A method and system for directional purification based on intelligent identification of pollutant components

Abstract

The application discloses a method and system for directional purification based on intelligent identification of pollutant components, and relates to the technical field of air purifiers. In the method, first, initial detection data of air to be purified is collected by a gas sensor array; then, short-time trial operation is sequentially controlled for each purification module corresponding to different pollutants to obtain response change data, and the removal response rate of each pollutant category is calculated accordingly; then, the rated purification capacity of the module is combined to calculate the predicted continuous operation time required to reduce each pollutant to a safety threshold; the target purification module is selected based on the time and operated at rated power; after the current module operation is completed, signals are re-collected to update the predicted time, and the next module is selected for purification in a cycle until the concentrations of all pollutants meet the standards. The technical scheme provided by the application can improve the directional purification efficiency of the air purifier.

Description

Technical Field

[0001] This application relates to the field of air purifier technology, specifically to a method, system, medium, and product for targeted purification based on intelligent identification of pollutant components. Background Technology

[0002] With the improvement of people's living standards and the enhancement of health awareness, indoor air quality is receiving increasing attention. There are many common pollutants in indoor air, including formaldehyde, benzene compounds, PM2.5, PM10, and bacteria, each with different degrees of harm and mechanisms of action to human health. Air purifiers, as important devices for improving indoor air quality, directly affect the speed of indoor environmental improvement and the health protection of residents.

[0003] Currently, air purifiers typically integrate multiple purification modules within the same device to address various types of air pollutants. For example, activated carbon filters adsorb formaldehyde and odors, HEPA filters filter particulate matter, photocatalyst modules decompose organic pollutants, and ultraviolet modules sterilize and disinfect. These purification modules operate simultaneously and continuously, covering the purification needs of various pollutants, and are widely used in homes and offices.

[0004] However, as users' demands for air purification performance continue to rise, the limitations of existing operating models are becoming increasingly apparent. In practical applications, the composition of pollutants varies significantly across different usage scenarios. For example, newly renovated rooms primarily suffer from excessive formaldehyde levels, while everyday living environments may be dominated by particulate matter pollution. Due to the lack of effective identification of current air pollution conditions and a reasonable allocation mechanism for the operation of each purification module, all purification modules operate continuously and simultaneously, resulting in dispersed purification resources. This prevents the purification capacity from being concentrated on addressing the most concentrated or harmful pollutants in the current environment. This operating mode leads to insufficient purification intensity targeting major pollutants, thereby reducing the targeted purification efficiency of the air purifier. Summary of the Invention

[0005] This application provides a method, system, medium, and product for targeted purification based on intelligent identification of pollutant components, which can improve the targeted purification efficiency of air purifiers.

[0006] The first aspect of this application provides a method for targeted purification based on intelligent identification of pollutant components, comprising: The air purifier acquires the mixed gas formed when the air to be purified enters the detection chamber of the air purifier, and obtains the initial response signal of the mixed gas through a gas sensor array to obtain initial detection data. The air purifier includes multiple purification modules corresponding to different pollutant categories. Each of the purification modules is controlled to operate at a preset detection power for a preset detection duration, and within the preset detection duration, the gas sensor array continuously collects the response signal sequence of the mixed gas to obtain the response change data corresponding to each of the purification modules. The rate of change of the response signal of each purification module within the preset detection time is calculated based on the response change data, and the rate of change is determined as the removal response rate of the corresponding pollutant category. Based on the removal response rate and the rated purification capacity parameters of the corresponding purification module, the predicted continuous running time required to reduce the concentration of each pollutant category to a preset safe concentration threshold is calculated. Based on the predicted continuous running time, the current target purification module is selected, and the current target purification module is controlled to run continuously at the rated purification power for the corresponding predicted continuous running time. After the current target purification module completes the predicted continuous running time, the response signal is re-acquired to update the predicted continuous running time of each pollutant category, and the next target purification module is selected in sequence for purification until the concentration of each pollutant category is reduced to below the preset safe concentration threshold.

[0007] By adopting the above technical solution, initial response signals of the air to be purified are first collected using a gas sensor array to obtain initial detection data, establishing a basic understanding of the current air pollution status. Subsequently, each purification module is sequentially controlled to operate at a preset detection power for a preset detection duration, continuously collecting the response signal sequence of the mixed gas during this process. Dynamic monitoring of response change data enables the precise establishment of the response relationship between different pollutant categories and their corresponding purification modules. The rate of change calculated based on the response change data accurately reflects the actual removal effect of each purification module on its corresponding pollutant category. After determining this as the removal response rate, combined with the rated purification capacity parameters of the purification modules, the predicted continuous operating time required to reduce the concentration of each pollutant category to a preset safe concentration threshold can be calculated specifically. This achieves effective identification of the current air pollution status and a quantitative assessment of the difficulty of removing each pollutant. By selecting the current target purification module based on the predicted continuous operating time, purification resources can be prioritized to treat the pollutants with the highest concentration or the greatest removal difficulty in the current environment. Controlling the target purification module to operate continuously at its rated purification power avoids the dispersion of purification capacity, concentrating purification intensity on treating major pollutants and significantly improving the purification intensity for major pollutants. After the current target purification module completes its predicted continuous operating time, the response signal is re-acquired to update the predicted continuous operating time for each pollutant category. The cyclical allocation mechanism, which sequentially selects the next target purification module for purification, ensures that all types of pollutants are fully treated and that purification resources are always concentrated. Compared with the existing technology where all purification modules work continuously and simultaneously, resulting in dispersed purification resources and insufficient purification intensity for major pollutants, this solution achieves dynamic focusing of purification capacity on the current major pollutants through intelligent identification of pollutant components and targeted allocation of purification modules, effectively improving the targeted purification efficiency of air purifiers.

[0008] Secondly, embodiments of this application provide a targeted purification system based on intelligent identification of pollutant components. The targeted purification system based on intelligent identification of pollutant components includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the targeted purification system based on intelligent identification of pollutant components to perform the method described in the first aspect and any possible implementation thereof.

[0009] Optionally, according to a preset pollutant detection sequence, a purification module to be detected is selected. The purification module includes an activated carbon filter module, a HEPA filter module, a photocatalyst module, and an ultraviolet disinfection module. The purification module to be detected is controlled to start at the preset detection power, and all other purification modules are controlled to remain off. Within a preset detection duration of the purification module to be detected, the response signal sequence of the mixed gas, including formaldehyde, benzene compounds, particulate matter, and microorganisms, is collected through the gas sensor array at a preset sampling frequency. The response signal sequence is used as the response change data corresponding to the purification module to be detected.

