A formaldehyde removal intelligent monitoring system and method for building decoration

Abstract

This invention belongs to the field of air monitoring and purification technology, and particularly relates to an intelligent formaldehyde removal monitoring system and method for building decoration. This invention achieves accurate formaldehyde pollution monitoring and pollution source location through distributed sensing and three-dimensional concentration field reconstruction. It employs dual-dimensional evaluation indicators and a standardized weighting mechanism to scientifically allocate purification priorities, improving purification efficiency and reducing energy consumption. Through operating condition identification and dynamic parameter adjustment, it enhances the system's environmental adaptability. Through zoned linkage control, it achieves intelligent scheduling of purification equipment, significantly enhancing control robustness under different environmental conditions. Simultaneously, the zoned linkage purification control mode avoids the resource waste caused by traditional global operation. Under the premise of ensuring indoor air quality safety, it achieves refined and intelligent scheduling of purification equipment, possessing high practical value and promising prospects for promotion.

Description

Technical Field

[0001] This invention belongs to the field of air monitoring and purification technology, specifically relating to an intelligent formaldehyde removal monitoring system and method for building decoration. Background Technology

[0002] With the rapid development of the building decoration industry, volatile organic pollutants such as formaldehyde released from decoration materials, furniture and decorative products have become the main cause of indoor air quality exceeding standards. Formaldehyde has the characteristics of long release period, uneven spatial distribution and susceptibility to environmental airflow and temperature and humidity. Long-term exposure will cause irreversible damage to the human respiratory system, immune system and mucous membrane tissue. Therefore, accurate monitoring and efficient purification of indoor formaldehyde after decoration has become an important research direction in the field of building environmental safety.

[0003] Current indoor formaldehyde monitoring and purification technologies still have many significant shortcomings. Monitoring methods mostly use single-point fixed sampling devices, which can only obtain concentration values ​​in a local space and cannot achieve full-space three-dimensional concentration field reconstruction. This makes it difficult to accurately locate the pollution source and release intensity, resulting in a lack of targeted purification. Moreover, traditional purification equipment often adopts manual start-stop or constant power operation modes, without combining multi-dimensional environmental parameters such as pollution concentration, airflow distribution, turbulence characteristics, and distance to the pollution source for intelligent decision-making. This leads to problems such as low purification efficiency, energy waste, and uneven regional purification. In addition, existing technologies often rely solely on real-time concentration as a single indicator in the purification strategy formulation process, without fully considering the impact of concentration change trends on the urgency of pollution, and ignoring fluid dynamic factors such as airflow direction effectiveness and distance attenuation. This makes the purification priority allocation inconsistent with the actual physical diffusion law, making it difficult to simultaneously meet the comprehensive needs of accurate perception, intelligent decision-making, zonal control, and closed-loop optimization.

[0004] To address the aforementioned issues, this application presents a formaldehyde removal intelligent monitoring system and method for building decoration. Summary of the Invention

[0005] To address the shortcomings of the prior art mentioned in the background section, this application proposes an intelligent formaldehyde removal monitoring system and method for building decoration. This invention achieves accurate formaldehyde pollution monitoring and source location through distributed sensing and three-dimensional concentration field reconstruction. It employs dual-dimensional evaluation indicators and a standardized weighting mechanism to scientifically allocate purification priorities, improving purification efficiency and reducing energy consumption. Through operating condition identification and dynamic parameter adjustment, it enhances the system's environmental adaptability. Finally, it achieves intelligent scheduling of purification equipment through zoned linkage control, thereby solving the problems mentioned in the background section.

