Safety monitoring method and system for dangerous goods storage tank group

Abstract

The invention discloses a safety monitoring method and system for a dangerous goods storage tank group, and the method ingeniously carries out the monitoring of the corrosion condition and crack initiation and expansion conditions of a storage tank bottom plate of the storage tank group through a corrosion monitoring sensing module and an acoustic monitoring sensing module, so that the storage tank is not liable to crack when being used for a long time. The thickness of the bottom plate and the acoustic abnormal condition can be monitored in time, so that a background manager can timely and reliably know the corrosion condition and the stress change condition of the storage tank body; according to the scheme, corrosion rate data, acoustic event characteristics, environmental parameters, inter-tank position parameters, storage tank bottom plate thickness difference data and other data of different dimensions are fused to generate multi-source fusion data, and on the basis, independent and combined judgment is conducted on the multi-source data through a corresponding algorithm so as to analyze whether the storage tank bottom plate is abnormal or not; and reliable mechanism guarantee is provided for safety monitoring of the storage tank group.

Description

Technical Field

[0001] The present invention relates to the technical field of LNG storage tank monitoring, and in particular to a method and system for safety monitoring of a group of hazardous goods storage tanks. Background Art

[0002] As a clean and efficient energy source, liquefied natural gas (LNG) plays an increasingly important role in the global energy structure. As the storage medium of LNG dangerous goods storage tanks has the characteristics of low temperature, flammability and potential high danger, its safe operation is related to the stability of the energy supply chain and the safety of the surrounding environment.

[0003] At present, the safety monitoring of LNG storage tanks focuses on structural integrity and sealing. Traditional tank safety management relies more on regular manual inspections and offline non-destructive testing (NDT) technologies, such as ultrasonic thickness measurement, magnetic particle testing, eddy current testing, and penetration testing. These technologies can detect corrosion, cracks, local thinning, and potential structural defects on the bottom plate of the tank to a certain extent. However, due to the long inspection cycle and insufficient inspection frequency, when corrosion or cracks develop rapidly between two inspections, it may lead to risk accumulation and fail to achieve early warning of accident signs. In addition, manual or regular offline inspections are often time-consuming, labor-intensive, and affect the normal operation of the tank.

[0004] With the development of sensor and Internet of Things technology, more and more online monitoring methods are being tried in the field of LNG tank safety. For example, electrochemical corrosion sensors, fiber grating sensors and ultrasonic thickness sensors can be used to continuously monitor the corrosion condition of the tank bottom plate; multi-point acoustic emission (AE) sensors can be used to capture acoustic event signals caused by microcrack initiation, expansion, pitting damage and potential leakage. On the other hand, the risk of LNG tank groups is not only affected by internal structural factors such as corrosion and crack expansion, but also by the coupling of external environmental factors (such as temperature and humidity, corrosive atmosphere components), safety distance and layout between tanks, and the overall operating conditions of the tank group. Traditional risk assessment is often based on a single factor or static conditions, and it is difficult to obtain accurate and dynamic risk assessment results in a complex and changeable actual operating environment.

[0005] With the parallel generation of multi-source data and the accumulation of massive information, it has become a trend to organically integrate corrosion monitoring, acoustic monitoring, environmental parameters, and tank operating parameters, and use big data analysis, machine learning, and probabilistic risk assessment models (such as Bayesian networks or risk benchmark assessment models) to conduct a comprehensive and multi-level safety assessment of LNG tank groups. This technical route can dynamically predict the corrosion rate based on real-time sensor data while realizing online monitoring of tank bottom corrosion, identify acoustic abnormal events (such as crack extension, pitting, and trace leakage), and fully consider external environmental factors such as gas composition, relative humidity, wind speed and direction, so as to provide risk indexes and early warning information for tank groups. This multi-factor comprehensive risk assessment method helps to achieve closed-loop control of the safety status of tanks, that is, to issue early warnings in a timely manner when safety hazards are detected, and to feed back inspection and maintenance data to the model for parameter correction and continuous optimization.

[0006] However, the existing monitoring and assessment methods are mostly fragmented, making it difficult to achieve the organic integration of corrosion monitoring, acoustic monitoring, environmental parameter detection and risk assessment. There is a lack of a systematic technical solution that can integrate hardware sensor equipment, data acquisition and transmission platforms, data analysis models and visual decision support tools in a unified framework, so as to grasp the safety status of LNG tank groups in real time, dynamically and in a closed loop during operation.

[0007] Therefore, it is necessary to propose a safety monitoring method and system that can integrate multiple sensing technologies, data processing methods and intelligent risk analysis models to achieve real-time monitoring, comprehensive analysis and dynamic early warning of the bottom plate corrosion of LNG hazardous goods storage tanks and the overall risk of the tank group, providing strong technical support for the long-term safe operation of storage tanks. Summary of the invention

[0008] In view of this, the purpose of the present invention is to propose a method and system for safety monitoring of dangerous goods storage tank groups which is reliable in implementation, flexible in application, efficient in early warning and good in reliability.

