Solid waste intelligent management system and method

Abstract

The invention discloses a solid waste intelligent management system and method. The system comprises an image acquisition module, an image recognition module and a data analysis module, wherein the image acquisition module is used for performing image acquisition on a target stacking area to obtain a target image; the image recognition module is used for recognizing the target image by adopting a pre-trained image recognition model and determining target recognition information; wherein the target identification information comprises first information of the target stacking area and second information of the solid waste stacked in the target stacking area; and the data analysis module is used for comparing the target identification information with pre-stored information, and performing early warning if the comparison result is inconsistent. According to the technical scheme, the stacking area of the solid waste is subjected to real-time image acquisition and recognition, so that automatic management of the stacking area and the solid waste is realized, the recognition accuracy of the solid waste is improved, and data support is provided for environmental protection and resource utilization.

Description

Technical Field

[0001] The present application relates to the field of machine vision technology, and in particular to an intelligent solid waste management system and method. Background Art

[0002] With the rapid development of industrialization, the amount of solid waste is increasing day by day. Moreover, there are some hazardous wastes in solid waste that are toxic, flammable, corrosive, reactive, radioactive and infectious. Therefore, how to safely manage and dispose of solid waste has become particularly important.

[0003] In the relevant technical solutions, there are usually the following methods for the management of solid waste. One is to use a video security monitoring system to monitor the storage of solid waste by deploying a video monitoring system in the solid waste management site. The second is to use waste management software to record the flow information of waste to achieve the monitoring of solid waste. The third is to use a warehouse management system to intelligently manage the flow of solid waste in the solid waste storage warehouse. However, the above solutions are highly dependent on manual operation and are easily affected by human factors, resulting in data errors and omissions. In addition, they lack automated real-time monitoring methods and cannot quickly and efficiently feedback the volume of hazardous waste storage piles, resulting in data lags.

[0004] Therefore, how to provide a technical solution that can intelligently manage solid waste storage is a technical problem that needs to be urgently solved by technical personnel in this field. Summary of the invention

[0005] The present application provides an intelligent solid waste management system and method, which realizes automated management of stacking areas and solid waste by real-time image acquisition and identification of solid waste stacking areas, improves the accuracy of solid waste identification, and provides data support for environmental protection and resource utilization.

[0006] According to one aspect of the present application, a solid waste intelligent management system is provided, the system comprising an image acquisition module, an image recognition module and a data analysis module; wherein:

[0007] The image acquisition module is used to acquire images of the target stacking area to obtain the target image;

[0008] The image recognition module is used to recognize the target image using a pre-trained image recognition model to determine target recognition information; wherein the target recognition information includes first information of the target stacking area and second information of the solid waste stacked in the target stacking area;

[0009] The data analysis module is used to compare the target identification information with the pre-stored information, and issue an early warning if the comparison result is inconsistent.

[0010] According to another aspect of the present application, a solid waste intelligent management method is provided, which is applied to the solid waste intelligent management system, and the system includes an image acquisition module, an image recognition module and a data analysis module; wherein the method includes:

[0011] Through the image acquisition module, the target stacking area is imaged to obtain the target image;

[0012] The target image is recognized by the image recognition module using a pre-trained image recognition model to determine target recognition information; wherein the target recognition information includes first information of the target stacking area and second information of the solid waste stacked in the target stacking area;

[0013] The target identification information is compared with the pre-stored information through the data analysis module, and an early warning is issued if the comparison result is inconsistent.

[0014] The technical solution provided by the present application is to acquire the target image by using the image acquisition module to acquire the target image of the target stacking area; to identify the target image by using the pre-trained image recognition model by using the image recognition module to determine the target identification information; wherein the target identification information includes the first information of the target stacking area and the second information of the solid waste stacked in the target stacking area; and to compare the target identification information with the pre-stored information by using the data analysis module, and to issue an early warning if the comparison result is inconsistent. The present technical solution realizes the automated management of the stacking area and solid waste by acquiring and identifying the solid waste stacking area in real time, improves the identification accuracy of the solid waste, and provides data support for environmental protection and resource utilization.

[0015] 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 application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a structural diagram of a solid waste intelligent management system provided in Example 1 of the present application.

