Security warning method, device, equipment, storage medium and computer program product

Abstract

The present application relates to the field of safety early warning technology, and discloses a safety early warning method, device, equipment, storage medium and computer program product, including: obtaining multi-source heterogeneous data of the current site, and inputting the multi-source heterogeneous data into a preset three-dimensional digital twin model to generate a three-dimensional visualization scene; matching the multi-source heterogeneous data with the safety operation and maintenance strategy, and determining the abnormal data and the corresponding risk level according to the matching result; marking the abnormal area in the three-dimensional visualization scene according to the abnormal data, and outputting a warning signal based on the risk level. By accurately matching the multi-source heterogeneous data with the safety operation and maintenance strategy, the status of the current monitoring site can be intuitively presented in a three-dimensional visualization scene, allowing security managers to understand the site situation more clearly and comprehensively, and enhancing the timeliness and effectiveness of safety early warnings.

Description

Technical Field

[0001] The present application relates to the field of security warning technology, and in particular to a security warning method, device, equipment, storage medium and computer program product. Background Art

[0002] Existing early warning notification methods are often presented in simple reports or two-dimensional graphics, which cannot effectively integrate the effective information of the monitoring site, and it is difficult to intuitively present the actual situation of the site. The judgment of abnormal data and the determination of risk levels are not accurate enough, and it is impossible to analyze the degree of deviation and risk value of the data in detail, resulting in a significant reduction in the accuracy and timeliness of the early warning and poor safety operation and maintenance effects. Summary of the invention

[0003] The main purpose of this application is to provide a safety warning method, device, equipment, storage medium and computer program product, which aims to solve the technical problems that the existing warning notification methods are often displayed in simple reports or two-dimensional graphics, cannot effectively integrate the effective information of the monitoring site, and are difficult to intuitively present the actual situation of the site. The judgment of abnormal data and the determination of risk levels are not accurate enough, and the degree of deviation and risk value of the data cannot be analyzed in detail, resulting in a significant reduction in the accuracy and timeliness of the warning and poor safety operation and maintenance effects.

[0004] To achieve the above objectives, the present application proposes a safety early warning method, which includes:

[0005] Acquire multi-source heterogeneous data of the current site, and input the multi-source heterogeneous data into a preset three-dimensional digital twin model to generate a three-dimensional visualization scene;

[0006] Matching the multi-source heterogeneous data with the security operation and maintenance strategy, and determining abnormal data and corresponding risk levels according to the matching results;

[0007] Abnormal areas are marked in the three-dimensional visualization scene according to the abnormal data, and a warning signal is output based on the risk level.

[0008] Optionally, the multi-source heterogeneous data includes operation data and behavior data;

[0009] The step of matching the multi-source heterogeneous data with the security operation and maintenance strategy and determining abnormal data and corresponding risk levels according to the matching results includes:

[0010] Set data operation scope and behavior standards based on preset security operation and maintenance strategies;

[0011] Matching the operation data in the multi-source heterogeneous data with the data operation range to obtain a first matching result;

[0012] Matching the behavior data in the multi-source heterogeneous data with the behavior standard to obtain a second matching result;

[0013] The abnormal data and the risk level are determined according to the first matching result and the second matching result.

[0014] Optionally, the step of determining the abnormal data and the risk level according to the first matching result and the second matching result includes:

[0015] If the first matching result is that there is a data point in the operation data that exceeds the data operation range, then the data point is recorded and the degree of deviation of the data point is calculated;

[0016] If the second matching result is that the action sequence in the behavior data is inconsistent with the behavior standard, then recording the action sequence and calculating the risk value of the action sequence;

[0017] determining abnormal data based on the data points and the action sequence;

[0018] A corresponding risk level is set for the abnormal data according to the deviation degree and the risk value.

[0019] Optionally, the step of acquiring multi-source heterogeneous data of the current site and inputting the multi-source heterogeneous data into a preset three-dimensional digital twin model to generate a three-dimensional visualization scene includes:

[0020] Based on the multimodal sensor network, multi-source heterogeneous data of the current site are collected according to the preset sampling period;

[0021] The multi-source heterogeneous data are allocated to corresponding spatial positions in the model according to the position labels of the multi-source heterogeneous data, and the three-dimensional digital twin model is rendered according to the data types of the multi-source heterogeneous data to generate the three-dimensional visualization scene.

[0022] Optionally, after the step of marking the abnormal area in the three-dimensional visualization scene according to the abnormal data and outputting a warning signal based on the risk level, the method further includes:

[0023] If the risk level is less than the preset danger level, designing a personnel inspection route based on the abnormal area and the site layout information in the three-dimensional visualization scene;

[0024] If the risk level is greater than the preset danger level, an evacuation route is designed based on the abnormal area and the personnel distribution information in the three-dimensional visualization scene.

