Monitoring and alarming method and system for garbage disposal points and intelligent garbage room

Abstract

The present invention relates to the technical field of garbage disposal supervision, and discloses a monitoring and alarm method, system and intelligent garbage room for garbage disposal points. The present invention continuously collects and analyzes monitoring area images of garbage disposal points through visual sensors, identifies real-time status characteristics of garbage disposal points, monitors the filling status of garbage storage areas in real time through filling status sensors, evaluates whether there are abnormal conditions in combination with real-time status characteristics and filling status characteristics, performs alarm analysis according to preset alarm standards, generates corresponding alarm response instructions, determines whether it is necessary to execute an alarm and the specific alarm method, realizes intelligent monitoring of garbage disposal points, improves management efficiency, can identify abnormal conditions in real time, supports flexible and accurate emergency response, improves the accuracy of garbage disposal and storage management, optimizes resource scheduling, reduces environmental pollution risks, and solves the problem of insufficient flexibility in automatic monitoring of garbage disposal points in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of garbage disposal supervision, and in particular to a monitoring and alarming method and system for garbage disposal points and an intelligent garbage room. Background Art

[0002] With the improvement of urban garbage management and environmental awareness, intelligent monitoring of garbage disposal points has gradually become an important technical requirement. The traditional management method of garbage disposal points mainly relies on manual inspections, which is time-consuming and labor-intensive, prone to omissions, and unable to monitor and correct garbage disposal behavior at all times. The automatic monitoring method currently used in garbage disposal points is relatively rigid, not flexible enough, and lacks effectiveness in supervising garbage disposal behavior. Summary of the invention

[0003] The purpose of the present invention is to provide a monitoring and alarm method and system for garbage disposal points and an intelligent garbage room, aiming to solve the problem of insufficient flexibility in automatic monitoring of garbage disposal points in the prior art.

[0004] The present invention is implemented in this way. In a first aspect, the present invention provides a monitoring and alarm method for a garbage placement point, comprising: Continuously collecting visual images of the designated monitoring area through a visual sensor preset at the garbage placement point to obtain an image monitoring sequence corresponding to the designated monitoring area; Performing recognition processing on the key dynamic features of the image monitoring sequence to obtain the real-time status features of the designated monitoring area; The filling state of the garbage storage area is collected in real time by a filling state sensor preset at the garbage delivery point to obtain the filling state characteristics corresponding to the garbage storage area; Performing abnormality assessment processing on the real-time status feature and the filling state feature according to a preset abnormality assessment standard to obtain an abnormality assessment result; An alarm behavior analysis is performed on the abnormal assessment result according to preset alarm standards to determine the alarm response instructions corresponding to the abnormal assessment result; wherein the alarm response instructions are used to describe whether an alarm behavior needs to be executed for the abnormal assessment result, and the specific alarm method when an alarm behavior needs to be executed for the abnormal assessment result.

[0005] In a second aspect, the present invention provides a monitoring and alarming system for a garbage disposal point, which is used to implement a monitoring and alarming method for a garbage disposal point as described in any one of the first aspects, including: An image monitoring module is used to continuously collect visual images of a designated monitoring area through a visual sensor preset at a garbage placement point to obtain an image monitoring sequence corresponding to the designated monitoring area; A real-time analysis module, used for identifying key dynamic features of the image monitoring sequence to obtain real-time status features of the designated monitoring area; A filling recognition module is used to collect the filling status of the garbage storage area in real time through a filling status sensor preset at the garbage delivery point to obtain the filling status characteristics corresponding to the garbage storage area; An abnormality assessment module, used for performing abnormality assessment processing on the real-time status feature and the filling state feature according to a preset abnormality assessment standard to obtain an abnormality assessment result; An alarm response module is used to perform alarm behavior analysis on the abnormal assessment results according to preset alarm standards to determine the alarm response instructions corresponding to the abnormal assessment results; wherein the alarm response instructions are used to describe whether an alarm behavior needs to be executed for the abnormal assessment results, and the specific alarm method when an alarm behavior needs to be executed for the abnormal assessment results.

[0006] In a third aspect, the present invention provides an intelligent garbage room, which adopts the monitoring and alarm system for garbage disposal points described in the second aspect.

[0007] The present invention provides a monitoring and alarm method for garbage disposal points, which has the following beneficial effects: The present invention continuously collects and analyzes images of the monitoring area of ​​the garbage disposal point through a visual sensor, identifies the real-time status characteristics of the garbage disposal point, monitors the filling status of the garbage storage area in real time through a filling status sensor, evaluates whether there is an abnormal situation in combination with the real-time status characteristics and the filling status characteristics, and performs alarm analysis according to preset alarm standards, generates corresponding alarm response instructions, determines whether it is necessary to execute an alarm and the specific alarm method, realizes intelligent monitoring of garbage disposal points, improves management efficiency, can identify abnormal conditions in real time, supports flexible and accurate emergency response, improves the accuracy of garbage disposal and storage management, optimizes resource scheduling, reduces environmental pollution risks, and solves the problem of insufficient flexibility in automatic monitoring of garbage disposal points in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a schematic diagram of the steps of a monitoring and alarm method for garbage placement points provided by an embodiment of the present invention; Figure 2 It is a structural schematic diagram of a monitoring and alarm system for garbage disposal points provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0009] 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.

[0010] The implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0011] Reference Figure 1 , Figure 2 As shown, a preferred embodiment of the present invention is provided.

[0012] In a first aspect, the present invention provides a monitoring and alarm method for a garbage disposal point, comprising: S1: continuously collecting visual images of a designated monitoring area through a visual sensor preset at a garbage placement point to obtain an image monitoring sequence corresponding to the designated monitoring area; S2: performing recognition processing of key dynamic features on the image monitoring sequence to obtain real-time status features of the designated monitoring area; S3: collecting the filling status of the garbage storage area in real time through a filling status sensor preset at the garbage delivery point to obtain a filling status feature corresponding to the garbage storage area; S4: performing abnormality assessment processing on the real-time status feature and the filling state feature according to a preset abnormality assessment standard to obtain an abnormality assessment result; S5: Perform an alarm behavior analysis on the abnormal assessment result according to the preset alarm standard to determine the alarm response instruction corresponding to the abnormal assessment result; wherein the alarm response instruction is used to describe whether an alarm behavior needs to be executed for the abnormal assessment result, and the specific alarm method when an alarm behavior needs to be executed for the abnormal assessment result.

[0013] Specifically, in step S1 of the embodiment provided by the present invention, a visual sensor, usually a high-definition camera, is deployed at a garbage disposal point (such as a garbage bin, a garbage disposal port, etc.), and an appropriate field of view and acquisition frequency are configured to ensure that the entire designated monitoring area can be covered. The angle, resolution and lighting conditions of the sensor are adjusted to ensure that image acquisition is not affected by environmental changes (such as lighting, weather, etc.) to ensure image clarity.

