Smart city garbage disposal determination method, internet of things system, device and medium

Abstract

The embodiment of the present specification provides a smart city garbage treatment determination method, an Internet of Things system, a device and a medium. The smart city garbage treatment determination Internet of Things system comprises a management platform, a sensing network platform and an object platform; the smart city garbage treatment determination method is executed by the management platform of the smart city garbage treatment determination Internet of Things system, and comprises the following steps: acquiring an image of at least one garbage aggregation point in a preset area and a size of the preset area based on the object platform; determining a current garbage amount of the at least one garbage aggregation point based on the image of the at least one garbage aggregation point; determining a garbage growth rate of the at least one garbage aggregation point based on the size of the preset area; determining a target garbage aggregation point based on the current garbage amount of the at least one garbage aggregation point and the garbage growth rate of the at least one garbage aggregation point; and controlling a garbage treatment device to treat garbage of the target garbage aggregation point.

Description

TECHNICAL FIELD

[0001] The present specification relates to the field of Internet of Things and cloud platform, in particular to a smart city garbage disposal determination method, an Internet of Things system, a device and a medium. BACKGROUND

[0002] With the development of urban construction and the improvement of the living standards of residents, the amount of urban household garbage is increasing day by day, and the cost of manpower and material resources for garbage disposal is also increasing accordingly. At present, the order of garbage disposal mainly relies on manual experience, which is time-consuming and laborious and inevitably has some omissions, resulting in lag in garbage disposal in some locations and easy pollution of the environment and impact on the city appearance.

[0003] Therefore, it is desirable to have a smart city garbage disposal determination method, an Internet of Things system, a device and a medium. The garbage disposal order can be determined in a timely and more scientific, reasonable and effective manner, the garbage disposal efficiency can be improved, and the city appearance can be improved, which is conducive to creating a cleaner, neater, more civilized and orderly urban environment. SUMMARY

[0004] One or more embodiments of the present specification provide a smart city garbage disposal determination method, which is implemented based on a smart city garbage disposal determination Internet of Things system. The smart city garbage disposal determination Internet of Things system includes a management platform, a sensor network platform and an object platform. The method is executed by the management platform. The method includes: obtaining images of at least one garbage accumulation point in a preset area and a size of the preset area based on the object platform; determining a current garbage amount of the at least one garbage accumulation point based on the images of the at least one garbage accumulation point; determining a garbage growth rate of the at least one garbage accumulation point based on the size of the preset area; determining a target garbage accumulation point based on the current garbage amount of the at least one garbage accumulation point and the garbage growth rate of the at least one garbage accumulation point; and controlling a garbage disposal device to dispose of garbage of the target garbage accumulation point.

[0005] One or more embodiments of the present specification provide a smart city garbage disposal determination Internet of Things system. The system includes a management platform, a sensor network platform and an object platform. The management platform is configured to perform the following operations: obtaining images of at least one garbage accumulation point in a preset area and a size of the preset area based on the object platform, and transmitting the images and the size to the management platform through the sensor network platform based on the object platform; determining a current garbage amount of the at least one garbage accumulation point based on the images of the at least one garbage accumulation point; determining a garbage growth rate of the at least one garbage accumulation point based on the size of the preset area; and determining a target garbage accumulation point based on the current garbage amount of the at least one garbage accumulation point and the garbage growth rate of the at least one garbage accumulation point.

[0006] One or more embodiments of the present specification provide a smart city garbage disposal determination device, the device comprising at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute at least part of the computer instructions to realize the method of smart city garbage disposal determination.

[0007] One or more embodiments of the present specification provide a computer readable storage medium, the storage medium stores computer instructions, when the computer reads the computer instructions in the storage medium, the computer executes the smart city garbage disposal determination method.

[0008] The present application is to overcome the problem of some garbage collection points processing lag, pollution environment. By acquiring the image of the garbage collection point in the preset area and the scale of the preset area, the current garbage amount and the garbage growth rate of the garbage collection point are determined, and then the target garbage collection point is determined. More accurate target garbage collection point can be obtained, and the garbage disposal device can be controlled to process the garbage in the area, improve the accuracy and timeliness of garbage disposal work, avoid some positions garbage processing lag, easy to pollute the environment and affect the city appearance and other problems. BRIEF DESCRIPTION OF DRAWINGS

[0009] The present specification will be further illustrated in the form of exemplary embodiments, which will be described in detail through the accompanying drawings. These embodiments are not limiting, in these embodiments, the same numbers represent the same structures, wherein:

[0010] Figure 1 is a schematic diagram of the application scene of the smart city garbage disposal determination Internet of Things system according to some embodiments of the present specification;

[0011] Figure 2 is a schematic diagram of the architecture of the smart city garbage disposal determination Internet of Things system according to some embodiments of the present specification;

[0012] Figure 3 is an exemplary flowchart of the smart city garbage disposal determination method according to some embodiments of the present specification;

[0013] Figure 4 is an exemplary flowchart of the method of determining the garbage growth rate of at least one garbage collection point according to some embodiments of the present specification;

[0014] Figure 5 is an exemplary flowchart of the method of determining the population activity of the preset area at the future time according to some embodiments of the present specification;

[0015] Figure 6 is an exemplary flowchart of the method of determining the household garbage generation level according to some embodiments of the present specification. Detailed Implementation

[0016] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0017] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0018] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0019] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0020] Figure 1 This is a schematic diagram illustrating an application scenario 100 of a smart city waste management IoT system based on some embodiments of this specification. In some embodiments, application scenario 100 may include a processing device 110, a network 120, a storage device 130, a monitoring device 140, a waste collection point 150, and a waste processing device 160.

[0021] In some embodiments, the determination of the application scenario 100 of the smart city waste management IoT system can be achieved by implementing the methods and / or processes disclosed in this specification.

[0022] The processing device 110 can be configured to process data related to the smart city garbage disposal determination Internet of Things system. For example, the processing device 110 can determine the current garbage amount and the garbage growth rate of at least one garbage aggregation point by performing the smart city garbage disposal determination method disclosed in the present specification. In some embodiments, the processing device 110 can be a single server or a group of servers. The group of servers can be centralized or distributed.