[0025] Optionally, before the step of acquiring multi-source heterogeneous data of the current site and inputting the multi-source heterogeneous data into a preset three-dimensional digital twin model to generate a three-dimensional visualization scene, the step further includes:

[0026] Obtain historical location information of equipment and employees in the current site, and construct an initial three-dimensional digital twin model based on the historical location information;

[0027] Collecting equipment location information and employee location data of the current site, wherein the employee location data is the location information of the employee when he / she starts working;

[0028] The initial three-dimensional digital twin model is updated according to the device location information and the employee location data to obtain a three-dimensional digital twin model.

[0029] In addition, to achieve the above purpose, the present application also proposes a safety warning device, which includes:

[0030] A scene generation module is used to obtain multi-source heterogeneous data of the current site and input the multi-source heterogeneous data into a preset three-dimensional digital twin model to generate a three-dimensional visualization scene;

[0031] An abnormal acquisition module is used to match the multi-source heterogeneous data with the security operation and maintenance strategy, and determine the abnormal data and the corresponding risk level according to the matching result;

[0032] The early warning module is used to mark the abnormal area in the three-dimensional visualization scene according to the abnormal data, and output a warning signal based on the risk level.

[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes a security warning device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the security warning method described above.

[0034] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the security warning method described above are implemented.

[0035] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the security warning method described above.

[0036] The present application discloses obtaining multi-source heterogeneous data of the current site, inputting the multi-source heterogeneous data into a preset three-dimensional digital twin model, generating a three-dimensional visualization scene; matching the multi-source heterogeneous data with the safety operation and maintenance strategy, determining abnormal data and corresponding risk levels according to the matching results; marking abnormal areas in the three-dimensional visualization scene according to the abnormal data, and outputting warning signals based on the risk level. By accurately matching multi-source heterogeneous data with the safety operation and maintenance strategy, the current site status can be intuitively presented in a three-dimensional visualization scene, allowing safety managers to understand the site status more clearly and comprehensively, and enhancing the timeliness and effectiveness of safety warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0039] Figure 1 This is a flowchart of the first embodiment of the safety warning method of this application;

[0040] Figure 2 A three-dimensional visualization scene diagram of the safety warning method of this application;

[0041] Figure 3 This is a flow chart of the second embodiment of the safety warning method of this application;

[0042] Figure 4 This is a flowchart of the third embodiment of the safety warning method of this application;

[0043] Figure 5 This is a schematic diagram of the module structure of the safety warning device according to an embodiment of the present application;

[0044] Figure 6 Schematic diagram of the device structure of the hardware operating environment involved in the security warning method in the embodiment of the present application.

[0045] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0046] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0047] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0048] The main solution of the embodiment of the present application is: obtain multi-source heterogeneous data of the current site, and input the multi-source heterogeneous data into a preset three-dimensional digital twin model to generate a three-dimensional visualization scene; match the multi-source heterogeneous data with the safety operation and maintenance strategy, and determine the abnormal data and the corresponding risk level according to the matching results; mark the abnormal area in the three-dimensional visualization scene according to the abnormal data, and output a warning signal based on the risk level.

[0049] As the requirements for safety warnings at various sites are increasing day by day. In traditional safety warning methods, there are limitations in data collection. Most of them can only obtain a single type of data, such as simple equipment operation data or personnel behavior data, and cannot achieve comprehensive collection of multi-source heterogeneous data, resulting in incomplete and inaccurate judgment of the site safety status, insufficient data processing and analysis capabilities, lack of effective integration and in-depth analysis methods for the collected data, and difficulty in associating and integrating different types of data, and unable to accurately identify potential safety risks. Moreover, traditional safety warning methods are often displayed in simple reports or two-dimensional graphics, which are not intuitive and vivid enough, making it difficult for safety managers to quickly and clearly grasp the overall safety situation of the site, affecting the efficiency and accuracy of decision-making.

[0050] Therefore, this application provides a global perception linkage alarm and dynamic safety warning method, which can intuitively present the current site conditions in a three-dimensional visualization scene, allowing security managers to understand the site conditions more clearly and comprehensively. By accurately matching multi-source heterogeneous data with security operation and maintenance strategies, the accuracy and efficiency of abnormal data identification are significantly improved, and the corresponding risk level can be accurately determined. Abnormal areas are marked in the three-dimensional visualization scene to make abnormal situations more eye-catching and intuitive, and convenient for timely processing. Based on the accurate risk level output warning signal, the timeliness and effectiveness of safety warnings are greatly enhanced, which can better prevent the occurrence of safety accidents and ensure the safe and stable operation of the site.

[0051] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, etc. The following takes the security early warning system as an example to illustrate this embodiment and the following embodiments.

[0052] Based on this, the present application embodiment provides a safety warning method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the safety warning method of the present application.

[0053] In this embodiment, the security early warning method includes:

[0054] Step S10, obtaining multi-source heterogeneous data of the current site, and inputting the multi-source heterogeneous data into a preset three-dimensional digital twin model to generate a three-dimensional visualization scene.

