet of Things, sensors can provide image data, including visible light cameras, infrared cameras, etc. Cloud computing and edge computing: Intelligent image recognition of the Internet of Things can use cloud computing platforms or edge servers for image processing and analysis. Cloud computing can provide high-performance computing and storage resources, which are suitable for large-scale image processing tasks. Edge computing moves some processing tasks to the device itself, providing real-time response and higher privacy protection.

[0005] Data security and privacy protection: Due to the massive amount of data generated by the large-scale devices and sensors connected to the IoT, data security and privacy protection have become critical issues. IoT image intelligent recognition requires encryption and privacy protection measures for image data to ensure data security and user privacy. With the continuous development of artificial intelligence and IoT technologies, IoT image intelligent recognition will be applied in more fields, providing us with a smarter, more convenient, and safer living and working environment.

[0006] Most existing security monitoring systems trigger an alarm immediately after detecting an abnormal signal, but due to the variability of environmental factors, false alarms are easily triggered; therefore, they do not meet existing needs. In this regard, we propose a security monitoring system for construction project management. Summary of the Invention

[0007] The present invention provides a security monitoring system for construction project management, which has intelligent identification and abnormal triggering of security monitoring, ensuring timely response when abnormal situations occur. It has the beneficial effects of strong intelligence and real-time performance, and solves the problem mentioned in the above background technology that most existing security monitoring systems trigger alarms immediately after detecting abnormal signals, but are prone to false alarms due to the changing environmental factors.

[0008] The present invention provides the following technical solution: a security monitoring system for construction project management, comprising:

[0009] Information acquisition module: acquires target information, including target monitoring area and image data information;

[0010] Image analysis module: Analyzes whether there is any abnormality in the target monitoring area based on the target information and generates abnormal signals;

[0011] Information processing module: forms data anomaly analysis strategy based on abnormal signals;

[0012] Abnormal alarm module: alarm based on the data after the execution of the data abnormality analysis strategy.

[0013] As an optional solution of the security monitoring system for construction project management described in the present invention, wherein: the analysis of whether an abnormality occurs in the target monitoring area based on the target information to generate an abnormality signal specifically includes:

[0014] Obtain target monitoring information of the target monitoring area;

[0015] The target area is the currently monitored area;

[0016] The target monitoring information includes a primary target area Q and a secondary target area W;

[0017] Obtain image data information of the target monitoring area;

[0018] Set the image frame rate threshold, denoted as E;

[0019] The image data information of the target monitoring area includes target image data of the target area per frame rate E and an occlusion area in the target image.

[0020] As an optional solution of the security monitoring system for construction project management according to the present invention, wherein: first target image data R of the first frame of the target area is obtained;

[0021] Acquire the second target image data T1 of the target area in the second frame;

[0022] Set the first occlusion rate threshold Y1;

[0023] Comparing the first target image data R with the second target image data T1 to see if they are the same;

[0024] If a shadow area I exists between the second target image data T1 and the first target image data R, first shadow area data U1 in the second target image data T1 is obtained;

[0025] Comparing the first shadow area data U1 with the first occlusion rate threshold Y1;

[0026] If the first shadow area data U1 is greater than the first occlusion rate threshold Y1;

[0027] Then the second target image data T is recorded as the first abnormal signal P;

[0028] If the first shadow area data U1 is less than the first occlusion rate threshold Y1;

[0029] Then determine whether the shadow area I is in the main target area Q;

[0030] If the shadow area I is not in the main target area Q, the second target image data T is determined to be a non-abnormal signal and the target area continues to be monitored;

[0031] If the shadow area I is in the main target area Q, the second target image data T is determined to be a second abnormal signal S;

[0032] Execute data anomaly analysis strategies.

