A sulfur hexafluoride material control method and system based on big data

Abstract

The present invention relates to a sulfur hexafluoride material management and control method and system based on big data, which includes the following steps: step S1, based on the full-cycle data of sulfur hexafluoride gas, using a distributed dynamic state perception model to perform data analysis, and establish a sulfur hexafluoride gas full life cycle information management and control big database including full-chain multi-dimensional information; step S2, based on the information management and control big database, establish a sulfur hexafluoride gas material accounting model; step S3, based on the information management and control big database, using a neural network algorithm, establish a sulfur hexafluoride gas quantity prediction model, predict the sulfur hexafluoride demand, and verify it on the sulfur hexafluoride digital twin model; step S4, based on blockchain technology, establish a comprehensive sulfur hexafluoride gas analysis and digital visualization system, and perform material management and control on the source, flow, and sink flow of sulfur hexafluoride gas. Compared with the existing technology, the present invention realizes material management and control of the source, flow, and sink flow of sulfur hexafluoride gas throughout the entire process.

Description

Technical Field

[0001] The present invention relates to the field of gas material management and control, and in particular to a sulfur hexafluoride material management and control method and system based on big data. Background Art

[0002] Sulfur hexafluoride is an excellent insulating and arc-extinguishing medium and has been widely used in the power industry. With the annual increase in the number of sulfur hexafluoride electrical equipment, the consumption of sulfur hexafluoride gas has continued to increase. The impact of sulfur hexafluoride gas emissions on the atmospheric environment has received widespread attention from all walks of life in recent years.

[0003] Digital management and control of sulfur hexafluoride (SF6) is still in its infancy. Currently, research focuses on the recovery, purification, and reuse of SF6 gas. However, there is no systematic research on the management and control of SF6 recovery and reuse based on big data technology.

[0004] The existing sulfur hexafluoride gas control and recycling work mainly has the following problems:

[0005] 1) It is difficult to accurately measure the amount of sulfur hexafluoride gas used or recovered

[0006] Accurately understanding the amount of sulfur hexafluoride gas used or recovered is the basis for achieving lean management and control of the entire life cycle of sulfur hexafluoride gas. However, at present, many municipal and county-level bureaus in my country still use or recover sulfur hexafluoride gas by simply visually observing the air pressure of the equipment to roughly estimate the gas usage. Some municipal and county-level bureaus that attach importance to the control of sulfur hexafluoride gas use the method of weighing sulfur hexafluoride gas cylinders to obtain accurate sulfur hexafluoride gas usage information. However, due to complex on-site working conditions, in some cases it is impossible to weigh the gas, and thus it is impossible to obtain accurate gas usage information.

[0007] 2) No record has been established between sulfur hexafluoride gas and power equipment

[0008] At present, there is no technology that mentions the integrated management and control of sulfur hexafluoride gas equipment and sulfur hexafluoride gas information. The source of the gas filled in the equipment and the gas replenishment / recovery information are difficult to trace, which affects the accurate assessment of the equipment status and work performance. The sulfur hexafluoride ledger is complicated and the degree of intelligence is not high. At present, the logistics department is generally responsible for the centralized procurement (distribution) and recovery of sulfur hexafluoride gas in enterprises, the chemical department is responsible for distribution and test supervision, and the operation and use of the operation department is responsible. In the use of sulfur hexafluoride gas, multiple professions and departments are involved, there are many ledgers, the management and control information is complicated, and cross-professional queries are inconvenient. There is a lack of an intelligent, integrated, and widely applicable integrated management and control platform.

[0009] In view of the current problems of unclear management and control mode, backward data collection method and imperfect operation and maintenance system in the "sulfur hexafluoride recycling process", it is urgent to develop a sulfur hexafluoride material management and control technology to realize the digital management and control of sulfur hexafluoride gas, reveal the key emissions and inefficient recovery links, grasp the gas loss in each link, and grasp the equipment health through modeling and analysis of big data, and predict the new gas purchase volume, gas recovery volume, purified gas demand and reuse volume. Summary of the Invention

[0010] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a sulfur hexafluoride material management method and system based on big data to realize the material management of the source, flow and sink of sulfur hexafluoride gas throughout the entire process.

