New energy smart enterprise safety management system

Abstract

The invention relates to the technical field of new energy enterprise safety management, and discloses a new energy smart enterprise safety management system, which comprises a plurality of key modules. The data acquisition module collects data of equipment operation, environment monitoring, personnel behavior and the like of the new energy enterprise. And the data analysis and processing module analyzes and mines data by applying a technical algorithm, identifies risks and predicts a fault probability. The safety early warning module carries out early warning through sound and light, short messages and the like when the risk appears or reaches a threshold value. And the emergency response module starts plans for different security events and performs coordinated disposal. The user management module is responsible for user registration, authority distribution and the like. And the data storage module stores various types of data by using a distributed and encryption technology. The system further comprises a data cleaning sub-module and a modeling sub-module, the data cleaning sub-module cleans data, and the modeling sub-module builds a model to lay a foundation for analysis and processing. The system has the advantages of accurate monitoring and efficient early warning, intelligent emergency and efficient operation and maintenance, collaborative management and data security.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy enterprise safety management, and in particular to a new energy smart enterprise safety management system. Background Art

[0002] Under the background of global advocacy of sustainable development, the new energy industry is booming. However, new energy enterprises face many thorny safety issues in production and operation. Take wind farms as an example. Wind turbines are exposed to the outdoors all year round and are affected by strong winds, low temperatures, sand and dust and other severe climates. Wind turbine blades are subjected to huge aerodynamic loads for a long time. The surface of the blades may be worn and cracked due to wind and sand erosion. If they are not discovered and handled in time, they are very likely to break under high-speed rotation, which will not only damage the wind turbine itself, but also pose a serious threat to surrounding facilities and personnel. At the same time, key components such as gearboxes and generators inside the wind turbine are prone to faults such as excessive oil temperature and bearing wear under long-term high-load operation. The traditional manual regular inspection method is difficult to detect hidden dangers in the early stage of the fault. It is often discovered only when the fault seriously affects the power generation efficiency or even causes shutdown.

[0003] Looking at solar power stations, they are distributed over a wide area, and photovoltaic panels are easily covered by dust and bird droppings, which reduces the efficiency of photoelectric conversion. In addition, the high temperature and high humidity environment in some areas will accelerate the aging of photovoltaic modules and cause safety risks such as leakage. In terms of personnel management, the production process of new energy enterprises is complex, involving many professional operation links. If employees have weak safety awareness and inadequate training on operating specifications, it is very easy to cause safety accidents due to illegal operations during the start-up, shutdown, and maintenance of equipment, such as electric shock caused by mistaken closing of the switch, and fire caused by hot work not following the process. Traditional safety management methods rely on manual experience and simple equipment, which are difficult to meet the complex and changeable safety management needs of new energy enterprises. It is urgent to develop an intelligent and comprehensive safety management system. Summary of the invention

[0004] In order to solve the above-mentioned problems, the present invention proposes a new energy smart enterprise safety management system.

[0005] In order to solve the above technical problems, the technical solution proposed by the present invention is: a new energy smart enterprise safety management system, comprising:

[0006] The data collection module is used to collect various data during the production and operation of new energy enterprises, including but not limited to equipment operation parameters, environmental monitoring data, and personnel behavior data; the equipment operation parameters include key indicators such as temperature, pressure, and speed of the equipment; the environmental monitoring data include air quality, humidity, and noise; the personnel behavior data involve personnel entry and exit records and operational process compliance information;

[0007] A data analysis and processing module is connected to the data acquisition module, receives the collected data, and uses big data analysis technology and artificial intelligence algorithms to perform real-time analysis and in-depth mining of the data, identify potential safety risks, and predict the probability of equipment failure and safety accidents;

[0008] A safety warning module is connected to the data analysis and processing module. When the data analysis and processing module identifies a safety risk or reaches a preset warning threshold, a warning is issued in a variety of ways, including but not limited to sound and light alarms, SMS notifications, and APP push notifications;

[0009] The emergency response module formulates corresponding emergency plans for different types and levels of security incidents. When a security incident occurs, the emergency plan is automatically activated and relevant resources are coordinated for emergency disposal, including but not limited to remote control of equipment shutdown, activation of fire-fighting facilities, and dispatch of rescue personnel;

[0010] The user management module is used to manage system users, including user registration, authority allocation, and login authentication. Different users are assigned different operation permissions based on their responsibilities and work content to ensure the security of system data and the standardization of operations.

