Intelligent inspection alarm method and device

Abstract

The present application relates to the technical field of intelligent inspection, and discloses an intelligent inspection alarm method and device, the present application divides the whole inspection area, constructs the subdivision area digital model and the whole area digital model, drives the intelligent inspection unit reciprocating motion to collect the inspection data and carries out the first round alarm, then substitutes into the corresponding digital model, simulates the state of each subdivision area, so as to further analyze to generate real-time inspection characteristics, and according to the preset standard, whether alarm is needed is judged, the method realizes the fine management of the inspection area through area division and digital model, combines the automatic data collection and real-time analysis processing of the intelligent inspection unit, can quickly and accurately judge the area state, timely alarm, is suitable for the inspection demand of complex environment, uses the relationship between multiple subdivision inspection areas to further predict and assist in inspection, solves the problem of low early warning capability when the automatic inspection equipment is used to process complex inspection area in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of smart inspection, and in particular to a smart inspection alarm method and device. Background Art

[0002] Inspection technology has long been a crucial tool for ensuring system operation and safety in areas such as industrial inspection, public safety, and intelligent surveillance. However, with the diversification of inspection targets, the expansion of inspection areas, and the increasing demands for inspections, traditional inspection methods are struggling to cope with complex inspection scenarios.

[0003] Traditional inspection methods mainly rely on manual inspections or simple automated tools. These methods have many defects when dealing with complex inspection areas, such as low efficiency, low degree of automation, and poor early warning capabilities. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent patrol alarm method and device, aiming to solve the problem of low early warning capability when using automatic patrol equipment to handle complex patrol areas in the existing technology.

[0005] The present invention is implemented as follows: In a first aspect, the present invention provides a smart patrol alarm method, comprising:

[0006] The overall inspection area is divided into several subdivided inspection areas, and an overall area digital model composed of several subdivided area digital models is constructed based on each of the subdivided inspection areas; wherein the subdivided area digital models are used to perform digital simulation feedback on the subdivided inspection areas, and the overall area digital model is used to perform digital simulation feedback on the overall inspection area;

[0007] Driving each prepared smart inspection unit to reciprocate in each of the subdivided inspection areas, while collecting inspection information data of the subdivided inspection areas to obtain inspection information data acquired by each of the smart inspection units, and performing a first round of analysis and processing based on the inspection information data to determine whether to issue an alarm message for the subdivided inspection area;

[0008] Substituting the inspection information data obtained by each of the smart inspection units into the subdivided area digital model corresponding to each of the subdivided inspection areas, and causing the overall area digital model to simulate the area status of each of the subdivided inspection areas according to each of the inspection information data, so as to obtain the area status of each of the subdivided inspection areas;

[0009] Perform inspection analysis on each inspection information data according to the regional status of each subdivided inspection area to obtain the real-time inspection characteristics of each inspection area, and judge and process the real-time inspection characteristics according to the preset standards to decide whether to issue an alarm information for the subdivided inspection area

[0010] In a second aspect, the present invention provides a smart patrol alarm device for implementing a smart patrol alarm method according to any one of the first aspects, comprising:

[0011] A region division module is used to divide the overall inspection area into several subdivided inspection areas, and to construct an overall area digital model composed of several subdivided area digital models based on each of the subdivided inspection areas; wherein the subdivided area digital models are used to provide digital simulation feedback for the subdivided inspection areas, and the overall area digital model is used to provide digital simulation feedback for the overall inspection area;

[0012] The regional inspection module is used to drive each of the intelligent inspection units to reciprocate in each of the subdivided inspection areas, and at the same time collect inspection information data for the subdivided inspection areas to obtain the number of inspection information obtained by each of the intelligent inspection units, and perform a first round of analysis and processing based on the inspection information data to determine whether to issue an alarm information for the subdivided inspection area;

[0013] a regional analysis module, configured to substitute the inspection information data acquired by each of the intelligent inspection units into the subdivided regional digital models corresponding to each of the subdivided inspection areas, and to cause the overall regional digital model to simulate the regional status of each of the subdivided inspection areas based on each of the inspection information data, so as to obtain the regional status of each of the subdivided inspection areas;

[0014] The inspection alarm module is used to perform inspection analysis and processing on each of the inspection information data according to the area status of each of the subdivided inspection areas to obtain the real-time inspection characteristics of each of the inspection areas, and to judge and process the real-time inspection characteristics according to preset standards to decide whether to issue an alarm information for the subdivided inspection area.

[0015] The present invention provides a smart patrol alarm method, which has the following beneficial effects:

[0016] The present invention divides the overall inspection area, constructs digital models of the subdivided areas and the overall area, drives the intelligent inspection unit to move back and forth to collect inspection data for an initial round of alarm, and then substitutes the corresponding digital model to simulate the status of each subdivided area, so as to perform further analysis to generate real-time inspection features, and judge whether an alarm is needed based on preset standards. This method realizes refined management of the inspection area through area division and digital models, and combines the automated data collection and real-time analysis and processing of the intelligent inspection unit to quickly and accurately judge the area status, issue an alarm in time, and improve inspection efficiency and safety. It is suitable for inspection needs in large areas and complex environments, and at the same time uses the relationship between multiple subdivided inspection areas to perform further prediction and auxiliary inspections, solving the problem of low early warning capability when using automatic inspection equipment to handle complex inspection areas in the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of the steps of a smart patrol alarm method provided by an embodiment of the present invention;

[0018] Figure 2 It is a structural diagram of a smart patrol alarm device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] The implementation of the present invention is described in detail below with reference to specific embodiments.

[0021] Reference Figure 1 、 Figure 2 As shown, a preferred embodiment of the present invention is provided.

