Distributed construction waste treatment method and system based on visual identification

Abstract

The invention relates to the technical field of computer vision, and provides a distributed construction waste treatment method and system based on visual recognition. The method comprises the following steps: determining a target area, installing a sensor group based on the target area, and obtaining real-time image parameter information; building a central processing unit, inputting the real-time image parameter information into the central processing unit for image analysis and processing, and obtaining real-time analysis data; constructing a real-time feedback mechanism, and dynamically adjusting the real-time analysis data to obtain real-time adjustment data; presetting a real-time adjustment rating, and performing real-time adjustment data judgment based on the real-time adjustment rating to obtain a judgment result; and performing construction waste treatment on the target area based on a judgment result. According to the invention, the central processing unit is established, and a real-time feedback mechanism is established, so that the technical problem that data processing is not timely due to dispersed data processing and lack of a unified coordination mechanism is solved, and the garbage processing efficiency and accuracy are improved.

Description

Technical Field

[0001] The present application relates to the field of computer vision technology, specifically to the field of visual recognition technology, and in particular to a distributed construction waste treatment method and system based on visual recognition. Background Art

[0002] The construction waste treatment method based on visual recognition mainly relies on advanced computer vision technology and image processing algorithms. Through the cameras and sensors installed at the treatment site, the system can capture the images and parameter information of construction waste in real time. This information is then transmitted to the central processing unit for in-depth analysis and processing. There are many types of construction waste, including concrete, masonry, wood, metal, plastic and other materials. These materials may have large differences in color, shape, texture, etc., making it difficult for the visual recognition system to accurately distinguish each type of garbage. Especially when the garbage is mixed together, or is contaminated or damaged, the accuracy of recognition may be seriously affected. The visual recognition system usually has certain requirements for lighting and environmental conditions. Under severe weather conditions such as strong light, shadows, rain and snow, the acquisition and processing of images may be disturbed, resulting in poor recognition results.

[0003] In this context, a distributed construction waste treatment method and system based on visual recognition came into being. It aims to solve the technical problem of untimely data processing caused by decentralized data processing and lack of a unified coordination mechanism by building a central processing unit and constructing a real-time feedback mechanism. Summary of the invention

[0004] This application provides a distributed construction waste treatment method and system based on visual recognition, aiming to solve the technical problem of untimely data processing caused by decentralized data processing and lack of a unified coordination mechanism by building a central processing unit and constructing a real-time feedback mechanism.

[0005] In view of the above problems, the present application provides a distributed construction waste treatment method and system based on visual recognition.

[0006] The first aspect disclosed in the present application provides a distributed construction waste treatment method based on visual recognition, the method comprising: determining a target area, installing a sensor group based on the target area, and acquiring real-time image parameter information; building a central processing unit, inputting the real-time image parameter information into the central processing unit for image analysis and processing, and acquiring real-time analysis data; constructing a real-time feedback mechanism, dynamically adjusting the real-time analysis data, and acquiring real-time adjustment data; presetting a real-time adjustment rating, and making a real-time adjustment data judgment based on the real-time adjustment rating to acquire a judgment result; and treating construction waste in the target area based on the judgment result.

