Oil storage pit flame retardant test process control method and system based on machine vision

Abstract

The present invention discloses a method and system for controlling a flame retardant test process for an oil storage pit based on machine vision, and relates to the technical field of machine vision. The method comprises: acquiring flame retardant data of flame retardant components and oil storage pit distribution data to analyze the safety of the oil storage pit, acquiring oil storage pit environmental data and oil storage pit evaporation data to analyze the fire spread hazard, analyzing the comprehensive fire hazard level according to the oil storage pit safety and the fire spread hazard, and analyzing whether to continue the oil storage pit flame retardant test according to the comprehensive fire hazard level. The present invention comprehensively predicts the fire hazard level according to the safety of the oil storage pit under the influence of the flame retardant components and the fire spread hazard under the influence of the environment, and promptly extinguishes the fire in the oil storage pit that shows signs of being out of control during the test, thereby achieving precise control of the flame retardant test process and improving the safety of the flame retardant test.

Description

Technical Field

[0001] The present invention relates to the technical field of machine vision, and in particular to a method and system for controlling a flame retardancy test process of an oil storage pit based on machine vision. Background Art

[0002] As a facility for storing flammable liquids, the flame retardant properties of oil storage pits are directly related to the control of fire risks. Flame retardant tests can evaluate the combustion characteristics of the materials used in oil storage pits in fire, including the burning rate, flame spread, and whether self-extinguishing will occur. However, during the flame retardant test, if there are signs of uncontrolled flame spread such as excessively fast flame spread, impending explosion limit, or excessively high temperature, fire extinguishing must be carried out in a timely manner to avoid accidents such as explosions.

[0003] Existing technologies for flame retardant test process control mostly determine whether the test is in an abnormal state by monitoring flame combustion and smoke data. They fail to consider the impact of flame retardant components and oil storage tanks on the entire test. As a result, they are unable to comprehensively predict the degree of fire hazard based on the safety of the oil storage pit under the influence of flame retardant components and the risk of fire spread under the influence of the environment, resulting in the inability to achieve the expected flame retardant test process control accuracy.

[0004] To address the above issues, no effective solutions have been proposed so far. Summary of the Invention

[0005] The present invention provides a machine vision-based oil storage pit flame retardant test process control system and method to solve the problem that the existing technology for flame retardant test process control mostly determines whether it is in an abnormal state by monitoring flame combustion and smoke data, but fails to consider the impact of flame retardant components and oil storage tanks on the entire test. As a result, it is impossible to comprehensively predict the degree of fire hazard based on the safety of the oil storage pit under the influence of flame retardant components and the fire spread hazard under the influence of the environment, resulting in the inability to achieve the expected flame retardant test process control accuracy.

[0006] In a first aspect, the present invention provides a method for controlling a flame retardant test process of an oil storage pit based on machine vision, comprising the following specific steps:

[0007] S1. Obtain flame retardant data of flame retardant components and distribution data of oil storage pits to analyze the safety of oil storage pits;

[0008] S2. Obtain oil pit environmental data and oil pit evaporation data to analyze the fire spread hazard;

[0009] S3. Analyze the comprehensive fire hazard level based on the safety of the oil storage pit and the risk of fire spread;

[0010] S4. Analyze whether to continue the oil storage pit flame retardant test based on the comprehensive fire hazard level.

[0011] Preferably, the S1 includes the following specific steps:

[0012] S11, obtaining flame retardant data of the flame retardant component, wherein the flame retardant data of the flame retardant component includes flame retardant component coordinates, response time, flame retardant efficiency, and coverage area;

[0013] Preferably, the acquisition of the flame retardant efficiency in S11 includes the following specific steps:

[0014] S111. Obtaining the flame area of ​​the flame retardant component before and after spraying, and obtaining a flame suppression rate by dividing the difference between the flame area before spraying and the flame area after spraying by the flame area before spraying;

[0015] S112, obtaining the grayscale standard deviation and average grayscale value of the pixels in the flame retardant coverage area, and obtaining the flame retardant diffusion uniformity by dividing the difference between the average grayscale value and the grayscale standard deviation of the pixels in the flame retardant coverage area by the average grayscale value;

[0016] S113. Obtain flame retardant efficiency based on the product of the flame suppression rate and the flame retardant diffusion uniformity plus a compensation factor.

