CN111680645B - Garbage classification treatment method and device - Google Patents
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Abstract
The embodiment of the application provides a garbage classification processing method and device, first tag prediction feature information of a plurality of garbage classification tags corresponding to a monitoring image stream information sequence is sequenced from high to low according to prediction confidence to obtain a first sequencing set, then function use information and partition feature information of a garbage region corresponding to each garbage classification tag for the tag source object corresponding to each garbage classification tag are determined based on the garbage region corresponding to each garbage classification tag, garbage regions are sequenced from early to late to obtain a second sequencing set, and accordingly the tag source objects in each garbage region are subjected to object process labeling between each garbage region according to the first sequencing set and the second sequencing set. Therefore, the object process labeling can be carried out on the garbage objects in each garbage area between each garbage area, so that the time sequence and the space sequence distribution condition of the garbage objects in each garbage area can be reflected.
Description
Technical Field
The application relates to the technical field of computers, in particular to a garbage classification processing method and device.
Background
At present, a deep learning mode is generally adopted to identify garbage objects in each garbage area, however, the traditional scheme does not have a technical means for marking the garbage objects in each garbage area in an object process between each garbage area, so that the time sequence and the space sequence distribution condition of the garbage objects in each garbage area are difficult to reflect.
Disclosure of Invention
In order to at least overcome the above-mentioned shortcomings in the prior art, an object of the present application is to provide a garbage classification processing method and apparatus, which can perform object process labeling on garbage objects in each garbage area between each garbage area, so as to reflect the time sequence and space sequence distribution situation of the garbage objects in each garbage area.
In a first aspect, the present application provides a garbage classification processing method, applied to a server, where the method includes:
determining prediction confidence degrees of a plurality of garbage classification labels corresponding to a monitoring image stream information sequence of a garbage classification monitoring terminal and first label prediction feature information corresponding to each garbage classification label in the monitoring image stream information sequence;
sequencing the first tag prediction characteristic information according to the sequence from high to low of the prediction confidence degree to obtain a first sequencing set;
Determining a garbage area where a tag source object corresponding to each garbage classification tag is located, and determining garbage distribution information of each garbage area according to function use information and partition characteristic information of each garbage area for the tag source object corresponding to each garbage classification tag; the garbage distribution information is used for representing the use distribution flow information of the garbage classification labels in each function use partition, each label source object corresponds to the garbage area where the label source object is located one by one, and the label source object is used for representing the garbage identification object corresponding to the garbage classification label;
sequencing the garbage areas according to the sequence from the early to the late of garbage distribution information to obtain a second sequencing set;
labeling the label source objects in each garbage area in an object process between each garbage area according to the first sorting set and the second sorting set; the number of the first tag prediction feature information corresponding to the first sorting set is the same as the number of the garbage areas corresponding to the second sorting set.
In a possible implementation manner of the first aspect, the labeling of the label source object in each garbage area according to the first sorting set and the second sorting set includes:
Sorting the label source objects corresponding to the garbage classification labels of each piece of first label prediction characteristic information according to the first sorting set to obtain a third sorting set used for representing the garbage occurrence frequency of the label source objects;
extracting a label source object in each garbage area, and importing the extracted label source object into a label library;
and labeling a first target label source object corresponding to a first label source object in the third sorting set in the labeling library into a first garbage area in the second sorting set, and repeatedly executing the steps according to the third sorting set until a second target label source object corresponding to a last label source object in the labeling library into a last garbage area in the second sorting set.