[0010] Optionally, the starting response signal and the ending response signal are extracted from the response change data; the signal difference between the starting response signal and the ending response signal is calculated; the ratio of the signal difference to the preset detection time is used as the initial change rate of the response signal; the environmental interference compensation coefficient of the gas sensor array is obtained, and the initial change rate is corrected using the environmental interference compensation coefficient to obtain the removal response rate of the corresponding pollutant category.

[0011] Optionally, based on a pre-established mapping relationship between response rate and purification capacity per unit time, the removal response rate is converted into the current purification capacity per unit time for the corresponding pollutant category; the current initial concentration of each pollutant category is determined based on the initial detection data; the concentration difference between the current initial concentration and the preset safe concentration threshold is calculated; and the predicted continuous operating time for the corresponding pollutant category is calculated based on the concentration difference, the current purification capacity per unit time, and the rated purification capacity parameter.

[0012] Optionally, the environmental parameters of the current environment in which the air purifier is located are obtained, including temperature and humidity; the corresponding rate conversion coefficient is found in a preset mapping coefficient table according to the environmental parameters; the removal response rate is multiplied by the rate conversion coefficient to obtain the current purification amount per unit time for the corresponding pollutant category.

[0013] Optionally, a runtime set containing each pollutant category and its corresponding predicted continuous runtime is constructed; the predicted continuous runtimes in the runtime set are sorted in descending order of numerical value; the predicted continuous runtime at the top of the sorted list is extracted, and the purification module corresponding to the predicted continuous runtime is determined as the current target purification module; a rated power operation command is sent to the current target purification module, and a stop command is sent when the operation time reaches the predicted continuous runtime.

[0014] Optionally, after the current target purification module stops operating, after a preset stabilization period, the current response signal of the mixed gas is collected by the gas sensor array; the remaining concentration of each pollutant category is determined based on the current response signal; it is determined whether each remaining concentration is less than the preset safe concentration threshold; if there is a target remaining concentration greater than or equal to the preset safe concentration threshold, the target predicted continuous running time for the corresponding pollutant category is calculated based on the target remaining concentration to update the predicted continuous running time.

[0015] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a system for targeted purification based on intelligent identification of pollutant components, cause the system to perform the method described in the first aspect and any possible implementation thereof.

[0016] Fourthly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a targeted purification system based on intelligent identification of pollutant components, causes the targeted purification system based on intelligent identification of pollutant components to perform the method described in the first aspect and any possible implementation thereof.

[0017] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: By adopting the above technical solution, initial response signals of the air to be purified are first collected using a gas sensor array to obtain initial detection data, establishing a basic understanding of the current air pollution status. Subsequently, each purification module is sequentially controlled to operate at a preset detection power for a preset detection duration, continuously collecting the response signal sequence of the mixed gas during this process. Dynamic monitoring of response change data enables the precise establishment of the response relationship between different pollutant categories and their corresponding purification modules. The rate of change calculated based on the response change data accurately reflects the actual removal effect of each purification module on its corresponding pollutant category. After determining this as the removal response rate, combined with the rated purification capacity parameters of the purification modules, the predicted continuous operating time required to reduce the concentration of each pollutant category to a preset safe concentration threshold can be calculated specifically. This achieves effective identification of the current air pollution status and a quantitative assessment of the difficulty of removing each pollutant. By selecting the current target purification module based on the predicted continuous operating time, purification resources can be prioritized to treat the pollutants with the highest concentration or the greatest removal difficulty in the current environment. Controlling the target purification module to operate continuously at its rated purification power avoids the dispersion of purification capacity, concentrating purification intensity on treating major pollutants and significantly improving the purification intensity for major pollutants. After the current target purification module completes its predicted continuous operating time, the response signal is re-acquired to update the predicted continuous operating time for each pollutant category. The cyclical allocation mechanism, which sequentially selects the next target purification module for purification, ensures that all types of pollutants are fully treated and that purification resources are always concentrated. Compared with the existing technology where all purification modules work continuously and simultaneously, resulting in dispersed purification resources and insufficient purification intensity for major pollutants, this solution achieves dynamic focusing of purification capacity on the current major pollutants through intelligent identification of pollutant components and targeted allocation of purification modules, effectively improving the targeted purification efficiency of air purifiers. Attached Figure Description

[0018] Figure 1 This is a schematic flowchart of the targeted purification method based on intelligent identification of pollutant components disclosed in the embodiments of this application; Figure 2 This is another schematic diagram of the targeted purification method based on intelligent identification of pollutant components disclosed in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of a system provided in an embodiment of this application.

[0019] Explanation of reference numerals in the attached drawings: 301, Central Processing Unit; 302, Read-Only Memory; 303, Random Access Memory; 304, Bus; 305, Input / Output Interface; 306, Input Section; 307, Output Section; 308, Storage Section; 309, Communication Section; 310, Driver; 311, Removable Media. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0021] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0022] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0023] This application provides a targeted purification method based on intelligent identification of pollutant components, referring to... Figure 1 , Figure 1 This is a flowchart illustrating a method for targeted purification based on intelligent identification of pollutant components, provided in an embodiment of this application. The method is applied to a system, which refers to a hardware and software integrated platform capable of executing a targeted purification program based on intelligent identification of pollutant components. The system can execute a targeted purification program based on intelligent identification of pollutant components. The method includes steps 101 to 106, as follows: Step 101: Obtain the mixed gas formed when the air to be purified enters the detection chamber of the air purifier, and collect the initial response signal of the mixed gas through the gas sensor array to obtain the initial detection data. The air purifier includes multiple purification modules corresponding to different pollutant categories.

[0024] In this embodiment, the detection chamber refers to an independent spatial area inside the air purifier used for gas sampling and detection. The mixed gas refers to the gas mixture formed when the air to be purified enters the detection chamber and mixes with the existing air inside, such as a composite gas containing multiple pollutants including formaldehyde, benzene, and PM2.5. The gas sensor array refers to a sensor combination composed of multiple gas sensors of different or the same type arranged in a specific manner, such as an array containing metal oxide sensors, electrochemical sensors, and optical sensors. The initial response signal represents the raw electrical or optical signal data output by the gas sensor array when detecting the mixed gas. The initial detection data represents the data set formed after collecting and recording the initial response signal. The pollutant category refers to the types of pollutants classified according to their chemical composition and physical properties, such as formaldehyde, benzene compounds, particulate matter, and microorganisms. The purification module refers to a purification functional unit designed for a specific pollutant category, such as an activated carbon filter module, a HEPA filter module, a photocatalyst module, and an ultraviolet disinfection module.

