An urban domestic waste management system and method based on the Internet of Things

Abstract

The application discloses a kind of urban domestic waste management system and method based on Internet of Things, it is related to big data analysis technical field, the basic data set of user historical garbage disposal record and garbage can full load quality is constructed by user collection in the application;After reaching recovery time node, according to site total accumulation quantity, construct garbage recovery sequence;Extract the information table of unfull garbage can, calculate remaining available quality and construct;According to user disposal preference, average disposal quality feature screening candidate user, push guide message, match remaining space;Through user reservation and actual disposal time calculation deviation score, combined with recovery sequence correction to obtain final recovery sequence;System includes data construction, accumulation analysis, empty volume statistics, disposal guide and sequence correction five big modules, each module cooperates to realize data acquisition processing, accumulation quantity analysis, guide disposal and recovery sequence optimization, improves urban domestic waste recovery utilization rate and management intelligent level.

Description

Technical Field

[0001] This invention relates to the field of big data analytics, specifically to an Internet of Things-based urban household waste management system and method. Background Technology

[0002] With the acceleration of urbanization and the improvement of residents' living standards, the amount of urban domestic waste generated continues to increase, and the complexity of waste management is increasing day by day. Traditional waste management models can no longer meet the needs of efficient, precise and environmentally friendly management, and have gradually exposed many problems that urgently need to be solved.

[0003] In the waste disposal and recycling process, under the current model, users' waste disposal behavior is entirely driven by their own subjective will and random needs. This results in a surplus of unused recycling space in the internal storage space of the trash cans after they have reached the recycling standards. This fails to maximize the utilization of the trash can storage space, leading to the idleness and waste of waste recycling resources and a low overall recycling rate of urban household waste. Summary of the Invention

[0004] The purpose of this invention is to provide an Internet of Things-based urban household waste management system and method to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for managing urban domestic waste based on the Internet of Things, the method comprising the following steps: By collecting users' historical waste disposal records and the full load capacity of different types of trash cans through IoT devices, a basic dataset is constructed. The historical waste disposal records include user ID, appointment time, actual disposal time, type of waste disposed of, waste weight disposed of, and trash can number. User identifiers are used to uniquely identify users and ensure the accurate association and attribution of user delivery records and delivery feature data. The appointment time is the time that users reserve in advance for garbage disposal. It is the basic data for calculating the deviation of disposal time and assessing the timeliness of users' disposal. The actual disposal time is the time when the user actually completes the disposal of garbage, which is used in conjunction with the scheduled time to calculate the deviation score; The deviation score is an indicator that measures the timeliness of a user's delivery time. It is calculated as the sum of the differences between the user's scheduled delivery time and the actual delivery time for each delivery, and the ratio of the number of deliveries made by that user. It reflects the degree to which users adhere to their scheduled delivery times. At the designated recycling time point, calculate the amount of waste accumulated in each bin at all waste collection sites and construct a waste recycling sequence. The recycling time node is the system's preset waste recycling start time, which serves as the time benchmark for calculating waste accumulation, screening user disposal combinations, and correcting the waste recycling sequence; Based on the waste collection sequence, the types of waste disposed of in the unfilled waste bins are recorded, and an information table of unfilled waste bins is constructed. Based on the type of waste that was not filled in the trash can, and combined with the user's disposal characteristics, a matching user was selected, and a disposal guidance message was pushed to the user. The deviation score is calculated based on the appointment time and actual disposal time in the user's historical waste disposal records, and the waste recycling sequence is corrected to form the final waste recycling sequence.

