Intelligent gas AI analysis and diagnosis system based on block chain

Abstract

The invention discloses an intelligent fuel gas AI analysis and diagnosis system based on a block chain, and relates to the technical field of fuel gas analysis, and the system comprises a construction module which is used for constructing a fuel gas use model for a client based on the block chain; the acquisition module is connected with the construction module and is used for acquiring fuel gas use information of the client in real time, the fuel gas use information comprises fuel gas use amount and time corresponding to fuel gas use, and the fuel gas use information is constructed into a fuel gas use model; and the prompting module is connected with the acquisition module and is used for analyzing the gas use information based on the gas use model to obtain an analysis result and prompting the analysis result to a client. According to the method, on the premise that the stability of an original system is not interfered, extra tasks can be executed or extra loads can be borne, gas use information collected subsequently can be better analyzed and sorted, data analysis, prediction and comparison of gas use can be better completed, and a prediction and diagnosis result of gas use is obtained.

Description

Technical Field

[0001] The present invention relates to the field of gas analysis technology, and in particular to a smart gas AI analysis and diagnosis system based on blockchain. Background Art

[0002] With the development of economy and the improvement of people's living standards, residents' demand for gas is increasing. At the same time, the demand for gas in industries such as industry and commerce is also growing. Especially in the field of new industrial gas, with the increasingly stringent environmental protection policies and the urgency of global climate change, the market share of new industrial gas represented by clean energy such as natural gas, liquefied petroleum gas (LPG), and biomass gas has increased significantly. This change in market demand provides a broad market space and development opportunities for gas analysis.

[0003] Smart gas refers to advanced information technologies such as artificial intelligence (AI), which deeply integrate traditional urban gas systems with modern digital means to achieve automated, intelligent, and remote monitoring and optimization of the entire process of gas facilities, supply, use, and management. Currently, gas is mostly used in households. In the process of household gas use, due to the large number of users and the complexity and interweaving of data, it is difficult to compare and analyze large amounts of gas usage data, and it is difficult to make diagnostic predictions based on gas usage data, and thus it is difficult to analyze the safety of gas use for corresponding users. Summary of the invention

[0004] The purpose of the present invention is to provide a smart gas AI analysis and diagnosis system based on blockchain to address the deficiencies in the background technology.

[0005] In order to achieve the above objectives, the present invention provides the following technical solutions: a smart gas AI analysis and diagnosis system based on blockchain, comprising: A building module for building a gas usage model based on the client; A collection module, connected to the construction module, is used to collect the gas usage information of the client in real time, wherein the gas usage information includes the gas usage amount and the time corresponding to the gas usage, and construct the gas usage information into the gas usage model; The prompt module is connected to the collection module, and is used to analyze the gas usage information based on the gas usage model to obtain analysis results, and prompt the analysis results to the client.

[0006] In a preferred embodiment, the building blocks include: A collection unit, used to collect user information of multiple users, wherein the user information includes user residence information and user gas meter information; A registration unit, used to register and obtain a client according to user information; A construction unit, used to construct a gas usage bar corresponding to a client, and to associate gas usage bars of multiple clients to obtain a gas usage model; The storage unit is used to configure the corresponding cloud servers of multiple clients as nodes, connect the multiple nodes through the protocol to obtain the blockchain, and store the gas usage model through the blockchain.

[0007] In a preferred embodiment, the building block comprises: A configuration unit, configured to configure a corresponding cloud space corresponding to a client, divide the cloud space into multiple stage spaces, and connect the multiple stage spaces to obtain a gas usage bar; A setting unit is used to set a location line corresponding to the gas use strip, differentiate the location line to obtain multiple attachment location lines, and correspond and bind the attachment location lines to the stage space one by one; The connection unit is used to connect the gas usage bars of multiple clients through the site line to obtain a gas usage model.

[0008] In a preferred embodiment, the configuration unit includes: An information setting unit, used to set at least two connection keys corresponding to the multiple stage spaces respectively, wherein the connection keys are identification codes; The sorting unit is used to sort multiple stage spaces, identify and connect the multiple stage spaces through connection keys according to the sorting, and bind the adjacent stage spaces by position through the connection keys to obtain the gas usage bar.

