Method and apparatus for cleaning oil stains

Abstract

The present disclosure relates to an oil stain cleaning method and device, comprising: obtaining current multi-modal oil stain perception data of a target device and current threshold index data corresponding to the target device; performing oil stain degree analysis on the target device based on the multi-modal oil stain perception data to obtain target oil stain index data corresponding to the target device; performing comparison processing on the target oil stain index data and the current threshold index data to obtain a target comparison result; and executing a preset cleaning operation on the target device based on the target comparison result. The embodiments of the present disclosure can enable the target device to improve the cleaning effect in different use environments, make accurate cleaning decisions according to the real-time state of the oil stain, and thus achieve efficient, accurate and energy-saving oil stain cleaning.

Description

Technical Field

[0001] This disclosure relates to the field of oil stain cleaning technology, and in particular to an oil stain cleaning method and apparatus. Background Technology

[0002] Currently, in the field of oil spill monitoring and cleaning, most traditional methods rely on experience-driven approaches, i.e., operation is carried out through preset timed cleaning or manual adjustments based on fixed thresholds. Common techniques include triggering cleaning based on the time of oil spill accumulation or periodic parameters (such as cleaning every 60 hours). These methods rely on human experience, cannot monitor oil spill changes in real time, and lack precision. Therefore, the above cleaning methods are inefficient and may lead to premature or delayed cleaning operations, resulting in wasted energy consumption. Summary of the Invention

[0003] In view of the above-mentioned technical problems, this disclosure proposes a method and apparatus for cleaning oil stains.

[0004] According to one aspect of the embodiments of this disclosure, an oil stain cleaning method is provided, the method comprising: Acquire the current multimodal oil pollution sensing data of the target device, as well as the current threshold index data corresponding to the target device; Based on the multimodal oil pollution sensing data, the oil pollution level of the target device is analyzed to obtain the target oil pollution index data corresponding to the target device; The target oil pollution index data and the current threshold index data are compared to obtain the target comparison result; Based on the target comparison results, a preset cleaning operation is performed on the target device.

[0005] Optionally, the method further includes: Obtain the device usage frequency data and the current ambient humidity data corresponding to the target device; Based on the device usage frequency data and the current ambient humidity data, determine the current adjustment coefficient; Update the current threshold index data based on the current adjustment coefficient.

[0006] Optionally, the method further includes: Acquire historical cleaning index data; the historical cleaning index data represents the cleaning effect after at least one preset cleaning operation performed before the current moment; Based on the historical cleaning index data, determine the current humidity adjustment coefficient and the current frequency adjustment coefficient; The step of determining the current adjustment coefficient based on the device usage frequency data and the current ambient humidity data includes: The current adjustment coefficient is determined based on the current humidity adjustment coefficient, the current frequency adjustment coefficient, the device usage frequency data, and the current ambient humidity data.

[0007] Optionally, the historical cleaning index data includes historical oil stain change data, historical pressure difference recovery data, historical humidity change data, and historical usage frequency change data. The step of determining the current humidity adjustment coefficient and the current frequency adjustment coefficient based on the historical cleaning index data includes: Based on the historical oil pollution change data, a first current learning weight is determined; The current humidity adjustment coefficient is determined based on the first current learning weight, the historical differential pressure recovery data, and the historical humidity change data; The current frequency adjustment coefficient is determined based on the first current learning weight, the historical differential pressure recovery data, and the historical usage frequency change data.

[0008] Optionally, the multimodal oil contamination sensing data includes current impeller vibration data, current duct pressure difference data, and current filter transmittance data; the step of analyzing the oil contamination level of the target equipment based on the multimodal oil contamination sensing data to obtain target oil contamination index data corresponding to the target equipment includes: Based on the first preset detection weight and the current air duct pressure difference data, the first oil pollution index data is determined; The second oil contamination index data is determined based on the second preset detection weight and the current impeller vibration data; Based on the third preset detection weight and the current filter transmittance data, the third oil stain index data is determined; The first oil pollution index data, the second oil pollution index data, and the third oil pollution index data are overlaid to obtain the target oil pollution index data.

