Central air conditioning water leakage prevention optimization installation method and system

Abstract

The present invention relates to the technical field of air conditioning, and discloses a central air-conditioning water leakage prevention optimization installation method and system, including receiving indoor house structure information sent by a central air-conditioning installation user; obtaining the power and pipe layout strategy of the central air-conditioning according to the indoor house structure information; obtaining the material information of the condensate drainage pipe according to the power of the central air-conditioning; forming installation parameters according to the power, material information and pipe layout strategy of the central air-conditioning; training a water leakage prediction model according to a historical data set, learning installation parameters through the water leakage prediction model, and obtaining the water leakage probability of the central air-conditioning; comparing the water leakage probability with a predetermined water leakage probability threshold, if the water leakage probability is less than the water leakage probability threshold, executing the central air-conditioning installation according to the installation parameters; otherwise, adjusting the installation parameters. Before installation, the system evaluates and dynamically adjusts the installation parameters of the central air-conditioning, thereby significantly reducing the occurrence of water leakage events and improving the safety and reliability of the entire central air-conditioning system.

Description

Technical Field

[0001] The invention relates to the technical field of air conditioning, and in particular to an optimized installation method and system for preventing water leakage of a central air conditioner. Background Art

[0002] Central air conditioning systems are indispensable air conditioning equipment in modern buildings. Their internal units and drainage pipes are laid inside the ceiling. Although this design is beautiful, it is extremely difficult to locate the problem and carry out repairs when a water leak occurs, causing great trouble to users.

[0003] The installation evaluation of the central air conditioning system depends on the initial structure of the building and the performance indicators of the air conditioning equipment. During the evaluation process before the equipment is installed, although the configuration of the central air conditioning can be reasonably planned through professional calculations and measurements, the actual operating conditions of the equipment may change as the use time increases. For example, long-term operation will cause bacteria and dirt to grow inside the condensate drain pipe, causing poor drainage. This phenomenon not only affects the normal operation of the air conditioner, but may also further cause water leakage and secondary damage to the building structure.

[0004] Traditional installation assessment methods focus on the static design stage, but lack effective dynamic assessment of potential problems in later operation. This makes managers fail to identify and solve potential water leakage risks in a timely manner, which leads to frequent water leakage incidents. Summary of the invention

[0005] To this end, the technical problem to be solved by the present invention is to overcome the central air-conditioning installation strategy in the prior art that relies on static design stage information, which will cause frequent water leakage incidents, and provide a central air-conditioning anti-leakage optimization installation method and system, which systematically evaluates and dynamically adjusts the installation parameters of the central air-conditioning before installation, thereby significantly reducing the occurrence of water leakage incidents and improving the safety and reliability of the entire central air-conditioning system.

[0006] In the first aspect, in order to solve the above technical problems, the present invention provides a central air conditioner water leakage prevention optimization installation method, comprising the following steps:

[0007] Receiving first information sent by a central air-conditioning installation user, wherein the first information carries indoor house structure information of the central air-conditioning installation user;

[0008] Obtaining the power of the central air conditioner according to the indoor house structure information;

[0009] Obtaining material information of the condensate drain pipe according to the power of the central air conditioner;

[0010] Obtaining a pipe layout strategy for the condensate drain pipe of the central air conditioner according to the indoor house structure information;

[0011] Establishing installation parameters according to the power of the central air conditioner, the material information and the pipe layout strategy;

[0012] Training a water leakage prediction model according to a historical data set, learning the installation parameters through the water leakage prediction model, and obtaining the water leakage probability of the central air conditioner;

[0013] Comparing the water leakage probability with a predetermined water leakage probability threshold, and if the water leakage probability is less than the water leakage probability threshold, performing the central air conditioning installation according to the installation parameters;

[0014] Otherwise, the installation parameters are adjusted until the water leakage probability is less than the water leakage probability threshold, and the central air conditioning installation is performed according to the adjusted installation parameters.

[0015] In one embodiment of the present invention, training a water leakage prediction model based on a historical data set includes:

[0016] Obtain a historical data set of the target brand of central air conditioners; wherein, taking households as units, one household data is a piece of historical data; merging multiple pieces of historical data into the historical data set; the piece of historical data includes house structure information, power of central air conditioners, material information of central air conditioners, layout strategy of condensate drainage pipes; and operation time, operation environment, maintenance records and water leakage events of central air conditioners;

[0017] A water leakage prediction model is trained based on the historical data set.