[0055] It should be noted that multi-source heterogeneous data can include data collected from different types of sensors (such as temperature sensors, pressure sensors, cameras, etc.), and can also include data generated by various professional subsystems of the power plant (such as production systems, fire protection systems, access control systems, etc.). The three-dimensional digital twin model is a digital mapping of the real physical power plant in the virtual space. It has the same appearance and geometry as the physical power plant, and also contains various attributes, behaviors and relationship information of the physical objects. The three-dimensional visualization scene is a dynamic and intuitive virtual environment generated by combining the three-dimensional digital twin model with relevant data. In this scene, users can use specific software platforms or tools to view the overall picture of the power plant from different perspectives and interactive methods, including information such as building layout, equipment distribution, personnel activities, and environmental status.

[0056] It is understandable that when acquiring multi-source heterogeneous data of the current site, the sensor network will continuously collect various physical quantity data, such as the camera capturing the image and video information of the scene in real time, the temperature sensor measuring the temperature value of the environment or equipment, and the pressure sensor monitoring the pressure change in the pipeline or container. At the same time, each professional subsystem will collect and organize the data generated during its own operation, such as the production system recording the power generation of the unit, the operating time of the equipment and other data, and the access control system recording the entry and exit time and identity information of the personnel. Based on the three-dimensional digital twin model with real-time data input, professional three-dimensional rendering engines and visualization software can be used to generate three-dimensional visualization scenes, and each frame of the image is calculated through real-time updated data (such as color changes caused by changes in the operating status of the equipment, movement of personnel, etc.). Enable users to operate in the scene through devices such as mouse, keyboard or touch screen, such as rotating, zooming, panning the perspective, clicking to view detailed information of the equipment, etc. Finally, a realistic and dynamic three-dimensional visualization scene of the overseas power plant is presented on the user terminal (such as a computer monitor, large-screen display system, etc.), and users can intuitively obtain various information of the power plant.

[0057] Step S20, matching the multi-source heterogeneous data with the security operation and maintenance strategy, and determining abnormal data and corresponding risk levels according to the matching results.

[0058] It should be noted that the safety operation and maintenance strategy is a collection of rules, processes and measures formulated to ensure the safe and stable operation of the site. It can be formulated based on the design specifications, industry standards, past experience and safety regulations of the power plant. Abnormal data refers to data in multi-source heterogeneous data that does not conform to the pre-set safety operation and maintenance strategy, indicating that the equipment operation status, personnel behavior or environmental conditions of the power plant may deviate from the normal range and there are potential safety risks. The risk level is used to measure the level of safety risk represented by abnormal data. It can be determined by comprehensive evaluation based on factors such as the degree to which the abnormal data deviates from the normal range, the scope and severity of the impact that the abnormal situation may have on the operation of the power plant, etc.

[0059] It should be understood that the matching of the multi-source heterogeneous data with the security operation and maintenance strategy can be performed by comparing the key features of the data to determine the abnormal feature vectors therein; for image data, key features (such as personnel identity, behavior, object shape, etc.) can be extracted through image recognition algorithms, and then matched with the provisions on personnel and environment in the strategy. For example, whether a person is wearing a helmet can be identified from video surveillance data and checked with the provisions in the security operation and maintenance strategy that require a helmet to be worn when entering a specific area.

[0060] It is understandable that when one or some data items in the multi-source heterogeneous data do not match the rules in the safety operation and maintenance strategy, these data are determined to be abnormal data. Taking the equipment operation data as an example, if the outlet pressure value of a pump is lower than the minimum pressure value specified in the safety operation and maintenance strategy, then the pressure data is abnormal data. For image data, if smoke features are detected in the monitoring screen of the prohibited fireworks area, the corresponding image data part will also be marked as abnormal data. At the same time, the system needs to consider the type of abnormal data, the degree of deviation from the normal range, and the duration to determine the risk level corresponding to the abnormal data. For example, for equipment temperature abnormality, if the temperature exceeds the normal range by a small amount and the duration is short, the equipment is non-critical equipment and may be judged as low risk; but if the temperature exceeds the standard seriously and continues to rise, and the equipment is the core power generation equipment of the power plant, then it may be judged as high risk. The determination of risk level helps safety managers quickly understand the severity of abnormal situations and take corresponding countermeasures.

[0061] In one example, the safe operation and maintenance strategy of a power plant stipulates that in dangerous areas such as high-voltage distribution rooms, personnel must be authorized and wear complete personal protective equipment (such as insulating gloves, safety helmets, goggles, etc.) to enter. The video surveillance system covers the entrances and exits of the high-voltage distribution room, and detects and identifies personnel in the monitoring screen through image recognition technology. When a person attempts to enter the high-voltage distribution room, the system extracts the person's features in the image, identifies whether he or she is wearing the prescribed protective equipment, and compares it with the personnel authorization database to determine whether he or she has access rights. If a person is found not to be wearing a safety helmet and entering without authorization, the relevant image data and personnel information are identified as abnormal data. At the same time, it is monitored that serious electric shock accidents may occur in high-voltage dangerous areas, and the risk level assessment model determines it as high risk according to preset rules.

[0062] Step S30: marking abnormal areas in the three-dimensional visualization scene according to the abnormal data, and outputting a warning signal based on the risk level.

