Aluminum liquid leakage prevention system based on laser sensor collaboration

Abstract

The present invention provides an aluminum liquid anti-leakage system based on the cooperation of laser sensors, which relates to the field of data detection technology. The analysis model building module builds a fluid dynamic analysis model. The leakage feature positioning module performs scenario dynamic simulation and aluminum liquid leakage analysis based on the fluid dynamic analysis model. The sensor layout module arranges the sensor array based on the installed capacity. The detection signal feedback module performs leakage point detection based on a preset interval and transmits the aluminum liquid detection signal. The target risk point positioning module performs signal recognition and liquid level variable analysis on the aluminum liquid detection signal, combines with the thermal map equipment for detection and verification, locates the target risk point, and the risk warning information generation module is used to generate risk warning information based on the target risk point. The technical problem that the detection accuracy of the aluminum liquid leakage detection method in the prior art is limited, resulting in the difficulty in effectively controlling the aluminum liquid leakage risk is solved. The technical effect of improving the detection accuracy of aluminum liquid leakage and reducing the risk of aluminum liquid leakage is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of data detection, and in particular to an aluminum liquid leakage prevention system based on the cooperation of laser sensors. Background Art

[0002] In the modern foundry industry, the flow and pouring of molten aluminum is one of the key steps in the production process. Liquid aluminum is transferred and poured under high temperature and high pressure, which is prone to leakage accidents. Liquid aluminum leakage will not only cause material waste and increase production costs, but may also cause equipment damage and even cause safety accidents. Most of the existing methods for detecting aluminum liquid leakage still rely mainly on manual inspection and simple liquid level sensors. These methods not only have limited detection accuracy, but also have poor real-time performance, and cannot timely warn and handle leakage incidents, increasing the safety risk of aluminum liquid leakage.

[0003] There are technical problems in the existing technology that the detection accuracy of aluminum liquid leakage detection methods is limited and leakage incidents cannot be warned and handled in a timely manner, resulting in the difficulty in effectively controlling the risk of aluminum liquid leakage. Summary of the invention

[0004] The present application provides an aluminum liquid anti-leakage system based on the cooperation of laser sensors, which is used to solve the technical problems in the prior art that the detection accuracy of aluminum liquid leakage detection methods is limited, and leakage incidents cannot be warned and handled in a timely manner, resulting in the difficulty in effectively controlling the risk of aluminum liquid leakage.

[0005] In view of the above problems, the present application provides an aluminum liquid anti-leakage system based on laser sensor collaboration, the system comprising: an analysis model building module, the analysis model building module is used to build a fluid dynamic analysis model for the aluminum liquid flow space based on the installed capacity of the casting production line; a leakage feature positioning module, the leakage feature positioning module is used to determine the multivariate operation scene of batch processing, based on the fluid dynamic analysis model, perform scenario dynamic simulation and aluminum liquid leakage analysis, locate the leakage feature, wherein the leakage feature includes a leakage point-critical feature; a sensor layout module, the sensor layout module is used to traverse the leakage feature, based on the installed capacity scale module, deploying a sensor array, wherein the sensor array includes a laser sensor array and a thermal map device; a detection signal feedback module, the detection signal feedback module is used to activate the laser sensor array, perform leakage point detection based on a preset interval, and feedback aluminum liquid detection signals; a target risk point positioning module, the target risk point positioning module is used to perform signal recognition and liquid level variable analysis on the aluminum liquid detection signal, perform detection and verification in combination with the thermal map device, and locate the target risk point; a risk warning information generation module, the risk warning information generation module is used to generate risk warning information based on the target risk point, and perform risk warning management of aluminum liquid leakage.

[0006] In a possible implementation, the leakage feature positioning module is used to: determine the associated influencing factors of fluid movement, wherein the associated influencing factors are identified with a weight distribution based on the associated influence degree; traverse the multivariate operation scenario to determine an element matrix based on the associated influencing factors; and perform scenario-based dynamic simulation using the element matrix as a constraint.

[0007] In a possible implementation, the leakage feature positioning module is also used to: obtain dynamic simulation data of the first scene and determine multiple dynamic node data; traverse the multiple dynamic node data and analyze and determine multiple groups of first leakage features; fit the multiple groups of first leakage features to determine the leakage features of the first scene.

