Method and device for intelligently monitoring operation process of reaction kettle based on visual analysis

Abstract

The invention discloses an intelligent monitoring method and device for the operation process of a reaction kettle based on visual analysis. The method comprises the following steps: acquiring a video sequence in a reaction kettle in a current period collected by a 5G explosion-proof camera deployed in the reaction kettle according to a preset period, so as to obtain a video sequence in the kettle to be analyzed; inputting the video sequence in the kettle to be analyzed into the chemical product visual analysis model for fuzzy video processing, and extracting product visual information of the current period to obtain the product visual information; determining whether the time distribution condition of the process operation nodes in the current period is consistent with the time distribution condition in preset process operation node information or not to obtain an analysis result; and if not, corresponding operation process prompt information is created and sent to the client. By implementing the method, the accurate control of the production process and the product quality stability can be effectively improved, and the method is particularly suitable for the production requirements of high-end chemical products.

Description

TECHNICAL FIELD

[0001] The present application relates to computers, and more particularly to a reaction kettle operation process intelligent monitoring method and device based on visual analysis. BACKGROUND

[0002] In the field of chemical production, the reaction kettle, as the core device of chemical reactions and production processes, has an extremely complex and crucial internal operation process. These operations include the addition of various raw materials and the precise control of reaction conditions such as temperature, pressure, and stirring speed. Accurate control and timely adjustment of these factors directly affect the quality of the final product.

[0003] In current technical practice, operators usually rely on observing the transparent viewing window installed on the reaction kettle to monitor the specific process. This method highly depends on the professional experience and personal attention level of the operator, and there is a risk of inaccurate monitoring due to operator fatigue or negligence. In addition, this visual-based manual monitoring method is difficult to accurately capture and record key details in the operation process, limiting the accuracy and timeliness of monitoring data, thereby affecting the consistency and stability of product quality.

[0004] Therefore, it is necessary to design a new method to effectively improve the precise control of the production process and the stability of product quality, especially suitable for the production requirements of high-end chemical products; to solve the technical problems in the prior art that the method of monitoring the operation process of the reaction kettle by the transparent viewing window through manual operation not only highly depends on the experience and attention of the operator, but also is difficult to accurately capture and record key process details in real time, thereby affecting the consistency and stability of product quality. SUMMARY

[0005] The purpose of the present application is to overcome the defects of the prior art and provide a reaction kettle operation process intelligent monitoring method and device based on visual analysis.

[0006] To achieve the above-mentioned purpose, the present application adopts the following technical solution: a reaction kettle operation process intelligent monitoring method based on visual analysis, comprising: acquiring a video sequence of the inside of the reaction kettle in the current period collected by a 5G explosion-proof camera deployed in the reaction kettle according to a preset period, to obtain a to-be-analyzed kettle video sequence; inputting the to-be-analyzed kettle video sequence into a chemical product visual analysis model for fuzzy video processing and extracting product visual information of the current period, to obtain product visual information; wherein the chemical product visual analysis model is obtained by model training using a neural network technology with operation process video segments of each process operation node in the historical video sequence as a sample set; analyzing the process operation nodes in the current period based on the preset process operation node information and the product visual information to determine whether the time distribution of the process operation nodes in the current period is consistent with the time distribution in the preset process operation node information, to obtain an analysis result; When the analysis result is that the time distribution of the process operation nodes in the current period is inconsistent with the time distribution in the preset process operation node information, corresponding operation process prompt information is created and sent to the client.

[0007] Further technical solutions thereof are that the training process of the chemical product visual analysis model comprises: acquiring a historical video sequence reflecting the whole-period operation process of a reaction kettle; determining the time period of each historical process operation node, and intercepting a corresponding operation process video segment from the historical video sequence; constructing a sample set for model training based on the operation process video segment; creating an initial model using a neural network technology; inputting the sample set into the initial model to train the initial model, adjusting the initial model until the loss value is minimized, to obtain a chemical product visual analysis model.

[0008] Further technical solutions thereof are that the preset period is a specific time period formed by dividing the production process by time, or a time period set by customization.

[0009] Further technical solutions thereof are that the process operation node comprises a time point with specific product visual information or an operation switching time point; one preset period contains one or more process operation nodes.

[0010] Further technical solutions thereof are that the analysis of the process operation nodes in the current period based on the preset process operation node information and the product visual information to determine whether the time distribution of the process operation nodes in the current period is consistent with the time distribution in the preset process operation node information, to obtain an analysis result, comprises: analyzing the process operation nodes in the current period based on the preset process operation node information and the product visual information to obtain process operation node information; wherein the process operation node information comprises a process operation node, a time position, and a product visual description parameter corresponding to the process operation node; determining whether the time distribution of the process operation nodes in the current period is consistent with the time distribution in the preset process operation node information according to the process operation node information, to obtain an analysis result.

[0011] A further technical solution is that the product visual description parameters include color parameters and state parameters, wherein the color parameters include RGB values and color change rates, and the state parameters include state labels and state change rates.

[0012] A further technical solution is that the analysis result is obtained by determining whether the time distribution of the process operation node in the current period is consistent with the time distribution in the preset process operation node information according to the process operation node information, including: The matching result of each index is calculated by comparing the product visual description parameters corresponding to the process operation node with the parameter range corresponding to the preset process operation node information according to the process operation node information. The analysis result is obtained by comprehensively evaluating whether the time distribution of the process operation node in the current period is consistent with the time distribution in the preset process operation node information according to the matching result.

[0013] A further technical solution is that the matching result of each index is calculated by comparing the product visual description parameters corresponding to the process operation node with the parameter range corresponding to the preset process operation node information according to the process operation node information, including: The color matching result is generated by comparing whether the color RGB value of the current material is in the color parameter range corresponding to the preset process operation node information according to the process operation node information. The color change rate matching result is generated by judging whether the color change rate of the current material conforms to the color change rate range corresponding to the preset process operation node information according to the process operation node information. The temperature matching result is obtained by judging whether the current material temperature is in the temperature range corresponding to the preset process operation node information according to the process operation node information. The state label matching result is obtained by calculating the matching degree between the current material state label and the state label corresponding to the preset process operation node information according to the process operation node information. The state change rate matching result is obtained by confirming whether the state change rate of the current material is in the state change rate range corresponding to the preset process operation node information according to the process operation node information. The matching result of each index includes the color matching result, the color change rate matching result, the temperature matching result, the state label matching result, and the state change rate matching result.

[0014] A further technical solution is that the analysis unit is configured to analyze the process operation nodes in the current period based on the preset process operation node information and the product visual information, to determine whether the time distribution of the process operation nodes in the current period is consistent with the time distribution in the preset process operation node information, to obtain an analysis result, and further comprising: When the analysis result is that the time distribution of the process operation nodes in the current period is consistent with the time distribution in the preset process operation node information, the video sequence of the inside of the reaction kettle in the current period collected by the 5G explosion-proof camera arranged in the reaction kettle is acquired according to the preset period, to obtain a to-be-analyzed kettle video sequence.

[0015] The application further provides a reaction kettle operation process intelligent monitoring device based on visual analysis, comprising: An acquisition unit is configured to acquire the video sequence of the inside of the reaction kettle in the current period collected by the 5G explosion-proof camera arranged in the reaction kettle according to a preset period, to obtain a to-be-analyzed kettle video sequence. An extraction unit is configured to input the to-be-analyzed kettle video sequence into a chemical product visual analysis model for fuzzy video processing and extract product visual information of the current period, to obtain the product visual information, wherein the chemical product visual analysis model is obtained by model training using a neural network technology with operation process video clips of each process operation node in historical video sequences as a sample set. An analysis unit is configured to analyze the process operation nodes in the current period based on the preset process operation node information and the product visual information, to determine whether the time distribution of the process operation nodes in the current period is consistent with the time distribution in the preset process operation node information, to obtain an analysis result. A creation unit is configured to create corresponding operation process prompt information and send it to a client when the analysis result is that the time distribution of the process operation nodes in the current period is inconsistent with the time distribution in the preset process operation node information.

