54results about How to "Ensure adaptability" patented technology

The invention discloses a connecting piece welding quality detection method and system, a medium and electronic equipment, and relates to the technical field of industrial automation and machine vision. The method specifically comprises the following steps: controlling a moving mechanism carrying an imaging device to move to a detection position, and obtaining a to-be-detected image containing a connecting sheet; inputting the to-be-detected image into a pre-trained target detection model, and obtaining a prediction bounding box of a connection sheet region; performing size expansion based on the prediction bounding box, and generating a region of interest covering the deformation region of the connecting sheet; in the region of interest, extracting a target feature point coordinate of a connecting sheet and a centroid coordinate of a welding spot by using an image processing algorithm; and calculating a distance value between the coordinates of the target feature point and the coordinates of the mass center of the welding spot, and judging the welding quality of the connecting piece based on the distance value. The invention aims to realize high-precision and high-efficiency welding quality detection of the connecting piece under the conditions of complex background interference and uncertain target form.

The invention belongs to the technical field of truss girder hoisting, and particularly relates to intelligent hoisting equipment for a suspension bridge truss girder in an extreme wind environment and a hoisting construction method.The intelligent hoisting equipment comprises a floating crane, and a hoisting tool component is fixedly installed at the lower end of a steel wire rope of the floating crane and comprises a posture adjusting device and an offset adjusting device. According to the intelligent hoisting equipment for the suspension bridge truss girder in the extreme wind environment and the hoisting construction method, by arranging the sling component and integrating the posture adjusting device and the offset adjusting device, multi-degree-of-freedom active adjustment can be conducted on the hoisted truss girder. The offset adjusting device generates reverse inertia force by driving the mass block to move, so that horizontal swinging of the truss girder caused by wind load is effectively counteracted; the posture adjusting device accurately corrects pitching and rolling postures of the truss girder by adjusting the length of the steel wire rope set, and therefore the overall wind resistance stability and posture keeping capacity of the hoisting system are remarkably enhanced in the extreme wind environment.

The invention discloses a method and a system for monitoring pressure abnormity of a fuel oil common rail pipe of a marine main engine. The method comprises the following steps: acquiring historical operation parameter data and constructing a data set; the historical operation parameter data comprises historical host load data, historical host rotating speed data and historical fuel oil common rail pressure data; historical operation parameter data are preprocessed, an SVDD model is constructed and trained based on the historical operation parameter data, the model obtains a historical normal data hyper-sphere in a three-dimensional feature space, and the center and the radius of the hyper-sphere are obtained; similarly, real-time operation parameter data collected in real time are preprocessed and then input into the model, the distance between the real-time operation parameter data and the center of the hypersphere is obtained, and the value of the distance is compared with the value of the radius of the hypersphere in historical normal data; and when the numerical value of the distance is large, it is judged that the real-time operation parameter data is within an abnormal range, the fuel oil common rail pressure of the ship main engine is in an abnormal state, and an alarm signal is triggered. The method has the effect of low error rate.

The invention relates to the field of equipment diagnosis, and discloses a submersible fault time sequence causal chain diagnosis method and system, and the method comprises the steps: obtaining multi-dimensional operation monitoring data in the operation process of a submersible; performing exception association analysis on the multi-dimensional operation monitoring data to obtain a cross-dimensional collaborative exception coefficient of an association unit group under each monitoring dimension; performing threshold comparison on all the cross-dimension collaborative anomaly coefficients, and determining each anomaly association unit group; and determining a diagnosis dimension trigger signal of the submersible according to the number of the abnormal association unit groups and the evolution characteristics of the cross-dimension collaborative abnormal coefficients of all the abnormal association unit groups. And a diagnosis dimension trigger signal is dynamically generated through the change and evolution characteristics of the cross-dimension collaborative abnormal coefficient, so that fault diagnosis scheduling of the submersible is triggered, and the response mechanism ensures the flexibility and adaptability in the diagnosis process.

The present application relates to the technical field of data processing and business intelligence, and discloses a multi-dimensional data driven self-brand growth value prediction method, which extracts a main feature vector of a previous time step, calculates a dynamic weight for a same-product white label control group to generate a dynamic benchmark, and accordingly peels off a pure brand-driven incremental index, and combines a repeat purchase decay rate and a search index to construct a three-dimensional state vector; through eigenvalue decomposition on a state covariance matrix, a current main feature vector and a maximum eigenvalue are obtained, and the main feature vector is fed back to a weight calculation link to form a closed loop; finally, the decomposition result and a historical state sequence are respectively input into a pre-trained machine learning model to output a brand life cycle stage and a future brand asset valuation. The present application accurately anchors a reference system through a dynamic feedback mechanism, and realizes comprehensive and accurate diagnosis and prediction of brand value.

