5412results about How to "Low failure rate" patented technology

The invention relates to the technical field of power systems, in particular to a substation main equipment self-checking system based on a multi-source heterogeneous data fusion technology. The self-inspection system comprises a data acquisition and preprocessing module, a self-adaptive data fusion algorithm module, an equipment fault mode dynamic identification and intelligent repair module, a multi-scale time sequence prediction and emergency plan generation module, an equipment health state evaluation and residual life prediction module, and a fault diagnosis and knowledge base updating module. A user interaction and remote monitoring management module, and an intelligent prediction and optimization scheduling module; according to the substation main equipment self-checking system based on the multi-source heterogeneous data fusion technology, comprehensive monitoring and intelligent management of substation equipment are achieved by integrating various data acquisition and processing technologies.

The invention relates to the technical field of artificial intelligence and Internet of Things, in particular to an inspection service management system based on artificial intelligence and Internet of Things, which comprises a data acquisition module, an edge calculation module, a task allocation module, a path planning module, an early warning module and a user interaction module. Wherein the data acquisition module is used for acquiring multi-dimensional data of environment and equipment in real time; the edge calculation module is used for carrying out preliminary analysis and processing on the multi-dimensional data; the task distribution module is used for generating inspection tasks according to inspection rules; the path planning module is used for generating an optimal path of the inspection equipment; the early warning module is used for generating an early warning signal when the potential risk is detected; and the user interaction module is used for displaying the state of the system through a visual interface. According to the invention, by realizing real-time data processing, dynamic task scheduling and path optimization, the response speed and efficiency of the system are improved, and meanwhile, the equipment fault prevention and decision-making capabilities of operators are enhanced.

The invention discloses a mobile energy storage power supply health state intelligent assessment method and system, and the method comprises the steps: inputting the multi-parameter monitoring data of a power supply into a battery physical model for preliminary assessment, and obtaining a first assessment value; performing feature extraction and feature selection on the obtained monitoring data and environmental data, and inputting an obtained principal component matrix into a health state evaluation model for prediction to obtain a second evaluation value; comparing the first evaluation value with the second evaluation value, and adjusting parameters of the battery physical model by using the health state evaluation model to obtain an optimized battery physical model; obtaining a third evaluation value based on the optimized battery physical model; and obtaining a final battery health state evaluation result based on the third evaluation value and the second evaluation value. By collecting various monitoring data and environmental data in real time, the health state of the battery is comprehensively known, so that the evaluation accuracy is improved; and the established physical model and the health state evaluation model are combined, so that the precision and robustness of the evaluation result are improved.

The invention provides a new energy station operation intelligent management method and system, and relates to the technical field of new energy station operation, and the method comprises the steps: collecting various data in real time through a sensor and monitoring equipment in a new energy station; transmitting the collected data to a central data center, carrying out iterative optimization processing, and determining final data classification, cleaning and compression parameter combination so as to construct a new energy station big data platform; and according to the constructed big data platform of the new energy station, constructing a digital twinborn model of the new energy station so as to reflect the operation state, the equipment health condition and the environment change of the station in real time. Through real-time data acquisition, digital twinborn model construction and simulation analysis, automatic generation and closed-loop management of an operation optimization scheme are realized, and the operation efficiency, prediction accuracy and fault handling capacity of the new energy station are effectively improved.

The invention relates to the field of transformer parameter regulation and control, in particular to a parameter optimization method and device for a coupling type transformer. The method comprises the following steps: acquiring multi-dimensional real-time working monitoring parameters and real-time working environment state parameters of the coupling type transformer; performing nonlinear dynamic analysis and global state sensing modeling on the multi-dimensional real-time working monitoring parameters so as to construct a transformer global state sensing model; performing multi-parameter combination definition on the multi-dimensional real-time working monitoring parameters, performing dynamic operation simulation based on a transformer global state sensing model, and collecting operation simulation response data of a plurality of schemes; performing multi-target dynamic balance analysis on the operation simulation response data of the plurality of schemes to obtain an optimal transformer working parameter combination; and carrying out environment dynamic parameter change analysis on the real-time working environment state parameters, and constructing a real-time environment condition distribution field. According to the invention, efficient and self-adaptive adjustment transformer parameter optimization is realized.

