Hydraulic particle intelligent separation control method and system

Abstract

The present invention provides a hydraulic particle intelligent separation control method and system, which relates to the field of hydraulic particle separation technology, including: when the water body to be separated enters the first detection area of ​​the transmission pipeline, obtaining the first water body information; matching in the preset separation standard library to obtain the first separation standard and the second separation standard; the preset separation standard library includes the preset floating separation standard and the preset sediment separation standard; activating the first separation screen set and the second separation screen set to perform primary particle separation and obtain the first separation result; transmitting to the separation component to obtain the second water body information; obtaining the sediment separation control scheme; controlling the centrifuge to perform secondary particle separation, obtaining the second separation result, and transmitting it to the clean water storage tank as the clean water body. The present invention solves the technical problems that the traditional method lacks effective particle size control in the particle separation process, and there is much manual intervention, resulting in poor separation effect and low efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of hydraulic particle separation, and in particular to a hydraulic particle intelligent separation control method and system. Background Art

[0002] Hydraulic particle separation is mainly used in environmental protection and water resource management such as sewage treatment and urban pipeline drainage management. It can improve water quality, protect the ecological environment, and improve the efficiency of water resource utilization. In the traditional hydraulic particle separation process, there are still some technical problems. On the one hand, due to the lack of effective automated control means, the traditional separation method is difficult to guarantee the separation effect under different impurities and water conditions, and is easily affected by external factors; on the other hand, traditional hydraulic particle separation often requires manual intervention and adjustment, which makes the operation complicated and time-consuming and labor-intensive. Summary of the invention

[0003] This application provides a hydraulic particle intelligent separation control method, aiming to solve the technical problems that traditional methods lack effective particle size control in the particle separation process and have a lot of manual intervention, resulting in poor separation effect and low efficiency.

[0004] In view of the above problems, the present application provides a hydraulic particle intelligent separation control method and system.

[0005] The first aspect disclosed in the present application provides a hydraulic particle intelligent separation control method, the method is applied to a hydraulic particle intelligent separation device, the hydraulic particle intelligent separation device includes a transmission pipeline and a separation component, the method includes: when the water body to be separated enters the first detection area of ​​the transmission pipeline, obtaining first water body information, wherein the first water body information includes floating object characteristics and sediment characteristics; based on the floating object characteristics and sediment characteristics, matching is performed in a preset separation standard library to obtain a first separation standard and a second separation standard, wherein the first separation standard includes a first separation particle size, and the second separation standard includes a second separation particle size; wherein the preset separation standard library includes a preset floating object separation standard and a preset sediment separation standard; based on the first separation According to the particle size, a first separation screen set is activated in the first separation area of ​​the transmission pipeline, and based on the second separation particle size, a second separation screen set is activated in the second separation area of ​​the transmission pipeline to perform primary particle separation on the water body to be separated to obtain a first separation result; the first separation result is transmitted to the separation component, and second water body information is obtained through the second detection area of ​​the separation component, wherein the second water body information includes sediment characteristics; based on the sediment characteristics, a sediment separation control analysis is performed to obtain a sediment separation control plan; based on the sediment separation control plan, the centrifuge of the separation component is controlled to perform secondary particle separation on the first separation result to obtain a second separation result, and the second separation result is transmitted to the clean water storage tank as a clean water body.

[0006] The second aspect disclosed in the present application provides a hydraulic particle intelligent separation control system, the system is applied to a hydraulic particle intelligent separation device, the hydraulic particle intelligent separation device includes a transmission pipeline and a separation component, the system is used for the above-mentioned hydraulic particle intelligent separation control method, the system includes: a first detection module, the first detection module is used to obtain first water body information when the water body to be separated enters the first detection area of ​​the transmission pipeline, wherein the first water body information includes floating object characteristics and sediment characteristics; a separation standard matching module, the separation standard matching module is used to match in a preset separation standard library based on the floating object characteristics and the sediment characteristics to obtain a first separation standard and a second separation standard, wherein the first separation standard includes a first separation particle size, and the second separation standard includes a second separation particle size; a standard library description module, the preset separation standard library is used for, wherein the preset separation standard library includes a preset floating object separation standard and a preset sediment separation standard; a primary particle separation module, The primary particle separation module is used to activate a first separation screen set in a first separation area of ​​the transmission pipeline based on the first separation particle size, and to activate a second separation screen set in a second separation area of ​​the transmission pipeline based on the second separation particle size, to perform primary particle separation on the water body to be separated, and obtain a first separation result; a second detection module, the second detection module is used to transmit the first separation result to the separation component, and obtain second water body information through the second detection area of ​​the separation component, wherein the second water body information includes sediment characteristics; a separation control analysis module, the separation control analysis module is used to perform sediment separation control analysis based on the sediment characteristics, and obtain a sediment separation control plan; a secondary particle separation module, the secondary particle separation module is used to control the centrifuge of the separation component to perform secondary particle separation on the first separation result based on the sediment separation control plan, to obtain a second separation result, and transmit the second separation result as a clean water body to the clean water storage tank.

[0007] According to a third aspect disclosed in the present application, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, any step of the first aspect disclosed in the present application is implemented.

