WO2023065985A1 - 热水器烟道风险预测方法、装置、计算机设备及介质 - Google Patents

热水器烟道风险预测方法、装置、计算机设备及介质 Download PDF

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WO2023065985A1
WO2023065985A1 PCT/CN2022/121484 CN2022121484W WO2023065985A1 WO 2023065985 A1 WO2023065985 A1 WO 2023065985A1 CN 2022121484 W CN2022121484 W CN 2022121484W WO 2023065985 A1 WO2023065985 A1 WO 2023065985A1
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Prior art keywords
flue
result
hidden danger
image information
water heater
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PCT/CN2022/121484
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English (en)
French (fr)
Inventor
李同治
何博睿
宋英豪
赵蕾
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新智我来网络科技有限公司
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Publication of WO2023065985A1 publication Critical patent/WO2023065985A1/zh

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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
    • F24H15/00—Control of fluid heaters
    • F24H15/30—Control of fluid heaters characterised by control outputs; characterised by the components to be controlled
    • F24H15/395—Information to users, e.g. alarms
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
    • F24H15/00—Control of fluid heaters
    • F24H15/40—Control of fluid heaters characterised by the type of controllers
    • F24H15/414—Control of fluid heaters characterised by the type of controllers using electronic processing, e.g. computer-based
    • F24H15/421—Control of fluid heaters characterised by the type of controllers using electronic processing, e.g. computer-based using pre-stored data
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
    • F24H15/00—Control of fluid heaters
    • F24H15/40—Control of fluid heaters characterised by the type of controllers
    • F24H15/414—Control of fluid heaters characterised by the type of controllers using electronic processing, e.g. computer-based
    • F24H15/443—Control of fluid heaters characterised by the type of controllers using electronic processing, e.g. computer-based using a central controller connected to several sub-controllers
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
    • F24H15/00—Control of fluid heaters
    • F24H15/40—Control of fluid heaters characterised by the type of controllers
    • F24H15/414—Control of fluid heaters characterised by the type of controllers using electronic processing, e.g. computer-based
    • F24H15/45—Control of fluid heaters characterised by the type of controllers using electronic processing, e.g. computer-based remotely accessible
    • F24H15/457—Control of fluid heaters characterised by the type of controllers using electronic processing, e.g. computer-based remotely accessible using telephone networks or Internet communication
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
    • F24H9/00—Details
    • F24H9/20—Arrangement or mounting of control or safety devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components

Definitions

  • the present disclosure relates to the technical field of flue risk prediction, in particular to a water heater flue risk prediction method, device, computer equipment and media.
  • the embodiments of the present disclosure provide a water heater flue risk prediction method, device, computer equipment and media to solve the problem in the prior art that manual inspection of hidden dangers in the flue leads to extremely low inspection efficiency.
  • the first aspect of the embodiments of the present disclosure provides a water heater flue risk prediction method, including: acquiring the original image information of the gas water heater; when the original image information contains the image information of the water heater, judging whether the original image information contains the flue Image information; when the original image information includes flue image information, the flue detection result is generated based on the preset non-flue hidden danger processing strategy; based on the target calculation strategy and the flue detection result, the target risk prediction result of the water heater flue.
  • the second aspect of the embodiments of the present disclosure provides a water heater flue risk prediction device, including: an acquisition module configured to acquire original image information of a gas water heater; a water heater image information judging module configured to When the water heater image information is included, it is judged whether the original image information contains the flue image information; the flue detection result generation module is configured to, when the original image information includes the flue image information, based on the preset non-flue hidden danger processing strategy Generate a flue detection result; the target risk prediction result generating module is configured to generate a target risk prediction result of the water heater flue based on the target calculation strategy and the flue detection result.
  • a third aspect of the embodiments of the present disclosure provides a computer device, including a memory, a processor, and a computer program stored in the memory and operable on the processor, and the processor implements the steps of the above method when executing the computer program.
  • a fourth aspect of the embodiments of the present disclosure provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above method are implemented.
  • the beneficial effects of the embodiment of the present disclosure compared with the prior art at least include: the embodiment of the present disclosure obtains the original image information of the gas water heater; when the original image information contains the image information of the water heater, it is judged whether the original image information contains the flue Image information; when the original image information includes flue image information, the flue detection result is generated based on the preset non-flue hidden danger processing strategy; based on the target calculation strategy and the flue detection result, the target risk prediction result of the water heater flue, The efficiency of the hidden danger detection of the flue can be greatly improved.
  • FIG. 1 is a schematic diagram of a scene of an embodiment of the present disclosure
  • Fig. 2 is a flow chart 1 of a water heater flue risk prediction method provided by an embodiment of the present disclosure
  • Fig. 3 is a second flow chart of another water heater flue risk prediction method provided by an embodiment of the present disclosure
  • Fig. 4 is a block diagram of a water heater flue risk prediction device provided by an embodiment of the present disclosure
  • Fig. 5 is a schematic diagram of a computer device provided by an embodiment of the present disclosure.
  • FIG. 1 is a schematic diagram of an application scenario of an embodiment of the present disclosure.
  • the application scenario may include a terminal device 1 , a server 2 and a network 3 .
  • the terminal device 1 can be hardware or software. When the terminal device 1 is hardware, it can be various electronic devices that have a display screen and support communication with the server 2, including but not limited to smart phones, tablet computers, laptop computers and desktop computers, etc.; when the terminal device 1 When it is software, it can be installed in the above-mentioned electronic equipment.