The invention discloses an electrical switch cabinet detection method based on the Internet of Things, and particularly relates to the technical field of electrical switch cabinets. A sensor is installed inside or outside a switch cabinet, temperature, humidity, current, voltage and gas concentration are collected, data are transmitted to an edge computing node for preprocessing by using a wireless communication technology, the preprocessed data are uploaded to a cloud platform, storage and trend analysis are performed at a cloud end, and the data are analyzed based on big data analysis and an artificial intelligence algorithm. According to the invention, the system can predict potential faults, a user can check the operation state and the prediction result of the equipment in real time through the cloud platform, and carries out the emergency shutdown or equipment scheduling operation remotely according to the early warning information, thereby effectively solving a reliability problem caused by communication interruption or data delay in an existing Internet of Things system, and improving the safety and intelligent level of the monitoring of the switch cabinet.

The invention belongs to the technical field of condensation prevention of power equipment, and discloses an intelligent induction type condensation prevention system and method for the power equipment, and the system comprises a multivariate data collection unit, an intelligent control unit and an execution unit, and achieves the comprehensive real-time monitoring of the internal and external environments of the equipment through the integration of the multivariate data collection unit; the intelligent control unit can accurately predict the condensation risk based on real-time and historical data by using a pre-trained machine learning model, and dynamically adjust the condensation prevention strategy according to the condensation risk, thereby ensuring that condensation can be effectively prevented under various complex environmental conditions; the accurate execution of the execution unit further guarantees the condensation prevention effect, the operation stability and safety of the power equipment are integrally improved, the equipment fault risk caused by condensation is reduced, the service life of the equipment is prolonged, and the unnecessary maintenance cost is reduced; the system has self-learning and self-adaptive capabilities, can continuously optimize a control strategy to adapt to different environmental conditions, and ensures an optimal anti-condensation effect.

The invention relates to an ice crystal evaporator and an ice crystal water cooling device producing from the same, belonging to the technical field of refrigeration. The ice crystal evaporator is characterized in that a liquid inlet channel is formed by an evaporator tube, a liquid inlet and a liquid outlet in a tubular casing; and the outer channel of the evaporator tube of the tubular casing is divided into more than one S-shaped channel forming a water return channel through a water inlet of a water divider and a water outlet of a water collector. The ice crystal water cooling device is characterized in that the return air inlet of the ice crystal evaporator is communicated with a compressor, a condenser, a liquid feeding valve, a dry filter, a liquid feeding electromagnetic valve anda thermostatic expansion valve in sequence, and then is communicated with the liquid inlet of the ice crystal evaporator; a cooling water inlet / outlet of the condenser is connected with a cooling tower in series; and a water inlet / outlet of the ice crystal evaporator is communicated with a water circulating pump and an ice crystal energy storage tank in series. The invention has the advantages ofhigh cooling efficiency, no icing and low cooling temperature and is suitable for production of energy storage central air-conditioning, marine fishing, seafood processing, fruit, vegetable or meat processing, and the like; the cooled medium is municipal water.

The invention discloses a data monitoring and processing method based on dam stability, and relates to the technical field of dam monitoring, and the method comprises the following steps: arranging sensors: arranging high-precision sensors, such as a strain gauge, a displacement meter and the like, at key positions according to the structural characteristics of a dam; comprehensively analyzing the structure of the dam; according to the method, the high-precision sensors are arranged at key positions of the dam, real-time acquisition of dam stability parameters including strain, displacement, pressure, temperature and the like is achieved, the real-time data acquisition mechanism remarkably improves the monitoring frequency and continuity, the real-time performance and the accuracy of data are ensured, and compared with traditional regular manual inspection, the real-time monitoring accuracy is improved. According to the invention, more accurate and continuous monitoring data can be provided, so that the stability state of the dam can be evaluated more effectively.

