Shower room deodorization control method and device based on intelligent perception

Abstract

The invention discloses a shower room deodorization control method and device based on intelligent perception, and relates to the technical field of intelligent deodorization, and the method comprises the following steps: interactively obtaining target construction characteristics of a target shower room; configuring an environment perception sensor to obtain a multi-dimensional perception sensing array; performing data mining by taking the target construction characteristics as constraints to obtain a plurality of sample shower rooms; interactively obtaining a plurality of historical deodorization control records of a plurality of sample shower rooms, obtaining a plurality of deodorization modes, and constructing a deodorization control judgment model; and carrying out deodorization mode matching analysis, outputting a real-time demand mode, calling real-time deodorization control parameters, and operating a shower room deodorization system to carry out deodorization treatment on the target shower room. The technical problems that in the prior art, deodorization control lacks accuracy and effectiveness, and energy conservation is difficult to achieve while the deodorization effect is guaranteed are solved, the accurate and effective deodorization effect is achieved, and the technical effect of efficient energy conservation is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent deodorization, and in particular to a shower room deodorization control method and device based on intelligent perception. Background Art

[0002] As people's living standards continue to improve, the requirements for comfort and sanitary conditions of the living environment are also increasing. As an important part of home life, the odor problem of shower rooms after use has always troubled people. Traditional shower room deodorization methods are often simple and extensive, such as relying solely on ventilation equipment and using chemical deodorants. However, these methods have many shortcomings. The effect of ventilation equipment is greatly affected by environmental factors, it is difficult to accurately control the deodorization effect, and it causes energy waste.

[0003] The existing technology has the technical problems of lack of precision and effectiveness in deodorization control and difficulty in achieving energy conservation while ensuring the deodorization effect. Summary of the invention

[0004] The present application provides a shower room deodorization control method and device based on intelligent perception, which is used to solve the technical problems in the prior art that deodorization control lacks accuracy and effectiveness and is difficult to achieve energy saving while ensuring the deodorization effect.

[0005] In view of the above problems, the present application provides a shower room deodorization control method and device based on intelligent perception.

[0006] The first aspect of the present application provides a shower room deodorization control method based on intelligent perception, the method comprising:

[0007] Interactively obtain target construction features of a target shower room; configure the target shower room with environmental perception sensors according to the target construction features to obtain a multi-dimensional perception sensor array; perform data mining with the target construction features as constraints to obtain multiple sample shower rooms; interactively obtain multiple historical deodorization control records of the multiple sample shower rooms, and perform deodorization control analysis according to the multiple historical deodorization control records to obtain multiple deodorization modes, wherein each deodorization mode includes an environmental control matrix and a multi-dimensional odor perception interval; construct a deodorization control judgment model based on the multiple deodorization modes; the deodorization control judgment model receives and determines the deodorization control matrix according to the multi-dimensional perception sensor array. The real-time multi-dimensional perception data uploaded by the array is subjected to deodorization pattern matching analysis, and a real-time demand pattern is output; the real-time deodorization control parameters are called according to the real-time demand pattern, and the real-time deodorization control parameters are used to operate the shower room deodorization system to deodorize the target shower room; a multi-dimensional safety and sanitation interval and a deodorization treatment frequency threshold are preset; if the real-time deodorization treatment frequency does not meet the deodorization treatment frequency threshold, it is determined in real time whether the process multi-dimensional perception data collected by the multi-dimensional perception sensor array meets the multi-dimensional safety and sanitation interval, until the process multi-dimensional perception data meets the multi-dimensional safety and sanitation interval, the operation of the shower room deodorization system is stopped.

[0008] The second aspect of the present application provides a shower room deodorization control device based on intelligent perception, the device comprising:

[0009] A target construction feature acquisition module, the target construction feature acquisition module is used to interactively obtain the target construction features of the target shower room; a multi-dimensional perception sensor array acquisition module, the multi-dimensional perception sensor array acquisition module is used to configure the environmental perception sensor of the target shower room according to the target construction features to obtain a multi-dimensional perception sensor array; a plurality of sample shower room acquisition modules, the plurality of sample shower room acquisition modules are used to perform data mining with the target construction features as constraints to obtain a plurality of sample shower rooms; a plurality of deodorization mode acquisition modules, the plurality of deodorization mode acquisition modules are used to interactively obtain a plurality of historical deodorization control records of the plurality of sample shower rooms, and perform deodorization control analysis according to the plurality of historical deodorization control records to obtain a plurality of deodorization modes, wherein each deodorization mode includes an environmental control matrix and a multi-dimensional odor perception interval; a deodorization control judgment model construction module, the deodorization control judgment model construction module constructs a deodorization control judgment model based on the plurality of deodorization modes; real-time demand A mode output module, the real-time demand mode output module is used for the deodorization control judgment model to receive and perform deodorization mode matching analysis according to the real-time multi-dimensional perception data uploaded by the multi-dimensional perception sensor array, and output a real-time demand mode; a deodorization treatment module, the deodorization treatment module is used to call the real-time deodorization control parameters according to the real-time demand mode, and use the real-time deodorization control parameters to run the shower room deodorization system to deodorize the target shower room; a deodorization treatment frequency threshold preset module, the deodorization treatment frequency threshold preset module is used to preset the multi-dimensional safety and sanitation interval and the deodorization treatment frequency threshold; a deodorization treatment frequency judgment module, the deodorization treatment frequency judgment module is used to judge in real time whether the process multi-dimensional perception data collected by the multi-dimensional perception sensor array meets the multi-dimensional safety and sanitation interval if the real-time deodorization treatment frequency does not meet the deodorization treatment frequency threshold, until the process multi-dimensional perception data meets the multi-dimensional safety and sanitation interval, the operation of the shower room deodorization system is stopped.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] Interactively obtain the target construction features of the target shower room, configure the environmental perception sensor for the target shower room, and obtain a multi-dimensional perception sensor array; perform data mining with the target construction features as constraints to obtain multiple sample shower rooms; interactively obtain multiple historical deodorization control records of multiple sample shower rooms, perform deodorization control analysis, and obtain multiple deodorization modes; construct a deodorization control judgment model based on the multiple deodorization modes; receive and perform deodorization mode matching analysis based on the real-time multi-dimensional perception data uploaded by the multi-dimensional perception sensor array, and output a real-time demand mode; call real-time deodorization control parameters according to the real-time demand mode, and use the real-time deodorization control parameters to run the shower room deodorization system to deodorize the target shower room; preset a multi-dimensional safety and sanitation interval and a deodorization treatment frequency threshold; if the real-time deodorization treatment frequency does not meet the deodorization treatment frequency threshold, then judge in real time whether the process multi-dimensional perception data collected by the multi-dimensional perception sensor array meets the multi-dimensional safety and sanitation interval, until the process multi-dimensional perception data meets the multi-dimensional safety and sanitation interval, the operation of the shower room deodorization system is stopped. It achieves precise and effective deodorization effect and realizes the technical effect of high efficiency and energy saving. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic diagram of a shower room deodorization control method based on intelligent perception provided in an embodiment of the present application;

[0014] Figure 2 A schematic diagram of a shower room deodorization control structure based on intelligent perception provided in an embodiment of the present application.

[0015] Explanation of the reference numerals: target construction feature acquisition module 10, multi-dimensional perception sensor array acquisition module 20, multiple sample shower room acquisition module 30, multiple deodorization mode acquisition module 40, deodorization control judgment model construction module 50, real-time demand mode output module 60, deodorization treatment module 70, deodorization treatment frequency threshold preset module 80, deodorization treatment frequency judgment module 90. DETAILED DESCRIPTION

[0016] The present application provides a shower room deodorization control method and device based on intelligent perception, which is used to solve the technical problems in the prior art that deodorization control lacks accuracy and effectiveness and is difficult to ensure deodorization effect while achieving energy saving.

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] Embodiment 1

[0019] like Figure 1 As shown, the present application provides a shower room deodorization control method based on intelligent perception, the method comprising:

[0020] Step S100: interactively obtaining target construction features of a target shower room.