[0017] S12, obtaining oil storage pit distribution data, wherein the oil storage pit distribution data includes oil storage pit coordinates, and obtaining oil storage pit distribution density according to the oil storage pit coordinates;

[0018] S13. Obtain a flame retardant efficiency score based on the distance between the flame retardant component coordinates and the oil storage pit coordinates and the flame retardant efficiency; obtain a response time score based on the response time; obtain a coverage area score based on the coverage area; obtain an oil storage pit distribution score based on the oil storage pit distribution density; and obtain the safety degree of the oil storage pit by multiplying the flame retardant efficiency score, the response time score, the coverage area score, and the oil storage pit distribution score by their corresponding weights.

[0019] Preferably, S2 includes the following specific steps:

[0020] S21, acquiring oil storage pit environmental data, wherein the oil storage pit environmental data includes temperature, humidity, and oxygen concentration;

[0021] S22, obtaining oil storage pit evaporation data, wherein the oil storage pit evaporation data includes average oil film thickness, oil and gas concentration, and volatilization rate;

[0022] S23. Obtain an environmental hazard assessment value based on temperature, humidity, and oxygen concentration; obtain an evaporation hazard assessment value based on the average oil film thickness, oil and gas concentration, and volatilization rate; and obtain a fire spread hazard degree by multiplying the environmental hazard assessment value and the evaporation hazard assessment value by their corresponding weights.

[0023] Preferably, S3 includes the following specific steps:

[0024] The comprehensive fire hazard value is obtained by multiplying the safety degree of the oil storage pit and the fire spread hazard degree by the corresponding weights.

[0025] Preferably, the S4 includes the following specific steps:

[0026] Based on the comparison results between the comprehensive fire hazard value and the preset comprehensive fire hazard threshold, it is analyzed whether to continue the oil storage pit flame retardant test. If the comprehensive fire hazard value is less than the comprehensive fire hazard threshold, the oil storage pit flame retardant test is continued. If the comprehensive fire hazard value is greater than or equal to the comprehensive fire hazard threshold, the oil storage pit flame retardant test is stopped and fire extinguishing operations are carried out.

[0027] In a second aspect, the present invention further provides a machine vision-based oil storage pit flame retardant test process control system, comprising:

[0028] The oil pit safety analysis module is used to obtain the flame retardant data of flame retardant components and the distribution data of oil pits to analyze the safety of oil pits;

[0029] Fire spread hazard analysis module, used to obtain oil pit environmental data and oil pit evaporation data to analyze fire spread hazard;

[0030] Comprehensive hazard analysis module, used to analyze the comprehensive fire hazard level based on the safety of the oil storage pit and the fire spread hazard;

[0031] The test control module is used to analyze whether to continue the oil storage pit flame retardant test based on the comprehensive fire hazard level.

[0032] In a third aspect, the present invention also provides an electronic device comprising a memory and a processor, wherein the processor and the memory are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to implement the steps of the above-mentioned oil storage pit flame retardant test process control method based on machine vision.

[0033] In a fourth aspect, the present invention further provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the steps of the above-mentioned oil storage pit flame retardant test process control method based on machine vision.

