Emergency response system, method and medium for urban storage tanks based on Internet of Things (IoT) big data model

Abstract

This invention provides an emergency response system, method, and medium for urban storage tanks based on a large-scale Internet of Things (IoT) model, relating to the field of urban storage tank emergency response. The method includes: acquiring the operating status of the storage tank at a first preset time; determining a state change value of the storage tank based on the operating status; in response to the state change value satisfying a first preset condition: acquiring storage tank monitoring data, and determining emergency control parameters based on the monitoring data, the emergency control parameters including the water mist spraying speed of a cooling device; and controlling the cooling device to spray water mist onto the outer wall of the storage tank based on the emergency control parameters. This method can promptly trigger an emergency mechanism in the early stages of an anomaly; and by using patrol robots to replace manual inspections of the area where the storage tank is located, while simultaneously collecting data in real time, it can improve inspection efficiency, the accuracy of safety warnings, and reduce the safety risks of the storage tank.

Description

Technical Field

[0001] This specification relates to the field of emergency response for urban storage tanks, and in particular to an emergency response system, method and medium for urban storage tanks based on a large-scale Internet of Things (IoT) model. Background Technology

[0002] With the rapid development of smart cities and industry, the number of storage tanks (such as gas storage tanks and oil storage tanks) in cities is also increasing. The continuous expansion of the scale of urban storage tanks places higher demands on the safety monitoring and emergency response capabilities of these tanks.

[0003] Traditional tank monitoring and emergency response systems typically rely on manual inspections or single sensor networks to detect tanks, and only take action after an anomaly occurs. They lack intelligent pre-emptive sensing and processing capabilities, making it difficult to meet the real-time and intelligent requirements of modern smart cities for tank emergency response.

[0004] Therefore, it is necessary to provide an emergency response system, method, and medium for urban storage tanks based on a large-scale Internet of Things (IoT) model, which can accurately and effectively improve the speed and accuracy of response to emergency events involving storage tanks. Summary of the Invention

[0005] This specification provides one or more embodiments of an emergency response system for urban storage tanks based on an Internet of Things (IoT) big data model. The system includes an emergency monitoring and management platform configured to: acquire the operating status of the storage tank at a first preset time; determine a state change value of the storage tank based on the operating status; in response to the state change value satisfying a first preset condition: acquire storage tank monitoring data and determine emergency control parameters based on the monitoring data, the emergency control parameters including the water mist spraying speed of a cooling device; and control the cooling device to spray water mist onto the outer wall of the storage tank based on the emergency control parameters.

[0006] This specification provides one or more embodiments of an emergency response method for urban storage tanks based on an Internet of Things (IoT) big data model. The method is executed by an emergency monitoring and management platform of the urban storage tank emergency response system based on the IoT big data model. The method includes: acquiring the operating status of the storage tank at a first preset time; determining a state change value of the storage tank based on the operating status; in response to the state change value satisfying a first preset condition: acquiring storage tank monitoring data, and determining emergency control parameters based on the monitoring data, the emergency control parameters including the water mist spraying speed of a cooling device; and controlling the cooling device to spray water mist onto the outer wall of the storage tank based on the emergency control parameters.

[0007] This specification provides one or more embodiments of a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes an emergency handling method for urban storage tanks based on an Internet of Things (IoT) big data model.

[0008] The beneficial effects of this manual include, but are not limited to: the urban storage tank emergency response system based on the Internet of Things (IoT) model enables data exchange between various functional platforms, forming a closed-loop information operation, and coordinating and operating systematically under unified management, significantly improving the efficiency and accuracy of emergency response. By acquiring the real-time operating status of storage tanks and calculating their status changes, the system can promptly trigger emergency mechanisms in the early stages of anomalies; by using patrol robots to replace manual labor for safety inspections of the storage tank areas and simultaneously collecting real-time data, inspection efficiency, the accuracy of safety warnings, and the reduction of safety risks associated with the storage tanks can be improved. Attached Figure Description

[0009] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0010] Figure 1 This is a platform structure diagram of an urban storage tank emergency response system based on an Internet of Things (IoT) big data model, as shown in some embodiments of this specification.

[0011] Figure 2 This is an exemplary flowchart of an emergency response method for urban storage tanks based on an Internet of Things (IoT) big data model, as shown in some embodiments of this specification.

[0012] Figure 3 This is an exemplary flowchart illustrating a patrol robot performing a patrol according to some embodiments of this specification;

[0013] Figure 4 This is an exemplary schematic diagram of a patrol parameter model according to some embodiments of this specification;

[0014] Figure 5 This is an exemplary schematic diagram illustrating the updating of water pump pressure according to some embodiments of this specification.

[0015] In the diagram, 100-Emergency handling system for storage tanks based on a large IoT model, 110-Emergency monitoring user platform, 120-Emergency monitoring service platform, 130-Emergency monitoring management platform, 140-Emergency monitoring sensor network platform, 150-Emergency monitoring object platform, 410-Candidate patrol parameters, 420-Storage tank operation data, 430-Storage tank characteristics, 440-Rest parameters, 450-Patrol parameter model, 460-Risk value, 510-Pipeline structure, 520-Storage liquid flow rate, 530-Pump pressure, 540-Edge prediction model, 550-Edge occurrence probability. Detailed Implementation

[0016] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0017] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0018] Figure 1 This is a platform structure diagram of an urban storage tank emergency response system based on an Internet of Things (IoT) big data model, as shown in some embodiments of this specification.

[0019] In some embodiments, the storage tank emergency response system 100 based on the Internet of Things big data model may include an emergency monitoring user platform 110, an emergency monitoring service platform 120, an emergency monitoring management platform 130, an emergency monitoring sensor network platform 140, and an emergency monitoring object platform 150.