[0008] The fourth aspect disclosed in the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any step of the first aspect disclosed in the present application.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] By automatically identifying the floating objects and sediments in the water body to be separated, the degree of automation of the separation process is improved, and the separation efficiency and accuracy are improved; by matching the floating objects and sediment features in the preset separation standard library, the first separation standard and the second separation standard are obtained, and the primary particle separation of the floating objects and sediments is performed, which realizes the precise separation of different particles and improves the separation effect and stability; by obtaining the sediment features in the second water body information and generating a sediment separation control plan, the centrifuge is controlled to perform secondary particle separation, which realizes the effective treatment and separation of sediment and improves the sediment treatment efficiency and accuracy. In summary, the hydraulic particle intelligent separation control method effectively solves the technical problems existing in the traditional hydraulic particle separation process through automatic identification, multi-stage particle size separation and other means, and achieves the technical effect of improving separation efficiency, stability and accuracy.

[0011] 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

[0012] Figure 1 A schematic diagram of a flow chart of a hydraulic particle intelligent separation control method provided in an embodiment of the present application;

[0013] Figure 2 A schematic diagram of the structure of a hydraulic particle intelligent separation control system provided in an embodiment of the present application;

[0014] Figure 3 This is a diagram of the internal structure of a computer device provided in an embodiment of the present application.

[0015] Explanation of the reference numerals: first detection module 10 , separation standard matching module 20 , standard library description module 30 , primary particle separation module 40 , second detection module 50 , separation control and analysis module 60 , secondary particle separation module 70 . DETAILED DESCRIPTION

[0016] The embodiment of the present application solves the technical problems that the traditional method lacks effective particle size control in the particle separation process and has a lot of manual intervention, resulting in poor separation effect and low efficiency by providing a hydraulic particle intelligent separation control method.

[0017] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0018] like Figure 1As shown, an embodiment of the present application provides a hydraulic particle intelligent separation control method, the method is applied to a hydraulic particle intelligent separation device, the hydraulic particle intelligent separation device includes a transmission pipeline and a separation component, and the method includes:

[0019] When the water to be separated enters the first detection area of ​​the transmission pipeline, first water body information is obtained, wherein the first water body information includes floating matter characteristics and sediment characteristics;

[0020] The hydraulic particle intelligent separation control method provided in the embodiment of the present application is applied to a hydraulic particle intelligent separation device, wherein the hydraulic particle intelligent separation device includes a transmission pipeline and a separation component, wherein the transmission pipeline is a pipeline through which the water to be treated passes, and the transmission pipeline includes a first detection area, a first separation area, and a second separation area. In this process, the transmission pipeline transports the water from the input end to the output end, and performs large particle separation treatment on the way; the separation component is a device for separating sediment in the water body, which includes equipment such as a centrifuge, and is used to separate and treat small particles of sediment in the water body to produce clean water.

[0021] First, the water body to be separated is guided to the set first detection area through the transmission pipeline. The first detection area includes an image acquisition device and a laser depth measurement device. The image acquisition device is used to capture images of the water body surface, and the image acquisition results are analyzed for floating object characteristics to obtain the floating object characteristics, wherein the floating object characteristics include floating object type characteristics, floating object size characteristics, and floating object quantity characteristics; the laser depth measurement device is used to perform laser depth measurement on the bottom of the water body to be separated, and the sediment characteristics are obtained based on the laser depth measurement data, wherein the sediment characteristics include sediment type characteristics, sediment size characteristics, and sediment quantity characteristics. The acquired floating object characteristics and sediment characteristics are integrated together to form complete first water body information, providing data support for subsequent processing steps.

[0022] Based on the floating object characteristics and the sediment characteristics, matching is performed in a preset separation standard library to obtain a first separation standard and a second separation standard, wherein the first separation standard includes a first separation particle size, and the second separation standard includes a second separation particle size;

[0023] The preset separation standard library includes already set separation standards, which correspond to different floating material characteristics and sediment characteristics. This library can be established based on experimental data or historical data and includes separation standard settings for various situations.

[0024] According to the obtained floating object characteristics and sediment characteristics, matching standards are found from the preset separation standard library. The floating object characteristics include floating object type characteristics, floating object size characteristics, and floating object quantity characteristics. The floating object characteristics are matched with the preset floating object separation standards. For example, according to multiple size intervals of floating objects, multiple first separation particle sizes are matched to select matching screen sizes. Sediment characteristics include sediment type characteristics, sediment size characteristics, and sediment quantity characteristics. Similarly, the sediment characteristics are matched with the preset sediment separation standards to obtain multiple second separation particle sizes. In this way, the characteristics of floating objects and sediments can be comprehensively considered to determine the separation standard that best suits the current situation.

[0025] Wherein, the preset separation standard library includes preset floating separation standards and preset sediment separation standards;

[0026] The preset separation standard library contains two main parts, namely the preset floating separation standard and the preset sediment separation standard. These two parts set a series of standards for the separation of floating matter and sediment respectively, so that the system can effectively separate particles according to the characteristics of the water body.

