A pressure tank overload protection method and protection system

Abstract

The application relates to the technical field of control systems, and discloses a pressure tank overload protection method and a protection system. The method comprises the following steps: collecting a real-time pressure signal of a pressure tank, performing standardization processing on the real-time pressure signal to obtain a standard pressure data sequence of the pressure tank; constructing a pressure state image of the pressure tank according to a pressure change trend and a pressure fluctuation feature in the standard pressure data sequence; performing hierarchical evaluation on an overload risk of the pressure tank based on the pressure state image to obtain a risk grade mark of the pressure tank; comparing the risk grade mark with a preset preventive intervention threshold value, and generating a hierarchical control instruction of the pressure tank according to a comparison result; and executing the hierarchical control instruction to coordinately control an air intake adjusting component and a pressure relief component connected with the pressure tank. The application can improve the efficiency of pressure tank overload protection.

Description

Technical Field

[0001] This invention relates to the field of control system technology, and in particular to a method and system for overload protection of pressure tanks. Background Technology

[0002] Existing technologies have significant shortcomings in the data processing and status identification stages of pressure tank overload protection. The collected real-time pressure signals are not systematically and standardized; they are simply filtered or used directly, resulting in severe interference from abnormal noise and an inability to form accurate pressure data sequences. Furthermore, a comprehensive pressure status profile is not constructed by combining pressure change trends and fluctuation characteristics; relying solely on a single pressure value to determine the status makes it difficult to capture the dynamic characteristics and potential risk factors of pressure tank operation. This results in a lack of scientific data support for overload risk assessment and an inability to predict potential overload hazards in advance.

[0003] Existing technologies have significant shortcomings in the risk assessment and control execution stages of pressure tank overload protection. They fail to conduct hierarchical assessments of overload risks, relying solely on a single overload threshold for simple judgment, which fails to differentiate the severity and urgency of risks, resulting in a lack of targeted protection responses. Furthermore, they do not generate tiered control commands based on risk levels, employing only fixed-intensity interventions, which easily leads to over- or under-intervention. During control execution, the intake regulation and pressure relief components are not coordinated, with only one component being operated, and the lack of pressure feedback and reset mechanisms prevents closed-loop control of the protection process. This results in poor accuracy and reliability of overload protection, making it difficult to ensure the safe and stable operation of the pressure tank. Summary of the Invention

[0004] This invention provides a method and system for overload protection of pressure tanks to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for overload protection of a pressure tank, comprising:

[0006] S1. Acquire the real-time pressure signal of the pressure tank, and perform standardization processing on the real-time pressure signal to obtain the standard pressure data sequence of the pressure tank;

[0007] S2. Based on the pressure change trend and pressure fluctuation characteristics in the standard pressure data sequence, construct a pressure state profile of the pressure tank;

[0008] S3. Based on the pressure state profile, the overload risk of the pressure tank is assessed hierarchically to obtain the risk level identifier of the pressure tank.

[0009] S4. Compare the risk level identifier with the preset preventive intervention threshold, and generate a graded control instruction for the pressure tank based on the comparison result.

[0010] S5. Execute the graded control command to coordinate the control of the air intake regulating component and the pressure relief component connected to the pressure tank.

[0011] In a preferred embodiment, the step of acquiring the real-time pressure signal of the pressure tank and standardizing the real-time pressure signal to obtain a standard pressure data sequence of the pressure tank includes:

[0012] The real-time pressure signal of the pressure tank is acquired, and abnormal noise data in the real-time pressure signal is removed to obtain the effective pressure data sequence of the pressure tank.

[0013] The effective pressure data sequence is subjected to moving average filtering to obtain a smoothed pressure sequence of the pressure tank;

[0014] Linear compensation is applied to the smoothed pressure sequence to obtain the corrected pressure signal sequence of the pressure tank.

[0015] The corrected pressure signal sequence is normalized to obtain the standard pressure data sequence of the pressure tank.

[0016] In a preferred embodiment, constructing a pressure state profile of the pressure tank based on the pressure change trend and pressure fluctuation characteristics in the standard pressure data sequence includes:

[0017] The time-domain features of the standard pressure data sequence are extracted to obtain the trend feature sequence of the pressure tank;

[0018] Fluctuation characteristic analysis is performed on the standard pressure data sequence to obtain the fluctuation characteristic set of the pressure tank;

[0019] The trend feature sequence and the fluctuation feature set are fused to obtain the multidimensional state vector of the pressure tank.

[0020] The multidimensional state vector is abstracted and mapped to obtain a pressure state profile of the pressure tank.

[0021] In a preferred embodiment, the step of abstracting and mapping the multidimensional state vector to obtain a pressure state profile of the pressure tank includes:

[0022] The time-frequency domain feature analysis of the standard pressure data sequence is performed to obtain the deterministic trend component and random fluctuation component of the pressure tank.

[0023] The deterministic trend component is input into the historical process stage knowledge base of the pressure tank, and the current operating stage of the pressure tank is identified by matching.

[0024] Based on the multi-dimensional dynamic association library of the pressure tank, the similarity of the random fluctuation components is measured to obtain the dominant load pattern of the pressure tank.

[0025] Based on the current operating stage and the dominant load mode, retrieve the corresponding state mapping rule set from the multi-dimensional dynamic association library;

[0026] Based on the state mapping rule set, the deterministic trend component, the random fluctuation component, the current operating stage, and the dominant load mode are fused and reconstructed to obtain the pressure state profile of the pressure tank.

[0027] In a preferred embodiment, the step of hierarchically assessing the overload risk of the pressure tank based on the pressure state profile to obtain a risk level identifier for the pressure tank includes:

[0028] Extract the steady-state characteristic values ​​that characterize pressure stability, the trend characteristic values ​​that characterize the rate of pressure change, and the fluctuation characteristic values ​​that characterize the pressure fluctuation pattern from the pressure state profile.

[0029] Based on the current operating stage and the dominant load mode, the risk factor weight coefficients corresponding to the pressure tank are determined, and the overload risk of the pressure tank is assessed in a hierarchical manner to obtain the comprehensive risk evaluation value of the pressure tank.

[0030] The comprehensive risk assessment value is mapped to the risk level range of the pressure tank to obtain the risk level identifier of the pressure tank.

[0031] In a preferred embodiment, the formula for calculating the comprehensive risk assessment value is as follows:

[0032] ;

[0033] In the formula, The comprehensive risk assessment value is... The weighting coefficient of the first risk factor is determined based on the current operating phase and dominant load pattern. The steady-state deviation is calculated based on the aforementioned steady-state eigenvalues. The weighting coefficient of the second risk factor is determined based on the current operating phase and dominant load pattern. The trend threat level is calculated based on the aforementioned trend characteristic values. The weighting coefficient of the third risk factor is determined based on the current operating phase and dominant load pattern. The fluctuation anomaly degree is calculated based on the fluctuation characteristic value.

