Air purification intelligent regulation and control system and method based on multi-sensor fusion

Abstract

The invention relates to the technical field of air purification intelligent regulation and control, and particularly discloses an air purification intelligent regulation and control system and method based on multi-sensor fusion. Comprising a multi-source environment data acquisition module, an environment dynamic coefficient generation module, a dominant pollution label identification module, a joint control instruction packet generation module, a purification efficiency verification module, an equipment regulation and control driving module and a database. By constructing a multi-dimensional environment sensing network, the concentration of suspended particles in air and the concentration of formaldehyde and volatile organic compounds can be monitored in real time, uneven spatial distribution of pollutants caused by environment temperature and humidity changes and human body activities can be captured, and meanwhile, an environment dynamic coefficient is generated; the dominant pollution type and the core pollution area in the current environment are accurately identified, and the optimal purification strategy is intelligently matched according to the dominant pollution type and the core pollution area. Through an efficiency evaluation mechanism, the system effectively balances operation energy consumption while ensuring efficient purification, and a green and sustainable air purification solution is realized.

Description

Technical Field

[0001] This invention belongs to the field of intelligent air purification control technology, and relates to an intelligent air purification control system and method based on multi-sensor fusion. Background Technology

[0002] With the acceleration of modern industrialization and urbanization, indoor air quality problems have become increasingly prominent, becoming a key environmental factor directly affecting human health. Indoor pollutants are numerous and complex in composition, and their sources are diverse. On the one hand, chemical gaseous pollutants continuously released from building materials, furniture, and decorations, such as formaldehyde, benzene compounds, and total volatile organic compounds, pose a long-term and insidious health threat. On the other hand, particulate pollutants generated by outdoor intrusion, human activity, cooking, and smoking, such as PM2.5 and PM10, are characterized by sudden onset and large concentration fluctuations. Furthermore, these pollutants of different properties do not exist in isolation; they undergo complex physicochemical interactions under specific temperature and humidity conditions. This complexity places extremely high demands on air purification equipment, requiring it not only to handle multiple pollutants but also to accurately identify the dominant pollution issues in the current environment and respond efficiently and energy-savingly.

[0003] The drawbacks of traditional air purification methods are mainly reflected in the following aspects. First, the purification strategy lacks specificity and precision. Traditional equipment cannot effectively distinguish the weight and urgency of different pollutants, often adopting a "one-size-fits-all" purification mode, that is, all purification units operate simultaneously without differentiation. Second, its control logic fails to fully consider the dynamic coupling effect of environmental factors. Finally, and most importantly, traditional equipment lacks an effective balance mechanism between purification efficiency and operating energy consumption. Its automatic mode is usually solely guided by achieving the purification target as quickly as possible, blindly pursuing high fan speeds and maximum power operation without assessing whether the energy consumption increase brought about by such high-performance output is reasonable. This results in maintaining high-energy-consuming operation even when the pollution level is not high or the marginal purification benefit is diminishing, causing serious energy waste and increasing the long-term operating costs for users. Summary of the Invention

[0004] In view of this, in order to solve the problems mentioned in the background technology, an intelligent air purification control system and method based on multi-sensor fusion is proposed.

[0005] The objective of this invention can be achieved through the following technical solution: The first aspect of this invention provides an intelligent air purification control system based on multi-sensor fusion, comprising: a multi-source environmental data acquisition module, which acquires multi-source raw data packages including suspended particulate concentration values, mixed concentration values ​​of formaldehyde and volatile organic compounds, ambient temperature and relative humidity values, and thermal distribution coordinate maps.

[0006] The environmental dynamic coefficient generation module calculates the humidity compensation weight, comprehensive pollution index, and local weight, which characterize the dynamic coupling relationship between pollutants, based on the multi-source raw data package, and generates an environmental dynamic coefficient.

[0007] The dominant pollution label identification module identifies and encapsulates the dominant type identifier and the core pollution area code based on the environmental dynamic coefficient, the comprehensive pollution index and the thermal distribution coordinate map, and generates a dominant pollution label.

[0008] The joint control instruction package generation module, based on the dominant pollution label, matches a preset coordination strategy and adjusts the air supply parameters to generate a joint control instruction package containing the main purification unit, the auxiliary purification unit, and the air supply angle.

