Dynamic garbage classified collection and transfer treatment system and method

Abstract

According to the dynamic garbage classified collection and transfer processing system and method, the processing parameters of the garbage processing station can be updated through the file guide reading processing module, the sample data set suitable for the current scene can be formed according to the newest garbage classification indication file, the data set is used for training the checking model, and the checking efficiency is improved. The accuracy of garbage classification quality monitoring can be improved, and the garbage classification treatment effect can be improved. Meanwhile, the scheduling module performs scheduling in combination with the updated processing parameters of the garbage processing stations, so that the garbage vehicles are scheduled according to the performance of each current garbage processing station, the garbage classification and transfer efficiency is effectively improved, and meanwhile, the dual improvement of environmental benefits and economic benefits can be ensured to the greatest extent; and the classified transfer scheme is flexibly configured according to scene requirements, and the scene adaptability of the scheme is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of dynamic garbage treatment, and in particular to a dynamic garbage classification, collection, transportation and treatment system and method. Background Art

[0002] Dynamic garbage classification, collection, transportation and treatment is a systematic and complex process, which involves multiple links and the participation of multiple parties, aiming to achieve effective classification, efficient collection, safe transportation and harmless treatment of garbage.

[0003] At present, the more prominent problems in dynamic garbage classification, collection, transfer and treatment are: low efficiency of classification and transfer; low flexibility, and the inability to flexibly configure the classification and transfer plan according to scene requirements, resulting in poor scene adaptability; transfer scheduling cannot be carried out according to the specific conditions of each garbage treatment station, and the dual guarantee of efficiency and benefits cannot be achieved; the quality of garbage classification is poor, which affects the garbage treatment effect. Summary of the invention

[0004] The present disclosure at least provides a dynamic garbage classification, collection, transportation and processing system and method to solve at least one of the above-mentioned technical problems.

[0005] According to one aspect of the present disclosure, a dynamic garbage classification collection, transfer and processing system is provided, comprising:

[0006] The file reading and processing module is used to import a new file and identify the file name. When the identification result indicates that the new file is a processing performance file of a garbage processing station, the file content is read and identified, and the processing parameters of the garbage processing station are extracted and stored; wherein the processing parameters include: the processing efficiency of the garbage processing station for various types of garbage, the weight of various types of garbage to be processed, the unit processing cost of processing various types of garbage, the environmental pollution value generated by processing various types of garbage, the unit benefit generated by processing various types of garbage, and the location of the garbage processing station; when the identification result indicates that the new file is a garbage classification instruction file , compare the new garbage classification index file with the stored historical garbage classification index file to obtain a difference text; identify the difference text, and when the difference text indicates that a new category has been added to a garbage category, obtain a picture of the new category from the Internet, and rotate and / or scale the obtained picture; use the picture obtained from the Internet, the picture formed by rotation and / or scaling as an initial sample training set and store it; send instruction information to the display module, so that the display module displays the added new category and the information forming the initial sample training set, and enable the user to train the corresponding verification model according to the initial sample training set;

[0007] A classification verification module is used to obtain a video of a garbage truck collecting garbage, extract type positioning key frames and classification verification key frames from the video file, wherein the type positioning key frames include at least two frames, and the classification verification key frames include a plurality of key frames randomly selected from frames after a predetermined number of frames after the type positioning key frames in the video; input the type positioning key frames into a trained classification model, use the classification model to determine the garbage category corresponding to each type positioning key frame, and when the garbage categories corresponding to each type positioning key frame are the same, use the garbage category determined by the classification model as the target garbage category; determine a target verification model that matches the target garbage category, and input the classification verification key frames into the trained target verification model, and use the target verification model to determine the proportion of garbage belonging to the target garbage type in each key frame; calculate the mean of the proportions corresponding to each key frame, and when the mean is greater than a preset threshold, determine that the currently collected garbage is classified as qualified; wherein different categories of garbage correspond to different verification models;

[0008] A weight assessment module is used to obtain images of garbage collectors of various categories on garbage trucks when garbage classification is qualified, and input the images corresponding to garbage collectors of various categories into corresponding weight assessment models, process the received images using the weight assessment model, obtain weight assessment values ​​of garbage of each category, and input the weight assessment values ​​corresponding to garbage of each category into the scheduling module and the early warning interaction module when the weight assessment values ​​of garbage of each category are greater than the first preset weight; wherein different weight assessment models correspond to different categories of garbage;

[0009] The early warning interaction module is used to store the total weight of each type of garbage in each time period of the garbage truck, and generate and display statistical charts according to the total weight of each type of garbage and each time period; and, when the total weight in N consecutive time periods is greater than the second preset weight of the corresponding garbage category, display and send early warning information to the control end; the early warning information includes the category of excess garbage and the weight of garbage exceeding the corresponding second preset weight; wherein N is a positive integer;