[0006] Furthermore, the amount of garbage accumulated is the ratio of the real-time mass in the garbage bin to the full load mass, and the real-time mass is obtained through a weight sensor built into the garbage bin; The full load capacity is the maximum amount of garbage that a trash can can hold, as specified at the time of manufacture. It is the benchmark parameter for calculating the amount of garbage accumulated and the remaining usable mass. The full load capacity of different types of trash cans is clearly specified through the factory parameters. Real-time quality is measured by the weight sensor built into the trash can, which collects the actual weight of the trash in the trash can in real time, reflecting the immediate loading status of the trash can. The amount of garbage accumulated is the core indicator for measuring the saturation of a garbage bin. It is calculated as the ratio of the real-time mass of the garbage bin to its full capacity. The closer the ratio is to 1, the more saturated the garbage bin is. The process of constructing the waste recycling sequence is as follows: obtain the amount of waste accumulated in each waste bin, sum the amount of waste accumulated in each waste bin according to the waste collection point, arrange them in descending order of the summation result, arrange each waste bin at the waste collection point in descending order of the amount of waste accumulated, and label the waste bin with the corresponding waste type to form a waste recycling sequence; The statistical summation involves iterating through every trash can at all waste collection points, calculating the amount of trash accumulated in each trash can based on the ratio of real-time mass to full load mass, summing the amount of trash accumulated in all trash cans at the same waste collection point, and obtaining the total amount of trash accumulated at that point, which reflects the overall degree of trash congestion at the point. All waste collection sites are arranged in descending order of total accumulated volume, with sites having higher recycling priority. For each waste collection site, the waste bins inside are sorted in descending order of their respective accumulated volume, and the type of waste to be disposed of in each bin is labeled. This results in a three-tiered waste recycling sequence: site priority, waste bin priority within the site, and waste type, providing clear routes and operational guidelines for the dispatch of recycling vehicles.

[0007] Furthermore, the process of constructing the information table for unfilled trash cans: Extract the types of waste disposed of in the waste collection sequence, obtain the full load mass based on the types of waste disposed of, subtract the current real-time mass of the waste bin from the full load mass, and record the result as the remaining available mass. Perform the same operation on all waste bins in sequence, bind the remaining available mass with the corresponding waste bin and label the types of waste disposed of, and form an information table of waste bins that are not full. From the established waste recycling sequence, select waste bins that have not reached full capacity, where the waste accumulation is less than 1. Based on the type of waste that can be disposed of in the trash can, and matching its rated full load capacity, the remaining amount of waste that each unfilled trash can can hold is calculated. The table format for information on unfilled trash cans is organized by trash station, type of trash disposed of, trash can number, and remaining available capacity.

[0008] Furthermore, the process of extracting user delivery features: The number of times each user disposes of garbage is counted, and the number of times garbage is disposed of is consistent with the total number of actual disposal times. The number of times garbage is disposed of is classified and counted according to the type of garbage disposed of, and recorded according to disposal preference and user identifier. The disposal preference is the type of garbage disposed of corresponding to the user's maximum number of times garbage is disposed of. The system tracks the quality and type of waste disposed of by each user each time, calculates the average quality of waste disposed of according to the type of waste disposed of, and records the data according to the type of waste disposed of, the average quality, and the user's identifier.

[0009] User delivery characteristics: Core behavioral characteristics extracted from users' historical delivery records, including delivery preferences and average delivery quality of various types of waste, are the core basis for screening and matching users and pushing guidance messages.

[0010] Furthermore, the method for implementing the user push notification and guidance message is as follows: Based on the types of waste and available remaining mass corresponding to the waste bins in the waste recycling sequence, statistics are compiled for each waste collection site according to the types of waste disposed of, and the data is recorded in the format of waste collection site, type of waste disposed of, waste bin number, and remaining available mass; Users with corresponding disposal preferences are selected based on the type of waste disposed of. The most recent actual disposal time of the selected users is retrieved, and it is determined whether the date is today. If it is today, the users are deleted, and the remaining users are used as a candidate user pool. The remaining available quality is split and matched from the candidate user pool. If there are multiple combinations, the recycling time node is obtained, and the combination with the largest difference between the actual delivery time and the recycling time node is selected, where the actual delivery time is less than the recycling time node. The matching step involves dividing the remaining available quality based on the remaining available quality of the unfilled trash cans and the average disposal quality of users in the candidate user pool, and then selecting user combinations whose total disposal quality is close to or equal to the remaining available quality. Retrieve user identifiers from the group and push messages; The message includes the location of the target trash can, the types of trash that can be disposed of, and information on the remaining available space, guiding the user to the trash can for disposal.