[0021] Specifically, a series of construction feature information about the target shower room is obtained through some interactive methods, such as user input, system questions and user answers, or selection from preset options. These construction features include but are not limited to the size (length, width, height), shape (square, curved, etc.), material (glass, plastic, metal, etc.), ventilation conditions (whether there are windows, vent size and location), drainage method (floor drain location, drainage pipe diameter), etc. of the shower room. For example, when the user operates the relevant software or interface, he fills in the specific parameters of the shower room, or describes the characteristics of the shower room by voice, so that the system can obtain these key construction feature information, provide targeted basis for subsequent system configuration and control, and realize personalized deodorization solutions to meet the specific needs of different users and different shower rooms.

[0022] Step S200: configuring the target shower room with environmental perception sensors according to the target construction features to obtain a multi-dimensional perception sensor array.

[0023] Specifically, the number and distribution of sensors are determined according to the size of the shower room. For example, a larger shower room requires more sensors to fully cover the space to accurately perceive the environmental conditions. For shower rooms with special shapes, such as arcs or special shapes, the arrangement of sensors will be optimized according to their geometric characteristics to ensure effective monitoring in corners and curved parts. The characteristics of the material will also affect the configuration of the sensor. For example, some materials have a specific effect on the adsorption or diffusion of odors, and the sensitivity or type of the sensor needs to be adjusted accordingly. The areas and parameters that the sensors focus on monitoring will also be different depending on the ventilation conditions. The density of sensors in well-ventilated areas is relatively low, while locations with poor ventilation require a denser arrangement. Through such comprehensive analysis and configuration, the multi-dimensional perception sensor array formed can obtain the environmental information of the target shower room in an all-round and accurate manner, providing an accurate data basis for subsequent deodorization control and analysis. It can ensure accurate monitoring of key locations and areas where odor problems may occur, and improve the accuracy and reliability of data.

[0024] Step S300: performing data mining with the target construction features as constraints to obtain a plurality of sample shower rooms.

[0025] Specifically, first, the system will conduct a detailed digital description and quantitative analysis of the construction features of the target shower room, convert the shape features into specific geometric parameters, represent the material properties with specific codes or values, measure the ventilation conditions with values ​​such as ventilation volume and wind speed, and the size is accurate to the specific length, width and height values. Then, the system will enter a database containing a large number of shower room data, which stores detailed information on various existing shower rooms, including construction features and related usage, maintenance records and other data. For example, if the target shower room is 2 meters long, 1.5 meters wide, curved, tempered glass and well ventilated, the system will search the database for shower rooms with similar size, shape, material and ventilation as samples. For some features, a certain deviation range is allowed to expand the number and diversity of samples. Finally, the system selects multiple sample shower rooms with high similarity in construction features with the target shower room from the database. The data of these sample shower rooms will be extracted for subsequent analysis and pattern extraction, such as studying their deodorization control methods, effects and possible problems. Through such a detailed and precise data mining process, it can be ensured that the sample shower rooms obtained have sufficient reference value and provide strong support for the formulation of deodorization control strategies for target shower rooms.

[0026] Step S400: interactively obtaining multiple historical deodorization control records of the multiple sample shower rooms, and performing deodorization control analysis based on the multiple historical deodorization control records to obtain multiple deodorization modes, wherein each deodorization mode includes an environmental control matrix and a multi-dimensional odor perception interval.

[0027] Specifically, by interacting with relevant data storage systems or databases, a large number of historical deodorization control records of multiple sample shower rooms are obtained. These records contain rich detailed information, such as deodorization measures taken at different time points and in different usage scenarios, changes in environmental parameters, etc. Then, these historical deodorization control records are analyzed in depth. During the analysis process, the focus will be on the deodorization effect and control strategy under different environmental conditions. On this basis, a variety of deodorization modes are summarized, and each deodorization mode consists of two key parts: the environmental control matrix and the multi-dimensional odor perception range. The environmental control matrix covers the combination range of multiple environmental factors, such as temperature range (for example, 20-25 degrees Celsius, 25-30 degrees Celsius, etc.), humidity range (such as 40%-50%, 50%-60%, etc.), microbial quantity range (50-200CFU / cm 3 , 200-300 CFU / cm 3 The multi-dimensional odor perception range is a quantitative range definition of various odor characteristics, including but not limited to the perception range of odor intensity, odor type, etc. corresponding to different combinations of temperature, humidity, microbial quantity and compound component content. For example, a deodorization mode is: environmental control matrix: temperature range 22-26 degrees Celsius, humidity range 45%-55%, microbial quantity range 50-200 CFU / cm 3 , the compound component content range is ammonia 0.2-0.4ppm. Multi-dimensional odor perception range: Under the above environmental conditions, the odor intensity is mild, and the main odor type is a weak organic decay odor. Through such precise definition and classification, it can provide targeted and effective deodorization control solutions for shower rooms under different environmental conditions.

[0028] Step S500: constructing a deodorization control judgment model based on the multiple deodorization modes.

[0029] Specifically, the generated deodorization modes are analyzed, and each mode corresponds to a specific temperature range, humidity range, microorganism quantity range, and compound component content range. For each deodorization mode, the corresponding specific deodorization program is determined, that is, the specific deodorization operation and control strategy that needs to be taken in this mode are clarified, and a model that can make judgments is constructed. The input of the model is the real-time monitored environmental data, such as the current temperature, humidity, microorganism quantity, and compound component content. The model will compare and match the input real-time data with the intervals set by various deodorization modes, and determine the deodorization mode data interval to which the real-time data belongs. Once the interval is determined, the specific deodorization mode to be adopted can be clarified. Finally, according to the judgment result, the corresponding deodorization program is triggered, and the corresponding deodorization operation and control strategy are started to achieve efficient and accurate deodorization control.

[0030] Step S600: the deodorization control judgment model receives and performs deodorization pattern matching analysis based on the real-time multi-dimensional sensing data uploaded by the multi-dimensional sensing sensor array, and outputs a real-time demand pattern.

[0031] Specifically, each sensor in the multi-dimensional sensing array works continuously and synchronously to accurately measure and collect real-time environmental and odor-related data in the target shower room. These sensors include temperature sensors, humidity sensors, microbial detection sensors, chemical analysis sensors, and special sensors specifically used to assess odor intensity and type. The collected data will be quickly uploaded to the deodorization control judgment model in the form of digital signals. These real-time multi-dimensional sensing data are a complex data set containing multiple dimensions and parameters. For example, the temperature was measured at 27.5 degrees Celsius, the humidity was 58%, and the number of microorganisms detected was 200 CFU / cm 3 , the component content of the specific compound is 0.3ppm, the odor intensity is evaluated as level 7 (assuming a 1-10 evaluation system), and the odor type is identified as a mixture of organic decay and chemical smells. After receiving these detailed and rich data, the deodorization control judgment model immediately starts its internal complex analysis and matching algorithm. The model will compare and calculate each parameter in the received real-time data with the environmental control matrix and multi-dimensional odor perception range set in the existing multiple deodorization modes. Taking a deodorization mode as an example, its environmental control matrix may stipulate that the temperature is in the range of 25-30 degrees Celsius, the humidity is in the range of 55%-65%, and the number of microorganisms is 50-200CFU / cm 3, the content of compound components is in the range of 0.2-0.5ppm, the odor intensity is in the range of 6-8 levels, and the odor type is a mixture of organic matter and chemical substances. The model will calculate the degree of match between the real-time data and the set interval of the mode, by calculating the deviation value, similarity score or using other statistical and mathematical methods. The model will perform such comparison and calculation for all deodorization modes, comprehensively considering the matching of each parameter in each mode. If most of the parameters of a mode are relatively matched with the real-time data, and the overall matching score is higher than that of other modes, then the mode is considered to be the most suitable deodorization mode for the current situation. Finally, the model outputs this most matching deodorization mode as the real-time demand mode. For example, the output mode indicates that it is necessary to start the high-speed ventilation equipment at the same time, release a specific dose of compound deodorant, and run it for 15 minutes. Through such a highly accurate and complex matching analysis process, it can ensure that the output real-time demand mode is closely matched with the actual situation of the current shower room, so as to achieve timely, effective and accurate deodorization control, and ensure that the shower room always maintains good air quality and a comfortable use environment.

[0032] Step S700: calling the real-time deodorization control parameters according to the real-time demand mode, and operating the shower room deodorization system with the real-time deodorization control parameters to deodorize the target shower room.

