3105results about How to "Flexible adjustment" patented technology

The invention relates to a multi-channel firmware upgrading method and system supporting breakpoint resume, and the method comprises the steps: segmenting a firmware file into a plurality of firmware data blocks, and generating a unique identifier and a checksum for each firmware data block; acquiring a network transmission channel, performing task matching with the network transmission channel according to the unique identifier to obtain a corresponding distribution transmission task, and establishing a corresponding initial record document; transmitting the firmware data block through the network transmission channel according to the distribution transmission task, monitoring the transmission state in real time, and carrying out breakpoint record updating on the initial record document to obtain a corresponding updated document; when it is detected that transmission is completed, verifying the firmware data block according to the checksum, and recombining the firmware data block to obtain the firmware file; and generating a corresponding upgrading report based on the updating document and the upgrading information. The upgrading strategy can be continuously optimized, and the system management efficiency can be improved.

The invention discloses a self-adaptive communication method and system for ubiquitous electric power Internet of Things intelligent metering equipment, relates to the technical field of Internet of Things communication, and solves the problems of inaccurate quantitative communication, insufficient communication efficiency and poor flexibility in existing intelligent metering equipment communication. According to the invention, the accuracy and reliability of data transmission are improved through a dynamic network self-organizing algorithm and a multi-hop communication mechanism, the communication efficiency is optimized through multi-path transmission and dynamic spectrum management, and the flexibility and adaptability of the network are enhanced through an adaptive modulation coding model and a real-time optimization link selection strategy. And secure transmission of the data is further guaranteed through a multi-layer security mechanism, and the communication performance and flexibility of the intelligent metering equipment are remarkably improved.

The invention relates to the technical field of environmental protection, in particular to a big data monitoring and intelligent early warning platform and method for environmental protection. Comprising a multi-source data acquisition module, a data integration and preprocessing module, an environmental risk factor identification module, a real-time monitoring and anomaly detection module, a prediction model and early warning module, a self-adaptive early warning and feedback module and a decision support and visualization module. The data integration and preprocessing module preprocesses multi-source data, the environmental risk factor identification module identifies key risk factors by using an XGBoost algorithm, and carries out accurate prediction and early warning on risks through a random forest and long and short term memory network combination model, and an adaptive feedback mechanism dynamically adjusts an early warning threshold value, so that the early warning efficiency is improved. According to the invention, the intelligent level and the scientific level of environmental protection are obviously improved.

The invention discloses an air conditioner energy-saving control method and system based on multi-objective optimization, a medium and a product, and relates to the field of data processing. The method comprises the steps that optimization targets for conducting energy-saving control on the air conditioner and corresponding constraint conditions are determined, and the optimization targets comprise target air conditioner energy consumption and target comfort degree; acquiring current environment data of the air conditioner; the equipment operation parameter combination of the air conditioner is used as particles in the multi-target particle swarm algorithm; determining an optimal solution through a multi-target particle swarm algorithm; the air conditioner is controlled according to the parameter combination corresponding to the optimal solution; the actual air conditioner energy consumption and the actual comfort degree are obtained; based on the target air conditioner energy consumption, the target comfort level, the actual air conditioner energy consumption and the actual comfort level, the equipment operation parameters of the air conditioner are adjusted through a PID control algorithm, and final equipment operation parameters are obtained; and controlling the air conditioner to work according to the final equipment operation parameters. According to the method, both the energy-saving effect and the comfort level of the air conditioner are balanced.

The invention provides an intelligent well completion multi-objective optimization method based on a multi-objective genetic algorithm, and the method comprises the steps: determining an optimization objective and a constraint condition according to a real environment of intelligent well completion; and constructing a multi-target genetic algorithm model, and executing optimization calculation of the multi-target genetic algorithm. And evaluating the influence of each ICV configuration on the optimization target by adopting a fitness function. And an initial population is generated for initialization, a current optimal individual is screened out through iterative operation, and the process is ended after data are output. The method has the beneficial effects that the calculation efficiency is improved by establishing the multi-target fitness model and optimizing the iteration strategy, so that the algorithm can quickly converge to the optimal solution under limited calculation resources. The fitness function of the scheme can accurately evaluate the influence of different parameter configurations on the optimization target, and the optimization result is more reliable and accurate. And an optimization method which is more efficient, more comprehensive and more suitable for actual production requirements is provided.

