Garbage treatment method, system and equipment based on big data and medium

Abstract

The invention provides a garbage disposal method, system and equipment based on big data and a medium, and relates to the technical field of garbage disposal, and the method comprises the following steps: obtaining monitoring videos of a plurality of garbage cans of a community and a community monitoring video; using a generative adversarial network to generate a subsequent garbage simulation growth video of each garbage can of the community based on the monitoring videos of the plurality of garbage cans of the community; based on the monitoring videos of the multiple garbage cans of the community, the community monitoring videos use a bearing capacity determination model to determine the maximum garbage bearing capacity of each garbage can and the comfort interval of the total garbage bearing capacity of the community; target garbage treatment time is determined based on the maximum garbage bearing capacity of each garbage can, the comfort interval of the total garbage bearing capacity of the community and the follow-up garbage simulation increase video of each garbage can; according to the method, the garbage treatment efficiency can be improved, and the intelligent management of the garbage treatment process can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of garbage disposal, and in particular to a garbage disposal method, system, equipment and medium based on big data. Background Art

[0002] With the acceleration of urbanization and the continuous increase in urban population density, the disposal of domestic waste has become an important issue in urban management. Traditional waste disposal methods mainly rely on regular manual inspections and empirical judgments to determine the time of waste collection. This method has many shortcomings. Traditional manual inspections and regular cleaning methods cannot dynamically adjust the cleaning time according to the actual changes in waste generation, resulting in the waste bins reaching saturation too early in some cases, while in other cases, resources may be idle. Due to the lack of accurate data support, there are often problems of over- or under-disposal in the waste disposal process, which not only increases unnecessary transportation costs, but also may cause environmental pollution. Therefore, how to improve the efficiency of garbage disposal and realize intelligent management of the garbage disposal process is a problem that needs to be solved urgently. Summary of the invention

[0003] The main technical problem solved by the present invention is how to improve the efficiency of garbage disposal and realize intelligent management of the garbage disposal process.

[0004] According to a first aspect, the present invention provides a garbage disposal method based on big data, comprising: obtaining surveillance videos of multiple garbage bins in a community and surveillance videos of the community; using a generative adversarial network based on the surveillance videos of the multiple garbage bins in the community to generate subsequent garbage simulation growth videos of each garbage bin in the community; determining the maximum garbage carrying capacity of each garbage bin and the comfortable range of the total garbage carrying capacity of the community based on the surveillance videos of the multiple garbage bins in the community and the surveillance videos of the community using a carrying capacity determination model; determining a target garbage disposal time based on the maximum garbage carrying capacity of each garbage bin, the comfortable range of the total garbage carrying capacity of the community, and the subsequent garbage simulation growth video of each garbage bin; and dispatching a vehicle to carry out garbage disposal based on the target garbage disposal time.

[0005] In one possible implementation, the target garbage disposal time is determined based on the maximum garbage carrying capacity of each garbage bin, the comfortable range of the total garbage carrying capacity of the community, and the subsequent garbage simulation growth video of each garbage bin, including: constructing a community graph structure, the community graph structure includes a community node and multiple garbage bin nodes, the community node is a central node, each garbage bin node establishes edges with the community node, the node characteristics of the community node include the comfortable range of the total garbage carrying capacity of the community, the node characteristics of each garbage bin node include the subsequent garbage simulation growth video of each garbage bin, and the characteristics of the edges between each garbage bin node and the community node include the maximum garbage carrying capacity of each garbage bin; the community graph structure is processed based on a graph neural network model to determine the target garbage disposal time.

[0006] In a possible implementation, the load capacity determination model is a long-term and short-term neural network model.

[0007] In one possible implementation, the input of the graph neural network model is the cell graph structure, and the output of the graph neural network model is the target garbage disposal time.

[0008] According to a second aspect, the present invention provides a garbage disposal system based on big data, comprising: An acquisition module is used to acquire surveillance videos of multiple garbage bins in a community and surveillance videos of the community; A generation module, configured to generate a subsequent garbage simulation growth video of each garbage bin in the community using a generative adversarial network based on the monitoring videos of the multiple garbage bins in the community; A carrying capacity determination module, configured to determine the maximum garbage carrying capacity of each garbage bin and the comfortable range of the total garbage carrying capacity of the community based on the surveillance videos of the plurality of garbage bins in the community and the carrying capacity determination model of the community surveillance videos; A time determination module, configured to determine a target garbage disposal time based on a maximum garbage carrying capacity of each garbage bin, a comfortable range of a total garbage carrying capacity of a community, and a subsequent garbage simulation growth video of each garbage bin; A processing module is used to dispatch vehicles to process garbage based on the target garbage processing time.

