133results about How to "Reduce response delay" patented technology

The invention relates to a switching power supply control method, system and device and a storage medium, and the method comprises the steps: monitoring the voltage and current of the output end of a switching power supply, and the rate and amplitude of load change, and obtaining the real-time data of the load change; according to the real-time data of the load change, the change of the current load is identified, and a load change mode is obtained; dynamically adjusting operation parameters of the controller according to the load change mode; based on the real-time data of the load change, predicting the load change trend of the switching power supply in real time by using a pre-trained load prediction model to obtain a prediction result, and generating a disturbance compensation signal based on the prediction result; and adjusting the switching state of a switching circuit of the switching power supply according to the control parameter adjusted by the controller and the disturbance compensation signal. The switching power supply has the effect of improving the stability of the switching power supply.

The invention discloses a wireless spectrum intelligent allocation and edge computing cooperation method, which relates to the technical field of mobile edge computing and wireless communication, and comprises the following steps: acquiring spectrum occupation data and service load data; constructing a spectrum resource allocation matrix according to the spectrum occupation data, and constructing an edge computing resource allocation matrix based on the service load data; and inputting the spectrum resource allocation matrix and the edge computing resource allocation matrix into a trained deep reinforcement learning model, outputting a joint optimization strategy, and performing collaborative scheduling of spectrum resources and computing tasks through a bidirectional matching algorithm. According to the invention, a multi-dimensional resource data acquisition mechanism is established, so that real-time monitoring and evaluation of a network resource state are realized; quantitative expression and association mapping of heterogeneous resource characteristics are realized by constructing a mathematical model of a spectrum resource allocation matrix and an edge computing resource allocation matrix; a deep reinforcement learning model is adopted for strategy optimization, and the limitation of a traditional fixed rule algorithm is overcome.

The invention relates to an edge data processing method of an information security monitoring and early warning AI platform and a related device. The method comprises the following steps: carrying out standardization processing on original security alarm data collected by a plurality of edge computing devices to obtain standardized feature data; constructing a target function comprising a processing efficiency item and a resource consumption item based on the standardized feature data, and decoupling the target function into a plurality of sub-optimization problems of edge computing nodes by using a Lagrange multiplier to obtain an initial task allocation scheme; performing local linearization processing and convex optimization decomposition on the initial task allocation scheme to obtain an optimal task decomposition scheme; constructing a state transition probability matrix according to the optimal task decomposition scheme, and generating a dynamic task scheduling strategy; and distributing the dynamic task scheduling strategy to each edge computing node, and performing time sequence analysis and spatial clustering on multi-source data to obtain a security situation assessment result. According to the invention, the utilization efficiency of edge computing resources is improved.

The invention discloses an adaptive modulation mode method for a terminal radio station, which comprises the following steps: acquiring original signal data of a receiving antenna, extracting feature points through time-frequency transformation and energy spectrum analysis, and carrying out noise reduction processing to obtain preprocessed signal data; extracting frequency, amplitude and phase features from the preprocessed signal data to generate feature matrix data; fusing the feature matrix data with historical channel feature data to obtain channel feature data; interference feature analysis is carried out based on the channel feature data, and interference feature data and interference confidence data are obtained through probability statistical modeling; performing multi-objective optimization in combination with the modulation strategy data to generate modulation parameter data and reliability index data; and performing resource optimization allocation according to the network state data and the service priority data, and outputting resource allocation data and time slot allocation data. The adaptive capacity and the resource utilization efficiency of the communication system in a complex electromagnetic environment are improved.