[0034] In a preferred embodiment, comparing the risk level identifier with a preset preventive intervention threshold and generating a graded control instruction for the pressure tank based on the comparison result includes:

[0035] Receive the risk level identifier, which includes risk level information representing the severity of the risk and risk trend information representing the urgency of the risk;

[0036] Obtain preset preventive intervention thresholds, wherein the preventive intervention thresholds include a first threshold set corresponding to the risk level information and a second threshold set corresponding to the risk trend information;

[0037] The risk level information is matched and compared with the first threshold set, and the risk trend information is matched and compared with the second threshold set to obtain the level comparison result and trend comparison result of the pressure tank;

[0038] Based on the hierarchical comparison results and the trend comparison results, the intervention type and intensity of the pressure vessel are determined;

[0039] Based on the intervention type and the intervention intensity, a hierarchical control instruction is generated that includes the specific action object, action direction, and action sequence.

[0040] In a preferred embodiment, determining the intervention type and intensity of the pressure vessel based on the hierarchical comparison results and the trend comparison results includes:

[0041] When the risk level information indicates high risk and the risk trend information indicates that the risk is escalating, it is determined that a strong intervention, primarily aimed at relieving pressure, is required.

[0042] When the risk level information indicates medium risk and the risk trend information indicates that the risk is stabilizing, it is determined that a smooth intervention, mainly adjusting the air intake, is required.

[0043] In a preferred embodiment, executing the graded control command to coordinate the control of the intake regulating component and the pressure relief component connected to the pressure tank includes:

[0044] The graded control command is parsed to obtain the intake regulation strategy identifier and the pressure relief strategy identifier corresponding to the risk level identifier;

[0045] Based on the intake regulation strategy identifier, a first drive command for the pressure tank is generated and sent to the intake regulation component to control the intake regulation component to perform operations to reduce and interrupt the intake airflow;

[0046] Based on the pressure relief strategy identifier, a second drive command for the pressure tank is generated and sent to the pressure relief component to control the pressure relief component to perform the operation of opening the relief channel;

[0047] After the pressure relief component performs the operation of opening the relief channel, the feedback pressure data of the pressure tank is obtained;

[0048] When the feedback pressure data meets the preset safety threshold conditions, a reset command for the pressure tank is generated, and the pressure relief component is controlled to close the relief channel based on the reset command.

[0049] To address the above problems, the present invention also provides a pressure tank overload protection system, the system comprising:

[0050] The data acquisition and processing module is used to acquire the real-time pressure signal of the pressure tank, perform standardization processing on the real-time pressure signal, and obtain the standard pressure data sequence of the pressure tank.

[0051] The status profile construction module is used to construct a pressure status profile of the pressure tank based on the pressure change trend and pressure fluctuation characteristics in the standard pressure data sequence.

[0052] The risk classification and assessment module is used to perform a hierarchical assessment of the overload risk of the pressure tank based on the pressure state profile, and obtain the risk level identifier of the pressure tank.

[0053] The control decision generation module is used to compare the risk level identifier with a preset preventive intervention threshold, and generate a graded control instruction for the pressure tank based on the comparison result.

[0054] The execution coordination control module is used to execute the hierarchical control commands and coordinate the control of the air intake regulating component and the pressure relief component connected to the pressure tank.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. This invention provides reliable data support for pressure tank overload protection through precise data processing and comprehensive condition characterization. After acquiring real-time pressure signals, a standard pressure data sequence is generated through anomaly removal, filtering, compensation, and normalization. Based on this sequence, trend and fluctuation characteristics are extracted, and a pressure state profile is constructed by integrating the current operating stage and dominant load mode, comprehensively capturing the dynamic operation of the pressure tank and laying a solid foundation for risk assessment.

[0057] 2. This invention significantly improves the efficiency and safety of overload protection by employing hierarchical risk assessment and precise graded control. Multi-dimensional feature values ​​are extracted based on pressure state profiles, and weighting coefficients are determined by combining operating stages and load modes. Hierarchical assessment yields risk level identifiers. Targeted graded control commands are generated based on the risk level, coordinating the control of intake regulation and pressure relief components. Closed-loop control is achieved through pressure feedback, enabling rapid response to different overload risks and ensuring stable operation of the pressure tank. Attached Figure Description

[0058] Figure 1 This is a schematic flowchart of a pressure tank overload protection method according to an embodiment of the present invention;

[0059] Figure 2 This is a functional block diagram of a pressure tank overload protection system provided in an embodiment of the present invention;

[0060] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0061] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0062] This application provides a method for overload protection of pressure tanks. The execution subject of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for overload protection of pressure tanks can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0063] Reference Figure 1 The diagram shown is a flowchart illustrating a pressure tank overload protection method according to an embodiment of the present invention. In this embodiment, the pressure tank overload protection method includes:

[0064] S1. Acquire the real-time pressure signal of the pressure tank, and perform standardization processing on the real-time pressure signal to obtain the standard pressure data sequence of the pressure tank;

[0065] In this embodiment of the invention, the step of acquiring the real-time pressure signal of the pressure tank and standardizing the real-time pressure signal to obtain a standard pressure data sequence of the pressure tank includes:

[0066] The real-time pressure signal of the pressure tank is acquired, and abnormal noise data in the real-time pressure signal is removed to obtain the effective pressure data sequence of the pressure tank.

[0067] The effective pressure data sequence is subjected to moving average filtering to obtain a smoothed pressure sequence of the pressure tank;

[0068] Linear compensation is applied to the smoothed pressure sequence to obtain the corrected pressure signal sequence of the pressure tank.

[0069] The corrected pressure signal sequence is normalized to obtain the standard pressure data sequence of the pressure tank.

[0070] Pressure sensors continuously capture pressure changes inside the pressure tank, generating a continuous real-time pressure signal. Based on the pressure fluctuation range under normal operating conditions, a fixed valid data judgment interval is set. Each data point in the real-time pressure signal is screened one by one, and abnormal noise data such as sudden pulse data and irregular fluctuation interference data that exceed the interval are all removed. Only data that are within the valid interval and conform to the normal pressure change pattern are retained, ultimately obtaining the valid pressure data sequence of the pressure tank.

[0071] Starting from the beginning of the effective pressure data sequence, according to a preset fixed data window length, continuous data segments of corresponding lengths are sequentially extracted. The arithmetic mean of all data within each segment is calculated, and this mean is used as the output data corresponding to the middle position of the data window. The data window is then moved forward by one data point, and the above extraction and calculation process is repeated until all data in the effective pressure data sequence is covered. This continuous sliding calculation method cancels out high-frequency fluctuations in the data, resulting in a smooth pressure sequence for the pressure tank.

[0072] The systematic error characteristics of the pressure sensor under different pressure values ​​were pre-calibrated through experiments, and a fixed linear compensation model was established. This model clearly defines the magnitude and direction of the compensation required in different pressure ranges. Each data point in the smoothed pressure sequence was substituted into this linear compensation model, and corresponding compensation corrections were applied according to the pressure range in which the data was located. This eliminated linear errors caused by factors such as insufficient sensor accuracy and installation deviations, resulting in the corrected pressure signal sequence of the pressure tank.

