Operation safety risk analysis system and method based on big data

Abstract

The invention discloses an operation safety risk analysis system and method based on big data, and the system comprises a central processor module, a storage module, a risk prevention module, and an operation monitoring module, and the central processor module is in bidirectional connection with the storage module, the operation monitoring module, and the risk prevention module. The risk prevention module is used for carrying out related processing on risk points possibly appearing in a working area, and the operation monitoring module is used for carrying out risk monitoring on working behaviors. The system has the beneficial effects that a risk point information database and a pre-control measure information database based on a big data platform are constructed by setting the risk prevention module, potential risk points in a working area can be preventively processed, an avoidance instruction can be sent in advance, and the possibility of accidents of workers is greatly reduced; meanwhile, the arranged operation monitoring module collects scene images of the operation area in real time through the acquisition unit, and the judgment unit analyzes the risk state, so that all-directional monitoring protection of the operation area is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid maintenance, and in particular to a big data-based operation safety risk analysis system and method. Background Art

[0002] With the development of the economy, the distribution of power grids is getting larger and larger, and it is developing towards complexity and multi-level. The power grid is a key national infrastructure and plays an important role in economic development and social stability. In order to ensure the normal operation of the power grid system, the power grid must be regularly inspected and maintained. However, power grid operation is a high-risk job, involving multiple risk factors such as high voltage and high-altitude operations. Safety risk analysis can identify potential safety hazards, take effective measures to prevent accidents, and protect the lives of operators. For example, factors such as equipment leakage and large-scale mechanical failures can not only cause casualties, but also cause huge economic losses. Through risk analysis, the probability of accidents can be reduced, thereby avoiding or reducing economic losses. Therefore, power grid operation safety risk analysis is a basic and vital task for power grid companies. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above problems or problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the purpose of the present invention is to provide a big data-based operation safety risk analysis system and method, which can preventively identify and process potential risk points in the work area through a risk prevention module, and use an operation monitoring module to achieve real-time monitoring and early warning of the operation area, and combine with a central processing unit module to achieve data interaction and closed-loop management between modules, effectively reduce safety risks in the operation process, ensure the personal safety of construction personnel, and improve the intelligence level of operation safety management.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a big data-based operation safety risk analysis system, which includes a central processing unit module, a storage module, a risk prevention module, and an operation monitoring module. The central processing unit module and the storage module, the operation monitoring module, and the risk prevention module are bidirectionally connected. The risk prevention module is used to perform relevant processing on risk points that may appear in the working area, and the operation monitoring module is used to perform risk monitoring on work behaviors.

[0007] As a preferred solution of the operation safety risk analysis system based on big data of the present invention, the risk prevention module includes a risk point unit, a pre-control measure unit, a configuration unit, a generation unit, and an indication unit;

[0008] The risk point unit is used to create a risk point information database based on the big data platform, which stores all the operational risk point information related to power grid maintenance;

[0009] The preventive measures unit is used to establish a preventive control measures information database based on the big data platform, which stores preventive control measures for all risk points;

[0010] The configuration unit is used to match risk point information with preventive control measures;

[0011] The generation unit is used to prompt the risk point information of the operating equipment during the simulation operation and generate risk point preventive control measures commands;

[0012] The instruction unit is used to issue commands to the staff to avoid risk points during the operation based on risk point preventive measures. It can also provide prevention and control suggestions to the staff who are at the risk points.

[0013] As a preferred solution of the operation safety risk analysis system based on big data of the present invention, the operation monitoring module includes an acquisition unit, a judgment unit, a detection unit, an alarm unit and an identification unit;

[0014] The acquisition unit is used to acquire the scene image of the staff working area and the scene image of the detection area;

[0015] The judgment unit is used to judge whether the operation area is in operation state according to the initial position and real-time position of the operator and heavy machinery in the scene image of the operation area through the preset big data platform;

[0016] The detection unit is used to determine whether there are workers violating regulations and being at risk points in the detection area based on the scene image of the detection area when the operation area is in operation;

[0017] The alarm unit is used to sound an alarm when a worker violates a rule and is at a risk point.

