Intelligent analysis method and system, computer equipment and readable storage medium

Abstract

The invention provides an intelligent analysis method and system, computer equipment and a readable storage medium. The intelligent analysis method comprises the steps that video monitoring data information of monitoring areas is acquired, and the monitoring areas comprise an oil depot entrance and exit area, an oil pump shed area, a storage tank area, a power transformation and distribution room area and a central control room area; performing data analysis on the video monitoring data information based on a preset AI identification algorithm, identifying a monitoring target in the monitoring area, and determining whether the monitoring target has a potential safety hazard; and in response to the potential safety hazard of the monitoring target, determining the level of the potential safety hazard, and outputting alarm information corresponding to the level of the potential safety hazard. According to the method, intelligent analysis of the video monitoring data information of the monitoring area can be realized, potential safety hazards are automatically identified according to the real-time data information, and early warning is provided, so that the safety management efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of oil and gas storage and transportation, and in particular to intelligent analysis methods and systems, computer equipment and readable storage media. Background Art

[0002] In the oil and gas storage and transportation environment, especially in high-risk environments such as oil depots, personnel safety monitoring is crucial. However, the current safety monitoring of personnel in oil depots only focuses on the location and entry and exit information of the staff, and lacks intelligent analysis and judgment capabilities, and cannot automatically assess risks and provide early warnings based on real-time data. Summary of the invention

[0003] Based on this, it is necessary to provide an intelligent analysis method and system, a computer device and a readable storage medium to solve the above technical problems.

[0004] An intelligent analysis method, comprising:

[0005] Obtaining video surveillance data information of the monitoring area, wherein the monitoring area includes the oil depot entrance and exit area, the oil pump shed area, the storage tank area, the transformer and distribution room area, and the central control room area;

[0006] Performing data analysis on the video surveillance data information based on a preset AI recognition algorithm, identifying the surveillance target within the surveillance area, and determining whether the surveillance target has a safety hazard;

[0007] In response to the existence of a potential safety hazard in the monitoring target, the level of the potential safety hazard is determined, and alarm information corresponding to the level of the potential safety hazard is output.

[0008] In one embodiment, the step of performing data analysis on the video surveillance data information based on a preset AI recognition algorithm, identifying the surveillance target in the surveillance area, and determining whether the surveillance target has a safety hazard includes:

[0009] Determining the preset AI recognition algorithm based on personnel database information and a preset algorithm;

[0010] Performing data analysis on the video surveillance data information based on the preset AI recognition algorithm to identify and determine the surveillance target within the surveillance area;

[0011] Based on the oil depot safety standards, it is determined whether the monitored target has safety hazards, wherein the safety hazards include whether the monitored target has climbed over fences, illegally entered restricted areas, left its post, fallen down, or failed to wear safety equipment, and the safety equipment includes at least one of a safety helmet, goggles, and work clothes.

[0012] In one embodiment, in response to the existence of a potential safety hazard in the monitoring target, the step of determining the level of the potential safety hazard and outputting alarm information corresponding to the level of the potential safety hazard includes:

[0013] In response to the existence of a potential safety hazard in the monitoring target, determining the type of the monitoring target based on a preset target information database, and determining the level of the potential safety hazard based on the type of the monitoring target, wherein the type of the monitoring target includes a staff member and a stranger;

[0014] Based on the level of the potential safety hazard, alarm information corresponding to the level of the potential safety hazard is output, wherein the alarm information includes video playback or abnormal sound alarm.

[0015] In one embodiment, the method further comprises:

[0016] Outputting alarm confirmation information based on the level of the potential safety hazard;

[0017] In response to the alarm confirmation information being confirmed, alarm information corresponding to the level of the potential safety hazard is output.

[0018] In one embodiment, the method further comprises:

[0019] The video surveillance data information of the surveillance area is collected and obtained through the surveillance equipment, wherein the surveillance equipment is a plurality of fixed cameras and a plurality of intelligent control balls.

[0020] In one embodiment, the method further comprises:

[0021] Storing the video surveillance data information according to the first storage time;

[0022] Uploading the alarm information to the emergency system for storage according to a second storage time;

[0023] The first storage time is no less than 30 days, and the second storage time is no less than 1 year.

