Dangerous goods container stacking method, device, system and storage medium

Abstract

This application provides a method, apparatus, system, and storage medium for stacking dangerous goods containers. The method includes: acquiring attribute information of all dangerous goods containers; determining the hazard impact contribution value of each dangerous goods container based on the attribute information, and sorting them in descending order of the hazard impact contribution values; iteratively calculating the stacking risk of moving each dangerous goods container to any stackable area using a quantitative risk analysis model in descending order of hazard impact contribution values, and determining whether the stacking risk corresponding to each layout scheme is less than the risk red line benchmark; stopping the calculation of the stacking risk when the iteration conditions are met, and outputting the layout scheme with the stacking risk less than the risk red line benchmark. This application embodiment, through an optimized QRA model and intelligent stacking decision technology, quickly evaluates the risk exceeding the risk red line benchmark, and realizes intelligent generation of dangerous goods container stacking schemes.

Description

Technical Field

[0001] This application relates to the field of intelligent decision-making technology for dangerous goods container stacking, and in particular to a method, apparatus, system and storage medium for dangerous goods container stacking. Background Technology

[0002] With the transformation and upgrading of domestic industrial structure and changes in international trade structure, as well as the influence of seasonal factors, the storage volume of various categories of dangerous goods at ports is substantial. For example, Shanghai Port, as the world's largest container terminal, handles nearly 1 million TEUs of dangerous goods containers annually, playing a vital role in international and domestic economic and trade activities. Currently, dangerous goods container storage companies at Shanghai Port strictly adhere to the categories and quantity restrictions approved by regulatory authorities, effectively managing the risks associated with dangerous goods at the port. However, when certain categories of goods arrive at the port in concentrated quantities, the limited number of approved cargo types at the storage yard cannot meet the storage demand. The current methods of unloading and transshipment of dangerous goods containers not only reduce operational efficiency but also increase the risks involved. The limited number of approved cargo types at the storage yard has become a bottleneck restricting the turnover of dangerous goods containers, limiting the port's ability to serve regional economic development as a shipping center.

[0003] A port group has established a three-dimensional twin system for dangerous goods containers and a technical system for optimizing dangerous goods container stacking decisions, which has improved control capabilities and transshipment efficiency to some extent. However, it still cannot solve the bottleneck problem of dangerous goods container turnover. Therefore, there is an urgent need for a method, device, system, and storage medium for storing dangerous goods containers in order to solve the above problems. Summary of the Invention

[0004] This application is made in view of at least one of the aforementioned technical problems existing in the prior art. According to one aspect of this application, a method for storing dangerous goods containers is provided, which can improve the turnover efficiency of dangerous goods container yards, the method comprising: Obtain attribute information for all dangerous goods containers; Based on the attribute information, the hazard impact contribution value of each dangerous goods container is determined, and the containers are sorted in descending order of the hazard impact contribution value; Based on the order of the dangerous goods container’s contribution value of danger impact from largest to smallest, the quantitative risk analysis model is used to iteratively calculate the storage risk when each dangerous goods container is moved to any storage area, and to determine whether the storage risk corresponding to each layout scheme is less than the risk red line benchmark. If the iteration conditions are met, stop calculating the heap risk and output a layout scheme where the heap risk is less than the risk red line baseline.

[0005] In some embodiments, determining the hazard impact contribution value for each dangerous goods container includes: The contribution value of the hazardous impact is determined based on the individual and social risks of each dangerous goods container.

[0006] In some embodiments, the method further includes: acquiring all stackable areas that match the current hazard classification of the dangerous goods container; This includes obtaining all stackable areas that match the current hazard classification of dangerous goods containers, including: Based on the current hazard classification of dangerous goods containers, candidate stackable areas that match the hazard classification are determined in all stacking areas; the candidate stackable areas must meet at least the following stacking conditions: meet spacing requirements, meet protective measures requirements, and meet dedicated storage area requirements. Check whether the candidate stackable area has available space; wherein, the available space includes at least: remaining stacking slots and empty slots that meet the stacking rules; If there are candidate stackable areas with available space, the candidate stackable areas with available space shall be designated as the stackable areas.

[0007] In some embodiments, the method further includes: If no candidate stackable regions with available space exist, search for candidate stackable regions with available space in neighboring regions and output the result.

[0008] In some embodiments, searching for the existence of candidate stackable areas with available space in the vicinity includes: Using the optimal stackable area of ​​the current dangerous goods container as the center, candidate stackable areas in the vicinity are found by expanding in concentric circles; wherein the distance between the vicinity area and the optimal stackable area of ​​the target container does not exceed a preset distance.

[0009] In some embodiments, the method further includes: In the case of multiple stackable areas, the multiple stackable areas are sorted according to their distance from each other; Prioritize the layout of the storage area that is furthest away.

[0010] In some embodiments, determining the heap risk corresponding to each layout scheme includes: Using a geometric cross algorithm, the intersection points of the risk contour lines and the red line baselines for each dangerous goods container are detected; Map the intersection points to the grid system of the quantitative risk analysis model; Using the quantitative risk analysis model and the location of the intersection point in the grid system, the expected consequences of moving each dangerous goods container to any of the stackable areas and the intensity of its impact on the intersection point, combined with the probability of the expected consequences, are used to derive the storage risk of each dangerous goods container moving to any of the stackable areas.

[0011] In some embodiments, the method further includes: When the intersection point is located in the near-field region of the leakage source, a 2m resolution is used to calculate the stacking risk; when the intersection point is located in the mid-field region of the leakage source, a 5m resolution is used to calculate the stacking risk; when the intersection point is located in the far-field region of the leakage source, a 10m resolution is used to calculate the stacking risk.

[0012] In some embodiments, the quantitative risk analysis model includes at least one of the following: a leakage analysis model, a fire analysis model, and an explosion analysis model.

[0013] In some embodiments, determining whether the heap risk corresponding to each layout scheme is less than the risk red line benchmark includes: Verify whether the storage risk corresponding to each stackable area meets the following conditions: a preset safety rule base, spacing requirements, yard stacking height standards, and a safe distance between dangerous goods containers and preset important protection targets; If the storage risk corresponding to each stackable area meets the above conditions, the storage risk corresponding to each stackable area is determined to be less than the risk red line benchmark.

[0014] In some embodiments, the calculation of the heap risk is stopped, and an optimized heap recommendation is output, including: If a dangerous goods container with a storage risk lower than the risk red line baseline is found, a corresponding layout suggestion will be output.

[0015] In some embodiments, the output of optimized heap storage recommendations includes: If a suitable stackable area exists, generate a layout image of the dangerous goods container and output the layout image.

[0016] In some embodiments, the method further includes: If no suitable storage area can be found, a recommendation to remove the dangerous goods container will be issued.

[0017] In some embodiments, the attribute information includes at least the dangerous goods container and hazard classification, load capacity, and current location.

[0018] According to another aspect of this application, a dangerous goods container storage device is also provided, the device comprising: The acquisition module is used to acquire attribute information for all dangerous goods containers; The sorting module is used to determine the hazard impact contribution value of each dangerous goods container based on the attribute information, and sort them in descending order of the hazard impact contribution value; The iterative calculation module is used to iteratively calculate the storage risk of moving each dangerous goods container to any stackable area according to the order of the dangerous impact contribution values ​​of all dangerous goods containers from largest to smallest using a quantitative risk analysis model, and to determine whether the storage risk corresponding to each layout scheme is less than the risk red line benchmark. The output module is used to stop calculating the heap risk when the iteration conditions are met, and to output the optimized heap suggestion.

