A method and system for evaluating the life of a key fitting of a gas appliance

Abstract

The application provides a kind of gas appliance key accessory life evaluation method and system, it is related to gas safety technical field, the gas appliance key accessory life evaluation method, comprising: obtaining each key node's hose accessory data uploaded according to sampling period by user;According to hose accessory data, the current hose state is evaluated, and state change data is obtained;According to state change data and hose basic data, obtain the hose simulation model containing each key node;Simulation is carried out through hose simulation model to predict the remaining life of hose accessory.The evaluation method and system provided by the application can accurately control the remaining life of hose accessory, and the hose accessory is replaced in time in the conservative time period, so as to prevent major accidents caused by hose accessory damage.

Description

Technical Field

[0001] This invention relates to the field of gas safety technology, and in particular to a method and system for assessing the lifespan of key components of gas appliances. Background Technology

[0002] Gas stove hoses are used to connect gas pipes or gas cylinders to gas stoves to deliver gas. The lifespan of gas stove hoses is typically short, generally 18 months to 2 years. However, due to the complex environmental conditions in which gas stove hoses are used, their lifespan is also affected by various factors.

[0003] Existing gas appliance key hose fittings, when used in environments such as kitchens, although having a limited service life during production, are subject to varying degrees of aging and wear due to various specific environmental factors. Because of these factors, it is impossible to assess the service life of the hose fittings within a conservative timeframe, leading to untimely replacement and posing significant safety hazards. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method and system for assessing the lifespan of key components of gas appliances, in order to solve the problem that in the prior art, it is not convenient to inspect the hose after installation, which makes it impossible to assess the lifespan of the hose components and thus impossible to replace the hose components within a conservative time period, resulting in significant safety hazards.

[0005] To achieve the above and other related objectives, the present invention provides a method for assessing the lifespan of key components of gas appliances, comprising: acquiring hose component data for each key node uploaded by the user according to a sampling period; assessing the current state of the hose based on the hose component data and acquiring state change data; acquiring a hose simulation model containing each key node based on the state change data and basic hose data; and performing simulation using the hose simulation model to predict the remaining lifespan of the hose components.

[0006] In one embodiment of the present invention, the method further includes: receiving safety hazard points recorded during the installation of hose fittings as key nodes; obtaining the importance of the points based on the key nodes and their corresponding layout status information; obtaining the comprehensive importance of the points based on each key layout that affects the key nodes in the layout status information and the importance of the points; and obtaining the sampling period based on the comprehensive importance of the points.

[0007] In one embodiment of the present invention, the importance of a point is obtained based on key nodes and their corresponding layout status information, including: finding key layouts that affect key nodes in the layout status information; obtaining the layout influence degree of key layouts on key nodes based on the type of key layouts; and obtaining the importance of a point based on the type influence degree of key layouts and the layout influence degree of key layouts on key nodes.

[0008] In one embodiment of the present invention, obtaining the layout influence degree of a key layout on a key node according to the type of the key layout includes: obtaining the influence mode of the key layout on the key node according to the type of the key layout; when the influence mode is direct influence, obtaining the first layout influence degree of the key layout on the key node according to the type of the key layout; when the influence mode is indirect influence, obtaining the second layout influence degree of the key layout on the key node according to the type of the key layout and the degree of weakening of the influence of the key layout on the key node; and using the first layout influence degree and / or the second layout influence degree as the layout influence degree.

[0009] In one embodiment of the present invention, obtaining the second layout influence degree of a critical layout on a critical node based on the type of the critical layout and the degree of weakening of the influence of the critical layout to the critical node includes: obtaining the degree of weakening of the influence of the critical layout to the critical node based on the type of the critical layout and the distance between the critical layout and the critical node; and obtaining the second layout influence degree of the critical layout on the critical node based on the type of the critical layout and the degree of weakening of the influence.