  • the terminal device 1 may be implemented as multiple software or software modules, or may be implemented as a single software or software module, which is not limited in this embodiment of the present disclosure. Further, various applications may be installed on the terminal device 1, such as data processing applications, instant messaging tools, social platform software, search applications, shopping applications, and the like.
  • Server 2 may be a server that provides various services, for example, a background server that receives requests sent by terminal devices that establish a communication connection with it, and the background server can receive and analyze requests sent by terminal devices, and generate processing result.
  • the server 2 may be one server, or a server cluster composed of several servers, or a cloud computing service center, which is not limited in this embodiment of the present disclosure.
  • the server 2 may be hardware or software. When the server 2 is hardware, it may be various electronic devices that provide various services for the terminal device 1 . When the server 2 is software, it can implement multiple software or software modules that provide various services for the terminal device 1, and can also implement a single software or software module that provides various services for the terminal device 1. No limit.
  • Network 3 can be a wired network connected by coaxial cable, twisted pair and optical fiber, or a wireless network that can realize the interconnection of various communication devices without wiring, such as Bluetooth, Near Field Communication , NFC), infrared (Infrared), etc., which are not limited in this embodiment of the present disclosure.
  • the user can establish a communication connection with the server 2 via the network 3 through the terminal device 1 to receive or send information and the like.
  • the server 2 can obtain the original image information of the gas water heater.
  • the server 2 can determine whether the original image information includes flue image information.
  • the server 2 can generate a flue detection result based on a preset non-smoke hidden danger processing strategy.
  • the server 2 can predict the target risk of the water heater flue based on the target calculation strategy and the flue detection result.
  • terminal device 1, the server 2, and the network 3 may be adjusted according to actual requirements of application scenarios, which is not limited in this embodiment of the present disclosure.
  • Fig. 2 is a flow chart of a water heater flue risk prediction method provided by an embodiment of the present disclosure.
  • the water heater flue risk prediction method in FIG. 2 can be executed by the terminal device or the server 2 in FIG. 1 .
  • the water heater flue risk prediction method includes:
  • the original image information of the gas water heater may refer to directly acquired image information related to the gas water heater.
  • the image information may be acquired photo information or the like.
  • the water heater image information may refer to image information related to water heaters in the original image information.
  • the flue image information may refer to the image information related to the flue in the original image information. If there is no image information of the water heater in the original image information, the image information of the water heater can be judged as a potential safety hazard, so it is necessary to first determine whether the image information of the flue exists. When the water heater image information exists, continue to judge whether there is flue image information in the original image information. If the flue image information does not exist, the original image information can be judged as a potential safety hazard.
  • the preset no-flue hidden danger processing strategy may refer to a preset method or step for generating a no-flue hidden danger result based on the original image information.
  • the No Flue Hazard result may refer to the probability result that no flue is installed.
  • the no-flue probability result may be 0.5. It should be pointed out that the result of the smoke-free hidden danger is a positive number between 0 and 1, which can be a percentage expression such as 60%, a common expression such as 0.38, a fractional expression such as 1/5 or other expressions, according to It needs to be set, and there is no specific limitation here.
  • Targeted treatment strategies may refer to steps or methods that generate targeted risk outcomes based on stack inspection results.
  • the target risk result can be the sum of the no-flue hazard result and the flue-installed hazard result, or the target risk result can be the product of the no-flue hazard result and the flue-installed hazard result, or the target risk
  • the result can be the result obtained through a series of calculation steps for the result of no flue hidden danger and the result of flue installed hidden danger, which can be set according to needs, and no specific limitation is set here.
  • the embodiments of the present disclosure obtain the original image information of the gas water heater; when the original image information contains the water heater image information, judge whether the original image information contains the flue image information; when the original image information When the flue image information is included, the flue detection result is generated based on the preset non-flue hidden danger processing strategy; based on the target calculation strategy and the flue detection result, the target risk prediction result of the water heater flue.
  • the preset non-flue hazard treatment strategy includes: importing the original image information into the water heater prediction model to obtain the water heater prediction result; obtaining the water heater threshold; when the water heater prediction result is greater than the water heater threshold, importing the original image information into the smoke Based on the flue prediction model, the flue prediction results are obtained; based on the non-flue hidden danger calculation strategy, the water heater prediction results and the flue prediction results, the flue-free hidden danger results are generated.
  • the water heater prediction model may refer to a mathematical formula that has been trained and can predict the existence of a water heater based on photo information.
  • the water heater prediction result may refer to a predicted probability value of existence of the water heater.
  • the prediction result of the water heater may be 0.83, and the probability of the existence of the water heater is 83%.
  • the water heater threshold may refer to a preset limit value for judging whether the water heater exists.
  • the water heater threshold may be 0.5, that is, 50%. When the water heater prediction result is less than 0.5, it may be considered that the water heater does not exist, and there is no need to predict whether the flue exists.
  • the flue prediction model may refer to a mathematical formula that has been trained and can predict the existence of a flue based on photo information.
  • the flue prediction result may refer to a predicted probability value of the existence of the flue.
  • the flue prediction result may be 0.59, and the probability of the existence of the flue is 59%.
  • the non-flue hazard calculation strategy may refer to a method or step for calculating whether there is a flue hazard based on the water heater prediction result and the flue prediction result.
  • the no-flue hidden danger calculation strategy includes: acquiring the water heater prediction result and the flue flue prediction result; and generating the no flue hidden danger result based on the water heater prediction result and the flue flue prediction result.
  • the water heater prediction result and the flue prediction result can be calculated based on the following calculation formula:
  • P 1 can refer to the result of no hidden danger in the flue
  • P 2 can refer to the prediction result of the water heater
  • P 3 can refer to the prediction result of the flue.