[0024] An intelligent analysis system, comprising:

[0025] Monitoring equipment, used to collect video monitoring data information of the monitoring area, wherein the monitoring area includes the oil depot entrance and exit area, the oil pump shed area, the storage tank area, the transformer and distribution room area and the central control room area;

[0026] A video analysis server is communicatively connected to the monitoring device, and is used to obtain the video monitoring data information, perform data analysis on the video monitoring data information based on a preset AI recognition algorithm, identify the monitoring target within the monitoring area, and determine whether there is a safety hazard in the monitoring target. In response to the presence of a safety hazard in the monitoring target, the server determines the level of the safety hazard and outputs an alarm message corresponding to the level of the safety hazard.

[0027] In one embodiment, the safety hazard includes whether the monitored target has at least one of climbed a fence, illegally entered a restricted area, left its post, fallen down, or failed to wear safety equipment, and the safety equipment includes at least one of a safety helmet, goggles, and work clothes.

[0028] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the above embodiments when executing the computer program.

[0029] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method described in any one of the above embodiments.

[0030] Compared with traditional technologies, the above intelligent analysis method and system, computer equipment and readable storage medium. First, the video surveillance data information of the monitoring area is obtained, wherein the monitoring area includes the oil depot entrance and exit area, the oil pump shed area, the storage tank area, the transformer and distribution room area and the central control room area; secondly, the video surveillance data information is analyzed based on the preset AI recognition algorithm to identify the monitoring target in the monitoring area, and determine whether the monitoring target has safety hazards; in response to the presence of safety hazards in the monitoring target, the level of the safety hazard is determined, and the alarm information corresponding to the level of the safety hazard is output. The above method can realize intelligent analysis of the video surveillance data information of the monitoring area, automatically identify safety hazards and provide early warnings based on real-time data information, thereby improving the efficiency of safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the conventional technology, the drawings required for use in the embodiments or the conventional technology descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 A flowchart of an intelligent analysis method provided in one embodiment of the present application;

[0033] Figure 2A structural block diagram of an intelligent analysis system provided in one embodiment of the present application;

[0034] Figure 3 This is a diagram of the internal structure of a computer device provided in one embodiment of the present application.

[0035] Description of reference numerals:

[0036] 10. Intelligent analysis system; 100. Monitoring equipment; 200. Video analysis server. DETAILED DESCRIPTION

[0037] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0038] The serial numbers assigned to the components herein, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning. The "connection" and "coupling" mentioned in this application, unless otherwise specified, include direct and indirect connections (couplings). In the description of this application, it should be understood that the orientation or position relationship indicated by the terms "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", etc. are based on the orientation or position relationship shown in the accompanying drawings, which is only for the convenience of describing this application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to this application.

[0039] In the present application, unless otherwise clearly specified and limited, a first feature being “above” or “below” a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being “above”, “above”, and “above” a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being “below”, “below”, and “below” a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0040] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be an intermediate element at the same time.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0042] See also Figure 1 , an embodiment of the present application provides an intelligent analysis method. The intelligent analysis method is applied to high-risk environments such as oil depots. The intelligent analysis method includes:

[0043] S102: Acquire video surveillance data information of a monitoring area, wherein the monitoring area includes an oil depot entrance and exit area, an oil pump shed area, a storage tank area, a transformer room area, and a central control room area.

[0044] It can be understood that there is no limit to the way of obtaining the video surveillance data information of the monitoring area, as long as the video surveillance data information can be obtained. In one implementation, the video surveillance data information of the monitoring area can be collected and obtained by a control ball. In one implementation, the video surveillance data information of the monitoring area can be collected and obtained by a camera. Specifically, the front-end camera in the video surveillance system that has been built in the oil depot can be used to collect the video surveillance data information of the monitoring area, and the collected video surveillance data information can be sent to the background server. In one embodiment, the monitoring area includes key areas such as the oil depot entrance and exit area, the oil pump shed area, the storage tank area, the transformer and distribution room area, and the central control room area.

[0045] S104: Perform data analysis on the video surveillance data information based on a preset AI recognition algorithm, identify the surveillance target within the surveillance area, and determine whether the surveillance target has a safety hazard.