[0009] In order to achieve the above technical objectives, the present invention adopts the following technical solutions: A method for safety monitoring of a hazardous goods storage tank group is applied to the monitoring of an LNG hazardous goods storage tank group. A corrosion monitoring sensor module and an acoustic monitoring sensor module are deployed in the tank bottom plate area of ​​the LNG hazardous goods storage tank group. An environmental sensor group is deployed in the placement site of the LNG hazardous goods storage tank group, which includes: S01. Real-time monitoring of the corrosion condition, crack initiation and expansion of the bottom plate area of ​​the storage tank is performed through the corrosion monitoring sensor module and the acoustic monitoring sensor module, and corrosion monitoring data and acoustic monitoring data are generated respectively; S02. Monitor the environmental corrosion factors and meteorological conditions around and between the storage tanks through the environmental sensor group to generate environmental monitoring data; S03, obtaining corrosion monitoring data, acoustic monitoring data and environmental monitoring data, performing data cleaning, filtering and feature extraction on the data, and then performing multi-source data fusion processing to generate multi-source fusion data; S04. Obtain multi-source fusion data, use risk assessment algorithms based on machine learning and statistical models to assess the corrosion degree of storage tanks and the overall risk of storage tank groups, and generate assessment results; S05. Obtain the assessment results, issue early warnings to storage tanks and hazardous goods storage tank groups based on the assessment results, and generate early warning information; S06. Build a digital twin model based on the hazardous materials storage tank group and present the assessment results and warning information in a visual form.

[0010] As a possible implementation mode, further, in the present solution S01, the corrosion monitoring sensor module includes a plurality of electrochemical sensors for monitoring the corrosion of the tank bottom plate; the acoustic monitoring sensor module includes a plurality of ultrasonic sensors for measuring the thickness, cracks and crack extension monitoring of the tank bottom plate; some of the plurality of ultrasonic sensors are deployed in a preset monitoring area of ​​the tank bottom plate in the form of a sensor array.

[0011] As a possible implementation mode, further, the corrosion monitoring data described in this scheme includes the corrosion current density and corrosion rate of the corresponding monitoring points on the bottom plate of the tank; the acoustic monitoring data includes the bottom plate thickness of the corresponding monitoring points on the bottom plate of the tank and the corresponding received sound wave data and sound source position data.

[0012] As a possible implementation mode, further, the method for the corrosion monitoring sensor module and the acoustic monitoring sensor module of the present solution to monitor and collect data on the bottom plate area of ​​the storage tank in real time includes: Multiple monitoring points are set on the bottom plate of the tank, and the location information set is defined as , n is the number of monitoring points, for each monitoring point , and regularly obtain its thickness through the acoustic monitoring sensor module , and the corrosion current density measured by the corrosion monitoring sensor module ; Based on the basic parameters of the tank bottom and the measured corrosion current density The corrosion rate of the tank bottom plate is calculated and is defined as follows: in, For monitoring point The instantaneous corrosion rate at is the unit conversion constant, is the molar mass of the metal used for the tank bottom, is the number of electrons lost in the corrosion reaction, is the density of the metal used for the tank bottom plate, For monitoring point The corrosion current density at The thickness of the bottom plate at the monitoring point is obtained through the acoustic monitoring sensor module to assist in verifying the reliability of the corrosion rate calculation. It can be fitted into a mathematical relationship between thickness and time, which is defined as follows: in, For acoustic monitoring sensor modules Monitoring point at all times The measured thickness of the base plate, For acoustic monitoring sensor modules Monitoring point at all times The measured thickness of the base plate, For thickness difference; The sensing point set of multiple ultrasonic sensors is defined as , n is the number of sensing points; For sound source positioning, the time difference between the sound signal emitted by the sound source and the sensing point where each ultrasonic sensor is located is recorded. Locate the sound source coordinates , at the sensing point of the known ultrasonic sensor Position and speed of sound In the case of, the coordinates of the sound source position are solved according to the least squares method. , whose formula is defined as follows: By solving the least squares method, the coordinates of the sound source position are obtained ,in, The sensing point of the ultrasonic sensor Corresponding x , y Coordinate location.

[0013] As a preferred implementation option, preferably, in this solution S03, when extracting the acoustic monitoring data, the sound wave data collected by the ultrasonic sensor is Perform wavelet transform, which is defined as follows: in, For sound wave data In scale and pan The wavelet coefficients under ; is the wavelet basis function; , which is the scale factor, is the translation factor, is complex conjugate; By selecting wavelet basis functions and characteristic coefficient statistics, characteristic parameters of acoustic events are extracted and set as acoustic event features, which include energy , duration , Ring Count One or more of the following; Among them, the extracted characteristic parameters and the sound source position coordinates The data are stored together and aggregated into the acoustic monitoring data for reuse.

