1273results about How to "Strong reliability" patented technology

The invention relates to the technical field of big data storage, in particular to a storage control method and device based on big data. The method comprises the following steps: acquiring an original data stream and application demand information; performing data dynamic correlation analysis on the original data flow according to the application demand information to obtain a dynamic update data correlation graph; performing feature vector generation on the dynamic update data association graph to obtain a data feature vector; performing intelligent quality evaluation on the data feature vector to obtain a data quality label; performing timeliness evaluation according to the data feature vector, and performing access popularity evaluation to obtain a timeliness score and an access popularity score; and acquiring system resource load condition information. The storage resource utilization rate, the data access performance and the overall efficiency of the system are improved by solving the limitation of a traditional method in the aspects of static data management and resource allocation.

The invention discloses a magnet surface defect detection equipment control method for AI optimization and a controller, and relates to the field of control adjusting systems.The method comprises the steps that according to a conveying monitoring image, surface brightness information of a to-be-detected magnet on a conveying platform is determined; determining detection light source parameters according to the surface brightness information, and adjusting an irradiation light source of the detection station according to the detection light source parameters; when the to-be-detected magnet arrives at the detection station, collecting a surface image of the to-be-detected magnet, and calculating a gray feature value of the surface image in real time; when the numerical deviation between the gray feature value and a preset gray target exceeds a preset deviation threshold value, finely adjusting the detection light source parameter according to the numerical deviation, and determining a target light source parameter; controlling the irradiation light source of the detection station to irradiate according to the target light source parameter, and obtaining the surface image of the to-be-detected magnet again as a target image; and inputting the target image into the AI detection model to obtain a defect detection result. By implementing the method, the detection effect on different magnets can be improved.

The invention provides a silicon-carbon negative electrode material fluidized bed reaction system, and relates to the technical field of fluidized bed equipment. Comprising a raw material conveying system, a fluidized bed reactor, a high-temperature discharging system, a process gas inlet pipeline and a protection system. According to the scheme, the uniformity and safety of the fluidization reaction can be improved, and the working efficiency can be improved.

The invention relates to the technical field of screen printing, in particular to a PCB screen printing ink layer thickness uniformity control method, system and device and a medium. The method comprises the following steps: firstly, collecting ink layer actual thickness data and screen tension data of a PCB screen printing area, then analyzing the relevance between the two data to obtain an ink layer uniformity analysis result, further determining proper compensation parameters through process compensation analysis, finally dynamically adjusting screen printing pressure, scraper angle and printing speed, bringing screen tension into a control system, and finally, controlling the screen printing uniformity. Compared with the prior art, the method has the advantages that the adaptability and the reliability are higher, and the product yield can be improved.

The invention discloses a pole climbing robot for a wind power tower pole, which is characterized in that the robot mainly comprises a robot body, 4-8 pole climbing mechanisms, a snake-shaped swing arm and a movable studio, wherein the snake-shaped swing arm and the movable studio are mounted on one side of the robot body; the robot body is an annular body formed by combining two semicircular cambered bodies or three equal cambered bodies; the diameter of an internal ring of the circular body is matched with the diameter of the wind power tower pole; the pole climbing mechanisms mainly comprise upper foot wheels, lower foot wheels, connecting arms, driving gears, thrust screw rods, nut gears and supporting seats; the upper foot wheels are connected with the lower foot wheels by the connecting arms through wheel shafts; the connecting arms are connected with the thrust screw rods by connecting pin shafts on the connecting arms, and form movable hinges; the 4-8 pole climbing mechanisms are distributed in the circular robot body uniformly; the snake-shaped swing arm consists of three swing arm parts, namely a large arm, a middle arm and a small arm; and the movable studio comprises two paralleled linear guide rails, a walk driving device and an operation room.

The invention relates to the technical field of medium and small span bridge construction and control, in particular to a precast beam size matching method and system based on a three-dimensional laser scanner, and the method comprises the steps: obtaining precast beam point cloud data, carrying out the preprocessing of the precast beam point cloud data, carrying out the registration of the preprocessed precast beam point cloud data with a BIM model, and obtaining the size of a precast beam. The automatic detection of the structure size is realized; then, by obtaining interface angular points of the precast beams, conducting virtual splicing by aligning the cross section angular points of the adjacent precast beams, simulating the field splicing process and obtaining key construction details, accurate and reliable data support is provided for follow-up construction, it is ensured that each beam section can conform to design specifications during splicing, and therefore potential safety hazards are reduced; the controllability and scientificity of the construction process are improved, the construction efficiency is improved, the construction period is shortened, the service life of the bridge structure is prolonged, and the structural reliability is improved. The problems of low precast beam parameter detection efficiency, high operation difficulty and low accuracy in the prior art are solved.