[0020] The emergency monitoring user platform 110 is a platform for interacting with users. In some embodiments, the emergency monitoring user platform 110 is configured as a terminal device, such as a mobile phone, computer, and applications, web pages installed on it.

[0021] In some embodiments, the emergency monitoring user platform 110 and the emergency monitoring service platform 120 exchange information.

[0022] The 120 Emergency Monitoring Service Platform is an interactive service platform for receiving and transmitting data.

[0023] In some embodiments, the emergency monitoring service platform 120 can interact with the emergency monitoring user platform 110 and the emergency monitoring management platform 130. For example, the emergency monitoring service platform 120 can obtain data such as the operating status of the storage tank, storage tank monitoring data, and emergency control parameters from the emergency monitoring management platform 130 and send them to the emergency monitoring user platform 110. In some embodiments, the emergency monitoring service platform 120 is configured as a server, switch, or other similar device.

[0024] The emergency monitoring and management platform 130 can be a platform that coordinates and integrates the connections and collaboration between various functional platforms, and aggregates all information from the Internet of Things (IoT), providing sensing, management, and control functions for the IoT. In some embodiments, the emergency monitoring and management platform 130 includes a processor and a storage medium. In some embodiments, the storage medium includes a hard disk drive, a storage array, etc. The storage medium can be used to store data such as the operating records and maintenance logs of the storage tank.

[0025] In some embodiments, the emergency monitoring and management platform 130 can interact with the emergency monitoring service platform 120 and the emergency monitoring sensor network platform 140 respectively. For example, the emergency monitoring and management platform 130 can send tank monitoring data to the emergency monitoring service platform 120. As another example, the emergency monitoring and management platform 130 can send instructions to the emergency monitoring sensor network platform 140 to obtain current tank operation data.

[0026] The emergency monitoring sensor network platform 140 can be a functional platform for managing sensor communication. In some embodiments, the emergency monitoring sensor network platform 140 can realize the functions of sensing information communication and control information communication, for example, realizing sensing information communication of storage tank operation data.

[0027] In some embodiments, the emergency monitoring sensor network platform 140 includes communication devices such as routing gateways.

[0028] The Emergency Supervision Target Platform 150 refers to the platform for emergency supervision data collection and execution of instructions.

[0029] In some embodiments, the emergency monitoring object platform 150 includes multiple emergency monitoring object sub-platforms. The emergency monitoring object sub-platforms are configured as devices such as temperature sensors, humidity sensors, and pressure sensors. The multiple emergency monitoring object sub-platforms can be respectively configured at cooling equipment, patrol robots, storage tank channel pipelines, and multiple storage tanks.

[0030] A storage tank is a storage device used in industrial production to store chemical substances, such as oil storage tanks and gas storage tanks. In some embodiments, temperature sensors may be installed on the outer wall of the storage tank to detect the tank temperature. Flow meters and pressure gauges may also be installed at the inlet and outlet of the storage tank to obtain data such as liquid flow rate and pump pressure. Environmental humidity meters, anemometers, and wind speed meters may also be installed near the storage tank to obtain ambient humidity and wind data.

[0031] Cooling equipment refers to devices used to lower the temperature of the outer wall of a storage tank, such as external spray systems. External spray systems spray water mist onto the outer wall of the storage tank, and the temperature of the tank is lowered through the evaporation of the water mist, which absorbs heat.

[0032] In some embodiments, the cooling device can obtain emergency control parameters for water mist spraying from the emergency monitoring and management platform 130.

[0033] Patrol robots are used for real-time monitoring, safety inspections, and anomaly warnings of the area where storage tanks are located. In some embodiments, the patrol robot is equipped with data acquisition devices (e.g., cameras, temperature sensors, humidity sensors, pressure sensors, etc.).

[0034] In some embodiments, the patrol robot can obtain patrol parameters for patrolling from the emergency monitoring and management platform 130 and send the tank monitoring data collected by the patrol robot to the emergency monitoring and management platform 130.

[0035] In some embodiments, the patrol robot includes a lidar sensor and an ultrasonic obstacle avoidance device. The patrol robot can use the lidar sensor and ultrasonic obstacle avoidance device to perceive obstacles in real time and automatically adjust its patrol path.

[0036] For more information on the aforementioned platform, sensors, devices, and apparatus, please refer to [link / reference]. Figures 2-5 And related content.

[0037] In some embodiments of this specification, the urban storage tank emergency response system based on an IoT big data model can exchange data and information between various functional platforms, forming a closed-loop information operation. Under unified management, it operates in a coordinated and regular manner, significantly improving the efficiency and accuracy of emergency response. By acquiring the real-time operating status of the storage tanks and calculating their status changes, the system can promptly trigger emergency mechanisms in the early stages of anomalies. By using patrol robots to replace manual labor in conducting safety inspections of the areas where the storage tanks are located, and simultaneously collecting data in real time, the system can improve inspection efficiency, the accuracy of safety warnings, and reduce the safety risks to the storage tanks.

[0038] It should be noted that the above description of the urban storage tank emergency response system based on an IoT big data model is for convenience only and should not be construed as limiting this specification to the scope of the embodiments described. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the various modules or construct subsystems connected to other modules without departing from these principles. In some embodiments, Figure 1 The emergency monitoring user platform 110, emergency monitoring service platform 120, emergency monitoring management platform 130, emergency monitoring sensor network platform 140, and emergency monitoring object platform 150 disclosed herein can be different platforms within the same system, or a single platform can implement the functions of two or more of the aforementioned platforms. For example, each platform can share a storage module, or each module can have its own storage module. Such variations are all within the scope of protection of this specification.