[0008] In a possible implementation, the leakage feature positioning module is further used to: traverse the associated influencing factors to determine the factor sensitivity; based on the factor sensitivity, expand the critical feature by a preset distance, wherein the preset distance is positively correlated with the factor sensitivity.

[0009] In a possible implementation, the detection signal feedback module is used to: determine a flow field cycle based on a real-time operation scenario; traverse the flow field cycle and determine periodic nodes based on fluid volatility; set the preset interval based on the periodic nodes, wherein the preset interval is a non-uniform interval; generate a sensing control instruction based on the preset interval, and respond to the laser sensor array for laser detection.

[0010] In a possible implementation, the target risk point positioning module is used to: identify the aluminum liquid detection signal and extract the liquid level characteristics; based on the liquid level characteristics, perform a dynamic and static liquid level abnormality analysis to locate the risk leakage point; based on the risk leakage point, trigger the thermal map device to perform detection and verification, determine the infrared thermal map and perform aluminum liquid fluid state analysis to determine the target leakage point.

[0011] In a possible implementation, a static liquid level anomaly is judged as over-limit based on a liquid level rise or a liquid level drop, and a dynamic liquid level anomaly is judged as over-limit based on a time phase liquid level trend.

[0012] In a possible implementation, the risk warning information generation module is used to: if the static liquid level is normal, predict the probability of occurrence of a leakage event based on the time-phase liquid level trend, and determine the risk prediction data; wherein, the occurrence probability prediction includes: obtaining homologous scene record data, determining a prediction proxy function, wherein the prediction proxy function is a linear trend relationship that approximates the dynamic change law of the liquid level; introducing the prediction proxy function, and performing linear prediction and probability calculation based on the time-phase liquid level trend.

[0013] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0014] The system provided in the embodiment of the present application is used to build a fluid dynamic analysis model for the aluminum liquid flow space based on the installed capacity of the casting production line through an analysis model building module, a leakage feature positioning module is used to determine the multivariate operation scene of batch processing, and based on the fluid dynamic analysis model, a scenario dynamic simulation and aluminum liquid leakage analysis are performed to locate the leakage feature, wherein the leakage feature includes a leakage point-critical feature, a sensor layout module is used to traverse the leakage feature, and a sensor array is arranged based on the installed capacity, wherein the sensor array includes a laser sensor array and a thermal map device, a detection signal return module is used to activate the laser sensor array, perform leakage point detection based on a preset interval, and return the aluminum liquid detection signal, a target risk point positioning module is used to perform signal recognition and liquid level variable analysis on the aluminum liquid detection signal, and perform detection and verification in combination with the thermal map device to locate the target risk point, and a risk warning information generation module is used to generate risk warning information based on the target risk point, and perform risk warning management of aluminum liquid leakage. The technical effect of improving the accuracy of aluminum liquid leakage detection, enhancing real-time warning and processing capabilities, reducing the risk of aluminum liquid leakage, and ensuring casting safety is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A schematic diagram of the structure of the aluminum liquid leakage prevention system based on the cooperation of laser sensors provided in this application.

[0017] Figure 2 A schematic diagram of the flow of leak point detection by the detection signal return module of the aluminum liquid anti-leakage system based on the cooperation of the laser sensor provided in this application;

[0018] Explanation of the accompanying drawings: analysis model building module 11, leakage feature positioning module 12, sensor deployment module 13, detection signal feedback module 14, target risk point positioning module 15, risk warning information generation module 16. DETAILED DESCRIPTION

[0019] This application provides an aluminum liquid anti-leakage system based on laser sensor collaboration, which is used to solve the technical problem that the aluminum liquid leakage detection method in the prior art has limited detection accuracy, cannot timely warn and handle leakage events, and makes it difficult to effectively control the risk of aluminum liquid leakage. It achieves the technical effect of improving the accuracy of aluminum liquid leakage detection, enhancing real-time warning and processing capabilities, reducing the risk of aluminum liquid leakage, and ensuring casting safety.

[0020] Below, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all of them.

[0021] like Figure 1 As shown, the present application provides an aluminum liquid leakage prevention system based on the cooperation of laser sensors, and the system includes:

[0022] The analysis model building module 11 is used to build a fluid dynamic analysis model for the aluminum liquid flow space based on the installed capacity of the casting production line.