[0010] The scheduling module is used to obtain the processing parameters of each garbage treatment station, and determine the transportation cost and transportation time of each garbage treatment station according to the location of each garbage treatment station; determine the timeliness index of each garbage treatment station in processing the garbage on the garbage truck based on the weight evaluation value corresponding to each type of garbage, the processing efficiency of each type of garbage at each garbage treatment station, the weight of each type of garbage to be processed, and the transportation time of each garbage treatment station; determine the economic benefit of each garbage treatment station in processing the garbage on the garbage truck based on the weight evaluation value corresponding to each type of garbage, the unit processing cost of each garbage treatment station for processing each type of garbage, the transportation cost of each garbage treatment station, and the unit benefit generated by each garbage treatment station for processing each type of garbage; determine the economic benefit of each garbage treatment station in processing the garbage on the garbage truck based on the weight evaluation value corresponding to each type of garbage, the unit processing cost of each garbage treatment station for processing each type of garbage, the transportation cost of each garbage treatment station, and the unit benefit generated by each garbage treatment station for processing each type of garbage; determine the timeliness index of each garbage treatment station in processing the garbage on the garbage truck based on the weight evaluation value corresponding to each type of garbage The corresponding weight assessment value and the environmental pollution index generated by each garbage treatment station in treating various types of garbage are used to determine the environmental benefit of each garbage treatment station in treating the garbage on the garbage vehicle; the weight values ​​corresponding to the timeliness index, economic benefit and environmental benefit are obtained respectively, and the scheduling priority value of each garbage treatment station in treating the garbage on the garbage vehicle is determined by using the weight values ​​corresponding to the timeliness index, economic benefit and environmental benefit respectively and the timeliness index, economic benefit and environmental benefit corresponding to each garbage treatment station, and the garbage treatment station with the highest scheduling priority value is taken as the target treatment station; the navigation route corresponding to the target treatment station is determined and sent to the garbage vehicle, so that the garbage vehicle transports the garbage to the target treatment station according to the navigation route.

[0011] In a possible implementation manner, when identifying the file name, the file guide processing module is specifically used to:

[0012] Get the file name;

[0013] Perform word segmentation on the file name to obtain multiple words;

[0014] Extract keywords from the multiple words to obtain multiple keywords;

[0015] The extracted keywords are matched with the vocabulary corresponding to each preset recognition result, and the preset recognition result with the highest matching degree is used as the recognition result of the new file.

[0016] In a possible implementation manner, when determining the navigation route corresponding to the target processing station, the scheduling module is specifically used to:

[0017] Acquire a current first position of the garbage truck and a second position of the target processing station;

[0018] Get pre-made maps;

[0019] A navigation route corresponding to a target processing station is generated according to the first location, the second location and the prefabricated map.

[0020] In a possible implementation manner, when acquiring a video of a garbage truck collecting garbage, the classification verification module is specifically used to:

[0021] When the garbage truck lifts the garbage can containing garbage, the video collection starts; and when the garbage truck puts down the garbage can, the video collection stops;

[0022] The type positioning key frames are two frames in the first M frames of the video; wherein M is a positive integer.

[0023] In a possible implementation, the weight assessment model determines the weight assessment value according to the height of the garbage in the garbage collector.

[0024] In a possible implementation manner, the early warning interaction module is further used for:

[0025] When the total weight in P consecutive time periods is less than the third preset weight of the corresponding garbage category, a praise message is displayed and sent to the management and control end; the praise message includes the category of garbage and the weight of garbage lower than the corresponding third preset weight; wherein P is a positive integer.

[0026] In a possible implementation, the scheduling module is further used for:

[0027] Get the processing parameters input by the user;

[0028] Stores processing parameters entered by the user.

[0029] According to another aspect of the present disclosure, a dynamic garbage classification collection and transfer processing method is provided, comprising:

[0030] Import a new file and identify the file name. When the identification result indicates that the new file is a processing performance file of a garbage treatment station, read the file content, identify the read file content, extract and store the processing parameters of the garbage treatment station; wherein the processing parameters include: the processing efficiency of the garbage treatment station for various types of garbage, the weight of various types of garbage to be processed, the unit processing cost of processing various types of garbage, the environmental pollution value generated by processing various types of garbage, the unit benefit generated by processing various types of garbage, and the location of the garbage treatment station; when the identification result indicates that the new file is a garbage classification instruction file, compare the new garbage classification indicator file with the stored historical garbage classification indicator file to obtain a difference text; identify the difference text, and when the difference text indicates that a new category is added to a garbage category, obtain a picture of the new category from the Internet, and rotate and / or scale the obtained picture; use the picture obtained from the Internet and the picture formed by rotation and / or scaling as an initial sample training set and store it; send instruction information to the display module so that the display module displays the added new category and the information forming the initial sample training set, and enable the user to train the target verification model according to the initial sample training set;

[0031] Obtain a video of a garbage truck collecting garbage, extract type positioning key frames and classification verification key frames from the video file, wherein the type positioning key frames include at least two frames, and the classification verification key frames include a plurality of key frames randomly selected from frames after a predetermined number of frames after the type positioning key frames in the video; input the type positioning key frames into a trained classification model, use the classification model to determine the garbage category corresponding to each type positioning key frame, and when the garbage categories corresponding to each type positioning key frame are the same, use the garbage category determined by the classification model as the target garbage category; determine a target verification model that matches the target garbage category, and input the classification verification key frames into the trained target verification model, and use the target verification model to determine the proportion of garbage belonging to the target garbage type in each key frame; calculate the mean of the proportions corresponding to each key frame, and when the mean is greater than a preset threshold, determine that the currently collected garbage is classified as qualified; wherein different categories of garbage correspond to different verification models;