[0033] As an optional solution of the security monitoring system for construction project management described in the present invention, the execution data anomaly analysis strategy specifically includes:

[0034] Obtain the surveillance camera angle of the second frame of the target area, recorded as the first angle A1;

[0035] The surveillance camera is moved to multiple angles, which are recorded as a second angle A2, a third angle A3, and a fourth angle A4;

[0036] Acquire a third target data image T2 of the surveillance camera at a second angle A2;

[0037] Acquire a fourth target data image T3 when the surveillance camera is at a third angle A3;

[0038] Acquire a fifth target data image T4 when the surveillance camera is at a fourth angle A4;

[0039] Set the second occlusion rate threshold Y2;

[0040] Acquire second shadow area data U2 of the third target data image T2;

[0041] Acquire third shadow area data U3 of the fourth target data image T3;

[0042] Acquire fourth shadow area data U4 of the fifth target data image T4;

[0043] Compare the second shadow area data U2, the third shadow area data U3, and the fourth shadow area data U4 with the second occlusion rate threshold value Y2;

[0044] If U2 is less than Y2, U3 is less than Y2, and U4 is less than Y2, then the second target image data T is determined to be a non-abnormal signal and the target area continues to be monitored;

[0045] If U2 is less than Y2, U3 is greater than Y2, and U4 is less than Y2, then the second target image data T is recorded as the first abnormal signal P;

[0046] Execute targeted anomaly analysis strategy.

[0047] As an optional solution of the security monitoring system for construction project management described in the present invention, the target abnormality analysis strategy is specifically as follows:

[0048] Obtain target occluder data in the occluded area of the target image;

[0049] Determining whether the target obstruction is moving in real time;

[0050] The specific steps for determining the real-time movement of the target occluder are:

[0051] Obtain a first distance F1 between the target blocking object and the surveillance camera in the second target image data T1;

[0052] Obtain a second distance F2 between the target blocking object and the surveillance camera in the third target data image T2;

[0053] Compare the values of the first distance F1 and the second distance F2 to see if they are consistent.

[0054] As an optional solution of the security monitoring system for construction project management according to the present invention, wherein: if the first distance F1 and the second distance F2 have the same value;

[0055] The first abnormal signal P is recorded as a fixed abnormality, and the first abnormal strategy is executed;

[0056] The first exception strategy is as follows:

[0057] Set a first time threshold G;

[0058] Calculate the first data picture H1 of the total target, where the first data picture H1 = the first time threshold G × the image frame rate threshold E;

[0059] Obtain the last target image data in the total target first data picture H1, and record it as the final first target image data J1;

[0060] Acquire the fifth shadow area data U5 in the final first target image data J1;

[0061] Comparing the fifth shadow area data U5 with the second occlusion rate threshold value Y2;

[0062] If the fifth shadow area data U5 is less than the second occlusion rate threshold Y2, the second target image data T is determined to be a non-abnormal signal, and the target area continues to be monitored.

[0063] As an optional solution of the security monitoring system for construction project management described in the present invention, if the fifth shadow area data U5 is greater than the second occlusion rate threshold Y2, the second abnormal strategy is executed;

[0064] The second exception strategy is as follows:

[0065] Obtain a third distance F3 between the fifth shadow area U5 and the surveillance camera;

[0066] Set a first distance threshold K;

[0067] Calculate the safety time L, safety time L = third distance F3 ÷ first distance threshold K;

[0068] Obtain the second data image H2 of the total target within the safety time L, where the second data image H2 of the total target = safety time L × image frame rate threshold E;

[0069] Obtain the last target image data in the total target second data picture H2, recorded as the final second target image data J2;

[0070] Acquire the sixth shadow area data U6 in the final second target image data J2;

[0071] Comparing the sixth shadow area U6 with the second occlusion rate threshold Y2;

[0072] If the sixth shadow area U6 is greater than or equal to the second occlusion threshold Y2, the abnormal alarm module issues an alarm.

[0073] As an optional solution of the security monitoring system for construction project management according to the present invention, wherein: if the values of the first distance F1 and the second distance F2 are inconsistent;

[0074] The first abnormal signal P is recorded as an unfixed abnormality, and the second abnormal strategy is executed;

[0075] The second exception strategy is as follows:

[0076] Setting a second time threshold Z;

[0077] Calculate the third data picture H3 of the total target, where the third data picture H3 = the second time threshold Z × the image frame rate threshold E;

[0078] Obtain the last target image data in the total target third data picture H3, and record it as the final third target image data J3;

[0079] Obtain a third distance F3 between the target obstructing object and the surveillance camera in the final third target image data J3;

[0080] Obtain a target frame number C of the final third target image data J3;

[0081] Calculate the target moving speed X of the target occluder: target moving speed X = (third distance F3 - first distance F1) ÷ (target frame number C - first frame).

