CN111680645A - Garbage classification processing method and device - Google Patents
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Abstract
The embodiment of the application provides a garbage classification processing method and device, wherein first label prediction characteristic information of a plurality of garbage classification labels corresponding to a monitored image stream information sequence is sequenced from high to low according to prediction confidence to obtain a first sequencing set, then garbage distribution information of each garbage area is determined according to function use information and partition characteristic information of a garbage area where a label source object corresponding to each garbage classification label is located for the label source object corresponding to each garbage classification label, the garbage areas are sequenced from early to late to obtain a second sequencing set, and therefore object process labeling is carried out on the label source object in each garbage area between the garbage areas 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 among the garbage areas, and the time sequence and the empty 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, the garbage objects of each garbage area are generally identified by adopting a deep learning mode, however, a technical means for performing object process labeling on the garbage objects in each garbage area among each garbage area does not exist in the conventional scheme, so that the time sequence and the empty sequence distribution condition of the garbage objects in each garbage area are difficult to reflect.
Disclosure of Invention
In order to overcome at least the above disadvantages in the prior art, an object of the present application is to provide a garbage classification processing method and apparatus, which can label garbage objects in each garbage area with object processes between each garbage area, so as to reflect the time sequence and the space sequence distribution 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, the method including:
determining a plurality of garbage classification labels corresponding to a monitoring image stream information sequence of a garbage classification monitoring terminal and a prediction confidence coefficient of first label prediction characteristic information corresponding to each garbage classification label in the monitoring image stream information sequence;
sequencing the first label prediction characteristic information according to the sequence of the prediction confidence coefficient from high to low to obtain a first sequencing set;
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; the garbage distribution information is used for representing the use distribution flow information of the garbage classification label in each function use partition, each label source object corresponds to a garbage area where the label source object is located one by one, and the label source object is used for representing a garbage identification object corresponding to the garbage classification label;
sorting the garbage areas according to the early-to-late sequence of the garbage distribution information to obtain a second sorting set;
performing object process labeling on the label source object in each garbage area between each garbage area according to the first sorting set and the second sorting set; the quantity of the first label prediction characteristic information corresponding to the first sorting set is the same as that of the garbage areas corresponding to the second sorting set.
In a possible implementation manner of the first aspect, performing object process labeling on the tag source object in each garbage area between 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 first label prediction characteristic information according to the first sorting set to obtain a third sorting set for representing the garbage occurrence frequency of the label source objects;
extracting the label source object in each garbage area, and importing the extracted label source object into a labeling library;
and marking a first target label source object corresponding to the first label source object in the third sorting set in the marking 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 the last label source object in the third sorting set in the marking library is marked into the last garbage area in the second sorting set.
In a possible implementation manner of the first aspect, determining garbage distribution information of each garbage area according to function usage information of a tag source object corresponding to each garbage classification tag of each garbage area and partition feature information includes:
acquiring program instruction information of a function use strategy program added to the function use information of each garbage area, and determining a first program function execution sequence corresponding to the program instruction information, wherein 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 the high-low order of a plurality of object control levels of the instruction object information;
determining a first function execution logic based on the input information and a second function execution logic based on the output information for each garbage area;
determining a distribution generation parameter for performing distribution generation on the first program function execution sequence according to a logic execution relation between the first function execution logic and the second function execution logic;
distributing the first program function execution sequence based on the distribution generation parameter to generate a second program function execution sequence;
splitting the second program function execution sequence to obtain a plurality of sequence objects, and extracting the characteristics of each sequence object to obtain list characteristics;
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 the 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 a space-order feature vector of the target node information area according to a set space interval;
generating a null-sequence distribution heat unit graph corresponding to the null-sequence feature vector and an area heat unit graph corresponding to a target node information area, wherein the null-sequence distribution heat unit graph and the area heat unit graph respectively comprise a plurality of heat units with different heat;
extracting description vector information of the empty-sequence feature vector in one of the heat units in the empty-sequence distribution heat unit diagram, and determining the heat unit with the minimum heat in the region heat unit diagram as a target heat unit;
marking the description vector information into the target heat unit to obtain marked feature information in the target heat unit, and then generating a spatial region distribution sequence between the space-sequence feature vector and the target node information region based on the description vector information and the marked feature information;
acquiring heat data segment information in the target heat unit by taking the marked feature information as an index object, mapping the heat data segment information to the heat unit where the description vector information is located according to the distribution incidence relation corresponding to the spatial region distribution sequence, and obtaining partition distinguishing information corresponding to the heat data segment information in the heat unit where the description vector information is located;
respectively listing the partition distinguishing objects in the partition distinguishing information in the 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 of the first aspect, the method further comprises:
acquiring second label prediction characteristic information sent by the garbage classification monitoring terminal; the second label prediction characteristic information is generated by the garbage classification monitoring terminal according to an operation instruction input by a user;
according to the second label prediction characteristic information, a plurality of garbage areas in the process of traversing object labeling are searched, a third target label source object is inquired, and the third target label source object is fed back to the garbage classification monitoring terminal;
and adding the second label prediction characteristic information into the monitoring image stream information sequence.
