Intelligent inspection alarm method and device

Abstract

The invention relates to the technical field of intelligent inspection, and discloses an intelligent inspection alarm method and device, and the method comprises the steps: dividing an overall inspection region, constructing a subdivided region digital model and an overall region digital model, driving an intelligent inspection unit to carry out the reciprocating motion, collecting the inspection data, carrying out the initial alarm, and substituting the inspection data into a corresponding digital model. According to the method, fine management of the inspection area is realized through area division and a digital model, automatic data acquisition and real-time analysis processing of the intelligent inspection unit are combined, and the inspection efficiency is improved. The method can quickly and accurately judge the area state and give an alarm in time, is suitable for the inspection requirement of a complex environment, carries out further prediction and auxiliary inspection by utilizing the relationship among a plurality of subdivided inspection areas, and solves the problem that the early warning capability is low when an automatic inspection device is adopted to process the 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] In the fields of industrial inspection, public safety, intelligent monitoring, etc., inspection technology has always been an important means to ensure the normal operation of the system and ensure safety. However, with the diversification of inspection objects, the expansion of inspection areas, and the improvement of inspection requirements, traditional inspection methods are unable 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 low early warning capabilities. Summary of the invention

[0004] The purpose of the present invention is to provide a smart 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 prior art.

[0005] The present invention is implemented in this way. In a first aspect, the present invention provides a smart patrol alarm method, comprising: 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 according to 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 of the prepared intelligent inspection units to reciprocate in each of the subdivided inspection areas, and collecting inspection information data of the subdivided inspection areas at the same time, so as to obtain the inspection information data acquired by each of the intelligent inspection units, and performing a first round of analysis and processing based on the inspection information data, so as to determine whether to issue an alarm information 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, so that the overall area digital model simulates 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; According to the regional status of each subdivided inspection area, each inspection information data is inspected and analyzed to obtain the real-time inspection characteristics of each inspection area, and the real-time inspection characteristics are judged and processed according to the preset standard to determine whether to issue an alarm information for the subdivided inspection area In a second aspect, the present invention provides a smart patrol alarm device, which is used to implement a smart patrol alarm method as described in any one of the first aspects, including: The area 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 according to 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; 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 message for the subdivided inspection area; A regional analysis module, used for substituting the inspection information data obtained by each of the intelligent inspection units into the subdivided regional digital models corresponding to each of the subdivided inspection areas, so that the overall regional digital model simulates the regional status of each of the subdivided inspection areas according to 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.

[0006] The present invention provides a smart patrol alarm method, which has the following beneficial effects: 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 reciprocate to collect inspection data for an initial round of alarms, and then substitutes the data into the corresponding digital model to simulate the status of each subdivided area, thereby performing further analysis to generate real-time inspection features, and judging whether an alarm is needed based on preset standards. The method achieves 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 a large range 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 prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1It is a schematic diagram of the steps of a smart patrol alarm method provided by an embodiment of the present invention; Figure 2 It is a structural diagram of a smart patrol alarm device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0008] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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.

[0009] The implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0010] Reference Figure 1 , Figure 2 As shown, a preferred embodiment of the present invention is provided.

[0011] In a first aspect, the present invention provides a smart patrol alarm method, comprising: 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 according to 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; S2: driving each of the smart inspection units to reciprocate in each of the subdivided inspection areas, and 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 areas; 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 allowing 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; S4: Performing inspection analysis and processing 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 judging and processing the real-time inspection characteristics according to preset standards to determine whether to issue an alarm information for the subdivided inspection area Specifically, in step S1 of the embodiment provided by the present invention, the boundaries and features 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 inspections and management.

[0012] 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.

[0013] 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 iterated and corrected multiple times to improve the accuracy of the simulation.

[0014] More specifically, the digital models of each segmented area are integrated to construct a digital model of the overall 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, which can ensure that the interaction between the segmented areas is correctly reflected in the overall simulation.