In a possible implementation manner of the first aspect, determining the garbage distribution information of each garbage area according to the function usage information and the partition characteristic information of the tag source object corresponding to each garbage classification tag of each garbage area includes:
program instruction information of a function use strategy program added on function use information of each garbage area is obtained, a first program function execution sequence corresponding to the program instruction information is determined, the program instruction information comprises instruction object information of a program service instruction determined according to input information and output information of the function use strategy program, and the first program function execution sequence comprises a plurality of object control levels of the instruction object information;
Determining first function execution logic of function use information of each garbage area based on input information and second function execution logic based on output information;
determining a distribution generation parameter for carrying out distribution generation on the first program function execution sequence according to the logic execution relation of the first function execution logic and the second function execution logic;
distributing the first program function execution sequence based on the distribution generation parameters to generate a second program function execution sequence;
splitting the second program function execution sequence to obtain a plurality of sequence objects, and extracting features of each sequence object to obtain list features;
determining first distribution information of each garbage area according to garbage distribution information corresponding to a plurality of list features corresponding to the second program function execution sequence;
determining a spatial distribution map of each garbage region from partition characteristic information of each garbage region, extracting spatial distribution node information of the spatial distribution map, and determining a target node information region corresponding to the spatial distribution node information;
extracting the space sequence feature vector of the target node information area according to a set space interval;
Generating an empty sequence distribution heat degree unit diagram corresponding to the empty sequence feature vector and a region heat degree unit diagram corresponding to the target node information region, wherein the empty sequence distribution heat degree unit diagram and the region heat degree unit diagram respectively comprise a plurality of heat degree units with different heat degrees;
extracting description vector information of one heat unit of the space sequence feature vector in the space sequence distribution heat unit diagram, and determining a heat unit with minimum heat in the area heat unit diagram as a target heat unit;
marking the description vector information into the target heat unit to obtain marked characteristic information in the target heat unit, and then generating a space region distribution sequence between the empty sequence characteristic vector and the target node information region based on the description vector information and the marked characteristic information;
acquiring heat data segment information in the target heat unit by taking the marking characteristic information as an index object, mapping the heat data segment information into the heat unit where the description vector information is located according to a distribution association relation corresponding to the spatial region distribution sequence, and acquiring partition discrimination information corresponding to the heat data segment information in the heat unit where the description vector information is located;
Respectively listing partition discrimination objects in the partition discrimination information in a garbage area distribution space, and determining second distribution information of each garbage area;
and determining garbage distribution information of each garbage area according to the first distribution information and the second distribution information.
In a possible implementation manner of the first aspect, the method further includes:
acquiring second tag prediction characteristic information sent by the garbage classification monitoring terminal; the second tag prediction characteristic information is generated by the garbage classification monitoring terminal according to an operation instruction input by a user;
traversing a plurality of garbage areas in the labeling process of the object process according to the second tag prediction characteristic information, inquiring a third target tag source object and feeding back the third target tag source object to the garbage classification monitoring terminal;
and adding the second tag prediction characteristic information to the monitoring image stream information sequence.
In a possible implementation manner of the first aspect, the step of determining a prediction confidence of the first tag prediction feature information corresponding to each garbage classification tag and a plurality of garbage classification tags corresponding to the monitoring image stream information sequence of the garbage classification monitoring terminal in the monitoring image stream information sequence includes:
Inputting the monitoring image stream information sequence of the garbage classification monitoring terminal into a garbage classification model to obtain a plurality of garbage classification labels corresponding to the monitoring image stream information sequence and the prediction confidence of the first label prediction characteristic information corresponding to each garbage classification label in the monitoring image stream information sequence;
the garbage classification model is obtained based on a pre-configured training sample and garbage classification labels corresponding to the training sample, and the training sample is monitoring image stream sample information.
In a second aspect, an embodiment of the present application provides a garbage classification processing apparatus, applied to a server, where the apparatus includes:
the first determining module is used for determining a plurality of garbage classification labels corresponding to the monitoring image flow information sequence of the garbage classification monitoring terminal and the prediction confidence of the first label prediction characteristic information corresponding to each garbage classification label in the monitoring image flow information sequence;
the first ordering module is used for ordering the first tag prediction characteristic information according to the order of the prediction confidence from high to low to obtain a first ordering set;
the second determining module is used for determining the garbage area where the tag source object corresponding to each garbage classification tag is located, and determining garbage distribution information of each garbage area according to function use information and partition characteristic information of each garbage area for the tag source object corresponding to each garbage classification tag; the garbage distribution information is used for representing the use distribution flow information of the garbage classification labels in each function use partition, each label source object corresponds to the garbage area where the label source object is located one by one, and the label source object is used for representing the garbage identification object corresponding to the garbage classification label;
The second ordering module is used for ordering the garbage areas according to the order of the garbage distribution information from the early to the late to obtain a second ordering set;
the labeling module is used for labeling the label source objects in each garbage area in the object process among the garbage areas according to the first sorting set and the second sorting set; the number of the first tag prediction feature information corresponding to the first sorting set is the same as the number of the garbage areas corresponding to the second sorting set.
In a third aspect, embodiments of the present application provide a server comprising a processor, a memory, and a network interface. The memory and the network interface processor can be connected through a bus system. The network interface is configured to receive a message, the memory is configured to store a program, instructions or code, and the processor is configured to execute the program, instructions or code in the memory to perform the operations described above in the first aspect or any of the possible designs of the first aspect.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having instructions stored therein, which when executed on a computer, cause the computer to perform the method of the first aspect or any of the possible designs of the first aspect.