[0082] As an optional solution of the security monitoring system for construction project management described in the present invention, wherein: a target pre-movement time length V of the target obstruction is calculated, and the target pre-movement time length V = the first distance F1 ÷ the target movement speed X;

[0083] Obtain the fourth data picture H4 of the total target within the target pre-movement duration V, where the fourth data picture H4 of the total target = target pre-movement duration V × image frame rate threshold E;

[0084] Setting a first image abnormality threshold N1;

[0085] Obtain the shadow areas of all images within the fourth data image H4 of the overall target;

[0086] All the pictures in the fourth data picture H4 of the total target whose shadow area is greater than the second threshold value Y2 of the occlusion rate are recorded as abnormal image data M.

[0087] If the number of abnormal image data M is greater than or equal to the first image abnormality threshold N1, the abnormality alarm module issues an alarm;

[0088] If the abnormal image data M is less than the first image abnormality threshold N1;

[0089] Setting a second image abnormality threshold N2;

[0090] If the abnormal image data M in the fourth data picture H4 of the overall target is continuous, and the number of the continuous abnormal image data M is greater than or equal to the second image abnormality threshold N2, the abnormal alarm module generates an alarm.

[0091] The present invention has the following beneficial effects:

[0092] 1. This security monitoring system for construction project management obtains monitoring information and image data from the target monitoring area to analyze whether there are any abnormal signals, and executes abnormal analysis strategies and abnormal alarms according to different situations. It determines whether there are any abnormal signals by comparing the characteristics of the target image data and the size of the shadow area, and performs abnormal analysis based on the angle of the monitoring camera and the movement of the target obstruction. It realizes intelligent identification and abnormal triggering of security monitoring, ensuring timely response when abnormal situations occur, and has strong intelligence and real-time performance.

[0093] 2. The security monitoring system for construction project management obtains target image data from different angles of the monitoring camera and compares the image data of different frames to determine whether there are abnormal signals. It then executes corresponding strategies for the abnormal signals, performs target abnormality analysis strategies, determines whether the target obstruction moves in real time, and issues abnormal alarms based on the movement. It executes corresponding abnormality analysis strategies based on the characteristics and movement of the abnormal signals, including fixed abnormality strategies and non-fixed abnormality strategies, and issues corresponding abnormality alarms. This improves the intelligence level of the security monitoring and helps to ensure the safety and stability of the monitored area.

[0094] 3. The security monitoring system for construction project management records and analyzes the data of the target monitoring area through the information acquisition module, providing data support for subsequent data analysis and abnormal signal judgment; through the information processing module, it performs intelligent analysis of image data, can automatically judge abnormal situations and take corresponding measures; through the abnormal alarm module, it can trigger an alarm immediately after an abnormal signal is detected, without waiting for manual intervention or processing, and make timely response in the shortest time, which helps to ensure the safety of the monitored area. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Figure 1 This is a block diagram of the security monitoring system for construction project management according to the present invention.

[0096] Figure 2 This is a schematic diagram of the second target data and third target data structures of the present invention. DETAILED DESCRIPTION

[0097] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0098] Example 1: This example aims to solve the problem that most existing security monitoring systems trigger an alarm immediately after detecting an abnormal signal, but due to the changing environmental factors, it is easy to trigger a false alarm. Figure 1 A security monitoring system for construction project management includes: an information acquisition module for acquiring target information, the target information including the target monitoring area and image data information; an image analysis module for analyzing whether the target monitoring area has any abnormality according to the target information, and forming an abnormality signal; an information processing module for forming a data abnormality analysis strategy according to the abnormal signal; and an abnormality alarm module for issuing an alarm according to the data after the execution of the data abnormality analysis strategy.

[0099] According to the target information, analyze whether there is any abnormality in the target monitoring area to form an abnormal signal, specifically including: obtaining the target monitoring information of the target monitoring area; the target area is the currently monitored area; the target monitoring information includes the main target area Q and the secondary target area W; obtaining the image data information of the target monitoring area; setting the image frame rate threshold, denoted as E; for example, the image frame rate threshold E is 30 frames / second.

[0100] The image data information of the target monitoring area includes the target image data of the target area per frame rate E and the occlusion area within the target image; the first target image data R when the first frame of the target area is obtained; and the second target image data T1 when the second frame of the target area is obtained.

