An intelligent monitoring and early warning method for construction waste process treatment flow

Abstract

The application provides an intelligent monitoring and early warning method for a construction waste process treatment flow, comprising: transmitting construction waste to a specified area to perform a first operation and first monitoring of a construction waste process treatment flow, collecting a current image of the specified area, obtaining a first position of recyclable waste existing in the specified area based on the current image, matching a second operation from the construction waste process treatment flow to the first position, and performing second monitoring, analyzing material hardness of remaining construction waste in the specified area based on the current image, matching a third operation from the construction waste process treatment flow to the material hardness, and performing third monitoring, judging whether all operations are qualified based on the monitoring results, and reminding, through the existing construction waste process treatment flow, the construction waste is treated and the operation is monitored, the unqualified operation can be effectively and timely reminded, the rationality of supervision is ensured, and the treatment efficiency is ensured.

Description

Technical Field

[0001] This invention relates to the field of waste treatment technology, and in particular to an intelligent monitoring and early warning method for the process of construction waste treatment. Background Technology

[0002] my country has been working on the disposal and resource utilization of construction waste for some time now. However, looking at various construction waste disposal sites, the processing technology and equipment vary widely, the site planning is chaotic, the processing lines are not running smoothly, the quality of the finished products is poor, and the recycling rate of recyclable waste is low. The reason for this is that most of the processes are based on the subjective judgment of engineers and the experience of predecessors, and are copied blindly. Furthermore, the lack of supervision over the waste disposal process leads to low processing efficiency.

[0003] In view of this, the present invention provides an intelligent monitoring and early warning method for the process of construction waste treatment. Summary of the Invention

[0004] This invention provides an intelligent monitoring and early warning method for construction waste processing procedures. By monitoring the processing and operation of construction waste in the existing construction waste processing procedures, it can effectively and promptly remind users of non-compliant operations, ensuring both the rationality of supervision and the efficiency of processing.

[0005] This invention provides an intelligent monitoring and early warning method for construction waste processing, comprising:

[0006] S1: Based on the first operation of the construction waste processing flow, the construction waste is transferred to a designated area, and the current image of the designated area is collected, and the first operation is monitored.

[0007] S2: Based on the current image, obtain the first location of recyclable waste existing in the specified area, match the second operation from the construction waste processing flow to the first location, and perform the second monitoring;

[0008] S3: Based on the current image analysis, analyze the material hardness of the remaining construction waste in the specified area, perform a third operation to match the material hardness from the construction waste processing flow, and execute a third monitoring;

[0009] S4: Based on the first monitoring result, the second monitoring result, and the third monitoring result, determine whether all operations of the construction waste processing flow on the construction waste are qualified, and execute the corresponding reminder according to the judgment result.

[0010] Preferably, obtaining the first location of recyclable waste within a specified area based on the current image includes:

[0011] Obtain the set of cells in the current image, fill each cell in the set with first data that matches the current image, and extract second data from the first data according to the set of regions in the specified region;

[0012] According to the material analysis thread, the second data is parsed to obtain the type identifier;

[0013] The second data is traversed, and the type is identified for each unit in the corresponding region set based on the traversal results;

[0014] Based on the specified area, the same type of calibration results are divided into regions, and the final region corresponding to recyclable waste is obtained according to the identification type in different regions.

[0015] A list of recyclable waste is created based on the final area and transmitted to a designated terminal for display.

[0016] In this context, each final area in the list of recyclable waste is considered the first position of recyclable waste.

[0017] Preferably, the process of matching the second operation from the construction waste processing flow to the first location includes:

[0018] Analyze and determine the waste space occupancy attributes and recyclability attributes of the recyclable waste at the first location;

[0019] Determine the initial location point for performing waste disposal at the first location, and determine the ease of operation based on the first location according to the minimum straight-line route, waste space occupancy attribute, and waste recyclability attribute;

[0020] Based on the ease of operation and the type of waste, a second operation is matched to the first location.

[0021] Preferably, before determining whether all operations of the construction waste processing procedure are qualified for the construction waste based on the first monitoring result, the second monitoring result, and the third monitoring result, the procedure further includes:

[0022] During the first monitoring, a first video stream is captured during the first operation process;

[0023] During the second monitoring, a second video stream is captured during the second operation process;

[0024] During the third monitoring process, a third video stream is collected during the third operation.