[0018] As a preferred solution of the operation safety risk analysis system based on big data of the present invention, the storage module records the data interaction process between the risk prevention module and the operation monitoring module in real time, and the central processing unit module analyzes the recorded data and feeds back the analysis results to the risk prevention module for optimizing preventive measures and to the operation monitoring module for adjusting monitoring parameters.

[0019] As a preferred solution of the operation safety risk analysis system based on big data of the present invention, wherein: the acquisition unit includes an acquisition subunit, a region determination subunit, a region image subunit, and a region detection subunit;

[0020] The acquisition subunit acquires the construction image of the construction area based on the preset panoramic camera equipment;

[0021] The area determination subunit performs image recognition based on the construction image to determine the detection target image;

[0022] The area image subunit is used to determine the scene image of the operation area according to the target image of the construction operation area;

[0023] The area detection subunit is used to determine the scene image of the detection area according to the target image of the detection area.

[0024] As a preferred solution of the operation safety risk analysis system based on big data of the present invention, wherein: the judgment unit includes a judgment subunit, an initial position subunit, a real-time position subunit and a difference calculation subunit;

[0025] The judgment subunit is used to obtain a scene image of the working area and identify the construction working area in the scene image of the working area;

[0026] The initial position subunit is used to obtain the preset initial position of the heavy machinery in the construction operation area based on the big data platform analysis system preset in the big data platform;

[0027] The real-time location subunit is used to dynamically calibrate the location of the construction area in real time based on the intelligent positioning system preset in the big data platform, and determine the preset real-time location of heavy machinery in the construction area;

[0028] The difference calculation subunit is used to calculate the position difference between the initial position and the real-time position of the construction operation area, and based on the preset operation fluctuation law of the heavy machinery, determine whether the position difference conforms to the operation fluctuation law.

[0029] As a preferred solution of the operation safety risk analysis system based on big data of the present invention, wherein: the acquisition unit is connected with the image monitoring unit and the delay judgment unit;

[0030] The image monitoring unit is used to continue monitoring the image acquisition module and acquire the monitoring image of the operating area when the operating area is in an intermittent period;

[0031] The delay judgment unit is used to start the timer, collect the monitoring information of the monitoring image, judge whether the monitoring information is delayed, and obtain the judgment result, wherein the judgment result includes a non-delay judgment result and a delay judgment result.

[0032] To solve the above technical problems, the present invention provides the following technical solutions: a method for analyzing operation safety risks based on big data, including a system for analyzing operation safety risks based on big data; and

[0033] Collecting operation scene image data through an acquisition unit;

[0034] Use the big data platform to conduct risk prediction analysis on the collected image data;

[0035] Implement corresponding prevention and control measures based on the risk analysis results;

[0036] Verify the execution results and feed them back to the database for update.

[0037] As a preferred solution of the operation safety risk analysis method based on big data of the present invention, the steps of risk prediction analysis specifically include:

[0038] Input the acquired work scene images into a preset big data analysis platform;

[0039] Risk identification is performed based on the initial position information of operators and heavy machinery in the scene image of the work area;

[0040] Dynamically calibrate the position of the operation area in real time to determine the preset real-time position of heavy machinery in the construction operation area;

[0041] Calculate the position difference between the initial position and the real-time position of the construction operation area;

[0042] Based on the preset mechanical operation fluctuation law, determine whether the position difference conforms to the operation fluctuation law;

[0043] When the judgment result is abnormal, a risk prevention and control instruction is generated.

