3700 results about "Process equipment" patented technology

The invention provides a semiconductor defect detection and process optimization method based on deep learning. The method comprises the following steps: acquiring semiconductor wafer surface multispectral image data, wafer reflectivity distribution data and thickness distribution data through multispectral imaging equipment; performing multi-modal fusion preprocessing to generate a defect enhanced image; extracting micro-nano scale topological features of the surface of the wafer through a lattice structure guided deep learning network; performing frequency domain-space domain joint modeling on the topological features to generate a defect feature energy distribution diagram; fusing real-time process parameter data to dynamically classify defect features and output defect types and confidence coefficients; generating a process equipment control instruction according to the defect classification result; a defect cause knowledge graph is constructed through a causal inference engine, and a process optimization scheme is output; and feeding back the optimized process parameters to the semiconductor manufacturing equipment to form closed-loop control. The semiconductor manufacturing efficiency and the product quality can be improved, and the production cost is reduced.

The invention provides a precision injection mold accessory production quality traceability management method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting material, process, equipment and environment data in real time through a distributed sensor; fusing multi-source data based on dynamic material characteristic parameters and process stability indexes, and quantifying melt flow and process fluctuation characteristics; constructing a mixed kernel function anomaly detection model to identify quality deviation; establishing a cross-process association map to reveal the space-time relationship among the raw materials, the process and the finished product; generating a three-dimensional traceability identifier containing the material hash, the process compression code and the block chain address; a block chain enhanced database is adopted to realize tamper-proof storage; and generating a visual traceability report through reverse analysis. Through dynamic modeling, cross-process association and block chain technologies, the problems of data isolation, detection lag and low traceability credibility in a traditional method are solved, the quality traceability efficiency and precision are remarkably improved, and precise injection molding full life cycle management is supported.

The invention relates to the technical field of manufacturing process control, and discloses a multi-dimensional process data co-simulation control method and system and a storage medium. The method comprises the following steps: firstly, collecting a multi-dimensional process parameter set of a plurality of process equipment in a manufacturing production line, carrying out cross-dimensional feature extraction, and generating a comprehensive feature matrix containing a time sequence feature, a spatial distribution feature and an energy consumption feature; according to feature relevance of different dimensions in the comprehensive feature matrix, a process parameter dynamic coupling model is constructed, co-simulation is carried out on interaction between process equipment, and a process state evolution sequence is output; extracting abnormal fluctuation characteristics in the sequence, and generating a process parameter adjustment instruction set; and finally, according to the adjustment instruction set and real-time process feedback data, dynamically correcting model simulation parameters, generating an optimized process control strategy, and executing the optimized process control strategy. According to the method, collaborative analysis and dynamic control of multi-dimensional process parameters are realized, and the accuracy and adaptability of process control are improved.

The invention provides a flexible production line adaptive scheduling control method fusing multi-agent reinforcement learning, and relates to the technical field of production line scheduling, and the method comprises the steps: obtaining the state of processing equipment and workpiece demand information in real time, constructing a state and action space, mapping the equipment into agents, achieving information sharing, and then selecting a scheduling action and executing the scheduling action. And the state-action value function is updated through the experience playback pool, and the multi-agent model is optimized. The system monitors the equipment state during operation, dynamically updates the state and action space to deal with faults or material shortage, and generates a compensation scheduling scheme. Finally, in combination with a real-time scheduling instruction, adaptive scheduling control of the flexible production line is realized, and the production efficiency and flexibility are improved.