[0073] The maximum and minimum values ​​in the corrected pressure signal sequence are determined, and the difference between them is calculated as the data fluctuation range. The minimum value of the sequence is subtracted from each data point in the corrected pressure signal sequence, and then divided by the data fluctuation range. Through this uniform scaling process, all data points are mapped to a fixed numerical range, giving the data a unified dimension and a comparable basis, ultimately yielding the standard pressure data sequence for the pressure tank.

[0074] The beneficial effects are that by collecting real-time pressure signals from pressure tanks and removing abnormal noise data, effective data that conforms to the normal pressure change pattern can be accurately selected, avoiding pressure data distortion caused by noise interference, providing clean and reliable basic data for subsequent standardization processing, and ensuring the accuracy of the final standard pressure data sequence.

[0075] The effective pressure data sequence is processed by moving average filtering. By continuously moving the average value of the data segments, high-frequency fluctuation components are offset, resulting in a smooth pressure sequence. This eliminates the interference of irregular fluctuations in the data, clearly presents the core trend of pressure changes, and improves the stability and analyzability of the data.

[0076] Linear compensation is performed on the smooth pressure sequence, and targeted corrections are made based on the error law of the sensor system to eliminate linear errors caused by factors such as insufficient sensor accuracy and installation deviation, so as to obtain a corrected pressure signal sequence, which further improves the measurement accuracy of pressure data and closely matches the actual pressure state of the pressure tank.

[0077] The corrected pressure signal sequence is normalized to map all data to a fixed numerical range, eliminating the dimensional differences caused by different pressure ranges, giving the data a unified basis for comparison, and finally forming a standard pressure data sequence. This provides standardized and normalized data support for subsequent pressure status profiling and overload risk assessment, ensuring the quality of analysis and decision-making in subsequent stages.

[0078] S2. Based on the pressure change trend and pressure fluctuation characteristics in the standard pressure data sequence, construct a pressure state profile of the pressure tank;

[0079] In this embodiment of the invention, constructing a pressure state profile of the pressure tank based on the pressure change trend and pressure fluctuation characteristics in the standard pressure data sequence includes:

[0080] The time-domain features of the standard pressure data sequence are extracted to obtain the trend feature sequence of the pressure tank;

[0081] Fluctuation characteristic analysis is performed on the standard pressure data sequence to obtain the fluctuation characteristic set of the pressure tank;

[0082] The trend feature sequence and the fluctuation feature set are fused to obtain the multidimensional state vector of the pressure tank.

[0083] The multidimensional state vector is abstracted and mapped to obtain a pressure state profile of the pressure tank.

[0084] The abstract mapping of the multidimensional state vector to obtain the pressure state profile of the pressure tank includes:

[0085] The time-frequency domain feature analysis of the standard pressure data sequence is performed to obtain the deterministic trend component and random fluctuation component of the pressure tank.

[0086] The deterministic trend component is input into the historical process stage knowledge base of the pressure tank, and the current operating stage of the pressure tank is identified by matching.

[0087] Based on the multi-dimensional dynamic association library of the pressure tank, the similarity of the random fluctuation components is measured to obtain the dominant load pattern of the pressure tank.

[0088] Based on the current operating stage and the dominant load mode, retrieve the corresponding state mapping rule set from the multi-dimensional dynamic association library;

[0089] Based on the state mapping rule set, the deterministic trend component, the random fluctuation component, the current operating stage, and the dominant load mode are fused and reconstructed to obtain the pressure state profile of the pressure tank.

[0090] The standard pressure data sequence is segmented chronologically, with each segment corresponding to a fixed time interval. The overall direction and magnitude of pressure change within each segment are calculated to determine whether the data is rising, falling, or stable, and the degree of change under each state is quantified. The change states and quantification results of each time period are arranged chronologically to form a continuous characteristic sequence reflecting the evolution of pressure over time, thus obtaining the trend characteristic sequence of the pressure tank.

[0091] The difference between each data point and its adjacent data points in the standard pressure data sequence is calculated to obtain the pressure change between adjacent data points. These changes are then used to statistically analyze the intensity of pressure fluctuations over the overall time period. Simultaneously, the maximum and minimum values ​​among all changes are determined to clarify the extreme range of pressure fluctuations. The average level of all changes is then calculated to understand the overall intensity of the fluctuations. By integrating information reflecting pressure fluctuation characteristics, such as fluctuation intensity, extreme range, and average intensity, a fluctuation characteristic set for the pressure tank is obtained.

[0092] A fixed feature fusion framework is established, which includes the corresponding association rules for trend features and fluctuation features, and clarifies the combination method of the two types of features. The feature data corresponding to each time segment in the trend feature sequence is matched one by one with the fluctuation characteristic data corresponding to the fluctuation feature set. The matched feature data are integrated and spliced ​​according to the association rules to form a comprehensive feature data set that simultaneously contains the time evolution pattern and fluctuation characteristics. Each set corresponds to the complete state description of the pressure tank at a specific stage, resulting in a multidimensional state vector of the pressure tank.

[0093] The system pre-defines the descriptive dimensions and expression standards for the pressure state profile. The descriptive dimensions cover core aspects such as pressure change patterns, fluctuation stability, and sustained state characteristics. The expression standards clearly define the standardized descriptive language for various states. Each feature data in the multi-dimensional state vector is mapped to a pre-descriptive dimension. Based on the specific characteristics of the feature data, it is transformed into intuitive and easy-to-understand state description information according to the expression standards. All descriptive information is then systematically integrated to form a comprehensive description that fully and accurately reflects the real-time operating status of the pressure tank, resulting in the pressure state profile of the pressure tank.

[0094] A comprehensive time-frequency domain feature analysis is performed on the standard pressure data sequence. By sorting out the continuous change pattern of the data in the time dimension, the component that is not subject to random interference and shows a fixed trend is separated. This component is the deterministic trend component of the pressure tank. At the same time, the fluctuation part in the data that does not have a fixed pattern and is caused by random factors is captured and extracted independently to obtain the random fluctuation component of the pressure tank.

[0095] The historical process stage knowledge base of the pressure tank stores standard deterministic trend components corresponding to different operating stages, as well as key information such as process characteristics and pressure change patterns of each stage. The analyzed deterministic trend components are compared one by one with the standard trend components of each stage in the knowledge base. By analyzing the degree of fit between the two in terms of change direction and pace, the historical process stage that perfectly matches the current deterministic trend component is accurately identified; this stage is the current operating stage of the pressure tank.

[0096] The multi-dimensional dynamic correlation library contains random fluctuation characteristics corresponding to various typical load patterns, each with a clear fluctuation pattern and characteristic description. The extracted random fluctuation components are comprehensively compared with the fluctuation characteristics of each typical load pattern in the correlation library. By measuring the similarity between the two in terms of fluctuation frequency, fluctuation amplitude, and fluctuation duration, the typical load pattern with the highest similarity is determined, which is the dominant load pattern of the pressure tank.