[0022] In a first aspect, the present invention provides a smart patrol alarm method, comprising:

[0023] S1: Divide the overall inspection area into several subdivided inspection areas, and construct an overall area digital model composed of several subdivided area digital models based on each of the subdivided inspection areas; wherein the subdivided area digital models are used to perform digital simulation feedback on the subdivided inspection areas, and the overall area digital model is used to perform digital simulation feedback on the overall inspection area;

[0024] S2: driving each of the smart inspection units to reciprocate in each of the subdivided inspection areas, and simultaneously collecting inspection information data for the subdivided inspection areas to obtain inspection information data acquired by each of the smart inspection units, and performing a first round of analysis and processing based on the inspection information data to determine whether to issue an alarm message for the subdivided inspection area;

[0025] S3: Substituting the inspection information data obtained by each of the smart inspection units into the subdivided area digital model corresponding to each of the subdivided inspection areas, and causing the overall area digital model to simulate the area status of each of the subdivided inspection areas according to each of the inspection information data, so as to obtain the area status of each of the subdivided inspection areas;

[0026] S4: Perform inspection analysis on each inspection information data according to the regional status of each subdivided inspection area to obtain the real-time inspection characteristics of each inspection area, and judge the real-time inspection characteristics according to the preset standards to decide whether to issue an alarm information for the subdivided inspection area

[0027] Specifically, in step S1 of the embodiment provided by the present invention, the boundaries and characteristics of the overall inspection area are first identified and divided into several subdivided inspection areas. The basis for the division may include factors such as geographical location, functional zoning, equipment distribution, and monitoring requirements to ensure that the division of each subdivided area is reasonable for subsequent inspection and management.

[0028] It should be noted that the execution of inspection work depends on the collaborative work of multiple smart inspection units. Therefore, when dividing the inspection areas into subdivided areas, the inspection capabilities of the smart inspection units need to be taken into consideration to make the subdivided inspection areas compatible with the inspection capabilities of the smart inspection units.

[0029] More specifically, a corresponding digital model is established for each subdivided inspection area. The digital model of each subdivided area should be able to reflect key information such as the layout of the equipment in the area, environmental conditions, inspection paths, etc. After the model is built, it is verified to ensure that it can accurately simulate the status of the actual inspection area. If necessary, the model can be iteratively corrected multiple times to improve the accuracy of the simulation.

[0030] More specifically, the digital models of each segmented area are integrated to construct a digital model of the entire area. This overall model can reflect the global status of the entire inspection area and support unified monitoring of equipment and environment in a large range. During the integration process, the relative position relationship between each segmented area and the logical rules of mutual influence are defined. This ensures that the interaction between the segmented areas is correctly reflected in the overall simulation.

[0031] More specifically, during the actual inspection process, digital models of each subdivided area are used to simulate feedback on real-time data collected. This process includes real-time calculation and visualization of information such as equipment status and environmental parameters. The overall regional digital model is then used to simulate feedback on the global status. The overall model integrates feedback information from subdivided areas to provide comprehensive inspection results and status assessments.

[0032] It's understandable that by using digital models of segmented areas, the system can precisely monitor the status of each individual area, capturing subtle changes and potential risks. This segmented management model improves inspection accuracy and reduces missed inspections and false alarms. The overall regional digital model provides a global perspective, enabling integrated analysis of the status of all segmented areas across a large scale. The system uses this digital model to achieve dynamic response and real-time feedback on the inspection area. During the inspection process, the digital model can immediately reflect changes in equipment and the environment, enabling inspectors and the system to take timely action.

[0033] It should be noted that in the embodiment provided by the present invention, the overall inspection area can be a designated area, which can be an industrial park, a school campus, a scenic park, etc. The designated area is divided into several subdivided inspection areas, and multiple smart inspection units are used to perform inspection work in each subdivided inspection area. The inspection work includes object identification of the inspection objects in the subdivided inspection area to determine whether there are inspection objects that do not meet the preset identification rules so as to issue an alarm.

[0034] More specifically, when the overall inspection area is an industrial park or school campus, the identification standard for the inspection object can be standard clothing and an identity information verification system. The first round of identification is performed by identifying the appearance. In addition, the second round of identification can be performed by inputting identity verification information. The two rounds of identification can take into account both the efficiency and accuracy of the inspection.

[0035] Specifically, in step S2 of the embodiment provided by the present invention, the prepared smart inspection units are deployed to each subdivided inspection area based on the results of the preliminary area division. Each inspection unit should be assigned to a suitable area to ensure that all key equipment and locations in the area can be covered. The smart inspection unit is started to perform system self-inspection and initialization settings. During the initialization process, the smart inspection unit will synchronize with the regional digital model to obtain the inspection path, monitoring points and data acquisition parameters.

[0036] More specifically, each smart inspection unit plans specific inspection routes, which should cover all important inspection points in the subdivided area to ensure that all monitoring needs can be fully covered during the inspection process. The smart inspection unit begins to move back and forth along the predetermined path in the subdivided inspection area. This process can be carried out according to the set time interval and frequency to ensure that the inspection of the entire area is completed within a certain time.

[0037] More specifically, during the inspection process, the smart inspection unit uses built-in sensors and cameras to collect inspection information data such as environmental parameters, equipment status, video images, etc. in real time. Data collection should be efficient and accurate, covering all key nodes. The smart inspection unit will transmit the collected inspection information data to the central control system in real time via wireless networks or other communication methods for subsequent analysis and processing.

[0038] More specifically, after receiving inspection data transmitted by the intelligent inspection units, the central control system first preprocesses it, filtering out noise and outliers and organizing it into a standardized format for subsequent analysis. The system then performs a first round of analysis on the preprocessed data to check for significant anomalies or potential risks. If any data exceeds preset safety thresholds or exhibits unusual trends, the system will flag the data and prepare for further analysis. Based on the results of this first round of analysis, it will determine whether an alarm is warranted. Alarm criteria are typically based on equipment operating status, abnormal changes in environmental parameters, and other factors to ensure timely detection of problems. If an alarm is deemed necessary, the system will immediately trigger the alarm mechanism, notifying relevant personnel or automatically initiating an emergency plan. Alarm information can be sent via various means, such as text messages, emails, and audio and visual alarms.

