Air purification method and device and terminal equipment

Abstract

The invention provides an air purification method and device and terminal equipment, and is suitable for the technical field of data processing. The method comprises the following steps: predicting and calculating an iteration number threshold, current pollutant concentration information, indoor space information, initial weight information, initial bias information and a preset gradient attenuation rate according to historical pollutant concentration information, indoor space information, randomly generated initial weight information and initial bias information, and a nonlinear mapping function; calculating target pollutant concentration prediction information according to a learning rate and a variable iteration frequency threshold value; and according to the target pollutant concentration prediction information, the indoor space information, the indoor activity personnel condition information, the fan parameter information, the pollutant concentration calibration information, the air speed adjustment empirical coefficient and the air cleaning amount per unit time, clean air speed adjustment amount information and air supply adjustment amount information are calculated so as to carry out air purification treatment. And by predicting the pollutant concentration, the parameters of the air purification equipment are dynamically adjusted, and the purification accuracy and effectiveness are ensured.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and in particular, relates to an air purification method, device and terminal equipment. Background Art

[0002] Operating room air purification refers to the use of certain technical means to reduce pollutants such as dust and microorganisms in the indoor air, so that the operating room reaches a certain cleanliness standard, thereby reducing interference from foreign matter and preventing cross infection, creating a stable operating environment for medical staff and patients.

[0003] In the prior art, during the use of the operating room, the air in the operating room is circulated with the outside air by setting an air purifier and a fan in a fixed mode, thereby purifying the air in the operating room.

[0004] However, in the prior art, air purifiers and fans are operated in a fixed mode, which makes it impossible to flexibly adjust the dynamically changing air quality conditions in the operating room, and it is difficult to ensure that the air quality in the operating room maintains the cleanliness standard requirements for a long time, thereby increasing the risk of medical staff and patients contracting diseases. Summary of the invention

[0005] In view of this, the embodiments of the present application provide an air purification method, apparatus and terminal device, which aim to solve the problems existing in the prior art that it is unable to adapt to the dynamically changing air quality in the operating room, it is difficult to monitor and flexibly control the air quality in the operating room in real time, and it is impossible to ensure that the air quality of the operating room is maintained within the cleanliness standard range during use, thereby increasing the risk of disease infection for medical staff and patients.

[0006] A first aspect of an embodiment of the present application provides an air purification method, comprising: Obtain historical pollutant concentration information, current pollutant concentration information, indoor space information, indoor activity personnel information, and fan parameter information; Randomly generate initial weight information and initial bias information; Calculating initial pollutant concentration prediction information according to the historical pollutant concentration information, indoor space information, initial weight information, initial bias information, a preset nonlinear mapping function, and a preset prediction calculation iteration number threshold; Calculating target weight information and target bias information according to the current pollutant concentration information, the initial pollutant concentration prediction information, the indoor space information, the initial weight information, the initial bias information, the preset gradient decay rate, the preset learning rate, and the preset variable iteration number threshold; Calculating target pollutant concentration prediction information according to the current pollutant concentration information, target weight information, target bias information, indoor space information, a preset nonlinear mapping function, and a preset prediction calculation iteration number threshold; According to the target pollutant concentration prediction information, indoor space information, indoor active personnel situation information, fan parameter information, preset pollutant concentration calibration information, preset wind speed adjustment empirical coefficient and preset air cleaning amount per unit time, the clean wind speed adjustment amount information and the supply air adjustment amount information are calculated to perform indoor air purification.

[0007] A second aspect of an embodiment of the present application provides an air purification device, comprising: An information acquisition module is used to obtain historical pollutant concentration information, current pollutant concentration information, indoor space information, indoor activity personnel information, and fan parameter information; An initial weight information and initial bias information generation module, used to randomly generate initial weight information and initial bias information; An initial pollutant concentration prediction information calculation module, used to calculate the initial pollutant concentration prediction information according to the historical pollutant concentration information, indoor space information, initial weight information, initial bias information, a preset nonlinear mapping function and a preset prediction calculation iteration number threshold; A target weight information and target bias information calculation module, used to calculate the target weight information and target bias information according to the current pollutant concentration information, the initial pollutant concentration prediction information, the indoor space information, the initial weight information, the initial bias information, the preset gradient decay rate, the preset learning rate and the preset variable iteration number threshold; a target pollutant concentration prediction information calculation module, configured to calculate the target pollutant concentration prediction information according to the current pollutant concentration information, target weight information, target bias information, indoor space information, a preset nonlinear mapping function, and a preset prediction calculation iteration number threshold; and The air purification processing module is used to calculate the clean wind speed adjustment amount information and the supply air adjustment amount information according to the target pollutant concentration prediction information, indoor space information, indoor active personnel situation information, fan parameter information, preset pollutant concentration calibration information, preset wind speed adjustment experience coefficient and preset unit time air cleaning amount, so as to perform indoor air purification.

[0008] A third aspect of an embodiment of the present application provides a terminal device, which includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the steps of the air purification method described in the first aspect above are implemented.

[0009] Compared with the prior art, the beneficial effects of the embodiments of the present application are as follows: considering the dynamic changes in indoor air quality caused by the activities of personnel in the operating room, combining the historical pollutant concentration information and the current pollutant concentration information collected by the sensors in the operating room, the changes in indoor pollutant concentration are accurately predicted, and then according to the indoor pollutant concentration prediction results combined with the fan parameters and the activities of the operating room users, the clean wind speed adjustment amount information and the air supply adjustment amount information are calculated, and then the wind speed of the air purifier and the air supply volume of the fan are adjusted to achieve purification of the air in the operating room. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0011] Figure 1 This is a schematic diagram of the implementation process of the air purification method provided in Example 1 of the present application; Figure 2 This is a schematic diagram of the implementation process of the air purification method provided in Example 2 of the present application; Figure 3 This is a schematic diagram of the implementation process of the air purification method provided in Example 3 of the present application; Figure 4 This is a schematic diagram of the implementation process of the air purification method provided in Example 4 of the present application; Figure 5 This is a schematic diagram of the implementation process of the air purification method provided in Example 5 of the present application; Figure 6 This is a schematic diagram of the implementation process of the air purification method provided in Example 6 of the present application; Figure 7 This is a schematic diagram of the implementation process of the air purification method provided in Example 7 of the present application; Figure 8 This is a schematic diagram of the implementation process of the air purification method provided in Example 8 of the present application; Fig. 9 is a schematic diagram of the structure of an air purification device provided in an embodiment of the present application; Fig.10 It is a schematic diagram of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0013] In order to illustrate the technical solution described in this application, a specific embodiment is provided below for illustration.