[0032] When the garbage classification is qualified, images of garbage collectors of various categories on the garbage truck are obtained, and the images corresponding to the garbage collectors of various categories are respectively input into the corresponding weight assessment models, and the received images are processed by the weight assessment model to obtain the weight assessment value of each category of garbage, and when the weight assessment value of each category of garbage is greater than the first preset weight, the weight assessment value corresponding to each category of garbage is input into the scheduling module and the early warning interaction module; wherein different categories of garbage correspond to different weight assessment models;

[0033] The total weight of each type of garbage of the garbage truck in each time period is stored, and a statistical chart is generated and displayed according to the total weight of each type of garbage and each time period; and, when the total weight in N consecutive time periods is greater than the second preset weight of the corresponding garbage category, an early warning message is displayed and sent to the control end; the early warning message includes the category of the excessive garbage and the weight of the garbage exceeding the corresponding second preset weight; wherein N is a positive integer;

[0034] Obtain the processing parameters of each garbage treatment station, and determine the transportation cost and transportation time of each garbage treatment station according to the location of each garbage treatment station; determine the timeliness index of each garbage treatment station in processing the garbage on the garbage truck based on the weight assessment value corresponding to each category of garbage, the processing efficiency of each category of garbage at each garbage treatment station, the weight of each category of garbage to be processed, and the transportation time of each garbage treatment station; determine the economic benefit of each garbage treatment station in processing the garbage on the garbage truck based on the weight assessment value corresponding to each category of garbage, the unit processing cost of each category of garbage at each garbage treatment station, the transportation cost of each garbage treatment station, and the unit benefit generated by each category of garbage at each garbage treatment station; determine the economic benefit of each garbage treatment station in processing the garbage on the garbage truck based on the weight assessment value corresponding to each category of garbage, the unit processing cost of each category of garbage at each garbage treatment station, the transportation cost of each garbage treatment station, and the unit benefit generated by each category of garbage at each garbage treatment station; determine the timeliness index of each garbage treatment station in processing the garbage on the garbage truck based on the weight assessment value corresponding to each category of garbage The weight assessment value and the environmental pollution index generated by each garbage treatment station in treating various types of garbage are used to determine the environmental benefit of each garbage treatment station in treating the garbage on the garbage vehicle; the weight values ​​corresponding to the timeliness index, economic benefit and environmental benefit are obtained respectively, and the scheduling priority value of each garbage treatment station in treating the garbage on the garbage vehicle is determined by using the weight values ​​corresponding to the timeliness index, economic benefit and environmental benefit respectively and the timeliness index, economic benefit and environmental benefit corresponding to each garbage treatment station, and the garbage treatment station with the highest scheduling priority value is taken as the target treatment station; the navigation route corresponding to the target treatment station is determined and sent to the garbage vehicle, so that the garbage vehicle transports the garbage to the target treatment station according to the navigation route.

[0035] The dynamic garbage classification collection and transportation processing system and method disclosed in the present invention can not only update the processing parameters of the garbage treatment station by using the file reading processing module, but also form a sample data set adapted to the current scene according to the latest garbage classification instruction file. The verification model is trained by using the data set, which can improve the accuracy of garbage classification quality monitoring and is conducive to improving the effect of garbage classification processing. At the same time, the scheduling module is combined with the processing parameters of the updated garbage treatment station for scheduling, and the garbage vehicles are dispatched according to the performance of each current garbage treatment station, which not only effectively improves the efficiency of garbage classification and transportation, but also can maximize the dual improvement of environmental benefits and economic benefits, and realizes the flexible configuration of classification and transportation schemes according to scene requirements, and improves the scene adaptability of the scheme. The classification verification module disclosed in the present invention extracts the type positioning key frame and the classification verification key frame, and uses the classification model to process the type positioning key frame to realize garbage category identification; on the basis of identification, the verification model matching the garbage category is enabled to process the classification verification key frame, and the garbage classification quality is monitored. Here, the key frame and deep learning method are used to effectively improve the efficiency and accuracy of garbage classification quality monitoring, providing strong support for the subsequent effective garbage treatment. The weight assessment module disclosed in the present invention can use the image in the garbage collector to quickly and accurately determine the weight of each category of garbage on the garbage truck, and send information to the scheduling module and the early warning interaction module after the weight of each category of garbage reaches a predetermined value, so as to perform garbage truck scheduling and statistical chart display, effectively avoiding the vehicle scheduling when the garbage truck is not full, and saving the cost of garbage transfer and processing. The early warning interaction module disclosed in the present invention can not only display the statistical chart of the total weight of each category of garbage in each time period, but also issue an early warning when the garbage weight exceeds the specified value for multiple consecutive time periods, so that the relevant departments or staff can quickly and accurately observe the changing trend of garbage weight, and control the generation of garbage from the source.

[0036] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0038] Figure 1 It is a structural schematic diagram of a dynamic garbage classification collection, transfer and processing system according to the present disclosure;

[0039] Figure 2 It is a flow chart of the dynamic garbage classification, collection, transportation and processing method disclosed in the present invention. DETAILED DESCRIPTION