[0039] It's understandable that the intelligent inspection unit, through its reciprocating motion, conducts real-time monitoring within a subdivided inspection area, quickly capturing any anomalies within the area. The real-time nature of data collection and transmission ensures the inspection system's ability to respond promptly, allowing problems to be discovered and addressed at an early stage. Through well-planned inspection routes and effective reciprocating motion, the intelligent inspection unit can cover all key areas within a subdivided area, ensuring no omissions. Simultaneously, the initial round of analysis and processing enables rapid and accurate preliminary assessment of the data, providing efficient alarm judgment. The inspection process is highly automated, reducing reliance on manual inspections and making it particularly suitable for use in dangerous, harsh, or complex environments. This not only improves inspection efficiency but also reduces the cost of manual intervention and potential safety risks.

[0040] Specifically, in step S3 of the embodiment provided by the present invention, the inspection information data collected by the smart inspection unit is matched to the corresponding subdivided inspection area according to the location and time of the inspection unit, ensuring that each data point can be accurately mapped to the digital model of the subdivided area to which it belongs.

[0041] More specifically, the inspection information data is used as input parameters and substituted into the corresponding digital model of the subdivided area. This process includes importing environmental parameters (such as temperature and humidity), equipment status (such as voltage and current), image data, etc. into the model for simulation calculation, activating the digital model of the subdivided area, and simulating its status based on the input data. The model will calculate the current status of the area according to preset algorithms and rules, including the operating status of the equipment, changes in the environment, etc.

[0042] More specifically, the simulation results of the digital models for each subdivided area are aggregated into the overall regional digital model. The overall model integrates the states of all subdivided areas to form a global simulation of the entire inspection area. Based on the aggregated states of the subdivided areas, the overall regional digital model performs higher-level simulation processing. It not only evaluates the independent states of each area but also considers the interactions between areas, thereby generating a more accurate global state map.

[0043] More specifically, based on the simulation results of the overall regional digital model, the status of each subdivided inspection area is evaluated. The evaluation results can be displayed through a visual interface, such as 3D graphics, status color coding, and trend charts. A status report for each subdivided inspection area is generated. The report content can include the health status of the area, potential risk points, and equipment requiring special attention. The report can be used for further decision support or automated response mechanisms.

[0044] It is understandable that by substituting inspection information data into the digital model of the subdivided area, the system can achieve refined management of each subdivided area. This precise control method ensures that the status of each area can be comprehensively monitored and evaluated. The combination of data and model improves the accuracy of regional status simulation. Through the input of actual data, the model can more accurately reflect the true status of the current area, thereby improving the reliability and credibility of the overall system. The overall regional digital model can integrate the status of each subdivided area and provide a global perspective. This global perspective helps to understand the interaction between different areas and the overall operating status, ensuring effective collaborative management at the system level. The regional status obtained after simulation processing can promptly identify potential risk points, and the system can trigger early warning or automatic response mechanisms based on this status information. This timely response capability can effectively prevent the occurrence of failures or accidents and improve the safety of the system.

[0045] Specifically, in step S4 of the embodiment provided herein, the regional status of the subdivided inspection area is integrated with the inspection information data. This process involves associating regional status data (such as equipment operating status and environmental parameters) with inspection information data (such as sensor readings and image data) to form a complete basic data set for inspection analysis. This consolidated data set is used for further analysis and processing to ensure data integrity and consistency. Data processing algorithms are used to analyze and process the integrated inspection information data. These algorithms may include signal processing, pattern recognition, anomaly detection, and other methods to extract key inspection features. Features can include equipment temperature, vibration frequency, and foreign object detection in images. Through analysis and processing, real-time inspection features at the current moment are extracted from the data. These features represent the key status of the equipment and environment within the current subdivided inspection area.

[0046] More specifically, the extracted real-time inspection features are compared against pre-set standards. These standards are typically based on historical data, equipment manuals, or industry specifications, covering the range of parameters for normal operation. Pre-set judgment rules are then applied to determine whether the inspection features meet these standards. Judgment rules can range from simple threshold comparisons to more complex multivariate conditional analysis. Based on the judgment results, if one or more inspection features exceed the pre-set standards, the system will evaluate whether the conditions for triggering an alarm are met. Alarm conditions may include a single anomaly exceeding a limit, multiple indicators being abnormal simultaneously, or the identification of specific patterns. Once an alarm is determined to be necessary, the system will immediately trigger an alarm message. Alarms can be sent through various channels, such as immediate notification of relevant personnel, automatic activation of emergency response plans, and logging. All inspection features, judgment results, and alarm information are recorded for subsequent analysis and system optimization. This data can also be used to refine the pre-set standards and judgment rules, improving the accuracy of the system's alarms.

[0047] It is understandable that the system realizes dynamic real-time monitoring of the inspection area by integrating regional status and inspection information data, precise inspection feature extraction and standardized comparison, ensuring that the system can accurately identify abnormal situations and provide timely and effective alarms. By comparing with preset standards, the system can effectively reduce false alarms and avoid unnecessary interruptions or interventions. At the same time, the system can respond quickly when real abnormalities occur to ensure that the problem can be dealt with in the first time. Through inspection analysis and processing, the system can not only identify current abnormalities, but also continuously optimize and adjust preset standards through the accumulation and analysis of historical data. This adaptive capability enhances the intelligence of the system, enabling it to better cope with complex and changing environments.

[0048] The present invention provides a smart patrol alarm method, which has the following beneficial effects:

[0049] The present invention divides the overall inspection area, constructs digital models of the subdivided areas and the overall area, drives the intelligent inspection unit to move back and forth to collect inspection data for an initial round of alarm, and then substitutes the corresponding digital model to simulate the status of each subdivided area, so as to perform further analysis to generate real-time inspection features, and judge whether an alarm is needed based on preset standards. This method realizes refined management of the inspection area through area division and digital models, and combines the automated data collection and real-time analysis and processing of the intelligent inspection unit to quickly and accurately judge the area status, issue an alarm in time, and improve inspection efficiency and safety. It is suitable for inspection needs in large areas and complex environments, and at the same time uses the relationship between multiple subdivided inspection areas to perform further prediction and auxiliary inspections, solving the problem of low early warning capability when using automatic inspection equipment to handle complex inspection areas in the existing technology.

