Human body allergen clearing time determination method, electronic equipment and storage medium

Abstract

The invention provides a human body allergen clearing time determination method, electronic equipment and a storage medium. The method comprises the following steps: acquiring an initial image corresponding to a target space acquired at one allergen clearing time closest to the current time; according to the acquisition time corresponding to the initial image, obtaining a target image corresponding to the target space every monitoring time period; performing image matching on the initial image and each target image, and determining a detection value corresponding to each target image; and if the sum of the detection values corresponding to all the target images is greater than the detection value threshold, determining the end time of the monitoring time period after the acquisition time corresponding to the last target image as the next allergen clearing time, and timely notifying a worker to carry out allergen detection and disinfection processing on the target object in the target space. The purposes of maintaining the physical and psychological health of the target personnel in the target space and eliminating life safety hidden dangers under the condition of reducing the allergen detection times of the working personnel are achieved.

Description

Background Art

[0002] Common human allergens include pollen, mites, dust, etc. If the content of allergens exceeds a certain concentration, it is possible to cause allergic reactions in people with allergic diseases, posing a life-threatening risk to the human body. Currently, there are more and more patients with allergic diseases, and severe allergic reactions can threaten the patient's life. Therefore, in order to reduce the onset of allergic diseases in patients, the World Health Organization has proposed a low-allergy home visit program. The low-allergy home visit program is to help users find allergens and irritants in the home environment and provide users with environmental intervention plans to cut off the causes of repeated allergies in family members from the source, and prevent and control allergens and irritants in the home environment through environmental and behavioral interventions.

[0003] In order to control the allergens in the home environment, it is necessary to conduct periodic allergen removal in the home environment (that is, the allergens must be removed once every specific time period to eliminate existing allergens). If the staff conducts periodic allergen removal for each family, the manpower cost will be very high, and the staff will need to negotiate the removal time with family members. During the removal period, family members are required to cooperate with the staff to remove allergens at home, which also increases the time cost for family members. Allergens may also appear during the specific removal time interval, threatening the life safety of allergy patients. Therefore, the allergen removal time needs to be optimized to determine the appropriate time to eliminate allergens. Summary of the invention

[0004] In view of the above technical problems, the technical solution adopted by the present invention is:

[0005] According to one aspect of the present application, a method for determining the time for clearing human allergens is provided, comprising the following steps:

[0006] Step S100, obtaining an initial image corresponding to the target space; the acquisition time corresponding to the initial image is the allergen removal time closest to the current time;

[0007] Step S200, acquiring a target image corresponding to the target space at every preset monitoring time period according to the acquisition time corresponding to the initial image;

[0008] Step S300, performing image matching on the initial image and each target image to determine the detection value corresponding to each target image;

[0009] Step S400: If the sum of the detection values ​​corresponding to all target images is greater than the preset detection value threshold, the end time of the next monitoring time period after the acquisition time corresponding to the last target image is determined as the next allergen removal time.

[0010] In an exemplary embodiment of the present application, step S200 includes:

[0011] Step S210: Obtain the acquisition time r corresponding to the initial image 01 ;

[0012] Step S220: Obtain the length r of the preset monitoring time period 02 ;

[0013] Step S230: every preset monitoring time period, obtain a number of target images collected from the collection time corresponding to the initial image to the current time, wherein the collection time of the vth target image is r 01 +r 02 ×v; v = 1, 2, ..., w; w is the number of target images collected from the collection time corresponding to the initial image to the current time.

[0014] In an exemplary embodiment of the present application, step S300 includes:

[0015] Step S310: performing image recognition on the initial image to determine a target object included in the initial image; the target object is an object that needs to be tested for allergens;

[0016] Step S320: Determine the detection value corresponding to each target image according to the matching degree between the image feature of the target object in each target image and the image feature of the target object in the initial image.

