A remote diagnosis method and system based on automated intelligent temperature control

Abstract

The present invention relates to the technical field of wall-mounted boiler temperature control, and specifically to a remote diagnosis method and system based on automated intelligent temperature control; a usage cycle is divided, and wall-mounted boiler usage parameters are obtained at the end moment of the usage cycle; the water supply water pipe is divided into a first pipe, a second pipe and a third pipe; a first water scale prediction model is constructed, and the wall-mounted boiler usage parameters of the first pipe and the third pipe are respectively identified to obtain first water scale data and third water scale data; a second water scale prediction model is constructed, and the wall-mounted boiler usage parameters of the second pipe are identified to obtain second water scale data; end-of-period water scale data is obtained according to the first water scale data, the second water scale data and the third water scale data, and a water scale warning coefficient is calculated for warning; a heating parameter correction model is constructed to obtain corrected heating parameters according to the end-of-period water scale data for adjustment. The present invention ensures the accuracy of wall-mounted boiler temperature control through the identification, warning and correction of water scale in the wall-mounted boiler.

Description

Technical Field

[0001] The present invention relates to the technical field of wall-mounted boiler temperature control, and specifically to a remote diagnosis method and system based on automated intelligent temperature control. Background Technique

[0002] A wall-mounted boiler is a common household device mainly used for providing indoor heating and domestic hot water. It generally uses gas as the main fuel and is also known as a gas wall-mounted boiler.

[0003] The wall-mounted boiler has many advantages. In terms of indoor heating, through the radiator or floor heating system, the wall-mounted boiler can provide a stable and uniform indoor temperature, avoiding the common phenomenon of uneven heating and cooling in traditional heating devices, and effectively improving the heating comfort. In terms of providing hot water, the wall-mounted boiler can quickly heat the water source and precisely adjust the water temperature, providing instant and stable hot water supply for users without waiting for the preheating of the water heater; thus, it is widely popular.

[0004] However, with the increase in the usage time of the wall-mounted boiler, affected by water quality, water pipe material, and the change in water temperature during heating, the water pipes are prone to scale formation. The scale adheres to the surface of the water pipes, affecting the movement of water flow in the pipes and the heating process, resulting in an error in the water temperature control accuracy of the wall-mounted boiler.

[0005] In the prior art, mainly a temperature sensor is set at the water outlet to collect the water temperature data of the water outlet; then, based on the comparison and feedback adjustment of the water temperature data of the water outlet and the expected water temperature data, the water temperature of the water outlet of the wall-mounted boiler is maintained near the expected water temperature. However, this method will cause the heating time of the water flow in the wall-mounted boiler to be too long, reduce the accuracy of water flow temperature control, and affect the user experience.

[0006] Therefore, a remote diagnosis method and system based on automated intelligent temperature control are proposed. Summary of the Invention

[0007] The object of the present invention is to provide a remote diagnosis method and system based on automatic intelligent temperature control. By dividing according to the usage rules of the wall-mounted boiler, the usage cycle is obtained; at the end moment of the usage cycle, the usage parameters of the wall-mounted boiler are acquired; the water supply pipe of the wall-mounted boiler is divided to obtain a first pipe, a second pipe and a third pipe; a first water scale prediction model is constructed to respectively identify the usage parameters of the wall-mounted boiler of the first pipe and the third pipe to obtain first water scale data and third water scale data; a second water scale prediction model is constructed to identify the usage parameters of the wall-mounted boiler of the second pipe to obtain second water scale data; according to the first water scale data, the second water scale data and the third water scale data, the end-of-period water scale data is obtained; the water scale warning coefficient is calculated based on the initial water scale data and the end-of-period water scale data for warning; a heating parameter correction model is constructed to identify the initial water temperature, the desired water temperature and the end-of-period water scale data to obtain the corrected heating parameters; the next usage cycle is adjusted according to the corrected heating parameters; through the identification, warning and correction of the water scale in the wall-mounted boiler, the accuracy and efficiency of the temperature control of the wall-mounted boiler are ensured.

[0008] To achieve the above object, the present invention provides a remote diagnosis method based on automatic intelligent temperature control, including:

[0009] S10. Divide the cycle according to the usage rules of the wall-mounted boiler to obtain the usage cycle; at the end moment of the usage cycle, acquire the usage parameters of the wall-mounted boiler; the usage parameters of the wall-mounted boiler include initial water scale data, pipe parameters, water flow parameters and water quality parameters;

[0010] S20. Divide the water supply pipe of the wall-mounted boiler to obtain a first pipe, a second pipe and a third pipe; the first pipe is the water inlet pipe, the second pipe is the heat exchange pipe, and the third pipe is the water outlet pipe;

[0011] S30. Construct a first water scale prediction model to respectively identify the usage parameters of the wall-mounted boiler of the first pipe and the third pipe to obtain first water scale data and third water scale data;

[0012] S40. Construct a second water scale prediction model to identify the usage parameters of the wall-mounted boiler of the second pipe; divide according to the temperature change data of the second pipe during the usage cycle to obtain variable temperature sub-regions; according to the usage parameters of the wall-mounted boiler of the second pipe, obtain the water scale sub-data, pipe sub-parameters, water flow sub-parameters and water quality sub-parameters of the variable temperature sub-regions, identify the variable temperature water scale sub-data, and obtain the second water scale data according to the variable temperature water scale sub-data;

[0013] S50. Obtain the end-of-period water scale data according to the first water scale data, the second water scale data and the third water scale data; calculate the water scale warning coefficient based on the initial water scale data and the end-of-period water scale data for warning;

[0014] S60. Build a heating parameter correction model to identify the final scale data, the initial water temperature and the desired water temperature in the next usage cycle, and obtain the corrected heating parameters; perform temperature control adjustment according to the corrected heating parameters.

[0015] The initial scale data is the scale distribution data of the water supply pipe at the start of the usage cycle of the wall-mounted boiler.