[0025] The first video stream, the second video stream, and the third video stream are transmitted to a designated terminal for display.

[0026] Preferably, based on the current image analysis of the material hardness of the remaining construction waste in the designated area, a third operation is performed to match the material hardness from the construction waste processing flow, and a third monitoring is executed, including:

[0027] Extract the target area containing the remaining construction waste from the current image;

[0028] Several sampling sub-regions are extracted from the target area, and the region texture corresponding to each sampling sub-region is obtained and the corresponding texture features are determined.

[0029] Based on the texture features, the surface texture direction of the remaining construction waste is analyzed, and the waste type of the remaining construction waste is determined by matching the surface texture direction with the texture database.

[0030] Based on the type-hardness database, the remaining construction waste is matched with the corresponding first material hardness;

[0031] Hardness tests were performed on each type of waste in the remaining construction waste to obtain the corresponding second material hardness.

[0032] Based on the first and second material hardness of the same type of waste, the operation to be processed is matched from the preset processing methods, and the adjustable range of the operation for the corresponding type of waste is determined according to the hardness difference between the first and second material hardness of the same type of waste.

[0033] Based on the adjustable range of the operation, each sub-operation in the operation to be processed is adaptively adjusted, and the best sub-operation that matches the same type of waste is selected, and a third monitoring is performed;

[0034] The optimal sub-operation is the corresponding matching third operation.

[0035] Preferably, the third operation includes:

[0036] The remaining construction waste is circulated and ground until the grinding particle size is smaller than the preset particle size, and the first sub-operation of the third operation is determined to be qualified.

[0037] Once the first sub-operation is successful, the remaining ground construction waste is transferred to a preset area for storage. After storage is completed, the second sub-operation of the third operation is confirmed to be successful.

[0038] Preferably, the second monitoring process also includes:

[0039] The second operation is analyzed to obtain the corresponding waste sorting method and the legal storage location corresponding to each sorting method;

[0040] Obtain the second video stream from which the second operation is performed;

[0041] The second video stream is parsed, and the current storage point for placing different types of garbage using different classification methods during the execution of the second operation is recorded. It is then determined whether the current storage point is consistent with the legal storage point.

[0042] If there is a discrepancy, the invalid storage location is obtained, a first reminder message is generated and sent to the designated terminal for display.

[0043] Preferably, the third monitoring process also includes:

[0044] Obtain the third video stream from which the third operation is performed;

[0045] The third video stream is analyzed, and the processing status of the remaining construction waste at each target time point during the execution of the third operation is recorded to establish a processing statistics table.

[0046] Analyze the processing statistics table to determine whether the progress of the third operation at each target time point is satisfactory.

[0047] If the result is unqualified, the time point of the unqualified result is obtained, a second reminder message is generated and sent to the designated terminal for display.

[0048] Preferably, based on the specified area, the same type of calibration results are divided into regions, and the final region corresponding to recyclable waste is obtained according to the identification type in different regions, including:

[0049] The calibration results of the same type are input into a preset coordinate system, and the coordinates and pixel values ​​corresponding to each pixel in the calibration results of the same type are obtained respectively. The coordinates and pixel values ​​corresponding to each pixel are paired to generate pixel information.

[0050] Based on the pixel information, establish classification layers corresponding to different class labeling results;

[0051] Analyze the coordinate distribution of each classification layer to obtain the occupied space based on the entire classification layer, and determine the first edge contour line and spatial density of the occupied space;

[0052] Simultaneously, it also identifies occupied areas that differ from those corresponding to the same type of calibration results within the divided regions, and determines the second edge contour line of the occupied areas;

[0053] The second edge contour line is determined based on the interference information of the first edge contour line, and combined with the spatial density, the corresponding adjustment area of ​​the same type of calibration result is determined;

[0054] The division region is adjusted based on the adjustment region to obtain the final region corresponding to the same type of calibration result.

[0055] Preferably, based on a type-hardness database, matching the remaining construction waste with a corresponding first material hardness includes:

[0056] Color training is performed on the surface texture direction to obtain the probability of occurrence of different pixel colors;

[0057]

[0058] in, This represents the probability of the m-th pixel color appearing along the surface texture direction; k represents the pixel color value of the m-th pixel color. This indicates the number of vertical pixels along the surface texture direction; The number of horizontal pixels representing the direction of the surface texture; The surface texture direction is represented by the pixel. The corresponding pixel color value;

[0059] Based on the occurrence probability of all different pixel colors, the corresponding color probability features are obtained, and then the corresponding surface color features are obtained, and the color range of the surface color features is analyzed.