[0010] In one embodiment of the present invention, acquiring hose accessory data for each key node uploaded by the user according to a sampling period includes: acquiring hose appearance images for each key node uploaded by the user according to the sampling period; performing image detection on the hose appearance images to obtain image anomaly features; obtaining derivative test steps for requirement testing based on the image anomaly features; sequentially receiving hose test data uploaded by the user according to the hose test steps; performing anomaly detection on the hose test data to obtain derivative anomaly features; continuing to execute the step of obtaining derivative test steps for requirement testing based on the derivative anomaly features to obtain all hose test data; and using the hose appearance images and all hose test data as hose accessory data.

[0011] In one embodiment of the present invention, obtaining derived test steps for requirement testing based on image anomaly features includes: obtaining all test steps corresponding to the feature type of the image anomaly features, and the requirement test weight corresponding to each test step; obtaining the requirement test degree of each test step based on the feature value of the image anomaly features and the requirement test weight corresponding to each test step; comparing the requirement test degrees of each test step, and obtaining the test step corresponding to the highest requirement test degree as the derived test step for requirement testing.

[0012] In one embodiment of the present invention, the hose fitting data includes a hose appearance image and hose test data; based on the hose fitting data, the current hose state is evaluated to obtain state change data, including: comparing the hose appearance image with the initial hose appearance image to obtain first type of state change data; comparing the hose test data with the initial hose test data to obtain test difference data; obtaining second type of state change data based on the test difference data and corresponding derived test steps; and using the first type of state change data and the second type of state change data as the state change data.

[0013] In one embodiment of the present invention, the remaining lifespan of a hose fitting is predicted by performing simulation using a hose simulation model. This includes: performing simulation using the hose simulation model to obtain predicted hose fitting data for key nodes in the next sampling period; determining whether the predicted hose fitting data is greater than a set value; if so, continuing to upload hose fitting data for each key node according to the sampling period and updating the hose simulation model; if not, adjusting the sampling period based on the predicted hose fitting data and the set value, uploading hose fitting data for each key node according to the adjusted sampling period, and updating the hose simulation model again; and when the adjusted sampling period is less than the set period, using the time of the adjusted sampling period corresponding to the less-than-set period as the remaining lifespan of the hose fitting.

[0014] To achieve the above and other related objectives, the present invention also provides a life assessment system for key components of gas appliances, comprising: an acquisition unit for acquiring hose component data for each key node uploaded by a user according to a sampling period; an assessment unit for assessing the current state of the hose based on the hose component data and acquiring state change data; a modeling unit for acquiring a hose simulation model containing each key node based on the state change data and basic hose data; and a prediction unit for performing simulation using the hose simulation model to predict the remaining life of the hose components.

[0015] As described above, the life assessment method and system for key components of gas appliances of the present invention has the following beneficial effects: By configuring key nodes in the installation process of hose fittings, the sampling period after hose installation can be conservatively determined based on the set key nodes. The hose condition is then assessed using the hose fitting data obtained during this sampling period to accurately determine the hose simulation model corresponding to the current hose condition. This allows for simulation using the corresponding hose simulation model to accurately predict the remaining life of the hose fittings. This enables precise control over the remaining life of the hose fittings and timely replacement of hose fittings within a conservative timeframe to prevent major accidents caused by hose fitting damage. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the life assessment method for key components of gas appliances provided in an embodiment of the present invention.

[0017] Figure 2 The diagram shown is a structural block diagram of a gas appliance critical component life assessment system provided in an embodiment of the present invention.

[0018] Figure 3 The diagram shown is a structural schematic of an electronic device according to an embodiment of the present invention.

[0019] Component designation explanation

[0020] Electronic device 1; Gas appliance key component life assessment system 11; Memory 12; Processor 13; Acquisition unit 111; Assessment unit 112; Modeling unit 113; Prediction unit 114. Detailed Implementation

[0021] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0022] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0023] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0024] This invention provides a method for assessing the lifespan of key components of gas appliances. By configuring key nodes in the installation process of hose components and obtaining hose component data for corresponding sampling periods based on these key nodes, the condition of the hose can be assessed to determine whether the hose has undergone a change in condition. Based on the condition change data, a hose simulation model can be constructed. This model can be used for simulation to accurately predict the remaining lifespan of the hose components, thereby enabling precise control over their remaining lifespan and timely replacement within a conservative timeframe to prevent major accidents caused by hose component damage.