[0097] The multi-dimensional dynamic association library pre-stores state mapping rule sets corresponding to different operating stages and various dominant load mode combinations. Each rule set clearly defines the feature fusion method and state description specification under different parameter combinations. Based on the identified current operating stage and dominant load mode, a precise search is performed in the association library to find the rule set that completely corresponds to the combination, thus obtaining the pressure tank's state mapping rule set.

[0098] Following the fusion logic and reconstruction requirements explicitly defined in the state mapping rule set, the fixed change characteristics reflected by the deterministic trend component, the random fluctuations reflected by the random fluctuation component, the technological background corresponding to the current operating stage, and the load characteristics represented by the dominant load mode are systematically integrated. During the integration process, the priority and association methods specified in the rule set are followed to organically combine the information from each part, forming a comprehensive description that fully and accurately reflects the operating status of the pressure tank, thus obtaining a pressure state profile of the pressure tank.

[0099] The beneficial effect is that by extracting time-domain features from standard pressure data sequences, the changes in pressure over time, such as rise, fall, or stabilization, can be accurately captured, forming a trend feature sequence, which provides core trend basis for comprehensively depicting the dynamic operation of pressure tanks.

[0100] Conduct fluctuation characteristic analysis of standard pressure data sequences, systematically sort out key characteristics such as the density, extreme range and average intensity of pressure fluctuations, form a fluctuation characteristic set, supplement the detailed information of the pressure tank's operating status, and avoid the one-sided state characterization caused by only focusing on trends.

[0101] By fusing trend feature sequences with fluctuation feature sets, and integrating macro-trend and micro-fluctuation information of pressure changes, a multi-dimensional state vector is formed, enabling a multi-dimensional and three-dimensional description of the pressure tank's operating status, thus laying a comprehensive data foundation for subsequent abstract mapping.

[0102] By abstracting and mapping multidimensional state vectors, scattered feature data are transformed into intuitive and standardized comprehensive state descriptions, resulting in a pressure state profile of the pressure tank. This clearly presents the operating mode, stability, and potential change tendencies of the pressure tank, providing accurate and easy-to-understand state support for overload risk assessment and improving the scientific nature and accuracy of risk prediction.

[0103] Time-frequency domain feature analysis is performed on standard pressure data sequences to accurately separate deterministic trend components that reflect fixed change patterns and random fluctuation components that reflect random disturbances. This comprehensively captures the core features and interference factors of pressure data, providing accurate basic data for subsequent state identification.

[0104] By matching deterministic trend components with a knowledge base of historical process stages, and leveraging mature historical process data, the current operating stage of the pressure tank can be accurately identified, clarifying the process background of pressure changes, avoiding judgments of status detached from actual operating scenarios, and improving the accuracy of status identification.

[0105] By using a multi-dimensional dynamic association library to measure the similarity of random fluctuation components, the dominant load pattern that best matches the current fluctuation characteristics is identified, and the core causes of pressure fluctuations are clearly located, providing key information for a comprehensive characterization of the pressure tank's operating status.

[0106] Based on the current operating stage and the dominant load mode, the corresponding state mapping rule set is retrieved to provide a standardized and normalized integration basis for multi-dimensional information fusion, ensuring that the fusion process is logically rigorous and has a clear direction, and avoiding information integration chaos.

[0107] According to the state mapping rule set, the deterministic trend component, random fluctuation component, current operating stage and dominant load mode are fused and reconstructed to transform the scattered features and scenario information into a comprehensive and systematic state description, thereby obtaining a pressure state profile of the pressure tank. This provides accurate and complete state support for overload risk assessment and improves the scientificity and reliability of risk assessment.

[0108] S3. Based on the pressure state profile, the overload risk of the pressure tank is assessed hierarchically to obtain the risk level identifier of the pressure tank.

[0109] In this embodiment of the invention, the step of hierarchically assessing the overload risk of the pressure tank based on the pressure state profile to obtain a risk level identifier for the pressure tank includes:

[0110] Extract the steady-state characteristic values ​​that characterize pressure stability, the trend characteristic values ​​that characterize the rate of pressure change, and the fluctuation characteristic values ​​that characterize the pressure fluctuation pattern from the pressure state profile.

[0111] Based on the current operating stage and the dominant load mode, the risk factor weight coefficients corresponding to the pressure tank are determined, and the overload risk of the pressure tank is assessed in a hierarchical manner to obtain the comprehensive risk evaluation value of the pressure tank.

[0112] The comprehensive risk assessment value is mapped to the risk level range of the pressure tank to obtain the risk level identifier of the pressure tank.

[0113] The formula for calculating the comprehensive risk assessment value is as follows:

[0114] ;

[0115] In the formula, The comprehensive risk assessment value is... The weighting coefficient of the first risk factor is determined based on the current operating phase and dominant load pattern. The steady-state deviation is calculated based on the aforementioned steady-state eigenvalues. The weighting coefficient of the second risk factor is determined based on the current operating phase and dominant load pattern. The trend threat level is calculated based on the aforementioned trend characteristic values. The weighting coefficient of the third risk factor is determined based on the current operating phase and dominant load pattern. The fluctuation anomaly degree is calculated based on the fluctuation characteristic value.

[0116] By deeply analyzing the core information in the pressure state profile, we extract characteristic indicators that directly reflect whether the pressure is continuous and stable without drastic fluctuations from the description of pressure stability. These indicators are the steady-state characteristic values ​​that characterize pressure stability. From the description of the rate of pressure change over time in the profile, we extract characteristic indicators that reflect the rate of pressure increase or decrease, obtaining trend characteristic values ​​that characterize the rate of pressure change. At the same time, from the description of pressure fluctuations recorded in the profile, we extract characteristic indicators that reflect the patterns of fluctuation frequency and amplitude distribution, forming fluctuation characteristic values ​​that characterize the patterns of pressure fluctuations.

[0117] By combining the current operating stage and dominant load mode of the pressure tank, and referring to historical operating data and overload accident cases, the impact of various load modes on overload risk under different operating stages is clarified. Factors with a significant impact on overload risk are assigned higher weight coefficients, while factors with a smaller impact are assigned lower weight coefficients, thus determining the corresponding risk factor weight coefficients for the pressure tank. The extracted steady-state characteristic values, trend characteristic values, and fluctuation characteristic values ​​are combined with their corresponding risk factor weight coefficients. Following a fixed evaluation logic, overload risk assessments are conducted progressively from different levels of risk (minor, moderate, and severe), comprehensively considering the risk contribution of each characteristic value under different weights, ultimately yielding the comprehensive risk assessment value for the pressure tank.