[0019] According to another aspect of this application, a dangerous goods container stacking system is also provided, the system comprising: The system includes a memory and a processor, wherein the memory stores a computer program that is executed by the processor, which, when run by the processor, causes the processor to perform the dangerous goods container stacking method as described above.

[0020] According to another aspect of this application, a storage medium is also provided, on which a computer program is stored, which, when run by a processor, causes the processor to perform the dangerous goods container stacking method as described above.

[0021] This application embodiment utilizes an optimized QRA model and intelligent stacking decision-making technology to quickly assess functions such as exceeding the risk red line benchmark, thereby enabling the intelligent generation of dangerous goods container stacking schemes. Attached Figure Description

[0022] Figure 1 A schematic flowchart illustrating a dangerous goods container stacking method 100 according to an embodiment of this application is shown. Figure 2 A schematic flowchart illustrating step S102 according to an embodiment of this application is shown; Figure 3 A schematic flowchart illustrating step S103 according to an embodiment of this application is shown; Figure 4 A schematic flowchart illustrating the acquisition of all stackable areas matching the current hazard classification of dangerous goods containers, according to an embodiment of this application; Figure 5 A schematic flowchart illustrating step S401 according to an embodiment of this application is shown; Figure 6A schematic flowchart illustrating step S401 according to an embodiment of this application is shown; Figure 7 A schematic flowchart of step S604 according to an embodiment of this application is shown; Figure 8 A schematic flowchart illustrating step S103 according to an embodiment of this application is shown; Figure 9 A schematic flowchart illustrating the selection of the farthest stackable area as the stacking recommendation in the case of multiple stackable areas according to embodiments of this application. Figure 10 A schematic flowchart illustrating step S104 according to an embodiment of this application is shown; Figure 11 Screenshots showing the results of calculating the risk baseline of a certain wharf yard using different assessment software according to embodiments of this application; Figure 12 A schematic block diagram of a dangerous goods container stacking device according to an embodiment of this application is shown; Figure 13 A schematic block diagram of a dangerous goods container storage system according to an embodiment of this application is shown. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions of the embodiments of this application, the application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] This application provides a method for stacking dangerous goods containers. The method includes: acquiring attribute information of all dangerous goods containers; calculating the hazard contribution value of each dangerous goods container using a quantitative risk analysis model based on the attribute information, and sorting the containers in descending order of hazard contribution values; iteratively calculating the stacking risk of moving each dangerous goods container to any stackable area in descending order of hazard contribution values; stopping the calculation of the stacking risk when the iteration conditions are met, and outputting an optimized stacking recommendation. The beneficial effects of this application's embodiments are: through an optimized QRA model and intelligent stacking decision-making technology, it can quickly assess functions such as exceeding the risk red line benchmark, and intelligently generate dangerous goods container stacking schemes.

[0025] Figure 1 A schematic flowchart illustrating a method for stacking dangerous goods containers according to an embodiment of this application is shown; as follows: Figure 1 As shown, the dangerous goods container stacking method 100 according to an embodiment of this application may include the following steps S101, S102, S103 and S104: In step S101, obtain the attribute information of all dangerous goods containers.

[0026] The attribute information includes at least the dangerous goods container and its hazard classification, loading capacity, and current location.

[0027] Because the accuracy of hazard classification and equivalent material data for dangerous goods directly affects the reliability of QRA calculation results, and given the diverse parameters of dangerous goods containers and the wide variety of dangerous goods types, it may be impossible to accurately establish material and accident models. Generally speaking, the greater the hazard of the goods, the wider the scope of personal risk impact they cause. Therefore, before determining the approval threshold, it is necessary to screen the hazard classifications of each category of goods. The principles for screening dangerous goods are as follows: (1) Qualitative determination of high-risk goods. For example, substances with a small critical quantity (q) in the determination of major hazard sources are usually highly dangerous, such as acetylene with a critical quantity of 1t in item 2.1, phosgene with a critical quantity of 0.3t in item 2.3, propylene oxide with a critical quantity of 10t in item 3, and acrolein with a critical quantity of 20t in item 6.1. In the "List of Dangerous Goods" (GB12268), the packaging category of the goods is Class I and can be transported by tank containers. At this time, the storage capacity of the goods is the largest and the danger is high, such as acetylene with a tank type of T75 in item 2, acrylonitrile with a tank type of T14 in item 3, and acrolein with a tank type of T7 in item 6.

[0028] (2) Quantitative Risk Assessment. For example, selecting cargo types with a high potential life loss contribution rate. Potential Life Loss (PLL) refers to the number of possible deaths caused by an accident or hazard within a specific time frame. It is usually expressed in persons per year, reflecting the risk level of a facility or activity to surrounding personnel. Cargo types that cause serious accident consequences, such as death zones, serious injury zones, and minor injury zones, generally indicate a higher level of danger for that type of cargo as the larger the affected area.

[0029] (3) Inapplicable substances should be excluded. Such as unapproved stockpiled goods; according to the requirements of the local regulatory authorities, dangerous goods containers containing ammonium nitrates in categories 1.1, 1.2, 7 and 5.1 of the List of Dangerous Goods (GB12268) should be directly loaded and unloaded and should not be stored in the port yard. Therefore, the above-mentioned goods are not within the scope of risk calculation; for Waigaoqiao Port Area, 228 kinds of dangerous chemicals that are completely prohibited from inland waterway transport, such as acrolein; prohibited substances such as 1,2-dibromo-3-chloropropane and dehydrophosphorus.

[0030] (4) Principles for selecting toxicity. Toxicity can enter the human body through oral, inhalation, injection or skin absorption. Based on the actual situation of the storage site, solid or liquid substances in the six categories of toxic substances that do not have inhalation hazards are not included in the calculation, such as the pesticide mancozeb; and acid and alkali substances in the eight categories that do not have inhalation hazards can be excluded.

[0031] (5) Update according to actual conditions. Based on the cargo types that have been transported in the port in the past and have a large turnover, obtain their MSDS parameters and enter them into the material database as typical cargo types; or obtain the key calculation parameters of the cargo based on relevant domestic and foreign journal literature.

[0032] In step S102, based on the attribute information, the hazard impact contribution value of each dangerous goods container is determined, and the containers are sorted in descending order of the hazard impact contribution value.

[0033] In one embodiment of this application, such as Figure 2 As shown, step S102, determining the hazard impact contribution value for each dangerous goods container, includes step S201: In step S201, the contribution value of the hazard impact is determined based on the individual risk and social risk of each dangerous goods container.

[0034] Individual risk refers to the probability of death of an individual at a fixed location in an area due to various potential fires, explosions, or toxic gas leaks from major hazardous sources of hazardous chemicals. It is the individual mortality rate per unit of time (usually per year), representing the frequency with which an individual dies in an accident, assuming that the individual has not taken any protective measures.