[0025] Figure 1 A flowchart illustrating a method for assessing the lifespan of critical components of gas appliances according to an exemplary embodiment of this application is shown. This method is applied to a system for assessing the lifespan of critical components of gas appliances and includes steps S10-S40. The following will be combined with… Figure 1 The technical solution of this application will be described in detail below.

[0026] First, execute step S10 to obtain the hose fitting data of each key node uploaded by the user according to the sampling period.

[0027] The key nodes in this invention can be locations in home kitchens that affect the use of hose fittings, i.e., potential safety hazards. Of course, they can also be scenarios such as commercial kitchens where gas appliances are used.

[0028] When the system's pre-set sampling period arrives, users can collect data on hose fittings corresponding to each key node using mobile devices. Alternatively, staff can periodically visit the site to collect data on hose fittings at key nodes. This data is then uploaded to the gas appliance key component lifespan assessment system, enabling the acquisition of hose fitting data at each key node and facilitating the assessment of hose fitting lifespan.

[0029] Before step S10, that is, before obtaining the hose fitting data of each key node uploaded by the user according to the sampling period, the process may further include:

[0030] The safety hazard points recorded during the installation of hose fittings are considered key points.

[0031] Based on the key nodes and their corresponding layout status information, the importance of each location can be determined.

[0032] Based on the key layouts that affect key nodes in the layout status information, and the importance of each point, the overall importance of the point is obtained;

[0033] The sampling period is determined based on the overall importance of the sampling points.

[0034] Before users upload data on hose fittings at key nodes to the gas appliance key component life assessment system, the system further determines the data collection cycle after hose fitting installation. In other words, after hose fitting installation, workers can identify potential safety hazards around the fittings and input their locations (hazard points) as key nodes into the system. This input also includes the layout status information of these key nodes. The system then determines the importance of each key node based on its layout status and, based on the influence of each key layout and its corresponding importance, first determines the overall importance of each key node. Finally, based on this overall importance, the sampling cycle for the hose fittings is determined. When determining the sampling period for hose fittings based on the overall importance of sampling points, the overall importance of all key nodes can be considered. For example, the overall importance of each key node can be compared to obtain a ranking from smallest to largest. Then, in a relatively conservative manner, the sampling period corresponding to the highest overall importance can be selected as the final sampling period. Furthermore, the overall importance of a sampling point is negatively correlated with its corresponding sampling period; that is, the higher the importance value, the more important the corresponding key node, and the shorter the sampling period required for investigation. Of course, the overall importance of sampling points can also be ranked from largest to smallest.

[0035] This includes determining the importance of a location based on key nodes and their corresponding layout status information, which may further include:

[0036] Based on the key nodes, identify the key layouts in the layout status information that affect the key nodes; the layout status information includes the key layouts that affect the key nodes and other layouts.

[0037] Based on the type of critical layout, obtain the degree of influence of the critical layout on the layout of critical nodes;

[0038] The importance of a location is determined by the type and influence of the critical layout and its impact on the layout of critical nodes.

[0039] The gas appliance critical component life assessment system of this invention determines the importance of each critical node based on its critical nodes and corresponding layout status information. It can then search the layout status information to identify the critical layouts that influence these nodes. Furthermore, based on the type of critical layout, the system determines the layout influence of each critical node. Finally, based on the type influence of the critical layout, the system determines the overall importance of each critical node.