[0014] More specifically, the visual sensor continuously collects images and generates image sequences at a set frequency (such as 1 frame per second or higher). The data captured by the images include but are not limited to environmental conditions, delivery behavior, garbage items, changes in the surrounding environment, etc.

[0015] More specifically, the collected image data is transmitted to a central processing unit (such as a cloud server or a local server) via a network, and the collected images are sorted in chronological order to form a continuous image monitoring sequence for subsequent dynamic feature analysis and behavior recognition.

[0016] It is understandable that through continuous image acquisition, the system can monitor the status of garbage disposal points in real time and ensure that garbage disposal behavior in the monitoring area is effectively recorded. The visual sensor provides high-definition images and can clearly capture the details of the disposal points, helping the system analyze specific disposal behaviors, environmental changes and possible abnormal situations. The image monitoring sequence provides basic data for subsequent dynamic behavior recognition, abnormality assessment and alarm analysis, enabling the system to perform more accurate real-time monitoring and abnormality warning.

[0017] Specifically, in step S2 of the embodiment provided by the present invention, a deep learning method (such as convolutional neural network CNN, YOLO, Faster R-CNN, etc.) is used to detect and identify objects in the image. Targets include trash cans, delivery ports, trash bags, etc. Non-target objects (such as objects in the surrounding environment) are removed or ignored. By comparing continuous frame images, optical flow, background subtraction or frame difference method are used to detect moving objects in the image. This can help identify the behavior of throwing garbage, combine object detection and motion analysis, and identify the behavioral characteristics of garbage throwing, such as the trajectory, frequency, speed, etc. of the object being thrown. At this time, a behavior recognition model based on deep learning (such as LSTM, 3D-CNN, etc.) can be used to analyze the behavior pattern in the image, such as whether there is littering, irregular throwing, etc.

[0018] Through image sequence analysis, the environmental changes of garbage disposal points are extracted, such as whether there is extra garbage around the garbage bin, whether it is blocked or blocked, and the key dynamic features extracted from the image are aggregated to generate real-time status features at that moment or in that area. These features usually include: disposal behavior (number of disposals, whether the disposal is standardized, etc.), surrounding environment (whether there are debris around, garbage accumulation, etc.).

[0019] It is understandable that through deep learning and computer vision technology, the system can extract key dynamic features from the image monitoring sequence in real time, helping the system to quickly identify the status of the garbage disposal point. This enables the system to understand the details of the garbage disposal behavior in real time and respond in a timely manner. Through in-depth analysis of the behavior of people and changes in objects in the image sequence, the system can accurately identify different garbage disposal behaviors (such as standardized disposal, littering, etc.) and detect abnormal situations in a timely manner according to the set rules. For example, frequent garbage disposal behaviors or excessive garbage accumulation at a certain moment will be identified as abnormal.

[0020] Specifically, in step S3 of the embodiment provided by the present invention, a suitable filling status sensor is selected according to different monitoring targets, and the sensor is installed at an appropriate position of the garbage disposal point, such as the top, side or bottom of the garbage can, to ensure that it can monitor the filling status in the garbage can in real time, and ensure that the sensor can be connected to the data collection system via a wireless or wired network to upload data in real time.

[0021] More specifically, the sensor data collection frequency is set as needed, and the sensor will continuously monitor the filling status of the trash can, and obtain the current filling level of the trash can in real time by measuring data such as height, weight, pressure or distance. These data are usually digital signals that reflect the empty or full status of the trash can. The data collected by the sensor is transmitted to the data management platform or cloud system in real time through wireless or wired networks for storage and processing.

[0022] More specifically, the collected sensor data is processed in real time, and the filling status features of the trash can are generated through the raw data collected by the sensor. Common filling status features include: Trash can filling height: measuring the height of the garbage pile in the trash can, garbage filling amount: estimating the amount of garbage in the trash can based on the measurement data of the weight sensor or volume sensor, filling percentage: calculating the filling percentage based on the ratio of the total capacity of the trash can to the current amount of garbage, reflecting the overflowing state of the trash can, garbage accumulation density: evaluating the density of the garbage by measuring the weight and volume ratio of the garbage, helping to determine the compactness of the garbage accumulation.

[0023] More specifically, the real-time filling status is displayed through a graphical interface or monitoring platform, and managers can view the filling status of each trash can in real time. According to the set threshold (such as when the trash can is filled more than 80%), the system will automatically trigger an alarm to remind the garbage collector to empty the trash can in time to avoid overflow or excessive accumulation.

[0024] It is understandable that the filling status sensor can monitor the filling status of the trash can in real time and provide accurate garbage accumulation data. This real-time monitoring can help the management system better understand the filling status of each trash can and avoid excessive accumulation or overflow. By collecting the filling status of the trash can in real time, the system can automatically optimize the garbage cleaning path and frequency according to the filling degree. By automatically collecting the filling status through sensors, the system can realize automatic monitoring and management of garbage disposal points and reduce the need for manual inspections.

[0025] Specifically, in step S4 of the embodiment provided by the present invention, reasonable threshold standards are formulated according to the filling status characteristics of the trash can (such as the amount of trash, filling height, weight, etc.), and the abnormal types are pre-defined. The filling status characteristics of the trash can (such as the trash height, weight, etc.) collected in real time are compared with the preset abnormal standards. The real-time collected data is compared and analyzed with the preset abnormal threshold to determine whether the current filling status of the trash can exceeds the predetermined threshold and whether it conforms to the abnormal mode. According to the deviation between the filling data and the threshold, the system can calculate an abnormal score to indicate the degree of abnormality of the trash can. The system can analyze the trend of the trash can filling status based on historical data and current filling data, and determine whether an abnormal pattern occurs by analyzing the changing speed or cycle of the trash can filling.

[0026] More specifically, based on the evaluation calculation, it is determined whether there are any problems with the real-time status characteristics of the garbage disposal operation to be performed at the garbage disposal point under the current filling state characteristics, and the corresponding methods.

[0027] Specifically, in step S5 of the embodiment provided by the present invention, multiple alarm types and levels are set according to the severity of the abnormality assessment results, the abnormality assessment results are classified according to different types (such as filling abnormality, overflow abnormality, cleaning time abnormality, etc.), and different alarm response strategies are set for each type. For each type of abnormality, the conditions for triggering the alarm and the alarm level are defined.

[0028] More specifically, the system compares and analyzes the abnormal assessment results collected in real time with the preset alarm standards to determine whether an alarm needs to be triggered. If the filling status of the trash can exceeds the set threshold and the abnormality is of a certain type (such as overflow, full bucket, etc.), an alarm will be triggered. Based on the type and severity of the abnormal assessment results, the corresponding alarm level and response measures will be determined.