The invention discloses an intelligent model data processing method applied to metal smelting, and relates to the technical field of metal smelting, and the method comprises the following specific steps: S100, collecting metal smelting data in real time, S200, establishing different sub-models, S300, outputting a prediction result based on each sub-model, and carrying out uncertainty evaluation. The invention designs a multi-target model output fusion mechanism capable of simultaneously considering the product quality, the energy consumption and the production efficiency in the metal smelting process, and through a weighted average method, the optimal decision output is obtained through the intelligent fusion of the output results, and the comprehensive optimal decision output is obtained, and S500, the actual data in the production process is continuously monitored, and the operation parameters of equipment are adjusted. The invention designs a multi-target model output fusion mechanism capable of simultaneously considering the product quality, the energy consumption and the production efficiency in the metal smelting process. And the model prediction results corresponding to different targets are intelligently fused to obtain a comprehensive optimal decision output, so that the metal smelting process can realize balance and optimization among a plurality of key indexes, and the demand of an enterprise for maximizing comprehensive benefits is met.

The invention discloses SYBR Green I real-time fluorescent quantitative PCR (polymerase chain reaction) detection primers and a kit for large yellow croaker pyramidal worm, and belongs to the technical field of biotechnology and diagnostic detection. A pair of specific primers designed by the invention comprises an upstream primer as shown in SEQ ID NO. 1 and a downstream primer as shown in SEQ ID NO. 2. By adopting the kit disclosed by the invention, the rapid quantitative detection of the large yellow croaker pyramidal worm can be realized, the detection specificity is strong, and the sensitivity is high. At present, a research report of a real-time fluorescent quantitative PCR detection method for quantitatively detecting the Pseudosciaena crocea pyramidal based on SYBR Green I does not exist, a basis can be provided for diagnosis of Pseudosciaena crocea pyramidal infection, an important role is played in guarantee of development of Pseudosciaena crocea breeding industry, and the method has a good application prospect.

The invention discloses a building construction quality control method, and relates to the technical field of building engineering, and the method comprises the steps: building a BIM model; setting an observation station, and performing three-dimensional laser scanning on the constructed building part on the site according to the observation station to obtain a plurality of scanning images; splicing the plurality of scanning images based on the reference point to obtain a three-dimensional point cloud model; selecting a common reference point, moving the BIM model and the three-dimensional point cloud model to the common reference point to enable the two models to coincide, and checking and comparing deviations existing in key point positions on model components; calculating a variance according to the deviation, and determining whether to re-customize the curtain wall plate or not according to the variance; and 3, on the basis of the three-dimensional point cloud model obtained in the step 3, determining whether to re-establish the model according to the variance. According to the method, the real object is converted into the model through field scanning, the model and the BIM model are subjected to superposition comparative analysis, the deviation between the model and the BIM model is rapidly and accurately measured, and BIM model parameters and special-shaped curtain wall plate customization parameters are conveniently corrected in time.

The invention belongs to the technical field of road engineering materials, and discloses a wide-temperature-range crack pouring adhesive based on rubber modified asphalt and a preparation method thereof.The wide-temperature-range crack pouring adhesive is prepared by taking the rubber modified asphalt as a matrix through a response surface method and adding SBS, basalt fibers and a modifier. The performance of the material is tested through a conventional test, and meanwhile, a test with more characteristics in northeast regions is provided, and the performance of the material is detected through injection depth, viscosity, low-temperature stretching after aging and freeze-thaw cycle stretching. Results show that a test designed by a response surface method has very strong reliability, and the modified heating type crack pouring adhesive added with the basalt fiber can significantly improve the high and low temperature performance of the crack pouring adhesive, and also has good anti-aging performance and freeze-thaw cycle resistance. And the filling depth and the viscosity of the sealant are not obviously lower than those of the same type of sealant.