[0025] Specifically, the air to be purified is guided into the detection chamber of the air purifier through the air inlet. The air to be purified mixes with the existing air in the detection chamber to form a gas mixture. Each sensor unit in the gas sensor array is activated, generating a corresponding electrical or optical signal upon contact with the gas mixture. The signals generated by each sensor unit are used as initial response signals. The initial response signals are converted from analog to digital and recorded by the signal acquisition circuit, storing the converted digital signals as initial detection data. The multiple purification modules within the air purifier are identified, and the pollutant category corresponding to each module is confirmed. For example, the activated carbon filter module corresponds to formaldehyde pollutants, and the HEPA filter module corresponds to particulate matter pollutants. This establishes a correspondence between purification modules and pollutant categories, obtaining the initial detection data.

[0026] Step 102: Sequentially control each purification module to run at a preset detection power for a preset detection time, and continuously collect the response signal sequence of the mixed gas through the gas sensor array within the preset detection time to obtain the response change data corresponding to each purification module.

[0027] In this embodiment, the preset detection power refers to the power value used by the purification module during the detection operation phase. This power is lower than the rated purification power of the purification module; for example, if the rated power is 50 watts, the preset detection power is 15 watts. The preset detection duration indicates the length of time the purification module continuously operates during the detection operation phase, such as 30 seconds, 60 seconds, or 90 seconds. The response signal sequence represents a sequence of data composed of multiple response signals continuously collected by the gas sensor array in chronological order within the preset detection duration. The response change data refers to a data set reflecting the trend of the response signal sequence over time, including information on signal amplitude changes and the rate of change.

[0028] Each purification module is set in an independent parallel air duct. Each air duct inlet is equipped with an electric air valve that can be independently controlled to open and close. By controlling the opening and closing of the corresponding air valve, the airflow can be selectively introduced into a specific purification module.

[0029] Specifically, each purification module is selected sequentially as the current target for detection according to a pre-set order. A start command is sent to the currently selected purification module, controlling it to start operating at a preset detection power, while keeping the remaining unselected purification modules in a closed state. During the operation of the current purification module, the gas sensor array is set to collect signals at a fixed sampling frequency, for example, once every 2 seconds, continuing until the end of the preset detection duration. All response signals collected within the preset detection duration are arranged in chronological order to form a response signal sequence. The changes in the values ​​of each signal in the response signal sequence are analyzed, and the amplitude changes and trend characteristics of the signals are calculated. These change characteristic data are used as the response change data corresponding to the current purification module. The above process is repeated until all purification modules are detected, obtaining the response change data corresponding to each purification module.

[0030] In one possible implementation, each purification module is sequentially controlled to operate at a preset detection power for a preset detection duration, and within the preset detection duration, the response signal sequence of the mixed gas is continuously collected through a gas sensor array to obtain the response change data corresponding to each purification module. Specifically, this includes steps 1021-1023, as follows: Step 1021: Select the purification module to be detected according to the preset pollutant detection sequence. The purification module includes an activated carbon filter module, a HEPA filter module, a photocatalyst module, and an ultraviolet disinfection module.

[0031] In this embodiment, the pollutant detection order refers to a pre-set sequence of purification modules arranged according to specific rules, such as detecting the activated carbon module, HEPA module, photocatalyst module, and ultraviolet disinfection module in that order. The purification module currently being detected refers to the purification module unit currently selected for detection during the execution of the detection task.

[0032] Specifically, the pre-configured pollutant detection sequence information is read from the memory. This sequence information includes the arrangement order of each purification module and its corresponding identification number. The location of the purification module to be detected is determined based on the current detection progress. If this is the first detection, the first purification module in the detection sequence is located; if partial detection has been completed, the next undetected purification module in the detection sequence is located. The purification module identification number corresponding to the location is extracted, and the corresponding physical purification module in the air purifier is located based on the identification number. The found physical purification module is marked as the current purification module to be detected, and its type information and corresponding pollutant category information are recorded. A data record containing the identification and status information of the current purification module to be detected is generated and stored in the detection task queue, providing target object information for subsequent detection and control operations.

[0033] Step 1022: Control the purification module to be detected to start at the preset detection power, and control the purification modules other than the one to be detected to remain in the off state.

[0034] In this embodiment, the off state refers to the working state in which the purification module does not receive power and does not perform purification functions.

[0035] Specifically, a start signal is sent to the control circuit of the currently detected purification module, containing a preset detection power value. The voltage and current output from the power supply circuit to the currently detected purification module are adjusted according to the preset detection power value, causing the module to start operating at the preset detection power. A list of identifiers for all other purification modules in the air purifier, excluding the currently detected module, is obtained, and the current operating status of each module in the list is read one by one. For purification modules in the list that are running, a shutdown command is sent to their control circuits, cutting off the power supply to these modules. For purification modules in the list that are already in the shutdown state, their power supply circuits are kept disconnected without any operation. The current input values ​​of each non-detected purification module are checked, and it is confirmed that all current input values ​​are zero, verifying that these purification modules have entered the shutdown state. After confirming that only the currently detected purification module is running and all other purification modules are in the shutdown state, the system's operating configuration information is recorded.

[0036] Step 1023: Within the preset detection time of the current purification module to be detected, collect the response signal sequence of the mixed gas through the gas sensor array at the preset sampling frequency. The mixed gas includes formaldehyde, benzene series compounds, particulate matter and microorganisms; use the response signal sequence as the response change data corresponding to the current purification module to be detected.

[0037] In this embodiment, the preset sampling frequency represents the number of times the gas sensor array collects response signals per unit time, such as 5 times per second or 10 times per second.

[0038] Specifically, a timer is started to record the running time of the purification module to be detected. The time interval between two adjacent samples is calculated based on a preset sampling frequency; for example, if the sampling frequency is 5 times per second, the time interval is 0.2 seconds. Simultaneously with the start of the timer, the gas sensor array is triggered to perform its first response signal acquisition, recording the signal values ​​output by each sensor unit at this time. After waiting for a certain time interval, the gas sensor array is triggered to perform a second response signal acquisition, continuing to record the signal values ​​output by each sensor unit. This process of waiting for the time interval and triggering acquisition is repeated until the running time displayed by the timer reaches the preset detection duration. All response signals acquired within the preset detection duration are arranged in chronological order of acquisition time, forming a response signal sequence containing response signal values ​​at multiple time points. This response signal sequence is then associated and bound with the identification information of the purification module to be detected, and stored as the response change data corresponding to that purification module.

[0039] Step 103: Calculate the rate of change of the response signal of each purification module within the preset detection time based on the response change data, and determine the rate of change as the removal response rate of the corresponding pollutant category.

