Gas safety management method and internet of things system for gas safety training

Abstract

The embodiment of the specification provides a gas safety management method and an Internet of Things system for gas safety training. The method is executed based on a wisdom gas safety management platform of a gas safety management Internet of Things system for gas safety training. The method comprises the following steps: acquiring gas use data of at least one gas use end, wherein the gas use data comprises at least one of a gas use amount, gas alarm data and gas maintenance data; determining a user type of each of the at least one gas use end based on the gas use data of the at least one gas use end, and determining a gas safety training scheme corresponding to each user type; pushing a safety training to a corresponding user terminal based on the gas safety training scheme; acquiring feedback information of the user terminal; determining a gas safety detection frequency of the gas use end of each user type and a gas safety risk level of the gas use end of each user type based on the feedback information corresponding to each user type and the gas use data corresponding to each user type.

Description

Technical Field

[0001] This manual relates to the field of gas safety management, and in particular to gas safety management methods and Internet of Things (IoT) systems used for gas safety training. Background Technology

[0002] Currently, gas companies mainly manage gas safety in households by disseminating gas safety knowledge to a wide range of gas users through articles, videos, and message notifications. However, there is a lack of targeted safety training for different user types (such as factories, residents, and businesses), different gas types, and different usage habits.

[0003] Therefore, it is necessary to propose gas safety management methods and IoT systems for gas safety training, so as to achieve targeted matching of appropriate training programs and push frequencies based on different user characteristics and gas characteristics, thereby improving training efficiency. At the same time, it is also possible to determine the frequency of gas safety detection based on the probability of safety risks, so as to comprehensively improve gas safety. Summary of the Invention

[0004] This specification provides one or more embodiments of a gas safety management method for gas safety training. The gas safety management method for gas safety training includes: acquiring gas usage data from at least one gas-consuming terminal, the gas usage data including at least one of gas consumption, gas alarm data, and gas maintenance data; based on the gas usage data from the at least one gas-consuming terminal, determining the user type for each of the at least one gas-consuming terminals, and determining a gas safety training plan corresponding to each user type; the gas safety training plan including at least one of training targets, training time, and push frequency; based on the gas safety training plan, pushing safety training to the corresponding user terminals; acquiring feedback information from the user terminals; and based on the feedback information corresponding to each user type and the corresponding gas usage data, determining the gas safety detection frequency for the gas-consuming terminal of each user type, and determining the gas safety risk level of the gas-consuming terminal of each user type.

[0005] One embodiment of this specification provides a gas safety management IoT system for gas safety training, comprising: a smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas indoor device sensor network platform, and a smart gas indoor device object platform; the smart gas indoor device object platform is used to acquire gas usage data from at least one gas user terminal; the gas usage data includes at least one of gas consumption, gas alarm data, and gas maintenance data; the smart gas indoor device sensor network platform is used to transmit the gas usage data from the at least one gas user terminal to the smart gas safety management platform; the smart gas safety management platform is used to: determine the user type of each of the at least one gas user terminals based on the gas usage data from the at least one gas user terminal, and determine the gas safety management system corresponding to each user type. A comprehensive training program is provided; the gas safety training program includes at least one of the following: training targets, training time, and push frequency; based on the gas safety training program, safety training is pushed to the corresponding user terminals; based on the feedback information of each user type on the safety training and the gas usage data corresponding to each user type, the gas safety detection frequency of the gas consumption terminal for each user type and the gas safety risk level of the gas consumption terminal for each user type are determined; the smart gas service platform is used to feed back the gas safety training program corresponding to each user type, the gas safety detection frequency of the gas consumption terminal for each user type, and the gas safety risk level of the gas consumption terminal for each user type to the smart gas user platform; the smart gas user platform is used to obtain the feedback information of each user type on the safety training. Attached Figure Description

[0006] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0007] Figure 1 This is a schematic diagram of a gas safety management Internet of Things system for gas safety training, shown according to some embodiments of this specification;

[0008] Figure 2 This is an exemplary flowchart of a gas safety management method for gas safety training, as shown in some embodiments of this specification.

[0009] Figure 3 This is an exemplary flowchart illustrating the determination of gas safety detection frequency according to some embodiments of this specification;

[0010] Figure 4 This is a schematic diagram illustrating the prediction of the probability of a security risk occurring, based on some embodiments of this specification;

[0011] Figure 5 This is a schematic diagram illustrating the determination of gas safety risk levels according to some embodiments of this specification. Detailed Implementation

[0012] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0013] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0014] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0015] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0016] Figure 1 This is a diagram of a gas safety management IoT system for gas safety training, illustrated according to some embodiments of this specification. Figure 1 As shown, the gas safety management IoT system for gas safety training includes: a smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas indoor equipment sensor network platform, and a smart gas indoor equipment object platform, which interact sequentially.

[0017] The smart gas user platform is a user-centric platform. In some embodiments, the smart gas user platform is configured as a terminal device.

[0018] In some embodiments, the smart gas user platform may include a gas user sub-platform, a regulatory user sub-platform, etc.

[0019] The gas user sub-platform provides gas users (e.g., gas consumers) with gas-related data and solutions to gas-related problems. Examples include gas usage and solutions for gas leaks. In some embodiments, the gas user sub-platform can also provide gas safety training content. In some embodiments, the gas user sub-platform can interact with and correspond to the smart gas service sub-platform to obtain safe gas usage services.