[0009] The purification efficiency verification module, based on the joint control instruction package and the environmental dynamic coefficient, calls the database to perform efficiency evaluation, and generates an execution instruction by comparing the predicted purification efficiency increment with the predicted energy consumption increment.

[0010] The equipment control and drive module drives the equipment operation unit to perform control according to the execution command, and collects the environmental parameters after control to generate feedback data packets and provide corresponding feedback.

[0011] The second aspect of the present invention provides an intelligent air purification control method based on multi-sensor fusion, including: S1, multi-source environmental data acquisition: acquiring multi-source raw data packets containing suspended particulate concentration values, mixed concentration values ​​of formaldehyde and volatile organic compounds, ambient temperature and relative humidity values, and thermal distribution coordinate maps.

[0012] S2. Generation of environmental dynamic coefficient: Based on the multi-source raw data package, calculate the humidity compensation weight, comprehensive pollution index and local weight that characterize the dynamic coupling relationship between pollutants, and generate an environmental dynamic coefficient.

[0013] S3. Dominant Pollution Label Identification: Based on the environmental dynamic coefficient, the comprehensive pollution index, and the thermal distribution coordinate map, identify and encapsulate the dominant type identifier and the core pollution area code to generate a dominant pollution label.

[0014] S4. Joint control instruction package generation: Based on the dominant pollution label, match the preset coordination strategy and adjust the air supply parameters to generate a joint control instruction package containing the main purification unit, auxiliary purification unit and air supply angle.

[0015] S5. Purification efficiency verification: Based on the joint control instruction package and the environmental dynamic coefficient, the database is called to evaluate the efficiency. By comparing the predicted purification efficiency increment with the predicted energy consumption increment, an execution instruction is generated.

[0016] S6. Equipment Control Drive: Drive the equipment operation unit to perform control according to the execution command, collect the environmental parameters after control, generate feedback data packets, and provide corresponding feedback.

[0017] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention constructs a multi-dimensional environmental sensing network by integrating particulate matter sensors, gas sensors, thermometers and hygrometers, and an innovative infrared activity detector. It can not only monitor particulate matter and gaseous pollutants simultaneously, but also capture humidity, a key variable affecting the dynamics of pollutants, as well as the uneven spatial distribution of pollutants caused by human activities. Furthermore, the present invention is not satisfied with simply listing data, but by calculating environmental dynamic coefficients, it mathematically models the compensation effect of humidity on formaldehyde release, the comprehensive influence of multiple pollutants, and the local weight of human activity areas, thereby transforming discrete sensor readings into a comprehensive index that can characterize the interaction and spatial dynamic distribution of pollutants.

[0018] (2) This invention innovatively introduces the concept of a dominant pollution label. Based on accurate diagnosis, it can quickly determine the core pollution type in the current environment, whether it is excessive particulate matter or excessive formaldehyde, and can pinpoint the most polluted "core area," that is, the place where human activity is most frequent or the pollution source is most concentrated. Based on this label, it can intelligently match the optimal combination of purification units. This "symptomatic treatment" targeted purification mode ensures that the most suitable purification technology is applied to the most critical problem and concentrates the purification efficiency on the most needed spatial points, greatly improving purification efficiency and user experience, and avoiding the ineffective or inefficient purification of traditional "flood irrigation" methods.

[0019] (3) This invention uses historical databases and dynamic coefficients of the current environment to conduct a "sandbox simulation" of the upcoming collaborative purification strategy, predicting the potential purification gains and corresponding energy consumption increases. The system has an internal intelligent decision-making threshold; the collaborative control command will only be approved for execution when the predicted purification gains significantly exceed its energy costs. Conversely, if the system determines that activating the complex collaborative mode is "not worth the effort," it will automatically switch to a low-energy alternative that only activates the basic filter. This forward-looking cost-benefit analysis mechanism effectively avoids paying excessive energy costs for minor purification improvements, fundamentally optimizing the operational economy of the equipment, extending the service life of core components, and truly achieving green and sustainable intelligent purification. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the system structure connection of the present invention.

[0022] Figure 2 This is a schematic diagram of the method steps of the present invention.