[0046] In one implementation, the video surveillance data information can be analyzed based on a preset AI recognition algorithm by a background server to identify the surveillance target in the surveillance area, and determine whether the surveillance target has a safety hazard. It can be understood that the surveillance target is a person entering the surveillance area. In one embodiment, the preset AI recognition algorithm can be algorithms such as human target monitoring, underlying feature extraction, human behavior modeling, and human behavior recognition. The preset AI recognition algorithm is used to analyze the video surveillance data information, identify the surveillance target in the surveillance area, and then further determine whether the surveillance target has a safety hazard.

[0047] In one embodiment, the wearing compliance of the monitoring target in the monitoring area can be identified, such as whether the monitoring target is wearing a helmet, whether the monitoring target is wearing identification goggles, etc. In this way, the monitoring target in the monitoring area can be tracked and potential safety hazards can be identified.

[0048] S106: In response to the existence of a potential safety hazard in the monitoring target, determining the level of the potential safety hazard, and outputting alarm information corresponding to the level of the potential safety hazard.

[0049] In one embodiment, the background server can be used to respond to the existence of safety hazards in the monitoring target, determine the level of the safety hazard, and output the alarm information corresponding to the level of the safety hazard. Specifically, when it is determined that the monitoring target has a safety hazard, the level of the safety hazard can be further determined. Among them, the level of the safety hazard can be divided into four levels: level I (major hidden danger), level II (large hidden danger), level III (serious hidden danger), and level IV (general hidden danger). After determining the corresponding level of the safety hazard, output the alarm information corresponding to the level of the safety hazard. For example, an alarm text message can be sent to the person in charge of safety in the relevant area to remind the management personnel to record and handle it. It is also possible to correct problems on the job site by remote shouting. In this embodiment, the above method can realize intelligent analysis of the video surveillance data information of the monitoring area, automatically identify safety hazards and provide early warnings based on real-time data information, thereby improving the efficiency of safety management.

[0050] In one embodiment, the steps of performing data analysis on the video surveillance data information based on a preset AI recognition algorithm, identifying the surveillance target within the surveillance area, and determining whether the surveillance target has safety hazards include: determining the preset AI recognition algorithm based on personnel database information and a preset algorithm; performing data analysis on the video surveillance data information based on the preset AI recognition algorithm to identify and determine the surveillance target within the surveillance area; and judging whether the surveillance target has safety hazards based on oil depot safety standards, wherein the safety hazards include whether the surveillance target has climbed a fence, illegally entered a restricted area, left its post, fallen down, or failed to wear safety equipment, and the safety equipment includes at least one of a safety helmet, goggles, and work clothes.

[0051] In one embodiment, the personnel database information includes information of all staff members of the oil depot, such as facial image information. The preset algorithm may be a basic recognition algorithm, and the basic recognition algorithm is trained and learned based on the personnel database information to determine the preset AI recognition algorithm. In one embodiment, the oil depot safety standard may be an oil depot safety operation manual.

[0052] In one embodiment, it is possible to determine whether the monitored target has potential safety hazards based on the oil depot safety standards. For example, the compliance of the monitored target's wearing can be intelligently identified, such as identifying a safety helmet, identifying work clothes, identifying whether goggles are worn, etc. When it is found that the monitored target is not wearing compliance, an alarm message corresponding to the level of the potential safety hazard can be output in a timely manner. Specifically, an alarm text message can be sent to the person in charge of security in the relevant area to remind the management personnel to record and handle the situation. In one embodiment, the illegal behaviors of the monitored target can also be identified and alarmed, including smoking, climbing fences, illegally entering restricted areas, personnel leaving their posts, personnel gathering, making phone calls and other potential safety hazards. In this embodiment, it is possible to identify in real time whether the monitored target has potential safety hazards, thereby improving the efficiency of safety management.

[0053] In one embodiment, the step of determining the level of the safety hazard in response to the existence of a safety hazard in the monitoring target, and outputting an alarm message corresponding to the level of the safety hazard includes: determining the type of the monitoring target based on a preset target information database in response to the existence of a safety hazard in the monitoring target, and determining the level of the safety hazard based on the type of the monitoring target, wherein the types of the monitoring targets include staff members and strangers; and outputting an alarm message corresponding to the level of the safety hazard based on the level of the safety hazard, wherein the alarm message includes a video playback or an abnormal sound alarm.

