Contact lens automatic detection method and system

Abstract

The invention is applicable to the technical field of contact lens detection, and particularly relates to an automatic detection method and system for contact lenses, and the method comprises the following steps: determining a plurality of manufacturing devices for manufacturing the contact lenses; under the condition that each piece of manufacturing equipment operates, monitoring operation data of each piece of manufacturing equipment in real time; determining the state data of each manufacturing device based on the operation state indicated by the operation data of each manufacturing device; determining to-be-detected data according to the abnormal state of the manufacturing equipment reflected by the state data of each manufacturing equipment; wherein the to-be-detected data is used for reflecting the to-be-detected contact lens and the to-be-detected part of the contact lens; and controlling a plurality of detection devices to detect the to-be-detected contact lenses and the to-be-detected parts of the contact lenses based on the to-be-detected data. According to the contact lens automatic detection method provided by the invention, the detection result can be prevented from being easily influenced by human factors, tiny defects are prevented from being easily ignored due to fatigue, and the detection efficiency is further improved.

Description

Technical Field

[0001] The present application belongs to the field of contact lens detection technology, and in particular relates to an automated contact lens detection method and system. Background Art

[0002] Contact lenses, also known as contact lenses, are lenses that attach directly to the surface of the cornea and are used for vision correction, cosmetic purposes, or to treat eye diseases. Contact lens testing methods use various methods to examine contact lens quality, defects, and performance parameters.

[0003] In related technologies, most contact lens inspection methods rely on manual experience to determine the inspection focus or key inspection positions of contact lenses. The inspection results are easily affected by human factors, and minor defects may be overlooked due to fatigue, resulting in low inspection efficiency. Summary of the Invention

[0004] The embodiments of the present application provide an automated contact lens inspection method and system, which can solve the problem that the inspection results are easily affected by human factors and minor defects are easily overlooked due to reliance on manual judgment of the inspection focus or the key inspection position of the contact lens.

[0005] In a first aspect, an embodiment of the present application provides a method for automated contact lens detection, comprising: Determining a plurality of manufacturing equipment for manufacturing contact lenses; wherein the plurality of manufacturing equipment is a plurality of different equipment used in manufacturing the contact lenses; When each of the manufacturing devices is in operation, monitoring the operating data of each of the manufacturing devices in real time; wherein the operating data is used to indicate the operating status of the manufacturing devices; Determining status data of each manufacturing device based on the operating status indicated by the operating data of each manufacturing device; wherein the status data is used to reflect whether the manufacturing device has an abnormal state, and the abnormal state includes an abnormal state or no abnormal state, and the abnormal state may have one or more abnormal states; Determining the data to be tested based on whether the abnormal state of the manufacturing equipment reflected by the status data of each manufacturing equipment exists; wherein the data to be tested is used to reflect the contact lens to be tested and the part of the contact lens to be tested; Based on the data to be detected, the plurality of detection devices are controlled to detect the contact lens to be detected and the part to be detected of the contact lens.

[0006] The present application provides an automated contact lens inspection method that identifies multiple manufacturing equipment for contact lenses and can identify each of the manufacturing equipment for contact lenses one by one. When each manufacturing equipment is in operation, the operating data of each manufacturing equipment is monitored in real time. The status data of each manufacturing equipment is determined based on the operating status indicated by the operating data of each manufacturing equipment. The data to be inspected is determined based on the abnormal status of the manufacturing equipment reflected by the status data of each manufacturing equipment. This can improve sensitivity to problems that may arise in the contact lens manufacturing process. Based on the status data of each manufacturing equipment, the contact lenses to be inspected and the parts to be inspected can be accurately determined, thereby performing targeted inspections, avoiding the subjectivity of manual judgment and the problem of easily overlooking minor defects. Based on the data to be inspected, multiple inspection devices are controlled to inspect the contact lenses to be inspected and the parts to be inspected of the contact lenses to be inspected. This can achieve automated and accurate detection of potential problems in the contact lens manufacturing process, greatly improving inspection efficiency and accuracy, reducing reliance on manual experience to determine inspection focus or key inspection locations of contact lenses, solving the problem of being unable to accurately locate the parts of potential contact lenses that need to be inspected, and avoiding repeated inspections of non-defective parts that result in excessive inspection time and low inspection efficiency.

[0007] In a second aspect, an embodiment of the present application provides an automated contact lens detection system, comprising: A first determining unit is configured to determine a plurality of manufacturing equipment for manufacturing contact lenses; wherein the plurality of manufacturing equipment are a plurality of different equipment used in manufacturing the contact lenses; A monitoring unit, configured to monitor operating data of each manufacturing device in real time when each manufacturing device is in operation; wherein the operating data is used to indicate an operating status of the manufacturing device; a second determining unit, configured to determine status data of each manufacturing device based on the operating status indicated by the operating data of each manufacturing device; wherein the status data is used to reflect whether the manufacturing device has an abnormal state, and the abnormal state includes an abnormal state or no abnormal state, and the abnormal state may have one or more abnormal states; A third determining unit is configured to determine data to be tested based on whether the abnormal state of the manufacturing equipment reflected by the status data of each manufacturing equipment exists; wherein the data to be tested is used to reflect the contact lens to be tested and the part of the contact lens to be tested; A control unit is used to control the plurality of detection devices to detect the contact lens to be detected and the contact lens portion to be detected based on the data to be detected.

[0008] In a third aspect, an embodiment of the present application provides an automated contact lens detection device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the method described in any one of the first aspects above.

[0009] In a fourth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on an automated contact lens detection device, the automated contact lens detection device executes the automated contact lens detection method described in any one of the first aspects above.