This invention relates to a method and system for automated generation of TSN network configurations based on a reflective agent. The system comprises a network state perception and intent reflection module, a YANG model configuration parameter and structure decision module, and a YANG model intelligent generation module. The network state perception and intent reflection module is responsible for structurally perceiving network state and resource information. The YANG model configuration parameter and structure decision module integrates multi-source knowledge and contextual information, intelligently generating deployable TSN scheduling parameters and YANG configuration structures based on business needs. The YANG model intelligent generation module automatically generates the corresponding YANG configuration model based on the optimized results, performs syntax and semantic verification, and then distributes it to the target device, achieving automated deployment of network configurations. This invention significantly improves the intelligence level and deployment automation capability of TSN network configuration by introducing reflective reasoning and feedback optimization techniques.

A power transmission line fault simulation and simulation test method and system, a transient simulation model of an actual power transmission line is established, and a wave speed is simulated and calculated; a simulation test is carried out on a test line, a fault source is output, a normal current at a power frequency is first output, a lightning impulse is injected at a peak of the normal current at the power frequency, and then a flashover discharge current of an insulator string is output, a duration of the flashover discharge current is calculated according to a lightning fault current set during simulation; if the test is normal, wave head times captured by two fault detection devices are obtained, corresponding two positioning errors are calculated in combination with the wave speed calculated by simulation, if the positioning errors exceed a set error threshold, it is indicated that the fault detection device is unqualified; otherwise, it is qualified, and a fault test is carried out by using the qualified fault detection device. The present application can efficiently carry out a detection test of a power transmission line fault positioning device.

The application discloses a digital-analog fusion double-drive digital-real fusion evaluation method for aerospace equipment, and belongs to the technical field of intelligent manufacturing. The method comprises the following steps: generating enhanced product data covering extreme working conditions through a conditional generative adversarial network, and constructing an environmental parameter matrix to realize systematic representation of assembly working conditions; secondly, a dynamic data scheduler and a hierarchical reward mechanism are designed to realize intelligent fusion of real product data and enhanced product data in the reinforcement learning training process; finally, a bidirectional collaborative training framework is constructed, the feature extraction capability of the graph neural network is combined with the decision-making capability of the reinforcement learning, and the model generalization capability is improved through the meta-course learning of environment perception. In the online evaluation stage, a hybrid prediction mechanism based on uncertainty estimation is adopted to ensure the prediction reliability of the system under different working conditions. The application significantly improves the quality prediction accuracy under the condition of small sample, and is especially suitable for the fields such as aerospace and precision instruments which have extremely high requirements for assembly precision.

The invention relates to the technical field of hybrid power, and discloses a vehicle torque compensation method and device, computer equipment and a storage medium. According to the method provided by the embodiment of the invention, when the vehicle is in the parallel mode and the gear switching is triggered, the connection state of the transmission chain is detected in real time, the connection condition of the power transmission path can be judged, and a prerequisite condition and a reliable basis are provided for subsequent torque compensation; the compensation torque currently needed by the vehicle is obtained, the accuracy and self-adaptability of the compensation amount are ensured, and torque disturbance caused by gear switching is effectively eliminated; and the vehicle is compensated in real time according to the compensation torque, so that engine rotating speed fluctuation and torque impact caused by gear shifting are restrained, high smoothness of the gear shifting process is achieved, and the driving comfort and the overall reliability of the vehicle are improved.

The embodiment of the invention discloses a big data processing method and system based on cloud computing, and the method is realized based on a cloud computing platform, and the method comprises the steps: decomposing a big data processing task into a plurality of micro-tasks, and distributing the micro-tasks to distributed computing nodes; each computing node selects an adjacent node based on a hyper-local algorithm, and constructs a local task processing model only by using local data and metadata of the adjacent node dynamically selected based on a fast game mechanism, the metadata including micro-task data semantic features and task dependency relationships of the adjacent node, and the task dependency relationships of the adjacent node are calculated; semantic features are extracted through a natural language processing technology, and a task dependency relationship is determined based on input and output association of the microtasks; according to the local task processing model, independently executing the micro-task on each computing node, and dynamically adjusting the distribution of the micro-task through a point-to-point local negotiation mechanism; and aggregating the micro-task processing results of the computing nodes to generate a global data processing result. According to the method, the operation and communication overhead is reduced.

