Toothbrush testing method, apparatus and system, and storage medium

Abstract

A toothbrush testing method. The cleaning capability of a toothbrush under test is quantized by means of a test result, and the cleaning degree regarding the occlusal surfaces of teeth is introduced, such that the test result can reflect the capability of said toothbrush in terms of prevention or alleviation of pit and fissure caries, thereby providing objective and efficient instructions for manufacturers and users, and thus promoting favorable development of the industry. The whole test process is standardized, and it is not necessary to perform long-term observational tests on a large number of test subjects, thereby improving the test efficiency and reducing test costs. The present application further relates to a toothbrush testing apparatus and system, and a storage medium.

Description

Toothbrush detection method, device, system and storage medium Cross-reference to related applications This disclosure claims priority to the Chinese patent application filed with the China Patent Office on December 8, 2023, with application number 202311685365.8, the entire contents of which are incorporated by reference into this disclosure. Technical Field

[0001] The present disclosure relates to the technical field of electronic equipment, and in particular to a toothbrush detection method, device, system and storage medium. Background Art

[0002] Toothbrushes are the most commonly used oral hygiene tools. Their cleaning power is a key indicator of their effectiveness, and effective cleaning can help prevent or alleviate oral diseases. However, prior art evaluations of toothbrush cleaning power are primarily based on subjective testers' perceptions, lacking an objective and effective standard for evaluating toothbrush cleaning power. This results in inconsistent toothbrush cleaning results, impacting oral health. Summary of the Invention

[0003] In order to effectively detect the cleaning power of a toothbrush and improve the reliability and guidance of toothbrush cleaning effect detection, the embodiments of the present disclosure provide a toothbrush detection method, device, system and storage medium.

[0004] In a first aspect, an embodiment of the present disclosure provides a toothbrush detection method, comprising: determining a first cleaning parameter of the toothbrush to be tested based on the difference between before and after cleaning a dental mold by the toothbrush to be tested; determining a second cleaning parameter of the toothbrush to be tested based on the degree of cleaning of the occlusal surface of the dental mold by the toothbrush to be tested; determining a third cleaning parameter of the toothbrush to be tested based on the proportion of a specific part of the occlusal surface of the tooth to the occlusal surface; and determining a detection result of the toothbrush to be tested based on the first cleaning parameter, the second cleaning parameter and the third cleaning parameter.

[0005] In some embodiments, the toothbrush detection method described in the present disclosure further includes: determining a fourth cleaning parameter of the toothbrush to be tested based on the time when the toothbrush to be tested cleans the occlusal surface of each tooth in the dental mold during the cleaning process; determining the detection result of the toothbrush to be tested based on the first cleaning parameter, the second cleaning parameter and the third cleaning parameter includes: determining the detection result of the toothbrush to be tested based on the first cleaning parameter, the second cleaning parameter, the third cleaning parameter and the fourth cleaning parameter.

[0006] In some embodiments, determining the first cleaning parameter of the toothbrush to be tested based on the difference before and after the toothbrush to be tested cleans the dental cast includes: obtaining a first dental cast image before the toothbrush to be tested cleans the dental cast, and a second dental cast image after the toothbrush to be tested cleans the dental cast; determining a first area of ​​an uncleaned area on the dental cast based on the first dental cast image; determining a second area of ​​a cleaned area on the dental cast based on the second dental cast image; and determining the first cleaning parameter based on the ratio of the second area to the first area.

[0007] In some embodiments, determining the first cleaning parameter of the toothbrush to be tested based on the difference before and after the toothbrush to be tested cleans the dental mold includes: obtaining a first weight of the dental mold before the toothbrush to be tested cleans the dental mold, and a second weight of the dental mold after the toothbrush to be tested cleans the dental mold; and determining the first cleaning parameter based on the difference between the first weight and the second weight.

[0008] In some embodiments, determining the first cleaning parameter of the toothbrush to be tested based on the difference between the dental molds before and after cleaning by the toothbrush to be tested includes: determining the cleaning visual parameter of the toothbrush to be tested based on the dental mold images before and after cleaning by the toothbrush to be tested; determining the cleaning effect parameter of the toothbrush to be tested based on the weight change of the dental mold before and after cleaning by the toothbrush to be tested; and determining the first cleaning parameter based on the cleaning visual parameter and the cleaning effect parameter.

[0009] In some embodiments, determining the second cleaning parameter of the toothbrush to be tested based on the degree of cleaning of the occlusal surface of the dental mold by the toothbrush to be tested includes: collecting a first cleaning image of the occlusal surface of the dental mold after the toothbrush to be tested cleans the dental mold; determining the second cleaning parameter of the toothbrush to be tested based on the first cleaning image and a pre-set correspondence, wherein the correspondence includes a correspondence between the degree of cleaning of the occlusal surface of the tooth and the second cleaning parameter.

[0010] In some embodiments, the specific portion includes pits and fissures on the occlusal surface of the tooth, and the determining of the third cleaning parameter of the toothbrush to be tested based on the proportion of the pits and fissures on the specific portion of the occlusal surface of the tooth to the occlusal surface of the tooth comprises: acquiring the second cleaning image, the second cleaning image including the image of the occlusal surface of the tooth mold; determining the third area of ​​the occlusal surface of the tooth and the fourth area of ​​the pits and fissures on the image based on the second cleaning image; and determining the third cleaning parameter based on the ratio of the fourth area to the third area.

[0011] In some embodiments, based on the time it takes for the toothbrush to be tested to clean the occlusal surface of each tooth in the dental mold during the cleaning process, the fourth cleaning parameter of the toothbrush to be tested is determined, including: obtaining the total duration of the cleaning process of the dental mold by the toothbrush to be tested, and the number of surfaces of the teeth cleaned; determining the cleaning time of the occlusal surface of each tooth in the cleaning process based on the total duration, the number of surfaces, and the third number of teeth simultaneously covered by the brush filaments of the toothbrush to be tested; and determining the fourth cleaning parameter based on the cleaning time.

[0012] In some embodiments, the cleaning process of the dental mold by the toothbrush to be tested includes: arranging attachments in a preset tooth area of ​​the dental mold, wherein the preset tooth area includes the occlusal surface area of ​​multiple teeth; controlling the robotic arm to clamp the toothbrush to be tested and clean the preset tooth area of ​​the dental mold in a preset action manner for a preset time.

[0013] In a second aspect, an embodiment of the present disclosure provides a toothbrush detection system, comprising: a manipulator for clamping a toothbrush to be tested and driving the toothbrush to be tested to perform a cleaning operation; an image acquisition device; and a controller, comprising a processor and a memory, wherein the memory stores computer instructions, and the computer instructions are used to enable the processor to execute the method described in any embodiment of the first aspect.

[0014] In a third aspect, an embodiment of the present disclosure provides a toothbrush detection device, comprising: a first determination module, configured to determine a first cleaning parameter of the toothbrush to be tested based on the difference between the toothbrush to be tested before and after cleaning the dental mold; a second determination module, configured to determine a second cleaning parameter of the toothbrush to be tested based on the degree of cleaning of the occlusal surface of the dental mold by the toothbrush to be tested; a third determination module, configured to determine a third cleaning parameter of the toothbrush to be tested based on the proportion of a specific part of the occlusal surface of the tooth to the occlusal surface; and a result determination module, configured to determine the detection result of the toothbrush to be tested based on the first cleaning parameter, the second cleaning parameter and the third cleaning parameter.

[0015] In some embodiments, the toothbrush detection device described in the present disclosure also includes a fourth determination module, which is configured to: determine the fourth cleaning parameter of the toothbrush to be tested based on the time when the toothbrush to be tested cleans the occlusal surface of each tooth in the dental mold during the cleaning process; the result determination module is configured to: determine the detection result of the toothbrush to be tested based on the first cleaning parameter, the second cleaning parameter, the third cleaning parameter and the fourth cleaning parameter.

[0016] In some embodiments, the first determination module is configured to: obtain a first dental model image before the toothbrush to be tested cleans the dental model, and a second dental model image after the toothbrush to be tested cleans the dental model; determine a first area of ​​an uncleaned area on the dental model based on the first dental model image; determine a second area of ​​a cleaned area on the dental model based on the second dental model image; and determine the first cleaning parameter based on a ratio of the second area to the first area.

[0017] In some embodiments, the first determination module is configured to: obtain a first weight of the dental cast before the toothbrush to be tested cleans the dental cast, and a second weight of the dental cast after the toothbrush to be tested cleans the dental cast; and determine the first cleaning parameter based on the difference between the first weight and the second weight.

[0018] In some embodiments, the first determination module is configured to: determine the cleaning visual parameters of the toothbrush to be tested based on the dental mold image before and after the toothbrush to be tested cleans the dental mold; determine the cleaning effect parameters of the toothbrush to be tested based on the weight change of the dental mold before and after the toothbrush to be tested cleans the dental mold; determine the first cleaning parameters based on the cleaning visual parameters and the cleaning effect parameters.