The invention provides a high-altitude energy storage monitoring system. The system comprises an environmental data acquisition module, an intelligent temperature control analysis module, an intelligent energy scheduling module, a health monitoring module, a self-adaptive adjustment module and a system optimization module. The invention provides an intelligent and environment-adaptive energy storage system monitoring and maintenance strategy, the temperature control strategy, the energy distribution and the maintenance plan can be dynamically adjusted according to the real-time change of the high-altitude environment, the efficient operation of the energy storage system is realized, and the service life of equipment is prolonged. A sensor technology is used for collecting multi-dimensional environment data, and an intelligent algorithm is combined to monitor the operation state of the equipment in real time, predict the health state of the equipment, optimize energy scheduling and formulate a self-adaptive maintenance strategy, so that the fault risk and the maintenance cost are reduced, and the stable operation of the system in a complex environment is ensured.

The invention relates to the technical field of agricultural machinery automation, in particular to an intelligent seeding machinery automation control system which comprises a soil variability analysis module, a dynamic seeding mode adjustment module, a soil structure adjustment module, a seeding control and optimization module, an intelligent fault diagnosis module and a visualization module. Soil data are collected in real time through a soil variability analysis module, spatial variability and seeding suitability of soil are analyzed by utilizing a machine learning algorithm, and a dynamic seeding mode adjustment module automatically adjusts the seeding depth, the seeding speed, the seed spacing and the feeding amount according to an analysis result. The soil structure adjusting module can intelligently adjust the structure according to the compaction or loosening condition of soil, the intelligent fault diagnosis module monitors the operation state of the seeder in real time and diagnoses and repairs faults, and the visualization module provides a remote monitoring interface. According to the invention, the seeding process can be accurately controlled according to different soil conditions, the seeding uniformity and the crop germination rate are improved, and the efficiency and precision of agricultural production are remarkably improved.

The invention relates to the technical field of monitoring or controlling a charging station, in particular to a charging pile safety monitoring method and system, and the method comprises the following steps: step 1, monitoring: step 1.1, electric leakage monitoring: installing a high-sensitivity electric leakage sensor in a charging pile and a charging gun line, continuously monitoring the electric leakage current in real time, and monitoring the electric leakage current in real time; the monitoring frequency is that data are collected once every 10 milliseconds; step 1.2, overload and short circuit monitoring: real-time current change of a charging circuit is monitored by using a current transformer, and the acquisition frequency is once every 5 milliseconds; step 1.3, monitoring the temperature; according to the scheme, through monitoring of electric leakage, overload and short circuit, temperature, smog and flame, charging gun safety, vision and the like in combination with intelligent analysis, the operation state and the surrounding environment condition of the charging pile can be comprehensively, timely and accurately mastered, electrical faults, temperature anomalies and potential safety hazards can be rapidly detected and judged, and the safety and reliability of the charging pile are improved. And all-around real-time monitoring of the potential safety hazard of the charging pile is realized.

The invention discloses intelligent production automatic wire drawing equipment which comprises a pay-off mechanism, a wire furnace, a mold furnace, a wire drawing mechanism, a take-up mechanism and a control system, and the pay-off mechanism comprises a pay-off wheel, a swing pulley, a buffer pulley and a pay-off speed measurement pulley; the pay-off wheel is driven by a pay-off servo motor to rotate and pay off The buffer pulley can vertically and linearly float and is provided with a floating detection sensor; the paying-off speed measurement pulley is provided with a first speed measurement sensor; the take-up mechanism comprises a take-up wheel and a take-up servo motor; the tension speed measurement mechanism comprises a first guide pulley and a post-drawing speed measurement pulley, the first guide pulley is connected with a second swing arm, the second swing arm is connected with a torsion control servo motor, and the control system controls the pay-off speed in real time according to the floating condition of the buffer pulley; meanwhile, the torque control servo motor is controlled to provide corresponding torque according to the current angle of the second swing arm, and the rotating speed of the take-up servo motor is controlled according to the angle change trend and the wire drawing speed of the second swing arm.