[0023] Figure 3 This is a schematic diagram of the thermal distribution coordinates of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Please see Figure 1 As shown, the first aspect of the present invention provides an intelligent air purification control system based on multi-sensor fusion, comprising: a multi-source environmental data acquisition module, an environmental dynamic coefficient generation module, a dominant pollution label identification module, a joint control instruction package generation module, a purification efficiency verification module, an equipment control drive module, and a database.

[0026] It should be noted that the present invention also includes a database for storing the purification efficiency benchmark value and energy consumption benchmark value corresponding to each environmental dynamic coefficient, and a mapping table for storing the coordinates of the core pollution area and the air supply angle of the directional fan.

[0027] The multi-source environmental data acquisition module is connected to the environmental dynamic coefficient generation module, which is also connected to the dominant pollution label identification module. The dominant pollution label identification module is connected to the joint control instruction package generation module. Both the environmental dynamic coefficient generation module and the joint control instruction package generation module are connected to the purification efficiency verification module. The purification efficiency verification module is connected to the equipment control and drive module. Both the joint control instruction package generation module and the purification efficiency verification module are connected to the database.

[0028] Please see Figure 3 As shown, the multi-source environmental data acquisition module acquires multi-source raw data packages containing suspended particulate concentration values, mixed concentration values ​​of formaldehyde and volatile organic compounds, ambient temperature and relative humidity values, and thermal distribution coordinate maps.

[0029] It should be noted that the concentration of suspended particles is measured by a particulate matter sensor, the mixed concentration of formaldehyde and volatile organic compounds is detected by a gas sensor, the ambient temperature and relative humidity are recorded by a thermometer and hygrometer, and the thermal distribution coordinate map is generated by an infrared activity detector.

[0030] It should also be noted that the air purifier's built-in integrated sensor group automatically activates upon device startup and collects real-time environmental parameters, generating a multi-source raw data package containing various data items. The specific implementation process is as follows: First, the particulate matter sensor directly measures the concentration of suspended particulate matter in the air based on the principle of laser scattering, including PM2.5 and PM10. Next, the gas sensor uses an electrochemical detection method to detect the mixed concentration of formaldehyde and volatile organic compounds. Then, the thermometer and hygrometer record the ambient temperature and relative humidity values ​​using a capacitive sensor. Finally, the infrared activity detector uses a passive infrared array to scan the trajectory of human activity within the space, generating a thermal distribution coordinate map by identifying changes in heat sources. This coordinate map represents the activity intensity in different areas of the space in a two-dimensional grid format. All collected data items are aggregated and packaged in real time by the microcontroller to form a multi-source raw data package, with data within the package aligned with timestamps to ensure synchronization.

[0031] The environmental dynamic coefficient generation module calculates the humidity compensation weight, comprehensive pollution index, and local weight, which characterize the dynamic coupling relationship between pollutants, based on the multi-source raw data packet, and generates an environmental dynamic coefficient.

[0032] In a specific embodiment of the present invention, the specific process of generating an environmental dynamic coefficient is as follows: based on the relative humidity value and the mixed concentration value of formaldehyde and volatile organic compounds, the humidity compensation weight is calculated.

[0033] It should be noted that the formula for calculating the humidity compensation weight is as follows: ,in It is the relative humidity value. This is a reference relative humidity value. It is a coefficient, set based on experimental data showing that every 10% increase in humidity leads to a 5% increase in the formaldehyde release rate. That is, a 5% increment divided by a 10% change in humidity, while also based on the ASHRAE indoor comfort standard. The value is 50.

[0034] The comprehensive pollution index is generated by combining the concentration values ​​of suspended particles, the mixed concentration values ​​of formaldehyde and volatile organic compounds, and the humidity compensation weight.

[0035] It should be noted that the formula for generating the comprehensive pollution index is: Where PM2.5 is the concentration of particulate matter, and PM10 is the concentration of particulate matter. It is the mixed concentration value of formaldehyde and volatile organic compounds. , and These represent the set reference concentrations of PM2.5, PM10, and the mixed concentration of formaldehyde and volatile organic compounds, respectively.

[0036] In one specific embodiment of the present invention, the setting can be based on the WHO air quality guidelines. Based on laboratory pollutant hazard test data, we can obtain... .

[0037] High-frequency activity areas are extracted from the thermal distribution coordinate map to calculate the local weights, and the local weights are combined with the comprehensive pollution index to generate the environmental dynamic coefficient.