[0011] Furthermore, the method for calculating the deviation score: The deviation score is calculated by dividing the sum of the difference between each user's appointment time and the actual delivery time by the number of deliveries.

[0012] Furthermore, the method for forming the final waste recycling sequence is as follows: Calculate the total deviation score of a certain waste collection site and sort all waste collection sites in ascending order of total deviation score; The reconstructing is based on the sum of the positions of the same waste site in the sorting results and the waste collection sequence. The position refers to the ranking of the waste site after sorting. If there is a sum of positions, the waste sites with the same sum of positions are sorted according to their order in the waste collection sequence.

[0013] Furthermore, the IoT device includes a user mobile terminal APP, a weight sensor built into the trash can, an NFC read / write module, a GPS positioning module, and an IoT communication module; the construction of the basic dataset includes standardization processing such as outlier removal and missing value completion, constructing a key-value pair structure dataset and storing it in a time-series database; the full-load capacity of the different types of trash cans is calibrated through factory parameters; The weight sensor is built into the core sensing device of the trash can to collect the real-time weight of the trash inside the trash can, providing basic data support for calculating the amount of trash accumulated and the remaining usable weight; The NFC read / write module is used to complete the identity verification and association between the user and the trash can when the user disposes of trash, ensuring that each trash disposal record can be accurately bound to the corresponding user and guaranteeing the accuracy of data ownership; The GPS positioning module is used to accurately obtain the geographical location information of trash cans and trash stations, providing location navigation basis when pushing disposal guidance messages to users, and also providing route reference for the dispatch of trash collection vehicles; The IoT communication module is responsible for transmitting the data collected by various IoT devices to the system backend in a unified manner, and at the same time realizes the message interaction between the system and the user terminal. It is the core channel for data transmission and command issuance. The outlier removal process involves statistically analyzing the collected data on waste disposal quality and appointment time to remove outliers that exceed a reasonable range. The missing value completion process uses linear interpolation to fill in the missing waste disposal quality data. Specifically, it fills in the data gaps based on the historical trend of the user's waste disposal quality for the same type, or the average disposal quality of users of the same type in the same area, to ensure the integrity of the dataset.

[0014] Furthermore, the urban domestic waste management system includes a data construction module, an accumulation analysis module, an empty quantity statistics module, a disposal guidance module, and a sequence correction module; The data construction module is used to build a basic dataset based on relevant data collected by IoT devices; The accumulation analysis module is used to calculate the amount of garbage accumulated in the garbage bins at each garbage station, and to construct a garbage recycling sequence based on the calculation results; The empty quantity statistics module is used to extract information about unfilled trash cans, calculate the remaining available mass, and construct an information table of unfilled trash cans. The waste disposal guidance module is used to extract user disposal characteristics, filter matching target users, and push waste disposal guidance messages. The sequence correction module is used to calculate the user's disposal time deviation score and correct the waste recycling sequence to form the final waste recycling sequence.

[0015] Furthermore, the data construction module includes an acquisition unit and a processing unit; The data acquisition unit is used to collect historical user waste disposal records and the full load capacity of different types of waste bins; the processing unit is used to perform outlier removal, missing value completion and standardization processing on the collected data, construct a key-value pair structured dataset and store it in a time-series database. The stacking analysis module includes a measurement unit and a sequence unit; The metering unit is used to obtain the real-time mass and full load mass of the trash cans, and calculate the amount of trash accumulated in each trash can; the sequencing unit is used to sum up the accumulated amounts according to the trash stations and sort them, and then sort the trash cans in each station and label the types of trash to form a trash recycling sequence. The empty quantity statistics module includes a calculation unit and a table creation unit; The calculation unit is used to obtain the full load mass based on the type of garbage put into the garbage bin, and calculate the remaining usable mass through the accumulation amount; the table building unit is used to bind the remaining usable mass with the corresponding garbage bin information, and form an information table of garbage bins that are not full after marking the type of garbage put into them. The delivery guidance module includes a feature unit and a push unit; The feature unit is used to count the number of times and quality of user garbage disposal, and extract user disposal features; the push unit is used to match suitable users from the candidate user pool, determine the optimal user combination, and push targeted disposal guidance messages. The sequence correction module includes a scoring unit and a reconstruction unit; The scoring unit is used to calculate the corresponding deviation score based on the difference between the user's scheduled disposal time and the actual disposal time each time; the reconstruction unit is used to reconstruct the waste recycling sequence by combining the total deviation scores of each waste station to obtain the final waste recycling sequence.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention uses multiple IoT devices to collaboratively collect user disposal records and trash can status data to build a complete basic dataset. This dataset provides reliable support for key steps such as calculating the amount of garbage accumulated and extracting user features, thereby improving management accuracy.