[0023] The network 120 can connect the components of the smart city garbage disposal determination Internet of Things system application scenario 100 and / or external resource parts. In some embodiments, information and / or data can be exchanged between one or more components of the smart city garbage disposal determination Internet of Things system application scenario 100 (e.g., the processing device 110, the storage device 130, the monitoring device 140, the garbage aggregation point 150, and the garbage disposal device 160) through the network 120. For example, the network 120 can transmit monitoring information of the garbage aggregation point 150 obtained by the monitoring device 140 to the processing device 110. For another example, the processing device 110 can control the garbage disposal device 160 to dispose of garbage at a target garbage aggregation point through the network 120. In some embodiments, the network 120 can be any one or more of a wired network or a wireless network.

[0024] The storage device 130 can store data, instructions, and / or any other information. For example, the size of a preset area can be saved in the storage device 130. In some embodiments, the storage device 130 can store data obtained from the monitoring device 140 and / or the processing device 110. For example, the storage device 130 can obtain entry and exit records of an entrance and exit of a preset area to be stored from the processing device 110. The storage device 130 can include one or more storage components, each of which can be a separate device or a part of other devices.

[0025] The monitoring device 140 refers to a device for monitoring a preset area. For example, the monitoring device 140 can include a panoramic camera, a monitoring camera, a drone, etc. The monitoring device 140 can obtain monitoring information related to a preset area. The monitoring information can include a combination of one or more of images, videos, voice, etc. In some embodiments, the monitoring device 140 can transmit the collected monitoring-related data information to other components of the smart city garbage disposal determination Internet of Things system application scenario 100 (e.g., the processing device 110) or other components outside the smart city garbage disposal determination Internet of Things system application scenario 100 through the network 120.

[0026] The garbage collection point 150 refers to a location where garbage is concentrated in a certain area. For example, the garbage collection point of A community can be the door of a building. In some embodiments, the garbage collection point 150 can include a garbage can, a garbage kiosk, a garbage house, and other garbage collection facilities. In some embodiments, the processing device 110 can obtain the monitoring image of the garbage collection point 150 through the monitoring device 140 via the network 120. More details about the garbage collection point 150 can be found in Figure 3 and the related description.

[0027] The garbage processing device 160 refers to a device or equipment that can process garbage. For example, a garbage truck 160-1, a garbage transfer robot 160-2, and the like. In some embodiments, the processing device 110 can control the garbage processing device 160 to go to the target garbage collection point to process garbage, etc. More details about the garbage processing device 160 can be found in Figure 3 and the related description.

[0028] Figure 2 The framework of the smart city garbage processing determination Internet of Things system 200 is shown according to some embodiments of the present specification.

[0029] As Figure 2 shown, the smart city garbage processing determination Internet of Things system 200 includes a user platform 210, a service platform 220, a management platform 230, a sensing network platform 240, and an object platform 250 that interact in turn.

[0030] The user platform 210 can refer to a user-oriented platform, including obtaining the user's demand and feeding back information to the user. In some embodiments, the user platform is configured as a terminal device, and the target garbage collection point is fed back to the user.

[0031] In some embodiments, the user platform 210 can interact downward with the service platform 220. For example, the control garbage processing device to process the garbage of the target garbage collection point instruction is issued to the service platform 220, and the target garbage collection point uploaded by the service platform 220 is received.

[0032] The service platform 220 can refer to a platform that preliminarily processes the user's query demand. In some embodiments, receiving, processing, and sending data or / and information are all uniformly performed by the platform.

[0033] In some embodiments, the service platform 220 can interact downward with the management platform 230. For example, the target garbage collection point query instruction is issued to the management platform 230, and the target garbage collection point uploaded by the management platform 230 is received.

[0034] In some embodiments, the service platform 220 can also interact with the user platform 210 upwards. For example, receiving the target garbage aggregation point query instruction issued by the user platform 210, uploading the target garbage aggregation point to the user platform 210, etc.

[0035] In some embodiments, the management platform 230 includes a plurality of management sub-platforms. Among them, the plurality of management sub-platforms correspond one-to-one to a plurality of management sub-platform databases, and the plurality of management sub-platforms correspond to different urban areas.

[0036] In some embodiments, the management platform 230 is a platform for executing the smart city garbage disposal determination method. In some embodiments, the management platform 230 can respond to the query needs of users, process the garbage monitoring related data uploaded by the sensor network platform 240, and determine the target garbage aggregation point.

[0037] In some embodiments, the management platform 230 is configured as a second server. It includes a plurality of independent sub-platforms, and the plurality of sub-platforms run and process data independently of each other and directly interact with the upper and lower functional platforms. The plurality of sub-platforms are divided according to urban areas and correspond one-to-one to the sensor network sub-platforms.

[0038] In some embodiments, the management platform 230 can interact with the sensor network platform 240 downwards. For example, receiving the garbage monitoring related data of each area uploaded by the sensor network platform 240 for processing, issuing garbage monitoring related data acquisition instructions to the sensor network platform 240, etc.

[0039] In some embodiments, the management platform 230 can interact with the service platform 220 upwards. For example, receiving the target garbage aggregation point query instruction issued by the service platform 220, uploading the target garbage aggregation point to the service platform 220, etc.

[0040] In some embodiments, by processing the garbage monitoring related data of different preset areas through the management sub-platform, the data processing pressure of the entire management platform can be reduced, and at the same time, the target garbage aggregation point of each area in the city can be independently managed by each area, which is more targeted.

[0041] The sensor network platform 240 can refer to a platform for transmitting garbage monitoring related data to the management platform 230. In some embodiments, the sensor network platform 240 is configured as a communication network and a gateway, which is provided with a total database and a plurality of sub-platforms (including self-database). The plurality of sub-platforms respectively store and process different types or different received object data sent by the object platform 250, the total database stores and processes the data of the plurality of sub-platforms after summarizing, and transmits the data to the management platform 230.