[0035] Social risk refers to the cumulative frequency (F) of an accident that can cause N or more deaths, or the number of deaths per unit of time (usually per year), representing the frequency of accidents that result in the simultaneous deaths of N or more people. It is typically represented by a social risk curve (FN curve), where F is the frequency and N is the number of casualties. The FN curve represents the acceptable level of risk—the relationship between frequency and the number of casualties caused by an accident. The FN curve values ​​are calculated cumulatively, for example, for a specific frequency corresponding to "N or more" deaths. The acceptable social risk standard adopts the ALARP (As Low As Reasonable Practice) principle as its acceptance criterion. The ALARP principle divides risk into three zones using two risk boundaries: the unacceptable zone, the minimized zone, and the acceptable zone. If the social risk curve enters the unacceptable zone, immediate safety improvements should be implemented to reduce the social risk; if the social risk curve enters the minimized zone, safety improvements should be implemented to reduce the social risk to the extent achievable; if the social risk curve falls entirely within the acceptable zone, the risk is acceptable. The socially acceptable risk standard is established based on the actual population distribution in the area surrounding a hazard source, to prevent the probability of mass casualties from exceeding the acceptable range for society and the public. In the social risk curve, the horizontal axis represents the number of deaths, and the vertical axis represents the cumulative probability of all accidents exceeding that number of deaths.

[0036] In step S103, according to the order of the dangerous goods container’s dangerous impact contribution value from largest to smallest, the storage risk of moving each dangerous goods container to any storage area is calculated iteratively using a quantitative risk analysis model, and it is determined whether the storage risk corresponding to each layout scheme is less than the risk red line benchmark.

[0037] In one embodiment of this application, such as Figure 3 As shown, step 103, which determines the stacking risk corresponding to each layout scheme, includes the following steps S301, S302, and S303. In step S301, the intersection point of the risk contour line and the red line baseline for each dangerous goods container is detected using a geometric cross algorithm. In step S302, the intersection points are mapped to the grid system of the quantitative risk analysis model; In step S303, based on the location of the intersection point in the grid system, the expected consequences and the intensity of the impact on the intersection point when each dangerous goods container is moved to any of the stackable areas are calculated using the quantitative risk analysis model, and combined with the probability of the expected consequences occurring.

[0038] Quantitative Risk Analysis (QRA) is a technique that uses mathematical models to quantify the probability and consequences of potential hazards. The formula for calculating quantitative risk is: Risk Value = Probability of Accident × Consequences of Accident. This QRA model calculates the storage risk arising when dangerous goods containers are moved to stackable areas by calculating equipment failure frequencies and simulating accident consequences.

[0039] Specifically, the process of calculating the storage risk of each dangerous goods container when placed in any of the stackable areas using a quantitative risk analysis model is as follows: First, the system employs a high-precision geometric intersection algorithm to detect intersection points between the risk contour lines and the red-line baseline. These intersection points signify the boundaries of risk diffusion exceeding limits, such as areas where thermal radiation influence extends beyond the safe zone or where overpressure values ​​exceed thresholds. Based on computational geometry principles, this application's embodiments use mathematical methods such as line segment intersection determination and curve interpolation to precisely locate the specific spatial positions where risks exceed limits.

[0040] For individual risks, intersection points represent the boundary locations where risk contour lines exceed safety limits; for social risks, intersection points reflect key areas where group risks exceed limits. After determining the coordinates of the intersection points, the system maps them to the grid system of the QRA model to obtain detailed calculation data for the corresponding grid cells. This data includes key information such as the source of risk contribution at that grid point, the distribution of influence intensity, and the superposition effect of various consequences, providing a data foundation for subsequent risk source analysis.

[0041] Secondly, by extracting out-of-limit grid data, a detailed risk contribution analysis is conducted on all containers that may affect the intersection grid. Grid data exceeding the risk red line is obtained from the QRA model, such as the risk thermal distribution of the yard area, to clarify the high-risk spatial range. By analyzing the impact intensity of various consequences (e.g., fire, explosion, toxic spread) generated by each container at that grid point, and combining this with the probability of occurrence, the specific contribution value of each dangerous goods container to the storage risk is derived.

[0042] Then, the storage risk is analyzed. Combining grid data, the consequences / risk contribution of each container to high-risk grids is quantitatively analyzed (e.g., the influence weight of dangerous goods type and storage location). Based on the type of goods, the corresponding quantitative risk analysis model (e.g., leakage analysis model, fire analysis model, explosion analysis model, etc.) is selected to calculate the storage risk of the current layout scheme. The quantitative risk analysis model is described in detail below.

[0043] Furthermore, when analyzing the stockpiling risk, a 2m resolution is used to calculate the stockpiling risk when the intersection point is located in the near-field region of the leak source; a 5m resolution is used when the intersection point is located in the mid-field region of the leak source; and a 10m resolution is used when the intersection point is located in the far-field region of the leak source. This hierarchical strategy ensures the calculation accuracy of critical areas while significantly reducing redundant calculations in the far-field region. Verification shows that the calculation accuracy deviation of the individual risk contour lines is controlled within ±5%, and the calculation accuracy of the impact distances (toxic lethal threshold distance, thermal radiation damage distance) is controlled within ±10%.

[0044] The inventors verified that, after the aforementioned fine-tuning of the grid, in complex storage yard conditions involving more than 500 containers and over 50 types of hazardous materials, the entire calculation process, including leakage source term calculation, atmospheric diffusion simulation, fire and explosion consequence analysis, frequency analysis, and comprehensive assessment of individual and social risks, was completed within a strictly controlled time of less than 30 minutes. This performance indicator is more than an order of magnitude higher than traditional QRA software, making dynamic storage decisions based on risk assessment possible.

[0045] To further verify the effectiveness of the network refinement, the inventors collected cargo storage data for Hudong Port from January 2025 to December 2025. The port stored approximately 1219 TEU of Class 8 (corrosive substances), approximately 1017 TEU of Class 3 (flammable liquids), and approximately 649 TEU of Class 6.1 (toxic substances) (plus 68 TEU of Class 2.2, 159 TEU of Class 4.1, 93 TEU of Class 5.1, and 395 TEU of Class 9 miscellaneous items, totaling approximately 3600 TEU).

[0046] The inventors discovered that when the intersection point is located in the near-field region (0–100m) of the leakage source, using a fine mesh with a 2m resolution, the accumulation risk calculated using a leakage analysis model is 3.00 × 10⁻⁶. -5 Using a 5m resolution, the heap risk is calculated to be 2.43 × 10⁻⁶. -5 The stacking risk calculated using a 10m resolution is 1.80 × 10⁻⁶. -5 The calculation of stacking risk is more accurate when using a 2m resolution (deviation from the baseline true value is <2%, while 5m underestimates by about 19% and 10m underestimates by about 40%).

[0047] When the intersection point is located in the mid-field region (100–500m) of the leakage source, the stockpiling risk calculated using a 2m resolution is 5.18 × 10⁻⁶. -7 Using a 5m resolution, the heap risk is calculated to be 5.00 × 10⁻⁶. -7The stacking risk calculated using a 10m resolution is 4.20 × 10⁻⁶. -7 The calculation of heap risk is more accurate when using a 5m resolution (2m provides only a limited improvement in accuracy but increases the computational load by about 6.25 times, while 10m underestimates it by about 16%).