[0034] Compared with the prior art, the beneficial effects of the present invention are: obtaining flame retardant data of flame retardant components and oil storage pit distribution data to analyze the safety of oil storage pits, obtaining oil storage pit environmental data and oil storage pit evaporation data to analyze the fire spread hazard, analyzing the comprehensive fire hazard level based on the safety of oil storage pits and the fire spread hazard, and analyzing whether to continue the oil storage pit flame retardant test based on the comprehensive fire hazard level. The present invention comprehensively predicts the fire hazard level based on the safety of the oil storage pit under the influence of flame retardant components and the fire spread hazard under the influence of the environment. During the test, oil storage pits that show signs of being out of control are promptly extinguished, thereby achieving precise control of the flame retardant test process and improving the safety of the flame retardant test. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive work. In the drawings:

[0036] Figure 1 A flow chart of a method for controlling an oil storage pit flame retardancy test process based on machine vision provided by an embodiment of the present invention;

[0037] Figure 2 A flow chart of a method S1 for controlling a flame retardant test process for an oil storage pit based on machine vision according to an embodiment of the present invention;

[0038] Figure 3 A flow chart of a method S11 for controlling a flame retardant test process for an oil storage pit based on machine vision according to an embodiment of the present invention;

[0039] Figure 4 A flow chart of a method S2 for controlling the oil storage pit flame retardancy test process based on machine vision provided in an embodiment of the present invention;

[0040] Figure 5 A schematic diagram of the structure of a machine vision-based oil storage pit flame retardancy test process control system provided in an embodiment of the present invention;

[0041] Figure 6 A schematic diagram of the computer device structure provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of the present invention.

[0043] See also Figure 1 The present invention provides an embodiment of a method for controlling a flame retardant test process of an oil storage pit based on machine vision, which includes the following specific steps:

[0044] S1. Obtain flame retardant data of flame retardant components and distribution data of oil storage pits to analyze the safety of oil storage pits;

[0045] In this example, see Figure 2 , S1 includes the following specific steps:

[0046] S11. Obtaining flame retardant data of the flame retardant component, where the flame retardant data includes flame retardant component coordinates, response time, flame retardant efficiency, and coverage area;

[0047] The flame retardant assembly is used to prevent the spread of fire, avoid fire or explosion, and protect the safety of oil storage pits and adjacent facilities. Most of them are installed on the oil storage pit vent pipe or the liquid inlet and outlet pipes at the bottom of the storage tank. In the specific implementation of this embodiment, the steps for obtaining the coordinates of the flame retardant assembly are: using a QR code-like mark on the flame retardant assembly, training the flame retardant assembly target detection model based on the YOLO series algorithm to identify the outline of the flame retardant assembly and the QR code mark, deploying a high-resolution industrial camera to calculate the three-dimensional coordinates of the flame retardant assembly based on triangulation. It should be noted that the coordinates of the oil storage pit can be obtained by the same steps;

[0048] The steps for obtaining the response time are as follows: obtaining the time when the flame retardant instruction is issued, deploying a high-speed camera to obtain the time when the flame retardant is initially injected, and obtaining the difference between the time when the flame retardant is initially injected and the time when the flame retardant instruction is issued;

[0049] The steps for obtaining the coverage area are as follows: deploy a depth camera to obtain multi-angle point cloud data, perform point cloud registration according to the IPC algorithm to generate a 3D model of the flame retardant spraying area, project the 3D point cloud vertically onto the plane of the oil storage pit (if the oil storage pit has a slope, perform coordinate transformation according to the slope), evenly divide the projected point cloud into several grids, analyze whether each grid is covered, and generate a binary mask image. The product of the number of covered pixels in the mask image and the actual area of ​​a single pixel is the coverage area.

[0050] In this example, see Figure 3 , the acquisition of flame retardant efficiency in S11 includes the following specific steps:

[0051] S111. Train a flame recognition model based on the YOLO series algorithm, calculate the flame area in real time, obtain the flame area before and after the flame retardant component is sprayed, and obtain the flame suppression rate by dividing the difference between the flame area before spraying and the flame area after spraying by the flame area before spraying;