[0009] Optionally, the method further includes: Acquire historical cleaning index data; the historical cleaning index data represents the cleaning effect after at least one preset cleaning operation performed before the current moment; Based on the historical cleaning index data, the current impeller vibration data, the current duct pressure difference data, and the current filter light transmittance data, the first preset detection weight, the second preset detection weight, and the third preset detection weight are updated.

[0010] Optionally, the historical cleaning index data includes historical oil stain change data; the step of updating the first preset detection weight, the second preset detection weight, and the third preset detection weight based on the historical cleaning index data, the current impeller vibration data, the current duct pressure difference data, and the current filter transmittance data includes: Based on the historical oil pollution change data, a second current learning weight is determined; The first preset detection weight is updated based on the second current learning weight, the historical oil pollution change data, and the current air duct pressure difference data; The second preset detection weight is updated based on the second current learning weight, the historical oil pollution change data, and the current impeller vibration data; The third preset detection weight is updated based on the second current learning weight, the historical oil stain change data, and the current filter transmittance data.

[0011] Optionally, the method further includes: If the current ambient humidity data is detected to be greater than or equal to the preset ambient humidity data, the current threshold index data is reduced.

[0012] Optionally, the method further includes: If the current period usage frequency is detected to be less than or equal to the preset usage frequency, the current threshold indicator data is increased; If the current cycle usage frequency is less than or equal to the preset usage frequency, and the operation control command corresponding to the preset cleaning operation is detected, the preset oil softening operation is executed. If the preset oil softening operation is detected to have ended, the preset cleaning operation is executed.

[0013] According to another aspect of the present disclosure, an oil stain cleaning device is provided, the device comprising: The first data acquisition module is used to acquire the current multimodal oil pollution sensing data of the target device, as well as the current threshold index data corresponding to the target device. The oil contamination level analysis module is used to analyze the oil contamination level of the target device based on the multimodal oil contamination sensing data, and obtain the target oil contamination index data corresponding to the target device; The comparison processing module is used to compare the target oil pollution index data and the current threshold index data to obtain the target comparison result; The first execution module is used to perform a preset cleaning operation on the target device based on the target comparison result.

[0014] According to another aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the above-described oil stain cleaning method.

[0015] According to another aspect of the present disclosure, a computer-readable storage medium is provided, which, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform the above-described oil stain cleaning method.

[0016] According to another aspect of the present disclosure, a computer program product containing instructions is provided that, when run on a computer, causes the computer to perform the above-described oil stain cleaning method.

[0017] The technical solutions provided by the embodiments of this disclosure have at least the following beneficial effects: By acquiring the current multimodal oil contamination sensing data of the target device and the corresponding current threshold index data of the target device, the oil contamination level of the target device is analyzed based on the multimodal oil contamination sensing data to obtain the target oil contamination index data of the target device. This enables the analysis of the oil contamination level of the target device. Then, the target oil contamination index data and the current threshold index data are compared to obtain the target comparison result, which allows the determination of the current cleaning needs of the target device. Next, based on the target comparison result, preset cleaning operations are executed for the target device, which can improve the cleaning effect of the target device in different usage environments. Accurate cleaning decisions are made based on the real-time status of oil contamination, thereby achieving efficient, accurate and energy-saving oil contamination cleaning.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0020] Figure 1 This is a schematic diagram illustrating an application system according to an exemplary embodiment; Figure 2 This is a flowchart illustrating an oil stain cleaning method according to an exemplary embodiment; Figure 3 This is a block diagram illustrating an oil stain cleaning device according to an exemplary embodiment; Figure 4 This is a block diagram illustrating an electronic device for cleaning oil stains from a target device according to an exemplary embodiment; Figure 5 This is a block diagram illustrating another electronic device for cleaning oil stains on a target device according to an exemplary embodiment. Detailed Implementation

[0021] Various exemplary embodiments, features, and aspects of this application will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0022] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0023] Furthermore, to better illustrate this application, numerous specific details are provided in the following detailed embodiments. Those skilled in the art should understand that this application can be implemented without certain specific details. In some instances, methods, means, components, and circuits well-known to those skilled in the art have not been described in detail in order to highlight the main points of this application.