[0042] In some embodiments, the sensing network platform 240 comprises a plurality of sensing network sub-platforms. The plurality of sensing network sub-platforms are divided according to the urban areas and correspond to the plurality of management sub-platforms one by one. In some embodiments, the plurality of sensing network sub-platforms are configured as independent gateways and can be used to obtain the garbage monitoring related data uploaded by the object platform 250.

[0043] In some embodiments, the sensing network platform 240 can interact with the object platform 250 downward. For example, receiving the garbage monitoring related data uploaded by the object platform 250, issuing an instruction to obtain garbage monitoring related data to the object platform 250, and the like.

[0044] In some embodiments, the sensing network platform 240 can interact with the management platform 230 upward. For example, receiving the instruction to obtain garbage monitoring related data issued by the management sub-platform, uploading the garbage monitoring related data of the sensing network platform total database to the corresponding management sub-platform, and the like.

[0045] The object platform 250 can refer to a functional platform for generating perception information and executing control information. In some embodiments, the object platform 250 is configured as a monitoring device. In some embodiments, the object platform 250 comprises a plurality of object sub-platforms, and the plurality of object sub-platforms correspond to the plurality of sensing network sub-platforms one by one. In some embodiments, the plurality of object sub-platforms can collect garbage monitoring related data in different preset areas of the city.

[0046] In some embodiments, the object platform 250 can interact with the sensing network platform 240 upward. For example, receiving the instruction to obtain garbage monitoring related data issued by the sensing network sub-platform, uploading the garbage monitoring related data to the corresponding sensing network sub-platform database, and the like.

[0047] Figure 3 is an exemplary flowchart of the smart city garbage disposal determination method according to some embodiments of the present specification. As shown in Figure 3 , the flow 300 comprises the following steps. In some embodiments, Figure 3 one or more operations of the flow 300 shown in Figure 1 application scenario 100 of the smart city garbage disposal determination Internet of Things system shown in. In some embodiments, the flow 300 can be executed by the management platform 230.

[0048] Step 310, obtaining an image of at least one garbage aggregation point in a preset area and a size of the preset area based on an object platform.

[0049] The preset area can refer to a pre-set geographical range. For example, a certain community, a certain neighborhood, etc. In some embodiments, the management platform 230 can determine a plurality of preset areas in a plurality of ways. In some embodiments, the preset area can correspond to the administrative division area in the city, for example, the preset area of Chengdu can include Qingyang District, Jinjiang District, Wuhou District, etc. In some embodiments, the preset area can correspond to the neighborhood in the city, for example, the preset area of Chengdu can include A neighborhood, B neighborhood, C neighborhood, etc.

[0050] In some embodiments, the management platform 230 can obtain the image of the garbage accumulation point photographed by the monitoring device 140 located in the preset area based on the object platform 250.

[0051] The scale of the preset area refers to a parameter reflecting the size of the preset area. For example, the scale of A community can be 35,000 in population.

[0052] In some embodiments, the management platform 230 can obtain the scale of the preset area through big data analysis. For example, a large amount of data can be obtained through telecom operator data for statistical analysis and other processing.

[0053] In some embodiments, the management platform 230 can obtain the scale of the preset area through third-party platform analysis. For example, the management platform 230 can obtain the registered population of A neighborhood through Chengdu government service platform, and determine the population of A neighborhood, and further determine the scale of A neighborhood.

[0054] Step 320, determining the current garbage amount of at least one garbage accumulation point based on the image of the at least one garbage accumulation point.

[0055] The current garbage amount refers to the total amount of garbage placed at a certain garbage accumulation point at a current time point. For example, the current garbage amount of the garbage accumulation point at the east gate of A neighborhood on January 1, 2025 at 08:00 in the morning is 500 liters.

[0056] In some embodiments, the management platform 230 can determine the current garbage amount of at least one garbage accumulation point based on the image of the at least one garbage accumulation point. For example, the garbage accumulation point at the east gate of A neighborhood includes 10 garbage cans of 200 liters. The image of the garbage accumulation point at 12:00 on January 1, 2025 shows that 5 are full, 1 is half full, and 4 are empty, so the current garbage amount of the garbage accumulation point is 1100 liters.

[0057] Step 330, determining the garbage growth rate of at least one garbage accumulation point based on the scale of the preset area.

[0058] The garbage growth rate refers to the amount of garbage growing in a unit of time, wherein the amount of growth can be expressed by volume. For example, the garbage growth rate of the garbage gathering point at the east gate of A community can be 100 liters / hour.

[0059] In some embodiments, the management platform 230 can determine the garbage growth rate of at least one garbage gathering point based on the scale of the preset area.

[0060] In some embodiments, the management platform 230 can arrange historical data such as historical scale and historical garbage growth rate of a plurality of preset areas into a data comparison table, and determine the garbage growth rate based on the data comparison table. For example, based on the historical scale of A community in the data comparison table being 1,000 in population and the historical garbage growth rate being 100 liters / hour, it is determined that the garbage growth rate can be 200 liters / hour when the scale of B community is 2,000 in population.

[0061] In some embodiments, the management platform 230 can determine the household garbage generation level of the preset area based on the water, electricity and gas data of the residents of the preset area, and determine the garbage growth rate of at least one garbage gathering point based on the scale of the preset area and the household garbage generation level of the preset area.

[0062] The residents refer to the personnel living in the preset area. The water, electricity and gas data of the residents refer to the data related to the water, electricity and gas used by the residents. For example, the water, electricity and gas data of the residents can be the total amount of water, electricity and gas used by the residents per year.

[0063] The household garbage generation level refers to a numerical value or a letter, etc. reflecting how much garbage is generated by the residents. For example, the household garbage generation level can be represented by a numerical value between 1 and 10, or a letter a-f, or a star rating. The larger the value, the larger the alphabetical order or the higher the star rating, indicating that the household garbage generation level is higher, and the residents generate more garbage.