[0014] Figure 1 The following is a flowchart of the air purification method according to the first embodiment of the present invention. Step S101, obtaining historical pollutant concentration information, current pollutant concentration information, indoor space information, indoor activity personnel information and fan parameter information.

[0015] In this embodiment, the pollutants may be pollutants such as dust and microorganisms in the air. The historical pollutant concentration information refers to the pollutant concentration information at multiple spatial points in the operating room at multiple different times in the past time period, which is used to characterize the indoor pollutant concentration distribution in the past time period, and the current pollutant concentration information refers to the pollutant concentration information at multiple spatial points in the operating room at the current moment, which is used to characterize the indoor pollutant concentration distribution at the current moment. The historical pollutant concentration information and the current pollutant concentration information can be collected by sensors set in the operating room and sent to the computer terminal to achieve acquisition. The indoor space information can be the volume information of the operating room, which is calculated by measuring the length, width and height inside the operating room. The indoor space information can also be the spatial rectangular coordinates of each spatial point in the operating room, and the horizontal axis coordinate, the vertical axis coordinate and the vertical axis coordinate represent the position of each spatial point in the operating room. The fan parameter information can be the air supply per unit time, the air supply pollutant concentration, the exhaust air volume per unit time and the indoor air exchange frequency. Among them, the air supply per unit time can be the volume of fresh air supplied by the fan every hour, which can be retrieved by retrieving the fan product manual; the air supply pollutant concentration can be the pollutant concentration in the fresh air supplied by the fan, which can be collected and obtained through outdoor environmental detection equipment; the indoor air exchange frequency can be the number of times the fan exchanges indoor and outdoor air per hour. Operating rooms with different cleanliness levels have different air exchange rate standards, which need to be set according to the cleanliness level of the operating room. Information on the situation of indoor active personnel may include information on the number of indoor active personnel, information on the average pollution amount of personnel per unit time, and information on the intensity coefficient of personnel activity. Among them, information on the number of indoor active personnel may refer to the number of personnel in the operating room, and information on the average pollution amount of personnel per unit time may refer to the amount of pollutants generated by a single person per unit time, which can be obtained through experimental measurement or reference to relevant standards. The personnel activity intensity coefficient information can be obtained by collecting images through an image acquisition device set up in the operating room, analyzing and processing the collected images to determine the specific activity status of the personnel, and then quantitatively determining the specific activity status of the personnel, or by monitoring and inspecting personnel to determine the activity status of the personnel in the room based on the monitoring screen, and scoring the activity status of the personnel in the room, and inputting the scoring results into a computer as the personnel activity intensity coefficient information. The personnel activity intensity coefficient information can have a value range between 0 and 1, with 0 when the personnel is stationary and 1 when the personnel is in intense activity.

[0016] Step S102, randomly generate initial weight information and initial bias information.

[0017] In this embodiment, the initial weight information and the initial bias information are parameter information used for preliminary prediction and calculation of air pollutant concentration in the subsequent process, and can be randomly generated by a computer. The initial weight information is used to quantify the importance of each information feature in the historical pollutant concentration information in a nonlinear space, and the initial bias information is used to quantify the displacement of each information feature in the historical pollutant concentration information in a nonlinear space, so as to increase the distance between each information feature, facilitate the full analysis of each information feature, and improve the accuracy of the pollutant concentration prediction result.

[0018] Step S103, calculating initial pollutant concentration prediction information according to the historical pollutant concentration information, indoor space information, initial weight information, initial bias information, a preset nonlinear mapping function and a preset prediction calculation iteration number threshold.

[0019] In this embodiment, the preset nonlinear mapping function can be a ReLU function or a Tanh function. The preset prediction calculation iteration number threshold can be set manually. The historical pollutant concentration information can be combined with the indoor space information to generate a data matrix, and then the data matrix is ​​weighted and summed by the initial weight information and the initial bias information is added, and then the calculation result is used as the independent variable of the nonlinear mapping function, and the calculated function value is used as the intermediate variable, and then the data matrix composed of the intermediate variables is weighted and summed with the initial weight information and the initial bias information is added for calculation, and then the calculation result is used as the independent variable of the nonlinear mapping function to calculate the function value, thereby realizing iterative calculation, and the final output result after multiple iterative calculations is used as the initial pollutant concentration prediction information, and the number of iterations can be the preset prediction calculation iteration number threshold.

[0020] Step S104, calculate the target weight information and the target bias information according to the current pollutant concentration information, the initial pollutant concentration prediction information, the indoor space information, the initial weight information, the initial bias information, the preset gradient decay rate, the preset learning rate and the preset variable iteration number threshold.

[0021] In this embodiment, the preset gradient decay rate, the preset learning rate, and the preset variable iteration number threshold value can be manually set. The prediction error value can be first calculated by using the current pollutant concentration information and the initial pollution concentration prediction information, and then the weight information and the bias information are iteratively updated multiple times by combining the prediction error value with the indoor space information, the preset gradient decay rate, the preset learning rate, and the preset variable iteration number threshold value, and then the target weight information and the target bias information are output, which are used to predict the concentration change of indoor pollutants at future moments according to the current indoor pollutant concentration.

[0022] Step S105, calculating target pollutant concentration prediction information according to the current pollutant concentration information, target weight information, target bias information, indoor space information, a preset nonlinear mapping function and a preset prediction calculation iteration number threshold.

[0023] In this embodiment, the current pollutant concentration information can be combined with the indoor space information to generate a data matrix, and then the data matrix is ​​weighted and summed by the target weight information and the target bias information is added. The calculation result is then used as the independent variable of the nonlinear mapping function, and the calculated function value is used as the intermediate variable. The data matrix composed of the intermediate variables is then weighted and summed with the target weight information and the target bias information is added for calculation. The calculation result is then used as the independent variable of the nonlinear mapping function to calculate the function value, thereby realizing iterative calculation. The final output result after multiple iterative calculations is used as the target pollutant concentration prediction information, and the number of iterations can be a preset prediction calculation iteration number threshold.

[0024] Step S106, based on the target pollutant concentration prediction information, indoor space information, indoor activity personnel situation information, fan parameter information, preset pollutant concentration calibration information, preset wind speed adjustment empirical coefficient and preset air cleaning amount per unit time, calculate the clean wind speed adjustment amount information and the supply air adjustment amount information to perform indoor air purification.