[0044] As a preferred solution of the operation safety risk analysis method based on big data of the present invention, the steps of executing prevention and control measures specifically include:

[0045] Acquire the working area scene image and the detection area scene image;

[0046] When the operation area is in operation, it is judged in real time whether there are workers in the detection area who violate the rules and are at risk points;

[0047] If a staff member is detected to have violated the rules, the image monitoring unit and the delay judgment unit are activated;

[0048] The image monitoring unit continues to monitor the image acquisition module to obtain the monitoring image of the operation area;

[0049] The delay judgment unit starts the timer to collect monitoring information of the monitoring image;

[0050] Determine whether the monitoring information generates a delay, and obtain a determination result including a non-delay determination result and a delay unit determination result;

[0051] Determine whether to trigger an alarm operation based on the judgment result.

[0052] Beneficial effects of the present invention: The present invention constructs a risk point information database and a preventive measure information database based on a big data platform by setting up a risk prevention module, which can preventively deal with potential risk points in the work area and issue avoidance instructions in advance, greatly reducing the possibility of accidents involving workers; at the same time, the set operation monitoring module collects scene images of the operation area in real time through the acquisition unit, and the judgment unit performs risk status analysis, thereby realizing all-round monitoring and protection of the operation area, avoiding accidental injuries to workers, and effectively ensuring the safety of construction workers; in addition, a two-way data connection between the risk prevention module and the operation monitoring module is realized through the central processing unit module, forming a closed-loop management mechanism for risk information collection, analysis, prevention and feedback, and using the storage module to record and analyze the data of risk prevention and operation monitoring, thereby realizing continuous optimization of risk management, significantly improving the management and control efficiency of operation safety risks, further reducing the risk of personal injury, and improving the overall operation safety level. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0054] Figure 1 This is a structural diagram of the operation safety risk analysis system based on big data.

[0055] Figure 2 This is a schematic diagram of the risk prevention module structure of the operation safety risk analysis system based on big data.

[0056] Figure 3 This is a schematic diagram of the structure of the operation monitoring module of the operation safety risk analysis system based on big data.

[0057] Figure 4 Schematic diagram of the acquisition unit structure of the operation safety risk analysis system based on big data.

[0058] Figure 5 This is a schematic diagram of the judgment unit structure of the operation safety risk analysis system based on big data.

[0059] Figure 6This is a schematic diagram of the overall process of the operation safety risk analysis method based on big data. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0063] Example 1

[0064] Reference Figures 1 to 5 , which is the first embodiment of the present invention, provides an operation safety risk analysis system based on big data, which includes a central processing unit module 100, a storage module 200, a risk prevention module 300, and an operation monitoring module 400.

[0065] Specifically, the central processing unit module 100, the storage module 200, the risk prevention module 300, and the operation monitoring module 400 are bidirectionally connected. The risk prevention module 300 is used to perform relevant processing on risk points that may appear in the work area, and the operation monitoring module 400 is used to perform risk monitoring on work behaviors.

[0066] It should be noted that the central processing unit module 100 can be a central processing unit CPU, or other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, field programmable gate arrays FPGA or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The central processing unit module 100 is the control center of the system, and uses various interfaces and lines to connect various parts of the electronic equipment in the entire system. The central processing unit module 100 can be connected to the communication unit contained in the device where the system is located, and can be connected to the big data analysis platform for communication. The big data analysis platform can parse the risk point information sent back and write it into the risk point of the device. In this way, a two-way communication can be formed. The risks generated during the actual operation process can be supplemented into the big data analysis platform, and then enter the risk point information library. After being analyzed by the big data analysis platform, the corresponding preventive measures can be configured and written into the corresponding equipment, which can be improved in time to achieve better safety effects.

[0067] Further, the risk prevention module 300 includes a risk point unit 301, a preventive measure unit 302, a configuration unit 303, a generation unit 304, and an indication unit 305;

[0068] The risk point unit 301 is used to create a risk point information database based on the big data platform, and the risk point information database stores all operation risk point information related to power grid maintenance;

[0069] The preventive measures unit 302 is used to establish a preventive measures information base based on the big data platform, and the preventive measures information base stores preventive measures for all risk points;

[0070] The configuration unit 303 is used to match the risk point information with the preventive control measures;

[0071] The generating unit 304 is used to prompt the risk point information of the operating equipment during the simulation operation and generate the risk point preventive control measure command;

[0072] The instruction unit 305 is used to issue risk point preventive measures to the staff to avoid risk points during the operation process, and can also provide prevention and control suggestions to the staff who are at the risk points.

