Deep-sea mining plume monitoring and early warning system based on infrared sensor

Abstract

The invention discloses a deep-sea mining plume monitoring and early warning system based on an infrared sensor, and relates to the technical field of plume monitoring and early warning. The plume monitoring and early warning center is in communication connection with an infrared sensor array module, a data acquisition and processing module, a concentration analysis and early warning module, a time recording and tracking module, a dissipation time evaluation module and an environment interference evaluation module. Through the characteristics of high sensitivity and non-contact measurement of the infrared sensor, the temperature distribution and concentration change of the plume are monitored in a deep sea environment, so that the system can quickly capture the dynamic change of the plume, including the starting, peak and dissipation processes, and calculate the dissipation time and concentration value of the plume, thereby obtaining an environmental interference coefficient, and further obtaining the environmental interference coefficient. And the interference degree of plume on the deep-sea mining environment is evaluated, so that high-precision early warning is realized, and timely and accurate information support is provided for environment management of deep-sea mining activities.

Description

Technical Field

[0001] The present invention relates to the technical field of plume monitoring and early warning, and in particular to a deep-sea mining plume monitoring and early warning system based on infrared sensors. Background Art

[0002] With the continued global demand for mineral resources, deep-sea mining has gradually become an important way to obtain new resources. However, the impact of deep-sea mining activities on the marine environment cannot be ignored. In particular, the plumes generated during the mining process may have long-term impacts on the seabed ecosystem and the marine environment. Therefore, real-time monitoring and early warning of deep-sea mining plumes have become the key to ensuring the safety of the marine environment and achieving sustainable deep-sea mining. Infrared sensors, as a non-contact measurement technology, can assist in achieving continuous and high-precision monitoring of plumes, improving the accuracy and timeliness of early warnings.

[0003] In the process of deep-sea mining plume monitoring, since the plume is in a dynamic process of change, the real-time monitoring feedback has a certain lag. Therefore, how to evaluate the plume dissipation time according to the change of plume concentration in order to timely discover and warn of potential environmental risks is the problem we need to solve. To this end, a deep-sea mining plume monitoring and early warning system based on infrared sensors is proposed. Summary of the Invention

[0004] The present invention aims to provide a deep-sea mining plume monitoring and early warning system based on infrared sensors to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: A deep-sea mining plume monitoring and early warning system based on infrared sensors includes a plume monitoring and early warning center, wherein the plume monitoring and early warning center is communicatively connected to an infrared sensor array module, a data acquisition and processing module, a concentration analysis and early warning module, a time recording and tracking module, a dissipation time assessment module, and an environmental interference assessment module, wherein the modules are electrically connected; The infrared sensor array module consists of two infrared sensors, which are used to detect light intensity in deep-sea environments. Since particulate matter in the plume scatters or absorbs light, the light intensity changes. By comparing the light intensity detected by infrared sensors at different locations, the concentration changes of the plume can be indirectly reflected. The data acquisition and processing module is used to collect the light intensity data output by the infrared sensor array module and perform pre-processing operations such as filtering and denoising to improve the accuracy and reliability of the data, providing a high-quality data basis for subsequent concentration analysis and dissipation time assessment; The concentration analysis and warning module analyzes the plume concentration trend based on the pre-processed light intensity data, converts the light intensity difference into a specific concentration parameter for intuitive understanding and analysis, and sets a concentration threshold. When the plume concentration trend exceeds the concentration threshold, the warning mechanism is triggered, and an early warning signal is sent to the operator through sound and light, reminding the operator to take timely measures to address potential environmental risks; The time recording and tracking module is used to record the time series data of the plume concentration change, including the starting time, peak time and dissipation time of the concentration change, providing the necessary time information for evaluating the plume dissipation time; The dissipation time assessment module uses statistical methods to assess the dissipation time of the plume based on the data provided by the time recording and tracking module, which helps to predict the duration and scope of the plume's impact on the environment; The environmental interference assessment module analyzes the degree of interference of the plume residence time on the deep-sea mining environment based on the assessed plume dissipation time, and warns of potential environmental risks.