[0020] The regulatory user sub-platform is used to monitor the operation of the entire Internet of Things (IoT) system for regulated users (e.g., gas companies). This includes monitoring the rationality of pipeline routes and the availability of faulty equipment. In some embodiments, the regulatory user sub-platform can interact with and correspond to the smart regulatory service sub-platform to obtain services related to safety supervision needs.

[0021] In some embodiments, the smart gas user platform can interact with the smart gas service platform. For example, it can receive indoor gas safety information and gas safety training programs uploaded by the smart gas service platform. Indoor gas safety information includes gas anomaly information, such as gas consumption or usage duration exceeding usual usage thresholds.

[0022] A smart gas service platform is a platform that provides users with services related to safe gas use. In some embodiments, a smart gas service platform may include a smart gas use service sub-platform and a smart monitoring service sub-platform.

[0023] The smart gas usage service sub-platform corresponds to the gas user sub-platform, providing gas users with safe gas usage services. For example, it allows users to upload gas safety training content to their platforms. The smart regulatory service sub-platform corresponds to the regulatory user sub-platform, providing services to meet the safety supervision needs of regulatory users. For example, regulatory users can obtain information such as gas pipeline layout, gas equipment usage and maintenance status through the smart regulatory service sub-platform.

[0024] In some embodiments, the smart gas service platform can interact downwards with the smart gas safety management platform. For example, it can receive indoor gas safety information and gas safety training plans uploaded by the smart gas safety management platform. In some embodiments, the smart gas service platform can also interact upwards with the smart gas user platform. For example, it can upload indoor gas safety information and gas safety training content to the smart gas user platform.

[0025] A smart gas safety management platform is used to manage indoor gas safety. Indoor gas safety management refers to the monitoring of indoor gas equipment and the handling of abnormal alarms. For example, a smart gas safety management platform can detect gas leaks and send an alarm to the gas user upon confirmation of a leak. In some embodiments, the smart gas safety management platform can also develop different gas safety training programs for different types of gas users; for more details, please refer to [link to relevant documentation]. Figure 2 and its related parts.

[0026] In some embodiments, the smart gas safety management platform may include a smart gas indoor safety management sub-platform and a smart gas data center, etc.

[0027] The Smart Gas Indoor Safety Management Sub-Platform is a sub-platform used for the safety management of indoor gas appliances for gas users. In some embodiments, the Smart Gas Indoor Safety Management Sub-Platform may include an intrinsically safe monitoring and management module, an information security monitoring and management module, a functional monitoring and management module, and an indoor safety inspection management module. The intrinsically safe monitoring and management module monitors the safety of the gas appliances themselves. For example, it monitors and manages explosion-proof safety features such as mechanical leaks, gas meter malfunctions, and valve control. The information security monitoring and management module monitors and manages gas data information. For example, it manages information such as data anomalies, unauthorized equipment information, and unauthorized access. The functional monitoring and management module monitors and manages gas functions. For example, it monitors and manages functional safety features such as prolonged inactivity, continuous flow exceeding timeout limits, flow overload, abnormally high flow rates, abnormally low flow rates, low gas pressure, strong magnetic interference, and low voltage. The indoor safety inspection management module manages the indoor gas safety status. For example, it manages the inspection time and frequency of indoor appliances for gas users.

[0028] In some embodiments, the smart gas indoor safety management sub-platform also includes a gas safety training management module. This module is used to develop different gas safety training programs for different types of gas users. For example, the safety training management module can develop training programs for users whose gas equipment is prone to damage, covering the correct use and maintenance of gas equipment. More details can be found in [link to relevant documentation]. Figure 2 and its related parts.

[0029] A smart gas data center is a platform used to aggregate and store various data, information, and instructions. For example, a smart gas data center can store gas data information, gas user categories, gas equipment information, and gas safety training programs.

[0030] In some embodiments, the smart gas indoor safety management sub-platform and the smart gas data center interact bidirectionally, including: the smart gas indoor safety management sub-platform acquiring and feeding back indoor equipment safety management data from the smart gas data center; the smart gas data center automatically sending the acquired relevant safety data to the corresponding safety monitoring and management module by identifying the safety parameter category (such as usage amount and usage duration); each safety monitoring and management module presets a safety monitoring threshold, and when the safety data exceeds the threshold, the smart gas safety management platform automatically alarms, and can optionally automatically push the alarm information to the gas user.

[0031] The data interaction between the smart gas safety management platform and the upper-level smart gas service platform and the lower-level smart gas indoor equipment sensor network platform is all conducted through the smart gas data center. In some embodiments, the data interaction of the smart gas safety management platform includes: the smart gas data center issuing instructions to obtain indoor gas usage-related data to the smart gas indoor equipment sensor network platform; the smart gas data center receiving indoor gas usage-related data uploaded by the smart gas indoor equipment sensor network platform; the smart gas data center sending the indoor gas usage-related data to the smart gas safety management sub-platform for analysis and processing; the smart gas indoor safety management sub-platform sending the processed data back to the smart gas data center; and the smart gas data center sending the aggregated and processed data to the smart gas service platform. The aggregated and processed data includes gas usage safety information, such as excessive or abnormal gas consumption or usage duration over a certain period (e.g., within a day or three days). In some embodiments, the smart gas data center can also transmit instructions to obtain gas user gas safety feedback information to the smart gas service platform; receive gas safety feedback information uploaded by the smart gas service platform and determine different gas safety training programs for different types of users; and send the determined gas safety training programs to the smart gas service platform. For more information on determining a gas safety training program, please participate in [the relevant event / organization]. Figure 2 and its related parts.