[0054] In one embodiment, the preset target information database includes information of all staff members of the oil depot, such as facial image information. When it is determined that the monitoring target has a safety hazard, the type of the monitoring target is further determined, that is, it is further determined whether the monitoring target is a staff member of the oil depot or a stranger (such as a visitor). If it is determined that the monitoring target is a stranger, an alarm message can be output based on the level of the safety hazard corresponding to the stranger. For example, the alarm output can be achieved through the stranger alarm function. If it is determined that the monitoring target is a staff member of the oil depot, an alarm message can be output based on the level of the safety hazard corresponding to the staff member.

[0055] In one embodiment, in response to the safety hazard of the monitored target being falling down (i.e., fainting and falling down due to long-term operation at the valves in the tank area, device area, etc., or fainting and falling down due to poisoning, electric shock during power maintenance, etc.), the corresponding alarm information can be output according to the safety hazard level corresponding to the falling down (such as level III, serious hazard). Specifically, the safety person in charge of the area where the monitored target is located can be notified by remote shouting or notifying the person in charge of safety in the area where the monitored target is located to handle it in time and avoid major accidents.

[0056] In one embodiment, in response to the monitoring target having a safety hazard of illegally entering a restricted area (such as when the monitoring target crosses a set warning surface), a corresponding alarm message can be output according to the safety hazard level corresponding to the illegal entry into the restricted area (such as level III, a serious hazard). Specifically, the person in charge of security in the area where the monitoring target is located can be notified by remote shouting or notifying the person in charge of security in the area where the monitoring target is located to handle it in a timely manner to avoid major accidents.

[0057] In one embodiment, in response to the existence of a safety hazard in the monitored target, such as a person staying in the oil tank area, oil pump shed, etc. for a long time, such as more than 5 minutes or 10 minutes, the corresponding alarm information can be output according to the safety hazard level corresponding to the person staying (such as level III, serious hazard). Specifically, the person in charge of the safety of the area where the monitored target is located can be notified by remote shouting or notifying the person in charge of safety in the area where the monitored target is located to handle it in time and avoid major accidents.

[0058] In one embodiment, in response to the existence of a safety hazard in the monitored target such as making a phone call or smoking, a corresponding alarm message can be output according to the safety hazard level corresponding to making a phone call or smoking (such as level III, a serious hazard). Specifically, timely processing can be carried out by remotely shouting or notifying the security person in charge of the area where the monitored target is located to avoid major accidents. It can be seen that this embodiment supports the intelligent identification of abnormal conditions such as open flames, smoke, climbing fences, illegally breaking into restricted areas, leaving the post, falling down, not wearing safety equipment, making phone calls, smoking, etc., to achieve tracking of monitored targets and identification of unsafe behaviors, and to provide graded warnings for unsafe behaviors, thereby improving the effectiveness of safety management.

[0059] In one embodiment, the method further includes: outputting alarm confirmation information based on the level of the potential safety hazard; in response to the alarm confirmation information being confirmed, outputting alarm information corresponding to the level of the potential safety hazard. Specifically, when outputting the alarm confirmation information, the management personnel can confirm the alarm confirmation information, thereby realizing manual review of violations or potential safety hazard during the operation process. At the same time, the real-time on-site images can be captured by manual capture, and the violation images can be saved and archived.

[0060] In one embodiment, the method further includes: collecting and acquiring video surveillance data information of the monitoring area through monitoring equipment. Wherein, the monitoring equipment is a plurality of fixed cameras and a plurality of intelligent control balls. The fixed cameras are used to collect and acquire video surveillance data information of the monitoring area, and the background server intelligently analyzes and alarms and prompts abnormal behaviors such as not wearing a safety helmet and falling to the ground during the operation in real time to avoid major safety accidents and casualties. The on-site mobile control ball supports the analysis and identification of safety hazards such as safety helmet detection, off-post detection, and intrusion into dangerous areas, as well as alarm prompts to avoid operational accidents. In one embodiment, the monitoring device supports the monitoring target tracking function, which can realize the trajectory tracking function of unsafe objects.