[0007] Another aspect disclosed in the present application provides a distributed construction waste treatment system based on visual recognition, the system comprising: an information acquisition component, the information acquisition component is used to determine a target area, install a sensor group based on the target area, and obtain real-time image parameter information; an image analysis component, the image analysis component is used to build a central processing unit, input the real-time image parameter information into the central processing unit for image analysis processing, and obtain real-time analysis data; a dynamic adjustment component, the dynamic adjustment component is used to build a real-time feedback mechanism, dynamically adjust the real-time analysis data, and obtain real-time adjustment data; a data judgment component, the data judgment component is used to preset a real-time adjustment rating, perform real-time adjustment data judgment based on the real-time adjustment rating, and obtain a judgment result; a garbage treatment component, the garbage treatment component is used to treat construction waste in the target area based on the judgment result.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: The above-mentioned distributed construction waste treatment method based on visual recognition first determines the target area where waste treatment is required, and installs a sensor group in the area to capture the image of the target area in real time and extract key parameter information. Subsequently, a central processing unit is built to receive the real-time image parameter information obtained from the sensor group. By using computer vision and image processing technology, the central processing unit analyzes and processes this information to generate real-time analysis data. After that, a real-time feedback mechanism is constructed. This mechanism can dynamically adjust the waste treatment strategy according to the real-time analysis data. By obtaining the real-time adjustment data, the treatment plan can be flexibly adjusted according to the actual situation of the target area. In addition, a real-time adjustment rating system is preset. This system quantitatively evaluates the effect of waste treatment based on real-time adjustment data. By comparing the preset rating standards, the judgment results can be obtained to understand the effect of the current treatment strategy. Finally, based on these judgment results, targeted construction waste treatment is carried out in the target area to improve the efficiency and accuracy of waste treatment.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A schematic diagram of a process of a distributed construction waste treatment method based on visual recognition in one embodiment; Figure 2 The present invention is an architecture diagram of a distributed construction waste treatment system based on visual recognition in one embodiment.

[0012] Explanation of the accompanying drawings: information collection component 1, image analysis component 2, dynamic adjustment component 3, data judgment component 4, garbage processing component 5. DETAILED DESCRIPTION

[0013] The embodiments of the present application solve the technical problem of untimely data processing caused by decentralized data processing and lack of a unified coordination mechanism by providing a distributed construction waste treatment method and system based on visual recognition.

[0014] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0015] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices. Embodiment 1

[0016] like Figure 1 As shown, the present application provides a distributed construction waste treatment method based on visual recognition, the method comprising: Determine a target area, install a sensor group based on the target area, and obtain real-time image parameter information; With the continuous advancement of computer vision and artificial intelligence technology, the performance of visual recognition systems will be further improved, and they will be able to more accurately identify and analyze construction waste. This will enable the method to more efficiently identify and classify different types of waste during the waste treatment process, providing more accurate data support for subsequent waste treatment.

[0017] In an embodiment of the present application, the system terminal determines the target area, which is the first step in the treatment of construction waste. It helps the system terminal to clarify the specific scope that needs attention and management. Subsequently, a sensor group is installed in the target area, and these sensors can capture and obtain image parameter information of the target area in real time. Through these sensors, the specific conditions of the construction waste in the target area, such as quantity, distribution and type, can be obtained. In this way, the system terminal can understand the garbage status of the target area in real time and provide accurate data support for subsequent processing work. In summary, determining the target area and installing the sensor group to obtain real-time image parameter information is one of the key steps in the treatment of construction waste. It helps the system terminal to grasp the garbage status of the target area in real time, and provides strong support for subsequent processing work.

[0018] Building a central processing unit, inputting the real-time image parameter information into the central processing unit for image analysis and processing, and obtaining real-time analysis data; In one embodiment, building a central processing unit is an important part of the construction waste treatment process. The main function of this unit is to take real-time image parameter information as input, and to conduct in-depth analysis and processing of this information by using image analysis and processing technology. This process aims to extract key data about construction waste from the image, such as the type, quantity, and distribution of the waste. In the process of building the central processing unit, the system terminal needs to perform scene evaluation for subsequent configuration of computing power and selection of image processing algorithms. After the central processing unit is built, the system terminal can obtain real-time analysis data through the processing of the central processing unit, which is crucial for understanding the garbage status of the target area. They provide accurate data support for subsequent garbage disposal decisions and help the system terminal to dispose of garbage more effectively. In summary, the existence of the central processing unit enables the system terminal to obtain key data about construction waste in real time, provides strong data support for garbage disposal, and improves processing efficiency and accuracy.

[0019] Furthermore, the present application provides for building a central processing unit, inputting the real-time image parameter information into the central processing unit for image analysis and processing, and obtaining real-time analysis data, and the method also includes: Performing a scene evaluation based on the target area to obtain a scene evaluation result; Perform computing power configuration according to the scenario evaluation results and obtain computing power configuration results; Select an image processing algorithm and build the central processing unit based on the computing power configuration result.