[0016] The pipe parameters include the inner diameter data of the pipe, the inner surface roughness of the pipe, and the pipe structure data.

[0017] The water flow parameters include the water flow velocity data and the water flow temperature data.

[0018] The water quality parameters include the pH value and the hardness data.

[0019] The training process of the first scale prediction model is as follows:

[0020] Obtain the historical usage parameters of the wall-mounted boiler, divide the historical usage parameters according to the usage cycle, and obtain a historical cycle division data set.

[0021] The historical cycle division data set includes historical initial scale data, historical final scale data, historical pipe parameters, historical water flow parameters, and historical water quality parameters.

[0022] Train and optimize the model according to the historical cycle division data set to obtain the first scale prediction model.

[0023] The second scale prediction model includes a pipe temperature identification layer, a variable temperature region division layer, a region data identification layer, and a region scale identification layer.

[0024] The pipe temperature identification layer collects the temperature data of the second pipe during the usage cycle through a temperature detection device to obtain temperature change data of the temperature over time.

[0025] The variable temperature region division layer identifies the temperature change data of the second pipe through a clustering algorithm, uses the temperature value and the temperature change law as clustering features to obtain temperature clustering data; divides the second pipe according to the temperature clustering data to obtain variable temperature sub-regions.

[0026] The region data identification layer collects the scale sub-data, pipe sub-parameters, water flow sub-parameters, and water quality sub-parameters of the variable temperature sub-region according to the wall-mounted boiler usage parameters of the second pipe.

[0027] The region scale identification layer obtains variable temperature scale sub-data according to the scale sub-data, pipe sub-parameters, water flow sub-parameters, and water quality sub-parameters of the variable temperature sub-region; obtains the second scale data according to the variable temperature scale sub-data of all variable temperature sub-regions.

[0028] The process of the scale warning coefficient is as follows: Based on the initial scale data of the first pipeline, the second pipeline, and the third pipeline, obtain the first initial scale thickness, the second initial scale thickness, and the third initial scale thickness; Based on the final scale data of the first pipeline, the second pipeline, and the third pipeline, obtain the first final scale thickness, the second final scale thickness, and the third final scale thickness;

[0029] Based on the first initial scale thickness, the second initial scale thickness, the third initial scale thickness, the first final scale thickness, the second final scale thickness, and the third final scale thickness, calculate and obtain the scale warning coefficient;

[0030] The calculation formula of the scale warning coefficient is:

[0031] ;

[0032] Wherein, represents the scale warning coefficient; represents the weight; represents the final scale thickness; represents the scale threshold; represents the initial scale thickness.

[0033] The training process of the heating parameter correction model is as follows:

[0034] Conduct a heating test on the water supply pipeline to obtain a test data set; The test data set includes a heating target temperature, an input water temperature, test scale data, and test heating parameters;

[0035] The test heating parameters are the parameter settings of the wall-mounted boiler for heating the water flow from the input water temperature to the heating target temperature under the condition of the test scale data, including gas parameters, air pressure parameters, and water pressure parameters;

[0036] Train the model according to the test data set to obtain a heating parameter correction model.

[0037] The present invention also proposes a remote diagnosis system based on automatic intelligent temperature control, including:

[0038] A data collection module divides the usage period according to the usage rules of the wall-mounted boiler to obtain a usage period; At the end moment of the usage period, obtain the wall-mounted boiler usage parameters; The wall-mounted boiler usage parameters include initial scale data, water pipe parameters, water flow parameters, and water quality parameters;

[0039] A pipeline division module divides the water supply pipeline of the wall-mounted boiler to obtain a first pipeline, a second pipeline, and a third pipeline; the first pipeline is the water inlet pipeline, the second pipeline is the heat exchange pipeline, and the third pipeline is the water outlet pipeline;

[0040] A first identification module constructs a first scale prediction model to respectively identify the wall-mounted boiler usage parameters of the first pipeline and the third pipeline, and obtains first scale data and third scale data;

[0041] A second identification module constructs a second scale prediction model to identify the wall-mounted boiler usage parameters of the second pipeline; divides according to the temperature change data of the second pipeline during the usage period to obtain variable temperature sub-regions; according to the wall-mounted boiler usage parameters of the second pipeline, obtains scale sub-data, water pipe sub-parameters, water flow sub-parameters, and water quality sub-parameters of the variable temperature sub-regions, identifies the variable temperature scale sub-data, and obtains second scale data according to the variable temperature scale sub-data;

[0042] A scale warning module obtains end-of-period scale data according to the first scale data, the second scale data, and the third scale data; calculates a scale warning coefficient according to the initial scale data and the end-of-period scale data, and issues a warning;

[0043] A parameter adjustment module constructs a heating parameter correction model to identify the end-of-period scale data, the initial water temperature of the next usage period, and the desired water temperature, and obtains corrected heating parameters; performs temperature control adjustment according to the corrected heating parameters.

[0044] The wall-mounted boiler usage parameters in the data collection module specifically include:

[0045] The initial scale data is the scale distribution data of the water supply pipeline at the start moment of the usage period of the wall-mounted boiler; the water pipe parameters include water pipe inner diameter data, inner surface roughness of the water pipe, and water pipe structure data; the water flow parameters include water flow velocity data and water flow temperature data; the water quality parameters include pH value and hardness data.

[0046] The training process of the first scale prediction model in the first identification module is as follows:

[0047] Obtain the historical usage parameters of the wall-mounted boiler, divide the historical usage parameters according to the usage period, and obtain a historical period division data set;

[0048] The historical period division data set includes historical initial scale data, historical end-of-period scale data, historical water pipe parameters, historical water flow parameters, and historical water quality parameters;

[0049] Train and optimize the model according to the historical period division data set to obtain a first scale prediction model.