[0040] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0041] The present invention provides a dynamic garbage classification, collection and transportation processing system and method in view of the defects of low efficiency of classification and transportation in the current dynamic garbage classification, collection and transportation processing scheme; low flexibility, inability to flexibly configure the classification and transportation scheme according to the scene requirements, resulting in poor scene adaptability; the transportation scheduling cannot be carried out according to the specific conditions of each garbage treatment station, and the dual guarantee of efficiency and benefit cannot be achieved; the garbage classification quality is poor, which affects the garbage treatment effect. The present invention uses a file reading processing module to not only update the processing parameters of the garbage treatment station, but also to form a sample data set adapted to the current scene according to the latest garbage classification instruction file, and to train the verification model using the data set, which can improve the accuracy of garbage classification quality monitoring and is conducive to improving the effect of garbage classification processing. At the same time, the scheduling module combines the processing parameters of the updated garbage treatment station for scheduling, and realizes the scheduling of garbage vehicles according to the performance of each current garbage treatment station, which not only effectively improves the efficiency of garbage classification and transportation, but also can maximize the dual improvement of environmental benefits and economic benefits, and realizes the flexible configuration of classification and transportation schemes according to scene requirements, and improves the scene adaptability of the scheme. The classification verification module of the present invention extracts the type positioning keyframe and the classification verification keyframe, and uses the classification model to process the type positioning keyframe to realize garbage category identification; on the basis of identification, the verification model matching the garbage category is enabled to process the classification verification keyframe, so as to realize the monitoring of garbage classification quality. Here, the keyframe and deep learning method are used to effectively improve the efficiency and accuracy of garbage classification quality monitoring, and provide strong support for the subsequent effective garbage disposal. The weight evaluation module of the present invention uses the image in the garbage collector to quickly and accurately determine the weight of each category of garbage on the garbage truck, and sends information to the scheduling module and the early warning interaction module after the weight of each category of garbage reaches a predetermined value, so as to perform garbage truck scheduling and statistical chart display, effectively avoiding the vehicle scheduling when the garbage truck is not full, and saving the garbage transfer and processing cost. The early warning interaction module of the present invention can not only display the statistical chart of the total weight of each type of garbage in each time period, but also issue an early warning when the garbage weight exceeds the specified value for multiple consecutive time periods, so that the relevant departments or staff can quickly and accurately observe the changing trend of garbage weight, and control the generation of garbage from the source.

[0042] The technical solution of the present disclosure is described below through specific embodiments.

[0043] like Figure 1 As shown, it is a structural diagram of the dynamic garbage classification, collection, transportation and processing system of this embodiment. Specifically, the dynamic garbage classification, collection, transportation and processing system of this embodiment includes a file reading and processing module 101, a classification verification module 102, a weight assessment module 103, an early warning interaction module 104, and a scheduling module 105.

[0044] The file reading and processing module 101 is used to import a new file and identify the file name. When the identification result indicates that the new file is a processing performance file of a garbage processing station, the file content is read and identified, and the processing parameters of the garbage processing station are extracted and stored. The processing parameters include: the processing efficiency of the garbage processing station for various types of garbage, the weight of various types of garbage to be processed, the unit processing cost of processing various types of garbage, the environmental pollution value generated by processing various types of garbage, the unit benefit generated by processing various types of garbage, and the location of the garbage processing station. When the identification result indicates that the new file is a garbage classification instruction file, In this case, the new garbage classification index file is compared with the stored historical garbage classification index file to obtain a difference text; the difference text is identified, and when the difference text represents that a new category has been added to a garbage category, a picture of the new category is obtained from the Internet, and the obtained picture is rotated and / or scaled; the picture obtained from the Internet and the picture formed by rotation and / or scaling are used as an initial sample training set and stored; an indication information is sent to a display module so that the display module displays the added new category and the information forming the initial sample training set, and the user trains the target verification model according to the initial sample training set.

[0045] When identifying a file name, the file reading processing module 101 is specifically used to: obtain the file name; perform word segmentation on the file name to obtain multiple words; extract keywords from the multiple words to obtain multiple keywords; match the extracted keywords with the vocabulary corresponding to each preset recognition result, and use the preset recognition result with the highest matching degree as the recognition result of the new file.

[0046] The above-mentioned word libraries corresponding to each preset recognition result are preset. The above-mentioned garbage classification instruction file may be a file issued by the government.

[0047] In some embodiments, the user labels the initial sample training set and adds some interference pictures to form a final sample training set, and then uses the final sample training set to train the verification model, which can effectively improve the accuracy of model recognition.

[0048] The file reading processing module 101 can specifically implement the above functions by using machine learning, deep learning and other methods.

[0049] The classification verification module 102 is used to obtain a video of a garbage truck collecting garbage, extract type positioning key frames and classification verification key frames from the video file, wherein the type positioning key frames include at least two frames, and the classification verification key frames include a plurality of key frames randomly selected from frames after a predetermined number of frames after the type positioning key frames in the video; input the type positioning key frames into a trained classification model, use the classification model to determine the garbage category corresponding to each type positioning key frame, and when the garbage categories corresponding to each type positioning key frame are the same, use the garbage category determined by the classification model as the target garbage category; determine a target verification model that matches the target garbage category, and input the classification verification key frames into the trained target verification model, and use the target verification model to determine the proportion of garbage belonging to the target garbage type in each key frame; calculate the mean of the proportions corresponding to each key frame, and when the mean is greater than a preset threshold, determine that the currently collected garbage is classified as qualified; wherein different categories of garbage correspond to different verification models.

[0050] When acquiring a video of a garbage truck collecting garbage, the classification verification module 102 is specifically used to: start collecting video when the garbage truck lifts a garbage can containing garbage; and stop collecting video when the garbage truck puts down the garbage can; the type positioning key frames are two frames of the first M frames of the video; where M is a positive integer.