[0020] Preferably, the system terminal analyzes the collected data to understand the distribution of construction waste in the target area, including its spatial distribution characteristics, quantity change trends, etc. At the same time, the composition of construction waste is analyzed to determine its physical and chemical properties. Subsequently, the environmental conditions of the target area are evaluated, including soil quality, groundwater conditions, air quality, etc. The quality of these environmental factors will directly affect the effect and feasibility of construction waste treatment. After that, the ecological sensitivity of the target area is analyzed to understand the resistance of the area to environmental pollution and damage. This helps to determine the environmental protection issues that need special attention in the process of construction waste treatment. Then, multiple evaluation results are sorted out to obtain detailed scene evaluation results about the target area, which provides an important reference for subsequent work. Further, based on the scene evaluation results, the system terminal analyzes the type and scale of computing power required to process construction waste. This includes determining the required calculation accuracy, processing speed, and concurrent processing capabilities. At the same time, it is also necessary to consider possible future data growth and changes in processing requirements. Through reasonable computing power configuration, it can be ensured that the central processing unit has sufficient performance when processing image information to meet the needs of garbage treatment. After the computing power configuration is completed, the system terminal selects a suitable image processing algorithm according to the actual situation. For example, when it is necessary to process label or mark information in construction waste, character recognition (OCR) algorithms are used. These algorithms are the core of the central processing unit for image analysis and processing. They can extract key data about construction waste based on the input image parameter information. Combined with the computing power configuration results, an efficient and accurate central processing unit can be built.

[0021] Furthermore, the present application provides a method for obtaining real-time analysis data, and the method further includes: The real-time image parameter information is transmitted to the central processing unit via a data interface; Performing real-time image parameter information data preprocessing in the central processing unit to obtain processed data; The processed data is subjected to image processing to obtain real-time analysis data.

[0022] Optionally, the system terminal quickly transmits the real-time image parameter information to the central processing unit through the data interface. The central processing unit first pre-processes the image parameter information, including data cleaning, format conversion and other steps to obtain data suitable for subsequent processing. Subsequently, the central processing unit uses the integrated image processing algorithm to perform in-depth analysis on the processed data to obtain real-time analysis data. These real-time analysis data not only reflect the real-time status of the image content, but also reveal some important information hidden behind the image, such as the humidity of the garbage. In summary, the entire process realizes the automated processing from the transmission of real-time image parameter information to the acquisition of real-time analysis data, providing strong data support for subsequent decision-making and application.

[0023] Building a real-time feedback mechanism to dynamically adjust the real-time analysis data and obtain real-time adjustment data; In one embodiment, in order to further optimize the processing flow and improve the accuracy of analysis, the system terminal constructs a real-time feedback mechanism. This mechanism can be dynamically adjusted according to the results of the real-time analysis data, thereby obtaining real-time adjustment data. Specifically, after the system terminal inputs the real-time analysis data into the real-time feedback mechanism, the real-time feedback mechanism will judge these data. When the real-time analysis data reflects certain problems or areas that need improvement, the feedback mechanism will be immediately activated to adjust the corresponding parameters or algorithms to ensure that subsequent processing is more accurate and efficient. In this way, the image processing process can be continuously optimized to make it more adaptable to the needs of actual scenes and improve the overall processing effect.

[0024] Furthermore, the present application provides a method for constructing a real-time feedback mechanism to dynamically adjust the real-time analysis data and obtain real-time adjustment data. The method also includes: The real-time feedback mechanism is in communication with the central processing unit to define the goals and performance indicators of the real-time feedback mechanism; The real-time analysis data is input into the real-time feedback mechanism, the real-time adjustment data is judged, and a data judgment result is obtained, wherein the data judgment result includes normal and abnormal; If the data judgment result is normal, the real-time analysis data is directly output; If the data judgment result is abnormal, the real-time analysis data is dynamically adjusted.