[0050] The training process of the heating parameter correction model in the parameter adjustment module is as follows:

[0051] Conduct a heating test on the water supply pipeline to obtain a test data set; the test data set includes the heating target temperature, the input water temperature, the test scale data, and the test heating parameters.

[0052] The test heating parameters are the parameter settings of the wall-mounted boiler for heating the water flow from the input water temperature to the heating target temperature under the condition of the test scale data; including gas parameters, air pressure parameters, and water pressure parameters.

[0053] Train the model according to the test data set to obtain the heating parameter correction model.

[0054] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0055] 1. In view of the relatively stable water flow temperature in the first pipeline and the third pipeline, the present invention constructs the first scale prediction model; the first scale prediction model identifies the initial scale data, water pipe parameters, water flow parameters, and water quality parameters of the first pipeline and the third pipeline to accurately obtain the first scale data and the third scale data.

[0056] 2. The present invention identifies the temperature data change situation of the second pipeline during the service cycle to obtain the temperature change data; the temperature numerical value and the temperature change law of the temperature change data are identified through the clustering algorithm to obtain the temperature clustering data; then the second pipeline is divided according to the temperature clustering data to obtain the variable temperature sub-region; the regions with similar temperature distributions and change laws are accurately divided, and the second scale data is accurately identified.

[0057] 3. The present invention respectively sets corresponding scale thresholds for the first pipeline, the second pipeline, and the third pipeline; measures the scale abnormal conditions of the first pipeline, the second pipeline, and the third pipeline according to the end-of-period scale data, the scale increase situation during the period, and the scale threshold; then comprehensively combines the scale abnormal conditions of the first pipeline, the second pipeline, and the third pipeline through weights to obtain the scale warning coefficient; accurately identifies and warns the abnormal scale in the water supply pipeline.

[0058] 4. The present invention obtains the relationship between the heating parameters and the scale data during the process of the wall-mounted boiler heating the water flow, and trains to obtain the heating parameter correction model; the initial water temperature, the desired water temperature, and the end-of-period scale data are identified through the heating parameter correction model to obtain the corrected heating parameters; through the corrected heating parameters, the water flow with the initial water temperature can be accurately heated to the desired temperature in the next service cycle, effectively improving the temperature control accuracy of the wall-mounted boiler. Description of the Drawings

[0059] Figure 1Schematic diagram of the process of a remote diagnosis method based on automated intelligent temperature control according to the present invention;

[0060] Figure 2 Schematic diagram of the structure of the second scale prediction model of the present invention;

[0061] Figure 3 Schematic diagram of the structure of a remote diagnosis system based on automated intelligent temperature control according to the present invention;

[0062] Figure 4 Schematic diagram of the second pipeline division of the present invention.

[0063] In the figure: 11. Second pipeline; 12. First section plane; 13. Second section plane; 14. Sub-pipeline. Detailed implementation manners

[0064] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0065] Embodiment 1

[0066] The present invention is a remote diagnosis method based on automated intelligent temperature control, and its process is as Figure 1 shown, including:

[0067] S10. Divide the cycle according to the usage rule of the wall-mounted boiler to obtain the usage cycle; at the end moment of the usage cycle, obtain the wall-mounted boiler usage parameters; the wall-mounted boiler usage parameters include initial scale data, water pipe parameters, water flow parameters, and water quality parameters.

[0068] In the process of dividing the usage cycle, the usage rule of the wall-mounted boiler is taken as the basis; specifically, the usage cycle can be divided according to "days", "months", "quarters", and "years", etc., or can be differentially divided according to specific usage rules.

[0069] The initial scale data is the scale distribution data of the water supply pipe at the start moment of the usage cycle of the wall-mounted boiler, and is obtained by ultrasonic detection equipment; the water pipe parameters include water pipe inner diameter data, inner surface roughness of the water pipe, and water pipe structure data; the water pipe structure data is the distribution data of the water pipe in space; the inner surface roughness of the water pipe is obtained by roughness detection equipment. The water flow parameters include water flow velocity data and water flow temperature data.

[0070] The water quality parameters include pH value and hardness data. The acidity and alkalinity of water affect the solubility of minerals such as calcium and magnesium in water. A higher pH value will cause precipitation of calcium and magnesium ions in water, thereby increasing the formation of water scale. The formation of water scale usually starts to increase significantly when the pH value is greater than 7. The hardness data of water is usually determined by the concentration of dissolved calcium and magnesium ions in water. The higher the hardness, the higher the rate and thickness of water scale formation generally are. Among them, the water quality parameters are the key factors affecting the formation of water scale, and the mineral components in the water quality directly affect the formation rate and thickness of water scale.

[0071] The present invention divides the usage cycle according to the usage rules of the wall-mounted boiler to obtain the usage cycle; collects the usage parameters of the wall-mounted boiler during the usage cycle, including initial water scale data, water pipe parameters, water flow parameters and water quality parameters; accurately identifies the usage conditions of the wall-mounted boiler during the usage cycle, providing a data basis for subsequent water scale identification.

[0072] S20. Divide the water supply pipe of the wall-mounted boiler to obtain a first pipe, a second pipe and a third pipe; the first pipe is the inlet pipe, the second pipe is the heat exchange pipe, and the third pipe is the outlet pipe.

[0073] During the heating process of the wall-mounted boiler, the flow process of water can be summarized as: cold water enters the wall-mounted boiler, the water exchanges heat with the heat exchanger, and hot water flows out of the wall-mounted boiler; the pipe through which the "cold water enters the wall-mounted boiler" process flows is used as the inlet pipe, that is, the first pipe; the pipe through which the "water exchanges heat with the heat exchanger" process flows is used as the heat exchange pipe, that is, the second pipe; the pipe through which the "hot water flows out for the wall-mounted boiler" process flows is used as the outlet pipe, that is, the third pipe.

[0074] S30. Construct a first water scale prediction model, respectively identify the usage parameters of the wall-mounted boiler for the first pipe and the third pipe to obtain first water scale data and third water scale data.