[0118] Based on the pressure tank's safety operation standards, overload tolerance limits, and actual application scenario requirements, multiple continuous and non-overlapping risk level intervals are pre-defined. Each interval corresponds to a clear risk level definition, including safety level, low risk level, medium risk level, and high risk level. The obtained comprehensive risk assessment value is compared with these pre-defined risk level intervals one by one to determine the specific interval to which the comprehensive risk assessment value belongs. Based on the risk level definition corresponding to the interval, a symbol or description that can clearly identify the current overload risk level of the pressure tank is generated, thus obtaining the pressure tank's risk level identifier.

[0119] The comprehensive risk assessment value is derived from the integrated calculation of steady-state deviation, trend threat, volatility anomaly, and corresponding risk factor weight coefficients. It is a comprehensive quantitative result of the pressure tank overload risk.

[0120] The weighting coefficient of the first risk factor is derived from the current operating stage and dominant load mode of the pressure tank. Combining the historical operating data and overload accident cases of the pressure tank, the influence of the dominant load mode on overload risk under the current operating stage is analyzed, and the specific value of the weighting coefficient of the first risk factor is determined accordingly.

[0121] The weighting coefficient of the second risk factor is also determined based on the current operating stage and dominant load mode of the pressure tank. By referring to past data on the strength of the effect of the pressure change rate on overload risk when the operating stage is combined with the dominant load mode, the magnitude of the weighting coefficient of the second risk factor is determined.

[0122] The weighting coefficient of the third risk factor is determined based on the current operating stage and dominant load mode of the pressure tank. The specific value of the weighting coefficient of the third risk factor is determined by analyzing the impact of pressure fluctuation patterns on overload risk in historical cases.

[0123] Steady-state deviation is derived from the steady-state characteristic value in the pressure state profile. First, the standard stable pressure range of the pressure tank in the current operating stage and under the dominant load mode is determined. The steady-state characteristic value is compared with this standard range, and the degree to which the steady-state characteristic value deviates from the standard range is calculated. This degree is the steady-state deviation.

[0124] The trend threat level is derived from the trend feature value in the stress state profile. First, determine the safe threshold of the rate of pressure change under the current operating stage and the dominant load mode. Then, analyze whether the rate of pressure change reflected by the trend feature value exceeds the safe threshold, and the extent of the exceedance or the degree close to the threshold. Based on this, the trend threat level is obtained.

[0125] The degree of fluctuation anomaly is derived from the fluctuation characteristic value in the pressure state profile. First, the normal pressure fluctuation pattern under the current operating stage and the dominant load mode is identified, including the fluctuation frequency, amplitude and other standards. The fluctuation characteristic value is compared with the standard pattern to determine the degree to which the fluctuation characteristic value deviates from the normal pattern. This degree is the degree of fluctuation anomaly.

[0126] The significance of the formula lies in comprehensively considering the three core factors affecting the overload risk of pressure tanks. By assigning the weights of the first, second, and third risk factors to the steady-state deviation, trend threat, and fluctuation anomaly, the formula ensures that the role of each risk factor is accurately reflected in different operating stages and load modes.

[0127] By combining the quantitative indicators and weighting coefficients corresponding to each risk factor, the system integrates the risk contributions from all aspects to obtain a comprehensive risk assessment value that can fully and objectively reflect the current overload risk level of the pressure tank. This provides a precise and quantitative basis for the subsequent determination of risk level identification, ensuring that the risk assessment results are scientific and consistent with actual operating scenarios.

[0128] The beneficial effects are that it can accurately extract steady-state characteristic values, trend characteristic values, and fluctuation characteristic values ​​from the pressure state profile, comprehensively covering the three core risk-related dimensions of pressure stability, rate of change, and fluctuation pattern, providing multi-dimensional and accurate feature support for overload risk assessment, and avoiding the one-sidedness of assessment caused by a single feature.

[0129] By combining the current operating stage and dominant load mode, the weight coefficients of risk factors are determined so that the risk contribution of each characteristic value is adapted to the actual operating scenario. This ensures that the hierarchical assessment is more in line with the actual working conditions of the pressure tank and improves the objectivity and pertinence of the comprehensive risk assessment value.

[0130] By gradually quantifying overload risk through a hierarchical assessment system, and integrating multi-dimensional features and scenario-based weighting coefficients to obtain a comprehensive risk assessment value, the system presents the overall level of risk, solving the problem that traditional single threshold judgment cannot distinguish the degree of risk.

[0131] The comprehensive risk assessment value is mapped to a preset risk level range, and the risk level identifier is clearly output, which intuitively presents the severity of the risk. This provides a clear and explicit decision-making basis for the generation of subsequent graded control instructions, ensuring the accuracy and efficiency of overload protection response.

[0132] The formula integrates three core risk indicators: steady-state deviation, trend threat, and fluctuation anomaly. It comprehensively covers the impact of pressure stability, rate of change, and fluctuation patterns on overload risk, avoiding the one-sidedness caused by single indicator assessment and ensuring that the comprehensive risk assessment value can fully reflect the overload risk level of the pressure tank.

[0133] The weight coefficients of the first, second, and third risk factors are determined based on the current operating stage and dominant load mode, so that the weights of each risk indicator are adapted to the actual operating scenario, solving the problem of different risk impacts under different operating conditions, making the comprehensive risk assessment value more consistent with the actual working state of the pressure tank, and improving the pertinence and objectivity of the assessment.

[0134] By using quantitative calculations, dispersed risk characteristics are transformed into a unified comprehensive risk assessment value, enabling precise quantification of overload risk. This breaks through the limitations of traditional single-threshold judgments that cannot distinguish the severity of risks, and provides a precise and comparable quantitative basis for the classification of risk levels.

[0135] The formula has clear logic and a simple calculation method. It can be directly calculated based on the feature data extracted from the stress state profile and the scenario-based weight coefficients, which facilitates rapid implementation. At the same time, it provides standardized calculation logic for hierarchical risk assessment, ensuring the consistency and reliability of the assessment results.

[0136] S4. Compare the risk level identifier with the preset preventive intervention threshold, and generate a graded control instruction for the pressure tank based on the comparison result.

[0137] In this embodiment of the invention, comparing the risk level identifier with a preset preventive intervention threshold and generating a graded control instruction for the pressure tank based on the comparison result includes:

[0138] Receive the risk level identifier, which includes risk level information representing the severity of the risk and risk trend information representing the urgency of the risk;

[0139] Obtain preset preventive intervention thresholds, wherein the preventive intervention thresholds include a first threshold set corresponding to the risk level information and a second threshold set corresponding to the risk trend information;

[0140] The risk level information is matched and compared with the first threshold set, and the risk trend information is matched and compared with the second threshold set to obtain the level comparison result and trend comparison result of the pressure tank;

[0141] Based on the hierarchical comparison results and the trend comparison results, the intervention type and intensity of the pressure vessel are determined;

[0142] Based on the intervention type and the intervention intensity, a hierarchical control instruction is generated that includes the specific action object, action direction, and action sequence.