[0025] In this embodiment, the preset pollutant concentration calibration information refers to the indoor pollutant concentration threshold value set by the cleanliness standard of the operating room, that is, when the pollutant concentration in the operating room is higher than the pollutant concentration calibration information, the air quality in the operating room will be judged as not up to standard, which will threaten the health of medical staff and patients. The preset wind speed adjustment experience coefficient can be set manually or obtained through multiple experimental measurements. The preset unit time air cleaning volume can refer to the volume of air that can be cleaned by the air purifier per hour, which can be uniformly set by the technical department where the operating room is located, so as to facilitate the unified management of the working state of the air purifier. The clean wind speed adjustment amount information refers to the wind speed adjustment amount of the air purifier, and the air supply adjustment amount information refers to the air supply adjustment amount of the fan. It can be understood that by adjusting the wind speed of the air purifier and the air supply amount of the fan, it is used to purify the air in the operating room. It can be calculated based on the prediction results of the indoor pollutant concentration combined with the fan parameters and the activities of the operating room users.

[0026] The air purification method provided in the embodiment of the present application takes into account the dynamic changes in indoor air quality caused by the activities of personnel in the operating room, and combines the historical pollutant concentration information and current pollutant concentration information collected by sensors in the operating room to accurately predict the changes in indoor pollutant concentration. Based on the prediction results of indoor pollutant concentration combined with fan parameters and the activities of personnel using the operating room, the wind speed adjustment amount of the air purifier and the air supply adjustment amount of the fan are calculated, and then the wind speed of the air purifier and the air supply amount of the fan are adjusted to achieve purification of the air in the operating room.

[0027] Figure 2 The flowchart of the air purification method provided in the second embodiment of the present application is shown, which differs from the first embodiment described above in that: The indoor space information includes indoor space horizontal coordinate information, indoor space vertical coordinate information and indoor space vertical coordinate information; The step S103 specifically includes: Step S201, generating an initial indoor pollution information matrix according to the historical pollutant concentration information, the indoor space horizontal coordinate information, the indoor space vertical coordinate information and the indoor space vertical coordinate information.

[0028] In the present embodiment, it can be understood that the historical pollutant concentration information is used to characterize the distribution of indoor pollutant concentrations at various moments in past time periods. The historical pollutant concentration information includes the indoor space horizontal coordinate information, the indoor space vertical coordinate information and the pollutant concentration of the spatial point represented by the indoor space vertical coordinate information corresponding to each time point. Therefore, each value in the historical pollutant concentration information can be extracted and matched one-to-one with the current indoor space horizontal coordinate information, the indoor space vertical coordinate information and the indoor space vertical coordinate information, so that the corresponding values ​​are combined to generate an initial indoor pollution information matrix.

[0029] Step S202: performing weighted summation on the initial indoor pollution information matrix according to the initial weight information to obtain an indoor pollution information representation variable matrix.

[0030] In this embodiment, a weighted sum calculation is performed by performing a weighted sum calculation on the initial weight information and the initial indoor pollution information matrix, and the calculation result is an indoor pollution information characterization variable matrix, which is used for subsequent further prediction calculation of indoor pollutant concentrations.

[0031] Step S203: Calculate an indoor pollution information intermediate variable matrix according to the indoor pollution information characterization variable matrix and initial bias information.

[0032] In this embodiment, the indoor pollution information characterization variable matrix and the initial bias information may be summed, and the summed result is the indoor pollution information intermediate variable matrix.

[0033] Step S204: generating a target indoor pollution information matrix according to the indoor pollution information intermediate variable matrix, a preset nonlinear mapping function and a preset prediction calculation iteration number threshold.

[0034] In this embodiment, the intermediate variable matrix of indoor pollution information can be used as the independent variable of the nonlinear mapping function, and the calculated function value is then operated with the initial weight information and the initial bias information, and then operated through the nonlinear mapping function to realize iterative calculation. The number of iterative calculations can be the predicted calculation iteration number threshold, and the result output after the iteration is completed is the target indoor pollution information matrix.

[0035] Step S205: Calculate initial pollutant concentration prediction information according to the target indoor pollution information matrix.

[0036] In this embodiment, the values ​​in the target indoor pollution information matrix are extracted, the concentration value part is extracted, and the spatial value corresponding to the concentration value is extracted, so as to obtain the initial pollutant concentration prediction information, which is used for subsequent error calculation with the current pollutant concentration information to adjust the weight and bias.

[0037] The air purification method provided in the embodiment of the present application makes a preliminary prediction of the pollutant concentration based on the historical pollutant concentration information and the operating room space information, maps and identifies the data features in the historical pollutant concentration information through a nonlinear mapping function, simulates the complex pollutant diffusion situation through the iterative transformation of the data features in the nonlinear space, and subsequently adjusts the weight and bias according to the preliminary prediction results, thereby providing accurate and effective data support for subsequent air purification decisions.

[0038] Figure 3 The flowchart of the air purification method provided in the third embodiment of the present application is shown. The difference between the third embodiment and the second embodiment is that the step S204 specifically includes: Step S301, mapping and transforming the indoor pollution information intermediate variable matrix according to a preset nonlinear mapping function to generate an intermediate indoor pollution information matrix.

[0039] In this embodiment, the intermediate variable matrix of indoor pollution information may be used as an independent variable of the nonlinear mapping function, and the function value obtained by calculation is the intermediate indoor pollution information matrix.

[0040] Step S302, counting the number of times the intermediate indoor pollution information matrix is ​​generated to obtain the number of prediction variable iterations.

[0041] In this embodiment, a counting operation may be performed each time the intermediate indoor pollution information matrix is ​​generated, thereby obtaining a statistical value of the number of times the intermediate indoor pollution information matrix is ​​generated, which is used as the number of prediction variable iterations to determine whether the iteration should be terminated later.

[0042] Step S303, determining whether the number of iterations of the prediction variable is less than a preset prediction calculation iteration number threshold; if so, proceeding to step S304; if not, proceeding to step S305.

[0043] In this embodiment, when the number of iterations of the prediction variable is less than the preset prediction calculation iteration number threshold, it means that the calculation result has not converged, and the iterative calculation should continue to improve the nonlinear fitting ability of the prediction calculation result and the accuracy of the pollutant prediction result; when the number of iterations of the prediction variable is greater than or equal to the preset prediction calculation iteration number threshold, it means that the calculation result has approached convergence, and the iterative calculation is stopped, and the intermediate indoor pollution information matrix is ​​output as the iterative calculation result.