Based on any one of the aspects, the method comprises the steps of sequencing first tag prediction feature information of a plurality of garbage classification tags corresponding to a monitoring image stream information sequence according to a prediction confidence from high to low to obtain a first sequencing set, determining garbage distribution information of each garbage region based on function use information and partition feature information of a garbage region of a tag source object corresponding to each garbage classification tag, and sequencing the garbage region from early to late to obtain a second sequencing set, so that object process labeling is carried out on the tag source object in each garbage region between each garbage region according to the first sequencing set and the second sequencing set. Therefore, the object process labeling can be carried out on the garbage objects in each garbage area between each garbage area, so that the time sequence and the space sequence distribution condition of the garbage objects in each garbage area can be reflected.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a garbage classification processing method according to an embodiment of the present application;
fig. 2 is a schematic functional block diagram of a garbage classification device according to an embodiment of the present application;
fig. 3 is a schematic block diagram of a server for executing the garbage classification processing method according to the embodiment of the present application.
Detailed Description
The following description is provided in connection with the accompanying drawings, and the specific operation method in the method embodiment may also be applied to the device embodiment or the system embodiment.
Referring to fig. 1, a flow chart of a garbage classification processing method according to an embodiment of the present application is shown, and the garbage classification processing method is described in detail below.
Step S110, determining a plurality of garbage classification labels corresponding to the monitoring image stream information sequence of the garbage classification monitoring terminal and the prediction confidence of the first label prediction characteristic information corresponding to each garbage classification label in the monitoring image stream information sequence.
And step S120, sequencing the first tag prediction characteristic information according to the sequence from high to low of the prediction confidence degree to obtain a first sequencing set.
Step S130, determining a garbage area where a label source object corresponding to each garbage classification label is located, and determining garbage distribution information of each garbage area according to function use information and partition characteristic information of each garbage area for the label source object corresponding to each garbage classification label.
In this embodiment, the garbage distribution information is used to characterize usage distribution flow information of garbage classification tags in each functional usage partition, each tag source object corresponds to a garbage area where the tag source object is located one by one, and the tag source object is used to represent a garbage identification object corresponding to the garbage classification tag.
And step S140, sorting the garbage areas according to the order of the garbage distribution information from the early to the late to obtain a second sorting set.
And step S150, labeling the label source objects in each garbage area according to the first sorting set and the second sorting set in the object process between each garbage area. In this embodiment, the number of the first tag prediction feature information corresponding to the first sorted set is the same as the number of the garbage areas corresponding to the second sorted set.
Based on the design, the embodiment sorts the first tag prediction feature information of the plurality of garbage classification tags corresponding to the monitoring image stream information sequence according to the order from high to low of the prediction confidence to obtain a first sorting set, then determines the garbage distribution information of each garbage region according to the function use information and the partition feature information of the garbage region of the tag source object corresponding to each garbage classification tag, sorts the garbage regions according to the order from early to late to obtain a second sorting set, and marks the tag source object in each garbage region in the object process between each garbage region according to the first sorting set and the second sorting set. Therefore, the object process labeling can be carried out on the garbage objects in each garbage area between each garbage area, so that the time sequence and the space sequence distribution condition of the garbage objects in each garbage area can be reflected.
In one possible implementation, for step S150, this may be achieved by the following sub-steps, described in detail below.
And step S151, sorting the label source objects corresponding to the garbage classification labels of the first label prediction characteristic information according to the first sorting set to obtain a third sorting set used for representing the garbage occurrence frequency of the label source objects.
And a substep S152, extracting the label source object in each garbage area, and importing the extracted label source object into a label library.
And step 153, labeling a first target label source object corresponding to a first label source object in a third sorting set in a labeling library to a first garbage area in a second sorting set, and repeatedly executing the steps according to the third sorting set until a second target label source object corresponding to a last label source object in the third sorting set in the labeling library is labeled to a last garbage area in the second sorting set.
In one possible implementation, for step S130, this may be achieved by the following sub-steps, described in detail below.
In the substep S131, program instruction information of the function usage policy program added to the function usage information of each garbage area is obtained, and a first program function execution sequence corresponding to the program instruction information is determined, where the program instruction information includes instruction object information of the program service instruction determined according to the input information and the output information of the function usage policy program, and the first program function execution sequence includes a plurality of object control levels of the instruction object information.
In a substep S132, a first function execution logic based on the input information and a second function execution logic based on the output information of the function usage information of each garbage area are determined.
Substep S133, determining a distribution generation parameter for performing distribution generation on the first program function execution sequence according to the logic execution relationship between the first function execution logic and the second function execution logic.
Sub-step S134 distributes the first program function execution sequence based on the distribution generation parameter to obtain a second program function execution sequence.