The invention relates to the technical field of industrial water system safety monitoring, in particular to an intelligent source tracing method for a process medium leaked in circulating water, which comprises the following steps of: acquiring multi-dimensional operating parameters such as conductivity, pH value, turbidity, dissolved oxygen, temperature, pressure and characteristic ion concentration; a standardized water quality parameter matrix is generated after space-time alignment and wavelet noise reduction; the method comprises the following steps: extracting an abnormal fluctuation signal by using a leakage feature recognition model based on transfer learning, simulating a diffusion process through a three-dimensional leakage diffusion model, realizing leakage source positioning by combining reverse particle tracking and kernel density estimation, associating a high-probability leakage region with upstream process equipment, extracting backtracking path features, and matching a process medium feature library, thereby realizing leakage source positioning. The leakage medium type is judged; and finally generating a structured traceability report. According to the method, high-precision identification, positioning and medium analysis of process leakage in a complex circulating water system can be realized, and the method has relatively high practicability and popularization value.

The invention relates to the technical field of road engineering, in particular to a construction positioning method and system for simulation equipment in a steel bridge deck pavement twinning scene, and the method comprises the steps: obtaining the positioning information and construction data of a construction site in real time, and constructing a construction site data set; performing primary processing on the construction site data set through a positioning algorithm to obtain a positioning position and construction dynamic characteristics of equipment; based on a GIS technology and a BIM architecture, constructing a three-dimensional twinborn scene model of the steel bridge deck, and numbering the three-dimensional model according to bridge deck pile numbers and pile-by-pile coordinate information; and matching the processed equipment positioning position data with the three-dimensional twin scene, and displaying the real-time position and the movement track of the equipment through a model. According to the invention, the problem of inaccurate positioning data caused by signal interference in the steel bridge deck pavement construction process is effectively solved, the accurate monitoring of the equipment position and the construction track is realized, and the comprehensive monitoring capability of the construction progress and quality is improved.

Dry development or dry removal of metal-containing extreme ultraviolet radiation (EUV) photoresist is performed in atmospheric conditions or performed in process tools without vacuum equipment. Dry removal of the metal-containing EUV photoresist may be performed under atmospheric pressure or over-atmospheric pressure. Dry removal of the metal-containing EUV photoresist may be performed with exposure to an air environment or with non-oxidizing gases. A process chamber or module may be modified or integrated to perform dry removal of the metal-containing EUV photoresist with baking, wafer cleaning, wafer treatment, or other photoresist processing function. In some embodiments, the process chamber for dry removal of the metal-containing EUV photoresist includes a heating assembly for localized heating of a semiconductor substrate and a movable discharge nozzle for localized gas delivery above the semiconductor substrate.

The invention discloses a high-purity quartz sand reinforced chlorination purification method, and belongs to the technical field of inorganic non-metallic material processing. The method comprises the following steps: putting a quartz sand raw material containing crystal structure impurities into a roasting furnace, and synchronously introducing a gas chlorinating agent and a gas reducing agent into the roasting furnace for roasting to obtain purified quartz sand. According to the method, on the basis of conventional chloridizing roasting purification, a gas reducing agent is introduced to regulate and control the reaction atmosphere, efficient dissociation and chlorination of crystal structure impurities difficult to remove are promoted, and deep efficient purification of quartz sand is achieved. The method has the advantages of simple process equipment, high raw material applicability, high impurity removal efficiency and the like, and can be widely applied to production of 4N5-grade and above high-purity quartz sand.

The invention discloses a multi-mode intelligent inspection system and device for an oil field gathering and transportation station yard, and belongs to the technical field of oil field gathering and transportation station yard inspection. The multi-mode intelligent inspection system comprises a data acquisition module, an abnormity judgment module, a station yard model simulation analysis module and a decision analysis and execution module; comprising a vibration camera sensor, a temperature camera sensor, a pressure camera sensor, a flow camera sensor, a liquid level camera sensor, a gas camera sensor, an ultraviolet flame camera sensor, an infrared thermal imaging camera sensor, a visible light camera sensor and a depth camera sensor and is used for collecting process equipment, environment states and personnel operation behavior data of a current oil field gathering and transportation station and recording the data collected by the sensors in a near-field edge server and a cloud server; the time difference of data acquisition is eliminated through a space-time alignment algorithm, and a time sequence health vector in a unified format is generated. According to the multi-mode intelligent inspection system and device for the oil field gathering and transportation station yard, multi-mode inspection can be carried out on the oil field gathering and transportation station yard based on the type of the oil field gathering and transportation station yard, and typical anomalies are recognized and processed.