[0029] More specifically, based on the analysis results, specific alarm response instructions are generated for each abnormal assessment result. The alarm response instructions contain the following parts: Whether to trigger an alarm: confirm whether the abnormal assessment result needs to trigger an alarm. If the alarm needs to be triggered, further execute the alarm behavior; if not, do not take any action. Alarm level: determine the alarm level based on the severity of the abnormality, and alarm method: define the execution method of the alarm behavior. Different levels of alarms use different notification methods. For example: More specifically, if an abnormal assessment result meets the conditions for triggering an alarm, the system will immediately execute the corresponding alarm behavior according to the alarm response instructions, such as: issuing a sound prompt or sending a notification to a designated person or team.

[0030] The present invention provides a monitoring and alarm method for garbage disposal points, which has the following beneficial effects: The present invention continuously collects and analyzes images of the monitoring area of ​​the garbage disposal point through a visual sensor, identifies the real-time status characteristics of the garbage disposal point, monitors the filling status of the garbage storage area in real time through a filling status sensor, evaluates whether there is an abnormal situation in combination with the real-time status characteristics and the filling status characteristics, and performs alarm analysis according to preset alarm standards, generates corresponding alarm response instructions, determines whether it is necessary to execute an alarm and the specific alarm method, realizes intelligent monitoring of garbage disposal points, improves management efficiency, can identify abnormal conditions in real time, supports flexible and accurate emergency response, improves the accuracy of garbage disposal and storage management, optimizes resource scheduling, reduces environmental pollution risks, and solves the problem of insufficient flexibility in automatic monitoring of garbage disposal points in the prior art.

[0031] Preferably, the step of continuously collecting visual images of a designated monitoring area by a visual sensor preset at a garbage placement point to obtain an image monitoring sequence corresponding to the designated monitoring area includes: S11: Collect and analyze information on garbage disposal points, and set visual sensors based on the analysis results; S12: Activate the set visual sensor to put the visual sensor in a visual monitoring state to continuously collect visual images of the designated monitoring area to obtain an image monitoring sequence corresponding to the designated monitoring area.

[0032] Specifically, it is necessary to determine the specific location of the garbage disposal point and the boundary of the monitoring area, obtain the spatial coordinates of the garbage disposal point and the area that needs to be monitored through on-site surveys, map data or sensor positioning technology, analyze the environmental characteristics of the garbage disposal point, such as lighting conditions, climate changes, whether there are obstructions, etc., and select the appropriate sensor type, installation angle and monitoring parameters based on the environmental characteristics.

[0033] More specifically, the target objects and monitoring details that need to be monitored are determined based on the usage and management needs of the garbage disposal points. Through detailed information collection and analysis, the installation location, configuration and monitoring strategy of the visual sensor can be accurately determined to ensure that the monitoring effect of the visual sensor is maximized. Understanding the environmental conditions in advance and choosing the appropriate type of visual sensor can improve the monitoring effect and reduce interference from environmental factors.

[0034] More specifically, different types of visual sensors are selected according to monitoring needs, such as ordinary cameras, infrared cameras, high-definition cameras, etc. According to the on-site conditions, fixed, pan-tilt or multi-angle adjustable sensors can be selected. The sensor's resolution, shooting angle, focal length, exposure time, image clarity and other parameters can be set according to the needs of target monitoring. Especially in an environment with large changes in light, it is necessary to automatically adjust the exposure and white balance.

[0035] More specifically, according to on-site needs, set the specific boundaries and scope of the monitoring area, such as choosing to monitor trash cans, disposal ports or wider garbage disposal scenarios, set the network connection method of the visual sensor (such as Wi-Fi, Bluetooth, Ethernet, etc.), and ensure that the data after image acquisition can be transmitted to the data storage platform or processing system in a timely manner.

[0036] Preferably, the steps of collecting and analyzing information on garbage disposal points and setting visual sensors according to the analysis results include: S111: collecting information about the surrounding environment of the garbage disposal point to obtain information about the surrounding environment of the garbage disposal point; S112: Analyzing the movement path of the garbage placement behavior at the garbage placement point according to the surrounding environment information to obtain the garbage placement movement path of the garbage placement point; wherein the garbage placement movement path is used to describe the movement path required before garbage placement at the garbage placement point; S113: marking the garbage delivery moving path as a designated monitoring area, performing a traceability analysis of visual monitoring on the designated monitoring area to obtain a visual monitoring node for visual monitoring of the garbage delivery moving path, and setting a visual sensor at a position corresponding to the visual monitoring node; S114: collecting structural specifications of the garbage placement opening of the garbage placement point to obtain structural information of the garbage placement opening of the garbage placement point; S115: performing a garbage placement area analysis of the garbage placement behavior at the garbage placement point according to the placement port structure information to obtain a garbage placement location area of ​​the garbage placement point; S116: Mark the garbage disposal location area as a designated monitoring area, perform a traceability analysis of visual monitoring on the designated monitoring area to obtain a visual monitoring node for visual monitoring of the garbage disposal behavior, and set a visual sensor at a position corresponding to the visual monitoring node.

[0037] Specifically, the surrounding environment of the garbage disposal point is collected through sensors, cameras, lidar, drones and other equipment. The goal is to obtain the location information and dynamic conditions of surrounding buildings, streets, roads, pedestrians, vehicles, etc. The collected data may include geographic location, ambient lighting, obstructions, traffic flow and other information.

[0038] More specifically, data processing and analysis algorithms are used to identify key features in the environment, such as peak traffic hours, pedestrian activity areas, weather influences, etc. By accurately obtaining environmental information around garbage disposal points, factors that affect the effectiveness of visual sensors, such as lighting, obstructions, traffic flow, etc., can be determined, providing a scientific basis for subsequent sensor deployment and settings.

[0039] More specifically, based on the surrounding environment information, the movement path of garbage disposal behavior is analyzed, and the path of the crowd or garbage disposal person from the approaching disposal point to the disposal port is determined through the data collection system (such as cameras, sensors or simulation data). These paths can include the common movement routes of pedestrians, the habitual behavior of garbage disposal persons, etc. The movement path before garbage disposal is modeled through algorithms (such as path planning algorithms, machine learning models, etc.). Common disposal paths are analyzed and potential disposal behavior patterns are predicted.

[0040] More specifically, the moving path is determined as the "designated monitoring area" of the garbage disposal point, and specific monitoring points are selected based on the characteristics of the path. The designated monitoring area is not limited to the garbage disposal point itself, but also includes the path segment and key behavior nodes (such as near the disposal port) during the disposal process. Based on the disposal behavior and moving path in the designated monitoring area, the possible behavior patterns of the garbage disposer are analyzed, and the monitoring strategy is further optimized accordingly. Through traceability analysis, the key monitoring nodes in the path (for example, in front of the disposal port, the crowd gathering area around it, etc.) are identified. According to the results of the traceability analysis, visual sensors are set at the key nodes. For example, sensors are set in areas with concentrated crowds to ensure that every link in the disposal process can be captured.

[0041] More specifically, collect information about the size, shape, structure, etc. of the garbage delivery port. This data can be obtained through on-site measurement, 3D scanning, or using CAD drawings. This data provides an important reference for subsequent analysis of delivery behavior. By analyzing the delivery port structure, it is evaluated whether it can carry the flow of garbage delivery and whether the delivery behavior is easily affected by structural limitations.