[0018] In one embodiment of the present invention, training a water leakage prediction model based on a historical data set also includes:

[0019] Performing water leakage influencing factor identification on the historical data set to obtain a water leakage influencing factor set;

[0020] Extracting a first water leakage influencing factor from the water leakage influencing factor set, extracting factor data and water leakage data from the historical data set according to the first water leakage influencing factor, and obtaining a first factor sample data set;

[0021] Performing cluster analysis on the first factor sample data set to obtain a first cluster analysis result, and performing similarity calculation with a cluster seed based on the first cluster analysis result to obtain a first similarity;

[0022] If the first similarity is less than a similarity threshold, adding the first water leakage influencing factor to a set of unrelated influencing factors;

[0023] Taking the difference between the water leakage influencing factor set and the unrelated influencing factor set as the target influencing factor set;

[0024] The water leakage prediction model is constructed by training according to the target influencing factor set.

[0025] In one embodiment of the present invention, cluster analysis is performed on the first factor sample data set to obtain a first cluster analysis result, and similarity calculation is performed with a clustering target based on the first cluster analysis result to obtain a first similarity, including:

[0026] Performing cluster seed recognition on the water leakage influencing factors to obtain seed points; and performing coordinate transformation on the seed points to obtain seed point positions;

[0027] Performing coordinate transformation on the first factor sample data set to obtain a first factor sample point data set;

[0028] Calculate the distance ED between each sample point in the first factor sample point data set and the seed point; the smaller the distance ED between the sample point and the seed point, the greater the similarity between the sample point and the seed point; otherwise, the smaller the similarity;

[0029] The distance calculation method is as follows:

[0030] ;

[0031] Where n is the coordinate dimension; is the position of the seed point on the kth coordinate axis; is the position of one of the sample points on the kth coordinate axis; k is the coordinate dimension number, and its value range is 1, 2, 3, ..., n.

[0032] In one embodiment of the present invention, obtaining the power of the central air conditioner according to the indoor house structure information includes:

[0033] Obtaining the area of ​​the indoor house;

[0034] Obtaining the properties of the indoor house;

[0035] Obtaining the average floor height of the indoor house;

[0036] The power of the central air conditioner is determined according to the indoor house area, the indoor house attributes and the average floor height of the indoor house, and the calculation method is:

[0037] ;

[0038] Among them, P is the power of central air conditioning; A is the area of ​​indoor houses; k is the property coefficient of indoor houses; H is the average floor height of indoor houses.

[0039] In one embodiment of the present invention, obtaining the material information of the condensate drain pipe according to the power of the central air conditioner includes:

[0040] Obtaining the average floor height of the indoor house;

[0041] The diameter of the condensate drainage pipe is determined according to the average floor height of the indoor house and the power of the central air conditioner, and the calculation method is:

[0042] ;

[0043] Among them, P is the power of the central air conditioner; is the condensation water generation coefficient of the central air conditioner; is the ratio of pi; H is the average floor height of indoor houses; is the coefficient of influence of floor height on lift, which is used to describe the pressure loss of the condensate drain pipe to overcome gravity; is the condensate flow rate.

[0044] In one embodiment of the present invention, the installation parameters are adjusted until the water leakage probability is less than a water leakage probability threshold, and the central air conditioning installation is performed according to the adjusted installation parameters, including adjusting the pipe layout strategy and / or the material information.

[0045] In one embodiment of the present invention, the pipe layout strategy of the condensate drain pipe of the central air conditioner is obtained according to the indoor building structure information, including:

[0046] Extracting outdoor drain outlet location information from the indoor house structure information to obtain a first location set; performing coordinate transformation on the first location set to obtain a first location point set;

[0047] Extract the location information of the indoor unit of the central air conditioner from the indoor house structure information to obtain a second location set; perform coordinate transformation on the second location set to obtain a second location point set;

[0048] Select any one position point in the second position point set as the second position point, calculate the straight-line distance between the second position point and all the position points in the first position point set, and match the central air-conditioning indoor unit with the outdoor drain outlet according to the straight-line distance calculation result;

[0049] Among them, the central air-conditioning indoor unit corresponding to the second position point matches the outdoor drainage outlet corresponding to the first position point having the smallest straight-line distance to the second position point.

[0050] In one embodiment of the present invention, after matching the central air conditioner indoor unit with the outdoor drain outlet, the method further includes:

[0051] The central air conditioner indoor unit and the outdoor drain outlet that match each other are respectively matched central air conditioner indoor unit and matched outdoor drain outlet;

[0052] A pipe layout path is planned with the matching central air-conditioning indoor unit as the starting point and the matching outdoor drain outlet as the end point.