[0038] In a specific embodiment of the present invention, the high-frequency activity region specifically refers to: the thermal distribution coordinate map represents the activity intensity of different regions in space in the form of a two-dimensional grid, the average activity intensity value of the thermal distribution coordinate map is obtained by averaging the activity intensity values ​​of each region, and the activity intensity values ​​of each region are compared with the average activity intensity value. If the activity intensity value of a certain region is greater than or equal to the average activity intensity value, then the region is recorded as a high-frequency activity region.

[0039] It should be noted that the calculation formula for generating the environmental dynamic coefficient is as follows: ,in It's local weights, local weights ,in It is the maximum activity intensity value corresponding to the high-frequency activity region in the thermal distribution coordinate map. This indicates the activity intensity value set as a reference. Based on multiple sets of experimental data, the activity intensity value set as a reference can be 100.

[0040] The dominant pollution label identification module identifies and encapsulates the dominant type identifier and the core pollution area code based on the environmental dynamic coefficient, the comprehensive pollution index, and the thermal distribution coordinate map, and generates a dominant pollution label.

[0041] In a specific embodiment of the present invention, the specific process of generating a dominant pollution label is as follows: comparing the normalized value of each pollutant component in the comprehensive pollution index with the preset exceedance threshold, and selecting the largest exceedance item as the dominant type identifier.

[0042] It should be noted that the normalized concentration values ​​of each pollutant component are extracted from the comprehensive pollution index, including the normalized values ​​of particulate matter PM2.5, particulate matter PM10, and the normalized values ​​of the mixed concentration of formaldehyde and volatile organic compounds. These normalized values ​​are compared with the preset exceedance thresholds, and the item with the largest normalized value is selected as the dominant type identifier.

[0043] In one specific embodiment of the present invention, the preset threshold value includes, but is not limited to, 1, and is set based on the pollutant safety limit.

[0044] The thermal distribution coordinate map is analyzed to identify high-frequency activity areas, and the coordinate points of the high-frequency activity areas are marked as the pollution core area code, that is, the pollution core area code is the set of coordinates of the high-frequency activity areas.

[0045] The dominant type identifier and the pollution core area code are encapsulated into a dominant pollution label data structure to generate a dominant pollution label.

[0046] The joint control instruction package generation module generates a joint control instruction package that includes the main purification unit, auxiliary purification unit, and air supply angle by matching the preset coordination strategy and adjusting the air supply parameters according to the dominant pollution label.

[0047] In a specific embodiment of the present invention, the specific method of matching the preset collaborative strategy and adjusting the air supply parameters is as follows: after obtaining the coordinate set of high-frequency activity areas, the corresponding air supply angle is matched by querying the mapping table of pollution core area coordinates and directional air supply angle of the pre-stored database, and then the directional air supply fan is controlled to adjust to the corresponding air supply angle to achieve precise directional air supply to the pollution core area. In this case, each specific coordinate point in the mapping table corresponds to a unique air supply angle value.

[0048] In a specific embodiment of the present invention, the specific process of generating a joint control command package including a main purification unit, an auxiliary purification unit and an air supply angle is as follows: if the dominant type is identified as formaldehyde and volatile organic compounds, then an activated carbon filter is selected as the main purification unit, and a negative ion generator is selected as the auxiliary purification unit.

[0049] If the dominant type is identified as a sudden burst of suspended particles, then a high-efficiency filter is selected as the main purification unit, and an electrostatic precipitator is selected as the auxiliary purification unit.

[0050] The matching main purification unit, auxiliary purification unit, and air supply angle are encapsulated in the joint control command package data.

[0051] The purification efficiency verification module, based on the joint control instruction package and the environmental dynamic coefficient, calls the database to perform efficiency evaluation, and generates an execution instruction by comparing the predicted purification efficiency increment with the predicted energy consumption increment.

[0052] In a specific embodiment of the present invention, the specific process of generating an execution instruction is as follows: matching the environmental dynamic coefficient with the purification efficiency benchmark value and energy consumption benchmark value corresponding to each environmental dynamic coefficient stored in the database to obtain the purification efficiency benchmark value and energy consumption benchmark value corresponding to the environmental dynamic coefficient.