The invention discloses an electrical element magnetic core loss prediction method based on a soft gating hybrid expert model, and relates to an electrical element magnetic core loss prediction method, which adaptively adjusts the expert weight, so that the model is more flexible and has strong adaptability when facing different types of data. Diversified expert model combinations can complement respective advantages, so that the overall robustness and generalization ability are improved, and the risk of overfitting is reduced; in addition, the modular design enables the model to be easy to expand and maintain, and the interpretability of the model can be enhanced by analyzing the output weight of the gating network; furthermore, automatic parameter adjustment and end-to-end training are carried out through an intelligent optimization algorithm, so that the performance of the model is further improved; and finally, the soft gating hybrid expert model can effectively process non-linear and complex relationships, the powerful non-linear modeling capability of the MLP is utilized to capture a complex mode in the data, and the calculation efficiency is improved through parallel calculation and dynamic resource allocation. The advantages enable the soft gating hybrid expert model to show higher accuracy and reliability when processing complex tasks such as magnetic core loss prediction and the like.

The invention discloses an underwater operation robot. The underwater operation robot comprises a multi-direction posture self-stabilization structural frame and a rotary cleaning system; the structural frame comprises a buoyancy pipe, and a flowing medium is arranged in the buoyancy pipe; the rotary cleaning system comprises a plurality of rotary cleaning discs and a water inlet, and the water inlet communicates with external water supply equipment to supply water to the rotary cleaning discs; the rotary cleaning disc comprises a cavitation nozzle obliquely arranged relative to the rotary cleaning disc, and when the cavitation nozzle sprays liquid, the counter-acting force of the cavitation nozzle drives the rotary cleaning disc to rotate. According to the underwater operation robot, the structural frame with the multi-direction posture self-stabilizing function is adopted, the flowing medium is injected, autonomous keeping of the underwater posture of the robot is achieved, and the control difficulty is reduced. In addition, the rotary cleaning system adopts the cavitation jet technology, the cleaning effect is good, the rotary cleaning disc can spin when the cavitation nozzle sprays water, no extra energy or no driving mechanism is needed to drive the rotary cleaning disc to rotate, the structure is simple, and the reliability is high.

The invention relates to the technical field of hydrogen fuel cell vehicle energy management, in particular to a multi-stack hydrogen fuel cell vehicle energy management method and device and a medium, and the method comprises the steps: obtaining the required power of a vehicle and the SOC of a power battery; determining a distribution quantity according to the SOC of the power battery, and distributing the required power to power of the power battery and power of a fuel battery system according to the distribution quantity; according to the operation data of each electric pile of the fuel cell system in the previous vehicle operation time period, determining the total performance attenuation rate of each electric pile in the previous vehicle operation time period; determining the service life type of each electric pile according to the total performance attenuation rate; and distributing the fuel cell system power to the electric piles participating in the power distribution after the electric piles participating in the power distribution are determined in the current vehicle running time period. The difference of the working states of the electric piles is small, and the service lives of the electric piles are more balanced, so that the service life of the fuel cell is prolonged.

The invention discloses a high-precision bearing life prediction method, system and device and a medium, and the method comprises the following steps: carrying out the denoising of original vibration signal data of a bearing, and calculating the RMS features of the denoised signal; bearing state division operation is executed based on the RMS features and a PELT algorithm; based on the original vibration signal data of the bearing, extracting a time-frequency graph corresponding to the degradation characteristics of the bearing; constructing an input data set based on the bearing state division result and the time-frequency diagram; training a bearing life prediction model by adopting the input data set, and predicting the bearing life by adopting the trained bearing life prediction model; according to the method, bearing state division operation with relatively low calculation amount can be adopted, meanwhile, model input data construction is realized by adopting simplified steps, and accurate prediction of the bearing life is performed through a prediction model based on an Informer deep learning framework.

The invention relates to a simulation test platform for a 1553B bus of a spacecraft, which is suitable for stand-alone test and system test of a 1553B bus interface of a satellite model, and comprises an application layer, a communication layer and a data interface connected with the application layer and the communication layer, the application layer comprises a main control module, a database module and a user interaction module; the communication layer comprises a non-periodic data communication module and a periodic data communication module. The method can be suitable for ground tests of multiple satellite models at the same time, the research and development cost is saved, great convenience is brought to the satellite ground tests, and therefore the development progress of the satellite models is effectively guaranteed.

