584results about How to "Timely processing" patented technology

The invention provides a first-aid platform work order management method based on digital twinning and artificial intelligence, and belongs to the technical field of information technology application innovation, and the method comprises the steps: collecting historical first-aid data and real-time data of a first-aid scene, and constructing a task scheduling system based on a knowledge graph; decomposing the task into a plurality of sub-tasks according to the work order data, and allocating an optimal first-aid resource combination for the sub-tasks; an optimization strategy is set to carry out collaborative allocation control on each resource, and task allocation is optimized; the optimized task allocation is monitored in real time, and meanwhile, in combination with real-time data flow monitored in real time, it is ensured that the change of the dynamic environment is responded at any time in the task execution process; and re-inputting the optimized task allocation and resource scheduling strategy into the knowledge graph, and carrying out closed-loop optimization. According to the invention, the problems of inaccurate task scheduling, weak real-time response capability, low resource utilization rate and the like in the existing work order management system of the emergency platform are effectively overcome, and the overall efficiency and the automation level of the emergency platform are greatly improved.

The invention relates to the technical field of traffic safety management, in particular to a vehicle abnormal driving recognition and early warning method and system. The method comprises the following steps: firstly, acquiring vehicle position, speed, direction and driver operation data in real time, then correcting time sequence data by adopting a smooth denoising method of adaptive segmented clustering, generating an information confirmation code through standardization, and then carrying out weighted fusion on multi-source data to obtain a multi-source data fusion model; the strategy decision-making module is used for extracting vehicle speed change rate, lane deviation degree and driver behavior characteristic key indexes, identifying and grading abnormal driving behaviors by setting a threshold value, calculating a comprehensive abnormal score, and generating a grading intervention strategy for slight, medium and serious anomalies according to a comparison result of the abnormal score and the set threshold value. And finally, combining automatic intervention in the vehicle with global coordinated intervention of a road manager. According to the invention, the intervention strategy is generated based on multi-source data acquisition, processing, analysis and decision, so that the vehicle driving safety and the road management efficiency are improved.

The invention provides an OracIeRAC-based CDC synchronization method and system, and relates to the technical field of OracleRAC, and the method comprises the steps: deploying an incremental log analyzer, a session state collector and a resource monitor, and respectively collecting atomic operation, concurrent transaction information and node performance indexes; and identifying data stream features by using an intelligent classification model, constructing a priority matrix, and performing fine-grained acquisition on high-priority data based on an adaptive sampling strategy. According to the method, multi-dimensional evaluation indexes are further utilized, a time sequence prediction model is constructed to predict node loads, data synchronization tasks are distributed to low-load nodes based on a load balancing matrix, and a two-stage submission protocol is adopted to ensure data consistency. By monitoring the synchronization state in real time and dynamically adjusting the allocation strategy, the CDC synchronization efficiency and stability in the Oracle RAC environment are effectively improved, and the synchronization delay is reduced.

The invention discloses a method for multi-AGV and multi-task allocation, and the method comprises the steps: obtaining real-time tasks, and sorting the tasks according to dynamic priorities; according to AGV states and task information, a charging decision is considered, an upper layer model of multi-AGV and multi-task allocation is established, scheduling optimization is carried out by using an APSO-NSGA-III hybrid algorithm, and a task queue to be executed is allocated for each AGV; according to task queue information generated by the upper-layer model, conflict resolution is considered, a lower-layer model with the lowest total path cost as a target is established, and an improved A * algorithm is used for performing optimal path planning of multiple AGVs; the optimal path planning result generated by the lower-layer model is fed back to the upper-layer model, the upper-layer model forms a new task queue scheme according to the feedback and continues to influence the lower-layer model, the upper-layer model and the lower-layer model mutually feed back, and the optimal scheme of multi-AGV multi-task allocation is generated.