[0010] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0012] Figure 1 1 is a flow chart of an automated contact lens detection method provided in one embodiment of the present application; Figure 2 4 is a schematic diagram of the implementation process of step S400 in the contact lens automated detection method provided in one embodiment of the present application; Figure 3 4 is a schematic diagram of the implementation process of step S430 in the contact lens automated detection method provided in one embodiment of the present application; Figure 4 4 is a schematic diagram of another implementation flow of step S400 in the contact lens automated detection method provided in one embodiment of the present application; Figure 5 Schematic diagram of the structure of the contact lens automated detection system provided in an embodiment of the present application; Figure 6 It is a structural schematic diagram of the control device of the contact lens automated detection equipment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0014] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0015] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0016] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0017] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0018] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0019] In the related art, the inspection methods for contact lenses mostly rely on manual experience to judge the inspection focus or the key inspection position of the contact lenses. The inspection results are easily affected by human factors, and the contact lenses need to be 100% inspected or randomly inspected. However, the actual defects of the contact lenses produced are often concentrated in specific parts affected by specific equipment, and it is impossible to accurately locate the parts of the contact lenses that need to be inspected. Manual inspection is prone to overlooking minor defects due to fatigue, and it is difficult to accurately inspect the parts of the contact lenses that need to be inspected. The poor targeting and repeated inspection of defect-free parts result in excessively long inspection time and low inspection efficiency.

[0020] To address the aforementioned issues, embodiments of the present application provide a method and system for automated contact lens inspection. In this method, multiple manufacturing equipment for contact lenses are identified, allowing each manufacturing equipment to be identified individually. While each manufacturing equipment is operating, operating data from each manufacturing equipment is monitored in real time. Status data for each manufacturing equipment is determined based on the operating status indicated by the operating data. Data to be inspected is determined based on whether the status data of each manufacturing equipment indicates abnormal conditions in the manufacturing equipment. This improves sensitivity to potential problems that may arise during the contact lens manufacturing process. Based on the status data of each manufacturing equipment, the contact lenses to be inspected and their locations to be inspected can be accurately determined, allowing for targeted inspections. This avoids the subjectivity of manual judgment and the tendency to overlook minor defects. Based on the data to be inspected, multiple inspection devices are controlled to inspect the contact lenses to be inspected and their locations to be inspected. This enables automated and accurate detection of potential problems during the contact lens manufacturing process, significantly improving inspection efficiency and accuracy. This reduces reliance on manual experience to determine inspection focus or critical inspection locations on contact lenses, addresses the inability to accurately locate the areas of potential contact lenses that require inspection, and avoids repeated inspections of non-defective areas, which results in excessive inspection time and low inspection efficiency.

[0021] The automated contact lens detection method provided in the embodiment of the present application can be applied to an automated contact lens detection device. In this case, the automated contact lens detection device is the executor of the automated contact lens detection method provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the automated contact lens detection device.

[0022] For example, an automated contact lens testing device includes manufacturing equipment and a control device; the control device is communicatively connected to the manufacturing equipment; the control device is located on a terminal device such as a tablet computer, laptop computer, netbook, desktop computer, smart screen, smart TV, or other computing device or other processing device connected to a wireless modem, a computer, laptop computer, handheld computing device, etc. The manufacturing equipment includes all devices involved in testing contact lenses, such as optical testing equipment, size and shape testing equipment, physical property testing equipment, cleaning and disinfection effect testing equipment, and fatigue testing equipment; the control device is communicatively connected to the optical testing equipment, size and shape testing equipment, physical property testing equipment, cleaning and disinfection effect testing equipment, and fatigue testing equipment.

[0023] In order to better understand the automated contact lens detection method provided in the embodiment of the present application, the specific implementation process of the automated contact lens detection method provided in the embodiment of the present application is exemplarily introduced below.

[0024] Figure 1 A schematic flow chart of an automated contact lens detection method provided in an embodiment of the present application is shown. The automated contact lens detection method includes: S100, determining a plurality of manufacturing equipment for manufacturing contact lenses; wherein the plurality of manufacturing equipment is a plurality of different equipment used in manufacturing contact lenses.

[0025] It is understood that the multiple manufacturing equipment used to manufacture contact lenses may include, but is not limited to, lens molding equipment, lens cutting equipment, lens edging equipment, lens cleaning equipment, lens disinfection equipment, and lens packaging equipment. The method for determining the multiple manufacturing equipment used to manufacture contact lenses may be to establish a communication connection with each manufacturing equipment via a communication interface, thereby determining the identity information, operating status information, and manufacturing parameters of each manufacturing equipment. The multiple manufacturing equipment refers to multiple different devices used to manufacture contact lenses, which can be understood as requiring different equipment depending on the specific contact lens being manufactured.

[0026] S200 , when each manufacturing device is in operation, monitoring the operation data of each manufacturing device in real time; wherein the operation data is used to indicate the operation status of the manufacturing device.

[0027] It is understood that operating data may include, but is not limited to, parameters such as equipment temperature, pressure, rotational speed, and vibration. Changes in these parameters can be used to determine the operating status of manufacturing equipment. Real-time monitoring of this operating data can promptly identify problems with manufacturing equipment. In practical implementation, operating data can be collected by installing sensors on various pieces of manufacturing equipment.

[0028] S300, determining the status data of each manufacturing device based on the operating status indicated by the operating data of each manufacturing device; wherein the status data is used to reflect whether the manufacturing device has an abnormal state, and whether the abnormal state exists includes an abnormal state or no abnormal state, and there are one or more abnormal states.

[0029] Understandable. When the abnormal state is abnormal, it can be abnormal operation or a state where multiple parameters have changed significantly (either increasing or decreasing), etc.; no abnormal state means normal operation.

[0030] For example, operating data can be collected by installing sensors on each manufacturing equipment. The sensors transmit the collected data to the control device through the communication interface. The control device analyzes the received data (i.e., analyzes the change amplitude and change pattern of the parameters) to determine the operating status of each manufacturing equipment.