[0027] Among them, the preset floating object separation standards include separation standards set for various characteristics of floating objects. These standards are based on the type, size, shape, density and other characteristics of floating objects. The preset floating object separation standards can be derived based on experimental data and historical data to ensure that the system can effectively separate floating objects from the water body.

[0028] The preset sediment separation standards include separation standards set for various characteristics of the sediment. These standards are based on the type, size, concentration, weight and other characteristics of the sediment. The preset sediment separation standards can also be derived based on experimental data and historical data to ensure that the system can effectively separate the sediment from the water body.

[0029] Through the preset floating matter separation standards and sediment separation standards, it is possible to quickly determine the appropriate separation scheme according to the characteristics detected in the water body in actual operation, thereby achieving efficient particle separation.

[0030] Based on the first separation particle size, a first separation screen set is activated in a first separation area of ​​the conveying pipeline, and based on the second separation particle size, a second separation screen set is activated in a second separation area of ​​the conveying pipeline to perform primary particle separation on the water body to be separated, and obtain a first separation result;

[0031] According to the first separation particle size determined in the first separation standard, a first separation screen set is activated in the first separation area of ​​the conveying pipeline, and the first separation screen set includes a series of screens set according to the standard, and the apertures of these screens can be screened according to the different sizes of floating objects. Similarly, according to the second separation particle size determined in the first separation standard, a second separation screen set is activated in the second separation area of ​​the conveying pipeline, and the second separation screen set includes a series of screens set according to the standard, and the apertures of these screens can be screened according to the different sizes of sediments.

[0032] The water body to be separated passes through the transmission pipeline, through the first separation area and the second separation area. In these areas, the first separation screen set and the second separation screen set separate the particles in the water body according to their size based on their specific sieve hole sizes, so that the particles are separated and after processing, the first separation result is obtained.

[0033] Furthermore, performing primary particle separation on the water body to be separated to obtain a first separation result includes:

[0034] Based on the first separation particle size, generating a first screen selection scheme, wherein the first screen selection scheme includes a first number of screens and a first screen hole size set;

[0035] Selecting a screen based on the first screen selection scheme to obtain a first separation screen set;

[0036] Based on the second separation particle size, generating a second screen selection scheme, wherein the second screen selection scheme includes the number of second screens and a second screen hole size set;

[0037] Selecting a screen based on the second screen selection scheme to obtain a second separation screen set;

[0038] The first separation screen set and the second separation screen set are used to perform primary particle separation of the water body to be separated to obtain a first separation result.

[0039] Transmitting the first separation result to a separation component, and obtaining second water body information through a second detection area of ​​the separation component, wherein the second water body information includes sediment characteristics;

[0040] The first separation result is transmitted to the separation component and detected by the second detection area of ​​the separation component. The second detection area includes a device for detecting sediment, such as a component detection device, a weight detection device, etc. In the second detection area, the first separation result is detected to obtain second water body information. The second water body information includes sediment characteristics, including sediment particle composition characteristics, sediment particle concentration characteristics, and sediment particle weight characteristics.

[0041] Based on the sediment characteristics, a sediment separation control analysis is performed to obtain a sediment separation control plan;

[0042] The obtained sediment characteristics are used to perform sediment separation control analysis. For example, the distribution of particle size in the sediment is analyzed to determine the appropriate separation parameters, and different sediment treatment schemes are evaluated, including the use of different control parameters to determine the best separation control scheme. The obtained scheme includes parameter settings for the separation device to achieve effective separation and treatment of sediment. The specific sediment separation control analysis method is described in detail in subsequent steps and is only briefly mentioned here.

[0043] Based on the sediment separation control scheme, the centrifuge of the separation component is controlled to perform secondary particle separation on the first separation result to obtain a second separation result, and the second separation result is transmitted as a clean water body to a clean water storage tank.

[0044] According to the parameters determined in the sediment separation control scheme, the centrifuge in the separation component is controlled. The centrifuge adjusts the rotation speed, tilt angle and other parameters according to the preset control scheme to further separate the sediment in the first separation result. During the separation process, according to the action of centrifugal force, the sediment is further separated to form a second separation result. The second separation result is a relatively purer water body that no longer contains heavier sediment and other particles. The second separation result is transmitted to the clean water storage tank for further processing or directly supplied to the department that needs clean water.

[0045] Furthermore, when the water to be separated enters the first detection area of ​​the transmission pipeline, first water body information is obtained, wherein the first water body information includes floating matter characteristics and sediment characteristics, including:

[0046] The first detection area includes an image acquisition device and a laser depth measurement device;

[0047] The image acquisition device is used to acquire an image of the surface of the water body to be separated, thereby obtaining an image acquisition result;

[0048] Performing floating object feature analysis on the image acquisition result to obtain the floating object features, wherein the floating object features include floating object type features, floating object size features, and floating object quantity features;

[0049] The laser depth measuring device is used to measure the bottom of the water body to be separated by laser to obtain laser depth measuring data;

[0050] Based on the laser bathymetric data, the sediment characteristics are obtained, wherein the sediment characteristics include sediment type characteristics, sediment size characteristics, and sediment quantity characteristics.