The invention discloses a numerical control machine tool motor operation monitoring method and system based on data analysis, which are used in the field of motor monitoring and fault diagnosis, and the method comprises the steps: collecting working condition data corresponding to the operation process of a numerical control machine tool motor; establishing a temperature change trend model, and predicting temperature change conditions of the motor of the numerical control machine tool under different working conditions; combining the extracted frequency and amplitude with the prediction result of the temperature trend model, and judging the current operation state of the numerical control machine tool motor; and setting a threshold value of the working condition data, comparing a classification analysis result with a preset threshold value, and automatically triggering a multi-stage early warning mechanism when monitoring that the working condition data of the motor of the numerical control machine tool exceeds the threshold value. According to the method, preventative maintenance is realized, sudden faults are avoided, the motor is ensured to operate under efficient and stable conditions by determining the optimal electrical working range and real-time monitoring data, energy consumption is reduced, the service life of equipment is prolonged, and intelligent motor state analysis and prediction are realized.

The invention relates to the technical field of building construction regulation and control, and discloses an efficient construction multi-element real-time dynamic regulation and control method based on an intelligent algorithm, and the method comprises the steps: building a personnel database, and recording the competent work types, skill levels and working experience of personnel; according to a construction project, screening personnel based on an intelligent algorithm, and performing personnel allocation; monitoring the material inventory of the construction project in real time; acquiring data of personnel position, material supply, equipment operation state, construction progress and construction quality of the construction site; based on an intelligent algorithm, adjusting task distribution of the personnel according to the acquired personnel position state data; according to the efficient construction multi-element real-time dynamic regulation and control method based on the intelligent algorithm, human resources can be reasonably utilized, supply of construction materials is guaranteed, the construction efficiency can be effectively improved, the construction quality is guaranteed, and the construction cost is reduced.

The invention relates to a data channel state sensing method based on multi-source time sequence data fusion and a topological structure. The method comprises the following steps: collecting multi-source time sequence data; cleaning is conducted; obtaining a multi-source fusion time sequence; constructing a knowledge graph structure, wherein the knowledge graph structure comprises data and operation conditions of an equipment link topological structure, an equipment body and topological association equipment; obtaining a trained long short-term memory network model; and inputting real-time data into the trained long-short-term memory network model, predicting the real-time data by the trained model, and dynamically sensing the running state of the equipment. According to the method, a knowledge graph structure is adopted, modeling is carried out on data and operation conditions of an equipment link topological structure, an equipment body and associated equipment, the complex association relation between the equipment can be expressed in the mode, the sensing capacity of the equipment state is improved in combination with the actual operation data, and the real-time performance of the equipment is improved. Therefore, the monitoring system can perform more comprehensive state analysis by using the existing professional knowledge and the association between the devices.

The invention provides an intelligent inspection warning method and system based on panoramic video fusion, and relates to the technical field of intelligent inspection, and the method comprises the steps: obtaining equipment fault first-level warning information through a panoramic video monitoring assembly; according to the abnormal equipment bit number list, constructing an equipment fault relation tree based on the upstream and downstream topological information of the equipment; based on the equipment fault relation tree, extracting first fault equipment, a mother node fault equipment set of the first fault equipment and a child node fault equipment set of the first fault equipment; based on the instrument monitoring state list, performing fault traceability analysis on the first fault equipment and the parent node fault equipment set to generate fault source equipment; adding the fault source equipment into a fault source equipment group; and generating an intelligent inspection warning signal according to the fault source equipment group. The technical problem that in the prior art, accurate traceability of the fault equipment is difficult to achieve is solved, and the technical effects of accurately positioning the fault source equipment and improving the alarm reliability are achieved.