[0031] S400, determining data to be inspected based on whether the status data of each manufacturing device reflects abnormal status of the manufacturing device; wherein the data to be inspected is used to reflect the contact lens to be inspected and the contact lens part to be inspected.

[0032] Exemplarily, when the abnormal state of the manufacturing equipment reflected by the status data of each manufacturing equipment is an abnormal state or a non-abnormal state, the data to be detected are respectively determined.

[0033] In one possible implementation, see Figure 2 S400 determines the data to be detected based on whether the status data of each manufacturing device reflects the abnormal status of the manufacturing device, including: S410 , when the abnormal state reflected by the status data of at least one manufacturing device among the plurality of manufacturing devices is an abnormal state, detecting the number of abnormal states.

[0034] It can be understood that the multiple manufacturing equipment can be understood as the multiple manufacturing equipment required for the production of the current contact lens; the number of abnormal states detected can be understood as the number of manufacturing equipment required for the production of the current contact lens that have abnormal states due to any reason; wherein any reason includes but is not limited to long operating time, high temperature, abnormal pressure, excessive vibration, etc.

[0035] S420: If the number of abnormal conditions detected is one, determine the manufacturing equipment that has caused the abnormal condition from the plurality of manufacturing equipments.

[0036] For example, a control device communicates with all equipment involved in production. Based on the abnormal parameters or other abnormal conditions (e.g., high temperature, abnormal pressure, excessive vibration) of the manufacturing equipment, the control device identifies the manufacturing equipment causing the abnormality. The control device monitors parameters across multiple manufacturing equipment in real time. Therefore, if an abnormality occurs in a manufacturing equipment parameter or other equipment location, the abnormality can be directly stored, facilitating subsequent identification of the manufacturing equipment causing the abnormality.

[0037] In one possible implementation, see Figure 2 , S420, the method further includes: S420A: When there are multiple detected abnormal states, determine similarity values ​​of the multiple abnormal states.

[0038] It can be understood that when the number of abnormal states detected is multiple, it can be understood that the manufacturing equipment has multiple abnormal states (for example, there may be abnormal parameter changes, abnormal temperature changes or other abnormal changes, etc.), and the multiple abnormal states are compared to see whether they are the same type of abnormal states (the same type of abnormal state can be understood as, if there are 3 abnormal states, comparing whether these three abnormal states are all abnormal parameter changes or other abnormal changes, etc.) to obtain the similarity value.

[0039] S421A, if the similarity values ​​of the multiple abnormal states are lower than a preset value, determine the equipment function of the manufacturing equipment that generates each abnormal state; wherein the equipment function is the function of the manufacturing equipment that generates each abnormal state for manufacturing a certain part of the contact lens.

[0040] For example, if the similarity values ​​of the multiple abnormal states judged are lower than a preset value, it is proved that the multiple abnormal states of the manufacturing equipment are all different abnormal states. The manufacturing equipment where the abnormal change occurs is determined to determine which equipment is used to manufacture contact lenses, so as to determine the specific batch (which can be one or more) of contact lenses produced during the period when the abnormal situation occurred, and determine the positions or parts of this batch of contact lenses that need to be inspected.

[0041] S422A, based on the equipment function of the manufacturing equipment that generates each abnormal state, determine the equipment functions of the two manufacturing equipments before and after the manufacturing equipment that generates each abnormal state.

[0042] It can be understood that the two adjacent manufacturing equipment can be understood as the two adjacent manufacturing equipment determined in the order of manufacturing; for example, the manufacturing equipment that produces each abnormal state is D, and there are 6 manufacturing equipment in total, namely A, B, C, D, E and F, then the two adjacent manufacturing equipment are C and E. After determining C and E, the functions of C and E are obtained to determine the equipment functions of the two adjacent manufacturing equipment before and after the manufacturing equipment that produces each abnormal state.

[0043] S423A, determining the data to be detected based on the equipment function of the manufacturing equipment that generates each abnormal state and the equipment functions of the two adjacent manufacturing equipments.

[0044] It can be understood that determining the data to be tested based on the equipment functions of the manufacturing equipment that produces each abnormal state and the equipment functions of the two adjacent manufacturing equipment can be understood as, citing the example of step S422A, determining the contact lenses and parts that need to be tested based on the functions of C and E, and the function of D of the manufacturing equipment that produces each abnormal state, and performing tests on three parts of the contact lenses that need to be tested, rather than just targeting C, D or E (the functions corresponding to the C, D or E equipment, the functions are the purpose of the corresponding equipment, such as E is to disinfect contact lenses, etc.). For example, the function of C is cutting, the function of D is accurate degree, and E is disinfection. Therefore, the contact lenses need to be tested at the cutting part, the degree of the contact lenses, and the bacteria situation. To determine the contact lenses that need to be tested, batches of three time periods need to be obtained; for example, the abnormal time of C is 8:15, the abnormal time of D is 9:15, or the abnormal time of E is 10:15, all of which lasted for 3 minutes. Then the contact lenses produced from 8:15 to 8:18, 9:15 to 9:18, and 10:15 to 10:18 in these three time periods need to be obtained for testing, and three positions need to be tested, namely cutting, degree, and disinfection.

[0045] With this setup, when multiple abnormalities occur on manufacturing equipment, the process of identifying the data to be tested can be further refined by determining the similarity values ​​of these abnormalities. The consideration of adjacent manufacturing equipment is based on the fact that in some cases, an abnormality on one piece of manufacturing equipment may indirectly affect the manufacturing processes of its adjacent equipment, causing these equipment to also experience abnormalities. Therefore, including adjacent equipment allows for a more comprehensive determination of the contact lens to be tested and its location, further improving the accuracy and specificity of testing.

[0046] S430: Determine data to be detected based on the manufacturing equipment that has an abnormal state.