[0025] Preferably, the real-time feedback mechanism is closely connected with the central processing unit, and they are jointly committed to improving the accuracy of image recognition. In order to ensure the realization of this goal, the system terminal clearly defines the performance indicators of the real-time feedback mechanism, including key parameters such as error rate, processing time and resource occupancy rate. When the real-time analysis data enters the real-time feedback mechanism, the mechanism will conduct an in-depth evaluation of these data to determine whether they need to be adjusted. Specifically, the system terminal inputs the real-time analysis data into the data judgment model, and the model performs multiple analyses through the internal weather judgment module, clarity judgment module and occupied resource judgment module to obtain the data judgment result. The results of this judgment process are divided into two categories: normal and abnormal. If the data judgment result is normal, it means that the image is clear and the recognition is accurate, and no adjustment is required, and it is directly output. On the contrary, if the result is abnormal, it means that there are problems caused by various factors, such as image recognition errors, insufficient clarity or too long processing time. In these cases, the real-time feedback mechanism will immediately start the adjustment strategy and dynamically adjust the corresponding parameters or algorithms to ensure the accuracy and efficiency of subsequent processing.

[0026] Further, the present application provides that the real-time analysis data is input into the real-time feedback mechanism, the real-time adjustment data is judged, and a data judgment result is obtained, wherein the data judgment result includes normal and abnormal, and the method further includes: Building a data judgment model, which includes an input layer, a judgment layer and an output layer, and is embedded in the real-time feedback mechanism, wherein the judgment layer includes a weather judgment module, a clarity judgment module and an occupied resource judgment module; The weather judgment module includes a strong light channel, a shadow channel, and a rain and snow channel, which are input according to the real-time weather conditions to obtain weather channel results; Optionally, in order to more accurately judge the status of the real-time analysis data, the system terminal builds a data judgment model, which is embedded in the real-time feedback mechanism. The model consists of an input layer, a judgment layer, and an output layer, forming a complete data processing flow. In the judgment layer, there are weather judgment modules, clarity judgment modules, and resource occupancy judgment modules, which are responsible for in-depth analysis of the real-time analysis data from different dimensions. Among them, the weather judgment module includes a strong light channel, a shadow channel, and a rain and snow channel; the clarity judgment module depends on the image clarity recognition model. When the system terminal inputs the real-time analysis data into the data judgment model, the weather judgment module extracts the real-time weather conditions from it, detects and analyzes the real-time weather conditions, and outputs the weather channel results. For example, when the extracted real-time weather conditions are sunny, the strong light channel will be activated to analyze the strong light part in the image; when there are shadows or rain and snow, the corresponding shadow channels and rain and snow channels will also play a role, helping the data judgment model to more accurately understand the impact of weather on image recognition. In this way, the weather judgment module can provide the system terminal with detailed information about weather conditions, which is crucial for subsequent image processing and recognition.

[0027] Based on the image clarity recognition model, the clarity judgment module is used to perform clarity judgment on the real-time analysis data to obtain a clarity judgment result; According to the clarity judgment result, a resource occupation result of the real-time analysis data is judged by a resource occupation judgment module; Optionally, when the real-time analysis data enters the data judgment model, the image clarity recognition model nested in the data judgment model will intercept the data, perform preliminary recognition on the data, and remove the abnormal data. Subsequently, the clarity judgment module is used to evaluate the clarity of the processed real-time analysis data. This module can analyze key features such as details, edges and contrast in the image, so as to accurately judge the clarity of the image. Through this step, the specific judgment result of the image clarity can be obtained. Afterwards, according to the clarity judgment result, the system terminal further analyzes the resource occupancy of the real-time analysis data through the occupied resource judgment module. This module can comprehensively consider factors such as the computational complexity, memory occupancy and processing time of image processing, so as to evaluate the occupation of system resources by the current image recognition task. Through this step, the specific performance of the real-time analysis data in terms of resource occupation can be understood. In summary, by using the image clarity recognition model and the relevant judgment module, the clarity and resource occupation of the real-time analysis data can be comprehensively evaluated. This provides the system terminal with important information about the performance of the image recognition task, which helps to better optimize and adjust the image processing process and improve the accuracy and efficiency of recognition.