The invention relates to the field of industrial automation, in particular to an industrial robot glass processing system based on large model intelligent decision, and relates to an industrial robot key technology based on multi-source information integration and intelligent decision. Glass cutting is optimized through multi-physics field monitoring, the edge defects are reduced by 70%, and the rejection rate is reduced by 50%. And the edge grinding of the special-shaped glass is assisted by three-dimensional reconstruction, and the precision deviation is controlled within 0.1 mm. And multi-modal data improves the coating uniformity, the thickness deviation is within 5 [mu] m, and the film layer shedding is reduced by 80%. Multi-robot cooperation is achieved through reinforcement learning, the machining efficiency is improved by 60%, and the cost is reduced by 40%. And a digital twinning optimization process is adopted, so that the equipment failure rate is reduced by 60%.

The invention relates to a high-flow gas mixing system and method and semiconductor process equipment. The large-flow gas mixing system comprises a first gas input unit, a second gas input unit, at least one first flow control unit, at least one second flow control unit, a first gas mixing unit, a first concentration analysis unit, a second gas mixing unit, a second concentration analysis unit, a first gas output unit and a second gas output unit. The flow control device has the advantages that the first gas input unit is communicated with the plurality of first flow control units, the second gas input unit is communicated with the plurality of second flow control units, and the plurality of second flow control units are used for controlling the flow at the same time, so that the flow control precision is improved, and the concentration precision is further improved; the first gas mixing unit is respectively communicated with the first flow control unit and the second flow control unit, and the first gas and the second gas are primarily mixed through the first gas mixing unit, so that the concentration uniformity of the whole system is improved.

The invention discloses a production safety accident emergency disposal scheme intelligent generation method and system, and the method comprises the steps: obtaining the spatial-temporal characteristics of historical accident cases, calculating the correlation degree, building an accident prediction network based on the correlation degree, simulating uncertain factors, calculating the similarity between a current accident and the historical cases, determining the prior probability, and carrying out the accident development reasoning. The method comprises the steps of tracing accident hazard degrees of different production links, obtaining an cause chain, generating an emergency resource allocation strategy set by utilizing Markov decision, constructing target function optimization, obtaining an emergency disposal scheme, monitoring production process, equipment state and personnel configuration changes in real time, and dynamically adjusting the emergency disposal scheme to adapt to the changes. According to the method, the historical data, the graph neural network and the Markov decision are combined, accident prediction and emergency resource intelligent allocation are realized, the method can adapt to the change of a production environment in real time, the disposal efficiency and safety are improved, and meanwhile, the method has relatively good interpretability.

The invention discloses a self-adaptive digital twin simulation device and method for semiconductor equipment. The device comprises a data acquisition module, a simulation model construction and operation module and a parameter updating module which are connected through signals. The data acquisition module is used for acquiring production information and process parameter information of a semiconductor product and acquiring actual processing data in real time; the simulation model construction and operation module constructs a process equipment energy consumption simulation model according to a preset process equipment configuration unit, fuses production information and process parameter information of a semiconductor product, operates the process equipment energy consumption simulation model, and gives energy consumption simulation data in real time; and the parameter updating module is used for updating the process parameter information in the process equipment energy consumption simulation model. A real-time data feedback mechanism is introduced by setting a parameter updating module, process parameter information is dynamically updated, and the accuracy and the real-time performance of the process equipment energy consumption simulation model are improved.