The invention discloses an energy storage cabinet temperature control method based on a cloud platform and a temperature control energy storage cabinet, and relates to the technical field of electric energy storage, the energy storage cabinet comprises a cabinet body, an energy storage module, a data acquisition module, a communication module and a temperature control module, and the temperature control method comprises the following steps: S1, acquiring environmental parameters and battery state data; s2, determining a temperature adjustment factor of the energy storage cabinet through the battery state data; s3, generating a composite temperature index based on the environmental parameters and the temperature adjustment factor; s4, performing region division on the energy storage cabinet, determining temperature requirements of different regions according to the composite temperature index, and formulating an energy storage cabinet temperature control strategy based on the temperature requirements in combination with the environment prediction temperature; s5, the cloud platform generates a control instruction based on the temperature control strategy and issues the control instruction to the energy storage cabinet to execute the control instruction; accurate control over the temperature in the energy storage cabinet can be achieved, it is ensured that energy storage equipment operates within the optimal temperature range, the energy storage efficiency is improved, and the service life of the equipment is prolonged.

The invention relates to the technical field of intelligent power grid control, and discloses an intelligent power grid control system and method based on gesture interaction, and the system comprises a multi-modal data collection and preprocessing module which is used for collecting gesture data of a user, including images, radar and inertial measurement unit data, the gesture data acquisition module is used for acquiring gesture data of a user, filtering, denoising and normalizing the acquired gesture data of the user, and transmitting the processed data to the multi-modal data synchronization and alignment module; and the multi-modal data synchronization and alignment module refers to the output data of the multi-modal data acquisition and preprocessing module, and performs time and space alignment on the multi-modal data through a timestamp synchronization and space calibration technology. According to the method, features of the image, the radar and the inertial measurement unit are subjected to weighted fusion to realize efficient integration of multi-modal information, so that the gesture recognition precision of the system in a complex and variable environment is remarkably improved, and the robustness and the recognition stability of the system are enhanced.

The invention discloses a photoelectric turntable servo tracking control method, a storage medium and a system, and relates to the technical field of servo control, and the method comprises the steps: receiving target deviation value information; caching the actual position information for a corresponding time length based on the target calculation and the transmission delay time length, and using the actual position before transmission and calculation when processing the target deviation information, so that the target deviation is consistent with the actual position information in time, and the target position is obtained; according to the target deviation and the actual position information, a predicted speed is calculated and output through feedforward control, the obtained target position is input into a position ring to obtain speed output, and the speed output and the predicted speed input are combined to a speed ring. According to the established data caching and feed-forward compensation model, the servo system compensates system predictive interference in advance, carrier motion and other interference factor influences are effectively restrained, response delay is reduced, the system tracking precision is improved, the dynamic response speed is increased, and real-time smooth tracking control is achieved.

The invention relates to an intelligent behavior analysis and anomaly detection system for Internet of Things security, which is applied to the technical field of Internet of Things security, and comprises a data acquisition module, a data preprocessing module, a behavior modeling module and an anomaly detection module, the data preprocessing module cleans the data, the behavior modeling module generates a vector sequence of equipment behaviors through feature embedding and position coding, and the anomaly detection module analyzes time sequence association of the equipment behaviors by utilizing a self-attention mechanism and a multi-head attention mechanism and judges whether the equipment has abnormal behaviors or not. The cooperative defense module realizes cross-device cooperative defense through an edge detection unit and a cloud analysis unit, and the threat intelligence sharing module realizes sharing and updating of threat intelligence through a local intelligence database and an external intelligence interface. And the response module carries out automatic defense and notifies a system administrator through the automatic defense unit and the alarm unit when an abnormity is detected.

The invention discloses a multi-tenant user management method and system supporting permission inheritance, and relates to the field of electrical digital data process.The method comprises the steps that a permission inheritance mapping table is created according to a role authorization request, and a permission inheritance relation is written in; generating a bidirectional mapping cache containing a first mapping table and a second mapping table; when it is detected that the permission of the source tenant role is changed, querying a target tenant user set associated with the source tenant role based on a first mapping table; constructing a permission update message for the target tenant user set, and asynchronously sending the permission update message to each user in the target tenant user set; receiving a permission verification request, and obtaining source tenant role information inherited by a target tenant user according to a second mapping table; and merging the current operation authority of the source tenant role corresponding to the source tenant role information with the operation authority of the target tenant user to obtain real-time authority data of the target tenant user. According to the invention, the real-time performance and accuracy of authority management are improved.