The utility model discloses a kind of binding tables for sofa manufacturing, it is related to sofa technical field, and it includes: workbench, the outer surface front end of the workbench is fixedly installed with installation box, the inside fixed mounting of the installation box is with motor, the output of the motor is fixedly installed with threaded rod, the shaft surface one end of the threaded rod is fixedly installed with moving block, and the outer surface bottom end both sides of the moving block are fixedly installed with second connecting plate, by adopting single motor drive precision mechanical linkage mechanism including threaded rod, moving block, second connecting plate, first connecting plate, positioning shaft, slide rail and elastic reset component reset spring, replace the multiple electric push rod and motor relied on in the prior art.This design significantly reduces the procurement cost of equipment, while core transmission components such as threaded rod, connecting plate, slider, slide rail and elastic element compression spring, reset spring are all high-reliability, low-maintenance mechanical structures.

The invention discloses a neural network proxy model-based power distribution area collaborative planning method and system. The method comprises the following steps of: constructing a power distribution area flexible interconnection and energy storage collaborative planning double-layer optimization model; candidate addressing and sizing schemes meeting constraint conditions of the double-layer optimization model are randomly generated, optimization calculation of the operation model is executed on each candidate addressing and sizing scheme, the real total life cycle cost corresponding to each candidate scheme is obtained, and a training sample set is formed; performing supervised training on a preset neural network agent model; and generating and iteratively updating the addressing and sizing scheme, evaluating the predicted total cost of the addressing and sizing scheme by using the neural network agent model, optimizing the addressing and sizing scheme according to the predicted total cost until a convergence condition is met, and outputting a final target addressing and sizing scheme and a corresponding cooperative operation strategy. The problems of'regulation-building-transportation 'disjunction and low calculation efficiency in traditional planning are effectively solved, and collaborative improvement of safety and solving efficiency is achieved.

The invention discloses a power distribution network line environment meteorological data prediction method and system, and belongs to the technical field of meteorological prediction, and the method comprises the steps: S1, carrying out the space-time alignment and quality control of collected historical environment meteorological data of a power distribution network line; s2, taking the lightweight long-short-term memory network as a learning framework of a time sequence evolution rule in the historical environmental meteorological data, and taking the physical constraint as a learning constraint of the time sequence evolution rule in the historical environmental meteorological data to construct a hybrid prediction model; and S3, inputting the historical environmental meteorological data into the hybrid prediction model for multi-scale space-time prediction to obtain an environmental meteorological field of each interval of the power distribution network line, obtaining a confidence interval of each interval according to the environmental meteorological field, and obtaining predicted environmental meteorological data of each interval according to the confidence interval. The technical problem that the environmental weather prediction accuracy is difficult to improve in the prior art is solved.

The utility model relates to the technical field of flue gas desulfurization, and specifically relates to a desulfurization system oxidation air multipurpose system. The primary circulation system and the secondary circulation system are connected by an inlet flue, and the original flue gas flows through the primary circulation system and the secondary circulation system in sequence to carry out the desulfurization process. The primary circulation system includes two primary oxidation air fans, each of which is connected to the absorption tower through the same outlet pipeline. The primary oxidation air fan is used to introduce oxidation air into the outlet pipeline. The outlet pipeline is connected with a cleaning air pipeline and a sealing air pipeline. The cleaning air pipeline is connected to the wet electric dust collector to introduce cleaning air into the wet electric dust collector. The sealing air pipeline is connected to the accident spraying pipeline of the absorption tower to introduce sealing air into the accident spraying pipeline. The system improves the resource utilization rate and energy efficiency of the oxidation air system and avoids the investment waste caused by the repeated construction of the auxiliary air system, thereby significantly reducing the system operation and maintenance costs.

The application discloses a mobile charging robot, and relates to the technical field of charging management, and aims to solve the technical problems of the existing mobile charging robot, that is, the overall output capacity of the battery pack is reduced, the charging speed is limited, and there are safety hazards due to the inconsistency of the health state when the mobile charging robot performs high-power fast charging service, and the mobile charging robot comprises a mobile charging robot body and a thermal management system integrated in the mobile charging robot body; the mobile charging robot body comprises a chassis, a driving system arranged on the chassis, a charging control unit for being connected with an electric vehicle, and a control system; the thermal management system comprises a battery pack comprising a plurality of battery units and used for storing electric energy; the application fundamentally solves the problems of the limited output capacity and safety hazards of the mobile charging robot in the fast charging process due to the inconsistency of the battery pack by introducing the differential two-way thermal management based on the health state of the battery unit.