[0018] Figure 2 A flow chart of a solid waste intelligent management method provided in Example 2 of the present application. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0020] It should be noted that the terms "target", "first", "second", "candidate", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or precedence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] Embodiment 1

[0022] Figure 1 This is a structural diagram of a solid waste intelligent management system provided in Example 1 of this application. Figure 1 As shown, the system includes an image acquisition module 110, an image recognition module 120 and a data analysis module 130; wherein,

[0023] The image acquisition module 110 is used to acquire images of the target stacking area to obtain a target image;

[0024] The image recognition module 120 is used to recognize the target image using a pre-trained image recognition model to determine target recognition information; wherein the target recognition information includes first information of the target stacking area and second information of the solid waste stacked in the target stacking area;

[0025] The data analysis module 130 is used to compare the target identification information with the pre-stored information, and issue an early warning if the comparison result is inconsistent.

[0026] The target stacking area can be a temporary stacking or transfer area for solid waste. For example, a special area for storing steel slag and iron filings is set up inside a steel plant; a warehouse for temporarily storing electronic waste such as waste circuit boards and discarded electronic components is set up inside an electronics factory. The target stacking area is usually classified and stored according to hazardous waste and general industrial waste, and measures such as rain protection, sun protection and leakage prevention are set up to prevent solid waste from polluting the environment during the stacking process.

[0027] The target image may be an image reflecting the spatial layout of the target stacking area, an image reflecting the stacking conditions of the stacked objects in the target stacking area, or an image reflecting the stacking facilities in the target stacking area.

[0028] Specifically, the image acquisition module can obtain a target image by installing at least one image acquisition device in the target stacking area and acquiring images of the target stacking area through the installed at least one image acquisition device. If multiple image acquisition devices are installed in the target stacking area, the target image can be obtained by fusing multiple images acquired at the same time.

[0029] The image recognition model may be a model obtained by learning and training the stacking area and solid waste characteristics in advance through a large amount of training data. For example, the training data may include images under different lighting conditions or in complex situations such as mixed solid waste stacking.

[0030] The image recognition model can be a convolutional neural network, such as AlexNet, VGG series, ResNet series, etc., which automatically extracts image features such as texture, shape, color and other information through the convolution layer, compresses and reduces the features through the pooling layer, and then performs classification and other operations through the fully connected layer. The image recognition model can also be a Transformer-based model, which divides the image into several small blocks, and then encodes the image blocks, performs attention calculations and other operations to capture the correlation features between different areas of the image. Recognizing the target image through the image recognition model greatly reduces the need for manual intervention and improves the efficiency and accuracy of identifying stacking areas and solid waste.

[0031] The target identification information may be the first information obtained by identifying the target stacking area, or the second information obtained by identifying the solid waste stacked in the target stacking area. Specifically, the information content to be identified may be determined according to the identification task of the system, or all the information in the target image may be directly identified.

[0032] The first information may be the spatial layout information of the target stacking area, such as shape, size, storage scale, etc., management subject information, and the types of solid waste that can be stored, etc. For example, the shape and size of a stacking area A is a rectangle with a length of 4 meters and a width of 3 meters, the storage scale is 30 cubic meters, the management subject is factory a, and the type of solid waste that can be stored is electronic waste.

[0033] Among them, the second information may be the type, quantity and distribution, morphological characteristics, relevant identification and marking, disposal requirements and historical disposal of the solid waste piled up in the target stacking area.

[0034] Optionally, the first information includes the effective volume of the target stacking area; accordingly, the image recognition module includes: a void volume determination unit, used to determine the void volume of the target stacking area according to the size of the voids in the target stacking area; wherein the voids include gaps and / or cushion layers; a stacking area volume determination unit, used to determine the total volume of the target stacking area according to the size of the target stacking area; and an effective volume determination unit, used to determine the effective volume of the target stacking area according to the void volume and the total volume.

[0035] Among them, the effective volume can be the actual volume of solid waste that can be stored in the target stacking area, which can directly reflect the actual storage capacity of the target stacking area, and is of great significance for the daily operation and management work such as the reasonable arrangement of the stacking volume of solid waste, planning of removal and transportation, and transfer plans. For example, in a landfill, only by determining the effective volume of the stacking area can we determine how much garbage the stacking area can continue to receive and when a new landfill area needs to be opened; for example, in the temporary storage area of ​​industrial solid waste, based on the effective volume of the stacking area, it can be judged whether there is enough space to store the waste generated later, whether it is necessary to timely deploy transportation vehicles to transfer the current solid waste to other treatment sites, etc.

[0036] In the present application, the total volume and void volume of the target stacking area are determined by performing image recognition on the target stacking area, and the effective volume is then determined on this basis.