The invention discloses a new energy station network security assessment method and system, and relates to the technical field of new energy stations, and the new energy station network security assessment method comprises the following steps: obtaining network operation feature data; identifying the change trend of the network stability; identifying key security risk factors influencing the network stability; and adjusting the network protection strategy of the new energy station based on the evaluation result. According to the method, the network behavior recognition algorithm is used for carrying out stability analysis on the network operation characteristic data, the stability change trend is recognized, potential unstable behaviors or hidden dangers in the new energy station network can be found in time, the network state change can be pre-judged in advance, sufficient time and basis are provided for risk control, and the risk control efficiency is improved. According to the method, the key security risk causing network instability is accurately identified, generalization or fuzzy risk assessment is avoided, a basis is provided for formulating an accurate network protection strategy, and the risk prevention and control cost is reduced.

The invention discloses a brain-like edge calculation-driven multi-mode intelligent security method and system, and aims to improve the security capability in a complex dynamic scene. According to the method, multi-modal data of videos, audios, sensors and the like are collected, preprocessing is carried out by utilizing a brain-like edge computing gateway, dynamic behavior characteristics are extracted, secondary confirmation is carried out through a lightweight visual large model, and then formal early warning is generated. And the system executes intelligent linkage operation according to the early warning information risk level, and records false alarm information for model optimization. According to the invention, brain-like calculation and multi-modal fusion are adopted to improve the recognition precision, edge calculation reduces delay, an intelligent closed loop realizes differentiated treatment, a low-power design and a modular architecture reduce the cost, and the system has a self-learning capability. The defects of an existing security and protection system in the aspects of dynamic behavior recognition, response speed, disposal intelligence, cost control and the like are effectively overcome. The system is suitable for scenes such as a campus and a community, and provides an accurate, efficient and intelligent security solution.

The invention relates to the technical field of touch screens, and discloses a multi-touch mode switching method, device and equipment for a touch screen, and the method comprises the steps: carrying out the minimum error processing of a touch signal collected by a touch screen, and obtaining a target feature component; inputting the target feature component into a multi-layer touch mode classifier for feature mapping to obtain a current touch mode feature; performing attenuation characteristic analysis and adaptive threshold adjustment on the target characteristic component to obtain a state transition parameter; carrying out delay response modulation and regularization zero interference on the response time, the response intensity and the response area of the touch point to obtain screen response change characteristics; performing fuzzy logic decision of a continuous sampling period to obtain a touch mode switching feasibility judgment result; the closed-loop control is performed on the response parameter of the touch screen to obtain the mode switching delay compensation control quantity, so that the reliability of touch mode switching is improved, the false triggering phenomenon is avoided, and the response delay in the mode switching process is remarkably reduced.

The invention provides an efficient cooling and fire extinguishing agent control method for an energy storage system fire, and relates to the field of fire extinguishing and cooling. Determining a cooling delay coefficient of the energy storage system during cooling control according to the dynamic temperature data; then the spraying adaptive amount during fire extinguishing spraying is determined through the spraying node; according to the target cooling strategy and the cooling delay coefficient, the distribution loss amount of each level of fire extinguishing agent under the fire extinguishing temperature control working condition is determined, and the cooling compensation characteristics of the automatic fire extinguishing system with the detection and alarm linkage during fire extinguishing task execution are determined according to the distribution loss amount; and determining a fire extinguishing cooperation efficiency index when the energy storage system carries out efficient cooling and fire extinguishing control by a fire extinguishing agent according to the spraying adaptation amount and the cooling compensation characteristics, and carrying out distribution control on a fire occurring in the energy storage system according to the fire extinguishing cooperation efficiency index. According to the invention, dynamic joint control adjustment can be carried out when the fire extinguishing strategy and resource allocation adjustment are delayed, so that the response efficiency of the fire extinguishing system is improved.