[0028] Perform weighted calculation according to the weather channel result, the clarity judgment result and the occupied resource result to obtain a calculation result; The calculation results are evaluated and a data judgment result is output.

[0029] Optionally, the system terminal performs weighted calculation based on the weather channel result, the clarity judgment result and the resource occupation result to obtain a comprehensive calculation result. Specifically, the system terminal analyzes the historical database to determine which of the weather, clarity and resource occupation has a greater impact on visual recognition, and assigns weights to the weather channel result, the clarity judgment result and the resource occupation result according to the degree of impact. Subsequently, each result is multiplied by its corresponding weight to obtain its respective weighted value. After that, all weighted results are added to obtain a sum. This sum represents the comprehensive impact of factors such as weather, clarity and resource occupation on image recognition performance. In this way, the system terminal can integrate the judgment results of different dimensions to form a comprehensive evaluation index. Then, the calculation result is evaluated. During the evaluation process, the system terminal compares the calculation result with the set evaluation criteria to determine whether the performance and status of image recognition are normal. According to the evaluation result, the final data judgment result is output, which reflects the overall situation of the real-time analysis data, including weather impact, image clarity and resource occupation. In summary, by weighted calculation and evaluation of weather channel results, clarity judgment results and resource occupancy results, a comprehensive data judgment result can be obtained, providing comprehensive reference information for the real-time feedback mechanism for subsequent adjustments and optimization operations.

[0030] Furthermore, the present application provides for evaluating the calculation result, the method further comprising: Obtain historical data for evaluation level training and obtain the target evaluation level; Evaluate the calculation result based on the target evaluation level to obtain a calculation result evaluation result; The dynamic adjustment is performed based on the calculation result evaluation result to obtain dynamic adjustment data.

[0031] Optionally, the system terminal collects a large amount of historical data from the historical database, organizes the collected historical data, and divides the data into different categories according to the accuracy and performance of the recognition results. For example, the data can be divided into different levels such as excellent, good, general, and poor according to the accuracy rate. Subsequently, the common features and rules in each category of data are analyzed, including statistics on the average clarity of the image under each category, the average recognition time, the average occupancy rate of system resources, and other indicators. After that, based on the feature analysis and statistical results, specific evaluation criteria are formulated for each evaluation level. These standards are based on the numerical range, for example, clarity above a certain threshold belongs to the excellent level. Through this process, the system terminal determines a target evaluation level, which will serve as a benchmark for subsequent evaluations. Then, the system terminal uses this target evaluation level to evaluate the calculation results previously obtained by weighted calculation. By comparing the calculation results and the target evaluation level, it is determined in which interval of the target evaluation level the calculation results can fall, and the evaluation results of the calculation results are obtained, which reflects the pros and cons of the current image recognition performance. Finally, if the evaluation result is abnormal, the system terminal makes dynamic adjustments. This adjustment process includes optimizing the parameters of the image processing algorithm, adjusting the allocation of system resources, or fine-tuning the image recognition model. Through these adjustments, new dynamic adjustment data is obtained, which will be used to update the central processing unit to improve its performance and accuracy.

[0032] Preset a real-time adjustment rating, perform real-time adjustment data judgment based on the real-time adjustment rating, and obtain a judgment result; In one embodiment, the system terminal pre-sets a set of real-time adjustment rating standards, which are used to evaluate whether the data meets the expected requirements after adjustment. Subsequently, the system terminal uses this set of rating standards to judge the real-time adjusted data to determine whether they meet the preset standards. Through this process, a judgment result can be obtained, which shows whether the adjusted data is qualified and whether further adjustment or optimization is required. In this way, the system terminal can ensure the accuracy and reliability of the data and provide strong support for subsequent image recognition or other applications.

[0033] The target area is subjected to construction waste treatment based on the judgment result.