[0011] The purpose of the present invention can be achieved by the following technical solutions:

[0012] According to a first aspect of the present invention, a method for managing and controlling sulfur hexafluoride materials based on big data is provided, the method comprising the following steps:

[0013] Step S1: Based on the full cycle data of sulfur hexafluoride gas, a distributed dynamic state perception model is used to perform data analysis to establish a large information management and control database for the entire life cycle of sulfur hexafluoride gas, including multi-dimensional information of the entire chain;

[0014] Step S2: establishing a sulfur hexafluoride gas material accounting model based on the information management and control database;

[0015] Step S3: Based on the information management and control database, a neural network algorithm is used to establish a sulfur hexafluoride gas quantity prediction model, predict the sulfur hexafluoride demand, and verify it on the sulfur hexafluoride digital twin model;

[0016] Step S4: Based on blockchain technology, a comprehensive analysis and digital visualization system for sulfur hexafluoride gas is established to conduct material control over the entire process of source, flow, and sink of sulfur hexafluoride gas.

[0017] Preferably, the full-chain multi-dimensional information in step S1 includes the historical inflation volume of the in-service equipment throughout the entire life cycle, the recycling volume under different mission states and different service years, the purification volume, flow rate, and in-and-out data.

[0018] Preferably, the step S1 includes the following steps:

[0019] Step S11: Based on the general coding rules for sulfur hexafluoride storage containers, physical IDs of power equipment, and sulfur hexafluoride gas auxiliary tools, as well as sulfur hexafluoride gas identification technology, determine the sulfur hexafluoride gas pressure container identification tag to form a multi-code-in-one distributed dynamic state perception model for sulfur hexafluoride;

[0020] Step S12: Collect physical IDs of gas-charging power equipment of different voltage levels, recovery devices, sulfur hexafluoride storage containers, and multi-level inventory information of sulfur hexafluoride, establish an equipment and gas inventory control mechanism, and integrate the data of gas-charging equipment and sulfur hexafluoride gas inventory;

[0021] Step S13: Based on the multi-code-in-one distributed dynamic state perception model of sulfur hexafluoride, the flow process of sulfur hexafluoride gas and the terminal are automatically identified and tracked, and the data is automatically recorded and uploaded;

[0022] Step S14: Based on the sulfur hexafluoride recovery and reuse scenarios under different management and control modes, corresponding database tables are designed to form a large information management and control database for the entire life cycle of sulfur hexafluoride gas.

[0023] Preferably, the sulfur hexafluoride gas identification technology in step S11 is a warehouse tracking technology based on radio frequency identification.

[0024] Preferably, step S2 specifically comprises: using a machine learning method to perform data mining on the massive full-chain multidimensional information collected using sensor technology, combining it with a state perception model of sulfur hexafluoride gas to perform data analysis, and establishing a sulfur hexafluoride gas material balance accounting model to provide data support for sulfur hexafluoride emission baseline, boundary definition, marginal emission reduction cost or obstacle analysis.

[0025] Preferably, the step S3 is specifically as follows:

[0026] Based on the information management and control big database of the entire life cycle of sulfur hexafluoride gas and digital twin technology, empirical mode decomposition and long short-term memory neural network algorithms are used to establish a sulfur hexafluoride gas prediction model to predict the demand for sulfur hexafluoride.

[0027] Preferably, the step S4 is specifically as follows:

[0028] Establish a blockchain ledger at every stage of sulfur hexafluoride gas production, and establish a traceability chain with multi-party participation, transparency, sharing, and authenticity;

[0029] Based on the sulfur hexafluoride big data map service, data visualization technology is used to realize the visualization of the entire life cycle of sulfur hexafluoride gas. Based on the sulfur hexafluoride gas material accounting model and prediction model, a comprehensive analysis of the entire process of sulfur hexafluoride gas source, flow, and sink is digitally displayed.

[0030] Preferably, the various steps of sulfur hexafluoride gas in step S4 include purchase, control, use, recovery, purification and reuse.

[0031] Preferably, the sulfur hexafluoride big data map service is a sulfur hexafluoride big data map service based on JQuery and Bootstraps framework.

[0032] According to a second aspect of the present invention, a system based on the above-mentioned big data-based sulfur hexafluoride material management and control method is provided, and the system includes the following modules:

[0033] Information control database module, used to collect and process data related to sulfur hexafluoride gas;

[0034] Gas material accounting module, used for material accounting of sulfur hexafluoride gas;

[0035] A blockchain-based data transmission module for trusted data transmission of sulfur hexafluoride gas;

[0036] The digital visualization module is used to visualize the entire process of source, flow, and sink of sulfur hexafluoride gas.