[0009] In a possible implementation manner, the time determination module is further used to: Constructing a cell graph structure, the cell graph structure includes a cell node and a plurality of trash bin nodes, the cell node is a central node, each trash bin node establishes edges with the cell node, the node characteristics of the cell node include a comfort range of the total trash carrying capacity of the cell, the node characteristics of each trash bin node include a subsequent trash simulation growth video of each trash bin, and the characteristics of the edge between each trash bin node and the cell node include a maximum trash carrying capacity of each trash bin; The community graph structure is processed based on a graph neural network model to determine the target garbage disposal time.

[0010] In a possible implementation, the load capacity determination model is a long-term and short-term neural network model.

[0011] In one possible implementation, the input of the graph neural network model is the cell graph structure, and the output of the graph neural network model is the target garbage disposal time.

[0012] According to a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor; a memory; and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement a method as described above, the method comprising: obtaining surveillance videos of multiple garbage bins in a community and surveillance videos of the community; using a generative adversarial network based on the surveillance videos of the multiple garbage bins in the community to generate subsequent garbage simulation growth videos of each garbage bin in the community; determining the maximum garbage carrying capacity of each garbage bin and a comfortable range of the total garbage carrying capacity of the community based on the surveillance videos of the multiple garbage bins in the community and the surveillance videos of the community using a carrying capacity determination model; determining a target garbage disposal time based on the maximum garbage carrying capacity of each garbage bin, the comfortable range of the total garbage carrying capacity of the community, and the subsequent garbage simulation growth video of each garbage bin; and dispatching a vehicle to dispose of the garbage based on the target garbage disposal time.

[0013] According to the fourth aspect, the present embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned garbage disposal method based on big data, the method comprising: obtaining surveillance videos of multiple garbage bins in a community and surveillance videos of the community; using a generative adversarial network to generate subsequent garbage simulation growth videos of each garbage bin in the community based on the surveillance videos of the multiple garbage bins in the community; determining the maximum garbage carrying capacity of each garbage bin and the comfortable range of the total garbage carrying capacity of the community based on the surveillance videos of the multiple garbage bins in the community and the surveillance videos of the community using a carrying capacity determination model; determining a target garbage disposal time based on the maximum garbage carrying capacity of each garbage bin, the comfortable range of the total garbage carrying capacity of the community, and the subsequent garbage simulation growth video of each garbage bin; and dispatching a vehicle to dispose of the garbage based on the target garbage disposal time.

[0014] The present invention provides a garbage disposal method and system based on big data. The method includes obtaining surveillance videos of multiple garbage bins in a community and surveillance videos of the community; using a generative adversarial network to generate subsequent garbage simulation growth videos of each garbage bin in the community based on the surveillance videos of the multiple garbage bins in the community; using a capacity determination model to determine the maximum garbage carrying capacity of each garbage bin and the comfortable range of the total garbage carrying capacity of the community based on the surveillance videos of the multiple garbage bins in the community and the surveillance videos of the community; determining a target garbage disposal time based on the maximum garbage carrying capacity of each garbage bin, the comfortable range of the total garbage carrying capacity of the community, and the subsequent garbage simulation growth video of each garbage bin; and dispatching a vehicle to dispose of the garbage based on the target garbage disposal time. The method can improve the efficiency of garbage disposal and realize intelligent management of the garbage disposal process. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of an application scenario of a garbage disposal method based on big data provided by an embodiment of the present invention; Figure 2 A schematic diagram of a process flow of a garbage disposal method based on big data provided by an embodiment of the present invention; Figure 3 A schematic diagram of a process for determining a target garbage disposal time provided by an embodiment of the present invention; Figure 4 A schematic diagram of a garbage disposal system based on big data provided by an embodiment of the present invention; Figure 5 A schematic diagram of an electronic device provided by an embodiment of the present invention; Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present invention better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present invention are not shown or described in the specification, this is to avoid the core part of the present invention being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0017] Figure 2 A schematic diagram of an application scenario of a garbage disposal method based on big data provided by an embodiment of the present invention. Figure 2 The application scenario of the garbage disposal method based on big data may include a server 11, a network 12, a terminal 13 and a storage device 14.

[0018] In some embodiments, the server 11 may be a single server or a server group. The server 11 may access information and / or data stored in the terminal 13 or the storage device 14 through the network 12. In some embodiments, the server 11 may be used to execute Figure 2 The garbage disposal method based on big data shown in.

[0019] The network 12 may facilitate the exchange of information and / or data. In some embodiments, the network 12 may be any form of wired or wireless network, or any combination thereof.

[0020] Terminal 13 may refer to one or more terminal devices used by a user. In some embodiments, terminal 13 may include one or more combinations of a mobile device, a tablet computer, a laptop computer, and the like.