[0042] More specifically, obtaining the structural information of the placement port helps to understand the physical limitations of garbage placement behavior. For example, the size, shape and opening angle of the placement port may affect the placement process. Based on this information, the installation angle and focal length of the monitoring equipment can be adjusted to ensure that the entire placement behavior can be captured. Accurate structural information also helps to optimize the monitoring strategy of garbage placement behavior and reduce monitoring blind spots caused by placement port restrictions.

[0043] More specifically, the spatial distribution area of ​​garbage placement behavior is analyzed based on the structural information of the placement port. For example, the opening size and position of the placement port determine the placement direction, placement height, and amount of garbage. Based on these analyses, the core areas of garbage placement are determined and marked as new designated monitoring areas to ensure that these areas are monitored. The focus of monitoring usually includes the placement port and its surrounding areas to detect any irregular placement behavior.

[0044] More specifically, according to the area and path of the garbage disposal behavior, the traceability analysis is conducted again to identify the key monitoring nodes of the garbage disposal behavior, which may be the entrance of the disposal port, the corners in the disposal path, the gathering points of people, etc. Visual sensors are set up at these key monitoring nodes. The deployment position and angle of the sensors should be precisely adjusted according to factors such as the behavior pattern, the structure of the disposal port, and the surrounding environment.

[0045] It is understandable that the visual sensors installed at key nodes can ensure comprehensive monitoring of garbage disposal behavior, especially the precise capture of details of the disposal ports and their surroundings. Through traceability analysis, the monitoring points can be adjusted more intelligently to ensure efficient and accurate collection of disposal data and reduce blind spots or false alarms.

[0046] Preferably, the step of performing recognition processing on the image monitoring sequence for key dynamic features to obtain the real-time status features of the designated monitoring area includes: S21: performing feature recognition of human portrait movement on the image monitoring sequence to obtain human portrait movement features in the designated monitoring area; S22: judging the movement tendency of the garbage disposal point on the moving features of the portrait to determine whether the content fed back by the moving features of the portrait is a movement behavior towards the garbage disposal point; if so, identifying the features of the objects carried by the portrait on the image monitoring sequence to obtain the features of the suspected target that will be placed at the garbage disposal point in the designated monitoring area; S23: performing content recognition on the suspected target feature to obtain a volume recognition result and a type recognition result of the suspected target feature; S24: performing delivery action recognition corresponding to the suspected target feature on the image monitoring sequence to obtain delivery action features in the designated monitoring area; S25: The volume recognition result and type recognition result of the suspected target feature and the delivery action feature together constitute the real-time status feature of the designated monitoring area.

[0047] Specifically, through a video surveillance system or real-time image acquisition equipment, a portrait recognition algorithm (such as Haar feature cascade classifier, YOLO, human detection model in OpenCV, etc.) is used to identify the portrait in the image, and the identified person is continuously tracked to determine its dynamic characteristics such as movement path, speed, direction, etc. in the monitoring area. Through the recognition of portrait movement, the movement trajectory of people in the monitoring area can be captured in real time to ensure that activities around the garbage disposal point are tracked in a timely manner.

[0048] More specifically, deep learning algorithms (such as convolutional neural networks (CNN)) or rule-based motion analysis are used to determine whether the movement of a person is directed toward a garbage disposal point, and to analyze whether the person is moving in the direction of the garbage disposal point. The person's intention is usually inferred by detecting the person's movement direction, speed, and position. If the person's movement path shows that he or she is approaching a garbage disposal point, the system will infer that he or she may be preparing to dispose of garbage. By analyzing the movement path, it can be determined whether the person has a behavioral tendency to dispose of garbage, thereby improving the response speed of the monitoring system.

[0049] More specifically, once it is determined that someone is approaching a garbage disposal point, the system will further analyze the items they are carrying, using image recognition technology (such as YOLOv5, Faster R-CNN and other target detection methods based on deep learning) to identify the features of the items in the image, determine whether it is garbage, the volume of the item, the category of the item, etc. After identifying the item, it uses image feature extraction algorithms (such as feature point matching, texture analysis, etc.) to determine whether the item meets the characteristics of garbage disposal.

[0050] More specifically, through advanced target detection technology, it is possible to effectively identify the type, size and other characteristics of an object, determine whether it is garbage, reduce misjudgment, identify possible objects at an early stage, and provide early warning and verify garbage disposal behavior.

[0051] More specifically, the volume of the identified carried objects is analyzed to determine whether they meet the volume range for garbage disposal. This step can be done through 3D modeling, deep learning algorithms, etc. to determine the three-dimensional volume characteristics of the objects, and through object classification models (such as convolutional neural networks CNN) to classify the objects and determine the type of the object (for example, paper, plastic bottles, food packaging, etc.). By identifying the volume and type, the nature of the garbage can be more accurately determined, recyclables can be distinguished from non-recyclables, and even abnormal disposal behavior can be identified in advance, thereby improving the system's recognition accuracy of garbage disposal behavior, especially in an environment with diverse objects.

[0052] More specifically, after the items are brought to the garbage disposal point, the imaging system further determines the occurrence of the disposal action, which usually includes the action of putting the items from the hands into the disposal port. Motion recognition technology (such as motion recognition based on convolutional neural networks or human posture analysis) is used to identify such actions, analyze the characteristics of the disposal action, and determine whether it is a standardized disposal or an irregular behavior (such as littering). At the same time, it can be combined with the type and volume data of the items to verify whether it complies with the garbage classification and disposal specifications. Through motion recognition technology, the disposal behavior can be accurately captured, thereby confirming the real-time dynamics of the disposal process. Disposal action recognition can effectively distinguish between standardized and irregular garbage disposal behaviors, thereby improving monitoring accuracy and the efficiency of garbage classification management.

[0053] More specifically, the results obtained from the above steps are integrated to synthesize the real-time situation characteristics in the designated monitoring area. These characteristics include information such as the movement characteristics of people, the type and volume of objects carried, and the occurrence of garbage disposal actions. Through real-time processing of multi-dimensional data, the monitoring system can determine whether the garbage disposal behavior complies with regulations, respond in a timely manner, and even trigger an alarm or notify relevant personnel.

[0054] It is understandable that through multi-dimensional data fusion, the system can monitor all dynamics of garbage disposal points in real time and accurately, ensuring that every detail is not overlooked. Combined with real-time dynamic data, the system can intelligently analyze and judge the compliance of disposal behavior, trigger early warnings or take corresponding management measures.

[0055] Preferably, the step of collecting the filling status of the garbage storage area in real time by means of a filling status sensor preset at the garbage delivery point to obtain the filling status characteristics corresponding to the garbage storage area includes: S31: Pre-arranging a filling state sensor in the garbage storage area of ​​the garbage delivery point so that the filling state sensor is in a position to monitor the state of the garbage storage area; wherein the filling state sensor includes an infrared detection sensor and a weight sensor, and the garbage storage area includes a plurality of unit areas, each unit area corresponding to a different type of garbage; S32: collecting the filling state of the garbage storage area in real time through the filling state sensor to obtain the filling state characteristics of the corresponding garbage storage area; wherein the filling state characteristics are used to describe the garbage volume and garbage weight in each unit area of ​​the garbage storage area.

