37results about How to "Comparable" patented technology

This application relates to the field of power trading technology, and provides an AI-based method and system for optimizing power trading decision-making strategies. After obtaining the electricity price sequence for the first day, a second day's electricity price sequence is obtained by performing time-series correlation prediction based on the first day's electricity price sequence and historical electricity price data. Candidate strategies are generated using mixed-integer linear programming based on the physical parameter set of the energy storage power station and the second day's electricity price sequence, employing both profit maximization and discharge maximization strategies. Profit statistics are performed on each objective priority strategy based on the first day's electricity price sequence and historical electricity price data to evaluate the historical profit win rate of each objective priority strategy. The optimal charging and discharging strategy is then selected from the candidate strategies based on the historical profit win rate. This application, through day-ahead electricity price prediction and charging and discharging constraint programming, ranks and filters candidate strategies based on historical profit win rates to obtain the optimal charging and discharging strategy that balances electricity price fluctuations and equipment constraints.

The invention discloses a geological disaster dynamic early warning method driven by multi-source data and an intelligent model, and belongs to the technical field of geological disaster early warning. The method comprises the following steps: acquiring multi-source heterogeneous geological monitoring data; performing space-time alignment and semantic standardization processing, and constructing a standardized data set; constructing a multi-modal deep fusion neural network model, extracting features through a parallel feature extraction sub-network, and carrying out feature fusion by adopting a cross-modal attention mechanism based on a historical accuracy rate dynamic adjustment weight; disaster risk prediction is carried out based on fusion features, and online model updating is realized through a sliding time window mechanism; and generating an early warning report according to the risk level. According to the invention, an early warning effect feedback closed loop mechanism is also established, and model iterative optimization is realized through postmortem verification of returned data. According to the method, a complete closed loop from data perception to model self-optimization is constructed, and the problems of poor semantic consistency, static feature fusion and insufficient early warning real-time performance of multi-source data are solved.

The application discloses an intelligent control method and system for precise positioning of a track robot, and belongs to the technical field of precise positioning of robots; key positioning nodes of the track robot in a positioning process are extracted, a node set is constructed, historical state attribute data is acquired, and a unified state feature space is constructed; the historical data is mapped to the unified state feature space, a node state vector is constructed, and a difference value of state attribute data between adjacent nodes is calculated; a historical positioning atlas is constructed, and a marginal precision value of each path is calculated through a positioning precision marginal effect modeling function; the marginal precision value is compared with a preset threshold value, if the marginal precision value is less than the threshold value, a hierarchical compensation mechanism is triggered, speed curve adjustment, filtering parameter optimization or insertion of an intermediate correction node is respectively executed according to the size of a compensation coefficient, and the atlas is updated, full-process intelligent control of the positioning process of the track robot is realized, and the comprehensive performance of positioning precision and operation efficiency is effectively improved.

The embodiment of the application discloses a grid water depth prediction method and device based on an LSH attention mechanism, and relates to the field of hydrological monitoring. The method disclosed by the application first collects B batches of boundary flow time series data and grid water depth time series data of a target basin output by a two-dimensional IFMS model; then, standardizes the collected data, constructs a first feature tensor and transposes the first feature tensor into a second feature tensor; then, a weighted feature tensor is calculated based on an attention algorithm by using a Reformer model; finally, the weighted feature tensor is decoded by using a pre-trained iTransformer to obtain prediction values of grid water depths at future time points. The application can realize cross-time period prediction, improve prediction efficiency and accuracy, and provide a reliable basis for flood control and disaster reduction.