[0053] The planned pipe layout path includes laying a condensate drainage pipe along the pipe layout path, and the condensate drainage pipe includes at least one straight segment. When the sinking slope of any of the straight segments is less than 2°, it also includes configuring a water pump for the matching central air-conditioning indoor unit.

[0054] In a second aspect, in order to solve the above technical problems, the present invention also provides a central air-conditioning water leakage prevention optimization installation system, comprising:

[0055] An information receiving module, which is used to receive first information sent by a central air-conditioning installation user, wherein the first information carries indoor house structure information of the central air-conditioning installation user;

[0056] A power calculation module, which is used to calculate the required central air-conditioning power according to the received indoor house structure information;

[0057] A material information acquisition module, which is used to obtain corresponding condensate drainage pipe material information according to the calculated central air-conditioning power;

[0058] Pipe layout strategy module, which is used to formulate a pipe layout strategy for the condensate drainage pipe of the central air conditioner based on the indoor building structure information;

[0059] An installation parameter generation module, which is used to generate installation parameters by combining the central air-conditioning power, the material information and the pipe layout strategy;

[0060] A water leakage prediction model training module is used to train a water leakage prediction model based on historical data sets, and predict the probability of water leakage by learning the current installation parameters through the central air conditioning water leakage prediction model;

[0061] The optimization installation module is used to compare the water leakage probability with a predetermined water leakage probability threshold. If the water leakage probability is less than the water leakage probability threshold, the central air conditioning installation is performed according to the generated installation parameters; otherwise, the generated installation parameters are adjusted until the water leakage probability is less than the water leakage probability threshold, and the central air conditioning installation is performed according to the adjusted installation parameters.

[0062] The above technical solution of the present invention has the following beneficial effects compared with the prior art:

[0063] The central air-conditioning water leakage prevention optimization installation method and system described in the present invention systematically evaluates before installation and dynamically adjusts the installation parameters of the central air-conditioning with the goal of reducing the probability of water leakage, thereby significantly reducing the occurrence of water leakage incidents and improving the safety and reliability of the entire central air-conditioning system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein:

[0065] Figure 1 It is a flow chart of the optimized installation method for preventing water leakage of a central air conditioner in a preferred embodiment of the present invention;

[0066] Figure 2 Flowchart for training a water leakage prediction model based on historical datasets;

[0067] Figure 3 A flow chart for obtaining the layout strategy of the condensate drainage pipe of the central air conditioner based on the indoor building structure information;

[0068] Figure 4 It is a structural block diagram of the central air-conditioning water leakage prevention optimization installation system in the preferred embodiment of the present invention. DETAILED DESCRIPTION

[0069] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0070] Embodiment 1

[0071] Reference Figure 1 As shown, the embodiment of the present invention discloses a central air conditioner water leakage prevention optimization installation method, comprising the following steps:

[0072] Receiving first information sent by a central air-conditioning installation user, wherein the first information carries indoor house structure information of the central air-conditioning installation user;

[0073] In specific application scenarios, the first information sent by the central air-conditioning installation user is collected and received by filling out a form or using a mobile phone application. The first information carries the user's indoor house structure information, which includes house type (apartment, villa, office building, etc.), building area (square meters), house structure diagram (line drawing and rendering), actual measured dimensions of each compartment (length, width, height), location and material information of partition walls, and layout of drainage system, etc. Receiving the first information sent by the user is the first and most critical step in the installation of central air-conditioning water leakage prevention, which provides a basis for subsequent installation optimization.

[0074] Obtaining the power of the central air conditioner according to the indoor house structure information;

[0075] In a specific application scenario, indoor house area extraction, indoor house attribute extraction, and indoor house average floor height extraction are performed according to the indoor house structure information; wherein; extracting the indoor house area includes extracting the area of ​​the entire house and the area of ​​each compartment; the house attributes include residential type (apartment, villa, office building), floor height, climate conditions in the area (such as year-round temperature and humidity, etc., the attribute coefficient in hot areas is usually higher), house insulation performance, etc.; the average floor height includes extracting the floor heights at multiple locations for each compartment and taking the average of multiple floor heights; the power of the central air conditioner is determined according to the indoor house area, the indoor house attributes, and the indoor house average floor height, and the calculation method is:

[0076] ;

[0077] Among them, P is the power of central air conditioning; A is the area of ​​indoor houses; k is the property coefficient of indoor houses; H is the average floor height of indoor houses.