[0017] 2. This invention uses an information table of unfilled trash cans to filter target users based on their disposal preferences and average disposal quality characteristics. It then pushes guidance messages containing information such as trash can location, types of trash that can be disposed of, and remaining space. This accurately matches user disposal needs with the remaining capacity of the trash cans, making full use of the remaining recycling capacity, avoiding the waste of resources when trash cans are not full but are idle, and effectively improving the overall recycling rate of urban household waste.

[0018] 3. This invention calculates the deviation score between user reservations and actual disposal times to correct the initial waste collection sequence. This sequence provides clear three-level priority guidance for collection vehicles, directing collection resources towards sites with severe congestion and high user cooperation, reducing waste overflow and ineffective scheduling, improving collection efficiency, reducing management costs, and optimizing the coordination and feasibility of the entire waste collection process. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating an Internet of Things-based urban household waste management method according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1: As Figure 1As shown, the present invention provides a technical solution, a method for managing urban domestic waste based on the Internet of Things, the method for managing urban domestic waste includes the following steps: By collecting users' historical waste disposal records and the full load capacity of different types of trash cans through IoT devices, a basic dataset is constructed. The historical waste disposal records include user ID, appointment time, actual disposal time, type of waste disposed of, waste weight disposed of, and trash can number. The IoT device includes a user mobile terminal APP, a weight sensor built into the trash can, an NFC read / write module, a GPS positioning module, and an IoT communication module; the construction of the basic dataset includes standardization processing such as outlier removal and missing value completion, constructing a key-value pair structure dataset and storing it in a time-series database; the full-load capacity of the different types of trash cans is calibrated through factory parameters; For example: The user's mobile app continuously collects comprehensive data on the user's waste disposal, including the scheduled time, actual disposal time, type of waste, and key information on disposal quality for each disposal. Built-in weight sensors in the trash cans collect real-time data on the load capacity of each can, combining this data with the factory-calibrated full-load capacity parameters to form basic equipment attribute data. An NFC reader / writer module verifies the user's identity and the trash can during the disposal process, ensuring accurate attribution of each disposal record. An IoT communication module transmits this data, scattered across different devices, to the system backend for centralized data aggregation. The system's backend data processing unit standardizes the collected data. In the outlier removal stage, reasonable data thresholds are set using statistical analysis methods to identify and remove outlier data exceeding these thresholds. For example, if the waste quality in a disposal record far exceeds the full capacity of the corresponding trash can, or if the deviation between the scheduled and actual disposal times exceeds a reasonable range, these are all considered outliers and removed. In the missing value completion stage, linear interpolation is used to fill in missing disposal quality data. Referencing the historical trends in waste quality for similar users or the average disposal levels of users in the same area and of the same type, data gaps are filled to ensure the completeness of the dataset. Finally, the processed user disposal records and device attribute data are standardized and stored in a time-series database using key-value pairs for subsequent data querying and analysis along the time dimension. Through multi-device collaborative data collection and standardized data processing, a complete, accurate, and standardized basic dataset was successfully constructed, eliminating the problems of data chaos, missing data, and inaccuracy in traditional waste management. This dataset not only provides reliable data support for key aspects such as subsequent waste accumulation calculation, user disposal feature extraction, and disposal time deviation analysis, but also lays a solid foundation for intelligent decision-making in the entire waste management system.