[0047] For example, the function or role of the manufacturing equipment is determined based on abnormal parameters or other abnormal behavior, where the function or role refers to the role played by the manufacturing equipment with abnormal parameters or other abnormal behavior in producing contact lenses. The contact lenses to be inspected and the locations of the contact lenses to be inspected are determined based on the function determined by the manufacturing equipment. For example, if the manufacturing equipment with abnormal parameters or other abnormal behavior is responsible for the clarity of the contact lenses, then a specific batch or several contact lenses can be determined by identifying the batch or several contact lenses during the time period when the abnormal behavior or parameters occurred. Based on the function, the locations of the batch or several contact lenses to be inspected, i.e., the clarity of the contact lenses, can be determined.

[0048] This setting allows for precise inspection, avoiding repeated inspections of non-defective areas and laying the foundation for subsequent follow-up.

[0049] In one possible implementation, see Figure 3 S430, determining data to be detected based on the manufacturing equipment generating the abnormal state, including: S431, determining the location data of the manufacturing equipment that generates the abnormal state; wherein the location data is used to indicate the location of the manufacturing equipment that generates the abnormal state among multiple manufacturing equipment, and the location of the manufacturing equipment that generates the abnormal state among the multiple manufacturing equipment is arranged according to the manufacturing process of manufacturing contact lenses.

[0050] It can be understood that the positions arranged according to the manufacturing process of manufacturing contact lenses can be understood as arranging multiple manufacturing equipment according to the manufacturing process of contact lenses and the sequence of each step.

[0051] S432, determining first manufacturing function data of the manufacturing equipment that generates the abnormal state according to the position data; wherein the first manufacturing function data is used to indicate the manufacturing equipment that manufactures a certain part of the contact lens.

[0052] It can be understood that the position of the manufacturing equipment that produces the abnormal state is determined according to the position arranged according to the manufacturing process of manufacturing contact lenses, and the function of the manufacturing equipment that produces the abnormal state is obtained according to its position, that is, the first manufacturing function data.

[0053] S433, obtaining manufacturing equipment associated with a certain part of the contact lens, and determining the manufacturing function of the manufacturing equipment associated with the certain part of the contact lens to obtain second manufacturing function data; wherein the second manufacturing function data is used to indicate the manufacturing equipment of other parts of the contact lens associated with the certain part of the contact lens.

[0054] It is understood that manufacturing equipment associated with a certain portion of a contact lens can be understood as manufacturing equipment associated with the manufacturing equipment that caused the abnormal condition. "Associated" can be understood as meaning that the two manufacturing equipment may have an operational relationship, i.e., the manufacturing function of one manufacturing equipment affects or is dependent on the manufacturing function of the other manufacturing equipment. For example, if the manufacturing equipment that caused the abnormal condition is used for lens cutting, then the manufacturing equipment associated with it may be equipment used for lens edging, as lens edging is performed after lens cutting, but this is not limited to this.

[0055] S434: Determine the data to be detected according to the first manufacturing function data and the second manufacturing function data.

[0056] Exemplarily, the manufacturing equipment for manufacturing a certain part of the contact lens indicated by the first manufacturing function data and the manufacturing equipment for other parts of the contact lens associated with the certain part of the contact lens indicated by the second manufacturing function data are used to determine the contact lens to be inspected and the part of the contact lens that needs to be inspected, that is, the data to be inspected.

[0057] This setup allows for a more comprehensive consideration of the interplay between various steps in the manufacturing process, leading to a more accurate identification of the contact lens and its location to be inspected. For example, if the manufacturing equipment experiencing the abnormal condition is used for lens molding, then the associated manufacturing equipment may include lens cutting equipment, lens edging equipment, and so on. By comprehensively considering the manufacturing functions of these devices, it can be determined that not only the lens molding area needs to be inspected, but also the cutting edges, edging quality, and other areas. This enables automated monitoring and inspection of the contact lens manufacturing process, improving the accuracy and specificity of inspections, reducing defective product rates, and improving production efficiency and product quality.

[0058] In one possible implementation, see Figure 4 S400 determines the data to be detected based on whether the status data of each manufacturing device reflects the abnormal status of the manufacturing device, including: S401, when the status data of multiple manufacturing devices among multiple manufacturing devices reflect abnormal states, determine the abnormality level of the status data of each manufacturing device; wherein the abnormality level is the degree of influence of the abnormal state of the manufacturing device on the contact lens manufacturing.

[0059] For example, when the status data of multiple manufacturing devices among multiple manufacturing devices reflect the presence or absence of abnormal conditions, the impact of the abnormal conditions is determined to obtain the degree of impact of the abnormal conditions of the manufacturing equipment on contact lens manufacturing, that is, the degree of abnormality of the status data of each manufacturing device.

[0060] S402 , sorting the plurality of manufacturing equipment in sequence according to the degree of abnormality to obtain a sorting result; wherein the sorting result indicates the order of the degree of influence of the abnormal state of each manufacturing equipment on the contact lens manufacturing.

[0061] For example, the manufacturing equipment corresponding to the obtained impact degree values ​​on contact lens manufacturing are sorted in order of impact to obtain a sorting result. In the sorting result, manufacturing equipment with a greater impact of abnormal conditions on contact lens manufacturing is ranked first, and manufacturing equipment with a smaller impact is ranked last.

[0062] S403 , selecting the manufacturing equipment with the highest abnormality and the manufacturing equipment with the lowest abnormality in the sorting results.

[0063] For example, the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality are extracted from the sorting results. The manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality are in the sorting results. For example, if the impact value of equipment A is 5, the impact value of equipment B is 3, the impact value of equipment C is 8, the impact value of equipment D is 7, and the impact value of equipment E is 6, then the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality are C and B, respectively.