The invention discloses an effect evaluation method for a non-dialysis diabetic kidney disease, which comprises the following steps: monitoring the change trend of data by collecting renal function related data of a patient, and generating a basic data set; a dynamic nonlinear weighted regression algorithm is adopted to analyze the multi-dimensional renal function data, data weights are dynamically adjusted, and influence weights of different time periods are distributed according to the change mode of the illness state; the data influence is adjusted in real time through a time correlation mechanism, and a disease assessment curve is generated; according to the method, historical data and real-time data are combined, a patient condition trend line and a prediction result are generated, personalized treatment feedback is output, and intervention suggestions are provided for clinicians based on long-term prognosis prediction. The method is suitable for disease monitoring and treatment feedback of patients with non-dialysis diabetic kidney diseases.

The invention discloses a coal mine fireproof early warning method based on intelligent monitoring, and relates to the technical field of coal mine fireproof early warning, and the method comprises the following steps: obtaining coal mine environment information data parameters in real time through a coal mine fireproof early warning system, including humidity dynamic parameter information and air pressure parameter information; the environment is intelligently evaluated through the generated environment safety change index, when the environment is judged to be a high-risk environment, the system dynamically adjusts the sampling frequency based on the preset sampling frequency, fine changes such as methane diffusion or air static pressure fluctuation in the early stage of a fire disaster are captured in time, rapid early warning is ensured, and the safety of the fire disaster is improved. The accuracy and timeliness of signal capture are improved, and early intervention time is won for managers. And in a low-risk environment, the system collects data at a fixed sampling frequency, so that unnecessary calculation tasks and energy consumption are reduced, and long-term monitoring and trend analysis are supported. Through the dual regulation and control mechanism, the system not only ensures the data integrity, but also optimizes the resource utilization and operation efficiency.

The invention discloses an intelligent medical and invasive project evaluation method and system based on natural language processing. The method comprises the steps that professional corpora of multiple sources of an intelligent medical and invasive project are collected; the knowledge graph construction module comprises a term library construction module, a term clustering module and a relationship construction module; taking the professional corpus as a core to construct a professional word library; segmenting the professional lexicon into a plurality of clusters with professional terms as the center; constructing a knowledge graph by taking vocabularies in the clusters as nodes; and constructing an intelligent evaluation module which comprises an intelligent scoring module and an intelligent evaluation module, and outputting AI comments. According to the invention, the project plan is automatically processed through a natural language processing technology, so that the auditing efficiency is greatly improved; and in combination with a knowledge graph technology, a professional knowledge base is systematically constructed, background knowledge support is provided for a large language model, and the professionality and consistency of evaluation results are ensured.

The invention relates to a humanoid robot lower limb structure and a robot. The lower limb structure comprises a hip unit, a thigh unit, a shank unit and a foot unit. The hip unit comprises a hip seat body, a first joint structure, a thigh swing drive, a thigh rotation drive and a thigh side-sway drive; the first joint structure comprises a first movable seat and a second movable seat; the thigh swing drive is mounted on the inner side of the hip seat body, and an output rotating shaft of the thigh swing drive is connected with the first movable seat; the thigh rotation drive is fixedly connected with the second movable seat, and an output rotating shaft of the thigh rotation drive is connected with the upper end of the thigh unit; the thigh side-sway drive is fixedly connected with the first movable seat, and an output rotating shaft of the thigh side-sway drive is connected with the second movable seat; the axial direction of a rotating shaft driven by thigh swing is parallel to the first direction; the axial direction of a rotating shaft of the thigh side-sway drive is perpendicular to the first direction; the axial direction of the rotating shaft of the thigh rotation drive is perpendicular to the first direction and the axial direction of the rotating shaft of the thigh side-sway drive.