[0063] It is understandable that the early warning signal can be output in a variety of ways, such as sound alarms (alarm sounds of different frequencies or volumes corresponding to different risk levels), light alarms (warning lights in specific areas light up or flash), SMS notifications (sending SMS messages containing abnormal information and risk levels to relevant responsible persons), system pop-up prompts (a window pops up on the monitoring terminal showing abnormal conditions and risk levels), etc.

[0064] It should be understood that marking the abnormal area in the three-dimensional visualization scene according to the abnormal data may be marking the area in the three-dimensional visualization scene in a specific manner, such as changing the color of the abnormal area (such as red for high-risk abnormal areas, yellow for medium-risk abnormal areas, etc.), adding a flashing effect, displaying a warning icon (such as an exclamation mark icon), or drawing a bounding box, etc.

[0065] In one example, reference Figure 2 , Figure 2 A three-dimensional visualization scene diagram of the safety warning method for this application. The figure shows a three-dimensional visualization scene of a computer room, with multiple rows of large cabinets or equipment compartments. There are some identification texts on the cabinets to distinguish the purpose, number or system of different cabinets. There are several triangular warning signs above the cabinets, and there is an exclamation mark in the middle of each sign. This sign is usually used to indicate the existence of potential dangers, failures or situations that require attention. There is a silhouette of a person in the aisle between the cabinets, showing that someone is active in this data center or computer room, indicating that inspections, maintenance or other related operations are being carried out.

[0066] Of course, in order to guide maintenance personnel to efficiently and quickly reach the abnormal area for inspection and repair, thereby ensuring the normal operation of the equipment or site, after step S30, the following steps are further included:

[0067] If the risk level is lower than the preset danger level, a personnel maintenance route is designed based on the abnormal area and the site layout information in the three-dimensional visualization scene; if the risk level is higher than the preset danger level, a personnel evacuation route is designed based on the abnormal area and the personnel distribution information in the three-dimensional visualization scene.

[0068] It should be noted that the site layout information includes detailed information such as the location, shape, size and relative position relationship of buildings, equipment, passages, obstacles and other elements in the site. This information is presented in the form of a three-dimensional model, which can intuitively display the overall layout structure of the power plant and provide basic data for designers to design maintenance routes. The personnel maintenance route is the optimal path planned for maintenance personnel to go from the current location to the abnormal area for equipment maintenance when the risk level is less than the preset danger level.

[0069] In addition, it should be noted that the personnel distribution information is the location, number, identity, and task of personnel in each area of ​​the site. The personnel evacuation route is the path planned from the current location of the personnel to the safe area to ensure the safety of the personnel in the power plant when the risk level is greater than the preset danger level.

[0070] It is understandable that when generating the personnel maintenance route, it is necessary to obtain the location and range information of the abnormal area and the site layout information in the three-dimensional visualization scene. Then, using the path planning algorithm, such as the A* algorithm, the Dijkstra algorithm, etc., with the current location of the maintenance personnel (which can be a preset starting point such as the maintenance workshop or the central control room) as the starting point, and the equipment in the abnormal area or the location that needs maintenance as the end point, search for the optimal path in the map composed of the site layout information. When the risk level is greater than the preset danger level, the system immediately obtains the location and range of the abnormal area and the personnel distribution information in the three-dimensional visualization scene. For each personnel location, the path planning algorithm is used to calculate the best evacuation path to the safe area (such as an open area outside the factory, a designated refuge place, etc.).

[0071] In one example, the monitoring system detected an abnormal vibration value of a water pump, and the risk level was assessed to be low risk (lower than the preset danger level). The maintenance personnel are currently in the maintenance workshop of the power plant. Through the path planning algorithm, the system calculates the optimal maintenance route from the maintenance workshop to the water pump location. Avoid the area of ​​large generator sets in operation and choose a channel with smoother traffic. In the three-dimensional visualization scene, the maintenance route is displayed with green lines, and the turning points and key equipment locations along the way are clearly marked and indicated with arrows. At the same time, the maintenance personnel received a push from the maintenance route navigation application on his mobile phone, and the voice prompted him to turn left after leaving the maintenance workshop, go along the channel, turn right at the second intersection, and other information.

[0072] In this embodiment, multi-source heterogeneous data of the current site is obtained, and the multi-source heterogeneous data is input into a preset three-dimensional digital twin model to generate a three-dimensional visualization scene; the multi-source heterogeneous data is matched with the safety operation and maintenance strategy, and the abnormal data and the corresponding risk level are determined according to the matching result; the abnormal area is marked in the three-dimensional visualization scene according to the abnormal data, and an early warning signal is output based on the risk level. By accurately matching the multi-source heterogeneous data with the safety operation and maintenance strategy, the current site status can be intuitively presented in a three-dimensional visualization scene, allowing security managers to understand the site status more clearly and comprehensively, and enhancing the timeliness and effectiveness of safety warnings.

[0073] Reference Figure 3 , Figure 3 This is a flow chart of the second embodiment of the security warning method of the present application. Based on the above-mentioned first embodiment, the second embodiment of the security warning method of the present application is proposed.