[0064] In some embodiments, the household garbage generation level can be determined according to the water, electricity and gas data of the residents of the preset area. The water, electricity and gas data can be a weighted average of the relevant data of the water, electricity and gas used by the residents. For example, the household garbage generation levels corresponding to the total amount of water, electricity and gas used by the residents per year in the ranges of 10,000-13,000, 13,000-16,000, 16,000-19,000, 19,000-22,000 and 22,000-25,000 are 1, 2, 3, 4 and 5 levels, respectively. If the amount of water used by the residents of A community per year is 7,500 tons, the amount of electricity used is 150,000 degrees, and the amount of gas used is 30,000 cubic meters, the water, electricity and gas data of the residents can be 7,500 x 75% + 150,000 x 5% + 30,000 x 20% = 19,125, and the corresponding household garbage generation level is 4.

[0065] In some embodiments, the management platform 230 can determine the household garbage generation level of the preset area based on the scale of the preset area and the water, electricity and gas data of the residents of the preset area. More details about determining the household garbage generation level based on the estimation model can be found in Figure 6 and the related description thereof.

[0066] In some embodiments, the management platform 230 can determine the garbage growth rate of at least one garbage collection point based on the household garbage generation level of the preset area. In some embodiments, the management platform 230 can organize historical data such as historical household garbage generation levels and historical garbage growth rates into a data comparison table, and determine the garbage growth rate based on the data comparison table. For example, based on the historical household garbage generation level of 2 levels in the data comparison table, the corresponding historical garbage growth rate is 100 L / h. The garbage growth rate of A community with a household garbage generation level of 2 is determined to be 100 L / h.

[0067] In some embodiments, the management platform 230 can determine the number of entries of the preset area based on the entry and exit records of the entrances and exits of the preset area, and determine the population activity of the preset area based on the number of entries of the preset area. Thus, the management platform 230 can determine the garbage growth rate of at least one garbage collection point based on the scale of the preset area and the population activity of the preset area. More details about determining the garbage growth rate of at least one garbage collection point based on the scale of the preset area and the population activity of the preset area can be found in Figure 4 and the related description thereof.

[0068] Step 340, determining a target garbage collection point based on the current garbage amount of at least one garbage collection point and the garbage growth rate of at least one garbage collection point.

[0069] The target garbage collection point refers to the garbage collection point to be processed. For example, the garbage collection point at the east gate of A community will be processed, and the garbage collection point can be the target garbage collection point.

[0070] In some embodiments, the management platform 230 can determine the predicted fullness time of each garbage collection point based on the current garbage amount and the corresponding garbage growth rate of each garbage collection point, and determine the garbage collection point with the earliest predicted fullness time as the target garbage collection point. For example, the current garbage amounts of garbage collection points A and B are 1000 L and 800 L, and the garbage growth rates are 100 L / h and 200 L / h, respectively, and the garbage amounts after being filled are 1500 L and 2000 L, respectively. The predicted fullness time of garbage collection point A is 5 h and 6 h, respectively. Since garbage collection point A is predicted to be filled first, garbage collection point A can be determined as the target garbage collection point.

[0071] Step 350, controlling the garbage disposal device to dispose of the garbage at the target garbage gathering point.

[0072] In some embodiments, the processing device 110 can issue a control instruction to the garbage disposal device to control the garbage disposal device to dispose of the garbage at the target garbage gathering point. The control instruction refers to an instruction for controlling the garbage disposal device to perform a specific operation, and at least includes time and location, and can also include one or any combination of accompanying personnel, amount of garbage to be disposed of, planned path, etc. For example, the control instruction for the garbage truck can be to go to the garbage gathering point at the east gate of A community on January 1, 2025 at 7:00 am to dispose of 2000 liters of garbage, accompanied by sanitation workers B and C.

[0073] Some embodiments of the present specification determine the current amount of garbage at the garbage gathering point and the garbage growth rate by obtaining images of the garbage gathering point in the preset area and the size of the preset area, and then determine the target garbage gathering point. A more accurate target garbage gathering point can be obtained, and the garbage disposal device can be controlled to dispose of the garbage in the area, improving the accuracy and timeliness of garbage disposal work, avoiding garbage disposal lag in some locations, easily polluting the environment and affecting the city appearance, etc.

[0074] Figure 4 is an exemplary flowchart of a method for determining the garbage growth rate of at least one garbage gathering point according to some embodiments of the present specification. As shown in Figure 4 , the flow 400 includes the following steps. In some embodiments, Figure 4 one or more operations of the flow 400 shown in Figure 1 can be implemented in the application scenario 100 of the smart city garbage disposal determination Internet of Things system shown in

[0075] Step 410, determining the number of entries and exits of the preset area based on the entry and exit records of the entrances and exits of the preset area.

[0076] The entrance and exit can refer to the passageway of the entrance and exit of the preset area, for example, the entrances and exits of A community can be No. 1 gate, No. 2 gate, east gate, north gate, etc. In some embodiments, the entrance and exit can be placed with security facilities, such as entrance and exit gates, monitoring devices, etc.

[0077] The entry and exit record refers to the relevant record information of the personnel entering and exiting the entrance and exit of the preset area. For example, the entry and exit record of A community can be the personnel entry and exit monitoring information at the east gate. In some embodiments, the entry and exit record can include one or a combination of images, videos, voices, etc.

[0078] The number of entries and exits refers to the total number of entries and exits of the preset area within a certain time interval. For example, the number of entries and exits of the east gate of A community from 08:00 to 12:00 on January 1, 2025 is 600 times.

[0079] In some embodiments, the management platform 230 can determine the number of entries and exits of the preset area based on the entry and exit records of the entrances and exits of the preset area. For example, the management platform 230 can determine the number of entries and exits of the preset area based on the entry and exit records of the entry and exit gates of the east gate of A community.

[0080] Step 420, determining the population activity of the preset area based on the number of entries and exits of the preset area.

[0081] The population activity refers to the activity level of the population in the preset area within a certain time interval. In some embodiments, the population activity of the preset area within a certain time interval is proportional to the number of entries and exits of the preset area.

[0082] In some embodiments, the management platform 230 can determine the population activity of the preset area based on the number of entries and exits of the preset area.