[0064] S404 , obtaining relevant important data based on the manufacturing equipment with the highest abnormality and the manufacturing equipment with the lowest abnormality; wherein the relevant important data is used to indicate the correlation value between the manufacturing equipment with the highest abnormality and the manufacturing equipment with the lowest abnormality.

[0065] For example, the correlation or mutual influence between the manufacturing equipment with the highest and lowest abnormalities is determined based on their functions or roles to obtain relevant important data. For example, if the manufacturing equipment with the highest abnormality is used for lens molding, while the manufacturing equipment with the lowest abnormality is used for lens packaging, the correlation between them may be low because molding and packaging are far apart in the manufacturing process. Conversely, if the manufacturing equipment with the highest and lowest abnormality is used for different stages of lens cutting, the correlation between them may be high because their manufacturing functions are closely related.

[0066] S405: Determine the data to be detected based on the associated important data.

[0067] Exemplarily, the contact lens to be inspected and the inspection position of the contact lens are determined based on the correlation value between the manufacturing equipment with the highest abnormality and the manufacturing equipment with the lowest abnormality.

[0068] This setup allows for comprehensive consideration of the abnormal conditions of different manufacturing equipment within the manufacturing process and their mutual influence. By assessing the degree of abnormality and correlation, the contact lenses to be inspected and their critical areas can be more accurately identified, helping to optimize the inspection process and improve inspection efficiency while ensuring the timely discovery and resolution of potential quality issues. For example, if correlated key data indicates a high correlation between the manufacturing equipment with the highest degree of abnormality and the equipment with the lowest degree of abnormality, it may be necessary to increase the frequency or depth of inspections of the contact lens areas affected by these equipment. This approach allows for comprehensive monitoring of the contact lens manufacturing process, further improving product quality and production efficiency.

[0069] In one possible implementation, see Figure 4 , S405, determining the data to be detected based on the associated important data, including: S4051, if the associated important data is greater than the preset data threshold, the manufacturing content data and manufacturing sequence of the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality are determined; wherein, the manufacturing content data is used to indicate the parts manufactured by the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality when manufacturing contact lenses, and the manufacturing sequence is the order in which the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality are actually manufacturing contact lenses.

[0070] Exemplarily, by comparing the associated important data with a preset data threshold, if the associated important data is greater than the preset data threshold, the manufacturing content data and manufacturing sequence of the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality are obtained; wherein the manufacturing content data has the same function and role as the manufacturing equipment, and the manufacturing sequence is the order in which the equipment for producing contact lenses works, and the positions and respective equipment functions of the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality are determined based on the order in which the equipment for producing contact lenses works.

[0071] S4052: Determine interval data based on the manufacturing content data and the manufacturing sequence; wherein the interval data is used to indicate the number of manufacturing equipment located between the manufacturing equipment with the highest abnormality and the manufacturing equipment with the lowest abnormality.

[0072] It can be understood that the interval data can be understood as other manufacturing equipment between the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality; for example, the manufacturing equipment with the highest degree of abnormality is equipment A, and the manufacturing equipment with the lowest degree of abnormality is equipment E, then the manufacturing equipment located between equipment A and equipment E are B, C and D, so the interval data is 3, then there are 3 manufacturing equipment between the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality, equipment E.

[0073] S4053: If the number indicated by the interval data is 0 or 1, the manufacturing equipment with the highest abnormality and the manufacturing equipment with the lowest abnormality are determined as data to be detected.

[0074] For example, if the number indicated by the interval data is 0, it means that the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality are adjacent to each other. At this time, the data to be tested is determined based on these two equipment; if the number indicated by the interval data is 1, it means that there is only one manufacturing equipment between the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality. Considering that the abnormality may also be reflected on this manufacturing equipment, or the function of this manufacturing equipment is closely related to these two equipment, determining the data to be tested based on these three equipment can cover potential quality problems more comprehensively, and can more accurately determine the contact lenses to be tested and their key parts, thereby improving the targetedness and efficiency of the detection, thereby helping to improve product quality and production efficiency.

[0075] In one possible implementation, see Figure 4 , the method further comprises: If the number indicated by the interval data is multiple, a manufacturing function analysis is performed on the multiple manufacturing equipment located between the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality to obtain multiple manufacturing function data; wherein the manufacturing function data is used to indicate the specific function of the manufacturing equipment located between the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality when manufacturing contact lenses.

[0076] For example, if the interval data indicates a large number of errors, this means there may be multiple potentially affected links between the manufacturing equipment with the highest and lowest levels of abnormality. Specifically, by determining the manufacturing utility data for each piece of equipment between the equipment with the highest and lowest levels of abnormality, it is possible to clarify their specific roles in the contact lens manufacturing process and the potential impacts they may be subject to. For example, if a piece of equipment is responsible for fine polishing of lenses, its utility data will show a direct correlation with lens surface quality. In abnormal situations, this equipment may suffer from parameter deviations or mechanical failures, leading to reduced polishing quality, which in turn affects the comfort or visual quality of the final product.

[0077] Key function data is determined based on a plurality of manufacturing function data, wherein the key function data is used to indicate a manufacturing device that is located between a manufacturing device with the highest degree of abnormality and a manufacturing device with the lowest degree of abnormality and has an impact higher than a preset value when manufacturing contact lenses.

[0078] For example, manufacturing equipment with an impact exceeding a preset threshold when manufacturing contact lenses can be identified from the obtained multiple manufacturing function data. This can be accomplished by comparing the impact of each manufacturing function data on the contact lens manufacturing process and selecting data with an impact exceeding a preset threshold as key function data. After determining the key function data, the correlation between the equipment and the manufacturing equipment with the highest and lowest abnormalities is determined. Based on the interactions and dependencies within the production process, a more comprehensive understanding of the impact of abnormal conditions on the overall manufacturing process can be achieved, allowing detection to cover all critical quality control points.