[0021] The storage device 14 may store data and / or instructions. For example, the storage device 14 may store data instructions of a garbage processing method based on big data.

[0022] In an embodiment of the present invention, there is provided Figure 1 A garbage processing method based on big data is shown, and the garbage processing method based on big data includes steps S1 to S5: Step S1, obtaining surveillance videos of multiple garbage bins in a community and surveillance videos of the community.

[0023] The surveillance videos of multiple garbage bins in the community are real-time video data collected by cameras installed near each garbage bin in the community.

[0024] The cell monitoring video is a monitoring video of the entire cell. In some embodiments, cameras can also be installed at the main entrances and exits of the cell to ensure that all important areas can be fully covered, and all video data can be spliced ​​to obtain the cell monitoring video.

[0025] Step S2: Based on the monitoring videos of the multiple garbage bins in the community, a generative adversarial network is used to generate a subsequent garbage simulation growth video for each garbage bin in the community.

[0026] The Generative Adversarial Network (GAN) consists of a generator and a discriminator. These two parts compete with each other, learn from each other, and jointly promote the training of the model. The input of the GAN is the surveillance video of multiple garbage bins in the community, and the output of the GAN is the subsequent garbage simulation growth video of each garbage bin in the community.

[0027] The generator generates new data samples by learning the distribution of real data. In the garbage simulation growth video generation task, the generator will learn the garbage growth pattern in the historical garbage bin monitoring video and generate a simulated video of garbage growth in the garbage bin in the future. The monitoring videos of multiple garbage bins in the community provide rich historical data, which include the changes in the amount of garbage in the garbage bins in different time periods. The generator can understand the pattern and law of garbage growth by learning these historical data. For example, the generator can learn that the amount of garbage grows faster in certain time periods (such as weekends or holidays), while the amount of garbage grows slower in certain time periods (such as weekdays). The monitoring video is continuous spatiotemporal data, which contains the state changes of the garbage bin at different time points. The generator can use this spatiotemporal continuity to generate a simulated video of garbage growth in the garbage bin in the future. The generator not only learns the static distribution of garbage volume, but also learns the dynamic trend of garbage growth.

[0028] Step S3, based on the surveillance videos of the plurality of garbage bins in the community and the community surveillance video use capacity determination model, determine the maximum garbage carrying capacity of each garbage bin and the comfortable range of the total garbage carrying capacity of the community.

[0029] The carrying capacity determination model is a long-term and short-term neural network model. The input of the carrying capacity determination model is the monitoring video of multiple garbage bins in the community and the community monitoring video, and the output of the carrying capacity determination model is the maximum garbage carrying capacity of each garbage bin and the comfortable range of the total garbage carrying capacity of the community.

[0030] The maximum waste capacity of each trash bin refers to the maximum amount of waste that the bin can hold without overflowing.

[0031] The comfort range of the total garbage carrying capacity of a community means that the total garbage volume of all garbage bins in the community is within a reasonable range, which can meet daily needs without causing garbage overflow or frequent cleaning. For example, the total garbage carrying capacity comfort range of a community may be 800-1200 kg, which means that when the total garbage volume of all garbage bins in the community is within this range, garbage disposal is most efficient and economical.

[0032] The long-term and short-term neural network model includes a long-term and short-term neural network (LSTM). The long-term and short-term neural network model can process the surveillance videos of the multiple garbage bins in the community and the surveillance videos of the community in a continuous time period, can better capture the surveillance videos of the multiple garbage bins in the community and the relationship in the time series of the surveillance videos of the community, and can output the features of the surveillance videos of the multiple garbage bins in the community and the correlation relationship of the surveillance videos of the community that comprehensively consider each time point, so that the output features are more accurate and comprehensive.

[0033] The surveillance videos of multiple garbage bins in the community and the community surveillance videos are all time series data, recording the status changes of the garbage bins at different time points.

[0034] In some embodiments, the carrying capacity determination model includes a maximum garbage carrying capacity determination layer, a community information determination layer, and a comfort interval determination layer. The input of the maximum garbage carrying capacity determination layer is the surveillance video of multiple garbage bins in the community, the output of the maximum garbage carrying capacity determination layer is the maximum garbage carrying capacity of each garbage bin, the input of the community information determination layer is the community surveillance video, the output of the community information determination layer is the greening degree of the community, the density of people in the community, and the housing grade of the community, the input of the comfort interval determination layer is the maximum garbage carrying capacity of each garbage bin, the greening degree of the community, the density of people in the community, and the housing grade of the community, and the output of the comfort interval determination layer is the comfort interval of the total garbage carrying capacity of the community.