[0056] Specifically, infrared detection sensors and weight sensors are pre-installed in the garbage storage area of ​​the garbage disposal point. The layout position of each sensor needs to cover each unit area of ​​the garbage storage area to ensure that the garbage filling situation of each unit can be accurately monitored. The garbage storage area is divided into several unit areas, each area specifically corresponds to a type of garbage (such as recyclable garbage, non-recyclable garbage, hazardous garbage, etc.), and each unit area can be independently monitored through sensors.

[0057] It can be understood that by reasonably arranging the sensor positions, it is ensured that the filling status of each unit area can be monitored in real time to avoid monitoring blind spots. The unit area division of the garbage storage area ensures that the filling status of each type of garbage can be tracked independently, thereby enhancing the accuracy of classification management.

[0058] More specifically, the infrared sensor is used to monitor the volume of garbage accumulation in the garbage storage area in real time. The sensor determines the filling degree of garbage by emitting infrared rays and detecting the intensity of the reflected signal. As the height of the garbage accumulation increases, the reflected infrared signal will change, thereby obtaining the volume of the garbage accumulation. The weight sensor is used to monitor the weight of garbage in each unit area in real time. Usually, the weight of the garbage is sensed by a pressure sensor on the ground (such as a strain gauge sensor). The weight data of the garbage can reflect the amount of garbage in the storage area and help determine the filling status of the garbage. By combining the volume monitoring of the infrared sensor and the weight monitoring of the weight sensor, the filling status of the garbage storage area can be fully understood, thereby providing more accurate data. The real-time data collection of the sensor can ensure that the system has a real-time understanding of the status of the garbage storage area, providing support for subsequent garbage disposal and cleaning work.

[0059] More specifically, the filling state sensors (infrared and weight sensors) continuously collect real-time data from each unit area of ​​the garbage storage area. The infrared sensor estimates the volume by measuring the height of the garbage pile, and the weight sensor directly measures the weight of the garbage. The collected data will be preliminarily processed by the sensor processing unit and transmitted to the central monitoring system through wireless networks or other communication methods. The monitoring system analyzes the data to obtain the specific filling state characteristics of each unit area, such as the volume and weight of the garbage.

[0060] More specifically, through the garbage volume data obtained by the infrared sensor and the garbage weight data obtained by the weight sensor, the monitoring system can describe the filling status characteristics of each unit area of ​​the garbage storage area. These characteristics include: the garbage volume (measured by the infrared sensor) and the garbage weight (measured by the weight sensor) of each unit area. These characteristics are used to monitor the saturation, capacity and garbage type of the garbage storage area in real time. According to the filling status characteristics of the garbage storage area, it can be determined whether it needs to be cleaned in time or whether there is an overfilling phenomenon. These data will help staff decide the timing and priority of garbage cleaning.

[0061] More specifically, the monitoring system generates real-time reports based on the filling status data collected in real time, and feeds back to management personnel or automatically triggers cleaning tasks. The system can determine whether the garbage storage area needs to be cleaned, whether there is a garbage disposal point overloaded or not filled, etc. through preset thresholds. If the garbage filling volume in a certain area exceeds the set threshold, the system will automatically issue a cleaning notification or alarm signal to deal with the problem in a timely manner and avoid excessive garbage accumulation.

[0062] Preferably, the step of performing abnormality assessment processing on the real-time status feature and the filling state feature according to a preset abnormality assessment standard to obtain an abnormality assessment result includes: S41: Retrieving the identification standard of the garbage station's unaccommodable objects in the preset abnormality assessment standard to perform abnormality identification processing on the real-time status feature, so as to obtain the abnormal feature of the placed object corresponding to the real-time status feature; S42: analyzing the remaining storage space of the garbage storage area of ​​the garbage disposal point according to the filling state characteristics to obtain the available storage space characteristics of the garbage disposal point; S43: Retrieving the storage feasibility analysis standard in the preset abnormality assessment standard to combine and analyze the real-time status feature and the placement space feature, so as to obtain the space accommodation abnormality feature corresponding to the real-time status feature and the placement space feature; S44: Retrieving the placement action analysis standard in the preset abnormal evaluation standard to analyze the placement action compliance of the real-time situation feature, so as to obtain the placement action compliance feature of the garbage placement point; S45: combining the abnormal characteristics of the placed object, the abnormal characteristics of the space accommodation and the compliance characteristics of the placing action to obtain an abnormality assessment result.

[0063] Specifically, the system will collect the real-time status characteristics of the garbage placement point based on sensors (such as weight sensors, infrared sensors, etc.), which include information such as the weight, volume, and type of garbage. Based on the preset identification standards for objects that cannot be contained in the garbage station, the system will identify and process the real-time status characteristics to determine whether there are items that do not meet the requirements for garbage placement (such as large garbage, hazardous substances, dangerous goods, etc.). For example, some types of garbage may exceed the capacity of the garbage placement point, or do not belong to the designated garbage classification. The identified non-compliant garbage types will be marked as "abnormal characteristics of the placed objects" and an abnormal mark will be generated.

[0064] It is understandable that this step can ensure that garbage disposal points will not be occupied by items that do not meet the regulations, and avoid problems caused by improper garbage disposal, such as obstruction of the operation of the garbage recycling system or pollution to the environment. Through real-time monitoring and automatic identification of non-compliant items, problems in the garbage disposal process can be effectively prevented, manual intervention can be reduced, and management efficiency can be improved.

[0065] More specifically, through the aforementioned sensors (infrared sensors and weight sensors), the system has collected the filling status characteristics of the garbage storage area in real time, including the volume and weight data of the garbage. According to the filling status characteristics, the system analyzes the remaining space in the current garbage storage area. The system will calculate the density of garbage accumulation and the current storage capacity in each unit area, and then estimate the remaining available storage space (available space), and obtain the remaining storage space characteristics of each unit area, indicating whether each area can accommodate more garbage.

[0066] It is understandable that by accurately calculating the remaining storage space, the system can manage garbage disposal more reasonably, avoid overfilling of garbage storage areas or waste of free space, and improve the efficiency of garbage storage space utilization. The system can analyze the remaining space based on real-time data, predict the storage needs of garbage disposal points, and help managers prepare in advance to avoid full loads.

[0067] More specifically, based on the real-time status characteristics (such as garbage type, volume, weight, etc.) and the remaining storage space characteristics, the system evaluates whether the current garbage storage area has enough space to accommodate the newly placed garbage based on the preset storage feasibility analysis standards. If the type and amount of the newly placed garbage do not match the existing remaining space, it may cause storage space overload or waste. If the analysis results show that the garbage storage space at the current placement point cannot effectively accommodate the garbage to be placed, the system will mark the "abnormal space capacity characteristics" and generate an alarm.