In a possible implementation manner of the first aspect, the determining a prediction confidence of a plurality of spam classification labels corresponding to a monitored image stream information sequence of a spam classification monitoring terminal and first label prediction feature information corresponding to each spam classification label in the monitored image stream information sequence includes:
inputting a monitoring image stream information sequence of the garbage classification monitoring terminal into a garbage classification model, and obtaining 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 a garbage classification label 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, which is applied to a server, and the apparatus includes:
the system comprises a first determining module, a second determining module and a judging module, wherein the first determining module is used for determining a plurality of garbage classification labels corresponding to a monitoring image stream information sequence of a garbage classification monitoring terminal 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 first ordering module is used for ordering the first label prediction characteristic information according to the order of the prediction confidence coefficient from high to low to obtain a first ordering set;
the second determining module is used for determining a garbage area where the label source object corresponding to each garbage classification label is located, and determining garbage distribution information of each garbage area according to the function use information and the partition characteristic information of each garbage area for the label source object corresponding to each garbage classification label; the garbage distribution information is used for representing the use distribution flow information of the garbage classification label in each function use partition, each label source object corresponds to a garbage area where the label source object is located one by one, and the label source object is used for representing a garbage identification object corresponding to the garbage classification label;
the second sorting module is used for sorting the garbage areas according to the garbage distribution information from early to late to obtain a second sorting set;
the labeling module is used for labeling the label source objects in each garbage area in an object process among the garbage areas according to the first sorting set and the second sorting set; the quantity of the first label prediction characteristic information corresponding to the first sorting set is the same as that of the garbage areas corresponding to the second sorting set.
In a third aspect, an embodiment of the present application provides a server, including 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 of the first aspect or any possible design of the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores instructions that, when executed on a computer, cause the computer to perform the method of the first aspect or any possible design manner of the first aspect.
Based on any one of the above aspects, the method includes the steps of sequencing first tag prediction feature information of multiple spam classification tags corresponding to a monitored image stream information sequence according to a sequence of prediction confidence degrees from high to low to obtain a first sequencing set, then determining spam distribution information of each spam region according to function use information and partition feature information of a spam region where a tag source object corresponding to each spam classification tag is located for the tag source object corresponding to each spam classification tag, sequencing the spam regions from early to late to obtain a second sequencing set, and performing object process labeling on the tag source object in each spam region between the spam regions 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 among the garbage areas, and the time sequence and the empty 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 required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
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 processing apparatus according to an embodiment of the present application;
fig. 3 is a block diagram schematically illustrating a structure of a server for executing the above-mentioned garbage classification processing method according to an embodiment of the present application.
Detailed Description
The present application will now be described in detail with reference to the drawings, and the specific operations in the method embodiments may also be applied to the apparatus embodiments or the system embodiments.
Please refer to fig. 1, which is a schematic flow chart of a garbage classification processing method according to an embodiment of the present application, and the following describes the garbage classification processing method in detail.
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 label prediction characteristic information according to the sequence of the prediction confidence degrees from high to low to obtain a first sequencing set.
Step S130, determining a garbage area where the label source object corresponding to each garbage classification label is located, and determining garbage distribution information of each garbage area according to the function use information and the 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 represent usage distribution flow information of the garbage classification label in each function usage partition, each label source object corresponds to a garbage area where the label source object is located, and the label source object is used to represent a garbage identification object corresponding to the garbage classification label.