[0035] The maximum garbage carrying capacity determination layer determines the maximum garbage carrying capacity of each garbage bin based on the historical monitoring videos of the garbage bins. The community information determination layer extracts the greening degree, population density, and housing grade information of the community based on the overall monitoring videos of the community. The comfort interval determination layer comprehensively considers the maximum garbage carrying capacity of each garbage bin, the greening degree, population density, and housing grade of the community, and finally determines the comfort interval of the total garbage carrying capacity of the community.

[0036] The greening degree of a community refers to the area and quality of the green areas within the community. The higher the greening degree, the better the green environment of the community. The higher the greening degree of a community, the better the environmental management of the community and the stronger the environmental awareness of the residents. In order to keep the environment clean, setting a lower garbage carrying capacity comfort range can ensure that garbage will not have a negative impact on the environment.

[0037] Population density refers to the number and distribution of residents and visitors in a community. The higher the population density, the denser the population in the community. The larger the population, the better the waste treatment effect is often required, and a lower waste load comfort range is set to reduce the impact on dense population.

[0038] Housing grade indicates the type and grade of houses in the community. The higher the housing grade, the higher the price and grade of the housing. Residents in high-end residential areas may pay more attention to environmental protection, so the value of the comfort range of the community's total garbage carrying capacity will be lower, and vice versa.

[0039] Step S4, determining the target garbage disposal time based on the maximum garbage carrying capacity of each garbage bin, the comfortable range of the total garbage carrying capacity of the community, and the subsequent garbage simulation growth video of each garbage bin.

[0040] In some embodiments, Figure 3 A schematic diagram of a process for determining a target garbage disposal time is provided in an embodiment of the present invention. The process for determining the target garbage disposal time includes steps S21 to S22: Step S21, constructing a cell graph structure, the cell graph structure includes a cell node and multiple garbage bin nodes, the cell node is a central node, each garbage bin node establishes an edge with the cell node, the node characteristics of the cell node include the comfort range of the total garbage carrying capacity of the cell, the node characteristics of each garbage bin node include the subsequent garbage simulation growth video of each garbage bin, and the characteristics of the edge between each garbage bin node and the cell node include the maximum garbage carrying capacity of each garbage bin.

[0041] The cell graph structure is a graph data structure used to represent the relationship and attributes between trash bins in a cell. The cell node represents the entire cell and serves as the central node. The trash bin node represents each trash bin and serves as a node in the graph. The edge connects the cell node and the trash bin node, representing the relationship between the trash bin and the cell. The node features of the cell node include the comfort range of the total trash carrying capacity of the cell. The node features of the trash bin node include the subsequent trash simulation growth video of each trash bin.

[0042] The characteristics of the cell node include the comfortable range of the total garbage carrying capacity of the cell, and the characteristics of the garbage bin node include the subsequent garbage simulation growth video of each garbage bin. These characteristics comprehensively reflect the status of the cell and the garbage bin. The characteristics of the edge include the maximum garbage carrying capacity of each garbage bin, reflecting the relationship between the garbage bin and the cell. By building a graph structure, multiple factors can be comprehensively considered to ensure that the selection of garbage disposal time is based on comprehensive data analysis and avoid inaccurate predictions caused by a single factor.

[0043] Step S22: Process the cell graph structure based on the graph neural network model to determine the target garbage disposal time.

[0044] The target garbage disposal time refers to the most appropriate garbage removal time while ensuring that the total amount of garbage in the community does not exceed the comfort range.

[0045] The input of the graph neural network model is the community graph structure, and the output of the graph neural network model is the target garbage disposal time. The graph neural network model is a deep learning model specifically used to process graph data, which can capture the relationships and features between nodes. The graph neural network model can process node relationships in the graph structure and capture the mutual influence between nodes. The graph neural network model can use the features of the edges, such as the maximum garbage carrying capacity of each garbage bin, to better understand the relationship between the garbage bin and the community. The graph neural network model can extract features at different levels through a multi-layer network. Through the graph neural network model, the garbage growth trend of each garbage bin and the changes in the total garbage volume in the community can be more accurately predicted, thereby determining the optimal garbage disposal time.

[0046] As an example, the comfort range of the total garbage load in the community is 800-1000 kg, and there are three garbage bins, each with a maximum garbage load of 100 kg, 120 kg, and 110 kg. The subsequent garbage simulation growth video of each garbage bin shows the growth trend of the garbage volume in the next few days. The target garbage disposal time output by the graph neural network model is 10 am on the third day.

[0047] Step S5: dispatching a vehicle to process the garbage based on the target garbage processing time.

[0048] Once the target garbage disposal time is determined, vehicles are dispatched to dispose of the garbage at the target garbage disposal time.

