A community garbage classification supervision method and system for property management

Abstract

This invention relates to the field of artificial intelligence technology and discloses a method and system for monitoring community waste sorting in property management. The method includes: using intelligent sensing terminals deployed at waste disposal nodes to respond to residents' waste disposal events and capture resident identity characteristics and multimodal behavioral characteristics of the disposal process linked to the waste disposal events; analyzing the degree of waste mixing and the standardization of disposal in each event to generate a waste disposal behavior score; sequentially associating resident identity characteristics and disposal behavior scores to construct a resident's waste sorting behavior profile; and generating and executing a tiered guidance strategy based on the waste sorting behavior profile. This tiered guidance strategy includes: triggering a positive incentive loop for residents whose waste sorting behavior profile is in the excellent range, and pushing personalized correction instructions to residents whose waste sorting behavior profile falls into the risk warning range. This invention can improve the efficiency of community waste sorting supervision.

Description

Technical Field

[0001] This invention relates to a community waste sorting and supervision method and system for property management, and belongs to the field of artificial intelligence technology. Background Technology

[0002] Community waste sorting supervision refers to a comprehensive system that uses intelligent technology and management strategies to accurately identify, quantitatively assess, and provide personalized guidance for residents' waste disposal behavior. Its profound significance lies not only in directly promoting the reduction, recycling, and harmless treatment of waste by improving sorting accuracy, but also in transforming environmentally friendly behavior into measurable and incentivized community credit. This data-driven approach reshapes residents' habits, cultivates community civilization, and ultimately upgrades the traditional management challenge of waste disposal into a key link in building modern, sustainable, and smart communities.

[0003] Traditional community waste sorting supervision mainly relies on on-site human supervision, that is, sanitation workers or volunteers open bags for inspection and verbal persuasion at the disposal points, supplemented by vague monitoring for post-event traceability. This approach remains a passive management model of discovery and correction, lacks data accumulation and behavior analysis capabilities, and cannot provide personalized guidance to residents, resulting in low supervision efficiency. Summary of the Invention

[0004] This invention provides a community waste sorting supervision method and system for property management, the main purpose of which is to improve the supervision efficiency of community waste sorting.

[0005] To achieve the above objectives, the present invention provides a community waste sorting and supervision method for property management, comprising: By deploying intelligent sensing terminals at waste disposal nodes, in response to residents' waste disposal events, the system captures resident identity features and multimodal behavioral features of the disposal process that are associated with the waste disposal events. Based on the multimodal behavioral characteristics, the degree of waste mixing and the degree of waste disposal standardization of the waste disposal event are analyzed to generate a waste disposal behavior score for the waste disposal event; The resident identity features and waste disposal behavior scores are correlated over time to construct a profile of the resident's waste sorting behavior. Based on the waste sorting behavior profile, a tiered guidance strategy is generated and executed. The tiered guidance strategy includes: triggering a positive incentive loop for residents whose waste sorting behavior profile is in the excellent range, and pushing personalized correction instructions to residents whose waste sorting behavior profile falls into the risk warning range.

[0006] Optionally, the analysis of the waste mixing degree and disposal standardization degree of the waste disposal event based on the multimodal behavioral characteristics includes: The multimodal behavioral features are preprocessed to extract the visual and behavioral features of the waste disposal event; The degree of waste mixing in the waste disposal event is analyzed based on the aforementioned visual features. Based on the aforementioned behavioral characteristics, the standardization of waste disposal events is analyzed.

[0007] Optionally, the extraction of visual and behavioral features of the waste disposal event includes: Keyframes are extracted from the video stream corresponding to the multimodal behavioral features of the garbage disposal event, and the keyframes are detected to obtain the contents and type of garbage in the garbage disposal event, so as to generate the visual features of the garbage disposal event. The movement trajectory, weight change, and disposal time of the waste in the multimodal behavioral characteristics are analyzed to generate the behavioral characteristics of the waste disposal event.

[0008] Optionally, analyzing the degree of waste mixing in the waste disposal event using the visual features includes: Analyze the classification confidence of the visual features corresponding to the contents of the waste; Based on the classification confidence level, calculate the content incompatibility index of the waste contents; The degree of waste mixing in the waste disposal event is determined based on the incompatibility index of the contents.