[0006] A further improvement of the technical solution of the present invention is that: in the infrared sensor array module, the process of detecting the light intensity in the deep sea environment is: The infrared sensor mounting bracket is made of sturdy and durable materials to ensure the sensor remains stable in the deep-sea high-pressure and strong water current environment. The infrared sensor is fixed to the bracket and, based on the characteristics of the deep-sea environment and the path of the plume, the two infrared sensors are divided into the first infrared sensor and the second infrared sensor at predetermined positions near the mining equipment; Before formal use, the sensitivity of the infrared sensor should be adjusted by adjusting the gain or amplification circuit inside the sensor to ensure that it has an appropriate response to changes in light intensity. Sensitivity adjustment is based on the specific lighting conditions and plume characteristics of the deep-sea environment to avoid over-sensitivity or under-sensitivity. The sensor's range and resolution should be adjusted to set the sensor's measurement range to ensure that it can accurately measure and record changes in light intensity when the plume passes. The sensor should be verified using a light source of known light intensity to ensure that its output data is accurate. The emitter (infrared light emitting diode) in the first infrared sensor continuously emits infrared rays. When the infrared rays hit the particulate matter in the plume in the deep-sea environment, part of the light is reflected. The receiver (infrared photodiode) of the second infrared sensor receives the transmitted infrared light and converts the received infrared light into an electrical signal.

[0007] A further improvement of the technical solution of the present invention is that in the data acquisition and processing module, the collection and preprocessing process of the light intensity data is as follows: The data acquisition and processing module is connected to the infrared sensor array module through a communication interface to ensure the reliability and stability of data transmission. It receives the light intensity data output by the infrared sensor array module through the communication interface and formats the received raw data into a unified format containing timestamp, sensor number and light intensity value fields to facilitate subsequent processing and analysis. Preprocessing the collected light intensity data includes filtering, denoising, and data smoothing. Digital filters (low-pass filters, band-pass filters, etc.) are used to filter the light intensity data to remove high-frequency noise and interference. Denoising algorithms (wavelet transform, Kalman filter, etc.) are used to further remove noise and outliers in the data. The filtered and denoised data are then smoothed to improve data continuity and smoothness. A database is constructed in the plume monitoring and early warning center, and the preprocessed light intensity data is uploaded to the plume monitoring and early warning center through a communication protocol (HTTP, HTTPS, FTP, etc.), and stored in the database of the plume monitoring and early warning center.

[0008] A further improvement of the technical solution of the present invention is that in the concentration analysis and early warning module, the analysis process of the plume concentration trend is: Extracting pre-processed light intensity data from the database of the plume monitoring and early warning center, parsing the extracted light intensity data, and obtaining timestamp, sensor number, and light intensity value information; Analyze the physical characteristics of the plume, including its formation, movement patterns, and influencing factors, clarify the potential relationship between light intensity and particulate matter concentration in the plume, and collect a large amount of plume-related data, including light intensity data and particulate matter concentration data, to integrate them into a plume concentration dataset; The plume concentration dataset is divided into a training set and a validation set. The training set is used to train the model, and the validation set is used to evaluate the model's performance. The neural network model is trained using the training set data to obtain a concentration analysis model. The trained model is then validated using the validation set data to evaluate the model's performance. Based on the validation results, the model is adjusted and optimized to improve its generalization ability and prediction accuracy. Deploy a concentration analysis model to analyze the physical properties of the plume and the relationship between light intensity and concentration. Changes in light intensity are correlated with the concentration of particulate matter in the plume. Light intensity data is input into the concentration analysis model to calculate the corresponding plume concentration value. The calculated plume concentration value is then verified to ensure it is within a reasonable range and conforms to physical laws. The concentration threshold of the plume is set at 0.2 based on comprehensive safety requirements and historical data. The calculated plume concentration value is continuously monitored and compared with the set concentration threshold to determine whether the plume concentration value exceeds the concentration threshold; When the monitored plume concentration value exceeds the concentration threshold, the early warning mechanism is triggered and a warning signal is sent to the operator through sound and light methods (buzzer, LED indicator light) to ensure the visibility and timeliness of the warning signal so that the operator can take measures quickly, and record the time, concentration value and sensor number information of the warning.

[0009] A further improvement of the technical solution of the present invention is that the calculation formula of the plume concentration value is: ; Among them, C is the plume concentration value, I is the light intensity value, which are input variables. is the baseline light intensity value, which is a constant representing the light intensity when there is no plume influence. k is a proportional constant used to adjust the speed of exponential decay. The value range of C is between 0 and 1.