[0049] Based on the same inventive concept, Figure 4 A schematic diagram of a garbage disposal system based on big data provided by an embodiment of the present invention, the garbage disposal system based on big data includes: An acquisition module 41 is used to acquire surveillance videos of multiple garbage bins in a community and surveillance videos of the community; A generation module 42 is used to generate a subsequent garbage simulation growth video of each garbage bin in the community using a generative adversarial network based on the monitoring videos of the multiple garbage bins in the community; A carrying capacity determination module 43 is used to determine the maximum garbage carrying capacity of each garbage bin and the comfortable range of the total garbage carrying capacity of the community based on the surveillance videos of the plurality of garbage bins in the community and the carrying capacity determination model of the community surveillance videos; A time determination module 44 is used to determine a target garbage disposal time based on the maximum garbage carrying capacity of each garbage bin, the comfort range of the total garbage carrying capacity of the community, and a subsequent garbage simulation growth video of each garbage bin; The processing module 45 is used to dispatch a vehicle to process garbage based on the target garbage processing time.

[0050] Based on the same inventive concept, an embodiment of the present invention provides an electronic device, such as Figure 5 As shown, including: It includes: a processor 51; a memory 52; and a computer program; wherein the computer program is stored in the memory 52 and is configured to be executed by the processor 51 to implement the garbage disposal method based on big data as provided above, the method comprising: obtaining surveillance videos of multiple garbage bins in a community and surveillance videos of the community; using a generative adversarial network based on the surveillance videos of the multiple garbage bins in the community to generate subsequent garbage simulation growth videos of each garbage bin in the community; determining the maximum garbage carrying capacity of each garbage bin and the comfortable range of the total garbage carrying capacity of the community based on the surveillance videos of the multiple garbage bins in the community and the surveillance videos of the community using a carrying capacity determination model; determining a target garbage disposal time based on the maximum garbage carrying capacity of each garbage bin, the comfortable range of the total garbage carrying capacity of the community, and the subsequent garbage simulation growth video of each garbage bin; and dispatching a vehicle to dispose of the garbage based on the target garbage disposal time.

[0051] Based on the same inventive concept, this embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by the processor 51, implements the aforementioned garbage disposal method based on big data, the method comprising: obtaining surveillance videos of multiple garbage bins in a community and surveillance videos of the community; using a generative adversarial network based on the surveillance videos of the multiple garbage bins in the community to generate subsequent garbage simulation growth videos of each garbage bin in the community; determining the maximum garbage carrying capacity of each garbage bin and the comfortable range of the total garbage carrying capacity of the community based on the surveillance videos of the multiple garbage bins in the community and the surveillance videos of the community using a carrying capacity determination model; determining the target garbage disposal time based on the maximum garbage carrying capacity of each garbage bin, the comfortable range of the total garbage carrying capacity of the community, and the subsequent garbage simulation growth video of each garbage bin; and dispatching a vehicle to dispose of the garbage based on the target garbage disposal time.

[0052] The garbage disposal method based on big data provided in the embodiments of the present application can be applied to terminal devices (such as mobile phones), tablet computers, laptops, ultra-mobile personal computers (UMPCs), handheld computers, netbooks, personal digital assistants (PDAs), wearable devices (such as smart watches, smart glasses or smart helmets, etc.), augmented reality (AR) and virtual reality (VR) devices, smart home devices, car computers and other electronic devices, and the embodiments of the present application do not impose any restrictions on this.

[0053] Taking the mobile phone 100 as an example of the electronic device, Figure 6 A schematic structural diagram of the mobile phone 100 is shown.

[0054] like Figure 6 As shown, the mobile phone 100 may include a processing module 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, a button 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc.

[0055] The processing module 110 may include one or more processing units, for example, the processing module 110 may include an application processor (AP), a modem processor, a graphics processor (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0056] The controller can be the nerve center and command center of the mobile phone 100, and is the decision maker that commands the various components of the mobile phone 100 to work in coordination according to the instructions. The controller can generate an operation control signal according to the instruction operation code and timing signal to complete the control of fetching and executing instructions.

[0057] The application processor may be installed with an operating system of the mobile phone 100, which is used to manage the hardware and software resources of the mobile phone 100. For example, it may manage and configure memory, determine the priority of system resource supply and demand, manage file systems, manage drivers, etc. The operating system may also be used to provide an operating interface for users to interact with the system. Various types of software may be installed in the operating system, such as drivers, applications (applications, apps), etc. Exemplarily, the operating system of the mobile phone 100 may be an Android system, a Linux system, etc.

[0058] A memory may also be provided in the processing module 110 for storing instructions and data. In some embodiments, the memory in the processing module 110 is a cache memory. The memory may store instructions or data that have just been used or are cyclically used by the processing module 110. If the processing module 110 needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processing module 110, and thus improves the efficiency of the system.