[0011] The data storage module is used to store collected data, analysis results, warning information, emergency plan data and other types of data. It adopts distributed storage technology and data encryption technology to ensure the security, integrity and traceability of the data.

[0012] Preferably, it also includes a data cleaning submodule, which cleans the collected raw data, removes noise data and abnormal data, and improves the quality and availability of the data; a data modeling submodule, which establishes an equipment operation model, a safety risk assessment model, and an accident prediction model based on the cleaned data, providing a basis for data analysis and processing.

[0013] Preferably, the warning information issued by the safety warning module includes risk type, risk level, risk occurrence location, and detailed information on possible hazards.

[0014] Preferably, during the emergency handling process, the emergency response module records the handling process and results in real time, generates an emergency handling report, and stores the report in the data storage module.

[0015] Preferably, the user management module adopts a multi-factor identity authentication method, including password, fingerprint recognition, and facial recognition, to improve the security of user login.

[0016] Preferably, the data storage module has data backup and recovery functions, backs up data regularly, and can quickly restore data when data is lost or damaged.

[0017] Compared with the prior art, the present invention has the following advantages:

[0018] Accurate monitoring and efficient early warning: With the help of high-precision sensors and real-time data collection technology in seconds, the system can obtain all-round operation and environmental data of new energy equipment. For example, real-time monitoring of key parameters of wind turbines in wind power companies and precise control of the status of photovoltaic panels in photovoltaic power stations. Through in-depth analysis of advanced algorithms, potential risks can be accurately identified, such as predicting the causes of wind turbine gear wear failure and reduced photovoltaic panel power generation efficiency. Once a risk occurs, the multi-channel early warning mechanism can quickly notify relevant personnel, greatly improving the timeliness and accuracy of risk detection.

[0019] Intelligent emergency response and efficient operation and maintenance: In the face of security incidents, the system can automatically launch targeted emergency plans. In wind power scenarios, it can remotely control the safe shutdown of wind turbines and dispatch maintenance teams; in photovoltaic power stations, it can start automatic cleaning and dehumidification equipment. This not only effectively avoids the deterioration of faults, but also greatly shortens the fault handling time, ensures the stable operation of equipment, reduces economic losses, and improves the operation and maintenance efficiency and economic benefits of new energy enterprises.

[0020] Collaborative management and data security: For comprehensive new energy enterprises, the user management module implements fine-grained authority allocation, making it easier for personnel in different positions to perform their duties. In complex situations such as extreme weather, departments can efficiently share information and collaborate through the system. At the same time, the data storage module uses advanced technology to ensure the safe storage of various types of data, providing reliable data support for enterprise security management and helping enterprises achieve all-round and intelligent security management upgrades. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of the working principle of the present invention. DETAILED DESCRIPTION

[0022] The present invention is further described in detail below with reference to the accompanying drawings.

[0023] Example 1

[0024] A large wind power company owns hundreds of wind turbines distributed in multiple wind farms along the coast. In the past, the company relied on manual inspections and simple sensors to monitor the operating status of equipment, and fault detection and processing were often delayed. After the introduction of the new energy smart enterprise safety management system, data acquisition modules were deployed on each wind turbine. These modules integrate high-precision wind speed sensors, wind direction sensors, vibration sensors, and oil temperature sensors, etc., and can collect key data such as wind speed, wind direction, blade speed, tower vibration, gearbox oil temperature, etc. in real time at a frequency of seconds, and send the data to the data analysis and processing module continuously through wireless transmission technology.

[0025] The data analysis and processing module builds a complex and accurate wind turbine fault prediction model based on big data analysis technology and machine learning algorithms. By learning from massive historical data, the model can identify the complex correlations and potential patterns between equipment operating parameters. One day, the system discovered through data analysis that the oil temperature of a wind turbine gearbox in wind farm A rose sharply in a short period of time, and the vibration amplitude of the tower also exceeded the normal range. After in-depth analysis and judgment of the model, the system predicted that the wind turbine may have a gear wear failure.

[0026] After capturing this risk signal, the safety warning module immediately sent warning information to the operation and maintenance personnel in various ways. The operation and maintenance personnel received a detailed warning text message on their mobile phones, including the specific location of the faulty fan, possible fault type and risk level. At the same time, the company's internal operation and maintenance management APP also popped up a striking warning prompt, accompanied by a rapid alarm sound.