[0040] In this embodiment, the rate of change refers to how quickly the response signal changes per unit time, and is characterized by the ratio of the difference in signal values ​​to the length of time. The removal response rate is used to represent the rate at which the gas sensor array response signal decreases when the purification module removes a specific type of pollutant; this rate reflects the speed at which the pollutant concentration decreases.

[0041] Specifically, the start and end response signals of the response signal sequence are extracted from the response change data corresponding to each purification module. The numerical difference between the start and end response signals is calculated and used as the signal change. The signal change is divided by a preset detection duration to obtain the signal change per unit time, which is the initially calculated rate of change. The temperature and humidity parameters of the gas sensor array's operating environment are acquired, and a pre-stored environmental interference compensation coefficient table is consulted based on these parameters. The compensation coefficient values ​​corresponding to the current temperature and humidity are extracted from the table, and the initially calculated rate of change is multiplied by the compensation coefficient for correction. The corrected rate of change is used as the removal response rate for the corresponding pollutant category targeted by the purification module. The above calculation process is repeated to process the response change data of all purification modules to obtain the removal response rate corresponding to each pollutant category.

[0042] In one possible implementation, the rate of change of the response signal of each purification module within a preset detection time is calculated based on the response change data, and the rate of change is determined as the removal response rate of the corresponding pollutant category. Specifically, this includes steps 1031-1033, as follows: Step 1031: Extract the initial response signal and the termination response signal from the response change data; calculate the signal difference between the initial response signal and the termination response signal.

[0043] In this embodiment, the starting response signal refers to the first response signal value collected by the gas sensor array at the beginning of the preset detection duration. The ending response signal represents the last response signal value collected by the gas sensor array at the end of the preset detection duration. The signal difference is used to represent the numerical difference between the starting response signal and the ending response signal.

[0044] Specifically, the complete dataset of the response signal sequence is read from the response change data. The first data point with the earliest time stamp in the response signal sequence is located, and the corresponding response signal value is extracted as the starting response signal. The last data point with the latest time stamp in the response signal sequence is located, and the corresponding response signal value is extracted as the ending response signal. The magnitudes of the starting and ending response signal values ​​are compared. If the starting response signal value is greater than the ending response signal value, a subtraction operation is performed; if the starting response signal value is less than the ending response signal value, a subtraction operation is performed. The result of the subtraction operation is recorded as the signal difference, which reflects the overall change amplitude of the response signal within a preset detection time. The signal difference data is associated with and stored with the corresponding purification module identification information, providing basic data for subsequent change rate calculations.

[0045] Step 1032: Use the ratio of the signal difference to the preset detection time as the initial rate of change of the response signal.

[0046] In the embodiments of this application, the initial rate of change represents the original rate of change of the response signal without environmental factor correction per unit time.

[0047] Specifically, the system reads the calculated signal difference data and the corresponding preset detection duration. It performs a division operation, dividing the signal difference by the preset detection duration, to calculate the signal change per unit time. For example, if the signal difference is 150 and the preset detection duration is 30 seconds, the signal change per unit time is 150 divided by 30, which equals 5. This calculated signal change per unit time is marked as the initial rate of change of the response signal. The sign of the initial rate of change is checked; a positive value indicates an upward trend in the response signal, while a negative value indicates a downward trend. The magnitude and trend of the initial rate of change are recorded, and this data is associated with the corresponding purification module identifier and pollutant category information. The data record containing the initial rate of change information is stored in the database. This initial rate of change serves as a fundamental parameter reflecting the purification effect during the detection phase of the purification module. This initial rate of change data is then used to prepare for subsequent environmental interference compensation and correction operations.

[0048] Step 1033: Obtain the environmental interference compensation coefficient of the gas sensor array, and use the environmental interference compensation coefficient to correct the initial change rate to obtain the removal response rate of the corresponding pollutant category.

[0049] In this embodiment, the environmental interference compensation coefficient refers to a correction parameter used to correct the influence of ambient temperature and humidity on the measurement results of the gas sensor array. Correction refers to the calculation process of adjusting the original measurement data using the compensation coefficient to eliminate environmental interference.

[0050] Specifically, the system reads the current temperature and humidity values ​​of the environment in which the gas sensor array is located. It then uses these values ​​to search a pre-stored compensation coefficient lookup table, which establishes a mapping between different temperature and humidity conditions and the compensation coefficients. The environmental disturbance compensation coefficient corresponding to the current temperature and humidity is extracted from the lookup table; for example, a compensation coefficient of 1.05 corresponds to a temperature of 25 degrees Celsius and a humidity of 60%. The initial rate of change to be corrected is read, and a multiplication operation is performed, multiplying the initial rate of change by the environmental disturbance compensation coefficient. The result of this multiplication is used as the corrected rate of change. Based on the identifier of the currently processed purification module, the system looks up the pollutant category information corresponding to that purification module and marks the corrected rate of change as the removal response rate for that pollutant category. The removal response rate data is then bound and stored with the pollutant category identifier, forming a data pair containing the pollutant category and its corresponding removal response rate.

[0051] Step 104: Based on the removal response rate and the rated purification capacity parameters of the corresponding purification module, calculate the predicted continuous running time required to reduce the concentration of each pollutant category to the preset safe concentration threshold.

[0052] In this embodiment, the rated purification capacity parameter represents the mass or volume of pollutants that the purification module can remove per unit time at its rated operating power, such as removing 20 mg of formaldehyde per hour or processing 300 cubic meters of air per hour. The preset safe concentration threshold refers to the upper limit of pollutant concentration required to determine if indoor air quality meets safety standards, such as a formaldehyde concentration of 0.08 mg / m³ or a PM2.5 concentration of 35 μg / m³. The predicted continuous operating time represents the length of time required for the purification module to continuously operate at its rated purification power to reduce the corresponding pollutant concentration to the preset safe concentration threshold.

[0053] Specifically, the conversion coefficient for the purification capacity per unit time corresponding to the removal response rate is retrieved from a pre-established mapping table. The removal response rate is multiplied by the conversion coefficient to obtain the current purification capacity per unit time, which represents the actual amount of pollutants removed per unit time under the detection power. The current concentration values ​​for each pollutant category are calculated based on the initial detection data. The difference between the current concentration value and the corresponding preset safe concentration threshold is calculated to obtain the concentration difference that needs to be reduced. The concentration difference is multiplied by the actual volume of the space to be purified to obtain the total amount of pollutants to be removed. The rated purification capacity parameters of each purification module are obtained, and the ratio of the rated purification capacity parameter to the purification capacity per unit time during the detection phase is calculated to obtain the power amplification factor. The total amount of pollutants to be removed is divided by the rated purification capacity parameter to obtain the basic operating time. The basic operating time is then corrected based on the power amplification factor to obtain the predicted continuous operating time corresponding to each pollutant category.