[0009] Optionally, the analysis of the standardization of waste disposal events based on the behavioral characteristics includes: The instantaneous velocity, maximum acceleration, and trajectory smoothness parameters before entering the bin are extracted from the motion trajectory corresponding to the behavioral features to calculate the motion posture score of the garbage disposal event; The weight change rate and impact coefficient are extracted from the weight changes corresponding to the behavioral characteristics to calculate the weight operation score of the waste disposal event. The total disposal time and effective operation time are extracted from the disposal time corresponding to the behavioral characteristics to calculate the time efficiency score of the waste disposal event; The standardization of the waste disposal event is analyzed by combining the motion posture score, the weight operation score, and the time efficiency score.

[0010] Optionally, the step of using intelligent sensing terminals deployed at waste disposal nodes to respond to residents' waste disposal events and capture resident identity features and multimodal behavioral features of the disposal process bound to the waste disposal event includes: In response to detecting that a resident has entered the preset sensing area of ​​the garbage disposal node, the smart sensing terminal is activated and enters working mode to collect the resident's identity characteristics through the identity recognition module of the smart sensing terminal; The multimodal sensors of the intelligent sensing terminal are activated simultaneously to collect multimodal behavioral characteristics from the time the resident's identity features are generated until the time the garbage is disposed of.

[0011] Optionally, the step of sequentially associating the resident's identity features and disposal behavior scores to construct a resident's waste sorting behavior profile includes: The resident's identity features are linked to a series of time-series waste disposal behavior scores to form a time-series behavior dataset of the resident. Based on the time-series behavior dataset, the resident's historical waste disposal scores, violation type preferences, and compliance stability indicators are calculated to generate a waste sorting behavior profile of the resident.

[0012] Optionally, the step of calculating the resident's historical waste disposal score, violation type preference, and compliance stability index based on the time-series behavior dataset to generate a waste sorting behavior profile of the resident includes: Within a preset historical time window, the scores of waste disposal behavior in the time-series behavior dataset are statistically calculated to generate the historical waste disposal score; A time-series analysis is performed on the waste mixing degree sub-score and waste disposal standardization degree sub-score that constitute the waste disposal behavior score in the time-series behavior dataset to identify the key violation dimensions that lead to low scores, thereby generating the residents' violation type preferences. Calculate the standard deviation of the delivery behavior scores within the historical time window to quantify the degree of fluctuation in the residents' behavior, thereby generating a compliance stability index for the residents. By combining the historical waste disposal scores, the violation type preferences, the compliance stability indicators, and the initial reputation data, a profile of the resident's waste sorting behavior is generated.

[0013] Optionally, the positive excitation closed loop includes: Reward notifications will be sent to the residents mentioned above; Record the residents' incentive events to update the initial reputation data in the waste sorting behavior profile.

[0014] To address the aforementioned problems, this invention also provides a community waste sorting and monitoring system for property management, the system comprising: The behavioral feature acquisition module is used to capture resident identity features and multimodal behavioral features of the disposal process in response to resident's garbage disposal events through intelligent sensing terminals deployed at garbage disposal nodes. The waste disposal behavior scoring module is used to analyze the waste mixing degree and disposal standardization degree of the waste disposal event based on the multimodal behavioral characteristics, so as to generate a waste disposal behavior score for the waste disposal event; The behavior profile building module is used to perform time-series correlation between the resident's identity features and the waste disposal behavior score to build a waste sorting behavior profile of the resident; The classification and grading guidance module is used to generate and execute a grading guidance strategy based on the waste classification behavior profile. The grading guidance strategy includes: triggering a positive incentive loop for residents whose waste classification behavior profile is in the excellent range, and pushing personalized correction instructions to residents whose waste classification behavior profile falls into the risk warning range.

[0015] First, by deploying intelligent sensing terminals, the system can seamlessly and accurately capture multi-dimensional data of each waste disposal behavior, avoiding the high cost, low efficiency, and subjective bias problems of traditional manual supervision. The waste disposal behavior score generated based on multi-modal features is more scientific than single-dimensional judgment. The core advantage lies in the fact that this invention constructs a time-series behavioral profile, deeply mining residents' behavioral patterns and potential problems, and implementing a tiered guidance strategy accordingly. This creates a virtuous cycle of positive incentives for high-performing residents and provides precise, personalized correction for residents with problems, greatly improving guidance efficiency and resident participation. Ultimately, it propels community waste sorting management from extensive supervision to a new stage of refined, intelligent, and efficient governance. Therefore, this invention can improve the supervision efficiency of community waste sorting. Attached Figure Description