[0051] The first detection area is used to detect the water body to be separated, including an image acquisition device and a laser depth measurement device, which are installed in a fixed position in the transmission pipeline or beside the pipeline. The image acquisition device is used to obtain image information of floating objects on the surface of the water body to be separated, and the laser depth measurement device is used to obtain laser depth measurement data of sediments at the bottom of the water body.

[0052] The image acquisition device is used to acquire images of the surface of the water body to be separated, including taking photos of the water body surface or recording videos, so as to obtain information such as the distribution, shape, size, etc. of floating objects in the water body and form an image acquisition result.

[0053] The image acquisition results are analyzed to identify and extract the features of floating objects in the image. This process includes the application of image processing technology, such as edge detection and feature extraction, to separate the information of floating objects from the image. The features of floating objects include the type features of floating objects, the size features of floating objects, and the number features of floating objects. Among them, the type features of floating objects include plastic, wood, fallen leaves, etc.; the size features of floating objects include the length, width, area, etc. of floating objects; the number features of floating objects are to calculate the number of floating objects, reflecting the density and distribution of floating objects in the water body.

[0054] The laser sounding device emits a laser beam to the water surface, which is then reflected back and used to detect sediments, including obtaining sediment types such as silt, mud, etc.; estimating the size or shape of sediments; and counting the number of bottom sediments. After analysis, characteristic information of bottom sediments is obtained, including characteristics such as type, size, and quantity.

[0055] Furthermore, performing floating object feature analysis on the image acquisition result to obtain the floating object feature includes:

[0056] Performing floating object edge detection on the image acquisition result to obtain a plurality of floating object images, wherein the plurality of floating object images correspond to a plurality of floating object areas;

[0057] Based on the multiple floating object areas, obtaining the floating object size characteristics and the floating object quantity characteristics;

[0058] Inputting the plurality of floating object images into a floating object type identifier, and outputting a plurality of floating object types as the floating object type features;

[0059] The construction process of the floating object type identifier is as follows:

[0060] According to the floating object processing record, a sample floating object image feature set is obtained;

[0061] Marking the sample floating object image feature set by floating object type to obtain a sample floating object type set;

[0062] Based on a convolutional neural network, the sample floating object image feature set is used as input data, and the sample floating object type set is used as label data to train and obtain the floating object type identifier.

[0063] The image is processed using an image processing algorithm to detect the edge of floating objects, including using an edge detection algorithm to identify the edge of floating objects in the image, identifying the outline of the floating objects through edge detection, and dividing the image into multiple floating object areas, each floating object area corresponding to a floating object, extracting these areas from the original image, and generating a corresponding floating object image.

[0064] For each floating object area, its size characteristics, such as length, width, area, etc., are calculated by measuring the bounding box of the floating object area; the number characteristics of floating objects are obtained by counting the number of floating object areas. Each floating object area corresponds to one floating object, so the number of floating object areas is the number of floating objects.

[0065] The floating object type identifier is a trained deep learning model used to identify the type of floating objects in an image. This model is built based on technologies such as convolutional neural networks (CNNs) and can extract features from images and match them with known floating object types. Multiple floating object images are used as input and input into the floating object type identifier one by one for identification. Each floating object image corresponds to a floating object. The type of each floating object is obtained by identifying these images separately. These identification results are used as floating object type features to describe the type of each floating object.

[0066] The construction process of the floating object type identifier is as follows:

[0067] A series of sample floating object image data are obtained from the previous floating object processing records. These sample images contain floating objects of various types and shapes, representing various situations of floating objects that have been processed in the past. Each sample floating object image is marked to determine its floating object type. After marking, each sample floating object image is associated with a specific floating object type, forming a sample floating object type set.

[0068] Convolutional neural network (CNN) is a deep learning model suitable for image recognition tasks. It consists of multiple convolutional layers and pooling layers stacked alternately, which can effectively extract image features.

[0069] The sample floating object image feature set is used as input data. These image features include the image pixel value, color distribution, texture, etc. The sample floating object type set is used as label data, which contains the true type of each sample floating object. For each floating object image, there is a corresponding label indicating the true type of the floating object.

[0070] The prepared input data and label data are input into the CNN model for training. During the training process, the CNN model adjusts the model parameters by learning the relationship between the input data and the label data, so that the model can accurately predict the type of floating objects. During the training process, optimization algorithms such as gradient descent are used to minimize the gap between the predicted results and the true labels. After the training is completed, the model is evaluated to ensure that it has good generalization ability on unseen data. Through this process, a floating object type identifier can be trained that can accurately predict the type of floating objects based on the characteristics of the floating object image.

[0071] Furthermore, performing floating object edge detection on the image acquisition results to obtain a plurality of floating object images includes:

[0072] Performing grayscale processing on the image acquisition result to obtain a grayscale value set;

[0073] Clustering the grayscale value set to obtain multiple clusters;

[0074] Connected region analysis is performed based on the multiple clusters to obtain multiple floating object regions, and image extraction is performed on the multiple floating object regions to obtain the multiple floating object images.

[0075] Grayscale processing is the process of converting a color image into a grayscale image. In a grayscale image, the value range of each pixel is 0 to 255, which represents the grayscale level of the pixel, 0 represents black, and 255 represents white. For a grayscale image, the grayscale value of each pixel is obtained to form a grayscale value set, which contains the grayscale value information of all pixels in the image.