And step S135, splitting the execution sequence of the second program function to obtain a plurality of sequence objects, and extracting the characteristics of each sequence object to obtain list characteristics.
In sub-step S136, the first distribution information of each garbage area is determined according to the garbage distribution information corresponding to the plurality of list features corresponding to the second program function execution sequence.
In the substep S137, the spatial distribution map of each garbage area is determined from the partition characteristic information of each garbage area, the spatial distribution node information of the spatial distribution map is extracted, and the target node information area corresponding to the spatial distribution node information is determined.
Sub-step S138 extracts the empty sequence feature vector of the target node information area according to the set space interval.
In sub-step S1391, an empty sequence distribution heat unit map corresponding to the empty sequence feature vector and a region heat unit map corresponding to the target node information region are generated, where the empty sequence distribution heat unit map and the region heat unit map respectively include a plurality of heat units with different heat degrees.
Sub-step S1392, extracts description vector information of an empty feature vector in one of the heat units of the empty distribution heat unit map and determines a heat unit having the smallest heat in the region heat unit map as a target heat unit.
Sub-step S1393, marking the description vector information in the target heat unit to obtain the marking feature information in the target heat unit, and then generating a spatial region distribution sequence between the empty feature vector and the target node information region based on the description vector information and the marking feature information.
And sub-step S1394, namely acquiring heat data segment information in a target heat unit by taking the marked characteristic information as an index object, mapping the heat data segment information into the heat unit where the description vector information is located according to the distribution association relation corresponding to the spatial region distribution sequence, and acquiring partition discrimination information corresponding to the heat data segment information in the heat unit where the description vector information is located.
In sub-step S1395, the partition discrimination objects in the partition discrimination information are listed in the garbage area distribution space, respectively, and the second distribution information of each garbage area is determined.
Sub-step S1396, determines garbage distribution information for each garbage area based on the first distribution information and the second distribution information.
In a possible implementation manner, the embodiment may further obtain second tag prediction feature information sent by the garbage classification monitoring terminal. The second tag prediction characteristic information is generated by the garbage classification monitoring terminal according to an operation instruction input by a user.
And then traversing a plurality of garbage areas in the labeling process of the object process according to the second tag prediction characteristic information, inquiring a third target tag source object, feeding the third target tag source object back to the garbage classification monitoring terminal, and adding the second tag prediction characteristic information to the monitoring image stream information sequence.
In a possible implementation manner, for step S110, the monitored image stream information sequence of the garbage classification monitoring terminal may be input into the garbage classification model, so as to obtain a plurality of garbage classification tags corresponding to the monitored image stream information sequence, and a prediction confidence of the first tag prediction feature information corresponding to each garbage classification tag in the monitored image stream information sequence.
The garbage classification model is obtained by training based on a pre-configured training sample and garbage classification labels corresponding to the training sample, wherein the training sample is monitoring image stream sample information. The detailed training process is the prior art and will not be described in detail herein.
Fig. 2 is a schematic diagram of functional modules of a garbage classification device 200 according to an embodiment of the present application, where the functional modules of the garbage classification device 200 may be divided according to the above-described method embodiment. For example, each functional module may be divided corresponding to each function, or two or more functions may be integrated in one processing module. The integrated modules may be implemented in hardware or in software functional modules. It should be noted that the division of the modules in this application is illustrative, and is merely a logic function division, and other division manners may be implemented in practice. For example, in the case of dividing the respective functional modules by the respective functions, the garbage classification processing apparatus 200 shown in fig. 2 is only one apparatus schematic. The garbage classification apparatus 200 may include a first determining module 210, a first sorting module 220, a second determining module 230, a second sorting module 240, and a labeling module 250, and the functions of the respective functional modules of the garbage classification apparatus 200 are described in detail below.
The first determining module 210 is configured to determine a prediction confidence of a plurality of garbage classification tags corresponding to the monitored image stream information sequence of the garbage classification monitoring terminal and the first tag prediction feature information corresponding to each garbage classification tag in the monitored image stream information sequence.
The first ranking module 220 is configured to rank the first tag prediction feature information according to the order of the prediction confidence from high to low to obtain a first ranking set.
The second determining module 230 is configured to determine a garbage area where the tag source object corresponding to each garbage classification tag is located, and determine garbage distribution information of each garbage area according to function usage information and partition feature information of each garbage area for the tag source object corresponding to each garbage classification tag. The garbage distribution information is used for representing the use distribution flow information of the garbage classification labels in each function use partition, each label source object corresponds to the garbage area where the label source object is located one by one, and the label source object is used for representing the garbage identification object corresponding to the garbage classification label.