[0015] More specifically, in the actual inspection process, the digital models of each subdivided area are used to simulate the feedback of the real-time collected data. This process includes the real-time calculation and visualization of information such as equipment status and environmental parameters, and the overall regional digital model is used to simulate the feedback of the global status. The overall model can integrate the feedback information of the subdivided area and provide comprehensive inspection results and status evaluation.

[0016] It is understandable that through the digital model of the subdivided area, the system can accurately monitor the status of each independent area and capture minor changes and potential risks. The subdivided management model can improve the accuracy of inspections and reduce missed inspections and false alarms. The overall regional digital model provides a global perspective and can integrate and analyze the status of all subdivided areas in a large range. The system realizes dynamic response and real-time feedback to the inspection area through the digital model. During the inspection process, the digital model can immediately reflect the changes in equipment and environment, helping the inspection personnel or system to take timely action.

[0017] 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 a number of subdivided inspection areas, and multiple smart inspection units are used to perform inspection work in each subdivided inspection area. The inspection work includes object recognition of the inspection objects in the subdivided inspection areas to determine whether there are inspection objects that do not meet the preset recognition rules so as to issue an alarm.

[0018] More specifically, when the overall inspection area is an industrial park or school campus, the identification standard for the inspection object can be standard dress 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 authentication information. The two rounds of identification can take into account both the efficiency and accuracy of the inspection.

[0019] Specifically, in step S2 of the embodiment provided by the present invention, the prepared smart inspection units are deployed to each subdivided inspection area according to 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 collection parameters.

[0020] 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 reciprocate 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.

[0021] More specifically, during the inspection process, the smart inspection unit collects inspection information data such as environmental parameters, equipment status, video images, etc. in real time through built-in sensors and cameras. 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 through wireless networks or other communication methods for subsequent analysis and processing.

[0022] More specifically, after receiving the inspection data transmitted by the intelligent inspection unit, the central control system first pre-processes it, filters out noise and abnormal values, and organizes it into a standardized format for subsequent analysis. The pre-processed inspection data is analyzed for the first round to check whether there are significant anomalies or potential risks. If some data is found to exceed the preset safety threshold or show abnormal change trends, the system will mark these data and prepare for more in-depth analysis. Based on the results of the first round of analysis, it is determined whether an alarm message needs to be issued. The alarm standard is usually based on the operating status of the equipment, abnormal changes in environmental parameters, etc., to ensure timely detection of problems. If it is determined that an alarm needs to be issued, the system will immediately trigger the alarm mechanism, notify relevant personnel or automatically start the emergency plan. The alarm information can be sent in a variety of ways, such as SMS, email, sound and light alarm, etc.

[0023] It is understandable that the intelligent inspection unit can quickly capture any abnormal situation in the subdivided inspection area by performing real-time monitoring through reciprocating motion. The real-time nature of data collection and transmission ensures the timely response capability of the inspection system, so that problems can be discovered and handled in the early stages. Through reasonably planned inspection paths and effective reciprocating motion, the intelligent inspection unit can cover all key parts in the subdivided area to ensure that nothing is missed. At the same time, the first round of analysis and processing can quickly and accurately evaluate the data and provide efficient alarm judgment. The inspection process is highly automated, reducing dependence on manual inspections, and is 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.

[0024] Specifically, in step S3 of the embodiment provided by the present invention, the inspection information data collected by the intelligent 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 accurately correspond to the digital model of the subdivided area to which it belongs.

[0025] More specifically, the inspection information data is used as an input parameter 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 the status according to 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.

[0026] More specifically, the simulation results of the digital models of each subdivided area are summarized into the overall regional digital model. The overall model integrates the status of all subdivided areas to form a global simulation of the entire inspection area. The overall regional digital model performs a higher-level simulation based on the summarized status of the subdivided areas. It not only evaluates the independent status of each area, but also considers the mutual influence between areas, thereby generating a more accurate global status diagram.