[0019] In some embodiments, the second determination module is configured to: collect a first cleaning image of the occlusal surface of the dental mold after the toothbrush to be tested cleans the dental mold; determine the second cleaning parameter of the toothbrush to be tested based on the first cleaning image and a pre-set correspondence, wherein the correspondence includes a correspondence between the degree of cleaning of the occlusal surface of the tooth and the second cleaning parameter.

[0020] In some embodiments, the specific part includes pits and fissures on the occlusal surface of the tooth, and the third determination module is configured to: obtain the second cleaning image, the second cleaning image including the image of the occlusal surface of the tooth of the dental mold; determine the third area of ​​the occlusal surface of the tooth and the fourth area of ​​the pits and fissures on the image based on the second cleaning image; determine the third cleaning parameter based on the ratio of the fourth area to the third area.

[0021] In some embodiments, the fourth determination module is configured to: obtain the total duration of the cleaning process of the toothbrush to be tested on the dental model, and the number of surfaces of the teeth cleaned; determine the cleaning time of the occlusal surface of each tooth during the cleaning process based on the total duration, the number of surfaces and the third number of teeth simultaneously covered by the brush wire of the toothbrush to be tested; and determine the fourth cleaning parameter based on the cleaning time.

[0022] In some embodiments, the toothbrush cleaning device described in the present disclosure further includes a cleaning module, which is configured to: arrange attachments in a preset tooth area of ​​the dental mold, wherein the preset tooth area includes the occlusal surface area of ​​multiple teeth; control the robotic arm to clamp the toothbrush to be tested and clean the preset tooth area of ​​the dental mold in a preset action manner for a preset time.

[0023] In a fourth aspect, an embodiment of the present disclosure provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the method according to any embodiment of the first aspect.

[0024] The toothbrush detection method of the disclosed embodiment includes determining a first cleaning parameter based on the difference between the toothbrush to be tested and the cleaning of the dental mold before and after, determining a second cleaning parameter based on the degree of cleaning of the occlusal surface of the dental mold by the toothbrush to be tested, determining a third cleaning parameter based on the proportion of a specific part of the occlusal surface of the tooth to be tested, and determining a detection result according to the first, second, and third cleaning parameters. In the disclosed embodiment, the cleaning ability of the toothbrush to be tested is quantified by the test results, and by introducing the degree of cleaning of the occlusal surface of the tooth, the test results can reflect the ability of the toothbrush to be tested to prevent or alleviate pit and fissure caries, providing objective and efficient guidance for manufacturers and users, and promoting the healthy development of the industry. In addition, in the disclosed embodiment, the entire detection process is standardized, eliminating the need for long-term observation tests on a large number of subjects, improving detection efficiency, and reducing detection costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the specific embodiments of the present disclosure, the following briefly introduces the drawings required for use in the description of the specific embodiments. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0026] Figure 1 is a schematic diagram of the human oral cavity structure.

[0027] Figure 2 is a schematic diagram of the human oral and mandibular structure.

[0028] FIG3 is a structural block diagram of a toothbrush detection system according to some embodiments of the present disclosure.

[0029] FIG4 is a schematic diagram of a dental model structure according to some embodiments of the present disclosure.

[0030] FIG5 is a flow chart of a toothbrush detection method according to some embodiments of the present disclosure.

[0031] FIG6 is a flow chart of a toothbrush detection method according to some embodiments of the present disclosure.

[0032] FIG7 is a flowchart of a toothbrush detection method according to some embodiments of the present disclosure.

[0033] FIG8 is a flow chart of a toothbrush detection method according to some embodiments of the present disclosure.

[0034] FIG9 is a schematic diagram of a toothbrush detection method according to some embodiments of the present disclosure.

[0035] FIG10 is a flowchart of a toothbrush detection method according to some embodiments of the present disclosure.

[0036] FIG11 is a flow chart of a toothbrush detection method according to some embodiments of the present disclosure.

[0037] FIG12 is a flow chart of a toothbrush detection method according to some embodiments of the present disclosure.

[0038] FIG13 is a flow chart of a toothbrush detection method according to some embodiments of the present disclosure.

[0039] FIG14 is a flow chart of a toothbrush detection method according to some embodiments of the present disclosure.

[0040] FIG15 is a structural block diagram of a toothbrush detection device according to some embodiments of the present disclosure.

[0041] FIG16 is a structural block diagram of a toothbrush detection system according to some embodiments of the present disclosure. DETAILED DESCRIPTION

[0042] The technical solutions of the present disclosure will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure. In addition, the technical features involved in the different embodiments of the present disclosure described below can be combined with each other as long as they do not conflict with each other.

[0043] The toothbrush is the most commonly used oral hygiene device. With the advancement of electronic technology, electric toothbrushes are increasingly replacing regular toothbrushes as the preferred dental cleaning device. However, whether using a regular or electric toothbrush, the effectiveness of the toothbrush's cleaning performance is a key indicator of its effectiveness. High cleaning efficiency ensures comprehensive cleaning of teeth and reduces the risk of oral diseases.

[0044] The occlusion surface of a tooth refers to the contact surface between the upper and lower teeth. The occlusion surface is uneven, with recessed areas known as pits and fissures. The shape and depth of pits and fissures vary from person to person, and even on the same tooth, they can be roughly categorized into two types: shallow, wide V-shaped pits and fissures, and deep, narrow I-shaped pits and fissures.

[0045] V-shaped pits and fissures are shallow, making them less likely to trap food debris and bacteria, thus preventing tooth decay. I-shaped pits and fissures, on the other hand, are narrow and long, making them highly susceptible to trapping food debris and bacteria, leading to tooth decay, a condition medically known as pit and fissure caries. Therefore, a toothbrush's ability to clean the occlusal surface (especially the pits and fissures) is crucial for preventing and alleviating pit and fissure caries.

[0046] In the prior art, toothbrush cleaning performance is typically evaluated through human efficacy testing. For example, a certain number of subjects are selected to brush their teeth with the toothbrush under test over a test period. The cleaning effect is observed to determine the toothbrush's cleaning performance. However, this testing process is costly and time-consuming, making it unsuitable for use during the product development phase.

[0047] More importantly, there is no industry standard in the relevant technology that can evaluate the cleaning effect of toothbrushes on specific parts (such as the occlusal surface of teeth), resulting in a lack of intuitive and effective guidance when people choose toothbrushes. Manufacturers also lack objective testing standards during the research and development stage, resulting in poor product performance.

[0048] Based on the defects of the above-mentioned related technologies, the embodiments of the present disclosure provide a toothbrush detection method, device, electronic device and storage medium, which aim to objectively quantify the ability of a toothbrush to clean specific parts of teeth (such as the occlusal surface of the teeth), thereby simulating the cleaning effect of specific parts of real teeth (such as the occlusal surface of the teeth), thereby reflecting the ability of the toothbrush to prevent oral diseases such as pit and fissure caries, and providing a complete set of toothbrush cleaning ability detection standards, so as to objectively and efficiently detect the cleaning power of the toothbrush.

[0049] To facilitate understanding of the embodiments of the present disclosure, some definitions of terms related to the human oral cavity are briefly described below with reference to FIG1 and FIG2 .

[0050] As shown in Figure 1, the human oral cavity is divided into the maxillary and mandibular parts, and the tooth distribution of the mandibular and mandibular parts is the same. Pit and fissure caries refers to the caries occurring on the occlusal surfaces of molars and premolars, that is, the caries occurring on the first premolars, second premolars, first molars, second molars and third molars (wisdom teeth) in Figure 1.

[0051] As shown in Figure 2, taking the mandibular teeth as an example, the side of the molar or premolar closest to the human cheek (or lip) is the labial (cheek) side M1, the side closest to the human tongue (or jaw) is the lingual (palatal) side M2, and the surface that occludes the maxillary teeth is the occlusal surface M3, which is distributed with pits and fissures M4. If the toothbrush does not clean the occlusal surface M3 sufficiently, food or bacteria can easily remain in the pits and fissures M4, causing pit and fissure caries.

[0052] In the disclosed embodiments, a toothbrush's ability to clean the occlusal surface of teeth can be objectively and efficiently tested and evaluated, thereby reflecting the toothbrush's cleaning effectiveness on the user's teeth and its ability and effectiveness in preventing or alleviating pit and fissure caries. This evaluation standard can serve as a universal industry standard, providing intuitive and efficient guidance for users in selecting toothbrush products. It can also provide objective guidance for manufacturers during the R&D phase, effectively prioritizing risks and forming an effective closed-loop system for product iteration.

[0053] After understanding the above, FIG3 shows an architecture diagram of a sanitary ware detection system in some embodiments of the present disclosure, which will be described below in conjunction with FIG3 .

[0054] As shown in FIG3 , in some embodiments, the toothbrush detection system of the present disclosure includes a toothbrush 100 to be tested, a dental mold 200 , a manipulator 300 , an image acquisition device 400 , and a controller 500 .