[0050] Preferably, the steps of dividing the overall inspection area into several subdivided inspection areas and constructing an overall area digital model composed of several subdivided area digital models according to each of the subdivided inspection areas include:

[0051] S11: Acquire overall area information of the overall inspection area, and construct an overall area digital model for performing digital simulation feedback on the overall inspection area according to the overall area information;

[0052] S12: Obtaining the inspection performance of the prepared smart inspection unit, and constructing a digital model of the inspection unit for performing digital simulation feedback on the smart inspection unit according to the inspection performance of the smart inspection unit;

[0053] S13: performing distribution setting processing on the digital models of the inspection units based on the overall area digital model to obtain a plurality of inspection unit distribution characteristics; wherein the inspection unit distribution characteristics are used to describe a distribution form of the smart inspection units in the overall inspection area;

[0054] S14: performing distribution rationality analysis on the various inspection unit distribution characteristics based on the overall regional digital model to obtain distribution rationality parameters corresponding to the various inspection unit distribution characteristics, and taking the inspection unit distribution characteristic with the best distribution rationality parameter as the actual set distribution characteristic;

[0055] S15: determining an initial setting position of each of the smart inspection units on the overall regional digital model according to the actual setting distribution characteristics, and performing an inspection range expansion process based on the initial setting position of each of the smart inspection units to obtain an inspection range corresponding to each of the smart inspection units in the overall regional digital model;

[0056] S16: Divide the overall inspection area into a plurality of subdivided inspection areas according to the inspection range of each of the smart inspection units, and construct subdivided area digital models corresponding to each of the subdivided inspection areas on the basis of the overall inspection area.

[0057] Specifically, various information about the entire inspection area is collected, including geographic information, equipment layout, and environmental parameters. This information provides the data foundation for subsequent digital simulations. Based on this collected information, a digital model of the entire area is constructed. This model is used to simulate the entire inspection area, reflecting its overall status and operational conditions.

[0058] More specifically, the inspection performance of the prepared smart inspection units is acquired and evaluated, including inspection speed, accuracy, detection range, and data transmission capacity. Based on the performance data of the smart inspection units, a corresponding digital model of the inspection units is constructed. This model is used to simulate the operating characteristics and operating range of each inspection unit.

[0059] More specifically, based on the digital model of the overall area, the digital models of the inspection units are reasonably distributed to generate the distribution characteristics of several types of inspection units, describing the different distribution forms of each smart inspection unit in the overall inspection area. Each distribution form will produce different inspection unit distribution characteristics, which are used to describe the configuration of the inspection units in the area. The rationality of the distribution characteristics of each inspection unit is analyzed to evaluate their coverage, inspection efficiency, resource utilization, etc., and the rationality parameters corresponding to each distribution characteristic are calculated.

[0060] More specifically, the inspection unit distribution feature with the best rationality parameters is selected as the actual setting distribution feature. This feature will serve as the final layout plan for the inspection units within the area. Based on the optimal distribution feature, the initial setting position of each smart inspection unit is determined on the overall regional digital model. Starting from this initial position, the inspection range of each inspection unit is determined, and regional coverage is expanded based on this range to ensure that each inspection unit can fully cover the designated area. Based on the inspection range of the smart inspection unit, the overall inspection area is divided into several sub-inspection areas, each of which corresponds to the coverage range of one or more inspection units.

[0061] More specifically, based on the divided inspection areas, corresponding digital models of the subdivided areas are constructed. Each digital model of the subdivided area is used to simulate the specific conditions in the area to achieve more refined regional management and monitoring.

[0062] It is understandable that through the rational division of the overall area and the precise configuration of the inspection units, the system has achieved refined management and precise coverage of the inspection area. This approach ensures that all subdivided areas can be fully monitored and potential problems are detected without omission. The optimal distribution characteristics are selected through rationality analysis, effectively optimizing the resource allocation of the smart inspection units. This can not only reduce unnecessary waste of resources, but also improve inspection efficiency and ensure that key areas receive key attention. The construction of digital models of subdivided areas and the precise layout of inspection units improve the reliability of the system. By monitoring each subdivided area, the system can identify problems faster and more accurately, improving overall safety. The hierarchical construction of the overall area digital model and the subdivided area digital model gives the system good dynamic adjustment capabilities. When the situation in the overall inspection area changes, the system can quickly adjust the subdivided area division and the inspection unit distribution to flexibly respond to new challenges.

[0063] Preferably, the steps of driving the prepared smart inspection units to reciprocate in each of the subdivided inspection areas and collecting inspection information data of the subdivided inspection areas to obtain the inspection information data acquired by each of the smart inspection units include:

[0064] S21: Analyzing and processing the inspection paths of the smart inspection units according to the digital model of the overall area and the digital models of each subdivided area to obtain the inspection paths of the smart inspection units in each subdivided inspection area, and driving each smart inspection unit to perform reciprocating motion in each subdivided inspection area according to the inspection paths of the smart inspection units in each subdivided inspection area;

[0065] S22: driving the intelligent inspection unit to perform reciprocating motion in the subdivided inspection area to collect inspection information data according to a preset inspection plan to obtain the inspection information data.

[0066] Specifically, based on the digital model of the overall area and the digital models of each subdivided area, the inspection path of each smart inspection unit is analyzed in detail. This includes considering factors such as the terrain, obstacles, equipment layout, etc. of each subdivided inspection area to ensure the rationality and effectiveness of the path. Based on the analysis results, a specific inspection path is formulated for each smart inspection unit. The path should cover the key locations of the subdivided inspection area to ensure that there are no blind spots and no missed inspections.

[0067] More specifically, according to the established inspection path, the smart inspection units are driven to move back and forth within the subdivided inspection areas they are responsible for. The system should have high-precision positioning and navigation capabilities to ensure that the inspection units can move strictly according to the path and control the smart inspection units to move back and forth on the specified path to ensure repeated inspections of the entire subdivided area to prevent missing potential problems.