[0023] Specifically, first, the overall layout and equipment configuration of the installed capacity of the casting production line are collected, including the die-casting machine model, mold size, pouring system, equipment quantity, distribution location and operating parameters, etc. The parameters can accurately reflect the actual scale of the production line. The analysis model building module 11 analyzes the collected installed capacity data, combines the casting process knowledge and fluid dynamics principles, calculates the required shape, size and appropriate inclination angle of the flow channel, that is, the spatial parameters of the aluminum liquid flow, to ensure that the aluminum liquid can smoothly, evenly and efficiently fill the mold cavity during the flow process. Then, by using computational fluid dynamics (CFD) technology, through numerical simulation software such as FLOW-3D, by establishing a flow model of aluminum liquid in the flow channel, its behavior and characteristics under different operating conditions are simulated. CFD technology can describe the velocity, pressure and temperature distribution of the fluid in detail, so as to capture the key features of the aluminum liquid flow process, including flow velocity, flow direction, pressure distribution and temperature field changes. Through repeated simulation and adjustment, the fluid dynamics model is optimized to accurately reflect the actual flow of aluminum liquid. Finally, a high-precision fluid dynamics analysis model is generated. The fluid dynamics analysis model refers to a physical model constructed through mathematical modeling and numerical simulation to describe the flow state of molten aluminum during the casting process and its interaction with the surrounding environment. It provides accurate data support for subsequent leakage feature positioning and risk warning, ensuring that the real flow behavior of molten aluminum can be simulated and potential leakage points can be identified.

[0024] The leakage feature positioning module 12 is used to determine the multivariate operation scenarios of batch processing, perform scenario-based dynamic simulation and aluminum liquid leakage analysis based on the fluid dynamics analysis model, and locate leakage features, wherein the leakage features include leakage point-critical features.

[0025] Specifically, the leakage feature positioning module 12 first comprehensively considers the processing environment, equipment configuration and diversity of process flow, identifies and determines the multivariate operation scenarios of the current batch processing, and the multivariate operation scenarios include different process parameter combinations, equipment configurations and operating conditions, which are used to reflect various working conditions that may occur during the casting process. Then, the fluid dynamics analysis model is used to simulate the flow behavior of the aluminum liquid in these scenarios and perform dynamic simulation. Through dynamic simulation, the leakage characteristics of the aluminum liquid in different scenarios are analyzed and located, that is, the leakage characteristics include leakage point-critical characteristics. The leakage point-critical characteristics refer to the specific location where the aluminum liquid leaks and the key influencing factors of the location, such as pressure peak, temperature anomaly, etc. In the dynamic simulation process, the leakage points and their critical characteristics in each scenario are identified and recorded to form a comprehensive set of leakage feature data. The leakage feature positioning module 12 traverses all multivariate operation scenarios to ensure that all possible leakage situations are fully covered and analyzed, and finally realizes the precise positioning of the aluminum liquid leakage characteristics, providing data support for subsequent sensor deployment and risk warning.

[0026] The sensor deployment module 13 is used to traverse the leakage characteristics and deploy a sensor array based on the installed capacity, wherein the sensor array includes a laser sensor array and a thermal imaging device.

[0027] Specifically, the sensor layout module 13 first traverses the leakage feature data determined by the leakage feature positioning module 12, and analyzes the dynamic flow state characteristics of the aluminum liquid leakage. According to the analyzed dynamic flow state characteristics of the leakage, based on the installed capacity of the current casting production line, the sensor array is laid out. Since the leakage of aluminum liquid not only involves abnormal changes in the liquid level, but also focuses on the high-speed and high-temperature dynamic flow of the fluid, the general laser sensor can still work stably under abnormal conditions such as high temperature and high pressure in the casting environment by using its non-contact detection advantages, but its detection ability is also relatively limited, and can only detect features such as liquid level. The sensor layout module 13 combines the laser sensor array with the infrared thermal image device to form a comprehensive sensor array detection system. When the laser sensor detects abnormal liquid level or suspected signs of leakage, the sensor layout module 13 will immediately start the infrared thermal image device, use infrared thermal imaging technology to capture the heat changes generated when the aluminum liquid leaks, and perform infrared thermal imaging detection, so as to more intuitively show the dynamic flow state of the leakage area, realize all-round and multi-angle monitoring of the leakage process, and improve the system's response to leakage events.