[0016] Figure 1 A flowchart illustrating a community waste sorting and supervision method for property management provided in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the generation of standardized waste disposal data for implementing the community waste sorting and supervision method for property management, as provided in an embodiment of the present invention. Figure 3 A schematic diagram of modules for implementing the community waste sorting and supervision method for property management, according to an embodiment of the present invention; Figure 4 A schematic diagram of a computer device for a community waste sorting and monitoring method for property management, provided in an embodiment of the present invention; The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0018] This application provides a community waste sorting and supervision method for property management. The implementing entity of this community waste sorting and supervision method for property management includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application embodiment: a server, a terminal, etc. In other words, the community waste sorting and supervision method for property management can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0019] Reference Figure 1 The diagram shown is a flowchart illustrating a community waste sorting and supervision method for property management according to an embodiment of the present invention. In this embodiment, the community waste sorting and supervision method for property management includes: S1. By deploying intelligent sensing terminals at waste disposal nodes, in response to residents' waste disposal events, capture resident identity features and multimodal behavioral features of the disposal process that are bound to the waste disposal event.

[0020] It should be explained that the garbage disposal node refers to an intelligent physical unit with sensing, computing and communication capabilities in the community garbage classification management network. The intelligent sensing terminal refers to the core hardware device deployed on the garbage disposal node, which is responsible for performing data collection and preliminary processing tasks, such as visual sensors, identity recognition modules, weight sensors and other devices. The garbage disposal event refers to a logical unit with a clear start and end time and a complete data packet.

[0021] This invention captures resident identity features and multimodal behavioral features of the disposal process associated with the waste disposal event, providing a basis for subsequent waste disposal analysis.

[0022] Specifically, the intelligent sensing terminals deployed at waste disposal nodes, in response to residents' waste disposal events, capture resident identity features and multimodal behavioral features of the disposal process bound to the waste disposal event, including: In response to detecting that a resident has entered the preset sensing area of ​​the garbage disposal node, the smart sensing terminal is activated and enters working mode to collect the resident's identity characteristics through the identity recognition module of the smart sensing terminal; The multimodal sensors of the intelligent sensing terminal are activated simultaneously to collect multimodal behavioral characteristics from the time the resident's identity features are generated until the time the garbage is disposed of.

[0023] The sensing area refers to a virtual three-dimensional space pre-defined in space, centered on the waste disposal node. When a resident or their object enters this area, it can be detected by the proximity sensor built into the intelligent sensing terminal, thereby generating a start signal to trigger subsequent processes. The working mode refers to a high-power, full-function operating state of the intelligent sensing terminal. In this mode, the terminal's core components, including the identity recognition module, multimodal sensors, data processing unit, and human-computer interaction interface, are activated and operate normally to perform data collection, analysis, and feedback tasks. The identity recognition module refers to the hardware and software combination integrated into the intelligent sensing terminal for uniquely identifying residents. The resident identity features refer to the raw data or processed identification information collected by the identity recognition module that can uniquely map to a specific community resident. The multimodal sensors refer to a set of sensors capable of sensing and collecting physical information related to the garbage disposal process from different dimensions. The multimodal behavioral features refer to the feature set synchronously collected by the multimodal sensors within a specified time period to comprehensively describe a garbage disposal event, including at least: continuous video streams captured by visual sensors recording residents from preparation to completion of disposal; and time-series data recorded by weight sensors reflecting weight changes before and after disposal.

[0024] S2. Based on the multimodal behavioral characteristics, analyze the garbage mixing degree and disposal standardization degree of the garbage disposal event to generate a disposal behavior score for the garbage disposal event.

[0025] Based on the aforementioned multimodal behavioral characteristics, this invention analyzes the degree of waste mixing and the degree of waste disposal standardization in waste disposal events, providing a solid and reliable data foundation for subsequent construction of waste sorting behavior analysis and implementation of hierarchical guidance strategies.

[0026] Specifically, the analysis of waste mixing degree and disposal standardization degree of the waste disposal event based on the multimodal behavioral characteristics includes: The multimodal behavioral features are preprocessed to extract the visual and behavioral features of the waste disposal event; The degree of waste mixing in the waste disposal event is analyzed based on the aforementioned visual features. Based on the aforementioned behavioral characteristics, the standardization of waste disposal events is analyzed.

