49 results about "Plant safety" patented technology

The invention relates to the technical field of industrial safety, in particular to a factory safety intelligent management and control method based on a heterogeneous multi-system cross service fusion technology, and the method comprises the steps: collecting factory multi-source heterogeneous data, and carrying out the time-space alignment and fusion; constructing a risk assessment model based on a deep learning algorithm, and performing quantitative risk analysis on personnel behaviors, environmental parameters and equipment states; a dynamic grading early warning mechanism is established, and warning and automatic handling are achieved according to the risk grade; introducing risk trend prediction, and analyzing a risk evolution trend; and a safe closed-loop optimization mechanism is constructed, and self-adaptive adjustment of the management and control strategy is realized. According to the method, a full-process and dynamically-optimized intelligent safety management and control scheme is constructed, and comprehensive perception, quick response and continuous optimization of factory safety management are realized.

The invention discloses a BIM-based power plant personnel position dynamic monitoring and early warning method and system, and relates to the related field of power plant safety alarm, and the method comprises the steps: constructing the approaching monitoring demands of unauthorized personnel and the entering safety monitoring demands of authorized personnel corresponding to a plurality of risk regions for a target power plant, and carrying out the BIM modeling marking, constructing a power plant BIM model; zero-trust personnel monitoring is carried out through personnel positioning equipment, and first temporary risk personnel corresponding to the first risk area are determined in combination with approaching monitoring requirements; verifying whether authorization exists or not; if the verification is passed, carrying out risk protection behavior detection; if the verification is not passed, reading the to-be-executed task, and connecting a historical personnel behavior monitoring log library to carry out abnormal detection on a personnel track; and performing abnormity early warning according to the first risk protection abnormal value or the second risk protection abnormal value. The technical problem that existing position monitoring is insufficient in monitoring and early warning capacity is solved, and the technical effect of improving the monitoring and early warning capacity is achieved.

The invention relates to the technical field of data early warning, in particular to an intelligent power plant safety early warning method based on multi-data coupling. The method comprises the following steps: acquiring multi-source coupling data including power plant production operation data, key equipment state monitoring data, environment monitoring data and historical accident records, and calculating acquisition time intervals and synchronous delays between data sources; defining the ratio of the acquisition time interval to the topological distance between the corresponding nodes of the key equipment as a coupling propagation rate; constructing a time sequence-topology association window based on the coupling propagation rate, and judging the state type of the key equipment according to the time sequence-topology association window; a key equipment risk parameter is obtained by quantifying the state type; according to the method, key equipment risk parameter quantification and potential risk marking are realized through multi-source coupling data and time sequence-topology correlation analysis, so that dynamic grading early warning is supported, and the operation safety and early warning real-time performance of a power plant are improved.

The invention discloses a thermal power plant safety operation early warning method and system, and relates to the technical field of power plant safety early warning. The method comprises the following steps: acquiring operation parameters from a DCS (Distributed Control System) configured by a target thermal power plant, and carrying out data preprocessing; training a multivariable Gaussian distribution model as a normal operation reference; the technical key points are as follows: the whole early warning scheme has the characteristics of self-adaption, learnability and high robustness, the safety monitoring level of the thermal power plant under complex working conditions is remarkably improved, the problem of high misjudgment rate of a fixed threshold value is solved by setting a dynamic threshold value, the early warning accuracy is improved, and the safety of the thermal power plant is improved. The first correction mechanism can solve the problem that a traditional scheme lacks self-adaptive ability, so that the current state is better matched, multi-level sensitive mode division can accurately respond to different risks, the second correction mechanism solves the problem that the traditional scheme lacks learning feedback, automatic threshold optimization is achieved, and the method better meets actual requirements.