[0055] The toothbrush to be tested 100 is the toothbrush whose cleaning performance is to be tested and evaluated. The toothbrush to be tested 100 can be a conventional toothbrush or an electric toothbrush, and this disclosure does not limit this. In the disclosed embodiments, the goal of testing the toothbrush to be tested 100 is to quantify the toothbrush's cleaning ability on the occlusal surfaces of teeth (particularly pits and fissures), that is, to quantify the toothbrush's effectiveness in preventing or alleviating pit and fissure caries.

[0056] Dental cast 200 is a standard dental model made of polymer materials, modeled after the human oral cavity. It includes both teeth and gums. For example, as shown in Figure 4 , the dental cast 200 in the disclosed embodiment utilizes a standard dental cast. The gums 1 can be made of a polymer material, while the teeth 2 can be made of materials such as zirconium dioxide or composite metals. The gums 1 wrap around the teeth 2, fully mimicking the structure of a real human oral cavity.

[0057] It is worth noting that in the toothbrush testing process in related technologies, most tooth model plates are used to simulate human teeth. For example, multiple small protrusions are formed on a stainless steel or other metal plate to simulate the tooth structure, and the cleaning effect of the toothbrush on the small protrusions is used to represent the cleaning effect of the toothbrush. However, this method does not fully conform to the actual human oral structure and cannot accurately represent the pit and fissure structure of the teeth, resulting in the final test results not reflecting the actual cleaning effect on human teeth. Therefore, in the embodiment of the present disclosure, a tooth model 200 that fully simulates the real human oral cavity is used to reflect the actual cleaning effect on human teeth and improve the detection accuracy.

[0058] The manipulator 300 is used to clamp the toothbrush 100 to be tested, and the manipulator 300 can be driven by the controller 500, so that the manipulator 300 can clamp the toothbrush 100 to be tested to clean the dental mold 200. In some embodiments of the present disclosure, the manipulator 300 can adopt a robotic arm with a memory function. For example, the user can drive the manipulator 300 to perform a brushing route in advance, so that the manipulator 300 can automatically record the movement distance and automatically repeat the brushing route to complete the automatic brushing process. In this way, the difficulty of task programming of the manipulator 300 can be reduced, and the detection cycle can be greatly shortened. As for the specific structure and control principle of the manipulator 300, those skilled in the art can undoubtedly understand and fully implement it with reference to relevant technologies, and this disclosure will not elaborate on this.

[0059] In some embodiments of the present disclosure, the controller 500 can at least control the manipulator 300 to clamp the toothbrush 100 to be tested to complete the "Bass method" process on the dental mold 200, which is described below in the present disclosure.

[0060] The image acquisition device 400 is used to capture frame images or video streams of the cleaning process of the toothbrush 100 to be tested on the dental mold 200. The image acquisition device 400 may include one or more industrial cameras, ToF (Time of flight) sensors, depth cameras, infrared cameras, lidars, etc., so that the planar or three-dimensional image information of the dental mold 200 can be collected in all directions during the cleaning process.

[0061] In an embodiment of the present disclosure, the image acquisition device 400 needs to at least acquire the dental mold images of the dental mold 200 before and after cleaning, so that the controller 500 performs corresponding image processing and analysis based on the dental mold images before and after cleaning, which is explained below in the present disclosure.

[0062] The controller 500 refers to the processing core of the toothbrush detection system, which may include a processor and a memory.

[0063] The processor can be any type of processor with one or more processing cores. It can perform single-threaded or multi-threaded operations and is used to parse instructions to perform operations such as acquiring data, performing logical operations, and issuing operation results.

[0064] The memory may include non-volatile computer-readable storage media, such as at least one disk storage device, flash memory device, distributed storage device remotely located relative to the processor, or other non-volatile solid-state storage device. The memory may have a program storage area for storing non-volatile software programs, non-volatile computer executable programs, and modules for the processor to call to enable the processor to perform one or more of the following method steps. The memory may also include a volatile random access memory medium or a storage portion such as a hard disk as a data storage area for storing operation processing results and data output by the processor.

[0065] In this embodiment, the memory stores computer-readable instructions that can be executed by the processor. When the computer-readable instructions are executed, the processor can perform the toothbrush detection method in the following embodiment. On the other hand, the memory can also store image data captured by the image capture device 400.

[0066] Based on the above, FIG5 shows a flow chart of a toothbrush detection method in some embodiments of the present disclosure. The method of the embodiments of the present disclosure will be described below with reference to FIG5 .

[0067] As shown in FIG. 5 , in some embodiments, the toothbrush detection method of the present disclosure includes steps S510 to S540 .

[0068] S510: Determine a first cleaning parameter of the toothbrush to be tested based on the difference between the cleaning effect of the toothbrush to be tested on the dental mold before and after cleaning.

[0069] It can be understood that in the embodiment of the present disclosure, the toothbrush 100 to be tested needs to be used to clean the dental model 200, so that the cleaning ability of the toothbrush 100 to be tested can be reflected to a certain extent by comparing the effects before and after cleaning.

[0070] In some embodiments, image detection technology can be used to determine the difference in cleaning results before and after cleaning, and the first cleaning parameter can be determined based on the change in computer vision. In other embodiments, the dental cast can be weighed before and after cleaning, and the first cleaning parameter can be determined based on the change in weight of the dental cast before and after cleaning. Each of these is described below in the present disclosure.

[0071] To facilitate clearer image comparison, before cleaning the dental model 200, a certain amount of attachments can be applied to the dental model 200 to simulate the presence of uncleaned dirt or residue on the teeth. For example, a certain amount of attachments can be applied to the dental model 200 to simulate the presence of dental plaque on human teeth. Specifically, how to arrange attachments on the dental model will be described in detail later.

[0072] 6 , the cleaning process of cleaning the dental mold 200 using the toothbrush 100 to be tested in the embodiment of the present disclosure will be described.

[0073] As shown in FIG. 6 , in some embodiments, the toothbrush detection method of the present disclosure includes steps S610 to S620 in the cleaning process of the dental mold 200 .

[0074] S610: Arrange attachments in the preset tooth area of ​​the dental model.

[0075] S620: Control the robot arm to hold the toothbrush to be tested and clean the preset tooth area of ​​the dental mold in a preset manner for a preset time.

[0076] For example, in some embodiments, an aqueous mixture of a dye and a thickener can be placed on the dental model 200 in a coating manner to simulate residues such as dental plaque.

[0077] It's worth noting that, based on the aforementioned information, pit and fissure caries are primarily caused by food and bacterial residue in the pits and fissures of the occlusal surfaces of molars and premolars. This residue is often caused by inadequate tooth cleaning. Therefore, the cleaning performance of areas such as the pits and fissures of the occlusal surfaces of teeth can particularly reflect the cleaning effectiveness of a toothbrush. In the disclosed embodiments, when testing the cleaning performance of a toothbrush on the occlusal surfaces of teeth, only teeth susceptible to pit and fissure caries can be cleaned.

[0078] For example, in some embodiments of the present disclosure, when applying attachments to the dental mold 200, the attachments may be applied to all teeth, or only to a preset tooth area. The preset tooth area may include, for example, the second premolar, first molar, second molar, and third molar shown in FIG1 , and no attachments are applied to other tooth areas.

[0079] After treating the predetermined tooth area of ​​the dental model 200 with the attachment, the dental model 200 can be cleaned using the toothbrush 100 to be tested. It should be understood that after applying the attachment, the dental model can be left to rest for a predetermined period of time to allow the coating to adhere better to the teeth. Alternatively, the attachment can be dried to enhance adhesion, thereby more effectively simulating the attachments remaining in the human oral cavity, particularly in pits and fissures.

[0080] In some feasible embodiments, a tester can hold the toothbrush 100 under test and perform a specific brushing method on the dental model 200 for a preset time. For example, the "Bass method" is an internationally recognized and effective method for cleaning teeth. The tester can hold the toothbrush 100 under test and perform a 2-minute Bass method on the dental model 200 to complete the cleaning operation. Of course, it should be understood that the preset time of 2 minutes is merely an example and can be adjusted as needed.

[0081] However, during manual cleaning, it is difficult to maintain a constant cleaning force and speed, which can easily introduce large detection errors and lead to poor detection accuracy. Therefore, in some other embodiments of the present disclosure, the detection system shown in Figure 3 can control the robot 300 to clamp the toothbrush 100 to be tested to achieve the cleaning operation of the dental mold 200.

[0082] For example, the controller 500 controls the manipulator 300 to hold the toothbrush 100 to be tested and perform a 2-minute Bass brushing process on the dental model 200 to complete the cleaning operation. Since the manipulator 300 has high control precision, it is more accurate than the manual cleaning process, and the subsequent test results are more accurate.

[0083] In some embodiments, the image acquisition device 400 may pre-capture one or more first dental model images of the dental model 200 before cleaning the dental model 200. After the dental model 200 is cleaned, the image acquisition device 400 may again capture one or more second dental model images of the dental model 200, thereby obtaining images of the dental model before and after cleaning.