[0027] The emergency response module quickly initiated the pre-established maintenance plan. First, it adjusted the wind turbine to a safe shutdown state through the remote control system to prevent the fault from further deteriorating. Then, the dispatch system rationally arranged an experienced maintenance team to go to the site based on the real-time location and skill level of the operation and maintenance personnel. The maintenance team carried professional testing equipment and spare parts and set off for the wind farm within 30 minutes after receiving the task. After arriving at the site, the maintenance personnel quickly disassembled and inspected the wind turbine gearbox based on the fault diagnosis information provided by the system. After careful investigation, it was confirmed that the gears were severely worn and needed to be replaced. The maintenance personnel used professional tools to efficiently complete the gear replacement work and fully debugged the wind turbine. After testing, the wind turbine resumed normal operation, successfully avoiding a major failure that could have caused long-term downtime and huge economic losses.

[0028] Example 2

[0029] On the edge of a desert in northwest my country, there is a large photovoltaic power station covering an area of ​​several square kilometers. It is dry and rainless all year round, but there are strong winds and sandstorms. The daily operation and maintenance of the photovoltaic power station faces many challenges. Before the adoption of the new energy smart enterprise safety management system, the power station mainly relied on manual regular cleaning of photovoltaic panels and simple temperature monitoring equipment, which was not accurate and timely in grasping the operating status of the power station.

[0030] After the introduction of this system, the data acquisition module played a key role. Highly sensitive temperature sensors, dust sensors and humidity sensors are installed on each row of photovoltaic panels. These sensors can monitor data such as the surface temperature of the photovoltaic panels, the degree of dust coverage and the humidity of the surrounding environment in real time. The data acquisition module transmits the collected data to the data analysis and processing module in real time.

[0031] The data analysis and processing module established a correlation model between the power generation efficiency of photovoltaic power stations and environmental factors by analyzing a large amount of historical data. For a period of time, the system analysis found that the power generation efficiency of photovoltaic panels in a certain area of ​​the power station continued to decline. Through further analysis of sensor data, it was found that the surface of the photovoltaic panels in this area had serious dust accumulation, and the humidity of the surrounding environment exceeded the normal range. The data analysis and processing module determined that dust coverage reduced the efficiency of photovoltaic panels in absorbing sunlight, while high humidity may accelerate the aging of photovoltaic components and affect their performance.

[0032] The safety warning module immediately issued a warning to the power station operation and maintenance personnel. After receiving the warning, the operation and maintenance personnel quickly notified the emergency response module. The emergency response module first started the automatic cleaning equipment, which moved along the track between the photovoltaic panels and used high-pressure airflow and rotating brushes to fully clean the surface of the photovoltaic panels. At the same time, in order to cope with the high humidity environment, the emergency response module controlled the ventilation system to increase the ventilation volume in the area and started the dehumidification equipment to reduce the ambient humidity.

[0033] During the cleaning and dehumidification process, the data acquisition module continuously monitors the data of the photovoltaic panels. After several hours of operation, the dust on the surface of the photovoltaic panels was completely removed, and the ambient humidity returned to the normal range. The data analysis and processing module shows that the power generation efficiency of the photovoltaic panels in this area gradually recovered and eventually returned to normal levels. Through the effective operation of the new energy smart enterprise safety management system, the stable power generation of the photovoltaic power station is guaranteed, and the economic benefits and safety of the power station are improved.

[0034] Example 3

[0035] A large-scale integrated new energy enterprise integrating wind power, photovoltaic and energy storage businesses, with business covering multiple regions, numerous production sites and complex operation management systems. In terms of safety management, it used to face problems such as data dispersion and difficulty in management coordination. After introducing the new energy smart enterprise safety management system, it achieved a comprehensive safety management upgrade.

[0036] The user management module assigns detailed permissions to different employees based on the company's organizational structure and job requirements. Operation and maintenance personnel are granted the authority to view and operate the real-time operation data of the equipment in their area, receive warning information, and perform basic operation and maintenance tasks; managers can view the company's overall security report, statistical analysis data, and make macro decisions.