[0060] Extract several standard color features within the color range of the surface color features;

[0061] Each standard color feature is superimposed on the surface color feature to obtain the corresponding superposition result. The superimposed color feature corresponding to each superposition result is obtained, and the target superimposed color feature with the highest similarity to the surface color feature is extracted as the texture color feature.

[0062]

[0063] in, This represents the extracted texture and color features; This indicates the total number of the standard color features; This represents the d-th standard color feature; This represents the surface color feature superimposed on the d-th standard color feature;

[0064] The surface texture corresponding to the surface texture direction is binarized to generate the corresponding binary texture.

[0065] Obtain the color distribution corresponding to the binary texture, and analyze the texture density features corresponding to the surface texture based on the color distribution;

[0066] The binary texture is input into a preset linear model and fitted to the preset linear model;

[0067] The fitting results are analyzed, the textureless sub-regions in the corresponding binary texture are removed, the target sub-regions corresponding to the corresponding binary texture are obtained, and then the texture curvature features of each target sub-region are established.

[0068] Based on the texture color characteristics, texture density characteristics, and texture curvature characteristics, match the material type that is consistent with the remaining construction waste material in the preset material library;

[0069] Based on the type-hardness database, the corresponding first material hardness is matched and transmitted to the designated terminal for display.

[0070] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0071] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0072] Figure 1 This is a schematic diagram illustrating the workflow of an intelligent monitoring and early warning method for construction waste processing in an embodiment of the present invention.

[0073] Figure 2 This is a structural diagram showing the space occupied in an embodiment of the present invention. Detailed Implementation

[0074] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0075] Example 1

[0076] This invention provides an intelligent monitoring and early warning method for the technological processing of construction waste, such as... Figure 1 As shown, it includes:

[0077] S1: Based on the first operation of the construction waste processing flow, the construction waste is transferred to a designated area, and the current image of the designated area is collected, and the first operation is monitored.

[0078] S2: Based on the current image, obtain the first location of recyclable waste existing in the specified area, match the second operation from the construction waste processing flow to the first location, and perform the second monitoring;

[0079] S3: Based on the current image analysis, analyze the material hardness of the remaining construction waste in the specified area, perform a third operation to match the material hardness from the construction waste processing flow, and execute a third monitoring;

[0080] S4: Based on the first monitoring result, the second monitoring result, and the third monitoring result, determine whether all operations of the construction waste processing flow on the construction waste are qualified, and execute the corresponding reminder according to the judgment result.

[0081] In this example, the designated area can be a temporary storage area for construction waste;

[0082] In this example, the current image represents the image of construction waste in the specified area;

[0083] In this example, the first operation represents the operation of separating construction waste in a specified area into two categories: recyclable waste and non-recyclable waste;

[0084] In this example, the second operation represents the process of classifying and categorizing construction waste;

[0085] In this example, the remaining construction waste represents non-recyclable waste;

[0086] In this example, the third operation represents the process of gradually crushing the remaining construction waste, and different steps are used to crush the remaining construction waste with different hardness.

[0087] This example is applicable to various construction waste treatment processes. The waste treatment device can be composed of heavy plate feeder, double-layer composite screen, water flotation, dewatering screen, impact crusher (fine crushing), finished product screen, manual sorting, impact crusher (coarse crushing), self-unloading iron separator, three-layer tension screen, integrated positive pressure air classifier, integrated negative pressure air classifier, dust removal system, sewage treatment system and belt conveyor, etc.