[0024] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating an application system according to an exemplary embodiment. The application system can be used for the oil stain cleaning method of this application. The application system can include at least an oil stain cleaning device and a target device. The oil stain cleaning device can be used to implement the aforementioned oil stain cleaning method. The target device can refer to the device to be cleaned. Specifically, the target device can include an oil fume treatment device (such as a range hood); for example, in this embodiment, a range hood is used as the target device. Figure 1 As shown, the oil stain cleaning equipment may include a controller, a multimodal oil stain sensing module, and a cleaning module. The controller can be used to implement the aforementioned oil stain cleaning method. The cleaning module can be used to clean the target equipment. The multimodal oil stain sensing module can be used to acquire the current multimodal oil stain sensing data of the target equipment. The multimodal oil stain sensing module may include a duct pressure differential detection submodule, an impeller vibration detection submodule, and a filter light transmittance detection submodule. Furthermore, the oil stain cleaning equipment may also include an oil stain softening module and a humidity acquisition module; the oil stain softening module may include a vibration module or a heating module. The vibration module can be used to vibrate the target equipment before cleaning to soften the oil stains. The heating module can be used to preheat the target equipment before cleaning to soften the oil stains. The humidity acquisition module can be used to acquire the humidity of the current environment of the target equipment.

[0025] It should be noted that this specification provides method operation steps as shown in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operation steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many steps and does not represent the only execution order.

[0026] Specifically, Figure 2This is a flowchart illustrating an oil stain cleaning method according to an exemplary embodiment. Figure 2 As shown, this oil stain cleaning method can be used in electronic devices such as terminals or servers, and specifically includes the following steps: S201: Obtain the current multimodal oil pollution sensing data of the target device, as well as the current threshold index data corresponding to the target device.

[0027] In one specific embodiment, multimodal oil contamination sensing data can be used to reflect the current oil contamination level of a target device from multiple different detection dimensions. Multimodal oil contamination sensing data may include at least two of various sensing data, such as current impeller vibration data, current duct pressure differential data, and current filter transmittance data.

[0028] In one specific embodiment, the current impeller vibration data can be used to indicate the degree of abnormal vibration during the operation of the target equipment. Specifically, the aforementioned current impeller vibration data can be obtained through an impeller vibration detection submodule. The impeller vibration detection submodule may include a vibration sensor. It is understood that when oil accumulates, the vibration mode of the target equipment will change, especially the impeller imbalance will increase.

[0029] In a specific embodiment, the current rotation signal of the impeller in the target device can be collected based on the vibration sensor in the impeller vibration detection submodule. The frequency component data, amplitude data and phase data of the current rotation signal can be obtained by performing spectrum analysis on the current rotation signal. Accordingly, the current impeller vibration data can be determined based on the frequency component data, amplitude data and phase data.

[0030] In a specific embodiment, the aforementioned current impeller vibration data can be obtained using the following formula:

[0031] in, E v This is the current impeller vibration data; A i It is the first i The amplitude of each frequency component; f i It is the first i The frequency of each frequency component; It is the first i The phase of each frequency component; n It is the number of frequency components.

[0032] In one specific embodiment, the current duct pressure differential data can be used to reflect the impact of oil accumulation within the target equipment on airflow. It is understood that when excessive oil accumulation occurs, the airflow resistance in the duct increases, leading to changes in pressure differential.