  • the preset strategy for dealing with hidden dangers of flue installation includes: obtaining the flue threshold; when the flue prediction result is greater than the flue threshold, importing the original image information into the flue port risk prediction model to obtain the flue port hidden danger Result: Based on the calculation strategy of flue installation hidden dangers, the results of no flue hidden dangers, and the results of flue port hidden dangers, the results of flue installation hidden dangers are generated.
  • the flue threshold may refer to a preset limit value for judging whether a flue exists.
  • the flue threshold may be 0.5, that is, 50%.
  • the hidden danger result at the flue port may refer to a predicted probability value that there is no hidden danger at the flue port.
  • the hidden danger result of the flue port may be 0.88, and the probability of a hidden danger at the flue port is 0.12.
  • the flue installation hidden danger calculation strategy may refer to a step or a method for generating a flue installation hidden danger result based on the no flue hidden danger result and the flue port hidden danger result.
  • the flue hazard result could be the sum of the no-flue hazard result and the flue port hazard result
  • the flue-mount hazard result could be the product of the no-flue hazard result and the flue port hazard result
  • the hidden danger result of duct installation can be the result obtained through a series of calculation steps from the result of no flue hidden danger and the result of flue port hidden danger, which can be set according to needs, and no specific limitation is set here.
  • the calculation strategy for the hidden danger of flue installation includes: obtaining the result of hidden danger of flue connection; when the result of hidden danger of flue connection is not connected normally, setting the result of hidden danger of flue connection as hidden danger; When it is a normal connection, set the result of the hidden danger of the flue connection to no hidden danger; based on the result of no hidden danger of the flue, the result of the hidden danger of the flue connection, the result of the hidden danger of the flue port and the target risk calculation strategy, the target risk result is generated.
  • the result of the hidden danger of the flue connection may refer to the result directly determined by the original image information.
  • the hidden danger result of the flue connection may include two cases of normal connection and abnormal connection.
  • a target risk calculation strategy may refer to steps or methods that are calculated to produce a target risk outcome.
  • the target risk calculation strategy includes: when the hidden danger result of the flue port is hidden danger, setting the target risk result as hidden danger; when the hidden danger result of the flue port is no hidden danger, based on the result of no hidden danger in the flue, Flue connection hazard results and flue port hazard results generate intermediate target risk results.
  • the result of the hidden danger of the flue port when the result of the hidden danger of the flue port is no hidden danger, the result of no hidden danger of the flue, the result of the hidden danger of the flue connection and the result of the hidden danger of the flue port can be calculated based on the following calculation formula, Generate intermediate target risk results:
  • P 4 can refer to the result of hidden dangers in the flue installation
  • P 5 can refer to the result of no hidden dangers in the flue
  • P 6 can refer to the result of hidden dangers in the flue port.
  • the target calculation strategy includes: when the result of the hidden danger of the flue connection is a hidden danger, the target risk result is the same as the result of no hidden danger in the flue; Installing hidden dangers results in the same.
  • Fig. 3 is a flowchart of a water heater flue risk prediction method provided by an embodiment of the present disclosure.
  • the water heater flue risk prediction method in FIG. 3 can be executed by the server 2 in FIG. 1 .
  • the water heater flue risk prediction method includes:
  • Fig. 4 is a schematic diagram of a water heater flue risk prediction device provided by an embodiment of the present disclosure. As shown in Figure 4, the water heater flue risk prediction device includes:
  • the obtaining module 401 is configured to obtain original image information.
  • the no-smoke hidden danger generation module 402 is configured to generate a no-smoke hidden danger result based on a preset no-smoke hidden danger processing strategy and original image information.
  • the generation module 403 of hidden dangers of flue installation is configured to generate the result of hidden dangers of flue installation based on the preset treatment strategy of hidden dangers of flue installation and original image information.
  • the target risk generation module 404 is configured to generate the target risk result based on the target calculation strategy, the result of no-flue hidden dangers and the result of flue-installed hidden dangers.
  • the embodiments of the present disclosure obtain the original image information of the gas water heater; when the original image information contains the water heater image information, judge whether the original image information contains the flue image information; when the original image information When the flue image information is included, the flue detection result is generated based on the preset non-flue hidden danger processing strategy; based on the target calculation strategy and the flue detection result, the target risk prediction result of the water heater flue.
  • the preset non-flue hazard treatment strategy includes: importing the original image information into the water heater prediction model to obtain the water heater prediction result; obtaining the water heater threshold; when the water heater prediction result is greater than the water heater threshold, importing the original image information into the smoke Based on the flue prediction model, the flue prediction results are obtained; based on the non-flue hidden danger calculation strategy, the water heater prediction results and the flue prediction results, the flue-free hidden danger results are generated.
  • the calculation strategy for no-flue hazards includes: acquiring the water heater prediction result and the flue prediction result; performing calculation based on the water heater prediction result and the flue prediction result, and generating the no-flue hazard result.
  • the preset strategy for dealing with hidden dangers of flue installation includes: obtaining the flue threshold; when the flue prediction result is greater than the flue threshold, importing the original image information into the flue port risk prediction model to obtain the flue port hidden danger Result: Based on the calculation strategy of flue installation hidden dangers, the results of no flue hidden dangers, and the results of flue port hidden dangers, the results of flue installation hidden dangers are generated.
  • the calculation strategy for the hidden danger of flue installation includes: obtaining the result of hidden danger of flue connection; when the result of hidden danger of flue connection is not connected normally, setting the result of hidden danger of flue connection as hidden danger; When it is a normal connection, set the result of the hidden danger of the flue connection to no hidden danger; based on the result of no hidden danger of the flue, the result of the hidden danger of the flue connection, the result of the hidden danger of the flue port and the target risk calculation strategy, the target risk result is generated.