The invention belongs to the technical field of e-commerce logistics management, and particularly relates to an e-commerce product storage full-process monitoring management method, which comprises the steps of synchronously acquiring multi-source heterogeneous monitoring data in real time through a sensing equipment network; performing space-time alignment preprocessing on the original data, and converting heterogeneous data into a uniform data format; semantic fusion is carried out on the standardized data, and a job behavior knowledge graph with a complete spatio-temporal context is constructed; inputting the atlas into a pre-training hybrid model, and detecting an inventory abnormal event; and generating a strategy instruction in combination with the real-time order and resource data, carrying out joint analysis on an operator, and outputting a compliance score and a specific violation operation type. Through adoption of the technical scheme, complete reconstruction of the context of the operation behavior can be realized, the anomaly detection accuracy is remarkably improved, the abnormal event response delay is shortened, and the inventory loss rate caused by human errors is reduced.

The invention relates to the field of new energy batteries, and discloses an efficient lossless lithium precipitation detection method and system for a battery, and the detection method comprises the following steps: S1, collecting the working data of the battery in real time through a plurality of sensors, including the internal resistance, temperature, signal intensity, voltage and current of the battery; s2, carrying out denoising and standardization processing on the collected data, and carrying out time sequence alignment on the data to ensure the consistency and accuracy of the data; and S3, based on the influence of the lithium precipitation process of the battery on the propagation of the high-frequency signal, establishing an attenuation model of the high-frequency signal, which describes the relationship between the conductivity of the battery and the signal attenuation coefficient. According to the method, a battery lithium precipitation detection technology based on the signal attenuation model and multi-dimensional variable coupling analysis is adopted, the effect of detecting the battery lithium precipitation phenomenon in real time on the premise of high precision is achieved, the detection accuracy and stability are effectively improved, and misjudgment or missed judgment caused by a single data source in a traditional scheme is avoided.

The invention provides an energy storage optimization scheduling method and system under microgrid priority fusion constraint, and the method comprises the steps: S1, carrying out the reading and preprocessing of multi-source data, carrying out the unified time axis of historical photovoltaic output, user load, future weather, electricity price, demand unit price, and completing the missing value interpolation and abnormal value elimination, and forming a time series data set; s2, setting decision variables, namely charging power, discharging power, maximum charging power, maximum discharging power, energy storage equipment capacity, commercial power purchase unit price and demand unit price; s3, the overall income is maximized under the specified priority, and the expenditure is reduced; s4, establishing a priority constraint, an energy storage hardware constraint, an electrical load constraint and other constraint conditions, and avoiding target scheduling conflicts according to the corresponding constraint conditions; s5, carrying out two-stage MINLP rapid solution, and searching a feasible solution in a short time by using a rapid exploration method; and deep optimization is carried out through a fine mode.

The invention discloses a remote management method and device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring real-time bandwidth information of the server; determining a compression strategy for the to-be-transmitted data according to the format of the to-be-transmitted data, and compressing the to-be-transmitted data by using the compression strategy to obtain compressed data; performing differential analysis on the compressed data and the transmitted data, and screening out incremental data from the compressed data according to the difference between the compressed data and the transmitted data; determining the transmission frequency of the data to be transmitted according to the real-time bandwidth information and the type of the data to be transmitted; and sending the incremental data corresponding to the to-be-transmitted data to a client according to the transmission frequency, so that the client remotely manages the server. The data transmission quantity can be reduced, the bandwidth occupation is reduced, the remote management real-time performance is improved, and the system resource consumption is optimized.