[0037] The void volume may be the volume occupied by gaps and / or cushions in the target stacking area. For example, in a construction waste dump, there may be irregular gaps between the construction waste blocks, and a sand and stone cushion may be laid at the bottom of the stacking area to prevent damage to the ground.

[0038] Specifically, image recognition technology can be used to first pre-process the image of the target stacking area, such as adjusting the image clarity and contrast, so as to better distinguish the gap part, and then use the image processing algorithm to identify the outline of the gap, and convert the pixel size into the actual physical size by combining the pixel size with the known image scale, and then calculate the volume of the gap.

[0039] The total volume of the target stacking area may be the maximum space size theoretically occupied by the target stacking area. For example, for a rectangular solid waste stacking warehouse with a regular shape, the length, width, height and other dimensions of the area may be obtained through image recognition, and the total volume of the stacking area may be obtained through the rectangular parallelepiped volume calculation formula.

[0040] It should be noted that when calculating the total volume of the target stacking area, it is usually necessary to consider the safety boundary line around the target stacking area to prevent accidents. For example, some target stacking areas will use yellow warning lines on the ground to mark the stacking area. When identifying through the target image, the yellow warning lines can be directly extracted to determine the total volume of the target stacking area.

[0041] On this basis, the effective volume can be obtained by subtracting the void volume from the total volume of the target stacking area. The advantage of the above technical solution is that by accurately calculating the effective volume of the target stacking area, the storage planning of solid waste can be optimized and the operating efficiency of the stacking area can be improved.

[0042] Based on the above scheme, the second information includes at least one of the type, quantity, and volume of the solid waste; accordingly, the image recognition module also includes: a solid waste label extraction unit, used to extract the label area in the target image to obtain the label image of the solid waste; a solid waste type identification unit, used to calculate the similarity between the label image and each standard image, and determine the type of the solid waste based on the similarity calculation result.

[0043] In accordance with relevant management policy requirements, some solid waste will have identification labels containing key attribute information of the solid waste, such as type, composition, hazardous characteristics, etc., posted in a prominent position on the outer packaging.

[0044] Specifically, the label area can be located based on edge detection, texture features, color features and other methods. For example, labels usually have regular shapes and relatively clear edges, and possible label outline boundaries can be found through edge detection algorithms; for another example, the text and patterns on the label often differ from the texture, color and other features of the surrounding solid waste and background, and the label area can be confirmed by using color feature differences. After locating the label area, it can be extracted through operations such as image cropping to obtain a label image.

[0045] Furthermore, a standard image library covering various common types of solid waste can be constructed. Each standard image in the standard image library corresponds to a type of solid waste, and the standard image content can also include typical features of the solid waste label of that type, such as text description, specific pattern logo, color matching, etc. Standard images can be obtained by collecting formal solid waste label samples or referring to relevant industry specification materials.

[0046] The similarity calculation between the label image and each standard image can be performed by using a method based on image pixels, a method based on feature vectors, or a method based on deep learning features. The present application embodiment does not limit this and can be selected according to actual needs. For example, when using color histogram similarity calculation, the color histograms of the label image and the standard image can be extracted respectively, and then the similarity value between the two can be calculated. The higher the value, the more similar it is.

[0047] After calculating the similarity between the label image and each standard image, the type of solid waste can be determined based on the set similarity threshold. Usually, the type of solid waste corresponding to the standard image with the highest similarity that exceeds a certain threshold is selected as the final recognition result. For example, if the threshold is set to 0.7, if the similarity between the label image and the standard image of a certain plastic solid waste reaches 0.8 and is the highest value among all comparison results, then the type of solid waste is determined to be plastic.

[0048] The advantage of the above technical solution is that by identifying the types of solid waste, it provides strong support for the scientific management of the stacking area and the subsequent treatment process.

[0049] On the basis of the above scheme, the image recognition module also includes: a solid waste shape and size unit, which is used to identify the shape of the solid waste and determine the shape of the solid waste; a first volume determination unit, which is used to determine the volume of the solid waste according to the size of the solid waste with respect to the solid waste of regular shape; and a second volume determination unit, which is used to approximate the shape of the solid waste with respect to the solid waste of irregular shape to obtain an approximate regular shape, and determine the volume of the solid waste according to the size of the approximate regular shape.

[0050] For solid wastes of different shapes, the volume calculation method is different. Therefore, the present application can determine the volume calculation method by identifying the shape of the solid waste.