  • the target risk calculation strategy includes: when the hidden danger result of the flue port is hidden danger, setting the target risk result as hidden danger; when the hidden danger result of the flue port is no hidden danger, based on the result of no hidden danger in the flue, Flue connection hazard results and flue port hazard results generate intermediate target risk results.
  • the target calculation strategy includes: when the result of the hidden danger of the flue connection is a hidden danger, the target risk result is the same as the result of no hidden danger in the flue; Installing hidden dangers results in the same.
  • FIG. 5 is a schematic diagram of a computer device 500 provided by an embodiment of the present disclosure.
  • the computer device 500 of this embodiment includes: a processor 501 , a memory 502 , and a computer program 503 stored in the memory 502 and capable of running on the processor 501 .
  • the processor 501 executes the computer program 503
  • the steps in the foregoing method embodiments are implemented.
  • the processor 501 executes the computer program 503
  • the functions of the modules/units in the foregoing device embodiments are realized.
  • the computer program 503 can be divided into one or more modules/units, and one or more modules/units are stored in the memory 502 and executed by the processor 501 to complete the present disclosure.
  • One or more modules/units may be a series of computer program instruction segments capable of accomplishing specific functions, and the instruction segments are used to describe the execution process of the computer program 503 in the computer device 500 .
  • the computer device 500 may be a computer device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • the computer device 500 may include, but is not limited to, a processor 501 and a memory 502 .
  • FIG. 5 is only an example of a computer device 500, and does not constitute a limitation to the computer device 500. It may include more or less components than those shown in the illustration, or combine certain components, or different components. , for example, computer equipment may also include input and output equipment, network access equipment, bus, and so on.
  • the processor 501 can be a central processing unit (Central Processing Unit, CPU), and can also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), on-site Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • a general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.
  • the memory 502 may be an internal storage unit of the computer device 500 , for example, a hard disk or a memory of the computer device 500 .
  • the memory 502 can also be an external storage device of the computer device 500, for example, a plug-in hard disk equipped on the computer device 500, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash memory card ( Flash Card), etc.
  • the memory 502 may also include both an internal storage unit of the computer device 500 and an external storage device.
  • the memory 502 is used to store computer programs and other programs and data required by the computer equipment.