[0037] Compared with the prior art, the present invention has the following advantages:

[0038] 1) The present invention realizes non-contact intelligent transmission of data from each link of sulfur hexafluoride recycling, constructs a non-contact large database of the entire life cycle of sulfur hexafluoride gas, and integrates scattered information such as electrical equipment, multi-level gas inventory, gas flow and recycling;

[0039] 2) The present invention uses diversified front-end sensing technology based on mobile intelligent terminals to track the process of sulfur hexafluoride gas entering the network, in operation, and recycling, achieving rapid identification of sulfur hexafluoride gas flow information and real-time automatic interaction;

[0040] 3) In response to the complexity and diversity of sulfur hexafluoride service scenarios, the present invention adopts an intelligent data transmission method for each link in the sulfur hexafluoride recycling process to avoid human tampering of data in each link. It also proposes a sulfur hexafluoride multi-dimensional data classification and accounting technology. This provides data support for key factors such as the establishment of sulfur hexafluoride emission baselines and boundary definition, marginal emission reduction costs, investment analysis, and barrier analysis, thereby achieving sustainable development of sulfur hexafluoride recovery and reuse.

[0041] 4) This invention improves the management and control of sulfur hexafluoride recycling. The storage tracking technology based on radio frequency identification principles controls the gas in service and in stock, solving the technical problem of inaccurate sulfur hexafluoride gas weight calculation in electrical equipment at the source.

[0042] 5) The present invention utilizes blockchain-based full data chain traceability technology to solve the current problem of accurate and reliable transmission of multi-channel and multi-source data during the full life cycle of sulfur hexafluoride recycling. It utilizes digital twin technology for production control process simulation to construct a digital twin model of the full life cycle control process of sulfur hexafluoride gas. By applying big data analysis to extract key characteristic functions of process control, the actual control process is simulated in the virtual world, realizing intelligent control of the entire process of sulfur hexafluoride recycling.

[0043] 6) The trusted data exchange and transmission adopted in the present invention establishes a traceability chain with transparent, shared, and authentic information among multiple parties; accounts are constructed in multiple links such as the purchase, control, use, recovery, purification, and reuse of sulfur hexafluoride gas, and a full-process chain path for the traceability of sulfur hexafluoride big data is established, which directly reaches the end user; and trusted data exchange and transmission of sulfur hexafluoride that can be promoted in a network environment is realized, forming a complete chain of evidence. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of the method of the present invention;

[0045] Figure 2 This is a diagram of the architecture of a large database for information management and control over the entire life cycle of sulfur hexafluoride gas in the embodiment;

[0046] Figure 3 FIG. 4 is a diagram of the system architecture in an embodiment. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0048] The present invention relates to a sulfur hexafluoride material management and control method and system based on big data.

[0049] The following combination Figure 1 , a method embodiment of the present invention is given, a sulfur hexafluoride material management and control method based on big data, the method comprising the following steps:

[0050] Step S1: Based on the full-cycle data of sulfur hexafluoride gas, a distributed dynamic state perception model is used to perform data analysis to establish a large information management and control database for the entire life cycle of sulfur hexafluoride gas, including full-chain multi-dimensional information. The full-chain multi-dimensional information includes the historical gas charging volume of in-service equipment throughout the entire life cycle, the recycling volume under different mission states and different service years, the purification volume, the flow rate, and the in-and-out data;

[0051] like Figure 2 As shown, step S1 is specifically as follows:

[0052] Step S11: Based on the general coding rules for sulfur hexafluoride storage containers, physical IDs of power equipment, and sulfur hexafluoride gas auxiliary tools, and the sulfur hexafluoride gas storage tracking technology based on radio frequency identification, an identification tag of the sulfur hexafluoride gas pressure container is determined to form a multi-code-in-one distributed dynamic state perception model of sulfur hexafluoride;

[0053] Step S12: Collect physical IDs of gas-charging power equipment of different voltage levels, recovery devices, sulfur hexafluoride storage containers, and multi-level inventory information of sulfur hexafluoride, establish an equipment and gas inventory control mechanism, and integrate the data of gas-charging equipment and sulfur hexafluoride gas inventory;

[0054] Step S13: Based on the multi-code-in-one distributed dynamic state perception model of sulfur hexafluoride, the flow process of sulfur hexafluoride gas and the terminal are automatically identified and tracked, and the data is automatically recorded and uploaded;

[0055] Step S14: Based on the sulfur hexafluoride recovery and reuse scenarios under different management and control modes, corresponding database tables are designed to form a large information management and control database for the entire life cycle of sulfur hexafluoride gas.

[0056] The distributed state perception model relies on front-end perception technology, which is manifested as information exchange between sensors and smart terminals. The process of its perception data interaction: using non-contact technology to transmit the information of the front-end built-in perception sensors to the terminal device; when the internal information of the terminal device changes, the front-end perception sensor data is updated through non-contact technology. The data in the interaction process does not change due to perception, realizing reversible data transmission.