[0053] Calculate the predicted purification efficiency increment and the predicted energy consumption increment of the joint control instruction package.

[0054] It should be noted that the formula for calculating the predicted purification efficiency increment of the joint control command package is as follows: ,in This is the predicted purification efficiency value from the current joint control command package. This represents the baseline value for purification efficiency.

[0055] It should also be noted that the formula for calculating the predicted energy consumption increment of the joint control command packet is as follows: ,in, It is a predicted energy consumption value. This represents the baseline value for energy consumption.

[0056] It should be further explained that the predicted purification efficiency value is calculated from the difference between the purification efficiency value corresponding to the current joint control instruction package and the purification efficiency benchmark value, while the predicted energy consumption value is calculated from the difference between the predicted energy consumption of the current joint control instruction package and the energy consumption benchmark value.

[0057] When the predicted purification efficiency increment exceeds the sum of the predicted energy consumption increment and the preset optimization threshold, the joint control instruction package is used as the execution instruction.

[0058] Otherwise, generate an alternative instruction that enables only the basic filter mode as the execution instruction.

[0059] It should be noted that when Output the joint control command packet; otherwise, output the alternative command. This indicates the preset optimization threshold.

[0060] In a specific embodiment of the present invention, the preset optimization threshold value includes, but is not limited to, 10%. The preset optimization threshold value is mainly based on the balance requirements of system performance evaluation: the threshold is used to judge the comparison relationship between the predicted purification efficiency increment and the predicted energy consumption increment. When the purification efficiency increment exceeds the sum of the energy consumption increment and the optimization threshold, a joint control command package is triggered; otherwise, the alternative mode is enabled. Among them, 10% is used as a typical reference value, which takes into account the marginal benefits of purification efficiency improvement and energy consumption growth in actual operation, and also reserves the flexibility to adjust the threshold according to different application scenarios. The ultimate goal is to achieve the optimal trade-off between pollution control effect and equipment operating cost.

[0061] The device control and drive module drives the device operation unit to perform control according to the execution command, and collects the environmental parameters after control to generate feedback data packets and provide corresponding feedback.

[0062] In a specific embodiment of the present invention, the specific method of controlling the device operation unit according to the execution instruction is as follows: if the execution instruction is the joint control instruction package, then the main purification unit, the auxiliary purification unit and the directional air supply fan are started simultaneously.

[0063] If the execution instruction is an alternative instruction, then only the basic filter mode will be activated.

[0064] In a specific embodiment of the present invention, the method for generating feedback data packets from the collected and regulated environmental parameters is as follows: the concentration values ​​of suspended particles and the mixed concentration values ​​of formaldehyde and volatile organic compounds are collected in real time by an integrated sensor group.

[0065] The rate of change of suspended particulate concentration is obtained by comparing the difference between the original suspended particulate concentration value and the adjusted suspended particulate concentration value with the original suspended particulate concentration value.

[0066] The difference between the original mixed concentration of formaldehyde and volatile organic compounds and the adjusted mixed concentration of formaldehyde and volatile organic compounds is compared with the original mixed concentration of formaldehyde and volatile organic compounds to obtain the rate of change of the mixed concentration of formaldehyde and volatile organic compounds. The rate of change in suspended particulate concentration, the rate of change in the mixed concentration of formaldehyde and volatile organic compounds, and the corresponding execution instructions are encapsulated together into a feedback data packet.

[0067] Reference Figure 2 As shown, the second aspect of the present invention provides an intelligent air purification control method based on multi-sensor fusion, including: S1, multi-source environmental data acquisition: acquiring multi-source raw data packets containing suspended particulate concentration values, mixed concentration values ​​of formaldehyde and volatile organic compounds, ambient temperature and relative humidity values, and thermal distribution coordinate maps.

[0068] S2. Generation of environmental dynamic coefficient: Based on the multi-source raw data package, calculate the humidity compensation weight, comprehensive pollution index and local weight that characterize the dynamic coupling relationship between pollutants, and generate an environmental dynamic coefficient.

[0069] S3. Dominant Pollution Label Identification: Based on the environmental dynamic coefficient, the comprehensive pollution index, and the thermal distribution coordinate map, identify and encapsulate the dominant type identifier and the core pollution area code to generate a dominant pollution label.