[0027] The visual features refer to a set of data extracted from video data that can describe the physical properties and classification information of the contents of the waste itself; the behavioral features refer to a set of data extracted from multimodal sensor data that can describe the dynamic behavioral patterns of residents throughout the waste disposal process; the waste mixing degree refers to a comprehensive indicator used to quantify the degree of coexistence of different types of waste in a single waste disposal event; and the disposal standardization degree refers to a comprehensive indicator used to assess whether residents' waste disposal behavior conforms to the preset management standards.

[0028] See Figure 2 The diagram illustrates the generation of the standardization of waste disposal in a community waste sorting and supervision method for property management, as provided in an embodiment of the present invention. The system first obtains scores corresponding to three dimensions of behavioral characteristics: movement posture score, weight handling score, and time efficiency score. The system checks whether all three scores meet preset minimum standards. If any score fails, the disposal behavior is directly determined to be non-standard, and the process ends. If no scores fail, the system performs a weighted sum of the three scores according to preset weights to obtain a comprehensive score. Based on the comprehensive score, the system categorizes it into three levels: high, medium, or low, and finally outputs the standardization of the waste disposal event.

[0029] Furthermore, the extraction of visual and behavioral features of the waste disposal event includes: Keyframes are extracted from the video stream corresponding to the multimodal behavioral features of the garbage disposal event, and the keyframes are detected to obtain the contents and type of garbage in the garbage disposal event, so as to generate the visual features of the garbage disposal event. The movement trajectory, weight change, and disposal time of the waste in the multimodal behavioral characteristics are analyzed to generate the behavioral characteristics of the waste disposal event.

[0030] The video stream refers to a continuous sequence of images with high temporal resolution captured by high-definition cameras deployed at garbage disposal nodes in response to garbage disposal events. The keyframe refers to a frame or a few frames of images containing the most important information, automatically selected from the video stream by a specific algorithm. The garbage contents refer to garbage items identified as independent individuals after instance segmentation of the keyframes. The garbage category refers to the attribute classification of the garbage contents. The motion trajectory refers to the spatial path formed by tracking the garbage bag or garbage contents in continuous video frames. The weight change refers to the numerical change generated at the moment of garbage disposal, recorded by the weight sensor built into the intelligent sensing terminal. The disposal time refers to the total time elapsed from the moment the garbage disposal event is triggered until the garbage is successfully placed in the garbage bin and the system detects that the resident has left.

[0031] Optionally, the extraction of keyframes from the video stream corresponding to the multimodal behavioral features of the garbage disposal event can be achieved using optical flow.

[0032] Furthermore, the analysis of the waste mixing degree of the waste disposal event through the visual features includes: Analyze the classification confidence of the visual features corresponding to the contents of the waste; Based on the classification confidence level, calculate the content incompatibility index of the waste contents; The degree of waste mixing in the waste disposal event is determined based on the incompatibility index of the contents.

[0033] Furthermore, as another embodiment of the present invention, the content incompatibility index is calculated using the following formula:

[0034] in, This indicates the incompatibility index of the contents of the junk file. Indicates the first item in the contents of the garbage Classification confidence of each content item Indicates the first item in the contents of the garbage Classification confidence of each content item This indicates the total number of objects containing garbage. Indicates the first item in the contents of the garbage The waste categories containing the contents, Indicates the first item in the contents of the garbage The waste categories containing the contents, Indicates the first item in the contents of the garbage The contents and the first Incompatibility index of contents.

[0035] The classification confidence level refers to the credibility of the model's analysis results. The content incompatibility index is a weighted score used to quantify the total incompatibility cost between all waste contents in a single disposal event due to different categories. The incompatibility index is an empirical value used to quantify the degree of adverse effects when any two different waste categories are mixed.

[0036] Furthermore, the analysis of the standardization of waste disposal events based on the behavioral characteristics includes: The instantaneous velocity, maximum acceleration, and trajectory smoothness parameters before entering the bin are extracted from the motion trajectory corresponding to the behavioral features to calculate the motion posture score of the garbage disposal event; The weight change rate and impact coefficient are extracted from the weight changes corresponding to the behavioral characteristics to calculate the weight operation score of the waste disposal event. The total disposal time and effective operation time are extracted from the disposal time corresponding to the behavioral characteristics to calculate the time efficiency score of the waste disposal event; The standardization of the waste disposal event is analyzed by combining the motion posture score, the weight operation score, and the time efficiency score.