[0022] At the designated recycling time point, calculate the amount of waste accumulated in each bin at all waste collection sites and construct a waste recycling sequence. The amount of garbage accumulated is the ratio of the real-time mass in the garbage bin to the full load mass, and the real-time mass is obtained by a weight sensor built into the garbage bin. The process of constructing the waste recycling sequence is as follows: obtain the amount of waste accumulated in each waste bin, sum the amount of waste accumulated in each waste bin according to the waste collection point, arrange them in descending order of the summation result, arrange each waste bin at the waste collection point in descending order of the amount of waste accumulated, and label the waste bin with the corresponding waste type to form a waste recycling sequence; For example: Once the preset recycling time point is reached, the built-in weight sensor of each trash can will collect real-time mass data and quickly transmit it to the system's accumulation analysis module via the Internet of Things communication module; at the same time, the full-load mass data of each trash can stored in the time series database will also be called to this module to provide basic parameters for the accumulation amount calculation. The accumulation analysis module's metering unit calculates the amount of waste accumulated in each bin based on the ratio of real-time mass to full load mass, clearly reflecting the saturation level of a single bin. The sequence unit statistically sums the accumulation amounts of all bins within a waste collection station to obtain the total accumulation amount for each station. The total accumulation amount directly reflects the overall waste congestion level of the station. All waste collection stations are sorted from largest to smallest based on their total accumulation amount, with stations having higher total accumulation amounts receiving higher collection priority. For each station, the bins within it are further sorted from largest to smallest based on their individual accumulation amounts, and the type of waste disposed of in each bin is labeled, ultimately forming a three-tiered waste collection sequence that includes station priority, bin priority within the station, and waste type. The establishment of a three-tiered waste collection sequence provides clear and explicit route guidance and operational standards for the dispatch of collection vehicles. Collection personnel can prioritize going to sites with high waste congestion according to the sequence, and prioritize collecting waste bins with high saturation levels at these sites. This effectively improves the targeting and efficiency of waste collection, and reduces waste overflow caused by untimely collection or resource waste caused by premature collection.

[0023] Based on the waste collection sequence, the types of waste disposed of in the unfilled waste bins are recorded, and an information table of unfilled waste bins is constructed. The process of constructing the information table for unfilled trash cans: Extract the types of waste disposed of in the waste collection sequence, obtain the full load mass based on the types of waste disposed of, subtract the current real-time mass of the waste bin from the full load mass, and record the result as the remaining available mass. Perform the same operation on all waste bins in sequence, bind the remaining available mass with the corresponding waste bin and label the types of waste disposed of, and form an information table of waste bins that are not full. For example: The real-time weight data of each trash can provided by the weight sensor, the full-load weight of the trash can and the garbage type labeling information stored in the time-series database are synchronously transmitted to the empty quantity statistics module through the Internet of Things communication module, providing data support for screening unfilled trash cans and calculating the remaining usable weight; The empty quantity statistics module's calculation unit filters out trash cans that have not reached full capacity from the constructed waste collection sequence; based on the type of waste disposed of in each unfilled trash can, it accurately matches its corresponding full capacity mass parameter, and calculates the remaining usable mass of each unfilled trash can by subtracting the current real-time mass from the full capacity mass, i.e., the maximum mass of waste it can still hold; the table building unit binds the remaining usable mass with the corresponding trash can information, marks the type of waste disposed of, and organizes it according to the standardized format of waste station, type of waste disposed of, trash can number, and remaining usable mass to form a standardized unfilled trash can information table; This information table provides clear data for subsequent targeted guidance of user disposal, ensuring that disposal guidance is no longer blind but accurately matches the remaining space in the trash can with the user's disposal needs.