The second sorting module 240 is configured to sort the garbage areas according to the order of the garbage distribution information from early to late to obtain a second sorted set.
The labeling module 250 is configured to label the tag source objects in each garbage area according to the first sorting set and the second sorting set, and perform object procedure labeling between each garbage area. The number of the first tag prediction feature information corresponding to the first sorting set is the same as the number of the garbage areas corresponding to the second sorting set.
In one possible implementation, the manner in which the label source object in each garbage area is subject to object procedure labeling between each garbage area according to the first ordering set and the second ordering set includes:
sorting the label source objects corresponding to the garbage classification labels of each piece of first label prediction characteristic information according to the first sorting set to obtain a third sorting set used for representing the garbage occurrence frequency of the label source objects;
extracting a label source object in each garbage area, and importing the extracted label source object into a label library;
and labeling a first target label source object corresponding to a first label source object in a third sorting set in the labeling library into a first garbage area in a second sorting set, and repeatedly executing the operation according to the third sorting set until a second target label source object corresponding to a last label source object in the third sorting set in the labeling library is labeled into a last garbage area in the second sorting set.
In one possible implementation manner, determining the garbage distribution information of each garbage area according to the function usage information and the partition characteristic information of the tag source object corresponding to each garbage classification tag of each garbage area includes:
program instruction information of a function use strategy program added on the function use information of each garbage area is obtained, a first program function execution sequence corresponding to the program instruction information is determined, the program instruction information comprises instruction object information of a program service instruction determined according to input information and output information of the function use strategy program, and the first program function execution sequence comprises a plurality of object control levels of the instruction object information;
determining first function execution logic of function use information of each garbage area based on input information and second function execution logic based on output information;
determining a distribution generation parameter for carrying out distribution generation on the first program function execution sequence according to the logic execution relation of the first function execution logic and the second function execution logic;
performing distributed generation on the first program function execution sequence based on the distributed generation parameters to obtain a second program function execution sequence;
Splitting a second program function execution sequence to obtain a plurality of sequence objects, and extracting features of each sequence object to obtain list features;
determining first distribution information of each garbage area according to garbage distribution information corresponding to a plurality of list features corresponding to the second program function execution sequence;
determining a spatial distribution map of each garbage region from partition characteristic information of each garbage region, extracting spatial distribution node information of the spatial distribution map, and determining a target node information region corresponding to the spatial distribution node information;
extracting the space sequence feature vector of the target node information area according to the set space interval;
generating an empty sequence distribution heat degree unit diagram corresponding to the empty sequence feature vector and a region heat degree unit diagram corresponding to the target node information region, wherein the empty sequence distribution heat degree unit diagram and the region heat degree unit diagram respectively comprise a plurality of heat degree units with different heat degrees;
extracting description vector information of a space sequence feature vector in one heat unit of a space sequence distribution heat unit diagram, and determining a heat unit with minimum heat in a region heat unit diagram as a target heat unit;
marking the description vector information into a target heat unit to obtain marked characteristic information in the target heat unit, and then generating a space region distribution sequence between the space sequence characteristic vector and the target node information region based on the description vector information and the marked characteristic information;
Acquiring heat data segment information in a target heat unit by taking the marking characteristic information as an index object, mapping the heat data segment information into the heat unit where the description vector information is located according to a distribution association relation corresponding to a spatial region distribution sequence, and acquiring partition discrimination information corresponding to the heat data segment information in the heat unit where the description vector information is located;
respectively listing partition discrimination objects in the partition discrimination information in a garbage area distribution space, and determining second distribution information of each garbage area;
and determining the garbage distribution information of each garbage area according to the first distribution information and the second distribution information.
In one possible implementation, the garbage classification apparatus 200 may further include a third acquisition module configured to:
acquiring second tag prediction characteristic information sent by the garbage classification monitoring terminal; the second tag prediction characteristic information is generated by the garbage classification monitoring terminal according to an operation instruction input by a user;
traversing a plurality of garbage areas in the labeling process of the object process according to the second tag prediction characteristic information, inquiring a third target tag source object and feeding back the third target tag source object to the garbage classification monitoring terminal;
And adding the second tag prediction characteristic information to the monitoring image stream information sequence.
In one possible implementation manner, the operation of determining the prediction confidence of the plurality of garbage classification labels corresponding to the monitoring image stream information sequence of the garbage classification monitoring terminal and the first label prediction feature information corresponding to each garbage classification label in the monitoring image stream information sequence includes:
inputting the monitoring image stream information sequence of the garbage classification monitoring terminal into a garbage classification model to obtain a plurality of garbage classification labels corresponding to the monitoring image stream information sequence and a prediction confidence coefficient of first label prediction characteristic information corresponding to each garbage classification label in the monitoring image stream information sequence;
the garbage classification model is obtained based on a pre-configured training sample and garbage classification labels corresponding to the training sample, wherein the training sample is monitoring image stream sample information.