[0037] The instantaneous velocity before entering the bin refers to the magnitude of the instantaneous velocity vector just before entering the plane containing the edge of the bin's disposal opening. The maximum acceleration refers to the maximum acceleration value reached by the garbage bag or its contents during the entire garbage disposal process. The trajectory smoothness parameter is a numerical indicator used to quantify the smoothness of the garbage's trajectory. The motion posture score is an indicator used to assess whether the resident's action posture when disposing of garbage is standardized. The weight change rate refers to the rate of change during a significant change in garbage weight. The impact coefficient is a dimensionless parameter used to quantify the degree of impact of garbage on the garbage bin. The weight handling score is used to assess the degree of care residents show towards the garbage bin when disposing of garbage. The total disposal time refers to the total time elapsed from the moment the garbage disposal event is triggered by the system until the garbage is completely placed in the bin and the system detects that the weight has stabilized. The effective operation time refers to the core time period during which residents actually perform disposal actions within the total disposal time. The time efficiency score is used to assess the efficiency of residents' disposal behavior.

[0038] This invention generates a waste disposal behavior score for the aforementioned waste disposal event to achieve accurate evaluation of the waste disposal event. The waste disposal behavior score is a normalized final evaluation index used to comprehensively quantify the overall quality of a single waste disposal event.

[0039] S3. Perform time-series correlation on the resident identity features and disposal behavior scores to construct a profile of the resident's waste sorting behavior.

[0040] This invention performs a time-series correlation between the resident's identity characteristics and waste disposal behavior scores to construct a waste sorting behavior profile of the resident as the basis for subsequent generation strategies.

[0041] In detail, the step of sequentially associating the resident's identity features and disposal behavior scores to construct a resident's waste sorting behavior profile includes: The resident identity features are bound to a series of time-series delivery behavior scores to form the resident's time-series behavior dataset; Based on the time-series behavior dataset, the residents' historical waste disposal scores, violation type preferences, and compliance stability indicators are calculated to generate a waste sorting behavior profile of the residents.

[0042] The time-series behavior dataset refers to an ordered data set formed by arranging the scores of each garbage disposal event of a single resident as a unique identifier in chronological order of the events. The historical garbage disposal score refers to a representative score obtained by statistically aggregating all disposal behavior scores in the time-series behavior dataset within a preset historical time window. The violation type preference refers to the main violation categories that lead to the resident's low score. The compliance stability index refers to a quantitative indicator that measures whether the resident's garbage sorting behavior is consistently stable. The garbage sorting behavior profile refers to a comprehensive and accurate characterization of the garbage sorting behavior features of an individual resident.

[0043] Further, the step of calculating the resident's historical waste disposal score, violation type preference, and compliance stability index based on the time-series behavior dataset to generate a waste sorting behavior profile of the resident includes: Within a preset historical time window, the scores of waste disposal behavior in the time-series behavior dataset are statistically calculated to generate the historical waste disposal score; A time-series analysis is performed on the waste mixing degree sub-score and waste disposal standardization degree sub-score that constitute the waste disposal behavior score in the time-series behavior dataset to identify the key violation dimensions that lead to low scores, thereby generating the residents' violation type preferences. Calculate the standard deviation of the delivery behavior scores within the historical time window to quantify the degree of fluctuation in the residents' behavior, thereby generating a compliance stability index for the residents. By combining the historical waste disposal scores, the violation type preferences, the compliance stability indicators, and the initial reputation data, a profile of the resident's waste sorting behavior is generated.

[0044] The historical time window refers to the preset time period for historical data statistical analysis. The waste mixing degree sub-score reflects the correctness of waste classification in this disposal, generated by the waste mixing degree analysis module. The disposal standardization degree sub-score reflects whether the residents' operation process in this disposal complies with the standards, generated by the disposal standardization degree analysis module. The key violation dimension refers to the dimension identified as the main reason for low scores when analyzing violation type preferences. The scoring standard deviation refers to the fluctuation of scores of all disposal behaviors within the historical time window. The degree of behavior fluctuation refers to the consistency of residents' waste sorting behavior. The initial reputation data refers to the initial stage of building a waste sorting behavior profile for residents, and a basic reputation score is set for them.