[0040] Specifically, when uploading user hose fitting information, all layout status information and key nodes of the hose fitting can be uploaded. Other key information can also be uploaded. For example, staff will enter various details of the hose fitting during installation as layout status information, including key layouts that affect key nodes. The system can then directly query key layouts that affect key nodes. For example, key layouts could be the layout of the sink, gas stove, water heater, or spatial layout. The sink, gas stove, and water heater layouts, among others, will have varying degrees of impact on the hose fitting during use due to differences in the distance between their installation location and key nodes; this type of impact can be defined as indirect. The impact of spatial layout on hose configuration is relatively fixed. For example, factors such as oil stains and temperature within the space caused by the spatial layout have a generally consistent impact on different locations of the hose fitting (including key nodes); therefore, this type of impact can be defined as direct.

[0041] Based on the type of critical layout, the impact of the critical layout on the layout of critical nodes can be obtained, which may further include:

[0042] Based on the type of critical layout, we can determine how the critical layout affects the critical nodes;

[0043] When the influence method is direct influence, the first layout influence degree of the key layout on the key node is obtained according to the type of key layout;

[0044] When the influence method is indirect, the second influence degree of the key layout on the key node is obtained based on the type of key layout and the degree of weakening of the influence of the key layout to the key node.

[0045] The first layout influence and / or the second layout influence are used as the layout influence.

[0046] The gas appliance critical component life assessment system determines the impact of critical layouts on critical nodes based on their type. The method of influence of a critical layout on a critical node can be determined by its type. For example, when a critical layout is a space containing oil or heat, it can be identified as having a direct impact. Then, based on the type of critical layout (e.g., an oil-contaminated space), the overall oil contamination impact can be calculated based on the space's volume and the oil contamination impact per unit volume, serving as one component of the first-level layout impact. Similarly, a temperature-contaminated space can also be included, and its impact can be calculated using the same method, also serving as one component of the first-level layout impact.

[0047] When critical layouts include the location of a water tank, gas stove, and water heater, their indirect influence can be identified. Then, based on the weakening effect per unit length for each critical layout, the degree of weakening from the critical layout to the critical node is determined. Further, the second degree of influence of the critical layout on the critical node is determined. For example, after determining the distance between the water tank and the critical node, the degree of weakening from the water tank to the critical node can be determined based on the weakening factor per unit length corresponding to the water tank. Then, the second degree of influence of the water tank on the critical node is obtained by subtracting the maximum degree of influence corresponding to the water tank. The first and / or second degree of influence are then integrated to obtain the overall degree of influence of the critical node. For example, if the first and second degree of influence exist independently, only one is considered; if both exist simultaneously, they are superimposed to obtain the overall degree of influence of the critical node.

[0048] Based on the type of critical placement and the degree of weakening of the critical placement's impact on critical nodes, the influence of the critical placement on the second placement of critical nodes can be obtained, which may further include:

[0049] Based on the type of critical placement and the distance between the critical placement and the critical node, obtain the degree of weakening of the impact from the critical placement to the critical node;

[0050] Based on the type and degree of weakening of the critical layout, obtain the degree of influence of the critical layout on the second layout of the critical node.

[0051] When calculating the second-order impact of a critical layout on a critical node using the gas appliance critical component life assessment system, a corresponding mitigation factor can be obtained based on the type of critical layout. Then, based on the mitigation factor and the distance between the critical layout and the critical node, the degree of impact reduction from the critical layout to the critical node can be calculated. Furthermore, based on the type of critical layout, the corresponding maximum layout impact can also be obtained. Finally, by subtracting the maximum layout impact from the impact reduction, the second-order impact of the critical layout on the critical node can be obtained.

[0052] Specifically, the formula for calculating the influence of the second layout is as follows: ,in, This is represented as the second layout influence. Represented as the type of critical layout The corresponding maximum layout impact This is represented as the distance between critical layouts and critical nodes. Represented as the type of critical layout The corresponding weakening factor, This represents the degree of weakening of the impact.

[0053] In step S10, the hose fitting data for each key node uploaded by the user according to the sampling period is obtained, including:

[0054] Obtain images of the tubing appearance at each key node uploaded by the user according to the sampling period;

[0055] Image detection is performed on the appearance image of the hose to obtain abnormal features;

[0056] Based on the abnormal features of the image, obtain the derivative test steps for the requirement test;

[0057] The system sequentially receives hose test data uploaded by users according to the hose testing procedures.