[0048] When the intersection point is located in the far-field region (more than 500m) of the leakage source, the stacking risk calculated using a 2m resolution is 5.04 × 10⁻⁶. -8 Using a 5m resolution, the heap risk is calculated to be 5.01 × 10⁻⁶. -8 Using a 10m resolution, the heap risk is calculated to be 5.00 × 10⁻⁶. -8 The calculation of heap risk is more accurate when using a 10m resolution (the accuracy of the three resolutions is comparable, with 10m requiring the least amount of computation).

[0049] It is evident that when the intersection point is located in the near-field region of the leakage source, a 2m resolution is more accurate for calculating the stockpiling risk; when the intersection point is located in the mid-field region of the leakage source, a 5m resolution is more accurate for calculating the stockpiling risk; and when the intersection point is located in the far-field region of the leakage source, a 10m resolution is more accurate for calculating the stockpiling risk.

[0050] Once the quantitative risk analysis model completes the risk assessment of the current layout plan for dangerous goods containers and obtains the storage risk, this storage risk is compared with a preset risk red line benchmark. The risk red line benchmark typically includes an individual risk limit (e.g., 10). -6 The system employs multi-dimensional safety standards, including annual and social risk limits (such as FN curve boundaries). If the storage risk meets the risk red line benchmark requirements (i.e., the storage risk does not exceed the risk red line benchmark), the system directly outputs storage recommendations and saves an image of the storage layout scheme to confirm the safety of the current storage scheme.

[0051] If the storage risk exceeds the risk red line benchmark, locate the dangerous goods container with the highest hazard impact contribution value. Move this container to any available storage area, and recalculate the storage risk using the quantitative risk analysis model. Verify whether the storage risk has been reduced to within the risk red line benchmark. If it has, directly output a storage recommendation and save an image of the storage layout plan to confirm the safety of the current storage plan. Otherwise, locate the dangerous goods container with the second highest hazard impact contribution value, move it to any available storage area, and recalculate the storage risk using the quantitative risk analysis model. Verify whether the risky storage has been reduced to within the risk red line benchmark. If it has, directly output a storage recommendation and save an image of the storage layout plan to confirm the safety of the current storage plan. Otherwise, locate the dangerous goods container with the third highest hazard impact contribution value, and so on, until a storage plan is found that reduces the storage risk to within the risk red line benchmark.

[0052] In some embodiments of this application, an iteration threshold can be calculated, and the number of iterations can be counted. If the iteration threshold is not exceeded (e.g., 3 times), the above steps are repeated. If the threshold is exceeded, it is determined that the container cannot reduce risk through migration, and a suggestion to remove the container is output. In other embodiments, other iteration termination conditions can be set. By setting iteration termination conditions, both the optimization effect and the computation time are controlled.

[0053] In other embodiments, when the anticipated consequences of a container accident affect important protected targets and exceed limits, an early warning message can be generated and pushed to the 3D monitoring system along with the risk assessment results. Important protected targets here may include locations such as office buildings, control rooms, and fire stations.

[0054] like Figure 11As shown, the inventors used four risk management software programs: CASST-QRA from the China Academy of Safety Science and Technology, TNO from the Netherlands Organization for Applied Scientific Research, RiskCloud from Shanghai Golue Software Technology Co., Ltd., and AutoFastRiskcloud (AFR) to conduct cargo hazard analysis and storage risk calculations, and compared the results. From the individual risk calculation results, the CASST-QRA software's result was slightly lower than expected. The results from TNO, RiskCloud, and AFR were within the same order of magnitude, with TNO's result slightly higher and AutoFastRiskcloud's result slightly lower. This is likely due to factors such as the software's calculation model, the level of detail in parameter settings, and the precision of the calculation. The results from TNO and RiskCloud were closer and more accurate; therefore, the results from TNO and RiskCloud are considered the most accurate.

[0055] The following introduces several commonly used quantitative risk analysis models, such as leakage analysis models, fire analysis models, and explosion analysis models.

[0056] The leakage analysis models include gas leakage models, liquid leakage models, liquid pool evaporation models, and diffusion models; the fire analysis models include pool fire models, jet fire models, fireball models, and solid fire models; and the explosion analysis models include TNT equivalent models and boiling liquid expanding vapor explosion (BLEVE) models.

[0057] The gas leakage model is used to determine the flow state (sonic / subsonic) of gas leaking through an orifice and to calculate the mass flow rate of the leaking gas under the corresponding state. Specifically, the gas leakage model distinguishes the flow type based on the pressure difference inside and outside the container and critical conditions, and then selects a formula to calculate the leakage amount, which is the basis for quantifying gas leakage risk.

[0058] Gas flow type is determined by the pressure inside the container. Environmental pressure The ratio, combined with the adiabatic index When satisfied At this time, the gas flow is sonic flow; for example, the flow velocity reaches the local speed of sound, and the flow rate does not change with the downstream pressure; when the following conditions are met... At this time, the gas flow is subsonic, for example, the flow velocity is lower than the local speed of sound, and the flow rate is affected by the downstream pressure.

[0059] Therefore, the mass flow rate of the flowing gas at the speed of sound is as follows: (1) Where Q represents the continuous discharge material mass flow rate, in kg / s; p represents the container pressure, in Pa; and A represents the leakage orifice area, in m². 2 ; Indicates the adiabatic index; C d R represents the gas leakage coefficient; M represents the relative molecular mass of the gas; g This represents the ideal gas constant, with units of J / (mol*K).

[0060] The mass flow rate of the subsonic flowing gas leakage is as follows: (2) Where Q represents the continuous discharge material mass flow rate, in kg / s; p represents the container pressure, in Pa; and A represents the leakage orifice area, in m². 2 ; Indicates the adiabatic index; C d R represents the gas leakage coefficient; M represents the relative molecular mass of the gas; g Y represents the ideal gas constant, with units of J / (mol*K); Y represents the effluent coefficient.

[0061] Therefore, in subsonic flow, the discharge coefficient The following complex formula needs to be used for calculation: (3) in, Indicates the pressure inside the container; Indicates environmental pressure; This indicates the adiabatic index.

[0062] This formula uses pressure ratio and insulation index This study corrects the influence of gas expansion and velocity changes on flow rate in subsonic flow, demonstrating the coupling relationship between pressure-driven and gas thermodynamic properties.

[0063] The liquid leakage model is used to calculate the instantaneous mass flow rate when liquid leaks through the tank orifice, reflecting the mass of liquid leaking per unit time. Therefore, the formula for the instantaneous mass flow rate is as follows: (4) Among them, Q m The value represents the mass flow rate of continuously discharged material, in kg / s; ρ represents the liquid density, in kg / m³; and A represents the leakage orifice area, in m². 2 C o P represents the liquid leakage coefficient; P represents the storage pressure, in Pa. oThe pressure represents ambient pressure, measured in Pa; g represents gravitational acceleration, measured in m / s². 2 h L This indicates the liquid height, in meters (m).

[0064] Regarding the liquid pool evaporation model, GB / T37243 specifies three types of evaporation for leaked liquids: flash evaporation, thermal evaporation, and mass evaporation. When flash evaporation is incomplete, some liquid forms a pool on the ground and absorbs heat from the ground, vaporizing; this is called thermal evaporation. When thermal evaporation ends, the liquid evaporates due to airflow over the pool surface; this is called mass evaporation. Besides some liquid flashing into gas, some liquid remains suspended in the gas as droplets.