[0045] S5. Based on the waste sorting behavior profile, generate and execute a graded guidance strategy, wherein the graded guidance strategy includes: triggering a positive incentive loop for residents whose waste sorting behavior profile is in the excellent range, and pushing personalized correction instructions to residents whose waste sorting behavior profile falls into the risk warning range.

[0046] Based on the aforementioned waste sorting behavior profile, this invention generates and executes a tiered guidance strategy to achieve efficient management of community residents' arrival and waste disposal.

[0047] Specifically, the generation and execution of a tiered guidance strategy based on the waste sorting behavior profile includes: Based on preset profile analysis rules, the garbage sorting behavior profile is mapped to a guidance strategy level; Based on the stated boot strategy level, a hierarchical boot strategy is generated and executed.

[0048] The profiling analysis rules refer to the judgment logic used to transform the abstract profile of garbage sorting behavior into a specific guidance strategy level. For example, when the historical garbage disposal score is higher than 85 points and the compliance stability index is lower than a certain value, it is judged as excellent; when the historical garbage disposal score is lower than 60 points three times in a row, it is judged as a risk warning. The guidance strategy level refers to a discrete category with a clear guidance orientation after evaluating the profile of residents' garbage sorting behavior according to the profiling analysis rules. The graded guidance strategy refers to a set of specific and executable intervention actions that correspond one-to-one with the guidance strategy level.

[0049] Specifically, the positive excitation closed loop includes: Reward notifications will be sent to the residents mentioned above; Record the residents' incentive events to update the initial reputation data in the waste sorting behavior profile.

[0050] It should be explained that the personalized correction instructions refer to highly targeted guidance or intervention information automatically generated by the system for residents identified as being in the risk warning zone. For example, if the profile shows that the main problem is "improper disposal", a video tutorial on gentle disposal will be pushed; if the problem is "serious mixing of kitchen waste", a picture and text guide on kitchen waste sorting will be pushed. The positive incentive closed loop refers to a virtuous cycle system designed for residents with "excellent" performance that can self-reinforce. The push reward notification refers to an instant message sent to residents informing them that they have received a reward, such as "Congratulations! You have received 50 environmental points for excellent sorting performance for a week." The incentive event refers to a complete incentive behavior unit determined and recorded by the system.

[0051] First, by deploying intelligent sensing terminals, the system can seamlessly and accurately capture multi-dimensional data of each waste disposal behavior, avoiding the high cost, low efficiency, and subjective bias problems of traditional manual supervision. The waste disposal behavior score generated based on multi-modal features is more scientific than single-dimensional judgment. The core advantage lies in the fact that this invention constructs a time-series behavioral profile, deeply mining residents' behavioral patterns and potential problems, and implementing a tiered guidance strategy accordingly. This creates a virtuous cycle of positive incentives for high-performing residents and provides precise, personalized correction for residents with problems, greatly improving guidance efficiency and resident participation. Ultimately, it propels community waste sorting management from extensive supervision to a new stage of refined, intelligent, and efficient governance. Therefore, this invention can improve the supervision efficiency of community waste sorting.

[0052] like Figure 3 The diagram shown is a functional module diagram of the community waste sorting and supervision system for property management according to the present invention.

[0053] The community waste sorting and monitoring system 300 for property management described in this invention can be installed in an electronic device. Depending on the functions implemented, the community waste sorting and monitoring system for property management may include a behavior feature acquisition module 301, a disposal behavior scoring module 302, a behavior profile construction module 30, and a classification and grading guidance module 304. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and is stored in the memory of the electronic device.

[0054] In this embodiment of the invention, the functions of each module / unit are as follows: The behavioral feature acquisition module 301 is used to capture resident identity features and multimodal behavioral features of the disposal process in response to resident's garbage disposal event through an intelligent sensing terminal deployed at the garbage disposal node. The waste disposal behavior scoring module 302 is used to analyze the waste mixing degree and disposal standardization degree of the waste disposal event based on the multimodal behavior characteristics, so as to generate a waste disposal behavior score for the waste disposal event. The behavior profile construction module 303 is used to perform time-series correlation between the resident's identity features and the disposal behavior score to construct the resident's waste sorting behavior profile; The classification and grading guidance module 304 is used to generate and execute a grading guidance strategy based on the waste classification behavior profile. The grading guidance strategy includes: triggering a positive incentive loop for residents whose waste classification behavior profile is in the excellent range, and pushing personalized correction instructions to residents whose waste classification behavior profile falls into the risk warning range.