The single-person oxygen generator comprises a compressor for compressing air and an adsorption assembly for separating nitrogen and oxygen in the air, the adsorption assembly comprises two adsorption tanks, a circulation tank is arranged between the two adsorption tanks, and a spiral sleeve, a rotating rod, a feeding plate and a collecting tank are arranged in the circulation tank; the rotating rod is coaxially and slidably connected with the spiral sleeve, the feeding plate is in threaded connection with the spiral sleeve, the collecting tank is located below the feeding plate, and the feeding plate can move in the circulation tank to screen out broken bodies and powder in molecular sieve particles and change positions of the screened-out molecular sieve particles. The circulation tank of the oxygen generator is used for screening broken bodies and powder of molecular sieve particles, meanwhile, the feeding plate can move the screened molecular sieve particles from the air inlet end to the air outlet end, the adsorption energy efficiency of the molecular sieve at the air inlet end is kept, and the collecting tank in the circulation tank can collect and timely treat the screened broken bodies and powder.

The embodiment of the invention discloses a task scheduling method and device based on process preemption, electronic equipment and a storage medium, and the method comprises the steps: under the condition that a first task needing to be executed through a target process is received, determining a first scheduling priority and an execution time slice of the target task, and executing the first scheduling priority and the execution time slice of the target task; obtaining a second scheduling priority of a second task being executed by the target process; and when the first scheduling priority is higher than the second scheduling priority, interrupting the second task, recording context information of the second task, and executing the first task through the target process, so that the first task with high priority can be preferentially processed, the real-time performance and the response speed of the system are improved, and the user experience is improved. And the context information of the interrupted second task is recorded, so that the continuity and the consistency when the tasks are recovered to be executed are ensured, and the stability and the reliability of the system are ensured.

The invention relates to a construction method of a customer complaint processing system for coping with supervision complaints, which belongs to the field of customer service and supervision compliance management, and comprises the following steps: (1) establishing diversified complaint channels, and integrating the complaint channels to a unified platform; (2) complaint information is comprehensively collected and intelligently analyzed; (3) establishing a quick response and processing mechanism based on an analysis result of the step (2); (4) performing closed-loop control and continuous improvement on complaints; (5) technical support and informatization construction; and (6) personnel training and ability improvement. The method has the advantages that the processing efficiency is improved, manual intervention is reduced through the automatic processing process, and the complaint response time is shortened; (2) the processing quality is enhanced: the complaint is ensured to be professionally and timely processed by a standardized processing flow and an intelligent distribution mechanism; and (3) resource configuration optimization: tasks are intelligently allocated according to the processing capability, and waste of human resources is avoided. (4) customer satisfaction is improved, complaints are quickly responded and effectively solved, and customer credibility and loyalty are enhanced.

The invention relates to the field of industrial equipment monitoring, and discloses a multi-sensor integrated system for remote monitoring of industrial equipment, which comprises a data acquisition module, a data processing module, a fault prediction module, a self-adaptive control module and a monitoring feedback module, the data acquisition module acquires multi-dimensional data from sensor arrays of a plurality of devices; the data processing module carries out noise reduction processing on the data and extracts key features; the fault prediction module predicts a short-term fault of the equipment and predicts a long-term operation trend; the self-adaptive control module adjusts control parameters of the equipment according to the prediction result and optimizes a control strategy; and the monitoring feedback module generates real-time fault alarm information by realizing remote monitoring and feedback of the equipment. The system can improve the safety and stability of equipment operation, optimize equipment management and reduce the equipment maintenance cost, and has high real-time performance, accuracy and intelligent level.

The invention relates to the technical field of breeding data transmission, in particular to a cattle breeding data transmission method and system based on a cloud platform, and the method comprises the steps: distinguishing an exhaled gas region and a cattle body region through analyzing the optical flow vector and temperature change degree of each region in continuous frames of images; obtaining a cattle position point in each frame of image and a physiological feature parameter of each cattle; further analyzing the variation of the cattle position points in the continuous frame images to obtain the activity amount of each cattle; according to the physiological feature parameters of the cattle, the abnormal degree of each cattle is recognized in combination with the environment temperature; in addition, the abnormal degrees are corrected by analyzing the group relevance among the abnormal degrees of all the cattle in the cattle pen range; and determining the data transmission priority at the current moment according to the corrected abnormal degree. According to the method disclosed by the invention, the timeliness and the accuracy of cattle body anomaly identification in the early stage of the contagious pleuropneumonia are improved, and the timeliness of treating the early stage of the contagious pleuropneumonia is further ensured.