[0032] The intelligent gas indoor equipment sensor network platform is used to acquire relevant data from gas equipment and gas usage data. It can be configured as a communication network and gateway. In some embodiments, the intelligent gas indoor equipment sensor network platform can be used to implement functions such as network management, protocol management, command management, and data parsing. Network management manages the network, enabling data and / or information flow between platforms and modules. Protocol management manages various network and communication protocols, enabling platforms and modules executing different network and communication protocols to exchange data and / or information. Command management manages various commands (e.g., commands to obtain gas usage data), and can store and execute various commands. Data parsing is used to parse various data and commands, allowing modules and platforms to successfully identify or execute them.

[0033] In some embodiments, the smart gas indoor device sensor network platform can interact downwards with the smart gas indoor device object platform. For example, it can receive indoor gas usage data uploaded by the smart gas indoor device object platform; or send instructions to the smart gas indoor device object platform to retrieve indoor gas usage data. In some embodiments, the smart gas indoor device sensor network platform can also interact upwards with the smart gas safety management platform. For example, it can receive instructions from the smart gas data center to retrieve indoor gas usage data, or upload indoor gas usage data to the smart gas data center.

[0034] A smart gas platform can be a functional platform for acquiring data and / or information related to gas users. This data primarily includes gas usage data. For example, gas usage data includes gas consumption, gas alarm data, and gas maintenance data. In some embodiments, the smart gas platform can be implemented based on corresponding device terminals, such as gas meters or valve control devices.

[0035] In some embodiments, the smart gas target platform includes a fair metering equipment target sub-platform, a safety monitoring equipment target sub-platform, and a safety valve control equipment target sub-platform. The fair metering equipment target sub-platform includes various metering devices such as gas flow meters to acquire data such as gas usage. The safety monitoring equipment target sub-platform includes gas alarm devices to acquire data such as the number and type of gas faults. The safety valve control equipment target sub-platform includes various valve control devices for the normal flow and shut-off of gas.

[0036] In some embodiments, the smart gas object platform can interact with the smart gas indoor device sensor network platform. For example, it can receive instructions from the smart gas indoor device sensor network platform to obtain gas usage data, and upload indoor gas usage data to the smart gas indoor device sensor network platform.

[0037] It should be noted that the above description of the gas safety management IoT system and its modules for gas safety training is for convenience only and should not be construed as limiting this specification to the scope of the embodiments described. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the various modules or construct subsystems connected to other modules without departing from these principles. In some embodiments, Figure 1 The smart gas user platform, smart gas service platform, smart gas safety management platform, smart gas indoor equipment sensor network platform, and smart gas indoor equipment object platform disclosed herein can be different modules within a single system, or a single module can implement the functions of two or more of the aforementioned modules. For example, the modules can share a single storage module, or each module can have its own separate storage module. Such variations are all within the scope of protection of this specification.

[0038] Figure 2 This is an exemplary flowchart illustrating a gas safety management method for gas safety training, according to some embodiments of this specification. Figure 2 As shown, process 200 includes the following steps. In some embodiments, process 200 may be executed by a smart gas safety management platform of a gas safety management IoT system for gas safety training.

[0039] Step 210: Obtain gas usage data from at least one gas user, including at least one of gas consumption, gas alarm data, and gas maintenance data.

[0040] Gas usage data refers to data generated by users when using gas. In some embodiments, gas usage data may include at least one of gas consumption, gas alarm data, and gas maintenance data.

[0041] Gas consumption refers to the amount of gas a user uses within a certain period of time (e.g., one week, one month, etc.). For example, a user's gas consumption in one month could be 20 cubic meters.

[0042] Gas alarm data refers to data related to alarms triggered by gas safety issues. Gas alarm data can include alarm time (e.g., alarm from 1 week ago, alarm from 1 month ago), alarm type (e.g., gas leak alarm, equipment aging alarm, etc.).

[0043] Gas maintenance data refers to data related to the maintenance of gas equipment by gas companies. Gas maintenance data can include the time of maintenance (e.g., maintenance one week ago, maintenance one month ago), maintenance type (e.g., replacement of gas valves, maintenance of gas pipelines, etc.).

[0044] In some embodiments, gas usage data can be obtained in various ways. For example, gas usage can be obtained through smart gas meters, gas alarm data can be obtained through gas alarm devices, and gas maintenance data can be obtained through gas maintenance departments.

[0045] Step 220: Based on the gas usage data of at least one gas-consuming terminal, determine the user type of each gas-consuming terminal in the at least one gas-consuming terminal, and determine the gas safety training plan corresponding to each user type.

[0046] User type refers to the type of user of gas. In some embodiments, different gas users can be classified into different user types based on different gas usage data. For example, users can be classified into residential users, commercial users, industrial users, etc., based on gas usage volume. Another example is that users can be classified into users prone to gas leaks, users whose gas equipment is easily damaged, etc., based on repair data. Yet another example is that users can be classified into frequent gas users, occasional gas users, etc., based on the frequency of gas use.