And step S140, sorting the garbage areas according to the early-to-late order of the garbage distribution information to obtain a second sorting set.
And S150, performing object process labeling on the label source object in each garbage area between each garbage area according to the first sorting set and the second sorting set. In this embodiment, 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.
Based on the above design, in this embodiment, the first tag prediction feature information of multiple spam classification tags corresponding to a monitored image stream information sequence is sorted according to the prediction confidence from high to low to obtain a first sorting set, then the spam distribution information of each spam region is determined according to the function use information and partition feature information of the spam region where the tag source object corresponding to each spam classification tag is located with respect to the tag source object corresponding to each spam classification tag, and the spam regions are sorted in the order from early to late to obtain a second sorting set, so that the tag source object in each spam region is subjected to object process labeling between each spam 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 among the garbage areas, and the time sequence and the empty sequence distribution condition of the garbage objects in each garbage area can be reflected.
In one possible embodiment, step S150 can be implemented by the following substeps, which are described in detail below.
And a substep S151, sorting the label source objects corresponding to the garbage classification labels of each first label prediction characteristic information according to the first sorting set, and obtaining a third sorting set 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 labeling library.
And a substep S153, labeling a first target label source object corresponding to the first label source object in the third sorting set in the label library to 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 the last label source object in the third sorting set in the label library is labeled to the last garbage area in the second sorting set.
In one possible implementation, step S130 can be implemented by the following sub-steps, which are described in detail below.
And a substep S131, obtaining the program instruction information of the function use policy program added to the function use information of each garbage region, and determining a first program function execution sequence corresponding to the program instruction information, wherein 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 use policy program, and the first program function execution sequence includes a high-low order of a plurality of object control levels of the instruction object information.
And a substep S132 of determining a first function execution logic based on the input information and a second function execution logic based on the output information for each garbage region.
And a substep S133, determining a distribution generation parameter for performing 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.
And a substep S134 of generating a second program function execution sequence by distributing the first program function execution sequence based on the distribution generation parameter.
In the substep S135, the second program function execution sequence is split to obtain a plurality of sequence objects, and feature extraction is performed on each sequence object to obtain list features.
And a substep S136, determining the first distribution information of each garbage region according to the garbage distribution information corresponding to the plurality of list features corresponding to the second program function execution sequence.
And a substep S137, determining the spatial distribution map of each garbage region from the partition characteristic information of each garbage region, extracting the spatial distribution node information of the spatial distribution map, and determining a target node information region corresponding to the spatial distribution node information.
And a substep S138 of extracting the space-order feature vector of the target node information region according to the set space interval.
In sub-step S1391, a space order distribution heat degree unit map corresponding to the space order feature vector and a region heat degree unit map corresponding to the target node information region are generated, where the space order distribution heat degree unit map and the region heat degree unit map respectively include a plurality of heat degree units with different heat degrees.
In the sub-step S1392, description vector information of the space-order feature vector in one of the heat units in the space-order distribution heat unit map is extracted, and the heat unit with the minimum heat in the region heat unit map is determined as the target heat unit.
And a substep S1393 of marking the description vector information into the target heat unit to obtain marked feature information in the target heat unit, and then generating a spatial region distribution sequence between the space-order feature vector and the target node information region based on the description vector information and the marked feature information.
And a substep S1394, obtaining heat data segment information in the target heat unit by taking the marked characteristic information as an index object, mapping the heat data segment information to the heat unit where the description vector information is located according to the distribution incidence relation corresponding to the spatial region distribution sequence, and obtaining partition distinguishing information corresponding to the heat data segment information in the heat unit where the description vector information is located.
In sub-step S1395, partition identification objects in the partition identification information are listed in the garbage region distribution space, and the second distribution information of each garbage region is determined.
And a substep S1396 of determining garbage distribution information of each garbage region according to the first distribution information and the second distribution information.
In a possible implementation manner, the embodiment may further obtain second label prediction feature information sent by the garbage classification monitoring terminal. And the second label 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 process of labeling the object according to the second label prediction characteristic information, inquiring a third target label source object, feeding the third target label source object back to the garbage classification monitoring terminal, and adding the second label prediction characteristic information to the information sequence of the monitored image stream.