[0061] In one embodiment, the method further includes: storing the video surveillance data information according to a first storage time; uploading the alarm information to the emergency system for storage according to a second storage time; wherein the first storage time is not less than 30 days, and the second storage time is not less than 1 year. Specifically, the stored video surveillance data information should be accompanied by location and time information, and the recorded images should be recorded in a frame-by-frame format.

[0062] See also Figure 2, another embodiment of the present application provides an intelligent analysis system 10. The intelligent analysis system 10 includes: a monitoring device 100 and a video analysis server 200. The monitoring device 100 is used to collect video monitoring data information of the monitoring area. Among them, the monitoring area includes the oil depot entrance and exit area, the oil pump shed area, the storage tank area, the transformer and distribution room area and the central control room area. The video analysis server 200 is communicatively connected to the monitoring device 100. The video analysis server 200 is used to obtain the video monitoring data information, perform data analysis on the video monitoring data information based on a preset AI recognition algorithm, identify the monitoring target in the monitoring area, and determine whether the monitoring target has a safety hazard. In response to the presence of a safety hazard in the monitoring target, the level of the safety hazard is determined, and an alarm message corresponding to the level of the safety hazard is output.

[0063] In one embodiment, the monitoring device 100 may be a plurality of fixed cameras and a plurality of intelligent control balls. Among them, the fixed cameras and the intelligent control balls support the GB / T28181-2016 national standard protocol, and have the functions of video preview, storage, playback, query, zoom, patrol, zoom and pan / tilt control. In one embodiment, the image captured by the selected fixed camera should account for more than 32x32 pixels on the 720P resolution image, and more than 48x48 pixels on the 1080P resolution image. In one embodiment, different resolutions are converted according to this rule. Fixed cameras can use zoom cameras to facilitate changes in scene size.

[0064] In one embodiment, the video analysis server 200 can be a server that supports accessing 16 channels of 1080P high-definition video, 4 channels of real-time video analysis, and 2 channels of concurrent analysis. Specifically, the video analysis server 200 can have the function of accessing video signals and performing real-time video analysis. In one embodiment, the power supply of the video analysis server 200 can be connected to the oil depot UPS system to ensure uninterrupted operation.

[0065] In one embodiment, the video analysis server 200 obtains the video surveillance data information, performs data analysis on the video surveillance data information based on a preset AI recognition algorithm, identifies the surveillance target within the surveillance area, and determines whether there is a safety hazard in the surveillance target. In response to the presence of a safety hazard in the surveillance target, determines the level of the safety hazard, and outputs alarm information corresponding to the level of the safety hazard. Please refer to the above embodiment and will not repeat it here.

[0066] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 3As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an intelligent analysis method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0067] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0068] See also Figure 3 Another embodiment of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the intelligent analysis method described in any one of the above embodiments when executing the computer program.

[0069] In one embodiment, the processor implements the following steps when executing the computer program:

[0070] S102: Acquire video surveillance data information of the monitoring area, wherein the monitoring area includes the oil depot entrance and exit area, the oil pump shed area, the storage tank area, the transformer and distribution room area, and the central control room area;

[0071] S104: Analyzing the video surveillance data information based on a preset AI recognition algorithm, identifying a surveillance target within the surveillance area, and determining whether the surveillance target has a safety hazard;

[0072] S106: In response to the existence of a potential safety hazard in the monitoring target, determining the level of the potential safety hazard, and outputting alarm information corresponding to the level of the potential safety hazard.

[0073] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the intelligent analysis method described in any one of the above embodiments are implemented.

[0074] In one embodiment, the computer program, when executed by a processor, implements the following steps:

[0075] S102: Acquire video surveillance data information of the monitoring area, wherein the monitoring area includes the oil depot entrance and exit area, the oil pump shed area, the storage tank area, the transformer and distribution room area, and the central control room area;

[0076] S104: Analyzing the video surveillance data information based on a preset AI recognition algorithm, identifying a surveillance target within the surveillance area, and determining whether the surveillance target has a safety hazard;

[0077] S106: In response to the existence of a potential safety hazard in the monitoring target, determining the level of the potential safety hazard, and outputting alarm information corresponding to the level of the potential safety hazard.