[0073] Furthermore, the operation monitoring module 400 includes an acquisition unit 401, a judgment unit 402, a detection unit 403, an alarm unit 404 and an identification unit 405;

[0074] The acquisition unit 401 is used to acquire the scene image of the staff working area and the scene image of the detection area;

[0075] The judgment unit 402 is used to judge whether the operation area is in operation state according to the initial position and real-time position of the operator and the heavy machinery in the scene image of the operation area through the preset big data platform;

[0076] The detection unit 403 is used to determine whether there is a worker violating the rules and being at a risk point in the detection area according to the scene image of the detection area when the operation area is in the operation state;

[0077] The alarm unit 404 is used to generate an alarm when a worker violates a rule and is at a risk point.

[0078] Furthermore, the storage module 200 records the data interaction process between the risk prevention module 300 and the operation monitoring module 400 in real time. After the central processing unit module 100 analyzes the recorded data, the analysis results are fed back to the risk prevention module 300 for optimizing preventive measures and to the operation monitoring module 400 for adjusting monitoring parameters.

[0079] Further, the acquisition unit 401 includes an acquisition subunit 401a, a region determination subunit 401b, a region image subunit 401c, and a region detection subunit 401d;

[0080] The acquisition subunit 401a acquires the construction image of the construction area based on the preset panoramic camera equipment;

[0081] The area determination subunit 401b performs image recognition based on the construction image to determine the detection target image;

[0082] The area image subunit 401c is used to determine the scene image of the operation area according to the target image of the construction operation area;

[0083] The area detection subunit 401d is used to determine the scene image of the detection area according to the target image of the detection area.

[0084] Preferably, the alarm unit 404 is used to alarm when there are violations by workers and they are at risk points. First, the construction image of the construction area is acquired through a preset panoramic camera device, and then image recognition is performed through the construction image to determine the detection target image, wherein the detection target includes the working area and the detection area. The entire acquisition module is an image acquisition and acquisition of the working area of ​​the construction area. Through dynamic acquisition of the construction area, the construction area is monitored in real time to provide the original monitoring data for the alarm module.

[0085] Further, the judgment unit 402 includes a judgment subunit 402a, an initial position subunit 402b, a real-time position subunit 402c and a difference calculation subunit 402d;

[0086] The judgment subunit 402a is used to obtain a scene image of the working area and identify the construction working area in the scene image of the working area;

[0087] The initial position subunit 402b is used to obtain the preset initial position of the heavy machinery in the construction operation area based on the big data platform analysis system preset in the big data platform;

[0088] The real-time position subunit 402c is used to dynamically calibrate the position of the construction operation area in real time based on the intelligent positioning system preset in the big data platform, and determine the preset real-time position of the heavy machinery in the construction operation area;

[0089] The difference calculation subunit 402d is used to calculate the position difference between the initial position and the real-time position of the construction operation area, and based on the preset operation fluctuation law of the heavy machinery, determine whether the position difference conforms to the operation fluctuation law.

[0090] Preferably, the operating fluctuation law is the regular fluctuation of the position difference formed on the image due to the regular work done by heavy machinery. The specific principle is: first obtain the operating area scene image, and identify the operating area in the operating area scene image, and locate the operating area through the position range unit based on the intelligent positioning system preset in the big data platform intelligent device, and determine the position range. Since the area of ​​interest is not a coordinate that can be calibrated, it is necessary to calibrate the position and determine an area. The most convenient area is generally a rectangle, but different areas can be selected for different working conditions, and areas of different shapes are determined. Then, the position area unit is used to calibrate the operating area according to the position range to determine the position area of ​​the operating area, based on the intelligent positioning system preset in the big data platform intelligent device. The system locates the position of the working area and determines the position area range of the working area; wherein the position area range represents the positioning coordinate interval of the working area; determining the area of ​​the position area of ​​the working area can help to focus on monitoring the area within the area during monitoring, thereby saving detection costs, and finally determining the initial position of the working area according to the position area through the initial position unit. Because the working area is generally the area where the machinery works and the position will change, when checking the relative position of the working area, it is necessary to determine the relatively unchanged initial position, and to judge whether the pixels within the area of ​​the first area have changed regularly, so as to determine whether the machinery is in operation.