[0016] Compared with the prior art, the present application has the beneficial effects that: the present application automatically acquires video sequences inside the reaction kettle according to a preset period, and processes these video data using a deep learning algorithm to extract key product visual information. Based on pre-defined process operation node information, the system can analyze the operation node time distribution in the current production cycle in real time, judge whether it meets the expected standard, and generate and send operation prompt information to relevant personnel immediately when deviation is detected, to ensure timely adjustment. Compared with the traditional method relying on manual monitoring, this automatic solution not only reduces the dependence on the experience and attention of the operator, but also can accurately capture and record key process details, realize precise control of the production process and significant improvement of product quality stability, especially suitable for strict production requirements of high-end chemical products. Therefore, the scheme effectively solves the problem of difficult to ensure product quality consistency and stability in the prior art, represents a more efficient and reliable production process monitoring means, and realizes automatic and intelligent monitoring of the operation process in the reaction kettle.

[0017] The present application will be further described below in combination with the drawings and specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0019] Figure 1 The flowchart of the reaction kettle operation process intelligent monitoring method based on visual analysis provided by the embodiment of the present application is shown in the figure. Figure 2 The schematic diagram of the video sequence provided by the embodiment of the present application is shown in the figure. Figure 3 The sub-flowchart of the reaction kettle operation process intelligent monitoring method based on visual analysis provided by the embodiment of the present application is shown in the figure. Figure 1 Figure 4 The sub-flowchart of the reaction kettle operation process intelligent monitoring method based on visual analysis provided by the embodiment of the present application is shown in the figure. Figure 2 Figure 5 The sub-flowchart of the reaction kettle operation process intelligent monitoring method based on visual analysis provided by the embodiment of the present application is shown in the figure. Figure 3 Figure 6 The schematic block diagram of the reaction kettle operation process intelligent monitoring device based on visual analysis provided by the embodiment of the present application is shown in the figure. Figure 7 ​​​A schematic block diagram of a computer device provided for an embodiment of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0021] It should be understood that, when used in the specification and the appended claims, the terms "comprise" and "include" indicate the presence of described features, integers, steps, operations, elements, and / or components, but do not exclude one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0022] It should also be understood that the terms used in the present application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] It should be further understood that the term "and / or" used in the present application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations thereof.

[0024] Please refer to Figure 1 , Figure 1 A schematic flowchart of a visual analysis-based intelligent monitoring method for a reaction kettle operation process provided for an embodiment of the present application. The visual analysis-based intelligent monitoring method for a reaction kettle operation process is applied to a server, which interacts with a camera, automatically acquires internal video sequences of the reaction kettle, and uses neural network technology to process fuzzy videos and extract key product visual information, thereby realizing accurate identification of each process operation node in the production process and real-time analysis of time distribution. This method can effectively improve the accurate control of the production process and the stability of product quality, and is particularly suitable for the production requirements of high-end chemical products. Compared with traditional methods that rely on manual monitoring, the present application not only reduces the dependence on the experience of operators, but also accurately captures and records key process details in real time, thereby overcoming the problems of poor product quality consistency and insufficient stability caused by human factors in the prior art. In addition, when the actual operation does not meet the preset standard, the system will automatically generate prompt information to feed back to the operator, further ensuring the accuracy and safety of production.

[0025] Figure 1 is a flowchart of the intelligent monitoring method for the reaction kettle operation process based on visual analysis provided by the embodiments of the present application. As shown in Figure 1 , the method comprises the following steps S110 to S140.

[0026] S110, acquire the video sequence of the inside of the reaction kettle in the current period collected by the 5G explosion-proof camera deployed in the reaction kettle according to a preset period, to obtain the to-be-analyzed kettle video sequence.

[0027] In this embodiment, the to-be-analyzed kettle video sequence refers to the video stream inside the reaction kettle collected by the 5G explosion-proof camera in the preset period, which is used to monitor and analyze the process operation node state change in the production process of chemical products. As shown in Figure 2 the picture.

[0028] Considering that the chemical production environment usually has the risk of being flammable and explosive, a 5G camera with explosion-proof function is used for video collection. The camera has the functions of customized cooling back-blow, circulating cooling water, supporting variable focal mode, explosion-proof, etc. This camera not only ensures safe work in high-risk environments, but also realizes real-time and efficient monitoring of the internal conditions of the reaction kettle by using the high speed and low delay characteristics of the 5G network.

[0029] According to the requirements of the actual production process, the entire production cycle is divided into several specific time periods (i.e. preset period), and different process operation nodes or product state changes are focused on in each period. The setting of these time periods can be based on time proportion division, or can be flexibly adjusted according to the specific process operation requirements. For example, some key process nodes may need to be monitored more frequently, while other stages can appropriately extend the monitoring period.

[0030] In each preset period, the 5G explosion-proof camera will automatically collect the video stream inside the reaction kettle and transmit it to the server as the data source for subsequent analysis. These video sequences contain rich visual information such as color change of materials, physical state transition, etc., which are important basis for evaluating whether the process operation is normal.

[0031] The to-be-analyzed kettle video sequence obtained through the above steps will be further input into a pre-trained chemical product visual analysis model to identify and extract key visual information in the video, so as to realize precise control and quality assurance of the production process. This method is particularly suitable for the production monitoring of high-end chemical products, because the production of such products has extremely strict requirements on environmental conditions and operation precision.

[0032] In this embodiment, the preset period is a specific time period formed by dividing the production process by time, or a time period set by custom. A series of specific time periods are obtained by systematically analyzing and dividing the entire production cycle. These time periods can be set according to the characteristics of the production process, the change law of the product state, and the monitoring needs. The establishment of the preset period aims to effectively capture and record the key visual information changes at each stage of the production process, so as to facilitate subsequent analysis and quality control.

[0033] Specifically, the preset period can be determined in two ways: one is to divide the entire production cycle by time in proportion, which is suitable for scenarios with relatively uniform state change patterns or low requirements for time distribution; the other is to set by custom according to specific process operation nodes, which is more flexible and can be customized according to different production process characteristics. For example, in a typical chemical production process, process operation node 1 to process operation node 3 may correspond to white, light yellow to other color changes, respectively, and the required attention time and monitoring frequency between each sub-node may be different. Therefore, the time period for process operation node 1 to process operation node 3 can be divided into period 1, while the operation of process operation node 4 is separately as a period 2, and so on.

[0034] By setting the preset period in this way based on the combination of time and process operation nodes, it not only helps to improve the monitoring efficiency and reduce unnecessary consumption of computing resources, but also ensures that the most accurate production state information is obtained at the key moment, thereby providing strong guarantee for the high-quality production of high-end chemical products. In addition, compared with the high resource consumption and frequent false alarms caused by real-time monitoring and early warning, periodic monitoring and analysis is more economical and efficient, especially suitable for chemical reaction processes that require high production control and frequent changes in product visual information.

[0035] In summary, by scientifically and reasonably setting the preset period, the product quality control level can be greatly improved while ensuring production efficiency, meeting the strict requirements of high-end chemical product production.

[0036] S120, input the to-be-analyzed video sequence in the chemical product visual analysis model for fuzzy video processing and extract the product visual information of the current period to obtain product visual information; wherein the chemical product visual analysis model is obtained by using the operation process video segments of each process operation node in the historical video sequence as a sample set and using neural network technology for model training.

[0037] In this embodiment, the product visual information refers to the key visual features and state change information extracted by the pre-trained chemical product visual analysis model after processing the video sequence in the reaction kettle.

[0038] The product visual description parameters include color parameters and state parameters, wherein the color parameters include RGB values and color change rates, and the state parameters include state labels and state change rates.

[0039] In the chemical industry, monitoring the color parameters and state parameters of materials in the reaction kettle is crucial for understanding the progress of chemical reactions. These parameters not only reflect changes in reaction conditions, but also indicate specific chemical processes or stages.