[0027] More specifically, based on the simulation results of the overall regional digital model, the status of each subdivided inspection area is evaluated, and the evaluation results can be displayed through a visual interface, such as three-dimensional graphics, status color coding, trend charts, etc., to generate a status report for each subdivided inspection area. The report content may include the health status of the area, potential risk points, equipment that needs to be focused on, etc. The report can be used for further decision support or automated response mechanisms.

[0028] It is understandable that by substituting the 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 fully 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 real 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 situation, and ensure effective collaborative management at the system level. The regional status obtained after simulation processing can timely identify potential risk points, and the system can trigger early warning or automatic response mechanisms based on these status information. This timely response capability can effectively prevent the occurrence of failures or accidents and improve the safety of the system.

[0029] Specifically, in step S4 of the embodiment provided by the present invention, the regional status of the subdivided inspection area is integrated with the inspection information data. This process includes associating the regional status data (such as equipment operating status, environmental parameters, etc.) with the inspection information data (such as sensor readings, image data, etc.) to form a complete inspection analysis basic data set. The sorted data set will be used for further analysis and processing to ensure the integrity and consistency of the data, and the integrated inspection information data will be analyzed and processed using data processing algorithms. These algorithms may include signal processing, pattern recognition, anomaly detection, etc., for extracting key inspection features. Features may be the temperature of the equipment, vibration frequency, foreign body recognition in the image, etc. Through analysis and processing, real-time inspection features at the current moment are extracted from the data. These features represent the key states of the equipment and environment in the current subdivided inspection area.

[0030] More specifically, the extracted real-time inspection features are compared with preset standards. These preset standards are usually based on historical data, equipment manuals or industry specifications, covering the range of various parameters for normal operation. The preset judgment rules are applied to judge whether the inspection features meet the standards. The judgment rules can be simple threshold comparisons or more complex multivariate conditional analysis. According to the judgment results, if one or more inspection features exceed the preset standards, the system will evaluate whether the conditions for triggering an alarm are met. The alarm conditions may include single abnormality exceeding the limit, multiple indicators being abnormal at the same time, and the recognition of specific patterns. Once it is determined that an alarm is needed, the system will immediately trigger the alarm information. The alarm can be sent through a variety of channels, such as immediate notification of relevant personnel, automatic activation of emergency plans, and recording in logs. All inspection features, judgment results, and alarm information will be recorded for subsequent analysis and system optimization. These data can also be used to improve the preset standards and judgment rules to improve the accuracy of the system's alarms.

[0031] It can be understood that the system realizes dynamic real-time monitoring of the inspection area by integrating regional status and inspection information data, accurate 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 a real abnormality occurs 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.

[0032] The present invention provides a smart patrol alarm method, which has the following beneficial effects: 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 reciprocate to collect inspection data for an initial round of alarms, and then substitutes the data into the corresponding digital model to simulate the status of each subdivided area, thereby performing further analysis to generate real-time inspection features, and judging whether an alarm is needed based on preset standards. The method achieves 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 a large range 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 prior art.

[0033] 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: 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; S12: obtaining the inspection performance of the prepared intelligent inspection unit, and constructing a digital model of the inspection unit for performing digital simulation feedback on the intelligent inspection unit according to the inspection performance of the intelligent inspection unit; S13: performing distribution setting processing of the digital models of each inspection unit based on the overall area digital model 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; 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 characteristics with the best distribution rationality parameters as the actual setting distribution characteristics; S15: 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 expansion process of the inspection range based on the initial setting position of each of the smart inspection units, so as to obtain the inspection range corresponding to each of the smart inspection units in the overall regional digital model; S16: Divide the overall inspection area into a plurality of subdivided inspection areas according to the inspection range of each of the intelligent inspection units, and construct subdivided area digital models corresponding to each of the subdivided inspection areas on the basis of the overall inspection area.

[0034] Specifically, various information about the overall inspection area is collected, including geographic information, equipment layout, environmental parameters, etc. This information provides a data basis for subsequent digital simulation. Based on the collected overall area information, a digital model of the overall area is constructed. This model will be used to simulate the entire inspection area and reflect the overall status and operation status of the area.