[0006] Firstly, to achieve the above objectives, this application provides a smart formaldehyde removal monitoring method for building decoration, which includes the following specific steps: Step S1: Continuously collect time-series data including formaldehyde concentration, temperature, humidity and airflow speed at a preset high-frequency sampling rate, and record the spatial coordinates of each sensor node. Step S2: Based on the collected time-series data and spatial coordinates, the three-dimensional spatial distribution field of indoor formaldehyde concentration is calculated in real time through a pre-constructed spatial field state reconstruction model, and the location and release intensity level of the main pollution release sources are determined by reverse deduction based on the concentration gradient change law. Step S3: Obtain the environmental condition information of the current decoration space, determine the mode category of the current environment based on the preset condition recognition rules, and dynamically adjust the boundary conditions of the spatial field reconstruction model in step S2 according to the determined mode category. Step S4: Based on the corrected three-dimensional spatial distribution field and airflow velocity data, calculate the purification priority index for different spatial sub-regions; The calculation of the purification priority index for different spatial sub-regions based on the corrected three-dimensional spatial distribution field and airflow velocity data includes the following specific steps: Step S41: Calculate the purification urgency index for each sub-region based on the ratio of the current formaldehyde concentration to the target safe concentration and the concentration change trend. Step S42: Calculate the disturbance dispersibility index of each sub-region based on the airflow velocity, turbulence intensity, and distance between the region and potential pollution sources. Step S43: Standardize the calculated purification urgency index and disturbance dissipation index. Step S44: Calculate the purification priority index for each sub-region based on the standardized purification urgency index and the standardized disturbance dissipation index. Step S5: Based on the calculation results of the purification priority index of each sub-area and the preset working characteristics of the formaldehyde removal equipment, generate zone linkage control strategy instructions for different areas. Step S6: Send the generated zone linkage control strategy command to the corresponding formaldehyde removal execution device; Step S7: Continuously monitor the concentration field change data after the execution of the partition linkage control strategy command, compare the measured change data with the theoretical expected data, and automatically optimize the parameters of the spatial field state reconstruction model in step S2 based on the comparison results.

[0007] Based on the above scheme, the preferred formula for calculating the purification urgency index is: Where Pi is the purification urgency index of the i-th sub-region, Ci is the current formaldehyde concentration of the i-th sub-region, Csafe is the formaldehyde safe concentration threshold, and Ct is the normalized reference concentration, used to map the difference to a reasonable range to avoid numerical explosion caused by an excessively small denominator. The concentration change rate is represented by a positive value indicating an increase and a negative value indicating a decrease. K (mode) is a model-dependent sensitivity coefficient used to dynamically adjust the weight of the trend term according to environmental conditions, taking a smaller value under static accumulation mode and a larger value under continuous release mode. It is a non-negative truncation function, used to ensure that urgency is generated only when the limit is exceeded or increases, avoiding interference from negative values.

[0008] Based on the above scheme, the preferred formula for calculating the disturbance dispersibility index is: Where Di is the disturbance dispersibility index of the i-th sub-region, representing the ease with which pollutants in the current region are dispersed and removed by airflow disturbance. The larger the value, the easier it is for pollutants in the region to be carried away by airflow, and the higher the purification efficiency. Vi is the average airflow velocity of the i-th sub-region, used to reflect the basic ability of airflow to carry pollutants. The higher the velocity, the stronger the pollutant dispersion potential. Let be the angle between the airflow direction and the target guidance direction in the i-th sub-region, Vref be the spatial reference wind speed, di be the straight-line distance between the i-th sub-region and the nearest pollution source, be the distance attenuation coefficient, TIi be the turbulence intensity in the i-th sub-region, and be the turbulence enhancement factor, which is an adjustable parameter > 0. Let be the absolute value of the cosine of the angle between the airflow direction of the i-th sub-region and the vector from the pollution source to the current region, with a value range of [0, 1]. This is the distance attenuation term.

[0009] Based on the above scheme, the standardized processing formulas for the purification urgency index and the disturbance dissipability index are as follows: , Wherein, Psi is the range of the standardized purification urgency index of the i-th sub-region [0, 1], Pmin is the minimum value of all sub-regions Pi in the current calculation batch, Pmax is the maximum value of all sub-regions Pi in the current calculation batch, Dsi is the range of the standardized disturbance dissipation index of the i-th sub-region [0, 1], Dmin is the minimum value of all sub-regions Di in the current calculation batch, and Dmax is the maximum value of all sub-regions Di in the current calculation batch.

[0010] Based on the above scheme, the preferred formula for calculating the purification priority index is: Where Qi is the purification priority index of the i-th sub-region, Ka and Kb are both weighting coefficients that can be flexibly adjusted according to the scenario, and Ka+Kb=1.

[0011] Based on the above scheme, the preferred mode category in step S3 includes static accumulation mode, continuous release mode, and forced intervention mode, and the boundary conditions include air convection diffusion coefficient and pollution source attenuation constant.