[0014] As a preferred implementation option, preferably, the environmental sensor group in this solution includes at least one of a gas composition sensor, a temperature sensor, a humidity sensor, and a wind speed and direction sensor; the environmental corrosion factors include hydrogen sulfide, sulfur dioxide, and chloride ions; In S02, environmental monitoring data is collected in the form of environmental parameter sets to form an environmental parameter vector , which is defined as follows: in, The gas concentration or ion concentration data of the corrosive gases hydrogen sulfide, sulfur dioxide, and chloride ions measured by the gas composition sensors respectively; is the ambient temperature data, is the relative humidity data, They are wind direction data and wind speed data respectively.

[0015] As a preferred implementation option, preferably, this solution S02 also includes: For the tank group, suppose there is a tank group set , obtain the center position of each tank through the laser ranging module or GPS positioning module , where the distance between tanks is It can be expressed as follows: in, For storage tank i The coordinate position of For storage tank j The coordinate position of Collect all the distances between tanks in the tank group , forming the tank position parameters .

[0016] As a preferred implementation option, preferably, this solution S03 includes: Obtain corrosion monitoring data, acoustic monitoring data and environmental monitoring data and set them as multi-source data; Detect and interpolate outliers in multi-source data to achieve data cleaning, then use sliding window averaging or median filtering to filter multi-source data, and then normalize data features so that all data features are mapped to the [0, 1] interval, so that data of different dimensions can be fused and processed; Establish multi-dimensional feature vectors to fuse data of different dimensions and generate multi-source fusion data , which includes corrosion rate data , Acoustic Event Characteristics , Environmental parameters 、Tank position parameters Thickness difference data of tank bottom plate , which is defined as follows: Among them, multi-source fusion data Used to assess the corrosion level of storage tanks and the overall risk of tank groups.

[0017] As a preferred implementation option, preferably, this solution S04 includes: S041. Obtain multi-source fusion data and extract corrosion rate data from it , Acoustic Event Characteristics , Environmental parameters 、Tank position parameters Thickness difference data of tank bottom plate ; S042. Use random forest RF or LSTM time series network to calculate the corrosion rate of future moments Make predictions to obtain predicted corrosion rates; Wherein, the input features include corrosion rate data, and environmental parameters and / or acoustic event characteristics; S043, classifying acoustic event features by a classifier including a SVM algorithm or a random forest algorithm, wherein the classification categories include: microcrack extension event Crack, pitting event Pitting, local leakage event Leakage, or mechanical noise event Noise; and obtaining acoustic event classification results; Among them, the input feature vector of the classifier , the output is the event category label ; S044. Comprehensively analyze multi-source fusion data through Bayesian network BN, and the analysis factors include corrosion rate data, acoustic event characteristics, environmental parameters, and tank position parameters; Among them, the Bayesian network nodes include: Base Plate Corrosion Status Node C ; Acoustic Event Node A ; Environmental Corrosion Factor Node E ; Distance between storage tanks and linkage risk nodes D ; Construct the joint probability distribution of the Bayesian network BN, which is defined as follows: By inferring the Bayesian network BN, when the current corrosion rate increases, the frequency of acoustic events increases, or the environmental parameters are unfavorable, the posterior inference of the Bayesian network BN will give a high-probability risk state, and finally the result of the Bayesian network BN is mapped to a risk index between 0 and 1. R , which is defined as follows: in, is the normalized mapping function; S045. Aggregate predicted corrosion rates, acoustic event classification results, and risk indices R , which is output as the evaluation result.

[0018] As a preferred implementation option, preferably, this solution S05 includes: Obtain assessment results, including predicted corrosion rate, acoustic event classification results, risk index R It is compared with the preset threshold or reference classification. When it is exceeded, an early warning message is generated and output to achieve early warning for storage tanks and hazardous goods storage tank groups.

[0019] Based on the above, the present invention also provides a method for managing a hazardous goods storage tank storage area, which includes the above-mentioned method for monitoring the safety of a hazardous goods storage tank group.

[0020] Based on the above, this solution also provides a dangerous goods storage tank group safety monitoring system, which includes: There are multiple corrosion monitoring sensor modules, which are deployed on the inner wall and / or outer wall of the tank bottom plate and are used to monitor the corrosion conditions of the tank bottom plate area in real time and generate corrosion monitoring data; There are multiple acoustic monitoring sensor modules, which are deployed on the inner wall and / or outer wall of the bottom plate of the storage tank and are used to monitor the initiation and expansion of cracks in the bottom plate area of ​​the storage tank in real time and generate acoustic monitoring data; Environmental sensor group, used to monitor environmental corrosion factors and meteorological conditions around and between storage tanks, and generate environmental monitoring data; The data processing module is used to obtain corrosion monitoring data, acoustic monitoring data and environmental monitoring data, perform data cleaning, filtering and feature extraction on them, and then perform multi-source data fusion processing to generate multi-source fusion data; The data evaluation module is used to obtain multi-source fusion data, evaluate the corrosion degree of the tank and the overall risk of the tank group using a risk assessment algorithm based on machine learning and statistical models, and generate evaluation results; The early warning information unit is used to obtain the evaluation results, issue early warnings to the storage tanks and dangerous goods storage tank groups according to the evaluation results, and generate early warning information; The digital twin module is used to build a digital twin model based on a group of hazardous materials storage tanks, and present the assessment results and warning information in a visual form.