[0051] Specifically, the outline of the solid waste in the target image can be extracted based on the edge detection algorithm, and then the shape characteristics of the solid waste can be determined by calculating the geometric characteristic parameters of the outline, such as perimeter, area, circularity or rectangularity, etc. For example, if the ratio of perimeter to area is close to the theoretical value of a circle and the circularity is high, it can be determined to be a circle; for another example, if the outline has four obvious sides and the rectangularity is high, it can be determined to be a rectangle.

[0052] For solid waste with regular shapes, such as cuboids, cubes, cylinders, spheres, etc., their volumes can be calculated using precise mathematical formulas. For solid waste with irregular shapes, they can be divided into multiple relatively regular shapes and determined by calculating the sum of the volumes of each regular shape; or their shapes can be fitted using known regular geometric models and their volumes calculated using the volume formula of the corresponding model.

[0053] The beneficial effect of the above technical solution is that, by calculating the volume of solid wastes of different shapes, the space utilization in the target stacking area can be more comprehensively understood, thereby facilitating the management of solid wastes.

[0054] The data analysis module can compare the image recognition results with the records in the solid waste intelligent management system in real time to promptly discover possible problems in the target stacking area and ensure the normal and orderly development of solid waste stacking management.

[0055] Specifically, the target identification information can be compared with the pre-stored information in the solid waste intelligent management system one by one, and a warning signal can be issued when any comparison result is inconsistent.

[0056] Among them, the early warning signal can be a visual prompt, such as popping up a striking warning box on the interface of the solid waste intelligent management system; it can also be a sound reminder, such as issuing a specific sound alarm; it can also be a message push, such as sending the early warning information to relevant responsible personnel through text messages, instant messaging software, etc.

[0057] On the basis of the above technical solution, optionally, the data analysis module includes: a first information comparison unit, used to compare the first information with the pre-stored area information to obtain a first comparison result, and if the first comparison result is inconsistent, an early warning is issued for the target stacking area; and / or, a second information comparison unit, used to compare the second information with the pre-stored waste information to obtain a second comparison result, and if the second comparison result is inconsistent, an early warning is issued for the solid waste.

[0058] For the first information comparison unit, the types of solid waste stored in the area and the effective volume of the area can be specifically compared. For example, if the type of the target storage area registered in the solid waste intelligent management system is a domestic waste transfer station, if it is observed from the image that the waste stored inside is mainly industrial waste residues rather than domestic waste, or if it is found that the layout is very different from the pre-stored layout information, and there are no corresponding transfer facilities such as compression equipment, then an early warning can be triggered to remind management personnel to verify and handle it.

[0059] For the second information comparison unit, the types, quantities and volumes of solid waste can be specifically compared. For example, in a certain construction solid waste disposal site, the pre-stored information shows that the main construction wastes are discarded bricks, concrete blocks, waste wood, etc., while through image recognition, it is found that chemical waste residues are actually piled up in the area. When the two information are inconsistent, it is possible that solid wastes may be mixed or dumped illegally in the area. Timely warnings are used to prevent different types of solid wastes from affecting each other and causing environmental hazards. For another example, if the quantity and volume displayed by the target identification information are significantly different from the pre-stored information, such as the actual quantity far exceeds the pre-stored carrying capacity, it may cause the stacking area to be overloaded and there is a risk of overflow. For another example, if the quantity suddenly decreases significantly, there may be unregistered illegal transfers, etc., then the data analysis module triggers an early warning to remind managers to pay attention to whether the circulation of solid waste is normal and whether it is necessary to adjust the operation strategy or strengthen supervision.

[0060] The beneficial effect of the above technical solution is that, by comparing the actual storage information with the pre-stored information, the solid waste stacking area can be monitored in real time to ensure the standardized use of the stacking area and the allocation and utilization efficiency of resources.

[0061] The embodiment of the present invention provides an intelligent management system for solid waste, which includes an image acquisition module, an image recognition module and a data analysis module; wherein the image acquisition module is used to acquire images of a target stacking area and obtain a target image; the image recognition module is used to recognize the target image using a pre-trained image recognition model and determine target recognition information; wherein the target recognition information includes first information of the target stacking area and second information of the solid waste stacked in the target stacking area; the data analysis module is used to compare the target recognition information with the pre-stored information, and issue an early warning if the comparison result is inconsistent. This technical solution, through real-time image acquisition and recognition of the solid waste stacking area, realizes the automated management of the stacking area and solid waste, improves the recognition accuracy of solid waste, and provides data support for environmental protection and resource utilization.