[0010] A further improvement of the technical solution of the present invention is that in the time recording and tracking module, the recording process of the plume concentration change time series data is as follows: Extract pre-processed light intensity data from the database and add a timestamp to each light intensity data record to ensure the time sequence of the data. The timestamp is key information to ensure data accuracy and traceability, making it easier to track the dynamic process of plume concentration changes. The light intensity data is input into the concentration analysis model to calculate the plume concentration value. The plume concentration value data is then processed and analyzed to identify the starting point, peak point, and dissipation point of the concentration change. For the identification of the starting point, the starting time is determined by detecting the point where the concentration value starts to rise significantly from a stable state. For the identification of the peak point, the point where the concentration value reaches the maximum value is found, and the time corresponding to this point is the peak time. For the identification of the dissipation point, the dissipation time is determined by identifying the point where the concentration value starts to drop significantly from the peak value and tends to stabilize. After identifying the starting point, peak point, and dissipation point of the concentration change, time series data including the starting time, peak time, and dissipation time are generated, and the generated time series data are stored in a database for subsequent analysis and query.

[0011] A further improvement of the technical solution of the present invention is that: in the dissipation time evaluation module, the evaluation process of the plume dissipation time is: Extract the time series data of plume concentration changes recorded by the time record tracking module from the database, including the start time, peak time and dissipation time of the concentration change; Using charting tools to visualize the trends in plume concentration over time helps identify patterns, trends, and outliers in the data. Time series analysis methods are then applied to smooth the data to identify long-term trends and analyze the overall trends in plume concentration over time. Combining the plume concentration value and the time series data of the plume concentration change, determine the time point when the plume concentration begins to decrease from the peak value and the time point when the plume concentration decreases to the plume concentration value. Calculate the time difference between the two time points to obtain the plume dissipation time. The calculated plume dissipation time is stored in a database and a report is generated for subsequent analysis and decision support. The report includes a chart of plume concentration changes and statistical data on dissipation time.

[0012] A further improvement of the technical solution of the present invention is that the calculation formula for the plume dissipation time is: ; in, represents the plume dissipation time, represents the peak value of the plume concentration, represents the plume concentration value at any time point t, and C represents the plume concentration value.

[0013] A further improvement of the technical solution of the present invention is that: in the environmental interference assessment module, the analysis process of the degree of interference of the plume residence time on the deep-sea mining environment is as follows: The residence time and diffusion range of the plume in the deep sea environment are analyzed according to the plume dissipation time, and the environmental interference coefficient is calculated by combining the plume concentration value and the plume dissipation time. Based on the size of the environmental interference coefficient, the degree of interference of the plume residence time on the deep-sea mining environment is quantitatively analyzed to assess the impact of the plume on the environment; Based on the assessment results of the impact of the plume on the environment, the interference degree is divided into different warning levels: low warning level, medium warning level, and high warning level. In combination with the environmental interference coefficient, the corresponding warning threshold is matched for each warning level. Based on the determined warning level, corresponding warning measures and recommendations are generated, including adjusting mining operations, reducing waste emissions, and strengthening environmental monitoring to prevent and mitigate the impact of deep-sea mining activities on the marine environment, and formulating emergency plans and response mechanisms to deal with sudden environmental incidents.

[0014] A further improvement of the technical solution of the present invention is that the calculation formula of the environmental interference coefficient is: ; Where E represents the environmental interference coefficient, represents the plume dissipation time, represents the peak value of the plume concentration, C represents the plume concentration value, and g represents the proportional constant used to adjust the speed of exponential decay. represents the regulation index, which indicates the degree of influence of the change of plume concentration on the environmental interference coefficient. The value of E ranges from 0 to 1. The plurality of warning levels correspond to the plurality of warning thresholds, wherein the warning thresholds include an upper threshold and a lower threshold, and the plurality of warning levels and the plurality of warning thresholds satisfy the following relationship: Low alert level ; Medium warning level ; High alert level ; Where E is the environmental interference coefficient, is the lower threshold corresponding to the medium warning level and the upper threshold corresponding to the low warning level, is the lower threshold corresponding to the high warning level and the upper threshold corresponding to the medium warning level, =0.3, =0.7.

[0015] Due to the adoption of the above technical solution, the present invention has the following technical advancements compared to the prior art: The present invention provides a deep-sea mining plume monitoring and early warning system based on infrared sensors. By utilizing the high sensitivity and non-contact measurement characteristics of infrared sensors, the temperature distribution and concentration changes of plumes are monitored in a deep-sea environment. This enables the system to quickly capture the dynamic changes of the plume, including its onset, peak value, and dissipation process, and calculate the dissipation time and concentration value of the plume. In addition, the environmental interference coefficient is obtained, and the degree of interference of the plume on the deep-sea mining environment is evaluated, thereby achieving high-precision early warning and providing timely and accurate information support for the environmental management of deep-sea mining activities.