[0074] In the second embodiment, the step S30 includes:

[0075] Step S301, setting the data operation scope and behavior standards based on the preset security operation and maintenance strategy.

[0076] It should be noted that the data operation range is the reasonable value range set for the equipment operation data in the safety operation and maintenance strategy. These ranges are determined based on the equipment's design parameters, performance indicators, and long-term operation experience, and are used to determine whether the equipment's operating status is normal. Behavioral standards refer to the specifications and guidelines that personnel should follow in various activities within the power plant as specified in the safety operation and maintenance strategy, including requirements for personnel's operating behavior, movement trajectory, and entry area restrictions.

[0077] Step S302: Match the operating data in the multi-source heterogeneous data with the data operating range to obtain a first matching result.

[0078] It is understood that the data acquisition system acquires the operating data collected by various sensors and monitoring equipment in the field in real time. After these data are transmitted to the data processing server, each parameter value in the operating data is automatically extracted and compared with the preset data operating range one by one.

[0079] In one example, for a device temperature value collected by a temperature sensor, the system will determine whether the temperature is within the normal operating temperature range of the corresponding device. If the temperature value is within the set range, the system records the first matching result as normal, and marks the data as normal operating data for storage and further analysis; if the temperature value is out of range, the system records the first matching result as abnormal, and records and saves the abnormal temperature data and related device information (such as device name, location, collection time, etc.).

[0080] Step S303: Match the behavior data in the multi-source heterogeneous data with the behavior standard to obtain a second matching result.

[0081] It is understandable that monitoring systems (such as video surveillance, personnel positioning systems, etc.) continuously collect behavioral data of personnel in the venue. After these data are transmitted to the data processing center, the system uses image recognition technology, location data analysis algorithms and other means to process the behavioral data. For the location information of personnel, the system determines whether they are in the authorized activity area; for the operation of personnel, the system uses image recognition technology to determine whether they comply with the prescribed operation procedures.

[0082] Step S304: determining the abnormal data and the risk level according to the first matching result and the second matching result.

[0083] It is understandable that determining the abnormal data and the risk level according to the first matching result and the second matching result may be to establish a risk assessment model to determine the risk level of the abnormal data, or may be to adopt a multi-factor comprehensive evaluation method such as hierarchical analysis method and fuzzy comprehensive evaluation method.

[0084] Of course, in order to achieve the quantitative classification of risks, the step S304 includes:

[0085] If the first matching result is that there are data points in the operating data that exceed the data operating range, the data points are recorded and the degree of deviation of the data points is calculated; if the second matching result is that the action sequence in the behavior data is inconsistent with the behavior standard, the action sequence is recorded and the risk value of the action sequence is calculated; abnormal data is determined based on the data points and the action sequence; and a corresponding risk level is set for the abnormal data according to the degree of deviation and the risk value.

[0086] It should be noted that a data point is a data value collected at a specific moment in the operating data. The degree of deviation is used to measure the degree to which a data point that exceeds the data operating range deviates from the normal range. It can usually be expressed by calculating the difference between the data point and the boundary value of the data operating range, or by using relative deviation ratios. An action sequence consists of a series of continuous human actions that are related in time and logic. The risk value is used to quantify the degree of risk caused by the inconsistency between the action sequence and the behavior standard.

[0087] It is understandable that when the operation data is matched with the data operation range, if there is a data point that exceeds the range, the detailed information of the data point is immediately recorded, including the value of the data point, the acquisition time, the device to which it belongs, and other information. Then, the degree of deviation is calculated according to the preset calculation method. When calculating the degree of deviation, the boundary value (upper and lower limits) of the data operation range is first determined. If the data point is greater than the upper limit, the degree of deviation may be calculated as the difference between the data point and the upper limit, or expressed in the form of a ratio of (data point-upper limit) / (upper limit-lower limit); if the data point is less than the lower limit, the difference or relative ratio with the lower limit is similarly calculated. Once the system detects that the action sequence in the behavior data does not conform to the behavior standard, it will completely record the entire process of the action sequence, including the time when each action occurs, the subject of the action (personnel identity), the location of the action, and other information. Subsequently, the risk value of the action sequence is calculated according to the pre-set risk value calculation model. This computational model takes into account multiple factors, such as the number of key actions in the action sequence that violate behavioral standards, the severity of the consequences that may result from each key action, and the frequency of occurrence of the action sequence within a certain period of time.