[0040] Color parameters: including RGB values (intensity of red, green, and blue primary colors) and their change rates. RGB values represent the intensity of red (Red), green (Green), and blue (Blue) respectively, with each color intensity ranging from 0 to 255. Color change rate refers to the speed of color change per unit time, which can be obtained by calculating the difference in RGB values between consecutive video frames.

[0041] State parameters: including state labels and state change rates. State labels are used to describe the physical state of materials, such as liquid, solid, emulsion state, etc.; state change rate refers to the speed of material transition from one state to another, such as the crystallization process from liquid to solid.

[0042] The process operation nodes include time points with specific product visual information or operation switching time points; one pre-set period contains one or more process operation nodes.

[0043] Specifically, the time point of specific product visual information refers to the time point when the color parameters (such as RGB values and their change rates) and state parameters (such as physical state labels and their change rates) of materials change significantly within a production cycle. These changes are usually closely related to the progress of chemical reactions, reflecting changes in reaction conditions or the progress of stages.

[0044] Color change: for example, in redox reactions, the color of the material may change from light to dark, and this color change can be monitored by significant changes in RGB values.

[0045] State transition: changes in physical state, such as the crystallization process from liquid to solid, can also be marked as important time nodes.

[0046] For example, the current product visual information is shown in Table 1.

[0047] Table 1. Current product visual information

[0048] Operation switch points refer to the transition moments between different operation steps in the production process. This includes but is not limited to raw material addition, temperature adjustment, pressure regulation, stirring speed change, etc. These operation switches not only mark different stages of the process, but also are important basis for evaluating whether the production process is executed according to the plan.

[0049] Raw material addition: Record when new raw materials are added to the reactor, which is very important for subsequent analysis of reaction results.

[0050] Temperature / pressure adjustment: Monitor and record the change points of temperature or pressure, which can help understand how these variables affect the speed and results of chemical reactions.

[0051] Stirring speed change: The adjustment of stirring speed may affect the mixing degree of materials, thereby affecting the quality of the final product.

[0052] In a preset production cycle, there may be one or more such process operation nodes. Each node represents an important event or change point in the production process. By identifying and recording these nodes, not only can the production process be better understood, but also a basis for subsequent data analysis can be provided.

[0053] The chemical product visual analysis model is a trained deep learning model specifically designed to analyze video data inside the reactor, automatically identifying and extracting key visual features in the video. Specifically, the model can process the following information: RGB values: The RGB values obtained by the camera can intuitively reflect the color change of the material, which is very useful for monitoring the progress of chemical reactions.

[0054] Color change rate: By calculating the change of RGB values between consecutive video frames, the color change rate can be determined, which helps to understand the occurrence of processes such as redox reactions.

[0055] State label and state change rate: The physical state of the material is another important visual feature in the reaction process. Changes in state can indicate different stages or processes of the reaction, such as phase transition or crystallization.

[0056] Although the camera can obtain more types of video data, such as color uniformity, bubble size and generation rate, the running state of the scraper and stirring device, temperature, pressure value, etc., this application particularly selects RGB values and color change rate, state label and state change rate as key monitoring parameters. This is because: Intuitiveness: Color and state are one of the most easily observed and recorded visual features.

[0057] Relevance: These parameters are closely related to specific stages of chemical reactions and can sensitively reflect changes in reaction conditions.

[0058] Cost-effectiveness: Selecting the most representative and sensitive parameters can reduce the complexity of data processing while ensuring monitoring effectiveness.

[0059] Easy to integrate and expand: Simplifying the complexity of the model and reducing the demand for computing resources facilitates system integration and subsequent expansion.

[0060] The training process of the chemical product visual analysis model includes: Obtain historical video sequences reflecting the full cycle operation process of the reactor; Determine the time period of each historical process operation node, and extract corresponding operation process video clips from the historical video sequence; Constructing a sample set for model training based on the video clips of the operation process; Create an initial model using neural network technology; The sample set is input into the initial model to train the initial model, and the initial model is adjusted until the loss value is minimized to obtain a chemical product visual analysis model.

[0061] In this example, we first need to acquire a series of historical video sequences that record the reactor's operation over the entire reaction cycle. These videos include important visual information such as material color changes and physical state transitions. Before formal use, the video quality must be checked to ensure there are no frame drops or corruption, thereby ensuring the accuracy of subsequent analysis.

[0062] Based on the preset process operation node information shown in Table 2, the specific time period of each process operation node is analyzed and determined. This step involves analyzing key events in the historical video, such as color changes and physical state transitions.

[0063] Table 2. Preset process operation node information

[0064] For the determined time period, the corresponding operation process video clips are accurately edited from the complete historical video sequence. These clips will be used to construct the model training sample set.

[0065] Next, relevant features are extracted from each video clip, including but not limited to material color (RGB value and its change rate), physical state (labels such as liquid and solid and their change rate), etc.

[0066] Each extracted feature is labeled to indicate the process operation node it corresponds to. This is an important part of supervised learning because it provides the "correct answer" required for model learning.

[0067] Select appropriate neural network architecture based on task requirements, e.g., Convolutional Neural Networks (CNNs) are suitable for image data, while Recurrent Neural Networks (RNNs) or their variants LSTM, GRU are suitable for time series data.

[0068] Set the number of network layers, the number of neurons in each layer, and the weight initialization strategy, etc.

[0069] Adjust model parameters through backpropagation algorithm, so that the error between model prediction results and actual labels (i.e., loss value) gradually decreases. This process may require multiple iterations until a predetermined stopping condition is reached, such as loss value no longer significantly decreasing or reaching maximum iteration number.

[0070] To evaluate the performance of the model, the dataset is usually divided into training set, validation set and test set. After training, the validation set is used to further optimize the model hyperparameters, and finally the test set is used to evaluate the generalization ability of the model.

[0071] The above steps constitute the complete training process of the visual analysis model of chemical products. This process not only covers the data preparation stage, but also includes the key links of model creation, training and evaluation, ensuring that the generated model can accurately identify the state changes of different process operation nodes in the reactor, and thus support the optimization of automated production processes.

[0072] In addition, in the process of training, first, the historical video sequences collected for the historical operation process of the reactor are obtained, which are used to reflect the visual information of the reactor in its historical reaction cycle. Then, the specific time period of each historical process operation node is analyzed from the historical reaction cycle, and the operation process video clips of each historical process operation node corresponding to the time period are extracted from the historical video sequences as operation process video clips. Based on these operation process video clips, samples are constructed, an initial model is created by using a neural network, and the samples are input into the initial model, and the model loss value is output. When the loss value reaches the minimum, the final model is generated.

[0073] As for how to analyze the historical time period of each historical process operation node, first, the historical video sequences are frame extracted in chronological order to obtain multiple original video frames carrying timestamps. Then, according to the preset process operation node information, the historical operation steps and the visual description parameters of the historical chemical products of each historical process operation node are obtained. Subsequently, the video frames matching the historical operation steps and the visual description parameters of the historical chemical products of each historical process operation node are searched in these original video frames to determine the starting frame and the ending frame of each historical process operation node. Finally, the timestamps of these frames are used to determine the historical time period of each historical process operation node.

[0074] Further, to find the video frames related to each historical process operation node from the original video frames, an inter-frame difference algorithm is employed to calculate the pixel change rate between adjacent video frames, and those video frames with pixel change rate exceeding a preset threshold are selected to form a sequence of video frames to be analyzed. For each video frame in this sequence, operation tool information and operation gesture information are defined to obtain first operation step information, and first chemical product visual description information is analyzed. By comparing these information with the related parameters of each historical process operation node, a correlation analysis is performed to determine which video frames to be analyzed are associated with a specific historical process operation node, and finally the video frames matching the historical operation step and chemical product visual description parameters are identified.