[0051] The above-mentioned type of positioning key frame is an image of a trash can containing trash. The classification model uses identification information on the trash can, or can determine the type of trash based on the trash can.

[0052] The above classification verification key frames are images taken during the process of dumping garbage from the garbage bin into the garbage collector of the garbage truck. The more classification verification key frames there are, the higher the accuracy of the verification. The number of classification verification key frames can be determined according to actual conditions. For example, the number of classification verification key frames can be half of the total number of frames in the collected video.

[0053] The above classification model is trained using sample data of trash cans containing different types of trash. The above verification model matches the type of trash, that is, different types of trash are verified using different verification models. This design is mainly due to the fact that different types of trash differ greatly in appearance, and verification using a matching verification model can effectively ensure the accuracy of verification. In the training structure, sample images of matching trash types are also used for training.

[0054] The weight assessment module 103 is used to obtain images of garbage collectors of various categories on the garbage truck when the garbage classification is qualified, and input the images corresponding to the garbage collectors of various categories into the corresponding weight assessment models respectively, and use the weight assessment model to process the received images to obtain the weight assessment value of each category of garbage, and when the weight assessment value of each category of garbage is greater than the first preset weight, the weight assessment value corresponding to each category of garbage is input into the scheduling module and the early warning interaction module; wherein different categories of garbage correspond to different weight assessment models.

[0055] The weight assessment model determines the weight assessment value based on the height of the garbage in the garbage collector. In addition to determining the weight assessment value based on the height, the weight assessment model can also combine information such as density and humidity of different types of garbage and input these information into the weight assessment model to determine the weight assessment value.

[0056] The weight estimation model is trained using sample images of garbage collectors with different levels of garbage.

[0057] The early warning interaction module 104 is used to store the total weight of various types of garbage on the garbage truck in various time periods, and generate and display statistical charts based on the stored total weight of various types of garbage and various time periods; and, when the total weight in N consecutive time periods is greater than the second preset weight of the corresponding garbage category, display and send early warning information to the management and control end; the early warning information includes the category of excess garbage and the weight of garbage exceeding the corresponding second preset weight; wherein N is a positive integer.

[0058] The early warning interaction module 104 is also used to: when the total weight in P consecutive time periods is less than the third preset weight of the corresponding garbage category, display and send praise information to the management and control end; the praise information includes the category of garbage and the weight of garbage lower than the corresponding third preset weight; wherein P is a positive integer.

[0059] Statistical charts can be used to intuitively and quickly observe the changing trends in the weight of various types of garbage in different time periods, which is conducive to the management and control of relevant departments, such as through bonuses or fines.

[0060] The scheduling module 105 is used to obtain the processing parameters of each garbage treatment station, and determine the transportation cost and transportation time of each garbage treatment station according to the location of each garbage treatment station; determine the timeliness index of each garbage treatment station in processing the garbage on the garbage truck in combination with the weight evaluation value corresponding to each type of garbage, the processing efficiency of each type of garbage at each garbage treatment station and the weight of each type of garbage to be processed, and the transportation time of each garbage treatment station; determine the economic benefit of each garbage treatment station in processing the garbage on the garbage truck in combination with the weight evaluation value corresponding to each type of garbage, the unit processing cost of each garbage treatment station in processing each type of garbage, the transportation cost of each garbage treatment station, and the unit benefit generated by each garbage treatment station in processing each type of garbage; determine the economic benefit of each garbage treatment station in processing the garbage on the garbage truck in combination with each type of garbage The weight assessment value corresponding to the garbage and the environmental pollution index generated by each garbage treatment station in treating each type of garbage are used to determine the environmental benefit of each garbage treatment station in treating the garbage on the garbage vehicle; the weight values ​​corresponding to the timeliness index, economic benefit and environmental benefit are obtained respectively, and the scheduling priority value of each garbage treatment station in treating the garbage on the garbage vehicle is determined by using the weight values ​​corresponding to the timeliness index, economic benefit and environmental benefit respectively and the timeliness index, economic benefit and environmental benefit corresponding to each garbage treatment station, and the garbage treatment station with the highest scheduling priority value is taken as the target treatment station; the navigation route corresponding to the target treatment station is determined and sent to the garbage vehicle, so that the garbage vehicle transports the garbage to the target treatment station according to the navigation route.

[0061] When determining the navigation route corresponding to the target processing station, the scheduling module 105 is specifically used to: obtain the current first position of the garbage truck and the second position of the target processing station; obtain a prefabricated map; and generate a navigation route corresponding to the target processing station based on the first position, the second position and the prefabricated map.

[0062] The scheduling module 105 is also used to: obtain the processing parameters input by the user through the interactive port; and store the processing parameters input by the user. Another solution for updating the processing parameters is provided here.

[0063] During the scheduling process, the scheduling module 105 utilizes factors such as the processing efficiency of each garbage treatment station for each type of garbage, the weight of each type of garbage to be processed, the unit processing cost of processing each type of garbage, the environmental pollution value generated by processing each type of garbage, the unit benefit generated by processing each type of garbage, and the location of the garbage treatment station. It not only achieves scheduling and allocation that takes into account both environmental and economic benefits, but also effectively guarantees the efficiency of garbage treatment.