In a possible implementation manner, for step S110, the monitoring image stream information sequence of the garbage classification monitoring terminal may be input into a garbage classification model, and a plurality of garbage classification tags corresponding to the monitoring image stream information sequence and a prediction confidence of the first tag prediction feature information corresponding to each garbage classification tag in the monitoring image stream information sequence are obtained.
Illustratively, the garbage classification model is obtained by training based on a preconfigured training sample and a garbage classification label corresponding to the training sample, and the training sample is monitoring image stream sample information. The detailed training process is prior art and will not be described herein.
Fig. 2 is a schematic diagram of functional modules of a garbage classification processing apparatus 200 according to an embodiment of the present application, and in this embodiment, the garbage classification processing apparatus 200 may be divided into the functional modules according to the above method embodiment. For example, the functional blocks may be divided for the respective functions, or two or more functions may be integrated into one processing block. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that, the division of the modules in the present application is schematic, and is only a logical function division, and there may be another division manner in actual implementation. For example, in the case of dividing each function module according to each function, the garbage classification processing apparatus 200 shown in fig. 2 is only a schematic apparatus. The garbage classification processing 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 functional modules of the garbage classification processing apparatus 200 are described in detail below.
The first determining module 210 is configured to determine a plurality of spam classification tags corresponding to a monitoring image stream information sequence of the spam classification monitoring terminal and a prediction confidence of first tag prediction feature information corresponding to each spam classification tag in the monitoring image stream information sequence.
The first sorting module 220 is configured to sort the first label prediction feature information according to a sequence from high to low of the prediction confidence to obtain a first sorting 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 the function usage information and the 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 label in each function use partition, each label source object corresponds to a garbage area where the label source object is located one by one, and the label source object is used for representing a garbage identification object corresponding to the garbage classification label.
And a second sorting module 240, configured to sort the garbage areas according to the early-to-late order of the garbage distribution information to obtain a second sorting set.
And the labeling module 250 is used for performing object process labeling on the label source object in each garbage area between each garbage area according to the first sorting set and the second sorting set. The quantity of the first label prediction characteristic information corresponding to the first sorting set is the same as that of the garbage areas corresponding to the second sorting set.
In a possible implementation manner, the way of performing object process labeling on the tag source object in each garbage area between 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 first label prediction characteristic information according to the first sorting set to obtain a third sorting set for representing the garbage occurrence frequency of the label source objects;
extracting the label source object in each garbage area, and importing the extracted label source object into a labeling library;
and marking a first target label source object corresponding to the first label source object in the third sorting set in the marking library into 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 the last label source object in the third sorting set in the marking library is marked into the last garbage area in the second sorting set.
In a possible implementation manner, the determining, according to the function usage information and the partition feature information of the tag source object corresponding to each spam classification tag in each spam area, the spam distribution information of each spam area includes:
acquiring program instruction information of a function use strategy program added to the function use information of each garbage area, and determining a first program function execution sequence corresponding to the program instruction information, wherein 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 the high-low order of a plurality of object control levels of the instruction object information;
determining a first function execution logic based on the input information and a second function execution logic based on the output information for each garbage area;
determining a distribution generation parameter for performing 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 parameter to generate a second program function execution sequence;
splitting the second program function execution sequence to obtain a plurality of sequence objects, and extracting the characteristics of each sequence object to obtain list characteristics;
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 the 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 a space-order feature vector of a target node information area according to a set space interval;
generating a space-order distribution heat unit graph corresponding to the space-order feature vector and an area heat unit graph corresponding to the target node information area, wherein the space-order distribution heat unit graph and the area heat unit graph respectively comprise a plurality of heat units with different heat degrees;
extracting description vector information of the empty-sequence feature vector in one of the heat units in the empty-sequence distribution heat unit diagram, and determining the heat unit with the minimum heat in the region heat unit diagram as a target heat unit;
marking description vector information into a target heat unit to obtain marked characteristic information in the target heat unit, and then generating a spatial region distribution sequence between a space-sequence characteristic vector and a 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 marked characteristic information as an index object, mapping the heat data segment information to a heat unit where description vector information is located according to a distribution incidence relation corresponding to a spatial region distribution sequence, and obtaining partition distinguishing information corresponding to the heat data segment information in the heat unit where the description vector information is located;
respectively listing the partition distinguishing objects in the partition distinguishing information in the 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 a possible implementation, the garbage classification processing apparatus 200 may further include a third obtaining module, configured to:
acquiring second label prediction characteristic information sent by the garbage classification monitoring terminal; the second label prediction characteristic information is generated by the garbage classification monitoring terminal according to an operation instruction input by a user;
according to the plurality of garbage areas of the object process labeling process traversed by the second label prediction characteristic information, inquiring a third target label source object and feeding the third target label source object back to the garbage classification monitoring terminal;
and adding the second label prediction characteristic information into the monitoring image stream information sequence.