[0068] It is understandable that by analyzing the spatial capacity of the garbage storage area, the system can effectively prevent the garbage storage area from being overfilled, causing garbage overflow or waste of area vacancy. The system can help reasonably arrange the timing and amount of garbage delivery, avoid insufficient capacity or waste of space at the delivery point, and thus improve the efficiency of garbage management.

[0069] More specifically, by analyzing the real-time situation characteristics, the system will evaluate whether the delivery action complies with the operating specifications based on the preset delivery action analysis standards (for example, the location and method of delivery of garbage, etc.). For example, the system checks whether the garbage is correctly delivered to the designated garbage bin, whether there is any littering or non-compliance with classification requirements. If the delivery action is not compliant, the system will mark it as "abnormal delivery action compliance characteristics" and generate an alarm to remind managers to take measures.

[0070] It can be understood that by analyzing the compliance of disposal actions, the system helps ensure that garbage disposal complies with relevant regulations, avoids littering and improper operation, and thus improves the standardization of the entire garbage management system. Automated analysis of the compliance of disposal actions can reduce human errors and ensure that garbage disposal behavior is more standardized.

[0071] More specifically, the abnormal characteristics of the objects placed, the abnormal characteristics of the space capacity and the compliance characteristics of the placement action are comprehensively analyzed to generate the final abnormal evaluation result. This evaluation result can not only show whether there is any non-compliant situation in the placement of garbage, but also point out whether the garbage storage area is overloaded or there are other abnormal situations. The system will feed back these abnormal evaluation results to the management personnel so that they can take timely measures to intervene, such as cleaning up the garbage, adjusting the storage space or improving the garbage placement behavior.

[0072] Preferably, the step of performing an alarm behavior analysis on the abnormal evaluation result according to a preset alarm standard to determine an alarm response instruction corresponding to the abnormal evaluation result includes: S51: classifying and analyzing the abnormal state of the abnormal evaluation result according to the evaluation and analysis standard in the preset alarm standard to obtain the abnormal type and abnormal index corresponding to the abnormal evaluation result; S52: matching and identifying the abnormality type and the abnormality index according to each alarm type condition in the preset alarm standard to determine the compliance parameter of each alarm type condition to the abnormality type and the abnormality index; S53: when all the compliance parameters do not reach the preset determination threshold, an alarm response instruction is generated without executing an alarm action; S54: When there is a compliance parameter that reaches a preset determination threshold, the alarm type condition corresponding to the compliance parameter is used as an alarm execution object, and an alarm execution instruction corresponding to the alarm execution object is scheduled from a database, and the alarm execution instruction is used as an alarm response instruction.

[0073] Specifically, the system has analyzed the real-time status characteristics, filling status characteristics, etc. according to the preset abnormal evaluation standards, and obtained the abnormal evaluation results. This result includes the abnormal type (such as abnormal placement, abnormal space capacity, etc.) and the abnormal index (such as the degree of exceeding the preset threshold). According to the preset evaluation and analysis standards, the system classifies and analyzes these abnormal evaluation results, and refines them into different abnormal types and corresponding abnormal indexes. For example, the abnormal type can be "garbage overflow" or "non-compliant placement", and the abnormal index can be expressed as the degree of garbage overflow or the percentage of garbage placement compliance deviation. Through classification and analysis, the system can accurately classify complex abnormal evaluation results into specific abnormal types and assign appropriate abnormal indexes to each abnormality, which is convenient for subsequent alarm behavior processing. The classified abnormal evaluation results provide clear basic data for alarm analysis, ensuring the pertinence of subsequent alarm response measures.

[0074] More specifically, the preset alarm standards define a variety of alarm types, each of which contains a series of conditions, which usually include the type of anomaly, the range of anomaly index, and the threshold reached. For example, if the "garbage storage space is full" exceeds 80%, the "storage space is full" alarm is triggered, and the system will match and identify the parsed anomaly type and anomaly index with the alarm type conditions. Each anomaly assessment result will be compared with a condition in the alarm type condition to check its compliance. Compliance can be understood as a quantitative value of the degree of matching. Usually, the threshold is used to determine whether to trigger an alarm. Through matching and identification, the system can select the appropriate alarm type according to different abnormal situations to ensure the accuracy of the alarm response.

[0075] More specifically, the system calculates the compliance parameters of each alarm type condition based on the degree of match between the anomaly type and the anomaly index. For example, if the anomaly index exceeds a preset threshold, the compliance of the alarm condition may be 100%; if it is lower than the threshold, the compliance will be low. The compliance parameter is usually a value between 0 and 100, which indicates the degree of match between a certain alarm type condition and the current anomaly assessment result. The system compares each compliance parameter with the preset judgment threshold. If the compliance does not reach the threshold, the abnormal situation is considered not serious enough and no alarm needs to be triggered; if the compliance reaches or exceeds the threshold, the abnormal situation is considered serious enough and an alarm needs to be triggered. Through the combination of compliance parameters and threshold judgment, the system can accurately control the triggering conditions of the alarm, avoid false alarms and missed alarms, and improve the accuracy of the alarm system.

[0076] More specifically, when each compliance parameter does not reach the preset judgment threshold, the system will generate an alarm response instruction of "no need to execute alarm behavior". This means that the current abnormal situation is not enough to trigger the response of the alarm system, and the system will not execute any alarm measures. When a certain compliance parameter reaches or exceeds the preset threshold, the system will use the alarm type condition corresponding to the compliance parameter as the alarm execution object, and dispatch the corresponding alarm execution instruction from the database according to the alarm type condition. For example, if the "garbage storage is full" alarm is triggered, the system may retrieve the "send cleanup personnel" instruction or the "adjust garbage storage capacity" operation instruction from the database as a response measure.

[0077] More specifically, after determining the alarm response instructions, the system will dispatch relevant execution instructions from the alarm management database according to the preset alarm type. For example, the dispatch system may issue an instruction to "clean up personnel on site" or trigger other emergency response operations. Once the instruction is dispatched, the system will execute the corresponding alarm response operation to ensure that abnormal situations can be handled in a timely manner to avoid more serious consequences. The automatic dispatch and execution of alarm response instructions helps ensure that abnormal situations can be responded to and handled in the shortest time possible to avoid human delays.

[0078] Preferably, the scheduling alarm execution instruction also includes: S71: acquiring filling state characteristics of other garbage disposal points in the vicinity through wireless data, and comparing the filling state characteristics of the other garbage disposal points with the filling state characteristics of the garbage disposal point to obtain a filling comparison result; S72: performing adaptability analysis of the abnormal evaluation results on the remaining garbage disposal points according to the filling comparison results, so as to obtain fitness parameters of the garbage disposal behaviors of the remaining garbage disposal points with respect to the abnormal evaluation results; S73: Analyze the garbage disposal behavior fed back by the abnormal evaluation result according to the fitness parameters of the remaining garbage disposal points to obtain a disposal suggestion instruction, and combine the disposal suggestion instruction with the alarm execution instruction to serve as an alarm response instruction.