[0078] The above-mentioned computer device and computer-readable storage medium obtain video surveillance data information of the monitoring area, perform data analysis on the video surveillance data information based on a preset AI recognition algorithm, identify the monitoring target in the monitoring area, and determine whether the monitoring target has a safety hazard; in response to the presence of a safety hazard in the monitoring target, determine the level of the safety hazard, and output an alarm message corresponding to the level of the safety hazard. The above-mentioned method can realize intelligent analysis of the video surveillance data information of the monitoring area, automatically identify safety hazards and provide warnings based on real-time data information, thereby improving the efficiency of security management.

[0079] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0080] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0081] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. An intelligent analysis method, characterized in that: include: Obtaining video surveillance data information of the monitoring area, wherein the monitoring area includes the oil depot entrance and exit area, the oil pump shed area, the storage tank area, the transformer and distribution room area, and the central control room area; Performing data analysis on the video surveillance data information based on a preset AI recognition algorithm, identifying the surveillance target within the surveillance area, and determining whether the surveillance target has a safety hazard; In response to the existence of a potential safety hazard in the monitoring target, the level of the potential safety hazard is determined, and alarm information corresponding to the level of the potential safety hazard is output.

2. The intelligent analysis method according to claim 1, characterized in that: The step of performing data analysis on the video surveillance data information based on a preset AI recognition algorithm, identifying the surveillance target in the surveillance area, and determining whether the surveillance target has a safety hazard includes: Determining the preset AI recognition algorithm based on personnel database information and a preset algorithm; Performing data analysis on the video surveillance data information based on the preset AI recognition algorithm to identify and determine the surveillance target within the surveillance area; Based on the oil depot safety standards, it is determined whether the monitored target has safety hazards, wherein the safety hazards include whether the monitored target has climbed over fences, illegally entered restricted areas, left its post, fallen down, or failed to wear safety equipment, and the safety equipment includes at least one of a safety helmet, goggles, and work clothes.

3. The intelligent analysis method according to claim 1, characterized in that: The step of determining the level of the potential safety hazard in response to the presence of a potential safety hazard in the monitoring target and outputting warning information corresponding to the level of the potential safety hazard includes: In response to the existence of a potential safety hazard in the monitoring target, determining the type of the monitoring target based on a preset target information database, and determining the level of the potential safety hazard based on the type of the monitoring target, wherein the type of the monitoring target includes a staff member and a stranger; Based on the level of the potential safety hazard, warning information corresponding to the level of the potential safety hazard is output, wherein the warning information includes video playback or abnormal sound alarm.

4. The intelligent analysis method according to claim 3, characterized in that: The method further comprises: Outputting alarm confirmation information based on the level of the potential safety hazard; In response to the alarm confirmation information being confirmed, alarm information corresponding to the level of the potential safety hazard is output.

5. The intelligent analysis method according to claim 4, characterized in that: The method further comprises: The video surveillance data information of the surveillance area is collected and obtained through the surveillance equipment, wherein the surveillance equipment is a plurality of fixed cameras and a plurality of intelligent control balls.

6. The intelligent analysis method according to claim 1, characterized in that: The method further comprises: Storing the video surveillance data information according to the first storage time; Uploading the alarm information to the emergency system for storage according to a second storage time; The first storage time is no less than 30 days, and the second storage time is no less than 1 year.

7. An intelligent analysis system, characterized in that: The intelligent analysis system comprises: Monitoring equipment, used to collect video monitoring data information of the monitoring area, wherein the monitoring area includes the oil depot entrance and exit area, the oil pump shed area, the storage tank area, the transformer and distribution room area and the central control room area; A video analysis server is communicatively connected to the monitoring device, and is used to obtain the video monitoring data information, perform data analysis on the video monitoring data information based on a preset AI recognition algorithm, identify the monitoring target within the monitoring area, and determine whether there is a safety hazard in the monitoring target. In response to the presence of a safety hazard in the monitoring target, the server determines the level of the safety hazard and outputs an alarm message corresponding to the level of the safety hazard.

8. The intelligent analysis system according to claim 7, characterized in that: The potential safety hazard includes at least one of whether the monitored target climbs a fence, illegally enters a restricted area, leaves his post, falls down, or fails to wear safety equipment, and the safety equipment includes at least one of a safety helmet, goggles, and work clothes.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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