Fig. 3 is a schematic structural diagram of a server 100 for performing the garbage classification processing method according to the embodiment of the present application, and as shown in fig. 3, the server 100 may include a network interface 110, a machine-readable storage medium 120, a processor 130, and a bus 140. The number of processors 130 may be one or more, one processor 130 being illustrated in fig. 3. The network interface 110, the machine-readable storage medium 120, and the processor 130 may be connected by a bus 140 or otherwise, as exemplified in fig. 3 by the bus 140.
The machine-readable storage medium 120 is a computer-readable storage medium that can be used to store a software program, a computer-executable program, and modules, such as program instructions/modules corresponding to the garbage classification method in the embodiments of the present application (e.g., the first determination module 210, the first ranking module 220, the second determination module 230, the second ranking module 240, and the labeling module 250 shown in fig. 2). The processor 130 performs various functional applications and data processing of the terminal device by detecting software programs, instructions and modules stored in the machine-readable storage medium 120, that is, implements the garbage classification processing method described above, and will not be described herein.
The machine-readable storage medium 120 may first comprise a storage program area and a storage data area, wherein the storage program area may store an operating system, a warehousing service process required by at least one function. The storage data area may store data created according to the use of the terminal, etc. Further, the machine-readable storage medium 120 may be volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM) which acts as an external high speed annotation store. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data rate Synchronous DRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), and direct memory bus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, memory of these and any other suitable moments. In some examples, the machine-readable storage medium 120 may further include memory located remotely from the processor 130, which may be connected to the terminal device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor 130 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above-described method embodiments may be performed by integrated logic circuitry in hardware or instructions in software in processor 130. The processor 130 may be a general purpose processor, a digital signal processor (Digital SignalProcessorDSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), an off-the-shelf programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor.
The server 100 may interact with other devices via a communication interface 110. Communication interface 110 may be a circuit, bus, transceiver, or any other device that may be used to exchange information. Processor 130 may transmit and receive information using communication interface 110.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital garbage classification processor (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the embodiments of the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the embodiments of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to encompass such modifications and variations.
Claims (8)
1. A garbage classification processing method, characterized by being applied to a server, the method comprising:
determining prediction confidence degrees of a plurality of garbage classification labels corresponding to a monitoring image stream information sequence of a garbage classification monitoring terminal and first label prediction feature information corresponding to each garbage classification label in the monitoring image stream information sequence;
sequencing the first tag prediction characteristic information according to the sequence from high to low of the prediction confidence degree to obtain a first sequencing set;
Determining a garbage area where a tag source object corresponding to each garbage classification tag is located, and determining garbage distribution information of each garbage area according to function use information and partition characteristic information of each garbage area for the tag source object corresponding to each garbage classification tag; the garbage distribution information is used for representing the use distribution flow information of the garbage classification labels in each function use partition, each label source object corresponds to the garbage area where the label source object is located one by one, and the label source object is used for representing the garbage identification object corresponding to the garbage classification label;
sequencing the garbage areas according to the sequence from the early to the late of garbage distribution information to obtain a second sequencing set;
labeling the label source objects in each garbage area in an object process between each garbage area according to the first sorting set and the second sorting set; the number of the first label prediction feature information corresponding to the first sorting set is the same as the number of the garbage areas corresponding to the second sorting set;
labeling the label source object in each garbage area according to the first sorting set and the second sorting set in an object process between each garbage area, including:
Sorting the label source objects corresponding to the garbage classification labels of each piece of first label prediction characteristic information according to the first sorting set to obtain a third sorting set used for representing the garbage occurrence frequency of the label source objects;
extracting a label source object in each garbage area, and importing the extracted label source object into a label library;
and labeling a first target label source object corresponding to a first label source object in the third sorting set in the labeling library into a first garbage area in the second sorting set, and repeatedly executing the steps according to the third sorting set until a second target label source object corresponding to a last label source object in the labeling library into a last garbage area in the second sorting set.