[0009] In a preferred embodiment, the setting unit includes: A corresponding configuration unit is used to set a site line corresponding to the gas usage strip in the cloud space, wherein the site line includes a line formed by combining and connecting multiple sites; A differentiation unit, used for replicating the site line to obtain a plurality of replica site lines, and establishing communication links between the plurality of replica site lines and the site line to obtain a plurality of attachment site lines; Binding unit, used to correspond and bind the attachment site line to the stage space one by one.

[0010] In a preferred embodiment, the connection unit comprises: An arrangement unit, used for obtaining the site lines corresponding to the plurality of clients and arranging the sites in the site lines; The connection unit is used to sequentially connect the site lines in the plurality of clients to communicate with each other through the sites according to the arrangement order of the sites in the site line, so as to obtain the connection relationship between the gas usage strips in the plurality of clients; The management unit is used to use the gas usage items in multiple clients with established involvement as the gas usage model.

[0011] In a preferred embodiment, the acquisition module includes: A collection setting unit, used to set the collection stage and bind the collection stage to the corresponding stage space; A collection unit, used for collecting the gas usage amount and the time corresponding to the gas usage of the client in real time based on the collection stage as the gas usage information, and storing the gas usage information in the stage space corresponding to the collection stage; The data construction unit is used to divide the gas usage information in the stage space into multiple data fragments, bind the sites in the attachment site line bound to the stage space to the data fragments, and interconnect the data fragments in multiple clients based on the gas usage model.

[0012] In a preferred embodiment, the prompt module includes: A comparison unit, configured to compare the data segments in the plurality of clients correspondingly based on the gas usage model to obtain a predicted diagnosis result, wherein the diagnosis result includes a gas usage distribution time period and a corresponding usage amount; The prompt unit uses the data segment corresponding to the diagnosis result that does not meet the preset condition as a prompt segment, and prompts the client corresponding to the prompt segment.

[0013] In the above technical solution, the technical effects and advantages provided by the present invention are: The multiple replicated site lines of the present invention respectively establish communication connections with the site lines, and then the multiple replicated site lines serve as attachment site lines. This differentiation does not represent replication, but rather a clone of the corresponding site line. The attachment site line after the clone can still establish a connection with the site line. The attachment site line not only retains the functional characteristics of the original site line, but can also perform additional tasks or bear additional loads without interfering with the stability of the original system. It can better analyze and sort out the subsequently collected gas usage information, and can better complete the data analysis, prediction and comparison of gas usage, and obtain the diagnosis results of gas usage. The diagnosis results are provided to users for their attention and they may take corresponding measures, such as adjusting usage habits, performing equipment maintenance or contacting professionals for inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0015] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Example 1, please refer to Figure 1 As shown, the blockchain-based smart gas AI analysis and diagnosis system described in this embodiment includes: A building module for building a gas usage model based on the client; A collection module, connected to the construction module, is used to collect the gas usage information of the client in real time, wherein the gas usage information includes the gas usage amount and the time corresponding to the gas usage, and construct the gas usage information into the gas usage model; The prompt module is connected to the collection module and is used to analyze the gas usage information based on the gas usage model to obtain analysis results, and prompt the analysis results to the client; It should be noted that with the development of the economy and the improvement of people's living standards, residents' demand for gas is increasing. At the same time, the demand for gas in industries such as industry and commerce is also growing. Especially in the field of new industrial gas, with the increasingly stringent environmental protection policies and the urgency of global climate change, the market share of new industrial gas represented by clean energy such as natural gas, liquefied petroleum gas (LPG), and biomass gas has increased significantly. This change in market demand provides a broad market space and development opportunities for gas analysis.

[0018] At present, the use of gas is mostly concentrated in households. In the process of household gas use, due to the large number of users, the complexity and interlacing of data, it is difficult to compare and analyze a large amount of gas usage data. There is no effective data combing function, and it is difficult to analyze the safety of gas use for the corresponding users. In this application, multiple replicated site lines are used as attachment site lines after establishing communication connections with the site lines respectively. This differentiation does not represent replication, but the clone of the corresponding site line. The attachment site line after the clone can still establish a connection with the site line. The attachment site line not only retains the functional characteristics of the original site line, but also can perform additional tasks or bear additional loads without interfering with the stability of the original system. It can better analyze and comb the subsequent collected gas usage information, and can better complete the data analysis and comparison of gas use, reminding users to pay attention and possibly take corresponding measures, such as adjusting usage habits, performing equipment maintenance or contacting professionals for inspection.