The invention discloses a welding piece anti-deformation fixing bottom bracket which comprises a bottom table plate, a supporting rod frame assembly and an exchange type extension mechanism, a detachable clamping assembly is arranged on the upper section of the supporting rod frame assembly, and the supporting rod frame assembly can be adjusted in a locking hole in the bottom table plate in a rotating or sliding mode. And the clamping assembly is driven by the exchange type extension mechanism to achieve free position adjustment in a three-dimensional space, the exchange type extension mechanism is used for adaptive extension connection between the two supporting rod frame assemblies, and the exchange type extension mechanism assists all-directional flexible adjustment in the clamping direction of the clamping assembly by conducting angle exchange on the supporting rod frame assemblies connected in an extension mode. According to the anti-deformation fixed bottom bracket for the welded part, for a fixed clamping structure of the welded part in the fixed bottom bracket, free position adjustment in a three-dimensional space is achieved, all-directional flexible adjustment in the clamping direction is achieved, and the anti-deformation fixed bottom bracket can adapt to different structural forms of all parts in the welded part and different butt joint requirements of all the parts.

The invention provides an automatic test system and method capable of being arranged and executed in batches, and the system comprises a test case arrangement plan table which is used for customizing the starting time, the precondition and the dependency relationship of each test item, whether execution is terminated or not when the test item fails, grouping execution, result archiving, product dependency and user-defined labels; the default configuration management module is used for parameterizing execution and supporting simple configuration to realize visual operation of the test script; the software framework supports an object-oriented language and provides multi-process processing to improve execution efficiency; and the distributed structure is used for realizing concurrent parallel execution across a plurality of processors or machines and supporting synchronous import and concurrent asynchronous execution of a plurality of test items. Through the support of multi-process processing and a distributed structure, the advantages of a multi-core processor and computing resources of cross-network nodes can be fully utilized, multiple test items are executed in parallel, the execution efficiency of automatic testing is greatly improved, and the overall testing period is shortened.

The invention discloses an intelligent reservoir scheduling method based on a near-end strategy optimization algorithm, and relates to the technical field of reservoir application. According to the method, a reservoir is modeled into an intelligent agent, the intelligent agent is composed of two neural networks of the same structure and interacts with a scheduling environment, network parameters are updated by adopting a near-end strategy optimization (PPO) algorithm, simulated reservoir inflow and forecast flow data are generated through an ARMA model according to historical actually-measured runoff data, and the intelligent agent is trained; the real-time scheduling performance of the intelligent agent is verified by using historical actually measured runoff data, behavior actions are expressed in a probability distribution form, the limitation of dimensionality disaster of a traditional algorithm is effectively avoided, and the Agent can more flexibly adjust a scheduling strategy by observing the water level and the incoming water condition in real time, so that the decision process is closer to an actual scheduling scene.

The invention relates to the technical field of laser cutting, in particular to intelligent cutting equipment for aluminum alloy pipes, which comprises a machine table, a laser cutting head is arranged on the machine table through a lead screw transmission system, and a chuck for fixing the pipes is arranged on the machine table. The intelligent cutting equipment for the aluminum alloy pipe further comprises an in-pipe buffering mechanism, the in-pipe buffering mechanism comprises a bearing unit arranged in the pipe, a first supporting unit is arranged on the side, facing the chuck, of the bearing unit, a second supporting unit is arranged on the other side of the bearing unit, and a maintaining unit is arranged at the position, corresponding to the bearing unit, of the machine table. Through the high precision and high speed of the laser cutting technology and the ingenious design of the buffer mechanism in the pipe, the problem that slag adheres to the interior of the pipe to form adhering slag is effectively avoided, so that the cutting quality and the product percent of pass are improved, meanwhile, the automation degree and the intelligence degree of the equipment are high, manual intervention is reduced, and the cutting efficiency is remarkably improved.