[0058] Anomaly detection is performed on hose test data to obtain derived anomaly characteristics;

[0059] Based on the derived anomaly characteristics, continue executing the derived test steps for obtaining the requirement test to acquire all hose test data;

[0060] Images of the hose's appearance and all hose test data are used as hose fitting data.

[0061] After determining the sampling period, the gas appliance key component life assessment system allows users to upload hose fitting data for each key node at the corresponding sampling period. Specifically, users upload images of the hose's appearance at each key node. The system then performs image detection to identify anomalous features, such as yellowing of the hose surface, cracks, or uneven thickness at the corresponding key node. Based on these anomalous features, further derivative testing steps are determined. For example, if cracks are present on the hose surface, a pressure test may be initiated to further test the hose material's properties. If the pressure test shows abnormal rebound, this is identified as a derivative anomalous feature, prompting further derivative testing steps to obtain all hose test data based on the hose's appearance image. Then, the hose appearance image and all hose test data are used as hose accessory data to evaluate the current state of the hose and determine the state change data to build a hose simulation model.

[0062] Based on the anomaly features of the image, derive the derivative test steps for the requirement test, including:

[0063] Based on the feature type of the image anomaly features, obtain all the steps to be tested corresponding to the feature type, and the required test weights for each step to be tested;

[0064] Based on the feature values ​​of image anomalies and the required test weights for each step to be tested, the required test level for each step to be tested is obtained.

[0065] The degree of requirement testing for each step to be tested is compared, and the step to be tested corresponding to the highest degree of requirement testing is obtained as the derivative test step of requirement testing.

[0066] After obtaining the image anomaly features, when determining the corresponding derivative test steps for demand testing using the gas appliance critical component life assessment system, all test steps corresponding to the feature type of the image anomaly features, and the demand test weight corresponding to each test step, can be obtained. For example, when the feature type of the image anomaly features is "cracks exist on the surface of critical nodes," all test steps corresponding to "cracks exist on the surface of critical nodes" can be found based on this feature type, such as the press-and-rebound test and the airtightness test. Then, based on the crack severity value of the critical node surface and the demand test weights corresponding to test steps such as the press-and-rebound test and the airtightness test, the demand test severity of each test step can be calculated. Then, based on the test steps, the demand test severity of each test step is compared, thereby obtaining the test step corresponding to the highest demand test severity as the derivative test step for demand testing, so as to achieve the most effective testing treatment for critical nodes and improve the efficiency of testing.

[0067] Specifically, the formula for calculating the degree of requirements testing is as follows: ,in, Represented as the first The degree of testing required for each step to be tested. The feature values ​​are represented as image anomaly features. This is represented as the requirement test weight.

[0068] Similarly, when continuing to execute the derivative test steps for obtaining requirement tests based on the derived anomaly features, the corresponding steps when executing the "obtain derivative test steps for requirement tests based on image anomaly features" step can also be adopted to obtain all the tubing test data corresponding to the image anomaly features and derived anomaly features.

[0069] Next, proceed to step S20: assess the current state of the hose based on the hose fitting data and obtain state change data.

[0070] Hose fitting data can include hose appearance images and hose test data. When evaluating the current condition of the hose based on this data, both the appearance images and test data can be evaluated separately. The results of both evaluations are then combined to determine the condition change data. This condition change data can then be used to accurately construct a hose simulation model.

[0071] In step S20, the current state of the hose is evaluated based on the hose fitting data, and state change data is obtained, including:

[0072] Compare the appearance image of the hose with the initial appearance image of the hose to obtain the first type of state change data;

[0073] Compare the hose test data with the initial hose test data to obtain the test difference data;

[0074] Based on the test difference data and the corresponding derivative test steps, obtain the second type of state change data;

[0075] The first type of state change data and the second type of state change data are used as state change data.