[0075] The first water scale prediction model is constructed based on a deep neural network model, and its training process is as follows:

[0076] Obtain the historical usage parameters of the wall-mounted boiler, divide the historical usage parameters according to the usage cycle to obtain a historical cycle division data set; the historical cycle division data set includes historical initial water scale data, historical end-of-period water scale data, historical water pipe parameters, historical water flow parameters and historical water quality parameters;

[0077] Train and optimize the model according to the historical cycle division data set to obtain the first water scale prediction model.

[0078] The first pipeline and the third pipeline are the water inlet pipeline and the water outlet pipeline respectively. During the flow of water, the influence of the pipeline on the water temperature is relatively small. In view of the relatively stable water temperature in the first pipeline and the third pipeline, the present invention constructs a first scale prediction model, and identifies the initial scale data, water pipe parameters, water flow parameters and water quality parameters of the first pipeline and the third pipeline through the first scale prediction model, so as to accurately obtain the first scale data and the third scale data.

[0079] S40. Construct a second scale prediction model to identify the wall-mounted boiler usage parameters of the second pipeline; divide according to the temperature change data of the second pipeline during the usage period to obtain variable temperature sub-regions; according to the wall-mounted boiler usage parameters of the second pipeline, obtain the scale sub-data, water pipe sub-parameters, water flow sub-parameters and water quality sub-parameters of the variable temperature sub-regions, identify the variable temperature scale sub-data, and obtain the second scale data according to the variable temperature scale sub-data.

[0080] The second scale prediction model is constructed based on a deep neural network model, and its structure is as Figure 2 shown, including a water pipe temperature identification layer, a variable temperature region division layer, a region data identification layer and a region scale identification layer;

[0081] The water pipe temperature identification layer collects the temperature data of the second pipeline during the usage period through a temperature detection device to obtain temperature change data of the temperature changing with time; the temperature detection device includes a temperature sensor and an infrared temperature measurement device;

[0082] The variable temperature region division layer identifies the temperature change data of the second pipeline through a clustering algorithm, uses the temperature value and the temperature change law as clustering features to obtain temperature clustering data, and divides the second pipeline according to the temperature clustering data to obtain variable temperature sub-regions;

[0083] The region data identification layer collects the scale sub-data, water pipe sub-parameters, water flow sub-parameters and water quality sub-parameters of the variable temperature sub-regions according to the wall-mounted boiler usage parameters of the second pipeline;

[0084] The region scale identification layer obtains variable temperature scale sub-data according to the scale sub-data, water pipe sub-parameters, water flow sub-parameters and water quality sub-parameters of the variable temperature sub-regions, and obtains the second scale data according to the variable temperature scale sub-data of all variable temperature sub-regions.

[0085] In order to verify the identification effect of the second scale prediction model, a comparative experiment is carried out on it;

[0086] There are two models in the comparative experiment, namely the first comparative model and the second comparative model;

[0087] Among them, the first comparison model is the second scale prediction model of the present invention. The temperature change data of the second pipeline is obtained, and the temperature change data of the second pipeline is identified through a clustering algorithm, and variable temperature sub-regions are divided; then the scale data is identified according to the variable temperature sub-regions to obtain the first comparison data. The second comparison model is based on the first model. Without considering the temperature change and regional subdivision of the second pipeline, the second comparison data is directly obtained through the initial scale data, water pipe parameters, water flow parameters and water quality parameters of the second pipeline.

[0088] The first comparison model and the second comparison model are respectively used to identify the same verification data set to obtain the first comparison data and the second comparison data. Then, the data similarity between the first comparison data, the second comparison data and the actual scale data in the verification data set is used as the recognition accuracy. The obtained data is shown in Table 1.

[0089] Table 1 Effect verification data table of the second scale prediction model

[0090]

[0091] Through the above comparative experiments, it can be obtained that the scale recognition accuracy of the first comparison model is relatively high; that is, considering the water pipe temperature value and the temperature change law for regional division and differential recognition, more accurate recognition results can be obtained.

[0092] The present invention identifies the temperature data change situation of the second pipeline during the service life to obtain the temperature change data; the temperature value and the temperature change law of the temperature change data are identified through a clustering algorithm to obtain the temperature clustering data; then the second pipeline is divided according to the temperature clustering data to obtain variable temperature sub-regions; regions with similar temperature distributions and change laws are accurately divided and accurate scale recognition is performed.

[0093] S50. According to the first scale data, the second scale data and the third scale data, the end-of-period scale data is obtained; the scale warning coefficient is calculated according to the initial scale data and the end-of-period scale data for warning.

[0094] The process of the scale warning coefficient is as follows:

[0095] According to the initial scale data of the first pipeline, the second pipeline and the third pipeline, the first initial scale thickness, the second initial scale thickness and the third initial scale thickness are obtained; according to the end-of-period scale data of the first pipeline, the second pipeline and the third pipeline, the first end-of-period scale thickness, the second end-of-period scale thickness and the third end-of-period scale thickness are obtained;

[0096] Based on the first initial scale thickness, the second initial scale thickness, the third initial scale thickness, the first end - of - period scale thickness, the second end - of - period scale thickness, and the third end - of - period scale thickness, a scale warning coefficient is calculated.

[0097] The calculation formula for the scale warning coefficient is as follows:

[0098] ;

[0099] Wherein, represents the scale warning coefficient; represents the weight; represents the end - of - period scale thickness; represents the scale threshold; represents the initial scale thickness.

[0100] In the present invention, corresponding scale thresholds are set for the first pipeline, the second pipeline, and the third pipeline respectively; according to the end - of - period scale data, the scale increase situation during the period, and the scale thresholds, the scale abnormal situations of the first pipeline, the second pipeline, and the third pipeline are measured; then, through weights, the scale abnormal situations of the first pipeline, the second pipeline, and the third pipeline are synthesized to obtain a scale warning coefficient, accurately identifying and warning the abnormality of the scale in the water supply pipeline.

