# US 2024/0201156 A1 · Methods and internet of things systems for visual management of smart gas data center processes > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2024/0201156 A1 - Document ID: US20240201156A1 - Status: Published application - Kind: A1 - Application number: 18/586,413 - Filed: 2024-02-23 - Published: 2024-06-20 - Earliest claimed: 2023-09-13 - Assignees: Chengdu Qinchuan Iot Technology Co., Ltd. - Applicants: Chengdu Qinchuan Iot Technology Co., Ltd. - Inventors: Zehua SHAO; Yong LI; Junyan ZHOU; Yuefei WU - Web page: https://patentgrep.com/patent/US20240201156A1 ## CPC - G01N 33/0063: {using a threshold to release an alarm or displaying means} - G01N 33/0034: {comprising neural networks or related mathematical techniques} - G06F 16/9024: {Graphs; Linked lists (G06F16/9027 takes precedence)} - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract The embodiment of the present disclosure provides methods and Internet of Things systems for visual management of smart gas data center processes. The method is executed by a smart gas management platform of the Internet of Things system. The method includes: obtaining a data acquisition feature and first sampling data; obtaining a data summary feature and second sampling data; obtaining a first task list and a second task list, determining a first processing feature of the first task list and a second processing feature and a queuing feature of the second task list; determining an estimation feature, the estimation feature at least including an estimated completion time of the at least one gas task, and an estimated queuing time of the at least one pending gas task; generating first visualization data; and controlling at least one target platform to display the first visualization data. ## Claims ### Claim 1 A method for visual management of smart gas data center processes, wherein the method is executed by a smart gas management platform of an Internet of Things system for visual management of smart gas data center processes, comprising: obtaining a data acquisition feature and first sampling data; obtaining a data summary feature and second sampling data; obtaining a first task list and a second task list, determining a first processing feature of the first task list and a second processing feature and a queuing feature of the second task list; wherein the first task list includes at least one gas task, and the second task list includes at least one pending gas task; determining an estimation feature based on the data acquisition feature, the data summary feature, the first processing feature, the second processing feature, and the queuing feature, the estimation feature at least including an estimated completion time of the at least one gas task, and an estimated queuing time of the at least one pending gas task; generating first visualization data according to the first sampling data, the second sampling data, and the estimation feature; and controlling at least one target platform to display the first visualization data. ### Claim 2 (depends on 1) The method according to claim 1, further comprising: determining abnormal data and an abnormal probability corresponding to the abnormal data according to the first sampling data, the second sampling data, the data acquisition feature, and the data summary feature; generating second visualization data based on the abnormal data and the abnormal probability; and controlling the at least one target platform to display the second visualization data. ### Claim 3 (depends on 2) The method according to claim 2, wherein the determining abnormal data and an abnormal probability corresponding to the abnormal data according to the first sampling data, the second sampling data, the data acquisition feature, and the data summary feature includes: determining the abnormal data and the abnormal probability through a pre-analysis model based on the first sampling data, the second sampling data, the data acquisition feature, and the data summary feature; and the pre-analysis model being a machine learning model, and the pre-analysis model including at least a pre-processing layer and an analysis layer; wherein an input of the pre-processing layer includes the first sampling data, the second sampling data, and historical probability data, and an output of the pre-processing layer includes a gas data processing feature; and an input of the analysis layer includes the gas data processing feature, the data acquisition feature, and the data summary feature, and an output of the analysis layer includes at least one set of abnormal data and an abnormal probability corresponding to the at least one set of abnormal data. ### Claim 4 (depends on 2) The method according to claim 2, wherein the second visualization data is related to a platform perception degree. ### Claim 5 (depends on 1) The method according to claim 1, further comprising: determining a warning mark according to the estimation feature; generating third visualization data according to the warning mark; and controlling the at least one target platform to display the third visualization data. ### Claim 6 (depends on 5) The method according to claim 5, wherein the warning mark includes a first warning mark and a second warning mark; the first warning mark is determined based on the estimated completion time through a preset algorithm; and the second warning mark is determined based on at least the estimated queuing time. ### Claim 7 (depends on 5) The method according to claim 5, wherein the determining a warning mark according to the estimation feature includes: generating a data flow graph based on the data acquisition feature, the data summary feature, and the estimation feature, the data flow graph including at least one node and at least one edge, the at least one node representing the at least one target platform, and the at least one edge representing a data flow feature between any two nodes; determining an accumulation degree of the at least one target platform based on the data flow graph; and determining the warning mark based on the