[0033] Specifically, when the real-time demand pattern is determined, the system will quickly enter the parameter call link. The system will search and match in the pre-set parameter database, and accurately extract a set of detailed real-time deodorization control parameters corresponding to the specific identification or code of the real-time demand pattern. These control parameters are a set of values ​​and instructions that have been carefully designed and optimized. For example, for ventilation equipment, the parameters include specific wind speed values ​​(such as 5 meters per second), continuous running time after startup (such as 15 minutes), and intermittent start-stop cycles (such as running for 5 minutes and pausing for 2 minutes). For the release of deodorant, the parameters specify the precise dosage of each release (such as 8 ml), the interval time of release (such as once every 8 minutes), and the release method (such as spray or diffusion). After obtaining the complete real-time deodorization control parameters, the system will accurately transmit these parameters to the various executive components of the shower room deodorization system. The ventilation equipment will receive instructions for wind speed and operating time, and immediately adjust the fan speed and working time; the deodorant release device will perform precise release operations according to the given dosage and time interval; if the system also includes other deodorization means, such as ion generators, activated carbon filters, etc., they will also receive their respective working parameters and start running accordingly. During the entire deodorization process, the system will continuously monitor the operating status of each component and the execution of parameters, and through sensor feedback, confirm in real time whether the ventilation equipment has reached the set wind speed and whether the release of the deodorant meets the predetermined dosage and time interval. If any deviation or abnormality occurs, the system will make timely adjustments and corrections to ensure that the deodorization process is always carried out according to the optimal real-time deodorization control parameters, so as to efficiently and stably deodorize the target shower room, quickly eliminate odors, and improve the quality of the shower environment.

[0034] Step S800: Preset the multi-dimensional safety and sanitation interval and deodorization treatment frequency threshold.

[0035] Specifically, the multi-dimensional safety and sanitation interval is a set of numerical ranges that comprehensively consider multiple environmental factors, aiming to ensure that the shower room maintains good hygiene and does not produce odor under these conditions. For temperature, the preset safety and sanitation interval is 20-28 degrees Celsius. Within this range, the growth and reproduction of microorganisms are relatively slow, and it is not easy to cause odor. If the temperature is too high or too low, exceeding this range, it will accelerate the decomposition of organic matter or affect the ventilation effect, thereby increasing the possibility of odor. The preset interval of humidity may be 40%-60%. Too high humidity is prone to breeding mold and bacteria, while too low humidity causes some materials to crack and release odorous substances. Staying within this range helps maintain environmental stability and hygiene. In terms of microorganisms, an upper limit is set for the number of common odor-causing microorganisms, such as certain specific bacteria and fungi. For example, the number of a certain bacteria does not exceed 100 CFU / cm 3, the number of spores of a certain fungus does not exceed 50 CFU / cm 3 Etc. For gas components, such as ammonia, hydrogen sulfide, volatile organic compounds (VOCs), etc., set their respective concentration limits. For example, the ammonia concentration does not exceed 0.1ppm, the hydrogen sulfide concentration does not exceed 0.01ppm, the total VOCs concentration does not exceed 0.5ppm, etc. The deodorization frequency threshold specifies the minimum and maximum number of deodorization operations within a certain period of time. For example, the number of deodorization treatments per 24 hours is not less than 2 times and not more than 5 times. The setting of this threshold must ensure sufficient deodorization effect while avoiding waste of resources and equipment loss caused by excessive treatment. By presetting these multi-dimensional safety and sanitation intervals and deodorization frequency thresholds, clear operating standards and constraints can be provided for the deodorization system of the shower room to ensure efficient, energy-saving and sustainable deodorization while maintaining a good sanitary environment.

[0036] Step S900: If the real-time deodorization treatment frequency does not meet the deodorization treatment frequency threshold, it is determined in real time whether the process multi-dimensional perception data collected by the multi-dimensional perception sensor array meets the multi-dimensional safety and sanitation interval, until the process multi-dimensional perception data meets the multi-dimensional safety and sanitation interval, the operation of the shower room deodorization system is stopped.

[0037] Specifically, the multi-dimensional sensing array will continuously collect multi-dimensional sensing data in the process, which includes information on temperature, humidity, the number of various microorganisms, and the concentration of various gas components. Then, the system will compare and evaluate the collected multi-dimensional sensing data of the process with the preset multi-dimensional safety and sanitation interval one by one. For example, compare the current temperature value with the preset temperature safety interval to determine whether it is within the allowable range; check whether the humidity data meets the humidity safety interval; verify whether the number of various microorganisms is lower than the preset upper limit; confirm whether the concentration of various gas components does not exceed the specified limit. If at a certain moment, all the multi-dimensional sensing data of the process meet the requirements of the multi-dimensional safety and sanitation interval, it means that the environment of the shower room is in a good and hygienic state at this time, and there is no risk of odor. At this time, the system will stop the operation of the shower room deodorization system to save energy and reduce unnecessary equipment loss, but after stopping the operation, the multi-dimensional sensing array will continue to work and continuously monitor the environmental conditions. Once any data exceeds the multi-dimensional safety and sanitation interval, or the deodorization treatment frequency does not meet the threshold requirements again, the system will restart the deodorization system for processing to ensure that the shower room is always kept in a safe, hygienic and odor-free state. This dynamic monitoring and control mechanism can achieve optimal utilization of resources and efficient operation of the system while ensuring environmental quality.

[0038] In a possible implementation, step S200 further includes:

[0039] Step S210: interactively obtaining an odor-related index set, wherein the odor-related index set includes temperature, humidity, multiple microorganisms, and multiple gas components.

[0040] Step S220: obtaining a plurality of environment perception sensors according to the odor-related indicator set.

[0041] Step S230: performing an environmental perception reliability analysis on the target shower room according to the odor-related index set and the target construction features to obtain an environmental perception node set.

[0042] Step S240: Arrange the plurality of environment perception sensors according to the environment perception node set, and construct a data transmission channel between the plurality of environment perception sensors and the deodorization control judgment model to generate the multi-dimensional perception sensor array.

[0043] Specifically, the system automatically obtains a widely recognized set of indicators related to odor, including temperature, humidity, various microorganisms, and various gas components, by connecting with professional environmental monitoring databases, research reports, or relevant standards. During the interaction, the system also provides a brief description of each indicator to help users better understand its meaning and function. For example, it explains how temperature affects the volatilization of chemicals and the activity of microorganisms to produce odors, the impact of humidity on mold growth and odor transmission, the mechanism by which different types of microbial metabolites cause odors, and the unique odor characteristics of various gas components themselves. Through this interactive operation, it ensures that a comprehensive and accurate set of key indicators closely related to odors is obtained, providing a clear basis and direction for subsequent environmental perception and deodorization control.

[0044] The system has a sensor library that contains various sensor types that can measure different parameters. When it is determined that the odor-related indicator set includes temperature, humidity, multiple microorganisms, and multiple gas components, the system will select specific sensors from the library that can accurately measure these indicators. For the temperature indicator, the system will call a high-precision digital temperature sensor that can accurately measure the temperature change in the shower room. For the humidity indicator, a humidity sensor with high sensitivity and fast response will be selected to accurately obtain the humidity value. In terms of microbial measurement, special microbial detection sensors will be called. These sensors are based on optical, electrical or biotechnology principles and can identify and quantify a variety of common microbial species and quantities. For multiple gas components, sensors that can detect different gases separately will be called, such as ammonia sensors for ammonia, special hydrogen sulfide sensors for hydrogen sulfide, and broad-spectrum VOC sensors for volatile organic compounds. When calling these sensors, the system will also obtain their relevant parameters and configuration information, such as measurement range, accuracy, response time, etc., to ensure that they can meet the measurement requirements of odor-related indicators.