[0083] In some embodiments, the management platform 230 can directly use the number of entries and exits of the preset area as the population activity of the preset area. For example, the number of entries and exits of the east gate of A community from 08:00 to 12:00 on January 1, 2025 is 600 times, and the population activity can be 600.

[0084] In some embodiments, the management platform 230 can determine the number of people on the roads around the preset area. And based on the number of entries and exits of the preset area and the number of people on the roads around the preset area, determine the population activity of the preset area.

[0085] The number of people on the roads around the preset area refers to the number of people passing through the roads around the preset area within a certain time interval. For example, the number of people on the roads around A community from 08:00 to 12:00 on January 1, 2025 is 500 people.

[0086] In some embodiments, the management platform 230 can determine the number of people on the roads around the preset area through an image recognition model. The image recognition model can be a machine learning model. The image recognition model can determine the number of people on the roads around the preset area by recognizing and processing the road images of the roads around the preset area collected within a certain time interval.

[0087] In some embodiments, the road images of the roads around the preset area within a certain time interval can be obtained through one or more monitoring devices installed on the roads around the preset area. The monitoring device can be a camera or a camera installed on the roads around the preset area, or other devices.

[0088] In some embodiments, the image recognition model can be a Convolutional Neural Network (CNN) model. In some embodiments, the input of the image recognition model can include a sequence of road pictures of the surrounding road of the preset area within a certain time interval, and the output can include the number of people on the surrounding road of the preset area within a certain time interval. Wherein, the sequence of road pictures of the surrounding road of the preset area within a certain time interval can include a sequence composed of pictures of the surrounding road of the preset area every certain time (e.g., 1s, 5s, 30s, etc.) within a certain time interval. The image recognition model can extract features from the sequence of road pictures of the surrounding road of the preset area within a certain time interval, and determine the number of people on the surrounding road of the preset area within a certain time interval based on the extracted feature values. Wherein, the feature values can be the contours of the detected personnel in the image, etc.

[0089] In some embodiments, the image recognition model can be trained by a plurality of labeled training samples. The plurality of labeled training samples can be input into an initial image recognition model. A loss function is constructed based on the labels and the results of the initial image recognition model, and the parameters of the initial image recognition model are iteratively updated based on the loss function. When the loss function of the initial image recognition model meets the preset condition, the model training is completed, and a trained initial image recognition model is obtained. In some embodiments, the training samples can at least include road images of the surrounding road of the sample area within a certain time interval, and the labels can be the number of people on the surrounding road of the sample area. The labels can be obtained by manually labeling the images of the sample area.

[0090] In some embodiments, the management platform 230 can determine the population activity of the preset area based on the number of entries and exits of the preset area and the number of people on the surrounding road of the preset area.

[0091] In some embodiments, the number of entries and exits of the preset area and the number of people on the surrounding road of the preset area are directly proportional to the population activity of the preset area. That is, the more the number of entries and exits of the preset area, the more the number of people on the surrounding road of the preset area, and the higher the population activity of the preset area.

[0092] In some embodiments, the management platform 230 can set a first preset rule between the number of entries and exits of the preset area, the number of people on the surrounding road of the preset area, and the population activity of the preset area. And determine the population activity of the preset area based on the first preset rule. For example, the management platform 230 can set the first preset rule as the population activity of the preset area is the sum of the number of entries and exits of the preset area and the number of people on the surrounding road of the preset area. For example, the number of entries and exits of A community 2025-01-01 08:00-12:00 is 600 times, and the number of people on the surrounding road is 500, then the population activity is 1100.

[0093] The population activity of the preset area is determined by the number of entries and exits of the preset area and the number of people on the surrounding roads of the preset area. The garbage generated by the surrounding roads is considered, so that the determined population activity of the preset area is more comprehensive and accurate.

[0094] In some embodiments, the number of people on the surrounding roads of the preset area can include the number of consumers of the ground floor shops on the surrounding roads of the preset area. The ground floor shops can refer to the ground floor shops near the entrance of the preset area, for example, the ground floor shops of the A community can be the ground floor shops in the range with the A community as the center and 300 meters as the radius.

[0095] In some embodiments, the management platform 230 can determine the number of consumers of the ground floor shops on the surrounding roads of the preset area by the image recognition model. In some embodiments, the input of the image recognition model can include a sequence of entrance pictures of the ground floor shops on the surrounding roads of the preset area in a certain time interval, and the output can include the number of consumers of the ground floor shops on the surrounding roads of the preset area in a certain time interval. For more information about the training of the image recognition model, please refer to the foregoing description of the determination of the number of people on the surrounding roads of the preset area by the image recognition model, which will not be repeated here.

[0096] In some embodiments, the garbage generated by the consumers of the ground floor shops on the surrounding roads of the preset area is more than the garbage generated by the consumers of the ground floor shops on the surrounding roads of the preset area. Therefore, the proportion of the number of consumers of the ground floor shops on the surrounding roads of the preset area to the number of people on the surrounding roads of the preset area is proportional to the population activity of the preset area, and the population activity of the preset area is proportional to the garbage growth rate of at least one garbage gathering point.

[0097] For example, if the number of consumers of the ground floor shops on the surrounding roads of the A community is 30, the number of people on the surrounding roads is 100, and the proportion of the number of consumers of the ground floor shops on the surrounding roads of the A community to the number of people on the surrounding roads is 3:10. The population activity of the A community can be 30x2+(100-30)=130, and the corresponding garbage growth rate can be 60L / h. If the number of consumers of the ground floor shops on the surrounding roads of the A community is 10, the number of people on the surrounding roads is 100, and the proportion of the number of consumers of the ground floor shops on the surrounding roads of the A community to the number of people on the surrounding roads is 1:10. The population activity of the A community can be 10x2+(100-10)=110, and the corresponding garbage growth rate can be 50L / h.

[0098] In determining the population activity of the preset area, the garbage generated by the consumers of the ground floor shops on the surrounding roads of the preset area is considered to be more than the garbage generated by the consumers of the ground floor shops on the surrounding roads of the preset area. The determined population activity of the preset area is more comprehensive and accurate.