[0017] In an exemplary embodiment of the present application, step S310 includes:

[0018] Step S311: Perform image recognition on the initial image to obtain the image features of each initial object included in the initial image, so as to obtain an initial image feature list set A = (A1, A2, ..., A m ,...,A n ), where m = 1, 2, ..., n, and n is the number of initial objects included in the initial image; A m is a list of image features corresponding to the mth initial object included in the initial image;

[0019] A m =(A m1 ,A m2 ,...,A mi ,...,A mj(m) ), i = 1, 2, ..., j(m), j(m) is the number of image features of the mth initial object included in the initial image, A mi is the i-th image feature of the m-th initial object included in the initial image;

[0020] Step S312: Obtain the image features of the mth initial object included in the initial image in each historical image to obtain a historical image feature list set B corresponding to the mth initial object. m =(B m1 ,B m2 ,...,B me ,...,B mf ), where e = 1, 2, ..., f, and f is the number of historical images; B me is the historical image feature list of the mth initial object in the eth historical image;

[0021] B me =(B me1 ,B me2 ,...,B meg ,...,B meh(me) ); g = 1, 2, ..., h(me); h(me) is the number of image features of the mth initial object in the eth historical image; B meg is the g-th image feature of the m-th initial object in the e-th historical image;

[0022] Step S313: A m and B me Perform feature comparison to obtain the corresponding feature matching degree C me ;

[0023] Step S314: If C me If the feature matching degree is greater than a preset threshold, the e-th historical image is determined as the target historical image corresponding to the m-th initial object;

[0024] Step S315, obtaining the neatness detection value of the mth initial object in each corresponding target historical image; the neatness detection value is determined according to a preset neatness detection value evaluation rule; the neatness of the initial object in the historical image is inversely proportional to the neatness detection value corresponding to the initial object in the historical image;

[0025] Step S316, determining the maximum neatness detection value corresponding to the mth initial object as the target neatness detection value of the mth initial object;

[0026] Step S317: If the target neatness detection value of the mth initial object is greater than a preset neatness detection threshold, the mth initial object is determined as the target object.

[0027] In an exemplary embodiment of the present application, step S320 includes:

[0028] Step S321, matching each target object in each target image with each target object in the initial image according to the image features of the target object in each target image and the image features of the target object in the initial image, so as to obtain a plurality of initial matching degrees corresponding to each target object;

[0029] Step S322, determining an image detection value corresponding to each target image according to a number of initial matching degrees corresponding to each target object;

[0030] Step S323, determining the physical sign detection value corresponding to each target image according to the physical sign data of the target person at the time of acquisition of each target image; the target person is a person whose stay time in the target space exceeds a preset time threshold every day;

[0031] Step S324: Determine the product of the image detection value corresponding to each target image and the vital sign detection value corresponding to the target image as the detection value corresponding to the target image.

[0032] In an exemplary embodiment of the present application, step S322 includes:

[0033] Step S3221, obtaining the initial matching degree corresponding to each target object in each target image;

[0034] Step S3222, averaging the initial matching degrees corresponding to a plurality of target objects in each target image to obtain an average matching degree corresponding to each target image;

[0035] Step S3223: Determine the image detection value corresponding to each target image according to a preset matching degree mapping table; the matching degree mapping table stores mapping relationships between a plurality of matching degrees and a plurality of image detection values.

[0036] In an exemplary embodiment of the present application, step S323 includes:

[0037] Step S3231, obtaining the vital sign data of the target person at the acquisition time of each target image;

[0038] Step S3232: determine the vital sign detection value corresponding to each target image according to a preset vital sign data mapping table; the vital sign data mapping table stores a mapping relationship between a plurality of vital sign data and a plurality of vital sign detection values.

[0039] In an exemplary embodiment of the present application, step S400 includes:

[0040] Step S410: Obtain the detection value corresponding to each target image to obtain a detection value list Z = (Z1, Z2, ..., Z v ,...,Z w ), where Zv is the detection value corresponding to the vth target image;

[0041] Step S420: If v＝1 w Z v If it is greater than the preset detection value threshold, r 01 +r 02 ×(w+1) is determined as the next allergen removal time.