[0075] Finally, regarding the construction of model training samples from the operation process video segments of each historical process operation node, all video frames need to be extracted from each operation process video segment. For each frame, historical color parameters of the material color (including historical RGB values, color change rate) and historical state parameters (such as state label and state change rate) are calculated. Using these data as labels, each video frame is labeled to construct model training samples. This process ensures that the training samples accurately reflect the color and state changes of the material during the historical operation process, providing a solid foundation for model training.

[0076] In an embodiment, the chemical product visual analysis model also has the function of processing blurred video sequences. Specifically, the model includes an inter-frame pixel analysis layer, a historical in-pot video sequence acquisition layer, a first pixel feature point extraction layer, a second pixel feature point extraction layer, a pixel feature offset vector analysis layer, an optical flow estimation layer, an image pixel parameter application layer, and a visual information extraction layer.

[0077] First, the video sequence to be analyzed is input into the chemical product visual analysis model, and the inter-frame pixel analysis layer is used to compare the image pixels between adjacent frames to calculate the image pixel contrast value. This step is used to detect blurring or significant changes in the video sequence. If the calculated image pixel contrast value is greater than a preset threshold, it indicates that the current video sequence may have blurring or other interference factors, and the historical in-pot video sequence acquisition layer needs to be called to find the nearest clear historical video sequence as a reference. Using this clear historical video sequence, the first pixel feature point extraction layer is used to extract first pixel feature points from each first image, and the second pixel feature point extraction layer is used to extract second pixel feature points from each second image in the video sequence to be analyzed.

[0078] Next, the pixel feature offset vector analysis layer calculates the difference between the first pixel feature point and the second pixel feature point, generating pixel feature offset vectors. These offset vectors are then used in the optical flow estimation layer, which combines the optical flow method to estimate the optical flow of the video sequence to be analyzed, to predict the image pixel parameters in the reaction kettle. Then, the predicted image pixel parameters are applied to the video sequence to be analyzed by the image pixel parameter application layer, generating a clear video sequence. Finally, the visual information extraction layer extracts and outputs the product visual information of the current period from the clear video sequence, such as the RGB value of the material color, the color change rate, the material physical state label, and the state change rate, etc.

[0079] If the image pixel contrast value is not greater than the preset threshold, the product visual information of the current period is directly extracted and output from the video sequence to be analyzed. It should be noted here that for the case of blurring, in addition to judging whether it is caused by environmental factors, further analysis is needed to determine whether the blurring is caused by inconsistent nodes (i.e., differences in time periods or operation steps between different process operation nodes). For this purpose, time stamp comparison analysis or consistency verification of process operation steps can be used to distinguish the specific cause of the blurring, so that targeted measures can be taken to correct or adjust, ensuring that the final extracted product visual information is accurate. This method not only improves the reliability and accuracy of the monitoring system, but also has important application value in complex chemical production environments.

[0080] Through the clear processing, the details in the video can be more obvious. The clear video sequence can more accurately identify the visual features of the product, thereby improving the accuracy of the chemical product visual analysis model in extracting product visual information. For example, when some small volume of product flocculation is produced in the reaction kettle, the blurred video may make it difficult for the monitoring system to accurately judge the size and distribution of the flocculation, while after clear processing, the system can more reliably monitor these conditions, avoid taking incorrect operation measures due to misjudgment, and enhance the reliability of the entire monitoring system.

[0081] The method provided by the present application can correct the clear historical video sequence in the case of blurring by combining inter-frame comparison and historical video sequences, thereby effectively solving the common visual interference problem in chemical production scenes. Compared with traditional video data processing methods, the method has more robustness and interpretability, and is more suitable for result-oriented chemical production processes in combination with production processes. In particular, the intelligent triggering method is more suitable for long-term monitoring scenarios in chemical production, greatly reducing the computational overhead and improving efficiency.

[0082] Further, in the chemical reaction process, the visual features such as color and state of the material change are the key to monitoring. The method can more accurately capture the changes of these visual features through multi-layer analysis (such as pixel feature point extraction, optical flow estimation, etc.) by clear processing of video sequences. This comprehensive processing method is more comprehensive and effective than single image sharpening technology (such as simple defogging or deblurring algorithm), and can better cope with complex chemical reaction scenes.

[0083] It should be noted that the above technical means can be used to sharpen the blurred video sequence to be analyzed in the tank, and other image sharpening strategies in the prior art can also be used for sharpening, which is not limited by the present application.

[0084] Of course, the above-mentioned fuzzy situation also needs to be distinguished from whether it is due to the improper control of factors such as temperature, pressure, material input, humidity, etc. in the complex environment in the reaction kettle, and the camera back-blowing device cannot cope with the large-scale state change in the reaction kettle, resulting in the fuzzy phenomenon of the video sequence collected by the camera, rather than the fuzzy phenomenon caused by the misalignment of nodes, etc. Specifically, ensure that enough sensors are deployed inside the reaction kettle to monitor the temperature, pressure, humidity and other key environmental parameters in real time, and record these data synchronously with the video frame timestamp.

[0085] A fast response module is added to the model, which can evaluate whether the current environmental state is likely to cause video quality degradation (such as lens fogging, air turbulence, etc.) at the moment of fuzzy phenomenon occurrence based on the real-time collected environmental data. This can be achieved through a pre-set threshold or a machine learning model, which analyzes which specific condition combinations are prone to cause fuzzy phenomena based on historical data.

[0086] A fuzzy type classifier is constructed to automatically analyze the fuzzy features in the video sequence using image processing technology. For example, through texture analysis of the fuzzy area, it can be distinguished whether it is caused by optical factors (such as lens contamination) or motion blur (such as liquid flow during stirring process). This classification helps to quickly locate the root cause of the problem. Once the fuzzy phenomenon caused by environmental factors is detected, the system should be able to provide immediate maintenance suggestions, such as adjusting the ventilation rate, optimizing the temperature control settings, etc., to reduce the occurrence of similar problems in the future.

[0087] For those fuzzy situations that cannot be immediately explained by environmental factors, they are marked for further analysis. When the operation cycle is over and all node information has been collected, a detailed node consistency check is performed. If misalignment of nodes is found, it will be considered as one of the potential causes of the fuzzy phenomenon.

[0088] This method can accurately identify and solve most video blur problems caused by environmental factors as much as possible without relying on real-time node alignment information, while retaining the ability to further explore other possible causes such as node misalignment. This can improve the response speed of the system while ensuring the comprehensiveness and accuracy of the final diagnosis.

[0089] In S130, the process operation nodes in the current period are analyzed based on the preset process operation node information and the product visual information to determine whether the time distribution of the process operation nodes in the current period is consistent with the time distribution in the preset process operation node information, to obtain an analysis result.

[0090] In this embodiment, the analysis result refers to determining the time distribution of the process operation nodes in the current period according to the time matching degree, and judging whether it meets the preset standard.

[0091] In an embodiment, referring to Figure 3 The above step S130 can include steps S131-S132.

[0092] In S131, the process operation nodes in the current period are analyzed based on the preset process operation node information and the product visual information to obtain process operation node information; wherein the process operation node information includes process operation nodes, time positions, and product visual description parameters corresponding to the process operation nodes. In this embodiment, the preset process operation node information includes specific operation steps that should be performed for each process operation node (for example, "add raw material A", "stir for 3 minutes"), corresponding chemical product visual description parameters (such as color range, color change rate, etc.), and chemical reaction conditions (such as temperature range).

[0093] Key visual description parameters are extracted from the product in the current period using video monitoring or image processing technology. This includes but is not limited to the color RGB value of the material, the state label (liquid, solid, etc.) and its change rate, etc.

[0094] The product visual description parameters extracted in the current period are matched with the preset chemical product visual description parameters to calculate the matching degree between them. This matching degree reflects the consistency degree of the actual production process and the pre-designed plan.

[0095] After the above steps, a detailed information set containing process operation node names, their time positions, and corresponding product visual description parameters is obtained.

[0096] S132, determine whether the time distribution of the process operation node in the current period is consistent with the time distribution in the preset process operation node information according to the process operation node information, to obtain an analysis result.