[0039] Figure 2 This is an exemplary flowchart illustrating an emergency response method for urban storage tanks based on a large-scale Internet of Things (IoT) model, according to some embodiments of this specification. Figure 2 As shown, steps S210 to S240 are included. In some embodiments, steps S210 to S240 may be executed by a processor.

[0040] Step S210: Obtain the working status of the storage tank at the first preset time.

[0041] The first preset time refers to a preset historical time period. In some embodiments, the first preset time can be set by a technician based on experience. For example, the past hour, the past day, etc.

[0042] The operating status of a storage tank reflects its current operational condition. In some embodiments, the operating status of a storage tank includes, but is not limited to, liquid inlet, liquid outlet, pressurization, and maintenance.

[0043] In some embodiments, the processor can obtain the operating status of the storage tank in various ways. For example, the processor can determine whether the liquid flow rate and pressure of the storage tank have changed based on the patrol robot, thereby determining whether the storage tank is currently in a state of liquid input, liquid output, pressurization, etc.; the processor can also use the patrol robot to collect on-site images of the storage tank, and through image recognition algorithms, determine the storage tank's operating status as under maintenance if it is determined that maintenance personnel are present. Another example is that the processor can retrieve the storage tank's operation records and maintenance logs from the storage medium, thereby determining the storage tank's operating status and storing it in the storage medium.

[0044] Step S220: Determine the state change value of the storage tank based on its working state.

[0045] The state change value can indicate whether the working state of the storage tank has changed. In some embodiments, when the working state of the storage tank changes within a first preset time period, the state change value corresponding to the first preset time period is 1; when the working state of the storage tank remains unchanged within the first preset time period, the state change value corresponding to the first preset time period is 0.

[0046] In some embodiments, the processor can obtain the initial working state of the storage tank at the start time of the first preset time, compare the working state of the storage tank at all time points within the first preset time with the initial working state of the storage tank, and if the working state of the storage tank at any time point is inconsistent with the initial working state of the storage tank, the state change value within the first preset time is 1; otherwise, the state change value within the first preset time is 0.

[0047] Step S230: In response to the state change value satisfying the first preset condition: acquire tank monitoring data, and determine emergency control parameters based on the tank monitoring data.

[0048] The first presupposition condition is the condition for determining whether emergency cooling of the storage tank is necessary.

[0049] In some embodiments, the first preset condition can be determined based on the actual application scenario and requirements. For example, the first preset condition can be: the state change value of the storage tank is 1 within a first preset time period.

[0050] Emergency control parameters are parameters used to control the operation of cooling equipment. In some embodiments, when a state change value meets a first preset condition, it indicates that the storage tank is prone to abnormality. The processor can determine the emergency control parameters and control the cooling equipment to cool the storage tank to ensure its safe operation. For more information on cooling equipment, please refer to [link to relevant documentation]. Figure 1 And related explanations.

[0051] In some embodiments, the emergency control parameters include the water mist spraying speed of the cooling equipment, that is, the water flow speed when the cooling equipment sprays water to cool the storage tank.

[0052] In some embodiments, the emergency control parameters may also include the water mist spray direction. For more information on the water mist spray direction, please refer to the relevant description below.

[0053] In some embodiments, the processor can determine emergency control parameters based on tank monitoring data.

[0054] Tank monitoring data can reflect the operational status characteristics of the tank. In some embodiments, tank monitoring data includes the temperature of the tank's outer wall.

[0055] In some embodiments, the processor can collect tank monitoring data in various ways, such as through temperature and humidity sensors on the tank. Another example is that the processor can collect tank monitoring data using a patrol robot. For more information on patrol robots collecting tank monitoring data, please refer to [link to relevant documentation]. Figure 3 And related explanations.

[0056] In some embodiments, the water mist spraying rate can be related to the temperature of the outer wall of the storage tank. For example, the water mist spraying rate can be positively correlated with the temperature of the outer wall of the storage tank; the higher the temperature of the outer wall of the storage tank, the faster the water mist spraying rate, so as to quickly reduce the temperature of the outer wall of the storage tank; the lower the temperature of the outer wall of the storage tank, the slower the water mist spraying rate, so as to save water.

[0057] In some embodiments, the processor may also periodically determine the water mist spraying speed based on the rate of temperature change and the rate of humidity change.

[0058] In some embodiments, the emergency monitoring and management platform is further configured to: within a preset period: acquire the tank operation data and ambient humidity of the previous period; determine the temperature change rate based on the tank operation data of the previous period, and determine the humidity change rate based on the ambient humidity of the previous period; and determine the water mist spraying speed of the current period based on the temperature change rate and the humidity change rate.

[0059] The preset cycle refers to the adjustment cycle of the water mist spraying speed, and the previous cycle refers to the cycle before the current preset cycle.

[0060] In some embodiments, the duration of the preset period can be determined in various ways. For example, the duration of the preset period can be determined based on the actual application scenario and requirements.

[0061] In some embodiments, the duration of the preset cycle can be determined based on patrol parameters. For example, the duration of the preset cycle is negatively correlated with the data acquisition time of the storage tank in the patrol parameters; the longer the data acquisition time of the storage tank, the shorter the duration of the preset cycle.

[0062] For more information on patrol parameters and tank data collection time, please see [link / reference]. Figure 3 Related descriptions.

[0063] According to some embodiments of this specification, the longer the data acquisition time of the storage tank, the more likely it is that the storage tank operation data will not be updated in a timely manner. Since the storage tank operation data can be obtained in real time through the Internet of Things, when the data acquisition time of the storage tank is long, shortening the period of the preset cycle can increase the acquisition frequency of the storage tank operation data and ambient humidity of the previous cycle. This can overcome the drawback of untimely updates of storage tank operation data, improve the efficiency and accuracy of storage tank cooling control, and ensure the safety of the storage tank.