The embodiment of the invention discloses a data transmission method and device, communication equipment and a computer readable storage medium, and the method comprises the steps that network equipment receives a first request sent by a terminal, and the first request is used for requesting three-dimensional model data; the first request comprises first information, and the first information at least comprises a model definition level of each object in a first object set under a first visual angle of the user; determining a first detail level corresponding to each object in the first object set based on the first information; wherein each object has three-dimensional model data of a plurality of detail levels; sending second information to the terminal, wherein the second information comprises the first data set; the first data set comprises first model data, corresponding to the first detail level, of each object in the first object set; the second information is used for the terminal to generate a three-dimensional model of each object in the first object set.

The invention discloses a dual-mode communication method and system based on intelligent decision making. The method comprises the following steps: acquiring historical operation data and actual operation data of HPLC (High Performance Liquid Chromatography) and HRF (High Performance Framework); creating a reinforcement learning model which comprises a state space, an action space and a reward function; the state space is a combined vector of a dual-mode channel quality index, the action space comprises HPLC priority, HRF priority and dual-mode self-adaption, and the reward function is a weighted calculation formula of throughput, power consumption and packet loss rate; and performing iterative training on the reinforcement learning model based on the historical operation data to obtain an intelligent optimization model, inputting the actual operation data into the intelligent optimization model to obtain an optimal action, and adjusting the dual-mode communication parameters according to a corresponding preset parameter adjustment rule. According to the invention, communication mode selection and parameter adjustment are more suitable for actual environment requirements, the communication efficiency is improved, the energy consumption and data loss risk are effectively reduced, the system architecture is simplified, and the deployment cost and response delay are reduced.

The application discloses a wearable split mobile computing and energy supply system based on dynamic resonance tracking and a dynamic wireless energy supply method thereof, relates to the technical field of mobile computing and wireless energy transmission, and aims to solve the problems of coupling deterioration and transmission efficiency reduction of a magnetic resonance wireless energy supply system in a receiving end motion. The system comprises a wearable host configured in a belt head form, which is internally integrated with a high-performance processing unit, a main transmitting coil unit, an adjustable matching network, a space perception module, a wireless energy supply control core, a mobile communication module, a short-distance wireless communication unit, a voice front end, a sensor unit and a touch display screen; a wearable belt internally contains a plurality of battery units to supply power to the host, and auxiliary transmitting coils are distributed on both sides of the belt to form a multi-array magnetic resonance transmitting coil array together with the main transmitting coil; and at least one interactive terminal is physically separated from the host and comprises a display unit, a magnetic resonance receiving coil, a rectification and voltage stabilization link, a buffer energy storage unit and a short-distance wireless communication module, and does not configure a main application processor. The wireless energy supply control core calculates the coupling state parameters of a magnetic resonance channel in real time based on the three-dimensional position and three-dimensional posture of the interactive terminal, generates frequency compensation instructions and power / phase distribution instructions, controls the adjustable matching network to dynamically adjust the working frequency to track the resonance point drift, controls the driving amplitude and phase of each unit of the multi-array coil array to form a directional energy beam, and realizes stable wireless energy supply in a dynamic scene.

The invention discloses a building vector graph multi-scene intelligent simplification method and system, and belongs to the technical field of geographic information systems. Comprising the following steps: establishing a four-level data system comprising an original data layer, an engineering cache layer, a display cache layer and a terminal view layer; monitoring data change and user interaction events through an event bus; the event is responded, the current application scene is intelligently recognized, and the corresponding simplified parameter configuration is dynamically loaded; based on the configuration, executing simplification logic on the target building vector graph; querying a topological relation to carry out cooperative processing on the contours of the adjacent buildings; and synchronously updating a processing result to the four-level data system. Through four-stage data caching, dual-drive triggering and scene self-adaptive judgment, efficient, high-quality and self-adaptive processing of building simplification in multiple scenes such as cartographic integration, real-time display and spatial analysis is realized, and the system flexibility, the resource utilization rate and the user experience are improved.