[0012] Based on the above scheme, the parameter of the spatial field state reconstruction model in step S2 is automatically optimized according to the comparison result in step S7. Specifically, if the actual concentration decay rate is lower than the theoretical decay rate and the deviation exceeds the preset threshold, the release intensity weight of the corresponding area in the pollution source inversion model is automatically enhanced, and the output power of the purification equipment in that area is increased in the subsequent linkage strategy.

[0013] Secondly, this application provides a formaldehyde removal intelligent monitoring system for building decoration, which specifically includes: a sensor sensing module, a three-dimensional concentration field reconstruction and pollution source inversion module, an environmental condition identification and boundary condition adjustment module, a purification priority index calculation module, a zone linkage control strategy generation module, a control command issuance and execution module, and a model closed-loop optimization module. The sensor sensing module includes multiple wireless sensor nodes deployed at key locations in space, which are used to continuously collect time-series data including formaldehyde concentration, temperature, humidity and airflow speed at a preset high-frequency sampling rate, and record the spatial coordinates of each sensor node. The three-dimensional concentration field reconstruction and pollution source inversion module is used to calculate the three-dimensional spatial distribution field of indoor formaldehyde concentration in real time based on the collected and recorded time-series data and spatial coordinates, and to determine the location and release intensity level of the main pollution release sources by reverse deduction combined with the concentration gradient change law. The environmental condition identification and boundary condition adjustment module is used to obtain the environmental condition information of the current decoration space, determine the mode category of the current environment based on the preset condition identification rules, and dynamically adjust the boundary conditions of the spatial field reconstruction model in step S2 according to the determined mode category. The purification priority index calculation module is used to calculate the purification urgency index of each sub-region based on the ratio of the current formaldehyde concentration to the target safe concentration in each sub-region, combined with the concentration change trend. The partition linkage control strategy generation module is used to generate partition linkage control strategy instructions for different areas based on the calculation results of the purification priority index of each sub-area and the preset working characteristics of the formaldehyde removal equipment. The control command issuance and execution module is used to send the generated zone linkage control strategy command to the corresponding formaldehyde removal execution device; The model closed-loop optimization module is used to continuously monitor the concentration field change data after the execution of the partition linkage control strategy command, compare the measured change data with the theoretical expected data, and automatically optimize the parameters of the spatial field state reconstruction model in step S2 based on the comparison results.

[0014] Thirdly, this application provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the aforementioned intelligent formaldehyde removal monitoring method for building decoration by calling the computer program stored in the memory.

[0015] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described intelligent formaldehyde removal monitoring method for building decoration.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention utilizes distributed multi-source sensing and three-dimensional concentration field reconstruction technology to accurately characterize the spatial distribution of formaldehyde in indoor environments and pinpoint pollution sources, thereby fundamentally improving the comprehensiveness and targeting of pollution monitoring. This invention innovatively proposes a weighted fusion of the purification urgency index and the disturbance dissipation index to generate a purification priority index. The former reflects the severity of pollution, while the latter reflects the remediability of pollution. The combination of these two indices optimizes decision-making by prioritizing areas with the heaviest pollution, avoiding the inefficiency of blindly prioritizing high-concentration but difficult-to-disturb areas. This ensures both the scientific rationality of purification priority allocation and the simplicity and engineering applicability of the algorithm, effectively improving purification efficiency and reducing equipment energy consumption. Relying on environmental condition recognition and dynamic boundary condition adjustment functions, the system can adapt to complex indoor scenarios such as door and window opening and closing and fresh air operation, which can significantly enhance the control robustness under different environmental conditions. By establishing a closed-loop optimization mechanism of control effect feedback and model parameter iteration, the system realizes continuous self-optimization of the field state reconstruction model and purification strategy, further improving the stability and long-term effectiveness of the treatment effect. At the same time, the zone-linked purification control mode avoids the resource waste caused by traditional global operation. Under the premise of ensuring indoor air quality safety, it realizes the refined and intelligent scheduling of purification equipment, which has high practical value and promotion prospects. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the overall process of an intelligent formaldehyde removal monitoring method for building decoration according to the present invention. Figure 2 This is a flowchart illustrating step S4 of a formaldehyde removal intelligent monitoring method for building decoration according to the present invention. Figure 3 This is a schematic diagram of the framework of an intelligent formaldehyde removal monitoring system for building decoration according to the present invention. Detailed Implementation