The invention discloses a transaction platform management system based on cloud service, and relates to the technical field of cloud computing, and the system comprises the following components: a user behavior analysis module which collects the browsing track, purchase record and evaluation information of a user through an e-commerce platform, collects the attribute data and sales data of a commodity, and sends the collected data to a server; the user behavior analysis module accurately extracts key features of user activeness and commodity popularity to provide powerful support for personalized recommendation, the intelligent recommendation module generates a personalized recommendation list based on real-time behavior data and interests of the user, and the intelligent customer service module realizes personalized recommendation through natural language processing and machine learning technologies. According to the system, the user problem is quickly and accurately responded, the customer service efficiency and the user satisfaction degree are remarkably improved, the predictive maintenance module identifies potential risks and abnormities in advance through real-time data analysis, an early warning and solution scheme is provided for an administrator, and the system failure rate and the maintenance cost are effectively reduced.

The invention relates to the technical field of piston ring group wear monitoring, in particular to a piston ring group wear life prediction method and system. The method comprises the following steps: collecting working state data of a piston ring group in real time, de-noising by using improved wavelet transform, processing missing data by using a KNN interpolation method, standardizing and normalizing the processed data, adopting a filtering label generation and auditing mechanism to ensure the ordered homogeneity of the data, and constructing an over-limit learning machine model to obtain a piston ring group model; a hidden layer weight and an offset vector are randomly generated to calculate a hidden layer output matrix, a whale optimization algorithm is adopted to optimize model parameters, the prediction precision is improved, then the residual life is predicted, a final prediction result is used for calculating a fault risk coefficient and comparing the fault risk coefficient with a preset threshold value, maintenance suggestions are provided, and real-time data and the prediction result are displayed. And a user is assisted to make a maintenance decision. The advanced signal processing technology and the machine learning algorithm are combined, and the maintenance efficiency of the piston ring set and the reliability of equipment operation are improved.

The invention relates to the technical field of sickbed control, in particular to a control system of an intelligent bed and a control method thereof. The method comprises the following steps: acquiring structural data of the intelligent bed; carrying out human body fitting area gravity sensor deployment on the intelligent bed structure data to obtain sickbed sensor deployment data; performing gravity sensor data acquisition on the sickbed sensor deployment data to obtain an initial gravity sensor data set; carrying out data matrix processing on the gravity sensor data set to generate a multi-dimensional pressure sensing matrix; acquiring user pain part data and bed board motor initial position data; and performing pressure distribution mode recognition on the multi-dimensional pressure sensing matrix to obtain a uniform distribution mode and a concentrated distribution mode. According to the invention, through the personalized support regulation, global attitude optimization and real-time feedback mechanism of the intelligent bed, the control accuracy and intelligence of the intelligent bed are improved.

The invention discloses a loom data monitoring method and system, computer equipment and a storage medium. The method comprises the following steps of S10, collecting operation data of a loom through a distributed sensor network; s20, optimizing the operation data of the weaving machine by utilizing edge computing equipment to obtain optimized operation data of the weaving machine; s30, the edge computing device transmits the optimized operation data of the weaving machine to a cloud server; s40, the cloud server analyzes the optimized operation data of the weaving machine based on a weaving machine operation state evaluation model to obtain an operation state evaluation result of the weaving machine; and S50, the cloud server performs predictive maintenance on the weaving machine based on the operation state evaluation result of the weaving machine. The technical problems of poor real-time performance, low maintenance efficiency and weak prediction performance of loom monitoring in the prior art can be solved.

The invention relates to the technical field of equipment management, in particular to a medical beauty equipment cloud management system and management method. The method comprises the following steps: acquiring a medical beauty equipment number and medical beauty equipment parameters; carrying out cloud platform data uploading on the medical beauty equipment parameters based on the medical beauty equipment number, and generating medical beauty equipment monitoring data; consumable information digital registration is carried out on the medical beauty equipment monitoring data, and a beauty equipment consumable digital registry is generated; the method comprises the following steps: acquiring beauty equipment consumable data through a beauty equipment consumable digital registry to obtain beauty equipment consumable sensing data; consumable loss influence analysis is carried out on the beauty equipment consumable sensing data, and a consumable state monitoring data set is generated; and performing consumable service life prediction on the consumable state monitoring data set, thereby generating consumable service life early warning data. According to the invention, through real-time monitoring, intelligent analysis and dynamic adjustment of the cloud platform, the accuracy and comprehensiveness of cloud management of the medical beauty equipment are improved.