[0088] The construction waste treatment process in this example can be summarized as follows: Construction waste raw materials are dumped into a raw material silo by a loader or bulldozer. A heavy-duty plate feeder located at the bottom of the silo transports the material to a double-layer composite screen. The first layer of the double-layer composite screen is a fixed screen with an all-bar structure, designed to separate large-sized lightweight materials from the raw material. The material is then conveyed to a manual sorting conveyor belt via vibration. Manual sorting removes large pieces of lightweight material from the larger materials. The second layer is a tension screen using polyurethane mesh, designed to grade the raw material. 0-20mm slag is conveyed to a slag pile via conveyor belt, while 20-100mm material is conveyed to a water flotation system for removing lightweight materials and further removing soil. The large pieces of material that have passed manual sorting are then fed into a specialized impact crusher for crushing, which removes the reinforcing steel bars from the construction waste. The crushed material is then conveyed to a third-layer tension screen via conveyor belt. During this conveying process, a self-unloading iron separator sucks the reinforcing steel bars removed by the impact crusher from the material, thus removing the reinforcing steel bars from the construction waste. The three-layer tension screen classifies the material crushed by the impact crusher into 0-10mm, 10-20mm, 20-60mm, and larger than 60mm. Material larger than 60mm is returned to the impact crusher via a return conveyor belt, forming a closed-loop crushing cycle. Manual sorting is provided on the return conveyor belt to further separate large, lightweight materials from the returned material. 20-60mm material is conveyed by conveyor belt to the impact crusher (fine crushing) for final product crushing. Positive pressure air classifiers are integrated at the head of the conveyor belt to further remove lightweight materials before entering the crusher. 10-20mm material is conveyed directly to the finished product via conveyor belt, also with positive pressure air classifiers integrated at the head of the conveyor belt for final lightweight material removal. 0-10mm material is conveyed by conveyor belt to the finished product screen. Materials aged 20-100mm undergo water flotation to remove impurities and soil, followed by dewatering via a dewatering screen. They are then conveyed by belt conveyor and mixed with the preceding 20-60mm materials before entering an impact crusher (fine crushing) for further crushing. The crushed materials, combined with the preceding 0-10mm materials, are then conveyed by belt conveyor to a finished product screen for final screening and output as the finished product. A dust removal system collects dust generated at the construction waste treatment site, while a wastewater treatment system treats the wastewater generated at the site. Ultimately, this achieves pollution-free treatment of construction waste.

[0089] The beneficial effects of the above technical solution are as follows: Due to the large volume and diverse types of construction waste, processing it is quite difficult. To simplify the process, the waste is first sorted before processing, and recyclable waste is collected to achieve green production. Then, a corresponding crushing process is matched to the hardness of the remaining construction waste to achieve the processing purpose. In this way, not only is the purpose of waste treatment achieved, but also the concept of sustainable environmental protection is reflected, which is in line with the current situation. In this process, the construction waste is mainly processed and monitored through the existing construction waste processing technology. This can effectively and timely remind users of non-compliant operations, ensuring not only the rationality of supervision but also the efficiency of processing.

[0090] Example 2

[0091] Based on Example 1, obtaining the first location of recyclable waste within a specified area based on the current image includes:

[0092] Obtain the set of cells in the current image, fill each cell in the set with first data that matches the current image, and extract second data from the first data according to the set of regions in the specified region;

[0093] According to the material analysis thread, the second data is parsed to obtain the type identifier;

[0094] The second data is traversed, and the type is identified for each unit in the corresponding region set based on the traversal results;

[0095] Based on the specified area, the same type of calibration results are divided into regions, and the final region corresponding to recyclable waste is obtained according to the identification type in different regions.

[0096] A list of recyclable waste is created based on the final area and transmitted to a designated terminal for display.

[0097] In this context, each final area in the list of recyclable waste is considered the first position of recyclable waste.

[0098] In this example, the cell set represents the set of every pixel cell in the specified area.

[0099] In this example, the first data represents all the information data on the current image, the second data refers to a portion of the data in a specified region, and the region set is a part of the cell set.

[0100] In this embodiment, the material analysis thread is pre-set, which mainly performs material analysis based on image information to determine the material type that can exist in the image, i.e., the type identifier.

[0101] The beneficial effects of the above technical solution are as follows: In order to effectively distinguish between recyclable and non-recyclable waste, the image is first filled with data, and then the filled image is parsed to obtain the type identifier, thereby obtaining the first position of recyclable waste in the specified area, laying the foundation for subsequent waste differentiation and ensuring the reliability of subsequent reminders.

[0102] Example 3

[0103] Based on Example 1, a second operation is matched from the construction waste processing flow to the first position, including:

[0104] Analyze and determine the waste space occupancy attributes and recyclability attributes of the recyclable waste at the first location;

[0105] Determine the initial location point for performing waste disposal at the first location, and determine the ease of operation based on the first location according to the minimum straight-line route, waste space occupancy attribute, and waste recyclability attribute;

[0106] Based on the ease of operation and the type of waste, a second operation is matched to the first location.