[0047] In some embodiments, user types can be obtained based on gas registration information, etc. In some embodiments, user types can also be determined based on preset gas usage. For example, users with monthly gas usage less than m1 cubic meters are residential users, users with monthly gas usage greater than m1 cubic meters but less than m2 cubic meters are commercial users, and users with monthly gas usage greater than m2 cubic meters are industrial users, etc. In some embodiments, user types can also be determined based on repair data over a period of time (e.g., 1 month, 1 year, etc.). For example, users with more than 3 gas leak repair reports within 1 year can be classified as gas equipment prone to failure users, and users with more than 2 gas leak incidents within 1 year can be classified as gas leak-prone users, etc. In some embodiments, user types can also be based on the frequency of change in gas statistics. For example, users whose gas statistics change daily are considered frequent gas users, and users whose gas statistics change within a week without exceeding a preset threshold are considered occasional gas users. Gas statistics can be obtained based on smart gas meters. Each user type includes at least one user.

[0048] A gas safety training program refers to a plan for providing gas safety training to users. In some embodiments, a gas safety training program includes at least one of the following: training content, target audience, training time, and frequency of notifications.

[0049] Training content refers to the gas safety training received by gas users, which may include quizzes, videos, articles, etc. For example, videos on how to properly use gas, gas safety knowledge quizzes, etc. In some embodiments, the training content received by different types of gas users may differ. For example, users whose gas appliances are prone to malfunction receive training content focusing more on the correct use and maintenance of gas appliances, while users who only occasionally use gas receive training content focusing more on the maintenance and upkeep of gas appliances when they are not frequently used.

[0050] The training is for users who require gas safety training. This includes, for example, residential users, business owners, and factory managers.

[0051] Training time refers to the length of time that the trainee receives gas safety training. For example, training time can be 5 minutes.

[0052] Push frequency refers to how often training content is pushed to the target audience. For example, the push frequency could be once every 3 days, once a week, etc.

[0053] In some embodiments, the training programs for different types of gas users may differ. For example, industrial users may receive training once a week on how to prevent and handle large-scale fires and explosions, while users whose gas equipment is prone to damage may receive training every three days on the correct use of gas and proper maintenance of gas equipment.

[0054] In some embodiments, gas companies may determine gas safety training programs based on historical data. For example, they may determine gas safety training programs based on historical gas accident information.

[0055] In some embodiments, gas companies may also provide different gas safety training programs for different types of users. For example, training programs for occasional gas users may focus more on the correct use and maintenance of gas pipelines, valves, etc. Training programs for users prone to gas leaks may focus more on how to prevent gas leaks and the procedures for handling gas leak incidents.

[0056] In some embodiments, the frequency of pushing out gas safety training programs can be positively correlated with data such as the alarm frequency in gas alarm data and the maintenance frequency in gas maintenance data.

[0057] Step 230: Based on the gas safety training program, push safety training to the corresponding user terminals.

[0058] User terminals refer to smart terminals used by gas users, such as smartphones and computers.

[0059] Safety training refers to the training received by gas users on the correct use of gas. Examples include training on preventing gas leaks, preventing gas explosions, and gas safety knowledge tests.

[0060] In some embodiments, the smart gas safety management platform can upload different safety training materials corresponding to different types of users to the smart gas service platform, which then pushes these different safety training materials to the corresponding users. The push methods may include official accounts, mini-programs, etc.

[0061] Step 240: Obtain feedback information from the user terminal.

[0062] Feedback information refers to information provided by gas users regarding safety training. Examples include the selection results of answers to safety training quizzes and questionnaires. In some embodiments, feedback information may be represented by scores or accuracy rates for each training session.

[0063] In some embodiments, gas users can send feedback information to the smart gas service platform through the smart gas user platform, and then the smart gas service platform sends it to the smart gas safety management platform for aggregation, processing and other related operations.

[0064] Step 250: Based on the feedback information and gas usage data corresponding to each user type, determine the gas safety detection frequency at the gas consumption end of each user type, and determine the gas safety risk level at the gas consumption end of each user type.

[0065] Gas safety inspection refers to determining different inspection contents based on different types of safety risks and their probabilities of occurrence. Examples include pipeline inspection, gas meter inspection, gas equipment inspection, and gas pressure testing. Gas safety inspection frequency refers to how often gas safety inspections are conducted. For example, once a week or once a month.

[0066] In some embodiments, gas companies may determine the inspection frequency manually based on historical experience. For example, gas companies may determine the inspection frequency of gas pipelines based on the lifespan of pipeline equipment. In some embodiments, the gas safety inspection frequency may also be determined based on feedback information and gas usage data. For more information on how to determine the gas safety inspection frequency at the gas consumption end, please refer to [link to relevant documentation]. Figure 3 And related content.

[0067] Gas safety risk refers to the risk of a gas safety accident. Examples include the risk of pipeline leaks, deflagration and fire, and equipment aging. Gas safety risk level refers to the degree of probability of a gas safety accident occurring. Gas safety risk level can be represented numerically, for example, using numbers 1 to 10. A higher gas safety risk level corresponds to a greater gas safety risk.

[0068] In some embodiments, gas companies can determine the gas safety risk level of different types of users based on historical data. For example, a smart gas safety management platform can determine the gas safety risk level of at least one gas user under a certain type based on feature comparison, vector retrieval, etc., and then determine the average gas safety risk level of all gas users under that type as the gas safety risk level of that type of user. In some embodiments, the gas safety risk level can also be determined based on a machine learning model. For more information on how to determine the gas safety risk level at the gas consumption end, please refer to [link to relevant documentation]. Figure 5 And related content.