The invention discloses an electronic fence positioning method and system applied to low-altitude flight control, and belongs to the technical field of data analysis. According to the embodiment of the invention, the low-altitude flight fence space is constructed and the flight early warning quantitative characteristics are set, so that the fine management basis of low-altitude flight is realized. The electronic fence monitoring route and the space-time cross sensing data thereof are acquired to determine the local management and control area, so that airspaces with different functions and risks can be accurately focused, and the management and control efficiency is improved. Alarm data of different monitoring routes are determined according to flight early warning quantitative characteristics, differences of tasks of different routes in the same area are considered, the method can adapt to complex and diversified flight scenes, and misjudgment is reduced. And after the trigger condition is responded, the monitoring risk decision feature is determined and the flight is controlled, so that the abnormal condition of the low-altitude aircraft can be handled in time in a targeted manner, the safety and orderliness of the low-altitude flight are effectively ensured, and potential threats to specific areas (such as airports and activity sites) are avoided.

The invention provides an electric power safety protection method and system for switch cabinet control, and relates to the technical field of electric power safety, and the method comprises the steps: arranging a vibration sensor, and collecting Q joint point vibration monitoring data sequences and P mechanical control point vibration monitoring data sequences; arranging an infrared thermal imager to collect an infrared temperature distribution diagram sequence; traversing the vibration monitoring data sequences of the Q joint points and the P mechanical control points to perform vibration anomaly analysis to obtain a first abnormal joint point set and a first abnormal mechanical control point set; abnormal temperature point identification is carried out according to the infrared temperature distribution diagram sequence, and an abnormal temperature point position set is obtained; and integrating all the abnormal positions to obtain an abnormal position point and sending the abnormal position point to a worker for safety detection. The technical problem that in the prior art, operation monitoring is carried out on the switch cabinet only from a single dimension, an abnormal position is difficult to locate accurately, and consequently the safety detection accuracy of the switch cabinet is insufficient is solved, and the safety protection efficiency and accuracy of the switch cabinet are improved.

The invention is suitable for the technical field of energy consumption analysis, and particularly relates to a smart energy consumption analysis method and system, and the method comprises the steps: drawing a technological process PID diagram, marking energy consumption equipment, positioning a control node of the energy consumption equipment, defining a standard working condition, reading a valve position of the control node through a preset DCS central control system, and carrying out the control of the energy consumption equipment. The fluctuation range of the valve position under the standard working condition is configured; and traversing the technological process PID diagram, determining the energy type of each control node, and clustering the fluctuation range based on the energy types. By generating the monitoring tree, the energy analysis result can be quickly output, the abnormal node can be found out, the energy consumption abnormal position can be accurately positioned, the more effective energy consumption management strategy can be formulated, and by drawing the change trend chart, the energy consumption condition can be visually displayed, the energy consumption state can be comprehensively known, and the energy consumption abnormity can be timely found and processed. And stable operation of a production system is ensured.

The embodiment of the invention provides a resource allocation method and device based on an intelligent computing cluster, equipment and a storage medium. The method comprises the steps of determining an average processing duration and a task allocation rate of processing historical tasks associated with each task queue by an intelligent computing service node cluster; each task queue corresponds to one target constraint function, and each target constraint function is used for limiting an average processing duration variable, a task allocation rate variable and a service node total quantity variable under the condition that a task delay processing proportion threshold value corresponding to the current task queue is met; distributing a function relationship between weight variables with the service nodes of the current task queue; calculating the target constraint function of each task queue to obtain a corresponding target service node allocation weight, and allocating a corresponding server node subset; and according to the maximum task delay threshold value of the to-be-processed task, determining a correspondingly allocated task queue and processing the to-be-processed task in the corresponding task queue. Therefore, system delay can be reduced, and system resources are fully utilized.