[0084] It can be understood that the first dental model image refers to the image of the dental model 200 before the cleaning operation is performed, and the second dental model image refers to the image of the dental model 200 after the cleaning operation is completed. Therefore, the difference between the two images before and after can reflect the effect of the attachment falling off during the cleaning process. By monitoring the effect of the attachment falling off, the cleaning effect of the toothbrush 100 to be tested on the dental model can be reflected, especially the cleaning effect on the occlusal surface of the teeth.

[0085] In the embodiment of the present disclosure, image analysis processing can be performed based on the first dental model image before cleaning and the second dental model image after cleaning to determine the first cleaning parameter of the test result. The first cleaning parameter can reflect the cleaning ability of the toothbrush 100 to be tested from a computer vision perspective.

[0086] In other embodiments, the detection system can weigh the dental cast 200 before cleaning it, and then weigh the dental cast 200 again after cleaning, and determine the first cleaning parameter based on the change in weight. For example, the first mass of the dental cast itself, the second mass of the dental cast after applying the coating, and the third mass of the dental cast after cleaning can be obtained. The difference between the second mass and the first mass can reflect the mass of the coating initially applied to the dental cast, the difference between the third mass and the second mass can reflect the mass of the coating that has been removed, and the difference between the third mass and the first mass can reflect the mass of the coating remaining on the dental cast. Accordingly, the ratio of the mass of the coating remaining on the dental cast / the mass of the coating removed to the mass of the coating initially applied to the dental cast can reflect the cleaning ability of the toothbrush on the dental cast.

[0087] In some other embodiments, the first cleaning parameter can be determined by combining the visual change and weight change of the dental cast before and after cleaning. That is, the two methods can be coupled, for example, by assigning a weight coefficient for weighting.

[0088] S520: Determine a second cleaning parameter of the toothbrush to be tested based on the cleaning degree of the toothbrush to be tested on the occlusal surface of the dental model.

[0089] It is worth noting that the first cleaning parameter obtained based on computer vision in the aforementioned S510 can reflect the cleaning ability of the toothbrush 100 to a certain extent. However, if the prevention or relief ability of the toothbrush 100 to be tested for pit and fissure caries is expressed only based on the first cleaning parameter, there may be deviations.

[0090] This is because, based on the aforementioned causes of pit and fissure caries, it's primarily caused by food or bacterial residue in the pits and fissures of the occlusal surface of the tooth, while the first cleaning parameter reflects the cleaning status of the entire tooth. Therefore, further refinement of the cleaning effect for specific areas of the tooth (e.g., pits and fissures) is necessary.

[0091] For example, after cleaning in scenario 1 and scenario 2, the residual plaque area of ​​the entire tooth is the same or similar, but in scenario 1, there is more plaque residue in the pits and fissures of the occlusal surface, while there is less plaque residue in the non-pits and fissures position (such as the buccal side of the teeth); in scenario 2, there is less plaque residue in the pits and fissures of the occlusal surface, while there is more plaque residue in the non-pits and fissures position (such as the buccal side of the teeth).

[0092] However, comparing Scenario 1 and Scenario 2, since there is more dental plaque residue in the pit and fissure positions in Scenario 1, Scenario 1 is more likely to cause pit and fissure caries. However, since the area of ​​dental plaque residue in the entire tooth area after cleaning in Scenario 1 and Scenario 2 is the same or similar, the first cleaning parameter obtained by S510 should also be the same or similar, that is, the cleaning ability of the toothbrush 100 to be tested in the two scenarios is the same or similar. However, it is obvious that compared with Scenario 1, in Scenario 2, the toothbrush to be tested has a better cleaning effect on the attachments on specific parts of the teeth (such as the pit and fissure positions). Although the overall effect of the toothbrushes on cleaning attachments in the two scenarios is similar, there is a big difference in the cleaning effect on specific parts. Obviously. This is not accurate enough for the prevention and relief of pit and fissure caries. In other words, only analyzing the dental plaque residue on the teeth is not detailed enough and cannot accurately reflect the cleaning effect of the toothbrush. The cleaning effect on specific parts of the teeth needs to be considered.

[0093] Therefore, in the embodiment of the present disclosure, a second cleaning parameter is introduced to evaluate the cleanliness of specific parts of the teeth, such as the occlusal surface of the teeth (especially the pits and fissures). The second cleaning parameter is used to reflect the cleaning effect of the toothbrush 100 to be tested on the occlusal surface of the teeth, and further reflect the cleaning effect of the toothbrush 100 to be tested, as well as its ability to prevent or alleviate pit and fissure caries.

[0094] In some embodiments, a correspondence can be pre-set for the cleanliness of the occlusal surface of the teeth. This correspondence refers to the correspondence between the cleanliness of the occlusal surface of the teeth and the second cleaning parameter. Based on this correspondence, the cleanliness of the occlusal surface of the teeth can be quantified to obtain the corresponding second cleaning parameter. The specific method for setting the correspondence is described below in this disclosure and is not detailed here.

[0095] S530: Determine a third cleaning parameter of the toothbrush to be tested based on the proportion of the specific portion of the tooth occlusal surface to the tooth occlusal surface.

[0096] In the disclosed embodiments, pits and fissures are used as an example of specific areas of the occlusal surface of teeth. As shown in FIG2 , the occlusal surface of human teeth is a three-dimensional structure. Furthermore, as previously mentioned, the shapes of pits and fissures vary from tooth to tooth, and even on the same tooth from different individuals. The larger the proportion of pits and fissures to the occlusal surface area, the more difficult it is to clean. Conversely, the smaller the proportion of pits and fissures to the occlusal surface area, the easier it is to clean.

[0097] Therefore, in the embodiment of the present disclosure, the area ratio of the pits and fissures to the occlusal surface of the teeth can be used as the third cleaning parameter, which can reflect the quality of the tooth cleaning effect to a certain extent.

[0098] In some embodiments, a second clean image including the occlusal surfaces of each tooth on the dental cast can be pre-taken, and then based on image detection technology, the area of ​​the occlusal surface and the area of ​​the pits and fissures included in the image can be determined. Then, based on the ratio of the pit and fissure area to the area of ​​the occlusal surface, the third cleaning parameter can be calculated. It should be understood that the second clean image of the occlusal surface of the tooth here can also reuse the dental cast image described in the aforementioned embodiment, that is, the area size of the occlusal surface and pits and fissures of the dental cast can be determined in the aforementioned dental cast image. This process is described in the following embodiments of the present disclosure and will not be described in detail here.

[0099] S540: Determine a detection result of the toothbrush to be tested based on the first cleaning parameter, the second cleaning parameter, and the third cleaning parameter.

[0100] In combination with the foregoing, it can be seen that in the embodiments of the present disclosure, the first cleaning parameter is the difference in dental plaque before and after cleaning, obtained using computer vision technology, which can reflect the cleaning power of the toothbrush under test on dental stains. The second cleaning parameter is a parameter obtained based on the cleaning degree of the occlusal surface of the teeth, which can reflect the ability of the toothbrush under test 100 to prevent or alleviate pit and fissure caries. The third cleaning parameter is a parameter obtained based on the area ratio of pits and fissures to the occlusal surface of the teeth, which can reflect the difficulty of the tooth cleaning process.

[0101] Therefore, the first cleaning parameter, the second cleaning parameter and the third cleaning parameter obtained from the above three positions can be integrated to determine the test results for the toothbrush to be tested. The test results can objectively and effectively reflect the ability and effect of the toothbrush to be tested in preventing pit and fissure caries and cleaning the oral cavity.

[0102] In some embodiments, a coefficient value can be set for each parameter in advance, and then the first cleaning parameter, the second cleaning parameter, and the third cleaning parameter can be fused to obtain a test result for the toothbrush to be tested. This disclosure will explain this below and will not be described in detail here.

[0103] It is worth noting that in the embodiment of the present disclosure, a test result M for the toothbrush to be tested is defined. The test result M can not only reflect the oral cleaning ability of the toothbrush to be tested, but also, by introducing the cleaning degree of the occlusal surface of the teeth, the test result M can reflect the ability of the toothbrush to be tested to prevent or alleviate pit and fissure caries. This is of guiding significance for the formulation of oral medicine industry standards and users' selection of oral hygiene appliances.

[0104] For example, in one example, a toothbrush manufacturer or a third-party testing agency can use the toothbrush testing method of the embodiment of the present disclosure to test the test result M of each gear of each toothbrush or electric toothbrush, and mark the test result M on the product for sale. On the one hand, the manufacturer can optimize the product in a closed loop according to the test result M during the research and development stage to prevent substandard products from entering the market and bringing uncontrollable risks to the corporate brand. On the other hand, when consumers purchase toothbrush products, they can also intuitively understand the product's prevention and relief effects on pit and fissure caries through the marked test result M and choose the appropriate product.