[0019] In one embodiment, the building blocks include: A collection unit, used to collect user information of multiple users, wherein the user information includes user residence information and user gas meter information; A registration unit, used to register and obtain a client according to user information; A construction unit, used to construct a gas usage bar corresponding to a client, and to associate gas usage bars of multiple clients to obtain a gas usage model; A storage unit is used to configure a plurality of clients with corresponding cloud servers as nodes, connect the plurality of nodes through a protocol to obtain a blockchain, and store the gas usage model through the blockchain; It should be noted that the use of gas is more targeted at household users. The house owner information is collected on a house basis, including the user's residence information and the user's gas meter information as user information. The user's residence information here includes the house address and the purpose of gas. The gas use includes cooking purposes and household hot water production purposes, such as gas water heaters and other appliances. The user's gas meter information includes the gas meter coding information corresponding to the user's residence. The client is then registered based on the user information. The client represents the gas collection port of a residence. The client registration method is: build a user registration form interface, including input fields, submit buttons, etc. After the user fills in the registration information, the data is sent to the server through form submission. This can be achieved through AJAX (Asynchronous JavaScript and XML) so that data can be sent without reloading the page. The registration request is made through the back-end framework, such as Node.js, Python (Django / Flask), Java (Spring Boot), etc., which is used to process the registration request sent by the front-end. The back-end sets up an API interface to receive the registration data, and after verifying the data, saves the user information in the database. After successful registration, the back-end can return a response containing user information or an authentication token to the front-end. Database: such as MySQL, PostgreSQL, MongoDB, etc., is used to store user information. Then, the corresponding client builds a gas usage bar, and the gas usage bars of multiple clients are implicated to obtain a gas usage model. Multiple clients are configured with corresponding cloud servers and used as nodes. Multiple nodes are connected through protocols to obtain a blockchain. The gas usage model is stored through the blockchain. The stability and security of the gas usage model storage can be guaranteed through the blockchain, and the gas usage analysis and prediction can be better performed to obtain the diagnostic results of the analysis. The gas usage model can be used to perform a health analysis of household gas use and assist users in the reasonable use of gas. In one embodiment, the building block comprises: A configuration unit, configured to configure a corresponding cloud space corresponding to a client, divide the cloud space into multiple stage spaces, and connect the multiple stage spaces to obtain a gas usage bar; A setting unit is used to set a location line corresponding to the gas use strip, differentiate the location line to obtain multiple attachment location lines, and correspond and bind the attachment location lines to the stage space one by one; An implicating unit, used to implicate gas usage bars of multiple clients through location lines to obtain a gas usage model; It should be noted that the cloud space corresponding to the client configuration is divided by virtualization technology (such as VMware, Hyper-V, etc.) to obtain multiple logically independent stage spaces, each of which can be regarded as an independent storage or computing unit for storing and managing gas usage data at different stages; then, database technology (such as MySQL, Oracle, etc.) or distributed file systems (such as HDFS, Ceph, etc.) are used to connect and manage multiple stage spaces; gas usage data at different stages are associated through database tables or file system directories to form gas usage bars; site lines are set on the gas usage bars, which can be regarded as setting marking points or indexes on the data sequence; these site lines can be used to identify different gas usage stages, events or abnormal points; data segmentation or clustering algorithms (such as K-means, DBSCAN, etc.) are used to differentiate the site lines to obtain multiple attachment site lines, which can further refine the gas usage data to make it more accurate and detailed; data mapping technology (such as database foreign keys, data table associations, etc.) is used to implicate and correspond the gas usage bars of multiple clients through site lines; In this way, a unified gas usage model can be formed to analyze and predict the gas usage of different clients; using big data analysis and mining technology (such as Hadoop, Spark, etc.) to deeply analyze and mine the gas usage model, the rules, trends and abnormal behaviors of gas usage can be discovered, and decision support can be provided for gas management and optimization. The cloud space is used to store the client's gas usage data in the future. Then, the cloud space is spatially divided to obtain multiple stage spaces. The multiple stage spaces are relatively independent, so the multiple stage spaces are connected to obtain gas usage bars. The stage space is used to store the client's gas usage information according to the stage. The site line is set in the cloud space, and the site line is differentiated to obtain multiple attachment site lines. The attachment site lines are matched and bound to the stage space one by one. Then, in order to better compare and sort out the data, the gas usage bars of multiple clients are implicated and matched through their respective site lines to obtain a gas usage model, which can better associate the clients in the same area, better understand the user-end gas usage in the area, and conduct better data sorting and analysis to facilitate subsequent gas supply preparation and client gas planning; In one embodiment, the configuration unit includes: An information setting unit, used to set at least two connection keys corresponding to the multiple stage spaces respectively, wherein