[0059] In some embodiments, the processing module 110 may include one or more interfaces. The interface may include an inter-integrated circuit (I2C) interface, an inter-integrated circuits sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0060] The processing module 110 can be used to: obtain surveillance videos of multiple garbage bins in a community and surveillance videos of the community; use a generative adversarial network to generate subsequent garbage simulation growth videos of each garbage bin in the community based on the surveillance videos of the multiple garbage bins in the community; determine the maximum garbage carrying capacity of each garbage bin and the comfortable range of the total garbage carrying capacity of the community based on the surveillance videos of the multiple garbage bins in the community and the surveillance videos of the community using a carrying capacity determination model; determine the target garbage disposal time based on the maximum garbage carrying capacity of each garbage bin, the comfortable range of the total garbage carrying capacity of the community, and the subsequent garbage simulation growth video of each garbage bin; and dispatch vehicles to dispose of garbage based on the target garbage disposal time.

[0061] The charging management module 140 is used to receive charging input from a charger. The charger may be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 may receive charging input from a wired charger through the USB interface 130. In some wireless charging embodiments, the charging management module 140 may receive wireless charging input through a wireless charging coil of the mobile phone 100. While the charging management module 140 is charging the battery 142, it may also power the electronic device through the power management module 141.

[0062] The power management module 141 is used to connect the battery 142, the charging management module 140 and the processing module 110. The power management module 141 receives input from the battery 142 and / or the charging management module 140, and supplies power to the processing module 110, the internal memory 121, the external memory, the display screen 194, the camera 193, and the wireless communication module 160. The power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle number, battery health status (leakage, impedance), etc. In some other embodiments, the power management module 141 can also be set in the processing module 110. In other embodiments, the power management module 141 and the charging management module 140 can also be set in the same device.

[0063] The wireless communication function of the mobile phone 100 can be implemented through the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor and the baseband processor.

[0064] Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in mobile phone 100 can be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve the utilization of antennas. For example, antenna 1 can be reused as a diversity antenna for a wireless local area network. In some other embodiments, the antenna can be used in combination with a tuning switch.

[0065] The mobile communication module 150 can provide solutions for wireless communications including 2G / 3G / 4G / 5G applied to the mobile phone 100. The mobile communication module 150 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), etc. The mobile communication module 150 can receive electromagnetic waves from the antenna 1, and filter, amplify, and process the received electromagnetic waves, and transmit them to the modulation and demodulation processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modulation and demodulation processor, and convert it into electromagnetic waves for radiation through the antenna 1. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be arranged in the processing module 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be arranged in the same device as at least some of the modules of the processing module 110.

[0066] The modem processor may include a modulator and a demodulator. Among them, the modulator is used to modulate the low-frequency baseband signal to be sent into a medium-high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After the low-frequency baseband signal is processed by the baseband processor, it is passed to the application processor. The application processor outputs a sound signal through an audio device (not limited to a speaker 170A, a receiver 170B, etc.), or displays an image or video through a display screen 194. In some embodiments, the modem processor may be an independent device. In other embodiments, the modem processor may be independent of the processing module 110 and be set in the same device as the mobile communication module 150 or other functional modules.

[0067] The wireless communication module 160 can provide wireless communication solutions including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared (IR), etc., which are applied to the mobile phone 100. The wireless communication module 160 can be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, modulates the frequency of the electromagnetic wave signal and performs filtering, and sends the processed signal to the processing module 110. The wireless communication module 160 can also receive the signal to be sent from the processing module 110, modulate the frequency of the signal, amplify it, and convert it into electromagnetic waves for radiation through the antenna 2.

[0068] In some embodiments, the antenna 1 of the mobile phone 100 is coupled to the mobile communication module 150, and the antenna 2 is coupled to the wireless communication module 160, so that the mobile phone 100 can communicate with the network and other devices through wireless communication technology. The wireless communication technology may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technology. The GNSS may include a global positioning system (GPS), a global navigation satellite system (GLONASS), a Beidou navigation satellite system (BDS), a quasi-zenith satellite system (QZSS) and / or a satellite based augmentation system (SBAS).

[0069] The mobile phone 100 implements the display function through a GPU, a display screen 194, and an application processor. The GPU is a microprocessor for image processing, which is connected to the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processing module 110 may include one or more GPUs, which execute program instructions to generate or change display information.

[0070] The display screen 194 is used to display images, videos, etc. The display screen 194 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode or an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the mobile phone 100 may include 1 or N display screens 194, where N is a positive integer greater than 1.

[0071] The mobile phone 100 can realize the shooting function through the ISP, the camera 193, the video codec, the GPU, the display screen 194 and the application processor. In some embodiments, the mobile phone 100 can realize the video communication function through the ISP, the camera 193, the video codec, the GPU and the application processor.