[0034] In one embodiment, the system terminal will perform corresponding construction waste treatment on the target area based on the judgment result. If the judgment result shows that the construction waste in the target area meets the treatment standard, the system terminal will generate a waste treatment plan and guide the transportation and resource utilization of the waste according to the content of the plan. In summary, the system terminal flexibly adjusts the treatment strategy according to the judgment result to ensure that the construction waste in the target area is properly treated to maintain a clean environment and public safety.

[0035] Furthermore, the present application provides for processing construction waste in the target area based on the judgment result, and the method further includes: Obtain historical treatment plans, and build a treatment plan knowledge base based on the historical treatment plans, wherein the treatment plan knowledge base data format includes construction waste conditions-weather-treatment plans; Preferably, the system terminal extracts a large number of historical construction waste treatment solutions from the historical database. These solutions include treatment measures taken for different construction waste situations under different weather conditions. Subsequently, a treatment solution knowledge base is constructed based on these historical treatment solutions. This knowledge base is a structured data storage system that establishes a close relationship between construction waste conditions, weather conditions, and treatment solutions. In the knowledge base, each piece of data conforms to the data format of construction waste conditions-weather-treatment solutions. This means that when the system terminal understands the current type of construction waste and weather conditions, it can quickly retrieve the corresponding treatment solution in the knowledge base. In this way, not only can the appropriate treatment solution for a specific situation be quickly found, but the knowledge base can also be continuously optimized and improved based on historical data to improve the efficiency and effectiveness of future construction waste treatment.

[0036] Searching for a treatment solution in the treatment solution knowledge base based on the judgment result; If it exists, construction waste disposal will be carried out; If it does not exist, the judgment result is fed back and processed, and then added to the processing solution knowledge base according to the established corresponding data format.

[0037] Preferably, based on the previously obtained judgment result, the system terminal first searches the treatment solution knowledge base for a matching treatment solution. If there is a corresponding treatment solution in the knowledge base, the system terminal immediately handles the construction waste according to these solutions to ensure that the waste is properly handled. However, if there is no treatment solution matching the judgment result in the knowledge base, this means that the system terminal is facing a new or relatively rare situation. In this case, the judgment result will be fed back to the system terminal for further analysis and processing. At the same time, based on this experience, a corresponding data format will be established, and new construction waste conditions, weather conditions, and treatment solutions will be added to the treatment solution knowledge base. In this way, through continuous feedback, learning, and updating, the treatment solution knowledge base will become richer and more complete, and will be able to better cope with various complex construction waste treatment situations.

[0038] In summary, the embodiments of the present application have at least the following technical effects: The embodiment of the present application obtains real-time image parameter information by installing a sensor group, and inputs this information into the central processing unit for image analysis and processing to generate real-time analysis data. In order to optimize the processing effect, a real-time feedback mechanism is constructed to dynamically adjust the real-time analysis data. During the construction of the central processing unit, the target area is evaluated for the scene, and the appropriate computing power is configured according to the evaluation results. Subsequently, a suitable image processing algorithm is selected, and the central processing unit is built in combination with the computing power configuration result. The real-time image parameter information is transmitted to the central processing unit through the data interface, and image processing is performed after preprocessing to obtain real-time analysis data. After that, the real-time analysis data is judged. If the data is normal, it is directly output. Otherwise, it is dynamically adjusted. During the judgment process, a data judgment model including modules such as weather, clarity and resource occupancy is used, and evaluation level training is performed based on historical data to obtain more accurate evaluation results. Finally, a processing solution knowledge base is built based on historical processing solutions to store processing solutions under different circumstances. After obtaining the judgment result, the corresponding processing solution is searched in the knowledge base. These technical effects jointly solve the technical problem of untimely data processing caused by decentralized data processing and lack of a unified coordination mechanism, and improve the efficiency and accuracy of garbage disposal. Embodiment 2