[0076] Cluster analysis is an unsupervised learning method that aims to divide samples in a data set into several categories so that samples in the same category have high similarity, while samples in different categories have large differences. The grayscale value set is used as input data and analyzed using a clustering algorithm. The clustering algorithm divides the pixels in the grayscale value set into multiple clusters, each of which has a specific grayscale value range and the pixels in it have similar grayscale features.

[0077] Using the connected region analysis method, adjacent pixels in the image are grouped according to their grayscale values ​​to form multiple connected regions. Each connected region represents a floating object region in the image, and the pixels in it have similar grayscale features. For each connected region, it is determined whether it is a floating object region based on the pixel grayscale value, size, shape and other features, and multiple floating object regions are obtained.

[0078] For each connected region identified as a floating object region, the corresponding pixels are extracted from the original image to form a new floating object image, the purpose of which is to separate the floating objects from the original image for subsequent analysis.

[0079] Furthermore, based on the sediment characteristics, a sediment separation control analysis is performed to obtain a sediment separation control solution, including:

[0080] The sediment characteristics include sediment particle composition characteristics, sediment particle concentration characteristics, and sediment particle weight characteristics;

[0081] Retrieving a set of sediment processing records, each sediment processing record including sample sediment characteristics and sample sediment separation control decisions, wherein the sample sediment characteristics include sample sediment particle composition, sample sediment particle concentration, and sample sediment particle weight;

[0082] Based on the sediment characteristics, performing sediment characteristic matching on the sediment processing record set to obtain a plurality of sample sediment characteristic matching results, wherein the plurality of sample sediment characteristic matching results include a plurality of matching coefficients;

[0083] Based on the multiple matching coefficients, decision extraction is performed on the sediment processing record set to obtain the sediment separation control solution.

[0084] The sediment particle composition characteristics describe the distribution of different particle sizes, shapes and components in the sediment, such as the distribution of various types of particles such as coarse particles, fine particles, clay particles, sand particles, etc. in the sediment; the sediment particle concentration characteristics describe the number of sediment particles contained in unit volume of water; the sediment particle weight characteristics describe the total mass of sediment particles contained in unit volume of water.

[0085] The sediment treatment record set is a data set of historical sediment treatment, which records the sediment treatment in the past. Each record corresponds to a sediment treatment process, and contains the characteristic information of the sample sediment and the sediment separation control decision at that time. Specifically, each sediment treatment record includes the characteristic information of the sample sediment, including the characteristics of the sample sediment particle composition, sample sediment particle concentration, and sample sediment particle weight. These characteristics describe the nature and content of the sediment at that time. Each sediment treatment record also contains the corresponding sediment separation control decision, which is a treatment plan formulated according to the characteristic information of the sediment at that time. The sediment separation control decision involves different equipment control parameters.

[0086] The characteristics of the current sediment are matched one by one with the characteristics of the sample sediment in the sediment processing record set. The matching process includes comparing the particle composition, particle concentration, particle weight and other characteristics of the sediment to determine the similarity between them. For each sample sediment characteristic, a matching coefficient is obtained in the matching process to indicate the degree of matching between the current sediment and the sample sediment.

[0087] Compare the sizes of multiple matching coefficients to obtain the largest matching coefficient. The sample corresponding to the matching coefficient is most similar to the current sediment characteristics. The sample sediment separation control decision of the sample is obtained as the sediment separation control plan for the current sediment characteristics to achieve effective treatment and separation of the current sediment.

[0088] Furthermore, based on the sediment characteristics, the sediment processing record set is subjected to sediment characteristic matching to obtain a plurality of sample sediment characteristic matching results, including:

[0089] Performing a similarity analysis on the sediment particle composition characteristics and the sediment particle composition of the first sample to obtain a first similarity;

[0090] Performing a similarity analysis on the sediment particle concentration feature and the sediment particle concentration of the first sample to obtain a second similarity;

[0091] Performing a similarity analysis on the sediment particle weight feature and the sediment particle weight of the first sample to obtain a third similarity;

[0092] A weighted sum is performed on the first similarity, the second similarity, and the third similarity to obtain a first matching coefficient, and the first matching coefficient is added to the multiple matching coefficients.

[0093] Compare the particle composition characteristics of the current sediment with the particle composition characteristics of the first sample sediment, and calculate the similarity between them. Various methods can be used for similarity analysis, such as cosine similarity, Euclidean distance, etc., to determine the similarity between the two. The first similarity is obtained through similarity analysis, which is used to represent the similarity between the current sediment particle composition characteristics and the first sample sediment particle composition characteristics. The higher the similarity value, the more similar the particle composition characteristics of the current sediment are to the particle composition characteristics of the first sample sediment.

[0094] In the same way, the sediment particle concentration feature is analyzed for similarity with the sediment particle concentration of the first sample, and the sediment particle weight feature is analyzed for similarity with the sediment particle weight of the first sample to obtain a second similarity and a third similarity.