[0078] The total power of the central air conditioner is determined based on the total area of ​​the house, and then different powers are allocated to each compartment based on the area. The influence of different regions, floors, and types of houses on the power of the central air conditioner is correlated through the property coefficient of the indoor house, thereby improving the accuracy of the central air conditioner power assessment and providing an accurate basis for subsequent water leakage prevention optimization.

[0079] Obtaining material information of the condensate drain pipe according to the power of the central air conditioner;

[0080] In a specific application scenario, the diameter of the condensate drain pipe is determined based on the extracted average floor height of the indoor house and the calculated central air conditioning power. The calculation method is:

[0081] ;

[0082] Among them, P is the power of the central air conditioner; is the condensation water generation coefficient of the central air conditioner; is the ratio of pi; H is the average floor height of indoor houses; is the coefficient of influence of floor height on lift, which is used to describe the pressure loss of the condensate drain pipe to overcome gravity; is the condensate flow rate.

[0083] Condensation water generation coefficient is the coefficient of performance of the central air conditioner, obtained through experiments, in liters per hour per kilowatt; is the influence coefficient of floor height on lift, which is used to describe the pressure loss of the condensate drainage pipe due to overcoming gravity and is related to the floor height of the room; The condensate flow rate is taken into consideration. As the air conditioner runs longer, the flow rate decreases due to bacteria growth in the drain pipe. The preferred setting is 0.5-1.5 m / s.

[0084] The influence coefficient of floor height on lift The influence of different building heights on drainage smoothness is correlated, thereby improving the accuracy of the evaluation of the diameter of the condensate drainage pipe and providing an accurate basis for subsequent water leakage prevention optimization. It should be noted here that the building height is the actual height of the space for decoration and living in the house. The higher the building height, the greater the sinking slope of the condensate drainage pipe can be, the smaller the pressure loss after overcoming gravity, and the smoother the drainage.

[0085] Condensation water generation coefficient The amount of condensate generated by the associated air conditioner can improve the accuracy of the assessment of the condensate discharge amount, indirectly improve the accuracy of the assessment of the diameter of the condensate drainage pipe, and then provide an accurate basis for the subsequent water leakage prevention optimization.

[0086] Obtaining a pipe layout strategy for the condensate drain pipe of the central air conditioner according to the indoor house structure information;

[0087] In specific application scenarios, the location of the outdoor drain outlet and the installation position of the indoor unit are extracted based on the indoor house structure information. The condensate drain pipe is laid with the indoor unit as the starting point and the outdoor drain outlet as the end point to ensure that the condensate generated by the indoor unit enters the outdoor drain outlet smoothly for discharge.

[0088] Establishing installation parameters according to the power of the central air conditioner, the material information and the pipe layout strategy;

[0089] Training a water leakage prediction model according to a historical data set, learning the installation parameters through the water leakage prediction model, and obtaining the water leakage probability of the central air conditioner;

[0090] Comparing the water leakage probability with a predetermined water leakage probability threshold, and if the water leakage probability is less than the water leakage probability threshold, performing the central air conditioning installation according to the installation parameters;

[0091] Otherwise, the installation parameters are adjusted until the water leakage probability is less than the water leakage probability threshold, and the central air conditioning installation is performed according to the adjusted installation parameters.

[0092] The central air-conditioning water leakage prevention optimization installation method described in the present invention systematically evaluates before installation and dynamically adjusts the installation parameters of the central air-conditioning with the goal of reducing the probability of water leakage, thereby significantly reducing the occurrence of water leakage incidents and improving the safety and reliability of the entire central air-conditioning system.

[0093] Further, the water leakage prediction model is trained based on the historical data set, including:

[0094] Obtain a historical data set of the target brand of central air conditioners, and train a water leakage prediction model based on the historical data set; wherein, taking households as units, one household data is one historical data; merge multiple historical data into the historical data set; the one historical data includes house structure information, central air conditioner power, central air conditioner material information, condensate drainage pipe layout strategy; as well as central air conditioner operating time, operating environment, maintenance records and water leakage events.

[0095] In the method of the present invention, a water leakage prediction model can be obtained by training based on any model training method in the prior art. However, the existing model training method often automatically learns and extracts features related to the probability of water leakage based on multi-source heterogeneous data such as historical water leakage events, house structures, and drainage pipe properties, and then uses a machine learning algorithm to train the prediction model. However, multi-source heterogeneous data such as historical water leakage events, house structures, and drainage pipe properties have different effects on the accuracy of the prediction model. If all the data are directly used for model training, it is easy to lead to insufficient prediction accuracy, thereby affecting the adjustment accuracy and accuracy of the installation parameters.