The application discloses a CC attack intelligent defense method, device and equipment based on a large language model, comprising: performing traffic semantic translation on captured real-time network traffic to generate a semantic description object including a time sequence behavior sequence and context features; inputting the semantic description object into a domain fine-tuned large language model to perform attack intention reasoning to obtain a threat judgment result, generating a corresponding dynamic defense strategy description based on the threat judgment result; compiling the dynamic defense strategy description into an executable instruction sequence, and issuing the executable instruction sequence to a downstream security device to drive execution to obtain an execution result; based on the execution result, monitoring network traffic situation and business indicators after the downstream security device executes the strategy to generate strategy effect evaluation data; and optimizing the large language model based on whole-link data including the semantic description object, the threat judgment result, the dynamic defense strategy description and the strategy effect evaluation data.

The present application relates to the technical field of robots, and in particular to a motion generation method, system, device and storage medium for a humanoid robot, comprising: physical simulation rendering generates a visual video containing visual representation of joint motion state of the robot; analyzing the visual video to obtain frame images, natural language instructions and motion sequences, decoding output trajectory data; and outputting control instructions through a hierarchical architecture of high-level motion planning and bottom-level dynamics tracking to drive the robot to perform actions. The present application implicitly encodes the joint state of the humanoid robot in the visual features, improves the inference efficiency and generalization ability of the model, and the hierarchical decoupling architecture of the large model high-level trajectory planning and the reinforcement learning bottom-level dynamics tracking controller pays a small performance cost, which can compress the inference time delay to within 50ms and the parameter quantity to below 2 billion, and balances the high generalization of the stateless VLA and the small model, few-step inference, edge low latency, and low power requirement.

The application provides a user interest-oriented fragmented learning time optimization distribution method and system, relates to the technical field of data processing, and the method comprises the following steps: acquiring user learning characteristic data; combining the user learning characteristic data, establishing a discrete time dynamic function for describing knowledge state evolution, and determining the knowledge state value of the user for different learning themes; combining the discrete time dynamic function to establish a virtual learning demand queue, and determining a Lyapunov control parameter for balancing the backlog degree of the virtual learning demand queue based on the virtual learning demand queue; constructing a convex optimizer with the output being the optimal distribution duration of the learning theme; determining the current time fragment, the current knowledge state value and the current virtual learning demand queue; inputting the current time fragment, the current knowledge state value and the current virtual learning demand queue into the convex optimizer to output the optimal distribution duration of each learning theme; and sequentially pushing each learning theme corresponding to the optimal distribution duration to the user.

The invention provides a coal quality parameter inversion method and system based on heterogeneous double-source collaboration and empirical self-evolution, and the method comprises the steps: building a parameter-waveband mapping relation, and outputting a hybrid coding sequence feature through heterogeneous feature attention fusion; inputting the mixed coding sequence features and the coal quality parameter coding features into a spectrum-coal quality combined learning module, and outputting spectral feature high-quality potential representation and coal quality parameters; when the to-be-inverted coal quality is inverted, if no new coal type appears, using the coal quality parameter as an inversion prediction result; when a new coal type appears, spectral feature high-quality potential representation of existing coal quality and encoded real coal quality parameters are fused to serve as historical spectrum-coal quality potential features, the historical spectrum-coal quality potential features and mixed encoding sequence features of the new coal type are input into an increment feature learning module together, and through cross-coal-type learning of experience transmission and gating feedback, the coal quality of the new coal type is obtained. And generating a coal quality parameter inversion prediction result considering historical knowledge and new features. The method can be used for coal quality parameter inversion.