[0037] During a rare extreme weather event, heavy rain, strong winds and lightning struck simultaneously, posing a serious threat to the company's multiple wind power and photovoltaic sites. The data acquisition module responded quickly, and the sensors at each site quickly collected data such as equipment operating status and environmental parameters, and transmitted them to the data analysis and processing module in real time.

[0038] The data analysis and processing module quickly analyzes a large amount of data and identifies safety risks at multiple sites. For example, wind turbines at some wind farms face the risk of overspeed due to strong winds, and photovoltaic panels at some photovoltaic power stations may be damaged due to heavy rain and strong winds. The safety warning module immediately issues a warning to relevant personnel, and the warning information details the location of the risk site, the type of risk, and the possible harm it may cause.

[0039] The emergency response module quickly launched the joint emergency plan. For wind farms, the wind turbine blade angle was adjusted through the remote control system to reduce the wind turbine speed so that it can maintain a safe operating state under strong winds; at the same time, the operation and maintenance personnel were arranged to go to the site to reinforce and inspect the wind turbines when safety conditions permit. For photovoltaic power stations, on the one hand, the dispatchers went to the site to check the fixing of photovoltaic panels and repair loose parts in time; on the other hand, the backup power system was started to ensure the stable operation of energy storage equipment when the power grid fluctuates.

[0040] Throughout the emergency response process, the various departments achieved efficient information sharing and collaborative work through the system. Managers were able to grasp the progress of emergency response at each site in real time and deploy resources and provide decision support based on actual conditions. After several hours of intense response, the safety crisis caused by the extreme weather was successfully resolved, ensuring the safe operation of the company. This incident fully demonstrated the powerful functions and effectiveness of the new energy smart enterprise safety management system in complex scenarios.

[0041] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A new energy smart enterprise safety management system, characterized in that: include: Data collection module, used to collect various data during the production and operation of new energy enterprises, including but not limited to equipment operation parameters, environmental monitoring data, and personnel behavior data; The equipment operation parameters include key indicators of equipment temperature, pressure, and speed; the environmental monitoring data include air quality, humidity, and noise; the personnel behavior data include personnel entry and exit records and operation process compliance information; A data analysis and processing module is connected to the data acquisition module, receives the collected data, and uses big data analysis technology and artificial intelligence algorithms to perform real-time analysis and in-depth mining of the data, identify potential safety risks, and predict the probability of equipment failure and safety accidents; A safety warning module is connected to the data analysis and processing module. When the data analysis and processing module identifies a safety risk or reaches a preset warning threshold, a warning is issued in a variety of ways, including but not limited to sound and light alarms, SMS notifications, and APP push notifications; The emergency response module formulates corresponding emergency plans for different types and levels of security incidents. When a security incident occurs, the emergency plan is automatically activated and relevant resources are coordinated for emergency disposal, including but not limited to remote control of equipment shutdown, activation of fire-fighting facilities, and dispatch of rescue personnel; User management module, used to manage system users, including user registration, authority allocation, and login authentication; Different users are assigned different operation permissions according to their responsibilities and work content to ensure the security of system data and the standardization of operations; The data storage module is used to store collected data, analysis results, warning information, emergency plan data and other types of data. It adopts distributed storage technology and data encryption technology to ensure the security, integrity and traceability of the data.

2. A new energy smart enterprise safety management system according to claim 1, characterized in that: Also includes: The data cleaning submodule cleans the collected raw data, removes noise data and abnormal data, and improves the quality and availability of the data; The data modeling submodule establishes equipment operation models, safety risk assessment models, and accident prediction models based on the cleaned data, providing a basis for data analysis and processing.

3. The new energy smart enterprise safety management system according to claim 1 is characterized by: The warning information issued by the safety warning module includes risk type, risk level, risk occurrence location, and detailed information on possible hazards.

4. The new energy smart enterprise safety management system according to claim 1 is characterized by: During the emergency handling process, the emergency response module records the handling process and results in real time, generates an emergency handling report, and stores the report in the data storage module.

5. The new energy smart enterprise safety management system according to claim 1 is characterized by: The user management module adopts a multi-factor identity authentication method, including password, fingerprint recognition, and facial recognition, to improve the security of user login.

6. The new energy smart enterprise safety management system according to claim 1 is characterized by: The data storage module has data backup and recovery functions, backs up data regularly, and can quickly restore data when data is lost or damaged.

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