[0088] In one example, in a foreign power plant, when an important cooling water pump is operating normally, the data operating range of its outlet pressure is set to 0.3-0.5MPa. The data acquisition system monitors the pump operation data in real time, and the outlet pressure value collected at a certain moment is 0.55MPa. The system detects that the data point exceeds the data operating range and immediately records the data point (0.55MPa, 9:20 am, cooling water pump number 003). Then the degree of deviation is calculated, and the absolute difference method is used, and the degree of deviation is 0.55-0.5=0.05MPa. When entering the fuel storage area, personnel must first register at the entrance, collect fireproof clothing and wear it correctly, and then enter with a special person. The monitoring system captured a staff member trying to enter the fuel storage area at 10:45 am. The action sequence was to walk directly to the entrance, not register, not collect fireproof clothing, and try to enter alone. The system detects that the action sequence is inconsistent with the behavior standard and records the action sequence (at 10:45 a.m., worker A was at the entrance of the fuel storage area, not registered, not wearing fireproof clothing, and walking alone to the entrance). Then the risk value is calculated. According to the risk value calculation model, considering that failure to comply with key safety regulations for entering the fuel storage area (not registered, not taking fireproof measures, and not being accompanied) may cause serious consequences such as fire or explosion (high severity of consequences), and that the behavior has not occurred in the recent period (low frequency), the risk value is calculated to be 9.

[0089] In this embodiment, the data operation range and behavior standard are set based on the preset security operation and maintenance strategy; the operation data in the multi-source heterogeneous data is matched with the data operation range to obtain a first matching result; the behavior data in the multi-source heterogeneous data is matched with the behavior standard to obtain a second matching result; the abnormal data and the risk level are determined according to the first matching result and the second matching result. The matching results of the operation data and the behavior data are recorded and quantitatively calculated respectively, which can comprehensively consider the different factors affecting the safety of the site, avoid the misjudgment or missed judgment that may be caused by general matching, and improve the accuracy and effectiveness of risk assessment.

[0090] Reference Figure 4 , Figure 4 This is a flow chart of the third embodiment of the security warning method of the present application. Based on the above second embodiment, the third embodiment of the security warning method of the present application is proposed.

[0091] In the third embodiment, the step S10 includes:

[0092] Step S101 : collecting multi-source heterogeneous data of the current site based on a multimodal sensor network according to a preset sampling period.

[0093] It is understandable that the preset sampling period can be a set time interval used to determine the frequency of data collection by the multimodal sensor network. For example, the temperature sensor can be set to collect data every 5 minutes, the pressure sensor can be set to collect data every 3 minutes, etc. The preset sampling period can also be determined by the change speed of the monitored parameter (fast-changing parameters may require a shorter sampling period) and the data processing capacity (a shorter sampling period will generate more data, requiring the system to have sufficient processing capacity).

[0094] Step S102: assign the multi-source heterogeneous data to corresponding spatial positions in the model according to the position labels of the multi-source heterogeneous data, and render the three-dimensional digital twin model according to the data types of the multi-source heterogeneous data to generate the three-dimensional visualization scene.

[0095] It is understandable that the location tag information in the multi-source heterogeneous data is read, and then the corresponding spatial position matching it is found in the three-dimensional digital twin model. For example, when data from a temperature sensor of a certain device is received, its location tag indicates that the sensor is located on a generator in a power plant, and the system finds the corresponding generator model in the power plant area of ​​the three-dimensional digital twin model. Then, the data collected by the temperature sensor is associated with the generator model to establish a connection between the data and the specific spatial position in the model. In this way, when processing and displaying the data later, the data can be accurately reflected in the corresponding position in the model, realizing the organic combination of data and model.

[0096] In one example, the personnel positioning system collects the location information of personnel in the main control room in real time (numeric type, with location tags such as personnel number and location coordinates). These data are assigned to the corresponding positions in the model. In the three-dimensional visualization scene, different personnel are represented by dots or icons of different colors, and their names and identity information are displayed. When personnel move, their positions in the model are also dynamically updated. Operation and maintenance personnel can clearly see the distribution and activity trajectory of personnel in the main control room, which is convenient for management and scheduling. In addition, some relevant text information can be displayed around the main control room model, such as the current list of on-duty personnel, recent operation records, etc.

[0097] Of course, in order to be able to reflect the latest site conditions in the model in a timely manner and ensure the synchronization and accuracy between the model and the actual site, before step S10, it also includes:

[0098] Obtain historical location information of equipment and employees in the current site, and construct an initial three-dimensional digital twin model based on the historical location information; collect equipment location information and employee location data of the current site, wherein the employee location data is the location information of the employees when they start working; update the initial three-dimensional digital twin model based on the equipment location information and the employee location data to obtain a three-dimensional digital twin model.

[0099] It is understandable that historical location information refers to the recorded data of the location of equipment and employees in overseas power plants over a period of time. The historical location information of equipment can include the location changes of equipment during installation, commissioning, operation, maintenance and relocation; the historical location information of employees covers the activity track records of different work areas in the power plant, such as the stay time and sequence in various workshops, offices, warehouses and other areas. The initial three-dimensional digital twin model is a virtual three-dimensional model preliminarily constructed based on the existing design data of overseas power plants (such as architectural drawings, equipment layout drawings, etc.) and the collected historical location information of equipment and employees.

[0100] It should be understood that employee location data specifically refers to the information record of the employee's location when he or she starts work. It can be obtained through the employee clock-in system (the clock-in device integrates the location collection function), specific location sensors triggered when the employee enters the work area (such as Bluetooth or Wi-Fi positioning devices installed at the door of the workshop), or the location actively reported by the employee using a mobile terminal (such as a mobile phone APP) when starting work.