This utility model discloses an experimental device for offline simulation of alkaline washing and degreasing of automotive body panels. It includes a main body, a pretreatment component, a first connecting component, a second connecting component, a temperature sensor, a pH sensor, and a processor. By separating the main body and the pretreatment component, the automotive body panel and the treatment liquid are placed in a first space and a second space respectively, effectively preventing the treatment liquid from contacting the automotive body panel without precise control, thus ensuring the stability of the experimental environment. Guided by the first connecting component, the treatment liquid can flow regularly from the second space into the first space, simulating the dynamic flow of the treatment liquid in actual production. This ensures the uniformity and consistency of the degreasing effect, improves the utilization rate of the treatment liquid, and ensures that the treatment liquid is always in an optimal degreasing state by continuously updating it, thus improving the stability of the degreasing effect. It can accurately simulate the degreasing process in actual production and monitor and control the process parameters in real time.

The application discloses a constant-temperature clean room design method and system and a storage medium, and the system comprises a scheme data acquisition module, a simulation model building and calculation module, a simulation result evaluation module, a scheme optimization adjustment module and a scheme comparison and output module. The simulation model of the clean room is established, the simulation result analysis and the temperature design effect judgment of the scheme are determined, the temperature distribution degree of the clean room under different schemes can be accurately quantitatively analyzed, the influence of different scheme measures on the temperature distribution effect can be reflected in time, and adjustment is made according to the simulation result. Compared with the traditional method, the dynamic design of the high-precision constant-temperature laboratory can be better realized.

This application relates to a numerical control input / output control method and system based on M-codes. The method includes acquiring M-code instructions, reading and parsing the M-code instructions to obtain parsing results; constructing task objects based on the parsing results and submitting the task objects to the execution queue of the task scheduling module; and executing corresponding control logic based on the task objects after the software state scheduling module receives the task objects. This application has the effect of improving task scheduling efficiency.

The invention relates to the technical field of intelligent control, and provides an intelligent spray control processing method and equipment based on temperature field spatial-temporal characteristics, which are used for improving the response timeliness and regulation and control accuracy of a spray control system. The method comprises the following steps: performing dimension mapping processing on original temperature field data of a target area to generate temperature field associated data; time variation trend characteristics and spatial distribution characteristics of the temperature field associated data are extracted and fused to generate temperature field spatial-temporal characteristics, and the temperature field spatial-temporal characteristics are imported into a gas concentration prediction model to output a gas concentration prediction result; the average temperature gradient intensity is determined through the context cohesion of the temperature field data between the adjacent space coordinate marks, and the space deviation distance is determined through the deviation situation comparison of the preset standard temperature field data and the temperature field associated data; spraying control strategy data are generated by fusing multiple parameters such as temperature field spatial-temporal characteristics, average temperature gradient intensity and spatial deviation distance and are transmitted to a spraying control system, and double-situation collaborative correction is carried out.

The invention relates to the cross technical field of artificial intelligence and metal plastic forming, and discloses a difficult-to-deform alloy multi-pass forging parameter optimization method based on machine learning, and the method comprises the following steps: constructing a physical constraint sub-model fusing dislocation density evolution, dynamic recrystallization and a grain growth mechanism; acquiring multi-pass time sequence process data through infrared temperature measurement and optical strain measurement; predicting the microstructure state of each pass by using a graph attention neural network; residual error correction is carried out by combining a physical model and data prediction, and a microstructure evolution trajectory is generated; calculating a grain boundary weakening index and constructing a multi-objective optimization function containing energy consumption, grain uniformity and grain boundary stability; and solving the optimal forging parameter sequence by adopting an improved non-dominated sorting genetic algorithm. The system comprises corresponding function modules. According to the method, the cracking risk can be remarkably reduced, the structure uniformity is improved, and energy consumption is reduced.

The utility model provides a kind of multi-channel battery charging and discharging device, belong to battery charging and discharging technical field, including installation shell, fixedly installed the connecting seat of installation shell side wall, fixedly installed the slide rail of installation shell inner wall, set in the charging and discharging subassembly of installation shell inner cavity, and set in the mobile subassembly of the slide rail inner wall cooperation charging and discharging subassembly use.The utility model is through the setting of charging and discharging subassembly box mobile subassembly, realized the efficient, stable and smooth of battery charging and discharging process, the design of installation box, contact head and spring in charging and discharging subassembly, ensure the stability and adaptability of battery and device connection, avoid the problem of poor contact or loose;Mobile subassembly is linked by motor, gear, rack and moving plate, realizes the automatic access and fixed of battery, easy to operate and accurate, reduces manual intervention;The ball design in rolling seat further improves the smoothness of battery movement, reduces friction loss.