[0064] Ambient humidity refers to the humidity of the area where the storage tank is located.

[0065] In some embodiments, the processor can retrieve the tank operation data from the previous cycle from the storage medium and obtain the ambient humidity via an ambient humidity meter. For more information on tank operation data, please refer to the relevant descriptions above; for more information on ambient humidity meters, please refer to [link to relevant documentation]. Figure 1 Related explanations.

[0066] The rate of temperature change refers to the rate at which the temperature of the outer wall of a storage tank changes.

[0067] In some embodiments, the processor can obtain the tank outer wall temperature at multiple time points in the previous cycle from the tank monitoring data, construct a linear fitting function based on the multiple time points and the tank outer wall temperature corresponding to each time point, and determine the slope of the linear fitting function as the temperature change rate.

[0068] The rate of change of humidity refers to the speed at which the ambient humidity in the area where the storage tank is located changes over time. The method for determining the rate of change of humidity is similar to that for determining the rate of change of temperature. For the specific steps for determining the rate of change of humidity, please refer to the aforementioned description of determining the rate of change of temperature.

[0069] In some embodiments, the processor can normalize the temperature change rate and the humidity change rate respectively, and then perform a weighted summation of the normalized temperature change rate and humidity change rate to obtain a change rate score.

[0070] For example, the rate of change score can also be obtained based on the following formula (1):

[0071] (1)

[0072] in, Indicates the rate of change score. B represents the normalized rate of temperature change, and A represents the normalized rate of humidity change. and The coefficient is greater than 0. and The value can be preset based on prior experience.

[0073] In some embodiments, the water mist spraying rate is negatively correlated with the rate of change score. A higher rate of change score indicates that the cooling and humidification of the tank's outer wall is faster, reducing the likelihood of a tank accident and allowing for a reduction in the water mist spraying rate to conserve water. Conversely, a lower rate of change score indicates that the cooling and humidification of the tank's outer wall is slower, making it difficult for the tank's outer wall temperature to decrease, increasing the likelihood of an accident and requiring a higher water mist spraying rate.

[0074] In some embodiments, to ensure the safety of the storage tank, the water mist spraying speed must not be lower than the lower limit of the water mist spraying speed. The lower limit of the water mist spraying speed can be determined based on presets by relevant personnel or adjusted according to the actual situation on site.

[0075] In some embodiments, when the processor detects abnormal conditions such as increased temperature or decreased humidity on the outer wall of the storage tank during water mist spraying, it indicates that an accident such as internal combustion or chemical reaction may occur inside the tank. The processor can then activate an emergency plan to promptly contain the serious accident. The emergency plan may include broadcast warnings, personnel evacuation, and activation of fire-fighting facilities. The emergency plan can be determined based on the actual needs of the scenario.

[0076] According to some embodiments of this specification, since ambient humidity affects thermal conductivity and thus the rate of temperature change, the water mist spraying speed for the current cycle is determined by comprehensively considering both the rate of temperature change and the rate of humidity change. Compared to considering only the rate of temperature change, the water mist spraying speed can be determined more comprehensively and intelligently, ensuring the safety of the storage tank. Periodically adjusting the water mist spraying speed can dynamically optimize the utilization rate of water resources, achieving efficient cooling while saving water resources.

[0077] Step S240: Based on emergency control parameters, control the cooling equipment to spray water mist onto the outer wall of the storage tank.

[0078] In some embodiments, the processor can control the cooling equipment to spray water mist onto the outer wall of the storage tank at the water mist spraying speed in the emergency control parameters.

[0079] In some embodiments, the emergency control parameters also include the water mist spraying direction, and the emergency monitoring and management platform is further configured to: determine ambient wind data; determine the water mist spraying direction based on the ambient wind data; and control the cooling equipment to spray water mist onto the outer wall of the storage tank based on the water mist spraying direction and the water mist spraying speed.

[0080] The direction of water mist spraying refers to the orientation of the spray nozzle when the cooling equipment is spraying water mist.

[0081] Environmental wind data refers to wind-related data for the area where the storage tank is located. In some embodiments, environmental wind data includes wind speed, wind direction, etc. The processor can determine wind speed and wind direction using devices such as anemometers and wind speed meters. More information about anemometers and wind speed meters can be found here. Figure 1 Related descriptions.

[0082] In some embodiments, since wind can interfere with water mist spraying, causing the actual spraying position of the water mist to deviate from the expected spraying position, the preset spraying direction of the water mist can be adjusted based on environmental wind data to obtain a water mist spraying direction that can resist the effect of wind.

[0083] In some embodiments, the processor can determine the water mist spraying direction based on ambient wind data in various ways. For example, the processor can construct a first preset table based on reference ambient wind data, an initial direction, and a reference adjusted direction. The first preset table includes the correspondence between reference ambient wind data, the initial direction, and different reference adjusted directions. The processor can determine the current adjusted direction by consulting the first preset table, based on the current ambient wind data and the initial direction. The initial direction refers to the default nozzle orientation, which can be the nozzle orientation when it is closest to the outer wall of the storage tank; the adjusted direction refers to the water mist spraying direction corrected after considering the influence of ambient wind force.

[0084] In some embodiments, the processor can test the degree of deviation of the initial reference direction under different reference wind speeds and directions in a computer simulation experiment, as well as the compensation angle required to correct the deviation. Based on the compensation angle, the initial reference direction is corrected, and the corrected direction is determined as the adjusted reference direction. By repeating the experiment several times, a first preset table can be obtained.