[0076] When evaluating the current condition of a hose based on its appearance image, the current appearance image can be compared with the initial appearance image to obtain first-type state change data. For example, comparing the current hose surface with the initial hose surface at installation reveals the surface differences. If cracks are present on the current hose surface, the degree of cracking can be used as first-type state change data based on these differences. During the derived testing steps, the hose is pressed to observe its rebound. This rebound data is then compared with the initial rebound data at installation to obtain the test rebound difference data. Further, based on the first-type and second-type state change data (i.e., other derived second-type state change data), the state change data for each critical node can be determined. For example, after obtaining the test rebound difference, further derived testing steps can be used to determine changes in the material of the hose fittings. For instance, a slower rebound of the hose fittings indicates a degree of hardening at the corresponding critical node. Then, a hose simulation model can be constructed based on this degree of hardening and other types of second-order state change data, combined with the hose's basic data.

[0077] Specifically, the difference between the hose test data and the initial hose test data is calculated to obtain the test difference data. The formula is as follows: ,in, This is represented as test difference data. This represents the initial test data for the hose. This is represented as hose test data.

[0078] Next, step S30 is executed to obtain a hose simulation model containing each key node based on the state change data and hose basic data.

[0079] After obtaining state change data, such as localized hardening of the hose, the corresponding state change data and basic hose data can be used to obtain a hose simulation model containing each key node. The basic hose data is constructed based on the materials used in the production of hose fittings, combined with the length and shape of the hose after installation.

[0080] Next, step S40 is executed to perform simulation using a hose simulation model to predict the remaining lifespan of the hose fittings.

[0081] When simulating using a hose simulation model, the remaining lifespan of hose fittings can be predicted based on the current state change data and the corresponding acquisition cycle time.

[0082] Specifically, simulations are performed using a hose simulation model to predict the remaining lifespan of hose fittings, including:

[0083] Simulation was performed using a hose simulation model to obtain predicted data for hose fittings at key nodes in the next sampling period.

[0084] Determine if the predicted data for the hose fittings is greater than the set value:

[0085] If so, continue uploading the hose fitting data for each key node according to the sampling cycle, and update the hose simulation model again;

[0086] If not, the sampling period is adjusted based on the predicted data and set values ​​of the hose fittings. The data of the hose fittings at each key node is uploaded according to the adjusted sampling period, and the hose simulation model is updated again. When the adjusted sampling period is less than the set period, the time of the adjusted sampling period corresponding to the time of the less than the set period is taken as the remaining life of the hose fittings.

[0087] When predicting the remaining lifespan of hose fittings, a hose simulation model based on state change data can be used to simulate the next sampling period, thereby obtaining predicted hose fitting data for key nodes in the next sampling period. Then, it is determined whether the predicted hose fitting data is greater than a set value. If the predicted hose fitting data is greater than the set value, it indicates that the current sampling period can continue, and the upload of hose fitting data for each key node can continue, with the hose simulation model updated again. At this point, it is not necessary to assess the remaining lifespan of the hose fittings. If the predicted hose fitting data is less than or equal to the set value, it indicates that the current sampling period is not conservative enough and needs to be shortened. This is achieved by calculating the difference between the predicted hose fitting data and the set value. This predicted hose fitting data corresponds to the state change data; that is, the predicted hose fitting data is obtained by simulating the predicted state change data according to the sampling period. Of course, if there is no state change data, the prediction can also be based on the natural aging period. Based on the difference data, the corresponding cycle adjustment amount for the predicted data of the hose fittings can be obtained by looking up a table. If multiple predicted data for hose fittings exist, the largest cycle adjustment amount is selected as the adjustment amount for adjusting the sampling cycle. After adjusting the sampling cycle using the cycle adjustment amount, the hose fitting data for each key node is uploaded using the adjusted sampling cycle, and the hose simulation model is updated again to ensure the prediction accuracy of the hose simulation model at all times. Then, if, within a certain adjusted sampling cycle, the predicted next adjusted sampling cycle is shorter than the set cycle, it indicates that the hose fitting can no longer be used in the next adjusted sampling cycle. Therefore, the hose fitting needs to be replaced in a timely manner to ensure that no safety hazards occur.