[0068] More specifically, according to the requirements of the inspection task, a preset inspection plan is configured for each smart inspection unit. The inspection plan should clearly stipulate details such as the movement speed, collection frequency, and data processing method of the inspection unit to adapt to different inspection environments and targets. During the reciprocating movement of the smart inspection unit, data collection of inspection information is carried out according to the preset inspection plan. The system should be able to record and process the collected data in real time to ensure the accuracy and completeness of the data.

[0069] More specifically, the collected inspection information data is preliminarily processed, such as data filtering, formatting, and classification, to ensure that the data can be directly used for subsequent analysis, reduce the storage and processing burden of useless data, and store the processed inspection information data in the database for subsequent analysis and retrieval. The storage system should have good scalability and security, and be able to support efficient storage and retrieval of large amounts of data.

[0070] It's understandable that by analyzing and developing optimal inspection routes, the system ensures that intelligent inspection units efficiently cover the entire segmented inspection area and conduct precise inspections. This efficient path planning reduces inspection time while improving inspection accuracy, ensuring no blind spots are missed. The system can dynamically adjust inspection routes and movement patterns based on real-time data and environmental changes, flexibly responding to varying inspection needs. This dynamic adaptability enhances the system's resilience, enabling timely responses to emergencies or environmental changes.

[0071] Preferably, the preset inspection scheme includes a basic inspection strategy and an additional inspection strategy, the basic inspection strategy is used to obtain basic inspection information, the additional inspection strategy is used to obtain additional inspection information, and the basic inspection information and the additional inspection information are both the inspection information data;

[0072] S221: The basic inspection strategy includes:

[0073] S2211: Collecting information through an information collection module preset on the smart inspection unit to obtain basic inspection information;

[0074] S2212: Analyze and process the basic inspection information to extract inspection object information and object identification tags corresponding to the inspection object information from the basic inspection information; wherein the inspection object information is used to provide multi-dimensional information feedback on the inspection object, and the object identification tags are used to identify and classify the inspection object based on the inspection object information;

[0075] S2213: judging and processing the object identification tag of the inspection object information according to a preset standard; when the object identification tag of the inspection object information meets the preset standard, marking the inspection object corresponding to the inspection object information as a key inspection object, and driving the smart inspection unit to execute the additional inspection strategy for the key inspection object;

[0076] S222: The additional inspection strategy includes:

[0077] S2221: driving the smart inspection unit to send a sound inspection instruction toward the key inspection object and move toward the key inspection object;

[0078] S2222: When the key inspection object refuses to execute the sound inspection instruction, generate and send an alarm instruction;

[0079] S2223: When the key inspection object receives and executes the sound inspection instruction, the key inspection object is required to display the target inspection information on the smart inspection unit. When the key inspection object fails to display the correct target inspection information, an alarm instruction is generated and sent. When the key inspection object displays the correct target inspection information, corresponding additional inspection information is generated according to the target inspection information; wherein, the additional inspection information is used to perform digital identity verification on the key inspection object to determine whether to issue an alarm message.

[0080] Specifically, the execution of the basic inspection strategy includes: starting the information collection module on the smart inspection unit to collect information on targets within the inspection area. The module may include cameras, sensors, RFID readers and other devices to capture multi-dimensional information of the inspection objects, such as images, temperature, location, etc.

[0081] More specifically, the collected basic inspection information is analyzed and processed to extract information related to the inspection object, and object identification tags are generated. These tags are classified based on the specific characteristics of the inspection object (such as color, shape, sound, etc.) for subsequent identification and processing. The judgment of object identification tags and the determination of key inspection objects: the object identification tags are judged and processed according to preset standards. When the tag meets a specific standard (such as abnormal appearance, abnormal behavior, etc.), the corresponding inspection object is marked as a key inspection object, and preparations are made to initiate additional inspection strategies.

[0082] More specifically, the intelligent inspection unit is driven to send a voice inspection instruction to the key inspection target. This can be a verbal command, warning, or inquiry issued through a speaker, aimed at confirming the status or behavior of the target. At the same time, the intelligent inspection unit begins tracking the key inspection target, performing close-range movements to ensure that the target is within the controllable range. This process is intended to further confirm the identity and status of the target. If the key inspection target refuses to execute the voice inspection instruction (such as not responding or exhibiting abnormal behavior), the system immediately generates and sends an alarm instruction to notify the management personnel or initiate automated response measures. If the key inspection target accepts and executes the voice inspection instruction, the system will require the target to display specific target inspection information (such as identity card, authorization code, etc.).

[0083] More specifically, when a key inspection object fails to display the correct target inspection information, the system will generate and send an alarm instruction, indicating that there is a potential risk. If the object displays the correct target inspection information, the system will generate corresponding additional inspection information based on the information and perform digital identity verification on the object to decide whether to continue the inspection or end the current task.

[0084] As you can see, the basic inspection strategy enables the system to perform preliminary identification and classification of all inspection targets and select key inspection targets based on pre-set criteria. This refined approach ensures efficient and accurate inspections, enabling timely detection of potential risks or anomalies. Additional inspection strategies further enhance monitoring of key inspection targets, ensuring compliance and authenticity through multiple verification methods (such as voice commands, target information display, and digital identity verification). This multi-layered verification mechanism significantly improves system security and reliability. By combining basic and additional inspection strategies, the system automates the entire inspection process, from initial identification to in-depth verification. This intelligent inspection approach reduces the need for manual intervention, improves inspection efficiency, and enhances the ability to manage inspection targets in complex environments. In additional inspection strategies, the system verifies target inspection information and generates additional inspection information, enabling digital identity verification of key inspection targets. This technology not only ensures the legitimacy of inspection targets but also provides reliable data support for subsequent security management and decision-making.