[0072] ISP is used to process the data fed back by camera 193. For example, when taking a photo, the shutter is opened, and the light is transmitted to the camera photosensitive element through the lens. The light signal is converted into an electrical signal, and the camera photosensitive element transmits the electrical signal to ISP for processing and converts it into an image visible to the naked eye. ISP can also perform algorithm optimization on the noise, brightness, and skin color of the image. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene. In some embodiments, ISP can be set in camera 193.

[0073] The camera 193 is used to capture still images or videos. The object generates an optical image through the lens and projects it onto the photosensitive element. The photosensitive element can be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, and then passes the electrical signal to the ISP to be converted into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into an image signal in a standard RGB, YUV or other format. In some embodiments, the mobile phone 100 may include 1 or N cameras 193, where N is a positive integer greater than 1.

[0074] The digital signal processor is used to process digital signals, and can process not only digital image signals but also other digital signals. For example, when the mobile phone 100 is selecting a frequency point, the digital signal processor is used to perform Fourier transform on the frequency point energy.

[0075] Video codecs are used to compress or decompress digital videos. Mobile phone 100 may support one or more video codecs. Thus, mobile phone 100 may play or record videos in various coding formats, such as Moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

[0076] NPU is a neural network (NN) computing processor. By drawing on the structure of biological neural networks, such as the transmission mode between neurons in the human brain, it can quickly process input information and can also continuously self-learn. Through NPU, the intelligent cognition of the mobile phone 100 can be realized, such as image recognition, face recognition, voice recognition, text understanding, etc.

[0077] The external memory interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the mobile phone 100. The external memory card communicates with the processing module 110 through the external memory interface 120 to implement a data storage function, such as storing music, video and other files in the external memory card.

[0078] The internal memory 121 can be used to store computer executable program codes, which include instructions. The processing module 110 executes various functional applications and data processing of the mobile phone 100 by running the instructions stored in the internal memory 121. The internal memory 121 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc. The data storage area can store data created during the use of the mobile phone 100 (such as audio data, a phone book, etc.), etc. In addition, the internal memory 121 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0079] The mobile phone 100 can implement audio functions such as music playing and recording through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the earphone interface 170D, and the application processor.

[0080] The audio module 170 is used to convert digital audio information into analog audio signal output, and is also used to convert analog audio input into digital audio signals. The audio module 170 can also be used to encode and decode audio signals. In some embodiments, the audio module 170 can be arranged in the processing module 110, or some functional modules of the audio module 170 can be arranged in the processing module 110.

[0081] The speaker 170A, also called a "speaker", is used to convert an audio electrical signal into a sound signal. The mobile phone 100 can listen to music or listen to a hands-free call through the speaker 170A.

[0082] The receiver 170B, also called a "earpiece", is used to convert audio electrical signals into sound signals. When the mobile phone 100 receives a call or voice message, the voice can be received by placing the receiver 170B close to the ear.

[0083] Microphone 170C, also called "microphone" or "microphone", is used to convert sound signals into electrical signals. When making a call or sending a voice message, the user can speak by putting their mouth close to the microphone 170C to input the sound signal into the microphone 170C. The mobile phone 100 can be provided with at least one microphone 170C. In other embodiments, the mobile phone 100 can be provided with two microphones 170C, which can not only collect sound signals but also realize noise reduction function. In other embodiments, the mobile phone 100 can also be provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify the sound source, realize directional recording function, etc.

[0084] The earphone interface 170D is used to connect a wired earphone and can be a USB interface 130 or a 3.5 mm open mobile terminal platform (OMTP) standard interface or a cellular telecommunications industry association of the USA (CTIA) standard interface.

[0085] The key 190 includes a power key, a volume key, etc. The key 190 can be a mechanical key or a touch key. The mobile phone 100 can receive key input and generate key signal input related to the user settings and function control of the mobile phone 100.

[0086] Motor 191 can generate vibration prompts. Motor 191 can be used for incoming call vibration prompts, and can also be used for touch vibration feedback. For example, touch operations acting on different applications (such as taking pictures, audio playback, etc.) can correspond to different vibration feedback effects. For touch operations acting on different areas of the display screen 194, motor 191 can also correspond to different vibration feedback effects. Different application scenarios (for example: time reminders, receiving messages, alarm clocks, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also support customization.

[0087] Indicator 192 may be an indicator light, which may be used to indicate charging status, power changes, messages, missed calls, notifications, etc.