[0091] Furthermore, the acquisition unit 401 is connected to an image monitoring unit 406 and a delay determination unit 407;

[0092] The image monitoring unit 406 is used to continue monitoring the image acquisition module and acquire the monitoring image of the operating area when the operating area is in an intermittent period;

[0093] The delay determination unit 407 is used to start a timer, collect monitoring information of the monitoring image, determine whether the monitoring information is delayed, and obtain a determination result, wherein the determination result includes a non-delay determination result and a delay determination result.

[0094] In summary, the present invention constructs a risk point information database and a preventive measure information database based on a big data platform by setting up a risk prevention module, which can preventively deal with potential risk points in the work area and issue avoidance instructions in advance, greatly reducing the possibility of accidents involving workers; at the same time, the set operation monitoring module collects scene images of the operation area in real time through the acquisition unit, and the judgment unit performs risk status analysis, thereby realizing all-round monitoring and protection of the operation area, avoiding accidental injuries to workers, and effectively ensuring the safety of construction workers; in addition, a two-way data connection between the risk prevention module and the operation monitoring module is realized through the central processing unit module, forming a closed-loop management mechanism for risk information collection, analysis, prevention and feedback, and using the storage module to record and analyze the data of risk prevention and operation monitoring, thereby realizing continuous optimization of risk management, significantly improving the management and control efficiency of operation safety risks, further reducing the risk of personal injury, and improving the overall operation safety level.

[0095] Example 2

[0096] Reference Figures 1 to 6 , which is the second embodiment of the present invention, and which provides a method for analyzing operation safety risks based on big data, comprising:

[0097] S1: Collecting the image data of the working scene through the acquisition unit.

[0098] Furthermore, collecting the work scene image data includes acquiring the construction image of the construction area based on a preset panoramic camera device through an acquisition subunit, determining the detection target image through image recognition through an area determination subunit, and determining the work area scene image using the area image subunit and the area detection subunit to determine the detection area scene image.

[0099] It should be noted that the collection of the work scene image data is completed by the acquisition unit of the work monitoring module 400. The acquisition unit collects dynamic data of the construction area through a preset panoramic camera device to achieve real-time monitoring of the construction area and provide original monitoring data for subsequent risk analysis. These data include key information such as worker activity data, equipment operation data, and environmental status data.

[0100] S2: Use the big data platform to conduct risk prediction analysis on the collected image data.

[0101] Furthermore, the risk prediction analysis includes using a judgment unit to obtain a scene image of the working area and identify the working area, obtaining the initial position and real-time position of the heavy machinery through an initial position subunit and a real-time position subunit respectively, and using a difference calculation subunit to calculate the position difference and determine whether it conforms to the operating fluctuation law.

[0102] It should be noted that the risk prediction analysis is completed through the risk prevention module 300, which first establishes a risk point information database through the risk point unit 301, and then establishes a preventive control measure information library through the preventive control measure unit 302. The configuration unit 303 matches the risk point information and the preventive control measures, and the generation unit 304 generates a risk preventive control measure command based on the analysis results.

[0103] S3: Implement corresponding prevention and control measures based on the risk analysis results.

[0104] Furthermore, the execution of prevention and control measures includes sending risk warning information to staff through the indication unit 305, starting the image monitoring unit for continuous monitoring when a violation is detected, judging whether the monitoring information is delayed through the delay judgment unit, and deciding whether to trigger an alarm operation based on the judgment result.