[0024] Based on the type of waste that was not filled in the trash can, and combined with the user's disposal characteristics, a matching user was selected, and a disposal guidance message was pushed to the user. The process of extracting user delivery features: The number of times each user disposes of garbage is counted, and the number of times garbage is disposed of is consistent with the total number of actual disposal times. The number of times garbage is disposed of is classified and counted according to the type of garbage disposed of, and recorded according to disposal preference and user identifier. The disposal preference is the type of garbage disposed of corresponding to the user's maximum number of times garbage is disposed of. The system collects statistics on the quality and type of waste disposed of by each user each time, calculates the average quality of waste disposed of according to the type of waste disposed of, and records the data according to the type of waste disposed of, the average quality, and the user's identifier. The method for implementing the user push notification message: Based on the types of waste and available remaining mass corresponding to the waste bins in the waste recycling sequence, statistics are compiled for each waste collection site according to the types of waste disposed of, and the data is recorded in the format of waste collection site, type of waste disposed of, waste bin number, and remaining available mass; Users with corresponding disposal preferences are selected based on the type of waste disposed of. The most recent actual disposal time of the selected users is retrieved, and it is determined whether the date is today. If it is today, the users are deleted, and the remaining users are used as a candidate user pool. The remaining available quality is split and matched from the candidate user pool. If there are multiple combinations, the recycling time node is obtained, and the combination with the largest difference between the actual delivery time and the recycling time node is selected, where the actual delivery time is less than the recycling time node. Retrieve user identifiers from the group and push messages; For example: User mobile terminal APP collects and uploads user disposal record data, which is processed by the system to form user disposal feature data and stored in a time series database; the empty quantity statistics module generates an information table of unfilled trash cans, which clarifies the target trash can information for guided disposal; the GPS positioning module provides accurate trash can location data to support navigation guidance; the Internet of Things communication module is responsible for data transmission between the modules and the final disposal guidance message push. The feature unit of the waste disposal guidance module extracts user disposal characteristics from the time-series database, clarifying each user's core disposal preferences and average disposal quality. The push unit filters users whose disposal preferences match the types of waste in the unfilled trash cans. It retrieves the most recent actual disposal time of these users. If the most recent disposal date is today, it is determined that the user has no disposal need and is excluded, with the remaining users forming a candidate user pool. Combining the average disposal quality of users in the candidate user pool, the remaining available quality of the unfilled trash cans is split and matched to filter user combinations whose total disposal quality is close to or equal to the remaining available quality. If multiple combinations meet the criteria, the optimal user combination is selected based on the recycling time node, where all users' actual disposal times are earlier than the recycling time node, and the combination with the largest total deviation between the actual disposal time and the recycling time node is selected. Finally, the IoT communication module pushes guidance messages to users in the optimal user combination. The message content includes the precise location of the target trash can, the types of waste that can be disposed of, and the remaining available space. Through precise user screening and personalized guidance, users with disposal needs were successfully directed to trash cans with remaining space, making full use of the trash cans' storage capacity and effectively reducing the problem of idle resources when trash cans are not full but no users are disposing of them, thus further improving the overall recycling rate of urban household waste.

[0025] Based on the user's historical waste disposal records, the deviation score is calculated to correct the waste collection sequence and form the final waste collection sequence. The method for calculating the deviation score: The deviation score is calculated by dividing the sum of the differences between each user's appointment time and the actual delivery time by the number of deliveries. The method for forming the final waste recycling sequence is as follows: Calculate the total deviation score of a certain waste collection site and sort all waste collection sites in ascending order of total deviation score; The reconstructing is based on the sum of the rankings of the same waste site in the sorting results and the waste collection sequence. The ranking is the position of the waste site after sorting. If there is a sum of rankings, the waste sites with the same sum of rankings are sorted according to their order in the waste collection sequence. For example: The user's mobile terminal APP collects user reservation time and actual disposal time data, which is transmitted to the sequence correction module via the Internet of Things communication module to provide raw data for the calculation of deviation score; the initial waste collection sequence constructed by the accumulation analysis module is synchronized to this module as the basis for sequence correction. The scoring unit of the sequence correction module calculates each user's individual deviation score based on the difference between their scheduled and actual disposal times. This individual deviation score directly reflects the punctuality of the user's disposal time. The individual deviation scores of all users at each waste collection point are aggregated to obtain the total deviation score for each point. This total deviation score reflects the overall punctuality level of the user group at that point. All waste collection points are sorted from smallest to largest by their total deviation scores. The smaller the total deviation score, the more punctual the users' disposal times at that point, and the higher the predictability of the recycling plan's execution. The reconstruction unit combines the site's ranking based on its total deviation score with its position in the initial waste collection sequence, calculates the sum of each site's position, and re-sorts the sites according to the sum of their positions from smallest to largest. If sites have the same sum of positions, they are arranged according to their order in the initial waste collection sequence, ultimately forming the corrected waste collection sequence.