[0045] The various parameters in the odor-related index set, such as specific temperature range, humidity threshold, common breeding conditions of various microorganisms, concentration standards of different gas components, etc., are deeply integrated and analyzed with the specific construction characteristics of the target shower room. For the construction characteristics of the target shower room, its spatial geometry (square, rectangular or irregular), size (length, width, height), internal structure (whether there are partitions, shelves, etc.), material characteristics (the influence of wall and floor materials on odor adsorption and emission), ventilation system design (location, number, wind speed, etc. of vents) and drainage system layout (floor drain location, drainage pipe direction) and other aspects will be considered in detail. In the analysis process, the three-dimensional model of the shower room is first constructed using computer simulation software, and the air flow trajectory in the room is simulated according to the location and wind speed of the vents. It is found that due to the angle of the vent position, the air circulation in the corner area of ​​the room far away from the vent is relatively weak, and odor substances are easy to accumulate. Considering the temperature distribution, due to the influence of the hot water of the shower head, the temperature near the shower head is higher, while the temperature in the corner far away from the shower head and poorly ventilated is relatively low. Combined with humidity analysis, it is found that the humidity is higher near the ground and in the corners, and these areas are more conducive to the growth and reproduction of microorganisms. For microorganisms, the possibility of microorganisms attaching to tile walls and marble floors is determined based on the hydrophilicity and surface roughness of the material. For example, tile gaps and rough marble surfaces are more likely to harbor dirt and breed microorganisms. For gas components, based on the simulated air flow and the airtightness of the room, it is predicted that the concentration of gases such as hydrogen sulfide in corners and low-level areas with poor ventilation is relatively high. Combining all the above factors, the reliability of each location in accurately reflecting the odor condition is evaluated, and those locations that are considered to be most sensitive to the generation, accumulation and change of odor and can provide the most valuable information are determined as environmental perception nodes. For example, the location below the vent, around the shower head, the low-level water accumulation in the corner, and the space above the area where people often stay, together constitute the environmental perception node set. Through such a comprehensive, detailed and multi-factor-based analysis process, the environmental perception node set can be accurately determined, providing a key location basis for subsequent environmental monitoring and deodorization control, ensuring that the odor problem in the shower room is effectively captured and dealt with.

[0046] According to the determined set of environmental perception nodes, the deployment of multiple environmental perception sensors begins. For each node location, select sensors of corresponding types and specifications according to the specific parameters that need to be monitored. For example, at the node below the vent, a comprehensive sensor that can simultaneously measure temperature, humidity and the concentration of multiple gas components is deployed; at the node around the shower head, a sensor that specifically detects humidity and the number of specific microorganisms is installed; at the node where water accumulates at a low position in the corner, a sensor that focuses on monitoring humidity and microbial growth is placed. When deploying sensors, ensure that they are firmly installed, accurately positioned, and do not affect the normal use and appearance of the shower room. At the same time, the sensors must be initialized and calibrated to ensure the accuracy and reliability of their measured data. Next, a data transmission channel is constructed between these environmental perception sensors and the deodorization control judgment model, which involves laying data cables, setting up wireless network connections, or using short-distance communication technologies such as Bluetooth. For the laying of data cables, the protection and concealment of the lines must be ensured to avoid being affected by factors such as water and moisture. If a wireless network connection is used, the network parameters need to be configured to ensure signal stability and timeliness of data transmission. In the process of building a data transmission channel, it is also necessary to set relevant parameters such as data format, transmission frequency and encryption. For example, it is stipulated that the sensor sends measurement data to the deodorization control judgment model once every certain period of time (such as 5 minutes), and the data is encapsulated in a specific format (such as JSON or XML), and an encryption algorithm is used to protect the transmitted data to prevent data leakage and tampering. By completing the layout of sensors and the construction of data transmission channels, multiple environmental perception sensors work together to collect environmental data at various key locations in the shower room in real time and accurately, and transmit these data to the deodorization control judgment model continuously. In this way, a powerful and efficient multi-dimensional perception sensor array is formed, which provides a solid data foundation for achieving precise deodorization control.

[0047] In a possible implementation, step S230 further includes:

[0048] The environment sensing node set includes a temperature sensing space node, a humidity sensing space node, a plurality of microorganism sensing space nodes and a plurality of gas sensing space nodes.

[0049] Specifically, temperature-sensing spatial nodes refer to those specific locations in the shower room that are more sensitive to temperature changes and can typically reflect the overall temperature conditions. For example, the area close to the shower head is usually hotter due to the direct impact of hot water; while the corners far away from the shower head and with good ventilation have relatively low temperatures and relatively stable changes. These areas are identified as temperature-sensing spatial nodes, which can provide key information for accurately understanding the temperature distribution and changes in the shower room. Humidity-sensing spatial nodes are usually located in places where water is easy to accumulate and water vapor is not easy to dissipate. For example, low-lying areas on the ground, the junction of corners and the ground, or confined spaces with poor ventilation. In these locations, humidity is easy to accumulate and remain at a high level. Monitoring humidity can well reflect the changes in humidity in the shower room, as well as the odor and microbial breeding problems that may be caused by it. Multiple microbial sensing spatial nodes focus on areas where microorganisms are easy to reproduce and survive. This includes damp tile gaps, near drains where water accumulates for a long time, corners that are not cleaned frequently, or surfaces that are frequently in contact with human skin debris and sebum. Placing sensors at these locations can detect changes in the types and quantities of microorganisms in a timely manner, providing an important basis for assessing odor risks. Multiple gas sensing space nodes are generally set in places where air flow is relatively weak and gas is easily accumulated, such as narrow storage spaces, vent return areas, or low spaces close to the ground. These locations can effectively capture changes in the concentration of various gas components, especially those gases that are closely related to odor generation, such as hydrogen sulfide and ammonia, thereby providing accurate gas composition data for deodorization control. By clearly dividing and determining these different types of environmental sensing space nodes, the environmental conditions in the shower room can be monitored comprehensively and targeted, providing accurate and detailed basic data support for subsequent deodorization control strategies.

[0050] In a possible implementation, step S230 further includes:

[0051] Step S231: extracting the target space size based on the target construction features.

[0052] Step S232: gridding the target shower room with 1 / 20 of the target space size to obtain multiple sensing spaces.

[0053] Step S233: Arrange a plurality of temperature sensors in the plurality of sensing spaces.

[0054] Step S234: interactively obtaining the historical showering habits of the target user, and using the historical showering habits to simulate the temperature change of the target shower room to obtain multiple temperature change sequences.

[0055] Step S235: decouple the multiple temperature change sequences, and locate the sensing space corresponding to the temperature extreme value based on the decoupling result to obtain the temperature sensing space node.

[0056] Step S236: Similarly, the environment perception node set is obtained through analysis.

[0057] Specifically, the construction features of the target shower room are reviewed and analyzed in detail. These construction features may be contained in design drawings, construction records, or information obtained through on-site measurements. From these rich construction feature data, the key length, width, and height dimensions are extracted. For example, through the annotations on the design drawings, it can be directly read that the length of the shower room is 3 meters, the width is 1.8 meters, and the height is 2.1 meters. Or by using professional measuring tools on site, such as laser rangefinders, the length from one end to the other, the width from one side to the other, and the height from the ground to the ceiling of the internal space of the shower room are measured respectively. The three dimensions of length, width, and height extracted will serve as the basic data for subsequent spatial analysis and processing, providing an important basis for further gridding and spatial layout analysis.

[0058] The spatial dimensions (length, width, and height) of the target shower room have been obtained and divided into units of 1 / 20 of these dimensions. Through such fine division in the three directions of length, width, and height, the entire target shower room is divided into many small cubic spaces, which are called sensing spaces. Each sensing space can be used as an independent monitoring unit for subsequent placement of sensors and more accurate environmental analysis.

[0059] For the many sensor spaces obtained by division, multiple temperature sensors are reasonably arranged. The total number of temperature sensors to be arranged is determined based on the budget, monitoring accuracy requirements and the actual situation of the shower room. Certain rules or strategies are used to arrange the location of sensors, and priority will be given to some key sensing spaces, such as near shower heads, vents, corners, etc., because the temperature changes in these places are usually more significant or representative. In addition, the mutual interference between sensors needs to be considered to avoid arranging multiple sensors too densely in adjacent sensing spaces to ensure the accuracy and reliability of the measurement data. Through such careful layout, real-time temperature data at multiple locations in the shower room can be effectively obtained, providing a basis for subsequent temperature analysis and environmental control.

[0060] Extensively collect temperature data of multiple shower rooms with similar structures and usage. The sources of these data can be existing monitoring systems, sensor records, or relevant information reported by users themselves. The collected data are preprocessed, including cleaning outliers, missing value processing, and data standardization to ensure the quality and consistency of the data. Data analysis techniques, such as cluster analysis or classification algorithms, are used to group these data according to different characteristics or patterns. For example, data can be classified according to factors such as shower time, water temperature, and ventilation conditions. For each type of data, further analyze the change pattern of its temperature over time, calculate the mean, median, and draw a temperature change curve, summarize the typical temperature change pattern under this type of condition, and thus form a temperature change sequence. By performing such analysis and processing on multiple different categories of data, multiple temperature change sequences corresponding to different shower habits can be obtained, providing strong support for subsequent work such as determining temperature perception space nodes.