[0107] In this example, the garbage space occupancy attribute indicates the space occupied by recyclable waste in a specified area;

[0108] In this example, the recyclable properties of the waste can be: plastic, paper, or iron.

[0109] The beneficial effects of the above technical solution are as follows: Based on the first location of recyclable waste in the designated area, as well as the space occupancy attribute and recyclability attribute of the waste, the initial point for waste disposal is determined, and then a second operation is matched for it, thus achieving the purpose of waste sorting and providing a basis for subsequent reminders.

[0110] Example 4

[0111] Based on Example 1, before determining whether all operations of the construction waste processing flow are qualified for the construction waste based on the first monitoring result, the second monitoring result, and the third monitoring result, the process further includes:

[0112] During the first monitoring, a first video stream is captured during the first operation process;

[0113] During the second monitoring, a second video stream is captured during the second operation process;

[0114] During the third monitoring process, a third video stream is collected during the third operation.

[0115] The first video stream, the second video stream, and the third video stream are transmitted to a designated terminal for display.

[0116] The working principle and beneficial effects of the above technical solution are as follows: In order to facilitate staff to follow up on the processing in a timely manner, the video of the monitoring process is transmitted to the designated terminal for staff reference, so as to facilitate the relevant personnel to carry out the next step of processing.

[0117] Example 5

[0118] Based on Example 1, the material hardness of the remaining construction waste in the designated area is analyzed based on the current image. A third operation is performed to match the material hardness from the construction waste processing flow, and a third monitoring is executed, including:

[0119] Extract the target area containing the remaining construction waste from the current image;

[0120] Several sampling sub-regions are extracted from the target area, and the region texture corresponding to each sampling sub-region is obtained and the corresponding texture features are determined.

[0121] Based on the texture features, the surface texture direction of the remaining construction waste is analyzed, and the waste type of the remaining construction waste is determined by matching the surface texture direction with the texture database.

[0122] Based on the type-hardness database, the remaining construction waste is matched with the corresponding first material hardness;

[0123] Hardness tests were performed on each type of waste in the remaining construction waste to obtain the corresponding second material hardness.

[0124] Based on the first and second material hardness of the same type of waste, the operation to be processed is matched from the preset processing methods, and the adjustable range of the operation for the corresponding type of waste is determined according to the hardness difference between the first and second material hardness of the same type of waste.

[0125] Based on the adjustable range of the operation, each sub-operation in the operation to be processed is adaptively adjusted, and the best sub-operation that matches the same type of waste is selected, and a third monitoring is performed;

[0126] The optimal sub-operation is the corresponding matching third operation.

[0127] In this example, the sampling sub-region refers to the sub-region containing the sampling results after random sampling of the target region;

[0128] In this example, the region texture represents the texture contained within the sampled sub-region;

[0129] In this example, the surface texture orientation refers to the orientation of the texture regions contained on the surface of the remaining construction waste, that is, the texture distribution.

[0130] In this example, the texture database represents a library of texture mappings for different materials;

[0131] In this example, the type-hardness database represents the hardness database for different types of waste.

[0132] In this embodiment, the preset processing method is a processing operation obtained after determining the hardness of the same type of waste using different methods, and the adjustable range of the operation is mainly adjusted according to the hardness difference, such as adjusting the magnitude of the impurity pressure of the waste.

[0133] The beneficial effects of the above technical solution are as follows: Since the hardness of different construction wastes varies, before performing the third operation, the current image is sampled to obtain the texture of multiple regions. Then, feature analysis is performed on the texture of each region to further obtain the material hardness of the remaining construction waste. Finally, the corresponding third operation is matched according to the different hardnesses, and different processing is carried out for materials with different hardnesses, which improves the targeting of processing, further ensures the efficiency of processing, and provides a basis for reminders.

[0134] Example 6

[0135] Based on Example 5, the third operation includes:

[0136] The remaining construction waste is circulated and ground until the grinding particle size is smaller than the preset particle size;

[0137] When the grinding particle size is smaller than the preset particle size, the remaining construction waste after grinding is transported to the preset area for storage.