The invention discloses a machine learning optimization method of a low-frequency sound absorption structure based on three-dimensional printing, and belongs to the technical field of artificial intelligence acoustics, the low-frequency sound absorption structure is prepared from a photosensitive resin material through photocuring three-dimensional printing, and comprises a side seam, an extension cavity, a circular through hole, a resonance cavity, a first partition plate, a second partition plate and a third partition plate, the method has good expansibility and can be adjusted according to different noise control requirements; through a machine learning optimization method, structure parameters can be quickly adjusted to cope with a noise control task on the premise of ensuring a small size, so that a remarkable sound absorption effect is obtained at a specific low-frequency band; according to the method, a machine learning method based on a multi-layer perceptron and a genetic algorithm is adopted, geometric parameters of the low-frequency sound absorption structure are optimized, simulation calculation is carried out by using a commercial finite element solver COMSOL Multiphysics, and the sound absorption performance of the structure is further verified and optimized.

The invention relates to the field of intelligent management, and particularly discloses a nutrition management system and method for a patient in a hepatobiliary and pancreatic surgery in a perioperative period, which uses a data analysis and coding technology based on deep learning to perform data arrangement and multi-dimensional time sequence association on vital sign data, and performs semantic embedding and association on a current test result at the same time. And then, fine-grained cross-modal coding is performed on the vital sign multi-dimensional time sequence association features and the test item semantic association features, so that a nutrition management scheme is intelligently generated based on semantic cross-modal coding representation and operation type labels. In this way, the physiological status and inspection characteristics of each patient can be comprehensively captured, and then an exclusive nutrition treatment scheme is made for the patient in a tailored mode. In addition, real-time monitoring and flexible adjustment of patient conditions are supported, and it is ensured that appropriate intervention measures are taken at critical moments.

The invention discloses a power transmission and transformation project cost intelligent review method based on cost fluctuation characteristics, and relates to the technical field of intelligent review, and the method comprises the following steps: collecting historical cost data, preprocessing the data based on NACEMD, and recognizing power transmission and transformation project cost influence factors; judging main influence factors influencing the cost by adopting correlation analysis, and carrying out clustering analysis on different project types to obtain different types of power transmission and transformation project sample sets; on the basis of the historical cost data and the transmission and transformation project sample set, cost intervals of different types of costs of the transmission and transformation project are calculated through a Cheff-Snow ratio inequality for different types of costs, and fluctuation intervals of different types of costs are judged; corresponding intelligent methods are set for different types of fees, and based on prediction analysis of the three types of fees, evaluation standard determination of the power transmission and transformation project cost is realized through input of related project parameters. And the scientificity and accuracy of the review process are ensured through model selection for different fluctuation types.

The invention discloses a modularized intelligent identification and configuration system for a portable Internet of Things electric energy metering box, and relates to the technical field of Internet of Things. Electric energy data are collected in real time through an integrated sensor network, and are cleaned, compressed and preliminarily filtered by a data processing module; a deep learning algorithm is adopted to perform feature extraction and abnormal power consumption behavior detection on the data, and rapid response is ensured; a graphical operation interface is provided through the user interface module, and a user is allowed to update configuration immediately according to requirements; the link state is monitored through the LACP, the optimal link is automatically selected, and stable data transmission is ensured; temporarily storing data through a local cache and backup module when the network fluctuates, and automatically synchronizing the data to the cloud after the network is recovered; the system load is monitored through the self-adaptive optimization module, resource allocation is dynamically adjusted, and efficient and stable operation of the system is guaranteed; the data processing speed of the metering box equipment of the electric power internet of things is greatly improved, and the flexibility and the stability are enhanced.