accumulation degree and the estimated feature. ### Claim 8 (depends on 7) The method according to claim 7, wherein the determining an accumulation degree of the at least one target platform based on the data flow graph includes: determining the accumulation degree based on the data flow graph using a flow graph model, wherein the flow graph model is a machine learning model. ### Claim 9 (depends on 7) The method according to claim 7, wherein the data flow feature also includes a data urgency degree. ### Claim 10 An Internet of Things system for visual management of smart gas data center processes, comprising a smart gas management platform, wherein the smart gas management platform includes a smart gas data center, and the smart gas management platform is configured to: obtain a data acquisition feature and first sampling data; obtain a data summary feature and second sampling data; obtain a first task list and a second task list, determine a first processing feature of the first task list and a second processing feature and a queuing feature of the second task list; wherein the first task list includes at least one gas task, and the second task list includes at least one pending gas task; determine an estimation feature based on the data acquisition feature, the data summary feature, the first processing feature, the second processing feature, and the queuing feature, the estimation feature at least including an estimated completion time of the at least one gas task, and an estimated queuing time of the at least one pending gas task; generate first visualization data according to the first sampling data, the second sampling data, and the estimation feature; and control at least one target platform to display the first visualization data. ### Claim 11 (depends on 10) The Internet of Things system according to claim 10, wherein the Internet of Things system further comprises a smart gas user platform, a smart gas service platform, a smart gas sensor network platform, and a smart gas object platform; the smart gas management platform transmits the first visualization data to at least one of the smart gas user platform, the smart gas service platform, the smart gas sensor network platform, and the smart gas object platform, for display. ### Claim 12 (depends on 11) The Internet of Things system according to claim 11, wherein the smart gas management platform is also configured to: determine abnormal data and an abnormal probability corresponding to the abnormal data according to the first sampling data, the second sampling data, the data acquisition feature, and the data summary feature; generate second visualization data based on the abnormal data and the abnormal probability; and control the at least one target platform to display the second visualization data. ### Claim 13 (depends on 12) The Internet of Things system according to claim 12, wherein the smart gas management platform is further configured to: determine the abnormal data and the abnormal probability through a pre-analysis model based on the first sampling data, the second sampling data, the data acquisition feature, and the data summary feature; and the pre-analysis model being a machine learning model, and the pre-analysis model including at least a pre-processing layer and an analysis layer; wherein an input of the pre-processing layer includes the first sampling data, the second sampling data, and historical probability data, and an output of the pre-processing layer includes a gas data processing feature; and an input of the analysis layer includes the gas data processing feature, the data acquisition feature, and the data summary feature, and an output of the analysis layer includes at least one set of abnormal data and an abnormal probability corresponding to the at least one set of abnormal data. ### Claim 14 (depends on 12) The Internet of Things system according to claim 12, wherein the second visualization data is also related to a platform perception degree. ### Claim 15 (depends on 11) The Internet of Things system according to claim 11, wherein the smart gas management platform is also configured to: determine a warning mark according to the estimation feature; generate third visualization data according to the warning mark; and control the at least one target platform to display the third visualization data. ### Claim 16 (depends on 15) The Internet of Things system according to claim 15, wherein the warning mark includes a first warning mark and a second warning mark; the first warning mark is determined based on the estimated completion time through a preset algorithm; and the second warning mark is determined based on at least the estimated queuing time. ### Claim 17 (depends on 15) The Internet of Things system according to claim 15, wherein the smart gas management platform is further configured to: generate a data flow graph based on the data acquisition feature, the data summary feature, and the estimation feature, the data flow graph including at least one node and at least one edge, the at least one node representing the at least one target platform, and the at least one edge representing a data flow feature between any two nodes; determine an accumulation degree of the at least one target platform based on the data flow graph; and determine the warning mark based on the accumulation degree and the estimated feature. ### Claim 18 (depends on 17) The Internet of Things system according to claim 17, wherein the smart gas management platform is further configured to: determine the accumulation degree based on the data flow graph using a flow graph model, wherein the flow graph model is a machine learning model. ### Claim 19 (depends on 17) The Internet of Things system according to claim 17, wherein the data flow feature also includes a data urgency degree. ### Claim 20 (depends on 1) A non-transitory computer-readable storage medium storing computer instructions, wherein when reading the computer instructions, a computer executes the method for visual management of smart gas data center processes according to claim 1.