[0138] The beneficial effects of the above technical solution are as follows: In order to facilitate recycling, the remaining construction waste is ground into small-particle materials through a circulating grinding method, which makes it easier for relevant personnel to carry out further processing and also serves as an effective early warning system.

[0139] Example 7

[0140] Based on Example 1, the second monitoring process also includes:

[0141] The second operation is analyzed to obtain the corresponding waste sorting method and the legal storage location corresponding to each sorting method;

[0142] Obtain the second video stream from which the second operation is performed;

[0143] The second video stream is parsed, and the current storage point for placing different types of garbage using different classification methods during the execution of the second operation is recorded. It is then determined whether the current storage point is consistent with the legal storage point.

[0144] If there is a discrepancy, the invalid storage location is obtained, a first reminder message is generated and sent to the designated terminal for display.

[0145] The beneficial effects of the above technical solution are as follows: In order to ensure that recyclable waste is effectively recycled, during the second operation, the second video stream is analyzed to determine whether each piece of recyclable waste is correctly classified, and corresponding reminder information is sent for incorrectly classified recyclable waste, so that relevant personnel can make quick adjustments.

[0146] Example 8

[0147] Based on Example 1, the third monitoring process also includes:

[0148] Obtain the third video stream from which the third operation is performed;

[0149] The third video stream is analyzed, and the processing status of the remaining construction waste at each target time point during the execution of the third operation is recorded to establish a processing statistics table.

[0150] Analyze the processing statistics table to determine whether the progress of the third operation at each target time point is satisfactory.

[0151] If the result is unqualified, the time point of the unqualified result is obtained, a second reminder message is generated and sent to the designated terminal for display.

[0152] The beneficial effects of the above technical solution are as follows: In order to ensure that the remaining construction waste is transformed into the target form after the third operation, the progress of the third operation is analyzed during the third operation process to determine whether it is up to standard, and relevant personnel are promptly reminded to carry out remedial work if it is not up to standard.

[0153] Example 9

[0154] Based on Example 2, the same type of calibration results are divided into regions based on the specified region, and the final region corresponding to recyclable waste is obtained according to the identification type in different regions, including:

[0155] The calibration results of the same type are input into a preset coordinate system, and the coordinates and pixel values ​​corresponding to each pixel in the calibration results of the same type are obtained respectively. The coordinates and pixel values ​​corresponding to each pixel are paired to generate pixel information.

[0156] Based on the pixel information, establish classification layers corresponding to different class labeling results;

[0157] Analyze the coordinate distribution of each classification layer to obtain the occupied space based on the entire classification layer, and determine the first edge contour line and spatial density of the occupied space;

[0158] Simultaneously, it also identifies occupied areas that differ from those corresponding to the same type of calibration results within the divided regions, and determines the second edge contour line of the occupied areas;

[0159] The second edge contour line is determined based on the interference information of the first edge contour line, and combined with the spatial density, the corresponding adjustment area of ​​the same type of calibration result is determined;

[0160] The division region is adjusted based on the adjustment region to obtain the final region corresponding to the same type of calibration result.

[0161] In this embodiment, the occupied space is mainly determined according to different coordinate distributions, and the space corresponding to different classification layers is different, such as... Figure 2 As shown, A1 represents the occupied space, and A2 represents the occupied area. A2 will cover A1, which may lead to incomplete determination of the final area. Therefore, it is necessary to determine the existing interference information and combine it with the density to estimate the existing adjustment area. Generally, the adjustment area is consistent with the occupied area.

[0162] The beneficial effects of the above technical solution are as follows: by establishing classification layers corresponding to different types of calibration results and determining the outline and density of the occupied space according to the coordinate distribution, and then determining the adjustment area through mixed information, the effective adjustment of the divided area is ensured, making the reliability of the final area obtained reliable and providing an effective basis for subsequent reminders.

[0163] Example 10

[0164] Based on Example 5, and using a type-hardness database, the remaining construction waste is matched with a corresponding first material hardness, including:

[0165] Color training is performed on the surface texture direction to obtain the probability of occurrence of different pixel colors;

[0166]

[0167] in, This represents the probability of the m-th pixel color appearing along the surface texture direction; k represents the pixel color value of the m-th pixel color. This indicates the number of vertical pixels along the surface texture direction; The number of horizontal pixels representing the direction of the surface texture; The surface texture direction is represented by the pixel. The corresponding pixel color value;

[0168] Based on the occurrence probability of all different pixel colors, the corresponding color probability features are obtained, and then the corresponding surface color features are obtained, and the color range of the surface color features is analyzed.