The invention discloses a power grid construction subpackage management and control method based on big data, and the method comprises the following steps: collecting the data of a power grid construction site in real time, carrying out the preprocessing, and carrying out the storage through a distributed storage system; generating a comprehensive evaluation report by using the residual neural network and the graph convolutional network; adopting a particle swarm optimization algorithm to carry out subpackage provider selection and task allocation; predicting the construction progress through a long short-term memory network; performing image analysis on the video stream of the construction site by using a residual neural network and a single-time multi-frame detection algorithm; a Bayesian network and a multi-layer perceptron are used to predict construction risks, an isolated forest algorithm is used to carry out abnormal value detection on data, and a risk early warning report is generated and notified to related personnel. According to the invention, big data, deep learning and reinforcement learning technologies are fully combined, the power grid construction subpackage management and control method based on big data is described in detail, and the method has the advantages of high construction efficiency, accurate risk prediction and resource optimization.

The invention relates to a closed-loop feedback transcranial alternating current stimulation system and method. The closed-loop feedback transcranial alternating current stimulation system comprises a phase stimulation system used for generating a plurality of electrical stimulation signals and stimulating different brain areas in the brain based on the electrical stimulation signals; a preset phase difference exists between the electrical stimulation signals; the electroencephalogram acquisition system is used for acquiring electroencephalogram signals of the brain after the phase stimulation system stimulates the brain for a preset time; and the evaluation feedback system is connected with the phase stimulation system and the electroencephalogram acquisition system and is used for evaluating the electroencephalogram signals acquired by the electroencephalogram acquisition system and adjusting the electrical stimulation signals based on an evaluation result. According to the closed-loop feedback transcranial alternating current stimulation system, the waveform signal can be flexibly adjusted based on the real-time oscillation signal of the brain, and the problems that in the prior art, the shock wave form is single, individual differences cannot be accurately matched, and dynamic brain network state changes cannot be adapted are solved.

The invention discloses a PID (Proportion Integration Differentiation) parameter optimization method based on an improved graffiti search algorithm, and relates to the technical field of PID. The invention aims to solve the problems of insufficient local search capability, slow convergence speed and poor system stability in the prior art, thereby realizing a better PID parameter optimization effect. Specifically, the PID parameter optimization method based on the improved graffiti search algorithm comprises the following steps: constructing a PID controller model; a step of replacing a fixed perception probability and a step length in the model with an adaptive perception probability and a Levy flight step length; a step of adding a crow misleading prevention factor into the model; and acquiring preset parameters in the PID controller model based on a particle swarm algorithm. The preset parameters comprise proportional (Kp), integral (Ki) and differential (Kd) parameters. Parameters of the Levy flight step length are dynamically adjusted through a Bayesian optimization algorithm. The method is suitable for being applied to PID parameter optimization work.

The invention relates to the technical field of wind power equipment manufacturing, in particular to a wind power concrete tower drum mold which comprises a bottom mold body, an outer mold body and an inner mold body. The outer mold comprises a plurality of first modules and a plurality of first connecting flexible plates, the first modules can be connected to form a cavity, one first connecting flexible plate is connected between every two adjacent first modules, the inner mold comprises a plurality of second modules and a plurality of second connecting flexible plates, and the inner mold and the outer mold form a pouring cavity. The arrangement mode of the inner mold is consistent with that of the outer mold; when the diameter of the outer mold needs to be decreased, the two adjacent first modules are folded towards the outer side of the cavity, when the diameter of the outer mold needs to be increased, the two adjacent first modules are unfolded towards the inner side of the cavity, and the area of the top plane of the cavity before the diameter is changed is equal to the area of the bottom plane of the cavity after the diameter is changed; the diameter changing mode of the inner mold is the same as that of the outer mold; the die has the effect of improving the defect that the diameter of the die is inconvenient to adjust.