[0085] In some embodiments, the processor can control the motor of the cooling device to rotate the spray nozzle to the water mist spraying direction, spraying water mist onto the outer wall of the storage tank according to the water mist spraying direction and speed. For example, the spray nozzle of the cooling device includes multiple nozzles, each with multiple different spraying directions. After determining the water mist spraying direction, the processor can activate the nozzle whose direction is closest to the water mist spraying direction to perform water mist spraying.

[0086] According to some embodiments of this specification, the water mist spraying direction is determined based on the direction of the ambient wind and the preset spraying direction of the water mist. This can reduce the influence of the ambient wind and allow the water mist to be sprayed onto the outer wall of the storage tank in the preset spraying direction, thereby avoiding the influence of the ambient wind on the cooling effect of the outer wall of the storage tank.

[0087] Changes in the operating status of storage tanks (such as changes in air pressure) often generate heat fluctuations, leading to temperature variations within the tank. In such cases, the tank is prone to problems. For example, if the tank's temperature has reached dynamic equilibrium, increasing pressure can cause a further temperature rise, disrupting the equilibrium and potentially leading to malfunctions or even an explosion. According to some embodiments in this specification, by further monitoring the data of storage tanks experiencing status changes, the safety of the storage tanks can be ensured while saving manpower and resources.

[0088] Figure 3 This is an exemplary flowchart illustrating how a patrol robot performs a patrol according to some embodiments of this specification. Figure 3 As shown, steps S310 to S340 are included. In some embodiments, steps S310 to S340 may be executed by a processor.

[0089] Step S310: Obtain the tank operation data and tank characteristics for the second preset time.

[0090] The second preset time refers to a preset historical time period. Similar to the first preset time, the second preset time can be set by technical personnel based on experience.

[0091] Tank operation data refers to data related to the operation of the tank, such as liquid output rate, liquid input rate, internal working temperature of the tank, and internal working air / hydraulic pressure of the tank.

[0092] The characteristics of a storage tank can reflect its inherent properties, such as tank type and tank capacity.

[0093] In some embodiments, the processor can retrieve the tank's operation records from the storage medium to determine the tank's operation data for a second preset time; the processor can also retrieve the tank's factory report, installation records, and other information from the storage medium to determine the tank's characteristics.

[0094] Step S320: Based on the tank operation data and tank characteristics at the second preset time, determine the patrol parameters.

[0095] Patrol parameters are the parameters that control the patrol robot to perform patrols.

[0096] In some embodiments, patrol parameters include patrol path and data collection time.

[0097] A patrol path refers to the pre-defined route a patrol robot takes when performing patrol tasks. The processor can generate patrol paths using path generation algorithms and other methods.

[0098] Data acquisition time refers to the time it takes for the patrol robot to collect monitoring data from the storage tank. The longer the data acquisition time, the more accurate the information collected about the storage tank; the shorter the data acquisition time, the faster the patrol robot can patrol.

[0099] In some embodiments, the processor can determine patrol parameters in a variety of ways. For example, the processor can determine the patrol path using a path planning algorithm; and determine the data collection time based on a second preset table.

[0100] In some embodiments, the processor can retrieve several sets of historical operating records of storage tanks from the storage medium, and the processor can exclude historical operating records in which the storage tanks experienced abnormalities or accidents. The processor can construct a second preset table based on the historical storage tank operating data, historical storage tank characteristics, and historical data acquisition time in the remaining historical operating records. The second preset table includes the correspondence between storage tank operating data, storage tank characteristics, and data acquisition time. The processor can determine the data acquisition time by querying the second preset table based on the storage tank operating data and storage tank characteristics at the second preset time.

[0101] In some embodiments, the patrol parameters also include the patrol priority of the storage tank, and the emergency monitoring and management platform is further configured to: determine the rest parameters of the storage tank; and determine the patrol parameters based on the storage tank operation data, storage tank characteristics, and rest parameters over a second preset time.

[0102] Patrol priorities can reflect the importance of different storage tanks.

[0103] Rest parameters reflect the duration of a storage tank's unavailability; they can be expressed as the duration of liquid input or output. The processor can retrieve the tank's operating records from the storage medium to determine the rest parameters.

[0104] In some embodiments, for each storage tank, the processor can also determine the data acquisition time of the storage tank through a second preset table. Based on the data acquisition time and adjustment parameters of the storage tank, normalization processing and weighted summation are performed to obtain an acquisition time score.

[0105] In some embodiments, the patrol priority of a storage tank is positively correlated with its data acquisition time score. That is, a higher data acquisition time score indicates a longer data acquisition time, less timely data acquisition, and a higher maintenance parameter, suggesting that the tank has undergone prolonged liquid input and output. Tanks with lower data acquisition time scores (e.g., those in static storage) are more likely to experience risks and require higher priority for patrol inspection.

[0106] For example, the time-collection score can be obtained based on the following formula (2):

[0107] (2)

[0108] in, Indicates the score based on the collection time. This represents the normalized data acquisition time, and N represents the normalized adjustment parameter. and The coefficient is greater than 0. and The value can be preset based on prior experience.

[0109] In some embodiments, if multiple storage tanks have the same patrol priority, multiple patrol robots can be dispatched to patrol the multiple storage tanks simultaneously.

[0110] According to some embodiments of this specification, the patrol robot patrols according to patrol priority, and can prioritize patrolling tanks with higher risks, which can make the patrol order of tanks more reasonable, improve the patrol efficiency of the patrol robot, and ensure the safety of tanks in a timely manner.

[0111] In some embodiments, the processor can also determine patrol parameters based on a patrol parameter model. For more information on patrol parameter models, see [link to relevant documentation]. Figure 4 Related descriptions.