[0021] By adopting the above technical scheme, the present invention has the following beneficial effects compared with the prior art: the present scheme cleverly monitors the corrosion condition, crack initiation and expansion condition of the bottom plate of the storage tank group through the corrosion monitoring sensor module and the acoustic monitoring sensor module, so that when the storage tank is used for a long time, the thickness of the bottom plate and the acoustic abnormality can be monitored in time, so that the back-end management personnel can timely and reliably know the corrosion condition and stress change condition of the storage tank body. At the same time, the present scheme also combines the environmental sensor group to monitor the environmental corrosion factors and meteorological conditions around and between the storage tanks, and generates environmental monitoring data, so that the placement environment of the storage tank group can be recorded, providing data basis and support for the maintenance and management of the storage tank; in addition, the present scheme also records the corrosion rate data , Acoustic Event Characteristics , Environmental parameters 、Tank position parameters Thickness difference data of tank bottom plate Fusion of data of different dimensions to generate multi-source fusion data On this basis, the corresponding algorithms are used to make separate and combined judgments on the multi-source data to analyze whether there are any abnormalities on the bottom plate of the tank, providing a reliable mechanism guarantee for the safety monitoring of the tank group. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 This is a brief implementation flow chart of the monitoring method of this program; Figure 2This is a schematic diagram of the unit module connection of the monitoring system of this scheme. DETAILED DESCRIPTION

[0024] The present invention will be further described in detail below in conjunction with the accompanying drawings and examples. It is particularly noted that the following examples are only used to illustrate the present invention, but are not intended to limit the scope of the present invention. Similarly, the following examples are only partial embodiments of the present invention rather than all embodiments, and all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0025] like Figure 1 As shown, the present embodiment provides a method for safety monitoring of a hazardous goods storage tank group, which is applied to the monitoring of an LNG hazardous goods storage tank group. A corrosion monitoring sensor module and an acoustic monitoring sensor module are deployed in the tank bottom plate area of ​​the LNG hazardous goods storage tank group, and an environmental sensor group is deployed in the placement site of the LNG hazardous goods storage tank group, which includes: S01. Real-time monitoring of the corrosion condition, crack initiation and expansion of the bottom plate area of ​​the storage tank is performed through the corrosion monitoring sensor module and the acoustic monitoring sensor module, and corrosion monitoring data and acoustic monitoring data are generated respectively; S02. Monitor the environmental corrosion factors and meteorological conditions around and between the storage tanks through the environmental sensor group to generate environmental monitoring data; S03, obtaining corrosion monitoring data, acoustic monitoring data and environmental monitoring data, performing data cleaning, filtering and feature extraction on the data, and then performing multi-source data fusion processing to generate multi-source fusion data; S04. Obtain multi-source fusion data, use risk assessment algorithms based on machine learning and statistical models to assess the corrosion degree of storage tanks and the overall risk of storage tank groups, and generate assessment results; S05. Obtain the assessment results, issue early warnings to storage tanks and hazardous goods storage tank groups based on the assessment results, and generate early warning information; S06. Build a digital twin model based on the hazardous materials storage tank group and present the assessment results and warning information in a visual form.

[0026] This solution cleverly uses corrosion monitoring sensor modules and acoustic monitoring sensor modules to monitor the corrosion, crack initiation and expansion of the tank bottom plates of the tank group, so that when the tank is used for a long time, the thickness of the bottom plate and acoustic abnormalities can be monitored in time, allowing the back-end management personnel to timely and reliably know the corrosion status and stress change status of the tank body. At the same time, this solution also combines the environmental sensor group to monitor the environmental corrosion factors and meteorological conditions around and between the tanks, and generate environmental monitoring data, so that the placement environment of the tank group can be recorded, providing data basis and support for the maintenance and management of the tanks.

[0027] In terms of hardware layout, as a possible implementation method, further, in this solution S01, the corrosion monitoring sensor module includes a plurality of electrochemical sensors for monitoring the corrosion of the tank bottom plate; the acoustic monitoring sensor module includes a plurality of ultrasonic sensors for measuring the thickness, cracks and crack extension monitoring of the tank bottom plate; some of the plurality of ultrasonic sensors are deployed in a preset monitoring area of ​​the tank bottom plate in the form of a sensor array.