[0055] In detail, the modules in the community waste sorting and monitoring system 300 for property management described in this embodiment of the invention adopt the same approach as described above when in use. Figure 1 The method used is the same as the community waste sorting and supervision method for property management described in the article, and can produce the same technical effect, so it will not be elaborated here.

[0056] In one embodiment, a computer device is provided, which may be a server or a client, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for communication with external clients via a network connection. When executed by the processor, the computer program implements functions or steps on the server or client side of a community waste sorting and monitoring method for property management.

[0057] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: By deploying intelligent sensing terminals at waste disposal nodes, in response to residents' waste disposal events, the system captures resident identity features and multimodal behavioral features of the disposal process that are associated with the waste disposal events. Based on the multimodal behavioral characteristics, the degree of waste mixing and the degree of waste disposal standardization of the waste disposal event are analyzed to generate a waste disposal behavior score for the waste disposal event; The resident identity features and waste disposal behavior scores are correlated over time to construct a profile of the resident's waste sorting behavior. Based on the waste sorting behavior profile, a tiered guidance strategy is generated and executed. The tiered guidance strategy includes: triggering a positive incentive loop for residents whose waste sorting behavior profile is in the excellent range, and pushing personalized correction instructions to residents whose waste sorting behavior profile falls into the risk warning range.

[0058] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: By deploying intelligent sensing terminals at waste disposal nodes, in response to residents' waste disposal events, the system captures resident identity features and multimodal behavioral features of the disposal process that are associated with the waste disposal events. Based on the multimodal behavioral characteristics, the degree of waste mixing and the degree of waste disposal standardization of the waste disposal event are analyzed to generate a waste disposal behavior score for the waste disposal event; The resident identity features and waste disposal behavior scores are correlated over time to construct a profile of the resident's waste sorting behavior. Based on the waste sorting behavior profile, a tiered guidance strategy is generated and executed. The tiered guidance strategy includes: triggering a positive incentive loop for residents whose waste sorting behavior profile is in the excellent range, and pushing personalized correction instructions to residents whose waste sorting behavior profile falls into the risk warning range.

[0059] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0060] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0061] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0062] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0063] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A community waste sorting and supervision method for property management, characterized in that, The method includes: By deploying intelligent sensing terminals at waste disposal nodes, in response to residents' waste disposal events, the system captures resident identity features and multimodal behavioral features of the disposal process that are associated with the waste disposal events. Based on the multimodal behavioral characteristics, the degree of waste mixing and the degree of waste disposal standardization of the waste disposal event are analyzed to generate a waste disposal behavior score for the waste disposal event; The resident identity features and waste disposal behavior scores are correlated over time to construct a profile of the resident's waste sorting behavior. Based on the waste sorting behavior profile, a tiered guidance strategy is generated and executed. The tiered guidance strategy includes: triggering a positive incentive loop for residents whose waste sorting behavior profile is in the excellent range, and pushing personalized correction instructions to residents whose waste sorting behavior profile falls into the risk warning range.

2. The community waste sorting and supervision method for property management as described in claim 1, characterized in that, The analysis of waste mixing degree and disposal standardization of the waste disposal event based on the multimodal behavioral characteristics includes: The multimodal behavioral features are preprocessed to extract the visual and behavioral features of the waste disposal event; The degree of waste mixing in the waste disposal event is analyzed based on the aforementioned visual features. Based on the aforementioned behavioral characteristics, the standardization of waste disposal events is analyzed.

3. The community waste sorting and supervision method for property management as described in claim 2, characterized in that, The extraction of visual and behavioral features of the waste disposal event includes: Keyframes are extracted from the video stream corresponding to the multimodal behavioral features of the garbage disposal event, and the keyframes are detected to obtain the contents and type of garbage in the garbage disposal event, so as to generate the visual features of the garbage disposal event. The movement trajectory, weight change, and disposal time of the waste in the multimodal behavioral characteristics are analyzed to generate the behavioral characteristics of the waste disposal event.