[0028] The detection signal feedback module 14 is used to activate the laser sensor array, perform leakage point detection based on a preset interval, and feedback aluminum liquid detection signals.

[0029] like Figure 2 As shown, in one possible implementation, the detection signal feedback module 14 is used to: determine a flow field cycle based on a real-time operation scenario; traverse the flow field cycle, and determine periodic nodes based on fluid volatility; based on the periodic nodes, set the preset interval, wherein the preset interval is a non-uniform interval; based on the preset interval, generate a sensing control instruction, and respond to the laser sensor array for laser detection.

[0030] Specifically, the detection signal return module 14, as a key component of the entire monitoring system, is responsible for activating the operation of the laser sensor array and detecting aluminum liquid leakage points. First, according to the real-time operation scenario, the flow field cycle is determined. The flow field cycle refers to the flow cycle time of aluminum liquid under different operating conditions. Then, the flow field cycle is traversed, and data analysis techniques such as time series analysis and spectrum analysis are used to determine the periodic nodes in the fluid fluctuation based on the fluid volatility of aluminum liquid. The fluid volatility refers to the changes in speed and pressure during the flow of aluminum liquid. The periodic nodes refer to the regular changes in the flow of aluminum liquid. Then, based on these periodic nodes, a preset interval is set. The preset interval is non-uniform. The specific setting is adjusted according to the volatility of the flow of aluminum liquid to ensure detection at critical moments. Subsequently, the detection signal return module 14 generates a sensor control instruction, which can activate the laser sensor array according to the preset interval so that it can perform laser detection at a specified time point. The laser sensor array detects the liquid level characteristics of aluminum liquid in a non-contact manner and transmits the detected signal back to the system in real time. Through this series of steps, the detection signal feedback module 14 realizes accurate monitoring of the aluminum liquid leakage point, and provides real-time monitoring data through signal feedback, providing reliable real-time data support for subsequent processing of the system.

[0031] The target risk point positioning module 15 is used to perform signal recognition and liquid level variable analysis on the aluminum liquid detection signal, and perform detection and verification in combination with the thermal map device to locate the target risk point.

[0032] Specifically, the target risk point positioning module 15 first receives the aluminum liquid detection signal from the detection signal feedback module 14, and then uses advanced signal recognition technology to perform signal analysis on the aluminum liquid detection signal to extract liquid level characteristics, including liquid level height, fluctuation amplitude and change trend, etc. Then, by comparing historical data, preset thresholds and the physical model of aluminum liquid flow, liquid level variable analysis is performed to identify potential risk signs such as abnormal liquid level fluctuations and potential leakage.

[0033] After identifying the potential risk leakage point, the target risk point positioning module 15 is combined with the thermal map device for detection and verification. The thermal map device uses infrared thermal imaging technology to further verify the abnormal liquid level point, generate infrared thermal maps, analyze the fluid state of the aluminum liquid, and confirm whether there is a leak. By comparing the liquid level characteristics and thermal map data, the target risk point can be accurately located. The target risk point positioning module 15 ensures the accuracy of the liquid level abnormality analysis, and improves the reliability of leakage detection through multiple verification methods, realizing the accurate positioning and timely warning of the aluminum liquid leakage risk.

[0034] The risk warning information generation module 16 is used to generate risk warning information based on the target risk point and perform risk warning management of aluminum liquid leakage.

[0035] Specifically, the risk warning information generation module 16 receives the target risk point from the target risk point positioning module 15, and comprehensively analyzes the risk characteristics of the target risk point, such as the leakage location, liquid level change trend and potential impact range, etc., based on the positioning result of the target risk point. According to the analysis results, it is judged whether it reaches the preset warning threshold or condition. When it is judged that there is a leakage of molten aluminum, the risk warning information generation module will immediately start the warning process, generate risk warning information, and conduct risk warning management of molten aluminum leakage. Through risk warning management, the risk of molten aluminum leakage can be discovered and handled in time to avoid accidents and ensure the safety and efficiency of the casting process.

[0036] In a possible implementation, the leakage feature positioning module 12 is used to: determine the associated influencing factors of fluid movement, wherein the associated influencing factors are identified with a weight distribution based on the associated influence degree; traverse the multivariate operation scenario to determine an element matrix based on the associated influencing factors; and perform scenario-based dynamic simulation using the element matrix as a constraint.