[0088] Please refer to 2. The present invention also provides a gas appliance key component life assessment system 11, comprising: an acquisition unit 111 for acquiring hose component data of each key node uploaded by the user according to the sampling period; an assessment unit 112 for assessing the current hose status based on the hose component data and acquiring status change data; a modeling unit 113 for acquiring a hose simulation model containing each key node based on the status change data and hose basic data; and a prediction unit 114 for performing simulation through the hose simulation model to predict the remaining life of the hose components.

[0089] It should be noted that the gas appliance key component life assessment system 11 provided in the above embodiments and the gas appliance key component life assessment method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the gas appliance key component life assessment system 11 provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0090] Please see Figure 3 The electronic device 1 may include a memory 12, a processor 13 and a bus, and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a life assessment program for critical components of gas appliances.

[0091] The memory 12 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 12 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 1. Furthermore, the memory 12 can include both internal and external storage units of the electronic device 1. The memory 12 can be used not only to store application software and various types of data installed on the electronic device 1, such as codes for life assessment of key components of gas appliances, but also to temporarily store data that has been output or will be output.

[0092] In some embodiments, the processor 13 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 13 is the control unit of the electronic device 1, connecting various components of the electronic device 1 via various interfaces and lines. It executes programs or modules stored in the memory 12 (e.g., life assessment programs for key components of gas appliances) and calls data stored in the memory 12 to perform various functions and process data in the electronic device 1.

[0093] The processor 13 executes the operating system of the electronic device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the above-described method for assessing the lifespan of key components of gas appliances.

[0094] For example, the computer program may be divided into one or more modules, which are stored in the memory 12 and executed by the processor 13 to complete this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into units in a gas appliance critical component life assessment system.

[0095] The integrated unit implemented as a software functional module can be stored in a computer-readable storage medium, which can be non-volatile or volatile. The software functional module, stored in the storage medium, includes several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute some functions of the gas appliance critical component life assessment method described in the various embodiments of this application.

[0096] In summary, the present invention discloses a method and system for assessing the lifespan of key components of gas appliances. By configuring key nodes during the installation process of hose fittings, it can conservatively determine the sampling period after hose installation based on the set key nodes. The hose fitting data obtained during this sampling period is then used to assess the hose condition, accurately determining the hose simulation model corresponding to the current hose condition. This model can then be used for simulation to accurately predict the remaining lifespan of the hose fittings. This allows for precise control over the remaining lifespan of the hose fittings and timely replacement within a conservative timeframe to prevent major accidents caused by hose fitting damage. Therefore, the present invention effectively overcomes the various shortcomings of existing technologies and has high industrial application value.

[0097] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method of gas appliance critical component life assessment, characterized in that, The method comprises the following steps: Obtaining the hose fitting data of each key node uploaded by the user according to the sampling period; Evaluating the current hose state according to the hose fitting data to obtain state change data; Obtaining a hose simulation model containing each key node according to the state change data and the hose basic data; Carrying out simulation simulation through the hose simulation model to predict the remaining life of the hose fitting; Further comprising: Receiving the safety hidden danger points entered during the hose fitting installation process as the key node; Obtaining the point importance degree according to the key node and the corresponding layout state information; Obtaining the comprehensive point importance degree according to each key layout in the layout state information which has an impact on the key node and the point importance degree; Obtaining the sampling period according to the comprehensive point importance degree; Obtaining the point importance degree according to the key node and the corresponding layout state information, comprising: Searching for the key layout in the layout state information which has an impact on the key node according to the key node; Obtaining the layout influence degree of the key layout on the key node according to the type of the key layout; Obtaining the point importance degree according to the type influence degree of the key layout and the layout influence degree of the key layout on the key node; Obtaining the layout influence degree of the key layout on the key node according to the type of the key layout, comprising: Obtaining the influence mode of the key layout on the key node according to the type of the key layout; When the influence mode is direct influence, obtaining the first layout influence degree of the key layout on the key node according to the type of the key layout; When the influence mode is indirect influence, obtaining the second layout influence degree of the key layout on the key node according to the type of the key layout and the influence weakening degree from the key layout to the key node; Taking the first layout influence degree and / or the second layout influence degree as the layout influence degree.