[0085] Preferably, the steps of substituting the inspection information data acquired by each of the smart inspection units into the subdivided area digital models corresponding to each of the subdivided inspection areas, and causing the overall area digital model to simulate the area status of each of the subdivided inspection areas according to each of the inspection information data to obtain the area status of each of the subdivided inspection areas include:

[0086] S31: Substituting the inspection information obtained by each of the smart inspection units into the subdivided area digital model corresponding to each of the subdivided inspection areas, and causing the subdivided area digital model to perform object recognition distribution analysis based on the inspection object information and object recognition tags in the inspection information to obtain object recognition distribution characteristics of the subdivided inspection area; wherein the object recognition distribution characteristics include object distribution characteristics and object recognition characteristics, the object distribution characteristics are used to describe the distribution status of the inspection objects in the subdivided inspection area, and the object recognition characteristics are used to describe the object status of the inspection objects in the subdivided inspection area;

[0087] S32: Instructing the overall area digital model to integrate and analyze the object recognition distribution features of the digital models of each subdivided area to obtain object statistical features in the overall inspection area; wherein the object statistical features are used to describe the overall number and overall status of the inspection objects in the overall inspection area;

[0088] S33: Analyzing and processing the object recognition distribution characteristics of the subdivided inspection area according to the object statistical characteristics of the overall inspection area to obtain the area proportion of the subdivided inspection area; wherein the area proportion is used to describe the proportion of the number and status of the inspection objects in the subdivided inspection area to the overall inspection area;

[0089] S34: The overall area digital model is used to perform object distribution trend analysis on the object recognition distribution characteristics of the subdivided inspections based on the relative positional relationship between the digital models of the subdivided areas; wherein the object distribution trend analysis is used to describe the possible change trend of the inspection recognition distribution characteristics of each subdivided inspection area in the future based on the current object recognition distribution characteristics of each subdivided inspection area;

[0090] S35: The area proportion of the subdivided inspection area and the object distribution trend characteristics are used together as the area status of the subdivided inspection area.

[0091] Specifically, the inspection information data obtained by each smart inspection unit is substituted into the digital model of the corresponding subdivided inspection area. Each subdivided area digital model then performs object recognition distribution analysis based on the inspection object information and object recognition tags contained in the inspection information. Through this processing, the system can extract the object recognition distribution characteristics of each subdivided inspection area.

[0092] More specifically, it describes the spatial distribution of inspection objects within a specific inspection area. For example, the density of objects in a specific area, the degree of concentration or dispersion, etc., and describes the status characteristics of the inspection objects, such as health status, working status, and whether there are any abnormalities.

[0093] More specifically, the object recognition distribution features of the digital models of each subdivided area are integrated into the digital model of the overall area, and statistical analysis is performed on the inspection objects in the overall inspection area. The system generates object statistical features based on these data to describe the overall number and status of objects in the entire inspection area.

[0094] More specifically, based on the object statistical characteristics of the overall inspection area, the object identification distribution characteristics of each subdivided inspection area are analyzed and processed in terms of regional proportion. The regional proportion is used to describe the proportion of the number and status of inspection objects in a subdivided inspection area in the entire inspection area. Through this analysis, the importance and potential risks of each area can be understood.

[0095] More specifically, based on the relative positional relationships between the digital models of the subdivided areas, the overall regional digital model calculates the object distribution trend characteristics within each subdivided inspection area. These characteristics describe the possible changing trends in the distribution of inspection object identification in the future, such as the direction of object movement within the area, and the trend of concentration or dispersion. The regional proportion is combined with the object distribution trend characteristics to comprehensively evaluate the regional status of the subdivided inspection area. This evaluation result will be output as the final status of the subdivided inspection area to guide subsequent inspection decisions and management.

[0096] It is understandable that by substituting inspection information data into the digital model of the subdivided area, the system can accurately reflect the real-time status of each inspection area. This precise assessment provides a reliable data basis for inspection management, and can effectively discover and warn of potential risk areas. The combination of regional proportion and object distribution trend characteristics enables the system to not only evaluate the status of a single subdivided area, but also understand the relative importance and development trend of each subdivided area in the entire inspection area through the correlation analysis of the overall area. The introduction of object distribution trend characteristics enables the system to predict the possible changing trends of inspection objects in the future. This predictive ability helps to respond to potential changes in advance, optimize the allocation and scheduling of inspection resources, and improve the initiative and foresight of inspection work.

[0097] Preferably, the step of performing inspection analysis and processing on each of the inspection information data according to the regional status of each of the subdivided inspection areas to obtain the real-time inspection characteristics of each of the inspection areas includes:

[0098] S41: Analyzing and processing the continuous proportion change characteristics of the inspection information data according to the regional proportion of the regional status of the subdivided inspection area to obtain a proportion distribution change characteristic for describing the change in the proportion of the inspection objects fed back by the inspection information data of the subdivided inspection area relative to the inspection objects of the overall inspection area within a continuous time period;

[0099] S42: Perform deviation analysis on the proportion distribution change feature according to the object distribution trend feature of the area status of the subdivided inspection area to obtain a deviation degree parameter between the proportion distribution change feature and the object distribution trend feature, and use the deviation degree parameter as the real-time inspection feature.

[0100] Specifically, inspection information data is collected from each subdivided inspection area. These data include the number and status of inspection objects in the area within a specific time period. According to the regional proportion of the subdivided inspection area, the collected inspection information data is continuously analyzed to calculate the changes in the proportion of inspection objects in the subdivided inspection area and the overall inspection area in each time period. The goal of this step is to identify the changing trend of the number or status of inspection objects in the subdivided inspection area over a period of time, and form the characteristics of the proportion distribution change.

[0101] More specifically, based on the object distribution trend characteristics of the subdivided inspection area, a deviation analysis is performed on the previously obtained proportion distribution change characteristics. The deviation analysis aims to evaluate whether the proportion distribution change characteristics are consistent with the expected object distribution trend characteristics. By comparing the difference between the proportion distribution change characteristics and the object distribution trend characteristics, the system calculates a deviation degree parameter. This parameter quantifies the degree of deviation between the actual inspection data and the expected distribution trend, reflecting the difference between the actual distribution of the inspection object and the predicted distribution.

[0102] More specifically, the calculated deviation parameters are integrated into real-time inspection features. These real-time inspection features reflect the degree of match between the actual and expected status of the inspection objects within the subdivided inspection area at the current point in time, providing real-time inspection status feedback. Ultimately, the system outputs these integrated real-time inspection features as a reference for inspection work. These real-time inspection features can be used to guide subsequent inspection operations, adjust inspection strategies, or trigger early warning mechanisms.