The rotary cross-flow ceramic membrane filtering device comprises a shell, a main shaft unit arranged in the shell and a power unit for driving the main shaft unit to rotate, the power unit provides rotating power for the main shaft unit and drives the filtering disc to rotate, the main shaft unit is installed in the shell and executes a specific filtering task, and the shell provides necessary support and protection for the main shaft unit and the power unit and controls inflow and outflow of materials. According to the rotary cross-flow ceramic membrane filtering device provided by the invention, the ceramic membrane filtering disc is arranged, so that the filtering efficiency and the system stability are improved, the operation cost is reduced, and the rotary cross-flow ceramic membrane filtering device is suitable for being widely applied to various industrial fields needing efficient filtering solutions.

The invention discloses an electrical cabinet with strong anti-electromagnetic interference capability, and relates to the technical field of electrical cabinets, the electrical cabinet comprises a cabinet body and further comprises a protection device, a sealing door slidably penetrates through the right side of the cabinet body, a moving hole is formed in the front side of the sealing door, and a limiting hole is formed in the front side of the sealing door. The protection device comprises an operation hole, an operation rod, a T-shaped rod, a protection rod, an L-shaped plate, a filter, a reset spring and a protection disc, the sealing door moves to enable the limiting hole to be aligned with the T-shaped rod, and after the T-shaped rod is aligned with the limiting hole, the T-shaped rod moves in the direction away from the protection rod under the elastic force effect of the reset spring; the T-shaped rod moves in the direction away from the protection rod to make contact with the inner wall of the limiting hole and restore limiting of the sealing door, the sealing performance of the cabinet body is kept through the L-shaped plate, the influence of external electromagnetic interference can be prevented, the equipment failure rate is reduced, and therefore the usability of the whole system is improved.

The invention relates to the technical field of artificial intelligence, and particularly provides an equipment predictive maintenance monitoring method for mine safety and electronic equipment. The method comprises the following steps: expanding marked operation data to obtain an expanded data set; training a feature extraction model through the expanded data set to obtain a trained feature extraction model and data after feature extraction; inputting the data after feature extraction into a feature dimensionality reduction model, and training the feature dimensionality reduction model to obtain the trained feature dimensionality reduction model and the data after feature dimensionality reduction; training a classifier model by using the data after feature dimension reduction to obtain a trained classifier model; the new sample is processed through the trained feature extraction model, the feature dimension reduction model and the classifier model to predict the operation of the mining equipment, and the method effectively performs predictive maintenance on the mining equipment, reduces the equipment failure rate and guarantees the safe operation of the equipment.

The invention discloses a fire-proof gas concentration detection device for a coal mine goaf, and relates to the technical field of environment detection, the fire-proof gas concentration detection device comprises a cylinder, the side wall of which is provided with an air inlet for introducing to-be-detected air; the air outlet cylinder is connected to the upper end of the cylinder body, fan blades are rotationally arranged in the air outlet cylinder, and rotation of the fan blades drives air to enter from the air inlet and flow through the whole device; the funnel-shaped dust collection cylinder is connected to the lower end of the cylinder body, and a plurality of separation blades are distributed on the inner circumference of the funnel-shaped dust collection cylinder and used for preliminarily filtering and blocking dust impurities in the air, so that the purified air enters the cylinder body to be detected; the dust collection box is connected to the bottom of the dust collection barrel; the plurality of sensor cylinders are uniformly distributed on the inner circumference of the cylinder body, a connecting seat is arranged in each sensor cylinder, and a gas semiconductor sensor is mounted on each connecting seat; the device can improve the precision of gas concentration detection, flexibly adapt to different working conditions and realize intelligent cleaning and maintenance.