[0101] S60. Construct a heating parameter correction model to identify the end - of - period scale data, the initial water temperature and the expected water temperature in the next usage cycle, and obtain the corrected heating parameters; perform temperature control adjustment according to the corrected heating parameters.

[0102] The heating parameter correction model is constructed based on a deep neural network model, and its training process is as follows:

[0103] Conduct heating tests on the water supply pipeline to obtain a test data set; the test data set includes the heating target temperature, the input water temperature, the test scale data, and the test heating parameters;

[0104] The test heating parameters are the parameter settings of the wall - hung boiler for heating the water flow from the input water temperature to the heating target temperature under the condition of the test scale data, including gas parameters, air pressure parameters, and water pressure parameters;

[0105] Train the model according to the test data set to obtain a heating parameter correction model.

[0106] In the process of obtaining the heating water flow of the wall-mounted boiler by the present invention, the relationship between the heating parameters and the scale data is obtained, and a heating parameter correction model is trained; the initial water temperature, the desired water temperature and the final scale data are identified through the heating parameter correction model to obtain the corrected heating parameters; the water flow with the initial water temperature can be accurately and quickly heated to the desired temperature in the next use cycle by the corrected heating parameters.

[0107] The remote diagnosis method based on automatic intelligent temperature control can be applied to the temperature control device of the wall-mounted boiler, and the current signal is received and output through the temperature control device; the gas parameters, the air pressure parameters and the water pressure parameters are controlled through the current signal.

[0108] The present invention also proposes a remote diagnosis system based on automatic intelligent temperature control, and its structure is as Figure 3 shown, including: a data collection module, a pipeline division module, a first identification module, a second identification module, a scale warning module and a parameter adjustment module.

[0109] The data collection module divides the cycle according to the usage rule of the wall-mounted boiler to obtain the usage cycle; at the end moment of the usage cycle, the usage parameters of the wall-mounted boiler are obtained; the usage parameters of the wall-mounted boiler include the initial scale data, the water pipe parameters, the water flow parameters and the water quality parameters.

[0110] The pipeline division module divides the water supply pipeline of the wall-mounted boiler to obtain a first pipeline, a second pipeline and a third pipeline; the first pipeline is the water inlet pipeline, the second pipeline is the heat exchange pipeline, and the third pipeline is the water outlet pipeline.

[0111] The first identification module constructs a first scale prediction model, and respectively identifies the usage parameters of the wall-mounted boiler of the first pipeline and the third pipeline to obtain the first scale data and the third scale data.

[0112] The second identification module constructs a second scale prediction model to identify the usage parameters of the wall-mounted boiler of the second pipeline; divides according to the temperature change data of the second pipeline in the usage cycle to obtain variable temperature sub-regions; obtains the scale sub-data, the water pipe sub-parameters, the water flow sub-parameters and the water quality sub-parameters of the variable temperature sub-regions according to the usage parameters of the wall-mounted boiler of the second pipeline, identifies the variable temperature scale sub-data, and obtains the second scale data according to the variable temperature scale sub-data.

[0113] The scale warning module obtains the final scale data according to the first scale data, the second scale data and the third scale data; calculates the scale warning coefficient according to the initial scale data and the final scale data for warning.

[0114] The parameter adjustment module constructs a heating parameter correction model to identify the final scale data, the initial water temperature and the desired water temperature of the next usage cycle to obtain the corrected heating parameters; performs temperature control adjustment according to the corrected heating parameters.

[0115] According to the usage pattern of the wall-mounted boiler, the present invention divides it to obtain the usage cycle; at the end moment of the usage cycle, the usage parameters of the wall-mounted boiler are obtained; the water supply pipe of the wall-mounted boiler is divided to obtain the first pipe, the second pipe, and the third pipe; a first water scale prediction model is constructed to respectively identify the usage parameters of the wall-mounted boiler in the first pipe and the third pipe to obtain the first water scale data and the third water scale data; a second water scale prediction model is constructed to identify the usage parameters of the wall-mounted boiler in the second pipe to obtain the second water scale data; according to the first water scale data, the second water scale data, and the third water scale data, the end-of-period water scale data is obtained; the water scale warning coefficient is calculated based on the initial water scale data and the end-of-period water scale data for warning; a heating parameter correction model is constructed to identify the initial water temperature, the desired water temperature, and the end-of-period water scale data to obtain the corrected heating parameters; according to the corrected heating parameters, the next usage cycle is adjusted to ensure the accuracy and efficiency of the temperature control of the wall-mounted boiler.

[0116] Example Two

[0117] Factory A mainly engages in the design and production of wall-mounted boilers and related accessories. In the product testing of wall-mounted boilers, Factory A found that with the accumulation of usage duration, water scale will appear in the water supply pipes of the wall-mounted boilers, resulting in the influence of heating efficiency during the process of heating water flow, bringing a bad usage experience to users.

[0118] To ensure the product quality, Factory A adopts a remote diagnosis method based on automated intelligent temperature control described in the present invention to identify, warn, and correct the water scale in the wall-mounted boiler pipes during the usage process.

[0119] The process of the remote diagnosis method based on automated intelligent temperature control is as Figure 1 shown, including:

[0120] S10. Divide the cycle according to the usage pattern of the wall-mounted boiler to obtain the usage cycle; at the end moment of the usage cycle, obtain the usage parameters of the wall-mounted boiler; the usage parameters of the wall-mounted boiler include initial water scale data, pipe parameters, water flow parameters, and water quality parameters;

[0121] During the process of dividing the usage cycle, Factory A conducts regular inspections on the water pipes of the wall-mounted boiler every year, including obtaining the water scale distribution data inside the water pipes using ultrasonic detection equipment; during the annual regular inspection period, Factory A divides according to the usage patterns in spring, summer, autumn, and winter to obtain the first usage cycle, the second usage cycle, the third usage cycle, and the fourth usage cycle.