[0101] In one example, in a large power plant, in order to achieve accurate management and safety monitoring of equipment, it was decided to build a three-dimensional digital twin model. First, the historical location information of the equipment was collected. The initial location coordinates of a large boiler at the time of installation and the location fine-tuning records during the past equipment upgrades and renovations were obtained from the equipment procurement archives; it was found from the equipment maintenance log that some auxiliary equipment had been temporarily moved due to pipeline maintenance needs. Using this historical location information, combined with the power plant building drawings and equipment layout drawings, the initial three-dimensional digital twin model was constructed using professional three-dimensional modeling software, and the three-dimensional model of the boiler and its auxiliary equipment was accurately placed in the model. When the data processing system receives the real-time location data of the boiler water level gauge and shows that its position has slightly shifted (possibly due to equipment vibration or other reasons), the corresponding water level gauge model is found in the initial three-dimensional digital twin model according to the equipment number, and its position is updated accordingly in the model to ensure that the water level gauge position in the model is always consistent with the actual equipment position. When employees go to work, the employee location data is collected through the punch-in device with integrated Wi-Fi positioning function installed at the entrance of the power plant. When employees punch in, the punch-in device automatically records the employee's Wi-Fi device MAC address and current location coordinates. At the same time, Bluetooth positioning beacons are installed at the entrances of each workshop and important work areas. When employees enter these areas with their mobile phones, the mobile phones communicate with the Bluetooth beacons to further pinpoint the employees' locations.

[0102] In this embodiment, multi-source heterogeneous data of the current site is collected based on a multimodal sensor network according to a preset sampling period; the multi-source heterogeneous data is assigned to the corresponding spatial position in the model according to the location tag of the multi-source heterogeneous data, and the three-dimensional digital twin model is rendered according to the data type of the multi-source heterogeneous data to generate the three-dimensional visualization scene. According to the location tag of the data, it is assigned to the corresponding spatial position in the three-dimensional digital twin model, and the model is rendered according to the data type, which realizes the efficient coordination of data collection and scene rendering, so that the collected data can be accurately and timely presented in the three-dimensional visualization scene, truly reflecting the actual status of the site, and providing security management personnel with intuitive and real-time site information display.

[0103] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the security warning method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0104] This application also provides a safety warning device, please refer to Figure 5 , the safety warning device comprises:

[0105] A scene generation module 10 is used to obtain multi-source heterogeneous data of the current site, and input the multi-source heterogeneous data into a preset three-dimensional digital twin model to generate a three-dimensional visualization scene;

[0106] The abnormal acquisition module 20 is used to match the multi-source heterogeneous data with the security operation and maintenance strategy, and determine the abnormal data and the corresponding risk level according to the matching result;

[0107] The early warning module 30 is used to mark the abnormal area in the three-dimensional visualization scene according to the abnormal data, and output an early warning signal based on the risk level.

[0108] The safety warning device provided by the present application adopts the safety warning method in the above embodiment, which can solve the problem that the existing warning notification method is often displayed in simple reports or two-dimensional graphics, which cannot effectively integrate the effective information of the monitoring site, and it is difficult to intuitively present the actual situation of the site. The judgment of abnormal data and the determination of risk level are not accurate enough, and the deviation degree and risk value of the data cannot be analyzed in detail, resulting in a significant reduction in the accuracy and timeliness of the warning, and poor safety operation and maintenance effects. Technical problems. Compared with the prior art, the beneficial effects of the safety warning device provided by the present application are the same as the beneficial effects of the safety warning method provided in the above embodiment, and the other technical features in the safety warning device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0109] The present application provides a security warning device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the security warning method in the above-mentioned embodiment one.

[0110] Reference below Figure 6 , which shows a schematic diagram of the structure of a safety warning device suitable for implementing the embodiment of the present application. The safety warning device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The safety warning device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0111] like Figure 6 As shown, the safety warning device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the safety warning device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the safety warning device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows a safety warning device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0112] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0113] The safety warning device provided by the present application adopts the safety warning method in the above embodiment, which can solve the problem that the existing warning notification method is often displayed in simple reports or two-dimensional graphics, which cannot effectively integrate the effective information of the monitoring site, and it is difficult to intuitively present the actual situation of the site. The judgment of abnormal data and the determination of risk level are not accurate enough, and the deviation degree and risk value of the data cannot be analyzed in detail, resulting in a significant reduction in the accuracy and timeliness of the warning, and poor technical problems in the safety operation and maintenance effect. Compared with the prior art, the beneficial effects of the safety warning device provided by the present application are the same as the beneficial effects of the safety warning method provided in the above embodiment, and the other technical features in the safety warning device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0114] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0115] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0116] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the security warning method in the above-mentioned embodiment.

[0117] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0118] The computer-readable storage medium may be included in the safety warning device; or it may exist independently without being assembled into the safety warning device.

[0119] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the security warning device, the security warning device executes the security warning method described above.