[0057] Step S2: establishing a sulfur hexafluoride gas material accounting model based on the information management and control database;

[0058] Machine learning methods are used to mine the massive amount of multi-dimensional information collected by sensor technology throughout the entire supply chain. Combined with the state perception model of sulfur hexafluoride gas, data analysis is conducted to establish a material balance accounting model for sulfur hexafluoride gas. This provides data support for sulfur hexafluoride emission baselines, boundary definition, and marginal emission reduction costs or barrier analysis.

[0059] The sulfur hexafluoride gas material accounting model uses sensor technology to collect and obtain historical gas volume of massive in-service equipment, recycling volume under different mission states and different service years, purification volume, flow rate and warehouse in and out data and other full-chain multi-dimensional information. Based on data preprocessing, a statistical machine learning method is used to mine massive sulfur hexafluoride full life cycle management data, revealing key emissions and inefficient recycling links, proposing optimization and improvement plans, and establishing a high-accuracy sulfur hexafluoride gas material accounting model.

[0060] Step S3: Based on the information management and control database, a neural network algorithm is used to establish a sulfur hexafluoride gas quantity prediction model, predict the sulfur hexafluoride demand, and verify it on the sulfur hexafluoride digital twin model, specifically:

[0061] Leveraging a large database of information management and control covering the entire lifecycle of sulfur hexafluoride gas, digital twin technology, and real-time data stream and time series data processing mechanisms, we explored the association rules between equipment failures and gas state changes. Using empirical mode decomposition and long-short-term memory neural network algorithms, we established a sulfur hexafluoride gas prediction model based on a full-equipment health state space model. This model was then validated on the sulfur hexafluoride digital twin model.

[0062] The sulfur hexafluoride gas prediction model is based on real-time data on sulfur hexafluoride gas density in electrical equipment, and conducts autonomous data cluster analysis in an unsupervised environment to establish a relationship model between equipment leakage and gas density changes. At the same time, based on information such as theoretical sulfur hexafluoride gas loss and loss during the purification process, combined with a large database of information management and control of the entire life cycle of sulfur hexafluoride gas established by the number of in-service equipment, historical failure probabilities, maintenance plans, and equipment replacement and elimination plans, a sulfur hexafluoride gas quantity simulation prediction model is established using digital twin technology to guide the gas control center in predicting and planning the amount of new gas purchased, gas recovered, purified gas demand, and reused gas.

[0063] Step S4: Based on blockchain technology, a comprehensive analysis and digital visualization system for sulfur hexafluoride gas is established to conduct material control over the entire process of source, flow, and sink of sulfur hexafluoride gas;

[0064] Establish a blockchain ledger for the purchase, control, use, recovery, purification, and reuse of sulfur hexafluoride gas, and establish a traceability chain with multi-party participation, transparency, sharing, and authenticity;

[0065] Relying on the sulfur fluoride big data map service based on JQuery and Bootstraps framework, data visualization technology is used to realize the visualization of the entire life cycle of sulfur hexafluoride gas. Based on the sulfur hexafluoride gas material accounting model and prediction model, a comprehensive analysis of the entire process of sulfur hexafluoride gas source, flow, and sink is digitally displayed.

[0066] Combine Figure 3 , a system embodiment of the present invention is given, a system based on the above-mentioned big data-based sulfur hexafluoride material management and control method, the system includes the following modules:

[0067] Information control database module, used to collect and process data related to sulfur hexafluoride gas;

[0068] Gas material accounting module, used for material accounting of sulfur hexafluoride gas;

[0069] A blockchain-based data transmission module for trusted data transmission of sulfur hexafluoride gas;

[0070] The digital visualization module is used to visualize the entire process of source, flow, and sink of sulfur hexafluoride gas.

[0071] This embodiment is based on the sulfur hexafluoride gas material accounting model and prediction model, and uses big data visualization presentation technology to develop a comprehensive analysis and digital display system for the entire process of sulfur hexafluoride gas source (new gas purchase), flow (gas application scenario), and convergence (recovery, purification, recycling and reuse). Data visualization technology is used to achieve an intuitive display of the entire life cycle of sulfur hexafluoride gas from purchase to use, recovery, purification and reuse, focus on monitoring key links such as emissions, inefficient recovery, and gas loss, and integrate the entire process data.

[0072] On this basis, a big data-based sulfur hexafluoride recovery and reuse evaluation system will be established to objectively evaluate and guide the implementation of sulfur hexafluoride recovery and utilization work.