The invention relates to the technical field of production of rotary installation filters, in particular to an intelligent control method for a production process of a rotary installation oil fuel filter, which comprises the following steps of: data acquisition: collecting a large amount of data during operation of an intelligent production line, including sensor readings, actuator states and control signals for establishing a system model and performing subsequent fault diagnosis; the advanced sensing technology and the PID control algorithm technology are introduced, fine management and collaborative optimization of a production line are achieved through a self-adaptive control algorithm, real-time monitoring of key parameters in the production process is achieved, machining parameters are automatically adjusted and corrected through the PID control algorithm, precision control over all key machining procedures is guaranteed, and the production efficiency is improved. The production process is controlled according to the real-time production state and parameter analysis, integrated production is achieved, the purposes of optimizing the production process, improving the product quality and improving the production efficiency are achieved, and meanwhile the functions of equipment fault detection and preventive maintenance are achieved.

The invention discloses a high-efficiency multiphase flow classification and separation integrated treatment method for argillaceous sandstone uranium ore. The crushed argillaceous sandstone uranium ore mixed material is sequentially subjected to hydraulic polishing and cluster breaking treatment, high-frequency linear vibration grading treatment, first-stage multiphase flow rotational flow grading treatment, deep low-frequency efficient ultrasonic scrubbing treatment, second-stage multiphase flow rotational flow grading treatment, jet flow uniform mixing treatment, micro-current activating treatment, hardening and tempering thickening and efficient squeezing solid-liquid separation treatment. According to the invention, mud and sand deep stripping and material fine grading separation of the crushed argillaceous sandstone uranium ore are realized, meanwhile, the fine-particle argillaceous material is subjected to efficient uniform mixing activation and deep efficient dehydration, and four core materials, namely a coarse sand material, a fine sand material, tailing dry slag and a high-concentration uranium ore leaching solution, are finally formed through continuous work; finally, efficient multi-phase flow fractional separation, concentration and purification treatment of the argillaceous sandstone uranium ore is achieved, residues of uranium elements in tailings are reduced, and the deep extraction rate of uranium ore resources is increased.

The invention belongs to the field of grid-connected converter control methods, and discloses a torque quantization-based low-frequency oscillation suppression method for a direct-current voltage synchronous inverter, which constructs a grid-connected damping torque model of the inverter, quantifies a damping torque and a synchronous torque of a system and describes oscillation characteristics of the system. When oscillation is unstable and damping or synchronous torque is a negative value, the provided compensation torque stabilizer is introduced through angular frequency deviation, a lag phase of the system at the oscillation frequency is compensated by using a lead-lag corrector, and then the compensation torque stabilizer is fed back to an active power reference value. By designing the compensation torque, it is ensured that the synchronous torque and the damping torque of the system are positive values all the time, and oscillation instability is effectively restrained. The method is suitable for a small-disturbance working condition, the steady-state characteristic of inverter operation is not changed, the grid-connected stability of the grid-forming type photovoltaic inverter is improved, and meanwhile scheduling optimization and energy efficiency evaluation of the system are not affected.

The invention provides a weather-proof test system for an abnormal sound vibration test. The weather-proof test system comprises an environment box and abnormal sound test equipment, the environment box comprises a base table and a box body, a temperature adjusting chamber is formed in the box body, the base table is provided with a carrying base face located on the lower side of the temperature adjusting chamber, the base table is provided with a heat insulation chamber sunken relative to the carrying base face, and the heat insulation chamber is separated from the temperature adjusting chamber through a heat insulation plate; the abnormal sound testing equipment comprises a driving mechanism, a transmission mechanism and a bearing mechanism, the driving mechanism comprises a vibration exciter, the vibration exciter is arranged in the isolation chamber, and the transmission mechanism and the bearing mechanism are arranged above the carrying base plane. Different parts of the abnormal sound testing equipment are separated by arranging the temperature adjusting chamber and the heat insulation chamber, the transmission mechanism and the bearing mechanism of the mechanical part are arranged in the temperature adjusting chamber, and the driving mechanism of the electrical part is arranged in the heat insulation chamber, so that the requirement of a weather-proof test can be met, and the working stability of the vibration exciter can be ensured.