In a possible embodiment, the method for determining a prediction confidence of a plurality of spam classification tags corresponding to a monitoring image stream information sequence of a spam classification monitoring terminal and first tag prediction feature information corresponding to each spam classification tag in the monitoring image stream information sequence includes:
inputting a monitoring image stream information sequence of a garbage classification monitoring terminal into a garbage classification model, and obtaining 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 a garbage classification label corresponding to the training sample, and the training sample is monitoring image stream sample information.
Fig. 3 is a schematic structural diagram of a server 100 for performing the above-mentioned garbage classification processing method according to an embodiment of the present disclosure, 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 the processors 130 may be one or more, and one processor 130 is illustrated in fig. 3 as an example. 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 by the connection by the bus 140 in fig. 3.
The machine-readable storage medium 120 is a computer-readable storage medium, and can be used to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the garbage classification processing method in the embodiment of the present application (for example, the first determining module 210, the first ordering module 220, the second determining module 230, the second ordering module 240, and the labeling module 250 shown in fig. 2). The processor 130 executes 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, the garbage classification processing method is implemented, and details are not described herein.
The machine-readable storage medium 120 may first include a storage program area and a storage data area, wherein the storage program area may store an operating system, a warehousing service process required for at least one function. The storage data area may store data created according to the use of the terminal, and the like. Further, the machine-readable storage medium 120 may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. The volatile Memory may be a Random Access Memory (RAM) which serves as an external high-speed label library. By way of example, but not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double data rate Synchronous Dynamic random access memory (DDR SDRAM), Enhanced Synchronous SDRAM (ESDRAM), Synchronous link SDRAM (SLDRAM), and direct memory bus RAM (DR RAM). It should be noted that the memories of the systems and methods described herein are intended to comprise, without being limited to, these and any other suitable memories at any other time. 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 having signal processing capabilities. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 130. The processor 130 may be a general-purpose processor, a digital signal processor (digital signal processor dsp), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components. The various methods, steps, and logic blocks disclosed 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 the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor.
The server 100 may interact with other devices via the 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 send and receive information using communication interface 110.
In the above embodiments, the implementation may be wholly or partially realized 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, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, digital spam processor line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 changes and modifications may be made in 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 of the present application and their equivalents, the present application is also intended to encompass such modifications and variations.
Claims (10)
1. A garbage classification processing method is applied to a server, and comprises the following steps:
determining a plurality of garbage classification labels corresponding to a monitoring image stream information sequence of a garbage classification monitoring terminal and a prediction confidence coefficient of first label prediction characteristic information corresponding to each garbage classification label in the monitoring image stream information sequence;
sequencing the first label prediction characteristic information according to the sequence of the prediction confidence coefficient from high to low to obtain a first sequencing set;
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; the garbage distribution information is used for representing the use distribution flow information of the garbage classification label in each function use partition, each label source object corresponds to a garbage area where the label source object is located one by one, and the label source object is used for representing a garbage identification object corresponding to the garbage classification label;
sorting the garbage areas according to the early-to-late sequence of the garbage distribution information to obtain a second sorting set;
performing object process labeling on the label source object in each garbage area between each garbage area according to the first sorting set and the second sorting set; the quantity of the first label prediction characteristic information corresponding to the first sorting set is the same as that of the garbage areas corresponding to the second sorting set.