[0065] The flash evaporation ratio of the leaked liquid is as follows: (5) in, Indicates the flash evaporation rate of the leaked liquid; Indicates storage temperature; Indicates the boiling point of the leaked liquid; The heat of vaporization of the leaked liquid is expressed in J / kg. The isobaric heat capacity of the leaked liquid is expressed in [kJ / (kg·K)].

[0066] The formula for the flash evaporation rate of superheated liquid is as follows: (6) in, Indicates the flash evaporation rate of the leaked liquid; Indicates storage temperature, in K; Indicates the boiling point of the leaked liquid, in Kelvin (K). The heat of vaporization of the leaked liquid is expressed in J / kg. The isobaric heat capacity of the leaked liquid is expressed in [kJ / (kg·K)]. This indicates the flash evaporation rate of a superheated liquid, expressed in kg / s. This indicates the rate of material leakage, expressed in kg / s.

[0067] Regarding thermal evaporation, when liquid flash evaporation is incomplete, some liquid forms a pool on the ground surface and absorbs heat from the ground, thus vaporizing; this is called thermal evaporation. The formula for calculating the evaporation rate of thermal evaporation is as follows: (7) in, This indicates the rate of heat evaporation, expressed in kg / s. Indicates the area of ​​the liquid pool, in units of ; This indicates the ambient temperature, expressed in Kelvin (K). This indicates the boiling point of a liquid, expressed in Kelvin (K). This represents the heat of vaporization of a liquid, expressed in J / kg. Represents the surface thermal diffusivity, with units of . / s; This represents the surface thermal conductivity, with units of [W / (m·K)]. This indicates the evaporation time, measured in seconds (s).

[0068] As for mass evaporation, it refers to the evaporation of the liquid after the heat evaporation is complete, where the evaporation is then caused by airflow on the surface of the liquid pool. Mass evaporation rate. The calculation formula is as follows: (8) in, This indicates the rate of evaporation by mass, expressed in kg / s. Indicates the atmospheric stability coefficient; This represents the vapor pressure at the liquid surface, expressed in Pa. This represents the gas constant, with units of [J / (mol·K)]. This indicates the ambient temperature, expressed in Kelvin (K). Wind speed is expressed in m / s. This indicates the radius of the liquid pool, in meters (m).

[0069] The diffusion model needs to analyze different stages, including active injection, expansion, gravitational settling, air entrainment, cloud heating, and passive diffusion. Different box models, similar models, and shallow models should be selected based on the gas density, temperature, topography and building conditions, surrounding environment, and assessment objectives. Diffusion models can include neutral gas diffusion models and heavy gas diffusion models.

[0070] Among them, the fire model can be used to calculate pool fire, liquid pool diameter, combustion rate, flame height, drag diameter, flame surface heat flux, atmospheric transmittance, incident heat radiation flux received by the target, etc. It can be calculated using fire models in traditional technologies, which will not be elaborated here.

[0071] The TNT equivalent model in explosion models refers to the TNT equivalent method proposed by Vanden Berg and Lannoy, a method frequently used in explosives research to compare the explosive effectiveness of different explosives. By comparing the explosion effects of different explosives with a specific amount of TNT (trinitrotoluene), the TNT equivalent of the explosive can be obtained through the TNT explosion efficiency fraction of a specific gas. Then, Kingery and Bulmash calculate the explosion consequences of different TNT equivalents. The BLEVE model, on the other hand, is used to predict and analyze the explosion effects caused by the rapid evaporation of liquids. BLEVE typically occurs in closed containers, such as vacuum or pressure vessels. When liquids such as oil, gases, or other hazardous chemicals reach high temperatures on the outer wall of the container due to external factors such as fire, the liquid inside begins to boil and form high-pressure gas. When the pressure accumulates to a certain level, exceeding the container's capacity, it causes the container to rupture. When the container ruptures, the internal liquid expands into gas instantaneously, creating a massive explosion effect—this is the BLEVE phenomenon. The resulting explosions are often devastating and can have serious impacts on organisms and the environment.

[0072] In one embodiment of this application, such as Figure 4 As shown, the method further includes step S401. In step S401, all stackable areas that match the current hazard classification of the dangerous goods container are obtained.

[0073] like Figure 5 As shown, step S401, obtaining all stackable areas that match the current hazard classification of the dangerous goods container, includes the following steps S501, S502, and S503: In step S501, based on the current hazard classification of the dangerous goods container, candidate stackable areas that match the hazard classification are determined among all stacking areas; wherein, the candidate stackable areas must at least meet the following stacking conditions: meet spacing requirements, meet protective measures requirements, and meet dedicated storage area requirements; In step S502, check whether the candidate stackable area has any available space; wherein, the available space includes at least: remaining stacking slots and empty slots that meet the stacking rules; In step S503, if there is a candidate stackable area with available space, the candidate stackable area with available space is selected as the stackable area.

[0074] For example, the system can query the yard configuration database to obtain candidate stackable areas that match the hazard classification of the current dangerous goods container. Then, based on the compatibility and segregation requirements of the dangerous goods container, it can filter out candidate stackable areas that meet safety regulations. If no stackable area is found, the search process ends, and a message indicating that no stackable area exists is displayed.

[0075] In one embodiment of this application, such as Figure 6 As shown, step S401, obtaining all stackable areas that match the current hazard classification of the dangerous goods container, includes the following steps S601, S602, S603, and S604: In step S601, based on the current hazard classification of the dangerous goods container, candidate stackable areas that match the hazard classification are determined among all stacking areas; wherein, the candidate stackable areas must at least meet the following stacking conditions: meet spacing requirements, meet protective measures requirements, and meet dedicated storage area requirements; In step S602, check whether there is any available space in the candidate stackable area; if so, proceed to step S603; otherwise, proceed to step S604. In step S603, if there is a candidate stackable area with available space, the candidate stackable area with available space is taken as the stackable area. In step S604, the system searches for candidate storage areas with available space in the neighboring regions and outputs a prompt result.

[0076] The storage space includes at least: remaining storage slots and empty slots that meet the storage rules.

[0077] In this embodiment, if a stackable area exists, a prompt is displayed to move the container to the available area, allowing the user to adjust the stacking location and reduce risk. If no stackable area exists, a prompt is displayed to search for available space in a nearby area, expanding the search scope to find a compliant stacking area.

[0078] In one embodiment of this application, such as Figure 7 As shown, step S604, which involves searching for candidate stackable regions with available storage space in neighboring areas, includes step S701: In step S701, candidate stackable areas in the vicinity are searched using the optimal stackable area of ​​the current dangerous goods container as the center and in a concentric circle expansion manner.

[0079] The distance between the adjacent area and the optimal stackable area of ​​the current dangerous goods container shall not exceed a preset distance.

[0080] The optimal stackable area can be the stackable area furthest from personnel, equipment, or important locations. In this embodiment, when there is no available space in the optimal stackable area, the system initiates a neighboring area search algorithm. This algorithm uses the current optimal stackable area as the center and expands in concentric circles to find nearby available areas, ensuring that the adjustment distance is minimized while meeting safety requirements.

[0081] In one embodiment of this application, such as Figure 8 As shown, step S103, determining whether the storage risk corresponding to each stackable area is less than the risk red line benchmark, includes steps S801 and S802: In step S801, verify whether the storage risk corresponding to each stackable area meets the following conditions: a preset safety rule base, spacing requirements, yard stacking height standards, and a safe distance between dangerous goods containers and preset important protection targets; In step S802, if the storage risk corresponding to each stackable area meets the above conditions, it is determined that the storage risk corresponding to each stackable area is less than the risk red line benchmark.