[0035] More specifically, the inspection performance of the prepared intelligent inspection units is obtained and evaluated, including inspection speed, accuracy, detection range, data transmission capacity, etc. According to the performance data of the intelligent inspection units, a corresponding digital model of the inspection units is constructed. The model is used to simulate the operating characteristics and working range of each inspection unit.

[0036] More specifically, based on the overall regional digital model, the digital models of the inspection units are reasonably distributed to generate distribution characteristics of several types of inspection units, describing the different distribution forms of each intelligent 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 each inspection unit distribution characteristic is analyzed to evaluate their coverage, inspection efficiency, resource utilization, etc., and the rationality parameters corresponding to each distribution characteristic are calculated.

[0037] More specifically, the inspection unit distribution feature with the best rationality parameter is selected as the actual setting distribution feature. This feature will be used as the final layout plan of the inspection unit in the area. According to the best distribution feature, the initial setting position of each smart inspection unit is determined on the overall regional digital model. Starting from the initial position, the inspection range of each inspection unit is determined, and the regional coverage is expanded according to these ranges to ensure that each inspection unit can fully cover the designated area. According to the inspection range of the smart inspection unit, the overall inspection area is divided into several subdivided inspection areas, and each subdivided area corresponds to the coverage range of one or more inspection units.

[0038] More specifically, based on the divided inspection areas, corresponding digital models of the subdivided areas are constructed, and each digital model of the subdivided area is used to simulate the specific situation in the area to achieve more refined regional management and monitoring.

[0039] It is understandable that through the reasonable 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 method 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 unit. 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. Through the monitoring of each subdivided area, the system can identify problems faster and more accurately, and improve overall safety. The hierarchical construction of the overall area digital model and the subdivided area digital model enables the system to have 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.

[0040] Preferably, the steps of driving each prepared intelligent inspection unit to reciprocate in each subdivided inspection area and collecting inspection information data of the subdivided inspection area to obtain the inspection information data acquired by each intelligent inspection unit include: S21: Analyze and process 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 drive each smart inspection unit to reciprocate in each subdivided inspection area according to the inspection paths of the smart inspection units in each subdivided inspection area; 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.

[0041] Specifically, according to the digital model of the overall area and the digital models of each subdivided area, the inspection paths of each smart inspection unit are analyzed in detail, which 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.

[0042] More specifically, according to the established inspection route, the smart inspection units are driven to reciprocate in 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 reciprocate on the specified path to ensure repeated inspections of the entire subdivided area to prevent missing potential problems.

[0043] More specifically, according to the requirements of the inspection tasks, a preset inspection plan is configured for each smart inspection unit. The inspection plan should clearly specify 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 motion of the smart inspection unit, data collection of inspection information is performed 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.

[0044] 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 call. The storage system should have good scalability and security, and be able to support efficient storage and retrieval of large amounts of data.

[0045] It is understandable that by analyzing and formulating the optimal inspection path, the system can ensure that the intelligent inspection unit efficiently covers the entire subdivided inspection area and conducts accurate inspection work. This efficient path planning can reduce inspection time, improve inspection accuracy, and ensure that there are no blind spots or missed inspections. The system can dynamically adjust the inspection path and movement mode according to real-time data and environmental changes, and flexibly respond to different inspection needs. This dynamic adaptability improves the system's resilience and can respond to emergencies or environmental changes in a timely manner.

[0046] 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; S221: The basic inspection strategy includes: S2211: Collecting information through an information collection module preset on the smart inspection unit to obtain basic inspection information; S2212: Analyze and process the basic inspection information to extract inspection object information and object identification tags corresponding to the inspection object information in the basic inspection information; wherein the inspection object information is used to provide multi-dimensional information feedback to the inspection object, and the object identification tags are used to identify and classify the inspection object according to the inspection object information; 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; S222: The additional inspection strategy includes: S2221: driving the smart inspection unit to send a sound inspection instruction toward the key inspection object, and move toward the key inspection object; S2222: When the key inspection object refuses to execute the sound inspection instruction, generate and send an alarm instruction; 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 in 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.