[0101] Set a first threshold value Y1 for the occlusion rate; the occlusion rate is the degree of occlusion in the occlusion area, such as setting the first threshold value Y1 for the occlusion rate to 45%; compare whether the first target image data R and the second target image data T1 are the same; if there is a shadow area I in the second target image data T1 and the first target image data R, obtain the first shadow area data U1 in the second target image data T1.

[0102] Compare the first shadow area data U1 with the first occlusion rate threshold Y1; if the first shadow area data U1 is 55%, the first shadow area data 55% is greater than the first occlusion rate threshold 45%; record the second target image data T as the first abnormal signal P.

[0103] If the first shadow area data U1 is 43%, the first shadow area data 43% is less than the first occlusion rate threshold 45%, and then it is determined whether the shadow area I is in the main target area Q.

[0104] If the shadow area I is not within the main target area Q, the second target image data T is determined to be a non-abnormal signal and the target area continues to be monitored; if the shadow area I is within the main target area Q, the second target image data T is determined to be a second abnormal signal S; and the data abnormality analysis strategy is executed.

[0105] Executing a data anomaly analysis strategy specifically includes: obtaining the surveillance camera angle of the second frame of the target area, recorded as the first angle A1; moving the surveillance camera to multiple angles, recorded as the second angle A2, the third angle A3, and the fourth angle A4; the above angles are achieved by adjusting the surveillance camera to capture multiple perspectives and view the occlusion of the shadow area I from multiple angles.

[0106] Obtain a third target data image T2 of the surveillance camera at a second angle A2; obtain a fourth target data image T3 of the surveillance camera at a third angle A3; obtain a fifth target data image T4 of the surveillance camera at a fourth angle A4.

[0107] The second occlusion rate threshold value Y2 is set; for example, the second occlusion rate threshold value Y2 is 25%.

[0108] The second shadow area data U2 of the third target data image T2 is obtained; the third shadow area data U3 of the fourth target data image T3 is obtained; and the fourth shadow area data U4 of the fifth target data image T4 is obtained.

[0109] Compare the second shadow area data U2, the third shadow area data U3, and the fourth shadow area data U4 with the second occlusion rate threshold Y2; if the second shadow area data U2 is 15%, the third shadow area data U3 is 12%, and the fourth shadow area data U4 is 20%.

[0110] The second shadow area data 15% is less than the second occlusion rate threshold of 25%, the third shadow area data 12% is less than the second occlusion rate threshold of 25%, and the fourth shadow area data 20% is less than the second occlusion rate threshold of 25%; the second target image data T is determined to be a non-abnormal signal, and the target area continues to be monitored.

[0111] If the second shadow area data U2 is 15%, the third shadow area data U3 is 27%, and the fourth shadow area data U4 is 20%; then the second shadow area data 15% is less than the second occlusion rate threshold of 25%, the third shadow area data 27% is less than the second occlusion rate threshold of 25%, and the fourth shadow area data 20% is less than the second occlusion rate threshold of 25%; the second target image data T is recorded as the first abnormal signal P; and the target abnormality analysis strategy is executed.

[0112] In this embodiment: by acquiring monitoring information and image data of the target monitoring area, it is analyzed whether abnormal signals appear, and abnormal analysis strategies and abnormal alarms are executed according to different situations; by comparing the characteristics of the target image data and the size of the shadow area, it is determined whether abnormal signals appear, and abnormal analysis is performed based on the angle of the monitoring camera and the movement of the target obstruction; intelligent identification and abnormal triggering of security monitoring are realized, ensuring that timely responses can be made when abnormal situations occur, with strong intelligence and real-time performance.

[0113] Example 2: This example aims to solve the problem of determining whether the target obstruction is moving in real time. This example is an improvement made on the basis of Example 1. For details, please refer to Figure 1 、 Figure 2The target anomaly analysis strategy is specifically as follows: obtaining target occlusion data in the occlusion area within the target image; determining whether the target occlusion moves in real time; determining whether the target occlusion moves in real time is specifically as follows: obtaining a first distance F1 between the target occlusion and the surveillance camera in the second target image data T1; obtaining a second distance F2 between the target occlusion and the surveillance camera in the third target data image T2; and comparing whether the values of the first distance F1 and the second distance F2 are consistent.