The application discloses a kind of identification and classification method for karst mountain city air pollution typical process, comprising: obtaining multi-source data to construct time series dataset;Pollutant concentration is identified based on pollution process start and end period;Extract time characteristics, meteorological statistical characteristics and diffusion condition characteristics to construct process feature vector;Time type principal component analysis is combined with clustering algorithm to carry out meteorological field objective typing;Based on Lagrange trajectory model, backward trajectory analysis is carried out, and transport path characteristics are extracted;Meteorological typing and transport path characteristics are integrated, and pollution process is divided into static stable cumulative type, regional transport type and composite influence type;Typical pollution process sample library is constructed.The application can accurately identify and classify karst mountain city air pollution typical process, provide data support for pollution mechanism analysis and prediction model training.

The invention provides a kidney transplantation rejection reaction serum polypeptide detection device and method, and the method comprises the steps: obtaining a kidney transplantation serum sample, and extracting a polypeptide characteristic signal group of the serum sample from a polypeptide mass spectrum of the serum sample in a preset mass-to-charge ratio range; the polypeptide characteristic signal group comprises three characteristic polypeptide peak signals corresponding to different protein fragments, and further determining the mass-to-charge ratio of each protein fragment and the relative intensity value of each characteristic polypeptide peak in the polypeptide characteristic signal group; carrying out fusion judgment on the rejection risk of the serum sample according to the mass-to-charge ratio of each protein fragment and the relative intensity value of each characteristic polypeptide peak in the polypeptide characteristic signal group to obtain a risk index representing the rejection state in the serum sample; and comparing the risk index with a rejection risk threshold value of the serum sample so as to make multi-level judgment on the rejection risk level to which the serum sample belongs. Based on the scheme, rejection reaction monitoring based on kidney transplantation serum polypeptide fingerprints can be realized.

This invention relates to the field of livestock management technology, specifically to a method for constructing a dairy cattle breeding index that integrates economic weights. The method includes the following steps: calculating the cash flow for each life stage (grooming period, first lactation period, peak lactation period, late lactation period, and culling period) based on feed costs, veterinary fees, labor costs, genetic assessment values ​​of milk yield, calf value, and culling residual value; and incorporating genetic assessment values ​​of mastitis resistance and conception rate. This invention provides a direct economic measure for breeding highly resilient cattle herds. Simultaneously, it integrates individual baseline net present value and heat stress tolerance values ​​to quantify the marginal economic contribution of each trait. The established dynamic economic weight set ensures that the importance of each trait is directly determined by its comprehensive impact on lifetime net profit and environmental adaptability, thereby improving breeding objectives and the long-term economic benefits of the ranch.

This invention proposes a multi-factor coupled comprehensive evaluation method and system for tire friction performance, comprising: S1, conducting a multi-stage progressive load test on the tire and collecting longitudinal force versus longitudinal displacement relationship curve data at each stage; S2, calculating a multi-stage dynamic weighted friction coefficient based on the longitudinal force data at each stage using progressively weighted coefficients; S3, considering the characteristics of the contact surface material, surface roughness, and contaminant coverage; S4, considering the test environment temperature and relative humidity; S5, determining the dynamic stiffness coupling coefficient of the tire-contact surface system based on the slope of the initial linear segment of the longitudinal force versus longitudinal displacement relationship curve; S6, calculating a comprehensive friction performance index based on the multi-stage dynamic weighted friction coefficient and the dynamic stiffness coupling coefficient; S7, calculating a friction performance stability index through repeated loading tests; and S8, comprehensively evaluating the tire friction performance level based on the comprehensive friction performance index and the friction performance stability index.

The invention discloses a bone mineral density detection method and device based on a CT image and a storage medium, and relates to the technical field of medical image data processing. The method comprises the following steps: firstly, acquiring a scanning data sequence of a plain-scan CT covering a target vertebral body; and delineating the region of interest of the cancellous bone on a plurality of parallel layers of the same target vertebral body to reduce the influence of interference factors. Linear transformation is carried out according to the scanning data sequence, the HU value of each pixel in the region of interest is converted pixel by pixel, and the CT mean value of the target vertebral body is obtained based on the HU values. And then determining a relative bone mineral density index of the target vertebral body based on the CT mean value by taking a preset reference value as a benchmark. And finally, based on the relative bone mineral density index, performing bone mineral density grading according to a set threshold value to obtain an evaluation result corresponding to each target vertebral body. Therefore, the bone mineral density detection method is simple in process, low in cost and universal.