[0061] Decoupling operations are performed on the multiple temperature change sequences obtained. The decoupling process is to separate the various factors that affect the temperature change in each temperature change sequence, so as to more clearly analyze the independent effect of each factor on the temperature. For example, the effects of factors such as shower time, water temperature, and ventilation conditions on temperature changes are analyzed separately. Then, based on the results of the decoupling, the time and location of the temperature extremes (highest temperature and lowest temperature) under different conditions are found, and by comparing the extreme values ​​in multiple temperature change sequences, the sensing spaces where temperature extremes frequently appear are located. These sensing spaces where temperature extremes frequently appear are identified as temperature perception space nodes, because the temperature changes at these locations are the most significant, and the key locations of temperature perception can be accurately determined, providing a targeted layout basis for subsequent temperature monitoring and control.

[0062] After the temperature perception space node is determined, similar methods and processes are used to determine the temperature perception space node to determine other nodes related to environmental perception, such as humidity perception space nodes, multiple microorganism perception space nodes, and multiple gas perception space nodes. Combining the above analysis results of temperature, humidity, microorganisms, and gases, a complete set of environmental perception nodes is finally integrated. This set covers the key spatial locations that can comprehensively and accurately reflect the environmental conditions of the shower room, providing an accurate layout basis for subsequent environmental monitoring and control, and can accurately determine the perception nodes of key environmental factors such as temperature, humidity, microorganisms, and gases in the shower room, realizing comprehensive and accurate monitoring of environmental conditions.

[0063] In a possible implementation, step S400 further includes:

[0064] Step S410: disassembling the plurality of historical deodorization control records to obtain M historical environment sensing arrays and M historical environment control arrays, where M is a positive integer.

[0065] Step S420: performing redundant parameter identification and elimination on the M historical environment control arrays to obtain multiple environment control matrices.

[0066] Step S430: Aggregate the M historical environment perception arrays according to the multiple environment control matrices to obtain multiple historical environment perception data sets, wherein each historical environment perception data set includes several historical environment perception arrays.

[0067] Step S440: performing extreme value calls on the multiple historical environmental perception data sets according to the odor association indicator set to obtain multiple multi-dimensional odor perception intervals.

[0068] Step S450: Associating the plurality of environmental control matrices with the plurality of multi-dimensional odor perception intervals to generate the plurality of deodorization modes.

[0069] Specifically, each historical deodorization control record is a data set containing rich information, which records the environmental conditions of the shower room and the control measures taken during a specific period of time. During the disassembly process, each record will be carefully analyzed and extracted. For environmental perception, data such as temperature values, humidity values, the number or concentration of different microorganisms, and the content of various gas components will be extracted from the records, and these data will be arranged in chronological order or a specific logical order to form a historical environmental perception array. At the same time, for control measures, control parameters such as the opening time, operating power, working mode of the deodorization equipment, and the wind speed setting of the ventilation equipment will be extracted, and they will also be organized into a historical environmental control array in a certain order. Suppose we have 100 historical deodorization control records. After disassembly, we will get 100 historical environmental perception arrays and 100 historical environmental control arrays. Here, 100 is the value of M (M=100), and M can be any positive integer, depending on the number of historical deodorization control records.

[0070] For the M historical environmental control arrays obtained by the previous disassembly, in-depth analysis is needed to identify the redundant parameters. Each control parameter in each historical environmental control array is reviewed one by one. These parameters include different operating mode settings of deodorization equipment, wind speed gears of ventilation equipment, specific settings of working hours, etc. Through observation and statistical analysis of a large amount of historical data, find out those parameters that have little effect on the final deodorization effect in most cases, or those that are repeated in different control records and have similar effects. For example, it is found that a certain deodorization equipment operating mode is almost the same as other modes in actual use, or a certain wind speed gear is rarely used and has no obvious improvement on the overall effect. After identifying these redundant parameters, they are removed from the historical environmental control array. After such processing, the remaining key and effective control parameters are rearranged and arranged to form multiple environmental control matrices.

[0071] A plurality of processed environmental control matrices are obtained, and based on these environmental control matrices, aggregation operations are performed on M historical environmental perception arrays. The environmental control matrices corresponding to each historical environmental perception array are compared one by one. If the environmental control matrices corresponding to two or more historical environmental perception arrays have high similarity (for example, the value range of control parameters, combination method, etc. are similar), these historical environmental perception arrays are grouped together. Through such grouping, multiple historical environmental perception data sets are eventually formed. Each historical environmental perception data set contains several historical environmental perception arrays generated under similar environmental control conditions. Such aggregation operations are helpful for the subsequent centralized analysis and processing of environmental perception data under similar control conditions to discover more regular and valuable information.

[0072] Clarify the specific indicators and threshold ranges covered by the odor-related indicator set. Then, conduct a detailed analysis of each historical environmental perception data set. For each environmental perception data in the data set, such as temperature, humidity, number of microorganisms, gas component concentration, etc., find its maximum and minimum values ​​in the data set. Using these maximum and minimum values ​​as boundaries, determine the value range of each environmental perception parameter, thereby constructing a multidimensional odor perception interval. This processing is performed on multiple historical environmental perception data sets, and finally multiple different multidimensional odor perception intervals are obtained. These multidimensional odor perception intervals can clearly reflect the range of changes in various environmental parameters related to odor generation under different environmental conditions in the past, providing an important reference for the subsequent formulation of odor control strategies and model optimization.

[0073] First, different combinations of odor perception intervals correspond to different combinations of control parameters that can achieve odor removal. Through the previous steps, multiple environmental control matrices are obtained, which represent various effective control parameter combinations, and the multidimensional odor perception interval is extracted from the historical environmental perception data set based on the odor association index set, indicating different odor ranges. By associating the two, the corresponding environmental control matrix is ​​found for each specific multidimensional odor perception interval. In this way, multiple deodorization modes are formed. For example, when it is detected that the current environment is in a specific odor perception interval, the control parameter combination specified by the corresponding environmental control matrix can be used to perform deodorization operations. These control parameters may include ventilation time, deodorant usage, air purifier operation mode and other factors. By establishing this association, the system can automatically select the appropriate deodorization mode according to the odor situation detected in real time to achieve the purpose of effectively removing odor. This can improve the efficiency and accuracy of deodorization and ensure that the most suitable control measures can be taken to achieve odor removal under different odor conditions.

[0074] In a possible implementation, step S900 further includes:

[0075] Step S910: Pre-constructing a mode duration analysis model.

[0076] Step S920: If the real-time deodorization frequency satisfies the deodorization frequency threshold, the real-time multi-dimensional perception data and the real-time demand pattern are synchronized to the pattern duration analysis model to analyze and obtain the real-time pattern operation duration.

[0077] Step S930: timing the operation of the shower room deodorization system based on the real-time mode operation duration as a constraint.

[0078] Specifically, use decision tree regression to pre-build a mode duration analysis model, and widely collect historical data including environmental conditions (such as temperature, humidity, odor concentration, etc.), deodorization mode parameters (such as deodorization equipment power, working frequency, ventilation volume, etc.) and the corresponding deodorization mode operation time. Clean the data, handle missing values ​​and outliers, and divide the data into features (X) and target variables (y, i.e., operation time). Perform necessary transformations or scaling on the features so that the decision tree can better process the data. For example, standardize or normalize numerical features. Randomly divide the prepared data into training sets and test sets. Usually, 70% of the data can be used as a training set and 30% of the data can be used as a test set. Use the decision tree regression algorithm to create a model. Set the parameters of the decision tree, such as the maximum depth, the minimum number of sample splits, etc., but the default values ​​can be used initially. Use the training set data to train the decision tree model so that the model learns the relationship between the features and the target variable. Use the test set data to evaluate the trained decision tree model and calculate evaluation indicators such as mean square error (MSE) and mean absolute error (MAE) to understand the prediction performance of the model. Select the model with the best performance on the test set as the final pattern duration analysis model.