[0097] By analyzing the real-time collected product visual information and comparing it with the preset process operation node information, a matching degree on the time division is obtained. If the actual visual description parameters in a certain time period are highly consistent with the preset visual description parameters, it is considered that the operation node in this time period is consistent with the preset; otherwise, there may be deviations.

[0098] Based on the results of the above matching degree, the actual time distribution of each process operation node in the current period is redefined. For example, an operation node in the original plan should be completed within 0-1 minutes, but if the analysis result shows that the node is actually completed within 0-2 minutes, it means that there is a delay or other problems in this node.

[0099] When the actual time distribution of the process operation node is found to be inconsistent with the preset, further analysis can be performed to find the specific problems. For example, in the 5-minute period mentioned in the example, if the original planned node 1 time is 0-1 minute, but the actual time is 0-2 minute, it can be preliminarily determined that node 1 has a problem.

[0100] Through such an analysis process, not only the current state of the reaction kettle can be accurately identified, but also the operation steps can be dynamically adjusted to reduce the errors caused by manual intervention, thereby improving the automation degree and accuracy of production. In addition, this method helps to find problems in time and take corrective measures to ensure the improvement of product quality and production efficiency.

[0101] In summary, the step S130 combines the preset process operation node information and the real-time acquired product visual information to realize accurate monitoring and management of each stage in the production process, which is of great significance for modern chemical production.

[0102] In an embodiment, please refer to Figure 4 The above step S132 can include steps S1321-S1322.

[0103] S1321, compare the product visual description parameters corresponding to the process operation node with the parameter range corresponding to the preset process operation node information according to the process operation node information, and calculate the matching result of each index.

[0104] In this embodiment, the matching result refers to the evaluation conclusion generated by comparing each index of the current material with the preset standard range, indicating whether each index meets the requirements.

[0105] In an embodiment, please refer toFigure 5 The step S1321 can include steps S13211-S13215.

[0106] S13211, compare the color RGB value of the current material with the color parameter range corresponding to the preset process operation node information according to the process operation node information, and generate a color matching result.

[0107] In this embodiment, the color matching result refers to the result obtained by confirming whether the color difference between the standard color sample and the material to be tested is within an acceptable range.

[0108] Specifically, the real-time color RGB value of the product in the current period is obtained; the RGB value is compared with the color parameter range specified in the preset process operation node information; if it is within the range, a positive color matching result is generated; otherwise, it is marked as not matching.

[0109] S13212, judge whether the color change rate of the current material conforms to the color change rate range corresponding to the preset process operation node information according to the process operation node information, and generate a color change rate matching result.

[0110] In this embodiment, the color change rate matching result refers to the result obtained by evaluating whether the speed of the color change of the material over time conforms to the preset standard.

[0111] Specifically, the color change rate of the current material is calculated; the change rate is compared with the preset standard range; and the corresponding color change rate matching result is generated according to the comparison result.

[0112] S13213, according to the process operation node information, judge whether the temperature is within the temperature range corresponding to the preset process operation node information, to obtain a temperature matching result.

[0113] In this embodiment, the temperature matching result refers to the result obtained by confirming whether the temperature performance of the material under a specific condition falls within the specified safe range.

[0114] Specifically, the real-time temperature data of the reaction kettle in the current period is obtained; it is checked whether the temperature falls within the preset temperature range; and the temperature matching result is generated according to the checking result.

[0115] S13214, according to the process operation node information, calculate the matching degree between the current material state label and the state label corresponding to the preset process operation node information, and obtain a state label matching result.

[0116] In this embodiment, the state label matching result refers to the result obtained by verifying whether the current physical or chemical state identification of the material is consistent with the expected state.

[0117] Specifically, the state label of the current material (e.g., liquid, solid, etc.) is determined; the state label is compared with the state label in the preset process operation node information; and the matching degree between the state labels is calculated and generated as the matching result.

[0118] S13215, according to the process operation node information, confirming whether the state change rate of the current material is within the state change rate range corresponding to the preset process operation node information, to obtain a state change rate matching result.

[0119] In this embodiment, the state change rate matching result refers to the result of measuring whether the speed of the material from one state to another state meets the established requirements.

[0120] Specifically, the change rate of the current material state is analyzed; the change rate is compared with the preset allowable range; and the state change rate matching result is obtained according to the analysis result.

[0121] The matching results of the various indicators include color matching results, color change rate matching results, temperature matching results, state label matching results, and state change rate matching results.

[0122] After all the above steps are completed, a series of matching results will be obtained, including but not limited to color matching results, color change rate matching results, temperature matching results, state label matching results, and state change rate matching results.

[0123] S1322, according to the matching results, comprehensively evaluating whether the time distribution of the process operation node in the current period is consistent with the time distribution in the preset process operation node information to obtain an analysis result.

[0124] Using the matching results obtained in step S1321, the time distribution of the process operation node in the current period is comprehensively evaluated to determine whether it is consistent with the preset information.

[0125] First, all the matching results from S1321 are summarized. This usually involves converting each individual matching result into a quantifiable numerical value or scoring system to facilitate subsequent comprehensive evaluation.

[0126] If one of them does not meet the requirements, it is determined that the time distribution of the process operation node in the current period is inconsistent with the preset. Otherwise, it is consistent.

[0127] In this way, the S132 step can not only accurately identify potential problems in the current production process, but also dynamically adjust the operation steps, thereby improving the degree of automation and accuracy of production, ensuring product quality and production efficiency.

[0128] In this embodiment, the method of the present embodiment determines the degree of matching of the visual description parameters over time by matching the preset product visual description parameters with the product visual description parameters in the current cycle, and determines the time distribution of the current process operation node according to the above-mentioned time matching degree. Specifically, if a cycle is divided into different time intervals, for example, 0-1 minutes for process operation node 1, 1-2 minutes for process operation node 2, and 2-5 minutes for process operation node 3, each process operation node has corresponding operation steps, control conditions and video information, then by analyzing the actual time distribution of these process operation nodes can be redefined, for example, 0-2 minutes for process operation node 1, 2-2.5 minutes for process operation node 2, and 2.5-4 minutes for process operation node 3, so as to identify the possible problem area, select the first process operation node that cannot be aligned, and then the process operation node where the problem is located as the error information.

[0129] S140, when the analysis result is that the time distribution of the process operation node in the current cycle is inconsistent with the time distribution in the preset process operation node information, corresponding operation process prompt information is created and sent to the client.

[0130] When the analysis result is that the time distribution of the process operation node in the current cycle is consistent with the time distribution in the preset process operation node information, the step S110 is executed.

[0131] The system first compares the time distribution of the process operation node identified in the current cycle with the time distribution specified in the preset process operation node information. This comparison not only involves the starting and ending time points of each node, but also includes the time interval between the conversion of each node.

[0132] Difference detection: If the actual operation time node (such as adding raw material A, stirring for 3 minutes, etc.) is found to deviate from the preset time arrangement, whether it is ahead of time or delayed, it is considered as inconsistent in time distribution.

[0133] Once the inconsistency is confirmed, the system will automatically generate one or more operation process prompt information. These prompt information includes but is not limited to: Specifically, which process operation node has a time deviation; The specific value of the deviation, such as how many time earlier or later than expected; Possible cause analysis and suggested adjustment measures, such as whether to speed up or slow down a certain step to restore normal flow.

[0134] According to the user's preference settings or specific requirements, the prompt information can be further customized to ensure that it has guiding significance to specific operators.

[0135] The generated prompt information will be immediately sent to the relevant client through a pre-set manner. This usually means that the information will be pushed to the display screen of the on-site monitoring system, or directly sent to the handheld device (such as a tablet computer, smartphone, etc.) of the operator responsible for the production line.

[0136] In order to increase reliability, the system may simultaneously use multiple communication channels to send notifications, such as SMS, email or dedicated APP push messages, to ensure that relevant personnel can timely receive important operation instructions.