The application discloses an electrochemical noise corrosion state identification and early warning method and system, and belongs to the technical field of corrosion monitoring and early warning. The method collects the potential noise signal and the current noise signal of the target component through an electrochemical workstation and calculates the noise resistance, adopts db4 wavelet for multi-layer wavelet decomposition and threshold denoising processing, extracts the kurtosis feature, the skewness feature and the wavelet coefficient feature, and constructs a corrosion state identification and early warning data set after feature screening; a fusion model including an LSTM time sequence feature extraction layer and an Attention attention weight distribution layer is constructed for training, and a corrosion state identification model is obtained; the real-time collected and processed feature parameters are input into the model for identification, the proportion of a specific corrosion state in a preset time period is counted, and a blue, orange or red graded early warning is triggered. The application realizes high-precision identification and accurate graded early warning of the corrosion state, the identification accuracy is above 85%, and reliable technical support is provided for industrial component corrosion protection.

The application discloses a CDC shock absorber whole vehicle abnormal sound quantification method, device, equipment and medium, relates to the automobile NVH test and diagnosis technical field, and the method comprises the following steps: obtaining subjective evaluation based on abnormal sound perception, vibration signals and sound signals measured by sensors under different driving conditions; obtaining vibration characteristic values based on the vibration signals, obtaining sound characteristic values based on the sound signals, and converting the subjective evaluation into standardized subjective score values; calculating standardized vibration values and standardized sound values, and calculating an abnormal sound comprehensive quantification index in combination with the standardized subjective score values; dividing the abnormal sound levels based on the abnormal sound comprehensive quantification index, combining the divided abnormal sound levels and the vibration characteristic values, and outputting adjustment suggestions for the CDC shock absorber. The application realizes objective and accurate quantification of the abnormal sound degree through multi-source data fusion and characteristic frequency analysis.

This invention discloses a scalable distributed multi-chamber open-path photosynthetic measurement system and method, relating to the field of plant physiological and ecological measurement technology. The system includes a main unit and at least two distributed chambers. The main unit centrally manages gas supply, concentration mixing, flow control, and high-precision concentration differential analysis. Each chamber is an independent unit, equipped with an adjustable LED light source, thermoelectric temperature control module, multiple types of sensors, independent power supply, and wireless communication module. An open path is formed through the gas supply pipeline and the return pipeline. The core of the method lies in the main unit polling and sampling the return gas from each chamber according to a preset program through a multi-channel automatic switching valve group. Combined with a "flushing stabilization" strategy, a single gas analysis unit is used to achieve high-precision, high-throughput parallel measurement of multiple chambers. This invention solves the problems of low throughput and high cost of traditional open-path instruments, achieving a balance between high precision and high throughput, and supports in-situ deployment in the field and remote monitoring.

The application provides an air conditioning equipment with a radar sensor and a processing device, which comprises: an entrance and exit identification module, which comprises: a grid division unit configured to establish a projection of a detection field in a ground direction, divide a region covered by the projection into multiple grids; a counting unit configured to record a detection frequency of an air conditioning room without detecting a valid element by using an unmanned time counting variable; a preliminary judgment unit configured to presume that a grid is a potential entrance and exit when a valid element is detected in the grid and no valid element is detected in continuous detection; record a number of times that the grid no longer has a valid element by using a potential entrance and exit credibility; and a selection unit configured to calculate a ratio of the potential entrance and exit credibility and the unmanned time counting variable when a valid element is detected in the potential entrance and exit again, and select the potential entrance and exit as a room entrance and exit when the ratio meets a preset condition of a scene. The application can improve long-term accuracy of detection.