  • the memory 502 can also be used to temporarily store data that has been output or will be output.
  • the disclosed apparatus/computer equipment and methods may be implemented in other ways.
  • the device/computer device embodiments described above are only illustrative, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, and multiple units or components can be Incorporation may either be integrated into another system, or some features may be omitted, or not implemented.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.
  • a unit described as a separate component may or may not be physically separated, and a component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.
  • an integrated module/unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • the present disclosure realizes all or part of the processes in the methods of the above embodiments, and can also be completed by instructing related hardware through computer programs.
  • the computer programs can be stored in computer-readable storage media, and the computer programs can be processed. When executed by the controller, the steps in the above-mentioned method embodiments can be realized.
  • a computer program may include computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate form, etc.
  • the computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory (Read-Only Memory, ROM), random access Memory (Random Access Memory, RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in computer readable media may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, computer readable media may not Including electrical carrier signals and telecommunication signals.

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Abstract

本公开提供了热水器烟道风险预测方法、装置、计算机设备及介质。该方法包括：通过获取原始图像信息；基于预设的无烟道隐患处理策略和原始图像信息，生成无烟道隐患结果；基于预设的烟道安装隐患处理策略和原始图像信息，生成烟道安装隐患结果；基于目标计算策略、无烟道隐患结果和烟道安装隐患结果，生成目标风险结果。本公开可以大大提高烟道隐患检测的效率。

Description

热水器烟道风险预测方法、装置、计算机设备及介质 技术领域
本公开涉及烟道风险预测技术领域，尤其涉及热水器烟道风险预测方法、装置、计算机设备及介质。
背景技术
随着经济的飞速发展，能源设备的铺设数量也越来越多。现有技术中对烟道隐患的排查主要靠人工处理。由于人工检测受人员数量、检测能力等方面的制约，排查效率极低。
发明内容
有鉴于此，本公开实施例提供了热水器烟道风险预测方法、装置、计算机设备及介质，以解决现有技术中人工排查烟道隐患导致排查效率极低的问题。
本公开实施例的第一方面，提供了一种热水器烟道风险预测方法，包括：获取燃气热水器的原始图像信息；当原始图像信息中包含热水器图像信息时，判断原始图像信息中是否包含烟道图像信息；当原始图像信息中包括烟道图像信息时，基于预设的无烟道隐患处理策略生成烟道检测结果；基于目标计算策略和烟道检测结果，热水器烟道的目标风险预测结果。
本公开实施例的第二方面，提供了一种热水器烟道风险预测装置，包括：获取模块，被配置为获取燃气热水器的原始图像信息；热水器图像信息判断模块，被配置为当原始图像信息中包含热水器图像信息时，判断原始图像信息中是否包含烟道图像信息；烟道检测结果生成模块，被配置为当原始图像信息中包括烟道图像信息时，基于预设的无烟道隐患处理策略生成烟道检测结果；目标风险预测结果生成模块，被配置为基于目标计算策略和烟道检测结果，生成热水器烟道的目标风险预测结果。
本公开实施例的第三方面，提供了一种计算机设备，包括存储器、处理器以及存储在存储器中并且可以在处理器上运行的计算机程序，该处理器执行计算机程序时实现上述方法的步骤。
本公开实施例的第四方面，提供了一种计算机可读存储介质，该计算机可读存储介质存储有计算机程序，该计算机程序被处理器执行时实现上述方法的步骤。
本公开实施例与现有技术相比存在的有益效果至少包括：本公开实施例通过获取燃气热水器的原始图像信息；当原始图像信息中包含热水器图像信息时，判断原始图像信息中是否 包含烟道图像信息；当原始图像信息中包括烟道图像信息时，基于预设的无烟道隐患处理策略生成烟道检测结果；基于目标计算策略和烟道检测结果，热水器烟道的目标风险预测结果，可以大大提高烟道隐患检测的效率。
附图说明
为了更清楚地说明本公开实施例中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本公开的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它的附图。
图1是本公开实施例的场景示意图；
图2是本公开实施例提供的一种热水器烟道风险预测方法的流程图一；
图3是本公开实施例提供的另一种热水器烟道风险预测方法的流程图二；
图4是本公开实施例提供的一种热水器烟道风险预测装置的框图；
图5是本公开实施例提供的计算机设备的示意图。