2. The garbage classification processing method according to claim 1, wherein the object process labeling of the label source object in each garbage region between each garbage region according to the first sorting set and the second sorting set comprises:
sorting the label source objects corresponding to the garbage classification labels of each first label prediction characteristic information according to the first sorting set to obtain a third sorting set for representing the garbage occurrence frequency of the label source objects;
extracting the label source object in each garbage area, and importing the extracted label source object into a labeling library;
and marking a first target label source object corresponding to the first label source object in the third sorting set in the marking 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 the last label source object in the third sorting set in the marking library is marked into the last garbage area in the second sorting set.
3. The method of claim 1, wherein determining the garbage distribution information of each garbage area according to the function usage information of the tag source object corresponding to each garbage classification tag of each garbage area and the partition feature information comprises:
acquiring program instruction information of a function use strategy program added to the function use information of each garbage area, and determining a first program function execution sequence corresponding to the program instruction information, wherein 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 the high-low order of a plurality of object control levels of the instruction object information;
determining a first function execution logic based on the input information and a second function execution logic based on the output information for each garbage area;
determining a distribution generation parameter for performing distribution generation on the first program function execution sequence according to a logic execution relation between the first function execution logic and the second function execution logic;
distributing the first program function execution sequence based on the distribution generation parameter to generate a second program function execution sequence;
splitting the second program function execution sequence to obtain a plurality of sequence objects, and extracting the characteristics of each sequence object to obtain list characteristics;
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 the 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 a space-order feature vector of the target node information area according to a set space interval;
generating a null-sequence distribution heat unit graph corresponding to the null-sequence feature vector and an area heat unit graph corresponding to a target node information area, wherein the null-sequence distribution heat unit graph and the area heat unit graph respectively comprise a plurality of heat units with different heat;
extracting description vector information of the empty-sequence feature vector in one of the heat units in the empty-sequence distribution heat unit diagram, and determining the heat unit with the minimum heat in the region heat unit diagram as a target heat unit;
marking the description vector information into the target heat unit to obtain marked feature information in the target heat unit, and then generating a spatial region distribution sequence between the space-sequence feature vector and the target node information region based on the description vector information and the marked feature information;
acquiring heat data segment information in the target heat unit by taking the marked feature information as an index object, mapping the heat data segment information to the heat unit where the description vector information is located according to the distribution incidence relation corresponding to the spatial region distribution sequence, and obtaining partition distinguishing information corresponding to the heat data segment information in the heat unit where the description vector information is located;
respectively listing the partition distinguishing objects in the partition distinguishing information in the 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.
4. A method of waste sorting treatment according to any of claims 1-3, characterised in that the method further comprises:
acquiring second label prediction characteristic information sent by the garbage classification monitoring terminal; the second label prediction characteristic information is generated by the garbage classification monitoring terminal according to an operation instruction input by a user;
according to the second label prediction characteristic information, a plurality of garbage areas in the process of traversing object labeling are searched, a third target label source object is inquired, and the third target label source object is fed back to the garbage classification monitoring terminal;
and adding the second label prediction characteristic information into the monitoring image stream information sequence.
5. The method according to any one of claims 1 to 4, wherein the step of determining the prediction confidence of the plurality of spam classification labels corresponding to the monitored image stream information sequence of the spam classification monitoring terminal and the first label prediction feature information corresponding to each spam classification label in the monitored image stream information sequence comprises:
inputting a monitoring image stream information sequence of the garbage classification monitoring terminal into a garbage classification model, and obtaining 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 a garbage classification label corresponding to the training sample, and the training sample is monitoring image stream sample information.
6. A garbage classification processing device, which is applied to a server, the device comprising:
the system comprises a first determining module, a second determining module and a judging module, wherein the first determining module is used for determining a plurality of garbage classification labels corresponding to a monitoring image stream information sequence of a garbage classification monitoring terminal 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 first ordering module is used for ordering the first label prediction characteristic information according to the order of the prediction confidence coefficient from high to low to obtain a first ordering set;
the second determining module is used for determining a garbage area where the label source object corresponding to each garbage classification label is located, and determining garbage distribution information of each garbage area according to the function use information and the partition characteristic information of each garbage area for the label source object corresponding to each garbage classification label; the garbage distribution information is used for representing the use distribution flow information of the garbage classification label in each function use partition, each label source object corresponds to a garbage area where the label source object is located one by one, and the label source object is used for representing a garbage identification object corresponding to the garbage classification label;
the second sorting module is used for sorting the garbage areas according to the garbage distribution information from early to late to obtain a second sorting set;
the labeling module is used for labeling the label source objects in each garbage area in an object process among the garbage areas according to the first sorting set and the second sorting set; the quantity of the first label prediction characteristic information corresponding to the first sorting set is the same as that of the garbage areas corresponding to the second sorting set.