The invention discloses an arctic channel route planning system and method based on a visual sea ice risk, and the system comprises a future sea ice data obtaining module which is used for obtaining the sea ice data of an arctic region in the future N days; the sea ice risk grade determination module is used for calculating to obtain a sea ice risk grade; the sea ice risk display module is used for displaying sea ice risk levels corresponding to different areas of the north pole; the route preliminary planning and drawing module is used for drawing an initial global route; and the route prediction display module is used for obtaining the risk condition of the initial global route in the cross-day period. Aiming at the complex ice condition environment of the ice area, the ship route design is carried out by considering the influence of sea ice on ship navigation, so that the whole ship route design process is visually displayed, the ship route can be flexibly adjusted, a more efficient, more economical and safer ship route is provided for ship navigation in the north pole, and the ship route design efficiency is improved. And the sailing safety and efficiency of the ship in the Arctic ice region are improved.

The invention provides a method for realizing contract intelligent analysis and maintenance based on image recognition, and relates to the technical field of image recognition, and the method comprises the steps: dividing contract information into contract basic information, customer information, contract object information and contract term information based on a purchase and sale contract template; key elements in the four types are further subdivided into different types, and attribute custom configuration of the elements is supported; the method comprises the following steps: forming a date format library, automatically identifying date data in various formats, converting the date data into formats required by contract maintenance, forming dictionary item configuration for common attributes in a purchase and sale contract, converting text data into standard dictionary item data, and customizing a data matching and conversion verification rule library; establishing a data verification rule, performing logic rationality verification according to contract terms after image recognition is completed, and providing verification result feedback; and in combination with a UML modeling method, page display elements are dynamically configured according to actual business requirements.

The invention provides a PCELL layout design optimization method and device, a medium and equipment, and is applied to the technical field of chip design. Flexible layout drawing is carried out by acquiring the PCELL containing the process parameters and the graph adjustable parameters, so that the layout structure parameters of the current PCELL are obtained. A three-dimensional device simulation model is constructed by using the parameters, and a simulation compact model is created by using an artificial neural network algorithm to describe the electrical behavior of the device. And verifying the electrical performance trend of the three-dimensional device simulation model by using the PCELL and the simulation compact model. And when the verification is passed, further verifying whether the module test circuit meets a preset PPA index. And if yes, fixing the structural parameters of the current PCELL layout. According to the method, process and design requirements are integrated through a construction and parameterization method, and simulation and verification of closed-loop feedback are utilized, so that the design precision and the process maturity are improved, the development period is shortened, and the reliability and efficiency of the process are enhanced.

The invention discloses a computer network information security monitoring method, which relates to the technical field of network information security monitoring, and comprises the following steps of: monitoring and collecting network flow data in real time by using a monitoring and collecting module, extracting and recording historical network flow data, judging the abnormal degree of the current flow by using a comprehensive evaluation and adjustment module, and judging the abnormal degree of the current flow. The method comprises the following steps: evaluating the overall risk level of network security, adjusting a network security policy, generating a policy adjustment report, executing the adjusted network security policy by using a security execution module based on the policy adjustment report, and continuously monitoring network traffic by using a monitoring acquisition module. The beneficial effects and optimization innovation of comprehensive assessment, dynamic risk assessment, flexible strategy adjustment and sensitivity improvement are realized, and the optimization and innovation not only solve the problems and defects in the prior art, but also improve the accuracy and reliability of the monitoring system. And a more comprehensive and effective guarantee is provided for computer network information security.

The invention discloses a multifunctional wind wave and flow coupling experiment platform, and belongs to the technical field of bridge engineering. The experimental platform comprises a wind power system and a water power system, the wind power system is of a two-layer structure, the upper layer is an unsteady wind tunnel, and the lower layer is a steady wind tunnel; the hydrodynamic system is located below the steady wind tunnel; the steady wind tunnel is of a sealed annular structure, and a fan, a first experimental section and a second experimental section are arranged in the steady wind tunnel; the unsteady wind tunnel is arranged above the second experiment section, and interaction windows A which are communicated with each other are formed between the unsteady wind tunnel and the steady wind tunnel; the hydrodynamic system is located below the second experiment section, and an interaction window B which is communicated with the steady wind tunnel is formed between the hydrodynamic system and the steady wind tunnel; the unsteady wind tunnel, the steady wind tunnel and the hydrodynamic system are communicated with one another through the two interactive windows. The experimental platform can simulate different coupling action environments of wind, waves and flows, and is used for testing the stability, reliability and durability of various ocean engineering structures and equipment.