[0105] As can be seen from the above, in the disclosed embodiments, the cleaning ability of the toothbrush under test is quantified through test results. Furthermore, by introducing a measure of the degree of cleaning specific to the occlusal surface, the test results can reflect the toothbrush's ability to prevent or alleviate pit and fissure caries, providing objective and efficient guidance to manufacturers and users, and promoting the healthy development of the industry. Furthermore, in the disclosed embodiments, the entire testing process is standardized, eliminating the need for long-term observation tests on a large number of subjects, improving testing efficiency and reducing testing costs.

[0106] In the above embodiment, the cleaning ability of the toothbrush under test is objectively quantified and evaluated by combining the above three dimensions. In some embodiments, the cleaning time can be further combined with the above three dimensions to determine a fourth cleaning parameter based on the time dimension. The first to fourth cleaning parameters are then combined to obtain the test result of the toothbrush under test, as illustrated below with reference to FIG7 .

[0107] As shown in FIG. 7 , in some embodiments, the toothbrush detection method of the present disclosure includes steps S710 to S720 .

[0108] S710: Determine a fourth cleaning parameter of the toothbrush to be tested based on the time taken by the toothbrush to be tested to clean the occlusal surface of each tooth in the dental mold during the cleaning process.

[0109] S720: Determine a test result of the toothbrush to be tested based on the first cleaning parameter, the second cleaning parameter, the third cleaning parameter, and the fourth cleaning parameter.

[0110] In the cleaning process of the aforementioned embodiment of Figure 6, the cleaning time of the toothbrush to be tested on the dental mold is a preset time. The preset time can be predetermined by the staff based on experience. For example, the real human brushing habits can be simulated. Generally, the brushing time is 2 minutes, so the preset time can be set to 2 minutes. That is, the robot 300 will control the toothbrush to be tested 100 to clean the dental mold 200 for 2 minutes.

[0111] It is understandable that the cleaning time will also affect the cleaning effect of the toothbrush. Generally speaking, the longer the toothbrush cleans the occlusal surface of each tooth, the better the cleaning effect. Therefore, in the embodiments of the present disclosure, the time dimension can be further considered, and the cleaning time of each tooth's occlusal surface during the cleaning process can be used as the fourth cleaning parameter. The final detection result is obtained based on this fourth cleaning parameter and the aforementioned first cleaning parameter, second cleaning parameter, and third cleaning parameter.

[0112] In some embodiments, the cleaning time for each tooth during the cleaning process can be determined based on the total duration of the cleaning process and the number of teeth being cleaned, combined with the number of teeth that the bristles of the toothbrush to be tested can cover at the same time, and then the cleaning time can be used as the fourth cleaning parameter.

[0113] In the following of this disclosure, the method process of determining the first to fourth cleaning parameters is described respectively.

[0114] As shown in FIG. 8 , in some embodiments, the toothbrush detection method of the present disclosure, the process of determining the first cleaning parameter includes steps S810 to S840 .

[0115] S810: Acquire a first dental cast image before the toothbrush to be tested cleans the dental cast, and a second dental cast image after the toothbrush to be tested cleans the dental cast.

[0116] 6 , after the dental cast is coated with attachments simulating dental plaque, the image acquisition device 400 may first be used to capture one or more first dental cast images A of the dental cast 200 . The first dental cast image A represents the image before the dental cast is cleaned.

[0117] After acquiring the first dental model image A, the controller 500 can control the manipulator 300 to clamp the toothbrush 100 to be tested and perform a cleaning operation on the dental model 200.

[0118] In one example, the toothbrush 100 to be tested can use the Bass brushing method to clean the dental mold 200. The Bass brushing method is an internationally recognized method of brushing teeth. When cleaning the tongue (jaw) side and the lip (cheek) side, the Bass brushing method requires the toothbrush to be tilted 45° to contact the teeth, and when cleaning the occlusal surface, the toothbrush needs to be kept 90° perpendicular to the occlusal surface. There are also detailed regulations for the direction and order of brushing teeth. Those skilled in the art can undoubtedly understand this by referring to relevant technologies, and this disclosure will not elaborate on it.

[0119] In order to enable the toothbrush 100 to be tested to implement the Bass brushing method, the manipulator 300 can be pre-programmed based on the action behavior of the Bass brushing method, so that the controller 500 can control the manipulator 300 to clamp the toothbrush 100 to be tested and perform cleaning operations on the dental mold 200 according to the preset action method specified by the Bass brushing method.

[0120] At the same time, the preset duration of the cleaning operation can also be standardized with corresponding regulations. For example, in one example, the preset duration of the cleaning operation can be defined as 1 minute, 3 minutes, or 5 minutes, and the present disclosure does not impose any restrictions on this.

[0121] In the embodiment of the present disclosure, after controlling the manipulator 300 to clamp the toothbrush 100 to be tested and complete the cleaning operation on the dental mold 200, the dental mold 200 can be rinsed with water to wash away the dental plaque adsorbed on the gums, and then dried for 1 minute to 5 minutes.

[0122] After the above-mentioned cleaning operation is completed, the image acquisition device 400 can capture another dental model image of the dental model 200, that is, the second dental model image B. It can be understood that the first dental model image A refers to the image of the dental model 200 before cleaning, and the second dental model image B refers to the image of the dental model 200 after cleaning. The difference between the first dental model image A and the second dental model image B can reflect the cleaning effect.

[0123] In some embodiments, after acquiring the first dental model image A and the second dental model image B, the tooth region may be cut out based on image processing technology, retaining only the tooth region in the image, thereby removing irrelevant background to improve calculation accuracy.

[0124] For example, Figure 9 shows the resulting image after the second dental model image B has been subjected to the cutout processing. The second dental model image B only includes the tooth region, with all other irrelevant areas removed. It should be understood that the image shown in Figure 9 has been grayscale processed to meet the requirements of the accompanying figure. In reality, the tooth and attachment regions in Figure 9 could also be color regions.

[0125] Combined with the above, it can be seen that in the second dental model image shown in Figure 9, it includes the occlusal surface area and the non-occlusal surface area. For example, the area in the white dotted box in Figure 9 is the occlusal surface M3, and the part outside the occlusal surface M3 is the non-occlusal surface area. The occlusal surface M3 includes pits and fissures. For example, the area shown in the black solid line in Figure 9 is the pits and fissures.

[0126] S820: Determine a first area of ​​an uncleaned region on the dental cast based on the first dental cast image.

[0127] S830: Determine a second area of ​​the cleaning region on the dental cast based on the second dental cast image.

[0128] In some embodiments, the first dental model image A and the second dental model image B may be processed using Image J image processing.

[0129] For example, image processing can be performed on the first dental model image A to determine a first area, area_1, of the uncleaned area on the image. Image processing can then be performed on the second dental model image B to determine a second area, area_2, of the cleaned area on the image. The second area, area_2, of the cleaned area represents the area of ​​the debris removed during the cleaning process.

[0130] S840: Determine a first cleaning parameter based on the ratio of the second area to the first area.

[0131] It can be understood that the first area area_1 represents the area of ​​the uncleaned area on the tooth, and the second area area_2 represents the area of ​​the cleaned area, so the ratio of the second area to the first area (area_2 / area_1) can reflect the cleaning effect on attachments during the cleaning process.

[0132] When the first area of ​​the uncleaned area, area_1, remains constant, a larger ratio indicates a larger second area, area_2, of the cleaned attachments. This indicates a better cleaning effect on the attachments, which in turn reflects a stronger cleaning power of the toothbrush against residual plaque on the teeth. Conversely, a smaller ratio indicates a smaller second area, area_2, of the cleaned attachments. This indicates a poorer cleaning effect on the attachments, which in turn reflects a weaker cleaning power of the toothbrush against residual plaque on the teeth. Therefore, in the disclosed embodiments, the ratio of the second area to the first area (area_2 / area_1) can be used as the first cleaning parameter K.

[0133] Of course, those skilled in the art will understand that the calculation process of the first cleaning parameter K is not limited to the above-mentioned Image J image processing method, and other suitable image processing methods can also be used, such as a neural network model based on deep learning to predict the first cleaning parameter K. Those skilled in the art can understand and fully implement it by referring to relevant technologies, and the present disclosure does not limit this.

[0134] As shown in FIG. 10 , in some embodiments, the toothbrush detection method of the present disclosure, the process of determining the first cleaning parameter, includes steps S1010 to S1020 .

[0135] S1010: Obtain a first weight of the dental cast before the toothbrush to be tested cleans the dental cast, and a second weight of the dental cast after the toothbrush to be tested cleans the dental cast.

[0136] S1020: Determine a first cleaning parameter based on a difference between the first weight and the second weight.

[0137] It can be understood that in the process of determining the first cleaning parameter by using image detection, the tooth area with obvious color difference has better detection accuracy, while there may be certain detection errors for lighter color changes.

[0138] For example, in an example scenario, the attachments at a certain position of the dental mold 200 are relatively thick. Although a part of the attachments at this position are cleaned during the cleaning process, they are not completely removed, so the color difference before and after cleaning is small. Therefore, the image detection will determine that this position belongs to the uncleaned area, but in fact, a part of the attachments at this position has been removed, thereby introducing an error.