[0069] In some embodiments, process 200 may further include the following steps:

[0070] Step 260: Based on the feedback information corresponding to each user type, update the gas safety training plan corresponding to each user type.

[0071] In some embodiments, the gas safety training program can be updated based on feedback from different users. For example, users with different scores or accuracy rates will receive different safety training content and at different frequencies.

[0072] In some embodiments, updating the gas safety training program based on feedback from different users includes: dividing the program into different intervals based on different scores, with each interval corresponding to different safety training content. The score thresholds for these intervals can be preset manually. For example, if a gas user scores below 60 points, retraining is required; if a gas user scores above 95 points on a particular safety training item, the training content is changed; and if a gas user consistently scores below 60 points, the frequency of push notifications is increased.

[0073] In some embodiments, the processor can also reclassify and determine different categories of training subjects based on feedback information. For example, the classification information of gas users can be further determined based on the scores.

[0074] In some embodiments of this specification, by pushing different gas safety training content to gas users and updating the safety training content based on feedback from different users, targeted safety management can be carried out for different users, thereby improving the efficiency of gas safety management and the safety awareness of gas users.

[0075] In some embodiments of this specification, different gas safety training programs are pushed out in a targeted manner based on different types of gas users and different gas usage data and risk levels of different users, which can improve the efficiency of gas safety management.

[0076] Figure 3 This is an exemplary flowchart illustrating the determination of gas safety detection frequency according to some embodiments of this specification. Figure 3 As shown, process 300 includes the following steps. In some embodiments, process 300 may be executed by a smart gas safety management platform of a gas safety management IoT system for gas safety training.

[0077] Step 310: Based on the feedback information and gas usage data corresponding to each user type, predict the probability of occurrence of safety risks for each user type.

[0078] The probability of a safety risk occurring refers to the probability of various gas safety accidents happening, such as the probability of a gas leak, the probability of an explosion, the probability of a fire, and the probability of equipment aging.

[0079] In some embodiments, the probability of safety risks occurring for each user type can be predicted manually based on feedback information and corresponding gas usage data. For example, if the feedback information from residential users shows "usually not checked" for the check item "check whether the gas stove switch is turned off after use," then the manual assessment is that the probability of a gas leak is relatively high.

[0080] In some embodiments, the probability of a security risk occurring can be predicted using a detection model. For more information on using detection models to predict the probability of a security risk, see [link to relevant documentation]. Figure 4 And its related descriptions.

[0081] Step 320: Based on the probability of occurrence of safety risks corresponding to each user type, determine the gas safety detection frequency at the gas consumption end of each user type.

[0082] In some embodiments, the higher the probability of safety risks occurring for each user type, the higher the frequency of gas safety inspections at the gas consumption end for each user type. For example, the higher the probability of equipment aging, the higher the inspection frequency of gas equipment at the gas consumption end.

[0083] In some embodiments, a mapping relationship may exist between the probability of a safety risk occurring and the frequency of gas safety inspections. For example, if the probability of a safety risk occurring is 0, the gas safety inspection frequency is once every six months; if the probability is 0-20%, the frequency is once every five months; if the probability is 20%-40%, the frequency is once every four months; if the probability is 40%-60%, the frequency is once every three months; if the probability is 60%-80%, the frequency is once every two months; and if the probability exceeds 80%, the frequency is once a week. The above mapping relationship is merely an example, and other mapping relationships may exist.

[0084] The mapping relationship between the probability of safety risks and the frequency of gas safety inspections can be stored as a mapping table on a storage device or in a database. Therefore, after determining the probability of safety risks for each user type, the gas safety inspection frequency for each user type can be determined by looking up the table.

[0085] Determining the frequency of gas safety inspections based on the probability of safety risks can improve the accuracy of determining the frequency and intelligently select an appropriate frequency, thereby rationalizing inspections, saving labor costs, and making gas safety management methods more economical and applicable.

[0086] It should be noted that the above descriptions of processes 200 and 300 are for illustrative purposes only and do not limit the scope of this specification. Those skilled in the art can make various modifications and changes to processes 200 and 300 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0087] Figure 4 This is a schematic diagram illustrating the prediction of the probability of a security risk occurring, based on some embodiments of this specification.

[0088] In some embodiments, the intelligent gas safety management platform can determine the gas usage characteristics of each user type based on gas usage data corresponding to each user type, through the first embedding layer of the detection model. In some embodiments, the intelligent gas safety management platform can predict the probability of occurrence of safety risks corresponding to each user type based on feedback information and gas usage characteristics corresponding to each user type, through the detection layer of the detection model, where the detection model is a machine learning model.

[0089] Detection model 400 is a model used to predict the probability of security risks occurring. The detection model is a machine learning model. In some embodiments, the detection model may include a convolutional neural network model or a deep neural network model.

[0090] In some embodiments, the detection model 400 includes a first embedding layer 420 and a detection layer 450. In some embodiments, the first embedding layer 420 and the detection layer 450 may be models obtained by convolutional neural networks or deep neural networks or combinations thereof.

[0091] In some embodiments, the input to the first embedding layer 420 may include gas usage data 410, and the output of the first embedding layer 420 may include gas usage features 430. For details regarding gas usage data 410, please refer to [link to relevant documentation]. Figure 2 And related descriptions. Gas usage characteristic 430 refers to characteristics related to gas usage, such as gas consumption, gas usage duration, and the number of times gas equipment was repaired. Gas usage characteristics can be represented by vectors. For example, (2,3,1) can represent gas usage characteristics including a gas consumption of 2 cubic meters, a gas usage duration of 3 hours, and gas equipment repaired once in the current month. Gas usage characteristics can also include other content, such as gas usage time periods and gas alarm status.