[0026] 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 invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for managing urban household waste based on the Internet of Things, characterized in that: The urban domestic waste management method includes the following steps: By collecting users' historical waste disposal records and the full load capacity of different types of trash cans through IoT devices, a basic dataset is constructed. The historical waste disposal records include user ID, appointment time, actual disposal time, type of waste disposed of, waste weight disposed of, and trash can number. At the designated recycling time point, calculate the amount of waste accumulated in each bin at all waste collection sites and construct a waste recycling sequence. Based on the waste collection sequence, the types of waste disposed of in the unfilled waste bins are recorded, and an information table of unfilled waste bins is constructed. Based on the type of waste that was not filled in the trash can, and combined with the user's disposal characteristics, a matching user was selected, and a disposal guidance message was pushed to the user. The deviation score is calculated based on the appointment time and actual disposal time in the user's historical waste disposal records, and the waste recycling sequence is corrected to form the final waste recycling sequence.

2. The method for managing urban domestic waste based on the Internet of Things according to claim 1, characterized in that: The amount of garbage accumulated is the ratio of the real-time mass in the garbage bin to the full load mass, and the real-time mass is obtained by a weight sensor built into the garbage bin. The process of constructing the waste recycling sequence is as follows: obtain the amount of waste accumulated in each waste bin, sum the amount of waste accumulated in each waste bin according to the waste collection point, arrange them in descending order of the summation result, arrange each waste bin at the waste collection point in descending order of the amount of waste accumulated, and label the waste type corresponding to each waste bin to form a waste recycling sequence.

3. The method for managing urban domestic waste based on the Internet of Things according to claim 1, characterized in that: The process of constructing the information table for unfilled trash cans: Extract the types of waste disposed of in the waste collection sequence, obtain the full load mass based on the types of waste disposed of, subtract the current real-time mass of the waste bin from the full load mass, and record the result as the remaining available mass. Perform the same operation on all waste bins in sequence, bind the remaining available mass with the corresponding waste bin and label the types of waste disposed of, and form an information table of waste bins that are not full.

4. The method for managing urban domestic waste based on the Internet of Things according to claim 1, characterized in that: The process of extracting user delivery features: The number of times each user disposes of garbage is counted, and the number of times garbage is disposed of is consistent with the total number of actual disposal times. The number of times garbage is disposed of is classified and counted according to the type of garbage disposed of, and recorded according to disposal preference and user identifier. The disposal preference is the type of garbage disposed of corresponding to the user's maximum number of times garbage is disposed of. The system tracks the quality and type of waste disposed of by each user each time, calculates the average quality of waste disposed of according to the type of waste disposed of, and records the data according to the type of waste disposed of, the average quality, and the user's identifier.

5. The method for managing urban domestic waste based on the Internet of Things according to claim 1, characterized in that: The method for implementing the user push notification message: Based on the types of waste and available remaining mass corresponding to the waste bins in the waste recycling sequence, statistics are compiled for each waste collection site according to the types of waste disposed of, and the data is recorded in the format of waste collection site, type of waste disposed of, waste bin number, and remaining available mass; Users with corresponding disposal preferences are selected based on the type of waste disposed of. The most recent actual disposal time of the selected users is retrieved, and it is determined whether the date is today. If it is today, the users are deleted, and the remaining users are used as a candidate user pool. The remaining available quality is split and matched from the candidate user pool. If there are multiple combinations, the recycling time node is obtained, and the combination with the largest difference between the actual delivery time and the recycling time node is selected, where the actual delivery time is less than the recycling time node. Retrieve user identifiers from the group and push messages.