[0088] The SIM card interface 195 is used to connect the SIM card. The SIM card can be connected to and separated from the mobile phone 100 by inserting it into the SIM card interface 195 or pulling it out from the SIM card interface 195. The mobile phone 100 can support 1 or N SIM card interfaces, where N is a positive integer greater than 1. The SIM card interface 195 can support Nano SIM cards, Micro SIM cards, SIM cards, etc. Multiple cards can be inserted into the same SIM card interface 195 at the same time. The types of the multiple cards can be the same or different. The SIM card interface 195 can also be compatible with different types of SIM cards. The SIM card interface 195 can also be compatible with external memory cards. The mobile phone 100 interacts with the network through the SIM card to realize functions such as calls and data communications. In some embodiments, the mobile phone 100 uses an eSIM, i.e., an embedded SIM card. The eSIM card can be embedded in the mobile phone 100 and cannot be separated from the mobile phone 100.

[0089] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0090] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0091] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0092] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0093] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A garbage disposal method based on big data, characterized in that: include: Obtain surveillance videos of multiple garbage bins in the community and surveillance videos of the community; Based on the surveillance videos of the multiple garbage bins in the community, a generative adversarial network is used to generate a subsequent garbage simulation growth video for each garbage bin in the community; Based on the surveillance videos of the plurality of garbage bins in the community, the community surveillance videos use a carrying capacity determination model to determine the maximum garbage carrying capacity of each garbage bin and the comfortable range of the total garbage carrying capacity of the community; Determine the target garbage disposal time based on the maximum garbage carrying capacity of each garbage bin, the comfortable range of the total garbage carrying capacity of the community, and the subsequent garbage simulation growth video of each garbage bin; A vehicle is dispatched to process the garbage based on the target garbage processing time.

2. The garbage disposal method based on big data according to claim 1, characterized in that: The determining of the target garbage disposal time based on the maximum garbage carrying capacity of each garbage bin, the comfortable range of the total garbage carrying capacity of the community, and the subsequent garbage simulation growth video of each garbage bin includes: Constructing a cell graph structure, the cell graph structure includes a cell node and a plurality of trash bin nodes, the cell node is a central node, each trash bin node establishes edges with the cell node, the node characteristics of the cell node include a comfort range of the total trash carrying capacity of the cell, the node characteristics of each trash bin node include a subsequent trash simulation growth video of each trash bin, and the characteristics of the edge between each trash bin node and the cell node include a maximum trash carrying capacity of each trash bin; The community graph structure is processed based on a graph neural network model to determine the target garbage disposal time.

3. The garbage disposal method based on big data according to claim 1, characterized in that: The load capacity determination model is a long-term and short-term neural network model.

4. The garbage disposal method based on big data according to claim 2, characterized in that: The input of the graph neural network model is the cell graph structure, and the output of the graph neural network model is the target garbage disposal time.

5. A garbage disposal system based on big data, characterized in that: include: An acquisition module is used to acquire surveillance videos of multiple garbage bins in a community and surveillance videos of the community; A generation module, configured to generate a subsequent garbage simulation growth video of each garbage bin in the community using a generative adversarial network based on the monitoring videos of the multiple garbage bins in the community; A carrying capacity determination module, configured to determine the maximum garbage carrying capacity of each garbage bin and the comfortable range of the total garbage carrying capacity of the community based on the surveillance videos of the plurality of garbage bins in the community and the carrying capacity determination model of the community surveillance videos; A time determination module, configured to determine a target garbage disposal time based on a maximum garbage carrying capacity of each garbage bin, a comfortable range of a total garbage carrying capacity of a community, and a subsequent garbage simulation growth video of each garbage bin; A processing module is used to dispatch vehicles to process garbage based on the target garbage processing time.

6. The garbage disposal system based on big data as claimed in claim 5, characterized in that: The time determination module is also used for: Constructing a cell graph structure, the cell graph structure includes a cell node and a plurality of trash bin nodes, the cell node is a central node, each trash bin node establishes edges with the cell node, the node characteristics of the cell node include a comfort range of the total trash carrying capacity of the cell, the node characteristics of each trash bin node include a subsequent trash simulation growth video of each trash bin, and the characteristics of the edge between each trash bin node and the cell node include a maximum trash carrying capacity of each trash bin; The community graph structure is processed based on a graph neural network model to determine the target garbage disposal time.

7. The garbage disposal system based on big data as claimed in claim 5, characterized in that: The load capacity determination model is a long-term and short-term neural network model.

8. The garbage disposal system based on big data as claimed in claim 6, characterized in that: The input of the graph neural network model is the cell graph structure, and the output of the graph neural network model is the target garbage disposal time.

9. An electronic device, characterized in that: include: processor; Memory; And a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the garbage processing method based on big data as described in any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the garbage processing method based on big data as described in any one of claims 1 to 4 is implemented.

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