[0099] A future time refers to a time point after a current time point. For example, if the current time point is January 1, 2025, 08:00, the future time can be January 1, 2025, 09:15.

[0100] In some embodiments, the greater the population activity of the preset area at the future time, the more likely it is to cause the future garbage growth rate to increase. To obtain a more accurate target garbage gathering point, the management platform 230 can determine the population activity of the preset area at the future time according to the community population activity at the current time point based on a second preset rule. In some embodiments, the management platform 230 can compile the historical time and historical population activity of multiple preset areas into a data table. And determine the population activity of the preset area at the future time based on the data table. For example, based on the historical population activity of A cell corresponding to the historical time of January 1, 2024, 08:00 and 10:15 in the data table is 1000 and 600 respectively. If the population activity at the current time point January 1, 2025, 08:00 is 1200, the population activity of the preset area at the future time January 1, 2025, 10:15 can be determined as 1200 x 600 ÷ 1000 = 720.

[0101] In some embodiments, the management platform 230 can determine the population activity of the preset area at the future time based on the size of the preset area, the number of active people and the activity range index vector within the preset time period, through a prediction model, which is a machine learning model. For more details on determining the population activity of the preset area at the future time through the prediction model, please refer to Figure 5 and the related description.

[0102] In some embodiments, the management platform 230 can determine the population activity of the preset area based on the number of entries and exits of the preset area and the population activity of the preset area at the future time.

[0103] In some embodiments, the management platform 230 can set a third preset rule for the number of entries and exits of the preset area and the population activity of the preset area at the future time, and the population activity of the preset area. And determine the population activity of the preset area based on the preset rule. For example, the management platform 230 can set the third preset rule as the population activity of the preset area being the average of the number of entries and exits of the preset area and the population activity of the preset area at the future time. For example, the number of entries and exits of A cell from January 1, 2025, 08:00 to 12:00 is 600, and the population activity at the future time January 1, 2025, 17:00 is expected to be 1000, then the population activity of A cell is 800.

[0104] The population activity level of the preset area is determined by the number of people entering and leaving the preset area and the population activity level of the preset area at the future time. The population activity level of the preset area at the future time is considered to change due to time change, so that the determined population activity level of the preset area is more comprehensive and accurate.

[0105] In some embodiments, the management platform 230 can obtain the activity range of each resident in the preset area. Based on the activity range of each resident, the activity range index of each resident is determined. Thus, based on the weighted calculation result of the activity range index of each resident, the population activity level of the preset area is determined.

[0106] The activity range refers to the spatial range of the daily activities of the resident in the preset area. In some embodiments, the activity range can include the number of times the resident appears in the preset area picture within a certain time interval, the number of different monitoring devices in which the resident appears, and the like. For example, a resident in A community appears in the east gate monitoring camera 3 times, the north gate camera 1 time, and the west gate camera 1 time from 08:00 to 12:00 on January 1, 2025, so the activity range of the resident in A community from 08:00 to 12:00 on January 1, 2025 can include the number of times the resident appears in the picture of A community, which is 5 times, and the number of different monitoring devices in which the resident appears, which is 3.

[0107] In some embodiments, the management platform 130 can obtain the activity range of each resident in the preset area through the monitoring device 140.

[0108] The activity range index refers to a relevant parameter that can reflect the activity range.

[0109] In some embodiments, the management platform 230 can determine the activity range index of each resident according to the activity range of each resident. For example, the management platform 230 can set the calculation formula of the activity range index as: activity range index = k x number of times the resident appears in the preset area picture within a certain time interval x number of different monitoring devices in which the resident appears. Wherein, k can be a constant, which can be set by the system automatically or manually according to actual needs. For example, a resident in A community appears in the east gate monitoring camera 3 times, the north gate camera 1 time, and the west gate camera 1 time from 08:00 to 12:00 on January 1, 2025, and k is set to 1, so the activity range index of the resident is 1 x 5 x 3 = 15.

[0110] In some embodiments, the management platform 230 can determine the population activity of the preset area based on the weighted calculation result of the activity range index of each resident. Wherein, the greater the activity range index of the resident, the greater the corresponding weight in calculating the population activity of the preset area. For example, the A community has three residents a, b and c, and the activity range indexes are 15, 30 and 30 respectively, and the population activity of the A community can be 15x20%+30x40%+30x40%=27.

[0111] The population activity of the preset area is determined by calculating the weighted calculation result of the activity range index of each resident. It is considered that the greater the activity range of the resident, the greater the activity, so that the determined population activity of the preset area is more comprehensive and accurate.

[0112] In some embodiments, the preset area can include a plurality of sub-areas, and each sub-area in the plurality of sub-areas is different in distance from at least one garbage aggregation point. The weight value corresponding to the activity range index of each resident is related to the plurality of sub-areas. Wherein, the distance of each sub-area from at least one garbage aggregation point is the sum of the distance of each sub-area from each garbage aggregation point. For example, the A community has three residents a, b and c, and the activity range indexes are 15, 30 and 30 respectively. Wherein, the distance of the sub-area where the resident a often acts from at least one garbage aggregation point is the smallest. Then the weight value of the resident a is larger, and the population activity of the A community can be 15x40%+30x30%+30x30%=24.

[0113] Step 430, determining the garbage growth rate of at least one garbage aggregation point based on the size of the preset area and the population activity of the preset area.

[0114] In some embodiments, the management platform 230 can set the calculation formula of the garbage growth rate as: garbage growth rate=k'x(preset area size+preset area population activity). Wherein, k' can be a constant, which can be set by the system automatically or manually according to actual needs. For example, if k' is 0.05, the size of the A community is 1500, and the population activity is 500, the corresponding garbage growth rate is 100L / h.

[0115] The population activity is determined based on the access records of the entrances and exits of the preset area, so as to determine the garbage growth rate of at least one garbage aggregation point based on the size of the preset area and the population activity of the preset area. Since not only the size of the preset area is considered, but also the population activity of the preset area is considered, the garbage growth rate of at least one garbage aggregation point can be more accurately determined.