[0112] Step S330: Based on the patrol parameters, drive the motor of the patrol robot to move, and use the patrol robot's lidar and ultrasonic obstacle avoidance device to perceive obstacles in real time and adjust the patrol path.

[0113] LiDAR (Light Detection and Ranging) is a sensing device that scans the environment and measures distance using a laser beam, while ultrasonic obstacle avoidance devices are sensing devices that measure distance based on ultrasonic echoes. Both LiDAR and ultrasonic obstacle avoidance devices can be used for autonomous navigation and obstacle avoidance in patrol robots.

[0114] In some embodiments, the patrol robot uses lidar and ultrasonic obstacle avoidance devices to detect whether there are obstacles on its patrol path. When an obstacle is detected, the patrol robot can adjust its patrol path to avoid the obstacle and continue to perform its patrol task according to the adjusted patrol path.

[0115] In some embodiments, the processor can determine the patrol path based on tank monitoring parameters and patrol parameters. For example, the processor can determine multiple tanks to be patrolled and their priority order based on the outer wall temperature of each tank in the tank monitoring parameters and the patrol priority in the patrol parameters, and determine the patrol path through methods such as path generation algorithms.

[0116] Step S340: Collect tank monitoring data within the patrol area to complete the patrol.

[0117] The patrol area refers to the spatial range that the patrol robot needs to patrol. For example, the patrol area could be the factory area where the storage tanks are located.

[0118] In some embodiments, the processor can determine the tanks to be patrolled based on tank monitoring data, and further determine the patrol area based on the tanks to be patrolled.

[0119] In some embodiments, the patrol robot can collect monitoring data from the storage tanks using its data acquisition device, which may include a camera, temperature sensor, humidity sensor, etc. The patrol is complete once the patrol robot has collected monitoring data from all the storage tanks.

[0120] According to some embodiments of this specification, the data collection time of the storage tank is determined based on the tank operation data and tank characteristics. This allows for the reasonable arrangement of the data collection time for each storage tank according to its different conditions, thereby improving the patrol efficiency of the patrol robot.

[0121] Figure 4This is an exemplary schematic diagram of a patrol parameter model shown according to some embodiments of this specification.

[0122] In some embodiments, the emergency monitoring and management platform is further configured to: determine candidate patrol parameters; determine a risk value 460 based on at least one candidate patrol parameter 410, tank operation data 420 for a second preset time, tank characteristics 430, and rest parameters 440, through a patrol parameter model 450, wherein the patrol parameter model is a machine learning model; and determine patrol parameters based on the risk value 460.

[0123] Candidate patrol parameters are patrol parameters that are to be selected.

[0124] In some embodiments, the processor can obtain several historical patrol parameters from historical patrol records, sort the historical patrol parameters according to their frequency of use, and select the top N historical patrol parameters as candidate patrol parameters. The preset selection number N can be set by technicians based on experience.

[0125] The risk value reflects the degree of risk of a storage tank. Tanks with higher risk values ​​require more timely patrols.

[0126] In some embodiments, the processor may determine the risk value based on a patrol parameter model.

[0127] The patrol parameter model is a predictive model used to determine the risk value corresponding to the patrol parameters. In some embodiments, the patrol parameter model can be any one or a combination of machine learning models, such as Convolutional Neural Networks (CNN) models or other custom model structures.

[0128] In some embodiments, the patrol parameter model takes as input candidate patrol parameters for future times, tank operation data for a second preset time, tank characteristics, and maintenance parameters, and outputs a risk value for future times.

[0129] In some embodiments, the patrol parameter model can be trained in various ways. For example, it can be trained using multiple first training samples with a first label. The first training sample may include sample patrol parameters from a second historical time, sample tank operation data from the first historical time, sample tank characteristics, and sample maintenance parameters. The first label corresponding to the first training sample is the sample risk value from the second historical time, where the first historical time is earlier than the second historical time.

[0130] In some embodiments, the processor can use historical patrol parameters, historical tank operation data, historical tank characteristics, and historical maintenance parameters obtained from historical patrol records for a first historical time as the first training sample, and use the historical risk value for a second historical time as the first label. The processor can count the frequency of abnormal events occurring during the historical patrols corresponding to the first training sample, and the historical risk value is positively correlated with the frequency of abnormal events. Abnormal events include failure to patrol tanks with lower safety, tank maintenance, tank malfunction, and serious accidents involving tanks.

[0131] In some embodiments, the processor can input candidate patrol parameters, sample tank operation data, sample tank features, and sample maintenance parameters into the initial patrol parameter model. Based on the risk value output by the initial patrol parameter model, a loss function is constructed with the first label. The initial patrol parameter model is then updated based on the loss function. When the training termination condition is met, the initial patrol parameter model training is complete, resulting in a trained patrol parameter model. The training termination condition can be loss function convergence, the number of iterations reaching a threshold, etc.

[0132] In some embodiments, the processor may select the candidate patrol parameter with the lowest risk value as the patrol parameter.

[0133] According to some embodiments of this specification, candidate patrol parameters are selected from historical patrol parameters, and then the patrol parameter model is used to further determine the risk value corresponding to the candidate patrol parameters. The final patrol parameters are determined based on the risk value, which can avoid some storage tanks not being patrolled or being patrolled in a timely manner, thereby improving the safety of the storage tanks.

[0134] Figure 5 This is an exemplary schematic diagram illustrating the updating of water pump pressure according to some embodiments of this specification.

[0135] In some embodiments, the emergency control parameters also include the pump pressure of the tank channel network, and the emergency monitoring and management platform is further configured to: calculate the eddy current occurrence probability 550 at a preset point in the tank channel network based on the tank's maintenance parameter 440; and update the pump pressure in response to the eddy current occurrence probability meeting a second preset condition to adjust the liquid flow rate in the tank.