[0037] Specifically, first, determine the associated influencing factors of fluid motion, which refer to parameter information that affects the flow state and leakage of aluminum liquid, such as flow rate, pressure, temperature, and external environment vibration. Then, through data analysis and expert experience, identify the associated influencing factors, and assign a weight distribution based on its associated influence to each factor, which reflects the importance of each factor in fluid motion. Traverse multiple operation scenarios, which include various operating conditions and process parameter combinations that may occur in the casting process. In each operation scenario, according to the determined associated influencing factors, construct an element matrix, which contains all associated influencing factors and their corresponding weights. Finally, use the element matrix as a constraint condition to perform scenario-based dynamic simulation to simulate the flow behavior of aluminum liquid in different operation scenarios. The leakage feature positioning module 12 can comprehensively and accurately analyze and predict the leakage risk of aluminum liquid under various working conditions through scenario-based dynamic simulation, analyze and obtain the flow characteristics of aluminum liquid, and provide reliable data support for subsequent risk warning and management.

[0038] In a possible implementation, the leakage feature positioning module 12 is also used to: obtain dynamic simulation data of the first scene and determine multiple dynamic node data; traverse the multiple dynamic node data and analyze and determine multiple groups of first leakage features; fit the multiple groups of first leakage features to determine the leakage features of the first scene.

[0039] Specifically, first, determine the first scenario of the multivariate operation scenario, perform dynamic simulation in the first scenario, and obtain dynamic simulation data, which includes the time series changes of parameters such as speed, pressure, and temperature during the flow of aluminum liquid. Then, determine multiple dynamic node data, that is, data points at key moments in the flow of aluminum liquid, based on the obtained dynamic simulation data. The dynamic node data reflects the important characteristics and change trends of fluid movement. Then, traverse and analyze multiple dynamic node data, and use data analysis techniques such as clustering and association rule mining to obtain the possible leakage point location, leakage conditions and influencing factors of each dynamic node, that is, multiple groups of first leakage features. In order to more accurately grasp the leakage situation of the entire scenario, mathematical models or machine learning algorithms, such as linear regression, decision trees, neural networks and other fitting methods, are used to fit multiple groups of first leakage features, and their spatial distribution and time variation laws are comprehensively considered to finally determine the leakage features under the first scenario. The leakage features under the first scenario not only describe the specific circumstances of the leakage, but also include an in-depth understanding of the cause of the leakage and the scope of influence. Based on this, the leakage feature positioning module 12 can accurately analyze the leakage risk of aluminum liquid in different operating scenarios, improve the assessment capability and response efficiency of the safety risk of the entire scenario, and ensure the effectiveness of early warning and emergency measures.

[0040] In a possible implementation, the leakage feature positioning module 12 is further used to: traverse the associated influencing factors to determine the factor sensitivity; based on the factor sensitivity, expand the critical feature by a preset distance, wherein the preset distance is positively correlated with the factor sensitivity.

[0041] Specifically, the leakage feature positioning module 12 further traverses all influencing factors associated with the leakage feature, such as flow rate, pressure, temperature, etc. By analyzing the degree of fluctuation of each influencing factor, that is, analyzing whether each influencing factor is prone to cause significant changes in the fluid state due to slight disturbances in the actual control process, the sensitivity of the factor is determined. The level of the sensitivity of the factor reflects the degree of influence of the factor on the risk of aluminum liquid leakage. The higher the sensitivity of the factor, the more significant the influence of the fluid state. Then, based on the sensitivity of the factor obtained by the analysis, the critical feature in the leakage analysis is dynamically adjusted, that is, according to the sensitivity level of the factor, the detection range of the critical feature is expanded by a preset spacing to expand the prediction of the influence range of the leakage feature. The preset spacing is not a fixed value, but is positively correlated with the sensitivity of the factor, that is, the higher the sensitivity of the factor, the larger the corresponding preset spacing, so as to fully consider the greater impact that these factors may have on the leakage feature boundary in extreme cases. For example, if the sensitivity of a certain factor is high and it has a great impact on the fluctuation of the fluid state, then the system will increase the monitoring range of the area and capture leakage features in a larger range by expanding the preset spacing. Through this method, the leakage feature positioning module 12 can dynamically adjust the scope of leakage detection to ensure that the leakage features under the influence of highly sensitive factors are fully captured and monitored. Ultimately, based on these adjustments, the accuracy and reliability of leakage feature positioning are improved, providing a more solid data foundation for the risk warning and management of the system.