[0103] It can be understood that by continuously analyzing the changing characteristics of the proportion, the system can provide dynamic distribution status of inspection objects at different time points. This real-time analysis enables inspection management to quickly respond to any changes or abnormal situations, improving the agility and effectiveness of inspection work. By comparing actual data with expected trends, deviation analysis provides accurate assessments. This analysis can identify deviations between the distribution of inspection objects and expectations within a specific inspection area, providing an important reference for inspection management and facilitating timely adjustment of inspection strategies. The calculation and integration of deviation degree parameters enable the system to provide more accurate inspection feature information. This precise guidance helps improve the efficiency of inspection work, reduce inspection blind spots, and optimize resource allocation and utilization. The output of real-time inspection features not only reflects the current inspection status but also triggers an early warning mechanism. When the deviation degree parameter exceeds the preset threshold, the system can automatically prompt inspection personnel to take measures, thereby improving the safety and reliability of inspection work. By combining proportion change analysis and deviation analysis processing, the system achieves comprehensive monitoring and management of inspection work. This comprehensive analysis and real-time feedback mechanism greatly improves the intelligence level of inspection management, making the overall inspection work more efficient and accurate.

[0104] Reference Figure 2 As shown, in a second aspect, the present invention provides a smart patrol alarm device for implementing a smart patrol alarm method according to any one of the first aspects, comprising:

[0105] A region division module is used to divide the overall inspection area into several subdivided inspection areas, and to construct an overall area digital model composed of several subdivided area digital models based on each of the subdivided inspection areas; wherein the subdivided area digital models are used to provide digital simulation feedback for the subdivided inspection areas, and the overall area digital model is used to provide digital simulation feedback for the overall inspection area;

[0106] The regional inspection module is used to drive each of the intelligent inspection units to reciprocate in each of the subdivided inspection areas, and at the same time collect inspection information data for the subdivided inspection areas to obtain the number of inspection information obtained by each of the intelligent inspection units, and perform a first round of analysis and processing based on the inspection information data to determine whether to issue an alarm information for the subdivided inspection area;

[0107] a regional analysis module, configured to substitute the inspection information data acquired by each of the intelligent inspection units into the subdivided regional digital models corresponding to each of the subdivided inspection areas, and to cause the overall regional digital model to simulate the regional status of each of the subdivided inspection areas based on each of the inspection information data, so as to obtain the regional status of each of the subdivided inspection areas;

[0108] The inspection alarm module is used to perform inspection analysis and processing on each of the inspection information data according to the area status of each of the subdivided inspection areas to obtain the real-time inspection characteristics of each of the inspection areas, and to judge and process the real-time inspection characteristics according to preset standards to decide whether to issue an alarm information for the subdivided inspection area.

[0109] In this embodiment, for the specific implementation of each module in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A smart patrol alarm method, characterized in that: include: The overall inspection area is divided into several subdivided inspection areas, and an overall area digital model composed of several subdivided area digital models is constructed based on each of the subdivided inspection areas; wherein the subdivided area digital models are used to perform digital simulation feedback on the subdivided inspection areas, and the overall area digital model is used to perform digital simulation feedback on the overall inspection area; Driving each prepared smart inspection unit to reciprocate in each of the subdivided inspection areas, while collecting inspection information data of the subdivided inspection areas to obtain inspection information data acquired by each of the smart inspection units, and performing a first round of analysis and processing based on the inspection information data to determine whether to issue an alarm message for the subdivided inspection area; Substituting the inspection information data obtained by each of the smart inspection units into the subdivided area digital model corresponding to each of the subdivided inspection areas, and causing the overall area digital model to simulate the area status of each of the subdivided inspection areas according to each of the inspection information data, so as to obtain the area status of each of the subdivided inspection areas; Performing inspection analysis and processing on each of the inspection information data according to the regional status of each of the subdivided inspection areas to obtain real-time inspection characteristics of each of the inspection areas, and judging and processing the real-time inspection characteristics according to preset standards to determine whether to issue an alarm message for the subdivided inspection area; The steps of driving each prepared smart inspection unit to reciprocate in each of the subdivided inspection areas and collecting inspection information data of the subdivided inspection areas to obtain the inspection information data obtained by each of the smart inspection units include: Analyzing and processing the inspection paths of the smart inspection units according to the digital model of the overall area and the digital models of each subdivided area to obtain the inspection paths of the smart inspection units in each subdivided inspection area, and driving each smart inspection unit to perform reciprocating motion in each subdivided inspection area according to the inspection paths of the smart inspection units in each subdivided inspection area; Driving the intelligent inspection unit to perform reciprocating motion in the subdivided inspection area to collect inspection information data according to a preset inspection plan to obtain the inspection information data; The preset inspection plan includes a basic inspection strategy and an additional inspection strategy, the basic inspection strategy is used to obtain basic inspection information, and the additional inspection strategy is used to obtain additional inspection information, and the basic inspection information and the additional inspection information are both the inspection information data; The basic inspection strategy includes: Collect information through the information collection module preset on the smart inspection unit to obtain basic inspection information; Analyzing and processing the basic inspection information to extract inspection object information and object identification tags corresponding to the inspection object information from the basic inspection information; wherein the inspection object information is used to provide multi-dimensional information feedback on the inspection object, and the object identification tags are used to identify and classify the inspection object based on the inspection object information; The object identification tag of the inspection object information is judged and processed according to a preset standard. When the object identification tag of the inspection object information meets the preset standard, the inspection object corresponding to the inspection object information is marked as a key inspection object, and the smart inspection unit is driven to execute the additional inspection strategy for the key inspection object; The additional inspection strategies include: Driving the smart inspection unit to send a sound inspection instruction toward the key inspection object and move toward the key inspection object; When the key inspection object refuses to execute the sound inspection instruction, generating and sending an alarm instruction; When the key inspection object receives and executes the sound inspection instruction, the key inspection object is instructed to display the target inspection information on the smart inspection unit. When the key inspection object fails to display the correct target inspection information, an alarm instruction is generated and sent. When the key inspection object displays the correct target inspection information, corresponding additional inspection information is generated according to the target inspection information; wherein the additional inspection information is used to perform digital identity authentication on the key inspection object to determine whether to issue an alarm message; Substituting the inspection information data acquired by each of the smart inspection units into the subdivided area digital model corresponding to each of the subdivided inspection areas, and causing the overall area digital model to simulate the area status of each of the subdivided inspection areas according to each of the inspection information data to obtain the area status of each of the subdivided inspection areas, the steps include: Substituting the inspection information obtained by each of the smart inspection units into the subdivided area digital model corresponding to each of the subdivided inspection areas, and causing the subdivided area digital model to perform object recognition distribution analysis based on the inspection object information and object identification tags in the inspection information to obtain object recognition distribution characteristics of the subdivided inspection area; wherein the object recognition distribution characteristics include object distribution characteristics and object identification characteristics, the object distribution characteristics are used to describe the distribution status of the inspection objects in the subdivided inspection area, and the object identification characteristics are used to describe the object status of the inspection objects in the subdivided inspection area; The overall area digital model is used to integrate and analyze the object recognition distribution characteristics of each of the subdivided area digital models to obtain object statistical characteristics in the overall inspection area; wherein the object statistical characteristics are used to describe the overall number and overall status of the inspection objects in the overall inspection area; Performing an analysis of the object recognition distribution characteristics of the subdivided inspection area based on the object statistical characteristics of the overall inspection area to obtain the area proportion of the subdivided inspection area; wherein the area proportion is used to describe the proportion of the number and status of the inspection objects in the subdivided inspection area to the overall inspection area; The overall area digital model is used to perform object distribution trend analysis on the object recognition distribution characteristics of the subdivided inspections based on the relative positional relationship between the digital models of each subdivided area; wherein the object distribution trend analysis is used to describe the possible change trend of the inspection recognition distribution characteristics of each subdivided inspection area in the future based on the current object recognition distribution characteristics of each subdivided inspection area; The area proportion of the subdivided inspection area and the object distribution trend characteristics are used together as the area status of the subdivided inspection area.