[0114] If the values of the first distance F1 and the second distance F2 are consistent, the first abnormal signal P is recorded as a fixed abnormality, and the first abnormal strategy is executed.

[0115] The first abnormal strategy is specifically: setting a first time threshold G; the first time threshold G is the time threshold G within which the blocked area in the target image can be safely monitored, such as the first time threshold G is 45 seconds.

[0116] The total target first data picture H1 is calculated. The total target first data picture H1 = the first time threshold G × the image frame rate threshold E, that is, the total target first data picture H1 is 45 × 30 = 1350 pictures.

[0117] The 1350th target image data in the total target first data picture is obtained and recorded as the final first target image data J1.

[0118] Obtain the fifth shadow area data U5 in the final first target image data J1; compare the fifth shadow area data U5 with the second occlusion rate threshold Y2; if the fifth shadow area data U5 is 21%, the fifth shadow area data 21% is less than the second occlusion rate threshold 25%, and the second target image data T is determined to be a non-abnormal signal, and the target area continues to be monitored.

[0119] If the fifth shadow area data U5 is 26%, the fifth shadow area data 26% is greater than the second occlusion rate threshold 25%, and the second abnormal strategy is executed.

[0120] The second abnormal strategy is specifically as follows: obtaining the third distance F3 between the fifth shadow area U5 and the surveillance camera; setting a first distance threshold K, which is the average rate at which the obstruction moves under normal circumstances.

[0121] Calculate the safety time L, safety time L = third distance F3 ÷ first distance threshold K; the safety time represents the shortest time for safely monitoring the fifth shaded area according to the first distance threshold within the fifth shaded area.

[0122] Obtain the total target second data picture H2 within the safety time length L, where the total target second data picture H2 = safety time length L×image frame rate threshold E.

[0123] The last target image data in the total target second data picture H2 is obtained, recorded as the final second target image data J2; and the sixth shadow area data U6 in the final second target image data J2 is obtained.

[0124] Compare the sixth shadow area U6 with the second occlusion rate threshold Y2; if the sixth shadow area U6 is 27%, the sixth shadow area 27% is greater than the second occlusion threshold 25%, and the abnormal alarm module issues an alarm.

[0125] like Figure 2 If the first distance F1 and the second distance F2 are inconsistent, the first abnormal signal P is recorded as an unfixed abnormality and the second abnormality strategy is executed. The second abnormality strategy is specifically as follows: setting a second time threshold Z; the second time threshold Z is the time threshold Z within which the occluded area in the target image can be safely monitored, such as the second time threshold Z is 65 seconds;

[0126] The total target third data picture H3 is calculated. The total target third data picture H3 = the second time threshold Z × the image frame rate threshold E, that is, the total target third data picture H3 is 65 × 30 = 1950 pictures.

[0127] The 1950th target image data in the third data picture of the total target is obtained, and recorded as the final third target image data J3; the third distance F3 between the target blocking object and the monitoring camera in the final third target image data J3 is obtained.

[0128] Calculate the target movement speed X of the target obstruction: target movement speed X = (third distance F3 - first distance F1) ÷ (second time threshold Z - 1). For example, if the first distance F1 is 38.8 meters, the third distance F3 is 42 meters, and the second time threshold Z is 65 seconds, then the target movement speed X is (42 - 38.8) ÷ (65 - 1) = 0.05 meters per second.

[0129] Calculate the target pre-movement time V of the target occluder, target pre-movement time V = first distance F1 ÷ target movement speed X; obtain the total target fourth data image H4 within the target pre-movement time V, total target fourth data image H4 = target pre-movement time V × image frame rate threshold E.

[0130] Set a first image abnormality threshold N1; the first image abnormality threshold N1 is a safe range of abnormal image values within the target pre-movement time V, such as the first image abnormality threshold N1 is 58 images.

[0131] Obtain the shadow areas of all pictures in the fourth data picture H4 of the total target; record the data pictures whose shadow areas are greater than the second occlusion rate threshold Y2 in the fourth data picture H4 of the total target as abnormal image data M.

[0132] If the number of abnormal image data M is 63, which is greater than the first image abnormality threshold of 58, the abnormal alarm module will issue an alarm.

