Safety evaluation method, system, equipment and program for chemical storage place

Abstract

The invention discloses a safety evaluation method, system, equipment and program for a chemical storage place, and relates to the technical field of dangerous chemical management, and the method comprises the steps: carrying out the time-space alignment of multi-source data; wherein the multi-source data comprises stored chemical data, environment data, equipment state data, management data and historical data; extracting spatial features of the multi-source data after space-time alignment, and generating a spatial risk weight map; pre-training a risk evaluation model; and mapping the weight in the spatial risk weight map to the risk evaluation model to obtain a risk level. Through the treatment scheme disclosed by the invention, potential risks of the storage place can be found in time, early warning can be performed in advance, emergency response can be started, the possibility of accidents can be effectively reduced, and the safety of the chemical storage place can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hazardous chemical management, and in particular to a safety evaluation method, system, equipment and program for chemical storage sites. Background Art

[0002] Explosive, radioactive, and highly toxic hazardous chemicals are extremely dangerous. If leaks or explosions occur during storage, they can cause immeasurable damage to human life and the ecological environment. Currently, safety assessments of chemical storage sites mostly rely on static evaluations, which primarily rely on regular manual inspections and simple data recording. These assessments are incomplete, lack real-time performance, and are difficult to effectively predict potential risks. Furthermore, the characteristics of different types of hazardous chemicals vary significantly, making it difficult for existing methods to comprehensively consider the risk factors of multiple chemicals and conduct accurate assessments.

[0003] Although the above static assessment method can perform security assessment to a certain extent, it is found that there are still some shortcomings in its actual use, which cannot achieve the best effect. Its shortcomings can be summarized as follows: 1. Relying on manual inspections and static assessment models, it is impossible to capture dynamic risks (such as sudden temperature rise, radiation leakage, container corrosion, etc.) in real time.

[0004] 2. There is a lack of systematic analysis tools for the chain reactions caused by vibration / friction of explosive materials and the mixed storage of radioactive materials and highly toxic substances.

[0005] 3. Accident scenario simulation relies on historical data and is difficult to cope with complex environmental variables (such as extreme weather and human errors).

[0006] Therefore, the above-mentioned existing static assessment methods still have inconveniences and defects in use and are in urgent need of further improvement. How to create a new safety assessment method for chemical storage sites has become a goal that the industry urgently needs to improve. Summary of the Invention

[0007] In view of this, an embodiment of the present disclosure provides a safety assessment method for a chemical storage site, which at least partially solves the problems existing in the prior art.

[0008] In a first aspect, an embodiment of the present disclosure provides a method for safety assessment of a chemical storage site, the method comprising the following steps: Performing spatiotemporal alignment on multi-source data, wherein the multi-source data includes stored chemical data, environmental data, equipment status data, management data, and historical data; Extract spatial features of multi-source data after spatiotemporal alignment and generate spatial risk weight maps; Pre-trained risk assessment model; The weights in the spatial risk weight map are mapped to the risk assessment model to obtain the risk level.

[0009] According to a specific implementation of the embodiment of the present disclosure, the environmental data includes: internal environmental data and external environmental data; wherein the internal environmental data includes three-dimensional point cloud data of the warehouse building structure; the external environmental data includes wind speed, rainfall, earthquake waves, groundwater level, and surrounding traffic flow; The device status data includes: temperature sensor data, humidity sensor data, gas concentration sensor data and vibration sensor data; The management data includes: chemical in-and-out information, storage quantity, storage location, shelf life, management personnel operation records, and safety training records.

[0010] According to a specific implementation of an embodiment of the present disclosure, the spatiotemporal alignment of multi-source data includes: Generate a BIM model of the building structure based on the 3D point cloud data of the warehouse building structure; Generate sensor time series data based on device status data; Performing spatiotemporal coordinate alignment, including: mapping sensor positions to the three-dimensional coordinates of the BIM model; Perform time window alignment, including: windowing the sensor data series into preset time periods and aligning them with the static data in the BIM model; and updating the external environment data at the minute level and interpolating them to the sensor timestamp.