[0052] S112. Add fluorescent dye to the flame retardant, obtain the distribution image of the flame retardant component after spraying using the UV camera, use the semantic segmentation model to mark the coverage area of ​​the flame retardant component, output the segmented binary mask image, obtain the product of the segmented binary mask image and each corresponding pixel of the original image, and extract the grayscale value of the flame retardant coverage area. The grayscale value calculation formula can be expressed as: ,in, Represents pixel points The gray value of Represents a binary mask image pixel The pixel value of Represents the original image pixel The pixel value of the flame retardant coverage area can be expressed as follows: ,in, Indicates the average gray value of the flame retardant coverage area, Represents a pixel in a binary mask image , It represents the number of pixels in the flame retardant covered area. The grayscale standard deviation of pixels in the flame retardant covered area can be calculated as: ,in, Indicates the grayscale standard deviation of the pixels in the flame retardant coverage area. Obtain the grayscale standard deviation and average grayscale value of the pixels in the flame retardant coverage area. Divide the difference between the average grayscale value and the grayscale standard deviation of the pixels in the flame retardant coverage area by the average grayscale value to obtain the flame retardant diffusion uniformity.

[0053] S113. Obtain flame retardant efficiency based on the product of the flame suppression rate and the flame retardant diffusion uniformity plus a compensation factor.

[0054] The compensation factor is calibrated based on historical data to eliminate algorithm deviations and improve the accuracy of flame retardant efficiency analysis. In the specific implementation of this embodiment, the steps for obtaining the compensation factor are: obtaining multiple sets of historical flame retardant spraying experimental data, quantifying the actual flame retardant efficiency based on the time from flame generation to complete flame extinction, calculating the product of the historical flame suppression rate and the flame retardant diffusion uniformity, and obtaining the average of the deviations between the multiple products and the actual flame retardant efficiency. After analyzing multiple sets of experiments, the compensation factor can be taken as 5.2%.

[0055] S12, obtaining oil storage pit distribution data, the oil storage pit distribution data including oil storage pit coordinates, and obtaining oil storage pit distribution density based on the oil storage pit coordinates;

[0056] During the specific implementation of this embodiment, the calculation formula for the oil storage pit distribution density can be expressed as: ,in, represents the distribution density of oil storage pits, Indicates the number of oil storage pits, represents the average distance between oil storage pits, represents the three-dimensional coordinates of the nth oil storage pit, It represents the three-dimensional coordinates of the oil storage pit closest to the nth oil storage pit. When the distribution density of oil storage pits is too high, flammable and explosive gases are likely to accumulate, which will accelerate the spread of fire and increase the difficulty of flame retardancy.

[0057] S13. Obtain a flame retardant efficiency score based on the distance between the flame retardant component coordinates and the oil storage pit coordinates and the flame retardant efficiency; obtain a response time score based on the response time; obtain a coverage area score based on the coverage area; obtain an oil storage pit distribution score based on the oil storage pit distribution density; and obtain the safety degree of the oil storage pit by multiplying the flame retardant efficiency score, the response time score, the coverage area score, and the oil storage pit distribution score by their corresponding weights.

[0058] In the specific implementation of this embodiment, the flame retardant efficiency score calculation formula can be expressed as: ,in, Indicates the flame retardant efficiency rating value, Indicates the distance between the nth oil storage pit and its closest flame retardant component, Indicates the optimal installation distance between the flame retardant component and the oil storage pit. If the flame retardant component is installed too close, it is easy to be threatened by high temperature or explosion. If it is installed too far away, the flame retardant may not be installed in time. The steps to obtain the optimal installation distance are: fix the flame retardant efficiency, analyze the flame retardant effect according to the adjustment of the installation distance, and obtain the installation distance corresponding to the best flame retardant effect. Indicates taking The minimum value between 1 and Indicates flame retardant efficiency. The flame retardant efficiency score becomes higher when the flame retardant component is installed near the optimal distance.

[0059] The response time score calculation formula can be expressed as: ,in, Represents the response time score value, represents the average response time of the flame retardant component, Indicates the latest response time of the flame retardant component. The latest response time affects whether the fire can be effectively controlled. In this embodiment, the latest response time can be fixed at 15s. The shorter the response time, the higher the score;

[0060] The calculation formula for the coverage area score value can be expressed as: ,in, Indicates the coverage area score value, Indicates the coverage area, represents the standard coverage area, Lm is the response distance of the flame retardant assembly, Lk is the distance from point k in the coverage area to the nearest fire point, hk is the thickness of the flame retardant assembly at point k in the coverage area, and hm is the average thickness of the flame retardant assembly. To avoid blind spots, the standard coverage area of ​​this embodiment can be 1.2 times the area of ​​the oil storage pit.