The invention provides a pressure control method of a chamber and semiconductor process equipment. The method comprises the following steps: monitoring the pressure of the chamber, monitoring the current opening degree of a pressure control valve of the chamber, and determining the actual flow introduced into the chamber based on the pressure of the chamber and the current opening degree; after the target pressure of the cavity is updated, the target opening degree of the pressure control valve is determined based on the actual flow currently introduced into the cavity and the target pressure, and the pressure control valve is controlled to be opened to the target opening degree; and performing closed-loop control on the pressure control valve based on the chamber pressure and the target pressure. According to the pressure control method for the cavity, the influence of the target pressure on the opening degree of the pressure control valve in open-loop control is considered, the influence of the real-time air inlet flow of the cavity on the opening degree of the pressure control valve is also considered, the open-loop control precision is improved, the opening degree of the pressure control valve in the open-loop control is close to the final steady-state opening degree position, and the pressure control accuracy is improved. The adjustment time of subsequent closed-loop control is shortened, the pressure control time of the cavity is shortened, and the process efficiency is improved.

The invention belongs to the technical field of machining equipment feeding, and particularly relates to punch press feeding equipment with an adjusting function, which comprises a material distribution bin and a punching lathe arranged on one side of the material distribution bin, and is characterized in that an outer frame bracket is arranged at the top of the material distribution bin in a sliding manner, a material frame is arranged in the outer frame bracket, and a material guide plate is arranged in the material distribution bin in a sliding manner; a material guiding plate is arranged on the base, a discharging adjusting plate is arranged at the top of the material guiding plate, a material distributing plate is arranged at the top of the discharging adjusting plate, a supporting plate is arranged at the bottom of the material guiding plate, and a mounting block is arranged on one side of the material guiding plate. And through arrangement of structures such as a mounting block and a detection block, the diameter and length of the bar can be detected, so that the stamping quality of the bar is improved, the bar can be cooled through arrangement of a water pipe and a water tank, and workers are prevented from being burnt by generated high temperature.

The invention discloses a radio frequency power adjusting system, an adjusting method thereof and semiconductor process equipment. The adjusting system comprises an interference monitoring module which obtains a time domain signal of radio frequency power in a reaction cavity and obtains interference data based on frequency domain characteristics of the time domain signal; the adaptive control module takes the interference data as a state input variable, updates an action output variable by learning an optimal strategy, and determines an optimal action output variable based on an objective function of interference suppression and energy consumption, and the action output variable comprises a frequency regulation variable, an amplitude regulation variable and a pulse width regulation variable; and the feedback adjusting module is used for dynamically adjusting a plurality of process parameters in the reaction cavity based on the optimal action output variable so as to enable the process parameters to reach the target process parameter range. According to the invention, the radio frequency power density in the reaction cavity can be adaptively adjusted, and the optimal dynamic balance between interference suppression and energy consumption is realized, so that the radio frequency interference suppression effect, the process stability and the equipment efficiency are improved.

The invention relates to the technical field of electric power energy monitoring, and discloses a remote monitoring analysis method and system based on electric power equipment operation data, and the method comprises the steps: collecting the multi-element energy operation parameters of a multi-source energy system and the multi-element energy use parameters of a user side in real time; after the collected data are preprocessed, the preprocessed equipment operation state data are evaluated, and an electric power equipment operation state index is obtained; whether equipment faults exist or not is judged according to power equipment operation state indexes, virtual mapping of a multi-source energy system is constructed, and system operation conditions under different energy scheduling strategies are simulated in real time by combining user energy demand prediction and external environment changes; when potential energy supply abnormity or equipment failure is detected, a multi-stage early warning mechanism is triggered, and a decision report including abnormity positioning, reason analysis, energy scheduling optimization and equipment maintenance disposal suggestions is generated, so that potential problems can be found in time, and the stability and reliability of energy system operation are improved.