[0082] The risk red line benchmark may include an individual risk limit (such as 10). -6 Safety standards include multi-dimensional measures such as annual risk limits and social risk limits (e.g., FN curve boundaries).

[0083] In step S104, if the iteration conditions are met, the calculation of the heap risk is stopped, and the layout scheme when the heap risk is less than the risk red line benchmark is output.

[0084] In some embodiments, the iteration count may be determined by reaching an iteration count threshold, for example, 3 iterations. If the threshold is not exceeded, steps S101 to S103 are repeated to find a layout scheme with a stacking risk less than the risk red line benchmark. If the threshold is exceeded and no layout scheme with a stacking risk less than the risk red line benchmark has been found, it is determined that the container cannot reduce risk through relocation, and a suggestion to remove the container is output. In other embodiments, the iteration condition may also be finding a layout scheme with a stacking risk less than the risk red line benchmark, or all stacking risks corresponding to all dangerous goods containers after relocation being greater than or equal to the risk red line benchmark.

[0085] In one embodiment of this application, such as Figure 9 As shown, the method further includes steps S901 and S902: In step S901, if there are multiple stackable areas, the multiple stackable areas are sorted according to their distance from each other. In step S902, the storage area that is furthest away is selected as the storage recommendation.

[0086] Since the farther away from the hazard source, the safer it is, the storage area with the greatest spatial distance should be prioritized when selecting a storage area to maximize the safety buffer space and reduce the risk and impact of accidents. In this embodiment, multiple storage areas are sorted according to their distance, and the storage area with the greatest distance is selected first to maximize the safety margin.

[0087] In one embodiment of this application, such as Figure 10 As shown, the optimized heap storage suggestion output in step S104 includes step S1001: In step S1001, if there is a stackable area that meets the requirements, a layout image of the dangerous goods container is generated and the layout image is output.

[0088] In this embodiment of the application, if a layout scheme with a stacking risk lower than the risk red line baseline is found, an optimized stacking suggestion is output and a layout image is generated and saved so that users can intuitively obtain the layout scheme.

[0089] The embodiments of this application can quickly evaluate functions such as exceeding the risk red line benchmark through an optimized QRA model and intelligent stacking decision technology, and realize intelligent generation of dangerous goods container stacking schemes.

[0090] After conducting on-site testing at a certain wharf, the inventors discovered that the approved cargo types and quantities (TEU / day) for the D001 storage yard at that wharf were as follows: Class 3 flammable liquids 280; Class 4 flammable solids, substances that are easily ignited and substances that release flammable gases upon contact with water 90; Class 8 corrosive substances 300; Class 9 miscellaneous dangerous substances and articles (unlimited).

[0091] Scenario 1: 280 acrylonitrile containers were selected for leakage at atmospheric pressure (3 types of cargo); Scenario 2: 90 independent fire models were selected for 4 types of cargo; Scenario 3: 300 anhydrous hydrogen fluoride containers were selected for leakage at atmospheric pressure (8 types of cargo); a total of 670 containers were selected, not exceeding the yard limit of 672 dangerous goods containers. The calculation model parameters are shown in Tables 1 and 2.

[0092] Table 1: Parameters of liquid leakage models for acrylonitrile (Class 3) and anhydrous hydrogen fluoride (Class 8)

[0093] Table 2: Parameters of the fire model

[0094] Different assessment software is used to calculate the risk baseline for a certain wharf yard, such as... Figure 11As shown in the figure, the results of individual risk calculations are as follows: CASST-QRA software results tend to be lower, while TNO, RiskCloud, and AFR software results are within the same order of magnitude. However, TNO software results are slightly higher, AFR software results are slightly lower, and RiskCloud software results are more accurate. This is mainly due to multiple factors such as the software calculation model, the level of detail in parameter settings, and the precision of the calculation.

[0095] In terms of risk contribution, the main risk of this storage yard is contributed by 300 anhydrous hydrogen fluoride tank containers of 8 types of cargo, mainly due to toxicity; its impact range far exceeds the individual risk superposition value of 280 acrylonitrile and 90 independent fire models, mainly due to thermal radiation.

[0096] The following describes the process of using RiskCloud software to simulate various major accidents and calculate accident risks: First, determine the types of goods and the selection of the storage yard. The items and categories mentioned in this article are all from GB6944-2025, "Classification and Numbering of Dangerous Goods".

[0097] Storage Yard (TEU / day): 2.2 The maximum quantity of non-flammable and non-toxic gases that can be stored is 10 TEU / day; 3. Flammable liquids that can be stored is 170 TEU / day; 4. Flammable solids that can be stored is 50 TEU / day; 5. Oxidizing substances that can be stored is 50 TEU / day; 6. Toxic substances that can be stored is 90 TEU / day; 8. Corrosive substances that can be stored is 100 TEU / day; 9. Miscellaneous hazardous substances and articles that can be stored is 140 TEU / day. The maximum storage capacity of the storage yard is 556 TEU / day.

[0098] Scenario 1: Leakage of pressure vessels of two types (10 sulfur hexafluoride); Scenario 2: Leakage of atmospheric pressure containers of three types of goods (170 acrylonitrile containers); Scenario 3: Select 50 independent models of 4 types of flammable solids (TNT); Scenario 4: Select 50 independent models (TNT) of 5 types of oxidizing substances and organic peroxides; Scenario 5: Leakage of 90 atmospheric pressure containers of 6 types of goods (epimyl chloride); Scenario 6: Select 8 types of goods for leakage in atmospheric pressure containers (100 anhydrous hydrogen fluoride containers); Scenario 7: Select 9 categories and 80 independent fire models; A total of 550 containers, not exceeding the yard's maximum capacity of 556 dangerous goods containers.

[0099] Second, since it is impossible to perform calculations based on various types of real accidents, this application selected several typical scenarios for storage conditions and accident risks to conduct scenario simulations.

[0100] 1. Storage device parameters Select the storage device parameters for each of the above scenarios. Dangerous goods container yards primarily contain tank containers, general containers, and gas cylinders. The preset scenario parameters for each storage device are as follows: (1) Tank container Pressure: 2 bar; Temperature: Room temperature (25°C); Quality: Pushed by the TOS system, not fixed; Volume: Calculated from mass and density of the material at a specific temperature; it is not fixed. Materials: Provided with the UN Number Lookup Table; Leakage height: 1.296m.

[0101] (2) Ordinary containers Pressure: Atmospheric pressure (1.01325 bar); Temperature: Room temperature (25°C); Quality: Pushed by the TOS system, not fixed; Volume: Calculated from mass and density of the material at a specific temperature; it is not fixed. Materials: Provided with the UN Number Lookup Table; Leakage height: Take 1 / 2 the height of the container (generally between 1m and 1.45m).

[0102] (3) Gas cylinder For compressed gases classified as Class 2 hazardous, container parameters are not used; instead, independent cylinder parameters are employed.

[0103] Pressure: 200 bar; Temperature: Room temperature (25°C); Volume: 0.5 m³; Mass: Calculated from volume and density of the material at a specific temperature; it is not fixed. Materials: Provided according to the UN Number Lookup Table.