具体实施方式
以下描述中，为了说明而不是为了限定，提出了诸如特定系统结构、技术之类的具体细节，以便透彻理解本公开实施例。然而，本领域的技术人员应当清楚，在没有这些具体细节的其它实施例中也可以实现本公开。在其它情况中，省略对众所周知的系统、装置、电路以及方法的详细说明，以免不必要的细节妨碍本公开的描述。
下面将参考附图并结合实施例来详细说明本公开。
图1是本公开实施例的应用场景的场景示意图。该应用场景可以包括终端设备1、服务器2以及网络3。
终端设备1可以是硬件，也可以是软件。当终端设备1为硬件时，其可以是具有显示屏且支持与服务器2通信的各种电子设备，包括但不限于智能手机、平板电脑、膝上型便携计算机和台式计算机等；当终端设备1为软件时，其可以安装在上述的电子设备中。终端设备1可以实现为多个软件或软件模块，也可以实现为单个软件或软件模块，本公开实施例对此不作限制。进一步地，终端设备1上可以安装有各种应用，例如数据处理应用、即时通信工具、社交平台软件、搜索类应用、购物类应用等。
服务器2可以是提供各种服务的服务器，例如，对与其建立通信连接的终端设备发送的请求进行接收的后台服务器，该后台服务器可以对终端设备发送的请求进行接收和分析等处 理，并生成处理结果。服务器2可以是一台服务器，也可以是由若干台服务器组成的服务器集群，或者还可以是一个云计算服务中心，本公开实施例对此不作限制。
需要说明的是，服务器2可以是硬件，也可以是软件。当服务器2为硬件时，其可以是为终端设备1提供各种服务的各种电子设备。当服务器2为软件时，其可以实现为终端设备1提供各种服务的多个软件或软件模块，也可以实现为终端设备1提供各种服务的单个软件或软件模块，本公开实施例对此不作限制。
网络3可以是采用同轴电缆、双绞线和光纤连接的有线网络，也可以是无需布线就能实现各种通信设备互联的无线网络，例如，蓝牙(Bluetooth)、近场通信(Near Field Communication，NFC)、红外(Infrared)等，本公开实施例对此不作限制。
用户可以通过终端设备1经由网络3与服务器2建立通信连接，以接收或发送信息等。具体地，首先，服务器2可以获取燃气热水器的原始图像信息。其次，当原始图像信息中包含热水器图像信息时，服务器2可以判断原始图像信息中是否包含烟道图像信息。再次，当原始图像信息中包括烟道图像信息时，服务器2可以基于预设的无烟道隐患处理策略生成烟道检测结果。最后，服务器2可以基于目标计算策略和烟道检测结果，热水器烟道的目标风险预测结果。
需要说明的是，终端设备1、服务器2以及网络3的具体类型、数量和组合可以根据应用场景的实际需求进行调整，本公开实施例对此不作限制。
图2是本公开实施例提供的一种热水器烟道风险预测方法的流程图。图2的热水器烟道风险预测方法可以由图1的终端设备或服务器2执行。如图2所示，该热水器烟道风险预测方法包括：
S201，燃气热水器的原始图像信息。
燃气热水器的原始图像信息可以指直接获取的与燃气热水器相关的图像信息。作为示例，该图像信息可以为获取的照片信息等。
S202，当原始图像信息中包含热水器图像信息时，判断原始图像信息中是否包含烟道图像信息。
热水器图像信息可以指该原始图像信息中与热水器相关的图像信息。烟道图像信息可以指该原始图像信息中与烟道相关的图像信息。若原始图像信息中没有热水器图像信息，则该热水器图像信息可以被判断为存在安全隐患，因此需要先判断烟道图像信息是否存在。当热水器图像信息存在时，继续判断该原始图像信息中是否存在烟道图像信息。若烟道图像信息不存在，则该原始图像信息可以被判断为存在安全隐患。
S203当原始图像信息中包括烟道图像信息时，基于预设的无烟道隐患处理策略生成烟 道检测结果。
预设的无烟道隐患处理策略可以指预先设置的基于该原始图像信息，生成无烟道隐患结果的方法或步骤。无烟道隐患结果可以指未安装烟道的概率结果。作为示例，该无烟道概率结果可以为0.5。需要指出的是，该无烟道隐患结果为0至1之间的正数，其可以为百分比表现形式如60％，普通表现形式如0.38，分数表现形式如1/5或其他表现形式，根据需要进行设置，在此不做具体限制。
S204，基于目标处理策略和烟道检测结果，热水器烟道的目标风险预测结果。
目标处理策略可以指基于烟道检测结果生成目标风险结果的步骤或方法。
作为示例，该目标风险结果可以为无烟道隐患结果和烟道安装隐患结果的和，或者，该目标风险结果可以为无烟道隐患结果和烟道安装隐患结果的乘积，或者，该目标风险结果可以为无烟道隐患结果和烟道安装隐患结果通过一系列计算步骤得到的结果，根据需要进行设置，在此不做具体限制。
根据本公开实施例提供的技术方案，本公开实施例通过获取燃气热水器的原始图像信息；当原始图像信息中包含热水器图像信息时，判断原始图像信息中是否包含烟道图像信息；当原始图像信息中包括烟道图像信息时，基于预设的无烟道隐患处理策略生成烟道检测结果；基于目标计算策略和烟道检测结果，热水器烟道的目标风险预测结果。
在一些实施例中，预设的无烟道隐患处理策略包括：将原始图像信息导入热水器预测模型，得到热水器预测结果；获取热水器阈值；当热水器预测结果大于热水器阈值时，将原始图像信息导入烟道预测模型，得到烟道预测结果；基于无烟道隐患计算策略、热水器预测结果和烟道预测结果，生成无烟道隐患结果。
热水器预测模型可以指已经训练好的，可以基于照片信息预测热水器是否存在的数学式。热水器预测结果可以指预测的热水器存在的概率值。作为示例，热水器预测结果可以为0.83，则热水器存在的概率为83％。热水器阈值可以指预先设置的判断热水器是否存在的限制值。作为示例，热水器阈值可以为0.5，即50％，当热水器预测结果小于0.5时，可以认为热水器不存在，则无需对烟道是否存在进行预测。烟道预测模型可以指已经训练好的，可以基于照片信息预测烟道是否存在的数学式。烟道预测结果可以指预测的烟道存在的概率值。作为示例，烟道预测结果可以为0.59，则烟道存在的概率为59％。无烟道隐患计算策略可以指基于热水器预测结果和烟道预测结果计算是否有烟道隐患的方法或步骤。
在一些实施例中，无烟道隐患计算策略包括：获取热水器预测结果和烟道预测结果；基于热水器预测结果和烟道预测结果，生成无烟道隐患结果。
在一些实施例的一个可选的实现方式中，生成该无烟道隐患结果时，可以基于以下计算 式对热水器预测结果和烟道预测结果进行计算：
P 1＝(1-P 2)*P 3
其中，P 1可以指无烟道隐患结果，P 2可以指热水器预测结果，P 3可以指烟道预测结果。
在一些实施例中，预设的烟道安装隐患处理策略包括：获取烟道阈值；当烟道预测结果大于烟道阈值时，将原始图像信息导入烟道端口风险预测模型，得到烟道端口隐患结果；基于烟道安装隐患计算策略、无烟道隐患结果、烟道端口隐患结果，生成烟道安装隐患结果。
烟道阈值可以指预先设置的判断烟道是否存在的限制值。作为示例，烟道阈值可以为0.5，即50％，当烟道预测结果小于0.5时，可以认为烟道不存在，则无需对烟道端口隐患是否存在进行预测。烟道端口隐患结果可以指预测的烟道端口不存在隐患的概率值。作为示例，烟道端口隐患结果可以为0.88，则烟道端口存在隐患的概率为0.12。烟道安装隐患计算策略可以指基于无烟道隐患结果和烟道端口隐患结果生成烟道安装隐患结果的步骤或方法。作为示例，烟道安装隐患结果可以为无烟道隐患结果和烟道端口隐患结果的和，或者，烟道安装隐患结果可以为无烟道隐患结果和烟道端口隐患结果的乘积，或者，烟道安装隐患结果可以为无烟道隐患结果和烟道端口隐患结果通过一系列计算步骤得到的结果，根据需要进行设置，在此不做具体限制。