The invention relates to an agricultural microbial preparation production line multi-device collaborative control method and system, and the method comprises the steps: calculating a device health score based on operation state information, combining the device health score with a biological risk index to generate a task release score, and generating a production task instruction when the task release score meets a preset release threshold value, constructing a cooperative interlocking matrix between the multiple types of process equipment and the material handling equipment based on the production task instruction, distributing a control execution instruction to the multiple types of process equipment and the material handling equipment, respectively obtaining equipment feedback state information and material circulation information, executing consistency verification on the equipment feedback state information and the material circulation information, and sending a verification result to the material handling equipment; a consistency verification result is obtained, during manual modification, a revision audit chain corresponding to the control event information is generated, and when the running state information or the control event information triggers a preset abnormal threshold value, a disposal control instruction is generated. The method has the effect of improving the production safety.

The invention provides a process knowledge graph construction method, which comprises the following steps of: acquiring original process data, establishing a process field dictionary by combining expert knowledge of a process production field, and respectively preprocessing structured original process data and unstructured original process data based on the dictionary to obtain standard process data; classifying the standard process data according to six dimensions of workpiece data, feature data, process equipment data, process method data, material characteristic data and process history and experience data so as to establish an ontology model of the process knowledge graph and form a mode layer of the process knowledge graph; inputting the various standard process data into a pre-trained deep learning model to identify and extract entities and relationships thereof so as to construct a data layer of the process knowledge graph; and integrating the mode layer and the data layer to form a complete process knowledge graph. The process knowledge graph constructed by the method is wide in applicable process range and high in intelligent degree.

The invention relates to the technical field of intelligent manufacturing, and discloses an intelligent control method and system for a stamping robot, and the method comprises the steps: constructing a knowledge graph containing the multi-dimensional relation of the quality of material technology equipment; the process knowledge is converted into a formalized logic rule, and the formalized logic rule is fused with a deep neural network to construct a neural symbol inference model; modeling the stamping process into a discrete event system, and verifying the correctness of a process control rule by using a linear temporal logic protocol; detecting and solving parameter constraint conflicts in the process control rule by using a satisfiability model theory solver; generating and executing a self-adaptive stamping control strategy based on the verified rule base and production process feedback; according to the method, symbol reasoning and deep learning are fused, the technical characteristics of high interpretability, high safety, high adaptive capacity and the like are achieved, and the quality consistency and production efficiency of stamping production can be effectively improved.

The invention discloses a valve processing and manufacturing method based on digital twinning, which comprises the following steps: S1, constructing a valve digital twinning model, creating a valve geometric model by using three-dimensional modeling software, and endowing the valve geometric model with physical attributes and material characteristics through finite element analysis software, carrying out design optimization through fluid mechanics simulation and stress analysis; s2, establishing a processing technology digital twin model, performing digital modeling on the device, integrating processing technology parameters, and simulating a material processing process; s3, the machining process is monitored in real time, multiple types of sensors are deployed on machining equipment, and operation data of the machining equipment are collected in real time; s4, dynamic simulation and comparative analysis are conducted, specifically, the digital twin model dynamically simulates the actual machining process according to real-time collected data, and a simulation result and design requirements are compared and analyzed; and S5, feedback control optimization: when the processing deviation exceeds a preset threshold value, automatically generating a process parameter adjustment scheme and feeding back the process parameter adjustment scheme to a processing equipment control system.

The utility model belongs to the technical field of water treatment, and particularly discloses a movable semiconductor ultrapure water preparation device which comprises a water tank part, a pretreatment part, a primary treatment part, a secondary treatment part, a functional treatment part, a terminal treatment part and a dosing part. All the parts are integrated, convenient to disassemble and assemble and compact in structure. The ultrapure water preparation device capable of integrating various cleaning and purifying functions comprises all process equipment for controlling the water quality index of the ultrapure water, strictly ensures that the water quality of produced water reaches the standard, and is designed with online detection equipment, so that the water quality of the ultrapure water is fully ensured. The whole device is convenient to move, and can be quickly conveyed to a factory for preparing ultrapure water for water preparation, so that the construction cost and the operation and maintenance cost are reduced while the requirements of the production process and the water quality are met. All functional parts in the device are recombined, disassembled and assembled, the market change requirements are met, the equipment cost is reduced by adopting the reasonable and integrated device, and the quality and efficiency of preparing ultrapure water are improved.