[0092] In some embodiments, the output of the first embedding layer 420 can be used as the input of the detection layer 450. The input of the detection layer 450 may include gas usage characteristics 430 and feedback information 440, and the output of the detection layer 450 may include the probability of safety risk occurrence 460. For details of the feedback information 440, please refer to [link / reference needed]. Figure 2 And its related description. For details on the probability of a security risk occurring at 460%, please refer to... Figure 3 And its related descriptions.

[0093] In some embodiments, the detection model can be obtained based on joint training of the first embedding layer 420 and the detection layer 450.

[0094] In some embodiments, the sample data used for jointly training the first embedding layer 420 and the detection layer 450 includes sample gas usage data and sample feedback information. The labels represent the occurrence of safety risks corresponding to the sample gas usage data and sample feedback information; if a gas safety incident occurs, the label value is 1, and if no gas safety incident occurs, the label value is 0. Training samples and labels can be retrieved from storage devices or databases, and labels can be obtained based on manual annotation.

[0095] During training, sample gas usage data is input into the first embedding layer 420 to obtain the gas usage features output by the first embedding layer 420. The gas usage features and sample feedback information are input into the detection layer 450 to obtain the probability of safety risks occurring, output by the detection layer 450. A loss function is constructed based on the probability of safety risks occurring and the probability of safety risks occurring in the samples. The first embedding layer 420 and the detection layer 450 are iteratively updated based on the loss function until a preset condition is met, at which point training is complete, resulting in a trained first embedding layer 420 and a trained detection layer 450. The preset condition can be that the loss function is less than a threshold, convergence occurs, or the training period reaches a threshold.

[0096] In some embodiments, the input to the detection layer 450 further includes a gas safety risk level 540. The gas safety risk level 540 can be obtained through a risk prediction model. For details regarding the gas safety risk level 540, please refer to [link / reference needed]. Figure 2 Step 250 and its related description. For more information on security prediction models, see [link to relevant documentation]. Figure 5 And its related descriptions.

[0097] Correspondingly, when jointly training the first embedding layer 420 and the detection layer 450, the training samples of the detection layer may also include the sample security risk level. For the rest of the training, please refer to the above text of this specification.

[0098] Based on gas usage data, feedback information, and gas safety risk levels, the probability of safety risks occurring can be predicted through detection models. This can accurately predict the probability of safety risks occurring, thereby improving the accuracy of determining the frequency of gas safety inspections and enhancing the safety of gas use.

[0099] Figure 5 This is a schematic diagram illustrating the determination of gas safety risk levels according to some embodiments of this specification.

[0100] In some embodiments, the intelligent gas safety management platform can determine the gas usage characteristics of each user type based on gas usage data corresponding to each user type, through the second embedding layer of a risk prediction model. In some embodiments, the intelligent gas safety management platform can determine the gas safety risk level of each user type's gas consumption end based on the gas usage characteristics corresponding to each user type, through the prediction layer of a risk prediction model, where the risk prediction model is a machine learning model.

[0101] A risk prediction model is a model used to predict the level of gas safety risk. The risk prediction model is a machine learning model. In some embodiments, the risk prediction model may include a convolutional neural network model or a deep neural network model.

[0102] In some embodiments, the risk prediction model 500 includes a second embedding layer 510 and a prediction layer 530. In some embodiments, the second embedding layer 510 and the prediction layer 530 may be models obtained from convolutional neural networks or deep neural networks or combinations thereof. The second embedding layer 510 may be obtained based on the parameters of the first embedding layer of a shared detection model.

[0103] In some embodiments, the input to the second embedding layer 510 may include gas usage data 410, and the output of the second embedding layer 510 may include gas usage features 430. For details regarding the gas usage data 410 and gas usage features 430, please refer to... Figure 4 And its related descriptions.

[0104] In some embodiments, the output of the second embedding layer 510 can be used as the input of the prediction layer 530. The input of the prediction layer 530 may include gas usage characteristics 430, and the output of the prediction layer 530 may include a gas safety risk level 540. For details regarding the gas safety risk level 540, please refer to... Figure 2 , Figure 4 And its related descriptions. Gas safety risk level 540 can be represented in the form of a level sequence. For example, the output of prediction layer 530 is a safety risk level sequence (a, b, c), where a, b, and c can correspond to pipeline leakage risk, deflagration and fire risk, and equipment aging risk, respectively. As an example only, the output safety risk level sequence (3, 4, 5) of prediction layer 530 indicates that the pipeline leakage risk level is 4, the deflagration and fire risk level is 4, and the equipment aging risk level is 5. Gas safety risk level 540 can also include other types of risk levels.

[0105] In some embodiments, the risk prediction model can be obtained based on a separately trained prediction layer 530, and the second embedding layer 510 in the risk prediction model can use the first embedding layer 420 trained in the detection model. In some embodiments, the training samples of the prediction layer 530 are sample gas usage features, and the labels are sample gas safety risk levels. The sample gas usage features can be obtained by processing gas usage data based on the first embedding layer 420 trained in the detection model. The training samples and labels can be retrieved from storage devices or databases, and the labels can be obtained based on manual annotation. The sample gas usage features are input into the prediction layer 530 to obtain the gas safety risk level output by the prediction layer 530. A loss function is constructed based on the gas safety risk level and the sample gas safety risk level, and the prediction layer 530 is iteratively updated based on the loss function until a preset condition is met, the training is completed, and a trained prediction layer 530 is obtained. The preset condition can be that the loss function is less than a threshold, convergence, or the training period reaches a threshold.