[0136] A storage tank pipeline network is a transport network that connects storage tanks to other equipment, pipelines, or facilities. Pump pressure refers to the pressure applied by the pumps in the storage tank pipeline network when transporting the stored liquid. Higher pump pressure results in a faster liquid flow rate and higher liquid transport efficiency; lower pump pressure results in a slower liquid flow rate and a lower probability of eddies. Liquid flow rate refers to the speed at which the liquid enters or exits the storage tank.

[0137] In some embodiments, the processor can obtain the water pump pressure based on a pressure gauge installed at the tank inlet / outlet.

[0138] Pre-defined locations refer to key nodes in the storage tank pipeline network. For example, key nodes may include the outlets and inlets of the storage tanks, pipeline branch points, and the installation locations of equipment such as pumps, valves, and flow meters.

[0139] In some embodiments, preset points can be preset based on prior experience. The processor can also determine preset points as points pre-entered by technicians.

[0140] Eddies refer to the eddies that occur when liquid flows through the pipeline network of a storage tank. When eddies occur, they can cause pressure waves in the storage tank during liquid input or output, reducing the stability of the storage tank during these processes.

[0141] The probability of eddy current occurrence refers to the likelihood of a eddy current occurring at a preset point.

[0142] In some embodiments, the processor can determine the probability of eddy current occurrence based on an eddy current prediction model.

[0143] Eddy current prediction models are models used to predict the probability of eddy current occurrence. In some embodiments, eddy current prediction models can be machine learning models, such as any one or a combination of regression models, neural network models (NNs), or other custom model structures.

[0144] In some embodiments, the eddy current prediction model 540 takes as input pump pressure 530, tank channel network structure 510, liquid flow velocity 520, and tank adjustment parameters 440, and outputs the eddy current occurrence probability 550 at a preset point. The tank channel network structure reflects the connection characteristics of the tank channel network, including the number of pipes, connection relationships, spatial distribution, and number of bends. The processor can obtain the network structure from data such as the tank channel network's laying records. More information regarding pump pressure, liquid flow velocity, adjustment parameters, and eddy current occurrence probability can be found in the relevant descriptions above.

[0145] In some embodiments, the eddy current prediction model can be trained using various methods. For example, it can be trained using multiple second training samples with second labels. The second training samples may include the sample pump pressure of the sample tank, the sample pipeline structure, the sample liquid flow rate, and sample conditioning parameters. The second label indicates whether eddies occur at a preset sample location in the sample tank; if eddies occur at the preset sample location, the second label is 1; otherwise, if no eddies occur at the preset sample location, the second label is 0.

[0146] In some embodiments, the processor can acquire tank operation data, and determine historical pump pressure, historical pipeline structure, historical liquid flow rate, and historical adjustment parameters from the tank operation data as second training samples, and determine the historical eddy current occurrence probability at historical preset points as a second label. The processor can determine the historical eddy current occurrence probability through methods such as manual annotation.

[0147] In some embodiments, since eddies can cause fluctuations in the liquid flow rate within the pipeline network, the processor can obtain the historical liquid flow rate at a historical preset point. When the historical liquid flow rate at a certain historical preset point is significantly different from the historical liquid flow rate at adjacent upstream and downstream historical preset points, the processor can consider that there is an eddy at that historical preset point and determine that the second label of that historical preset point is 1; otherwise, it determines that the second label of that historical preset point is 0.

[0148] The training method for the eddy current prediction model is similar to that for the patrol parameter model. For the training process of the eddy current prediction model, please refer to the relevant description of the training process of the patrol parameter model mentioned above.

[0149] The second pre-set condition is the criterion for determining whether the water pump pressure needs to be adjusted. When the possibility of eddies generating in the storage tank pipeline network is too high, the water pump pressure needs to be updated.

[0150] In some embodiments, the probability of eddy current occurrence can be either satisfied or not satisfied with a second preset condition. When the probability of eddy current occurrence satisfies the second preset condition, the water pump pressure is updated to adjust the liquid flow rate in the storage tank. When the probability of eddy current occurrence does not satisfy the second preset condition, the water pump pressure is not updated.

[0151] In some embodiments, the second preset condition may be: the average probability of eddy current occurrence at any preset point is greater than a preset probability threshold. The second preset condition may also be: the number of preset points with an eddy current occurrence probability greater than the preset probability threshold is greater than a quantity M. The preset probability threshold and the quantity M (the number of preset points) can be set by technical personnel based on experience.

[0152] In some embodiments, the processor can update the pump pressure based on the probability of eddy current occurrence. For example, the decrease in pump pressure is positively correlated with the probability of eddy current occurrence. For instance, the higher the probability of eddy current occurrence, the greater the decrease in pump pressure.

[0153] In some embodiments, when there are multiple water pumps in the tank channel network, if the average probability of eddy current occurrence at a preset point is greater than a preset probability threshold, the processor can reduce the water pump pressure of all water pumps in batches based on the eddy current occurrence probability at the preset point.

[0154] In some embodiments, in response to the probability of eddy current occurrence at a certain preset point being greater than a preset probability threshold, the processor may reduce the water pump pressure only at that certain preset point.

[0155] In some embodiments, the reduction in pump pressure is directly proportional to the extent to which the probability of eddy occurrence exceeds a preset probability threshold. The greater the extent to which the probability of eddy occurrence exceeds the preset probability threshold, the greater the likelihood of eddy occurrence, requiring a significant reduction in pump pressure to decrease the liquid flow rate and suppress eddy formation.