[0039] Based on the same inventive concept as the distributed construction waste treatment method based on visual recognition in the aforementioned embodiment, Figure 2 As shown, the present application provides a distributed construction waste treatment system based on visual recognition, the system comprising: Information collection component 1: the information collection component 1 is used to determine the target area, install a sensor group based on the target area, and obtain real-time image parameter information; Image analysis component 2: The image analysis component 2 is used to build a central processing unit, input the real-time image parameter information into the central processing unit for image analysis and processing, and obtain real-time analysis data; Dynamic adjustment component 3: the dynamic adjustment component 3 is used to build a real-time feedback mechanism, dynamically adjust the real-time analysis data, and obtain real-time adjustment data; Data judgment component 4: the data judgment component 4 is used to preset a real-time adjustment rating, perform real-time adjustment data judgment based on the real-time adjustment rating, and obtain a judgment result; Garbage disposal component 5: The garbage disposal component 5 is used to dispose of construction waste in the target area based on the judgment result.

[0040] Furthermore, the image analysis component 2 is used to perform the following method: Performing a scene evaluation based on the target area to obtain a scene evaluation result; Perform computing power configuration according to the scenario evaluation results and obtain computing power configuration results; Select an image processing algorithm and build the central processing unit based on the computing power configuration result.

[0041] Furthermore, the image analysis component 2 is used to perform the following method: The real-time image parameter information is transmitted to the central processing unit via a data interface; Performing real-time image parameter information data preprocessing in the central processing unit to obtain processed data; The processed data is subjected to image processing to obtain real-time analysis data.

[0042] Furthermore, the dynamic adjustment component 3 is used to perform the following method: The real-time feedback mechanism is in communication with the central processing unit to define the goals and performance indicators of the real-time feedback mechanism; The real-time analysis data is input into the real-time feedback mechanism, the real-time adjustment data is judged, and a data judgment result is obtained, wherein the data judgment result includes normal and abnormal; If the data judgment result is normal, the real-time analysis data is directly output; If the data judgment result is abnormal, the real-time analysis data is dynamically adjusted.

[0043] Furthermore, the dynamic adjustment component 3 is used to perform the following method: Building a data judgment model, which includes an input layer, a judgment layer and an output layer, and is embedded in the real-time feedback mechanism, wherein the judgment layer includes a weather judgment module, a clarity judgment module and an occupied resource judgment module; The weather judgment module includes a strong light channel, a shadow channel, and a rain and snow channel, which are input according to the real-time weather conditions to obtain weather channel results; Based on the image clarity recognition model, the clarity judgment module is used to perform clarity judgment on the real-time analysis data to obtain a clarity judgment result; According to the clarity judgment result, a resource occupation result of the real-time analysis data is judged by a resource occupation judgment module; Perform weighted calculation according to the weather channel result, the clarity judgment result and the occupied resource result to obtain a calculation result; The calculation results are evaluated and a data judgment result is output.

[0044] Furthermore, the dynamic adjustment component 3 is used to perform the following method: Obtain historical data for evaluation level training and obtain the target evaluation level; Evaluate the calculation result based on the target evaluation level to obtain a calculation result evaluation result; The dynamic adjustment is performed based on the calculation result evaluation result to obtain dynamic adjustment data.

[0045] Furthermore, the garbage disposal component 5 is used to perform the following method: Obtain historical treatment plans, and build a treatment plan knowledge base based on the historical treatment plans, wherein the treatment plan knowledge base data format includes construction waste conditions-weather-treatment plans; Searching for a treatment solution in the treatment solution knowledge base based on the judgment result; If it exists, construction waste disposal will be carried out; If it does not exist, the judgment result is fed back and processed, and then added to the processing solution knowledge base according to the established corresponding data format.

[0046] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. Other embodiments are within the scope of the attached claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0047] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0048] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A distributed construction waste treatment method based on visual recognition, characterized in that: The method comprises: Determine a target area, install a sensor group based on the target area, and obtain real-time image parameter information; Building a central processing unit, inputting the real-time image parameter information into the central processing unit for image analysis and processing, and obtaining real-time analysis data; Building a real-time feedback mechanism to dynamically adjust the real-time analysis data and obtain real-time adjustment data; Preset a real-time adjustment rating, perform real-time adjustment data judgment based on the real-time adjustment rating, and obtain a judgment result; The target area is subjected to construction waste treatment based on the judgment result.