The invention relates to a battery anomaly detection method, device and equipment, a storage medium and a program product. The method comprises the following steps: acquiring voltage data of each battery in a battery pack in a preset time period, determining an abnormal battery of which the voltage data is greater than or equal to a first threshold value, and dividing the batteries of which the voltage data is less than the first threshold value into a to-be-analyzed set, and determining the dispersion degree of the battery voltage data by calculating the standard deviation of the battery data in the set, if the standard deviation is greater than or equal to a second threshold value, considering that an abnormal value possibly exists, performing analysis through an entropy algorithm, and determining an abnormal battery through the characteristic value of each battery under the entropy algorithm. By setting the first threshold value, the obviously abnormal batteries can be preliminarily screened, then the abnormal possibility of the remaining batteries is determined based on the standard deviation, and finally, the abnormal batteries can be more accurately detected through overall analysis of the entropy algorithm and based on the relevance between the voltage and the thermal runaway, and the thermal runaway risk of the batteries can be timely processed and reduced.

The invention relates to the field of photovoltaic detection, in particular to a maritime photovoltaic real-time detection system, which is characterized in that aerial images are acquired to calibrate moving targets of image frames, and the relative position relationship between the moving targets and the included angle between corresponding moving vectors are determined to verify whether migration and aggregation tendencies exist or not; acquiring area image data of the photovoltaic panel, predicting a moving path of a moving target based on an image frame, and determining a maximum distribution distance of the moving target relative to the moving path so as to determine an inspection area for the photovoltaic panel; the method comprises the following steps: carrying out mobile acquisition on an inspection area, obtaining detail image data of a photovoltaic panel area, extracting panel texture features, determining a corrosion area of a photovoltaic panel, calling electric energy conversion data of the photovoltaic panel in the corrosion area, and judging whether to send out an early warning signal or not. On the premise of ensuring the reliability, the inspection efficiency is improved, and the influence of bird feces or scratches on the photovoltaic panel is reduced.

The invention discloses a construction engineering construction site robot safety inspection and early warning method based on machine vision, and the method employs a tracked robot, integrates a plurality of sensors, employs an improved autonomous navigation and positioning technology, and combines a deep learning driven machine vision detection technology. And real-time monitoring and potential safety hazard identification of the construction site are realized. The system further comprises a real-time early warning system, a cloud platform data analysis and user customization interface, and a complete automatic safety monitoring solution is formed. According to the method, the inspection efficiency and accuracy are remarkably improved, the safety risk of manual inspection is reduced, and innovative technical support is provided for safety management of a building engineering construction site.

The embodiment of the invention provides a signal processing circuit, a signal processing system and a mobile carrier, which can be particularly used for processing signals of a wheel speed sensor. The signal processing circuit comprises a signal screening circuit, and the signal screening circuit comprises a first comparison element, a second comparison element and a first switch assembly. The first comparison element is used for outputting a first signal larger than a first threshold value, the second comparison element is used for outputting a second signal larger than a second threshold value, and the first signal and the second signal are effective signals of the WSS. When the first signal and the second signal are both output, the first switch assembly is used for outputting effective signals corresponding to two current values in the first signal and the second signal from the first output end and the second output end of the signal screening circuit respectively. Therefore, the circuit is simple, the cost is low, and effective signals with different current values can be screened and output according to wheel speed signals under different protocols.