6. The method for managing urban domestic waste based on the Internet of Things according to claim 1, characterized in that: The method for calculating the deviation score: The deviation score is calculated by dividing the sum of the difference between each user's appointment time and the actual delivery time by the number of deliveries.

7. The method for managing urban domestic waste based on the Internet of Things according to claim 1, characterized in that: The method for forming the final waste recycling sequence is as follows: Calculate the total deviation score of a certain waste collection site and sort all waste collection sites in ascending order of total deviation score; The reconstructing is based on the sum of the positions of the same waste site in the sorting results and the waste collection sequence. The position refers to the ranking of the waste site after sorting. If there is a sum of positions, the waste sites with the same sum of positions are sorted according to their order in the waste collection sequence.

8. The method for managing urban domestic waste based on the Internet of Things according to claim 1, characterized in that: The IoT devices include a user mobile terminal APP, a weight sensor built into the trash can, an NFC read / write module, a GPS positioning module, and an IoT communication module; the construction of the basic dataset includes standardization processing such as outlier removal and missing value completion, constructing a key-value pair structure dataset and storing it in a time-series database; the full-load capacity of the different types of trash cans is calibrated through factory parameters.

9. An Internet of Things (IoT)-based urban domestic waste management system, applied to the IoT-based urban domestic waste management method described in any one of claims 1-8, characterized in that: The urban domestic waste management system includes a data construction module, an accumulation analysis module, an empty quantity statistics module, a disposal guidance module, and a sequence correction module; The data construction module is used to build a basic dataset based on relevant data collected by IoT devices; The accumulation analysis module is used to calculate the amount of garbage accumulated in the garbage bins at each garbage station, and to construct a garbage recycling sequence based on the calculation results; The empty quantity statistics module is used to extract information about unfilled trash cans, calculate the remaining available mass, and construct an information table of unfilled trash cans. The waste disposal guidance module is used to extract user disposal characteristics, filter matching target users, and push waste disposal guidance messages. The sequence correction module is used to calculate the user's disposal time deviation score and correct the waste recycling sequence to form the final waste recycling sequence.

10. A city household waste management system based on the Internet of Things according to claim 1, characterized in that: The data construction module includes an acquisition unit and a processing unit; The data acquisition unit is used to collect historical user waste disposal records and the full load capacity of different types of waste bins; the processing unit is used to perform outlier removal, missing value completion and standardization processing on the collected data, construct a key-value pair structured dataset and store it in a time-series database. The stacking analysis module includes a measurement unit and a sequence unit; The metering unit is used to obtain the real-time mass and full load mass of the trash cans, and calculate the amount of trash accumulated in each trash can; the sequencing unit is used to sum up the accumulated amounts according to the trash stations and sort them, and then sort the trash cans in each station and label the types of trash to form a trash recycling sequence. The empty quantity statistics module includes a calculation unit and a table creation unit; The calculation unit is used to obtain the full load mass based on the type of garbage put into the garbage bin, and calculate the remaining usable mass through the accumulation amount; the table building unit is used to bind the remaining usable mass with the corresponding garbage bin information, and form an information table of garbage bins that are not full after marking the type of garbage put into them. The delivery guidance module includes a feature unit and a push unit; The feature unit is used to count the number of times and quality of user garbage disposal, and extract user disposal features; the push unit is used to match suitable users from the candidate user pool, determine the optimal user combination, and push targeted disposal guidance messages. The sequence correction module includes a scoring unit and a reconstruction unit; The scoring unit is used to calculate the corresponding deviation score based on the difference between the user's scheduled disposal time and the actual disposal time each time; the reconstruction unit is used to reconstruct the waste recycling sequence by combining the total deviation scores of each waste station to obtain the final waste recycling sequence.

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