The invention belongs to the field of computer aided design, and discloses a design method of a multistable metamaterial with an ideal energy absorption characteristic, which comprises the following steps of: firstly, carrying out theoretical analysis on a buckle to establish a mathematical model; then, according to the theoretical solution of the rigidity value of the buckle structure, reverse topological optimization design is carried out on the curved beam, so that a force displacement curve of the curved beam has negative rigidity and peak force corresponding to the buckle. The buckle structure and the optimized and customized curved beam are assembled through the functional part, and the multistable metamaterial unit cell with the ideal energy absorption characteristic can be obtained. And programming design is carried out on the unit cells, so that the required metamaterial can be constructed to meet the energy absorption requirement under the complex load. The technical scheme is widely applicable to the fields of aerospace, automobile industry, building protection and the like, and excessive rebound of impact energy is avoided while the impact resistance of equipment is improved.

The invention relates to a network abnormal behavior detection method based on dynamic data balance and generation, and the method comprises the steps: obtaining original network flow data, inputting the original network flow data into a dynamic abnormal data balance model, and obtaining an enhanced data set; the method comprises the steps of marking original network flow data based on an abnormal data balance model, performing selective undersampling on marked data, adjusting a ratio of normal data to abnormal data, obtaining a balance data set, generating new abnormal data according to the balance data set, and merging the new abnormal data with the balance data set to obtain an enhanced data set; and inputting the enhanced data set into a TimeDiT noise reduction model for denoising to obtain a target data set after noise reduction, inputting the target data set into an LSTM-Transform weighted training model for network abnormal behavior detection, and weighting abnormal data in the target data set in combination with a weighted loss function to obtain a detection result. According to the invention, the network traffic abnormal behavior detection efficiency and accuracy can be improved.

The invention belongs to the field of wastewater treatment, and provides a tunnel construction wastewater treatment method, which uses a system, the system comprises a secondary sedimentation tank, a tertiary sedimentation tank, an integrated device, three on-line sensors for wastewater pH value monitoring and two on-line sensors for wastewater SS value monitoring, the system further comprises a pH first-stage adding device, a pH second-stage adding device, a coagulant adding device and a flocculant adding device, and the online sensor and the adding devices are electrically connected with the controller. According to the treatment method provided by the invention, the dosage of chemicals can be accurately metered according to the change of the concentration and the water quantity of the tunnel construction wastewater, the concentration of the chemical liquid is automatically configured, the flow of the chemical liquid is accurately controlled, and real-time adjustment can be realized according to the change of the water quality and the water quantity of the wastewater. According to the treatment method, a dead-corner-preventing wastewater treatment device is used, so that the problems of sludge deposition and hardening at the bottoms of tank bodies of the coagulation tank and the flocculation tank, especially at the four corners of the tank bodies can be effectively solved.

The invention discloses a group behavior recognition method based on dynamic and static characteristics and multiple interactive networks, and relates to the technical field of computer vision, comprising the following steps: preprocessing original video data and skeleton node data to obtain dynamic and static characteristics of participants; multi-level character features are obtained by extracting coarse-grained key character information and restoring fine-grained character information; the method comprises the following steps of: obtaining a complex space-time interaction feature by applying a Space-Transform and a Temporal-Transform, and obtaining the complex space-time interaction feature by applying the Space-Transform and the Temporal-Transform; predicting an identification result through data fusion; dynamic features are utilized to supplement static features, and group behaviors of participants are comprehensively and meticulously analyzed; in order to emphasize the key figure information, the key figure information is used as a consistency constraint to guide the focus of the space-time interaction network, and the recognition capability of the model on the space-time dynamic state of the key figure is enhanced.