[0064] Based on the same inventive concept, the present disclosure provides a flow chart of a dynamic garbage classification collection and transfer processing method. The steps performed by the method are the same or similar to those of the above system, so similar parts are not repeated. Figure 2 As shown, the dynamic garbage classification, collection and transfer processing method of this embodiment includes:

[0065] S210, intelligently process various files to update or store the processing parameters of the garbage treatment station, or form an initial sample training set for the training verification model: import a new file and identify the file name, and when the recognition result indicates that the new file is a processing performance file of the garbage treatment station, read the file content, identify the read file content, extract and store the processing parameters of the garbage treatment station; wherein the processing parameters include: the processing efficiency of the garbage treatment station for various types of garbage, the weight of various types of garbage to be processed, the unit processing cost of processing various types of garbage, the environmental pollution value generated by processing various types of garbage, the unit benefit generated by processing various types of garbage, and the location of the garbage treatment station; in the recognition result In the case where the new file is characterized as a garbage classification indication file, the new garbage classification index file is compared with the stored historical garbage classification index file to obtain a difference text; the difference text is identified, and in the case where the difference text represents that a new category has been added to a garbage category, a picture of the new category is obtained from the Internet, and the obtained picture is rotated and / or scaled; the picture obtained from the Internet, the rotated and / or scaled picture is used as an initial sample training set and stored; an indication information is sent to a display module so that the display module displays the added new category and the information forming the initial sample training set, and the user trains the corresponding verification model according to the initial sample training set.

[0066] S220, determine the type of garbage and conduct garbage classification quality verification: obtain a video of a garbage truck collecting garbage, extract type positioning key frames and classification verification key frames from the video file, wherein the type positioning key frames include at least two frames, and the classification verification key frames include multiple key frames randomly selected from frames after a predetermined number of frames after the type positioning key frames in the video; input the type positioning key frames into a trained classification model, use the classification model to determine the garbage category corresponding to each type positioning key frame, and when the garbage categories corresponding to each type positioning key frame are the same, use the garbage category determined by the classification model as the target garbage category; determine a target verification model that matches the target garbage category, and input the classification verification key frames into the trained target verification model, and use the target verification model to determine the proportion of garbage belonging to the target garbage type in each key frame; calculate the mean of the proportions corresponding to each key frame, and when the mean is greater than a preset threshold, determine that the currently collected garbage classification is qualified; wherein different categories of garbage correspond to different verification models;

[0067] S230, garbage weight assessment and garbage classification processing scheduling: When the garbage classification is qualified, obtain the images of the garbage collectors of each category on the garbage truck, and input the images corresponding to the garbage collectors of each category into the corresponding weight assessment model, and use the weight assessment model to process the received images to obtain the weight assessment value of each category of garbage, and when the weight assessment value of each category of garbage is greater than the first preset weight, perform the following steps:

[0068] The total weight of each type of garbage of the garbage truck in each time period is stored, and a statistical chart is generated and displayed according to the total weight of each type of garbage and each time period; and, when the total weight in N consecutive time periods is greater than the second preset weight of the corresponding garbage category, an early warning message is displayed and sent to the control end; the early warning message includes the category of the excessive garbage and the weight of the garbage exceeding the corresponding second preset weight; wherein N is a positive integer;

[0069] Obtain the processing parameters of each garbage treatment station, and determine the transportation cost and transportation time of each garbage treatment station according to the location of each garbage treatment station; determine the timeliness index of each garbage treatment station in processing the garbage on the garbage truck based on the weight assessment value corresponding to each category of garbage, the processing efficiency of each category of garbage at each garbage treatment station, the weight of each category of garbage to be processed, and the transportation time of each garbage treatment station; determine the economic benefit of each garbage treatment station in processing the garbage on the garbage truck based on the weight assessment value corresponding to each category of garbage, the unit processing cost of each category of garbage at each garbage treatment station, the transportation cost of each garbage treatment station, and the unit benefit generated by each category of garbage at each garbage treatment station; determine the economic benefit of each garbage treatment station in processing the garbage on the garbage truck based on the weight assessment value corresponding to each category of garbage, the unit processing cost of each category of garbage at each garbage treatment station, the transportation cost of each garbage treatment station, and the unit benefit generated by each category of garbage at each garbage treatment station; determine the timeliness index of each garbage treatment station in processing the garbage on the garbage truck based on the weight assessment value corresponding to each category of garbage The weight assessment value and the environmental pollution index generated by each garbage treatment station in treating various types of garbage are used to determine the environmental benefit of each garbage treatment station in treating the garbage on the garbage vehicle; the weight values ​​corresponding to the timeliness index, economic benefit and environmental benefit are obtained respectively, and the scheduling priority value of each garbage treatment station in treating the garbage on the garbage vehicle is determined by using the weight values ​​corresponding to the timeliness index, economic benefit and environmental benefit respectively and the timeliness index, economic benefit and environmental benefit corresponding to each garbage treatment station, and the garbage treatment station with the highest scheduling priority value is taken as the target treatment station; the navigation route corresponding to the target treatment station is determined and sent to the garbage vehicle, so that the garbage vehicle transports the garbage to the target treatment station according to the navigation route.