[0133] If the number of abnormal image data M is 53, which is less than the first image abnormality threshold of 58, then the second image abnormality threshold N2 is set. The second image abnormality threshold N2 is a safe range of abnormal image values that appear continuously within the target pre-movement time V, such as the second image abnormality threshold N2 is 10.

[0134] If the abnormal image data M in the fourth data picture H4 of the total target is continuous, and the number of the continuous abnormal image data M is 12, which is greater than the second image abnormality threshold of 10, the abnormal alarm module will sound an alarm.

[0135] In this embodiment: target image data from different angles of the surveillance camera is obtained, and image data from different frames are compared to determine whether there is an abnormal signal, and a corresponding strategy is executed on the abnormal signal to perform a target abnormality analysis strategy to determine whether the target obstruction is moving in real time, and an abnormality alarm is issued according to the movement situation; a corresponding abnormality analysis strategy is executed according to the characteristics and movement situation of the abnormal signal, including a fixed abnormality strategy and a non-fixed abnormality strategy, and a corresponding abnormality alarm is issued; the intelligent level of the security monitoring is improved, which helps to ensure the safety and stability of the monitored area.

[0136] Example 3: This example is intended to facilitate the solution of the coordination problem between modules. This example is an improvement made on the basis of Example 1. For details, please refer to Figure 1 ,Information acquisition module: obtain target information, which includes target monitoring area and image data information; ,collect various information and data of the monitoring area for subsequent analysis, ,processing and decision-making.

[0137] This module uses a camera or surveillance camera array to capture video image data from the monitored area. This video image acquisition allows for real-time monitoring of dynamic changes in the target area and subsequent image processing and analysis. In addition to video images, the information acquisition module can also collect sound data within the monitored area, such as voice information and ambient noise.

[0138] The image analysis module is a key component in IoT-based security monitoring systems. Its primary function is to intelligently analyze and process collected surveillance images to implement functions such as target detection, behavior recognition, and anomaly detection.

[0139] This module can identify and analyze the behavior of target objects in surveillance images, such as people entering or exiting, vehicles traveling, and objects moving. Through behavior recognition, it can determine whether abnormal behavior is occurring, providing timely warnings and implementing appropriate measures. By analyzing and comparing surveillance images, the image analysis module can determine whether anomalies exist, such as people entering restricted areas or objects moving in an unusual manner. When anomalies are detected, the system can trigger an alarm.

[0140] The information processing module is responsible for receiving, processing, storing, and analyzing various data and information obtained from the information acquisition and image analysis modules, including environmental data, image data, sensor data, etc. The module will integrate this data to form a complete monitoring data set.

[0141] This module is responsible for storing and managing the received data. Data can be stored on a local server or cloud platform for subsequent query, analysis, and decision-making. Data storage can be structured in a database or other form to provide fast and reliable data access and retrieval.

[0142] The Abnormal Alarm Module's primary function is to automatically generate alerts when abnormalities occur in the monitored area, prompting personnel to take timely action. Working in conjunction with the Information Collection and Image Analysis Modules, it automatically detects and identifies abnormalities in the monitored area. Real-time analysis and comparison of data and images can determine whether abnormalities, such as intrusion or theft, have occurred.

[0143] The abnormal alarm module will select different alarm methods based on different monitoring scenarios and user needs, such as sound and light alarms, SMS alarms, and email alarms. At the same time, the module will also trigger corresponding emergency response measures, such as notifying relevant personnel to handle the situation and launching patrols.

[0144] The abnormal alarm module can set different alarm levels and priorities according to different types of abnormal situations. For example, for abnormal situations in important areas, higher alarm levels and priorities can be set so that timely measures can be taken.

[0145] The exception alarm module also supports user-defined alarm conditions and parameters, allowing for adjustments to alarm logic for different scenarios. Users can set different alarm thresholds, time periods, and conditions based on actual needs. The exception handling process can also be recorded and analyzed for subsequent targeted analysis and improvement. For example, the occurrence time, handling process, and alarm results of various alarm situations can be recorded, providing a basis for subsequent safety management and decision-making.

[0146] In this embodiment: through the information acquisition module, the data of the target monitoring area is recorded and analyzed to provide data support for subsequent data analysis and abnormal signal determination; through the information processing module, the image data is intelligently analyzed to automatically determine abnormal situations and take corresponding measures; through the abnormal alarm module, an alarm can be triggered immediately after an abnormal signal is discovered, without waiting for manual intervention or processing, and a timely response can be made in the shortest time possible to ensure that the alarm can be triggered in a timely and accurate manner, thereby improving the emergency response capability and safety level of the monitoring system.