The invention relates to the technical field of power generation and energy storage, and discloses liquid cooling type new energy power generation and energy storage equipment which comprises a cabinet body. The energy storage equipment is mounted in the cabinet body; the temperature sensor is mounted on the side surface of the energy storage equipment; the cooling structure is arranged on the side surface of the energy storage equipment; the water removal structure is arranged on the side surface of the energy storage equipment; the water receiving structure is arranged on the side face of the energy storage equipment and located below the cooling structure; and the feedback structure is arranged on the side surface of the energy storage equipment. The mounting plate and the absorbent cotton on the mounting plate are driven to move through the connecting plate, the vertical plate and the connecting piece, the absorbent cotton is tightly attached to the side face of the outer-layer pipe, the absorbent cotton is clamped to the outer-layer pipe through the grooves in the absorbent cotton, and therefore condensate water generated on the surface of the outer-layer pipe can be effectively absorbed in the moving process; by means of the design, the situation that water mist is condensed into water drops and drips is avoided, and it is ensured that condensate water is effectively treated in time.

The invention relates to the field of big data super-concurrent processing, in particular to an education big data super-concurrent processing system and method. The method comprises the following steps: collecting and preprocessing original request data to obtain actual request traffic; using historical data in actual request traffic to train a weighted autoregression model, and predicting the traffic; dynamically adjusting the concurrency based on the obtained flow prediction value, calculating the scheduling priority of the data packets, and sorting and distributing the data packets; a dynamic back pressure control mechanism based on flow prediction is introduced, flow input is dynamically adjusted, and overload is prevented. The problems that a traditional scheduling algorithm and a traditional load control strategy lack enough accurate flow prediction, concurrency cannot be adjusted in time, and system resources are excessively distributed or insufficient are solved; the priority processing of the key task cannot be ensured because the flow fluctuation and the scheduling priority are not fully considered; the problems that flexibility and self-adaptive capacity are lacked, the flow cannot be effectively limited when the load is too high, and the overload risk of the system is increased are solved.

The invention discloses an automatic detection method and system for small-size pests, and relates to the technical field of automatic detection of pests, environmental condition data in a pest monitoring area are collected, and pest data are collected by a sensor network; identifying insect types and position information in the insect pest image by using the trained insect pest target detection model; the insect types are verified according to the insect pest characteristics of the small-size insects, the insect intensity in the area is calculated according to the position information of the target insect pests and the time nodes, when the insect intensity exceeds the expectation, the trained insect pest outbreak prediction model is used for predicting the number of the insect pests, and if the prediction result exceeds the expectation, an alarm value is constructed according to the alarm data. And when the warning value exceeds a warning threshold value, outputting an insect pest emergency treatment scheme by the small-size insect pest treatment knowledge graph. On the basis of mutual cooperation and verification of a plurality of different characteristics, the detection reliability is improved when small-size insects are detected and identified.

The invention discloses a memory monitoring method, device, system and equipment and a medium, and relates to the technical field of computers. When it is monitored that a hardware error exists in the memory, hardware interruption is triggered through a preset programmable logic device, so that the micro-control unit is awakened to enter a working state; obtaining error data related to hardware errors in a preset target component by using the substrate management controller, and sending the error data to the awakened micro-control unit; and performing error type analysis on the error data based on the awakened micro-control unit, and sending an error processing request triggered based on an error type analysis result to an operating system of a preset target component, so that the operating system responds to the error processing request to obtain a corresponding error processing result. Through the technical scheme of the invention, the memory error can be quickly and accurately detected and timely processed, and the reliability, availability and energy efficiency management of the system are improved.

The invention discloses a multi-dimensional intelligent adjustment system and method applied to power equipment, and relates to the technical field of power equipment inspection, and the method comprises the steps: generating an equipment performance value of the power equipment through obtained performance data, and predicting the operation state of the power equipment if the correlation between an environment risk value and the equipment performance value exceeds the expectation; the health state of the power equipment is evaluated according to the prediction data, and if the power equipment is in an unhealthy state, a multi-level alarm instruction is sent to the outside according to the unhealthy degree; planning and acquiring a routing inspection path for the power equipment in the industrial park so as to perform routing inspection on each abnormal equipment, and verifying the abnormity of the power equipment according to the acquired routing inspection data; and after the trained parameter control model is used for automatically controlling the abnormal equipment, a maintenance scheme is output for the abnormal equipment. When the abnormal equipment is maintained according to the inspection path obtained through planning, the abnormal equipment which is poor in health degree and frequently abnormal is maintained preferentially.