2. The garbage classification processing method according to claim 1, wherein determining garbage distribution information of each garbage region based on function usage information and partition characteristic information of a tag source object corresponding to each garbage classification tag for each garbage region, comprises:
program instruction information of a function use strategy program added on function use information of each garbage area is obtained, a first program function execution sequence corresponding to the program instruction information is determined, the program instruction information comprises instruction object information of a program service instruction determined according to input information and output information of the function use strategy program, and the first program function execution sequence comprises a plurality of object control levels of the instruction object information;
Determining first function execution logic of function use information of each garbage area based on input information and second function execution logic based on output information;
determining a distribution generation parameter for carrying out distribution generation on the first program function execution sequence according to the logic execution relation of the first function execution logic and the second function execution logic;
distributing the first program function execution sequence based on the distribution generation parameters to generate a second program function execution sequence;
splitting the second program function execution sequence to obtain a plurality of sequence objects, and extracting features of each sequence object to obtain list features;
determining first distribution information of each garbage area according to garbage distribution information corresponding to a plurality of list features corresponding to the second program function execution sequence;
determining a spatial distribution map of each garbage region from partition characteristic information of each garbage region, extracting spatial distribution node information of the spatial distribution map, and determining a target node information region corresponding to the spatial distribution node information;
extracting the space sequence feature vector of the target node information area according to a set space interval;
Generating an empty sequence distribution heat degree unit diagram corresponding to the empty sequence feature vector and a region heat degree unit diagram corresponding to the target node information region, wherein the empty sequence distribution heat degree unit diagram and the region heat degree unit diagram respectively comprise a plurality of heat degree units with different heat degrees;
extracting description vector information of one heat unit of the space sequence feature vector in the space sequence distribution heat unit diagram, and determining a heat unit with minimum heat in the area heat unit diagram as a target heat unit;
marking the description vector information into the target heat unit to obtain marked characteristic information in the target heat unit, and then generating a space region distribution sequence between the empty sequence characteristic vector and the target node information region based on the description vector information and the marked characteristic information;
acquiring heat data segment information in the target heat unit by taking the marking characteristic information as an index object, mapping the heat data segment information into the heat unit where the description vector information is located according to a distribution association relation corresponding to the spatial region distribution sequence, and acquiring partition discrimination information corresponding to the heat data segment information in the heat unit where the description vector information is located;
Respectively listing partition discrimination objects in the partition discrimination information in a garbage area distribution space, and determining second distribution information of each garbage area;
and determining garbage distribution information of each garbage area according to the first distribution information and the second distribution information.
3. The garbage classification processing method according to claim 1 or 2, characterized in that the method further comprises:
acquiring second tag prediction characteristic information sent by the garbage classification monitoring terminal; the second tag prediction characteristic information is generated by the garbage classification monitoring terminal according to an operation instruction input by a user;
traversing a plurality of garbage areas in the labeling process of the object process according to the second tag prediction characteristic information, inquiring a third target tag source object and feeding back the third target tag source object to the garbage classification monitoring terminal;
and adding the second tag prediction characteristic information to the monitoring image stream information sequence.
4. The garbage classification processing method according to claim 1 or 2, wherein the step of determining a prediction confidence of a plurality of garbage classification tags corresponding to a monitored image stream information sequence of a garbage classification monitoring terminal and first tag prediction feature information corresponding to each garbage classification tag in the monitored image stream information sequence includes:
Inputting the monitoring image stream information sequence of the garbage classification monitoring terminal into a garbage classification model to obtain a plurality of garbage classification labels corresponding to the monitoring image stream information sequence and the prediction confidence of the first label prediction characteristic information corresponding to each garbage classification label in the monitoring image stream information sequence;
the garbage classification model is obtained based on a pre-configured training sample and garbage classification labels corresponding to the training sample, and the training sample is monitoring image stream sample information.
5. A garbage classification processing apparatus, for application to a server, the apparatus comprising:
the first determining module is used for determining a plurality of garbage classification labels corresponding to the monitoring image flow information sequence of the garbage classification monitoring terminal and the prediction confidence of the first label prediction characteristic information corresponding to each garbage classification label in the monitoring image flow information sequence;
the first ordering module is used for ordering the first tag prediction characteristic information according to the order of the prediction confidence from high to low to obtain a first ordering set;
the second determining module is used for determining the garbage area where the tag source object corresponding to each garbage classification tag is located, and determining garbage distribution information of each garbage area according to function use information and partition characteristic information of each garbage area for the tag source object corresponding to each garbage classification tag; the garbage distribution information is used for representing the use distribution flow information of the garbage classification labels in each function use partition, each label source object corresponds to the garbage area where the label source object is located one by one, and the label source object is used for representing the garbage identification object corresponding to the garbage classification label;
The second ordering module is used for ordering the garbage areas according to the order of the garbage distribution information from the early to the late to obtain a second ordering set;
the labeling module is used for labeling the label source objects in each garbage area in the object process among the garbage areas according to the first sorting set and the second sorting set; the number of the first label prediction feature information corresponding to the first sorting set is the same as the number of the garbage areas corresponding to the second sorting set;
the method for labeling the label source object in each garbage area according to the first sorting set and the second sorting set in the object process between each garbage area comprises the following steps:
sorting the label source objects corresponding to the garbage classification labels of each piece of first label prediction characteristic information according to the first sorting set to obtain a third sorting set used for representing the garbage occurrence frequency of the label source objects;
extracting a label source object in each garbage area, and importing the extracted label source object into a label library;
and labeling a first target label source object corresponding to a first label source object in the third sorting set in the labeling library to a first garbage area in the second sorting set, and repeatedly executing the operation according to the third sorting set until a second target label source object corresponding to a last label source object in the labeling library to a last garbage area in the second sorting set.