[0079] The manufacturing equipment with the highest degree of abnormality, the manufacturing equipment with the lowest degree of abnormality, and the manufacturing equipment with an impact higher than a preset value are determined as data to be detected.

[0080] Exemplarily, the contact lenses that need to be inspected and the positions or parts of the contact lenses that need to be inspected are determined based on the manufacturing equipment with the highest degree of abnormality, the manufacturing equipment with the lowest degree of abnormality, and the manufacturing equipment with an impact higher than a preset value.

[0081] This setup allows for a more in-depth analysis of the potential impacts of different links in the manufacturing process, allowing for more accurate identification of contact lenses and their critical components for testing. For example, if key functional data indicates that a piece of manufacturing equipment located between the most abnormal and least abnormal manufacturing equipment has a significant impact on a key contact lens property (such as oxygen permeability or wear resistance), this equipment and the contact lens component it manufactures should be prioritized in the data for testing. This not only improves the targetedness and efficiency of testing, but also enables comprehensive monitoring of potential quality issues in the manufacturing process, further enhancing product quality and production efficiency.

[0082] In one possible implementation, see Figure 4 , the method further comprises: If the associated important data is not greater than the preset data threshold, a first manufacturing device adjacent to the manufacturing device with the highest degree of abnormality and a second manufacturing device adjacent to the manufacturing device with the lowest degree of abnormality are obtained.

[0083] Exemplarily, if the associated important data is not greater than the preset data threshold, it proves that the correlation between the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality is low. Therefore, the first manufacturing equipment and the second manufacturing equipment adjacent to the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality, and the equipment adjacent to the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality may also be affected by the abnormal state.

[0084] The manufacturing equipment with the highest abnormality, the manufacturing equipment with the lowest abnormality, the first manufacturing equipment, and the second manufacturing equipment are determined as data to be detected.

[0085] Illustratively, the contact lens that needs to be inspected and the position or portion of the contact lens that needs to be inspected are determined based on the manufacturing equipment with the highest degree of abnormality, the manufacturing equipment with the lowest degree of abnormality, the first manufacturing equipment, and the second manufacturing equipment.

[0086] This setup can further broaden the scope of detection, ensuring comprehensive monitoring and detection of all links in the manufacturing process that may be affected by abnormal conditions, thereby improving product quality and production efficiency.

[0087] In one possible implementation, 400, determining the data to be detected based on whether the status data of each manufacturing device reflects abnormal status of the manufacturing device includes: S410A: When the abnormality status reflected by the status data of the plurality of manufacturing equipment is no abnormality, the operating time of each manufacturing equipment is detected.

[0088] For example, when the abnormal state is non-abnormal, the control device obtains the effective operating time of each manufacturing device and determines the operating time of each manufacturing device based on the effective time. Determining the operating time of each manufacturing device based on the effective time can be performed by obtaining the start time of each manufacturing device and the current time to obtain the operating time of each manufacturing device.

[0089] S420A, obtaining historical record data of each manufacturing device; wherein the historical record data is used to indicate the probability of abnormal conditions occurring in each manufacturing device in the past.

[0090] It can be understood that historical record data may include the number, frequency, type, and handling results of each abnormal state of each manufacturing equipment in the past period of time (which can be one month, one week, or the completion of one production task, two production tasks, or multiple production tasks, etc., or the duration of each device, etc.). This information can be used to evaluate the stability and reliability of each manufacturing equipment and predict the risk of abnormal states in the future, thereby providing a basis for formulating targeted inspection and maintenance plans.

[0091] S430A, determining, from a plurality of manufacturing devices, a manufacturing device with the highest probability of an abnormal state occurring based on historical record data.

[0092] Exemplarily, the manufacturing equipment that has the most abnormal states or the highest frequency in the historical record data is determined as the manufacturing equipment with the highest probability of having the abnormal state.

[0093] S440A, determining the equipment function of the manufacturing equipment with the highest probability of an abnormal state, and obtaining third manufacturing function data; wherein the third manufacturing function data is used to indicate that the manufacturing equipment with the highest probability of an abnormal state is a device for manufacturing a certain part of the contact lens.

[0094] Exemplarily, the function of the manufacturing equipment with the highest probability of an abnormal state is determined to determine its specific role in the contact lens manufacturing process and the possible impact on the quality of the contact lenses, so as to obtain third manufacturing function data. The third manufacturing function data describes the specific part or process flow that the manufacturing equipment with the highest probability of an abnormal state is responsible for when manufacturing contact lenses.

[0095] S450A, obtaining data to be tested according to the third manufacturing function data.

[0096] Exemplarily, the contact lens to be inspected and the part of the contact lens to be inspected are determined based on the fact that the manufacturing equipment with the highest probability of an abnormal state occurring indicated by the third manufacturing function data is the equipment for manufacturing a certain part of the contact lens.

[0097] This setup allows potential quality risk points to be predicted, even without clear abnormal conditions, through a comprehensive analysis of manufacturing equipment's operating time and historical abnormal condition probabilities. Specifically, equipment with long operating times may be more prone to failure due to wear and aging, while equipment with a history of frequent abnormal conditions may have design flaws or improper maintenance. By focusing on these devices, potential quality issues can be promptly identified and addressed, preventing them from impacting the final product. This allows for refined monitoring of the contact lens manufacturing process, further improving product quality and production efficiency.

[0098] S500, controlling a plurality of detection devices to detect the contact lens to be detected and the contact lens portion to be detected based on the data to be detected.

[0099] For example, the control device can control corresponding detection devices to detect the contact lens and the contact lens portion to be detected based on the data to be detected. For example, if the data to be detected indicates the shape of the lens, the control device can control the shape detection device to detect the shape of the lens; if the data to be detected indicates the physical properties of the lens, the control device can control the physical property detection device to detect the physical properties of the lens.