[0061] The calculation formula for the oil storage pit distribution score can be expressed as: ,in, Indicates the oil storage pit distribution score, The standard density of oil storage pit distribution is obtained by obtaining the coordinates of all oil storage pits in several factories within a set range, calculating the oil storage pit distribution density of each factory, and obtaining the average value. The lower the oil storage pit distribution density, the higher the score.

[0062] The safety calculation formula of the oil storage pit can be expressed as: ,in, Indicates the safety level of the oil storage pit. represents the flame retardant efficiency weight, represents the response time weight, represents the coverage area weight, represents the distribution weight of oil storage pits, 、 、 、 are greater than 0 and ;

[0063] The safety calculation of oil storage pits conducts a multi-dimensional analysis of the fire risk resistance of oil storage pits based on response time, flame retardant efficiency, coverage area and oil storage pit distribution density.

[0064] S2. Obtain oil pit environmental data and oil pit evaporation data to analyze the fire spread hazard;

[0065] In this example, see Figure 4 , S2 includes the following specific steps:

[0066] S21. Acquire oil storage pit environmental data, including temperature, humidity, and oxygen concentration;

[0067] In the specific implementation of this embodiment, the temperature is measured and obtained by an infrared thermal imager or a distributed optical fiber temperature sensor deployed near the surface of the oil storage pit, the humidity is measured and obtained by a capacitive humidity sensor deployed above the oil storage pit, and the oxygen concentration is measured and obtained by an electrochemical oxygen sensor deployed at the vent of the oil storage pit;

[0068] S22, obtaining oil storage pit evaporation data, the oil storage pit evaporation data including average oil film thickness, oil and gas concentration, and volatilization rate;

[0069] In the specific implementation of this embodiment, the steps for obtaining the average thickness of the oil film are as follows: obtaining a 3D point cloud of the oil surface using an RGB-D camera, fitting the oil film thickness distribution, and obtaining the average thickness of the oil film based on the height difference of the point cloud. The formula for calculating the average thickness of the oil film can be expressed as: ,in, Indicates the average thickness of the oil film, Indicates the number of oil levels. Indicates the current oil surface point cloud height, Indicates the empty base height of the oil storage pit;

[0070] The oil and gas concentration is obtained by measuring hydrocarbons such as methane using a flame ionization detector deployed above the oil storage pit;

[0071] The calculation formula of the volatilization rate can be expressed as: ,in, represents the evaporation rate, It represents the mass transfer coefficient. The mass transfer coefficient is the rate of mass transfer from the liquid phase to the gas phase during the volatilization process. In this embodiment, the oil loss and vapor concentration can be analyzed based on the constant temperature evaporation chamber experiment within the set time. The oil volatilization model can also be established and the inversion fitting can be used to obtain the mass transfer coefficient. Value, measure the volatilization rate, and record the maximum volatilization rate, Represents the current vapor pressure, which is obtained according to the Antonio equation calculation method in this embodiment and will not be repeated here. Indicates the number of oil film pixels, Represents the actual area of ​​a single pixel, Represents the surface area of ​​the oil film. In this embodiment, the depth point cloud of the oil storage pit is obtained by the RGB-D camera, the oil film color range is set, the binary mask of the oil film area is extracted, and the number of oil film pixels in the binary mask is counted. Indicates the current temperature. Add 273 to the current temperature to convert between Celsius and Kelvin.

[0072] S23. Obtain an environmental hazard assessment value based on temperature, humidity, and oxygen concentration; obtain an evaporation hazard assessment value based on the average oil film thickness, oil and gas concentration, and volatilization rate; and obtain a fire spread hazard degree by multiplying the environmental hazard assessment value and the evaporation hazard assessment value by their corresponding weights.