[0028] In terms of data monitoring, as a possible implementation method, further, the corrosion monitoring data described in this scheme includes the corrosion current density and corrosion rate of the corresponding monitoring points on the bottom plate of the tank; the acoustic monitoring data includes the bottom plate thickness of the corresponding monitoring points on the bottom plate of the tank and the corresponding received sound wave data and sound source position data.

[0029] Accordingly, as a possible implementation method, further, the method for the corrosion monitoring sensor module and the acoustic monitoring sensor module of the present solution to monitor and collect data on the bottom plate area of ​​the storage tank in real time includes: Multiple monitoring points are set on the bottom plate of the tank, and the location information set is defined as , n is the number of monitoring points, for each monitoring point , and regularly obtain its thickness through the acoustic monitoring sensor module , and the corrosion current density measured by the corrosion monitoring sensor module ; Based on the basic parameters of the tank bottom and the measured corrosion current density The corrosion rate of the tank bottom plate is calculated and is defined as follows: in, For monitoring point The instantaneous corrosion rate at is the unit conversion constant, is the molar mass of the metal used for the tank bottom, is the number of electrons lost in the corrosion reaction, is the density of the metal used for the tank bottom plate, For monitoring point The corrosion current density at The thickness of the bottom plate at the monitoring point is obtained through the acoustic monitoring sensor module to assist in verifying the reliability of the corrosion rate calculation. It can be fitted into a mathematical relationship between thickness and time, which is defined as follows: in, For acoustic monitoring sensor modules Monitoring point at all times The measured thickness of the base plate, For acoustic monitoring sensor modules Monitoring point at all times The measured thickness of the base plate, For thickness difference; The sensing point set of multiple ultrasonic sensors is defined as , n is the number of sensing points; For sound source positioning, the time difference between the sound signal emitted by the sound source and the sensing point where each ultrasonic sensor is located is recorded. Locate the sound source coordinates , at the sensing point of the known ultrasonic sensor Position and speed of sound In the case of, the least squares method is used to solve the sound source position coordinates ( ), whose formula is defined as follows: By solving the least squares method, the coordinates of the sound source position are obtained ,in, The sensing point of the ultrasonic sensor Corresponding x , y Coordinate location.

[0030] In order to improve the reliability of the data, reduce the signal noise contained therein and facilitate the extraction of characteristic signals in the data, as a preferred implementation option, preferably, in this solution S03, when extracting the acoustic monitoring data, the sound wave data collected by the ultrasonic sensor is Perform wavelet transform, which is defined as follows: in, For sound wave data In scale and pan The wavelet coefficients under ; is the wavelet basis function; , which is the scale factor, is the translation factor, is complex conjugate; By selecting wavelet basis functions and characteristic coefficient statistics, characteristic parameters of acoustic events are extracted and set as acoustic event features, which include energy , duration , Ring Count One or more of the following; Among them, the extracted characteristic parameters and the sound source position coordinates The data are stored together and aggregated into the acoustic monitoring data for reuse.

[0031] In terms of environmental monitoring, as a better implementation option, preferably, the environmental sensor group described in this scheme includes one or more of a gas composition sensor, a temperature sensor, a humidity sensor, and a wind speed and direction sensor; the environmental corrosion factors include hydrogen sulfide, sulfur dioxide, and chloride ions.

[0032] Accordingly, in this solution S02, the environmental monitoring data is collected in the form of an environmental parameter set to form an environmental parameter vector , which is defined as follows: in, The gas concentration or ion concentration data of the corrosive gases hydrogen sulfide, sulfur dioxide, and chloride ions measured by the gas composition sensors respectively; is the ambient temperature data, is the relative humidity data, They are wind direction data and wind speed data respectively.

[0033] As a preferred implementation option in terms of the tank spacing, preferably, this solution S02 also includes: For the tank group, suppose there is a tank group set , obtain the center position of each tank through the laser ranging module or GPS positioning module , where the distance between tanks is It can be expressed as follows: in, For storage tank i The coordinate position of For storage tank j The coordinate position of Collect all the distances between tanks in the tank group , forming the tank position parameters .

[0034] As for data fusion analysis, as a better implementation option, preferably, this solution S03 includes: Obtain corrosion monitoring data, acoustic monitoring data and environmental monitoring data and set them as multi-source data; Detect and interpolate outliers in multi-source data to achieve data cleaning, then use sliding window averaging or median filtering to filter multi-source data, and then normalize data features so that all data features are mapped to the [0, 1] interval, so that data of different dimensions can be fused and processed; Establish multi-dimensional feature vectors to fuse data of different dimensions and generate multi-source fusion data , which includes corrosion rate data , Acoustic Event Characteristics , Environmental parameters 、Tank position parameters Thickness difference data of tank bottom plate , which is defined as follows:

[0035] Among them, multi-source fusion data Used to assess the corrosion level of storage tanks and the overall risk of tank groups.