[0156] In a complex network of interconnected tanks connected by a network of pipes, pressure waves are generated when liquid is input or output from each tank. These pressure waves not only affect the pressure stability of the tank itself but also propagate along the pipe network, impacting other tanks downstream and on adjacent branches. According to some embodiments of this specification, by predicting the probability of eddy currents at different preset points in advance and reducing the pump pressure at preset points with a higher probability of eddy current occurrence, the probability of eddy currents can be reduced, ensuring the stability of the liquid flow velocity during liquid input or output and improving the safety of the tanks during these processes.

[0157] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0158] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0159] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0160] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0161] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. An emergency response system for urban storage tanks based on an Internet of Things (IoT) big data model, characterized in that: The urban storage tank emergency response system based on the Internet of Things (IoT) big data model includes an emergency monitoring and management platform, which is configured as follows: Obtain the operating status of the storage tank at the first preset time; Based on the operating status of the storage tank, determine the state change value of the storage tank; In response to the state change value satisfying a first preset condition: Acquire tank monitoring data, and based on the tank monitoring data and ambient wind data, determine emergency control parameters, including the water mist spraying speed and water mist spraying direction of the cooling equipment; Determining the direction of the water mist spray specifically includes: Based on the ambient wind data and the initial direction of the cooling device, the water mist spraying direction is determined using a first preset table; The first preset table is constructed based on multiple simulation experiment results, which include the degree of deviation of different reference wind speeds, reference wind directions and reference initial directions, and the corresponding relationship of compensation angles. Determining the water mist spraying speed specifically includes: Within a preset period: Obtain the tank operation data and ambient humidity from the previous cycle; The rate of temperature change is determined based on the tank operation data from the previous period, and the rate of humidity change is determined based on the ambient humidity from the previous period; and, Based on the rate of temperature change and the rate of humidity change, the water mist spraying speed for the current cycle is determined; and, Based on the water mist spraying direction and the water mist spraying speed of the current cycle, the cooling device is controlled to spray water mist onto the outer wall of the storage tank.

2. The urban storage tank emergency response system based on an Internet of Things (IoT) big data model as described in claim 1, characterized in that, The emergency monitoring and management platform is further configured as follows: Acquire tank operation data and tank characteristics at a second preset time; Based on the tank operation data and tank characteristics at the second preset time, patrol parameters are determined, including the tank's data collection time. as well as, Based on the patrol parameters, the motor driving the patrol robot moves, and through the patrol robot's lidar and ultrasonic obstacle avoidance device, it senses obstacles in real time and adjusts the patrol path, collecting the monitoring data of the storage tank within the patrol area to complete the patrol.

3. The urban storage tank emergency response system based on an Internet of Things (IoT) big data model according to claim 2, characterized in that, The patrol parameters also include the patrol priority of the storage tanks, and the emergency monitoring and management platform is further configured as follows: Determine the maintenance parameters for the storage tank; The patrol parameters are determined based on the tank operation data, tank characteristics, and rest parameters at the second preset time.

4. The urban storage tank emergency response system based on an Internet of Things (IoT) big data model according to claim 3, characterized in that, The emergency monitoring and management platform is further configured as follows: Determine candidate patrol parameters; Based on at least one of the candidate patrol parameters, the tank operation data for the second preset time, the tank characteristics, and the rest parameters, a risk value is determined through a patrol parameter model, wherein the patrol parameter model is a machine learning model; and, The patrol parameters are determined based on the risk value.

5. The urban storage tank emergency response system based on an Internet of Things (IoT) big data model according to claim 1, characterized in that, The emergency monitoring and management platform is further configured as follows: The duration of the preset cycle is determined based on the patrol parameters.

6. The urban storage tank emergency response system based on an Internet of Things (IoT) big data model according to claim 1, characterized in that, The emergency control parameters also include the water pump pressure of the storage tank pipeline network, and the emergency monitoring and management platform is further configured as follows: Based on the maintenance parameters of the storage tank, calculate the probability of eddy current occurrence at preset points in the storage tank channel network; In response to the probability of eddy current occurrence meeting the second preset condition, the water pump pressure is updated to adjust the liquid flow rate in the storage tank.

7. An emergency response method for urban storage tanks based on an Internet of Things (IoT) big data model, characterized in that, The emergency monitoring and management platform of the urban storage tank emergency response system based on the Internet of Things (IoT) big data model, as described in any one of claims 1 to 6, is used to execute the method, which includes: Obtain the operating status of the storage tank at the first preset time; Based on the operating status of the storage tank, determine the state change value of the storage tank; In response to the state change value satisfying a first preset condition: Acquire tank monitoring data, and based on the tank monitoring data and ambient wind data, determine emergency control parameters, including the water mist spraying speed and water mist spraying direction of the cooling equipment; Determining the direction of the water mist spray specifically includes: Based on the ambient wind data and the initial direction of the cooling device, the water mist spraying direction is determined using a first preset table; The first preset table is constructed based on multiple simulation experiment results, which include the degree of deviation of different reference wind speeds, reference wind directions and reference initial directions, and the corresponding relationship of compensation angles. Determining the water mist spraying speed specifically includes: Within a preset period: Obtain the tank operation data and ambient humidity from the previous cycle; The rate of temperature change is determined based on the tank operation data from the previous period, and the rate of humidity change is determined based on the ambient humidity from the previous period; and, Based on the rate of temperature change and the rate of humidity change, the water mist spraying speed for the current cycle is determined; and, Based on the water mist spraying direction and the water mist spraying speed of the current cycle, the cooling device is controlled to spray water mist onto the outer wall of the storage tank.

8. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. When the computer reads the computer instructions from the storage medium, the computer executes the urban storage tank emergency handling method based on the Internet of Things big model as described in claim 7.

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