The invention provides a laser array type soil surface displacement field identification method, which comprises the following steps of: determining a plurality of observation points according to a laser array and a target on the soil surface, establishing a coordinate system by taking a soil body mark point without load as an original point, and recording the original coordinates of each observation point; after the soil body displaces, the laser array records coordinates of the observation points after displacement according to a scanning period; extracting coordinates of a plurality of observation points after soil displacement according to the target displacement field of the soil surface and the spatial change surface data; and interpolating and outputting the plurality of coordinates into a surface by using an interpolation method. According to the invention, high-precision and non-contact scanning is carried out on the land surface by utilizing laser displacement sensor equipment through a laser array type soil surface displacement field identification technology, and the displacement condition of the land surface can be monitored in real time through processing and analyzing scanned data, so that potential geological disaster risk points can be identified. Therefore, the purpose of continuously monitoring the soil surface displacement in real time is achieved.

The invention discloses an automatic voltage regulation method and device for a portable on-load transformer, and the method comprises the steps: collecting images corresponding to a push rod when a gear-shifting knob of the on-load transformer is located at different gears in the process that the push rod of a gear-shifting mechanism pushes the gear-shifting knob for gear shifting, so as to build a feature image library of the images of the push rod under each gear; when gear adjustment is carried out each time, whether gear adjustment is completed or not is comprehensively judged according to the resistance change condition of swing of the push rod when the gear adjustment knob is pushed and the push rod image collected in real time, and the current push rod image collected when gear adjustment is completed is compared with the image of the corresponding gear in the feature image library to determine whether gear adjustment is correct or not. Whether the gear shifting process is completed or not can be comprehensively judged, and the accuracy of each gear shifting operation is ensured; the gear corresponding to the current push rod is accurately recognized, so that whether the gear is correctly shifted to the target gear or not is accurately judged, the gear recognition precision is high, and the reliability is high.

The utility model relates to the technical field of PVD (Physical Vapor Deposition) coating, in particular to a PVD coating vacuum system, which comprises a feeding table, a sheet inlet / outlet chamber, a first buffer chamber, a process chamber, a second buffer chamber and an isolating valve, and the sheet inlet / outlet chamber, the first buffer chamber, the process chamber and the second buffer chamber are sequentially arranged behind the feeding table. Isolation valves are arranged between the feeding table and the sheet inlet and outlet chamber, between the sheet inlet and outlet chamber and the first buffer chamber, between the first buffer chamber and the process chamber, and between the process chamber and the second buffer chamber; and when the isolation valves are closed, all the chambers form closed chambers. According to the PVD coating vacuum system disclosed by the utility model, the isolating valves are arranged among the vacuum chambers, so that the vacuum degree of each vacuum chamber can be well controlled; the vacuum coating system is simple in structure, high in reliability, low in cost and capable of being used for research and development of PVD coating products, continuous production can be achieved, cost is saved to a great extent, and working efficiency is improved.

The invention relates to a dual-computer hot-standby satellite-borne cryptographic device, a main-standby switching method, a satellite and a communication system, and the dual-computer hot-standby satellite-borne cryptographic device adopts a single-board dual-computer hot-standby hardware architecture; comprising a cryptographic module A and a cryptographic module B which are mutually active and standby, and an RAM chip which is used as a shared memory of the cryptographic module A and the cryptographic module B, two one-way signal lines are connected between the password module A and the password module B, one signal line transmits main and standby signals of the password module A from the password module A to the password module B, the other signal line transmits main and standby signals of the password module B from the password module B to the password module A, and the main and standby signals are used for autonomous switching of dual-computer hot standby; and the RAM chip is used for storing state information required by main / standby switching. According to the invention, the reliability of satellite load service password processing is improved.

The invention discloses a model pruning method based on channel attention and adaptive learning, and the method comprises the steps: firstly introducing a channel attention mechanism SENet, adding the SENet to each convolution layer of a target model, and training the SENet until the model converges; then, extracting the middle output of the SENet of the training sample, combining with a scaling factor in a BN layer to obtain a channel importance score, and then performing pruning according to the channel importance score; and finally, training the pruned model by using an adaptive learning loss function until convergence. According to the method, the model pruning principle and the channel attention principle are fully utilized, and the high-precision model pruning method in the remote sensing field is achieved. The method can greatly reduce the consumption of computing resources, improve the processing efficiency and reduce the hardware cost while maintaining the model precision or even exceeding the model precision before pruning.