The application relates to the technical field of smart grids, in particular to a power distribution network equipment insulation state monitoring method and system, which comprises the following steps: synchronously collecting the instantaneous load current and the partial discharge amplitude of electrical equipment at each sampling moment; obtaining the thermal cumulative current at each sampling moment; determining the load level and the discharge level to which each sampling moment belongs; judging the effective discharge event according to the size of the partial discharge amplitude, and constructing a discharge distribution matrix; after the element values in the discharge distribution matrix are binarized and mapped, the effective connected domains are extracted and screened out through connected domain analysis; the minimum excitation load rate and the discharge trend degree of each effective connected domain are determined, the insulation state of the electrical equipment is monitored and evaluated. The application can accurately identify early insulation hidden dangers with physical continuity in a strong noise background.

The application discloses a crosstalk tolerance capability test method and system of SerDes IP, and belongs to the technical field of network communication. The method comprises the following steps: a first test topology is established based on an EVB board, a crosstalk board and an insertion loss board; further, equalization parameters of a SerDes sending end corresponding to different crosstalk intensities are obtained based on the first test topology; a second test topology is established based on the EVB board, the crosstalk board and the insertion loss board; and based on the second test topology, a first insertion loss value corresponding to a target bit error rate of a SerDes link under a crosstalk-free scene and a plurality of second insertion loss values corresponding to the target bit error rate of the SerDes link under different crosstalk intensities are obtained respectively; and the crosstalk tolerance capability of the SerDes under the corresponding crosstalk intensity is obtained according to the difference between each second insertion loss value and the first insertion loss value. The application can quantify crosstalk, eliminate platform differences and support performance evaluation and SerDes IP selection.

The utility model discloses a simple positioner for positioning yeast two-hybrid colonies, which relates to the technical field of molecular biology experimental equipment and comprises a high-pressure plastic ring, a clamping groove is arranged on the inner side of the high-pressure plastic ring, and elastic clamping components are arranged on two sides of the high-pressure plastic ring. The elastic clamping assembly comprises elastic plastic sheets fixedly arranged on the two sides of the high-pressure plastic ring. A marking card is clamped in the clamping groove of the high-pressure plastic ring; clamping grooves formed in the inner sides of the high-pressure plastic rings are used for fixing marking cards, and clamping assemblies composed of elastic plastic sheets are arranged on the two sides of the high-pressure plastic rings to fix culture dishes of different specifications; the positioner solves the problems of low precision and error proneness of a traditional bacterial colony positioning method through standardized coordinate marking, and has the advantages of simple structure, convenience in operation, low cost and the like. The experimental process is more flexible and efficient due to the lightweight design and the replaceable mark card system, and the system is particularly suitable for bacterial colony positioning work in a high-throughput yeast two-hybrid screening system.

The invention relates to the technical field of welding quality online monitoring, and discloses an exothermic welding quality monitoring method based on sensing data. Arranging a temperature sensor at a welding station to be aligned with a heat release center, and collecting a calibration temperature and an original voltage sample to generate a conversion slope and an intercept; determining sampling starting and ending according to starting and working step ending signals, forming a voltage sequence according to a sampling interval, converting temperature, generating temperature rise by taking the first temperature as a reference, and normalizing the temperature rise; self-adaptive triangular smoothing is carried out according to the number of sampling points, a computable interval is limited, and differential change rate and local second-order bending rate energy samples are constructed; samples are sorted to obtain self-baseline statistics, an abnormal index, a quality value and an abnormal time period are generated in combination with maximum energy, and a quality grade is output and filed. Cross-device comparability is achieved, noise is resisted, invalid sampling is reduced, and compared with a peak value method, process abnormity is easier to recognize and locate and traceability is achieved.