[0054] Step 105: Filter the current target purification module based on the predicted continuous running time, and control the current target purification module to run continuously at the rated purification power for the corresponding predicted continuous running time.

[0055] In this embodiment, the current target purification module refers to the purification module selected to perform the purification task in the current purification cycle. Rated purification power refers to the standard operating power specified in the design of the purification module, which corresponds to the maximum purification capacity output of the purification module, such as 50 watts or 100 watts.

[0056] Specifically, a runtime dataset is constructed by combining each pollutant category with its corresponding predicted continuous runtime. All predicted continuous runtime values ​​in the runtime dataset are sorted in descending order. The largest predicted continuous runtime value at the top of the sorted dataset is extracted, and the corresponding pollutant category is identified. Based on the correspondence between pollutant categories and purification modules, the purification module responsible for processing that pollutant category is determined and marked as the current target purification module. A start control command containing rated purification power parameters and runtime parameters is sent to the current target purification module, controlling it to start operating at the rated purification power. A timer is started to record the actual running time of the current target purification module, continuously monitoring whether the actual running time reaches the corresponding predicted continuous runtime. When the timer shows that the actual running time has reached the predicted continuous runtime, a stop command is sent to the current target purification module, causing it to stop operating.

[0057] In one possible implementation, the current target purification module is selected based on the predicted continuous running time, and the current target purification module is controlled to continuously run at the rated purification power for the corresponding predicted continuous running time. Specifically, this includes steps 1051-1053, as follows: Step 1051: Construct a runtime set containing each pollutant category and its corresponding predicted continuous runtime.

[0058] In this embodiment of the application, the runtime set is used to represent a data set structure containing the identifiers of each pollutant category and their corresponding predicted continuous runtime values.

[0059] Specifically, an empty data set structure is created to store runtime information. All completed purification modules are traversed, and the pollutant category identifier for each module is read one by one. For each pollutant category identifier, the previously calculated predicted continuous runtime value is retrieved. The pollutant category identifier and its corresponding predicted continuous runtime value are paired, for example, formaldehyde and its corresponding 180 seconds are paired. The paired data is added to the data set structure, and the process continues for the next pollutant category. The process of reading category identifiers, retrieving runtime, building paired data, and adding to the set is repeated until all pollutant categories are processed. The number of paired data in the data set structure is checked to ensure it matches the total number of purification modules. The integrity of each paired data in the data set structure is verified to ensure that each paired data contains a valid pollutant category identifier and a non-negative predicted continuous runtime value. The completed data set containing all pollutant categories and their corresponding runtimes is marked as the runtime set.

[0060] Step 1052: Sort the predicted continuous runtimes in the runtime set in descending order of their numerical values; extract the first predicted continuous runtime in the sorted set, and determine the purification module corresponding to the predicted continuous runtime as the current target purification module.

[0061] In this embodiment, descending order sorting refers to the operation of rearranging data according to their numerical values ​​from largest to smallest. The first position in the sorting sequence represents the data item with the largest numerical value that is located at the first position after descending order sorting.

[0062] Specifically, the process involves reading all data pairs from the runtime set. Predicted continuous runtime values ​​are extracted from each data pair, and these extracted runtime values ​​are used to construct a list of values ​​to be sorted. A descending sorting algorithm is applied to this list, comparing the values ​​and adjusting their positions from largest to smallest. This results in a descending sequence of runtime values, with the first value being the maximum value among all predicted continuous runtimes. The maximum predicted continuous runtime value at the top of the sorted sequence is then extracted. Based on this maximum predicted continuous runtime value, the corresponding pollutant category identifier is searched in the runtime set. The identifier of the purification module responsible for processing that pollutant category is then searched in the purification module configuration information. The purification module corresponding to the found identifier is designated as the current target purification module, and the predicted continuous runtime value required for this purification module is recorded.

[0063] Step 1053: Send a rated power operation command to the current target purification module, and send a stop command when the operation time reaches the predicted continuous operation time.

[0064] In this embodiment, the rated power operation command represents a control signal that controls the purification module to operate at its rated purification power. The stop command is a control signal that controls the purification module to terminate operation. The running time represents the cumulative operating time of the purification module from startup to the current moment.

[0065] Specifically, a control command data packet containing rated purification power and runtime parameters is constructed. This data packet is sent to the control circuit of the current target purification module. The control circuit parses the power parameters in the data packet and adjusts the power supply output. The power supply circuit provides the corresponding voltage and current to the current target purification module according to the rated purification power parameters, enabling the purification module to start operating at its rated power. A timer is synchronously started to record the running time of the current target purification module, with the initial value set to zero. During the continuous timing, the current value of the timer is read at fixed time intervals, for example, once every 1 second. The read timer value is compared with the predicted continuous runtime. If the timer value is less than the predicted continuous runtime, the purification module continues to operate. If the timer value reaches or exceeds the predicted continuous runtime, a stop command data packet is immediately constructed and sent to the control circuit of the current target purification module. Upon receiving the stop command, the control circuit cuts off the power supply to the purification module, causing the purification module to stop operating.

[0066] Step 106: After the current target purification module has completed its predicted continuous running time, the response signal is re-acquired to update the predicted continuous running time of each pollutant category, and the next target purification module is selected in sequence for purification until the concentration of each pollutant category is reduced to below the preset safe concentration threshold.

[0067] In this embodiment of the application, the next target purification module is used to refer to the purification module that is selected to perform subsequent purification tasks after the current purification cycle is completed.

[0068] Specifically, after the current target purification module stops operating, a preset stabilization time is waited for the gas distribution in the detection chamber to reach a uniform state, such as 10 or 20 seconds. The gas sensor array is activated to collect the current response signal of the mixed gas in the detection chamber, and this signal is recorded as updated detection data. The remaining concentration values ​​of each pollutant category are calculated based on the correspondence between the current response signal and the pollutant concentration. The remaining concentration values ​​of each pollutant category are compared with preset safety concentration thresholds. If the remaining concentration values ​​of all pollutant categories are less than the preset safety concentration threshold, the purification task is considered complete. If there are pollutant categories with remaining concentration values ​​greater than or equal to the preset safety concentration threshold, their remaining concentration values ​​are extracted. Based on the difference between the remaining concentration values ​​of each pollutant exceeding the standard and the preset safety concentration threshold, and combined with the rated purification capacity parameters of the corresponding purification module, the predicted continuous operating time is recalculated, thus updating the predicted continuous operating time. The updated predicted continuous operating times are then sorted in descending order, and the purification module corresponding to the first position in the sort is selected as the next target purification module, which then begins purification operation.