[0011] According to a specific implementation of the embodiment of the present disclosure, extracting spatial features of the multi-source data after time-space alignment to generate a spatial risk weight map includes: Extract spatial features of BIM models based on GCN; Extract time series features from temperature sensor data, humidity sensor data, and gas concentration sensor data based on long short-term memory networks; Extract environmental features of environmental data, including: calculating the radial component of wind speed; extracting features from vibration sensor data based on wavelet transform; and extracting the main frequency energy spectrum from seismic wave signals through Fourier transform. Map BIM spatial features, sensor temporal features, and environmental features to a shared latent space and compress them into unified dimensional features through a fully connected layer; Generate weight coefficients of spatial features, temporal features, and environmental features based on the gating mechanism; Output spatial risk weight map through deep learning model.

[0012] According to a specific implementation of the embodiment of the present disclosure, the weight coefficients of the spatial features, temporal features, and environmental features generated based on the gating mechanism include: The gating mechanism dynamically assigns weight coefficients based on the following formula: ; in, It is the weighted sum of spatial features, temporal features, and environmental features; The weight coefficient generated for the gate satisfies ; is the spatial feature; is the time series feature; For environmental characteristics; The spatial feature weight is obtained based on the following formula : ; in, is the spatial feature weight The learnable weight matrix of is the spatial feature; is the environmental parameter; The time series feature weight is obtained based on the following formula : ; in, is the time series feature weight The learnable weight matrix of is the time series feature; is the environmental parameter encoder; It is the feature splicing operation; The environmental feature weights are obtained based on the following formula : ; in, is the environmental feature weight The learnable weight matrix of is the tensor product operation; It is a graph neural network.

[0013] According to a specific implementation of the embodiment of the present disclosure, the risk assessment model is a CNN model; The pre-trained risk assessment model includes the following steps: Acquiring historical data; the historical data includes historical stored chemical data, historical environmental data, historical equipment status data, historical management data, and historical safety evaluation data; Normalize historical data so that the input value is scaled to the range of [-1, 1] or [0, 1]; The normalized historical data is divided into training set, validation set and test set in a ratio of 7:2:1; Train the risk assessment model based on the training set; The trained risk assessment model is verified based on the validation set. When the validation set loss does not decrease for five consecutive rounds, the training is terminated. The verified risk assessment model is tested based on the test set; when the test result meets the preset threshold, the training is stopped; when the test result does not meet the preset threshold, the hyperparameters of the risk assessment model are tuned based on Bayesian optimization, and the tuned risk assessment model is re-trained, verified, and tested until the test result meets the preset threshold.

[0014] In a second aspect, an embodiment of the present disclosure provides a safety assessment system for chemical storage sites, the system comprising: a data processing module configured to perform spatiotemporal alignment on multi-source data, wherein the multi-source data includes stored chemical data, environmental data, equipment status data, management data, and historical data; The weight module is configured to extract spatial features of multi-source data after spatiotemporal alignment and generate a spatial risk weight map; A model training module is configured to pre-train the risk assessment model; The risk prediction module is configured to map the weights in the spatial risk weight map to the risk assessment model to obtain a risk level.

[0015] According to a specific implementation of the embodiment of the present disclosure, the system further includes: The spatiotemporal alignment module is configured to generate a BIM model of the building structure based on the three-dimensional point cloud data of the warehouse building structure; generate a sensor time series data sequence based on the equipment status data; perform spatiotemporal coordinate alignment, including: mapping the sensor position to the three-dimensional coordinates of the BIM model; perform time window alignment, including: windowing the sensor data sequence according to a preset time period and aligning it with the static data in the BIM model; and update the external environment data at the minute level and interpolate it to the sensor timestamp.

[0016] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor. When the instructions are executed by the at least one processor, the at least one processor implements the safety assessment method for chemical storage sites as described in any one of the first aspect or any one of the implementations of the first aspect.

[0017] In a fourth aspect, an embodiment of the present disclosure further provides a non-transitory computer-readable storage medium, which stores computer instructions, and when the computer instructions are executed by at least one processor, the at least one processor executes the safety assessment method for chemical storage sites in the aforementioned first aspect or any implementation of the first aspect.

[0018] In a fifth aspect, an embodiment of the present disclosure further provides a computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions, which, when executed by a computer, cause the computer to execute the safety assessment method for chemical storage sites in the aforementioned first aspect or any implementation of the first aspect.