[0169] Extract several standard color features within the color range of the surface color features;

[0170] Each standard color feature is superimposed on the surface color feature to obtain the corresponding superposition result. The superimposed color feature corresponding to each superposition result is obtained, and the target superimposed color feature with the highest similarity to the surface color feature is extracted as the texture color feature.

[0171]

[0172] in, This represents the extracted texture and color features; This indicates the total number of the standard color features; This represents the d-th standard color feature; This represents the surface color feature superimposed on the d-th standard color feature;

[0173] The surface texture corresponding to the surface texture direction is binarized to generate the corresponding binary texture.

[0174] Obtain the color distribution corresponding to the binary texture, and analyze the texture density features corresponding to the surface texture based on the color distribution;

[0175] The binary texture is input into a preset linear model and fitted to the preset linear model;

[0176] The fitting results are analyzed, the textureless sub-regions in the corresponding binary texture are removed, the target sub-regions corresponding to the corresponding binary texture are obtained, and then the texture curvature features of each target sub-region are established.

[0177] Based on the texture color characteristics, texture density characteristics, and texture curvature characteristics, match the material type that is consistent with the remaining construction waste material in the preset material library;

[0178] Based on the type-hardness database, the corresponding first material hardness is matched and transmitted to the designated terminal for display.

[0179] In this example, the surface color feature represents the surface color of the remaining construction waste;

[0180] In this example, the purpose of generating the color range is to avoid the influence of external light on the imaging effect when the image is captured. Therefore, the color range is generated, the target color features within the color range are extracted, and the next step is performed.

[0181] In this example, the binarized texture representation uses black and white colors to represent the surface texture.

[0182] In this example, the fitting information represents the fitting length and fitting position between each line type and the binary region texture;

[0183] In this example, the textureless region represents a smooth region in the binary texture.

[0184] The beneficial effects of the above technical solution are as follows: In order to analyze the texture and hardness of building materials, it is necessary to perform corresponding analysis on the texture. First, the color features of the texture are extracted using formulas. Then, the density features of the texture are obtained based on the color distribution and formulas. Next, the curvature features of the texture are obtained through fitting. Finally, materials with these features are searched in the material library to obtain the remaining building materials and their hardness, which lays the foundation for the subsequent matching third operation.