[0122] The initial water scale data is the water scale distribution data of the water supply pipe at the start moment of the first usage cycle of the wall-mounted boiler, which is obtained by detecting with ultrasonic detection equipment;

[0123] The water pipe parameters include the inner diameter data of the water pipe, the inner surface roughness of the water pipe, and the water pipe structure data; the water pipe structure data is the distribution data of the water pipe in space; the inner surface roughness of the water pipe is obtained by detecting with a roughness detection device.

[0124] The water flow parameters include the water flow velocity data and the water flow temperature data; the water quality parameters include the pH value and the hardness data.

[0125] S20. Divide the water supply pipe of the wall-mounted boiler to obtain the first pipe, the second pipe, and the third pipe; the first pipe is the inlet pipe, the second pipe is the heat exchange pipe, and the third pipe is the outlet pipe;

[0126] S30. Build a first water scale prediction model, respectively identify the usage parameters of the wall-mounted boiler for the first pipe and the third pipe to obtain the first water scale data and the third water scale data;

[0127] The training process of the first water scale prediction model is as follows:

[0128] Obtain the historical usage parameters of the wall-mounted boiler, divide the historical usage parameters according to the usage cycle to obtain a historical cycle division data set; the historical cycle division data set includes historical initial water scale data, historical end-of-period water scale data, historical water pipe parameters, historical water flow parameters, and historical water quality parameters;

[0129] Train and optimize the model according to the historical cycle division data set to obtain the first water scale prediction model.

[0130] S40. Build a second water scale prediction model to identify the usage parameters of the wall-mounted boiler for the second pipe; divide according to the temperature change data of the second pipe during the usage cycle to obtain variable temperature sub-regions; according to the usage parameters of the wall-mounted boiler for the second pipe, obtain the water scale sub-data, water pipe sub-parameters, water flow sub-parameters, and water quality sub-parameters of the variable temperature sub-regions, identify the variable temperature water scale sub-data, and obtain the second water scale data according to the variable temperature water scale sub-data;

[0131] The second water scale prediction model is built based on a deep neural network model and includes a water pipe temperature identification layer, a variable temperature region division layer, a region data identification layer, and a region water scale identification layer;

[0132] The water pipe temperature identification layer collects the temperature data of the second pipe during the usage cycle through a temperature detection device to obtain temperature change data of the temperature changing with time;

[0133] The collection process of the temperature change data of Factory A is as follows: obtain the structural distribution of the second pipeline, divide it into sub-pipelines of equal length, detect and collect the temperature data of the sub-pipelines during their use cycle, and obtain the temperature change sub-data; summarize the temperature change sub-data of all sub-pipelines to obtain the temperature change data of the second pipeline.

[0134] Taking the horizontally distributed second pipeline as an example, pipeline division is performed, such as Figure 4 As shown; the length of the second pipe 11 is obtained, which is 50 cm; the second pipe 11 is divided into sub-pipes of equal length, wherein the length of the sub-pipe 14 is 2 cm, and a total of 25 sub-pipes are obtained; the sub-pipe 14 is determined according to the first section 12 and the second section 13; the temperature data of the sub-pipe during the use period is detected and collected to obtain temperature change sub-data; the temperature change sub-data of all the sub-pipes are summarized to obtain temperature change data.

[0135] The temperature-varying area division layer identifies the temperature variation data of the second pipeline by a clustering algorithm, takes the temperature value and the temperature variation law as clustering features, and obtains temperature clustering data; divides the second pipeline according to the temperature clustering data to obtain the temperature-varying sub-areas;

[0136] The temperature change time data of the sub-pipelines are identified by a clustering algorithm, and clustered according to their temperature values ​​and temperature change rules to obtain temperature clustering data; the connected sub-pipelines in the temperature clustering data are divided into the same temperature change sub-area.

[0137] The regional data recognition layer collects scale sub-data, water pipe sub-parameters, water flow sub-parameters and water quality sub-parameters of the variable temperature sub-region according to the wall-mounted boiler usage parameters of the second pipeline;

[0138] The regional scale identification layer obtains variable temperature scale sub-data based on the scale sub-data of the variable temperature sub-region, water pipe sub-parameters, water flow sub-parameters and water quality sub-parameters; and obtains second scale data based on the variable temperature scale sub-data of all variable temperature sub-regions.

[0139] S50. According to the first scale data, the second scale data and the third scale data, the end scale data is obtained; according to the initial scale data and the end scale data, the scale warning coefficient is calculated and an early warning is issued;

[0140] The process of the scale warning coefficient is as follows: according to the initial scale data of the first pipeline, the second pipeline and the third pipeline, the first initial scale thickness, the second initial scale thickness and the third initial scale thickness are obtained; according to the end-of-period scale data of the first pipeline, the second pipeline and the third pipeline, the first end-of-period scale thickness, the second end-of-period scale thickness and the third end-of-period scale thickness are obtained;

[0141] The scale thickness is the average value of the scale distribution data in the corresponding area.

[0142] Based on the first initial scale thickness, the second initial scale thickness, the third initial scale thickness, the first end - of - period scale thickness, the second end - of - period scale thickness, and the third end - of - period scale thickness, a scale warning coefficient is calculated and obtained;

[0143] The calculation formula for the scale warning coefficient is as follows:

[0144] ;

[0145] Wherein, represents the scale warning coefficient; represents the weight; represents the end - of - period scale thickness; represents the scale threshold; represents the initial scale thickness.

[0146] Obtain the initial scale thickness and the end - of - period scale thickness of the first pipeline, the second pipeline, and the third pipeline during the first usage cycle of the wall - hung boiler; then set the scale threshold according to the design parameters of the product and the relevant data of scale faults; the obtained data is shown in Table 2.