[0019] The safety evaluation method for chemical storage sites in the disclosed embodiment realizes a comprehensive, real-time and accurate evaluation of the safety status of storage sites for explosive-making hazardous chemicals, radioactive hazardous chemicals and highly toxic hazardous chemicals by integrating multi-source data and a weighted dynamic intelligent algorithm, discovers potential risks in advance and issues timely warnings, and effectively reduces the probability of accidents. The multi-source data integrated by the present invention covers multiple dimensions such as environment, equipment, and management, and comprehensively considers various risk factors in the storage process of explosive-making, radioactive sources and highly toxic hazardous chemicals. Compared with traditional static evaluation methods, the evaluation is more comprehensive and accurate. Through real-time data collection and dynamic evaluation, it is possible to timely discover potential risks in storage sites, issue early warnings and initiate emergency responses, effectively reduce the possibility of accidents, and improve the safety of chemical storage sites. The weight coefficients of spatial characteristics, temporal characteristics and environmental characteristics are generated based on the gating mechanism, so that the weight distribution is more scientific and reasonable, and the reliability of the evaluation results is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic flow chart of a safety assessment method for a chemical storage site provided in an embodiment of the present disclosure; Figure 2 A flowchart of a safety assessment method for a chemical storage site provided in an embodiment of the present disclosure; Figure 3 A flowchart of a method for performing spatiotemporal alignment of multi-source data provided in an embodiment of the present disclosure; Figure 4 A schematic diagram of a safety assessment system for chemical storage sites provided in an embodiment of the present disclosure; Figure 5 A schematic diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0021] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0022] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0023] It should be noted that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or functionality described herein is illustrative only. Based on this disclosure, those skilled in the art will appreciate that one aspect described herein may be implemented independently of any other aspect, and that two or more of these aspects may be combined in various ways. In addition, other structures and / or functionality other than one or more of the aspects described herein may be used to implement this apparatus and / or practice this method.

[0024] Additionally, in the following description, specific details are provided to provide a thorough understanding of the examples. However, one skilled in the art will appreciate that the aspects described can be practiced without these specific details.

[0025] Figure 1 A schematic diagram of the process of a safety assessment method for a chemical storage site provided in an embodiment of the present disclosure.

[0026] Figure 2 For Figure 1 Corresponding flow chart of the safety assessment method for chemical storage sites.

[0027] like Figure 1 As shown, at step S110, the multi-source data is temporally and spatially aligned; wherein the multi-source data includes stored chemical data, environmental data, equipment status data, management data and historical data.

[0028] More specifically, required data is acquired, including stored chemical data, environmental data, equipment status data, management data, and historical data.

[0029] In an embodiment of the present invention, the environmental data includes: internal environmental data and external environmental data; wherein the internal environmental data includes three-dimensional point cloud data of the warehouse building structure; the external environmental data includes wind speed, rainfall, earthquake waves, groundwater level, and surrounding traffic flow; The equipment status data includes: temperature sensor data, humidity sensor data, gas concentration sensor data, and vibration sensor data; the equipment status data also includes: storage container material data, storage container structure data, storage container sealing data, building structure data, firefighting equipment data, and leakage detection data; The management data includes: chemical in-and-out information, storage quantity, storage location, shelf life, management personnel operation records, safety training records, safety inspection data and emergency response plan data.

[0030] More specifically, among the external environmental data, meteorological data is connected to authoritative meteorological platforms (such as the China Meteorological Administration) through API interfaces to obtain real-time meteorological data (temperature and humidity, wind speed, rainfall, thunderstorm probability, etc.) and disaster warnings (typhoons, earthquakes, floods); geological data is collected in real time by deploying vibration sensors and geological monitoring equipment to collect seismic waves, soil subsidence, and groundwater level change data around the warehouse.

[0031] Internal environmental data includes real-time temperature and humidity, air pressure, ventilation conditions, lighting, gas concentration, radiation, and surrounding building structures (BIM models). The warehouse's internal sensor network (temperature, humidity, air pressure, and vibration) is linked and calibrated with external data to eliminate environmental interference (such as sensor errors caused by temperature and humidity gradients).

[0032] By inputting the type of stored chemicals, the chemical properties and storage condition data are obtained, such as the friction sensitivity of explosives (such as ammonium nitrate), the half-life of radioactive sources (such as iridium-192), the volatility threshold of highly toxic substances (such as sodium cyanide), and factors that may affect the safety and stability of stored chemicals. For example, ammonium nitrate is hygroscopic and easily explosive. Factors affecting the safety and stability of ammonium nitrate include humidity, temperature, thunderstorms, and the sealing of storage containers.