The invention relates to a low-voltage cable with a protection structure applied to the technical field of cables, and the low-voltage cable comprises a low-voltage cable body and an anti-pull-damage protection mechanism, the anti-pull-damage protection mechanism comprises an anti-damage buffer box, a semi-cylindrical curved cable column is movably arranged in the anti-damage buffer box, and a pull detection supporting cylinder is embedded in the anti-damage buffer box in a penetrating manner; the anti-pulling-damage protection mechanism can provide a buffering protection effect for the low-voltage cable body, when the low-voltage cable body is subjected to large pulling force, the low-voltage cable body can be protected to a great extent, the low-voltage cable body is prevented from being deformed and damaged, the anti-pulling-damage protection mechanism further has an alarm function, and when the low-voltage cable body is subjected to large pulling force, the low-voltage cable body is prevented from being damaged. When the low-voltage cable body is pulled, the loss prevention intelligent alarm device starts the alarm to make an alarm sound to warn and remind related personnel and promote the related personnel to stop pulling the low-voltage cable body, so that the low-voltage cable body can be further prevented from being deformed and damaged due to large pulling force, and the safety of the low-voltage cable is greatly improved.

The invention discloses a battery capacity online estimation method based on model parameters, and belongs to the technical field of battery capacity estimation. Comprising the following steps: acquiring voltage and current data of a battery, and performing parameter identification and feature processing on the voltage and current data; performing correlation analysis on the battery model parameter and the maximum available capacity thereof, and extracting a battery health factor; building a bidirectional gating cycle unit model based on a double attention mechanism, and dividing battery data into a training data set and a test data set; and inputting the training data set into the bidirectional gating cycle unit model for training until the loss function converges and then carries out battery capacity online estimation together with the test data set. According to the method, a double attention mechanism is introduced, the correlation degree of battery model parameters, historical information and capacity is analyzed from the two aspects of features and time sequences, the features and time sequences with high contribution degree are highlighted through dynamic weight distribution, and the model precision is improved.

The invention discloses a vehicle moving track data compression method, which comprises the following steps of: acquiring vehicle driving data which comprises vehicle original track data; performing first analysis processing on the vehicle driving data to obtain vehicle travel information; performing first impurity removal processing on the vehicle original trajectory data to obtain first trajectory impurity removal data; performing track redundant point merging processing on the first track impurity-removed data to obtain first track compressed data; and performing similar track merging processing on the first track compressed data to output second track compressed data. According to the technical scheme, compared with the prior art, the compression rate of the vehicle form track can be increased, and the pressure of data processing and transmission can be reduced. In addition, the invention also relates to a vehicle movement track data compression system and electronic equipment, which also have the above beneficial effects.

The invention relates to a direct current insulation resistance detection method and a detection device. The method comprises the following steps: establishing a mathematical model of insulation resistance and bus voltage of a direct current bus to be measured, acquiring voltage to ground values of positive and negative buses when a positive bridge arm and a negative bridge arm of an unbalanced bridge are not connected and when the negative bridge arm of the unbalanced bridge is connected, introducing noise influence, and establishing a calculation method for eliminating the noise influence based on the acquired measurement values. And performing iterative calculation to obtain a to-be-identified parameter theta. And theta is substituted into the mathematical model to solve the insulation resistance value of the direct current system to be measured. According to the method, environment noise generally existing in the test process and pulse noise caused by circuit switch switching are fully considered, so that the insulation resistance detection error of the direct current system to be detected is reduced, and the reliability of a detection result is improved.

The invention discloses a continuous unsupervised domain adaptive fault diagnosis method. The method comprises the following steps: acquiring source domain and target domain fault data of rotating mechanical equipment; a prototype comparison learning strategy based on measurement and characterization learning is adopted, a fault diagnosis model is pre-trained through source domain data, a uniformly-divided unit hyperspherical feature space is obtained, and the fault diagnosis model comprises a feature extractor and a cosine similarity classifier; freezing a prototype obtained by source domain pre-training, and carrying out supervised training combined with source domain data on the fault diagnosis model to realize continuous domain adaptation to a target domain; source feature knowledge distillation and local domain alignment are carried out, mutual information loss between features and a prototype is maximized, and supervised training is carried out on a fault diagnosis model in combination with source domain data; and the fault diagnosis model is applied to actual fault diagnosis after completing learning of all current target domains. According to the method, new data distribution can be quickly and effectively adapted, the performance of the model on historical data distribution is kept, and knowledge forgetting is avoided.