[0069] In one possible implementation, after the current target purification module has completed its predicted continuous runtime, the response signal is re-acquired to update the predicted continuous runtime for each pollutant category. This includes steps 1061-1063, as follows: Step 1061: After the current target purification module stops running, after a preset stabilization period, the current response signal of the mixed gas is collected through the gas sensor array.

[0070] In this embodiment, the preset stabilization time represents the time required for the gas distribution in the detection chamber to reach a uniform and stable state after the purification module stops operating, such as 10 seconds or 15 seconds. The current response signal is used to represent the response signal value collected in real time by the gas sensor array after the stabilization wait ends.

[0071] Specifically, the system detects the operating status of the current target purification module, confirming that it has completely stopped operating and its power supply circuit has been disconnected. A wait timer is started to record the elapsed time since the module stopped operating, with the initial value set to zero. During the continuous timing of the wait timer, the gas sensor array remains in standby mode and does not perform signal acquisition. The change in the wait timer value is continuously monitored, and the timing stops when the value reaches a preset stable duration. Immediately after the wait timer stops, a trigger signal is sent to the gas sensor array. Upon receiving the trigger signal, the gas sensor array activates each sensor unit to detect the state of the mixed gas within the detection chamber. Each sensor unit outputs a corresponding electrical or optical signal upon contact with the mixed gas, and these signals are converted into digital signal values ​​by the signal acquisition circuit. The converted digital signal values ​​are recorded as the current response signal, which reflects the real-time state of the mixed gas within the detection chamber after the target purification module has stopped operating.

[0072] Step 1062: Determine the remaining concentration of each pollutant category based on the current response signal.

[0073] In this embodiment of the application, the remaining concentration refers to the current concentration value of each type of pollutant in the detection chamber after purification treatment.

[0074] Specifically, the system reads the newly acquired current response signal data, which includes the output values ​​of each sensor unit in the gas sensor array. A pre-established mapping table between response signals and pollutant concentrations is loaded from memory, recording the pollutant concentration values ​​corresponding to different response signal values. For each pollutant category, the output values ​​of the sensor units related to that pollutant category in the current response signal are extracted. The extracted sensor output values ​​are then matched against the mapping table to find the corresponding concentration value. For example, a sensor output value of 320 corresponds to a formaldehyde concentration of 0.06 mg / m³. The obtained concentration value is recorded as the remaining concentration for that pollutant category. This process of extracting sensor values, querying mapping relationships, and recording remaining concentrations is repeated to determine the remaining concentration value for each pollutant category. All pollutant categories and their corresponding remaining concentration values ​​are then combined into a remaining concentration dataset for storage. This dataset contains real-time concentration status information for various pollutants within the current detection chamber.

[0075] Step 1063: Determine whether each remaining concentration is less than the preset safe concentration threshold; if there is a target remaining concentration greater than or equal to the preset safe concentration threshold, calculate the target prediction continuous running time for the corresponding pollutant category based on the target remaining concentration, so as to update the prediction continuous running time.

[0076] In this embodiment, the target remaining concentration represents the pollutant concentration value that is greater than or equal to a preset safe concentration threshold. The target predicted continuous running time is used to represent the running time required for continued purification, recalculated based on the target remaining concentration.

[0077] Specifically, the remaining concentration values ​​for each pollutant category and their corresponding preset safe concentration threshold values ​​are read. The remaining concentration of each pollutant category is compared with the preset safe concentration threshold. If the remaining concentration of a pollutant category is less than the corresponding preset safe concentration threshold, that pollutant category is marked as having met the standard. If the remaining concentration of a pollutant category is greater than or equal to the corresponding preset safe concentration threshold, that remaining concentration is marked as the target remaining concentration. The comparison results for all pollutant categories are statistically analyzed to determine if all remaining concentrations are less than the preset safe concentration threshold. If all remaining concentrations are less than the threshold, the purification process ends. If target remaining concentrations exist, these target remaining concentration values ​​and their corresponding pollutant category identifiers are extracted. For each target remaining concentration, the difference between that concentration and the preset safe concentration threshold is calculated. Based on the concentration difference and the rated purification capacity parameters of the corresponding purification module, the time required to reduce the pollutant concentration below the threshold is calculated; this time is the target predicted continuous runtime. The calculated target predicted continuous runtime replaces the original predicted continuous runtime value in the runtime set, completing the update operation for the predicted continuous runtime.

[0078] In the above embodiments, the identification of pollutant categories and the quantitative evaluation of removal response rates were achieved through the acquisition of probing purification operation and response signal sequences. To further improve the air purifier's ability to predict purification efficiency under complex pollution conditions and enhance the system's directional purification decision-making level in situations with multiple pollutants coexisting, this application also provides a method for directional purification based on intelligent identification of pollutant components. This method establishes a dynamic conversion model of purification capacity by mapping the removal response rate to the purification amount per unit time, constructs a multi-dimensional rate correction system by combining the temperature and humidity parameters of the air purifier's environment, and uses a concentration difference calculation and rated capacity parameter correlation algorithm to achieve accurate prediction of purification time. This allows the system to accurately quantify the operating time required for each pollutant category to decrease from its current concentration to a safe threshold while adapting to environmental changes, thus supporting the priority scheduling decision of the purification module. The following section combines... Figure 2 Another method for targeted purification based on intelligent identification of pollutant components in the embodiments of this application is described below: Please see Figure 2 This is another schematic diagram of a targeted purification method based on intelligent identification of pollutant components in an embodiment of this application.

[0079] Step 201: Based on the pre-established mapping relationship between response rate and purification amount per unit time, convert the removal response rate into the current purification amount per unit time for the corresponding pollutant category.

[0080] In this embodiment, the mapping relationship refers to a pre-established correspondence table between response rate values ​​and purification capacity per unit time, based on experimental data. For example, a response rate of 5 corresponds to a purification capacity of 0.8 mg / s. The current purification capacity per unit time represents the mass of pollutants that the purification module can remove per unit time, such as removing 1.2 mg of formaldehyde per second.

[0081] Specifically, a pre-established mapping table between response rate and purification rate per unit time is read from memory. This mapping table is stored according to different pollutant categories, with each pollutant category corresponding to a set of response rate and purification rate mapping data. The removal response rate value corresponding to the currently processed pollutant category is read. Based on the pollutant category identifier, the corresponding mapping sub-table in the mapping table is located. The mapping sub-table searches for a response rate item that matches the removal response rate value. If a perfectly matching response rate value exists in the mapping sub-table, the corresponding purification rate value per unit time is directly extracted. If no perfectly matching value exists in the mapping sub-table, the two closest adjacent response rate values ​​are found, and the purification rates per unit time corresponding to these two adjacent values ​​are extracted and linearly interpolated. The found or calculated purification rate value per unit time is marked as the current purification rate per unit time for the current pollutant category, and this value is associated with the pollutant category identifier and stored.