[0044] Step S304: Use the intermediate indoor pollution information matrix as the initial indoor pollution information matrix, and return to step S202.

[0045] In this embodiment, when the number of prediction variable iterations is less than the preset prediction calculation iteration number threshold, it means that the calculation result has not converged, and the iterative calculation should continue to improve the nonlinear fitting ability of the prediction calculation result and improve the accuracy of the pollutant prediction result.

[0046] Step S305: generating a target indoor pollution information matrix according to the intermediate indoor pollution information matrix.

[0047] In this embodiment, when the number of iterations of the prediction variable is greater than or equal to the preset prediction calculation iteration number threshold, it means that the calculation result has approached convergence, and the iterative calculation is stopped, and the intermediate indoor pollution information matrix is ​​output as the iterative calculation result.

[0048] The air purification method provided in the embodiment of the present application performs multiple iterative calculations on historical pollutant concentration information, performs deep feature extraction on the complex information in the historical pollutant concentration information, and explores the potential rules behind the historical pollutant concentration information, thereby improving the accuracy of pollutant concentration prediction and providing reliable data support for the precise control of purification equipment.

[0049] Figure 4 The flowchart of the air purification method provided in the fourth embodiment of the present application is shown, which differs from the first embodiment in that: The preset gradient decay rate includes a preset first gradient decay rate and a preset second gradient decay rate; The step S104 specifically includes: Step S401, calculating predicted loss value information according to the current pollutant concentration information, the initial pollutant concentration prediction information and the indoor space information.

[0050] In this embodiment, the data constraint loss can be obtained by calculating the variance of the current pollutant concentration information and the initial pollutant concentration prediction information, and then the operating room space can be discretized through the indoor space information. Specifically, the length, width, and height of the operating room can be divided according to a certain interval, so as to divide multiple grids in the three directions respectively, and then the residual between the initial pollutant concentration prediction information and the current pollutant concentration information in each grid point is calculated, and then the multiple residual values ​​are summed to obtain the physical constraint loss, and then the data constraint loss and the physical constraint loss are added to obtain the predicted loss value information.

[0051] Step S402, calculating a weight gradient variable and a bias gradient variable according to the predicted loss value information, the initial weight information and the initial bias information.

[0052] In this embodiment, an automatic differentiation tool, such as tensorflow or pytorch, may be used to take the predicted loss value information, initial weight information, and initial bias information as input data to calculate the weight gradient variable and the bias gradient variable.

[0053] Step S403, calculating a weight gradient mean and a bias gradient mean according to the weight gradient variable, the bias gradient variable and a preset first gradient decay rate.

[0054] In this embodiment, the first-order moment estimates of the weight gradient variable and the bias gradient variable can be calculated using an automatic differentiation tool, such as tensorflow or pytorch, and then the first gradient decay rate is multiplied by the weight gradient variable and the bias gradient variable respectively, and then the multiplication result is added to the first-order moment estimates of the weight gradient variable and the bias gradient variable respectively to obtain the weight gradient mean and the bias gradient mean.

[0055] Step S404: Calculate the weight gradient variance and the bias gradient variance according to the weight gradient variable, the bias gradient variable and a preset second gradient decay rate.

[0056] In this embodiment, the second-order moment estimates of the weight gradient variable and the bias gradient variable can be calculated using an automatic differentiation tool, such as tensorflow or pytorch, and then the second gradient decay rate is multiplied by the weight gradient variable and the bias gradient variable respectively, and then the multiplication result is added to the second-order moment estimates of the weight gradient variable and the bias gradient variable respectively to obtain the weight gradient variance and the bias gradient variance.

[0057] Step S405, calculating an intermediate weight variable and an intermediate bias variable according to the weight gradient mean, the bias gradient mean, the weight gradient variance, the bias gradient variance and a preset learning rate.

[0058] In this embodiment, the preset learning rate can be set manually. The weight gradient variance and the bias gradient variance can be squared first, and then the square root results are used as denominators, and the preset learning rate is used as the numerator, so as to form two fractions, and the two fractions are multiplied by the weight gradient mean and the bias gradient mean to obtain the multiplication results, and then the initial weight information and the initial bias information are respectively subtracted from the two multiplication results, and the subtraction results are the intermediate weight variables and the intermediate bias variables.

[0059] Step S406, calculating target weight information and target bias information according to the intermediate weight variable, the intermediate bias variable and a preset variable iteration number threshold.

[0060] In this embodiment, the intermediate weight variables and the intermediate bias variables may be first combined with the historical pollutant concentration information, and the pollutant concentration may be predicted and calculated again, and then the loss value may be calculated again using the concentration prediction calculation result, and then the intermediate weight variables and the intermediate bias variables may be calculated again using the loss value, thereby achieving iteration. The number of iterations is determined by a preset variable iteration number threshold. When the iterative calculation is completed, the output result, namely the target weight information and the target bias information, is used to use the current pollutant concentration information as input data for the prediction calculation, thereby predicting and calculating future changes in pollutant concentrations.

[0061] The air purification method provided in the embodiment of the present application calculates the prediction error value, updates the weight information and bias information, and thus optimizes the operation parameters in the prediction calculation process, so that the prediction result of the indoor pollutant concentration is closer to the true value, which is used to enable the air purification equipment to adjust the operating parameters according to the more accurate prediction result in the future, thereby effectively improving the indoor air purification effect.

[0062] Figure 5 The flowchart of the air purification method provided in the fifth embodiment of the present application is shown. The difference between the fifth embodiment and the fourth embodiment is that the step S401 specifically includes: Step S501, collecting statistics on the data volume of the current pollutant concentration information.

[0063] In this embodiment, the data volume information of the current pollutant concentration information, that is, the number of data points actually collected, may also be the total number of pollutant concentration data collected at different indoor spatial locations at the current time point.

[0064] Step S502: Calculate a first intermediate prediction loss value based on the data volume information, current pollutant concentration information, and initial pollutant concentration prediction information.

[0065] In this embodiment, the variance can be calculated using the current pollutant concentration information and the initial pollutant concentration prediction information, and then the calculated variance value is divided by the data volume information to obtain a first intermediate prediction loss value, which is used to measure the difference between the predicted output result and the actual collected data.