[0033] The equipment status data includes: storage container material data, storage container structure data, storage container sealing data, building structure data, firefighting equipment data and leakage detection data; Historical accident data includes the spread range, impact time, and data at the time of the accident of similar chemical leakage accidents.

[0034] More specifically, the above data are preprocessed, including interpolation of missing values; wavelet transform (Daubechies wavelet) is used to remove high-frequency noise and retain key risk signals.

[0035] like Figure 3 As shown, in an embodiment of the present invention, the spatiotemporal alignment of multi-source data includes: generating a BIM model of the building structure based on the three-dimensional point cloud data of the warehouse building structure; generating a sensor time series data sequence based on the equipment status data; performing spatiotemporal coordinate alignment, including: mapping the sensor position to the three-dimensional coordinates of the BIM model; performing time window alignment, including: windowing the sensor data sequence according to a preset time period and aligning it with the static data in the BIM model; and updating the external environment data at the minute level and interpolating it to the sensor timestamp.

[0036] More specifically, mapping the sensor position to the 3D coordinates of the BIM model for spatiotemporal coordinate alignment includes: First, obtain the physical coordinates and sensor type of the sensor. Then, synchronize the sensor timestamp with the BIM model timeline based on the NTP protocol (Network Time Protocol) and convert it to the BIM model coordinate system using a coordinate conversion tool (such as Proj4 or GDAL).

[0037] Then, perform spatial alignment, which includes the following steps: 1. Point cloud matching: The sensor coordinates are matched with the point cloud of the BIM model using the ICP algorithm (Iterative Closest Point).

[0038] 2. Feature matching: Utilize key geometric features in the BIM model (such as wall edges and equipment outlines) to perform nearest neighbor matching with sensor locations.

[0039] 3. Manual calibration: Manually place sensor icons in BIM software (such as Revit) and verify position accuracy by cutting through the view.

[0040] 4. Error analysis: The Euclidean distance between the sensor coordinates and the corresponding points on the model is calculated, and the average error is counted. When the error is greater than the preset threshold, key points (such as beam-column nodes) are selected for field measurement, and the position deviation between the model and the sensor is compared to re-match the space.

[0041] More specifically, the process proceeds to step S120.

[0042] In step S120, the spatial features of the multi-source data after time-space alignment are extracted to generate a spatial risk weight map.

[0043] In an embodiment of the present invention, the spatial features of the multi-source data after time and space alignment are extracted to generate a spatial risk weight map, including: extracting spatial features of the BIM model based on GCN (Graph Convolutional Network); extracting time series features of temperature sensor data, humidity sensor data and gas concentration sensor data based on a long short-term memory network; extracting environmental features of environmental data, including: calculating the radial component of wind speed; extracting features of vibration sensor data based on wavelet transform; extracting the main frequency energy spectrum by Fourier transform of seismic wave signals; mapping BIM spatial features, sensor time series features and environmental features to a shared latent space, and compressing them into unified dimensional features through a fully connected layer; generating weight coefficients of spatial features, time series features and environmental features based on a gating mechanism; and outputting a spatial risk weight map through a deep learning model.

[0044] More specifically, spatial feature extraction of the BIM model based on GCN includes the following steps: Based on the BIM model, relevant data is extracted, including geometric information of building components (such as coordinates, dimensions, shapes, etc.), topological information (such as connection relationships and adjacency relationships between components, etc.), and semantic information (such as component type and function, etc.). These data are then organized and preprocessed to ensure that the data is on the same scale to facilitate subsequent calculations.

[0045] Each building component in the BIM model is considered a node in a graph. Edges in the graph are defined based on the actual connectivity or spatial adjacency between components. If two components are directly connected spatially or meet certain adjacency conditions (such as a distance less than a certain threshold), an edge is established between their corresponding nodes. This creates a graph that reflects the spatial structure of the BIM model.

[0046] The graph that can reflect the spatial structure of the BIM model is input into the trained GCN model for spatial feature extraction. The GCN model will output the spatial feature representation of each node (building component) and analyze and visualize the extracted spatial features.

[0047] Time series feature extraction is performed on temperature sensor data, humidity sensor data, and gas concentration sensor data based on the long short-term memory network, including: Collect data from temperature sensors, humidity sensors, and gas concentration sensors. These sensors usually sample data at preset time intervals, such as once every minute or once every hour.