[0143] The determination of the intervention type and intensity of the pressure vessel based on the hierarchical comparison results and the trend comparison results includes:

[0144] When the risk level information indicates high risk and the risk trend information indicates that the risk is escalating, it is determined that a strong intervention, primarily aimed at relieving pressure, is required.

[0145] When the risk level information indicates medium risk and the risk trend information indicates that the risk is stabilizing, it is determined that a smooth intervention, mainly adjusting the air intake, is required.

[0146] Receive the risk level label output from the pressure tank overload risk stratification assessment stage. This label clearly includes risk level information that directly reflects the severity of the current overload risk, as well as risk trend information that reflects the future development trend of the risk and whether it will continue to intensify. Completely receive both types of information to support subsequent comparison operations.

[0147] Pre-set preventive intervention thresholds are retrieved from the safety control preset parameter library of the pressure tank. This threshold system includes a first threshold set corresponding to risk level information and a second threshold set corresponding to risk trend information. The first threshold set is divided into corresponding intervention triggering standards according to different levels of risk severity, and the second threshold set sets corresponding intervention initiation benchmarks based on the rate of change and the likelihood of aggravation of risk trends, ensuring that the two types of threshold sets fully cover the core dimensions of risk assessment.

[0148] The risk level information in the risk level identifier is compared one by one with the various intervention triggering criteria in the first threshold set to clarify the threshold standards reached by the risk level information, thus forming the level comparison result of the pressure vessel. At the same time, the risk trend information in the risk level identifier is comprehensively compared with the various intervention initiation benchmarks in the second threshold set to determine whether the risk trend information meets the threshold requirements, thus obtaining the trend comparison result of the pressure vessel. This ensures that the two types of comparison results can accurately reflect the matching status between the risk and the preset threshold.

[0149] Based on the hierarchical comparison results, the intervention needs corresponding to the severity of the risk are clearly defined. If the hierarchical comparison results show that the risk has reached a high threshold standard, then more targeted and forceful intervention is required. If it is at a low threshold standard, then mild intervention is required. Combined with the trend comparison results, the need for supplementary intervention based on the risk development trend is judged. If the trend comparison results show that the risk is rapidly escalating, then the timeliness and intensity of the intervention need to be enhanced on the basis of the corresponding level of intervention. If the risk situation is stable, the intervention intensity of the corresponding level is maintained. The intervention type and intensity of the pressure vessel are determined by combining these two aspects to ensure that the intervention measures are highly adapted to the actual risk situation.

[0150] Based on the identified intervention type, the specific components or systems requiring intervention actions are identified, such as pressure regulating valves and pressure relief devices. The direction of the action is determined according to the intervention objective, such as opening or closing the regulating valve or activating the pressure relief device. The sequence of actions is planned in conjunction with the risk development pace and equipment response characteristics, clarifying the order and duration of each intervention action. The action objects, directions, and sequences are then structurally integrated to form clear, directly executable hierarchical control instructions, ensuring that intervention actions are implemented accurately and orderly to mitigate overload risks.

[0151] A comprehensive review of the risk level comparison results confirmed that the risk level information clearly indicated that the pressure tank was in a high-risk state. Simultaneously, a check of the trend comparison results confirmed that the risk trend information clearly indicated that the high risk was continuously escalating. At this point, the overload risk faced by the pressure tank had reached a level requiring emergency handling. Intervention measures that could quickly reduce pressure and curb the worsening of the risk were necessary. Therefore, it was determined that a strong intervention primarily focused on pressure relief was required. By rapidly releasing some of the pressure inside the pressure tank, the escalating high-risk situation could be directly and efficiently alleviated, ensuring equipment safety.

[0152] A careful review of the risk level comparison results revealed that the pressure tank is currently in a medium-risk state. Further examination of the trend comparison results confirmed that the risk trend information indicates that the medium-risk situation is stabilizing and there are no signs of further aggravation. At this point, there is no need to take aggressive intervention measures. It is sufficient to maintain pressure stability through gentle adjustments to gradually reduce the risk. Therefore, it is determined that a smooth intervention mainly based on adjusting the intake air is required. By precisely controlling the amount of gas entering the pressure tank, the pressure inside the tank can be kept within a safe range, and the medium-risk situation can be steadily resolved.

[0153] The beneficial effects are that it receives risk level labels that include risk level information and risk trend information, comprehensively covering the core dimensions of risk severity and urgency, avoiding the one-sidedness of intervention decisions caused by a single dimension judgment, and laying the foundation for the accurate generation of control instructions.

[0154] Obtain a dual threshold set corresponding to the risk level and trend. The first threshold set clarifies the intervention trigger criteria for the severity of the risk, while the second threshold set defines the intervention initiation benchmark for the urgency of the risk, forming a comprehensive threshold determination system to ensure that the comparison basis is standardized and meets actual needs.

[0155] The system uses a two-dimensional matching and comparison of risk information and threshold sets to accurately output hierarchical comparison results and trend comparison results. It clearly presents the fit between risks and preset standards, providing objective and accurate decision support for determining intervention types and intensities, and avoiding subjective judgment errors.

[0156] The type and intensity of intervention are determined based on the results of the two comparisons, so that the intervention measures are both appropriate to the severity of the risk and in line with the development trend of the risk, which solves the problem of the lack of targeting of traditional fixed interventions and ensures that the intervention actions are efficient and appropriate.

[0157] Generate hierarchical control commands that include specific action objects, directions, and timing, clarify the core elements of intervention execution, make operations such as air intake regulation and pressure relief follow a set pattern, ensure that control commands can be directly implemented, improve the execution efficiency and accuracy of overload protection, and ensure the safe and stable operation of the pressure tank.

[0158] High-risk and escalating scenarios are identified and addressed with strong intervention, primarily through pressure relief. This involves taking efficient measures to directly address the core issues that exacerbate the risk, rapidly reducing the pressure inside the pressure tank, curbing further escalation of the risk, maximizing equipment safety, and preventing overload accidents.

[0159] For medium-risk and relatively stable scenarios, a smooth intervention approach is adopted, mainly by adjusting the air intake. No aggressive operation is required. By precisely controlling the air intake, pressure stability is maintained. This approach mitigates risks while avoiding excessive intervention that could disrupt normal equipment operation and ensures operational continuity.

[0160] Develop differentiated intervention strategies based on a combination of risk levels and trends, ensuring that the type and intensity of intervention are fully adapted to the actual risk situation. This addresses the lack of specificity in traditional fixed intervention models and improves the accuracy and rationality of overload protection.

[0161] Clearly defining the intervention direction and intensity standards for the two core risk scenarios provides a clear and fixed decision-making basis for the generation of subsequent graded control instructions, ensuring that intervention actions are carried out in a regulated manner and guaranteeing the standardization and efficiency of the protection process.

[0162] S5. Execute the graded control command to coordinate the control of the air intake regulating component and the pressure relief component connected to the pressure tank.