[0147] Table 2 Scale data table of the water supply pipeline

[0148]

[0149] S60. Construct a heating parameter correction model to identify the end - of - period scale data, the initial water temperature of the next usage cycle, and the desired water temperature, and obtain the corrected heating parameters; perform temperature control adjustment according to the corrected heating parameters.

[0150] The training process of the heating parameter correction model is as follows:

[0151] Conduct a heating test on the water supply pipeline to obtain a test data set; the test data set includes the heating target temperature, the input water temperature, the test scale data, and the test heating parameters; the test heating parameters are the parameter settings of the wall - hung boiler for heating the water flow from the input water temperature to the heating target temperature under the condition of the test scale data; including gas parameters, air pressure parameters, and water pressure parameters;

[0152] Train a model according to the test data set to obtain a heating parameter correction model.

[0153] In order to verify the temperature control effect of the method described in the present invention, a verification experiment is conducted; there are two temperature control methods in the experiment, namely the first temperature control method and the second temperature control method;

[0154] The first temperature control method is a remote diagnosis method based on automated intelligent temperature control according to the present invention. The first pipeline, the second pipeline, and the third pipeline are obtained by dividing the water supply pipeline of the wall-mounted boiler. The final scale data is obtained by respectively identifying the first pipeline, the second pipeline, and the third pipeline through the model. Then, the temperature control adjustment is performed by the heating parameter correction model according to the final scale data, the initial water temperature, and the desired water temperature. The second temperature control method is to set a temperature sensor at the water outlet to collect the water temperature data at the water outlet. Then, the comparison and feedback adjustment are performed according to the water temperature data at the water outlet and the desired water temperature.

[0155] A total of three groups of verification experiments are carried out. The initial water temperature of the first group of verification experiments is 12 °C, and the desired water temperature is 25 °C. The initial water temperature of the second group of verification experiments is 15 °C, and the desired water temperature is 25 °C. The initial water temperature of the third group of verification experiments is 18 °C, and the desired water temperature is 25 °C. During the temperature control process of the water flow, based on the desired water temperature, a desired temperature range is set, and the time when the water temperature at the water outlet first reaches the desired temperature range is used as the temperature control duration. According to the difference between the average water temperature at the water outlet and the desired water temperature during the period from the moment when the water temperature at the water outlet first reaches the desired temperature range to the end of the test, the temperature control accuracy is obtained. The calculation formula for the temperature control accuracy is:

[0156] ;

[0157] where, represents the temperature control accuracy; represents the average water temperature; represents the desired water temperature.

[0158] In the three groups of verification experiments, multiple experiments are respectively carried out, and the experimental data are averaged for effect measurement, and the experimental data are shown in Table 3.

[0159] Table 3 Verification data table of temperature control effect

[0160]

[0161] From the data in Table 3, it can be seen that the temperature control duration of the first temperature control method in the three groups of verification tests is less than that of the second temperature control method. At the same time, the temperature control accuracy of the first temperature control method is higher than that of the second temperature control method, that is, the first temperature control method can heat the water flow more accurately and efficiently.

[0162] Through a remote diagnosis method based on automated intelligent temperature control according to the present invention, during the annual regular inspection period, Factory A can accurately identify and warn of scale according to the usage situation of the wall-mounted boiler of users in a quarter, and at the same time adjust the temperature control parameters according to the scale situation, effectively improving the temperature control accuracy and efficiency of the wall-mounted boiler and ensuring the user experience.

[0163] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will appreciate that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A remote diagnosis method based on automated intelligent temperature control, characterized in that: include: S10. Divide the boiler into cycles according to the usage pattern to obtain the usage cycle; at the end of the usage cycle, obtain the boiler usage parameters; the boiler usage parameters include initial scale data, water pipe parameters, water flow parameters and water quality parameters; the initial scale data is the scale distribution data of the water supply pipe; S20. Divide the water supply pipe of the wall-mounted boiler into a first pipe, a second pipe and a third pipe; the first pipe is a water inlet pipe, the second pipe is a heat exchange pipe, and the third pipe is a water outlet pipe; S30. Constructing a first scale prediction model, identifying the wall-mounted boiler usage parameters of the first pipeline and the third pipeline, respectively, to obtain first scale data and third scale data; S40. Construct a second scale prediction model, identify the wall-mounted boiler usage parameters of the second pipeline; divide the second pipeline according to the temperature change data during the use period to obtain variable temperature sub-areas; obtain scale sub-data, water pipe sub-parameters, water flow sub-parameters and water quality sub-parameters of the variable temperature sub-areas according to the wall-mounted boiler usage parameters of the second pipeline, identify and obtain variable temperature scale sub-data, and obtain second scale data according to the variable temperature scale sub-data; S50. According to the first scale data, the second scale data and the third scale data, the end scale data is obtained; according to the initial scale data and the end scale data, the scale warning coefficient is calculated and an early warning is issued; S60. Construct a heating parameter correction model to identify the end-of-period scale data, the initial water temperature of the next use cycle, and the expected water temperature, and obtain corrected heating parameters, including gas parameters, wind pressure parameters, and water pressure parameters; and adjust the temperature control according to the corrected heating parameters.

2. A remote diagnosis method based on automated intelligent temperature control according to claim 1, characterized in that: The initial scale data is the scale distribution data of the water supply pipe at the beginning of the use cycle of the wall-mounted boiler; The water pipe parameters include water pipe inner diameter data, water pipe inner surface roughness and water pipe structure data; The water flow parameters include water flow velocity data and water flow temperature data; The water quality parameters include pH value and hardness data.

3. The remote diagnosis method based on automated intelligent temperature control according to claim 1, characterized in that: The training process of the first scale prediction model is: Acquire historical usage parameters of the wall-mounted boiler, divide the historical usage parameters according to usage cycles, and obtain a historical cycle division data set; The historical period division data set includes historical initial scale data, historical end-of-period scale data, historical water pipe parameters, historical water flow parameters and historical water quality parameters; The data set is divided according to the historical period to train and tune the model to obtain a first scale prediction model.