The invention provides a data interaction method and device, and the data interaction method is applied to a server, and comprises the steps: receiving an interaction request sent by a client, and sending the interaction request to a target interaction model; receiving byte interaction response data fed back by the target interaction model according to the interaction request in a streaming manner, and converting the byte interaction response data into text interaction response data in real time; the byte interaction response data comprises at least one byte; sequentially processing the converted text interaction response data according to a preset blocking strategy, and determining to-be-audited blocking data; after each piece of block data to be audited is determined, according to a preset auditing strategy, auditing the block data to be audited; and according to a preset display format, sending the to-be-audited block data passing the audit to a client in a streaming manner. In this way, the instantaneity and safety of the dynamically generated content can be ensured through the technical means of streaming transmission, block checking, real-time processing and the like.

The application discloses an emoticon package selection and text generation method based on prompt learning and a multi-modal model, and comprises the following steps: S1, intercepting a big model agent and a user's recent round of dialogue, and compressing the dialogue into a dialogue prompt mark; S2, arranging an emoticon package set into a candidate emoticon package input set graph and pairing the candidate emoticon package input set graph into an emoticon package reasoning sample; S3, accessing an improved RT-DETR model to extract and fuse multi-scale visual features; S4, an object query group organization structure is truncated, independently detected, and inquired into an entry, and is grouped into a query group; S5, a Transformer decoder with a query group cross-layer inheritance structure forms a candidate emoticon package to undertake a query; S6, a prediction head reads out a path constraint candidate reply text generation; and S7, after dialogue closure verification, emoticon package images and reply texts are output. The application relates to the technical field of artificial intelligence multi-modal content processing, and realizes emoticon package selection and reply text generation collaborative processing.

The invention discloses a cloud local application non-inductive switching method, system and device and a storage medium, and relates to the technical field of cloud virtualization and intelligent vehicle machine interaction, and the method comprises the steps: loading and uniformly displaying cloud and local applications when a vehicle machine is started; when the network is available, preloading the high-frequency cloud application based on the historical use data, and obtaining the unique serial number of the process of the high-frequency cloud application; when a user calls any cloud application, response data (such as application video streams) are directly obtained from the cloud according to whether the serial number is cached or not. According to the scheme, complete cloud mirror image pre-loading is abandoned, the single application serves as the minimum operation unit, the response time is reduced to be within the driving safety threshold value, and safety is improved; the delay is obviously reduced through intelligent preloading and instance multiplexing; seamless fusion of cloud local experience is realized and interaction splitting is eliminated through unified entrance, adaptive vehicle machine UI and cockpit hardware linkage.

The invention relates to a TSN (Time Sensitive Network) timestamp recovery method based on an FPGA (Field Programmable Gate Array), which belongs to the technical field of TSNs and comprises the following steps of: S1, collecting synchronous messages of different TSN nodes in parallel by multiple ports of the FPGA, and analyzing timestamp data by taking a message serial number as an anchor point; s2, constructing an incomplete timestamp matrix and a mask matrix by adopting a time sliding window mechanism, and automatically marking missing data; s3, a random index set is generated through LFSR, and matrix sparse sampling is achieved through a bitmap judgment unit; and S4, based on an FPGA accelerated CUR matrix decomposition algorithm, solving a pseudo-inverse matrix through parallel MGS-QR decomposition, and reconstructing a complete timestamp matrix through block parallel matrix chain multiplication. The FPGA parallel architecture, ping-pong cache and fixed-point number processing are adopted, microsecond-level low-delay recovery is achieved, hardware resource occupation is greatly reduced, robustness in a severe network environment is improved, and the method is suitable for a high-precision clock synchronization scene of an industrial TSN node.