[0116] Figure 5This is a schematic diagram of an exemplary process 500 for determining the population activity of a preset area at a future time, according to some embodiments of this specification.

[0117] In some embodiments, the management platform 230 can predict the population activity of the preset area at future times using a prediction model based on the size of the preset area, the number of active people within a preset time period, and the activity range index vector.

[0118] In some embodiments, the predictive model can be used to predict population activity in a predetermined area at future times. The predictive model can be a machine learning model, such as a deep neural network (DNN) model, a recurrent neural network (RNN) model, or any combination thereof.

[0119] In some embodiments, such as Figure 5 As shown, the inputs to the prediction model 520 may include the size of the preset area 510-1, the number of active people within a preset time period 510-2, and the activity range index vector 510-3. The output of the prediction model 520 may include the population activity level of the preset area at a future time 530.

[0120] The number of people active within a preset time period (510-2) can refer to the number of people active within a preset area during a pre-defined time interval. For example, the number of people active in Community A from 09:00 to 12:00 on January 1, 2025 could be 100 different people captured by the surveillance cameras at the east, north, west, and south gates.

[0121] In some embodiments, the management platform 230 can determine the number of people active within a preset time period using an image recognition model. In some embodiments, the input to the image recognition model may include a sequence of images captured by a monitoring device in a preset area within the preset time period, and the output may include the number of people active within the preset time period. Further details regarding the training of the image recognition model can be found in the foregoing description of determining pedestrian traffic flow around a preset area using an image recognition model, and will not be repeated here.

[0122] The activity range index vector 510-3 can refer to the vector composed of the activity range index of each person captured by the monitoring device in the preset area. For example, the activity range index vector of Community A from 09:00 to 10:00 on January 1, 2025 can be a vector composed of the activity range indices of 5 people [4,1,15,8,4].

[0123] In some embodiments, the input to the prediction model 520 may further include pedestrian traffic flow 510-4 on roads surrounding a preset area. More details regarding pedestrian traffic flow on roads surrounding a preset area can be found in [reference needed].Figure 4 and the related description.

[0124] The population activity of the preset area at the future time is determined by adding the traffic volume of the road around the preset area. It is considered that the greater the traffic volume of the road around the preset area, the more comprehensive and accurate the determined population activity of the preset area is.

[0125] In some embodiments, the prediction model 520 can be trained by a plurality of labeled training samples. A plurality of first training samples 540 with labels can be input into an initial prediction model 550, and a loss function is constructed by the labels and the results of the initial prediction model 550. The parameters of the initial prediction model 550 are iteratively updated based on the loss function. When the loss function of the initial prediction model 550 meets a preset condition, the model training is completed, and the trained prediction model 520 is obtained. The preset condition can be that the loss function converges, the number of iterations reaches a threshold, etc.

[0126] In some embodiments, the first training sample 540 can include the size of the sample preset area, the number of active people in the sample preset time period, and the sample activity range index vector. The label can be the population activity of the sample preset area at the future time. In some embodiments, the first training sample 540 can be obtained by big data analysis. For example, a large amount of data can be obtained and statistically analyzed after processing, such as through a third-party platform, historical input information of a plurality of preset areas, etc., and the label can be obtained by manual annotation.

[0127] When the input of the prediction model 520 can also include the traffic volume 510-4 of the road around the preset area, the first training sample 540 can also include the traffic volume of the road around the sample preset area.

[0128] Through some embodiments of the present specification, the size of the preset area, the number of active people in the preset time period, the activity range index vector, and the traffic volume of the road around the preset area are processed by the prediction model, which can more conveniently and accurately determine the population activity of the preset area at the future time.

[0129] Figure 6 is a schematic diagram of an example process 600 of determining a household garbage generation level according to some embodiments of the present specification.

[0130] In some embodiments, the management platform 230 can determine the household garbage generation level by the prediction model based on the size of the preset area and the water, electricity and gas data of the residents of the preset area.

[0131] In some embodiments, the prediction model can be used to predict the household garbage generation level. The prediction model can be a machine learning model. For example, a DNN model, an RNN model, etc., or any combination thereof.

[0132] In some embodiments, as shown in FIG. 6, the input of the estimation model 620 can include the size of the preset area 610-1 and the water, electricity and gas data of the residents of the preset area 610-2. The output of the estimation model 620 can include the household garbage generation level 630. Figure 6 In some embodiments, the input of the estimation model 620 can further include the population activity of the preset area 610-3. Since the greater the population activity of the preset area, the shorter the time of the residents of the preset area at home, the lower the household garbage generation level. That is, the household garbage generation level of the preset area is inversely proportional to the population activity of the preset area.

[0133] By adding the population activity of the preset area to determine the household garbage generation level. It is considered that the greater the population activity, the lower the household garbage generation level, so that the determined household garbage generation level is more comprehensive and accurate.

[0134] In some embodiments, the estimation model 620 can be trained by a plurality of labeled training samples. A plurality of labeled second training samples 640 can be input into an initial estimation model 650, a loss function can be constructed by the label and the result of the initial estimation model 650, and the parameters of the initial estimation model 650 can be iteratively updated based on the loss function. When the loss function of the initial estimation model 650 meets the preset condition, the model training is completed, and the trained estimation model 620 is obtained. The preset condition can be that the loss function converges, the number of iterations reaches a threshold, etc.

[0135] In some embodiments, the second training sample 640 can include the size of the sample preset area and the water, electricity and gas data of the residents of the sample preset area. The label can be the sample household garbage generation level. In some embodiments, the second training sample 640 can be obtained by big data analysis. For example, a large amount of data can be obtained and statistically analyzed after processing, such as through a third-party platform, historical input information of a plurality of preset areas, etc., and the label can be obtained by manual annotation.

[0136] When the input of the estimation model 620 can further include the population activity of the preset area 610-3, the first training sample 540 can further include the population activity of the sample preset area.