[0095] For the first similarity, the second similarity and the third similarity, they are weighted and summed to obtain a comprehensive matching coefficient. The weight of the weighted summation can be set according to the actual situation and specific needs to show the importance of each similarity. The comprehensive matching coefficient is the first matching coefficient. The first matching coefficient combines the information of multiple similarities and is used to evaluate the overall similarity between the current sediment and the first sample sediment. The first matching coefficient is added to the multiple matching coefficients to comprehensively evaluate the matching degree between the current sediment and multiple sample sediments.

[0096] In summary, the hydraulic particle intelligent separation control method provided in the embodiment of the present application has the following technical effects:

[0097] 1. Through the automatic identification of floating objects and sediments in the water to be separated, the automation degree of the separation process is improved, and the separation efficiency and accuracy are improved;

[0098] 2. Match the characteristics of floating objects and sediments in the preset separation standard library to obtain the first separation standard and the second separation standard, and perform primary particle separation of floating objects and sediments, thereby achieving accurate separation of different particles and improving separation effect and stability;

[0099] 3. By acquiring the sediment characteristics in the second water body information and generating a sediment separation control plan, the centrifuge is controlled to perform secondary particle separation, thereby achieving effective treatment and separation of sediment and improving sediment treatment efficiency and accuracy.

[0100] In summary, the hydraulic particle intelligent separation control method effectively solves the technical problems existing in the traditional hydraulic particle separation process through automatic identification, multi-stage particle size separation and other means, and achieves the technical effect of improving separation efficiency, stability and accuracy.

[0101] Based on the same inventive concept as the hydraulic particle intelligent separation control method in the aforementioned embodiment, Figure 2 As shown, the present application provides a hydraulic particle intelligent separation control system, the system is applied to a hydraulic particle intelligent separation device, the hydraulic particle intelligent separation device includes a transmission pipeline and a separation component, and the system includes:

[0102] A first detection module 10, the first detection module 10 is used to obtain first water body information when the water body to be separated enters the first detection area of ​​the transmission pipeline, wherein the first water body information includes floating object characteristics and sediment characteristics;

[0103] A separation standard matching module 20, wherein the separation standard matching module 20 is used to match the floating object characteristics and the sediment characteristics in a preset separation standard library to obtain a first separation standard and a second separation standard, wherein the first separation standard includes a first separation particle size and the second separation standard includes a second separation particle size;

[0104] A standard library description module 30, wherein the preset separation standard library is used, and the preset separation standard library includes a preset floating separation standard and a preset sediment separation standard;

[0105] A primary particle separation module 40, the primary particle separation module 40 is used to activate a first separation screen set in a first separation area of ​​the conveying pipeline based on the first separation particle size, and activate a second separation screen set in a second separation area of ​​the conveying pipeline based on the second separation particle size, to perform primary particle separation on the water body to be separated, and obtain a first separation result;

[0106] A second detection module 50, the second detection module 50 is used to transmit the first separation result to a separation component, and obtain second water body information through a second detection area of ​​the separation component, wherein the second water body information includes sediment characteristics;

[0107] A separation control analysis module 60, wherein the separation control analysis module 60 is used to perform sediment separation control analysis based on the sediment characteristics to obtain a sediment separation control solution;

[0108] The secondary particle separation module 70 is used to control the centrifuge of the separation component to perform secondary particle separation on the first separation result based on the sediment separation control scheme, obtain a second separation result, and transmit the second separation result as a clean water body to a clean water storage tank.

[0109] Furthermore, the system further includes a feature acquisition module to perform the following operation steps:

[0110] The first detection area includes an image acquisition device and a laser depth measurement device;

[0111] The image acquisition device is used to acquire an image of the surface of the water body to be separated, thereby obtaining an image acquisition result;

[0112] Performing floating object feature analysis on the image acquisition result to obtain the floating object features, wherein the floating object features include floating object type features, floating object size features, and floating object quantity features;

[0113] The laser depth measuring device is used to measure the bottom of the water body to be separated by laser to obtain laser depth measuring data;

[0114] Based on the laser bathymetric data, the sediment characteristics are obtained, wherein the sediment characteristics include sediment type characteristics, sediment size characteristics, and sediment quantity characteristics.

[0115] Furthermore, the system further includes an image analysis module to perform the following operation steps:

[0116] Performing floating object edge detection on the image acquisition result to obtain a plurality of floating object images, wherein the plurality of floating object images correspond to a plurality of floating object areas;

[0117] Based on the multiple floating object areas, obtaining the floating object size characteristics and the floating object quantity characteristics;

[0118] Inputting the plurality of floating object images into a floating object type identifier, and outputting a plurality of floating object types as the floating object type features;

[0119] The construction process of the floating object type identifier is as follows:

[0120] According to the floating object processing record, a sample floating object image feature set is obtained;

[0121] Marking the sample floating object image feature set by floating object type to obtain a sample floating object type set;

[0122] Based on a convolutional neural network, the sample floating object image feature set is used as input data, and the sample floating object type set is used as label data to train and obtain the floating object type identifier.