[0062] On the basis of the above embodiment, optionally, the system further includes: a data integration module, which is used to connect the candidate solid waste management system using a preset interface to achieve data interaction with the candidate solid waste business system.

[0063] The preset interface may be a data interaction format, protocol, and specific calling method, etc. The candidate solid waste management system may be an existing solid waste management system, or a solid waste intelligent management system in other regions.

[0064] Specifically, the interface of the candidate solid waste management system can be identified and adapted first. After the interface adaptation is completed, a connection request can be sent to the candidate solid waste management system through the data integration module, and a communication link can be established after verification.

[0065] The advantage of the above technical solution is that it can not only utilize existing system resources, but also ensure the real-time and accuracy of data, and improve the efficiency and reliability of the entire solid waste management process.

[0066] Based on the above embodiment, optionally, the system further includes: a data saving unit, configured to save the target image when the comparison results are inconsistent.

[0067] Specifically, folders can be created according to time for classified storage, which makes it easier to find inconsistencies in comparison that occur in a specific time period. The storage path can also be further subdivided based on the name and number of the stacking area. For example, main folders can be created according to different solid waste stacking areas, and images can be stored in each main folder according to time.

[0068] For example, if domestic waste is found in an industrial solid waste storage area, the relevant management personnel can confirm when and how the domestic waste entered the area by viewing the saved target images, and then determine whether it was caused by accidental unloading during transportation or negligence in management, so as to take targeted corrective measures.

[0069] The advantage of the above technical solution is that it retains key original data for subsequent further verification, analysis and tracing of abnormal situations, which helps relevant managers to fully understand the abnormal situations and make accurate judgments and decisions.

[0070] Embodiment 2

[0071] Figure 2 The second embodiment of the present application provides a flow chart of a solid waste intelligent management method. This embodiment can be applied to the case of intelligent management of solid waste storage areas. The method can be applied to a solid waste intelligent management system, which can be implemented in the form of hardware and / or software. Specifically, the solid waste intelligent management system includes an image acquisition module, an image recognition module, and a data analysis module. Figure 2 As shown, the intelligent solid waste management method includes the following steps.

[0072] S210: Capture an image of the target stacking area through the image acquisition module to obtain a target image.

[0073] S220. Using the image recognition module, a pre-trained image recognition model is used to recognize the target image to determine target recognition information; wherein the target recognition information includes first information of the target stacking area and second information of the solid waste stacked in the target stacking area.

[0074] S230: Compare the target identification information with the pre-stored information through the data analysis module, and issue a warning if the comparison results are inconsistent.

[0075] The embodiment of the present invention provides a method for intelligent management of solid waste, which uses an image acquisition module to acquire an image of a target stacking area to obtain a target image; uses an image recognition module to recognize the target image using a pre-trained image recognition model to determine target recognition information; wherein the target recognition information includes first information of the target stacking area and second information of the solid waste stacked in the target stacking area; uses a data analysis module to compare the target recognition information with the pre-stored information, and issues an early warning if the comparison result is inconsistent. This technical solution, through real-time image acquisition and recognition of the solid waste stacking area, realizes automated management of the stacking area and solid waste, improves the recognition accuracy of solid waste, and provides data support for environmental protection and resource utilization.

[0076] Optionally, the first information includes the effective volume of the target stacking area; accordingly, the target image is recognized by a pre-trained image recognition model to determine target recognition information, including: determining the void volume of the target stacking area according to the size of the voids in the target stacking area; wherein the voids include gaps and / or cushion layers; determining the total volume of the target stacking area according to the size of the target stacking area; and determining the effective volume of the target stacking area according to the void volume and the total volume.

[0077] Optionally, the second information includes at least one of the type, quantity, and volume of the solid waste; accordingly, the target image is identified using a pre-trained image recognition model to determine the target identification information, and also includes: extracting the label area in the target image to obtain a label image of the solid waste; calculating the similarity between the label image and each standard image, and determining the type of the solid waste based on the similarity calculation result.

[0078] Optionally, a pre-trained image recognition model is used to identify the target image and determine the target recognition information, which also includes: identifying the shape of the solid waste and determining the shape of the solid waste; for solid waste with a regular shape, determining the volume of the solid waste according to the size of the solid waste; for solid waste with an irregular shape, approximating the shape of the solid waste to obtain an approximate regular shape, and determining the volume of the solid waste according to the size of the approximate regular shape.