[0079] Continuously monitor the frequency of real-time deodorization treatment. Compare this real-time deodorization treatment frequency with the pre-set deodorization treatment frequency threshold. If the real-time deodorization treatment frequency reaches or exceeds the set deodorization treatment frequency threshold, it means that the current deodorization situation requires a more accurate operation duration analysis. At this time, the real-time multi-dimensional perception data obtained at the current moment (such as various environmental parameters such as the current temperature, humidity, odor concentration, etc.) and the real-time demand mode currently adopted (that is, the specific deodorization mode being executed, such as the operation mode of a deodorization device of a certain power, specific ventilation settings, etc.) are input into the previously pre-built mode duration analysis model. After receiving these data, the mode duration analysis model will use the laws and relationships learned internally to conduct a comprehensive analysis and calculation of the input data, and finally obtain the most appropriate real-time mode operation duration under the current real-time multi-dimensional perception data and real-time demand mode. This real-time mode operation duration will provide an important reference basis for the subsequent operation control of the deodorization system to achieve more accurate and effective deodorization treatment.

[0080] This real-time mode running time is used as a clear constraint to set the running timer of the shower room deodorization system, so that the deodorization system can run according to this determined time. For example, if the real-time mode running time is 30 minutes, the deodorization system will be set to run continuously for 30 minutes after startup. During operation, the system will strictly control the running time through an internal timing mechanism. When 30 minutes are reached, the deodorization system will automatically stop running, or switch to other modes or states according to preset strategies. By timing the operation based on the real-time mode running time, it can be ensured that the operation of the deodorization system can not only effectively remove odors, but also avoid excessive operation causing energy waste or unnecessary equipment loss, thereby achieving more efficient, energy-saving and precise deodorization control.

[0081] In a possible implementation, step S910 further includes:

[0082] Step S911: when the frequency of running the shower room deodorization system to deodorize the target shower room meets the deodorization frequency threshold, extracting multiple historical multi-dimensional perception data, multiple historical demand patterns and multiple historical pattern operation durations from the shower room deodorization system.

[0083] Step S912: Aggregate the multiple historical multidimensional perception data and the multiple historical mode operation durations according to the mapping relationship between the multiple historical demand patterns and the multiple deodorization patterns to obtain multiple deodorization operation association number sets, wherein each deodorization operation association number set includes multiple groups of historical multidimensional perception data-historical mode operation durations.

[0084] Step S913: constructing multiple running time analysis branches based on the multiple deodorization running association number sets.

[0085] Step S914: connecting the multiple running duration analysis branches in parallel to obtain the mode duration analysis model.

[0086] Specifically, in actual situations, even if the same environmental adjustment mode is used (for example, the same deodorization equipment settings and operation mode), due to different initial environmental conditions (such as differences in environmental parameters such as temperature, humidity, and odor concentration), the execution time of the environmental adjustment mode required to achieve the desired standard environmental requirements will also be different. This is because different initial environmental conditions will have different effects on the deodorization process. For example, higher temperature and humidity will cause odors to spread faster and require longer deodorization treatment; while lower odor concentrations can achieve the ideal deodorization effect in a shorter time. Based on this phenomenon, we build a model by analyzing the environmental control time corresponding to various different combinations of environmental parameters in the same mode. When the number of times the shower room deodorization system performs deodorization operations on the target shower room reaches or exceeds the pre-set deodorization frequency threshold, it means that the system has accumulated enough operating data for meaningful analysis. At this time, a series of important historical information is extracted from the shower room deodorization system. Multiple historical multi-dimensional perception data cover multiple measurements of the shower room environment at different time points, such as temperature, humidity, specific components and concentrations of odors at different times, and other detailed data. Multiple historical demand patterns refer to the various specific deodorization strategies and patterns adopted in different situations in the past, including specific working modes of deodorization equipment (such as power level, operation cycle, etc.) and ventilation system settings (wind speed, ventilation time, etc.). Multiple historical mode operation durations record the actual operation duration of each historical demand mode, that is, the time period from the start to the end of operation. By extracting these rich historical data, a solid foundation is provided for subsequent in-depth analysis and the construction of an effective mode duration analysis model, so as to more accurately predict the appropriate deodorization mode operation duration under similar environmental conditions in the future.

[0087] It is clear that there is a specific mapping relationship between multiple historical demand patterns and multiple deodorization patterns. Based on this mapping relationship, the multiple historical multi-dimensional perception data and multiple historical pattern operation times obtained are sorted and combined. After such an aggregation operation, multiple different sets will be obtained, that is, multiple deodorization operation association number sets. Each deodorization operation association number set contains multiple sets of historical multi-dimensional perception data-historical pattern operation time pairs. This means that in each set, the relationship between different environmental perception data and corresponding operation times in a specific deodorization mode is clearly presented. In this way, the data can be analyzed and processed in a more organized manner, providing an organized data foundation for the subsequent construction of an effective model.

[0088] For each deodorization operation association data set, it will be used as an independent data set to build a runtime analysis branch, select a decision tree algorithm, use the historical multi-dimensional perception data in the data set as input features, and the historical mode runtime as the target output, and train and optimize the selected algorithm. During the training process, the model will learn the potential relationship and pattern between the input features and the output duration. In this way, a branch that can analyze and predict the runtime for each deodorization operation association data set is constructed for that specific data set.

[0089] After completing the construction of multiple runtime analysis branches, these branches are connected in parallel. This means that they exist simultaneously and work independently in the final pattern duration analysis model. When new environmental perception data is input into the model, all branches will receive and process this data at the same time, and each will analyze and predict the runtime according to the rules and relationships learned in training. Then, the prediction results of each branch are combined, and the final pattern duration prediction result is obtained through a certain fusion strategy (such as taking the average, weighted average, etc.). By connecting multiple runtime analysis branches in parallel to build a pattern duration analysis model, the characteristics and rules of different data sets are fully utilized to improve the generalization ability and prediction accuracy of the model, so that it can more effectively cope with various complex environmental conditions and deodorization needs.

[0090] In a possible implementation, step S913 further includes:

[0091] Step S9131: extracting and obtaining a first deodorization operation related number set based on the plurality of deodorization operation related number sets.

[0092] Step S9132: performing linear regression analysis on multiple groups of historical multidimensional perception data-historical mode operation time in the first deodorization operation association data set to obtain a first operation control fitting function.

[0093] Step S9133: Synchronize the first operation control fitting function to a pre-built standard operation duration analysis branch to obtain a first operation duration analysis branch.

[0094] Step S9134: Similarly, the multiple running time analysis branches are constructed based on the multiple deodorization running association number sets.

[0095] Specifically, among multiple deodorization run-related data sets, one data set is selected as the object of initial processing according to a predetermined order or rule, and it is marked as the first deodorization run-related data set. This selection can be random, or it can be purposefully selected based on certain characteristics of the data set (such as the amount of data contained, specific pattern types, etc.). The purpose is to be able to conduct specific analysis and processing based on this data set in the future, thereby taking the first step in building the run time analysis branch.

[0096] Obtain multiple groups of historical multidimensional perception data-historical mode operation time data combinations in the first deodorization operation association data set, use these historical multidimensional perception data as independent variables, and the historical mode operation time as the dependent variable, and apply the linear regression method for analysis. The goal of linear regression is to find a straight line (a hyperplane in the case of multidimensional data) such that the sum of the squares of the distances from these data points to this straight line (or hyperplane) is minimized. Through calculation and optimization, a linear equation is obtained, which can approximately describe the relationship between the independent variable (historical multidimensional perception data) and the dependent variable (historical mode operation time) in a linear manner. This linear equation is the so-called first operation control fitting function, which can be used to predict the corresponding historical mode operation time based on the newly input historical multidimensional perception data.

[0097] The first operation control fitting function has been obtained. The pre-built standard operation duration analysis branch is a framework or template with certain basic structure and functions. Integrating or loading the first operation control fitting function into this pre-built branch is to embed the fitting function obtained specifically for the first deodorization operation associated number set into the corresponding position or module of the standard branch, making it the core analysis component of the branch. After such a synchronization operation, this originally general standard operation duration analysis branch is endowed with specific analysis capabilities for the first deodorization operation associated number set, thereby transforming into the first operation duration analysis branch that can specifically handle situations related to the first deodorization operation associated number set. In this way, the foundation is laid for the subsequent construction of corresponding operation duration analysis branches for different deodorization operation associated number sets, and ultimately multiple branches with specific analysis functions are formed to jointly provide support for the entire pattern duration analysis model.