[0047] 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 writers and other equipment to capture multi-dimensional information of the inspection objects, such as images, temperature, location, etc.

[0048] More specifically, the collected basic inspection information is analyzed and processed, information related to the inspection object is extracted, 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 start additional inspection strategies.

[0049] More specifically, the smart inspection unit is driven to send a sound inspection instruction to the key inspection object. This can be a verbal command, warning or inquiry issued through a speaker, which is intended to confirm the status or behavior of the object. At the same time, the smart inspection unit begins to track the key inspection object and performs close-range movements to ensure that the object is within the controllable range. This process is intended to further confirm the identity and status of the object. If the key inspection object refuses to execute the sound inspection instruction (such as no response or 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 object accepts and executes the sound inspection instruction, the system will require the object to display specific target inspection information (such as identity card, authorization code, etc.).

[0050] More specifically, when a key inspection object fails to display the correct target inspection information, the system will generate and send an alarm instruction to indicate the existence of 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.

[0051] It is understandable that through the basic inspection strategy, the system can preliminarily identify and classify all inspection objects, and select key inspection objects according to preset standards. This refined processing method ensures the efficiency and accuracy of the inspection work, and can timely discover potential risks or abnormal situations. The additional inspection strategy further enhances the monitoring of key inspection objects, and ensures the behavioral compliance and identity authenticity of the inspection objects through multiple verification methods (such as voice commands, target information display, digital identity verification, etc.). This multi-level verification mechanism significantly improves the security and reliability of the system. By combining the basic and additional inspection strategies, the system realizes the full process of automated inspection from preliminary identification to in-depth verification. This intelligent inspection method reduces the need for manual intervention, improves the inspection efficiency, and enhances the control ability of inspection objects in complex environments. In the additional inspection strategy, the system realizes digital identity verification of key inspection objects through the verification of target inspection information and the generation of additional inspection information. This technology not only ensures the legitimacy of the inspection object, but also provides reliable data support for subsequent security management and decision-making.

[0052] Preferably, the inspection information data acquired by each of the smart inspection units is substituted into the subdivided area digital model corresponding to each of the subdivided inspection areas, and the overall area digital model is made 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, including: S31: Substitute the inspection information acquired by each of the smart inspection units into the subdivided area digital model corresponding to each of the subdivided inspection areas, and make the subdivided area digital model perform object recognition distribution analysis and processing according to the inspection object information and the object recognition tag in the inspection information, so as to obtain the 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; S32: Instructing the overall area digital model to integrate and analyze the object recognition distribution features of each of the subdivided area digital models 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; S33: Analyze and process the area proportion of 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 in the overall inspection area; S34: The overall area digital model is used to perform object distribution trend characteristics on the object recognition distribution characteristics of the subdivided inspection according to the relative position relationship between the digital models of each subdivided area; wherein the object distribution trend characteristics are used to describe the possibility of the 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; 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.

[0053] Specifically, the inspection information data obtained by each intelligent inspection unit is substituted into the digital model of the corresponding subdivided inspection area, and each subdivided area digital model performs object recognition distribution analysis based on the inspection object information and object recognition tags in the inspection information. Through this processing, the system can extract the object recognition distribution characteristics of each subdivided inspection area.

[0054] More specifically, it describes the spatial distribution of the inspection objects in the subdivided inspection area. For example, the density of objects in a specific area, the degree of concentration or dispersion of distribution, etc. It also describes the state characteristics of the inspection objects, such as health status, working status, and whether there are any abnormalities.

[0055] More specifically, the object recognition distribution characteristics 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 characteristics based on these data to describe the overall number and status of objects in the entire inspection area.

[0056] 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 for regional proportion. The regional proportion is used to describe the proportion of the number and status of inspection objects in a certain subdivided inspection area in the entire inspection area. Through this analysis, the importance and potential risks of each area can be understood.