the connection keys are identification codes; A sorting unit is used to sort multiple stage spaces, identify and connect multiple stage spaces through connection keys according to the sorting, and positionally bind adjacent stage spaces through connection keys to obtain gas usage bars; It should be noted that at least two connection keys are set for each of the multiple stage spaces. The connection key is an identification code loaded on the stage space. One stage space carries at least two connection keys. The connection key, as an identification code, can be regarded as a unique identifier or index of the stage space. A hash function or a UUID (universally unique identifier) ​​generation algorithm can be used to generate at least two connection keys for each stage space. The connection keys will be used for subsequent stage space sorting and connection. After that, a sorting algorithm (such as quick sort, merge sort, heap sort, etc.) can be used to sort the stage space. The basis for sorting can be the creation time of the stage space, gas usage, or other related attributes. After sorting, the connection key is used to identify and connect adjacent stage spaces. This can be achieved by storing the mapping relationship between the stage space and its connection key in the data structure. The connection between adjacent stage spaces can be represented by establishing a pointer, linked list or graph relationship in the data structure; according to the results of sorting and connection, the adjacent stage spaces are positionally bound through the connection key to form a continuous gas usage bar, in which each stage space is connected to its adjacent space through the connection key. The gas usage bar can be regarded as a time series or event sequence for recording the gas usage information of the client; this is not limited to the sorting of multiple stage spaces. The arrangement here is a virtual arrangement of the stage space, which is used to list the position relationship of the stage space, and then the multiple stage spaces are identified and connected through the connection key according to the sorting. The adjacent stage spaces are positionally bound through the connection key to obtain the gas usage bar. The gas usage bar here is used to record the gas usage information of the client, has a good periodic recording function, and is convenient for analyzing and predicting the gas usage information; In one embodiment, the setting unit includes: A corresponding configuration unit is used to set a site line corresponding to the gas usage strip in the cloud space, wherein the site line includes a line formed by combining and connecting multiple sites; A differentiation unit, used for replicating the site line to obtain a plurality of replica site lines, and establishing communication links between the plurality of replica site lines and the site line to obtain a plurality of attachment site lines; A binding unit, used to correspond and bind the attachment site line to the stage space one by one; It should be noted that a site line corresponding to a gas bar is set in the cloud space, and the site line includes a line formed by combining and connecting multiple sites. The site serves as a management port, and multiple management ports are connected in a linear combination. The combination connection is a communication connection. The specific operation method of setting a management port in the cloud space and connecting multiple management ports is as follows: In SAN, each storage device (or storage location) can be configured with one or more management ports, which are interconnected through the SAN network to achieve communication and data transmission. The site line is then differentiated to obtain multiple attached site lines. The differentiation process is to first copy multiple copy site lines. The copying process is not a simple data copy, but a copy that is almost identical to the original site line in structure and function. These copies are given a new identity as a replication site line at the moment of replication. After multiple replication site lines establish communication with the site line, the multiple replication site lines serve as attachment site lines. This differentiation does not represent replication, but the doppelganger of the corresponding site line. The attachment site line after the doppelganger can still establish a connection with the site line. The attachment site line not only retains the functional characteristics of the original site line, but also can perform additional tasks or bear additional loads without interfering with the stability of the original system, so as to better analyze and sort out the gas usage information collected subsequently, and better complete the data analysis of gas usage; In one embodiment, the connection unit comprises: An arrangement unit, used for obtaining the site lines corresponding to the plurality of clients and arranging the sites in the site lines; The connection unit is used to sequentially connect the site lines in the plurality of clients to communicate with each other through the sites according to the arrangement order of the sites in the site line, so as to obtain the connection relationship between the gas usage strips in the plurality of clients; A management unit, used to use the gas usage items in the multiple clients with established involvement as a gas usage model; It should be noted that the sites equipped on the site line are organized and arranged in an orderly manner. Since the sites on the site line have been sorted and communicated, they can be virtually organized into a clear order. The purpose is to build a clear and orderly communication structure foundation; follow the set arrangement order of the sites, and realize the interconnection of the site lines between multiple clients one by one; this process is connected through their respective sites, aiming to build a network of interrelated gas usage status items between clients, that is, to establish the relationship model between them; based on the successfully established relationship network, the gas usage items in these interrelated clients are further integrated into a unified gas usage model; this model not only reflects the gas usage of each client, but also includes the dynamic mutual influence relationship between them, which provides strong support for subsequent monitoring, analysis and management.