[0082] In one possible implementation, based on a pre-established mapping relationship between response rate and purification capacity per unit time, the removal response rate is converted into the current purification capacity per unit time for the corresponding pollutant category. Specifically, this includes steps 2011-2012, as follows: Step 2011: Obtain the environmental parameters of the current environment where the air purifier is located, including temperature and humidity.

[0083] In this embodiment, environmental parameters are used to represent the physical state characteristics of the current working environment of the air purifier. Temperature refers to the thermodynamic temperature of the environment, such as 25 degrees Celsius or 30 degrees Celsius. Humidity represents the percentage of water vapor content in the air relative to the saturated water vapor content, such as a relative humidity of 50% or 65%.

[0084] Specifically, the air purifier's temperature sensor is activated to detect the ambient temperature. The temperature sensor converts the detected analog temperature signal into a digital temperature value using an analog-to-digital converter (ADC). The digital temperature value output by the ADC is read, representing the current ambient temperature in degrees Celsius. The air purifier's humidity sensor is then activated to detect the ambient humidity. The humidity sensor converts the detected analog humidity signal into a digital humidity value using an ADC. The digital humidity value output by the ADC is read, representing the current relative humidity as a percentage. The acquired temperature and humidity values ​​are combined to form an environmental parameter data pair, containing both temperature and humidity fields. The environmental parameter data pair is validated to ensure the temperature value is within a reasonable range (e.g., between -20°C and 60°C) and the humidity value is between 0 and 100%. Upon successful validation, the environmental parameter data pair is stored in a temporary cache, providing input data for subsequent mapping coefficient lookup operations.

[0085] Step 2012: Find the corresponding rate conversion coefficient in the preset mapping coefficient table according to the environmental parameters; multiply the removal response rate with the rate conversion coefficient to obtain the current purification amount per unit time for the corresponding pollutant category.

[0086] In this embodiment, the mapping coefficient table refers to a lookup table pre-stored between environmental parameters and rate conversion coefficients. The rate conversion coefficient represents the multiplication factor used when converting the response rate into the purification amount per unit time, for example, a coefficient of 0.15 or 0.18.

[0087] Specifically, the process reads the temperature and humidity values ​​from the acquired environmental parameter data pairs. A pre-defined mapping coefficient table is loaded from memory. This table contains multiple entries, each recording the rate conversion coefficient corresponding to a specific temperature and humidity range. Each entry in the mapping coefficient table is traversed, comparing the temperature range in each entry with the current temperature value. Once a temperature range containing the current temperature value is found, the humidity range in that entry is compared with the current humidity value. When both temperature and humidity match, the rate conversion coefficient value recorded in that entry is extracted. The removal response rate value to be converted is read. A multiplication operation is performed, multiplying the removal response rate by the rate conversion coefficient. For example, if the removal response rate is 6.5 and the rate conversion coefficient is 0.16, the result is 6.5 multiplied by 0.16, which equals 1.04. The result of the multiplication operation is marked as the current purification amount per unit time for the corresponding pollutant category. The current purification amount per unit time is associated and bound with the pollutant category identifier and stored in the purification amount database, completing the conversion from response rate to purification amount per unit time.

[0088] Step 202: Determine the current initial concentration of each pollutant category based on the initial detection data; calculate the concentration difference between the current initial concentration and the preset safe concentration threshold.

[0089] In this embodiment, the initial detection data refers to the response signal data of each pollutant collected by the gas sensor array before the purification process begins. The current initial concentration represents the concentration value of each pollutant category before purification, calculated based on the initial detection data; for example, the initial formaldehyde concentration is 0.15 mg / m³. The concentration difference is used to represent the numerical difference between the current initial concentration and a preset safe concentration threshold.

[0090] Specifically, the initial detection data collected before the purification process begins is read from the data storage area. This initial detection data contains the response signal values ​​of each sensor unit in the gas sensor array for different pollutant categories. A pre-established concentration mapping table between response signals and pollutant concentrations is loaded from memory. For each pollutant category, the corresponding response signal value for that pollutant category is extracted from the initial detection data. The extracted response signal value is matched against the concentration mapping table to obtain the corresponding concentration value, which is recorded as the current initial concentration for that pollutant category. The response signal extraction and concentration query operations are repeated until the current initial concentrations for all pollutant categories are determined. The preset safe concentration threshold value for each pollutant category is read. For each pollutant category, a subtraction operation is performed between the current initial concentration and the preset safe concentration threshold. The result of the subtraction operation is recorded as the concentration difference for that pollutant category. The concentration difference data for each pollutant category is associated and stored with the corresponding category identifier, providing basic data for subsequent runtime calculations.

[0091] Step 203: Calculate the predicted continuous operating time for the corresponding pollutant category based on the concentration difference, the current purification rate per unit time, and the rated purification capacity parameters.

[0092] In this embodiment, the rated purification capacity parameter refers to the performance index of the air volume or pollutant mass that the purification module can process per unit time at rated power. The predicted continuous operating time is used to represent the length of time the purification module needs to operate continuously, calculated based on the current pollution conditions, such as 180 seconds or 240 seconds.

[0093] Specifically, the process involves reading the concentration difference value, the current purification rate per unit time, and the rated purification capacity parameter of the corresponding purification module for each pollutant category. For each pollutant category, the concentration difference is first divided by the current purification rate per unit time to obtain the basic time required to reduce the pollutant concentration to a safe threshold at the current purification rate. The rated purification capacity parameter of the corresponding purification module is then read, reflecting the module's processing capacity under standard operating conditions. The basic time is multiplied by a correction factor for the rated purification capacity parameter, which is determined based on the degree of deviation between the rated purification capacity parameter and the actual operating state. The calculated time value is rounded up to the nearest integer in seconds to obtain the predicted continuous runtime for that pollutant category. The predicted continuous runtime for each pollutant category is then associated and stored with its corresponding category identifier, constructing a complete dataset containing all pollutant categories and their runtimes.

[0094] The following describes a targeted purification system based on intelligent identification of pollutant components from the perspective of hardware processing in an embodiment of this invention. Please refer to [link / reference]. Figure 3 This is a schematic diagram of a targeted purification system based on intelligent identification of pollutant components in an embodiment of this application.