[0139] Therefore, in some embodiments of the present disclosure, after the dental cast 200 is placed with attachments and dried and allowed to stand, the dental cast 200 may be weighed for the first time to obtain a first weight P1. Then, after the dental cast 200 is cleaned through the above process, it may be rinsed and dried, and weighed for a second time to obtain a second weight P2.

[0140] It can be understood that the difference between the first weight P1 and the second weight P2 represents the weight of the attached matter removed during the cleaning process, so the difference between the first weight P1 and the second weight P2 can be determined as the first cleaning parameter K.

[0141] As shown in FIG. 11 , in some embodiments, the toothbrush detection method of the present disclosure, the process of determining the first cleaning parameter, includes steps S1110 to S1130 .

[0142] S1110 : Determine the cleaning visual parameters of the toothbrush to be tested based on the dental mold images before and after the toothbrush to be tested cleans the dental mold.

[0143] S1120: Determine a cleaning effect parameter of the toothbrush to be tested based on a weight change of the dental mold before and after the toothbrush to be tested cleans the dental mold.

[0144] S1130 : Determine a first cleaning parameter based on the cleaning visual parameter and the cleaning effect parameter.

[0145] In some embodiments of the present disclosure, the above-mentioned image detection and weight change process may be combined to calculate the first cleaning parameter K.

[0146] For example, first, referring to the embodiment of FIG8 , the cleaning visual parameter Q1 can be determined by analyzing the images before and after cleaning. Then, referring to the embodiment of FIG10 , the cleaning effect parameter Q2 can be determined by the weight change before and after cleaning.

[0147] Afterwards, the cleaning visual parameter and the cleaning effect parameter can be fused to calculate the first cleaning parameter K, which is expressed as: K = α*Q1+θ*Q2. Wherein, α and θ represent fusion coefficients, and their specific values ​​can be selected as needed and are not limited by the present disclosure.

[0148] From the above, it can be seen that in the embodiment of the present disclosure, when calculating the first cleaning parameter K, weight changes and visual changes are fully considered to eliminate or reduce calculation errors and improve detection accuracy.

[0149] As shown in FIG. 12 , in some embodiments, the toothbrush detection method of the present disclosure, the process of determining the first cleaning parameter, includes steps S1210 to S1220 .

[0150] S1210: Collect a first cleaned image of the occlusal surface of the dental mold after the dental mold is cleaned by the toothbrush to be tested.

[0151] S1220: Determine a second cleaning parameter of the toothbrush to be tested based on the first cleaning image and a preset corresponding relationship.

[0152] As can be seen from the foregoing, the second cleaning parameter Z is used to reflect the pit and fissure caries prevention or alleviation capability of the toothbrush 100 to be tested. Therefore, the second cleaning parameter Z focuses on the evaluation of the cleaning effect on the occlusal surface of the teeth.

[0153] As shown in FIG3 , after the dental cast 200 is cleaned through the aforementioned process, an image capture device 400 can be used to capture an image of the cleaned dental cast, such as the aforementioned second dental cast image B. Since this process only targets the occlusal surface of the teeth, to reduce the computational complexity of image processing, image detection can be performed on the aforementioned second dental cast image B, and only a portion of the image of the occlusal surface can be cropped to obtain a first cleaned image C of the occlusal surface. For example, in the second dental cast image shown in FIG9 , only the image area of ​​the occlusal surface M3, indicated by the white dashed line, can be cropped. This allows the aforementioned dental cast image to be reused to calculate the second cleaning parameter Z, eliminating the need for repeated image capture.

[0154] In some embodiments, the detection, recognition and image cropping of the tooth occlusal surface can be achieved through a neural network model based on deep learning, which will not be described in detail in this disclosure.

[0155] It is worth noting that, in the embodiment of the present disclosure, for quantifying the cleaning effect of the tooth occlusal surface, a correspondence relationship needs to be pre-set, and the correspondence relationship includes the correspondence between the cleaning degree of the tooth occlusal surface and the second cleaning parameter Z. For example, in an example, the correspondence relationship can be shown in the following Table 1: Table 1

[0156] Through the correspondence shown in Table 1 above, after obtaining the first clean image C of the occlusal surface of the tooth mold 200, the image of the occlusal surface can be feature analyzed to determine the plaque area based on image processing technology, and then the corresponding second cleaning parameter Z can be determined through the correspondence described in Table 1.

[0157] For example, in one example, image processing is performed based on the first cleaning image C to determine that the dental plaque coverage area at the pit and fissure position is less than 1 / 5. By comparing the corresponding relationship described in Table 1, the second cleaning parameter Z=1 can be determined.

[0158] Of course, those skilled in the art will appreciate that the correspondence between the degree of cleanliness of the occlusal surface of the tooth and the second cleaning parameter Z is not limited to the example in Table 1. Those skilled in the art can independently set it according to specific scenarios, and this disclosure will not elaborate on this. In addition, the process of image processing for the first clean image C can undoubtedly be understood and fully implemented by those skilled in the art with reference to relevant technologies, and this disclosure will also not elaborate on this.

[0159] As shown in FIG. 13 , in some embodiments, the toothbrush detection method of the present disclosure, in which the process of determining the third cleaning parameter D of the toothbrush to be tested, includes steps S1310 to S1330 .

[0160] S1310. During the cleaning process of the toothbrush to be tested on the dental mold, a second cleaning image of the toothbrush to be tested and the occlusal surface of the dental mold is obtained.

[0161] In combination with the foregoing, it can be seen that the third cleaning parameter D refers to the ratio of the pits and fissures on the dental model to the occlusal surface of the tooth. In some embodiments of the present disclosure, the third cleaning parameter D can be determined using image detection technology.

[0162] For example, as shown in FIG3 , before the dental mold 200 is cleaned, an image of the dental mold can be captured using the image capture device 400 , and the image is the second cleaning image.

[0163] It can be understood that the second clean image needs to include the occlusal surface of each tooth. Therefore, in order to clearly display the occlusal surface of each tooth on the dental mold 200, the dental mold 200 can be placed as shown in Figure 1, and then the second clean image captured by the image acquisition device 400 is as shown in Figure 1, so that the occlusal surface of each molar can be clearly presented.

[0164] S1320: Determine a third area of ​​the occlusal surface of the teeth and a fourth area of ​​the pits and fissures in the image based on the second cleaned image.

[0165] In the disclosed embodiments, image detection technology can be used to perform image detection on the second clean image. First, a first pixel region of the occlusal surface of each molar included in the image can be determined. By counting the number of pixels within the first pixel region, the total area of ​​the occlusal surface included in the image can be obtained. This area is the third area, area_3, of the occlusal surface. For example, referring to FIG. 9 , image detection technology can be used to identify the occlusal surface region M3 in the image. Then, the area of ​​the occlusal surface M3 can be counted to obtain the third area, area_3.

[0166] Next, image detection is performed on the first pixel region of the tooth's occlusal surface to determine the second pixel region of each pit and groove on the occlusal surface. By counting the number of pixels within the second pixel region, the total area of ​​the pits and grooves in the image is obtained. This area is the fourth area of ​​the pits and grooves, area_4. For example, referring to FIG. 9 , image detection technology can be used to further identify pit and groove region M4 in the image, and then the area of ​​pit and groove M4 can be counted to obtain the fourth area, area_4.

[0167] In some embodiments, the above-described image detection process can be implemented using an image detection model based on a deep neural network (DNN). For example, an image detection model for detecting the occlusal surface and pits and fissures can be pre-trained. After acquiring a second clean image, the second clean image is input into the pre-trained image detection model to obtain the image detection model's predicted outputs of the third area of ​​the occlusal surface, area_3, and the fourth area of ​​the pits and fissures, area_4. Those skilled in the art will undoubtedly understand and fully implement this process, and this disclosure will not elaborate further.

[0168] S1330: Determine a third cleaning parameter based on a ratio of the fourth area to the third area.

[0169] In the embodiment of the present disclosure, combined with the foregoing, it can be seen that the third cleaning parameter D refers to the area ratio of the pits and fissures on the dental model to the occlusal surface of the tooth. Therefore, after determining the third area area_3 of the occlusal surface of the tooth and the fourth area area_4 of the pits and fissures, the third cleaning parameter D can be calculated using the ratio of the two, expressed as: D = area_4 / area_3.

[0170] As shown in FIG. 14 , in some embodiments, the toothbrush detection method of the present disclosure, the process of determining the fourth cleaning parameter T, includes steps S1410 to S1430 .

[0171] S1410: Obtain the total duration of the cleaning process of the toothbrush to be tested on the dental mold, and the second number of teeth cleaned.

[0172] S1420: Determine the cleaning time for the occlusal surface of each tooth during the cleaning process based on the total time, the second number, and the third number of teeth simultaneously covered by the brush filaments of the toothbrush to be tested.

[0173] S1430: Determine a fourth cleaning parameter based on the cleaning time.