[0018] 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1 To address the technical problems raised in the background art, this application provides a preferred embodiment: such as Figures 1-3 As shown, a smart formaldehyde monitoring method for building decoration includes the following specific steps: Step S1: Continuously collect time-series data including formaldehyde concentration, temperature, humidity and airflow speed at a preset high-frequency sampling rate, and record the spatial coordinates of each sensor node. Step S2: Based on the collected time-series data and spatial coordinates, the three-dimensional spatial distribution field of indoor formaldehyde concentration is calculated in real time through a pre-constructed spatial field state reconstruction model, and the location and release intensity level of the main pollution release sources are determined by reverse deduction based on the concentration gradient change law. Step S3: Obtain the environmental condition information of the current decoration space, determine the mode category of the current environment based on the preset condition recognition rules, and dynamically adjust the boundary conditions of the spatial field reconstruction model in step S2 according to the determined mode category. Step S4: Based on the corrected three-dimensional spatial distribution field and airflow velocity data, calculate the purification priority index for different spatial sub-regions; The calculation of the purification priority index for different spatial sub-regions based on the corrected three-dimensional spatial distribution field and airflow velocity data includes the following specific steps: Step S41: Calculate the purification urgency index for each sub-region based on the ratio of the current formaldehyde concentration to the target safe concentration and the concentration change trend. Step S42: Calculate the disturbance dispersibility index of each sub-region based on the airflow velocity, turbulence intensity, and distance between the region and potential pollution sources. Step S43: Standardize the calculated purification urgency index and disturbance dissipation index. Step S44: Calculate the purification priority index for each sub-region based on the standardized purification urgency index and the standardized disturbance dissipation index. Step S5: Based on the calculation results of the purification priority index of each sub-area and the preset working characteristics of the formaldehyde removal equipment, generate zone linkage control strategy instructions for different areas. Step S6: Send the generated zone linkage control strategy command to the corresponding formaldehyde removal execution device; Step S7: Continuously monitor the concentration field change data after the execution of the partition linkage control strategy command, compare the measured change data with the theoretical expected data, and automatically optimize the parameters of the spatial field state reconstruction model in step S2 based on the comparison results.

[0020] The advantages of this embodiment compared to the prior art are as follows: This invention achieves accurate monitoring and source location of formaldehyde pollution through distributed sensing and three-dimensional concentration field reconstruction; it adopts dual-dimensional evaluation indicators and a standardized weighting mechanism to scientifically allocate purification priorities, improve purification efficiency, and reduce energy consumption; it enhances the system's environmental adaptability through operating condition identification and dynamic parameter adjustment; it improves the long-term effectiveness of treatment by continuously iterating models and strategies based on a closed-loop optimization mechanism; and it achieves intelligent scheduling of purification equipment through zoned linkage control, combining safety and practicality, and has significant promotional value.

[0021] Furthermore: In an optional embodiment, the purification urgency index is calculated using the following formula: Where Pi is the purification urgency index of the i-th sub-region, Ci is the current formaldehyde concentration of the i-th sub-region, Csafe is the formaldehyde safe concentration threshold, and Ct is the normalized reference concentration, used to map the difference to a reasonable range to avoid numerical explosion caused by an excessively small denominator. The concentration change rate is represented by a positive value indicating an increase and a negative value indicating a decrease. K (mode) is a model-dependent sensitivity coefficient used to dynamically adjust the weight of the trend term according to environmental conditions, taking a smaller value under static accumulation mode and a larger value under continuous release mode. It is a non-negative truncation function, used to ensure that urgency is generated only when the limit is exceeded or increases, avoiding interference from negative values.

[0022] The advantages of this embodiment over the prior art are as follows: the urgency is determined by the degree of concentration exceeding the standard and the trend of concentration change, and a non-negative cutoff function is used for non-negative cutoff, which avoids invalid negative values ​​below the safe concentration and can predict the trend of pollution deterioration in advance. At the same time, the weight is dynamically adjusted in combination with the operating conditions, so that the index is more in line with the actual pollution risk, and the decision is safer and more robust.