[0073] In the specific implementation of this embodiment, the environmental risk assessment value calculation formula can be expressed as: ,in, Indicates the environmental hazard assessment value, Indicates the flash point of oil in the oil storage pit. Indicates the current oxygen concentration. The oxygen concentration threshold that generates explosion risk can be determined by looking up the oxygen concentration control and oil storage pit design specifications in relevant engineering materials. Indicates the current humidity. The historical average humidity, temperature and oxygen concentration are positively correlated with environmental risks. When any of the temperature, humidity and oxygen concentration parameters approaches the risk limit, the overall environmental risk will increase significantly. The above formula reflects the multi-factor coupling effect.

[0074] The calculation formula for evaporation risk assessment value can be expressed as: ,in, Indicates the evaporation risk assessment value, Indicates the current volatilization rate, Indicates the maximum volatilization rate measured historically, Indicates the current average thickness of the oil film. Indicates the maximum historical oil film thickness. Increased oil film thickness will delay combustion time and increase the risk of evaporation. Indicates the current oil and gas concentration. The lower explosion limit of oil and gas concentration is determined using standard test methods, such as ASTM E681. The oil film thickness, oil and gas concentration, and volatilization rate are positively correlated with the evaporation risk. When any of these parameters approaches the risk limit, the overall evaporation risk increases significantly. The above formula reflects the multi-factor coupling effect.

[0075] The fire spread risk calculation formula can be expressed as: ,in, Indicates the fire spread risk. represents the environmental hazard weight, represents the evaporation risk weight, 、 are greater than 0 and .

[0076] S3. Analyze the comprehensive fire hazard level based on the safety of the oil storage pit and the risk of fire spread;

[0077] In this embodiment, S3 includes the following specific steps:

[0078] The comprehensive fire hazard value is obtained by multiplying the safety degree of the oil storage pit and the fire spread hazard degree by the corresponding weights.

[0079] In the specific implementation of this embodiment, the calculation formula of the comprehensive fire risk value can be expressed as: ,in, Indicates the comprehensive fire hazard value, represents the safety weight of the oil storage pit, represents the fire spread hazard weight, 、 are greater than 0 and .

[0080] S4. Analyze whether to continue the oil storage pit flame retardant test based on the comprehensive fire hazard level.

[0081] In this embodiment, S4 includes the following specific steps:

[0082] Based on the comparison results between the comprehensive fire hazard value and the preset comprehensive fire hazard threshold, it is analyzed whether to continue the oil storage pit flame retardant test. If the comprehensive fire hazard value is less than the comprehensive fire hazard threshold, the oil storage pit flame retardant test is continued. If the comprehensive fire hazard value is greater than or equal to the comprehensive fire hazard threshold, the oil storage pit flame retardant test is stopped and fire extinguishing operations are carried out.

[0083] When this embodiment is implemented, 、 、 、 、 、 、 、 The steps for obtaining the comprehensive fire hazard threshold are as follows: screen fifty groups of factories containing oil storage pits and flame retardant components, obtain the flame retardant data of the flame retardant components and the oil storage pit distribution data to analyze the safety of the oil storage pits, obtain the oil storage pit environmental data and the oil storage pit evaporation data to analyze the fire spread hazard, analyze the comprehensive fire hazard value based on the oil storage pit safety and fire spread hazard, preset the comprehensive fire hazard threshold, obtain the analysis result of whether to continue the oil storage pit flame retardant test, judge whether a dangerous situation actually occurs in the next stage of testing, and import the analysis result and judgment result into the trained fitting software to obtain the corresponding maximum judgment accuracy. 、 、 、 、 、 、 、 and comprehensive fire danger thresholds.