[0048] Check whether there are missing values ​​in the dataset; if so, choose to fill them using mean filling, median filling, or time series-based interpolation methods, depending on the specific situation.

[0049] The data collected by different sensors are normalized and mapped to a specific interval such as [0,1] or [-1,1] to eliminate the dimensional differences between different data dimensions and speed up the convergence of the model.

[0050] The preprocessed data is fed into a trained LSTM (Long Short-Term Memory) model for feature extraction. The input data is represented as time series features, and the extracted features are analyzed and visualized. For example, methods such as principal component analysis (PCA) can be used to map high-dimensional features into a two- or three-dimensional space, observing the distribution of different sensor data in the feature space and their temporal trends. Cluster analysis can also be used to cluster data points with similar characteristics to uncover underlying patterns and regularities in the data. Furthermore, the extracted features can be used for subsequent tasks such as anomaly detection and predictive analysis, and their effectiveness can be further verified by evaluating the model's performance on these tasks.

[0051] Map BIM spatial features, sensor temporal features, and environmental features to a shared latent space and compress them into unified dimensional features through a fully connected layer. This involves the following steps: Use the fully connected layer to map the reduced BIM features into the shared latent space:

[0052] in, is the shared latent space feature vector after BIM model mapping; is the learnable weight matrix applied to BIM; The spatial characteristics of the BIM model; It is a bias term that enhances the expressiveness of the model.

[0053] Output dimension ,in, represents a d-dimensional real vector space, where d can be 128.

[0054] In the process of sensor feature embedding, the temporal features Applying Multi-Layer Perceptron (MLP):

[0055] in, is the shared latent space feature vector after sensor mapping; MLP is a multi-layer perceptron used for nonlinear mapping of sensor time series features; is the sensor time series feature vector Apply an embedding layer or shallow network to the normalized environment features.

[0056] Use Multi-Head Attention to calculate the inter-modality association weights and dynamically adjust feature importance.

[0057] Query: Shared latent space feature vector after BIM mapping .

[0058] Key: shared latent space feature vector after sensor mapping .

[0059] Value: shared latent space feature vector after environment mapping .

[0060] Output: weighted fusion features .

[0061] The fusion features Gradually compress the dimensions (e.g., d→64→32), and each layer is followed by ReLU activation and Dropout (e.g., 0.3).

[0062]

[0063] Output unified dimensional features .

[0064] In the embodiment of the present invention, the weight coefficients of spatial features, temporal features, and environmental features generated based on the gating mechanism include: The gating mechanism dynamically assigns weight coefficients based on the following formula: ; in, It is the weighted sum of spatial features, temporal features, and environmental features; The weight coefficient generated for the gate satisfies ; is the spatial feature; is the time series feature; For environmental characteristics; The spatial feature weight is obtained based on the following formula : ; in, is the spatial feature weight The learnable weight matrix of is the spatial feature; is the environmental parameter, which includes a set of internal and external environmental parameters; The time series feature weight is obtained based on the following formula : ; in, is the time series feature weight The learnable weight matrix of is the time series feature; is the environmental parameter encoder; It is the feature splicing operation; The environmental feature weights are obtained based on the following formula : ; in, is the environmental feature weight The learnable weight matrix of For environmental characteristics; is the tensor product operation; It is a graph neural network.

[0065] Next, go to step S130.

[0066] In step S130 , a risk assessment model is pre-trained.

[0067] In an embodiment of the present invention, the risk assessment model is a CNN model; the pre-trained risk assessment model includes the following steps: obtaining historical data; the historical data includes historical storage chemical data, historical environmental data, historical equipment status data, historical management data and historical safety assessment data; normalizing the historical data so that the input value is scaled to the interval of [-1,1] or [0,1]; dividing the normalized historical data into a training set, a validation set and a test set in a ratio of 7:2:1; training the risk assessment model based on the training set; validating the trained risk assessment model based on the validation set, and terminating the training when the validation set loss does not decrease for five consecutive epochs; testing the validated risk assessment model based on the test set; wherein, when the test result meets the preset threshold, stopping the training; when the test result does not meet the preset threshold, tuning the hyperparameters of the risk assessment model based on Bayesian optimization, and retraining, validating and testing the optimized risk assessment model until the test result meets the preset threshold.

[0068] Next, go to step S140.

[0069] In step S140, the weights in the spatial risk weight map are mapped to the risk assessment model to obtain the risk level.