2. A gas appliance key fitting life assessment method according to claim 1 characterised in that: Obtaining the second layout influence degree of the key layout on the key node according to the type of the key layout and the influence weakening degree from the key layout to the key node, comprising: Obtaining the influence weakening degree from the key layout to the key node according to the type of the key layout and the distance between the key layout and the key node; Obtaining the second layout influence degree of the key layout on the key node according to the type of the key layout and the influence weakening degree.

3. A gas appliance key fitting life assessment method according to claim 1 characterised in that: Obtaining the hose fitting data of each key node uploaded by the user according to the sampling period, comprising: Obtaining the hose appearance image of each key node uploaded by the user according to the sampling period; Carrying out image detection on the hose appearance image to obtain image abnormal features; Obtaining derived test steps required for testing according to the image abnormal features; Receiving the hose test data uploaded by the user in sequence according to the hose test steps; Carrying out abnormality detection on the hose test data to obtain derived abnormal features; According to the derived abnormal feature, the step of acquiring the derived test step of the requirement test is continued to acquire all the hose test data; The hose appearance image and all the hose test data are taken as the hose accessory data.

4. A gas appliance key fitting life assessment method according to claim 3 characterised in that: According to the image abnormal feature, the derived test step of the requirement test is acquired, including: According to the feature type of the image abnormal feature, all the to-be-tested steps corresponding to the feature type and the requirement test weight corresponding to each to-be-tested step are acquired; According to the feature value of the image abnormal feature and the requirement test weight corresponding to each to-be-tested step, the requirement test degree of each to-be-tested step is acquired; The requirement test degrees of each to-be-tested step are compared, and the to-be-tested step corresponding to the maximum requirement test degree is taken as the derived test step of the requirement test.

5. A gas appliance key fitting life assessment method according to claim 1 characterised in that: The hose accessory data includes a hose appearance image and hose test data; According to the hose accessory data, the current hose state is evaluated to acquire state change data, including: The hose appearance image is compared with a hose initial appearance image to acquire first-type state change data; The hose test data is compared with hose initial test data to acquire test difference data; According to the test difference data and the corresponding derived test step, second-type state change data is acquired; The first-type state change data and the second-type state change data are taken as the state change data.

6. A gas appliance key fitting life assessment method according to claim 1 characterised in that: Simulation simulation is performed through the hose simulation model to predict the remaining life of the hose accessory, including: Simulation simulation is performed through the hose simulation model to acquire hose accessory prediction data of the key node in the next sampling period; It is judged whether the hose accessory prediction data is greater than a set value: If yes, the uploading of the hose accessory data of each key node is continued to be performed according to the sampling period, and the hose simulation model is re-updated; If not, the sampling period is adjusted according to the hose accessory prediction data and the set value, the uploading of the hose accessory data of each key node is performed according to the adjusted sampling period, the hose simulation model is re-updated, and when the adjusted sampling period is less than a set period, the time corresponding to the adjusted sampling period when the set period is less than the set period is taken as the remaining life of the hose accessory.

7. An evaluation system for the evaluation method of the service life of critical components of a gas appliance according to any one of claims 1 to 6, characterized in that Including: An acquisition unit is configured to acquire hose accessory data of each key node uploaded by a user according to a sampling period; An evaluation unit is configured to evaluate a current hose state according to the hose accessory data to acquire state change data; A modeling unit is configured to acquire a hose simulation model containing each key node according to the state change data and hose basic data; and A prediction unit is configured to perform simulation simulation through the hose simulation model to predict the remaining life of the hose accessory. ​

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