[0104] 2. Preset Scene (1) Calculation standard: For example, in cargo accident scenarios of Category 2, Category 3, Item 4.3, Item 6.1, and Item 6.1 (secondary hazard in Category 8), gases and liquids are handled according to leakage models. Using a 25-cubic-meter tank container as the basic unit, risk calculations are performed based on leakage scenarios for tank trucks and warehouses according to the "Guidelines for Quantitative Risk Assessment of Chemical Enterprises" (AQT+3046-2013) and the "Method for Determining External Safety Protection Distances for Hazardous Chemical Production and Storage Facilities" (GB / T 37243-2019). Scenario 1 is a continuous leakage with a 100mm orifice diameter, and Scenario 2 is a complete rupture.

[0105] For example, in accident scenarios for goods in categories 4.1, 4.2, and 9, flammable solids are calculated using an independent fire model. A combustion model is set up using a 15-ton container as the basic unit. Referring to literature such as "Development of Sprinkler Protection Guidance or Lithium Ion Based Energy Storage Systems" and "Simulation of Lithium Battery Cell Ignition and Combustion Process," the combustion rate is taken as 0.03 kg / s, and the heat of combustion as 6 MJ / kg.

[0106] For example, in item 4.1, category 5 substances, a TNT independent model is used for calculation. A 15-ton container is used as the basic unit for setting up a TNT explosion model. Referring to the Yellow Book "Methods for the calculation of Physical Effects Due to releases of hazardous materials (liquids and gases)" and the TNO guidance manual, a reactivity coefficient of 0.05 is used for low reactivity, 0.1 for medium reactivity, and 0.15 for high reactivity. The average equivalence coefficient is taken as 0.1.

[0107] (2) Leakage time The weather scenarios are divided into daytime and nighttime scenarios, each counted as one group. According to Article 1.2 of the safety production industry standard AQ30468.1.2, Table F.2 determines the leakage time according to the detection level C. The maximum handling time for a 100mm leakage is 40 minutes, and the maximum handling time for a complete rupture leakage is 20 minutes.

[0108] (3) Independent model For example, solid-state fires involving lithium batteries, BLEVE for Class 2.2 inert gases, and TNT equivalent for other types. These parameters fluctuate with the material mapping table and are not fixed.

[0109] 3. Failure frequency (1) Continuous leakage scenario For example, the failure frequency of ordinary containers is determined according to Table C.7 of GBT37243 warehouse, with a release rate of 1*10-5 for solid packaging powder and liquid packaging for scenario 1.

[0110] For example, referring to GBT37243 Warehouse Table C.8 Cao Cheng, the scenario and frequency of pressure tank trucks are 5*10-5.

[0111] For example, the failure frequency of a regular container is 1*10-5, while the failure frequency of a pressurized container is 5*10-5.

[0112] (2) Catastrophic rupture scenario For example, the failure frequency of ordinary containers is determined according to Table C.7 of GBT37243 Warehouse: Scenario 1 solid packaging powder, Scenario 2 liquid packaging release: 1*10-5.

[0113] For example, referring to GBT37243 Warehouse Table C.8 Cao Cheng, the scenario and frequency of pressure tank trucks are 5*10-5.

[0114] For example, the failure frequency of a regular container is 1*10-5, while the failure frequency of a pressurized container is 5*10-5.

[0115] (3) All other independent model scenarios The failure frequency of the independent model scenario is 1*10-5.

[0116] 4. Ignition probability Refer to GB / T37243 F.5 Motor vehicle ignition frequency 0.4.

[0117] Third, the following are examples of parameter settings for several scenarios involving the storage of hazardous materials.

[0118] Table 3: Calculation model parameters for sulfur hexafluoride (Type 2)

[0119] Table 4: Calculation model parameters for acrylonitrile (Class 3), epichlorohydrin (Class 6), and anhydrous hydrogen fluoride (Class 8)

[0120] Table 5: Parameter Table of Calculation Model

[0121] Fourth, environmental scene parameters In the calculation, the ignition source was set to have an ignition probability of 0.4 within 1 minute, referencing motor vehicles. For meteorological conditions, the wind speed was assessed at 5.4 m / s in Shanghai, and the atmospheric stability was set to D. The meteorological conditions are shown in Table 6. The ignition probability of 0.4 within 1 minute was used, referencing motor vehicles. Daytime wind frequencies are shown in Table 7, and nighttime wind frequencies are shown in Table 8.

[0122] Table 6.5-2 Selection of Typical Diffusion Meteorological Conditions

[0123] Table 6.5-3 Daytime Wind Frequency Data

[0124] Table 6.5-4 Nighttime Wind Frequency Data

[0125] The inventors also compiled statistics on the distribution of personnel within a port's internal network. The population was categorized into rectangular and polygonal populations, including factors such as population type, population density, and percentage of indoor and outdoor populations. The port had 1000 people during the day and 500 people at night, and there were no highly sensitive, important, or general security targets in the surrounding area.

[0126] Figure 12 A schematic block diagram of a dangerous goods container stacking device according to an embodiment of this application is shown; as follows: Figure 12 As shown, the dangerous goods container stacking device 1200 according to an embodiment of this application may include an acquisition module 1201, a sorting module 1202, an iterative calculation module 1203, and an output module 1204: Module 1201 is used to acquire attribute information of all dangerous goods containers; The sorting module 1202 is used to determine the hazard impact contribution value of each dangerous goods container based on the attribute information, and sort them in descending order of the hazard impact contribution value; The iterative calculation module 1203 is used to iteratively calculate the storage risk of each dangerous goods container when it is moved to any storage area according to the order of the dangerous impact contribution values ​​of all dangerous goods containers from largest to smallest using a quantitative risk analysis model, and to determine whether the storage risk corresponding to each layout scheme is less than the risk red line benchmark. The output module 1204 is used to stop calculating the heap risk when the iteration conditions are met, and to output the layout scheme when the heap risk is less than the risk red line benchmark.

[0127] The following is combined Figure 13 This application describes a dangerous goods container storage system, wherein,Figure 13 A schematic block diagram of a dangerous goods container storage system according to an embodiment of this application is shown.

[0128] like Figure 13 As shown, the dangerous goods container stacking system 1300 includes: one or more memories 1301 and one or more processors 1302. The memories 1301 store a computer program that is executed by the processors 1302. When the computer program is executed by the processors 1302, the processors 1302 perform the dangerous goods container stacking method described above.

[0129] The dangerous goods container stacking system 1300 may be part or all of a computer device that can realize the dangerous goods container stacking method through software, hardware or a combination of software and hardware.

[0130] like Figure 13 As shown, the dangerous goods container stacking system 1300 includes one or more memories 1301, one or more processors 1302, displays (not shown), and communication interfaces, etc., which are interconnected via a bus system and / or other forms of connection mechanisms (not shown). It should be noted that... Figure 13 The components and structure of the dangerous goods container stacking system 1300 shown are merely exemplary and not limiting. The dangerous goods container stacking system 1300 may also have other components and structures as needed.

[0131] Memory 1301 is used to store various data and executable program instructions generated during the operation of related methods, such as storing various application programs or algorithms that implement various specific functions. It may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0132] The processor 1302 may be a central processing unit (CPU), a graphics processing unit (GPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other processing units with data processing and / or instruction execution capabilities, and may be other components in the dangerous goods container stacking system 1300 to perform the desired functions.