[0096] In order to solve the above problems, the central air conditioner water leakage prevention optimization installation method described in the present invention trains a water leakage prediction model based on historical data sets, and refers to Figure 2 As shown, the water leakage prediction model training method includes the following steps:

[0097] Step 1: performing water leakage influencing factor identification on the historical data set to obtain a water leakage influencing factor set;

[0098] In the specific application scenario, the target brand central air conditioner is the central air conditioner brand to be installed, such as Gree, Mitsubishi, Daikin, etc.; historical data refers to the leakage record data of the target brand central air conditioner in historical time, including house structure information, central air conditioner power, central air conditioner material information (material type, pipe diameter, etc.), condensate drainage pipe layout strategy (laying path, number of pipes, pipe sinking slope and whether to install a pump, etc.); and central air conditioner operation time, operation environment, maintenance records, etc. Further, the leakage influencing factors are identified on the historical data to obtain a set of leakage influencing factors. Specifically, the historical data can be analyzed through existing analysis methods, such as factor analysis, to obtain multiple indicators that are associated with the probability of leakage, such as power, pipe layout path, pipe sinking slope, etc. as a set of leakage influencing factors.

[0099] Step 2: extracting a first water leakage influencing factor from the water leakage influencing factor set, extracting factor data and water leakage data from the historical data set according to the first water leakage influencing factor, and obtaining a first factor sample data set;

[0100] In a specific application scenario, the first water leakage influencing factor in the water leakage influencing factor set is extracted, and the factor data and water leakage data are extracted from the historical data set according to the first water leakage influencing factor, that is, the factor and water leakage data corresponding to the first water leakage influencing factor are extracted. The corresponding factor data and water leakage data are extracted for all water leakage influencing factors to form a first factor sample data set. For example, if the first water leakage influencing factor is the pipe sinking slope, it is only necessary to extract the pipe sinking slope and water leakage data from the historical data set and put them into the first factor sample data set.

[0101] Step 3: performing cluster analysis on the first factor sample data set to obtain a first cluster analysis result, and performing similarity calculation with a cluster seed based on the first cluster analysis result to obtain a first similarity;

[0102] In a specific application scenario, cluster analysis is performed on the first factor sample data set to obtain a first cluster analysis result, that is, data with correlation in the first factor sample data set is clustered into one category through cluster analysis; the cluster seed is the influencing factor that will definitely cause water leakage; the cluster seed here is determined based on historical experience data; first, the cluster seed is converted into data that can be operated on the first factor sample data set, and the similarity between the cluster seed and each clustered first factor sample data is calculated to obtain a first similarity; wherein, cluster analysis is performed first, and then the similarity calculation with the cluster seed is performed based on the cluster analysis result, which can reduce the amount of calculation and improve the operating efficiency; illustratively, the cluster seed is calculated once with the first factor sample data in the same category after clustering to obtain the first similarity.

[0103] Step 4: If the first similarity is less than a similarity threshold, add the first water leakage influencing factor to a set of unrelated influencing factors;

[0104] In a specific application scenario, the first similarity is used to describe the similarity between the first factor sample and the cluster seed. The larger the first similarity is, the more similar the first factor sample is to the cluster seed, and the more likely the first factor sample is to cause a water leakage event.

[0105] Step 5: taking the difference between the water leakage influencing factor set and the unrelated influencing factor set as the target influencing factor set;

[0106] In a specific application scenario, the difference between the water leakage influencing factor set and the unrelated influencing factor set is to eliminate the first factor samples that are not related to the water leakage event, and the first factor samples of the difference thickness are all samples related to the water leakage event.

[0107] Step 6: Construct the water leakage prediction model based on the target influencing factor set training.

[0108] In specific application scenarios, samples related to water leakage events are used to train the water leakage prediction model, thereby improving the prediction accuracy and prediction precision of the water leakage prediction model.