[0105] It should be noted that the system coordinates the work of each functional unit through the central processor module 100 to ensure the timely implementation of risk prevention and control measures. When a potential risk is found, the system will immediately notify relevant personnel through the early warning mechanism and initiate the emergency response process to minimize the possibility of safety accidents.

[0106] S4: Verify the execution results and feed them back to the database for update.

[0107] Furthermore, the verification and feedback process includes recording the process data of risk prevention and operation monitoring through the storage module 200, analyzing the recorded data by the central processing unit module 100, and feeding back the analysis results to the risk prevention module and the operation monitoring module respectively, so as to achieve continuous optimization of the system.

[0108] It should be noted that the system establishes a complete data closed loop, and timely feeds back the verification information of the execution results to the big data platform, continuously updating and improving the risk point information database and the preventive measures information database. This dynamic update mechanism ensures that the system can continuously learn and optimize, and improve the accuracy and efficiency of risk prevention and control. Through the complete closed loop of risk information collection, analysis, prevention and feedback, intelligent management of operational safety risks is achieved.

[0109] Example 3

[0110] One embodiment of the present invention provides a method for analyzing operational safety risks based on big data. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0111] In order to verify the actual effect of the system of the present invention in the maintenance of power equipment, a maintenance work site of a power company in a province was selected as a test scene. The experiment selected four different substation operation areas (A, B, C, and D areas), and each area deployed the operation safety risk analysis system of the present invention and the traditional safety monitoring system for a three-month comparative test.

[0112] The test area is equipped with 4K high-definition panoramic cameras (model DH-IPC-PF83443-A180), covering an area of ​​about 2,000 square meters. After the system is deployed, the risk point unit is used to analyze historical operation data and establish a risk point information database containing 15 types of high-risk scenarios, 12 types of equipment operation risks, and 8 types of environmental risks. The preventive measures unit simultaneously established a targeted library of 35 standard preventive measures.

[0113] In actual operation, the acquisition unit collects 12 frames of image data per second through panoramic cameras, and the area determination subunit performs real-time image segmentation and target recognition to accurately locate the spatial location information of operators and equipment. The judgment unit analyzes the operation behavior through a deep learning algorithm. When it is detected that the distance between the operator and the equipment is less than the safety threshold (1.5 meters) or the operation posture is in violation of regulations, the system automatically activates the risk warning mechanism.

[0114] At the same time, the system records in real time key indicator data such as the frequency of occurrence of various risk events, early warning response time, and risk avoidance success rate. The system also innovatively introduces an abnormal behavior recognition algorithm based on the law of operation fluctuations, and discovers potential illegal operations in advance by analyzing the changing laws of equipment movement trajectories.

[0115] Refer to Table 1, test data record table:

[0116]

[0117]

[0118] Through comparative experimental data analysis, it can be concluded that the operation safety risk analysis system of the present invention is significantly superior to the traditional monitoring system in multiple key indicators. First, in terms of risk identification accuracy, the average identification accuracy of the system of the present invention in the four test areas reached 97.4%, which is 15 percentage points higher than the 82.4% of the traditional system. This is mainly due to the deep learning algorithm adopted by the system and the abnormal behavior identification mechanism based on the law of operation fluctuations, which can more accurately capture potential risk behaviors. Secondly, in terms of system response time, the average response time of the system of the present invention is only 1.875 seconds, which is much better than the 8.775 seconds of the traditional system. This significant improvement is of great significance for preventing sudden safety accidents.

[0119] It is particularly noteworthy that the false alarm rate and false alarm rate of the system of the present invention are reduced to 2.65% and 4.075% respectively, while the traditional system is as high as 12.575% and 15.725% respectively, indicating that the system of the present invention has greatly improved the reliability of risk warning. The final risk prevention success rate data more directly proves the actual effect of the system. The system of the present invention achieves an average prevention success rate of 95.35%, which is nearly 20 percentage points higher than the 75.375% of the traditional system.