[0073] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for controlling sulfur hexafluoride materials based on big data, characterized in that: The method comprises the following steps: Step S1: Based on the full cycle data of sulfur hexafluoride gas, a distributed dynamic state perception model is used to perform data analysis to establish a large information management and control database for the entire life cycle of sulfur hexafluoride gas, including multi-dimensional information of the entire chain; Step S2: establishing a sulfur hexafluoride gas material accounting model based on the information management and control database; Step S3: Based on the information management and control database, a neural network algorithm is used to establish a sulfur hexafluoride gas quantity prediction model, predict the sulfur hexafluoride demand, and verify it on the sulfur hexafluoride digital twin model; Step S4: Based on blockchain technology, a comprehensive analysis and digital visualization system for sulfur hexafluoride gas is established to conduct material control over the entire process of source, flow, and sink of sulfur hexafluoride gas; The step S1 comprises the following steps: Step S11: Based on the general coding rules for sulfur hexafluoride storage containers, physical IDs of power equipment, and sulfur hexafluoride gas auxiliary tools, as well as sulfur hexafluoride gas identification technology, determine the sulfur hexafluoride gas pressure container identification tag to form a multi-code-in-one distributed dynamic state perception model for sulfur hexafluoride; Step S12: Collect physical IDs of gas-charging power equipment of different voltage levels, recovery devices, sulfur hexafluoride storage containers, and multi-level inventory information of sulfur hexafluoride, establish an equipment and gas inventory control mechanism, and integrate the data of gas-charging equipment and sulfur hexafluoride gas inventory; Step S13: Based on the multi-code-in-one distributed dynamic state perception model of sulfur hexafluoride, the flow process of sulfur hexafluoride gas and the terminal are automatically identified and tracked, and the data is automatically recorded and uploaded; Step S14: Based on the sulfur hexafluoride recovery and reuse scenarios under different management and control modes, corresponding database tables are designed to form a large information management and control database for the entire life cycle of sulfur hexafluoride gas; Step S2 specifically comprises: using machine learning methods to conduct data mining on the massive amount of full-chain multi-dimensional information collected using sensor technology, combining it with a state perception model of sulfur hexafluoride gas, conducting data analysis, and establishing a sulfur hexafluoride gas material balance accounting model to provide data support for sulfur hexafluoride emission baselines, boundary definition, marginal emission reduction costs, or barrier analysis; The step S3 specifically includes: based on the information management and control big database of the entire life cycle of sulfur hexafluoride gas and digital twin technology, using empirical mode decomposition and long short-term memory neural network algorithm, establishing a sulfur hexafluoride gas prediction model to predict the demand for sulfur hexafluoride.

2. The method for managing and controlling sulfur hexafluoride materials based on big data according to claim 1, characterized in that: The full-chain multi-dimensional information in step S1 includes the historical inflation volume of the in-service equipment throughout its entire life cycle, the recycling volume under different mission states and different service years, the purification volume, flow rate, and in-and-out data.

3. The method for managing and controlling sulfur hexafluoride materials based on big data according to claim 1, characterized in that: The sulfur hexafluoride gas identification technology in step S11 is a warehouse tracking technology based on radio frequency identification.

4. The method for managing and controlling sulfur hexafluoride materials based on big data according to claim 1, characterized in that: The step S4 is specifically as follows: Establish a blockchain ledger at every stage of sulfur hexafluoride gas production, and establish a traceability chain with multi-party participation, transparency, sharing, and authenticity; Based on the sulfur hexafluoride big data map service, data visualization technology is used to realize the visualization of the entire life cycle of sulfur hexafluoride gas. Based on the sulfur hexafluoride gas material accounting model and prediction model, a comprehensive analysis of the entire process of sulfur hexafluoride gas source, flow, and sink is digitally displayed.

5. The method for managing and controlling sulfur hexafluoride materials based on big data according to claim 4, characterized in that: The various links of sulfur hexafluoride gas in step S4 include purchase, control, use, recovery, purification and reuse.

6. The method for managing and controlling sulfur hexafluoride materials based on big data according to claim 4, characterized in that: The sulfur hexafluoride big data map service is a sulfur hexafluoride big data map service based on JQuery and Bootstraps framework.

7. A system based on the big data-based sulfur hexafluoride material management and control method according to claim 1, characterized in that: The system includes the following modules: Information control database module, used to collect and process data related to sulfur hexafluoride gas; Gas material accounting module, used for material accounting of sulfur hexafluoride gas; A blockchain-based data transmission module for trusted data transmission of sulfur hexafluoride gas; The digital visualization module is used to visualize the entire process of source, flow, and sink of sulfur hexafluoride gas.

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