[0066] Step S503: Calculate a second intermediate predicted loss value according to the indoor space information and the initial pollutant concentration prediction information.

[0067] In this embodiment, the operating room space is discretized through indoor space information. Specifically, the length, width, and height of the operating room can be divided at a certain interval, thereby dividing multiple grids in the three directions. Then, the residual between the initial pollutant concentration prediction information and the current pollutant concentration information at each grid point is calculated, and then the multiple residual values ​​are summed, and the summed result is divided by the data volume information to obtain a second intermediate prediction loss value, which is used to constrain the output result of the prediction loss value information to avoid falling into a feature mapping relationship that does not conform to physical reality and causing large errors.

[0068] Step S504: Calculate prediction loss value information according to the first intermediate prediction loss value and the second intermediate prediction loss value.

[0069] In this embodiment, the first intermediate predicted loss value is added to the second intermediate predicted loss value to obtain predicted loss value information, which is used to characterize the error between the predicted value of the pollutant concentration and the actual value of the pollutant concentration.

[0070] The air purification method provided in the embodiment of the present application, by respectively calculating the first intermediate predicted loss value and the second intermediate predicted loss value, effectively characterizes the gap between the predicted value of pollutant concentration and the actual value of pollutant concentration, ensures that the physical laws are followed while fitting the data, and avoids the output value exceeding the dimension and being unable to perform subsequent calculations, thereby improving the effectiveness and accuracy of the prediction of pollutant concentration, and facilitating the provision of more reliable data support for efficient and scientific indoor air purification methods.

[0071] Figure 6 The flowchart of the air purification method provided in the sixth embodiment of the present application is shown. The difference between the sixth embodiment and the fourth embodiment is that the step S406 specifically includes: Step S601, counting the number of calculations of the intermediate weight variables and the intermediate bias variables to obtain the number of intermediate variable iterations.

[0072] In this embodiment, a counting operation may be performed each time an intermediate weight variable and an intermediate bias variable are calculated, thereby obtaining the number of intermediate variable iterations.

[0073] Step S602, determining whether the number of intermediate variable iterations is less than a preset variable iteration number threshold; if so, proceeding to step S603; if not, proceeding to step S604.

[0074] In this embodiment, when the number of intermediate variable iterations is less than the preset number of variable iterations, it means that the number of updates for the weight information and the bias information is not enough, and the validity of the weight information and the bias information is still insufficient, and it is necessary to continue the iterative calculation, so as to continue to update the weight information and the bias information. When the number of intermediate variable iterations is greater than or equal to the preset number of variable iterations, it means that the number of updates for the weight information and the bias information is sufficient to ensure the validity of the weight information and the bias information, and it is not necessary to continue the iterative calculation, so as to stop continuing to update the weight information and the bias information.

[0075] Step S603, using the intermediate weight variable as initial weight information, using the intermediate bias variable as initial bias information, and returning to step S103.

[0076] In this embodiment, when the number of intermediate variable iterations is less than the preset number of variable iterations, it means that the number of updates for the weight information and the bias information is not enough, and the validity of the weight information and the bias information is still insufficient, and it is necessary to continue the iterative calculation to continue updating the weight information and the bias information.

[0077] Step S604: Using the intermediate weight variables and intermediate bias variables as target weight information and target bias information.

[0078] In this embodiment, when the number of intermediate variable iterations is greater than or equal to the preset number of variable iterations, it means that the number of updates to the weight information and bias information is sufficient to ensure the validity of the weight information and bias information, and there is no need to continue the iterative calculation, thereby stopping the updating of the weight information and bias information. The currently calculated weight information and bias information are then output as the target weight information and target bias information, which are used for subsequent prediction and calculation of pollutant concentrations to predict and calculate changes in indoor pollutant concentrations.

[0079] The air purification method provided in the embodiment of the present application realizes multiple updates of weight information and bias information through multiple iterative calculations, thereby optimizing the operation parameters in the prediction calculation process, making the prediction results of indoor pollutant concentrations closer to the true values, thereby improving the accuracy and effectiveness of predicting indoor air quality conditions, and enabling the air purification equipment to adjust the operating parameters based on more accurate prediction results, effectively improving the indoor air purification effect.

[0080] Figure 7 The flowchart of the air purification method provided in the seventh embodiment of the present application is shown, which differs from the first embodiment described above in that: The indoor space information includes indoor space volume information; The indoor activity personnel information includes the number of indoor activity personnel, the average pollution amount per unit time, and the personnel activity intensity coefficient information; The fan parameter information includes air supply volume per unit time, air supply pollutant concentration, air exhaust volume per unit time and indoor air exchange frequency; The step S106 specifically includes: Step S701, calculating the air purification efficiency adjustment amount according to the target pollutant concentration prediction information and the preset pollutant concentration calibration information.

[0081] In this embodiment, the difference between the target pollutant concentration prediction information and the pollutant concentration calibration information is calculated first, and then the difference is used as the numerator and the pollutant concentration calibration information is used as the denominator. The result of the fractional operation is the air purification efficiency adjustment amount.

[0082] Step S702, calculating the cleaning wind speed adjustment amount information according to the air purification efficiency adjustment amount and the preset wind speed adjustment empirical coefficient.

[0083] In this embodiment, the preset wind speed adjustment empirical coefficient may include a first wind speed empirical coefficient and a second wind speed empirical coefficient. The difference between the air purification efficiency adjustment amount and the first wind speed empirical coefficient may be calculated first, and then the difference is divided by the second wind speed empirical coefficient, and the division result is the cleaning wind speed adjustment amount information.

[0084] Step S703, calculating the pollutant generation rate of the active persons according to the information on the number of active persons in the room, the average pollution amount of the persons per unit time, and the information on the coefficient of the activity intensity of the persons.

[0085] In this embodiment, the information on the number of indoor active persons, the information on the average pollution amount of persons per unit time, and the information on the coefficient of the activity intensity of persons are multiplied together, and the multiplication result is the pollutant generation rate of the active persons.

[0086] Step S704, calculates the air supply adjustment amount information based on the target pollutant concentration prediction information, indoor space volume information, indoor air exchange frequency, pollutant generation rate of active personnel, air supply volume per unit time, air supply pollutant concentration, preset air cleaning volume per unit time and preset pollutant concentration calibration information.