[0174] In the embodiment of the present disclosure, combined with the above, it can be seen that the fourth cleaning parameter T reflects the cleaning time of the occlusal surface of each tooth during the cleaning process. The total cleaning time T0 is also the preset time, and its value can be predetermined by the staff.

[0175] As shown in Figure 2 , each tooth has multiple cleaning surfaces. For example, molars have three cleaning surfaces: the labial (cheek) side M1, the lingual (palatal) side M2, and the occlusal surface M3. The remaining teeth have the labial (cheek) side M1 and the lingual (palatal) side M2, but no occlusal surface M3. A standard dental model 200 includes 28 teeth, each of which has 28 lingual (palatal) sides, 28 labial (cheek) sides, and 16 occlusal surfaces.

[0176] In the embodiment of the present disclosure, the number of surfaces for cleaning teeth refers to the total number of cleaning surfaces of all teeth that need to be cleaned. For example, when all 28 teeth on the dental model are cleaned, the number of surfaces for cleaning teeth is: 28+28+16=72.

[0177] Understandably, assuming that the toothbrush head can only brush one cleaning surface at a time during brushing, the cleaning time for each cleaning surface is calculated as: total brushing time divided by the number of teeth cleaned. However, in reality, the toothbrush head can often cover multiple cleaning surfaces simultaneously during brushing. Therefore, the cleaning time for each cleaning surface needs to be combined with the number of teeth simultaneously covered by the toothbrush filaments under test.

[0178] In the disclosed embodiment, the total duration of the cleaning process is defined as T0, the number of tooth surfaces being cleaned is K2, and the number of teeth that the toothbrush can simultaneously cover is a third number K3. Therefore, the cleaning time per tooth is expressed as: T = T0 / K2 * K3. This cleaning time T can be determined as the fourth cleaning parameter T.

[0179] For example, in one example, the total duration of the cleaning process is T0 = 2 min, the number of tooth surfaces to be cleaned is K2 = 72, and the number of teeth that the bristles of the toothbrush to be tested can cover at the same time is the third number K3 = 3, then the fourth cleaning parameter T = (2*60) / 72*3 = 5s.

[0180] Through the above process, the first cleaning parameter K, the second cleaning parameter Z, the third cleaning parameter D, and the fourth cleaning parameter T are respectively obtained. In some embodiments, in the process of respectively determining the first cleaning parameter K, the second cleaning parameter Z, the third cleaning parameter D, and the fourth cleaning parameter T, the data accuracy can be improved by taking the average value through multiple calculations. Those skilled in the art will understand that this disclosure will not elaborate on this.

[0181] After obtaining the first cleaning parameter K, the second cleaning parameter Z, the third cleaning parameter D, and the fourth cleaning parameter T, the detection result M can be calculated based on the first cleaning parameter K, the second cleaning parameter Z, the third cleaning parameter D, and the fourth cleaning parameter T, which can be expressed as: y(M) = f(K, Z, -D, T) (1)

[0182] In formula (1), f() represents a fusion function, which is used to perform fusion calculation on the first cleaning parameter K, the second cleaning parameter Z, the third cleaning parameter D, and the fourth cleaning parameter T.

[0183] In some embodiments, corresponding preset coefficient values ​​may be set in advance for the first cleaning parameter K, the second cleaning parameter Z, the third cleaning parameter D, and the fourth cleaning parameter T. Then, the first cleaning parameter K, the second cleaning parameter Z, the third cleaning parameter D, and the fourth cleaning parameter T are fused according to the preset coefficient values ​​to obtain a detection result M, which can be expressed as: y(M) = αK + βZ - ωD + θT (2)

[0184] In formula (2), α represents the coefficient value corresponding to the first cleaning parameter K, β represents the coefficient value corresponding to the second cleaning parameter Z, ω represents the coefficient value corresponding to the third cleaning parameter D, and θ represents the coefficient value corresponding to the fourth cleaning parameter T. In the embodiments of the present disclosure, there is no limitation on the specific values ​​of α, β, ω, and θ. Those skilled in the art can select corresponding coefficient values ​​according to specific application scenarios, and this disclosure will not elaborate on this.

[0185] As can be seen from the above, in the disclosed embodiments, the cleaning ability of the toothbrush under test is quantified through test results. Furthermore, by introducing a measure of the degree of cleaning specific to the occlusal surface, the test results can reflect the toothbrush's ability to prevent or alleviate pit and fissure caries, providing objective and efficient guidance to manufacturers and users, and promoting the healthy development of the industry. Furthermore, in the disclosed embodiments, the entire testing process is standardized, eliminating the need for long-term observation tests on a large number of subjects, improving testing efficiency and reducing testing costs.

[0186] It is worth noting that electric toothbrushes nowadays often have multiple cleaning gears according to different functions, and different cleaning gears have different cleaning effects. Therefore, when the toothbrush 100 to be tested is an electric toothbrush, the method described above in the present disclosure can be specifically used to test each cleaning gear of the electric toothbrush.

[0187] That is, as shown in FIG3 , when the robot arm 300 holds the toothbrush 100 to be tested and cleans the dental mold 200, the toothbrush 100 can be adjusted to a specific cleaning position, so that the test result M obtained through the above method is the test result for that cleaning position. If further testing of other cleaning positions is required, the above method can be repeated by simply changing the cleaning position of the electric toothbrush. This disclosure will not further elaborate on this.

[0188] From the above, it can be seen that in the embodiment of the present disclosure, not only the cleaning effect of the toothbrush can be tested, but also different cleaning gears of the toothbrush can be further tested. The test results are objective, effective, highly instructive, and promote the healthy development of the industry.

[0189] In some embodiments, the present disclosure provides a toothbrush detection device, which can be applied to the detection system shown in FIG. 3 .

[0190] As shown in Figure 15, in some embodiments, the toothbrush detection device of the example of the present disclosure includes: a first determination module 10, configured to determine the first cleaning parameter of the toothbrush to be tested based on the difference between the toothbrush to be tested before and after cleaning the dental mold; a second determination module 20, configured to determine the second cleaning parameter of the toothbrush to be tested based on the degree of cleaning of the occlusal surface of the dental mold by the toothbrush to be tested; a third determination module 30, configured to determine the third cleaning parameter of the toothbrush to be tested based on the proportion of pits and fissures on the occlusal surface of the tooth to the occlusal surface of the tooth; and a result determination module 40, configured to determine the detection result of the toothbrush to be tested based on the first cleaning parameter, the second cleaning parameter and the third cleaning parameter.

[0191] As can be seen from the above, in the disclosed embodiments, the cleaning ability of the toothbrush under test is quantified through test results. Furthermore, by introducing a measure of the degree of cleaning specific to the occlusal surface, the test results can reflect the toothbrush's ability to prevent or alleviate pit and fissure caries, providing objective and efficient guidance to manufacturers and users, and promoting the healthy development of the industry. Furthermore, in the disclosed embodiments, the entire testing process is standardized, eliminating the need for long-term observation tests on a large number of subjects, improving testing efficiency and reducing testing costs.

[0192] In some embodiments, the toothbrush detection device described in the present disclosure also includes a fourth determination module, which is configured to: determine the fourth cleaning parameter of the toothbrush to be tested based on the time when the toothbrush to be tested cleans the occlusal surface of each tooth in the dental mold during the cleaning process; the result determination module is configured to: determine the detection result of the toothbrush to be tested based on the first cleaning parameter, the second cleaning parameter, the third cleaning parameter and the fourth cleaning parameter.

[0193] In some embodiments, the first determination module 10 is configured to: obtain a first dental cast image before the toothbrush to be tested cleans the dental cast, and a second dental cast image after the toothbrush to be tested cleans the dental cast; determine a first area of ​​an uncleaned area on the dental cast based on the first dental cast image; determine a second area of ​​a cleaned area on the dental cast based on the second dental cast image; and determine the first cleaning parameter based on a ratio of the second area to the first area.

[0194] In some embodiments, the first determination module 10 is configured to: obtain a first weight of the dental cast before the toothbrush to be tested cleans the dental cast, and a second weight of the dental cast after the toothbrush to be tested cleans the dental cast; and determine the first cleaning parameter based on the difference between the first weight and the second weight.

[0195] In some embodiments, the first determination module 10 is configured to: determine the cleaning visual parameters of the toothbrush to be tested based on the dental mold image before and after the toothbrush to be tested cleans the dental mold; determine the cleaning effect parameters of the toothbrush to be tested based on the weight change of the dental mold before and after the toothbrush to be tested cleans the dental mold; determine the first cleaning parameters based on the cleaning visual parameters and the cleaning effect parameters.

[0196] In some embodiments, the second determination module 20 is configured to: collect a first cleaning image of the occlusal surface of the dental mold after the toothbrush to be tested cleans the dental mold; determine the second cleaning parameter of the toothbrush to be tested based on the first cleaning image and a pre-set correspondence, wherein the correspondence includes a correspondence between the degree of cleaning of the occlusal surface of the tooth and the second cleaning parameter.