[0042] According to one aspect of the present application, a non-transitory computer-readable storage medium is provided, in which at least one instruction or at least one program is stored. The at least one instruction or the at least one program is loaded and executed by a processor to implement the aforementioned method for determining the time for clearing human allergens.

[0043] According to one aspect of the present application, an electronic device is provided, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0044] The present invention has at least the following beneficial effects:

[0045] The method for determining the time for clearing allergens for human body of the present invention obtains an initial image corresponding to the target space acquired at the most recent allergen clearing time from the current time, and obtains the target image corresponding to the target space at each monitoring time period according to the acquisition time corresponding to the initial image, performs image matching between the initial image and each target image, so as to determine the detection value corresponding to each target image. If the sum of the detection values ​​corresponding to all target images is greater than a preset detection value threshold, it indicates that the content of allergens covered by the target object in the current target space exceeds the standard, and allergen detection and elimination are required. Therefore, the end time of the next monitoring time period after the acquisition time corresponding to the last target image is determined as the time for clearing allergens for the next time, so as to timely discover allergens, promptly notify the staff to perform allergen detection and elimination treatment on the target object in the target space, timely eliminate the hazards caused by the allergens, and perform allergen prevention and control on the target space, so as to achieve the purpose of maintaining the physical and mental health of the target personnel in the target space and eliminating the hidden dangers to life safety while reducing the number of allergen detections performed by the staff. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1This is a flow chart of a method for determining the time to clear human allergens provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0049] A method for determining the time of clearing human allergens described in this application is as follows: Figure 1 As shown, the following steps are included:

[0050] Step S100, obtaining an initial image corresponding to the target space;

[0051] Among them, the acquisition time corresponding to the initial image is the allergen removal time most recent to the current time, that is, the image of the target space acquired at the last allergen removal time. The allergen removal time is the time when the staff detects and removes the allergens of the target objects in the target space. The target space may be the space corresponding to the home environment. The target object is the object covered with allergens in the target space determined by the staff, that is, the staff detects and removes the allergens of the target objects in the target space. The initial image includes several target objects in the target space.

[0052] The target object can be an object of a specific type or an object placed on a specific type, or an object covered with allergens that may cause allergic diseases (such as respiratory allergies, skin itching allergies, eye stinging allergies, etc.), such as beds, carpets, coffee tables, flowers, etc.

[0053] Step S200, acquiring a target image corresponding to the target space at every preset monitoring time period according to the acquisition time corresponding to the initial image;

[0054] Further, step S200 includes steps S210 to S230:

[0055] Step S210: Obtain the acquisition time r corresponding to the initial image 01 ;

[0056] Step S220: Obtain the length r of the preset monitoring time period 02 ;

[0057] Step S230: every preset monitoring time period, obtain a number of target images collected from the collection time corresponding to the initial image to the current time, wherein the collection time of the vth target image is r 01 +r 02×v; v = 1, 2, ..., w; w is the number of target images collected from the collection time corresponding to the initial image to the current time.

[0058] A current image (i.e., target image) corresponding to the target space is captured every monitoring period since the last allergen removal time. The angle and position of the target space captured by the target image are the same as those of the target space captured by the initial image. For example, a target image is acquired every other day so that the next time the allergen removal of the target object is determined based on image matching.

[0059] Step S300, performing image matching on the initial image and each target image to determine the detection value corresponding to each target image;

[0060] The detection value indicates a trend of a difference in neatness between the target object in the target image and the target object in the initial image.