[0087] In this embodiment, the exhaust volume of the fan can be calculated first by the indoor air exchange frequency and the indoor space volume information. Then, the air supply adjustment volume information is calculated by the exhaust volume, indoor space volume information, pollutant generation rate of active personnel, air supply volume per unit time, air supply pollutant concentration, air cleaning volume per unit time and pollutant concentration calibration information. According to the law of conservation of mass, the change of indoor air quality satisfies: Wherein, V is the indoor space volume of the operating room, in cubic meters; The air supply volume provided to the fan; is the concentration of pollutants in the supply air; The exhaust volume of the fan can be calculated by multiplying the indoor air exchange frequency by the indoor volume. It is understandable that in actual application scenarios, the indoor air exchange frequency is usually fixed, and the indoor volume is also fixed, so the exhaust volume of the fan is also fixed, which is convenient for the hospital building to uniformly control, manage and maintain each fan; is the indoor pollutant concentration; The pollutant generation rate of personnel involved in pollutant-generating activities; is the amount of air cleaning per unit time; the amount of air supply adjustment that needs to be increased That is, the calculation formula of the air supply adjustment information can be expressed as: in, Indicates the predicted information of target pollutant concentration; Indicates pollutant concentration calibration information.

[0088] The air purification method provided in the embodiment of the present application targets the dynamic and changing air quality caused by human activities in the operating room environment. By timely calculating the wind speed adjustment amount of the air purifier and the increase in air supply of the fan that need to be regulated based on the real-time prediction results of the pollutant concentration, the method adapts to the changes in air quality in a timely manner and maintains the air cleanliness in the operating room, thereby improving the indoor air purification efficiency and the indoor environmental safety, increasing the success rate of surgery and reducing the infection rate, and providing a reliable medical environment for medical staff and patients.

[0089] Figure 8 The flowchart of the air purification method provided in the eighth embodiment of the present application is shown. The difference between the eighth embodiment and the seventh embodiment is that the step S704 specifically includes: Step S801, calculating the air cleaning rate according to the target pollutant concentration prediction information, the indoor space volume information and the preset air cleaning amount per unit time.

[0090] In this embodiment, the air cleaning amount per unit time may be first divided by the indoor space volume information, and then the division result is multiplied by the target pollutant concentration prediction information to calculate the air cleaning rate.

[0091] Step S802, calculating the air supply purification rate according to the target pollutant concentration prediction information, the air supply volume per unit time, the air supply pollutant concentration and the indoor space volume information.

[0092] In this embodiment, the air supply volume per unit time may be first divided by the indoor space volume information, and then the division result is multiplied by the difference between the target pollutant concentration prediction information and the pollutant concentration calibration information to calculate the air supply purification rate.

[0093] Step S803, calculating the air purification rate according to the air cleaning rate and the air supply purification rate.

[0094] In this embodiment, the air cleaning rate and the air supply purification rate may be added together to obtain the air purification rate, which is used for subsequent calculation of the air supply adjustment amount of the fan.

[0095] Step S804: Calculate exhaust volume information according to the indoor air exchange frequency and indoor space volume information.

[0096] In this embodiment, the indoor air exchange frequency and the indoor space volume information may be multiplied to calculate the exhaust volume information, which is used for subsequent calculation of the air supply adjustment amount of the fan.

[0097] Step S805, calculate the air supply adjustment amount information based on the target pollutant concentration prediction information, indoor space volume information, exhaust volume information, pollutant generation rate of active personnel, air purification rate, supply air pollutant concentration and preset pollutant concentration calibration information.

[0098] In this embodiment, the difference between the target pollutant concentration prediction information and the pollutant concentration calibration information can be first calculated as the indoor pollutant concentration adjustment amount, and then the indoor pollutant concentration adjustment amount is multiplied by the indoor space volume information to obtain the multiplication result of the indoor pollutant concentration adjustment amount and the indoor space volume information, and then the exhaust volume is multiplied by the target pollutant concentration prediction information to obtain the multiplication result of the exhaust volume and the target pollutant concentration prediction information, and then the multiplication result of the indoor pollutant concentration adjustment amount and the indoor space volume information and the multiplication result of the exhaust volume and the target pollutant concentration prediction information are added, and the sum result is added with the pollutant generation rate of active personnel, and the air purification rate is subtracted from the addition result, and then the subtraction result is divided by the pollutant concentration calibration information, so as to calculate the air supply adjustment amount of the fan, that is, the air supply adjustment amount information, which is used to increase the air supply of the fan, that is, to increase the fresh air supply of the fan to the room, which can be achieved by increasing the working power of the fan to achieve indoor air purification.

[0099] The air purification method provided in the embodiment of the present application quantifies the air purifier's processing capacity for pollutant concentration in the current state by calculating the air purification rate, and adjusts the wind speed of the air purifier in real time according to the calculated air purification rate, thereby improving the purification effect on indoor air, and ensuring that more fresh air is introduced into the room by calculating the air supply volume of the fan that needs to be increased to reduce the indoor pollutant concentration and promote the discharge of pollutants to the outside, so as to ensure that the air cleanliness requirements of the operating room are met in the complex and changeable indoor air quality changes, and ensure that the operating room environment always meets strict standards to reduce the risk of infection and improve the safety and reliability of the medical environment.

[0100] Corresponding to the method of the above embodiment, Fig. 9 A structural block diagram of an air purification device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown. Fig. 9 The exemplary air purification device may be the execution body of the air purification method provided in the aforementioned embodiment 1.

[0101] Reference Fig. 9 , the air purification device comprises: The information acquisition module 910 is used to obtain historical pollutant concentration information, current pollutant concentration information, indoor space information, indoor activity personnel information and fan parameter information; An initial weight information and initial bias information generating module 920, used to randomly generate initial weight information and initial bias information; An initial pollutant concentration prediction information calculation module 930 is used to calculate the initial pollutant concentration prediction information according to the historical pollutant concentration information, indoor space information, initial weight information, initial bias information, a preset nonlinear mapping function and a preset prediction calculation iteration number threshold; A target weight information and target bias information calculation module 940 is used to calculate the target weight information and target bias information according to the current pollutant concentration information, the initial pollutant concentration prediction information, the indoor space information, the initial weight information, the initial bias information, the preset gradient decay rate, the preset learning rate and the preset variable iteration number threshold; A target pollutant concentration prediction information calculation module 950 is used to calculate the target pollutant concentration prediction information according to the current pollutant concentration information, target weight information, target bias information, indoor space information, a preset nonlinear mapping function and a preset prediction calculation iteration number threshold; and The air purification processing module 960 is used to calculate the clean wind speed adjustment amount information and the supply air adjustment amount information based on the target pollutant concentration prediction information, indoor space information, indoor active personnel situation information, fan parameter information, preset pollutant concentration calibration information, preset wind speed adjustment empirical coefficient and preset unit time air cleaning amount, so as to perform indoor air purification.