[0070] In an embodiment of the present invention, the method further includes: presetting a linkage response strategy; the linkage response strategy can be divided into linkage strategies according to risk levels or application scenarios.

[0071] For example: 1. Linkage strategies based on risk levels 1. When the risk is low and the only time series characteristics are fluctuations in temperature and humidity, and the fluctuation is less than 5%, the system records the risk data, generates a daily report, and notifies the safety officer to perform a routine inspection.

[0072] If the risk indicator indicates low risk and the radioactive material activity concentration is less than 0.1 μSv / h, or the toxic gas leakage rate is less than 10 g / min (or the threshold set for the type of toxic chemical), activate the local exhaust system to dilute the toxic gas concentration. Initiate drone inspections to verify the location of the leak, notify on-site personnel to wear protective equipment, and increase monitoring frequency to every 15 minutes.

[0073] 2. When the risk is medium and the gas concentration increases at a rate greater than 10 ppm / h, start the local exhaust system (such as increasing the vent opening by 30%), simulate the risk evolution path, and notify the emergency team to stand by.

[0074] When the risk is medium, the radioactivity is greater than or equal to 0.1μSv / h and less than 1mSv / h, or the toxic gas leakage rate is greater than or equal to 10g / min and less than 100g / min, close the adjacent ventilation ducts, start the iodine tablet distribution device (for radioactive iodine leakage), call a robot to enter the contaminated area to collect samples, and define the radius of the warning zone based on the real-time wind speed. Unrelated personnel are prohibited from entering, and the emergency team enters the scene wearing chemical protective clothing.

[0075] 3. When a high risk is indicated and environmental parameters change suddenly (such as wind speed greater than 10m / s, rainfall greater than 20mm / h, etc.), the entire area sprinkler system (such as the ammonium nitrate storage area) is triggered, the power supply to adjacent shelves is cut off, an evacuation route is generated, an emergency broadcast is initiated, and personnel are evacuated to a safe area.

[0076] When a high risk is indicated, the radioactivity is greater than 1mSv / h, or toxic substances spread to densely populated areas, and the concentration of volatile poisons (such as hydrogen cyanide) reaches the IDLH (immediately threatening life and health) threshold, the entire storage area will be triggered to spray dilution (for acidic poisons) or inert gas blanketing (such as nitrogen to inhibit cyanide combustion), and emergency broadcasting will be started to guide personnel to evacuate in the upwind direction. The fire brigade will wear positive pressure breathing apparatus (SCBA) to seal off the scene, the medical team will prepare the decontamination station, and the environmental protection department will activate the regional radiation monitoring network.

[0077] 4. When an extreme risk is indicated and there is a risk of a chain reaction (such as dust explosion or spread of toxic gases), the highest level emergency protocol will be activated, the fire protection system will be linked, and the government emergency department will be notified to forcibly evacuate all personnel in the factory and block the surrounding area.

[0078] 2. Linkage Strategies by Application Scenario 1. When a hazardous chemical leak is detected, the leak source is located based on a spatial weight map, and the relevant pipeline valves are automatically closed. When wind speeds exceed 5m / s, a mobile fog cannon is activated to dilute the gas. At night, when there are few people, audible and visual alarms are triggered first to avoid panic.

[0079] 2. When a fire is detected, the fire spread path is analyzed based on the BIM model (such as ventilation ducts supporting combustion), and the opening and closing status of the fire shutter doors are dynamically adjusted to isolate the fire source; the fire water monitor is automatically activated and targeted at high-temperature areas.

[0080] 3. When a natural disaster is detected, such as an earthquake wave intensity greater than level 6, emergency lighting and evacuation route indications are triggered; when the groundwater level rises to the warning line, the underground warehouse waterproof gate is automatically closed; when a thunderstorm warning is issued, the warehouse ventilation system is automatically shut down and the nitrogen inerting device is activated (to prevent oxidation of explosive materials); when an earthquake warning is issued, the shelf locking mechanism is triggered, stopping the AGV transport robot operation.

[0081] The above examples are merely embodiments of the present invention and do not limit the present invention. Linkage strategies for different situations can be preset according to different stored chemicals.