The invention provides a multi-mode electronic fence forbidden zone intrusion identification method and system based on AI. The method comprises the following steps: firstly, acquiring synchronous multi-modal data acquired by a heterogeneous sensor network; secondly, according to the real-time production activity state, the type attribute and the real-time vehicle scheduling information, a differential perception focusing strategy is generated; performing weighted focusing fusion on the synchronous multi-modal data according to a differential sensing focusing strategy to generate dynamic intrusion sensing features; and then performing pattern analysis through an artificial intelligence model based on the dynamic intrusion sensing features, and finally identifying an intrusion behavior which accords with a preset factory safety regulation definition. According to the technical scheme provided by the invention, the identification precision of real threats in a complex industrial environment and the overall adaptability of the system are improved, and high-precision and self-adaptive intelligent identification of forbidden zone intrusion behaviors in the complex industrial environment is also realized.

The utility model relates to the technical field of thermal power plant safety protection, in particular to a safety protection device for a thermal power plant. The protective fence comprises a mounting base and a protective fence body. The number of the mounting seats is two, and the two mounting seats are arranged in parallel; the protective fence is installed between the two installation bases in a sliding mode. And a driving assembly for driving the protective fence to move along the mounting seat is arranged on the mounting seat. By arranging the movable protective fence and the guide rail structure, the protective fence can be conveniently driven to linearly move along the guide rail under the action of the driving assembly, the distance between internal equipment and the protective fence can be further conveniently adjusted, a worker can conveniently overhaul and maintain the equipment through the space, and the working efficiency is improved.

The invention discloses a power plant safety intelligent early warning method based on a dynamic electronic fence, and the method comprises the steps: firstly analyzing the historical work ticket data of a power plant through a BERT-BiLSTM-CRF hybrid model, and recognizing an equipment entity, an operation type and a risk level; in combination with space coordinates of a power plant equipment three-dimensional model, an initial virtual safety boundary is automatically divided according to work ticket operation content, operation personnel operation actions, multi-person position relations and equipment states are fused in real time, and a buffer distance is dynamically adjusted to form a dynamic electronic fence; based on a dynamic electronic fence and high-precision positioning data, a group operation anti-collision model and a safe path planning strategy are established, a graded early warning mechanism is constructed, and corresponding grade early warning is triggered according to the real-time distance between an operator and the electronic fence through linkage of an intelligent wearable device and a power plant monitoring system. According to the invention, the intelligence and refinement level of power plant safety management can be significantly improved, and the occurrence rate of operation safety accidents is effectively reduced.

The invention discloses an intelligent factory personnel positioning and safety early warning method, which comprises the following steps of: accurately identifying a risk source, dividing direct and indirect risk induction areas, and marking a position and an influence range by a three-dimensional map to plan a personnel route, so that a high-risk area is effectively avoided, path planning is optimized to enable personnel to be far away from potential danger, and an exposure risk is reduced; a risk chain model is constructed to quantify a risk transmission path and an influence weight, a path deviation value is calculated in combination with real-time positioning, and a risk value can be dynamically and accurately calculated based on the constructed risk assessment model, so that the risk assessment accuracy is improved; when the risk value exceeds the threshold value, early warning is performed in time, the accident response time is shortened, the accident occurrence probability is greatly reduced, personnel and equipment safety is comprehensively guaranteed, and a firm defense line is built for factory safety production.

The invention relates to a ship production workshop safety monitoring device and method and computer equipment. According to the application, the video stream, the sound data and the environmental parameters are acquired through the multi-mode monitoring terminal, and the procedure exclusive behavior recognition module performs special illegal behavior recognition, so that precise monitoring of procedures such as welding, coating, hoisting and the like is realized, and the recognition accuracy of the special illegal behaviors is improved. When a suspected illegal behavior is detected, the system automatically triggers the authority face recognition camera to carry out secondary verification of the identity and the operation authority of the person, so that closed-loop control of behavior recognition-identity verification-authority verification is realized, and the safety risk caused by operation of an unauthorized person is effectively avoided. The industrial-grade super computer carries out centralized processing on identification results and sends violation information to the grading early warning module, and the grading early warning module carries out three-stage grading early warning and corresponding processing measures according to violation risk grades, so that automation and timeliness of a response mechanism are realized, and safety management is promoted.