[0036] As a preferred implementation option, preferably, this solution S04 includes: S041. Obtain multi-source fusion data and extract corrosion rate data from it , Acoustic Event Characteristics , Environmental parameters 、Tank position parameters Thickness difference data of tank bottom plate ; S042. Use random forest RF or LSTM time series network to calculate the corrosion rate of future moments Make predictions to obtain predicted corrosion rates; Wherein, the input features include corrosion rate data, and environmental parameters and / or acoustic event characteristics; S043, classifying acoustic event features by a classifier including a SVM algorithm or a random forest algorithm, wherein the classification categories include: microcrack extension event Crack, pitting event Pitting, local leakage event Leakage, or mechanical noise event Noise; and obtaining acoustic event classification results; Among them, the input feature vector of the classifier , the output is the event category label ; S044. Comprehensively analyze multi-source fusion data through Bayesian network BN, and the analysis factors include corrosion rate data, acoustic event characteristics, environmental parameters, and tank position parameters; Among them, the Bayesian network nodes include: Base Plate Corrosion Status Node C ; Acoustic Event Node A ; Environmental Corrosion Factor Node E ; Distance between storage tanks and linkage risk nodes D ; Construct the joint probability distribution of the Bayesian network BN, which is defined as follows: By inferring the Bayesian network BN, when the current corrosion rate increases, the frequency of acoustic events increases, or the environmental parameters are unfavorable, the posterior inference of the Bayesian network BN will give a high-probability risk state, and finally the result of the Bayesian network BN is mapped to a risk index between 0 and 1. R , which is defined as follows: in, is the normalized mapping function; S045. Aggregate predicted corrosion rates, acoustic event classification results, and risk indices R , which is output as the evaluation result.

[0037] Based on the above content, as a better implementation option, preferably, this solution S05 includes: Obtain assessment results, including predicted corrosion rate, acoustic event classification results, risk index R It is compared with the preset threshold or reference classification. When it is exceeded, an early warning message is generated and output to achieve early warning for storage tanks and hazardous goods storage tank groups.

[0038] This solution cleverly converts corrosion rate data , Acoustic Event Characteristics , Environmental parameters 、Tank position parameters Thickness difference data of tank bottom plate Fusion of data of different dimensions to generate multi-source fusion data On this basis, the corresponding algorithms are used to make separate and combined judgments on the multi-source data to analyze whether there are any abnormalities on the bottom plate of the tank, providing a reliable mechanism guarantee for the safety monitoring of the tank group.

[0039] Based on the above, the present invention also provides a method for managing a hazardous goods storage tank storage area, which includes the above-mentioned method for monitoring the safety of a hazardous goods storage tank group.

[0040] Combination Figure 2 As shown, based on the above, this solution also provides a dangerous goods storage tank group safety monitoring system, which includes: There are multiple corrosion monitoring sensor modules, which are deployed on the inner wall and / or outer wall of the tank bottom plate and are used to monitor the corrosion conditions of the tank bottom plate area in real time and generate corrosion monitoring data; There are multiple acoustic monitoring sensor modules, which are deployed on the inner wall and / or outer wall of the bottom plate of the storage tank and are used to monitor the initiation and expansion of cracks in the bottom plate area of ​​the storage tank in real time and generate acoustic monitoring data; Environmental sensor group, used to monitor environmental corrosion factors and meteorological conditions around and between storage tanks, and generate environmental monitoring data; The data processing module is used to obtain corrosion monitoring data, acoustic monitoring data and environmental monitoring data, perform data cleaning, filtering and feature extraction on them, and then perform multi-source data fusion processing to generate multi-source fusion data; The data evaluation module is used to obtain multi-source fusion data, evaluate the corrosion degree of the tank and the overall risk of the tank group using a risk assessment algorithm based on machine learning and statistical models, and generate evaluation results; The early warning information unit is used to obtain the evaluation results, issue early warnings to the storage tanks and dangerous goods storage tank groups according to the evaluation results, and generate early warning information; The digital twin module is used to build a digital twin model based on a group of hazardous materials storage tanks, and present the assessment results and warning information in a visual form.

[0041] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0042] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0043] The above descriptions are only some embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Any equivalent device or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly used in other related technical fields, are also included in the protection scope of the present invention.