通过设置烟道阈值，可以灵活调节是否进行后续预测的条件，调高该阈值可以提高处理效率。调低该阈值可以提高处理精度。根据需要进行设置，在此不作具体限制。
在一些实施例中，烟道安装隐患计算策略包括：获取烟道连接隐患结果；当烟道连接隐患结果为未正常连接时，将烟道连接隐患结果设置为有隐患；当烟道连接隐患结果为正常连接时，将烟道连接隐患结果设置为无隐患；基于无烟道隐患结果、烟道连接隐患结果、烟道端口隐患结果和目标风险计算策略，生成目标风险结果。
烟道连接隐患结果可以指由该原始图像信息直接确定的结果。该烟道连接隐患结果可以包括正常连接和未正常连接两种情况。目标风险计算策略可以指经过计算可以生成目标风险结果的步骤或方法。其中，上述各种结果为有隐患可以指该结果为0，结果为无隐患可以指该结果为1。上述各种结果还可以设置为其他形式或数值，根据需要进行设置，在此不做具体限制。
在一些实施例中，目标风险计算策略包括：当烟道端口隐患结果为有隐患时，将目标风险结果设置为有隐患；当烟道端口隐患结果为无隐患时，基于无烟道隐患结果、烟道连接 隐患结果和烟道端口隐患结果，生成中间目标风险结果。
在一些实施例的一个可选的实现方式中，当烟道端口隐患结果为无隐患时，可以基于以下计算式对无烟道隐患结果、烟道连接隐患结果和烟道端口隐患结果进行计算，生成中间目标风险结果：
P 4＝1-(1-P 5)*P 6
其中，P 4可以指烟道安装隐患结果，P 5可以指无烟道隐患结果，P 6可以指烟道端口隐患结果。
在一些实施例中，目标计算策略包括：当烟道连接隐患结果为有隐患时，目标风险结果与无烟道隐患结果相同；当烟道连接隐患结果为无隐患时，目标风险结果与烟道安装隐患结果相同。
将目标风险结果通过概率生成，可以大大提高处理的效率和精度。
图3是本公开实施例提供的热水器烟道风险预测方法的流程图。图3的热水器烟道风险预测方法可以由图1服务器2执行。如图3所示，该热水器烟道风险预测方法包括：
S301，获取原始图像信息。
S302，将原始图像信息导入热水器预测模型，得到热水器预测结果。
S303，当热水器预测结果大于热水器阈值时，将原始图像信息导入烟道预测模型，得到烟道预测结果。
S304，基于无烟道隐患计算策略、热水器预测结果和烟道预测结果，生成无烟道隐患结果。
S305，当烟道预测结果大于烟道阈值时，将原始图像信息导入烟道端口风险预测模型，得到烟道端口隐患结果。
S306，获取烟道连接隐患结果。
S307，当烟道连接隐患结果为未正常连接时，将烟道连接隐患结果设置为有隐患。
S308，当烟道连接隐患结果为正常连接时，将烟道连接隐患结果设置为无隐患。
S309，基于无烟道隐患结果、烟道连接隐患结果、烟道端口隐患结果和目标风险计算策略，生成目标风险结果。
S310，基于目标计算策略、无烟道隐患结果和烟道安装隐患结果，生成目标风险结果。
上述所有可选技术方案，可以采用任意结合形成本申请的可选实施例，在此不再一一赘述。
下述为本公开装置实施例，可以用于执行本公开方法实施例。对于本公开装置实施例中未披露的细节，请参照本公开方法实施例。
图4是本公开实施例提供的热水器烟道风险预测装置的示意图。如图4所示，该热水器烟道风险预测装置包括：
获取模块401，被配置为获取原始图像信息。
无烟道隐患生成模块402，被配置为基于预设的无烟道隐患处理策略和原始图像信息，生成无烟道隐患结果。
烟道安装隐患生成模块403，被配置为基于预设的烟道安装隐患处理策略和原始图像信息，生成烟道安装隐患结果。
目标风险生成模块404，被配置为基于目标计算策略、无烟道隐患结果和烟道安装隐患结果，生成目标风险结果。
根据本公开实施例提供的技术方案，本公开实施例通过获取燃气热水器的原始图像信息；当原始图像信息中包含热水器图像信息时，判断原始图像信息中是否包含烟道图像信息；当原始图像信息中包括烟道图像信息时，基于预设的无烟道隐患处理策略生成烟道检测结果；基于目标计算策略和烟道检测结果，热水器烟道的目标风险预测结果。
在一些实施例中，预设的无烟道隐患处理策略包括：将原始图像信息导入热水器预测模型，得到热水器预测结果；获取热水器阈值；当热水器预测结果大于热水器阈值时，将原始图像信息导入烟道预测模型，得到烟道预测结果；基于无烟道隐患计算策略、热水器预测结果和烟道预测结果，生成无烟道隐患结果。
在一些实施例中，无烟道隐患计算策略包括：将获取热水器预测结果和烟道预测结果；基于热水器预测结果和烟道预测结果进行计算，生成无烟道隐患结果。
在一些实施例中，预设的烟道安装隐患处理策略包括：获取烟道阈值；当烟道预测结果大于烟道阈值时，将原始图像信息导入烟道端口风险预测模型，得到烟道端口隐患结果；基于烟道安装隐患计算策略、无烟道隐患结果、烟道端口隐患结果，生成烟道安装隐患结果。
在一些实施例中，烟道安装隐患计算策略包括：获取烟道连接隐患结果；当烟道连接隐患结果为未正常连接时，将烟道连接隐患结果设置为有隐患；当烟道连接隐患结果为正常连接时，将烟道连接隐患结果设置为无隐患；基于无烟道隐患结果、烟道连接隐患结果、烟道端口隐患结果和目标风险计算策略，生成目标风险结果。
在一些实施例中，目标风险计算策略包括：当烟道端口隐患结果为有隐患时，将目标风险结果设置为有隐患；当烟道端口隐患结果为无隐患时，基于无烟道隐患结果、烟道连接隐患结果和烟道端口隐患结果，生成中间目标风险结果。
在一些实施例中，目标计算策略包括：当烟道连接隐患结果为有隐患时，目标风险结果与无烟道隐患结果相同；当烟道连接隐患结果为无隐患时，目标风险结果与烟道安装隐患结果相同。
应理解，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本公开实施例的实施过程构成任何限定。
图5是本公开实施例提供的计算机设备500的示意图。如图5所示，该实施例的计算机设备500包括：处理器501、存储器502以及存储在该存储器502中并且可以在处理器501上运行的计算机程序503。处理器501执行计算机程序503时实现上述各个方法实施例中的步骤。或者，处理器501执行计算机程序503时实现上述各装置实施例中各模块/单元的功能。
示例性地，计算机程序503可以被分割成一个或多个模块/单元，一个或多个模块/单元被存储在存储器502中，并由处理器501执行，以完成本公开。一个或多个模块/单元可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述计算机程序503在计算机设备500中的执行过程。
计算机设备500可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算机设备。计算机设备500可以包括但不仅限于处理器501和存储器502。本领域技术人员可以理解，图5仅仅是计算机设备500的示例，并不构成对计算机设备500的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如，计算机设备还可以包括输入输出设备、网络接入设备、总线等。
处理器501可以是中央处理单元(Central Processing Unit，CPU)，也可以是其它通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)或者其它可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