[0120] These data fully demonstrate that the present invention has achieved accurate identification and rapid response to operational safety risks by establishing a complete risk prevention and monitoring system, significantly improving the safety performance of power equipment maintenance operations. In particular, the improvement of the system's real-time performance and accuracy provides more reliable safety protection for operators. In addition, through the accumulation and analysis of data, the system can also continuously optimize risk prevention strategies and further improve the level of safety management.

[0121] Importantly, it should be noted that the construction and arrangement of the present application shown in a plurality of different exemplary embodiments are only exemplary. Although only a few embodiments are described in detail in this disclosure, it should be readily understood by those who refer to this disclosure that many modifications are possible (e.g., the size, scale, structure, shape and proportion of various elements, and parameter values ​​(e.g., temperature, pressure, etc.), installation arrangement, use of materials, color, directional changes, etc.) without substantially departing from the novel teachings and advantages of the subject matter described in the application. For example, the element shown as integrally formed can be composed of multiple parts or elements, the position of the element can be inverted or otherwise changed, and the nature or number or position of the discrete element can be changed or changed. Therefore, all such modifications are intended to be included in the scope of the present invention. The order or sequence of any process or method steps can be changed or reordered according to alternative embodiments. In the claims, any "device plus function" clause is intended to cover the structure of performing the function described herein, and is not only structurally equivalent but also equivalent structure. Without departing from the scope of the present invention, other replacements, modifications, changes and omissions can be made in the design, operating conditions and arrangement of the exemplary embodiments. Therefore, the invention is not limited to a specific embodiment, but extends to numerous modifications still falling within the scope of the appended claims.

[0122] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those that are not relevant to implementing the invention) may not be described.

[0123] It will be appreciated that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will be a routine task of design, fabrication, and production for those of ordinary skill having the benefit of this disclosure without undue experimentation.

[0124] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A big data-based operation safety risk analysis system, characterized by: include, A central processing unit module (100), a storage module (200), a risk prevention module (300), and an operation monitoring module (400), wherein the central processing unit module (100), the storage module (200), the operation monitoring module (400), and the risk prevention module (300) are bidirectionally connected, the risk prevention module (300) is used to perform relevant processing on risk points that may appear in a work area, and the operation monitoring module (400) is used to perform risk monitoring on work behavior.

2. The operation safety risk analysis system based on big data according to claim 1, characterized in that: The risk prevention module (300) comprises a risk point unit (301), a preventive measure unit (302), a configuration unit (303), a generation unit (304), and an indication unit (305); The risk point unit (301) is used to create a risk point information database based on a big data platform, and the risk point information database stores all operation risk point information related to power grid maintenance; The preventive measures unit (302) is used to establish a preventive measures information database based on a big data platform, wherein the preventive measures information database stores preventive measures for all risk points; The configuration unit (303) is used to match risk point information with preventive measures; The generating unit (304) is used to prompt risk point information of the operating equipment during the simulation operation process and generate risk point preventive control measures commands; The instruction unit (305) is used to issue a command to the staff to avoid the risk points in the operation process according to the risk point prevention measures, and can also provide prevention and control suggestions to the staff who are at the risk point.

3. The operation safety risk analysis system based on big data according to claim 2, characterized in that: The operation monitoring module (400) comprises an acquisition unit (401), a judgment unit (402), a detection unit (403), an alarm unit (404) and an identification unit (405); The acquisition unit (401) is used to acquire a scene image of a worker's work area and a scene image of a detection area; The judgment unit (402) is used to judge whether the operation area is in an operation state according to the initial position and real-time position of the operator and the heavy machinery in the scene image of the operation area through a preset big data platform; The detection unit (403) is used to determine whether there are any staff members violating regulations and being at risk points in the detection area according to the scene image of the detection area when the operation area is in an operation state; The alarm unit (404) is used to generate an alarm when a worker violates a rule and is at a risk point.