6. The garbage classification processing apparatus according to claim 5, wherein the means for determining garbage distribution information of each garbage area based on function usage information and partition characteristic information of a tag source object corresponding to each garbage classification tag for each garbage area, comprises:
program instruction information of a function use strategy program added on function use information of each garbage area is obtained, a first program function execution sequence corresponding to the program instruction information is determined, the program instruction information comprises instruction object information of a program service instruction determined according to input information and output information of the function use strategy program, and the first program function execution sequence comprises a plurality of object control levels of the instruction object information;
determining first function execution logic of function use information of each garbage area based on input information and second function execution logic based on output information;
determining a distribution generation parameter for carrying out distribution generation on the first program function execution sequence according to the logic execution relation of the first function execution logic and the second function execution logic;
Distributing the first program function execution sequence based on the distribution generation parameters to generate a second program function execution sequence;
splitting the second program function execution sequence to obtain a plurality of sequence objects, and extracting features of each sequence object to obtain list features;
determining first distribution information of each garbage area according to garbage distribution information corresponding to a plurality of list features corresponding to the second program function execution sequence;
determining a spatial distribution map of each garbage region from partition characteristic information of each garbage region, extracting spatial distribution node information of the spatial distribution map, and determining a target node information region corresponding to the spatial distribution node information;
extracting the space sequence feature vector of the target node information area according to a set space interval;
generating an empty sequence distribution heat degree unit diagram corresponding to the empty sequence feature vector and a region heat degree unit diagram corresponding to the target node information region, wherein the empty sequence distribution heat degree unit diagram and the region heat degree unit diagram respectively comprise a plurality of heat degree units with different heat degrees;
extracting description vector information of one heat unit of the space sequence feature vector in the space sequence distribution heat unit diagram, and determining a heat unit with minimum heat in the area heat unit diagram as a target heat unit;
Marking the description vector information into the target heat unit to obtain marked characteristic information in the target heat unit, and then generating a space region distribution sequence between the empty sequence characteristic vector and the target node information region based on the description vector information and the marked characteristic information;
acquiring heat data segment information in the target heat unit by taking the marking characteristic information as an index object, mapping the heat data segment information into the heat unit where the description vector information is located according to a distribution association relation corresponding to the spatial region distribution sequence, and acquiring partition discrimination information corresponding to the heat data segment information in the heat unit where the description vector information is located;
respectively listing partition discrimination objects in the partition discrimination information in a garbage area distribution space, and determining second distribution information of each garbage area;
and determining garbage distribution information of each garbage area according to the first distribution information and the second distribution information.
7. A waste classification processing apparatus according to claim 5 or 6, further comprising:
a third acquisition module, configured to:
Acquiring second tag prediction characteristic information sent by the garbage classification monitoring terminal; the second tag prediction characteristic information is generated by the garbage classification monitoring terminal according to an operation instruction input by a user;
traversing a plurality of garbage areas in the labeling process of the object process according to the second tag prediction characteristic information, inquiring a third target tag source object and feeding back the third target tag source object to the garbage classification monitoring terminal;
and adding the second tag prediction characteristic information to the monitoring image stream information sequence.
8. The garbage classification processing apparatus according to claim 5 or 6, wherein the operation of determining the plurality of garbage classification tags corresponding to the monitored image stream information sequence of the garbage classification monitor terminal and the prediction confidence of the first tag prediction feature information corresponding to each garbage classification tag in the monitored image stream information sequence includes:
inputting the monitoring image stream information sequence of the garbage classification monitoring terminal into a garbage classification model to obtain a plurality of garbage classification labels corresponding to the monitoring image stream information sequence and the prediction confidence of the first label prediction characteristic information corresponding to each garbage classification label in the monitoring image stream information sequence;
The garbage classification model is obtained based on a pre-configured training sample and garbage classification labels corresponding to the training sample, and the training sample is monitoring image stream sample information.
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