[0137] When the analysis result is that the time distribution of the process operation node in the current period is consistent with the time distribution in the preset process operation node information, step S110 is executed.

[0138] If, after analysis, it is found that the time distribution of the process operation node in the current period fully conforms to the preset information, i.e. there is no deviation or only a small fluctuation within an acceptable range, the system will continue to operate according to the predetermined plan for the next period, repeating step S110. This means that the entire production process is running efficiently according to the plan, without the need for additional manual intervention or adjustment.

[0139] In this way, not only can the production process be effectively monitored to ensure that each link is strictly executed according to the preset standard, but also the response can be quickly made when an abnormality occurs, reducing the quality problems or production delays caused by operation errors.

[0140] The above-mentioned reaction kettle operation process intelligent monitoring method based on visual analysis automatically acquires video sequences inside the reaction kettle according to a preset period, and processes these video data using a deep learning algorithm to extract key product visual information. Based on pre-defined process operation node information, the system can analyze the time distribution of the operation node in the current production period in real time, judge whether it meets the expected standard, and generate and send operation prompt information to relevant personnel immediately when a deviation is detected, ensuring timely adjustment. Compared with traditional methods that rely on manual monitoring, this automated solution not only reduces the dependence on the experience and attention of operators, but also accurately captures and records key process details, achieving precise control of the production process and significant improvement in product quality stability, especially suitable for strict production requirements of high-end chemical products. Therefore, this scheme effectively solves the problem of difficulty in ensuring product quality consistency and stability in the prior art, represents a more efficient and reliable production process monitoring method, and realizes automatic and intelligent monitoring of the operation process in the reaction kettle.

[0141] Figure 6is a schematic block diagram of an intelligent monitoring device 300 for a reaction kettle operation process based on visual analysis provided by an embodiment of the present application. As shown, corresponding to the above intelligent monitoring method for a reaction kettle operation process based on visual analysis, the present application also provides an intelligent monitoring device 300 for a reaction kettle operation process based on visual analysis. The intelligent monitoring device 300 for a reaction kettle operation process based on visual analysis includes units for executing the above intelligent monitoring method for a reaction kettle operation process based on visual analysis, and the device can be configured in a server. Specifically, please refer to Figure 6 , the intelligent monitoring device 300 for a reaction kettle operation process based on visual analysis includes an acquisition unit 301, an extraction unit 302, an analysis unit 303, and a creation unit 304. Figure 6

[0142] The acquisition unit 301 is configured to acquire a video sequence of the inside of a reaction kettle in a current period collected by a 5G explosion-proof camera deployed in the reaction kettle according to a preset period, to obtain a to-be-analyzed kettle video sequence. The extraction unit 302 is configured to input the to-be-analyzed kettle video sequence into a chemical product visual analysis model for fuzzy video processing and extract product visual information of the current period, to obtain product visual information. The chemical product visual analysis model is obtained by model training using a neural network technology with operation process video segments of each process operation node in historical video sequences as a sample set. The analysis unit 303 is configured to analyze a process operation node in the current period based on preset process operation node information and the product visual information, to determine whether the time distribution of the process operation node in the current period is consistent with the time distribution in the preset process operation node information, to obtain an analysis result. When the analysis result is that the time distribution of the process operation node in the current period is consistent with the time distribution in the preset process operation node information, the acquisition of the video sequence of the inside of the reaction kettle in the current period collected by the 5G explosion-proof camera deployed in the reaction kettle according to the preset period is performed, to obtain the to-be-analyzed kettle video sequence.

[0143] The creation unit 304 is configured to, when the analysis result is that the time distribution of the process operation node in the current period is not consistent with the time distribution in the preset process operation node information, create corresponding operation process prompt information and send it to a client.

[0144] In an embodiment, the analysis unit 303 includes: ​a node analysis subunit configured to analyze a process operation node in a current period based on preset process operation node information and the product visual information to obtain process operation node information, wherein the process operation node information comprises a process operation node, a time position, and a product visual description parameter corresponding to the process operation node; and a time distribution analysis subunit configured to determine whether a time distribution of the process operation node in the current period is consistent with a time distribution in the preset process operation node information according to the process operation node information to obtain an analysis result.

[0145] In an embodiment, the time distribution analysis subunit comprises: a parameter matching module configured to compare the product visual description parameter corresponding to the process operation node with a parameter range corresponding to the preset process operation node information according to the process operation node information to calculate a matching result of each index; and a consistency comparison module configured to comprehensively evaluate whether the time distribution of the process operation node in the current period is consistent with the time distribution in the preset process operation node information according to the matching result to obtain the analysis result.

[0146] In an embodiment, the parameter matching module comprises: a color matching sub-module configured to compare whether a color RGB value of a current material is within a color parameter range corresponding to the preset process operation node information according to the process operation node information to generate a color matching result; a color change rate matching sub-module configured to determine whether a color change rate of the current material conforms to a color change rate range corresponding to the preset process operation node information according to the process operation node information to generate a color change rate matching result; a temperature matching sub-module configured to determine whether a current temperature is within a temperature range corresponding to the preset process operation node information according to the process operation node information to obtain a temperature matching result; a state label matching sub-module configured to calculate a matching degree between a current material state label and a state label corresponding to the preset process operation node information according to the process operation node information to obtain a state label matching result; and a state change rate matching sub-module configured to determine whether a state change rate of the current material is within a state change rate range corresponding to the preset process operation node information according to the process operation node information to obtain a state change rate matching result. The matching result of each index comprises the color matching result, the color change rate matching result, the temperature matching result, the state label matching result, and the state change rate matching result.

[0147] It should be noted that the specific implementation process of the reaction kettle operation process intelligent monitoring device 300 and each unit based on visual analysis can be clearly understood by those skilled in the art, and can be referred to the corresponding description in the foregoing method embodiments. For the convenience and brevity of description, it will not be repeated here.

[0148] The reaction kettle operation process intelligent monitoring device 300 based on visual analysis can be implemented in the form of a computer program, which can run on a computer device as shown in the figure. Figure 7

[0149] Please refer to Figure 7 , Figure 7 is a schematic block diagram of a computer device provided by an embodiment of the present application. The computer device 500 can be a server, wherein the server can be a stand-alone server or a server cluster composed of multiple servers.

[0150] Refer to Figure 7 , the computer device 500 includes a processor 502, a memory and a network interface 505 connected through a system bus 501, wherein the memory can include a non-volatile storage medium 503 and an internal memory 504.

[0151] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions which, when executed, can cause the processor 502 to perform a reaction kettle operation process intelligent monitoring method based on visual analysis.

[0152] The processor 502 is configured to provide computing and control capabilities to support the operation of the entire computer device 500.

[0153] The internal memory 504 provides an environment for the running of the computer program 5032 in the non-volatile storage medium 503, which, when executed by the processor 502, can cause the processor 502 to perform a reaction kettle operation process intelligent monitoring method based on visual analysis.

[0154] The network interface 505 is configured to perform network communication with other devices. Those skilled in the art can understand that Figure 7 the structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device 500 to which the scheme of the present application is applied. The specific computer device 500 can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0155] The processor 502 is configured to run the computer program 5032 stored in the memory to implement the following steps:​ According to a preset period, a video sequence of the inside of the reaction kettle in the current period collected by the 5G explosion-proof camera arranged in the reaction kettle is acquired to obtain a to-be-analyzed kettle video sequence; the to-be-analyzed kettle video sequence is input into a chemical product visual analysis model for fuzzy video processing and extraction of product visual information in the current period to obtain product visual information; wherein the chemical product visual analysis model is obtained by model training using a neural network technology with operation process video clips of each process operation node in a historical video sequence as a sample set; based on preset process operation node information and the product visual information, process operation nodes in the current period are analyzed to determine whether the time distribution of the process operation nodes in the current period is consistent with the time distribution in the preset process operation node information to obtain an analysis result; when the analysis result is that the time distribution of the process operation nodes in the current period is inconsistent with the time distribution in the preset process operation node information, corresponding operation process prompt information is created and sent to the client.