[0023] In an optional embodiment, the perturbation dispersibility index is calculated using the following formula: Where Di is the disturbance dispersibility index of the i-th sub-region, representing the ease with which pollutants in the current region are dispersed and removed by airflow disturbance. The larger the value, the easier it is for pollutants in the region to be carried away by airflow, and the higher the purification efficiency. Vi is the average airflow velocity of the i-th sub-region, used to reflect the basic ability of airflow to carry pollutants. The higher the velocity, the stronger the pollutant dispersion potential. Let be the angle between the airflow direction and the target guidance direction in the i-th sub-region, Vref be the spatial reference wind speed, di be the straight-line distance between the i-th sub-region and the nearest pollution source, be the distance attenuation coefficient, TIi be the turbulence intensity in the i-th sub-region, and be the turbulence enhancement factor, which is an adjustable parameter > 0. Let be the absolute value of the cosine of the angle between the airflow direction of the i-th sub-region and the vector from the pollution source to the current region, with a value range of [0, 1]. This is the distance attenuation term.

[0024] The advantages of this embodiment over the prior art are as follows: the calculation formula of the disturbance dispersibility index integrates airflow velocity, airflow direction effectiveness, pollution source distance attenuation and turbulence intensity, which conforms to the real physical laws of indoor pollutant diffusion. The introduction of the absolute value of the cosine of the airflow direction to filter ineffective airflow allows the index to accurately reflect the actual purification potential of the area, thereby improving the rationality and pertinence of the purification strategy.

[0025] In an optional embodiment, the standardized formulas for the purification urgency index and the disturbance dissipation index are respectively: , Wherein, Psi is the range of the standardized purification urgency index of the i-th sub-region [0, 1], Pmin is the minimum value of all sub-regions Pi in the current calculation batch, Pmax is the maximum value of all sub-regions Pi in the current calculation batch, Dsi is the range of the standardized disturbance dissipation index of the i-th sub-region [0, 1], Dmin is the minimum value of all sub-regions Di in the current calculation batch, and Dmax is the maximum value of all sub-regions Di in the current calculation batch.

[0026] The advantages of this embodiment over the prior art are as follows: by standardizing the processing formula, the purification urgency index and disturbance dispersibility index of different magnitudes and physical meanings can be uniformly mapped to the [0, 1] interval, which can eliminate the weighting distortion caused by the difference in dimensions and values, make the allocation of purification priority fair and controllable, and make the engineering implementation simpler and more stable.

[0027] In an optional embodiment, the purification priority index is calculated using the following formula: Where Qi is the purification priority index of the i-th sub-region, Ka and Kb are both weighting coefficients that can be flexibly adjusted according to the scenario, and Ka+Kb=1.

[0028] Furthermore: In an optional embodiment, the mode categories in step S3 include static accumulation mode, continuous release mode, and forced intervention mode, and the boundary conditions include air convection diffusion coefficient and pollution source attenuation constant.

[0029] In an optional embodiment, the parameters of the spatial field state reconstruction model in step S2 are automatically optimized based on the comparison results in step S7. Specifically, if the actual concentration decay rate is lower than the theoretical decay rate and the deviation exceeds a preset threshold, the release intensity weight of the corresponding area in the pollution source inversion model is automatically increased, and the output power of the purification equipment in that area is increased in the subsequent linkage strategy.