[0016] The present invention provides a deep-sea mining plume monitoring and early warning system based on infrared sensors. The system automatically divides warning levels according to the size of the environmental interference coefficient and generates corresponding early warning measures and suggestions. At the same time, based on real-time monitoring data, it adapts to changes in deep-sea mining activities and environmental fluctuations, improving the accuracy and timeliness of early warnings. Once the system issues an early warning signal, the emergency plan is immediately activated and corresponding emergency measures can be taken to adjust mining operations and reduce waste emissions, etc., to mitigate the impact of plumes on the deep-sea environment, help reduce environmental risks, and maintain the safety and stability of the deep-sea ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a module structure diagram of the present invention; Figure 2 This is a flow chart for analyzing plume concentration trends of the present invention; Figure 3 This is a flow chart for evaluating the plume dissipation time of the present invention; Figure 4 This is a flow chart for analyzing the degree of interference of plume residence time on deep-sea mining environment in the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] Example 1, as Figure 1 、 Figure 2 As shown, the present invention provides a deep-sea mining plume monitoring and early warning system based on infrared sensors, including a plume monitoring and early warning center, which is communicatively connected to an infrared sensor array module, a data acquisition and processing module, a concentration analysis and early warning module, a time recording and tracking module, a dissipation time assessment module, and an environmental interference assessment module, wherein the modules are electrically connected; The infrared sensor array module consists of two infrared sensors and is used to detect light intensity in deep-sea environments. Since particulate matter in the plume scatters or absorbs light, the light intensity changes. By comparing the light intensity differences detected by infrared sensors at different positions, the concentration changes of the plume can be indirectly reflected. The infrared sensor mounting bracket is made of sturdy and durable materials to ensure that the sensor remains stable in the deep-sea high-pressure and strong water flow environment. The infrared sensor is fixed on the bracket and divided into the first infrared sensor and the second infrared sensor according to the characteristics of the deep-sea environment and the path of the plume at predetermined positions near the mining equipment. The predetermined positions should ensure that the sensors can accurately capture the changes in light intensity when the plume passes and avoid other unnecessary interference. The angle and position of the infrared sensor are adjusted to maximize the reception of infrared rays reflected by the plume before formal use; Adjusting the sensitivity of the infrared sensor by adjusting the gain or amplification circuit within the sensor to ensure that it responds appropriately to changes in light intensity. Sensitivity adjustment is based on the specific lighting conditions and plume characteristics of the deep-sea environment to avoid oversensitivity or undersensitivity. The sensor's range and resolution are adjusted to set the sensor's measurement range to ensure that it can accurately measure and record changes in light intensity as the plume passes. The sensor is verified using a light source of known light intensity to ensure that its output data is accurate. The emitter (infrared light emitting diode) in the first infrared sensor continuously emits infrared light. When the infrared light hits particulate matter in the plume in the deep-sea environment, some of the light is reflected. The second infrared sensor's receiver (infrared photodiode) receives the transmitted infrared light and converts the received infrared light into an electrical signal whose strength is proportional to the intensity of the received infrared light. The data acquisition and processing module is used to collect the light intensity data output by the infrared sensor array module and perform pre-processing operations such as filtering and denoising to improve the accuracy and reliability of the data, providing a high-quality data basis for subsequent concentration analysis and dissipation time assessment. The data acquisition and processing module is connected to the infrared sensor array module through a communication interface (RS-232, RS-485, Ethernet, etc.) to ensure the reliability and stability of data transmission. It receives the light intensity data output by the infrared sensor array module through the communication interface and formats the received raw data into a unified format containing timestamp, sensor number and light intensity value fields, facilitating subsequent processing and analysis; Preprocessing the collected light intensity data includes filtering, denoising, and data smoothing. A digital filter (low-pass filter, band-pass filter, etc.) is applied to the light intensity data to remove high-frequency noise and interference. A denoising algorithm (wavelet transform, Kalman filter, etc.) is used to further remove noise and outliers from the data. The filtered and denoised data is then smoothed to improve data continuity and smoothness. A database is constructed in the plume monitoring and early warning center. The preprocessed light intensity data is uploaded to the plume monitoring and early warning center via a communication protocol (HTTP, HTTPS, FTP, etc.) and stored in the database of the plume monitoring and early warning center. The concentration analysis and early warning module analyzes the plume concentration trend based on the pre-processed light intensity data, converts the light intensity difference into a specific concentration parameter for intuitive understanding and analysis, and sets a concentration threshold. When the plume concentration