[0070] S4. Joint control instruction package generation: Based on the dominant pollution label, match the preset coordination strategy and adjust the air supply parameters to generate a joint control instruction package containing the main purification unit, auxiliary purification unit and air supply angle.

[0071] S5. Purification efficiency verification: Based on the joint control instruction package and the environmental dynamic coefficient, the database is called to evaluate the efficiency. By comparing the predicted purification efficiency increment with the predicted energy consumption increment, an execution instruction is generated.

[0072] S6. Equipment Control Drive: Drive the equipment operation unit to perform control according to the execution command, collect the environmental parameters after control, generate feedback data packets, and provide corresponding feedback.

[0073] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. An air purification intelligent regulation system based on multi-sensor fusion, characterized in that, The method comprises the following steps: A multi-source environmental data acquisition module acquires a multi-source original data packet containing suspended particle concentration values, mixed concentration values of formaldehyde and volatile organic compounds, environmental temperature and relative humidity values, and a thermal distribution coordinate graph; An environmental dynamic coefficient generation module calculates humidity compensation weights, comprehensive pollution indexes, and local weights representing the dynamic coupling relationship between pollutants based on the multi-source original data packet, and generates an environmental dynamic coefficient; A dominant pollution label identification module identifies and encapsulates a dominant type identifier and a pollution core area code based on the environmental dynamic coefficient, the comprehensive pollution index, and the thermal distribution coordinate graph, and generates a dominant pollution label; A joint control instruction packet generation module matches a preset collaborative strategy and adjusts the air supply parameters according to the dominant pollution label, and generates a joint control instruction packet containing a main purification unit, an auxiliary purification unit, and an air supply angle; A purification efficiency verification module calls a database for efficiency evaluation based on the joint control instruction packet and the environmental dynamic coefficient, compares the predicted purification efficiency increment and the predicted energy consumption increment, and generates an execution instruction; A device control and driving module drives the device running unit for control according to the execution instruction, and collects the environmental parameters after the control to generate a feedback data packet for corresponding feedback.

2. The multi-sensor fusion based air purification intelligent regulation system according to claim 1, characterized in that: The specific process of generating an environmental dynamic coefficient is as follows: Based on the relative humidity values and the mixed concentration values of formaldehyde and volatile organic compounds, the humidity compensation weights are calculated; The comprehensive pollution index is generated by fusing the suspended particle concentration values, the mixed concentration values of formaldehyde and volatile organic compounds, and the humidity compensation weights; The local weights are calculated from the high-frequency activity areas extracted from the thermal distribution coordinate graph, and the local weights are combined with the comprehensive pollution index to generate the environmental dynamic coefficient.

3. The multi-sensor fusion based air purification intelligent regulation system according to claim 2, characterized in that: The high-frequency activity area specifically refers to that the thermal distribution coordinate graph represents the activity intensity of different regions in space in the form of a two-dimensional grid, the activity intensity values of each region are averaged to obtain the average activity intensity value of the thermal distribution coordinate graph, and the activity intensity values of each region are compared with the average activity intensity value. If the activity intensity value of a certain region is greater than or equal to the average activity intensity value, the region is recorded as a high-frequency activity region.

4. The multi-sensor fusion based air purification intelligent regulation system according to claim 1, wherein: The specific process of generating a dominant pollution label is as follows: The normalized values of each pollutant component in the comprehensive pollution index are compared with the preset exceeding threshold value, and the largest exceeding item is selected as the dominant type identifier; The thermal distribution coordinate graph is analyzed to identify high-frequency activity areas, and the coordinate points of the high-frequency activity areas are marked as the pollution core area code, i.e., the pollution core area code is the coordinate set of the high-frequency activity areas; The dominant type identifier and the pollution core area code are encapsulated into the dominant pollution label data structure, thereby generating a dominant pollution label.

5. The multi-sensor fusion based air purification intelligent regulation system according to claim 4, wherein: The specific manner of matching the preset cooperative strategy and adjusting the air supply parameter is: after the coordinate set of the high-frequency activity area is obtained, the corresponding air supply angle is matched by querying the mapping table of the pollution core area coordinate and the air supply angle of the directional air supply machine pre-stored in the database, and then the directional air supply machine is controlled to adjust to the corresponding air supply angle, so as to realize the directional air supply accurately aiming at the pollution core area, wherein each specific coordinate point in the mapping table corresponds to a unique air supply angle value.