[0042] In a possible implementation, the target risk point positioning module 15 is used to: identify the aluminum liquid detection signal and extract the liquid level characteristics; based on the liquid level characteristics, perform a dynamic and static liquid level abnormality analysis to locate the risk leakage point; based on the risk leakage point, trigger the thermal map device to perform detection and verification, determine the infrared thermal map and perform aluminum liquid fluid state analysis to determine the target leakage point.

[0043] In a possible implementation, a static liquid level anomaly is determined based on whether the liquid level rises or falls, and a dynamic liquid level anomaly is determined based on whether the liquid level exceeds the limit.

[0044] Specifically, the target risk point positioning module 15 first uses signal processing technology to identify and analyze the aluminum liquid detection signal detected by the detection signal feedback module 14, and extracts the liquid level height, fluctuation amplitude and change trend as features. Then, a dynamic and static analysis method is used to analyze the liquid level abnormality of the liquid level characteristics. In static analysis, an over-limit judgment is made by monitoring the rise or fall of the liquid level, that is, whether the liquid level exceeds the normal range. When the liquid level rises abnormally and exceeds the safety upper limit or falls below the warning line, it is immediately judged as a static liquid level abnormality. In dynamic analysis, by observing the time series data of the liquid level, by comparing historical data with real-time data, a phase liquid level trend analysis is performed, so as to make an over-limit judgment, that is, to judge whether the liquid level change trend is abnormal. When it is judged that the liquid level fluctuates abnormally or continues to deviate from the normal trend, it is judged as a dynamic liquid level abnormality, further revealing potential fluid dynamics problems or hidden leakage points. Through the combination of static and dynamic analysis of liquid level anomalies, when the liquid level anomaly is identified, the risk leakage point is immediately located, and the thermal map detection equipment is automatically triggered for precise verification. The thermal map equipment uses infrared imaging technology to capture the temperature distribution of the aluminum liquid and its surrounding environment, and generates a high-definition infrared thermal map. Through the detailed analysis of the infrared thermal map, the fluid temperature state of the aluminum liquid can be intuitively displayed, and the temperature abnormality area can be identified. The temperature abnormality area is often directly related to the heat accumulation or loss caused by the aluminum liquid leakage, and the leakage situation can be further determined. Finally, combined with the comprehensive analysis of the liquid level characteristics and the infrared thermal map, the target leakage point can be accurately determined to ensure the accurate positioning and timely warning of the aluminum liquid leakage.

[0045] In one possible implementation, the risk warning information generation module is used to: if the static liquid level is normal, predict the probability of occurrence of a leakage event based on the time-phase liquid level trend, and determine risk prediction data; wherein, the occurrence probability prediction includes: obtaining homologous scene record data, determining a prediction proxy function, wherein the prediction proxy function is a linear trend relationship that approximates the dynamic change law of the liquid level; introducing the prediction proxy function, and performing linear prediction and probability calculation based on the time-phase liquid level trend.

[0046] Specifically, when the static liquid level is judged to be within the normal range, in order to prospectively evaluate the potential leakage risk, the scene recording data of the same source is obtained, and the scene recording data of the same source includes the historical liquid level change information under similar operating conditions. Then, the scene recording data of the same source is used for regression analysis to determine the prediction proxy function, which is an approximate linear trend relationship of the dynamic change law of the liquid level. For example, a linear regression model is used, and the liquid level change data is used as input. Through mathematical methods such as the least squares method, an optimal linear function is fitted. This linear function, namely the prediction proxy function, can reflect the overall trend of the liquid level change over time to a certain extent. Although the liquid level change is actually nonlinear, in this way, determining an approximate linear function for trend prediction analysis can reflect the overall direction and speed of the liquid level change to a certain extent, and provide a feasible framework for subsequent prediction analysis. After the introduction of the prediction proxy function, a linear prediction is performed based on the phase liquid level trend, that is, the future liquid level change is estimated using the linear function, and the probability of the occurrence of liquid level anomalies (leakage events) in a certain period of time in the future is calculated. Based on this, a series of risk prediction data are generated, which include not only the predicted future liquid level value, but also the corresponding probability of leakage incidents, providing a scientific basis for risk warning and management. This process ensures that under the condition of normal static liquid level, the potential leakage risk can still be effectively predicted through the analysis of the phase liquid level trend, which helps relevant staff to take measures in advance to prevent or reduce the losses caused by potential leakage incidents and ensure the safety and efficiency of the casting process.