[0084] See also Figure 5 , the oil storage pit flame retardant test process control system based on machine vision, including:

[0085] The oil pit safety analysis module is used to obtain the flame retardant data of flame retardant components and the distribution data of oil pits to analyze the safety of oil pits;

[0086] Fire spread hazard analysis module, used to obtain oil pit environmental data and oil pit evaporation data to analyze fire spread hazard;

[0087] Comprehensive hazard analysis module, used to analyze the comprehensive fire hazard level based on the safety of the oil storage pit and the fire spread hazard;

[0088] The test control module is used to analyze whether to continue the oil storage pit flame retardant test based on the comprehensive fire hazard level.

[0089] See also Figure 6 An embodiment of the present invention also provides a computer device, which may include an input device, a processor and a memory, wherein the memory is used to store processor-executable instructions, and when the processor executes the instructions, the steps of the oil storage pit flame retardant test process control method based on machine vision in any of the above embodiments are implemented.

[0090] In this embodiment, the input device may include a keyboard, a mouse, a camera, a scanner, a light pen, a handwriting input board, a voice input device, etc. The input device is used to input raw data and data processing programs into the computer. The input device can also obtain and receive data transmitted by other modules, units, and devices. The processor can be implemented in any appropriate manner. For example, the processor can take the form of a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, etc. The memory can specifically be a memory device used to store information in modern information technology. The memory can include multiple levels. In a digital system, anything that can store binary data can be considered a memory. In an integrated circuit, a circuit with a storage function without a physical form is also called a memory, such as a RAM, a FIFO, etc. In a system, a storage device with a physical form is also called a memory, such as a memory stick, a TF card, etc.

[0091] In this embodiment, the specific functions and effects achieved by the computer device can be explained in comparison with other embodiments and will not be repeated here.

[0092] A computer-readable storage medium is also provided in an embodiment of the present invention. The computer-readable storage medium has computer program instructions, which, when executed, implement the steps of the oil storage pit flame retardancy test process control method based on machine vision in any of the above embodiments.

[0093] In this embodiment, the above-mentioned computer-readable storage medium includes but is not limited to random access memory (RAM), read-only memory (ROM), cache, hard disk drive (HDD) or memory card. The memory can be used to store computer program instructions, and the network communication unit can be an interface set according to the standards specified by the communication protocol for network connection communication.

[0094] In this embodiment, the functions and effects specifically implemented by the program instructions of the computer-readable storage medium can be explained in comparison with other embodiments and will not be repeated here.

[0095] Those skilled in the art also know that in addition to implementing the controller in the form of computer-readable program code, logic programming can also be performed according to the method steps to enable the controller to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be regarded as structures within the hardware component, or the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0096] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In distributed computing environments, program modules may be located in local and remote computer storage media, including storage devices.

[0097] According to the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented with the help of software plus a necessary general hardware platform. Based on this understanding, the technical solution of the present invention can essentially be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device to execute the methods described in various embodiments of the present invention or certain parts of the embodiments.

[0098] The various embodiments of the present invention are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. The present invention can be used in many general or special computer system environments or configurations, such as: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0099] Although the present invention provides method operation steps as described in the embodiments or flow charts, more or fewer operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiments is only one way of executing the steps and does not represent the only execution order. When executed in an actual device or client product, the methods may be executed in the order shown in the embodiments or drawings or in parallel. The terms "include", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, product or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, product or device. Without further restrictions, it does not exclude the presence of other identical or equivalent elements in the process, method, product or device including the elements. Words such as first and second are used to indicate names and do not indicate any specific order.