[0070] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0071] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0072] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0073] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0074] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0075] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0076] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0077] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A dynamic garbage classification, collection, transportation and processing system, characterized in that: include: The file reading and processing module is used to import a new file and identify the file name. When the identification result indicates that the new file is a processing performance file of a garbage processing station, the file content is read and identified, and the processing parameters of the garbage processing station are extracted and stored; wherein the processing parameters include: the processing efficiency of the garbage processing station for various types of garbage, the weight of various types of garbage to be processed, the unit processing cost of processing various types of garbage, the environmental pollution value generated by processing various types of garbage, the unit benefit generated by processing various types of garbage, and the location of the garbage processing station; when the identification result indicates that the new file is a garbage classification instruction file , compare the new garbage classification index file with the stored historical garbage classification index file to obtain a difference text; identify the difference text, and when the difference text indicates that a new category has been added to a garbage category, obtain a picture of the new category from the Internet, and rotate and / or scale the obtained picture; use the picture obtained from the Internet, the picture formed by rotation and / or scaling as an initial sample training set and store it; send instruction information to the display module, so that the display module displays the added new category and the information forming the initial sample training set, and enable the user to train the corresponding verification model according to the initial sample training set; A classification verification module is used to obtain a video of a garbage truck collecting garbage, extract type positioning key frames and classification verification key frames from the video file, wherein the type positioning key frames include at least two frames, and the classification verification key frames include a plurality of key frames randomly selected from frames after a predetermined number of frames after the type positioning key frames in the video; input the type positioning key frames into a trained classification model, use the classification model to determine the garbage category corresponding to each type positioning key frame, and when the garbage categories corresponding to each type positioning key frame are the same, use the garbage category determined by the classification model as the target garbage category; determine a target verification model that matches the target garbage category, and input the classification verification key frames into the trained target verification model, and use the target verification model to determine the proportion of garbage belonging to the target garbage type in each key frame; calculate the mean of the proportions corresponding to each key frame, and when the mean is greater than a preset threshold, determine that the currently collected garbage is classified as qualified; wherein different categories of garbage correspond to different verification models; A weight assessment module is used to obtain images of garbage collectors of various categories on garbage trucks when garbage classification is qualified, and input the images corresponding to garbage collectors of various categories into corresponding weight assessment models, process the received images using the weight assessment model, obtain weight assessment values ​​of garbage of each category, and input the weight assessment values ​​corresponding to garbage of each category into the scheduling module and the early warning interaction module when the weight assessment values ​​of garbage of each category are greater than the first preset weight; wherein different weight assessment models correspond to different categories of garbage; The early warning interaction module is used to store the total weight of each type of garbage in each time period of the garbage truck, and generate and display statistical charts according to the total weight of each type of garbage and each time period; and, when the total weight in N consecutive time periods is greater than the second preset weight of the corresponding garbage category, display and send early warning information to the control end; the early warning information includes the category of excess garbage and the weight of garbage exceeding the corresponding second preset weight; wherein N is a positive integer; The scheduling module is used to obtain the processing parameters of each garbage treatment station, and determine the transportation cost and transportation time of each garbage treatment station according to the location of each garbage treatment station; determine the timeliness index of each garbage treatment station in processing the garbage on the garbage truck based on the weight evaluation value corresponding to each type of garbage, the processing efficiency of each type of garbage at each garbage treatment station, the weight of each type of garbage to be processed, and the transportation time of each garbage treatment station; determine the economic benefit of each garbage treatment station in processing the garbage on the garbage truck based on the weight evaluation value corresponding to each type of garbage, the unit processing cost of each garbage treatment station for processing each type of garbage, the transportation cost of each garbage treatment station, and the unit benefit generated by each garbage treatment station for processing each type of garbage; determine the economic benefit of each garbage treatment station in processing the garbage on the garbage truck based on the weight evaluation value corresponding to each type of garbage, the unit processing cost of each garbage treatment station for processing each type of garbage, the transportation cost of each garbage treatment station, and the unit benefit generated by each garbage treatment station for processing each type of garbage; determine the timeliness index of each garbage treatment station in processing the garbage on the garbage truck based on the weight evaluation value corresponding to each type of garbage The corresponding weight assessment value and the environmental pollution index generated by each garbage treatment station in treating various types of garbage are used to determine the environmental benefit of each garbage treatment station in treating the garbage on the garbage vehicle; the weight values ​​corresponding to the timeliness index, economic benefit and environmental benefit are obtained respectively, and the scheduling priority value of each garbage treatment station in treating the garbage on the garbage vehicle is determined by using the weight values ​​corresponding to the timeliness index, economic benefit and environmental benefit respectively and the timeliness index, economic benefit and environmental benefit corresponding to each garbage treatment station, and the garbage treatment station with the highest scheduling priority value is taken as the target treatment station; the navigation route corresponding to the target treatment station is determined and sent to the garbage vehicle, so that the garbage vehicle transports the garbage to the target treatment station according to the navigation route.

2. The system according to claim 1, characterized in that When identifying the file name, the file reading processing module is specifically used to: Get the file name; Perform word segmentation on the file name to obtain multiple words; Extract keywords from the multiple words to obtain multiple keywords; The extracted keywords are matched with the vocabulary corresponding to each preset recognition result, and the preset recognition result with the highest matching degree is used as the recognition result of the new file.

3. The system according to claim 1, characterized in that When determining the navigation route corresponding to the target processing station, the scheduling module is specifically used to: Acquire a current first position of the garbage truck and a second position of the target processing station; Get pre-made maps; A navigation route corresponding to a target processing station is generated according to the first location, the second location and the prefabricated map.

4. The system according to claim 1, characterized in that When obtaining the video of the garbage truck collecting garbage, the classification verification module is specifically used to: When the garbage truck lifts the garbage can containing garbage, the video collection starts; and when the garbage truck puts down the garbage can, the video collection stops; The type positioning key frames are two frames in the first M frames of the video; wherein M is a positive integer.