trend exceeds the concentration threshold, the early warning mechanism is triggered, and an early warning signal is sent to the operator through sound and light, reminding the operator to take timely measures to deal with potential environmental risks. The pre-processed light intensity data is extracted from the database of the plume monitoring and early warning center, and the extracted light intensity data is parsed to obtain the timestamp, sensor number and light intensity value information. The physical characteristics of the plume are analyzed, including its formation, movement patterns and influencing factors, and the potential relationship between light intensity and particulate matter concentration in the plume is clarified. A large amount of plume-related data, including light intensity data and particulate matter concentration data, is collected and integrated to obtain a plume concentration dataset. The plume concentration dataset is divided into a training set and a validation set. The training set is used to train the model, and the validation set is used to evaluate the performance of the model. The training set data is used to train the neural network model to obtain a concentration analysis model. The trained model is validated using the validation set data to evaluate the performance of the model. Adjust and optimize the model to improve its generalization ability and prediction accuracy. Deploy a concentration analysis model to analyze the physical properties of the plume and the relationship between light intensity and concentration. Correlate changes in light intensity with the concentration of particulate matter in the plume. Input light intensity data into the concentration analysis model to calculate the corresponding plume concentration value. Verify the calculated plume concentration value to ensure it is within a reasonable range and conforms to physical laws. Set a plume concentration threshold of 0.2 based on comprehensive safety requirements and historical data. Continuously monitor the calculated plume concentration value and compare it with the set concentration threshold to determine whether the plume concentration value exceeds the concentration threshold. When the monitored plume concentration value exceeds the concentration threshold, trigger the early warning mechanism, and send a warning signal to the operator through sound and light methods (buzzer, LED indicator light) to ensure the visibility and timeliness of the warning signal so that the operator can take quick action. Record the time, concentration value, and sensor number of the warning. Furthermore, the calculation formula of the plume concentration value is: ; Among them, C is the plume concentration value, I is the light intensity value, which are input variables. is the reference light intensity value, which is a constant representing the light intensity when there is no plume influence. k is a proportional constant used to adjust the speed of exponential decay. The value range of C is between 0 and 1. It means that as the light intensity I increases, the plume concentration value C will decay exponentially, simulating the absorption and scattering effect of particulate matter in the plume on light. It is used to simulate the nonlinear relationship of the change of plume concentration value when the light intensity increases, which increases the complexity and adaptability of the model. Used to adjust the output range of the formula to ensure that when the light intensity I is close to or exceeds the reference value When I is close to 0, C is close to 0, indicating that there is almost no plume. As I increases, C increases, but the rate of increase gradually slows down. When I is much larger than When , C tends to an upper limit, but never exceeds 1, reflecting the physical limitation between light intensity and plume concentration; The time record tracking module is used to record the time series data of plume concentration changes, including the starting time, peak time and dissipation time of the concentration change, providing the necessary time information for evaluating the plume dissipation time. It extracts the pre-processed light intensity data from the database and adds a timestamp to each light intensity data record to ensure the temporal nature of the data. The timestamp is key information to ensure data accuracy and traceability, facilitating the tracking of the dynamic process of plume concentration changes. The light intensity data is integrated into the concentration analysis model to calculate the plume concentration value, and the plume concentration value data is processed and analyzed to identify the starting point, peak point and dissipation point of the concentration change. Among them, for the identification of the starting point, the starting time is determined by detecting the point where the concentration value starts to rise significantly from a stable state. For the identification of the peak point, the point where the concentration value reaches the maximum value is found, and the time corresponding to this point is the peak time. For the identification of the dissipation point, the point where the concentration value starts to drop significantly from the peak and tends to be stable is identified to determine the dissipation time. After identifying the starting point, peak point and dissipation point of the concentration change, time series data containing the starting time, peak time and dissipation time is generated, and the generated time series data is stored in the database for subsequent analysis and query; The dissipation time assessment module uses statistical methods to estimate the dissipation time of the plume based on the data provided by the time recording and tracking module, which helps to predict the duration and scope of the plume's impact on the environment; The environmental interference assessment module analyzes the degree of interference of the plume residence time on the deep-sea mining environment based on the assessed plume dissipation time, and warns of potential environmental risks.