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

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

Claims

1. Aluminum liquid anti-leakage system based on laser sensor cooperation, characterized in that: The system comprises: An analysis model building module, wherein the analysis model building module is used to build a fluid dynamic analysis model for the aluminum liquid flow space based on the installed capacity of the casting production line; A leakage feature positioning module, which is used to determine the multivariate operation scenarios of batch processing, perform scenario-based dynamic simulation and aluminum liquid leakage analysis based on the fluid dynamics analysis model, and locate leakage features, wherein the leakage features include leakage point-critical features; A sensor deployment module, the sensor deployment module is used to traverse the leakage characteristics and deploy a sensor array based on the installed capacity, wherein the sensor array includes a laser sensor array and a thermal image device; A detection signal feedback module, the detection signal feedback module is used to activate the laser sensor array, perform leakage point detection based on a preset interval, and feedback an aluminum liquid detection signal; A target risk point positioning module, which is used to perform signal recognition and liquid level variable analysis on the aluminum liquid detection signal, and perform detection and verification in combination with the thermal map device to locate the target risk point; A risk warning information generation module, the risk warning information generation module is used to generate risk warning information based on the target risk point, and perform risk warning management of aluminum liquid leakage; The detection signal return module is used for: Determine the flow field cycle based on real-time operation scenarios; Traversing the flow field period, and determining periodic nodes based on fluid volatility; Based on the periodic node, setting the preset interval, wherein the preset interval is a non-uniform interval; Based on the preset interval, generating a sensing control instruction, responding to the laser sensor array to perform laser detection; The target risk point positioning module is used for: Identifying the aluminum liquid detection signal and extracting liquid level characteristics; Based on the liquid level characteristics, the liquid level anomaly analysis is performed in combination with static and dynamic analysis to locate the risk leakage point. The static liquid level anomaly is determined to be out of limit based on the liquid level rise or drop, and the dynamic liquid level anomaly is determined to be out of limit based on the time phase liquid level trend. Based on the risk leakage point, the thermal image equipment is triggered to perform detection and verification, determine the infrared thermal image and perform aluminum liquid fluid state analysis to determine the target leakage point.

2. The aluminum liquid leakage prevention system based on laser sensor cooperation according to claim 1 is characterized in that: The leakage feature positioning module is used for: Determining correlation influencing factors of fluid motion, wherein the correlation influencing factors are identified with weight distribution based on correlation influence degree; Traversing the multivariate operation scenario, determining a factor matrix based on the associated influencing factors; Taking the element matrix as a constraint, a scenario-based dynamic simulation is performed.

3. The aluminum liquid anti-leakage system based on laser sensor cooperation as claimed in claim 1 is characterized in that: The leakage feature positioning module is also used for: Acquire dynamic simulation data of the first scene and determine multiple dynamic node data; Traversing the plurality of dynamic node data, analyzing and determining a plurality of groups of first leakage features; The plurality of groups of first leakage features are fitted to determine leakage features of the first scene.

4. The aluminum liquid anti-leakage system based on laser sensor cooperation as claimed in claim 2, characterized in that: The leakage feature positioning module is also used for: Traversing the associated influencing factors to determine the sensitivity of the factors; Based on the sensitivity of the element, the critical feature is expanded by a preset distance, wherein the preset distance is positively correlated with the sensitivity of the element.

5. The aluminum liquid anti-leakage system based on laser sensor cooperation as claimed in claim 1, characterized in that: The risk warning information generation module is used to: If the static liquid level is normal, the probability of leakage is predicted based on the phase liquid level trend to determine the risk prediction data; Among them, the occurrence probability prediction includes: Obtaining scene record data from the same source and determining a prediction proxy function, wherein the prediction proxy function is a linear trend relationship that approximates the dynamic change law of the liquid level; The prediction agent function is introduced to perform linear prediction and probability calculation based on the temporal liquid level trend.

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