7. The garbage classification processing device according to claim 6, wherein the way of labeling the label source objects in each garbage region with object process between each garbage region according to the first sorting set and the second sorting set comprises:
sorting the label source objects corresponding to the garbage classification labels of each first label prediction characteristic information according to the first sorting set to obtain a third sorting set for representing the garbage occurrence frequency of the label source objects;
extracting the label source object in each garbage area, and importing the extracted label source object into a labeling library;
and marking a first target label source object corresponding to the first label source object in the third sorting set in the marking library into a first garbage area in the second sorting set, and repeatedly executing the operations according to the third sorting set until a second target label source object corresponding to the last label source object in the third sorting set in the marking library is marked into the last garbage area in the second sorting set.
8. The garbage classification processing apparatus according to claim 6, wherein the manner of determining the garbage distribution information of each garbage region according to the function usage information of the tag source object corresponding to each garbage classification tag of each garbage region and the partition feature information includes:
acquiring program instruction information of a function use strategy program added to the function use information of each garbage area, and determining a first program function execution sequence corresponding to the program instruction information, wherein 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 the high-low order of a plurality of object control levels of the instruction object information;
determining a first function execution logic based on the input information and a second function execution logic based on the output information for each garbage area;
determining a distribution generation parameter for performing distribution generation on the first program function execution sequence according to a logic execution relation between the first function execution logic and the second function execution logic;
distributing the first program function execution sequence based on the distribution generation parameter to generate a second program function execution sequence;
splitting the second program function execution sequence to obtain a plurality of sequence objects, and extracting the characteristics of each sequence object to obtain list characteristics;
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 the 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 a space-order feature vector of the target node information area according to a set space interval;
generating a null-sequence distribution heat unit graph corresponding to the null-sequence feature vector and an area heat unit graph corresponding to a target node information area, wherein the null-sequence distribution heat unit graph and the area heat unit graph respectively comprise a plurality of heat units with different heat;
extracting description vector information of the empty-sequence feature vector in one of the heat units in the empty-sequence distribution heat unit diagram, and determining the heat unit with the minimum heat in the region heat unit diagram as a target heat unit;
marking the description vector information into the target heat unit to obtain marked feature information in the target heat unit, and then generating a spatial region distribution sequence between the space-sequence feature vector and the target node information region based on the description vector information and the marked feature information;
acquiring heat data segment information in the target heat unit by taking the marked feature information as an index object, mapping the heat data segment information to the heat unit where the description vector information is located according to the distribution incidence relation corresponding to the spatial region distribution sequence, and obtaining partition distinguishing information corresponding to the heat data segment information in the heat unit where the description vector information is located;
respectively listing the partition distinguishing objects in the partition distinguishing information in the 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.
9. A waste sorting device according to any of claims 6-8, characterised in that the device further comprises:
a third obtaining module configured to:
acquiring second label prediction characteristic information sent by the garbage classification monitoring terminal; the second label prediction characteristic information is generated by the garbage classification monitoring terminal according to an operation instruction input by a user;
according to the second label prediction characteristic information, a plurality of garbage areas in the process of traversing object labeling are searched, a third target label source object is inquired, and the third target label source object is fed back to the garbage classification monitoring terminal;
and adding the second label prediction characteristic information into the monitoring image stream information sequence.
10. The apparatus according to any one of claims 6 to 8, wherein the operation of determining the prediction confidence of the plurality of spam classification labels corresponding to the monitored image stream information sequence of the spam classification monitoring terminal and the first label prediction feature information corresponding to each spam classification label in the monitored image stream information sequence comprises:
inputting a monitoring image stream information sequence of the garbage classification monitoring terminal into a garbage classification model, and obtaining 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 a garbage classification label corresponding to the training sample, and the training sample is monitoring image stream sample information.
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