5. The system according to claim 1, characterized in that The weight assessment model determines a weight assessment value based on the height of the garbage in the garbage collector.

6. The system according to claim 1, characterized in that The early warning interaction module is also used for: When the total weight in P consecutive time periods is less than the third preset weight of the corresponding garbage category, a praise message is displayed and sent to the management and control end; the praise message includes the category of garbage and the weight of garbage lower than the corresponding third preset weight; wherein P is a positive integer.

7. The system according to claim 1, characterized in that The scheduling module is also used for: Get the processing parameters input by the user; Stores processing parameters entered by the user.

8. A dynamic garbage classification, collection, transportation and processing method, characterized in that: include: Import a new file and identify the file name. When the identification result indicates that the new file is a processing performance file of a garbage treatment station, read the file content, identify the read file content, extract and store the processing parameters of the garbage treatment station; wherein the processing parameters include: the processing efficiency of the garbage treatment station for various types of garbage, the weight of various types of garbage to be processed, the unit processing cost of processing various types of garbage, the environmental pollution value generated by processing various types of garbage, the unit benefit generated by processing various types of garbage, and the location of the garbage treatment station; when the identification result indicates that the new file is a garbage classification instruction file, compare the new garbage classification indicator file with the stored historical garbage classification indicator file to obtain a difference text; identify the difference text, and when the difference text indicates that a new category has been added to a garbage category, obtain a picture of the new category from the Internet, and rotate and / or scale the obtained picture; use the picture obtained from the Internet and the picture formed by rotation and / or scaling as an initial sample training set and store it; send instruction information to the display module so that the display module displays the added new category and the information forming the initial sample training set, and enable the user to train the corresponding verification model according to the initial sample training set; Obtain a video of a garbage truck collecting garbage, extract type positioning key frames and classification verification key frames from the video file, wherein the type positioning key frames include at least two frames, and the classification verification key frames include a plurality of key frames randomly selected from frames after a predetermined number of frames after the type positioning key frames in the video; input the type positioning key frames into a trained classification model, use the classification model to determine the garbage category corresponding to each type positioning key frame, and when the garbage categories corresponding to each type positioning key frame are the same, use the garbage category determined by the classification model as the target garbage category; determine a target verification model that matches the target garbage category, and input the classification verification key frames into the trained target verification model, and use the target verification model to determine the proportion of garbage belonging to the target garbage type in each key frame; calculate the mean of the proportions corresponding to each key frame, and when the mean is greater than a preset threshold, determine that the currently collected garbage is classified as qualified; wherein different categories of garbage correspond to different verification models; When the garbage classification is qualified, images of garbage collectors of various categories on the garbage truck are obtained, and the images corresponding to the garbage collectors of various categories are respectively input into the corresponding weight assessment models, and the received images are processed by the weight assessment models to obtain the weight assessment value of each category of garbage, and when the weight assessment value of each category of garbage is greater than the first preset weight, the following steps are performed: The total weight of each type of garbage of the garbage truck in each time period is stored, and a statistical chart is generated and displayed according to the total weight of each type of garbage and each time period; and, when the total weight in N consecutive time periods is greater than the second preset weight of the corresponding garbage category, an early warning message is displayed and sent to the control end; the early warning message includes the category of the excessive garbage and the weight of the garbage exceeding the corresponding second preset weight; wherein N is a positive integer; Obtain the processing parameters of each garbage treatment station, and determine the transportation cost and transportation time of each garbage treatment station according to the location of each garbage treatment station; determine the timeliness index of each garbage treatment station in processing the garbage on the garbage truck based on the weight assessment value corresponding to each category of garbage, the processing efficiency of each category of garbage at each garbage treatment station, the weight of each category of garbage to be processed, and the transportation time of each garbage treatment station; determine the economic benefit of each garbage treatment station in processing the garbage on the garbage truck based on the weight assessment value corresponding to each category of garbage, the unit processing cost of each category of garbage at each garbage treatment station, the transportation cost of each garbage treatment station, and the unit benefit generated by each category of garbage at each garbage treatment station; determine the economic benefit of each garbage treatment station in processing the garbage on the garbage truck based on the weight assessment value corresponding to each category of garbage, the unit processing cost of each category of garbage at each garbage treatment station, the transportation cost of each garbage treatment station, and the unit benefit generated by each category of garbage at each garbage treatment station; determine the timeliness index of each garbage treatment station in processing the garbage on the garbage truck based on the weight assessment value corresponding to each category of garbage The weight assessment value and the environmental pollution index generated by each garbage treatment station in treating various types of garbage are used to determine the environmental benefit of each garbage treatment station in treating the garbage on the garbage vehicle; the weight values ​​corresponding to the timeliness index, economic benefit and environmental benefit are obtained respectively, and the scheduling priority value of each garbage treatment station in treating the garbage on the garbage vehicle is determined by using the weight values ​​corresponding to the timeliness index, economic benefit and environmental benefit respectively and the timeliness index, economic benefit and environmental benefit corresponding to each garbage treatment station, and the garbage treatment station with the highest scheduling priority value is taken as the target treatment station; the navigation route corresponding to the target treatment station is determined and sent to the garbage vehicle, so that the garbage vehicle transports the garbage to the target treatment station according to the navigation route.

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