[0061] The image matching method in step S300 can be implemented by the following source code:

[0062] import cv2

[0063] import numpy as np def match_images(base_image_path,target_image_paths):

[0064] #Read the initial image

[0065] base_image=cv2.imread(base_image_path,cv2.IMREAD_GRAYSCALE)

[0066] if base_image is None:

[0067] raise ValueError

[0068] #Create ORB feature detector and descriptor

[0069] orb = cv2.ORB_create()

[0070] #Calculate the feature points and descriptors of the initial image

[0071] kp_base,des_base=orb.detectAndCompute(base_image,None)

[0072] #Store the matching results for each target image

[0073] results = {}

[0074] for target_image_path in target_image_paths:

[0075] #Read the target image

[0076] target_image=cv2.imread(target_image_path,cv2.IMREAD_GRAYSCALE)

[0077] if target_image is None:

[0078] raise ValueError

[0079] #Calculate the feature points and descriptors of the target image

[0080] kp_target,des_target=orb.detectAndCompute(target_image,None)

[0081] #Use BFMatcher for matching

[0082] bf=cv2.BFMatcher(cv2.NORM_HAMMING,crossCheck=True)

[0083] matches=bf.match(des_base,des_target)

[0084] # Sort the matching results by distance

[0085] matches=sorted(matches,key=lambda x:x.distance)

[0086] #Store matching results (such as the number of matching points)

[0087] results[target_image_path]=len(matches)

[0088] return results

[0089] Further, step S300 includes step S310-step S320:

[0090] Step S310: performing image recognition on the initial image to determine the target object included in the initial image;

[0091] The target object is an object that needs to be tested for allergens, and step S310 includes steps S311 to S317:

[0092] Step S311: Perform image recognition on the initial image to obtain the image features of each initial object included in the initial image, so as to obtain an initial image feature list set A = (A1, A2, ..., A m ,...,A n ), where m = 1, 2, ..., n, and n is the number of initial objects included in the initial image; A m is a list of image features corresponding to the mth initial object included in the initial image;

[0093] A m =(A m1 ,A m2 ,...,A mi ,...,A mj(m) ), i = 1, 2, ..., j(m), j(m) is the number of image features of the mth initial object included in the initial image, A mi is the i-th image feature of the m-th initial object included in the initial image;

[0094] Step S312: Obtain the image features of the mth initial object included in the initial image in each historical image to obtain a historical image feature list set B corresponding to the mth initial object. m =(B m1 ,B m2 ,...,B me ,...,B mf ), where e = 1, 2, ..., f, and f is the number of historical images; B me is the historical image feature list of the mth initial object in the eth historical image;

[0095] B me =(B me1 ,B me2 ,...,B meg ,...,B meh(me) ); g = 1, 2, ..., h(me); h(me) is the number of image features of the mth initial object in the eth historical image; B meg is the g-th image feature of the m-th initial object in the e-th historical image;

[0096] The historical images are images of the target objects in the target space collected during the historical time period at various angles, positions, and levels of neatness.

[0097] Step S313: Am and B me Perform feature comparison to obtain the corresponding feature matching degree C me ;

[0098] In addition, an image object recognition model (such as a CNN convolutional neural network model) can be used to perform image recognition and object recognition on the initial image. The initial image is input into the image object recognition model, which extracts features from the initial image and compares them with the image features of the training samples of the image object recognition model (the training samples are the entire image or partial image of each target object in different orientations, i.e., historical images). If the comparison is successful, the corresponding target object in the initial image is determined.

[0099] Step S314: If C me If the feature matching degree is greater than a preset threshold, the e-th historical image is determined as the target historical image corresponding to the m-th initial object;

[0100] Step S315, obtaining the neatness detection value of the mth initial object in each corresponding target historical image;

[0101] The neatness detection value is determined according to a preset neatness detection value evaluation rule; the neatness of the initial object in the historical image is inversely proportional to the neatness detection value corresponding to the initial object in the historical image.

[0102] The neatness detection value of the initial object is determined according to the neatness of the initial object in the corresponding historical image. The neatness of the initial object is detected by the neatness detection value evaluation rule to infer the allergen content of the initial object. That is, the lower the neatness of the initial object, the higher the allergen content of the initial object, and the higher the corresponding neatness detection value, which means that the time required for the next allergen detection and removal is shorter.