[0030] In practice: the indoor living space after the renovation is completed is divided into multiple sub-areas in advance, and wireless sensor nodes are deployed at key locations in each sub-area to build a distributed sensor perception network. Each sensor node collects environmental time-series data such as formaldehyde concentration, temperature and humidity, airflow speed, airflow direction and turbulence intensity in the corresponding area according to a preset sampling frequency. At the same time, the spatial coordinate information of the sensor node is collected and connected to the communication interface between the door and window status sensing unit and the fresh air purification equipment to complete the system hardware deployment and data initialization. Next, the collected time-series data and spatial coordinates are processed through the three-dimensional concentration field reconstruction and pollution source inversion module. Based on the pre-constructed spatial field reconstruction model, an indoor three-dimensional formaldehyde concentration distribution field is generated. Combined with the concentration gradient change characteristics, the distribution location and release intensity level of the main pollution sources in the space are identified. At the same time, the current space ventilation status, door and window opening and closing status and fresh air system operation status are obtained through the environmental condition identification module. The current environmental condition mode is determined and the boundary conditions of the spatial field reconstruction model are adjusted accordingly to make the model adapt to the current actual environmental condition. The purification priority index calculation module performs index calculations on each sub-region. First, based on the relative relationship between the formaldehyde concentration and the preset safe concentration in each sub-region, and combined with the concentration change trend, the purification urgency index is calculated. During the calculation process, the values ​​are truncated to ensure the rationality of the index results. Second, based on the airflow motion parameters, turbulence intensity, and relative spatial distance between the sub-region and the pollution source in each sub-region, and combined with the airflow direction effectiveness factor and distance attenuation factor, the disturbance dissipation index is calculated. Then, the purification urgency index and the disturbance dissipation index are standardized and mapped to the same numerical range. Finally, they are linearly weighted and fused using preset weights to obtain the purification priority index for each sub-region. Secondly, the partition linkage control strategy generation module generates partition linkage control instructions adapted to each sub-region based on the purification priority index of each sub-region and the operating characteristics and constraints of the purification equipment. The control instruction issuing and execution module transmits the control instructions to the corresponding fresh air unit, air purifier and other execution equipment. The equipment performs partitioned and differentiated purification operations according to the instructions. Finally, the environmental concentration field data after the purification is carried out in real time through the model closed-loop optimization module. The measured data is compared with the theoretical prediction data of the model. Based on the comparison deviation results, the relevant parameters of the spatial field state reconstruction model are adaptively adjusted to optimize the subsequent pollution source inversion accuracy and purification strategy formulation logic, so as to realize the closed-loop iteration and self-optimization of the entire system.

[0031] Example 2 Based on the same inventive concept as in Embodiment 1, such as Figure 1 As shown, this embodiment provides a formaldehyde removal intelligent monitoring system for building decoration, which specifically includes: a sensor sensing module, a three-dimensional concentration field reconstruction and pollution source inversion module, an environmental condition identification and boundary condition adjustment module, a purification priority index calculation module, a zone linkage control strategy generation module, a control command issuance and execution module, and a model closed-loop optimization module. The sensor sensing module includes multiple wireless sensor nodes deployed at key locations in space, which are used to continuously collect time-series data including formaldehyde concentration, temperature, humidity and airflow speed at a preset high-frequency sampling rate, and record the spatial coordinates of each sensor node. The three-dimensional concentration field reconstruction and pollution source inversion module is used to calculate the three-dimensional spatial distribution field of indoor formaldehyde concentration in real time based on the collected and recorded time-series data and spatial coordinates, and to inversely deduce the location and release intensity level of the main pollution release sources by combining the concentration gradient change law. The environmental condition identification and boundary condition adjustment module is used to obtain the environmental condition information of the current decoration space, determine the mode category of the current environment based on the preset condition identification rules, and dynamically adjust the boundary conditions of the spatial field reconstruction model in step S2 according to the determined mode category. The purification priority index calculation module is used to calculate the purification urgency index of each sub-region based on the ratio of the current formaldehyde concentration to the target safe concentration and the concentration change trend. The zone linkage control strategy generation module is used to generate zone linkage control strategy instructions for different zones based on the calculation results of the purification priority index of each sub-zone and the preset working characteristics of the formaldehyde removal equipment. The control command issuance and execution module is used to send the generated zone linkage control strategy commands to the corresponding formaldehyde removal execution devices. The model closed-loop optimization module is used to continuously monitor the concentration field change data after the execution of the partition linkage control strategy command, compare the measured change data with the theoretical expected data, and automatically optimize the parameters of the spatial field state reconstruction model in step S2 based on the comparison results.

[0032] The parameters and steps of each unit module in the above-described intelligent formaldehyde removal monitoring system for building decoration of the present invention to achieve the corresponding functions can be referred to the parameters and steps in the embodiments of the intelligent formaldehyde removal monitoring method for building decoration described above, and will not be repeated here.

[0033] Example 3 Based on the same inventive concept as Embodiment 1, this embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-described intelligent formaldehyde removal monitoring method for building decoration by calling the computer program stored in the memory.

[0034] It should be noted that all computer programs for a formaldehyde removal intelligent monitoring method used in building decoration are implemented using the C language.

[0035] Example 4 Based on the same inventive concept as in Embodiment 1, this embodiment proposes a computer-readable storage medium having an erasable and rewritable computer program stored thereon. When the computer program runs on the computer device, it causes the computer device to perform the aforementioned intelligent formaldehyde removal monitoring method for building decoration.