[0057] More specifically, based on the relative position relationship between the digital models of the subdivided areas, the overall regional digital model calculates the object distribution trend characteristics in each subdivided inspection area. These characteristics describe the possible changing trends in the inspection object identification distribution in the future, such as the direction of object movement in the area, the tendency of concentration or dispersion, etc. 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 used as the final status output of the subdivided inspection area to guide subsequent inspection decisions and management.

[0058] It can be understood that by substituting the 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 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 capability 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.

[0059] 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: S41: Analyze and process the continuous proportion change characteristics of the inspection information data according to the regional proportion of the regional status of the subdivided inspection area, so as to obtain a proportion distribution change characteristic for describing the proportion change of the inspection objects fed back by the inspection information data of the subdivided inspection area in a continuous time period relative to the inspection objects of the overall inspection area; 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.

[0060] 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 to form the characteristics of the change in proportion distribution.

[0061] More specifically, based on the object distribution trend characteristics of the segmented 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.

[0062] More specifically, the calculated deviation degree parameters are integrated into the real-time inspection features. These real-time inspection features reflect the degree of match between the actual state and the expected state of the inspection object in the subdivided inspection area at the current time point, providing real-time inspection status feedback. Finally, the system outputs these integrated real-time inspection features as a reference for inspection work. Real-time inspection features can be used to guide subsequent inspection operations, adjust inspection strategies, or trigger early warning mechanisms.

[0063] It can be understood that by continuously analyzing the characteristics of the change in the proportion, the system can provide dynamic distribution status of inspection objects at different time points. This real-time analysis enables inspection management to respond quickly to any changes or abnormal situations, improving the agility and effectiveness of inspection work. By comparing actual data with expected trends, deviation analysis provides an accurate assessment. This analysis can identify the deviation between the distribution of inspection objects and expectations in the subdivided inspection area, providing an important reference for inspection management and helping to adjust the inspection strategy in a timely manner. The calculation and integration of deviation degree parameters enable the system to provide more accurate inspection feature information. This precise guidance helps to improve the efficiency of inspection work, reduce inspection blind spots, and optimize the allocation and use of resources. The output of real-time inspection features can not only reflect the current inspection status, but also trigger an early warning mechanism. When the deviation degree parameter exceeds the preset threshold, the system can automatically prompt the inspection personnel to take measures, thereby improving the safety and reliability of the inspection work. By combining the proportion change analysis and deviation analysis processing, the system realizes all-round 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.

[0064] Reference Figure 2 As shown, in a second aspect, the present invention provides a smart patrol alarm device, which is used to implement a smart patrol alarm method as described in any one of the first aspects, including: The area 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 according to 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; 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 message for the subdivided inspection area; A regional analysis module, used for substituting the inspection information data obtained by each of the intelligent inspection units into the subdivided regional digital models corresponding to each of the subdivided inspection areas, so that the overall regional digital model simulates the regional status of each of the subdivided inspection areas according to 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.

[0065] In this embodiment, for the specific implementation of each module in the above-mentioned device embodiment, please refer to the above-mentioned method embodiment, which will not be described in detail here.

[0066] 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 protection scope 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 according to 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 of the prepared intelligent inspection units to reciprocate in each of the subdivided inspection areas, and collecting inspection information data of the subdivided inspection areas at the same time, so as to obtain the inspection information data acquired by each of the intelligent inspection units, and performing a first round of analysis and processing based on the inspection information data, so as to determine whether to issue an alarm information 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, so that the overall area digital model simulates 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; The inspection information data are inspected and analyzed according to the area status of each subdivided inspection area to obtain the real-time inspection characteristics of each inspection area, and the real-time inspection characteristics are judged and processed according to preset standards to decide whether to issue an alarm message for 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: Acquire the 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; Acquire the inspection performance of the prepared intelligent inspection unit, and construct a digital model of the inspection unit for performing digital simulation feedback on the intelligent inspection unit according to the inspection performance of the intelligent inspection unit; Based on the overall area digital model, distribution setting processing of the digital models of each inspection unit is performed 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; Determine 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 perform inspection range expansion processing based on the initial setting position of each of the smart inspection units to obtain the inspection range corresponding to each of the smart inspection units in the overall regional digital model; The overall inspection area is divided into a plurality of subdivided inspection areas according to the inspection range of each of the intelligent 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 driving each prepared intelligent inspection unit to reciprocate in each subdivided inspection area and collecting inspection information data of the subdivided inspection area to obtain the inspection information data acquired by each intelligent inspection unit include: Analyze and process the inspection paths of each of the smart inspection units according to the digital model of the overall area and the digital models of each of the subdivided areas to obtain the inspection paths of each of the smart inspection units in each of the subdivided inspection areas, and drive each of the smart inspection units to reciprocate in each of the subdivided inspection areas according to the inspection paths of each of the smart inspection units in each of the subdivided inspection areas; When the intelligent inspection unit is driven to perform reciprocating motion in the subdivided inspection area, data collection of inspection information is performed according to a preset inspection plan to obtain the inspection information data.