The utility model provides a membrane changing device and a sampler with the same, the membrane changing device comprises a membrane pushing mechanism for driving a filter membrane to move left and right, a filter membrane lifting mechanism for driving a new filter membrane and an old filter membrane to move up and down, a sampling head lifting mechanism for driving a sampling head to move up and down and a controller, and the controller uniformly controls the operation of the mechanisms. According to the membrane replacing device, the membrane pushing mechanism, the filter membrane lifting mechanism and the sampling head lifting mechanism are matched with one another, so that multiple filter membranes can be automatically replaced. A cam is matched with a lever structure to realize lifting of a sampling head, the structure is stable and reliable, a horizontal lead screw drives a horizontal push rod to push a filter membrane to move, a new membrane cylinder and an old membrane cylinder are arranged below a demolding mechanism, the filter membrane falls into a corresponding filter membrane barrel after being pushed to a corresponding position, and accurate positioning is not needed. The whole die changing device is compact in structure and high in reliability.

The invention provides a deep learning method and system based on a station-city fusion space energy consumption model, and the method comprises the steps: analyzing a core energy consumption system of a core space region in a station-city fusion three-dimensional network space through employing a space region analysis method, and constructing a station-city fusion three-dimensional network energy consumption model of the core energy consumption system; constructing a carbon emission mechanism model through a station-city fusion three-dimensional network energy consumption model, defining a loss function of the carbon emission mechanism model according to a deep learning neural network structure of an initial input variable and an output variable in the carbon emission mechanism model, and performing iterative training on the loss function by adopting an iterative optimization function to obtain a carbon emission mechanism model; and outputting a deep learning neural network agent model until the loss value is lower than an allowable value, and finally evaluating the green operation performance of the station-city fusion three-dimensional network space by adopting the deep learning neural network agent model according to specification requirements to obtain an evaluation result. Compared with the prior art, the method is higher in evaluation efficiency and higher in reliability.

The invention relates to the technical field of transport rolling unloading operation, in particular to a ship unloader berthing rolling unloading system and method.The ship unloader berthing rolling unloading system comprises a rigid string protection mechanism and a rolling unloading mechanism, and the rigid string protection mechanism is arranged at the edge of a transport ship; the rolling unloading mechanism comprises a deck bridge head, a sleeper, an ashore bridge head, a slope track and a rigid gap bridge, and the deck bridge head is fixedly arranged on the transport ship and corresponds to the deck track; the sleeper is fixedly arranged on the berthing wharf, the ashore bridge head is arranged at the top, one end of the rigid gap bridge is hinged to the ashore bridge head, the other end of the rigid gap bridge is in free lap joint with the deck bridge head, one end of the slope track is located on the wharf track, and the other end of the slope track is located on the sleeper and connected with the ashore bridge head. By changing the traditional roll-roll loading mode of jacking, steering and rail falling and the conventional berthing mode of the transport ship, the effect that the ship unloader unloads the ship from the transport ship conveniently is achieved, the ship unloading risk of the ship unloader is reduced, and the working efficiency of roll unloading operation of the ship unloader is improved.

The invention provides a chronic disease management-oriented dynamic causal model construction and application method, which comprises the following steps of: continuously acquiring multi-modal dynamic health data of a patient, taking the multi-modal dynamic health data as a node set, taking a causal relationship among the multi-modal dynamic health data as an edge set, and establishing a dynamic causal model; the causal intensity between the multi-modal dynamic health data is used as a weight matrix, and a dynamic causal model is constructed based on the node set, the edge set and the weight matrix; for the new multi-modal dynamic health data, calculating marginal causal effect values of the current new multi-modal dynamic health data and all node sets in the dynamic causal model, and updating the dynamic causal model based on the marginal causal effect values; for each time step, the comprehensive causal weight of each side of each time step is calculated according to the long-term component features and the short-term component features of the multi-modal dynamic health data corresponding to the same side, the dynamic causal model is updated based on the comprehensive causal weight, and an application method for the dynamic causal model is provided.

The invention discloses a joint simulation method for researching the influence of an urban wind environment on urban building energy consumption. The method comprises the following steps: automatically generating a block model and calculating parameters; simulating selected block scale urban heat island intensity, and updating temperature and humidity data; simulating wind speed data of eight wind directions of the selected block for 8760h in the whole year; the average wind speed around each building is automatically obtained, and a new EPW file is generated for each building; building energy consumption of the selected block under three time scales is simulated and calculated; using the updated EPW file to simulate and calculate the building energy consumption of the selected block under three time scales; the influence of the wind environment on the urban building energy consumption is quantified according to the two energy consumption calculation results; and obtaining an influence rule of the stroke environment of the case city (climate zone) on the urban building energy consumption according to a result. According to the method, the speed and accuracy of urban wind environment calculation are both considered, and quantitative analysis of simulation results is simply achieved.