Claims

1. A safety monitoring method for a hazardous goods storage tank group is applied to the monitoring of an LNG hazardous goods storage tank group. A corrosion monitoring sensor module and an acoustic monitoring sensor module are deployed in the tank bottom plate area of ​​the LNG hazardous goods storage tank group. An environmental sensor group is deployed in the placement site of the LNG hazardous goods storage tank group. The method is characterized in that: It includes: S01. Real-time monitoring of the corrosion condition, crack initiation and expansion of the bottom plate area of ​​the storage tank is performed through the corrosion monitoring sensor module and the acoustic monitoring sensor module, and corrosion monitoring data and acoustic monitoring data are generated respectively; S02. Monitor the environmental corrosion factors and meteorological conditions around and between the storage tanks through the environmental sensor group to generate environmental monitoring data; S03, obtaining corrosion monitoring data, acoustic monitoring data and environmental monitoring data, performing data cleaning, filtering and feature extraction on the data, and then performing multi-source data fusion processing to generate multi-source fusion data; S04. Obtain multi-source fusion data, use risk assessment algorithms based on machine learning and statistical models to assess the corrosion degree of storage tanks and the overall risk of storage tank groups, and generate assessment results; S05. Obtain assessment results, issue early warnings to storage tanks and hazardous goods storage tank groups based on the assessment results, and generate early warning information.

2. The method for safety monitoring of a group of dangerous goods storage tanks according to claim 1, characterized in that: It also includes: S06. Build a digital twin model based on the hazardous materials storage tank group and present the assessment results and warning information in a visual form.

3. The method for safety monitoring of a group of dangerous goods storage tanks according to claim 2, characterized in that: In S01, the corrosion monitoring sensor module includes a plurality of electrochemical sensors for monitoring the corrosion of the tank bottom plate; the acoustic monitoring sensor module includes a plurality of ultrasonic sensors for measuring the thickness, cracks and crack extension of the tank bottom plate; some of the plurality of ultrasonic sensors are deployed in a preset monitoring area of ​​the tank bottom plate in the form of a sensor array; The corrosion monitoring data includes the corrosion current density and corrosion rate of the corresponding monitoring points on the bottom plate of the storage tank; the acoustic monitoring data includes the bottom plate thickness of the corresponding monitoring points on the bottom plate of the storage tank and the corresponding received sound wave data and sound source position data; The method for the corrosion monitoring sensor module and the acoustic monitoring sensor module to monitor and collect data from the bottom plate area of ​​the storage tank in real time includes: Multiple monitoring points are set on the bottom plate of the tank, and the location information set is defined as , n is the number of monitoring points, for each monitoring point , regularly obtain its thickness through the acoustic monitoring sensor module , and the corrosion current density measured by the corrosion monitoring sensor module ; Based on the basic parameters of the tank bottom and the measured corrosion current density The corrosion rate of the tank bottom plate is calculated and is defined as follows: in, For monitoring point The instantaneous corrosion rate at is the unit conversion constant, is the molar mass of the metal used for the tank bottom, is the number of electrons lost in the corrosion reaction, is the density of the metal used for the tank bottom plate, For monitoring point The corrosion current density at The thickness of the bottom plate at the monitoring point is obtained through the acoustic monitoring sensor module to assist in verifying the reliability of the corrosion rate calculation. It can be fitted into a mathematical relationship between thickness and time, which is defined as follows: in, For acoustic monitoring sensor modules Monitoring point at all times The measured thickness of the base plate, For acoustic monitoring sensor modules Monitoring point at all times The measured thickness of the base plate, For thickness difference; The sensing point set of multiple ultrasonic sensors is defined as , n is the number of sensing points; For sound source positioning, the time difference between the sound signal emitted by the sound source and the sensing point where each ultrasonic sensor is located is recorded. Locate the sound source coordinates , at the sensing point of the known ultrasonic sensor Position and speed of sound In the case of, the coordinates of the sound source position are solved according to the least squares method. , whose formula is defined as follows: By solving the least squares method, the coordinates of the sound source position are obtained ,in, The sensing point of the ultrasonic sensor Corresponding x , y Coordinate location.

4. The method for safety monitoring of a group of dangerous goods storage tanks according to claim 3, characterized in that: In S03, when extracting the acoustic monitoring data, the sound wave data collected by the ultrasonic sensor is Perform wavelet transform, which is defined as follows: in, For sound wave data In scale and pan The wavelet coefficients under ; is the wavelet basis function; , which is the scale factor, is the translation factor, is complex conjugate; By selecting wavelet basis functions and characteristic coefficient statistics, characteristic parameters of acoustic events are extracted and set as acoustic event features, which include energy , duration , Ring Count One or more of the following; Among them, the extracted characteristic parameters and the sound source position coordinates The data are stored together and aggregated into the acoustic monitoring data for reuse.

5. The method for safety monitoring of a group of dangerous goods storage tanks according to claim 3 or 4, characterized in that: The environmental sensor group includes at least one of a gas composition sensor, a temperature sensor, a humidity sensor, and a wind speed and direction sensor; the environmental corrosion factors include hydrogen sulfide, sulfur dioxide, and chloride ions; In S02, environmental monitoring data is collected in the form of environmental parameter sets to form an environmental parameter vector , which is defined as follows: in, The gas concentration or ion concentration data of the corrosive gases hydrogen sulfide, sulfur dioxide, and chloride ions measured by the gas composition sensors respectively; is the ambient temperature data, is the relative humidity data, They are wind direction data and wind speed data respectively; S02 also includes: For the tank group, suppose there is a tank group set , obtain the center position of each tank through the laser ranging module or GPS positioning module , where the distance between tanks is It can be expressed as follows: in, For storage tank i The coordinate position of For storage tank j The coordinate position of Collect all the distances between tanks in the tank group , forming the tank position parameters .