[0197] In some embodiments, the third determination module 30 is configured to: obtain the second cleaning image, which includes the image of the occlusal surface of the tooth of the dental mold; determine the third area of ​​the occlusal surface of the tooth on the image and the fourth area of ​​the pits and fissures based on the second cleaning image; and determine the third cleaning parameter based on the ratio of the fourth area to the third area.

[0198] In some embodiments, the fourth determination module is configured to: obtain the total duration of the cleaning process of the toothbrush to be tested on the dental model, and the number of surfaces of the teeth cleaned; determine the cleaning time of the occlusal surface of each tooth during the cleaning process based on the total duration, the number of surfaces and the third number of teeth simultaneously covered by the brush wire of the toothbrush to be tested; and determine the fourth cleaning parameter based on the cleaning time.

[0199] In some embodiments, the toothbrush cleaning device described in the present disclosure further includes a cleaning module, which is configured to: apply attachments to a preset tooth area of ​​the dental mold, wherein the preset tooth area includes the occlusal surface area of ​​multiple teeth; control the robotic arm to clamp the toothbrush to be tested and clean the preset tooth area of ​​the dental mold in a preset action manner for a preset time.

[0200] As can be seen from the above, in the disclosed embodiments, the cleaning ability of the toothbrush under test is quantified through test results. Furthermore, by introducing a measure of the degree of cleaning specific to the occlusal surface, the test results can reflect the toothbrush's ability to prevent or alleviate pit and fissure caries, providing objective and efficient guidance to manufacturers and users, and promoting the healthy development of the industry. Furthermore, in the disclosed embodiments, the entire testing process is standardized, eliminating the need for long-term observation tests on a large number of subjects, improving testing efficiency and reducing testing costs.

[0201] In some embodiments, the present disclosure provides a toothbrush detection system, which may be as shown in FIG3 above, and will not be described in detail in the present disclosure.

[0202] In some embodiments, the present disclosure provides a storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method according to any of the aforementioned embodiments.

[0203] Specifically, FIG16 shows a schematic structural diagram of a system 600 suitable for implementing the method disclosed herein. Through the structure shown in FIG16 , the corresponding functions of the above-mentioned controller and storage medium can be realized.

[0204] As shown in FIG16 , the system 600 includes a processor 601 that can perform various appropriate actions and processes according to a program stored in a memory 602 or a program loaded from a storage unit 608 into the memory 602. Various programs and data required for the operation of the system 600 are also stored in the memory 602. The processor 601 and the memory 602 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0205] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, and the like; an output section 607 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 608 including a hard disk; and a communication section 609 including a network interface card such as a LAN card or a modem. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 610 as needed, so that computer programs read therefrom can be installed into the storage section 608 as needed.

[0206] In particular, according to embodiments of the present disclosure, the above method process can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program comprising program code for executing the above method. In such embodiments, the computer program can be downloaded and installed from a network via the communication portion 609 and / or installed from a removable medium 611.

[0207] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0208] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the embodiments. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present disclosure.

Claims

1. A toothbrush detection method, comprising: Determining a first cleaning parameter of the toothbrush to be tested based on the difference between the toothbrush to be tested and the tooth model to be cleaned before and after; Determining a second cleaning parameter of the toothbrush to be tested based on the cleaning degree of the tooth occlusal surface of the dental model by the toothbrush to be tested; Determining a third cleaning parameter of the toothbrush to be tested based on the proportion of the specific portion of the tooth occlusal surface to the tooth occlusal surface; The detection result of the toothbrush to be tested is determined according to the first cleaning parameter, the second cleaning parameter and the third cleaning parameter.

2. The method according to claim 1, further comprising: determining a fourth cleaning parameter of the toothbrush to be tested based on the time taken by the toothbrush to be tested to clean the occlusal surface of each tooth of the dental model during the cleaning process; The step of determining the detection result of the toothbrush to be tested according to the first cleaning parameter, the second cleaning parameter and the third cleaning parameter comprises: The detection result of the toothbrush to be tested is determined according to the first cleaning parameter, the second cleaning parameter, the third cleaning parameter and the fourth cleaning parameter.

3. The method according to claim 1, wherein: The determining of the first cleaning parameter of the toothbrush to be tested based on the difference between the toothbrush to be tested and the tooth model to be cleaned before and after the toothbrush to be tested cleans the tooth model comprises: Acquire a first dental mold image before the dental mold is cleaned by the toothbrush to be tested, and a second dental mold image after the dental mold is cleaned by the toothbrush to be tested; determining a first area of ​​an uncleaned region on the dental model based on the first dental model image; determining a second area of ​​the cleaning region on the dental model based on the second dental model image; The first cleaning parameter is determined based on a ratio of the second area to the first area.

4. The method according to claim 1, wherein: The determining of the first cleaning parameter of the toothbrush to be tested based on the difference between the toothbrush to be tested and the tooth model to be cleaned before and after the toothbrush to be tested cleans the tooth model comprises: Acquire a first weight of the dental mold before the toothbrush to be tested cleans the dental mold, and a second weight of the dental mold after the toothbrush to be tested cleans the dental mold; The first cleaning parameter is determined based on a difference between the first weight and the second weight.

5. The method according to any one of claims 1 to 4, wherein: The determining of the first cleaning parameter of the toothbrush to be tested based on the difference between the toothbrush to be tested and the tooth model to be cleaned before and after the toothbrush to be tested cleans the tooth model comprises: Determining the cleaning visual parameters of the toothbrush to be tested based on the dental mold images before and after the dental mold is cleaned by the toothbrush to be tested; Determining a cleaning effect parameter of the toothbrush to be tested based on a weight change of the dental mold before and after the toothbrush to be tested cleans the dental mold; Based on the cleaning visual parameter and the cleaning effect parameter, the first cleaning parameter is determined.

6. The method according to claim 1, wherein: The step of determining a second cleaning parameter of the toothbrush to be tested based on the cleaning degree of the tooth occlusal surface of the dental model by the toothbrush to be tested comprises: Collecting a first cleaning image of the tooth occlusal surface of the dental mold after the dental mold is cleaned by the toothbrush to be tested; Based on the first cleaning image and a preset corresponding relationship, the second cleaning parameter of the toothbrush to be tested is determined, and the corresponding relationship includes a corresponding relationship between the cleaning degree of the occlusal surface of the teeth and the second cleaning parameter.

7. The method according to claim 1, wherein: The specific part includes the pits and fissures on the occlusal surface of the tooth, and the third cleaning parameter of the toothbrush to be tested is determined based on the proportion of the specific part on the occlusal surface of the tooth to the occlusal surface of the tooth, including: Acquire the second clean image, where the second clean image includes an image of the tooth occlusal surface of the dental model; Determine a third area of ​​the occlusal surface of the tooth and a fourth area of ​​the pits and fissures on the image based on the second cleaned image; The third cleaning parameter is determined based on a ratio of the fourth area to the third area.

8. The method according to claim 2, wherein: The determining of the fourth cleaning parameter of the toothbrush to be tested based on the time taken by the toothbrush to be tested to clean the occlusal surface of each tooth of the dental model during the cleaning process includes: Obtaining the total duration of the cleaning process of the toothbrush to be tested on the tooth model, and the number of surfaces of the teeth cleaned; Determine the cleaning time of the occlusal surface of each tooth during the cleaning process based on the total time, the number of surfaces, and a third number of teeth simultaneously covered by the brush wires of the toothbrush to be tested; The fourth cleaning parameter is determined based on the cleaning time.

9. The method according to any one of claims 1 to 4, wherein: The cleaning process of the toothbrush to be tested on the dental model includes: Arranging attachments in a preset tooth area of ​​the dental model, wherein the preset tooth area includes an occlusal surface area of ​​a plurality of teeth; The robot arm is controlled to clamp the toothbrush to be tested and clean the preset tooth area of ​​the dental model in a preset manner for a preset time.

10. A toothbrush detection system, comprising: A manipulator, used for holding the toothbrush to be tested and driving the toothbrush to be tested to perform a cleaning operation; Image acquisition equipment; as well as A controller comprises a processor and a memory, wherein the memory stores computer instructions, and the computer instructions are used to enable the processor to execute the method according to any one of claims 1 to 9.

11. A toothbrush detection device, comprising: A first determination module is configured to determine a first cleaning parameter of the toothbrush to be tested based on the difference between the toothbrush to be tested and the tooth mold before and after cleaning; A second determination module is configured to determine a second cleaning parameter of the toothbrush to be tested based on the cleaning degree of the toothbrush to be tested on the tooth occlusal surface of the dental model; A third determination module is configured to determine a third cleaning parameter of the toothbrush to be tested based on the proportion of the specific part of the tooth occlusal surface to the tooth occlusal surface; The result determination module is configured to determine the detection result of the toothbrush to be tested according to the first cleaning parameter, the second cleaning parameter and the third cleaning parameter.

12. A storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 9.

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