4. The community waste sorting and supervision method for property management as described in claim 2, characterized in that, The analysis of the waste mixing degree of the waste disposal event through the visual features includes: Analyze the classification confidence of the visual features corresponding to the contents of the waste; Based on the classification confidence level, calculate the content incompatibility index of the waste contents; The degree of waste mixing in the waste disposal event is determined based on the incompatibility index of the contents.

5. The community waste sorting and supervision method for property management as described in claim 2, characterized in that, The analysis of the standardization of waste disposal events based on the aforementioned behavioral characteristics includes: The instantaneous velocity, maximum acceleration, and trajectory smoothness parameters before entering the bin are extracted from the motion trajectory corresponding to the behavioral features to calculate the motion posture score of the garbage disposal event; The weight change rate and impact coefficient are extracted from the weight changes corresponding to the behavioral characteristics to calculate the weight operation score of the waste disposal event. The total disposal time and effective operation time are extracted from the disposal time corresponding to the behavioral characteristics to calculate the time efficiency score of the waste disposal event; The standardization of the waste disposal event is analyzed by combining the motion posture score, the weight operation score, and the time efficiency score.

6. The community waste sorting and supervision method for property management as described in claim 1, characterized in that, The intelligent sensing terminals deployed at waste disposal nodes respond to residents' waste disposal events and capture resident identity features and multimodal behavioral features of the disposal process associated with the waste disposal events, including: In response to detecting that a resident has entered the preset sensing area of ​​the garbage disposal node, the smart sensing terminal is activated and enters working mode to collect the resident's identity characteristics through the identity recognition module of the smart sensing terminal; The multimodal sensors of the intelligent sensing terminal are activated simultaneously to collect multimodal behavioral characteristics from the time the resident's identity features are generated until the time the garbage is disposed of.

7. The community waste sorting and supervision method for property management as described in claim 1, characterized in that, The step of sequentially associating the resident's identity features and waste disposal behavior scores to construct a profile of the resident's waste sorting behavior includes: The resident's identity features are linked to a series of time-series waste disposal behavior scores to form a time-series behavior dataset of the resident. Based on the time-series behavior dataset, the resident's historical waste disposal scores, violation type preferences, and compliance stability indicators are calculated to generate a waste sorting behavior profile of the resident.

8. The community waste sorting and supervision method for property management as described in claim 7, characterized in that, The step of calculating the resident's historical waste disposal score, violation type preference, and compliance stability index based on the time-series behavior dataset to generate a waste sorting behavior profile of the resident includes: Within a preset historical time window, the scores of waste disposal behavior in the time-series behavior dataset are statistically calculated to generate the historical waste disposal score; A time-series analysis is performed on the waste mixing degree sub-score and waste disposal standardization degree sub-score that constitute the waste disposal behavior score in the time-series behavior dataset to identify the key violation dimensions that lead to low scores, thereby generating the residents' violation type preferences. Calculate the standard deviation of the delivery behavior scores within the historical time window to quantify the degree of fluctuation in the residents' behavior, thereby generating a compliance stability index for the residents. By combining the historical waste disposal scores, the violation type preferences, the compliance stability indicators, and the initial reputation data, a profile of the resident's waste sorting behavior is generated.

9. The community waste sorting and supervision method for property management as described in claim 1, characterized in that, The positive excitation closed loop includes: Reward notifications will be sent to the residents mentioned above; Record the residents' incentive events to update the initial reputation data in the waste sorting behavior profile.

10. A community waste sorting and monitoring system for property management, characterized in that: The system includes: The behavioral feature acquisition module is used to capture resident identity features and multimodal behavioral features of the disposal process in response to resident's garbage disposal events through intelligent sensing terminals deployed at garbage disposal nodes. The waste disposal behavior scoring module is used to analyze the waste mixing degree and disposal standardization degree of the waste disposal event based on the multimodal behavioral characteristics, so as to generate a waste disposal behavior score for the waste disposal event; The behavior profile building module is used to perform time-series correlation between the resident's identity features and the waste disposal behavior score to build a waste sorting behavior profile of the resident; The classification and grading guidance module is used to generate and execute a grading guidance strategy based on the waste classification behavior profile. The grading guidance strategy includes: triggering a positive incentive loop for residents whose waste classification behavior profile is in the excellent range, and pushing personalized correction instructions to residents whose waste classification behavior profile falls into the risk warning range.

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