The present application provides a semiconductor process device and a calibration apparatus therefor, the calibration apparatus being used for calibrating the position of a wafer. The calibration apparatus comprises pushing mechanisms and a driving mechanism. There are two or more pushing mechanisms, and each pushing mechanism comprises an abutting push part. The abutting push part can move from an initial position thereof to a calibration position thereof, and, while moving, pushes the wafer to move, so as to calibrate the position of the wafer. The driving mechanism is connected to the abutting push parts of at least two pushing mechanisms and is used for driving the abutting push parts of the at least two pushing mechanisms to synchronously move between the respective initial positions and the respective calibration positions. In a calibration process, the driving mechanism acts and simultaneously drives the two or more pushing mechanisms to act, such that the wafer is pushed to move to a calibration position by means of the abutting push parts. The two or more abutting push parts are driven to act by the same driving mechanism, such that the synchronization rate of action is higher, avoiding wafer position deviation caused by asynchronous actions of the pushing mechanisms and improving the accuracy of wafer position calibration, thereby increasing the process yield.

The invention relates to the technical field of composite material surface magnetron sputtering coating, and particularly discloses a composite material surface magnetron sputtering coating process and method, and the composite material surface magnetron sputtering coating process comprises the steps of S1, surface treatment, S2, process equipment condition setting, S3, intelligent control, S4, multi-target co-sputtering process, S5, post-treatment and S6, surface topography detection. Through the multi-target co-sputtering technology, multiple layers of composite coatings such as TiN, CrN, Al2O3, ITO and other different functional layers can be deposited on the surface of the composite material, the base material is endowed with the properties such as abrasion resistance, corrosion resistance, insulation and electric conduction, and the comprehensive performance of the coatings is further improved through the synergistic effect of all the layers; the intelligent control system monitors and adjusts sputtering parameters in real time, so that the compactness and uniformity of the film are ensured, the defect rate of the film is reduced, the protection effect of a coating is enhanced, the service life of the coating is prolonged, key parameters such as sputtering power and temperature can be accurately adjusted, fluctuation caused by manual intervention is reduced, and the service life of the coating is prolonged. And the result of each process operation has high consistency.

The invention relates to the technical field of surface defect detection, and discloses an aluminum profile surface defect analysis method based on machine vision, and the method comprises the steps: obtaining equipment operation parameters, environment parameters, aluminum bar processing procedure parameters and aluminum profile surface snapshots of processing equipment of a target aluminum profile in real time; obtaining a corresponding equipment operation risk value according to the equipment operation parameter; according to the aluminum bar machining procedure parameters, the equipment operation risk values and the environment parameters, procedure score values of the corresponding aluminum bar machining key procedures are obtained; the aluminum profile surface snapshots, obtained from multiple angles, of the target aluminum profile are combined and then sent to the pre-trained surface recognition model for evaluation, the quality inspection defect value output by the surface recognition model is obtained, and the surface quality defects can be continuously, accurately and rapidly found in a non-contact detection mode. The aluminum profile production efficiency and quality are fully improved, and compared with manual detection, a machine vision system has lower cost and higher efficiency.

The invention relates to the field of industrial automation, in particular to an industrial robot semiconductor grinding and polishing system based on intelligent control, which reduces the flatness deviation of the wafer surface by 60% through multi-sensor fusion and real-time feedback and reaches the nanoscale. Multi-modal data acquisition assists material removal uniformity optimization, the wafer thickness deviation is controlled within 1 nm, and the yield is increased by 30%. The grinding time is shortened by 40% through multi-target reinforcement learning, and the efficiency is greatly improved. And the equipment failure rate is reduced by 50% through a digital twinning pre-planning process. And through multi-robot cooperation, the production efficiency is improved by 50%, and the resource utilization rate is increased by 40%. And the production level is comprehensively improved, and intelligent upgrading demonstration is provided for the semiconductor manufacturing industry.