The utility model provides a water spraying system for fire extinguishing in a power plant, which belongs to the technical field of power plant safety and comprises a water conveying pipeline, a fire-fighting water pipeline and a fire-fighting pool. The water inlet end of the fire-fighting water pipeline is communicated with a fire-fighting pool arranged underground through an underground pipeline; the water inlet end of the water conveying pipeline is communicated with a fire-fighting water pipeline, the fire-fighting water pipeline is used for providing fire extinguishing water for the water conveying pipeline, and the water outlet end of the water conveying pipeline is communicated with a spray head. According to the utility model, the fire extinguishing water delivery pipeline is communicated with the fire-fighting water pipeline, and the fire-fighting water pipeline is used for taking water from the underground fire-fighting water pool, so that the problem that the water delivery header pipe is frozen due to lower outdoor temperature is effectively avoided, and the fire extinguishing system can quickly respond and effectively deliver water even in a low-temperature environment; therefore, a fire source is extinguished in time, and economic loss and casualties possibly caused by the fire are greatly reduced.

Strengthen safety management for workers. [Solution] A mobile body equipped with an imaging device, a control device, and a notification device, wherein the control device includes a movement control unit that controls the body to move within the factory based on a patrol route for patrolling the work area within the factory, a detection unit that processes motion image data captured by the imaging device after the start of the patrol to detect events that increase the risk of occupational accidents for workers, and a notification instruction unit that, when an event that increases the risk of occupational accidents for workers is detected, provides notification via the notification device to encourage the reduction of the risk of occupational accidents.

The utility model relates to the technical field of power plant safety protection, disclose a kind of power plant production safety management protection device, including base, the bottom four corners of base are uniformly provided with universal wheel, the top of base is provided with foldable fence, the front side of foldable fence is installed with warning sign, the left and right sides of foldable fence are provided with main vertical rod, and the inside of main vertical rod is provided with extension assembly, and the extension assembly includes control rod, the outside of control rod is equipped with pressurized spring, and the middle part of control rod is fixedly connected with limit plate. In the utility model, through foldable fence, and install warning sign at front end, cooperate with the infrared sensor of base front side and the loudspeaker in control box, can effectively prompt the pedestrian close to construction area. Meanwhile, extension assembly is arranged in the main vertical rod of fence two sides, can flexibly adjust protection range according to need, ensure the safety and isolation effect of work area.

The application discloses a power plant safety intelligent monitoring device based on the Internet of Things, which comprises annular guide vane shaft connecting plates and wheel shaft connecting plates, the guide vane shaft connecting plates can be connected to each guide vane shaft of an impeller machine, the wheel shaft connecting plates can be sleeved in the wheel shaft of the impeller machine, and three inclined deformation sensors are uniformly distributed between the guide vane shaft connecting plates and the wheel shaft connecting plates in a circumferential direction. Compared with the prior art, the application can meet the monitoring requirement, reduce the manufacturing and maintenance costs of the device, and provides an efficient and economical safety monitoring solution for a power plant.

The invention discloses a power plant safety monitoring system based on STC space-time coding. The power plant safety monitoring system comprises a sensor data acquisition module, an STC space-time coding module, a data analysis module, an alarm generation module, a user interface module, a communication module, a storage module and a control module. By introducing the STC space-time coding technology, the performance of the power plant safety monitoring system is remarkably improved, the system can efficiently code and process space-time data, reduce misinformation and missed information and achieve more accurate threat detection and real-time response, meanwhile, data storage and transmission are optimized, the system load is reduced, the overall reliability and efficiency are improved, and the power plant safety monitoring system is suitable for popularization and application. And the system can be easily integrated into the existing infrastructure, so that the safety management level of the power plant is enhanced, and self-adaptive adjustment is supported to adapt to different operation conditions.