4. The intelligent patrol alarm method according to claim 3, characterized in that: The preset inspection scheme includes a basic inspection strategy and an additional inspection strategy, wherein the basic inspection strategy is used to obtain basic inspection information, and the additional inspection strategy is used to obtain additional inspection information, and both the basic inspection information and the additional inspection information are the inspection information data; The basic inspection strategy includes: Collecting information through an information collection module preset on the smart inspection unit to obtain basic inspection information; Analyze and process the basic inspection information to extract the inspection object information and the object identification tag corresponding to the inspection object information in the basic inspection information; wherein the inspection object information is used to provide multi-dimensional information feedback to the inspection object, and the object identification tag is used to identify and classify the inspection object according to the inspection object information; The object identification tag of the inspection object information is judged and processed according to the 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 intelligent 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 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 authentication on the key inspection object to determine whether to issue an alarm message.

5. The intelligent patrol alarm method according to claim 4, characterized in that: Substituting the inspection information data obtained by each of the intelligent inspection units into the subdivided area digital model corresponding to each of the subdivided inspection areas, and making the overall area digital model 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: Substitute 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 make the subdivided area digital model perform object recognition distribution analysis and processing according to the inspection object information and the object recognition tag in the inspection information, so as to obtain the 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; The overall area digital model is required to integrate and analyze the object recognition distribution characteristics of each of the subdivided area digital models to obtain the 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; According to the object statistical characteristics of the overall inspection area, the object recognition distribution characteristics of the subdivided inspection area are analyzed and processed in terms of area proportion 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 in the overall inspection area; The overall area digital model is used to perform object distribution trend characteristics on the object recognition distribution characteristics of the subdivided inspection according to the relative position relationship between the digital models of each subdivided area; wherein the object distribution trend characteristics are used to describe the possibility of change trend of the inspection recognition distribution characteristics of each subdivided inspection area in the future based on the object recognition distribution characteristics of each current 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.

6. The intelligent patrol alarm method according to claim 5, 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: Analyze and process the continuous proportion change characteristics of the inspection information data according to the regional proportion of the regional status of the subdivided inspection area, so as to obtain the proportion distribution change characteristics used to describe the proportion change of the inspection objects fed back by the inspection information data of the subdivided inspection area in a continuous time period relative to the inspection objects of the overall inspection area; The proportion distribution change characteristics are subjected to deviation analysis processing according to the object distribution trend characteristics of the area status of the subdivided inspection area to obtain a deviation degree parameter between the proportion distribution change characteristics and the object distribution trend characteristics, and the deviation degree parameter is used as the real-time inspection feature.

7. A smart patrol alarm device, characterized in that: A smart patrol alarm method for implementing any one of claims 1 to 6, comprising: The area 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 according to 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; 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 message for the subdivided inspection area; A regional analysis module, used for substituting the inspection information data obtained by each of the intelligent inspection units into the subdivided regional digital models corresponding to each of the subdivided inspection areas, so that the overall regional digital model simulates the regional status of each of the subdivided inspection areas according to 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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