[0133] In one example, the dangerous goods container stacking system 1300 also includes output devices that can output various information (such as images or sounds) to the outside (e.g., a user), and may include one or more of a display device, a speaker, etc.

[0134] The communication interface can be any known communication protocol interface, such as a wired interface or a wireless interface. The communication interface may include one or more serial ports, USB interfaces, Ethernet ports, WiFi, wired networks, DVI interfaces, device integrated interconnect modules, or other suitable ports, interfaces, or connections.

[0135] Furthermore, according to embodiments of this application, a storage medium is also provided, on which program instructions are stored. When the program instructions are executed by a computer or processor, they are used to perform corresponding steps of the dangerous goods container stacking method of this application. The storage medium may, for example, include a memory card of a smartphone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media.

[0136] Furthermore, according to embodiments of this application, a computer program product is also provided, which, when executed by a processor, implements the steps of the method described above.

[0137] The dangerous goods container stacking device, dangerous goods container stacking system, computer program product, and storage medium of the present application embodiments have the same advantages as the aforementioned dangerous goods container stacking method because they can implement the aforementioned dangerous goods container stacking method.

[0138] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of this application. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of this application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.

[0139] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0141] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0142] Similarly, it should be understood that, in order to streamline this application and aid in understanding one or more of the various inventive aspects, features of this application may sometimes be grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of this application. However, this approach should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with features fewer than all features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0143] Those skilled in the art will understand that, apart from the mutual exclusion of features, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or elements of any method or apparatus so disclosed may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0144] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0145] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules according to the embodiments of this application. This application can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0146] It should be noted that the above embodiments are illustrative of this application and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0147] The above description is merely a specific embodiment or illustration of the embodiments of this application. The scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. The scope of protection of this application shall be determined by the scope of the claims.

Claims

1. A method for storing dangerous goods containers, characterized in that, The method includes: Obtain attribute information for all dangerous goods containers; Based on the attribute information, the hazard impact contribution value of each dangerous goods container is determined, and the containers are sorted in descending order of the hazard impact contribution value; Based on the order of the dangerous goods container’s contribution value of danger impact from largest to smallest, the quantitative risk analysis model is used to iteratively calculate the storage risk when each dangerous goods container is moved to any storage area, and to determine whether the storage risk corresponding to each layout scheme is less than the risk red line benchmark. If the iteration conditions are met, stop calculating the heap risk and output a layout scheme where the heap risk is less than the risk red line benchmark.

2. The method according to claim 1, characterized in that, Determine the hazard impact contribution value for each dangerous goods container, including: The contribution value of the hazardous impact is determined based on the individual and social risks of each dangerous goods container.

3. The method according to claim 1, characterized in that, The method further includes: acquiring all stackable areas that match the current hazard classification of the dangerous goods container; This includes obtaining all stackable areas that match the current hazard classification of dangerous goods containers, including: Based on the current hazard classification of dangerous goods containers, candidate stackable areas that match the hazard classification are determined in all stacking areas; the candidate stackable areas must meet at least the following stacking conditions: meet spacing requirements, meet protective measures requirements, and meet dedicated storage area requirements. Check whether the candidate stackable area has available space; wherein, the available space includes at least: remaining stacking slots and empty slots that meet the stacking rules; If there are candidate stackable areas with available space, the candidate stackable areas with available space shall be designated as the stackable areas.

4. The method according to claim 3, characterized in that, The method further includes: If no candidate stackable regions with available space exist, search for candidate stackable regions with available space in neighboring regions and output the result.

5. The method according to claim 4, characterized in that, Search for potential storage areas with available space in the vicinity, including: Using the optimal stackable area of ​​the current dangerous goods container as the center, candidate stackable areas in the vicinity are found by expanding in concentric circles; wherein the distance between the vicinity area and the optimal stackable area of ​​the target container does not exceed a preset distance.

6. The method according to claim 3, characterized in that, The method further includes: In the case of multiple stackable areas, the multiple stackable areas are sorted according to their distance from each other; Prioritize the layout of the storage area that is furthest away.

7. The method according to claim 1, characterized in that, Identify the heap risks associated with each layout scheme, including: Using a geometric cross algorithm, the intersection points of the risk contour lines and the red line baselines for each dangerous goods container are detected; Map the intersection points to the grid system of the quantitative risk analysis model; Based on the location of the intersection point in the grid system, the expected consequences and the intensity of the impact on the intersection point when each dangerous goods container is moved to any of the stackable areas are calculated using the quantitative risk analysis model, combined with the probability of the expected consequences occurring.

8. The method according to claim 7, characterized in that, The method further includes: When the intersection point is located in the near-field region of the leakage source, a 2m resolution is used to calculate the stacking risk; when the intersection point is located in the mid-field region of the leakage source, a 5m resolution is used to calculate the stacking risk; when the intersection point is located in the far-field region of the leakage source, a 10m resolution is used to calculate the stacking risk.

9. The method according to claim 7, characterized in that, in, The quantitative risk analysis model includes at least one of the following: leakage analysis model, fire analysis model, and explosion analysis model.

10. The method according to claim 1, characterized in that, Determine whether the heap risk corresponding to each layout scheme is less than the risk red line benchmark, including: Verify whether the storage risk corresponding to each stackable area meets the following conditions: a preset safety rule base, spacing requirements, yard stacking height standards, and a safe distance between dangerous goods containers and preset important protection targets; If the storage risk corresponding to each stackable area meets the above conditions, the storage risk corresponding to each stackable area is determined to be less than the risk red line benchmark.

11. The method according to claim 1, characterized in that, Stop calculating the heap risk and output optimized heap recommendations, including: If a dangerous goods container with a storage risk lower than the risk red line baseline is found, a corresponding layout suggestion will be output.

12. The method according to claim 11, characterized in that, Output optimized heap storage recommendations, including: If a suitable stackable area exists, generate a layout image of the dangerous goods container and output the layout image.

13. The method according to claim 10, characterized in that, The method further includes: If no suitable storage area can be found, a recommendation to remove the dangerous goods container will be issued.

14. The method according to claim 1, characterized in that, in, The attribute information includes at least the dangerous goods container and its hazard classification, load capacity, and current location.

15. A dangerous goods container stacking device, characterized in that, The device includes: The acquisition module is used to acquire attribute information for all dangerous goods containers; The sorting module is used to determine the hazard impact contribution value of each dangerous goods container based on the attribute information, and sort them in descending order of the hazard impact contribution value; The iterative calculation module is used to iteratively calculate the storage risk of moving each dangerous goods container to any stackable area according to the order of the dangerous impact contribution values ​​of all dangerous goods containers from largest to smallest using a quantitative risk analysis model, and to determine whether the storage risk corresponding to each layout scheme is less than the risk red line benchmark. The output module is used to stop calculating the heap risk when the iteration conditions are met, and to output the optimized heap suggestion.

16. A dangerous goods container stacking system, characterized in that, The system includes: A memory and a processor, wherein the memory stores a computer program that is executed by the processor, the computer program, when executed by the processor, causes the processor to perform the dangerous goods container stacking method as described in any one of claims 1 to 14.

17. A storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, causes the processor to perform the dangerous goods container stacking method as described in any one of claims 1 to 14.

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