The invention belongs to the technical field of power equipment safety monitoring and intelligent sensing, and particularly relates to an overhead line bird repelling device and method based on multi-mode sensing dynamic fusion and edge calculation. The system comprises a sensing layer which is used for sensing the activity of birds around the overhead line and the ambient brightness in real time; the edge calculation layer is used for processing the data of the sensing layer by adopting a prediction model, outputting bird behavior prediction, and outputting a corresponding bird repelling strategy by adopting a self-adaptive bird repelling decision model according to the bird behavior prediction; the execution layer is used for executing bird repelling operation according to the bird repelling strategy and the ambient brightness; the power supply layer is used for supplying power. According to the bird repelling system, through combination of the microwave radar unit, the camera unit and the photosensitive sensor, multi-mode sensing dynamic fusion is formed to capture bird activities, a prediction model and a self-adaptive decision model are locally operated in combination with an edge calculation layer, bird behaviors are predicted in advance, response delay is compressed, and the bird repelling effect is improved. The delay problem caused by a traditional scheme depending on the cloud is solved.

The invention relates to the technical field of big data management, in particular to a whole-process performance data management method based on big data. The specific implementation process comprises the steps of obtaining heterogeneous behavior traces, deconstructing and packaging the heterogeneous behavior traces into space-time cross-domain data streams, and inputting the space-time cross-domain data streams into a distributed cooperative monitoring model; an asynchronous pause frequency and a cross-domain retrieval track are extracted, a cooperative friction coefficient is calculated, and a cooperative retardation degree is generated; inputting the collaborative friction coefficient and the collaborative retardation degree into a performance evolution engine, generating a targeted dynamic weight, and outputting a targeted collaborative performance report; and outputting a resource intervention instruction when the cooperative retardation degree change slope exceeds a preset warning threshold. According to the method, real-time unified access of multi-source heterogeneous process data, continuous association modeling of cross-system behavior tracks, detection of flow blocking nodes and online dynamic updating of evaluation weights are realized by utilizing a distributed collaborative monitoring model and a performance evolution engine, so that the association processing delay of the cross-system data is reduced; and the timeliness and accuracy of flow anomaly detection are improved.

The invention discloses a working condition self-adaptive injection molding process energy consumption optimization control device and method, and belongs to the technical field of injection molding process control. The method comprises the following steps: acquiring current quasi-static working condition data, and calculating a theoretical energy consumption reference value and a theoretical melt viscosity fingerprint reference value based on the quasi-static working condition data; in the plasticizing stage of the current injection molding period, a torque signal of a screw driving motor is collected, integral operation is conducted on the torque signal, and melt viscosity fingerprints in the current period are extracted; calculating to obtain a plasticizing power deviation value based on the current-period melt viscosity fingerprint and a theoretical melt viscosity fingerprint reference value; and based on the plasticizing power deviation value and the theoretical energy consumption reference value, a corrected energy consumption target for the next injection molding period is generated, and a corresponding equipment control instruction is output. According to the method, cycle-by-cycle feed-forward correction is performed on the energy consumption target by utilizing the process fluctuation information, so that the problem of failure of the preset energy consumption model caused by non-uniform components of the recycled materials is solved, and the energy-saving precision and the product percent of pass are improved.

The invention is applicable to the technical field of information processing, and provides a design consultation service system based on case similarity comparison, comprising: a case database module for storing multi-dimensional design case data including text description, images, 3D models, user feedback data and metadata tags; according to the method, high-precision matching of multi-modal cases is achieved through the similarity comparison module, the accuracy and efficiency of design consultation services are remarkably improved, an embedding extraction unit of the similarity comparison module adopts a multi-modal embedding model, texts, images and 3D models are uniformly converted into vector representation, cross-modal matching is supported, and the accuracy and efficiency of design consultation services are improved. Limitations of traditional keyword retrieval are overcome, a similarity calculation unit applies a weighted cosine similarity algorithm, different modal information is fused, it is ensured that a matching result meets design requirements, and a dynamic weight adjustment sub-module automatically optimizes weight parameters according to historical preferences of a user and provides a personalized matching strategy. And the cross-domain transfer learning subunit realizes transfer of knowledge in the field of building design to the field of product design through fine tuning of a pre-training model.