4. The remote diagnosis method based on automated intelligent temperature control according to claim 1, characterized in that: The second scale prediction model includes a water pipe temperature identification layer, a variable temperature area division layer, a regional data identification layer and a regional scale identification layer; The water pipe temperature identification layer collects temperature data of the second pipe during the use period through the temperature detection device to obtain temperature change data of the temperature changing with time; The temperature-varying area division layer identifies the temperature variation data of the second pipeline by a clustering algorithm, takes the temperature value and the temperature variation law as clustering features, and obtains temperature clustering data; divides the second pipeline according to the temperature clustering data to obtain the temperature-varying sub-areas; The regional data recognition layer collects scale sub-data, water pipe sub-parameters, water flow sub-parameters and water quality sub-parameters of the variable temperature sub-region according to the wall-mounted boiler usage parameters of the second pipeline; The regional scale identification layer obtains variable temperature scale sub-data based on the scale sub-data of the variable temperature sub-region, water pipe sub-parameters, water flow sub-parameters and water quality sub-parameters; and obtains second scale data based on the variable temperature scale sub-data of all variable temperature sub-regions.

5. The remote diagnosis method based on automated intelligent temperature control according to claim 1, characterized in that: The process of the scale warning coefficient is as follows: according to the initial scale data of the first pipeline, the second pipeline and the third pipeline, the first initial scale thickness, the second initial scale thickness and the third initial scale thickness are obtained; according to the end-of-period scale data of the first pipeline, the second pipeline and the third pipeline, the first end-of-period scale thickness, the second end-of-period scale thickness and the third end-of-period scale thickness are obtained; the scale thickness is the average value of the corresponding pipeline scale data; The scale warning coefficient is calculated according to the first initial scale thickness, the second initial scale thickness, the third initial scale thickness, the scale thickness at the end of the first period, the scale thickness at the end of the second period, and the scale thickness at the end of the third period; The calculation formula of the scale warning coefficient is: ； in, Indicates scale warning coefficient; Indicates Weight; Indicates Scale thickness at the end of the period; Indicates Scale threshold; Indicates Initial scale thickness.

6. A remote diagnosis method based on automated intelligent temperature control according to claim 1, characterized in that: The training process of the heating parameter correction model is: Performing a heating test on the water supply pipeline to obtain a test data set; the test data set includes a heating target temperature, an input water temperature, test scale data, and a test heating parameter; The test heating parameters are the parameters of the wall-mounted boiler for heating the water flow from the input water temperature to the heating target temperature when testing the scale data, including gas parameters, wind pressure parameters and water pressure parameters; The model is trained according to the test data set to obtain a heating parameter correction model.

7. A remote diagnosis system based on automated intelligent temperature control, characterized in that: include: The data collection module divides the period according to the usage rule of the wall-mounted boiler to obtain the usage period; at the end of the usage period, the usage parameters of the wall-mounted boiler are obtained; the usage parameters of the wall-mounted boiler include initial scale data, water pipe parameters, water flow parameters and water quality parameters; The pipeline division module divides the water supply pipe of the wall-mounted boiler into a first pipeline, a second pipeline and a third pipeline; the first pipeline is a water inlet pipeline, the second pipeline is a heat exchange pipeline, and the third pipeline is a water outlet pipeline; The first identification module constructs a first scale prediction model, identifies the wall-mounted boiler usage parameters of the first pipeline and the third pipeline respectively, and obtains the first scale data and the third scale data; The second identification module constructs a second scale prediction model to identify the wall-mounted boiler usage parameters of the second pipeline; divides the second pipeline according to the temperature change data during the usage period to obtain variable temperature sub-areas; obtains scale sub-data, water pipe sub-parameters, water flow sub-parameters and water quality sub-parameters of the variable temperature sub-areas according to the wall-mounted boiler usage parameters of the second pipeline, identifies the variable temperature scale sub-data, and obtains the second scale data according to the variable temperature scale sub-data; A scale warning module obtains end-of-period scale data according to the first scale data, the second scale data and the third scale data; The scale warning coefficient is calculated based on the initial scale data and the final scale data to issue a warning; The parameter adjustment module constructs a heating parameter correction model to identify the end-of-period scale data, the initial water temperature of the next use cycle, and the expected water temperature to obtain the corrected heating parameters; and adjusts the temperature control according to the corrected heating parameters.

8. A remote diagnosis system based on automated intelligent temperature control according to claim 7, characterized in that: The wall-mounted boiler usage parameters in the data collection module specifically include: The initial scale data is the scale distribution data of the water supply pipe at the beginning of the use cycle of the wall-mounted boiler; the water pipe parameters include water pipe inner diameter data, water pipe inner surface roughness and water pipe structure data; the water flow parameters include water flow velocity data and water flow temperature data; the water quality parameters include pH value and hardness data.

9. The remote diagnosis system based on automated intelligent temperature control according to claim 7, characterized in that: The training process of the first scale prediction model in the first recognition module is: Acquire historical usage parameters of the wall-mounted boiler, divide the historical usage parameters according to usage cycles, and obtain a historical cycle division data set; The historical period division data set includes historical initial scale data, historical end-of-period scale data, historical water pipe parameters, historical water flow parameters and historical water quality parameters; The data set is divided according to the historical period to train and tune the model to obtain a first scale prediction model.

10. The remote diagnosis system based on automated intelligent temperature control according to claim 7, characterized in that: The training process of the heating parameter correction model in the parameter adjustment module is as follows: Performing a heating test on the water supply pipeline to obtain a test data set; the test data set includes a heating target temperature, an input water temperature, test scale data, and a test heating parameter; The test heating parameters are the parameters of the wall-mounted boiler that heat the water flow from the input water temperature to the heating target temperature when testing the scale data; including gas parameters, wind pressure parameters and water pressure parameters; The model is trained according to the test data set to obtain a heating parameter correction model.

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