The invention discloses a sound wave positioning device and method for a coal mine auxiliary transportation digital twin system. According to the device, two loudspeakers are installed on the side wall of a roadway of an auxiliary transportation equipment operation area at intervals; the omnidirectional microphone and the edge computing node are mounted on the auxiliary transportation equipment; the method comprises installing the sound wave positioning device; constructing an asymmetric sound field; off-line RSS acquisition is carried out; constructing a multi-dimensional RSS fingerprint vector according to the received interference intensity, and constructing a gradient fingerprint image based on the multi-dimensional RSS fingerprint vector; establishing a fingerprint database; acquiring an RSS vector Rtest of a current to-be-positioned point, converting the RSS vector Rtest into a corresponding to-be-matched gradient fingerprint image gtest, and performing fingerprint matching by using a fingerprint image matching algorithm based on a discrete Frechet distance to obtain position information; and digital twinning integration. According to the device and the method, the auxiliary transportation equipment can be quickly and accurately positioned in a complex coal mine environment, and the safety and the efficiency of mine operation can be effectively improved through real-time feedback and visual monitoring.

The invention relates to the technical field of air pollution prevention and control, in particular to a sewage deodorization system and method based on a biological filter, and the system comprises a data acquisition module, a system preprocessing module, a system deodorization effect evaluation module, a system operation performance evaluation module, a system efficiency comprehensive analysis and processing module and a system database. According to the sewage deodorization system and method based on the biological filter, the system performance and the deodorization efficiency are comprehensively analyzed, and comprehensive system energy efficiency evaluation is provided. By analyzing the two indexes, the strong and weak points of the system can be identified, so that the resource configuration is optimized, and the overall efficiency of the system is improved. The deodorization effect and the operation performance are comprehensively considered, and more comprehensive and more accurate decisions can be made. By improving the deodorization effect, adverse effects on the surrounding environment and residents can be reduced. Meanwhile, the good operation performance can help to reduce the maintenance and operation cost.

The invention relates to the technical field of glass bottle defect detection, and particularly discloses glass wine bottle body defect optical detection equipment and detection method.The optical detection equipment comprises a conveying belt, and a clamping mechanism, a lifting rotating mechanism, a detection mechanism and a sorting mechanism are sequentially arranged on the conveying belt from the feeding end to the discharging end. The detection end of the detection mechanism is attached to the bottle body of the glass wine bottle for optical detection, the receiving end of the detection mechanism receives the light in the glass wine bottle, the light directly reaches the receiving end in the bottle after passing through a bottle body material, and a signal path is relatively fixed, so that the interference of ambient light or reflection of surrounding objects on an optical signal can be effectively reduced. The bottle body of the glass bottle has a certain thickness, deviation of light scattering and refraction is easy to occur when light passes through the thickness of the glass in traditional external detection, the error can be reduced by matching of an external detection end and an internal receiving end, fine defects such as bubbles, cracks or tiny scratches are helped to be captured, and the detection stability is improved.

The invention discloses a segmented energy storage direct-hanging convergence method based on photovoltaic direct-current networking, and relates to the technical field of segmented energy storage of photovoltaic direct-current networking, and the method comprises the steps: constructing a direct-current networking system, collecting the operation data of the direct-current networking, and constructing a data set; based on the data set, dynamically adjusting the output power of a plurality of energy storage modules in real time through an adaptive power distribution algorithm, and balancing each energy storage module according to the use frequency and depth; and constructing a long-term optimization strategy of the direct-current networking system based on real-time adjustment and control of the direct-current networking system, and continuously optimizing system configuration and algorithm performance. By combining direct current networking, segmented energy storage and an artificial intelligence algorithm, the electric energy conversion efficiency is remarkably improved, the equipment loss is reduced, load variable frequency driving is realized, the system structure is simplified, and the fault rate and the equipment cost are reduced. And meanwhile, energy storage utilization is optimized, the response speed and stability of the system are improved, and access of renewable energy sources is better adapted.