2. The intelligent patrol alarm method according to claim 1, characterized in that: The steps of dividing the overall inspection area into several subdivided inspection areas and constructing an overall area digital model composed of several subdivided area digital models according to each of the subdivided inspection areas include: Acquiring overall regional information of the overall inspection area, and constructing an overall regional digital model for performing digital simulation feedback on the overall inspection area based on the overall regional information; Obtaining the inspection performance of the prepared smart inspection unit, and constructing a digital model of the inspection unit for performing digital simulation feedback on the smart inspection unit based on the inspection performance of the smart inspection unit; Based on the overall area digital model, distribution setting processing is performed on the digital models of each inspection unit to obtain several inspection unit distribution characteristics; wherein the inspection unit distribution characteristics are used to describe a distribution form of each smart inspection unit in the overall inspection area; Based on the overall regional digital model, the distribution rationality of the various inspection unit distribution characteristics is analyzed and processed to obtain the distribution rationality parameters corresponding to the various inspection unit distribution characteristics, and the inspection unit distribution characteristics with the best distribution rationality parameters are used as the actual setting distribution characteristics; Determining the initial setting position of each of the smart inspection units on the overall regional digital model according to the actual setting distribution characteristics, and performing an inspection range expansion process based on the initial setting position of each of the smart inspection units to obtain an inspection range corresponding to each of the smart inspection units in the overall regional digital model; The overall inspection area is divided into a number of subdivided inspection areas according to the inspection range of each of the smart inspection units, and subdivided area digital models corresponding to each of the subdivided inspection areas are constructed on the basis of the overall inspection area.

3. The intelligent patrol alarm method according to claim 1, characterized in that: The steps of performing inspection analysis and processing on each of the inspection information data according to the regional status of each of the subdivided inspection areas to obtain the real-time inspection characteristics of each of the inspection areas include: Analyzing and processing the continuous proportion change characteristics of the inspection information data according to the regional proportion of the regional status of the subdivided inspection area to obtain a proportion distribution change characteristic for describing the change in the proportion of the inspection objects fed back by the inspection information data of the subdivided inspection area relative to the inspection objects of the overall inspection area within a continuous time period; The proportion distribution change feature is subjected to deviation analysis processing according to the object distribution trend feature of the area status of the subdivided inspection area to obtain a deviation degree parameter between the proportion distribution change feature and the object distribution trend feature, and the deviation degree parameter is used as the real-time inspection feature.

4. A smart patrol alarm device, characterized in that: A smart inspection alarm method for implementing any one of claims 1 to 3, comprising: A region division module is used to divide the overall inspection area into several subdivided inspection areas, and to construct an overall area digital model composed of several subdivided area digital models based on each of the subdivided inspection areas; wherein the subdivided area digital models are used to provide digital simulation feedback for the subdivided inspection areas, and the overall area digital model is used to provide digital simulation feedback for the overall inspection area; The regional inspection module is used to drive each of the intelligent inspection units to reciprocate in each of the subdivided inspection areas, and at the same time collect inspection information data for the subdivided inspection areas to obtain the number of inspection information obtained by each of the intelligent inspection units, and perform a first round of analysis and processing based on the inspection information data to determine whether to issue an alarm information for the subdivided inspection area; a regional analysis module, configured to substitute the inspection information data acquired by each of the intelligent inspection units into the subdivided regional digital models corresponding to each of the subdivided inspection areas, and to cause the overall regional digital model to simulate the regional status of each of the subdivided inspection areas based on each of the inspection information data, so as to obtain the regional status of each of the subdivided inspection areas; The inspection alarm module is used to perform inspection analysis and processing on each of the inspection information data according to the area status of each of the subdivided inspection areas to obtain the real-time inspection characteristics of each of the inspection areas, and to judge and process the real-time inspection characteristics according to preset standards to decide whether to issue an alarm information for the subdivided inspection area.

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