[0147] The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as laptop computers, PADs (tablet computers), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and the like, as well as fixed terminals.

[0148] An electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) or a program loaded from a storage device into a random access memory (RAM). In RAM, various programs and data required for the operation of the electronic device are also stored. The processing device, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0149] Typically, the following devices can be connected to the I / O interface: input devices such as touch screens, touchpads, image sensors, and microphones; output devices such as liquid crystal displays (LCDs) and speakers; storage devices such as magnetic tapes and hard disks; and communication devices. The communication devices can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data.

[0150] An embodiment of the present disclosure also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the high-precision map-based driving assistance recognition method in the aforementioned method embodiment.

[0151] It should be noted that more specific examples of computer-readable storage media may include portable computer disks, hard disks, erasable programmable read-only memories (EPROMs or flash memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable medium may be included in the electronic device described above, or may exist independently without being incorporated into the electronic device.

[0152] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device can implement the solution provided by the above method embodiment.

[0153] Computer program code for performing operations of the present disclosure may be written in one or more programming languages, or a combination thereof, and may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. Where a remote computer is involved, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0154] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0155] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A security monitoring system for construction project management, characterized in that: include: Information acquisition module: acquires target information, including target monitoring area and image data information; Image analysis module: Analyzes whether there is any abnormality in the target monitoring area based on the target information and generates abnormal signals; Information processing module: forms data anomaly analysis strategy based on abnormal signals; Abnormal alarm module: alarm based on the data after the execution of the data abnormality analysis strategy.

2. A method for using the security monitoring system for construction project management according to claim 1, characterized in that: The analysis of whether an abnormality occurs in the target monitoring area based on the target information to generate an abnormality signal specifically includes: Obtain target monitoring information of the target monitoring area; The target area is the currently monitored area; The target monitoring information includes a primary target area Q and a secondary target area W; Obtain image data information of the target monitoring area; Set the image frame rate threshold, denoted as E; The image data information of the target monitoring area includes target image data of the target area per frame rate E and an occlusion area in the target image.

3. The method of the construction project management security monitoring system according to claim 2, characterized in that: Acquire first target image data R of the first frame of the target area; Acquire the second target image data T1 of the target area in the second frame; Set the first occlusion rate threshold Y1; Comparing the first target image data R with the second target image data T1 to see if they are the same; If a shadow area I exists between the second target image data T1 and the first target image data R, first shadow area data U1 in the second target image data T1 is obtained; Comparing the first shadow area data U1 with the first occlusion rate threshold Y1; If the first shadow area data U1 is greater than the first occlusion rate threshold Y1; Then the second target image data T is recorded as the first abnormal signal P; If the first shadow area data U1 is less than the first occlusion rate threshold Y1; Then determine whether the shadow area I is in the main target area Q; If the shadow area I is not in the main target area Q, the second target image data T is determined to be a non-abnormal signal and the target area continues to be monitored; If the shadow area I is in the main target area Q, the second target image data T is determined to be a second abnormal signal S; Execute data anomaly analysis strategies.

4. The method of using a security monitoring system for construction project management according to claim 3, characterized in that: The execution data anomaly analysis strategy specifically includes: Obtain the surveillance camera angle of the second frame of the target area, recorded as the first angle A1; The surveillance camera is moved to multiple angles, which are recorded as a second angle A2, a third angle A3, and a fourth angle A4; Acquire a third target data image T2 of the surveillance camera at a second angle A2; Acquire a fourth target data image T3 when the surveillance camera is at a third angle A3; Acquire a fifth target data image T4 when the surveillance camera is at a fourth angle A4; Set the second occlusion rate threshold Y2; Acquire second shadow area data U2 of the third target data image T2; Acquire third shadow area data U3 of the fourth target data image T3; Acquire fourth shadow area data U4 of the fifth target data image T4; Compare the second shadow area data U2, the third shadow area data U3, and the fourth shadow area data U4 with the second occlusion rate threshold value Y2; If U2 is less than Y2, U3 is less than Y2, and U4 is less than Y2, then the second target image data T is determined to be a non-abnormal signal and the target area continues to be monitored; If U2 is less than Y2, U3 is greater than Y2, and U4 is less than Y2, then the second target image data T is recorded as the first abnormal signal P; Execute targeted anomaly analysis strategy.