[0185] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for intelligent monitoring and early warning of construction waste processing procedures, characterized in that, include: S1: The first operation based on the construction waste processing flow is to transfer the construction waste to a designated area, collect the current image of the designated area, and perform the first monitoring on the first operation. The first operation means the operation of dividing the construction waste in the designated area into two categories: recyclable waste and non-recyclable waste. S2: Based on the current image, obtain the first location of recyclable waste in the specified area, match the second operation from the construction waste processing flow to the first location, and perform the second monitoring. The second operation represents the process of classifying and summarizing construction waste. S3: Based on the current image analysis, analyze the material hardness of the remaining construction waste in the specified area, match the material hardness to the construction waste processing flow, and perform third monitoring. The third operation represents the process of gradually crushing the remaining construction waste, and different steps are used to crush the remaining construction waste with different hardness. During the first monitoring, a first video stream is captured during the first operation process; During the second monitoring, a second video stream is captured during the second operation process; During the third monitoring process, a third video stream is collected during the third operation. The first video stream, the second video stream, and the third video stream are transmitted to a designated terminal for display. S4: Based on the first monitoring result, the second monitoring result, and the third monitoring result, determine whether all operations of the construction waste processing flow on the construction waste are qualified, and execute the corresponding reminder according to the judgment result; The process of matching the second operation from the construction waste processing flow to the first location includes: Analyze and determine the waste space occupancy attributes and recyclability attributes of the recyclable waste at the first location; Determine the initial location point for performing waste disposal at the first location, and determine the ease of operation based on the first location according to the minimum straight-line route, waste space occupancy attribute, and waste recyclability attribute; Based on the ease of operation and the type of waste, match the second operation to the first location; Based on the current image analysis, the material hardness of the remaining construction waste in the designated area is analyzed, and a third operation is performed to match the material hardness from the construction waste processing flow, and a third monitoring is executed, including: Extract the target area containing the remaining construction waste from the current image; Several sampling sub-regions are extracted from the target area, and the region texture corresponding to each sampling sub-region is obtained and the corresponding texture features are determined. Based on the texture features, the surface texture direction of the remaining construction waste is analyzed, and the waste type of the remaining construction waste is determined by matching the surface texture direction with the texture database. Based on the type-hardness database, the remaining construction waste is matched with the corresponding first material hardness; Hardness tests were performed on each type of waste in the remaining construction waste to obtain the corresponding second material hardness. Based on the first and second material hardness of the same type of waste, the operation to be processed is matched from the preset processing methods, and the adjustable range of the operation for the corresponding type of waste is determined according to the hardness difference between the first and second material hardness of the same type of waste. Based on the adjustable range of the operation, each sub-operation in the operation to be processed is adaptively adjusted, and the best sub-operation that matches the same type of waste is selected, and a third monitoring is performed; The optimal sub-operation is the corresponding matching third operation; The third operation includes: The remaining construction waste is circulated and ground until the grinding particle size is smaller than the preset particle size, and the first sub-operation of the third operation is determined to be qualified. Once the first sub-operation is successful, the remaining ground construction waste is transferred to a preset area for storage. After storage is completed, the second sub-operation of the third operation is confirmed to be successful. Among them, based on the type-hardness database, matching the remaining construction waste with the corresponding first material hardness includes: Color training is performed on the surface texture direction to obtain the probability of occurrence of different pixel colors; in, This represents the probability of the m-th pixel color appearing along the surface texture direction; k represents the pixel color value of the m-th pixel color. This indicates the number of vertical pixels along the surface texture direction; The number of horizontal pixels representing the direction of the surface texture; The surface texture direction is represented by the pixel. The corresponding pixel color value; Based on the occurrence probability of all different pixel colors, the corresponding color probability features are obtained, and then the corresponding surface color features are obtained, and the color range of the surface color features is analyzed. Extract several standard color features within the color range of the surface color features; Each standard color feature is superimposed on the surface color feature to obtain the corresponding superposition result. The superimposed color feature corresponding to each superposition result is obtained, and the target superimposed color feature with the highest similarity to the surface color feature is extracted as the texture color feature. in, This represents the extracted texture and color features; This indicates the total number of the standard color features; This represents the d-th standard color feature; This represents the surface color feature superimposed on the d-th standard color feature; The surface texture corresponding to the surface texture direction is binarized to generate the corresponding binary texture. Obtain the color distribution corresponding to the binary texture, and analyze the texture density features corresponding to the surface texture based on the color distribution; The binary texture is input into a preset linear model and fitted to the preset linear model; The fitting results are analyzed, the textureless sub-regions in the corresponding binary texture are removed, the target sub-regions corresponding to the corresponding binary texture are obtained, and then the texture curvature features of each target sub-region are established. Based on the texture color characteristics, texture density characteristics, and texture curvature characteristics, match the material type that is consistent with the remaining construction waste material in the preset material library; Based on the type-hardness database, the corresponding first material hardness is matched and transmitted to the designated terminal for display.

2. The intelligent monitoring and early warning method for construction waste processing as described in claim 1, characterized in that, The second monitoring process also includes: The second operation is analyzed to obtain the corresponding waste sorting method and the legal storage location corresponding to each sorting method; Obtain the second video stream from which the second operation is performed; The second video stream is parsed, and the current storage point for placing different types of garbage using different classification methods during the execution of the second operation is recorded. It is then determined whether the current storage point is consistent with the legal storage point. If there is a discrepancy, the invalid storage location is obtained, a first reminder message is generated and sent to the designated terminal for display.

3. The intelligent monitoring and early warning method for construction waste processing as described in claim 1, characterized in that, The third monitoring process also includes: Obtain the third video stream from which the third operation is performed; The third video stream is analyzed, and the processing status of the remaining construction waste at each target time point during the execution of the third operation is recorded to establish a processing statistics table. Analyze the processing statistics table to determine whether the progress of the third operation at each target time point is satisfactory. If the result is unqualified, the time point of the unqualified result is obtained, a second reminder message is generated and sent to the designated terminal for display.

Images