6. The method for safety monitoring of a group of dangerous goods storage tanks according to claim 5, characterized in that: S03 includes: Obtain corrosion monitoring data, acoustic monitoring data and environmental monitoring data and set them as multi-source data; Detect and interpolate outliers in multi-source data to achieve data cleaning, then use sliding window averaging or median filtering to filter multi-source data, and then normalize data features so that all data features are mapped to the [0, 1] interval, so that data of different dimensions can be fused and processed; Establish multi-dimensional feature vectors to fuse data of different dimensions and generate multi-source fusion data , which includes corrosion rate data , Acoustic Event Characteristics , Environmental parameters 、Tank position parameters Thickness difference data of tank bottom plate , which is defined as follows: Among them, multi-source fusion data Used to assess the corrosion level of storage tanks and the overall risk of tank groups.

7. The method for safety monitoring of a group of dangerous goods storage tanks according to claim 6, characterized in that: S04 includes: S041. Obtain multi-source fusion data and extract corrosion rate data from it , Acoustic Event Characteristics , Environmental parameters 、Tank position parameters Thickness difference data of tank bottom plate ; S042. Use random forest RF or LSTM time series network to calculate the corrosion rate of future moments Make predictions to obtain predicted corrosion rates; Wherein, the input features include corrosion rate data, and environmental parameters and / or acoustic event characteristics; S043, classifying acoustic event features by a classifier including a SVM algorithm or a random forest algorithm, wherein the classification categories include: microcrack extension event Crack, pitting event Pitting, local leakage event Leakage, or mechanical noise event Noise; and obtaining acoustic event classification results; Among them, the input feature vector of the classifier , the output is the event category label ; S044. Comprehensively analyze multi-source fusion data through Bayesian network BN, and the analysis factors include corrosion rate data, acoustic event characteristics, environmental parameters, and tank position parameters; Among them, the Bayesian network nodes include: Base Plate Corrosion Status Node C ; Acoustic Event Node A ; Environmental Corrosion Factor Node E ; Distance between storage tanks and linkage risk nodes D ; Construct the joint probability distribution of the Bayesian network BN, which is defined as follows: By inferring the Bayesian network BN, when the current corrosion rate increases, the frequency of acoustic events increases, or the environmental parameters are unfavorable, the posterior inference of the Bayesian network BN will give a high-probability risk state, and finally the result of the Bayesian network BN is mapped to a risk index between 0 and 1. R , which is defined as follows: in, is the normalized mapping function; S045. Aggregate predicted corrosion rates, acoustic event classification results, and risk indices R , which is output as the evaluation result.

8. The method for safety monitoring of a group of dangerous goods storage tanks according to claim 7, characterized in that: S05 includes: Obtain assessment results, including predicted corrosion rate, acoustic event classification results, risk index R It is compared with the preset threshold or reference classification. When it is exceeded, an early warning message is generated and output to achieve early warning for storage tanks and hazardous goods storage tank groups.

9. A method for managing a storage area of ​​a hazardous goods storage tank, characterized in that: It includes the safety monitoring method for a group of dangerous goods storage tanks as described in any one of claims 1 to 8.

10. A safety monitoring system for dangerous goods storage tanks, characterized in that: It includes: There are multiple corrosion monitoring sensor modules, which are deployed on the inner wall and / or outer wall of the tank bottom plate and are used to monitor the corrosion conditions of the tank bottom plate area in real time and generate corrosion monitoring data; There are multiple acoustic monitoring sensor modules, which are deployed on the inner wall and / or outer wall of the bottom plate of the storage tank and are used to monitor the initiation and expansion of cracks in the bottom plate area of ​​the storage tank in real time and generate acoustic monitoring data; Environmental sensor group, used to monitor environmental corrosion factors and meteorological conditions around and between storage tanks, and generate environmental monitoring data; The data processing module is used to obtain corrosion monitoring data, acoustic monitoring data and environmental monitoring data, perform data cleaning, filtering and feature extraction on them, and then perform multi-source data fusion processing to generate multi-source fusion data; The data evaluation module is used to obtain multi-source fusion data, evaluate the corrosion degree of the tank and the overall risk of the tank group using a risk assessment algorithm based on machine learning and statistical models, and generate assessment results; The early warning information unit is used to obtain the evaluation results, issue early warnings to the storage tanks and dangerous goods storage tank groups according to the evaluation results, and generate early warning information; The digital twin module is used to build a digital twin model based on a group of hazardous materials storage tanks, and present the assessment results and warning information in a visual form.

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