[0120] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0121] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0122] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0123] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned safety warning method, which can solve the problems that the existing warning notification method is often displayed in simple reports or two-dimensional graphics, cannot effectively integrate the effective information of the monitoring site, and is difficult to intuitively present the actual situation of the site. The judgment of abnormal data and the determination of risk levels are not accurate enough, and the degree of deviation and risk value of the data cannot be analyzed in detail, resulting in a significant reduction in the accuracy and timeliness of the warning, and poor technical problems in the safety operation and maintenance effect. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the safety warning method provided by the above-mentioned embodiment, which will not be repeated here.

[0124] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned security warning method when executed by a processor.

[0125] The computer program product provided by this application can solve the technical problems that the existing early warning notification methods are often displayed in simple reports or two-dimensional graphics, which cannot effectively integrate the effective information of the monitoring site, and it is difficult to intuitively present the actual situation of the site. The judgment of abnormal data and the determination of risk levels are not accurate enough, and the deviation degree and risk value of the data cannot be analyzed in detail, resulting in a significant reduction in the accuracy and timeliness of the early warning, and poor security operation and maintenance effects. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the security early warning method provided by the above embodiment, which will not be repeated here.

[0126] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A safety early warning method, characterized in that: The safety early warning method comprises: Acquire multi-source heterogeneous data of the current site, and input the multi-source heterogeneous data into a preset three-dimensional digital twin model to generate a three-dimensional visualization scene; Matching the multi-source heterogeneous data with the security operation and maintenance strategy, and determining abnormal data and corresponding risk levels according to the matching results; Abnormal areas are marked in the three-dimensional visualization scene according to the abnormal data, and a warning signal is output based on the risk level.

2. The safety warning method according to claim 1, characterized in that: The multi-source heterogeneous data includes operation data and behavior data; The step of matching the multi-source heterogeneous data with the security operation and maintenance strategy and determining abnormal data and corresponding risk levels according to the matching results includes: Set data operation scope and behavior standards based on preset security operation and maintenance strategies; Matching the operation data in the multi-source heterogeneous data with the data operation range to obtain a first matching result; Matching the behavior data in the multi-source heterogeneous data with the behavior standard to obtain a second matching result; The abnormal data and the risk level are determined according to the first matching result and the second matching result.

3. The safety warning method according to claim 2, characterized in that: The step of determining the abnormal data and the risk level according to the first matching result and the second matching result includes: If the first matching result is that there is a data point in the operation data that exceeds the data operation range, then the data point is recorded and the degree of deviation of the data point is calculated; If the second matching result is that the action sequence in the behavior data is inconsistent with the behavior standard, then recording the action sequence and calculating the risk value of the action sequence; determining abnormal data based on the data points and the action sequence; A corresponding risk level is set for the abnormal data according to the deviation degree and the risk value.

4. The safety early warning method according to claim 1, characterized in that: The step of acquiring multi-source heterogeneous data of the current site and inputting the multi-source heterogeneous data into a preset three-dimensional digital twin model to generate a three-dimensional visualization scene includes: Based on the multimodal sensor network, multi-source heterogeneous data of the current site are collected according to the preset sampling period; The multi-source heterogeneous data are allocated to corresponding spatial positions in the model according to the position labels of the multi-source heterogeneous data, and the three-dimensional digital twin model is rendered according to the data types of the multi-source heterogeneous data to generate the three-dimensional visualization scene.

5. The safety warning method according to claim 1, characterized in that: After the step of marking the abnormal area in the three-dimensional visualization scene according to the abnormal data and outputting a warning signal based on the risk level, the method further includes: If the risk level is less than the preset danger level, designing a personnel inspection route based on the abnormal area and the site layout information in the three-dimensional visualization scene; If the risk level is greater than the preset danger level, an evacuation route is designed based on the abnormal area and the personnel distribution information in the three-dimensional visualization scene.

6. The safety early warning method according to claim 1, characterized in that: Before the step of acquiring multi-source heterogeneous data of the current site and inputting the multi-source heterogeneous data into a preset three-dimensional digital twin model to generate a three-dimensional visualization scene, the step further includes: Obtain historical location information of equipment and employees in the current site, and construct an initial three-dimensional digital twin model based on the historical location information; Collecting equipment location information and employee location data of the current site, wherein the employee location data is the location information of the employee when he / she starts working; The initial three-dimensional digital twin model is updated according to the device location information and the employee location data to obtain a three-dimensional digital twin model.

7. A safety warning device, characterized in that: The device comprises: A scene generation module is used to obtain multi-source heterogeneous data of the current site and input the multi-source heterogeneous data into a preset three-dimensional digital twin model to generate a three-dimensional visualization scene; An abnormal acquisition module is used to match the multi-source heterogeneous data with the security operation and maintenance strategy, and determine the abnormal data and the corresponding risk level according to the matching result; The early warning module is used to mark the abnormal area in the three-dimensional visualization scene according to the abnormal data, and output a warning signal based on the risk level.

8. A safety warning device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the safety warning method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the safety warning method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the safety warning method according to any one of claims 1 to 6 are implemented.

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