[0163] In this embodiment of the invention, executing the graded control command to coordinate the control of the intake regulating component and the pressure relief component connected to the pressure tank includes:

[0164] The graded control command is parsed to obtain the intake regulation strategy identifier and the pressure relief strategy identifier corresponding to the risk level identifier;

[0165] Based on the intake regulation strategy identifier, a first drive command for the pressure tank is generated and sent to the intake regulation component to control the intake regulation component to perform operations to reduce and interrupt the intake airflow;

[0166] Based on the pressure relief strategy identifier, a second drive command for the pressure tank is generated and sent to the pressure relief component to control the pressure relief component to perform the operation of opening the relief channel;

[0167] After the pressure relief component performs the operation of opening the relief channel, the feedback pressure data of the pressure tank is obtained;

[0168] When the feedback pressure data meets the preset safety threshold conditions, a reset command for the pressure tank is generated, and the pressure relief component is controlled to close the relief channel based on the reset command.

[0169] The graded control commands are fully decomposed, and the core control information corresponding to the risk level identifiers is extracted. The operational requirements and strategy types for the intake regulating components in the commands are clarified, and intake regulating strategy identifiers are formed. At the same time, the action specifications and strategy orientations of the pressure relief components in the commands are sorted out to obtain pressure relief strategy identifiers. This ensures that the two identifiers can accurately reflect the control requirements of the graded control commands for different components.

[0170] Based on the control requirements specified in the intake air conditioning strategy, and according to the working principle and control logic of the intake air conditioning component, a first drive command is generated, including the action type, execution range, and operation sequence. The action type strictly corresponds to the requirement of reducing or interrupting the intake airflow, and the execution range and operation sequence match the risk level indicated by the risk level label. The generated first drive command is sent to the intake air conditioning component through a preset signal transmission channel. After receiving the command, the intake air conditioning component activates its internal actuator to gradually reduce the intake airflow or directly cut off the intake airflow channel according to the command requirements, thus completing the operation of reducing or interrupting the intake airflow.

[0171] Based on the operating procedures specified by the pressure relief strategy identifier, and considering the structural characteristics and release logic of the pressure relief component, a second driving command is generated, including the channel opening degree, opening speed, and maintenance duration. This ensures that the command parameters are compatible with the intervention intensity corresponding to the risk level identifier. The second driving command is sent to the pressure relief component via a dedicated command transmission path. Upon receiving the command, the pressure relief component activates its own driving mechanism and gradually opens the release channel according to the opening degree and speed set in the command, allowing the pressure inside the pressure tank to be released outward through the channel, thus executing the operation of opening the release channel.

[0172] After the pressure relief component begins to open the relief channel, the pressure detection device on the pressure tank is activated to continuously capture the pressure change data inside the tank. The detection device collects pressure information at fixed time intervals to ensure that the pressure drop trend can be tracked in real time. Each set of pressure data collected is recorded and transmitted in real time to obtain the feedback pressure data of the pressure tank.

[0173] A pre-set safety threshold condition for the pressure tank is established, based on the equipment's safe operating standards and normal operating pressure range, clearly defining the safe pressure range that the pressure tank needs to maintain. The acquired feedback pressure data is continuously compared with the pre-set safety threshold condition. When the feedback pressure data falls within the safe pressure range and remains stable, i.e., the pre-set safety threshold condition is met, a reset command containing the closing sequence and closing amplitude is immediately generated. Based on this reset command, it is sent to the pressure relief component through a signal transmission channel. The pressure relief component, according to the command requirements, gradually reduces the opening degree of the relief channel until it is completely closed, restoring the pressure tank to its normal sealing state.

[0174] The beneficial effects are that by analyzing the graded control commands, intake regulation strategy identifiers and pressure relief strategy identifiers are obtained, and the core control requirements of components corresponding to the risk level identifiers are accurately extracted, providing a clear basis for the subsequent generation of targeted drive commands and ensuring that component control is highly adapted to the risk situation.

[0175] Based on the intake regulation strategy identifier, a first drive command is generated and sent to the intake regulation component to control it to perform operations to reduce or interrupt the intake airflow, thereby curbing pressure rise at the source, quickly responding to overload risks, and preventing the risks from escalating further.

[0176] The second driving command is generated based on the pressure relief strategy identifier and sent to the pressure relief component to control it to open the relief channel and directly release the excess pressure in the tank. This works in conjunction with the air intake adjustment operation to improve the efficiency of overload risk mitigation and ensure the safety of the pressure tank.

[0177] After the pressure relief component is activated, it acquires feedback pressure data in real time and dynamically tracks pressure changes, providing accurate data support for subsequent reset operations. This avoids blindly closing the relief channel, which could lead to a rebound in risk and ensures the controllability of the protection process.

[0178] When the feedback pressure data meets the safety threshold conditions, a reset command is generated to control the pressure relief component to close the relief channel, so that the pressure tank returns to the normal sealing state, realizing closed-loop control of overload protection, which not only ensures equipment safety, but also does not affect subsequent normal operation, thus improving the integrity and practicality of overload protection.

[0179] like Figure 2 The diagram shown is a functional block diagram of a pressure tank overload protection system provided in an embodiment of the present invention.

[0180] The pressure tank overload protection system 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the pressure tank overload protection system 100 may include a data acquisition and processing module 101, a status profile construction module 102, a risk classification and assessment module 103, a control decision generation module 104, and an execution coordination and control module 105. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0181] In this embodiment, the functions of each module / unit are as follows:

[0182] The data acquisition and processing module 101 is used to acquire the real-time pressure signal of the pressure tank, perform standardization processing on the real-time pressure signal, and obtain the standard pressure data sequence of the pressure tank.

[0183] The state profile construction module 102 is used to construct a pressure state profile of the pressure tank based on the pressure change trend and pressure fluctuation characteristics in the standard pressure data sequence.

[0184] The risk classification assessment module 103 is used to perform a hierarchical assessment of the overload risk of the pressure tank based on the pressure state profile, and obtain the risk level identifier of the pressure tank.

[0185] The control decision generation module 104 is used to compare the risk level identifier with a preset preventive intervention threshold, and generate a graded control instruction for the pressure tank based on the comparison result.

[0186] The execution coordination control module 105 is used to execute the hierarchical control command and coordinate the control of the air intake regulating component and the pressure relief component connected to the pressure tank.

[0187] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0188] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0189] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0190] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0191] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0192] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for overload protection of a pressure tank, characterized in that, The method includes: S1. Acquire the real-time pressure signal of the pressure tank, and perform standardization processing on the real-time pressure signal to obtain the standard pressure data sequence of the pressure tank; S2. Based on the pressure change trend and pressure fluctuation characteristics in the standard pressure data sequence, construct a pressure state profile of the pressure tank; S3. Based on the pressure state profile, the overload risk of the pressure tank is assessed hierarchically to obtain the risk level identifier of the pressure tank. S4. Compare the risk level identifier with the preset preventive intervention threshold, and generate a graded control instruction for the pressure tank based on the comparison result. S5. Execute the graded control command to coordinate the control of the air intake regulating component and the pressure relief component connected to the pressure tank.