[0020] In one embodiment, the acquisition module includes: A collection setting unit, used to set the collection stage and bind the collection stage to the corresponding stage space; A collection unit, used for collecting the gas usage amount and the time corresponding to the gas usage of the client in real time based on the collection stage as the gas usage information, and storing the gas usage information in the stage space corresponding to the collection stage; A data construction unit, used for dividing the gas usage information in the stage space to obtain multiple data fragments, binding the sites in the attachment site line bound to the stage space to the data fragments, and interconnecting the data fragments in multiple clients based on the gas usage model; It should be noted that a collection stage is set, for example, the collection stage can be a one-day time period, a quarterly time period, or a one-year time period, and then the collection stage is bound to the corresponding stage space. There are multiple stage spaces. For example, the collection stage can be a one-day time period, then the first stage space is the gas usage information of the first day of collection, and the second stage space of sorting is the gas usage information of the second day of collection. The collection is carried out in this way, and the gas usage information in the stage space is divided to obtain multiple data fragments. The division here can be divided according to the time in the collection stage. Then, based on the gas usage model, the data fragments in multiple clients are interconnected. The data fragments of gas in the same collection stage in multiple clients can be interconnected through the site, which can be used for subsequent comparison and comprehensive analysis, and can also be used for individual analysis of the client, or comprehensive analysis between multiple clients. It has a good information sorting effect, can better predict the client's gas usage information, and complete the diagnosis of gas usage; In one embodiment, the prompt module includes: A comparison unit, configured to compare the data segments in the plurality of clients correspondingly based on the gas usage model to obtain a predicted diagnosis result, wherein the diagnosis result includes a gas usage distribution time period and a corresponding usage amount; A prompting unit, which uses the data segment corresponding to the diagnosis result that does not meet the preset condition as a prompt segment and prompts the client corresponding to the prompt segment; It should be noted that in order to optimize the efficiency and safety of gas use, an advanced gas use data analysis model has been constructed; the model aims to reveal the gas use distribution pattern and specific usage in different time periods by deeply comparing and analyzing the gas use data fragments uploaded by multiple clients; this process not only helps us understand the gas consumption characteristics of each client, but also provides a scientific basis for the rational allocation and management of energy. In the comparison process, a series of preset conditions based on energy efficiency, safety standards or historical average levels are set, among which the preset conditions are the concentrated gas use distribution time period and the preset usage. The concentrated gas use distribution time period here is the concentrated gas use distribution time period with the highest proportion among multiple clients, which is used as the benchmark for evaluating the rationality of the data fragment; based on AI, the gas use distribution time period and its corresponding usage in each diagnosis result are automatically compared with these preset conditions one by one; for those diagnosis results that fail to meet the preset standards, they are marked as prompt fragments, which are prediction information for a diagnosis. The specific problem needs to be monitored by the client itself, and here it is used as a predicted gas use diagnosis. Finally, the prompt information will be pushed to the client user of the corresponding data fragment in real time, such as aging equipment, improper usage habits or potential leakage risks, to remind users to pay attention and take corresponding measures, such as adjusting usage habits, performing equipment maintenance or contacting professionals for inspection, etc. Through such an intelligent and automated gas usage analysis and prompt system, it can not only promote the effective use of gas resources, but also significantly enhance users' safety awareness and energy-saving awareness, and jointly promote the construction of a greener and safer energy usage environment.