This invention discloses a safe hoisting system for factory buildings, belonging to the field of construction technology. The system includes baffles at both ends of a crossbeam. The crane comprises a body with a recessed groove on its top surface. Drive wheels are rotatably mounted on both sides of the groove. Drive protrusions are located on the support surfaces on both sides of the crossbeam, positioned along the movement lines of the drive wheels. When the crane moves to the end of the crossbeam, the drive wheels contact the drive protrusions. Slots are opened at both ends of the body, each containing an airbag. Air cylinders are fixedly connected to the ends of the airbags. A rotating wheel is coaxially mounted at the ends of the two air cylinders at both ends of the body. Threaded posts are coaxially mounted on both sides of the rotating wheel, with threaded sealing plates connected to the posts. The sealing plates are movable within the air cylinders. The drive wheels drive the rotating wheels to rotate. The airbag-type buffer ensures a soft impact and minimal rebound distance at the edge of the crossbeam. Furthermore, with the presence of an anti-collision structure, the crane's overall structure can be retracted, improving its tightness.

This invention discloses an artificial intelligence-based safety monitoring method and system for thermal power plants, comprising: firstly, responding to a thermal power plant safety monitoring start command, acquiring current boiler parameters at preset time intervals; then, calling a pre-trained boiler status assessment model to determine whether the current boiler parameters are in a boiler warning state; and finally, when the current boiler parameters are in a boiler warning state, acquiring and executing a target warning resolution strategy for the current boiler parameters. This design, compared to the prior art's process of reporting first and then processing, can improve the efficiency of safety monitoring and processing for thermal power plants.

The invention provides a multi-source target inspection method for inter-plant safety. According to the method, an unmanned aerial vehicle carries high-definition camera shooting to carry out target identification, and the target is transmitted to a ground station cloud platform to carry out component defect diagnosis, so that the problems of high labor intensity, low efficiency and easiness in being influenced by the environment in a traditional inspection mode are solved, and the problems of error detection and missing detection caused by inspection personnel through pictures on a remote controller are solved; and unmanned aerial vehicle inspection can enter a high-risk area, so that personnel are prevented from directly contacting a dangerous environment, and the working safety is remarkably improved.

The invention relates to a deep learning-based factory safety device identification method. The method comprises the following steps of S1, acquiring a to-be-identified image in a factory environment through image acquisition equipment; s2, inputting the to-be-recognized image into a pre-trained target detection model; wherein the target detection model is a neural network model which is based on a YOLOv8 architecture and introduces an attention optimization mechanism, and is used for identifying the category and the position of a safety device in the image; and S3, receiving and outputting a safety device identification result returned by the target detection model. The method has the advantages that the safety equipment can be detected and recognized in real time in a factory through a deep learning algorithm and in combination with a factory management department, it is ensured that a worker wears necessary safety equipment and whether the equipment is correctly placed or not, potential safety hazards are reduced, and the overall safety level of the factory is improved.

The present invention provides a primary air volume coupling and adjustment system for mixed coal volatile matter and ignition distance, relating to the field of energy conservation and environmental protection technology for power plants. The system uses a relational database interface to read the blended coal types and ratios established by the coal blending system, as well as the corresponding coal volatile matter, moisture, and ash parameters for the coal types in the coal yard, to calculate the pulverizer mixed coal fuel ratio. It also uses a real-time system database interface to read primary air volume and coal feed rate data from the SIS system and calculate the pulverizer mixed coal air-coal ratio. Based on experimental data, a fuzzy system is constructed to establish the coupling relationship between ignition distance, mixed coal volatile matter, and primary air volume. Using sliding window module technology, the system calculates the offset of the primary air volume set value in real time using a real-time weighted average, real-time primary air volume set value, and real-time ignition distance data. The system uses an external plug-in mode to interact with the original pulverizer control system, ensuring power plant safety.