[0106] In some embodiments, the risk prediction model can be obtained based on joint training of the second embedding layer 510 and the prediction layer 530.

[0107] In some embodiments, the sample data for joint training of the second embedding layer 510 and the prediction layer 530 includes sample gas usage data, labeled as sample gas safety risk level. The sample gas usage data can be obtained from the gas usage data in the training data of the first embedding layer of the shared detection model. Training samples and labels can be retrieved from storage devices or databases, and labels can be obtained based on manual annotation. The sample gas usage data is input into the second embedding layer 510 to obtain the gas usage features output by the second embedding layer 510. The gas usage features are input into the prediction layer 530 to obtain the gas safety risk level output by the prediction layer 530. A loss function is constructed based on the gas safety risk level and the sample gas safety risk level, and the second embedding layer 510 and the prediction layer 530 are iteratively updated based on the loss function until a preset condition is met, training is complete, and the trained second embedding layer 510 and prediction layer 530 are obtained. The preset condition can be that the loss function is less than a threshold, convergence occurs, or the training period reaches a threshold.

[0108] By using a trained risk prediction model to determine the gas safety risk level, instead of manually calculating it, the time required to predict the gas safety risk level can be shortened, thereby improving processing efficiency.

[0109] In some embodiments, the input to the prediction layer 530 further includes feedback information 440. For details regarding the feedback information 440, please refer to [link to relevant documentation]. Figure 4 And its related descriptions.

[0110] In some implementations, the input to the prediction layer 530 also includes detection information 520.

[0111] Inspection information refers to information obtained based on the frequency of gas safety inspections. For example, pipeline inspections, gas meter inspections, gas equipment inspections, and gas pressure inspections are conducted every three months, and the corresponding inspection results constitute the inspection information. As an example, inspecting a gas pipeline every three months yields information on the pipeline's condition (e.g., whether it is deformed or damaged) (a, b, c, ...); 'a' indicates whether the gas meter is operating normally; different values ​​represent different operating conditions, such as a value of 0 indicating abnormal operation and a value of 1 indicating normal operation; 'b' indicates whether the gas equipment is damaged; 'c' indicates whether the gas pressure is within a reasonable range, etc.

[0112] When the input to the prediction layer 530 also includes feedback information 440, the training samples of the prediction layer may also include sample feedback information during the training of the second embedding layer 510 and the prediction layer 530. For the remaining training components, please refer to the above description. When the input to the prediction layer 530 also includes detection information 520, the training samples of the prediction layer may also include sample detection information during the training of the second embedding layer 510 and the prediction layer 530. For the remaining training components, please refer to the above description.

[0113] Using feedback and detection information as input to the model can improve the accuracy of the risk prediction model in predicting the gas safety risk level, making the predicted gas safety risk level more consistent with the actual situation.

[0114] In some embodiments, the intelligent gas safety management platform can determine the corresponding safety reminder information for each user type based on the gas safety risk level of each user's gas consumption terminal. In some embodiments, the intelligent gas safety management platform can push safety reminders to the corresponding user terminals based on the safety reminder information corresponding to each user type.

[0115] Safety alerts refer to information used to remind users of gas safety-related matters. Examples include information related to pipeline safety, gas meter safety, and the safety of gas appliances.

[0116] In some embodiments, if the gas safety risk level is greater than or equal to the gas safety risk level threshold, the corresponding safety reminder information is determined, where the gas safety risk level threshold can be set to 5. For example, if the gas safety risk level related to pipeline safety is greater than or equal to the gas safety risk level threshold, the safety reminder information is determined to be a reminder information related to pipeline safety (such as pipeline abnormality). As another example, if the gas safety risk level related to gas equipment safety is greater than or equal to the gas safety risk level threshold, the safety reminder information is determined to be a reminder information related to gas equipment safety (such as thermometer or flow meter abnormality). As an example only, when the safety risk level sequence is (2,5,7), it indicates that the pipeline leakage risk level is 2, the deflagration and fire risk level is 5, and the equipment aging risk level is 7. Where both the deflagration and fire risk level and the equipment aging risk level are greater than or equal to the gas safety risk level threshold (5), the safety reminder information is determined to be a reminder message related to deflagration and fire risk, and a reminder message related to gas equipment safety. More details on the safety risk level sequence can be found in the relevant description above.

[0117] Safety alerts are information pushed to and displayed on the user's device to remind them of gas safety. In some embodiments, safety alerts may be presented in any one or a combination of images, text, audio, video, etc.

[0118] In some embodiments, the intelligent gas safety management platform can push safety alerts to corresponding user terminals based on safety reminder information corresponding to each user type. For example, the intelligent gas safety management platform can push safety alerts about gas leaks to users at risk of gas leaks, and push safety alerts about fire prevention to users at risk of fire.

[0119] Pushing safety reminders to relevant user terminals can alert users to gas safety. These reminders are tailored to specific types of gas safety incidents, providing more targeted guidance and enabling users to use gas-related equipment more effectively. This, in turn, improves the efficiency of gas safety management methods.