[0100] Such a setting can more accurately locate the key parts of the contact lens, reduce invalid detection, improve sensitivity to problems that may occur in the contact lens manufacturing process, avoid the subjectivity of manual judgment and the problem of easily overlooking minor defects, reduce the situation of relying on manual experience to judge the detection focus or the key detection position of the contact lens, and solve the problem of not being able to accurately locate the parts of the potential contact lens that need to be inspected, greatly improving detection efficiency and accuracy.

[0101] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0102] Corresponding to the automated contact lens detection method described in the above embodiment, the embodiment of the present application further provides an automated contact lens detection system, and each unit of the system can implement each step of the automated contact lens detection method. Figure 5 The structural block diagram of the contact lens automated detection system provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0103] Reference Figure 5 , the contact lens automated detection system includes: A first determining unit is configured to determine a plurality of manufacturing equipment for manufacturing contact lenses; wherein the plurality of manufacturing equipment are a plurality of different equipment used in manufacturing contact lenses; A monitoring unit, configured to monitor operating data of each manufacturing device in real time when each manufacturing device is in operation; wherein the operating data is used to indicate the operating status of the manufacturing device; a second determining unit, configured to determine status data of each manufacturing device based on an operating status indicated by operating data of each manufacturing device; wherein the status data is used to reflect whether the manufacturing device has an abnormal state, and whether the abnormal state exists includes an abnormal state or no abnormal state, and there may be one or more abnormal states; A third determining unit is configured to determine data to be tested based on whether the status data of each manufacturing device reflects the presence or absence of abnormal status of the manufacturing device; wherein the data to be tested is used to reflect the contact lens to be tested and the contact lens part to be tested; The control unit is used to control multiple detection devices to detect the contact lens to be detected and the contact lens part to be detected based on the data to be detected.

[0104] It should be noted that the information interaction, execution process, etc. between the above-mentioned systems / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0105] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0106] The embodiment of the present application also provides a control device, Figure 6 This is a schematic diagram of the structure of a control device provided in one embodiment of the present application. Figure 6 As shown, the control device 6 of this embodiment includes: at least one processor 60 ( Figure 6 Only one is shown), at least one memory 61 ( Figure 6 Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the control device 6 implements the steps of any of the above-mentioned embodiments of the automated contact lens detection method, or implements the functions of the modules / units in the above-mentioned system embodiments.

[0107] For example, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to implement the present application. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program 62 in the control device 6.

[0108] The control device 6 can be a computing device such as a desktop computer or a notebook. The control device can include, but is not limited to, a processor 60 and a memory 61. It will be understood by those skilled in the art that Figure 6 This is merely an example of the control device 6 and does not constitute a limitation on the control device 6 . The control device 6 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, buses, etc.

[0109] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0110] In some embodiments, the memory 61 may be an internal storage unit of the control device 6, such as a hard drive or memory of the control device 6. In other embodiments, the memory 61 may also be an external storage device of the control device 6, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the control device 6. Furthermore, the memory 61 may include both the internal storage unit of the control device 6 and an external storage device. The memory 61 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory 61 may also be used to temporarily store data that has been output or is about to be output.

[0111] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0112] An embodiment of the present application provides a computer program product. When the computer program product is run on an automated contact lens testing device, the automated contact lens testing device is enabled to implement the steps of any of the above method embodiments.

[0113] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to the contact lens automated testing equipment, a recording medium, computer memory, read-only memory (ROM), random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Examples include a USB flash drive, a removable hard drive, a magnetic disk, or an optical disk.

[0114] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0115] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0116] In the embodiments provided herein, it should be understood that the disclosed automated contact lens detection systems, devices, and methods can be implemented in other ways. For example, the embodiments of the automated contact lens detection systems and devices described above are merely illustrative. For example, the division of modules or units is merely a logical functional division. In actual implementation, other division methods may be used, such as combining or integrating multiple units or components into another system, or ignoring or not implementing certain features. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device, or unit, and may be electrical, mechanical, or other forms.

[0117] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0118] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A contact lens automated detection method, characterized in that: include: Determining a plurality of manufacturing equipment for manufacturing contact lenses; wherein the plurality of manufacturing equipment is a plurality of different equipment used in manufacturing the contact lenses; When each of the manufacturing devices is in operation, monitoring the operating data of each of the manufacturing devices in real time; wherein the operating data is used to indicate the operating status of the manufacturing devices; Determining status data of each manufacturing device based on the operating status indicated by the operating data of each manufacturing device; wherein the status data is used to reflect whether the manufacturing device has an abnormal state, and the abnormal state includes an abnormal state or no abnormal state, and the abnormal state may have one or more abnormal states; Determining the data to be tested based on whether the abnormal state of the manufacturing equipment reflected by the status data of each manufacturing equipment exists; wherein the data to be tested is used to reflect the contact lens to be tested and the part of the contact lens to be tested; Based on the data to be detected, multiple detection devices are controlled to detect the contact lens to be detected and the part to be detected of the contact lens.

2. The automated contact lens detection method according to claim 1, wherein: The determining of the data to be detected based on whether the abnormal state of the manufacturing equipment reflected by the status data of each manufacturing equipment exists includes: detecting the number of abnormal states when the abnormal state reflected by the status data of at least one of the plurality of manufacturing equipment is the abnormal state; If the number of the abnormal conditions detected is one, determining the manufacturing equipment that generates the abnormal condition from the plurality of manufacturing equipment; The data to be detected is determined based on the manufacturing equipment that generates the abnormal state.