[0036] For example, computer-readable storage media can be read-only memory, random access memory, read-only optical disc, magnetic tape, floppy disk, and optical data storage devices.

[0037] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, the embodiments for IoT devices and media are relatively simple in description because they are fundamentally similar to the method embodiments; relevant parts can be referred to the descriptions in the method embodiments.

[0038] The systems, media, and methods provided in the embodiments of the present invention are in one-to-one correspondence. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0039] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0040] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0041] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0042] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0043] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0044] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0045] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0046] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, including an element by a statement does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element. The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for intelligent formaldehyde monitoring in building decoration, characterized in that, Includes the following steps: Step S1: Continuously collect time-series data including formaldehyde concentration, temperature, humidity and airflow speed at a preset high-frequency sampling rate, and record the spatial coordinates of each sensor node. Step S2: Based on the collected time-series data and spatial coordinates, the three-dimensional spatial distribution field of indoor formaldehyde concentration is calculated in real time through a pre-constructed spatial field state reconstruction model, and the location and release intensity level of the main pollution release sources are determined by reverse deduction based on the concentration gradient change law. Step S3: Obtain the environmental condition information of the current decoration space, determine the mode category of the current environment based on the preset condition recognition rules, and dynamically adjust the boundary conditions of the spatial field reconstruction model in step S2 according to the determined mode category. Step S4: Based on the corrected three-dimensional spatial distribution field and airflow velocity data, calculate the purification priority index for different spatial sub-regions; The calculation of the purification priority index for different spatial sub-regions based on the corrected three-dimensional spatial distribution field and airflow velocity data includes the following specific steps: Step S41: Calculate the purification urgency index for each sub-region based on the ratio of the current formaldehyde concentration to the target safe concentration and the concentration change trend. Step S42: Calculate the disturbance dispersibility index of each sub-region based on the airflow velocity, turbulence intensity, and distance between the region and potential pollution sources. Step S43: Standardize the calculated purification urgency index and disturbance dissipation index. Step S44: Calculate the purification priority index for each sub-region based on the standardized purification urgency index and the standardized disturbance dissipation index. Step S5: Based on the calculation results of the purification priority index of each sub-area and the preset working characteristics of the formaldehyde removal equipment, generate zone linkage control strategy instructions for different areas. Step S6: Send the generated zone linkage control strategy command to the corresponding formaldehyde removal execution device; Step S7: Continuously monitor the concentration field change data after the execution of the partition linkage control strategy command, compare the measured change data with the theoretical expected data, and automatically optimize the parameters of the spatial field state reconstruction model in step S2 based on the comparison results.

2. The intelligent formaldehyde removal monitoring method for building decoration according to claim 1, characterized in that: The formula for calculating the urgency index of purification is as follows: Where Pi is the purification urgency index of the i-th sub-region, Ci is the current formaldehyde concentration of the i-th sub-region, Csafe is the formaldehyde safe concentration threshold, and Ct is the normalized reference concentration, used to map the difference to a reasonable range to avoid numerical explosion caused by an excessively small denominator. The concentration change rate is represented by a positive value indicating an increase and a negative value indicating a decrease. K (mode) is a model-dependent sensitivity coefficient used to dynamically adjust the weight of the trend term according to environmental conditions, taking a smaller value under static accumulation mode and a larger value under continuous release mode. It is a non-negative truncation function, used to ensure that urgency is generated only when the limit is exceeded or increases, avoiding interference from negative values.

3. The intelligent formaldehyde removal monitoring method for building decoration according to claim 2, characterized in that: The formula for calculating the disturbance dispersibility index is as follows: Where Di is the disturbance dispersibility index of the i-th sub-region, representing the ease with which pollutants in the current region are dispersed and removed by airflow disturbance. The larger the value, the easier it is for pollutants in the region to be carried away by airflow, and the higher the purification efficiency. Vi is the average airflow velocity of the i-th sub-region, used to reflect the basic ability of airflow to carry pollutants. The higher the velocity, the stronger the pollutant dispersion potential. Let be the angle between the airflow direction and the target guidance direction in the i-th sub-region, Vref be the spatial reference wind speed, di be the straight-line distance between the i-th sub-region and the nearest pollution source, be the distance attenuation coefficient, TIi be the turbulence intensity in the i-th sub-region, and be the turbulence enhancement factor, which is an adjustable parameter >

0. Let be the absolute value of the cosine of the angle between the airflow direction of the i-th sub-region and the vector from the pollution source to the current region, with a value range of [0, 1]. This is the distance attenuation term.