[0137] Through some embodiments of the present specification, the size of the preset area, the water, electricity and gas data of the residents of the preset area, and the population activity of the preset area are processed by the estimation model. A plurality of preset areas can be analyzed at the same time, which can improve the operation efficiency, make the determination process of the household garbage generation level more efficient, and also significantly improve the accuracy of the determined household garbage generation level.

[0138]

[0139] ​It should be noted that different embodiments can produce different beneficial results, and in different embodiments, the beneficial results that can be produced can be any one or a combination of the above, or any other beneficial result that can be obtained.

[0140] Some embodiments of the present specification also disclose a smart city garbage disposal determination device, comprising at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute at least part of the computer instructions to realize the method of smart city garbage disposal determination.

[0141] Some embodiments of the present specification also disclose a computer readable storage medium, which stores computer instructions, when a computer reads the computer instructions in the storage medium, the computer executes the smart city garbage disposal determination method as described in any one of the above embodiments.

[0142] The above has described the basic concept, and it is obvious that the above detailed disclosure is only used as an example, and does not limit the present specification. Although it is not explicitly stated here, those skilled in the art can make various modifications, improvements and corrections to the present specification. Such modifications, improvements and corrections are suggested in the present specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of the present specification.

[0143] In addition, unless the claim explicitly states, the order of the processing elements and sequences described in the present specification, the use of numerals and letters, or the use of other names, is not used to limit the order of the processes and methods of the present specification. Although some currently considered useful embodiments are discussed in the above disclosure through various examples, it should be understood that such details are only for the purpose of illustration, and the additional claims are not limited to the disclosed embodiments, on the contrary, the claims are intended to cover all modifications and equivalent combinations that meet the spirit and scope of the embodiments of the present specification. For example, although the system components described above can be realized by hardware devices, they can also be realized only by software solutions, such as installing the described system on existing servers or mobile devices.

[0144] Finally, it should be understood that the embodiments described in the present specification are only used to illustrate the principles of the embodiments of the present specification. Other variations can also belong to the scope of the present specification. Therefore, as an example but not limitation, alternative configurations of the embodiments of the present specification can be considered consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to the embodiments explicitly introduced and described in the present specification.

Claims

1. A smart city garbage disposal determination method, characterized in that, The smart city garbage disposal determination Internet of Things system comprises a management platform, a sensing network platform and an object platform; The method is executed by the management platform; The method comprises: acquiring images of at least one garbage accumulation point in a preset area and a size of the preset area based on the object platform; determining a current garbage amount of the at least one garbage accumulation point based on the images of the at least one garbage accumulation point; determining a garbage growth rate of the at least one garbage accumulation point based on the size of the preset area; determining a target garbage accumulation point based on the current garbage amount of the at least one garbage accumulation point and the garbage growth rate of the at least one garbage accumulation point; controlling a garbage disposal device to dispose of garbage of the target garbage accumulation point; wherein the determining of the garbage growth rate of the at least one garbage accumulation point based on the size of the preset area comprises: determining a number of entries and exits of the preset area based on entry and exit records of entrances and exits of the preset area; determining a population activity level of the preset area based on the number of entries and exits of the preset area; determining the garbage growth rate of the at least one garbage accumulation point based on the size of the preset area and the population activity level of the preset area; or determining a household garbage generation level of the preset area based on water, electricity and gas data of residents of the preset area; determining the garbage growth rate of the at least one garbage accumulation point based on the household garbage generation level of the preset area.

2. The method of claim 1, wherein, The management platform comprises a plurality of management sub-platforms, wherein the plurality of management sub-platforms correspond to a plurality of management sub-platform databases one by one; the plurality of management sub-platforms correspond to different urban areas.

3. The method of claim 2, wherein, The smart city garbage disposal determination Internet of Things system further comprises a service platform and a user platform; the user platform is configured to receive a query demand for garbage disposal initiated by a user and transmit the query demand to the management platform based on the service platform.

4. The method of claim 2, wherein, The sensing network platform comprises a sensing network total platform database, a plurality of sensing network sub-platforms and a plurality of sensing network sub-platform databases, wherein the plurality of sensing network sub-platforms correspond to the plurality of sensing network sub-platform databases one by one; the plurality of sensing network sub-platforms correspond to different urban areas.

5. The method of claim 1, wherein, The determining of the population activity level of the preset area based on the number of entries and exits of the preset area comprises: acquiring an activity range of each resident in the preset area; determining an activity range index of each resident based on the activity range of each resident; determining the population activity level of the preset area based on a weighted calculation result of the activity range index of each resident. 6.A smart city waste processing determination Internet of Things system, characterized in that, comprise a management platform, a sensing network platform and an object platform; the management platform is configured to perform the following operations: acquiring images of at least one garbage accumulation point in a preset area and a size of the preset area based on the object platform, and transmitting the images and the size to the management platform through the sensing network platform based on the object platform; determining a current garbage amount of the at least one garbage accumulation point based on the images of the at least one garbage accumulation point; determine a garbage growth rate of the at least one garbage gathering point based on the size of the preset area; determine a target garbage gathering point based on the current garbage amount of the at least one garbage gathering point and the garbage growth rate of the at least one garbage gathering point; wherein the determining the garbage growth rate of the at least one garbage gathering point based on the size of the preset area comprises: determine a number of entries and exits of the preset area based on entry and exit records of entrances and exits of the preset area; determine a population activity of the preset area based on the number of entries and exits of the preset area; determine the garbage growth rate of the at least one garbage gathering point based on the size of the preset area and the population activity of the preset area; or determine a household garbage generation level of the preset area based on water, electricity and gas data of residents of the preset area; determine the garbage growth rate of the at least one garbage gathering point based on the household garbage generation level of the preset area. 7.A smart city garbage disposal determination device, characterized in that, The device comprises at least one processor and at least one memory; The at least one memory is used to store computer instructions; The at least one processor is used to execute at least part of the computer instructions to implement the method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions, and when a computer reads the computer instructions in the storage medium, the computer executes the smart city garbage disposal determination method according to any one of claims 1-5.

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