The invention relates to the technical field of plant safety, and discloses a plant micro-vibration prevention intelligent monitoring and control method and system, and the method comprises the steps: S1, collecting the dynamic information of a plant in real time; s2, according to the collected dynamic information of the factory building, obtaining an amplitude characteristic value of the factory building; s3, establishing an amplitude prediction model according to the amplitude characteristic value; s4, predicting the vibration of the factory building through the amplitude prediction model to obtain an amplitude prediction value; s5, according to the amplitude predicted value and a standard vibration threshold value, determining the acceleration level of the factory building amplitude; s6, according to the dynamic information peak value of the factory building, determining the displacement grade of the factory building amplitude; s7, determining the state of the factory building according to the acceleration level and the displacement level; s8, determining to adopt an active control strategy or a passive control strategy according to the plant state; and S9, controlling the amplitude of the plant by adopting an active control strategy or a passive control strategy. The vibration level of the factory building can be effectively controlled.

The invention belongs to the technical field of power plant safety monitoring, and discloses a power plant safety monitoring system based on artificial intelligence. Comprising the following modules: a data acquisition module used for acquiring environment data and equipment data of all monitoring areas in a power plant in real time; according to the invention, through setting the fire occurrence remaining time prediction model and the support score, the fire prediction is promoted from simple probability evaluation to accurate time prediction, and the time interval from the current moment to the fire in the unit area is accurately predicted. The prediction of the time dimension enables the implementation of prevention and control measures to be more targeted, on this basis, a support score is established, state parameters such as the battery electric quantity, the fire extinguishing agent remaining amount, the historical reliability and the arrival time of the robot are comprehensively considered, the optimal scheduling of the fire extinguishing robot is realized, and the intelligent scheduling method improves the utilization rate of the robot and reduces the labor intensity of the robot. And meanwhile, prevention and control deployment is completed before a fire disaster occurs.

The invention relates to the technical field of wastewater treatment, and particularly discloses a wastewater treatment plant safety protection system which comprises a smoke exhaust mechanism, and a fire extinguishing mechanism is arranged on the surface of the smoke exhaust mechanism; a detection mechanism is further arranged on the front face of the smoke exhaust mechanism. Through the smoke exhaust mechanism, when the concentration of harmful gas in a plant exceeds the standard or smoke is generated in a fire disaster, a shaft rod can drive a main exhaust blade to rotate through driving of a motor, an upward air supply effect can be generated, and smoke in the plant can be sucked and exhausted into an outdoor smoke pipe through air inlet nozzles inside and outside an indoor smoke pipe; and finally, smoke is discharged outdoors through smoke exhaust holes in the smoke exhaust head, dust screen structures are installed on the air inlet part at the lower end in the indoor smoke pipe and the interior of the air inlet nozzle, and batting and dust can be prevented from entering the indoor smoke pipe to be gathered.

The invention discloses a relative position-based method applied to factory photoelectric personnel tracking, and belongs to the technical field of modern communication and geographic information. In order to solve the problems that in the prior art, an existing scheme generally has the defects that the positioning precision is limited, indoor and outdoor scenes are difficult to switch, the equipment monitoring and guiding efficiency is low, and the movement trend of personnel is lack of real-time calculation and tracking and the like, the technical scheme provided by the invention comprises the following steps of: inputting point position attributes of photoelectric equipment; the visible range of indoor photoelectricity and the maximum visible distance of outdoor photoelectricity are set; establishing front-line operator information; acquiring position information of front-line operators in a preset period; calculating the acceleration according to the position information of two consecutive times, and determining the movement direction to predict the position of the person in the next period; and guiding the corresponding personnel according to the personnel position in the next period. The method is suitable for being applied to industrial plant safety management work for carrying out real-time accurate tracking and monitoring on front-line operators.