[0109] Further, performing cluster analysis on the first factor sample data set to obtain a first cluster analysis result, and performing similarity calculation with a clustering target based on the first cluster analysis result to obtain a first similarity, including:

[0110] Performing cluster seed recognition on the water leakage influencing factors to obtain seed points; and performing coordinate transformation on the seed points to obtain seed point positions;

[0111] Performing coordinate transformation on the first factor sample data set to obtain a first factor sample point data set;

[0112] Calculate the distance ED between each sample point in the first factor sample point data set and the seed point; the smaller the distance ED between the sample point and the seed point, the greater the similarity between the sample point and the seed point; otherwise, the smaller the similarity;

[0113] The distance calculation method is as follows:

[0114] ;

[0115] Where n is the coordinate dimension; is the position of the seed point on the kth coordinate axis; is the position of one of the sample points on the kth coordinate axis; k is the coordinate dimension number, and its value range is 1, 2, 3, ..., n.

[0116] In a specific application scenario, by obtaining the distance between the seed point and the sample point in the same coordinate system, the similarity between the clustering seed and the first factor sample point is described by the distance. The smaller the distance ED between the sample point and the seed point, the greater the similarity between the sample point and the seed point; otherwise, the similarity is smaller.

[0117] In other embodiments of the present invention, the installation parameters are adjusted until the water leakage probability is less than a water leakage probability threshold, and the central air conditioning installation is performed according to the adjusted installation parameters, including adjusting the pipe layout strategy and / or the material information.

[0118] Specifically, the pipe layout strategy of the condensate drain pipe of the central air conditioner is obtained according to the indoor house structure information, referring to Figure 3 As shown, obtaining the pipe layout strategy includes the following steps:

[0119] Step 1: extracting outdoor drain outlet location information from the indoor house structure information to obtain a first location set; performing coordinate transformation on the first location set to obtain a first location point set;

[0120] In a specific application scenario, consider the situation where a household has multiple outdoor drainage outlets. The location information of all outdoor drainage outlets is extracted and coordinate conversion is performed to prepare for subsequent calculations.

[0121] Step 2: extract the location information of the indoor central air conditioner from the indoor house structure information to obtain a second location set; perform coordinate transformation on the second location set to obtain a second location point set;

[0122] In a specific application scenario, consider a household with multiple compartments, and the developer has pre-planned air conditioner installation locations for the compartments, or the decoration company has planned the air conditioner installation locations in advance, extract the location information of all indoor units, and perform coordinate conversion to prepare for subsequent calculations.

[0123] Step 3: Select any one position point in the second position point set as the second position point, calculate the straight-line distance between the second position point and all the position points in the first position point set, and match the central air-conditioning indoor unit with the outdoor drain outlet according to the straight-line distance calculation result;

[0124] Among them, the central air-conditioning indoor unit corresponding to the second position point matches the outdoor drainage outlet corresponding to the first position point having the smallest straight-line distance to the second position point.

[0125] In a specific application scenario, the central air-conditioning indoor unit corresponding to the second position point matches the outdoor drain outlet corresponding to the first position point having the smallest straight-line distance to the second position point, that is, finding the outdoor drain outlet closest to the current indoor unit position, thereby reducing the length of the drainage path, facilitating smooth drainage, and reducing the probability of water leakage incidents.

[0126] Step 4: The central air-conditioning indoor unit and the outdoor drain outlet that match each other are respectively a matching central air-conditioning indoor unit and a matching outdoor drain outlet; a pipe layout path is planned with the matching central air-conditioning indoor unit as the starting point and the matching outdoor drain outlet as the end point.

[0127] The planned pipe layout path includes laying a condensate drainage pipe along the pipe layout path, and the condensate drainage pipe includes at least one straight segment. When the sinking slope of any of the straight segments is less than 2°, it also includes configuring a water pump for the matching central air-conditioning indoor unit.

[0128] In specific application scenarios, the pipe layout strategy configures the condensate drainage pipe to have a sinking slope of no less than 2°. In addition, the sinking slope is related to the building height. When the building height limits the sinking slope to less than 2°, a water pump is configured to ensure smooth drainage of condensate and reduce the probability of water leakage.