[0082] The safety assessment method for chemical storage sites proposed in this paper integrates multi-source data, encompassing multiple dimensions such as the environment, equipment, and management. It comprehensively considers various risk factors associated with the storage of explosives, radioactive sources, and highly toxic hazardous chemicals. Compared to traditional static assessment methods, this method is more comprehensive and accurate. Furthermore, through real-time data collection and dynamic assessment, potential risks at storage sites can be promptly identified, providing early warnings and initiating emergency responses, effectively reducing the likelihood of accidents, improving the safety of chemical storage sites, and enhancing the reliability of assessment results.

[0083] Figure 4 The safety assessment system 400 for chemical storage sites provided by the present invention is shown, including a data processing module 410 , a weight module 420 , a model training module 430 and a risk prediction module 440 .

[0084] The data processing module 410 is used to perform spatiotemporal alignment on multi-source data; wherein the multi-source data includes stored chemical data, environmental data, equipment status data, management data and historical data; The weight module 420 is used to extract the spatial features of the multi-source data after time and space alignment and generate a spatial risk weight map; The model training module 430 is used to pre-train the risk assessment model; The risk prediction module 440 is used to map the weights in the spatial risk weight map to the risk assessment model to obtain the risk level.

[0085] See also Figure 5 The present disclosure further provides an electronic device 50, which includes: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to execute the safety assessment method for chemical storage sites in the aforementioned method embodiment.

[0086] The embodiment of the present disclosure also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the safety assessment method for chemical storage sites in the aforementioned method embodiment.

[0087] An embodiment of the present disclosure also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, which, when executed by a computer, enable the computer to execute the safety assessment method for chemical storage sites in the aforementioned method embodiment.

[0088] Reference below Figure 5 , which shows a schematic structural diagram of an electronic device 50 suitable for implementing an embodiment of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0089] like Figure 5As shown, electronic device 50 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 502 or programs loaded from a storage device 508 into a random access memory (RAM) 503. RAM 503 also stores various programs and data required for the operation of electronic device 50. Processing device 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to bus 504.

[0090] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; a storage device 508 including, for example, a magnetic tape, hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 50 to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows the electronic device 50 with various devices, it should be understood that not all of the devices shown are required to be implemented or present. More or fewer devices may alternatively be implemented or present.

[0091] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication device 509, or installed from the storage device 508, or installed from the ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0092] It should be noted that the computer-readable medium described above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable storage media may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wire, optical cable, RF (radio frequency), or any suitable combination thereof.

[0093] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0094] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains at least two Internet Protocol addresses; sends a node evaluation request including the at least two Internet Protocol addresses to a node evaluation device, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein the obtained Internet Protocol address indicates an edge node in a content distribution network.

[0095] Alternatively, the computer-readable medium carries one or more programs, which, when executed by the electronic device, causes the electronic device to: receive a node evaluation request including at least two Internet Protocol addresses; select an Internet Protocol address from the at least two Internet Protocol addresses; and return the selected Internet Protocol address; wherein the received Internet Protocol address indicates an edge node in a content distribution network.

[0096] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0097] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0098] The units involved in the embodiments described in this disclosure may be implemented in software or hardware. In some cases, the name of a unit does not limit the unit itself. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses."

[0099] It should be understood that various parts of the present disclosure can be implemented in hardware, software, firmware, or a combination thereof.

[0100] The above description is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with this technical field within the technical scope disclosed in this disclosure should be covered by the protection scope of the present disclosure.

Claims

1. A safety assessment method for chemical storage sites, characterized in that: The method comprises the following steps: Performing spatiotemporal alignment on multi-source data, wherein the multi-source data includes stored chemical data, environmental data, equipment status data, management data, and historical data; Extract spatial features of multi-source data after spatiotemporal alignment and generate spatial risk weight maps; Pre-trained risk assessment model; The weights in the spatial risk weight map are mapped to the risk assessment model to obtain the risk level.

2. The safety assessment method for chemical storage sites according to claim 1, characterized in that: The environmental data includes: internal environmental data and external environmental data; wherein the internal environmental data includes three-dimensional point cloud data of the warehouse building structure; the external environmental data includes wind speed, rainfall, seismic waves, groundwater level, and surrounding traffic flow; The device status data includes: temperature sensor data, humidity sensor data, gas concentration sensor data and vibration sensor data; The management data includes: chemical in-and-out information, storage quantity, storage location, shelf life, management personnel operation records, and safety training records.