[0103] The preset neatness detection value evaluation rules may be detection value evaluation rules defined by staff or users, that is, the user determines the neatness detection value corresponding to the neatness by viewing the neatness of the initial object in the historical image. For example, the neatness is divided into five levels, and the higher the level, the lower the neatness and the higher the corresponding neatness detection value.

[0104] Step S316, determining the maximum neatness detection value corresponding to the mth initial object as the target neatness detection value of the mth initial object;

[0105] In order to reduce the judgment error of allergens, the maximum neatness detection value corresponding to the initial object is determined as the target neatness detection value.

[0106] Step S317: If the target neatness detection value of the mth initial object is greater than a preset neatness detection threshold, the mth initial object is determined as the target object.

[0107] Step S320, determining a detection value corresponding to each target image according to a matching degree between an image feature of a target object in each target image and an image feature of the target object in the initial image;

[0108] Wherein, step S320 includes steps S321 to S324:

[0109] Step S321, matching each target object in each target image with each target object in the initial image according to the image features of the target object in each target image and the image features of the target object in the initial image, so as to obtain a plurality of initial matching degrees corresponding to each target object;

[0110] Step S322, determining an image detection value corresponding to each target image according to a number of initial matching degrees corresponding to each target object;

[0111] Wherein, step S322 includes steps S3221 to S3223:

[0112] Step S3221, obtaining the initial matching degree corresponding to each target object in each target image;

[0113] Step S3222, averaging the initial matching degrees corresponding to a plurality of target objects in each target image to obtain an average matching degree corresponding to each target image;

[0114] Step S3223: Determine the image detection value corresponding to each target image according to a preset matching degree mapping table;

[0115] The preset matching degree mapping table stores mapping relationships between a plurality of matching degrees and a plurality of image detection values, and the staff can set the image detection values ​​for different matching degrees.

[0116] Step S323, determining the physical sign detection value corresponding to each target image according to the physical sign data of the target person at the time of acquisition of each target image;

[0117] The target person is a person who stays in the target space for more than a preset time threshold every day, and may be a family member in a home environment.

[0118] When determining the allergen removal time, in addition to judging the cleanliness of the target object, it is also necessary to consider the target person's vital sign data, that is, the target person's breathing and / or heart rate and / or blood pressure data at the time of target image acquisition. By checking the change trend of the target person's vital sign data, the determination of the allergen removal time is affected. If the changes in the target person's vital sign data for several consecutive days tend to develop abnormally, the next time the staff comes to the target space to remove allergens will be shortened, so as to further accurately determine the allergen removal time, maintain the physical and mental health of the target person, and eliminate life safety hazards.

[0119] Wherein, step S323 includes step S3231-step S3232:

[0120] Step S3231, obtaining the vital sign data of the target person at the acquisition time of each target image;

[0121] Step S3232: Determine the vital sign detection value corresponding to each target image according to a preset vital sign data mapping table;

[0122] The physical sign data mapping table stores mapping relationships between a plurality of physical sign data and a plurality of physical sign detection values, and the staff can set corresponding physical sign detection values ​​according to the plurality of physical sign data.

[0123] Step S324: Determine the product of the image detection value corresponding to each target image and the vital sign detection value corresponding to the target image as the detection value corresponding to the target image.

[0124] Step S400: if the sum of the detection values ​​corresponding to all target images is greater than the preset detection value threshold, the end time of the next monitoring time period after the acquisition time corresponding to the last target image is determined as the next allergen removal time;

[0125] Further, step S400 includes step S410-step S420:

[0126] Step S410: Obtain the detection value corresponding to each target image to obtain a detection value list Z = (Z1, Z2, ..., Z v ,...,Z w ), where Z v is the detection value corresponding to the vth target image;

[0127] Step S420: If v＝1 w Z v If it is greater than the preset detection value threshold, r 01 +r 02 ×(w+1) is determined as the next allergen removal time.