[0021] Example 2, as Figure 3 、 Figure 4 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in the dissipation time evaluation module, the evaluation process of the plume dissipation time is: Extract the time series data of plume concentration changes recorded by the time record tracking module from the database, including the starting time, peak time, and dissipation time of the concentration change. Use chart tools to visualize the trend of plume concentration changes over time, which helps to identify patterns, trends, and anomalies in the data. Apply time series analysis methods to smooth the data to identify long-term trends. Analyze the overall trend of plume concentration changes over time. Combine the plume concentration value with the time series data of plume concentration changes to determine the time point when the plume concentration starts to decrease from the peak value and the time point when the plume concentration decreases to the plume concentration value. Calculate the time difference between the two time points to obtain the plume dissipation time. Store the calculated plume dissipation time in the database and generate a report for subsequent analysis and decision support. The report includes a chart of plume concentration changes and statistical data on dissipation time. Furthermore, the calculation formula for the plume dissipation time is: ; in, represents the plume dissipation time, represents the peak value of the plume concentration, represents the plume concentration value at any time point t, C represents the plume concentration value, that is, when the plume concentration drops below this value, the plume is considered to have dissipated. It is used to simulate the relative change of the concentration value from the peak value to any time point t, reflecting the attenuation characteristics of the plume concentration. Used to adjust the output range of the formula to ensure that when the plume concentration When approaching trend value C, the dissipation time The changes are smoother and limit The maximum value of does not exceed 1. near hour, Close to 0, indicating that the plume has not yet begun to dissipate significantly. The decrease, Increase, when When approaching C, Reaching the maximum value indicates that the plume has almost completely dissipated, ensuring The value range of is between 0 and 1, which is consistent with the physical meaning of the plume dissipation time; In the environmental interference assessment module, the analysis process of the degree of interference of plume residence time with the deep-sea mining environment is as follows: The residence time and diffusion range of the plume in the deep-sea environment are analyzed based on the plume dissipation time. The environmental interference coefficient is calculated by combining the plume concentration value and the plume dissipation time. Based on the size of the environmental interference coefficient, the degree of interference of the plume residence time on the deep-sea mining environment is quantitatively analyzed, and the impact of the plume on the environment is evaluated. Based on the assessment results of the impact of the plume on the environment, the interference level is divided into different warning levels, namely low warning level, medium warning level and high warning level. In combination with the environmental interference coefficient, the corresponding warning threshold is matched for each warning level. According to the determined warning level, corresponding warning measures and suggestions are generated, including adjusting mining operations, reducing waste emissions, and strengthening environmental monitoring to prevent and mitigate the impact of deep-sea mining activities on the marine environment. Emergency plans and response mechanisms are also formulated to deal with sudden environmental incidents. Furthermore, the calculation formula of the environmental interference coefficient is: ; Where E represents the environmental interference coefficient, represents the plume dissipation time, represents the peak value of the plume concentration, C represents the plume concentration value, and g represents the proportional constant used to adjust the speed of exponential decay. It represents the regulation index, which indicates the degree of influence of the change of plume concentration on the environmental interference coefficient. The value of E ranges from 0 to 1. This indicates that as the dissipation time increases, the impact of the plume on the environment gradually weakens. Indicates the contribution of the plume concentration to the environmental interference coefficient during the process of plume concentration decreasing from the peak value to the plume concentration value, the adjustment index Used to adjust the influence of concentration change on the environmental interference coefficient. When E is close to 0, it is close to 1, indicating that the plume has the greatest impact on the environment. As the value of E increases, E decreases gradually, indicating that the impact of the plume on the environment weakens over time. The larger the value of , the more significant the impact of plume concentration changes on the environmental interference coefficient; Multiple warning levels correspond to multiple warning thresholds, where the warning thresholds include an upper threshold and a lower threshold. The multiple warning levels and the multiple warning thresholds satisfy the following relationship: Low alert level ; Medium warning level ; High alert level ; Where E is the environmental interference coefficient, is the lower threshold corresponding to the medium warning level and the upper threshold corresponding to the low warning level, is the lower threshold corresponding to the high warning level and the upper threshold corresponding to the medium warning level, =0.3, =0.7.