[0156] The preset period is a specific time period formed by dividing the production process by time, or a time period set by the user.

[0157] The process operation node includes a time point with specific product visual information or an operation switching time point; one preset period contains one or more process operation nodes.

[0158] The product visual description parameter includes a color parameter and a state parameter, wherein the color parameter includes an RGB value and a color change rate, and the state parameter includes a state label and a state change rate.

[0159] In an embodiment, the processor 502 specifically implements the following steps when implementing the training step of the chemical product visual analysis model: A historical video sequence reflecting the full-cycle operation process of the reaction kettle is acquired; the time period of each historical process operation node is determined, and the corresponding operation process video clip is intercepted from the historical video sequence; a sample set for model training is constructed based on the operation process video clip; an initial model is created using a neural network technology; the sample set is input into the initial model to train the initial model, and the initial model is adjusted until the loss value is minimized to obtain a chemical product visual analysis model.

[0160] In an embodiment, the processor 502, when implementing the step of analyzing the process operation node in the current period based on the preset process operation node information and the product visual information to determine whether the time distribution of the process operation node in the current period is consistent with the time distribution in the preset process operation node information to obtain an analysis result, specifically implements the following steps: analyzing the process operation node in the current period based on the preset process operation node information and the product visual information to obtain process operation node information; wherein the process operation node information comprises a process operation node, a time position, and a product visual description parameter corresponding to the process operation node; and determining whether the time distribution of the process operation node in the current period is consistent with the time distribution in the preset process operation node information according to the process operation node information to obtain an analysis result.

[0161] In an embodiment, the processor 502, when implementing the step of determining whether the time distribution of the process operation node in the current period is consistent with the time distribution in the preset process operation node information according to the process operation node information to obtain an analysis result, specifically implements the following steps: comparing the product visual description parameter corresponding to the process operation node with a parameter range corresponding to the preset process operation node information according to the process operation node information to calculate a matching result of each index; and comprehensively evaluating whether the time distribution of the process operation node in the current period is consistent with the time distribution in the preset process operation node information according to the matching result to obtain an analysis result.

[0162] In an embodiment, the processor 502, when implementing the step of comparing the product visual description parameter corresponding to the process operation node with a parameter range corresponding to the preset process operation node information according to the process operation node information to calculate a matching result of each index, specifically implements the following steps: According to the process operation node information, whether the color RGB value of the current material is in the color parameter range corresponding to the preset process operation node information is compared, and a color matching result is generated; according to the process operation node information, whether the color change rate of the current material conforms to the color change rate range corresponding to the preset process operation node information is judged, and a color change rate matching result is generated; according to the process operation node information, whether the current material temperature is in the temperature range corresponding to the preset process operation node information is determined, so as to obtain a temperature matching result; according to the process operation node information, the matching degree between the current material state label and the state label corresponding to the preset process operation node information is calculated, so as to obtain a state label matching result; according to the process operation node information, whether the state change rate of the current material is in the state change rate range corresponding to the preset process operation node information is confirmed, so as to obtain a state change rate matching result; wherein, the matching results of the indicators include the color matching result, the color change rate matching result, the temperature matching result, the state label matching result and the state change rate matching result.

[0163] In an embodiment, the processor 502, after analyzing the process operation node in the current period based on the preset process operation node information and the product visual information to determine whether the time distribution of the process operation node in the current period is consistent with the time distribution in the preset process operation node information to obtain an analysis result, further implements the following steps: When the analysis result is that the time distribution of the process operation node in the current period is consistent with the time distribution in the preset process operation node information, the video sequence of the inside of the reaction kettle in the current period collected by the 5G explosion-proof camera deployed in the reaction kettle is acquired according to the preset period to obtain a to-be-analyzed kettle inside video sequence.

[0164] It should be understood that, in the embodiments of the present application, the processor 502 can be a central processing unit (CPU), and the processor 502 can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), ready-to-program gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0165] Those skilled in the art can understand that all or part of the processes in the method of implementing the above embodiments can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the above method embodiments.

[0166] Therefore, the present application also provides a storage medium. The storage medium can be a computer readable storage medium. The storage medium stores a computer program, wherein the computer program is executed by a processor to make the processor execute the following steps: According to a preset period, a video sequence of a reaction kettle inside in a current period collected by a 5G explosion-proof camera arranged in the reaction kettle is acquired to obtain a to-be-analyzed kettle video sequence; the to-be-analyzed kettle video sequence is input into a chemical product visual analysis model for fuzzy video processing and extraction of product visual information of the current period to obtain product visual information; wherein the chemical product visual analysis model is obtained by model training using a neural network technology with operation process video clips of each process operation node in a historical video sequence as a sample set; based on preset process operation node information and the product visual information, a process operation node in the current period is analyzed to determine whether a time distribution of the process operation node in the current period is consistent with a time distribution in the preset process operation node information, to obtain an analysis result; when the analysis result is that the time distribution of the process operation node in the current period is inconsistent with the time distribution in the preset process operation node information, corresponding operation process prompt information is created and sent to a client.

[0167] The preset period is a specific time period formed by dividing the production process by time, or a time period set by the user.

[0168] The process operation node includes a time point with specific product visual information or an operation switching time point; one preset period contains one or more process operation nodes.

[0169] The product visual description parameter includes a color parameter and a state parameter, wherein the color parameter includes an RGB value and a color change rate, and the state parameter includes a state label and a state change rate.

[0170] In an embodiment, when the processor executes the computer program to implement the training step of the chemical product visual analysis model, the following steps are specifically implemented: acquire a historical video sequence reflecting a whole-cycle operation process of a reaction kettle; determine a time period of each historical process operation node and cut a corresponding operation process video segment from the historical video sequence; construct a sample set for model training based on the operation process video segment; create an initial model using a neural network technology; input the sample set into the initial model to train the initial model, adjust the initial model until a loss value is minimized, and obtain a chemical product visual analysis model.

[0171] In an embodiment, when the processor implements the step of analyzing the process operation node in the current cycle based on the preset process operation node information and the product visual information to obtain an analysis result by executing the computer program, the following steps are specifically implemented: analyzing the process operation node in the current cycle based on the preset process operation node information and the product visual information to obtain process operation node information, wherein the process operation node information comprises a process operation node, a time position, and a product visual description parameter corresponding to the process operation node; and determining whether the time distribution of the process operation node in the current cycle is consistent with the time distribution in the preset process operation node information according to the process operation node information to obtain an analysis result.

[0172] In an embodiment, when the processor implements the step of determining whether the time distribution of the process operation node in the current cycle is consistent with the time distribution in the preset process operation node information according to the process operation node information to obtain an analysis result by executing the computer program, the following steps are specifically implemented: comparing the product visual description parameter corresponding to the process operation node with a parameter range corresponding to the preset process operation node information according to the process operation node information, calculating a matching result of each index, and comprehensively evaluating whether the time distribution of the process operation node in the current cycle is consistent with the time distribution in the preset process operation node information according to the matching result to obtain an analysis result.

[0173] In an embodiment, when the processor implements the step of comparing the product visual description parameter corresponding to the process operation node with a parameter range corresponding to the preset process operation node information according to the process operation node information, calculating a matching result of each index by executing the computer program, the following steps are specifically implemented: According to the process operation node information, whether the color RGB value of the current material is in the color parameter range corresponding to the preset process operation node information is compared, and a color matching result is generated; according to the process operation node information, whether the color change rate of the current material conforms to the color change rate range corresponding to the preset process operation node information is judged, and a color change rate matching result is generated; according to the process operation node information, whether the current material temperature is in the temperature range corresponding to the preset process operation node information is determined, so as to obtain a temperature matching result; according to the process operation node information, the matching degree between the current material state label and the state label corresponding to the preset process operation node information is calculated, so as to obtain a state label matching result; according to the process operation node information, whether the state change rate of the current material is in the state change rate range corresponding to the preset process operation node information is confirmed, so as to obtain a state change rate matching result; The matching results of the indicators include the color matching result, the color change rate matching result, the temperature matching result, the state label matching result, and the state change rate matching result.