The invention discloses a wafer bearing device, a temperature control method thereof and semiconductor process equipment. The wafer bearing device comprises a heating assembly, the heating assembly comprises a heater body, the heater body comprises an inner heating area and an outer heating area, the outer heating area is located outside the inner heating area in the radial direction, and an inner area heating part and an inner area temperature measuring part are arranged in the inner heating area; an outer area heating part and an outer area temperature measuring part are arranged in the outer heating area; an annular protruding structure is arranged on the edge of the water cooling disc, the water cooling disc is connected with the heater body in a sealed mode through the annular protruding structure, the width and the height of the annular protruding structure are set according to the working condition temperature needed by the wafer bearing device, an inner cooling area is arranged in the annular protruding structure, and the annular protruding structure is an outer cooling area. And a gap is formed between the inner cooling area and the heater body. The cooling device has high cooling capacity under the high-temperature working condition and the low-temperature working condition, and the high-temperature working condition and the low-temperature working condition can be considered simultaneously.

The present application belongs to the field of semiconductor technology and provides a load port for a wafer pod. To address the problem of lower wafer transfer efficiency caused by limited operational efficiency of load ports, the present application provides said load port for a wafer pod, comprising: a support plate; a first loading module and a second loading module are vertically arranged on the support plate; a control module is simultaneously connected to the first loading module and the second loading module and alternately controls the first loading module and the second loading module to convey wafer pods; the control module is arranged on a first side face of the support plate; and an accommodating space is formed between a second side face of the support plate and the first and second loading modules. The present application improves operational efficiency of the load port by means of the first loading module and the second loading module working in alternation, uninterrupted wafer conveyance by a wafer conveyance mechanical arm among process equipment is implemented, and thereby wafer transfer efficiency is improved.

The invention relates to a carbon footprint management method and system of a lithium battery, electronic equipment and a storage medium, and belongs to the technical field of carbon footprint management, and the method comprises the steps: obtaining first carbon footprint data of each link of the whole life cycle of the lithium battery; the first carbon footprint data comprises energy used in each link, materials input and output in each link, wastes generated in each link, metering equipment of the energy material wastes, process equipment used in each link, basic information of the process equipment, carbon emission factors of each link, test equipment of the carbon emission factors, a test environment and a test method; carbon emissions of all links and a calculation method of the carbon emissions; generating a carbon footprint report according to the first carbon footprint data; and uploading the first carbon footprint data and the carbon footprint report to a block chain. According to the invention, block chain storage is carried out on the measurement information of each link, the carbon emission factor and the test / calculation process of the carbon emission, so that the credibility of the carbon footprint is ensured.

The invention relates to a full-period collaborative optimization method, system and device for a water treatment process chain and an energy system and a medium, and the method comprises the steps: obtaining and processing multi-dimensional data, and obtaining a data set; constructing a photovoltaic panel two-degree-of-freedom adjustment model, and introducing generated power feedback; calculating an electric quantity self-sufficiency rate based on the data set, when the self-sufficiency rate is lower than a critical threshold value, executing elastic adjustment of discharge depth constraint of an energy storage unit and priority dynamic distribution of power of non-critical process equipment, and monitoring and triggering a compensation strategy of working point correction and load redistribution of a photovoltaic panel in real time to form dynamic balance of power distribution; by constructing a water quality and energy consumption prediction model, a parameter optimization instruction of process equipment is generated, and a power distribution state is associated synchronously, so that multi-dimensional collaborative convergence is formed. By integrating multi-dimensional data acquisition, photovoltaic power generation dynamic adjustment, electric power elastic distribution and process parameter closed-loop optimization, the energy self-consistency and operation efficiency of the water treatment system are remarkably improved.