6. The multi-sensor fusion based air purification intelligent regulation system according to claim 5, wherein: The specific process of generating a joint control instruction package including a main purification unit, an auxiliary purification unit and an air supply angle is: If the dominant type identifier is formaldehyde and volatile organic matter dominant, an active carbon filter screen is selected as the main purification unit, and a negative ion generator is selected as the auxiliary purification unit; If the dominant type identifier is a suspended particle burst, a high-efficiency filter screen is selected as the main purification unit, and an electrostatic dust collector is selected as the auxiliary purification unit; The matched main purification unit, auxiliary purification unit and air supply angle are packaged into the joint control instruction package data.

7. The multi-sensor fusion based air purification intelligent regulation system according to claim 1, wherein: The specific process of generating an execution instruction is: The environmental dynamic coefficient is matched with the purification efficiency reference value and the energy consumption reference value corresponding to each environmental dynamic coefficient stored in the database to obtain the purification efficiency reference value and the energy consumption reference value corresponding to the environmental dynamic coefficient; The predicted purification efficiency increment and the predicted energy consumption increment of the joint control instruction package are calculated; When the predicted purification efficiency increment exceeds the sum of the predicted energy consumption increment and the preset optimization threshold, the joint control instruction package is taken as the execution instruction; Otherwise, a backup instruction of only enabling the basic filter screen mode is generated as the execution instruction.

8. The multi-sensor fusion based air purification intelligent regulation system according to claim 7, wherein: The specific manner of driving the equipment operation unit according to the execution instruction is: If the execution instruction is the joint control instruction package, the main purification unit, the auxiliary purification unit and the directional air supply machine are started synchronously; If the execution instruction is the backup instruction, only the basic filter screen mode is activated.

9. The multi-sensor fusion based air purification intelligent regulation system according to claim 8, wherein: The specific manner of generating a feedback data package by collecting the environmental parameters after regulation is: The suspended particle concentration value and the mixed concentration value of formaldehyde and volatile organic matter after regulation are collected in real time by the integrated sensor group; The difference between the original suspended particle concentration value and the suspended particle concentration value after regulation is divided by the original suspended particle concentration value to obtain the suspended particle concentration change rate; The difference between the original mixed concentration value of formaldehyde and volatile organic matter and the mixed concentration value of formaldehyde and volatile organic matter after regulation is divided by the original mixed concentration value of formaldehyde and volatile organic matter to obtain the mixed concentration change rate of formaldehyde and volatile organic matter; The suspended particle concentration change rate, the mixed concentration change rate of formaldehyde and volatile organic matter and the corresponding execution instruction are packaged together as a feedback data package.

10. The air purification intelligent regulation method based on multi-sensor fusion, characterized in that, It includes: S1, multi-source environmental data acquisition: acquire a multi-source original data package including suspended particle concentration value, mixed concentration value of formaldehyde and volatile organic matter, environmental temperature and relative humidity value, and thermal distribution coordinate graph; S2, environmental dynamic coefficient generation: based on the multi-source original data packet, the humidity compensation weight, the comprehensive pollution index and the local weight representing the dynamic coupling relationship between pollutants are calculated to generate an environmental dynamic coefficient; S3, dominant pollution label identification: based on the environmental dynamic coefficient, the comprehensive pollution index and the thermal distribution coordinate diagram, the dominant type identification and the pollution core area code are identified and packaged to generate a dominant pollution label; S4, joint control instruction packet generation: according to the dominant pollution label, the preset cooperative strategy is matched and the air supply parameter is adjusted to generate a joint control instruction packet including the main purification unit, the auxiliary purification unit and the air supply angle; S5, purification efficiency verification: based on the joint control instruction packet and the environmental dynamic coefficient, the database is called for efficiency evaluation, and by comparing the predicted purification efficiency increment and the predicted energy consumption increment, an execution instruction is generated; S6, equipment regulation and control driving: according to the execution instruction, the equipment operation unit is driven for regulation and control, and the feedback data packet is generated by collecting the environmental parameters after regulation and control for corresponding feedback.

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