3. The safety assessment method for chemical storage sites according to claim 2, characterized in that: The spatiotemporal alignment of multi-source data includes: Generate a BIM model of the building structure based on the 3D point cloud data of the warehouse building structure; Generate sensor time series data based on device status data; Performing spatiotemporal coordinate alignment, including: mapping sensor positions to the three-dimensional coordinates of the BIM model; Perform time window alignment, including: windowing the sensor data series into preset time periods and aligning them with the static data in the BIM model; and updating the external environment data at the minute level and interpolating them to the sensor timestamp.

4. The safety assessment method for chemical storage sites according to claim 2, characterized in that: The step of extracting spatial features of the multi-source data after time-space alignment and generating a spatial risk weight map includes: Extract spatial features of BIM models based on GCN; Extract time series features from temperature sensor data, humidity sensor data, and gas concentration sensor data based on long short-term memory networks; Extract environmental features of environmental data, including: calculating the radial component of wind speed; extracting features from vibration sensor data based on wavelet transform; and extracting the main frequency energy spectrum from seismic wave signals through Fourier transform. Map BIM spatial features, sensor temporal features, and environmental features to a shared latent space and compress them into unified dimensional features through a fully connected layer; Generate weight coefficients of spatial features, temporal features, and environmental features based on the gating mechanism; Output spatial risk weight map through deep learning model.

5. The safety assessment method for chemical storage sites according to claim 4, characterized in that: The weight coefficients of spatial features, temporal features, and environmental features generated based on the gating mechanism include: The gating mechanism dynamically assigns weight coefficients based on the following formula: ； in, It is the weighted sum of spatial features, temporal features, and environmental features; The weight coefficient generated for the gate satisfies ; is the spatial feature; is the time series feature; For environmental characteristics; The spatial feature weight is obtained based on the following formula : ； in, is the spatial feature weight The learnable weight matrix of is the spatial feature; is the environmental parameter; The time series feature weight is obtained based on the following formula : ； in, is the time series feature weight The learnable weight matrix of is the time series feature; is the environmental parameter encoder; It is the feature splicing operation; The environmental feature weights are obtained based on the following formula : ； in, is the environmental feature weight The learnable weight matrix of For environmental characteristics; is the tensor product operation; It is a graph neural network.

6. The safety assessment method for chemical storage sites according to claim 1, characterized in that: The risk assessment model is a CNN model; The pre-trained risk assessment model includes the following steps: Acquiring historical data; the historical data includes historical stored chemical data, historical environmental data, historical equipment status data, historical management data, and historical safety evaluation data; Normalize historical data so that the input value is scaled to the range of [-1, 1] or [0, 1]; The normalized historical data is divided into training set, validation set and test set in a ratio of 7:2:1; Train the risk assessment model based on the training set; The trained risk assessment model is verified based on the validation set. When the validation set loss does not decrease for five consecutive rounds, the training is terminated. The verified risk assessment model is tested based on the test set; when the test result meets the preset threshold, the training is stopped; when the test result does not meet the preset threshold, the hyperparameters of the risk assessment model are tuned based on Bayesian optimization, and the tuned risk assessment model is re-trained, verified, and tested until the test result meets the preset threshold.

7. A safety assessment system for chemical storage sites, characterized in that: The system comprises: a data processing module configured to perform spatiotemporal alignment on multi-source data, wherein the multi-source data includes stored chemical data, environmental data, equipment status data, management data, and historical data; The weight module is configured to extract spatial features of multi-source data after spatiotemporal alignment and generate a spatial risk weight map; A model training module is configured to pre-train the risk assessment model; The risk prediction module is configured to map the weights in the spatial risk weight map to the risk assessment model to obtain a risk level.

8. The safety assessment system for chemical storage sites according to claim 7, characterized in that: The system further comprises: The spatiotemporal alignment module is configured to generate a BIM model of the building structure based on the three-dimensional point cloud data of the warehouse building structure; generate a sensor time series data sequence based on the equipment status data; perform spatiotemporal coordinate alignment, including: mapping the sensor position to the three-dimensional coordinates of the BIM model; perform time window alignment, including: windowing the sensor data sequence according to a preset time period and aligning it with the static data in the BIM model; and update the external environment data at the minute level and interpolate it to the sensor timestamp.

9. An electronic device, characterized in that: The electronic device includes: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor. When the instructions are executed by the at least one processor, the at least one processor is caused to perform the safety assessment method for a chemical storage site according to any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer is caused to execute the safety assessment method for chemical storage sites according to any one of claims 1 to 6.

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