5. The method of the construction project management security monitoring system according to claim 4, characterized in that: The target anomaly analysis strategy is specifically as follows: Obtain target occluder data in the occluded area of the target image; Determining whether the target obstruction is moving in real time; The specific steps for determining the real-time movement of the target occluder are: Obtain a first distance F1 between the target blocking object and the surveillance camera in the second target image data T1; Obtain a second distance F2 between the target blocking object and the surveillance camera in the third target data image T2; Compare the values of the first distance F1 and the second distance F2 to see if they are consistent.

6. The method of the security monitoring system for construction project management according to claim 5, characterized in that: If the first distance F1 and the second distance F2 have the same value; The first abnormal signal P is recorded as a fixed abnormality, and the first abnormal strategy is executed; The first exception strategy is as follows: Set a first time threshold G; Calculate the first data picture H1 of the total target, where the first data picture H1 = the first time threshold G × the image frame rate threshold E; Obtain the last target image data in the total target first data picture H1, and record it as the final first target image data J1; Acquire the fifth shadow area data U5 in the final first target image data J1; Comparing the fifth shadow area data U5 with the second occlusion rate threshold value Y2; If the fifth shadow area data U5 is less than the second occlusion rate threshold Y2, the second target image data T is determined to be a non-abnormal signal, and the target area continues to be monitored.

7. The method of using a security monitoring system for construction project management according to claim 6, characterized in that: If the fifth shadow area data U5 is greater than the second occlusion rate threshold Y2, the second exception strategy is executed; The second exception strategy is as follows: Obtain a third distance F3 between the fifth shadow area U5 and the surveillance camera; Set a first distance threshold K; Calculate the safety time L, safety time L = third distance F3 ÷ first distance threshold K; Obtain the second data image H2 of the total target within the safety time L, where the second data image H2 of the total target = safety time L × image frame rate threshold E; Obtain the last target image data in the total target second data picture H2, recorded as the final second target image data J2; Acquire the sixth shadow area data U6 in the final second target image data J2; Comparing the sixth shadow area U6 with the second occlusion rate threshold Y2; If the sixth shadow area U6 is greater than or equal to the second occlusion threshold Y2, the abnormal alarm module issues an alarm.

8. The method of using a security monitoring system for construction project management according to claim 7, characterized in that: If the first distance F1 and the second distance F2 have different values; The first abnormal signal P is recorded as an unfixed abnormality, and the second abnormal strategy is executed; The second exception strategy is as follows: Setting a second time threshold Z; Calculate the third data picture H3 of the total target, where the third data picture H3 = the second time threshold Z × the image frame rate threshold E; Obtain the last target image data in the total target third data picture H3, and record it as the final third target image data J3; Obtain a third distance F3 between the target obstructing object and the surveillance camera in the final third target image data J3; The target moving speed X of the target obstruction is calculated, where target moving speed X=(third distance F3−first distance F1)÷(second time threshold Z−1).

9. The method of using a security monitoring system for construction project management according to claim 8, characterized in that: Calculate the target pre-movement duration V of the target occluder, where: target pre-movement duration V = first distance F1 ÷ target movement speed X; Obtain the fourth data picture H4 of the total target within the target pre-movement duration V, where the fourth data picture H4 of the total target = target pre-movement duration V × image frame rate threshold E; Setting a first image abnormality threshold N1; Obtain the shadow areas of all images within the fourth data image H4 of the overall target; All the pictures in the fourth data picture H4 of the total target whose shadow area is greater than the second threshold value Y2 of the occlusion rate are recorded as abnormal image data M. If the number of abnormal image data M is greater than or equal to the first image abnormality threshold N1, the abnormality alarm module issues an alarm; If the abnormal image data M is less than the first image abnormality threshold N1; Setting a second image abnormality threshold N2; If the abnormal image data M in the fourth data picture H4 of the overall target is continuous, and the number of the continuous abnormal image data M is greater than or equal to the second image abnormality threshold N2, the abnormal alarm module generates an alarm.

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