The invention discloses a student actual combat homework AI intelligent evaluation method, system and device based on a hybrid expert model and a storage medium. The method comprises the following steps: pre-constructing a structured expected answer containing dimension activation information for a teaching task; when the homework is evaluated, a dimension expert subset needing to be activated is determined according to dimension activation information in the expected answer; calling each activation dimension expert to output a structured evaluation evidence; converting the structured evaluation evidence into a dimension score according to a preset calculation rule; and only carrying out normalization processing on the weight of the activation dimension expert subset, and then carrying out weighted fusion to obtain a final scoring result. According to the invention, through solidifying the evaluation benchmark, dynamically activating the dimension subset, outputting the structured evidence and carrying out weight normalization fusion on the activation set, the problems of different scoring standards, total scale drift and unreviewable evaluation results in the prior art are solved, and automatic, consistent and explainable intelligent evaluation of student actual combat homework is realized.

The invention provides a resistance characteristic test device of a nuclear power large indirect air cooling tower. The resistance characteristic test device for the nuclear power large indirect air cooling tower comprises a test chamber, a resistance characteristic test device and a resistance characteristic test device, the support frame is fixedly mounted in the mounting space; the testing tower is arranged on the supporting frame, and the testing tower is provided with a first air inlet facing the ground in the height direction of the testing tower and a first air outlet away from the ground; the lifting device is arranged on the supporting frame and is in transmission connection with the testing tower so as to drive the testing tower to move in the height direction of the testing tower; the exhaust pipe is at least arranged in the mounting space, the exhaust pipe comprises a second air inlet and a second exhaust outlet, the second air inlet is connected with the first exhaust outlet, the second exhaust outlet is provided with a fan, and the exhaust pipe is provided with at least one bent section. According to the resistance characteristic test device of the nuclear power large indirect air cooling tower, flexible adjustment of the height of the test tower is realized, and meanwhile, the authenticity of an air inlet boundary condition and the accuracy of airflow measurement are ensured.

This application provides a method and system for predicting urban air pollutant concentrations. By performing sensor-level standardization processing on air pollution monitoring data, the raw signals output from different types of smart sensors are uniformly mapped into standardized pollutant concentration feature vectors. Based on these feature vectors, a pollution state characterization sequence integrating temporal evolution characteristics and spatial correlation characteristics is constructed. Based on this sequence, the pollutant concentration variation patterns under different meteorological backgrounds are dynamically adjusted to form predictive input features that match real-time environmental conditions. Based on these predictive input features, pollutant concentrations are predicted, and the predicted future air pollutant concentrations for the target city within a preset timescale are output. Using this approach, collaborative modeling and adaptive prediction of the temporal evolution characteristics and spatial correlation characteristics of pollutant concentrations can be achieved in complex urban environments where multi-type sensor data and variable meteorological conditions are coupled.

The application relates to the technical field of angle-resolved photoelectron spectroscopy experimental instruments, and discloses an angle-resolved multi-electron coincidence measurement system and method based on an event-type delay line detector, which comprises an angle-resolved energy analyzer, an event-type delay line detector, an external trigger and synchronization module and a time digitization readout module. The detection surface of the event-type delay line detector is divided into at least two independent areas, which are used for receiving photoelectrons in parallel and independently outputting event signals; the external trigger and synchronization module is used for generating a unified trigger period to establish a global time reference; the time digitization readout module is used for receiving the event signals and generating time stamps; and the data acquisition and processing module is used for performing correlation selection based on the time stamps in the same trigger period. The application reduces the dead time through multi-area parallel readout, realizes unified time base calibration through time baseline calibration and delay compensation, can realize multi-electron coincidence measurement while maintaining the angle-resolving capability, and is suitable for pulsed or continuous light sources.