[0100] The embodiments of the present invention disclosed above are only used to help illustrate the present invention. The embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technical personnel in the relevant technical field can better understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. The oil storage pit flame retardant test process control method based on machine vision is characterized by: The specific steps include: S1. Obtain flame retardant data of flame retardant components and distribution data of oil storage pits to analyze the safety of oil storage pits; The specific steps include: S11. Obtaining flame retardant data of a flame retardant component, wherein the flame retardant data includes flame retardant component coordinates, response time, flame retardant efficiency, and coverage area; obtaining the flame retardant efficiency includes the following specific steps: S111. Obtaining the flame area of ​​the flame retardant component before and after spraying, and obtaining a flame suppression rate by dividing the difference between the flame area before spraying and the flame area after spraying by the flame area before spraying; S112, obtaining the grayscale standard deviation and average grayscale value of the pixels in the flame retardant coverage area, and obtaining the flame retardant diffusion uniformity by dividing the difference between the average grayscale value and the grayscale standard deviation of the pixels in the flame retardant coverage area by the average grayscale value; S113. Obtain flame retardant efficiency based on the product of flame suppression rate and flame retardant diffusion uniformity plus a compensation factor; S12, obtaining oil storage pit distribution data, wherein the oil storage pit distribution data includes oil storage pit coordinates, and obtaining oil storage pit distribution density according to the oil storage pit coordinates; S13. Obtain a flame retardant efficiency score based on the distance between the flame retardant component coordinates and the oil storage pit coordinates and the flame retardant efficiency; obtain a response time score based on the response time; obtain a coverage area score based on the coverage area; obtain an oil storage pit distribution score based on the oil storage pit distribution density; and obtain an oil storage pit safety degree by multiplying the flame retardant efficiency score, the response time score, the coverage area score, and the oil storage pit distribution score by their corresponding weights. S2. Obtain oil pit environmental data and oil pit evaporation data to analyze the fire spread hazard; The specific steps include: S21, acquiring oil storage pit environmental data, wherein the oil storage pit environmental data includes temperature, humidity, and oxygen concentration; S22, obtaining oil storage pit evaporation data, wherein the oil storage pit evaporation data includes average oil film thickness, oil and gas concentration, and volatilization rate; S23. Obtain an environmental risk assessment value based on the temperature, humidity, and oxygen concentration; obtain an evaporation risk assessment value based on the average oil film thickness, oil and gas concentration, and volatilization rate; and obtain a fire spread risk degree by multiplying the environmental risk assessment value and the evaporation risk assessment value by their respective weights. S3. Analyze the comprehensive fire hazard level based on the safety of the oil storage pit and the risk of fire spread; S4. Analyze whether to continue the oil storage pit flame retardant test based on the comprehensive fire hazard level.

2. The oil storage pit flame retardant test process control method based on machine vision according to claim 1 is characterized in that: The S3 includes the following specific steps: The comprehensive fire hazard value is obtained by multiplying the safety degree of the oil storage pit and the fire spread hazard degree by the corresponding weights.

3. The oil storage pit flame retardant test process control method based on machine vision according to claim 2, characterized in that: The S4 includes the following specific steps: Whether to continue the oil storage pit flame retardant test is analyzed based on the comparison results of the comprehensive fire hazard value and the preset comprehensive fire hazard threshold. If the comprehensive fire hazard value is less than the comprehensive fire hazard threshold, the oil storage pit flame retardant test is continued. If the comprehensive fire hazard value is greater than or equal to the comprehensive fire hazard threshold, the oil storage pit flame retardant test is stopped.

4. A machine vision-based oil storage pit flame retardant test process control system, used to implement the machine vision-based oil storage pit flame retardant test process control method according to any one of claims 1 to 3, characterized in that: include: The oil pit safety analysis module is used to obtain the flame retardant data of flame retardant components and the distribution data of oil pits to analyze the safety of oil pits; Fire spread hazard analysis module, used to obtain oil pit environmental data and oil pit evaporation data to analyze fire spread hazard; Comprehensive hazard analysis module, used to analyze the comprehensive fire hazard level based on the safety of the oil storage pit and the fire spread hazard; The test control module is used to analyze whether to continue the oil storage pit flame retardant test based on the comprehensive fire hazard level.

5. An electronic device, characterized in that: include: A memory and a processor, wherein the processor and the memory are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to implement the steps of the oil storage pit flame retardancy test process control method based on machine vision according to any one of claims 1 to 3.

6. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the steps of the oil storage pit flame retardancy test process control method based on machine vision according to any one of claims 1 to 3 are implemented.

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