4. The operation safety risk analysis system based on big data according to claim 3, characterized in that: The storage module (200) records the data interaction process between the risk prevention module (300) and the operation monitoring module (400) in real time. After the central processing unit module (100) analyzes the recorded data, the analysis results are fed back to the risk prevention module (300) for optimizing preventive control measures and to the operation monitoring module (400) for adjusting monitoring parameters.

5. The operation safety risk analysis system based on big data according to claim 4, characterized in that: The acquisition unit (401) comprises an acquisition subunit (401a), a region determination subunit (401b), a region image subunit (401c), and a region detection subunit (401d); The acquisition subunit (401a) acquires a construction image of the construction area based on a preset panoramic camera device; The area determination subunit (401b) performs image recognition based on the construction image to determine the detection target image; The area image subunit (401c) is used to determine the scene image of the operation area according to the target image of the construction operation area; The area detection subunit (401d) is used to determine the scene image of the detection area according to the target image of the detection area.

6. The operation safety risk analysis system based on big data according to claim 5, characterized in that: The judgment unit (402) includes a judgment subunit (402a), an initial position subunit (402b), a real-time position subunit (402c) and a difference calculation subunit (402d); The judgment subunit (402a) is used to obtain the working area scene image and identify the construction working area in the working area scene image; The initial position subunit (402b) is used to obtain the preset initial position of the heavy machinery in the construction operation area based on the big data platform analysis system preset in the big data platform; The real-time position subunit (402c) is used to dynamically calibrate the position of the construction operation area in real time based on the intelligent positioning system preset in the big data platform, and determine the preset real-time position of the heavy machinery in the construction operation area; The difference calculation subunit (402d) is used to calculate the position difference between the initial position and the real-time position of the construction operation area, and based on the preset operation fluctuation law of the heavy machinery, determine whether the position difference conforms to the operation fluctuation law.

7. The operation safety risk analysis system based on big data according to claim 6, characterized in that: The acquisition unit (401) is connected to an image monitoring unit (406) and a delay determination unit (407); The image monitoring unit (406) is used to continue monitoring the image acquisition module and acquire the monitoring image of the operating area when the operating area is in an intermittent period; The delay judgment unit (407) is used to start the timer, collect the monitoring information of the monitoring image, judge whether the monitoring information is delayed, and obtain the judgment result, wherein the judgment result includes a non-delay judgment result and a delayed judgment result.

8. A method for analyzing operational safety risks based on big data, characterized in that: The operation safety risk analysis system based on big data as claimed in any one of claims 1 to 7; and Collecting operation scene image data through an acquisition unit; Use the big data platform to conduct risk prediction analysis on the collected image data; Implement corresponding prevention and control measures based on the risk analysis results; Verify the execution results and feed them back to the database for update.

9. The method for analyzing operational safety risks based on big data according to claim 8, characterized in that: The steps of risk prediction analysis specifically include: Input the acquired work scene images into a preset big data analysis platform; Risk identification is performed based on the initial position information of operators and heavy machinery in the scene image of the work area; Dynamically calibrate the position of the operation area in real time to determine the preset real-time position of heavy machinery in the construction operation area; Calculate the position difference between the initial position and the real-time position of the construction operation area; Based on the preset mechanical operation fluctuation law, determine whether the position difference conforms to the operation fluctuation law; When the judgment result is abnormal, a risk prevention and control instruction is generated.

10. The method for analyzing operational safety risks based on big data according to claim 9, characterized in that: The steps for implementing the prevention and control measures specifically include: Acquire the working area scene image and the detection area scene image; When the operation area is in operation, it is judged in real time whether there are workers in the detection area who violate the rules and are at risk points; If a staff member is detected to have violated the rules, the image monitoring unit and the delay judgment unit are activated; The image monitoring unit continues to monitor the image acquisition module to obtain the monitoring image of the operation area; The delay judgment unit starts the timer to collect monitoring information of the monitoring image; Determine whether the monitoring information generates a delay, and obtain a determination result including a non-delay determination result and a delay unit determination result; Determine whether to trigger an alarm operation based on the judgment result.

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