[0129] Embodiment 2

[0130] Reference Figure 4 As shown, the embodiment of the present invention discloses a central air-conditioning water leakage prevention optimization installation system, comprising:

[0131] An information receiving module, which is used to receive first information sent by a central air-conditioning installation user, wherein the first information carries indoor house structure information of the central air-conditioning installation user;

[0132] A power calculation module, which is used to calculate the required central air-conditioning power according to the received indoor house structure information;

[0133] A material information acquisition module, which is used to obtain corresponding condensate drainage pipe material information according to the calculated central air-conditioning power;

[0134] Pipe layout strategy module, which is used to formulate a pipe layout strategy for the condensate drainage pipe of the central air conditioner based on the indoor building structure information;

[0135] An installation parameter generation module, which is used to generate installation parameters by combining the central air-conditioning power, the material information and the pipe layout strategy;

[0136] A water leakage prediction model training module is used to train a water leakage prediction model based on historical data sets, and predict the probability of water leakage by learning the current installation parameters through the central air conditioning water leakage prediction model;

[0137] The optimization installation module is used to compare the water leakage probability with a predetermined water leakage probability threshold. If the water leakage probability is less than the water leakage probability threshold, the central air conditioning installation is performed according to the generated installation parameters; otherwise, the generated installation parameters are adjusted until the water leakage probability is less than the water leakage probability threshold, and the central air conditioning installation is performed according to the adjusted installation parameters.

[0138] The central air conditioning water leakage prevention optimization installation system disclosed in the embodiment of the present invention can effectively realize the central air conditioning water leakage prevention optimization, and the technical effects that can be achieved are as described in the above embodiments and will not be repeated here.

[0139] In summary, the central air-conditioning anti-leakage optimization installation method and system described in the present invention systematically evaluates the installation parameters of the central air-conditioning before installation and dynamically adjusts the installation parameters of the central air-conditioning with the goal of reducing the probability of leakage, thereby significantly reducing the occurrence of leakage incidents and improving the safety and reliability of the entire central air-conditioning system.

[0140] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0141] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0142] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0143] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

[0144] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. A central air conditioning water leakage prevention optimization installation method, characterized in that: The following steps are included: Receiving first information sent by a central air-conditioning installation user, wherein the first information carries indoor house structure information of the central air-conditioning installation user; Obtaining the power of the central air conditioner according to the indoor house structure information; Obtaining material information of a condensate drain pipe according to the power of the central air conditioner; Obtaining a pipe layout strategy for the condensate drain pipe of the central air conditioner according to the indoor house structure information; Establishing installation parameters according to the power of the central air conditioner, the material information and the pipe layout strategy; Training a water leakage prediction model according to a historical data set, learning the installation parameters through the water leakage prediction model, and obtaining the water leakage probability of the central air conditioner; Comparing the water leakage probability with a predetermined water leakage probability threshold, and if the water leakage probability is less than the water leakage probability threshold, performing the central air conditioning installation according to the installation parameters; Otherwise, the installation parameters are adjusted until the water leakage probability is less than the water leakage probability threshold, and the central air conditioning installation is performed according to the adjusted installation parameters; Wherein, obtaining the power of the central air conditioner according to the indoor house structure information includes: Obtaining the area of ​​the indoor house; Obtaining the properties of the indoor house; Obtaining the average floor height of the indoor house; The power of the central air conditioner is determined according to the indoor house area, the indoor house attributes and the average floor height of the indoor house, and the calculation method is: ； Among them, P is the power of central air conditioning; A is the area of ​​indoor houses; k is the property coefficient of indoor houses; H is the average floor height of indoor houses; Obtaining material information of the condensate drain pipe according to the power of the central air conditioner, including: Obtaining the average floor height of the indoor house; The diameter of the condensate drainage pipe is determined according to the average floor height of the indoor house and the power of the central air conditioner, and the calculation method is: ; Among them, P is the power of the central air conditioner; is the condensation water generation coefficient of the central air conditioner; is the ratio of pi; H is the average floor height of indoor houses; is the coefficient of influence of floor height on lift, which is used to describe the pressure loss of the condensate drain pipe to overcome gravity; is the condensate flow rate; Obtaining a pipe layout strategy for the condensate drain pipe of the central air conditioner according to the indoor building structure information, including: Extracting outdoor drain outlet location information from the indoor house structure information to obtain a first location set; performing coordinate transformation on the first location set to obtain a first location point set; Extract the location information of the indoor unit of the central air conditioner from the indoor house structure information to obtain a second location set; perform coordinate transformation on the second location set to obtain a second location point set; Select any one position point in the second position point set as the second position point, calculate the straight-line distance between the second position point and all the position points in the first position point set, and match the central air-conditioning indoor unit with the outdoor drain outlet according to the straight-line distance calculation result; Among them, the central air-conditioning indoor unit corresponding to the second position point matches the outdoor drainage outlet corresponding to the first position point having the smallest straight-line distance to the second position point.