2. The pressure tank overload protection method as described in claim 1, characterized in that, The process involves acquiring the real-time pressure signal from the pressure tank, standardizing the real-time pressure signal to obtain a standard pressure data sequence for the pressure tank, including: The real-time pressure signal of the pressure tank is acquired, and abnormal noise data in the real-time pressure signal is removed to obtain the effective pressure data sequence of the pressure tank. The effective pressure data sequence is subjected to moving average filtering to obtain a smoothed pressure sequence of the pressure tank; Linear compensation is applied to the smoothed pressure sequence to obtain the corrected pressure signal sequence of the pressure tank. The corrected pressure signal sequence is normalized to obtain the standard pressure data sequence of the pressure tank.

3. The pressure tank overload protection method as described in claim 1, characterized in that, The step of constructing a pressure state profile of the pressure tank based on the pressure change trend and pressure fluctuation characteristics in the standard pressure data sequence includes: The time-domain features of the standard pressure data sequence are extracted to obtain the trend feature sequence of the pressure tank; Fluctuation characteristic analysis is performed on the standard pressure data sequence to obtain the fluctuation characteristic set of the pressure tank; The trend feature sequence and the fluctuation feature set are fused to obtain the multidimensional state vector of the pressure tank. The multidimensional state vector is abstracted and mapped to obtain a pressure state profile of the pressure tank.

4. The pressure tank overload protection method as described in claim 3, characterized in that, The abstract mapping of the multidimensional state vector to obtain the pressure state profile of the pressure tank includes: The time-frequency domain feature analysis of the standard pressure data sequence is performed to obtain the deterministic trend component and random fluctuation component of the pressure tank. The deterministic trend component is input into the historical process stage knowledge base of the pressure tank, and the current operating stage of the pressure tank is identified by matching. Based on the multi-dimensional dynamic association library of the pressure tank, the similarity of the random fluctuation components is measured to obtain the dominant load pattern of the pressure tank. Based on the current operating stage and the dominant load mode, retrieve the corresponding state mapping rule set from the multi-dimensional dynamic association library; Based on the state mapping rule set, the deterministic trend component, the random fluctuation component, the current operating stage, and the dominant load mode are fused and reconstructed to obtain the pressure state profile of the pressure tank.

5. The pressure tank overload protection method as described in claim 4, characterized in that, The step of hierarchically assessing the overload risk of the pressure tank based on the pressure state profile to obtain a risk level identifier for the pressure tank includes: Extract the steady-state characteristic values ​​that characterize pressure stability, the trend characteristic values ​​that characterize the rate of pressure change, and the fluctuation characteristic values ​​that characterize the pressure fluctuation pattern from the pressure state profile. Based on the current operating stage and the dominant load mode, the risk factor weight coefficients corresponding to the pressure tank are determined, and the overload risk of the pressure tank is assessed in a hierarchical manner to obtain the comprehensive risk evaluation value of the pressure tank. The comprehensive risk assessment value is mapped to the risk level range of the pressure tank to obtain the risk level identifier of the pressure tank.

6. The pressure tank overload protection method as described in claim 5, characterized in that, The formula for calculating the comprehensive risk assessment value is as follows: ； In the formula, The comprehensive risk assessment value is... The weighting coefficient of the first risk factor is determined based on the current operating phase and dominant load pattern. The steady-state deviation is calculated based on the aforementioned steady-state eigenvalues. The weighting coefficient of the second risk factor is determined based on the current operating phase and dominant load pattern. The trend threat level is calculated based on the aforementioned trend characteristic values. The weighting coefficient of the third risk factor is determined based on the current operating phase and dominant load pattern. The fluctuation anomaly degree is calculated based on the fluctuation characteristic value.

7. The pressure tank overload protection method as described in claim 1, characterized in that, The step of comparing the risk level identifier with a preset preventive intervention threshold and generating a graded control instruction for the pressure tank based on the comparison result includes: Receive the risk level identifier, which includes risk level information representing the severity of the risk and risk trend information representing the urgency of the risk; Obtain preset preventive intervention thresholds, wherein the preventive intervention thresholds include a first threshold set corresponding to the risk level information and a second threshold set corresponding to the risk trend information; The risk level information is matched and compared with the first threshold set, and the risk trend information is matched and compared with the second threshold set to obtain the level comparison result and trend comparison result of the pressure tank; Based on the hierarchical comparison results and the trend comparison results, the intervention type and intensity of the pressure vessel are determined; Based on the intervention type and the intervention intensity, a hierarchical control instruction is generated that includes the specific action object, action direction, and action sequence.

8. The pressure tank overload protection method as described in claim 7, characterized in that, The determination of the intervention type and intensity of the pressure vessel based on the hierarchical comparison results and the trend comparison results includes: When the risk level information indicates high risk and the risk trend information indicates that the risk is escalating, it is determined that a strong intervention, primarily aimed at relieving pressure, is required. When the risk level information indicates medium risk and the risk trend information indicates that the risk is stabilizing, it is determined that a smooth intervention, mainly adjusting the air intake, is required.

9. A method for overload protection of a pressure tank as described in claim 1, characterized in that, The execution of the graded control command, coordinating the control of the intake regulating component and the pressure relief component connected to the pressure tank, includes: The graded control command is parsed to obtain the intake regulation strategy identifier and the pressure relief strategy identifier corresponding to the risk level identifier; Based on the intake regulation strategy identifier, a first drive command for the pressure tank is generated and sent to the intake regulation component to control the intake regulation component to perform operations to reduce and interrupt the intake airflow; Based on the pressure relief strategy identifier, a second drive command for the pressure tank is generated and sent to the pressure relief component to control the pressure relief component to perform the operation of opening the relief channel; After the pressure relief component performs the operation of opening the relief channel, the feedback pressure data of the pressure tank is obtained; When the feedback pressure data meets the preset safety threshold conditions, a reset command for the pressure tank is generated, and the pressure relief component is controlled to close the relief channel based on the reset command.

10. A pressure tank overload protection system, characterized in that, The system for implementing the pressure tank overload protection method according to claim 1 includes: The data acquisition and processing module is used to acquire the real-time pressure signal of the pressure tank, perform standardization processing on the real-time pressure signal, and obtain the standard pressure data sequence of the pressure tank. The status profile construction module is used to construct a pressure status profile of the pressure tank based on the pressure change trend and pressure fluctuation characteristics in the standard pressure data sequence. The risk classification and assessment module is used to perform a hierarchical assessment of the overload risk of the pressure tank based on the pressure state profile, and obtain the risk level identifier of the pressure tank. The control decision generation module is used to compare the risk level identifier with a preset preventive intervention threshold, and generate a graded control instruction for the pressure tank based on the comparison result. The execution coordination control module is used to execute the hierarchical control commands and coordinate the control of the air intake regulating component and the pressure relief component connected to the pressure tank.

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