[0022] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A deep-sea mining plume monitoring and early warning system based on infrared sensors, including a plume monitoring and early warning center, characterized by: The plume monitoring and early warning center is communicatively connected to an infrared sensor array module, a data acquisition and processing module, a concentration analysis and early warning module, a time recording and tracking module, a dissipation time assessment module, and an environmental interference assessment module, wherein electrical signals are connected between the modules; The infrared sensor array module is composed of two infrared sensors and is used to detect the light intensity in the deep sea environment; The data acquisition and processing module is used to collect the light intensity data output by the infrared sensor array module and perform pre-processing operations such as filtering and denoising; The concentration analysis and warning module analyzes the plume concentration trend based on the pre-processed light intensity data and sets a concentration threshold. When the plume concentration trend exceeds the concentration threshold, the warning mechanism is triggered; The time recording and tracking module is used to record the time series data of the changes in plume concentration; The dissipation time evaluation module uses statistical methods to evaluate the dissipation time of the plume based on the data provided by the time recording and tracking module; The environmental interference assessment module analyzes the degree of interference of the plume residence time on the deep-sea mining environment based on the assessed plume dissipation time, and warns of potential environmental risks.

2. The deep-sea mining plume monitoring and early warning system based on infrared sensors according to claim 1, characterized in that: In the infrared sensor array module, the process of detecting light intensity in the deep sea environment is as follows: The infrared sensor mounting bracket is made of sturdy and durable materials to ensure the sensor remains stable in the deep-sea high-pressure and strong water current environment. The infrared sensor is fixed to the bracket and, based on the characteristics of the deep-sea environment and the path of the plume, the two infrared sensors are divided into the first infrared sensor and the second infrared sensor at predetermined positions near the mining equipment; Before formal use, adjust the sensitivity of the infrared sensor, and adjust the sensor's range and resolution to set the sensor's measurement range. Use a light source with known light intensity to verify the sensor. The emitter in the first infrared sensor is used to continuously emit infrared rays, and the receiver in the second infrared sensor is used to receive the transmitted infrared light, and the receiver converts the received infrared light into an electrical signal.

3. The deep-sea mining plume monitoring and early warning system based on infrared sensors according to claim 2, characterized in that: In the data acquisition and processing module, the collection and preprocessing process of light intensity data is as follows: The data acquisition and processing module is connected to the infrared sensor array module through a communication interface, receives the light intensity data output by the infrared sensor array module through the communication interface, and formats the received raw data into a unified format including a timestamp, a sensor number, and a light intensity value field; Preprocessing the collected light intensity data includes filtering, denoising, and data smoothing. A digital filter is applied to the light intensity data to remove high-frequency noise and interference. A denoising algorithm is used to further remove noise and outliers in the data. The filtered and denoised data are then smoothed. A database is constructed in the plume monitoring and early warning center, and the preprocessed light intensity data is uploaded to the plume monitoring and early warning center through a communication protocol and stored in the database of the plume monitoring and early warning center.

4. The deep-sea mining plume monitoring and early warning system based on infrared sensors according to claim 3 is characterized by: In the concentration analysis and early warning module, the analysis process of plume concentration trend is as follows: Extracting pre-processed light intensity data from the database of the plume monitoring and early warning center, parsing the extracted light intensity data, and obtaining timestamp, sensor number, and light intensity value information; Analyze the physical characteristics of the plume, including its formation, movement patterns, and influencing factors, clarify the potential relationship between light intensity and particulate matter concentration in the plume, and collect plume-related data, including light intensity data and particulate matter concentration data, to integrate them into a plume concentration dataset; The plume concentration dataset is divided into a training set and a validation set. The training set is used to train the model, and the validation set is used to evaluate the performance of the model. The neural network model is trained using the training set data to obtain a concentration analysis model. The trained model is validated using the validation set data to evaluate the performance of the model. Based on the validation results, the model is adjusted and optimized. Deploy a concentration analysis model to analyze the physical properties of the plume and the relationship between light intensity and concentration. Correlate changes in light intensity with the concentration of particulate matter in the plume. Input light intensity data into the concentration analysis model to calculate the corresponding plume concentration value. Verify the calculated plume concentration value. The concentration threshold of the plume is set at 0.2 based on comprehensive safety requirements and historical data. The calculated plume concentration value is continuously monitored and compared with the set concentration threshold to determine whether the plume concentration value exceeds the concentration threshold; When the monitored plume concentration value exceeds the concentration threshold, the early warning mechanism is triggered, and an early warning signal is sent to the operator through sound and light, and the time, concentration value, and sensor number information of the early warning are recorded.