2. The method according to claim 1, characterized in that A central processing unit is constructed, and the real-time image parameter information is input into the central processing unit for image analysis and processing to obtain real-time analysis data. The method includes: Performing a scene evaluation based on the target area to obtain a scene evaluation result; Perform computing power configuration according to the scenario evaluation results and obtain computing power configuration results; Select an image processing algorithm and build the central processing unit based on the computing power configuration result.

3. The method according to claim 2, characterized in that include: The real-time image parameter information is transmitted to the central processing unit via a data interface; Performing real-time image parameter information data preprocessing in the central processing unit to obtain processed data; The processed data is subjected to image processing to obtain real-time analysis data.

4. The method according to claim 1, characterized in that A real-time feedback mechanism is constructed to dynamically adjust the real-time analysis data and obtain real-time adjustment data, the method comprising: The real-time feedback mechanism is in communication with the central processing unit to define the goals and performance indicators of the real-time feedback mechanism; The real-time analysis data is input into the real-time feedback mechanism, the real-time adjustment data is judged, and a data judgment result is obtained, wherein the data judgment result includes normal and abnormal; If the data judgment result is normal, the real-time analysis data is directly output; If the data judgment result is abnormal, the real-time analysis data is dynamically adjusted.

5. The method according to claim 4, characterized in that The real-time analysis data is input into the real-time feedback mechanism, the real-time adjustment data is judged, and a data judgment result is obtained, wherein the data judgment result includes normal and abnormal data. The method includes: Building a data judgment model, which includes an input layer, a judgment layer and an output layer, and is embedded in the real-time feedback mechanism, wherein the judgment layer includes a weather judgment module, a clarity judgment module and an occupied resource judgment module; The weather judgment module includes a strong light channel, a shadow channel, and a rain and snow channel, which are input according to the real-time weather conditions to obtain weather channel results; Based on the image clarity recognition model, the clarity judgment module is used to perform clarity judgment on the real-time analysis data to obtain a clarity judgment result; According to the clarity judgment result, a resource occupation result of the real-time analysis data is judged by a resource occupation judgment module; Perform weighted calculation according to the weather channel result, the clarity judgment result and the occupied resource result to obtain a calculation result; The calculation results are evaluated and a data judgment result is output.

6. The method according to claim 5, characterized in that Methods include: Obtain historical data for evaluation level training and obtain the target evaluation level; Evaluate the calculation result based on the target evaluation level to obtain a calculation result evaluation result; The dynamic adjustment is performed based on the calculation result evaluation result to obtain dynamic adjustment data.

7. The method according to claim 1, characterized in that Based on the judgment result, the target area is treated with construction waste, and the method includes: Obtain historical treatment plans, and build a treatment plan knowledge base based on the historical treatment plans, wherein the treatment plan knowledge base data format includes construction waste conditions-weather-treatment plans; Searching for a treatment solution in the treatment solution knowledge base based on the judgment result; If it exists, construction waste disposal will be carried out; If it does not exist, the judgment result is fed back and processed, and then added to the processing solution knowledge base according to the established corresponding data format.

8. A distributed construction waste treatment system based on visual recognition, characterized in that: The method for distributing construction waste based on visual recognition according to any one of claims 1 to 7 comprises: Information collection component: determine the target area, install a sensor group based on the target area, and obtain real-time image parameter information; Image analysis component: build a central processing unit, input the real-time image parameter information into the central processing unit for image analysis and processing, and obtain real-time analysis data; Dynamic adjustment component: building a real-time feedback mechanism to dynamically adjust the real-time analysis data and obtain real-time adjustment data; Data judgment component: preset real-time adjustment rating, perform real-time adjustment data judgment based on the real-time adjustment rating, and obtain judgment results; Waste disposal component: based on the judgment result, the target area is subjected to construction waste disposal.

Images