The utility model belongs to the technical field of back flushing systems of filter presses, and particularly relates to a pressure relief device of a back flushing system of a program-controlled filter press, which comprises a third chute, a first chute is arranged on the left rear side of the upper portion of the third chute, and a second chute is arranged on the upper portion of the third chute and located on the right side of the first chute. An air outlet and a liquid outlet pipeline are arranged in the first chute, and adjusting mechanisms are arranged on the air outlet and the liquid outlet pipeline. According to the scheme, the air outlet pipeline, the liquid outlet pipeline and the connecting pipeline are connected in series, and the end openings of the air outlet pipeline and the liquid outlet pipeline are adjustably shielded by using the baffles, so that reverse blowing solution and airflow pressure entering the chute are reduced, and the problem that the solution is splashed and scattered in a plant and pollutes the factory environment is solved; the factory safety production standardization dynamic standard reaching is improved, the safety risk of follow-up operating personnel is reduced, and the workload of the operating personnel for cleaning a working site is reduced.

The utility model belongs to the technical field of monitoring and early warning, and particularly relates to an industrial factory building safety intelligent connection panoramic monitoring and early warning device which comprises an MCU microcontroller, an infrared temperature sensor, a signal processing module and an alarm module. The infrared temperature sensor is connected with the MCU microcontroller through the signal processing module; the microcontroller is connected with the alarm module; the signal processing module comprises a signal amplifying circuit and a signal filtering circuit; the signal amplification circuit comprises a signal amplification chip; the slide rheostat is connected between the-Rg pin and the + Rg pin of the signal amplification chip, so that the amplification factor of the signal amplification circuit can be changed by adjusting the resistance value of the slide rheostat, the detection sensitivity is improved, detection and amplification of small voltage are facilitated, and when a fire disaster is in the bud, the fire disaster can be prevented from being damaged. Therefore, the fire hazard can be predicted as early as possible.

The application discloses a kind of fire power plant fine treatment centralized control auxiliary early warning system of fire power plant safety protection technical field, including bottom plate, the upper surface of the bottom plate both sides is equipped with slide rail, its structure is reasonable, the auxiliary early warning component is installed on the moving platform by being equipped with moving platform and auxiliary early warning component, and moving platform is collocated motor A, so that motor A drives control gear rotation, to reach the automatic movement processing of moving platform between cabinet, by auxiliary early warning component taking camera, smoke sensor and temperature sensor, so that camera carries out routine data monitoring and inspection to the cabinet on both sides, during smoke sensor and temperature sensor detect whether temperature in cabinet is excessive and there is smoke phenomenon, if there is generation, alarm can be used to alarm and remind processing, to avoid the situation that fire power plant fine treatment centralized control equipment early warning is not timely as far as possible.

The application belongs to the technical field of unbalanced classification, and discloses an improved LightGBM unbalanced classification method for factory area safety early warning.The overall method has high performance, low cost, flexibility, easy deployment and other advantages, and is very suitable for unbalanced image classification tasks in industrial scenes.Especially in the high-demand application scene of factory safety monitoring, the model can not only ensure high classification accuracy, but also balance resource consumption, so that the method can also perform outstanding performance in the actual environment with limited computing resources.Compared with the traditional method, the application can complete efficient classification of complex data under lower computing overhead, ensure the stability and accuracy of the factory area safety early warning system, and still maintain high classification accuracy and reliability in the case of unbalanced data and many noise points.

The utility model discloses an industrial plant safety energy -conserving mixed ventilation device belongs to plant ventilation device field, including ventilation pipeline main part, the lower extreme fixed mounting of ventilation pipeline main part has rotary base, the rotary base is fixedly installed on the plant, the lower extreme fixed mounting of rotary base has the fan guard shield, the fan guard shield is fixedly installed and has the ventilation fan, the output shaft fixed mounting of ventilation fan has the ventilation vane, through the main body ventilation structure that ventilation pipeline main part, rotary base, fan guard shield, ventilation fan, ventilation vane constitute, collocation closed assembly and adjusting assembly, can accurate control ventilation start -stop, ventilation volume size and ventilation direction, and closed assembly can adjust the opening -closing angle of multiple sets of second pivot flexibly, as needed control airflow enters, and adjusting assembly can realize the steering adjustment of ventilation pipeline main part relative rotary base, can be in the natural ventilation situation according to the wind direction flow and carry out the sleeve joint.