[0123] Furthermore, the system further comprises a floating object image acquisition module to perform the following operation steps:

[0124] Performing grayscale processing on the image acquisition result to obtain a grayscale value set;

[0125] Clustering the grayscale value set to obtain multiple clusters;

[0126] Connected region analysis is performed based on the multiple clusters to obtain multiple floating object regions, and image extraction is performed on the multiple floating object regions to obtain the multiple floating object images.

[0127] Furthermore, the system further includes a first separation result acquisition module to perform the following operation steps:

[0128] Based on the first separation particle size, generating a first screen selection scheme, wherein the first screen selection scheme includes a first number of screens and a first screen hole size set;

[0129] Selecting a screen based on the first screen selection scheme to obtain a first separation screen set;

[0130] Based on the second separation particle size, generating a second screen selection scheme, wherein the second screen selection scheme includes the number of second screens and a second screen hole size set;

[0131] Selecting a screen based on the second screen selection scheme to obtain a second separation screen set;

[0132] The first separation screen set and the second separation screen set are used to perform primary particle separation of the water body to be separated to obtain a first separation result.

[0133] Furthermore, the system further includes a sediment separation control scheme acquisition module to perform the following operation steps:

[0134] The sediment characteristics include sediment particle composition characteristics, sediment particle concentration characteristics, and sediment particle weight characteristics;

[0135] Retrieving a set of sediment processing records, each sediment processing record including sample sediment characteristics and sample sediment separation control decisions, wherein the sample sediment characteristics include sample sediment particle composition, sample sediment particle concentration, and sample sediment particle weight;

[0136] Based on the sediment characteristics, performing sediment characteristic matching on the sediment processing record set to obtain a plurality of sample sediment characteristic matching results, wherein the plurality of sample sediment characteristic matching results include a plurality of matching coefficients;

[0137] Based on the multiple matching coefficients, decision extraction is performed on the sediment processing record set to obtain the sediment separation control solution.

[0138] Furthermore, the system further includes a matching coefficient acquisition module to perform the following operation steps:

[0139] Performing a similarity analysis on the sediment particle composition characteristics and the sediment particle composition of the first sample to obtain a first similarity;

[0140] Performing a similarity analysis on the sediment particle concentration feature and the sediment particle concentration of the first sample to obtain a second similarity;

[0141] Performing a similarity analysis on the sediment particle weight feature and the sediment particle weight of the first sample to obtain a third similarity;

[0142] A weighted sum is performed on the first similarity, the second similarity, and the third similarity to obtain a first matching coefficient, and the first matching coefficient is added to the multiple matching coefficients.

[0143] Through the above-mentioned detailed description of the hydraulic particle intelligent separation control method in this specification, those skilled in the art can clearly understand the hydraulic particle intelligent separation control system in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0144] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3As shown. The computer device includes a processor, a memory and a network interface connected through a system bus, wherein the processor of the computer device is used to provide computing and control capabilities; the memory of the computer device includes a non-volatile storage medium and an internal memory, wherein the non-volatile storage medium stores an operating system, a computer program and a database, and the internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium; the network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the hydraulic particle intelligent separation control method.