The application discloses an enhanced recognition method in a multi-source heterogeneous scene, and relates to the technical field of intelligent sensing, comprising the following steps: S1, dynamic acquisition and preprocessing of multi-source data; S2, dynamic modal weight distribution; S3, cross-modal feature enhancement fusion; S4, lightweight model reasoning and optimization; S5, dynamic environment adaptive learning. The enhanced recognition method in the multi-source heterogeneous scene effectively solves the problems of heterogeneous data time alignment and format unification by synchronously collecting multi-modal data in real time and performing standardization processing. In cooperation with the unique dynamic modal weight distribution mechanism, the reliability of each modal data can be evaluated in real time according to the scene change, and the fusion weight can be dynamically adjusted, so as to further ensure the accuracy and adaptability of data fusion. In addition, more discriminative joint features are generated through cross-modal feature enhancement fusion, which significantly improves the recognition accuracy and robustness.

This invention discloses a multi-dimensional trust assessment method for enhancing the security and stability of SDN, belonging to the field of network security technology. The method includes: S1: collecting traffic data on device behavior in the network using a controller and querying its trust value; S2: initiating a trust assessment mechanism to perform multi-dimensional trust assessments on devices; S3: entering a trust fusion mechanism to comprehensively process the results of each trust assessment; S4: based on the updated trust value, implementing corresponding blocking strategies for hosts or devices with low trust values ​​or exhibiting malicious behavior. This invention overcomes the limitations of traditional methods through multi-dimensional trust assessment and fusion, enabling a more comprehensive and accurate assessment of device behavior. By dynamically adjusting trust values ​​in real time and implementing an automated response mechanism for malicious devices, it improves network security and adaptability, and maintains stable network operation even in the face of complex attack scenarios.

This utility model relates to the technical field of engine lubrication structure, and discloses an oil cooler, including a housing with a coolant outlet pipe and a coolant inlet pipe fixedly connected to its outer wall. A heat exchanger is disposed inside the housing, and a heat dissipation assembly is disposed inside the heat exchanger. The heat dissipation assembly includes a first heat sink, disposed inside the heat exchanger, and a second heat sink on one side of the outer wall of the first heat sink. The first and second heat sinks are arranged alternately. A heat dissipation groove is formed inside the first heat sink. A heat dissipation plate is fixedly connected to the upper surface of both the first and second heat sinks. The heat dissipation plate is elongated, and multiple heat dissipation blocks, which are star-shaped, are fixedly connected to its upper surface. In this utility model, by setting the first and second heat sinks in an alternate arrangement inside the heat exchanger, the internal heat exchange density is significantly improved, and the contact area with the coolant is effectively expanded.

The utility model relates to a fabricated building component connecting structure, and relates to the field of building industrialization. The device comprises a building component and a connecting piece, the connecting piece comprises a connecting disc, a connecting column, a supporting column, a connecting screw rod, an elastic lining, an expansion sliding sleeve, a fixing nut and a tension spring, the periphery of the expansion sliding sleeve is arranged to be in a wedge shape, and the elastic lining is used for fixing the other building component. According to the fabricated building component connecting structure, firstly, one building component is fixed to the connecting disc through the embedded steel bars, then the connecting column is sleeved with the elastic lining, the state of the elastic lining is adjusted and controlled through the expansion sliding sleeve and the fixing nut, and it is ensured that the elastic lining can be tightly attached to the inner wall of the other building component; meanwhile, the tension spring provides auxiliary support when the elastic lining deforms, the stability and adaptability of the whole structure are ensured, and therefore reliable connection of the two building components is achieved.

The invention provides a bypass pipeline type selection method and device of a liquid cooling system, a controller and a data center. The method comprises the following steps: acquiring a lower limit value of required flow of a main pipeline of the liquid cooling system and a flow limit value of the main pipeline of the liquid cooling system under the lowest operation frequency of a water pump; based on a flow-lift formula and the flow limit value of the main pipeline under the lowest operation frequency of the water pump, determining a bypass pipeline pressure drop limit value corresponding to the main pipeline demand flow lower limit value; and selecting the minimum circulation specification of the bypass pipeline when the total pressure drop is not greater than the pressure drop limit value of the bypass pipeline as the selected circulation specification. According to the method, the shunting boundary needing to be borne by the bypass pipeline of the system under the most demanding low-load working condition can be accurately positioned, the problem of flow capacity mismatching caused by neglecting system individual difference due to empirical selection is avoided, and therefore it is guaranteed that the liquid cooling system can accurately, stably and efficiently operate within a wide flow range.