The invention relates to the technical field of percutaneous electrical stimulation, in particular to a portable eye movement detection and analysis system integrated with percutaneous ear vagus nerve stimulation, which comprises a stimulation output module, an eye movement acquisition module, a response association module, a parameter adjustment module and a detection and analysis module, according to the method, the stimulation parameters are continuously recorded in the combination form of the pulse width, the pulse interval and the stimulation amplitude and form a clear time sequence corresponding relation with the stimulation period, so that the stimulation condition has traceability and comparability, in addition, the eye movement data are subjected to windowing interception around the stimulation starting and ending time, and the accuracy of the eye movement data is improved. Pupil changes, horizontal eye movement and instantaneous eye tracks are limited in corresponding stimulation sections, interference of non-stimulation factors is reduced, under the same parameter condition, stability of multiple eye movement responses is subjected to statistical analysis, consistent nerve response characteristics are recognized, association between stimulation parameters and response stability is established, the stimulation parameters are adjusted based on stable response intervals, and the stability of the eye movement responses is improved. The stable expression capability under the repeated stimulation condition is improved.

The embodiment of the invention provides a multi-channel optical fiber diagnosis method and system based on single photon counting, and belongs to the technical field of optical fiber sensing. A multi-channel optical fiber diagnosis method based on single photon counting comprises the following steps: acquiring link identification information of a multi-branch optical fiber link, and distributing a corresponding single photon counting channel and a corresponding channel time delay calibration parameter for each optical fiber link based on the link identification information; injecting a detection pulse into each optical fiber link based on a single photon pulse light source, and starting a counting window of each single photon counting channel to obtain a corresponding photon arrival time sequence; constructing alignment echo data corresponding to each optical fiber link; and performing adaptive counting control on each single photon counting channel based on the alignment echo data, and extracting diagnosis features to output a diagnosis result of the multi-branch optical fiber link. According to the scheme of the invention, multi-channel single-photon echo data which can be aligned and comparable can be obtained, and the method is used for supporting unified diagnosis of a multi-branch optical fiber link.

This application belongs to the field of sedimentary basin analysis technology, and relates to a method and system for characterizing magma underplating uplift based on seismic data. The method includes: acquiring drilling and logging data and seismic data volumes of the study area; extracting seismic profiles from the seismic data volumes; interpreting anomalous and non-anomalous seismic data volumes on the seismic profiles; constructing a first trend surface based on the anomalous seismic data volumes and a second trend surface based on the non-anomalous seismic data volumes; calculating the uplift caused by magma underplating based on the first and second trend surfaces; and quantitatively characterizing the degree of magma underplating uplift based on the uplift caused by magma underplating. The technical solution of this application, by constructing two mathematical surfaces representing the actual morphology and the regional background trend respectively and performing spatial difference calculations, achieves a fundamental shift from qualitative description to objective quantitative characterization of magma underplating uplift, providing accurate and reliable technical support for deep basin process research and oil and gas exploration.

The application discloses a soybean bollworm prediction and forecasting method based on machine learning, relates to the technical field of pest situation prediction, and comprises the following steps: collecting trapping count and state information, summarizing the total amount of trapping on the current day, and adopting S-shaped attenuation punishment to calculate an observation reliability index; aligning the phase of the total amount of trapping historical data in a year to determine a key occurrence window, and calculating a phenology continuity index of a future prediction window based on the sowing date and daily air temperature; establishing a mechanism constraint machine learning model based on a feedforward fully connected neural network, and outputting continuous time trapping intensity prediction values and change trend information; integrating the prediction values to obtain future pest situation accumulations, combining environmental suitability and phenological suitability to calculate pest situation risk quantities; generating maintenance instructions according to the observation reliability index, and outputting early warning and patrol frequency adjustment instructions according to the pest situation risk quantities. The application realizes quantitative suppression of low-quality trapping data, mechanism constraint stabilization of trapping intensity prediction, and comparable and reviewable risk early warning.