[0145] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0146] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0147] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A hydraulic particle intelligent separation control method, characterized in that: The method is applied to a hydraulic particle intelligent separation device, the hydraulic particle intelligent separation device includes a transmission pipeline and a separation component, and the method includes: When the water to be separated enters the first detection area of ​​the transmission pipeline, first water body information is obtained, wherein the first water body information includes floating matter characteristics and sediment characteristics; Based on the floating object characteristics and the sediment characteristics, matching is performed in a preset separation standard library to obtain a first separation standard and a second separation standard, wherein the first separation standard includes a first separation particle size, and the second separation standard includes a second separation particle size; Wherein, the preset separation standard library includes preset floating separation standards and preset sediment separation standards; Based on the first separation particle size, a first separation screen set is activated in a first separation area of ​​the conveying pipeline, and based on the second separation particle size, a second separation screen set is activated in a second separation area of ​​the conveying pipeline to perform primary particle separation on the water body to be separated, and obtain a first separation result; Transmitting the first separation result to a separation component, and obtaining second water body information through a second detection area of ​​the separation component, wherein the second water body information includes sediment characteristics; Based on the sediment characteristics, a sediment separation control analysis is performed to obtain a sediment separation control plan; Based on the sediment separation control scheme, controlling the centrifuge of the separation component to perform secondary particle separation on the first separation result to obtain a second separation result, and transmitting the second separation result as a clean water body to a clean water storage tank; When the water to be separated enters the first detection area of ​​the transmission pipeline, first water body information is obtained, wherein the first water body information includes floating matter characteristics and sediment characteristics, including: The first detection area includes an image acquisition device and a laser depth measurement device; The image acquisition device is used to acquire an image of the surface of the water body to be separated, thereby obtaining an image acquisition result; Performing floating object feature analysis on the image acquisition result to obtain the floating object features, wherein the floating object features include floating object type features, floating object size features, and floating object quantity features; The laser depth measuring device is used to measure the bottom of the water body to be separated by laser to obtain laser depth measuring data; Based on the laser bathymetric data, the sediment characteristics are obtained, wherein the sediment characteristics include sediment type characteristics, sediment size characteristics, and sediment quantity characteristics; Performing floating object feature analysis on the image acquisition result to obtain the floating object feature includes: Performing floating object edge detection on the image acquisition result to obtain a plurality of floating object images, wherein the plurality of floating object images correspond to a plurality of floating object areas; Based on the multiple floating object areas, obtaining the floating object size characteristics and the floating object quantity characteristics; Inputting the plurality of floating object images into a floating object type identifier, and outputting a plurality of floating object types as the floating object type features; The construction process of the floating object type identifier is as follows: According to the floating object processing record, a sample floating object image feature set is obtained; Marking the sample floating object image feature set by floating object type to obtain a sample floating object type set; Based on a convolutional neural network, the sample floating object image feature set is used as input data, and the sample floating object type set is used as label data to train and obtain the floating object type identifier; Performing floating object edge detection on the image acquisition results to obtain multiple floating object images, including: Performing grayscale processing on the image acquisition result to obtain a grayscale value set; Clustering the grayscale value set to obtain multiple clusters; Performing connected region analysis based on the multiple clusters to obtain multiple floating object regions, and performing image extraction on the multiple floating object regions to obtain the multiple floating object images; Based on the sediment characteristics, a sediment separation control analysis is performed to obtain a sediment separation control solution, including: The sediment characteristics include sediment particle composition characteristics, sediment particle concentration characteristics, and sediment particle weight characteristics; Retrieving a set of sediment processing records, each sediment processing record including sample sediment characteristics and sample sediment separation control decisions, wherein the sample sediment characteristics include sample sediment particle composition, sample sediment particle concentration, and sample sediment particle weight; Based on the sediment characteristics, performing sediment characteristic matching on the sediment processing record set to obtain a plurality of sample sediment characteristic matching results, wherein the plurality of sample sediment characteristic matching results include a plurality of matching coefficients; Based on the multiple matching coefficients, decision extraction is performed on the sediment treatment record set to obtain the sediment separation control solution; Based on the sediment characteristics, the sediment processing record set is matched with sediment characteristics to obtain a plurality of sample sediment characteristic matching results, including: Performing a similarity analysis on the sediment particle composition characteristics and the sediment particle composition of the first sample to obtain a first similarity; Performing a similarity analysis on the sediment particle concentration feature and the sediment particle concentration of the first sample to obtain a second similarity; Performing a similarity analysis on the sediment particle weight feature and the sediment particle weight of the first sample to obtain a third similarity; A weighted sum is performed on the first similarity, the second similarity, and the third similarity to obtain a first matching coefficient, and the first matching coefficient is added to the multiple matching coefficients.

2. The method according to claim 1, characterized in that Performing primary particle separation on the water body to be separated to obtain a first separation result, comprising: Based on the first separation particle size, generating a first screen selection scheme, wherein the first screen selection scheme includes a first number of screens and a first screen hole size set; Selecting a screen based on the first screen selection scheme to obtain a first separation screen set; Based on the second separation particle size, generating a second screen selection scheme, wherein the second screen selection scheme includes the number of second screens and a second screen hole size set; Selecting a screen based on the second screen selection scheme to obtain a second separation screen set; The first separation screen set and the second separation screen set are used to perform primary particle separation of the water body to be separated to obtain a first separation result.

3. Hydraulic particle intelligent separation control system, characterized in that: The system is applied to a hydraulic particle intelligent separation device, the hydraulic particle intelligent separation device includes a transmission pipeline and a separation component, and is used to implement the hydraulic particle intelligent separation control method according to any one of claims 1 to 2. The system includes: A first detection module, the first detection module is used to obtain first water body information when the water body to be separated enters the first detection area of ​​the transmission pipeline, wherein the first water body information includes floating object characteristics and sediment characteristics; A separation standard matching module, wherein the separation standard matching module is used to match the floating object characteristics and the sediment characteristics in a preset separation standard library to obtain a first separation standard and a second separation standard, wherein the first separation standard includes a first separation particle size and the second separation standard includes a second separation particle size; A standard library description module, in which the preset separation standard library is used, and the preset separation standard library includes a preset floating separation standard and a preset sediment separation standard; a primary particle separation module, the primary particle separation module being used to activate a first separation screen set in a first separation region of the conveying pipeline based on the first separation particle size, and to activate a second separation screen set in a second separation region of the conveying pipeline based on the second separation particle size, to perform primary particle separation on the water body to be separated, and to obtain a first separation result; a second detection module, the second detection module being used to transmit the first separation result to a separation component, and obtain second water body information through a second detection area of ​​the separation component, wherein the second water body information includes sediment characteristics; A separation control analysis module, the separation control analysis module is used to perform sediment separation control analysis based on the sediment characteristics to obtain a sediment separation control solution; A secondary particle separation module is used to control the centrifuge of the separation component to perform secondary particle separation on the first separation result based on the sediment separation control scheme, obtain a second separation result, and transmit the second separation result as a clean water body to a clean water storage tank.

4. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the hydraulic particle intelligent separation control method described in any one of claims 1 to 2 are implemented.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the hydraulic particle intelligent separation control method described in any one of claims 1 to 2 are implemented.

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