[0095] It should be noted that, Figure 3 The structure of a targeted purification system based on intelligent identification of pollutant components shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0096] like Figure 3 As shown, a targeted purification system based on intelligent identification of pollutant components includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a Read-Only Memory (ROM) 302 or a program loaded from a storage section 308 into a Random Access Memory (RAM) 303, such as executing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0097] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0098] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0099] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0100] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0101] Specifically, the targeted purification system based on intelligent identification of pollutant components in this embodiment includes a processor and a memory. The memory stores a computer program, and when the computer program is executed by the processor, it implements the targeted purification method based on intelligent identification of pollutant components provided in the above embodiment.

[0102] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the targeted purification system based on intelligent identification of pollutant components described in the above embodiments; or it may exist independently and not assembled into the targeted purification system based on intelligent identification of pollutant components. The storage medium carries one or more computer programs, which, when executed by a processor of the targeted purification system based on intelligent identification of pollutant components, cause the system to implement the targeted purification method based on intelligent identification of pollutant components provided in the above embodiments.

Claims

1. A method for targeted purification based on intelligent identification of pollutant components, characterized in that, The method includes: The air purifier acquires the mixed gas formed when the air to be purified enters the detection chamber of the air purifier, and obtains the initial response signal of the mixed gas through a gas sensor array to obtain initial detection data. The air purifier includes multiple purification modules corresponding to different pollutant categories. Each of the purification modules is controlled to operate at a preset detection power for a preset detection duration in sequence, and the response signal sequence of the mixed gas is continuously collected through the gas sensor array within the preset detection duration to obtain the response change data corresponding to each of the purification modules; The rate of change of the response signal of each purification module within the preset detection time is calculated based on the response change data, and the rate of change is determined as the removal response rate of the corresponding pollutant category. Based on the removal response rate and the rated purification capacity parameters of the corresponding purification module, the predicted continuous running time required to reduce the concentration of each pollutant category to a preset safe concentration threshold is calculated. Based on the predicted continuous running time, the current target purification module is selected, and the current target purification module is controlled to run continuously at the rated purification power for the corresponding predicted continuous running time. After the current target purification module completes the predicted continuous running time, the response signal is re-acquired to update the predicted continuous running time of each pollutant category, and the next target purification module is selected in sequence for purification until the concentration of each pollutant category is reduced to below the preset safe concentration threshold.

2. The method according to claim 1, characterized in that, The system sequentially controls each of the purification modules to operate at a preset detection power for a preset detection duration, and continuously collects the response signal sequence of the mixed gas through the gas sensor array within the preset detection duration to obtain the response change data corresponding to each purification module, including: According to the preset pollutant detection sequence, the purification module to be detected is selected. The purification module includes an activated carbon filter module, a HEPA filter module, a photocatalyst module, and an ultraviolet disinfection module. The purification module to be detected is controlled to start at the preset detection power, and all other purification modules except the one to be detected are kept off. Within a preset detection time of the current purification module to be detected, the response signal sequence of the mixed gas is collected by the gas sensor array at a preset sampling frequency. The mixed gas includes formaldehyde, benzene compounds, particulate matter, and microorganisms. The response signal sequence is used as the response change data corresponding to the purification module currently being detected.

3. The method according to claim 1, characterized in that, The step of calculating the rate of change of the response signal of each purification module within the preset detection time based on the response change data, and determining the rate of change as the removal response rate for the corresponding pollutant category, includes: Extract the start response signal and the end response signal from the response change data; Calculate the signal difference between the start response signal and the termination response signal; The ratio of the signal difference to the preset detection duration is used as the initial rate of change of the response signal; The environmental interference compensation coefficient of the gas sensor array is obtained, and the initial change rate is corrected using the environmental interference compensation coefficient to obtain the removal response rate of the corresponding pollutant category.

4. The method according to claim 1, characterized in that, The step of calculating the predicted continuous operating time required to reduce the concentration of each pollutant category to a preset safe concentration threshold based on the removal response rate and the rated purification capacity parameters of the corresponding purification module includes: Based on the pre-established mapping relationship between response rate and purification capacity per unit time, the removal response rate is converted into the current purification capacity per unit time for the corresponding pollutant category. The current initial concentration of each pollutant category is determined based on the initial detection data; Calculate the concentration difference between the current initial concentration and the preset safe concentration threshold; Based on the concentration difference, the current purification rate per unit time, and the rated purification capacity parameter, the predicted continuous operating time for the corresponding pollutant category is calculated.

5. The method according to claim 4, characterized in that, The step of converting the removal response rate into the current purification rate per unit time for the corresponding pollutant category based on a pre-established mapping relationship between the response rate and the purification capacity per unit time includes: The environmental parameters of the current environment in which the air purifier is located are obtained, including temperature and humidity; The corresponding rate conversion coefficient is found in the preset mapping coefficient table based on the environmental parameters. The removal response rate is multiplied by the rate conversion coefficient to obtain the current purification amount per unit time for the corresponding pollutant category.

6. The method according to claim 1, characterized in that, The step of selecting the current target purification module based on the predicted continuous running time and controlling the current target purification module to continuously run at rated purification power for the corresponding predicted continuous running time includes: Construct a runtime set that includes each pollutant category and its corresponding predicted continuous runtime; The predicted consecutive runtimes in the runtime set are sorted in descending order of their numerical values. Extract the predicted continuous runtime of the first ranked item, and determine the purification module corresponding to the predicted continuous runtime as the current target purification module; Send a rated power operation command to the current target purification module, and send a stop command when the operation time reaches the predicted continuous operation time.

7. The method according to claim 1, characterized in that, The step of re-collecting response signals to update the predicted continuous runtime for each pollutant category after the current target purification module has completed the predicted continuous runtime includes: After the current target purification module stops operating, and after a preset stabilization period, the current response signal of the mixed gas is collected by the gas sensor array; The remaining concentration of each pollutant category is determined based on the current response signal; Determine whether each of the remaining concentrations is less than the preset safe concentration threshold; If there is a target residual concentration greater than or equal to the preset safe concentration threshold, the target predicted continuous running time for the corresponding pollutant category is calculated based on the target residual concentration to update the predicted continuous running time.

8. A system for targeted purification based on intelligent identification of pollutant components, characterized in that, The targeted purification system based on intelligent identification of pollutant components includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the targeted purification system based on intelligent identification of pollutant components to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on a system for targeted purification based on intelligent identification of pollutant components, the system for targeted purification based on intelligent identification of pollutant components performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on a targeted purification system based on intelligent identification of pollutant components, the system performs the method as described in any one of claims 1-7.

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