5. The deep-sea mining plume monitoring and early warning system based on infrared sensors according to claim 4 is characterized by: The calculation formula for the plume concentration value is: ； Where C is the plume concentration value, I is the light intensity value, is the baseline light intensity value, which is a constant representing the light intensity when there is no plume influence. k is a proportional constant used to adjust the speed of exponential decay. The value range of C is between 0 and 1.

6. The deep-sea mining plume monitoring and early warning system based on infrared sensors according to claim 5, characterized in that: In the time recording and tracking module, the recording process of the plume concentration change time series data is as follows: Extract pre-processed light intensity data from the database and add a timestamp to each light intensity data record; Integrate the light intensity data into the concentration analysis model to calculate the plume concentration value, and process and analyze the plume concentration value data to identify the starting point, peak point and dissipation point of the concentration change; After identifying the starting point, peak point, and dissipation point of the concentration change, time series data including the starting time, peak time, and dissipation time are generated, and the generated time series data are stored in a database.

7. The deep-sea mining plume monitoring and early warning system based on infrared sensors according to claim 6, characterized in that: In the dissipation time evaluation module, the evaluation process of the plume dissipation time is: Extract the time series data of plume concentration changes recorded by the time record tracking module from the database, including the start time, peak time and dissipation time of the concentration change; Use charting tools to visualize the temporal trends of plume concentrations, apply time series analysis methods to smooth the data to identify long-term trends, and analyze the overall temporal trends of plume concentrations; Combining the plume concentration value and the time series data of the plume concentration change, determine the time point when the plume concentration begins to decrease from the peak value and the time point when the plume concentration decreases to the plume concentration value. Calculate the time difference between the two time points to obtain the plume dissipation time. The calculated plume dissipation time is stored in a database and a report is generated, which includes a graph of the plume concentration change and statistical data of the dissipation time.

8. The deep-sea mining plume monitoring and early warning system based on infrared sensors according to claim 7, characterized in that: The calculation formula for the plume dissipation time is: ； in, represents the plume dissipation time, represents the peak value of the plume concentration, represents the plume concentration value at any time point t, and C represents the plume concentration value.

9. The deep-sea mining plume monitoring and early warning system based on infrared sensors according to claim 8, characterized in that: In the environmental interference assessment module, the analysis process of the degree of interference of plume residence time with the deep-sea mining environment is as follows: The residence time and diffusion range of the plume in the deep sea environment are analyzed according to the plume dissipation time, and the environmental interference coefficient is calculated by combining the plume concentration value and the plume dissipation time. Based on the size of the environmental interference coefficient, the degree of interference of the plume residence time on the deep-sea mining environment is quantitatively analyzed to assess the impact of the plume on the environment; Based on the assessment results of the impact of the plume on the environment, the interference degree is divided into different warning levels: low warning level, medium warning level, and high warning level. In combination with the environmental interference coefficient, the corresponding warning threshold is matched for each warning level. Based on the determined warning level, corresponding warning measures and suggestions are generated, including adjusting mining operations, reducing waste emissions, strengthening environmental monitoring, and formulating emergency plans and response mechanisms to deal with sudden environmental incidents.

10. The deep-sea mining plume monitoring and early warning system based on infrared sensors according to claim 9, characterized in that: The calculation formula of the environmental interference coefficient is: ； Where E represents the environmental interference coefficient, represents the plume dissipation time, represents the peak value of the plume concentration, C represents the plume concentration value, and g represents the proportional constant used to adjust the speed of exponential decay. represents the regulation index, which indicates the degree of influence of the change of plume concentration on the environmental interference coefficient. The value of E ranges from 0 to 1. The plurality of warning levels correspond to the plurality of warning thresholds, wherein the warning thresholds include an upper threshold and a lower threshold, and the plurality of warning levels and the plurality of warning thresholds satisfy the following relationship: Low alert level ; Medium warning level ; High alert level ; Where E is the environmental interference coefficient, is the lower threshold corresponding to the medium warning level and the upper threshold corresponding to the low warning level, is the lower threshold corresponding to the high warning level and the upper threshold corresponding to the medium warning level, =0.3, =0.7.

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