[0021] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. The blockchain-based smart gas AI analysis and diagnosis system is characterized by: include: A construction module for building a gas usage model based on the blockchain corresponding client; A collection module, connected to the construction module, is used to collect the gas usage information of the client in real time, wherein the gas usage information includes the gas usage amount and the time corresponding to the gas usage, and construct the gas usage information into the gas usage model; The prompt module is connected to the collection module, and is used to analyze the gas usage information based on the gas usage model to obtain analysis results, and prompt the analysis results to the client.

2. The blockchain-based smart gas AI analysis and diagnosis system according to claim 1 is characterized by: The building blocks include: A collection unit, used to collect user information of multiple users, wherein the user information includes user residence information and user gas meter information; A registration unit, used to register and obtain a client according to user information; A construction unit, used to construct a gas usage bar corresponding to a client, and to associate gas usage bars of multiple clients to obtain a gas usage model; The storage unit is used to configure the corresponding cloud servers of multiple clients as nodes, connect the multiple nodes through the protocol to obtain the blockchain, and store the gas usage model through the blockchain.

3. The blockchain-based smart gas AI analysis and diagnosis system according to claim 1 is characterized by: The building block comprises: A configuration unit, configured to configure a corresponding cloud space corresponding to a client, divide the cloud space into multiple stage spaces, and connect the multiple stage spaces to obtain a gas usage bar; A setting unit is used to set a location line corresponding to the gas use strip, differentiate the location line to obtain multiple attachment location lines, and correspond and bind the attachment location lines to the stage space one by one; The connection unit is used to connect the gas usage bars of multiple clients through the site line to obtain a gas usage model.

4. The blockchain-based smart gas AI analysis and diagnosis system according to claim 1 is characterized by: The configuration unit comprises: An information setting unit, used to set at least two connection keys corresponding to the multiple stage spaces respectively, wherein the connection keys are identification codes; The sorting unit is used to sort multiple stage spaces, identify and connect the multiple stage spaces through connection keys according to the sorting, and bind the adjacent stage spaces by position through the connection keys to obtain the gas usage bar.

5. The blockchain-based smart gas AI analysis and diagnosis system according to claim 1 is characterized by: The setting unit comprises: A corresponding configuration unit is used to set a site line corresponding to the gas usage strip in the cloud space, wherein the site line includes a line formed by combining and connecting multiple sites; A differentiation unit, used for replicating the site line to obtain a plurality of replica site lines, and establishing communication links between the plurality of replica site lines and the site line to obtain a plurality of attachment site lines; Binding unit, used to correspond and bind the attachment site line to the stage space one by one.

6. The blockchain-based smart gas AI analysis and diagnosis system according to claim 1 is characterized by: The connection unit comprises: An arrangement unit, used for obtaining the site lines corresponding to the plurality of clients and arranging the sites in the site lines; The connection unit is used to sequentially connect the site lines in the plurality of clients to communicate with each other through the sites according to the arrangement order of the sites in the site line, so as to obtain the connection relationship between the gas usage strips in the plurality of clients; The management unit is used to use the gas usage items in multiple clients with established involvement as the gas usage model.

7. The blockchain-based smart gas AI analysis and diagnosis system according to claim 1 is characterized by: The acquisition module comprises: A collection setting unit, used to set the collection stage and bind the collection stage to the corresponding stage space; A collection unit, used for collecting the gas usage amount and the time corresponding to the gas usage of the client in real time based on the collection stage as the gas usage information, and storing the gas usage information in the stage space corresponding to the collection stage; The data construction unit is used to divide the gas usage information in the stage space into multiple data fragments, bind the sites in the attachment site line bound to the stage space to the data fragments, and interconnect the data fragments in multiple clients based on the gas usage model.

8. The blockchain-based smart gas AI analysis and diagnosis system according to claim 1 is characterized by: The prompt module comprises: A comparison unit, configured to compare the data segments in the plurality of clients correspondingly based on the gas usage model to obtain a predicted diagnosis result, wherein the diagnosis result includes a gas usage distribution time period and a corresponding usage amount; The prompt unit uses the data segment corresponding to the diagnosis result that does not meet the preset condition as a prompt segment, and prompts the client corresponding to the prompt segment.

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