This invention belongs to the field of electric vehicle device and component manufacturing technology, specifically relating to an intelligent monitoring method and system for motor rotor production based on the Internet of Things. The method includes: setting benchmark values ​​for vibration and current load using the third and fourth quartiles of historical data; calculating the vibration risk factor per second in real time; and weighting the current load ratio accordingly to obtain the instantaneous operating intensity; subsequently, fusing the average intensity values ​​of short, medium, and long-term windows, dynamically determining the weights through the window change rate, calculating the cumulative operating intensity, and comparing this intensity with historical cumulative normal intensity to obtain an abnormal risk factor; finally, dynamically scaling the benchmark alarm threshold of the LSTM self-encoder using the abnormal risk factor, and achieving intelligent monitoring of electronic rotor production by comparing the reconstruction error with the dynamic threshold in real time.

The invention provides a quail health state early warning and management method based on individual behavior monitoring, and belongs to the field of animal health monitoring, and the method comprises the steps: constructing a multi-modal monitoring scheme, synchronously collecting quail data, building identity mapping, carrying out the early warning through an edge end and a cloud end model after preprocessing and feature extraction and screening, and carrying out the early warning through a cloud end model. And then dynamically calibrating a threshold value, tracing pathology and grading, and finally implementing individual and group level intervention according to a result to form a closed loop. According to the quail health state early warning and management method based on individual behavior monitoring, early recognition of the quail health state, disease cause tracing and accurate management are achieved, and the breeding efficiency is improved.

This invention relates to the field of electric drive system design and development for new energy vehicles, specifically to a method for testing and analyzing the dynamic stiffness of electric drive assembly mounts and the accuracy of simulation. The method involves standardized frequency response testing of the electric drive assembly mounts, identification of minimum dynamic stiffness, and the construction of a dynamic simulation model consistent with the test boundaries to extract the minimum dynamic stiffness on the simulation side. Then, it sequentially performs frequency and amplitude error analysis of the minimum dynamic stiffness points in the tests and simulations, as well as average dynamic stiffness error analysis. Finally, it comprehensively calculates accuracy performance coefficients based on multi-dimensional error coefficients and establishes a scoring system to achieve a quantitative evaluation of the test and simulation accuracy. This method solves the technical problems in existing technologies where evaluating the dynamic stiffness of electric drive assembly mounts requires multiple physical impact tests, which are resource-intensive, time-consuming, and lack a systematic and quantitative analysis method for testing and quantifying the accuracy of mount dynamic stiffness, thus failing to utilize the preliminary analysis results to guide subsequent mount design and development.

The invention discloses a cross-border e-commerce multi-country data collaborative management method and system, and belongs to the field of computer network and data processing, and the method comprises the steps: obtaining local original e-commerce service data by each edge node, and obtaining a standardized feature vector; each edge node uses the standardized feature vector to train a local model, generates a model parameter update package indicating a model optimization direction, executes privacy protection processing including differential privacy noise injection on the model parameter update package, and sends the model parameter update package to a collaborative management center server; the collaborative management center server receives the model parameter updating packages from the multiple edge nodes, calculates an adaptive aggregation weight coefficient, and executes weighted aggregation to update a global collaborative model; and the collaborative management center server predicts the commodity demand quantity of the future time domain based on the updated global collaborative model, and generates a deterministic service scheduling instruction. According to the invention, the total cost of cross-border logistics is reduced, and the inventory turnover rate is improved.

The embodiment of the application provides a test method and system of a compulsory parent node based on a Mesh network, the method comprises the following steps: defining a topology structure of the Mesh network based on a test requirement, the topology structure containing an association list of a child node and a target parent node; extracting configuration information of each child node, generating a configuration instruction based on the configuration information, and enabling the child node side to establish a connection relationship with the target parent node based on the configuration instruction; checking based on a connection result periodically fed back by the child node, generating a health state level corresponding to each link, and generating a corresponding response strategy according to different health state levels; executing a corresponding test case based on the test requirement, and recording test data; after receiving a test requirement update instruction, determining a change mode based on the update instruction, modifying a parent node configuration of the child node based on the change mode, recording a change time stamp, and determining a dynamic response capability of the child node.