[0174] In an embodiment, after the processor executes the computer program to analyze the process operation node in the current period based on the preset process operation node information and the product visual information to determine whether the time distribution of the process operation node in the current period is consistent with the time distribution in the preset process operation node information to obtain an analysis result, the processor further performs the following steps: When the analysis result is that the time distribution of the process operation node in the current period is consistent with the time distribution in the preset process operation node information, the processor performs the step of acquiring the video sequence of the inside of the reaction kettle in the current period collected by the 5G explosion-proof camera deployed in the reaction kettle according to the preset period to obtain a to-be-analyzed kettle video sequence.

[0175] The storage medium can be a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, and various computer readable storage media that can store program codes.

[0176] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware, computer software, or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in general terms in the above description. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application. However, such implementation should not be considered beyond the scope of the present application.

[0177] In several embodiments provided by the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. For example, the embodiments of the apparatus described above are merely schematic. For example, the division of the units is merely a logical function division. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In this way, the inventive idea can be implemented.

[0178] The steps in the method embodiments of the present application can be executed in sequence, combined, or deleted according to actual needs. The units in the apparatus embodiments of the present application can be combined, divided, or deleted according to actual needs. In addition, each functional unit in the various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically, or two or more units can be integrated in one unit.

[0179] If the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on such an understanding, the technical solutions of the present application essentially, or the part that contributes to the prior art, or all or a part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.

[0180] The above describes only the specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An intelligent monitoring method for reactor operation process based on visual analysis, characterized in that: include: According to a preset period, the video sequence of the interior of the reactor in the current period, captured by the 5G explosion-proof camera deployed in the reactor, is obtained to obtain the video sequence inside the reactor to be analyzed; Inputting the video sequence of the kettle to be analyzed into a chemical product visual analysis model to perform fuzzy video processing and extract product visual information of the current cycle to obtain product visual information; wherein the chemical product visual analysis model is obtained by using the operation process video clips of each process operation node in the historical video sequence as a sample set and using neural network technology to train the model; Analyzing the process operation nodes in the current cycle based on the preset process operation node information and the product visual information to determine whether the time distribution of the process operation nodes in the current cycle is consistent with the time distribution in the preset process operation node information, so as to obtain an analysis result; When the analysis result shows that the time distribution of the process operation nodes in the current cycle is inconsistent with the time distribution in the preset process operation node information, corresponding operation process prompt information is created and sent to the client.

2. The method for intelligent monitoring of reactor operation process based on visual analysis according to claim 1, characterized in that: The training process of the chemical product visual analysis model includes: Obtain historical video sequences reflecting the full cycle operation process of the reactor; Determine the time period of each historical process operation node, and extract corresponding operation process video clips from the historical video sequence; Constructing a sample set for model training based on the video clips of the operation process; Create an initial model using neural network technology; The sample set is input into the initial model to train the initial model, and the initial model is adjusted until the loss value is minimized to obtain a chemical product visual analysis model.

3. The method for intelligent monitoring of reactor operation process based on visual analysis according to claim 2, characterized in that: The preset cycle is a specific time period formed by dividing the production process into time periods, or a time period set by a user.

4. The method for intelligent monitoring of reactor operation process based on visual analysis according to claim 3 is characterized in that: The process operation node includes a time point with specific product visual information or an operation switching time point; a preset cycle includes one or more process operation nodes.

5. The method for intelligent monitoring of reactor operation process based on visual analysis according to claim 1, characterized in that: The analyzing the process operation nodes in the current cycle based on the preset process operation node information and the product visual information to determine whether the time distribution of the process operation nodes in the current cycle is consistent with the time distribution in the preset process operation node information to obtain an analysis result includes: Analyzing the process operation nodes in the current cycle based on the preset process operation node information and the product visual information to obtain process operation node information; wherein the process operation node information includes the process operation node, the time position, and the product visual description parameters corresponding to the process operation node; It is determined based on the process operation node information whether the time distribution of the process operation nodes in the current cycle is consistent with the time distribution in the preset process operation node information to obtain an analysis result.

6. The method for intelligent monitoring of reactor operation process based on visual analysis according to claim 5, characterized in that: The product visual description parameters include color parameters and state parameters, wherein the color parameters include RGB values ​​and color change rates, and the state parameters include state labels and state change rates.

7. The method for intelligent monitoring of reactor operation process based on visual analysis according to claim 6, characterized in that: The determining, based on the process operation node information, whether the time distribution of the process operation nodes in the current cycle is consistent with the time distribution in the preset process operation node information to obtain an analysis result includes: Comparing the product visual description parameters corresponding to the process operation node with the preset parameter range corresponding to the process operation node information according to the process operation node information, and calculating the matching results of various indicators; Based on the matching result, it is comprehensively evaluated whether the time distribution of the process operation nodes in the current cycle is consistent with the time distribution in the preset process operation node information to obtain an analysis result.

8. The method for intelligent monitoring of reactor operation process based on visual analysis according to claim 7, characterized in that: The step of comparing the product visual description parameter corresponding to the process operation node with the preset parameter range corresponding to the process operation node information according to the process operation node information, and calculating the matching results of various indicators includes: Comparing the color RGB value of the current material according to the process operation node information to determine whether it is within the color parameter range corresponding to the preset process operation node information, and generating a color matching result; Determining whether the current material color change rate meets the preset color change rate range corresponding to the process operation node information based on the process operation node information, and generating a color change rate matching result; Verifying whether the current temperature is within a preset temperature range corresponding to the process operation node information according to the process operation node information to obtain a temperature matching result; Calculating the matching degree between the current material status label and the status label corresponding to the preset process operation node information based on the process operation node information to obtain a status label matching result; Confirming, based on the process operation node information, whether the state change rate of the current material is within a state change rate range corresponding to the preset process operation node information, and obtaining a state change rate matching result; The matching results of the various indicators include color matching results, color change rate matching results, temperature matching results, state label matching results, and state change rate matching results.

9. The method for intelligent monitoring of reactor operation process based on visual analysis according to claim 1, characterized in that: After analyzing the process operation nodes in the current cycle based on the preset process operation node information and the product visual information to determine whether the time distribution of the process operation nodes in the current cycle is consistent with the time distribution in the preset process operation node information, and obtaining the analysis results, the method further includes: When the analysis result shows that the time distribution of the process operation nodes in the current cycle is consistent with the time distribution in the preset process operation node information, the video sequence inside the reactor in the current cycle captured by the 5G explosion-proof camera deployed in the reactor is obtained according to the preset cycle to obtain the video sequence inside the reactor to be analyzed.

10. The intelligent monitoring device for the operation process of the reactor based on visual analysis is characterized in that: include: An acquisition unit is configured to acquire, according to a preset period, a video sequence of the interior of the reactor captured by a 5G explosion-proof camera deployed in the reactor in the current period, so as to obtain a video sequence of the interior of the reactor to be analyzed; an extraction unit, configured to input the video sequence of the interior of the reactor to be analyzed into a chemical product visual analysis model for fuzzy video processing and extract product visual information of the current cycle to obtain product visual information; wherein the chemical product visual analysis model is obtained by using video clips of the operation process of each process operation node in the historical video sequence as a sample set and using neural network technology to train the model; an analyzing unit, configured to analyze the process operation nodes in the current cycle based on the preset process operation node information and the product visual information, to determine whether a time distribution of the process operation nodes in the current cycle is consistent with a time distribution in the preset process operation node information, so as to obtain an analysis result; The creation unit is used to create corresponding operation process prompt information and send it to the client when the analysis result shows that the time distribution of the process operation nodes in the current cycle is inconsistent with the time distribution in the preset process operation node information.

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