存储器502可以是计算机设备500的内部存储单元，例如，计算机设备500的硬盘或内存。存储器502也可以是计算机设备500的外部存储设备，例如，计算机设备500上配备的插接式硬盘，智能存储卡(Smart Media Card，SMC)，安全数字(Secure Digital，SD)卡，闪存卡(Flash Card)等。进一步地，存储器502还可以既包括计算机设备500的内部存储单元也包括外部存储设备。存储器502用于存储计算机程序以及计算机设备所需的其它程序和数据。存储器502还可以用于暂时地存储已经输出或者将要输出的数据。
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单元、 模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中，上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。另外，各功能单元、模块的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范围。上述系统中单元、模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本公开的范围。
在本公开所提供的实施例中，应该理解到，所揭露的装置/计算机设备和方法，可以通过其它的方式实现。例如，以上所描述的装置/计算机设备实施例仅仅是示意性的，例如，模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通讯连接可以是通过一些接口，装置或单元的间接耦合或通讯连接，可以是电性，机械或其它的形式。
作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本公开各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读存储介质中。基于这样的理解，本公开实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，计算机程序可以存储在计算机可读存储介质中，该计算机程序在被处理器执行时，可以实现上述各个方法实施例的步骤。计算机程序可以包括计算机程序代码，计算机程序代码可以为源代码形式、对象代码形 式、可执行文件或某些中间形式等。计算机可读介质可以包括：能够携带计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(Read-Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、电载波信号、电信信号以及软件分发介质等。需要说明的是，计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如，在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括电载波信号和电信信号。
以上实施例仅用以说明本公开的技术方案，而非对其限制；尽管参照前述实施例对本公开进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本公开各实施例技术方案的精神和范围，均应包含在本公开的保护范围之内。

Claims (10)

  1. 一种热水器烟道风险预测方法，其特征在于，所述方法包括：
    获取燃气热水器的原始图像信息；
    当所述原始图像信息中包含热水器图像信息时，判断所述原始图像信息中是否包含烟道图像信息；
    当所述原始图像信息中包括所述烟道图像信息时，基于预设的无烟道隐患处理策略生成烟道检测结果；
    基于目标处理策略和所述烟道检测结果，热水器烟道的目标风险预测结果。
  2. 根据权利要求1所述的方法，其特征在于，所述当所述原始图像信息中包含热水器图像信息时，判断所述原始图像信息中是否包含烟道图像信息，包括：
    当所述原始图像信息中包含热水器图像信息时，获取热水器阈值；
    将所述原始图像信息导入热水器预测模型，得到热水器预测结果；
    当所述热水器预测结果大于所述热水器阈值时，判断所述原始图像信息中是否包含烟道图像信息。
  3. 根据权利要求2所述的方法，其特征在于，所述当所述原始图像信息中包括所述烟道图像信息时，基于预设的无烟道隐患处理策略生成烟道检测结果，包括：
    当所述原始图像信息中包括所述烟道图像信息时，获取烟道阈值；
    将所述原始图像信息导入烟道预测模型，得到烟道预测结果；
    当所述烟道预测结果大于所述烟道阈值时，基于所述热水器预测结果和所述烟道预测结果，生成无烟道隐患结果；
  4. 根据权利要求1所述的方法，其特征在于，所述目标处理策略包括：
    基于预设的烟道安装隐患处理策略和所述原始照片信息，生成烟道安装隐患结果；
    基于所述烟道安装隐患结果和所述烟道检测结果，热水器烟道的目标风险预测结果。
  5. 根据权利要求4所述的方法，其特征在于，所述烟道安装隐患计算策略包括：
    获取烟道连接隐患结果；
    当所述烟道连接隐患结果为未正常连接时，将所述烟道连接隐患结果设置为有隐患；
    当所述烟道连接隐患结果为正常连接时，将所述烟道连接隐患结果设置为无隐患；
    基于所述无烟道隐患结果、所述烟道连接隐患结果、所述烟道端口隐患结果和目标风险计算策略，生成所述目标风险结果。
  6. 根据权利要求5所述的方法，其特征在于，所述目标风险计算策略包括：
    当所述烟道端口隐患结果为有隐患时，将所述目标风险结果设置为有隐患；
    当所述烟道端口隐患结果为无隐患时，基于所述无烟道隐患结果、所述烟道连接隐患结果和所述烟道端口隐患结果，生成所述目标风险结果。
  7. 根据权利要求6所述的方法，其特征在于，所述目标处理策略包括：
    当所述烟道连接隐患结果为有隐患时，所述目标风险结果与所述所述无烟道隐患结果相同；
    当所述烟道连接隐患结果为无隐患时，所述目标风险结果与所述烟道安装隐患结果相同。
  8. 一种热水器烟道风险预测装置，其特征在于，包括：
    获取模块，被配置为获取燃气热水器的原始图像信息；
    热水器图像信息判断模块，被配置为当所述原始图像信息中包含热水器图像信息时，判断所述原始图像信息中是否包含烟道图像信息；
    烟道检测结果生成模块，被配置为当所述原始图像信息中包括所述烟道图像信息时，基于预设的无烟道隐患处理策略生成烟道检测结果；
    目标风险预测结果生成模块，被配置为基于目标计算策略和所述烟道检测结果，生成热水器烟道的目标风险预测结果。
  9. 一种计算机设备，包括存储器、处理器以及存储在所述存储器中并且可以在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1所述方法的步骤。
  10. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1所述方法的步骤。
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