2. The central air conditioner water leakage prevention optimization installation method according to claim 1 is characterized in that: Training the water leakage prediction model based on historical data sets includes: Obtain a historical data set of the target brand of central air conditioners; wherein, taking households as units, one household data is a piece of historical data; merging multiple pieces of historical data into the historical data set; the piece of historical data includes house structure information, power of central air conditioners, material information of central air conditioners, layout strategy of condensate drainage pipes; and operation time, operation environment, maintenance records and water leakage events of central air conditioners; A water leakage prediction model is trained based on the historical data set.

3. The central air conditioner water leakage prevention optimization installation method according to claim 2 is characterized in that: Training a water leakage prediction model based on historical data sets also includes, Performing water leakage influencing factor identification on the historical data set to obtain a water leakage influencing factor set; Extracting a first water leakage influencing factor from the water leakage influencing factor set, extracting factor data and water leakage data from the historical data set according to the first water leakage influencing factor, and obtaining a first factor sample data set; Performing cluster analysis on the first factor sample data set to obtain a first cluster analysis result, and performing similarity calculation with a cluster seed based on the first cluster analysis result to obtain a first similarity; If the first similarity is less than a similarity threshold, adding the first water leakage influencing factor to a set of unrelated influencing factors; Taking the difference between the water leakage influencing factor set and the unrelated influencing factor set as the target influencing factor set; The water leakage prediction model is constructed by training according to the target influencing factor set.

4. The central air conditioner water leakage prevention optimization installation method according to claim 3 is characterized in that: Performing cluster analysis on the first factor sample data set to obtain a first cluster analysis result, and performing similarity calculation with a clustering target based on the first cluster analysis result to obtain a first similarity, including: Performing cluster seed identification on the water leakage influencing factors to obtain seed points; and performing coordinate transformation on the seed point to obtain the seed point position; Performing coordinate transformation on the first factor sample data set to obtain a first factor sample point data set; Calculate the distance ED between each sample point in the first factor sample point data set and the seed point; the smaller the distance ED between the sample point and the seed point, the greater the similarity between the sample point and the seed point; otherwise, the smaller the similarity; The distance calculation method is as follows: ； Where n is the coordinate dimension; is the position of the seed point on the kth coordinate axis; is the position of one of the sample points on the kth coordinate axis; k is the coordinate dimension number.

5. The central air conditioner water leakage prevention optimization installation method according to claim 1 is characterized in that: The installation parameters are adjusted until the water leakage probability is less than a water leakage probability threshold, and the central air conditioning installation is performed according to the adjusted installation parameters, including adjusting the pipe layout strategy and / or the material information.

6. The central air conditioner water leakage prevention optimization installation method according to claim 1 is characterized in that: After the central air conditioner indoor unit is matched with the outdoor drain outlet, the method further includes: The central air conditioner indoor unit and the outdoor drain outlet that match each other are respectively matched central air conditioner indoor unit and matched outdoor drain outlet; Planning a pipe layout path with the matching central air conditioner indoor unit as the starting point and the matching outdoor drain outlet as the end point; The planned pipe layout path includes laying a condensate drainage pipe along the pipe layout path, and the condensate drainage pipe includes at least one straight segment. When the sinking slope of any of the straight segments is less than 2°, it also includes configuring a water pump for the matching central air-conditioning indoor unit.

7. A central air conditioner water leakage prevention optimization installation system, used to execute the central air conditioner water leakage prevention optimization installation method according to any one of claims 1 to 6, characterized in that: include, An information receiving module, which is used to receive first information sent by a central air-conditioning installation user, wherein the first information carries indoor house structure information of the central air-conditioning installation user; A power calculation module, which is used to calculate the required central air-conditioning power according to the received indoor house structure information; A material information acquisition module, which is used to obtain corresponding condensate drainage pipe material information according to the calculated central air-conditioning power; Pipe layout strategy module, which is used to formulate a pipe layout strategy for the condensate drainage pipe of the central air conditioner based on the indoor building structure information; An installation parameter generation module, which is used to generate installation parameters by combining the central air-conditioning power, the material information and the pipe layout strategy; A water leakage prediction model training module is used to train a water leakage prediction model based on historical data sets, and predict the probability of water leakage by learning the current installation parameters through the central air conditioning water leakage prediction model; The optimization installation module is used to compare the water leakage probability with a predetermined water leakage probability threshold. If the water leakage probability is less than the water leakage probability threshold, the central air conditioning installation is performed according to the generated installation parameters; otherwise, the generated installation parameters are adjusted until the water leakage probability is less than the water leakage probability threshold, and the central air conditioning installation is performed according to the adjusted installation parameters.

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