[0079] Specifically, the system collects the filling status characteristics of other garbage disposal points in the vicinity through wireless communication technology (such as Internet of Things technology, wireless sensor networks, etc.). These characteristics include the current filling volume of each garbage disposal point, garbage disposal frequency, garbage type and other information. Data collection can be real-time. The system obtains data through embedded sensors, cameras and other devices to ensure timely updating and accuracy of the filling status characteristics. Using wireless communication technology, the system can collect the filling status information of neighboring garbage disposal points in real time to ensure the timeliness and accuracy of the data.

[0080] More specifically, the system compares the filling status characteristics of the target garbage disposal point corresponding to the current abnormal assessment result with the filling status characteristics of other nearby garbage disposal points. For example, if the target garbage disposal point has reached a state close to full load, the system will compare the filling levels of other disposal points to check whether they have similar problems, including filling ratio, garbage disposal mode, similarity of garbage types and other standards. If the filling status of multiple garbage disposal points is similar, it may mean that there are certain common problems in the overall garbage disposal behavior in the area. By comparing the filling status of different disposal points, potential systemic problems can be discovered more comprehensively, avoiding focusing on a single disposal point and improving the accuracy of the alarm.

[0081] More specifically, after obtaining the filling comparison results, the system further compares the filling status characteristics of other garbage disposal points with the abnormal evaluation results of the target garbage disposal point to analyze whether the remaining disposal points will also be affected by similar problems. For example, if a disposal point has a "garbage accumulation" problem, the system will evaluate whether similar disposal points around it also have similar garbage accumulation conditions, and perform abnormal evaluation based on this similarity. The adaptive parameters of the evaluation may include "garbage filling overflow degree", "garbage accumulation speed", "storage space remaining", etc., in order to determine the adaptability of the remaining disposal points to the target abnormal evaluation results.

[0082] More specifically, based on the adaptability analysis in the previous step, the system will calculate a fitness parameter for each adjacent garbage disposal point. This fitness parameter represents the degree of adaptability of the disposal point to the target anomaly assessment result. The fitness parameters may include "whether similar anomaly types have been reached" and "the degree of anomaly impact". The higher the value, the closer the anomaly problem of the disposal point is to the target disposal point. These fitness parameters will affect the subsequent disposal recommendations and alarm strategy formulation, and determine whether more sophisticated management or adjustments are needed for certain disposal points.

[0083] More specifically, based on the fitness parameters of the remaining garbage disposal points, the system can generate disposal recommendations. For example, if the fitness parameter of a garbage disposal point is high, it means that it is more likely to have similar problems. The system can recommend increasing the monitoring frequency of the disposal point or adjusting the time and capacity of garbage disposal. Based on the results of the fitness analysis, the system will provide specific disposal recommendations to managers, such as "recommend adjusting the garbage disposal time of X disposal point" or "recommend increasing the cleaning frequency of Y disposal point".

[0084] More specifically, the alarm execution instruction and the delivery suggestion instruction are combined to form a comprehensive alarm response instruction. This instruction not only includes the response measures to the current abnormal situation, but also covers the preventive measures for potential problems. For example, the alarm instruction may be "perform garbage accumulation cleaning", while the delivery suggestion instruction is "adjust the delivery mode of certain delivery points". The system will coordinate the implementation of these two instructions as a whole to ensure that not only the current abnormal situation can be handled, but also potential problems in the future can be effectively prevented.

[0085] Reference Figure 2 As shown, in a second aspect, the present invention provides a monitoring and alarm system for garbage disposal points, which is used to implement a monitoring and alarm method for garbage disposal points as described in any one of the first aspects, including: An image monitoring module is used to continuously collect visual images of a designated monitoring area through a visual sensor preset at a garbage placement point to obtain an image monitoring sequence corresponding to the designated monitoring area; A real-time analysis module, used for identifying key dynamic features of the image monitoring sequence to obtain real-time status features of the designated monitoring area; A filling recognition module is used to collect the filling status of the garbage storage area in real time through a filling status sensor preset at the garbage delivery point to obtain the filling status characteristics corresponding to the garbage storage area; An abnormality assessment module, used for performing abnormality assessment processing on the real-time status feature and the filling state feature according to a preset abnormality assessment standard to obtain an abnormality assessment result; An alarm response module is used to perform alarm behavior analysis on the abnormal assessment results according to preset alarm standards to determine the alarm response instructions corresponding to the abnormal assessment results; wherein the alarm response instructions are used to describe whether an alarm behavior needs to be executed for the abnormal assessment results, and the specific alarm method when an alarm behavior needs to be executed for the abnormal assessment results.

[0086] In this embodiment, for the specific implementation of each module in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0087] In a third aspect, the present invention provides an intelligent garbage room, which adopts the monitoring and alarm system for garbage disposal points described in the second aspect.

[0088] 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 monitoring and alarm method for garbage disposal points, characterized in that: include: Continuously collecting visual images of the designated monitoring area through a visual sensor preset at the garbage placement point to obtain an image monitoring sequence corresponding to the designated monitoring area; Performing recognition processing on the key dynamic features of the image monitoring sequence to obtain the real-time status features of the designated monitoring area; The filling state of the garbage storage area is collected in real time by a filling state sensor preset at the garbage delivery point to obtain the filling state characteristics corresponding to the garbage storage area; Performing abnormality assessment processing on the real-time status feature and the filling state feature according to a preset abnormality assessment standard to obtain an abnormality assessment result; An alarm behavior analysis is performed on the abnormal assessment result according to preset alarm standards to determine the alarm response instructions corresponding to the abnormal assessment result; wherein the alarm response instructions are used to describe whether an alarm behavior needs to be executed for the abnormal assessment result, and the specific alarm method when an alarm behavior needs to be executed for the abnormal assessment result.

2. The method for monitoring and warning garbage disposal points according to claim 1, characterized in that: The steps of continuously collecting visual images of a designated monitoring area by using a visual sensor preset at a garbage placement point to obtain an image monitoring sequence corresponding to the designated monitoring area include: Collect and analyze information on garbage disposal points, and set up visual sensors based on the analysis results; The visual sensor is activated to be in a visual monitoring state, so as to continuously collect visual images of the designated monitoring area, so as to obtain an image monitoring sequence corresponding to the designated monitoring area.