[0098] After completing steps S9131 to S9133 for the first deodorization operation association number set and successfully constructing the first operation duration analysis branch, the remaining deodorization operation association number sets are processed in sequence according to the same process and method. Similarly, the above operation is repeated for each subsequent deodorization operation association number set, and multiple different operation duration analysis branches are gradually constructed. In this way, all deodorization operation association number sets are fully utilized, and corresponding analysis branches are established for each specific situation, and finally a branch set covering multiple situations and capable of comprehensive and accurate operation duration analysis is formed, which provides a solid foundation for the effectiveness and accuracy of the entire pattern duration analysis model.

[0099] Embodiment 2

[0100] Based on the same inventive concept as the shower room deodorization control method based on intelligent perception in the aforementioned embodiment, Figure 2 As shown, the present application provides a shower room deodorization control device based on intelligent perception, and the device and method embodiments in the present application embodiments are based on the same inventive concept. The device includes:

[0101] The target construction feature acquisition module 10 is used to interactively obtain the target construction features of the target shower room.

[0102] A multi-dimensional sensing sensor array acquisition module 20 is used to configure the environment sensing sensors for the target shower room according to the target construction features to obtain a multi-dimensional sensing sensor array.

[0103] A plurality of sample shower room acquisition modules 30 are used to perform data mining with the target construction features as constraints to obtain a plurality of sample shower rooms.

[0104] A multiple deodorization mode acquisition module 40 is used to interactively obtain multiple historical deodorization control records of the multiple sample shower rooms, and perform deodorization control analysis based on the multiple historical deodorization control records to obtain multiple deodorization modes, wherein each deodorization mode includes an environmental control matrix and a multi-dimensional odor perception interval.

[0105] The deodorization control judgment model building module 50 builds a deodorization control judgment model based on the multiple deodorization modes.

[0106] The real-time demand pattern output module 60 is used for the deodorization control judgment model to receive and perform deodorization pattern matching analysis based on the real-time multi-dimensional sensing data uploaded by the multi-dimensional sensing sensor array, and output the real-time demand pattern.

[0107] The deodorization processing module 70 is used to call the real-time deodorization control parameters according to the real-time demand mode, and use the real-time deodorization control parameters to operate the shower room deodorization system to perform deodorization on the target shower room.

[0108] The deodorization treatment frequency threshold preset module 80 is used to preset the multi-dimensional safety and sanitation interval and the deodorization treatment frequency threshold.

[0109] The deodorization treatment frequency judgment module 90 is used to judge in real time whether the process multi-dimensional perception data collected by the multi-dimensional perception sensor array meets the multi-dimensional safety and sanitation interval if the real-time deodorization treatment frequency does not meet the deodorization treatment frequency threshold, and stop the operation of the shower room deodorization system until the process multi-dimensional perception data meets the multi-dimensional safety and sanitation interval.

[0110] Furthermore, the multi-dimensional sensing array acquisition module 20 further includes:

[0111] The odor-related index set acquisition unit is used to interactively obtain an odor-related index set, wherein the odor-related index set includes temperature, humidity, multiple microorganisms and multiple gas components.

[0112] An environment perception sensor acquisition unit is used to obtain multiple environment perception sensors according to the odor association indicator set.

[0113] An environment perception node set acquisition unit is used to perform an environment perception reliability analysis on the target shower room according to the odor association index set and the target construction characteristics to obtain an environment perception node set.

[0114] A multi-dimensional sensing sensor array generation unit, wherein the multi-dimensional sensing sensor array generation unit is used to deploy the multiple environmental sensing sensors according to the environmental sensing node set, and to construct a data transmission channel between the multiple environmental sensing sensors and the deodorization control judgment model to generate the multi-dimensional sensing sensor array.

[0115] Furthermore, the environment perception node set acquisition unit further includes:

[0116] The environment sensing node set includes a temperature sensing space node, a humidity sensing space node, a plurality of microorganism sensing space nodes and a plurality of gas sensing space nodes.

[0117] Furthermore, the environment perception node set acquisition unit further includes:

[0118] A target space size acquisition unit is used to extract and obtain the target space size according to the target construction feature.

[0119] A sensing space acquisition unit is used to grid the target shower room with a size of 1 / 20 of the target space to obtain multiple sensing spaces.

[0120] A temperature sensor arrangement unit is used to arrange a plurality of temperature sensors in the plurality of sensing spaces.

[0121] A temperature change sequence acquisition unit is used to interactively acquire the historical shower habits of a target user, and use the historical shower habits to simulate the temperature changes of the target shower room to acquire multiple temperature change sequences.

[0122] A temperature sensing space node acquisition unit is used to decouple the multiple temperature change sequences, and locate the sensing space corresponding to the temperature extreme value based on the decoupling result to obtain the temperature sensing space node.

[0123] In this way, the environment perception node set is obtained through analysis.

[0124] Furthermore, the multiple deodorization mode acquisition module 40 also includes:

[0125] A historical deodorization control record disassembly unit is used to disassemble the multiple historical deodorization control records to obtain M historical environment perception arrays and M historical environment control arrays, where M is a positive integer.

[0126] An environmental control matrix acquisition unit is used to identify and eliminate redundant parameters of the M historical environmental control arrays to obtain multiple environmental control matrices.

[0127] A historical environment perception data set acquisition unit is used to aggregate the M historical environment perception arrays according to the multiple environmental control matrices to obtain multiple historical environment perception data sets, wherein each historical environment perception data set includes several historical environment perception arrays.

[0128] A multi-dimensional odor perception interval acquisition unit is used to perform extreme value calls on the multiple historical environmental perception data sets according to the odor association indicator set to obtain multiple multi-dimensional odor perception intervals.

[0129] A deodorization mode generating unit is used to associate the plurality of environmental control matrices with the plurality of multi-dimensional odor perception intervals to generate the plurality of deodorization modes.

[0130] Furthermore, the deodorization frequency determination module 90 further includes:

[0131] A pattern duration analysis model pre-construction unit is used to pre-construct a pattern duration analysis model.

[0132] A real-time mode operation duration obtaining unit is used to synchronize the real-time multi-dimensional perception data and the real-time demand mode to the mode duration analysis model if the real-time deodorization treatment frequency meets the deodorization treatment frequency threshold, and analyze and obtain the real-time mode operation duration.

[0133] An operation timing unit is used to set the operation timing of the shower room deodorization system based on the operation time of the real-time mode as a constraint.

[0134] Furthermore, the mode duration analysis model pre-construction unit also includes:

[0135] A historical data acquisition unit is used to extract and obtain multiple historical multi-dimensional perception data, multiple historical demand patterns and multiple historical pattern operation durations from the shower room deodorization system when the frequency of running the shower room deodorization system to perform deodorization treatment on the target shower room meets the deodorization treatment frequency threshold.

[0136] A deodorization operation associated number set acquisition unit is used to aggregate the multiple historical multidimensional perception data and the multiple historical mode operation durations according to the mapping relationship between the multiple historical demand patterns and the multiple deodorization patterns to obtain multiple deodorization operation associated number sets, wherein each deodorization operation associated number set includes multiple groups of historical multidimensional perception data-historical mode operation durations.

[0137] A running time analysis branch construction unit is provided, wherein the running time analysis branch construction unit constructs a plurality of running time analysis branches based on the plurality of deodorization running associated number sets.

[0138] A pattern duration analysis model acquisition unit is used to connect the multiple operation duration analysis branches in parallel to obtain the pattern duration analysis model.

[0139] Furthermore, the running time analysis branch construction unit also includes:

[0140] The first deodorization operation related number set acquisition unit is configured to extract and obtain a first deodorization operation related number set based on the plurality of deodorization operation related number sets.

[0141] The first operation control fitting function acquisition unit is used to perform linear regression analysis on multiple groups of historical multidimensional perception data-historical mode operation time in the first deodorization operation association number set to obtain a first operation control fitting function.

[0142] The first running time analysis branch acquisition unit is used to synchronize the first running control fitting function to a pre-built standard running time analysis branch to obtain a first running time analysis branch.

[0143] The running time analysis branch construction unit constructs the multiple running time analysis branches based on the multiple deodorization running related number sets by analogy.