The invention relates to a method, a device, equipment and a medium for optimizing an SSD (Solid State Disk) write buffer area, and the method comprises the following steps: acquiring a newly written operation request to obtain written data; judging whether the new write-in is host write-in or not; if so, writing the write-in data into a data preparation area; judging whether the data volume in the data preparation area reaches a concurrent write-in threshold value or not; if yes, concurrence data is formed, the concurrence data prepared in the data preparation area is transferred into the data programming area, and the concurrence data is transmitted to a specified Page of each DIE specified WL; whether loading of the Page data corresponding to the WL is completed or not is judged; and if the loading is completed, initiating a programming command of the WL corresponding to each DIE until the data programming is completed. According to the invention, the key data can be processed in time, the delay of data processing is reduced, the idle or excessive allocation of resources is avoided, and the optimal configuration of the resources is realized.

The invention relates to the technical field of electronic digital data processing, in particular to a data responsibility traceability method and system based on edge computing, and the method comprises the steps: collecting the surrounding environment data of a production line, and obtaining a regional environment influence feature value through processing; obtaining operation data of each edge device, processing the operation data to obtain a performance evaluation index of each edge device, and carrying out bearable data volume matching on each edge device; basic data of each edge device is collected, and abnormal characteristic values of each edge device are obtained through processing; performing comprehensive analysis to obtain a fault risk assessment index of each edge device, and performing risk assessment and feedback on each edge device; according to the data responsibility tracing method and system based on the edge computing, the demand processing data of each edge device is obtained, data distribution is carried out on each edge device and the cloud processing device, the data can be ensured to be effectively processed in time, and the overall efficiency of data processing and the overall utilization rate of resources are improved.

The invention relates to a smoke purifier, and belongs to the field of purification. A smoke dust purifier comprises a shell assembly, and the shell assembly is provided with an air inlet, a containing cavity, an air draft cavity and an air outlet; the filtering assembly is arranged on the shell assembly and located in the containing cavity, and the filtering assembly is provided with a filtering air inlet and a filtering cavity; the air inlet assembly is arranged on the shell assembly, the air inlet assembly is provided with an air inlet channel, and the air inlet channel, the air inlet, the filtering air inlet, the filtering cavity, the air draft cavity and the air outlet are sequentially communicated; the fan assembly is arranged on the shell assembly and located in the air draft cavity; the display assembly is arranged on the shell assembly; the electric control assembly is arranged on the shell assembly, and the electric control assembly is electrically connected with the filtering assembly and the display assembly. The invention discloses a smoke dust purifier which is used for absorbing fine smoke dust generated in a working environment and reducing the risk that workers are exposed in a harmful dust environment.

The invention discloses an AI-based agricultural disease and insect pest identification method. The disease and insect pest identification method comprises the following specific steps: S100, transmitting image data of various diseases and insect pests to a disease and insect pest standard database of identification equipment; s200, carrying out image acquisition on the crops by using identification equipment, carrying out feature extraction on the shot image by using the identification equipment, and processing the extracted image data to enable the image data type to be the same as the image type in the pest and disease damage standard database; s300, comparing the acquired image features with data in a pest and disease damage standard database to determine the type of pest and disease damage; and S400, transmitting the determined pest and disease damage type to terminal equipment in a wireless transmission mode. According to the method, crops are monitored in real time through recognition equipment, the recognition equipment can collect images of the crops around the recognition equipment, and the collected data are compared with data in a database.