[0128] If the sum of the detection values ​​corresponding to all target images is greater than the preset detection value threshold, it means that the target object in the target space needs to be cleared of allergens, and a detection and clearing notification is sent to the staff to remind them to come to the target space at the end time of the next monitoring time period after the acquisition time corresponding to the last target image, and clear the allergens of the target object. By acquiring the target image of the target space, the time for the next allergen clearing is adjusted in time. The better the cleanliness of the target object and the better the physical sign data of the target person, the later the time for the next allergen clearing will be.

[0129] The method for determining the time for clearing allergens for human body of the present invention obtains an initial image corresponding to the target space acquired at the most recent allergen clearing time from the current time, and according to the acquisition time corresponding to the initial image, obtains the target image corresponding to the target space at each monitoring time period, performs image matching on the initial image and each target image, so as to determine the detection value corresponding to each target image. If the sum of the detection values ​​corresponding to all target images is greater than a preset detection value threshold, it indicates that the content of allergens covered by the target object in the current target space exceeds the standard, and allergen detection and elimination are required. Therefore, the end time of the next monitoring time period after the acquisition time corresponding to the last target image is determined as the time for clearing allergens for the next time, so as to promptly notify the staff to perform allergen detection and elimination on the target object in the target space, and perform allergen prevention and control on the target space, so as to achieve the purpose of maintaining the physical and mental health of the target personnel in the target space and eliminating the hidden dangers to life safety while reducing the number of allergen detections performed by the staff.

[0130] An embodiment of the present invention further provides a computer program product, which includes program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method according to various exemplary embodiments of the present invention described above in this specification.

[0131] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0132] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0133] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0134] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0135] The electronic device according to this embodiment of the present invention is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0136] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor mentioned above, the at least one storage device mentioned above, and a bus connecting different system components (including storage devices and processors).

[0137] The storage stores program codes, which can be executed by the processor, so that the processor executes the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0138] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read only memory (ROM).

[0139] The storage may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0140] The bus may represent one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0141] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter.

[0142] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0143] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0144] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0145] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0146] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0147] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0148] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0149] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0150] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for determining the time of clearing human allergens, characterized in that: The method comprises the following steps: Step S100, obtaining an initial image corresponding to the target space; the acquisition time corresponding to the initial image is the allergen removal time closest to the current time; Step S200, acquiring a target image corresponding to the target space at every preset monitoring time period according to the acquisition time corresponding to the initial image; Step S300: performing image matching on the initial image and each of the target images to determine a detection value corresponding to each of the target images; Step S400: If the sum of the detection values ​​corresponding to all the target images is greater than a preset detection value threshold, the end time of the next monitoring time period after the acquisition time corresponding to the last target image is determined as the next allergen removal time.

2. The method according to claim 1, characterized in that The step S200 includes: Step S210: Obtain the acquisition time r corresponding to the initial image 01 ; Step S220: Obtain the length r of the preset monitoring time period 02 ; Step S230: every preset monitoring time period, obtain a number of target images collected from the collection time corresponding to the initial image to the current time, wherein the collection time of the vth target image is r 01 +r 02 ×v; v=1, 2, ..., w; w is the number of target images collected from the collection time corresponding to the initial image to the current time.

3. The method according to claim 2, characterized in that The step S300 includes: Step S310: performing image recognition on the initial image to determine a target object included in the initial image; the target object is an object that needs to be tested for allergens; Step S320: Determine a detection value corresponding to each of the target images according to a matching degree between an image feature of the target object in each of the target images and an image feature of the target object in the initial image.