[0079] Optionally, the target identification information is compared with the pre-stored information, and an early warning is issued if the comparison result is inconsistent, including: comparing the first information with the pre-stored area information to obtain a first comparison result, and if the first comparison result is inconsistent, an early warning is issued for the target stacking area; and / or, comparing the second information with the pre-stored waste information to obtain a second comparison result, and if the second comparison result is inconsistent, an early warning is issued for the solid waste.

[0080] Optionally, the method further includes: using a preset interface to connect the candidate solid waste management system to achieve data interaction with the candidate solid waste business system.

[0081] Optionally, the method further includes: saving the target image when the comparison results are inconsistent.

[0082] 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 application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution of this application can be achieved, and this document is not limited here.

[0083] The above specific implementations do not constitute a limitation on the protection scope of this application. 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 modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A solid waste intelligent management system, characterized in that: The system includes an image acquisition module, an image recognition module and a data analysis module; wherein, The image acquisition module is used to acquire images of the target stacking area to obtain the target image; The image recognition module is used to recognize the target image using a pre-trained image recognition model to determine target recognition information; wherein the target recognition information includes first information of the target stacking area and / or second information of the solid waste stacked in the target stacking area; The data analysis module is used to compare the target identification information with the pre-stored information, and issue an early warning if the comparison result is inconsistent.

2. The system according to claim 1, characterized in that The first information includes the effective volume of the target stacking area; Accordingly, the image recognition module includes: A void volume determination unit, configured to determine the void volume of the target stacking area according to the size of the void in the target stacking area; wherein the void includes a gap and / or a cushion layer; A stacking area volume determination unit, used to determine the total volume of the target stacking area according to the size of the target stacking area; The effective volume determination unit is used to determine the effective volume of the target stacking area according to the void volume and the total volume.

3. The system according to claim 1, characterized in that The second information includes at least one of the type, quantity, and volume of the solid waste; Accordingly, the image recognition module further includes: A solid waste label extraction unit, used to extract the label area in the target image to obtain a label image of the solid waste; The solid waste type identification unit is used to calculate the similarity between the label image and each standard image, and determine the type of the solid waste according to the similarity calculation result.

4. The system according to claim 3, characterized in that The image recognition module further includes: A solid waste shape and size unit, used to identify the shape of the solid waste and determine the shape of the solid waste; A first volume determination unit, for determining the volume of the solid waste according to the size of the solid waste in a regular shape; The second volume determination unit is used for approximating the shape of the solid waste in an irregular shape to obtain an approximate regular shape, and determining the volume of the solid waste according to the size of the approximate regular shape.

5. The system according to claim 1, characterized in that The data analysis module comprises: A first information comparison unit, configured to compare the first information with pre-stored area information to obtain a first comparison result, and to issue an early warning to the target stacking area if the first comparison result is inconsistent; And / or, a second information comparison unit is used to compare the second information with pre-stored waste information to obtain a second comparison result, and if the second comparison result is inconsistent, an early warning is issued for the solid waste.

6. The system according to claim 1, characterized in that The system further comprises: The data integration module is used to connect the candidate solid waste management system using a preset interface to achieve data interaction with the candidate solid waste business system.

7. The system according to claim 1, characterized in that The system further comprises: The data storage unit is used to store the target image when the comparison results are inconsistent.

8. A solid waste intelligent management method, characterized in that: Applied to the solid waste intelligent management system, the system includes an image acquisition module, an image recognition module and a data analysis module; the method includes: Through the image acquisition module, the target stacking area is imaged to obtain the target image; The target image is recognized by the image recognition module using a pre-trained image recognition model to determine target recognition information; wherein the target recognition information includes first information of the target stacking area and second information of the solid waste stacked in the target stacking area; The target identification information is compared with the pre-stored information through the data analysis module, and an early warning is issued if the comparison result is inconsistent.

9. The method according to claim 8, characterized in that The first information includes the effective volume of the target stacking area; Accordingly, the target image is recognized using a pre-trained image recognition model to determine target recognition information, including: Determine the void volume of the target stacking area according to the size of the void in the target stacking area; wherein the void includes a gap and / or a cushion layer; Determining the total volume of the target stacking area according to the size of the target stacking area; The effective volume of the target stacking area is determined according to the void volume and the total volume.

10. The method according to claim 8, characterized in that The second information includes at least one of the type, quantity, and volume of the solid waste; Accordingly, the target image is recognized using a pre-trained image recognition model to determine target recognition information, and further includes: Extracting the label area in the target image to obtain the label image of the solid waste; The similarity between the label image and each standard image is calculated, and the type of the solid waste is determined according to the similarity calculation result.

Images