[0144] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0145] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0146] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A shower room deodorization control method based on intelligent perception, characterized in that: The method comprises: Interactively obtain target construction features of a target shower room; Performing environmental perception sensor configuration on the target shower room according to the target construction features to obtain a multi-dimensional perception sensor array; Performing data mining with the target construction features as constraints to obtain multiple sample shower rooms; Interactively obtaining a plurality of historical deodorization control records of the plurality of sample shower rooms, and performing deodorization control analysis according to the plurality of historical deodorization control records to obtain a plurality of deodorization modes, wherein each deodorization mode includes an environment control matrix and a multi-dimensional odor perception interval; constructing a deodorization control judgment model based on the multiple deodorization modes; The deodorization control judgment model receives and performs deodorization pattern matching analysis based on the real-time multi-dimensional sensing data uploaded by the multi-dimensional sensing sensor array, and outputs a real-time demand pattern; Calling the real-time deodorization control parameters according to the real-time demand mode, and operating the shower room deodorization system to deodorize the target shower room using the real-time deodorization control parameters; Preset multi-dimensional safety and sanitation intervals and deodorization treatment frequency thresholds; If the real-time deodorization treatment frequency does not meet the deodorization treatment frequency threshold, it is determined in real time whether the process multi-dimensional perception data obtained by the multi-dimensional perception sensor array meets the multi-dimensional safety and sanitation interval, until the process multi-dimensional perception data meets the multi-dimensional safety and sanitation interval, the operation of the shower room deodorization system is stopped.

2. The shower room deodorization control method based on intelligent perception according to claim 1, characterized in that: According to the target construction features, the target shower room is configured with an environmental perception sensor to obtain a multi-dimensional perception sensor array, and the method further includes: Interactively obtaining a set of odor-related indicators, wherein the set of odor-related indicators includes temperature, humidity, multiple microorganisms, and multiple gas components; Calling and obtaining a plurality of environment perception sensors according to the odor-related indicator set; Performing an environmental perception reliability analysis on the target shower room according to the odor-related index set and the target construction features to obtain an environmental perception node set; The plurality of environment perception sensors are arranged according to the environment perception node set, and data transmission channels between the plurality of environment perception sensors and the deodorization control judgment model are constructed to generate the multi-dimensional perception sensor array.

3. The shower room deodorization control method based on intelligent perception as claimed in claim 2, characterized in that: The environment sensing node set includes a temperature sensing space node, a humidity sensing space node, a plurality of microorganism sensing space nodes and a plurality of gas sensing space nodes.

4. The shower room deodorization control method based on intelligent perception as claimed in claim 3, characterized in that: Performing an environmental perception reliability analysis on the target shower room according to the odor-related index set and the target construction features to obtain an environmental perception node set, the method further comprising: Extracting the target space size according to the target construction features; The target shower room is gridded with 1 / 20 of the target space size to obtain multiple sensing spaces; Arrange a plurality of temperature sensors in the plurality of sensing spaces; interactively obtaining the historical showering habits of the target user, and using the historical showering habits to simulate the temperature change of the target shower room to obtain multiple temperature change sequences; Decoupling the multiple temperature change sequences, and locating the sensing space corresponding to the temperature extreme value based on the decoupling result to obtain the temperature sensing space node; In this way, the environment perception node set is obtained through analysis.

5. The shower room deodorization control method based on intelligent perception as claimed in claim 2, characterized in that: Interactively obtaining multiple historical deodorization control records of the multiple sample shower rooms, and performing deodorization control analysis according to the multiple historical deodorization control records to obtain multiple deodorization modes, wherein each deodorization mode includes an environment control matrix and a multi-dimensional odor perception interval, and the method further includes: Disassembling the plurality of historical deodorization control records to obtain M historical environment sensing arrays and M historical environment control arrays, where M is a positive integer; Performing redundant parameter identification and elimination on the M historical environment control arrays to obtain multiple environment control matrices; Aggregating the M historical environment sensing arrays according to the multiple environment control matrices to obtain multiple historical environment sensing data sets, wherein each historical environment sensing data set includes a plurality of historical environment sensing arrays; According to the odor association indicator set, the multiple historical environmental perception data sets are called for extreme values ​​to obtain multiple multi-dimensional odor perception intervals; The plurality of environmental control matrices and the plurality of multi-dimensional odor perception intervals are associated to generate the plurality of deodorization modes.

6. The shower room deodorization control method based on intelligent perception according to claim 1, characterized in that: The method further comprises: Pre-built mode duration analysis model; If the real-time deodorization frequency satisfies the deodorization frequency threshold, synchronizing the real-time multi-dimensional perception data and the real-time demand pattern to the pattern duration analysis model, and analyzing to obtain the real-time pattern operation duration; The shower room deodorization system is timed to operate based on the real-time mode operation duration.

7. The shower room deodorization control method based on intelligent perception according to claim 6, characterized in that: A pre-built mode duration analysis model, the method further comprising: When the frequency of running the shower room deodorization system to perform deodorization treatment on the target shower room meets the deodorization treatment frequency threshold, extracting and obtaining a plurality of historical multi-dimensional perception data, a plurality of historical demand patterns and a plurality of historical pattern operation durations from the shower room deodorization system; Aggregating the plurality of historical multidimensional perception data and the plurality of historical mode operation durations according to the mapping relationship between the plurality of historical demand modes and the plurality of deodorization modes, to obtain a plurality of deodorization operation association number sets, wherein each deodorization operation association number set includes a plurality of sets of historical multidimensional perception data-historical mode operation durations; constructing a plurality of operation duration analysis branches based on the plurality of deodorization operation association number sets; The multiple operation duration analysis branches are connected in parallel to obtain the mode duration analysis model.

8. The shower room deodorization control method based on intelligent perception according to claim 7, characterized in that: Based on the multiple deodorization operation association number sets, multiple operation duration analysis branches are constructed, and the method further includes: Extracting and obtaining a first deodorization operation related number set based on the plurality of deodorization operation related number sets; Performing linear regression analysis on multiple groups of historical multidimensional perception data-historical mode operation duration in the first deodorization operation association data set to obtain a first operation control fitting function; Synchronizing the first operation control fitting function to a pre-built standard operation duration analysis branch to obtain a first operation duration analysis branch; By analogy, the multiple running time analysis branches are constructed based on the multiple deodorization running associated number sets.

9. A shower room deodorization control device based on intelligent perception, characterized in that: The device comprises: A target construction feature acquisition module, wherein the target construction feature acquisition module is used to interactively obtain target construction features of a target shower room; A multi-dimensional sensing sensor array acquisition module, which is used to configure the environment sensing sensor of the target shower room according to the target construction characteristics to obtain a multi-dimensional sensing sensor array; A plurality of sample shower room acquisition modules, wherein the plurality of sample shower room acquisition modules are used to perform data mining with the target construction features as constraints to obtain a plurality of sample shower rooms; A multiple deodorization mode acquisition module, the multiple deodorization mode acquisition module is used to interactively obtain multiple historical deodorization control records of the multiple sample shower rooms, and perform deodorization control analysis according to the multiple historical deodorization control records to obtain multiple deodorization modes, wherein each deodorization mode includes an environment control matrix and a multi-dimensional odor perception interval; a deodorization control judgment model construction module, wherein the deodorization control judgment model construction module constructs a deodorization control judgment model based on the multiple deodorization modes; A real-time demand pattern output module, which is used for the deodorization control judgment model to receive and perform deodorization pattern matching analysis based on the real-time multi-dimensional sensing data uploaded by the multi-dimensional sensing sensor array, and output a real-time demand pattern; A deodorization processing module, the deodorization processing module is used to call the real-time deodorization control parameters according to the real-time demand mode, and use the real-time deodorization control parameters to operate the shower room deodorization system to perform deodorization processing on the target shower room; A deodorization frequency threshold preset module, which is used to preset a multi-dimensional safety and sanitation interval and a deodorization frequency threshold; A deodorization treatment frequency judgment module is used to judge in real time whether the process multi-dimensional perception data collected by the multi-dimensional perception sensor array meets the multi-dimensional safety and sanitation interval if the real-time deodorization treatment frequency does not meet the deodorization treatment frequency threshold, and stop the operation of the shower room deodorization system until the process multi-dimensional perception data meets the multi-dimensional safety and sanitation interval.

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