4. The intelligent formaldehyde removal monitoring method for building decoration according to claim 3, characterized in that: The standardized formulas for the purification urgency index and the disturbance dissipation index are as follows: , Wherein, Psi is the range of the standardized purification urgency index of the i-th sub-region [0, 1], Pmin is the minimum value of all sub-regions Pi in the current calculation batch, Pmax is the maximum value of all sub-regions Pi in the current calculation batch, Dsi is the range of the standardized disturbance dissipation index of the i-th sub-region [0, 1], Dmin is the minimum value of all sub-regions Di in the current calculation batch, and Dmax is the maximum value of all sub-regions Di in the current calculation batch.

5. The intelligent formaldehyde removal monitoring method for building decoration according to claim 4, characterized in that: The formula for calculating the purification priority index is as follows: Where Qi is the purification priority index of the i-th sub-region, Ka and Kb are both weighting coefficients that can be flexibly adjusted according to the scenario, and Ka+Kb=1.

6. The intelligent formaldehyde removal monitoring method for building decoration according to claim 1, characterized in that: The mode categories in step S3 include static accumulation mode, continuous release mode, and forced intervention mode, and the boundary conditions include air convection diffusion coefficient and pollution source attenuation constant.

7. The intelligent formaldehyde removal monitoring method for building decoration according to claim 1, characterized in that: The automatic optimization of the parameters of the spatial field state reconstruction model in step S2 based on the comparison results in step S7 is as follows: if the actual concentration decay rate is lower than the theoretical decay rate and the deviation exceeds the preset threshold, the release intensity weight of the corresponding area in the pollution source inversion model is automatically increased, and the output power of the purification equipment in that area is increased in the subsequent linkage strategy.

8. A formaldehyde removal intelligent monitoring system for building decoration, which is based on the formaldehyde removal intelligent monitoring method for building decoration as described in any one of claims 1-7, characterized in that, Specifically, it includes: a sensor perception module, a three-dimensional concentration field reconstruction and pollution source inversion module, an environmental condition identification and boundary condition adjustment module, a purification priority index calculation module, a zone linkage control strategy generation module, a control command issuance and execution module, and a model closed-loop optimization module; The sensor sensing module includes multiple wireless sensor nodes deployed at key locations in space, which are used to continuously collect time-series data including formaldehyde concentration, temperature, humidity and airflow speed at a preset high-frequency sampling rate, and record the spatial coordinates of each sensor node. The three-dimensional concentration field reconstruction and pollution source inversion module is used to calculate the three-dimensional spatial distribution field of indoor formaldehyde concentration in real time based on the collected and recorded time-series data and spatial coordinates, and to determine the location and release intensity level of the main pollution release sources by reverse deduction combined with the concentration gradient change law. The environmental condition identification and boundary condition adjustment module is used to obtain the environmental condition information of the current decoration space, determine the mode category of the current environment based on the preset condition identification rules, and dynamically adjust the boundary conditions of the spatial field reconstruction model in step S2 according to the determined mode category. The purification priority index calculation module is used to calculate the purification urgency index of each sub-region based on the ratio of the current formaldehyde concentration to the target safe concentration in each sub-region, combined with the concentration change trend. The partition linkage control strategy generation module is used to generate partition linkage control strategy instructions for different areas based on the calculation results of the purification priority index of each sub-area and the preset working characteristics of the formaldehyde removal equipment. The control command issuance and execution module is used to send the generated zone linkage control strategy command to the corresponding formaldehyde removal execution device; The model closed-loop optimization module is used to continuously monitor the concentration field change data after the execution of the partition linkage control strategy command, compare the measured change data with the theoretical expected data, and automatically optimize the parameters of the spatial field state reconstruction model in step S2 based on the comparison results.

9. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor, characterized in that: the processor executes a formaldehyde removal intelligent monitoring method for building decoration as described in any one of claims 1-7 by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that: The device stores instructions that, when executed on a computer, cause the computer to perform a formaldehyde removal intelligent monitoring method for building decoration as described in any one of claims 1-7.

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