[0102] The process of each module in the air purification device provided in the embodiment of the present application realizing its own function can be specifically referred to the aforementioned Figure 1 The description of the first embodiment is not repeated here.

[0103] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0104] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0105] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0106] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0107] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish descriptions, and cannot be understood as indicating or suggesting relative importance. It should also be understood that although the terms "first", "second", etc. are used to describe various elements in some embodiments of the present application in the text, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, the first table can be named as the second table, and similarly, the second table can be named as the first table without departing from the scope of the various described embodiments. The first table and the second table are both tables, but they are not the same table.

[0108] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0109] The air purification method provided in the embodiments of the present application can be applied to terminal devices such as mobile phones, tablet computers, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptop computers, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA), etc. The embodiments of the present application do not impose any restrictions on the specific types of terminal devices.

[0110] For example, the terminal device can be a station (STAION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a vehicle networking terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a TV set top box (STB), a customer premises equipment (CPE) and / or other devices for communicating on a wireless system and a next-generation communication system, such as a mobile terminal in a 5G network or a mobile terminal in a future evolved Public Land Mobile Network (PLMN) network, etc.

[0111] As an example but not limitation, when the terminal device is a wearable device, the wearable device can also be a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not just hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0112] Fig.10 Schematic diagram of the structure of a terminal device provided by an embodiment of the present application. Fig.10 As shown, the terminal device 10 of this embodiment includes: at least one processor 1000 ( Fig.10 Only one is shown in the figure), a memory 1010, wherein the memory 1010 stores a computer program 1020 that can be run on the processor 1000. When the processor 1000 executes the computer program 1020, the steps in the above-mentioned air purification method embodiments are implemented, such as Figure 1 Alternatively, when the processor 1000 executes the computer program 1020, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0113] The terminal device 10 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device may include, but is not limited to, a processor 1000 and a memory 1010. Those skilled in the art will appreciate that Fig.10 It is only an example of the terminal device 10 and does not constitute a limitation of the terminal device 10. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include an input sending device, a network access device, a bus, etc.

[0114] The processor 1000 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0115] In some embodiments, the memory 1010 may be an internal storage unit of the terminal device 10, such as a hard disk or memory of the terminal device 10. The memory 1010 may also be an external storage device of the terminal device 10, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 10. Further, the memory 1010 may also include both an internal storage unit of the terminal device 10 and an external storage device. The memory 1010 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 1010 may also be used to temporarily store data that has been sent or is to be sent.

[0116] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0117] An embodiment of the present application also provides a terminal device, which includes at least one memory, at least one processor, and a computer program stored in the at least one memory and executable on the at least one processor, wherein when the processor executes the computer program, the terminal device implements the steps in any of the above-mentioned method embodiments.

[0118] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0119] An embodiment of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0120] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0121] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0122] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0123] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0124] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An air purification method, characterized in that: include: Obtain historical pollutant concentration information, current pollutant concentration information, indoor space information, indoor activity personnel information, and fan parameter information; Randomly generate initial weight information and initial bias information; Calculating initial pollutant concentration prediction information according to the historical pollutant concentration information, indoor space information, initial weight information, initial bias information, a preset nonlinear mapping function, and a preset prediction calculation iteration number threshold; Calculating target weight information and target bias information according to the current pollutant concentration information, the initial pollutant concentration prediction information, the indoor space information, the initial weight information, the initial bias information, the preset gradient decay rate, the preset learning rate, and the preset variable iteration number threshold; Calculating target pollutant concentration prediction information according to the current pollutant concentration information, target weight information, target bias information, indoor space information, a preset nonlinear mapping function, and a preset prediction calculation iteration number threshold; According to the target pollutant concentration prediction information, indoor space information, indoor active personnel situation information, fan parameter information, preset pollutant concentration calibration information, preset wind speed adjustment empirical coefficient and preset air cleaning amount per unit time, the clean wind speed adjustment amount information and the supply air adjustment amount information are calculated to perform indoor air purification.

2. The air purification method according to claim 1, characterized in that: The indoor space information includes indoor space horizontal coordinate information, indoor space vertical coordinate information and indoor space vertical coordinate information; The step of calculating the initial pollutant concentration prediction information according to the historical pollutant concentration information, the indoor space information, the initial weight information, the initial bias information, the preset nonlinear mapping function and the preset prediction calculation iteration number threshold specifically includes: Generate an initial indoor pollution information matrix according to the historical pollutant concentration information, the indoor space horizontal coordinate information, the indoor space vertical coordinate information and the indoor space vertical coordinate information; According to the initial weight information, weighted summing is performed on the initial indoor pollution information matrix to obtain an indoor pollution information representation variable matrix; Calculating an indoor pollution information intermediate variable matrix according to the indoor pollution information characterization variable matrix and initial bias information; Generate a target indoor pollution information matrix according to the indoor pollution information intermediate variable matrix, a preset nonlinear mapping function and a preset prediction calculation iteration number threshold; Initial pollutant concentration prediction information is calculated based on the target indoor pollution information matrix.

3. The air purification method according to claim 2, characterized in that: The step of generating a target indoor pollution information matrix according to the indoor pollution information intermediate variable matrix, a preset nonlinear mapping function and a preset prediction calculation iteration number threshold specifically includes: According to a preset nonlinear mapping function, mapping transformation is performed on the intermediate variable matrix of indoor pollution information to generate an intermediate indoor pollution information matrix; Counting the number of times the intermediate indoor pollution information matrix is ​​generated to obtain the number of iterations of the prediction variables; Determining whether the number of iterations of the prediction variable is less than a preset prediction calculation iteration number threshold; If yes, the intermediate indoor pollution information matrix is ​​used as the initial indoor pollution information matrix, and the process returns to the step of performing weighted summation on the initial indoor pollution information matrix according to the initial weight information to obtain an indoor pollution information characterization variable matrix; If not, a target indoor pollution information matrix is ​​generated according to the intermediate indoor pollution information matrix.