3. The automated contact lens detection method according to claim 2, wherein: The determining the data to be detected based on the manufacturing equipment generating the abnormal state includes: Determining position data of the manufacturing equipment that generates the abnormal state; wherein the position data is used to indicate the position of the manufacturing equipment that generates the abnormal state among the plurality of manufacturing equipment, and the position of the manufacturing equipment that generates the abnormal state among the plurality of manufacturing equipment is arranged according to the manufacturing process of manufacturing the contact lens; Determine first manufacturing function data of the manufacturing equipment that generates the abnormal state according to the position data; wherein the first manufacturing function data is used to indicate the manufacturing equipment that manufactures a certain part of the contact lens; Obtaining the manufacturing equipment associated with a certain portion of the contact lens, and determining a manufacturing function of the manufacturing equipment associated with the certain portion of the contact lens to obtain second manufacturing function data; wherein the second manufacturing function data is used to indicate manufacturing equipment of other portions of the contact lens associated with the certain portion of the contact lens; The data to be detected is determined according to the first manufacturing function data and the second manufacturing function data.

4. The automated contact lens detection method according to claim 2, wherein: The method further comprises: In the case where the number of the abnormal states detected is multiple, determining similarity values ​​of the multiple abnormal states; If the similarity values ​​of the plurality of abnormal states are lower than a preset value, determining the equipment function of the manufacturing equipment that generates each abnormal state; wherein the equipment function is the function of the manufacturing equipment that generates each abnormal state for manufacturing a certain part of the contact lens; Based on the equipment function of the manufacturing equipment that generates each of the abnormal states, determining the equipment functions of two manufacturing equipments that are adjacent to and before the manufacturing equipment that generates each of the abnormal states; The data to be detected is determined according to the equipment function of the manufacturing equipment that generates each abnormal state and the equipment functions of the two adjacent manufacturing equipments.

5. The automated contact lens detection method according to claim 1, wherein: The determining of the data to be detected based on whether the abnormal state of the manufacturing equipment reflected by the status data of each manufacturing equipment exists includes: When the status data of multiple manufacturing devices among the multiple manufacturing devices reflect the presence or absence of the abnormal state, determining the abnormality degree of the status data of each manufacturing device; wherein the abnormality degree is a value indicating the degree of influence of the abnormal state of the manufacturing device on the manufacturing of the contact lens; The plurality of manufacturing devices are sequentially sorted according to the abnormality levels to obtain a sorting result; wherein the sorting result indicates the order of the degree of influence of the abnormal state of each manufacturing device on the contact lens manufacturing; selecting the manufacturing equipment with the highest abnormality and the manufacturing equipment with the lowest abnormality in the sorting results; Obtaining associated important data based on the manufacturing equipment with the highest abnormality and the manufacturing equipment with the lowest abnormality; wherein the associated important data is used to indicate a correlation value between the manufacturing equipment with the highest abnormality and the manufacturing equipment with the lowest abnormality; The data to be detected is determined based on the associated important data.

6. The automated contact lens detection method according to claim 5, wherein: The determining the data to be detected based on the associated important data includes: If the associated important data is greater than a preset data threshold, determining the manufacturing content data and manufacturing sequence of the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality; wherein the manufacturing content data is used to indicate the parts manufactured by the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality when manufacturing the contact lens, and the manufacturing sequence is the order in which the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality are actually manufacturing the contact lens; Determining interval data based on the manufacturing content data and the manufacturing sequence; wherein the interval data is used to indicate the number of the manufacturing equipment located between the manufacturing equipment with the highest abnormality and the manufacturing equipment with the lowest abnormality; If the number indicated by the interval data is 0 or 1, the manufacturing equipment with the highest abnormality and the manufacturing equipment with the lowest abnormality are determined as the data to be detected.

7. The automated contact lens detection method according to claim 5, wherein: The method further comprises: If the associated important data is not greater than a preset data threshold, obtaining a first manufacturing device adjacent to the manufacturing device with the highest abnormality and a second manufacturing device adjacent to the manufacturing device with the lowest abnormality; The manufacturing equipment with the highest abnormality, the manufacturing equipment with the lowest abnormality, the first manufacturing equipment, and the second manufacturing equipment are determined as the data to be detected.

8. The automated contact lens detection method according to claim 6, wherein: The method comprises: If the number indicated by the interval data is multiple, performing a manufacturing function analysis on the multiple manufacturing equipment located between the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality to obtain multiple manufacturing function data; wherein the manufacturing function data is used to indicate the specific functions of the manufacturing equipment located between the manufacturing equipment with the highest degree of abnormality and the manufacturing equipment with the lowest degree of abnormality when manufacturing the contact lens; Determining key utility data based on the plurality of manufacturing utility data; wherein the key utility data is used to indicate the manufacturing equipment located between the manufacturing equipment with the highest abnormality level and the manufacturing equipment with the lowest abnormality level, and having an impact higher than a preset value when manufacturing the contact lens; The manufacturing equipment with the highest abnormality, the manufacturing equipment with the lowest abnormality, and the manufacturing equipment with an influence higher than a preset value are determined as the data to be detected.

9. The automated contact lens detection method according to claim 1, wherein: The determining of the data to be detected based on whether the abnormal state of the manufacturing equipment reflected by the status data of each manufacturing equipment exists includes: detecting the operating time of each of the manufacturing equipment when the abnormal state reflected by the status data of the plurality of manufacturing equipment indicates that there is no abnormal state; Acquiring historical record data of each of the manufacturing devices; wherein the historical record data is used to indicate the probability of the abnormal state occurring in the history of each of the manufacturing devices; Determining, from the plurality of manufacturing devices, the manufacturing device with the highest probability of the abnormal state occurring according to the historical record data; Determining the function of the manufacturing equipment with the highest probability of the abnormal state occurring to obtain third manufacturing function data; wherein the third manufacturing function data is used to indicate that the manufacturing equipment with the highest probability of the abnormal state occurring is a device for manufacturing a certain part of the contact lens; The data to be detected is obtained according to the third manufacturing function data.

10. An automated contact lens detection device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

Images