4. The method according to claim 3, characterized in that The step S310 includes: Step S311: Perform image recognition on the initial image to obtain image features of each initial object included in the initial image, so as to obtain an initial image feature list set A = (A1, A2, ..., A m ,...,A n ), wherein m=1, 2, ..., n, and n is the number of initial objects included in the initial image; A m is a list of image features corresponding to the mth initial object included in the initial image; A m =(A m1 ,A m2 ,...,A mi ,...,A mj(m) ), i = 1, 2, ..., j(m), j(m) is the number of image features of the mth initial object included in the initial image, A mi is the i-th image feature of the m-th initial object included in the initial image; Step S312: Obtain the image features of the mth initial object included in the initial image in each historical image to obtain a historical image feature list set B corresponding to the mth initial object. m =(B m1 ,B m2 ,...,B me ,...,B mf ), wherein e=1, 2, ..., f, and f is the number of the historical images; B me is a list of historical image features of the mth initial object in the eth historical image; B me =(B me1 ,B me2 ,...,B meg ,...,B meh(me) ); g = 1, 2, ..., h(me); h(me) is the number of image features of the mth initial object in the eth historical image; B meg is the g-th image feature of the m-th initial object in the e-th historical image; Step S313: A m and B me Perform feature comparison to obtain the corresponding feature matching degree C me ; Step S314: If C me If the feature matching degree is greater than a preset threshold, the e-th historical image is determined as the target historical image corresponding to the m-th initial object; Step S315, obtaining a neatness detection value of the mth initial object in each corresponding target historical image; the neatness detection value is determined according to a preset neatness detection value evaluation rule; the neatness of the initial object in the historical image is inversely proportional to the neatness detection value corresponding to the initial object in the historical image; Step S316, determining the maximum neatness detection value corresponding to the mth initial object as the target neatness detection value of the mth initial object; Step S317: If the target neatness detection value of the mth initial object is greater than a preset neatness detection threshold, the mth initial object is determined as the target object.

5. The method according to claim 4, characterized in that The step S320 includes: Step S321, matching each target object in each target image with each target object in the initial image according to the image features of the target object in each target image and the image features of the target object in the initial image, so as to obtain a plurality of initial matching degrees corresponding to each target object; Step S322: determining an image detection value corresponding to each of the target images according to a number of initial matching degrees corresponding to each of the target objects; Step S323, determining the physical sign detection value corresponding to each target image according to the physical sign data of the target person at the time of acquisition of each target image; the target person is a person who stays in the target space for a period of time exceeding a preset time threshold every day; Step S324: Determine the product of the image detection value corresponding to each target image and the vital sign detection value corresponding to the target image as the detection value corresponding to the target image.

6. The method according to claim 5, characterized in that The step S322 includes: Step S3221, obtaining an initial matching degree corresponding to each target object in each target image; Step S3222, averaging the initial matching degrees corresponding to the target objects in each target image to obtain an average matching degree corresponding to each target image; Step S3223: Determine the image detection value corresponding to each of the target images according to a preset matching degree mapping table; the matching degree mapping table stores mapping relationships between a plurality of matching degrees and a plurality of image detection values.

7. The method according to claim 6, characterized in that The step S323 includes: Step S3231, obtaining the vital sign data of the target person at the acquisition time of each of the target images; Step S3232: determine the vital sign detection value corresponding to each of the target images according to a preset vital sign data mapping table; the vital sign data mapping table stores a mapping relationship between a plurality of vital sign data and a plurality of vital sign detection values.

8. The method according to claim 7, characterized in that The step S400 includes: Step S410: Obtain the detection value corresponding to each target image to obtain a detection value list Z=(Z1, Z2, ..., Z v ,...,Z w ), where Z v is the detection value corresponding to the vth target image; Step S420: If v＝1 w Z v If it is greater than the preset detection value threshold, r 01 +r 02 ×(w+1) is determined as the next allergen removal time.

9. A non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the method as claimed in any one of claims 1 to 8.

10. An electronic device, characterized in that: The invention comprises a processor and the non-transitory computer-readable storage medium as claimed in claim 9.

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