4. The air purification method according to claim 1, characterized in that: The preset gradient decay rate includes a preset first gradient decay rate and a preset second gradient decay rate; The step of calculating the target weight information and the target bias information according to the current pollutant concentration information, the initial pollutant concentration prediction information, the indoor space information, the initial weight information, the initial bias information, the preset gradient decay rate, the preset learning rate and the preset variable iteration number threshold specifically includes: Calculating predicted loss value information based on the current pollutant concentration information, the initial pollutant concentration prediction information, and the indoor space information; Calculating a weight gradient variable and a bias gradient variable according to the predicted loss value information, the initial weight information, and the initial bias information; Calculating a weight gradient mean and a bias gradient mean according to the weight gradient variable, the bias gradient variable and a preset first gradient decay rate; Calculate the weight gradient variance and the bias gradient variance according to the weight gradient variable, the bias gradient variable and a preset second gradient decay rate; Calculate intermediate weight variables and intermediate bias variables according to the weight gradient mean, bias gradient mean, weight gradient variance, bias gradient variance and a preset learning rate; Calculate target weight information and target bias information based on the intermediate weight variable, the intermediate bias variable and a preset variable iteration number threshold.

5. The air purification method according to claim 4, characterized in that: The step of calculating the predicted loss value information according to the current pollutant concentration information, the initial pollutant concentration prediction information and the indoor space information specifically includes: Counting the data volume information of the current pollutant concentration information; Calculating a first intermediate prediction loss value according to the data volume information, current pollutant concentration information, and initial pollutant concentration prediction information; Calculating a second intermediate predicted loss value according to the indoor space information and the initial pollutant concentration prediction information; The prediction loss value information is calculated based on the first intermediate prediction loss value and the second intermediate prediction loss value.

6. The air purification method according to claim 4, characterized in that: The step of calculating target weight information and target bias information according to the intermediate weight variable, the intermediate bias variable and a preset variable iteration number threshold specifically includes: Counting the number of calculations of the intermediate weight variables and the intermediate bias variables to obtain the number of intermediate variable iterations; Determine whether the number of intermediate variable iterations is less than a preset variable iteration number threshold; If so, the intermediate weight variable is used as the initial weight information, the intermediate bias variable is used as the initial bias information, and the process returns to the step of calculating the initial pollutant concentration prediction information according to the historical pollutant concentration information, the indoor space information, the initial weight information, the initial bias information, the preset nonlinear mapping function and the preset prediction calculation iteration number threshold; If not, the intermediate weight variable and the intermediate bias variable are used as the target weight information and the target bias information.

7. The air purification method according to claim 1, characterized in that: The indoor space information includes indoor space volume information; The indoor activity personnel information includes the number of indoor activity personnel, the average pollution amount per unit time, and the personnel activity intensity coefficient information; The fan parameter information includes air supply volume per unit time, air supply pollutant concentration, air exhaust volume per unit time and indoor air exchange frequency; The step of calculating the clean wind speed adjustment amount information and the supply air adjustment amount information according to the target pollutant concentration prediction information, indoor space information, indoor activity personnel situation information, fan parameter information, preset pollutant concentration calibration information, preset wind speed adjustment experience coefficient and preset unit time air cleaning amount specifically includes: Calculating the air purification efficiency adjustment amount according to the target pollutant concentration prediction information and the preset pollutant concentration calibration information; Calculating the clean wind speed adjustment amount information according to the air purification efficiency adjustment amount and the preset wind speed adjustment empirical coefficient; Calculate the pollutant generation rate of the active personnel according to the number of active personnel information, the average pollution amount of personnel per unit time, and the personnel activity intensity coefficient information; The air supply adjustment amount information is calculated based on the target pollutant concentration prediction information, indoor space volume information, indoor air exchange frequency, pollutant generation rate of active personnel, air supply volume per unit time, air supply pollutant concentration, preset air cleaning volume per unit time and preset pollutant concentration calibration information.

8. The air purification method according to claim 7, characterized in that: The step of calculating the air supply adjustment amount information according to the target pollutant concentration prediction information, indoor space volume information, indoor air exchange frequency, pollutant generation rate of active personnel, air supply volume per unit time, air supply pollutant concentration, preset air cleaning volume per unit time and preset pollutant concentration calibration information specifically includes: Calculating the air cleaning rate according to the target pollutant concentration prediction information, the indoor space volume information and the preset air cleaning amount per unit time; Calculate the air supply purification rate according to the target pollutant concentration prediction information, the air supply volume per unit time, the air supply pollutant concentration and the indoor space volume information; Calculating an air purification rate according to the air cleaning rate and the air supply purification rate; Calculating exhaust volume information according to the indoor air exchange frequency and indoor space volume information; The air supply adjustment amount information is calculated based on the target pollutant concentration prediction information, indoor space volume information, exhaust volume information, pollutant generation rate of active personnel, air purification rate, supply air pollutant concentration and preset pollutant concentration calibration information.

9. An air purification device, characterized in that: include: An information acquisition module is used to obtain historical pollutant concentration information, current pollutant concentration information, indoor space information, indoor activity personnel information, and fan parameter information; An initial weight information and initial bias information generation module, used to randomly generate initial weight information and initial bias information; An initial pollutant concentration prediction information calculation module, used to calculate the initial pollutant concentration prediction information according to the historical pollutant concentration information, indoor space information, initial weight information, initial bias information, a preset nonlinear mapping function and a preset prediction calculation iteration number threshold; A target weight information and target bias information calculation module, used to calculate the target weight information and target bias information according to the current pollutant concentration information, the initial pollutant concentration prediction information, the indoor space information, the initial weight information, the initial bias information, the preset gradient decay rate, the preset learning rate and the preset variable iteration number threshold; A target pollutant concentration prediction information calculation module, used to calculate the target pollutant concentration prediction information according to the current pollutant concentration information, target weight information, target bias information, indoor space information, a preset nonlinear mapping function and a preset prediction calculation iteration number threshold; as well as The air purification processing module is used to calculate the clean wind speed adjustment amount information and the supply air adjustment amount information according to the target pollutant concentration prediction information, indoor space information, indoor active personnel situation information, fan parameter information, preset pollutant concentration calibration information, preset wind speed adjustment experience coefficient and preset unit time air cleaning amount, so as to perform indoor air purification.

10. A terminal device, characterized in that: The terminal device includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor implements the steps of the method according to any one of claims 1 to 8 when executing the computer program.

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