[0120] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0121] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0122] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0123] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0124] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0125] For each patent, patent application, patent application publication, and other material, such as articles, books, specifications, publications, and documents, referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0126] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A gas safety management method for gas safety training, executed based on a smart gas safety management platform of a gas safety management IoT system for gas safety training; the method includes: Through the smart gas indoor equipment sensor network platform, gas usage data of at least one gas user is obtained from the smart gas indoor equipment object platform. The gas usage data includes at least one of gas consumption, gas alarm data and gas maintenance data. Based on the gas usage data of the at least one gas-consuming terminal, determine the user type of each gas-consuming terminal and determine the gas safety training plan corresponding to each user type. The gas safety training program includes at least one of the following: training targets, training time, and frequency of dissemination. Based on the aforementioned gas safety training program, safety training is pushed to the corresponding user terminals through the smart gas service platform; The smart gas service platform obtains feedback information from the user terminal from the smart gas user platform. Based on the feedback information and gas usage data corresponding to each user type, the gas safety detection frequency at the gas consumption end of each user type is determined, and the gas safety risk level at the gas consumption end of each user type is determined.

2. The method according to claim 1, wherein the feedback information is issued based on image selection and / or text selection; The method further includes: Based on the feedback information corresponding to each user type, the gas safety training program for each user type is updated.

3. The method according to claim 2, wherein determining the gas safety detection frequency of the gas consumption terminal for each user type based on the feedback information and corresponding gas usage data for each user type includes: Based on the feedback information and gas usage data corresponding to each user type, the probability of occurrence of safety risks corresponding to each user type is predicted. Based on the probability of occurrence of safety risks corresponding to each user type, the gas safety detection frequency at the gas consumption end of each user type is determined.

4. The method according to claim 3, wherein predicting the probability of occurrence of safety risks corresponding to each user type based on the feedback information and the corresponding gas usage data for each user type includes: Based on the gas usage data corresponding to each user type, the gas usage characteristics corresponding to each user type are determined through the first embedding layer of the detection model. Based on the feedback information and gas usage characteristics corresponding to each user type, the probability of occurrence of safety risks corresponding to each user type is predicted through the detection layer of the detection model. The detection model is a machine learning model.

5. The method according to claim 1, wherein determining the gas safety risk level of the gas-consuming end for each user type includes: Based on the gas usage data corresponding to each user type, the gas usage characteristics corresponding to each user type are determined through the second embedding layer of the risk prediction model; Based on the gas usage characteristics corresponding to each user type, the gas safety risk level of each user type's gas consumption end is determined through the prediction layer of the risk prediction model; the risk prediction model is a machine learning model.

6. A gas safety management IoT system for gas safety training, including: Smart gas user platform, smart gas service platform, smart gas safety management platform, smart gas indoor equipment sensor network platform, smart gas indoor equipment object platform; The smart gas indoor equipment object platform is used to acquire gas usage data from at least one gas user; the gas usage data includes at least one of gas consumption, gas alarm data, and gas maintenance data; The smart gas indoor equipment sensor network platform is used to transmit gas usage data from at least one gas user terminal to the smart gas safety management platform. The intelligent gas safety management platform is used for: Based on the gas usage data of the at least one gas-consuming terminal, determine the user type of each gas-consuming terminal and determine the gas safety training plan corresponding to each user type. The gas safety training program includes at least one of the following: training targets, training time, and frequency of dissemination. Based on the gas safety training program, safety training is pushed to the corresponding user terminals through the smart gas service platform; Based on the feedback information of each user type on the safety training and the gas usage data corresponding to each user type, the gas safety detection frequency of the gas consumption terminal of each user type is determined, and the gas safety risk level of the gas consumption terminal of each user type is determined. The smart gas service platform is used to feed back the gas safety training program corresponding to each user type, the gas safety detection frequency of the gas consumption terminal of each user type, and the gas safety risk level of the gas consumption terminal of each user type to the smart gas user platform. The intelligent gas user platform is used to obtain feedback information from various user types regarding the safety training.

7. The Internet of Things system according to claim 6, wherein the feedback information is issued based on image selection and / or text selection; The intelligent gas safety management platform is further used for: Based on the feedback information corresponding to each user type, the gas safety training program for each user type is updated.

8. The Internet of Things system according to claim 7, wherein the intelligent gas safety management platform is further configured to: Based on the feedback information and gas usage data corresponding to each user type, the probability of occurrence of safety risks corresponding to each user type is predicted. Based on the probability of occurrence of safety risks corresponding to each user type, the gas safety detection frequency at the gas consumption end of each user type is determined.

9. The Internet of Things system according to claim 8, wherein the intelligent gas safety management platform is further used for: Based on the gas usage data corresponding to each user type, the gas usage characteristics corresponding to each user type are determined through the first embedding layer of the detection model. Based on the feedback information and gas usage characteristics corresponding to each user type, the probability of safety risks occurring for each user type is predicted through the detection layer of the detection model; the detection model is a machine learning model.

10. The Internet of Things system according to claim 6, wherein the intelligent gas safety management platform is further configured to: Based on the gas usage data corresponding to each user type, the gas usage characteristics corresponding to each user type are determined through the second embedding layer of the risk prediction model; Based on the gas usage characteristics corresponding to each user type, the gas safety risk level of each user type's gas consumption end is determined through the prediction layer of the risk prediction model; the risk prediction model is a machine learning model.

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