3. The monitoring and alarm method for garbage disposal points according to claim 2, characterized in that: The steps of collecting and analyzing information about garbage disposal points and setting up visual sensors based on the analysis results include: Collecting the surrounding environment of the garbage disposal point to obtain the surrounding environment information of the garbage disposal point; Performing a moving path analysis of the garbage placement behavior at the garbage placement point according to the surrounding environment information to obtain a garbage placement moving path at the garbage placement point; wherein the garbage placement moving path is used to describe the moving path required before garbage placement at the garbage placement point; Marking the garbage delivery moving path as a designated monitoring area, performing a traceability analysis of visual monitoring on the designated monitoring area to obtain a visual monitoring node for visual monitoring of the garbage delivery moving path, and setting a visual sensor at a position corresponding to the visual monitoring node; Collecting structural specifications of the garbage placement opening of the garbage placement point to obtain structural information of the garbage placement opening of the garbage placement point; Performing a garbage placement area analysis of the garbage placement behavior at the garbage placement point according to the placement port structure information to obtain a garbage placement location area of ​​the garbage placement point; The garbage disposal location area is marked as a designated monitoring area, and a traceability analysis of visual monitoring is performed on the designated monitoring area to obtain a visual monitoring node for visually monitoring the garbage disposal behavior, and a visual sensor is set at a position corresponding to the visual monitoring node.

4. The monitoring and alarm method for garbage disposal points according to claim 1, characterized in that: The step of performing recognition processing on the image monitoring sequence for key dynamic features to obtain the real-time status features of the designated monitoring area comprises: Performing feature recognition of human portrait movement on the image monitoring sequence to obtain human portrait movement features in the designated monitoring area; The moving tendency of the garbage disposal point is judged on the moving feature of the portrait to determine whether the content fed back by the moving feature of the portrait is a movement behavior toward the garbage disposal point. If so, the feature recognition of the object carried by the portrait is performed on the image monitoring sequence to obtain the features of the suspected target that will be placed at the garbage disposal point in the designated monitoring area; Performing content recognition on the suspected target feature to obtain a volume recognition result and a type recognition result of the suspected target feature; Performing delivery action recognition corresponding to the suspected target feature on the image monitoring sequence to obtain delivery action features in the designated monitoring area; The volume recognition result and type recognition result of the suspected target feature and the delivery action feature together constitute the real-time status feature of the designated monitoring area.

5. The monitoring and alarm method for garbage disposal points according to claim 1, characterized in that: The steps of collecting the filling status of the garbage storage area in real time by using a filling status sensor preset at the garbage delivery point to obtain the filling status characteristics corresponding to the garbage storage area include: A filling state sensor is pre-arranged in the garbage storage area of ​​the garbage delivery point so that the filling state sensor is in a position to monitor the state of the garbage storage area; wherein the filling state sensor includes an infrared detection sensor and a weight sensor, and the garbage storage area includes a plurality of unit areas, each unit area corresponding to a different type of garbage; The filling state sensor collects the filling state of the garbage storage area in real time to obtain the filling state characteristics of the corresponding garbage storage area; wherein the filling state characteristics are used to describe the garbage volume and garbage weight in each unit area of ​​the garbage storage area.

6. The method for monitoring and warning garbage disposal points according to claim 1, characterized in that: The step of performing abnormality assessment processing on the real-time status feature and the filling state feature according to a preset abnormality assessment standard to obtain an abnormality assessment result includes: Retrieving the identification standard of the garbage station's non-containable objects in the preset abnormality assessment standard to perform abnormality identification processing on the real-time status feature, so as to obtain the abnormal feature of the placed object corresponding to the real-time status feature; Analyzing the remaining storage space of the garbage storage area of ​​the garbage placement point according to the filling state characteristics to obtain the characteristics of the available space for the garbage placement point; Retrieving the storage feasibility analysis standard in the preset abnormality assessment standard to combine and analyze the real-time status feature and the deployable space feature, so as to obtain the space accommodation abnormality feature corresponding to the real-time status feature and the deployable space feature; Retrieving the placement action analysis standard in the preset abnormal evaluation standard to analyze the placement action compliance of the real-time situation characteristics, so as to obtain the placement action compliance characteristics of the garbage placement point; The abnormal characteristics of the placed objects, the abnormal characteristics of the space accommodation and the compliance characteristics of the placing action are combined to obtain an abnormal evaluation result.

7. The method for monitoring and warning garbage disposal points according to claim 1, characterized in that: The step of performing an alarm behavior analysis on the abnormal evaluation result according to a preset alarm standard to determine an alarm response instruction corresponding to the abnormal evaluation result comprises: Classify and analyze the abnormal state of the abnormal evaluation result according to the evaluation and analysis standard in the preset alarm standard to obtain the abnormal type and abnormal index corresponding to the abnormal evaluation result; Matching and identifying the abnormality type and the abnormality index according to each alarm type condition in the preset alarm standard to determine the compliance parameter of each alarm type condition to the abnormality type and the abnormality index; When each of the compliance parameters does not reach the preset determination threshold, an alarm response instruction is generated without executing an alarm action; When there is a compliance parameter that reaches a preset determination threshold, the alarm type condition corresponding to the compliance parameter is used as an alarm execution object, and an alarm execution instruction corresponding to the alarm execution object is scheduled from a database, and the alarm execution instruction is used as an alarm response instruction.

8. The method for monitoring and warning garbage disposal points as claimed in claim 7, characterized in that: The dispatch alarm execution instructions also include: Acquire filling state characteristics of other garbage disposal points within the vicinity through wireless data, and compare the filling state characteristics of other garbage disposal points with the filling state characteristics of the garbage disposal point to obtain a filling comparison result; Performing adaptability analysis of the abnormal evaluation results on the remaining garbage disposal points according to the filling comparison results to obtain fitness parameters of the garbage disposal behaviors of the remaining garbage disposal points with respect to the abnormal evaluation results; The garbage disposal behavior fed back by the abnormal evaluation result is analyzed for disposal suggestions according to the fitness parameters of the remaining garbage disposal points to obtain a disposal suggestion instruction, and the disposal suggestion instruction is combined with the alarm execution instruction to serve as an alarm response instruction.

9. A monitoring and warning system for garbage disposal points, characterized in that: A method for monitoring and warning a garbage disposal point according to any one of claims 1 to 8, comprising: An image monitoring module is used to continuously collect visual images of a designated monitoring area through a visual sensor preset at a garbage placement point to obtain an image monitoring sequence corresponding to the designated monitoring area; A real-time analysis module, used for identifying key dynamic features of the image monitoring sequence to obtain real-time status features of the designated monitoring area; A filling recognition module is used to collect the filling status of the garbage storage area in real time through a filling status sensor preset at the garbage delivery point to obtain the filling status characteristics corresponding to the garbage storage area; An abnormality assessment module, used for performing abnormality assessment processing on the real-time status feature and the filling state feature according to a preset abnormality assessment standard to obtain an abnormality assessment result; An alarm response module is used to perform alarm behavior analysis on the abnormal assessment results according to preset alarm standards to determine the alarm response instructions corresponding to the abnormal assessment results; wherein the alarm response instructions are used to describe whether an alarm behavior needs to be executed for the abnormal assessment results, and the specific alarm method when an alarm behavior needs to be executed for the abnormal assessment results.

10. An intelligent garbage room, characterized in that: The intelligent garbage room adopts the monitoring and alarm system for garbage disposal points as described in claim 9.

Images