[0033] In one specific embodiment, the current duct pressure difference detection submodule can be used to obtain the duct pressure difference data. Specifically, multiple pressure sensors in the duct pressure difference detection submodule can be used to collect duct inlet pressure data and duct outlet pressure data; correspondingly, the difference between the aforementioned duct inlet pressure data and duct outlet pressure data can be used as the current duct pressure difference data.

[0034] In one specific embodiment, the current filter transmittance data can be used to indicate the filter's transmittance. It is understood that the filter's transmittance can indirectly reflect the accumulation of oil; oil accumulation leads to a decrease in the filter's transmittance.

[0035] In one specific embodiment, the photoelectric sensor in the filter transmittance detection submodule can collect light intensity data after passing through the filter. Based on the preset light intensity data corresponding to the lighting device set in front of the filter and the aforementioned light intensity data after passing through the filter, the current filter transmittance data can be obtained. The aforementioned preset light intensity data can characterize the total light intensity before passing through the filter.

[0036] In a specific embodiment, the current filter transmittance data can be obtained using the following formula:

[0037] Where G represents the current light transmittance data of the filter; I filtered This is the light intensity data after passing through the filter; This is the preset light intensity data.

[0038] In one specific embodiment, the current threshold index data can be used to determine whether the current level of oil contamination requires cleaning. Specifically, the current threshold index data can be updated based on the target device's current usage frequency data and the current ambient humidity data. The device usage frequency data indicates the current cycle usage frequency of the target device. The current ambient humidity data indicates the humidity of the environment in which the target device is currently located.

[0039] In a specific embodiment, the aforementioned current threshold indicator data may be obtained in the following ways: Acquire the device usage frequency data and the current ambient humidity data corresponding to the target device; The current adjustment coefficient is determined based on equipment usage frequency data and current ambient humidity data; Update the current threshold index data based on the current adjustment coefficient.

[0040] In one specific embodiment, device operation data of the target device can be acquired; based on the aforementioned device operation data, the number of times the target device operates within the current period can be determined; the number of times the target device operates within the current period can be used as device usage frequency data. The device operation data may include the operating time of each instance of the target device. The current period may refer to a preset time range preceding the current moment. Specifically, the preset time range can be set according to actual application needs, and this disclosure does not impose any limitations.

[0041] In one specific embodiment, the current ambient humidity data corresponding to the target device can be collected based on the humidity acquisition module.

[0042] In one specific embodiment, the current adjustment coefficient can be determined by the following formula:

[0043] in, k This is the current adjustment coefficient; This is the current humidity adjustment factor; This is the current frequency adjustment coefficient; H This refers to the current ambient humidity data. F This refers to the frequency data used by the equipment.

[0044] In one specific embodiment, the current threshold indicator data can be determined by the following formula:

[0045] in, This is the current threshold indicator data; k This is the current adjustment coefficient.

[0046] In the above embodiments, by acquiring the device usage frequency data and the current ambient humidity data corresponding to the target device, determining the current adjustment coefficient based on the device usage frequency data and the current ambient humidity data, and updating the current threshold index data based on the current adjustment coefficient, it is possible to update the current threshold index data by combining humidity and usage frequency, thereby enabling more accurate cleaning decisions based on the current degree of oil contamination under different usage environments, improving cleaning effect, cleaning efficiency and energy saving effect.

[0047] In one specific embodiment, the above method may further include: Obtain historical cleaning metrics data; Based on historical cleaning index data, determine the current humidity adjustment coefficient and the current frequency adjustment coefficient; Accordingly, determining the current adjustment coefficient based on equipment usage frequency data and current ambient humidity data can include: The current adjustment coefficient is determined based on the current humidity adjustment coefficient, the current frequency adjustment coefficient, the equipment usage frequency data, and the current ambient humidity data.

[0048] In one specific embodiment, historical cleaning index data can characterize the cleaning effect after at least one preset cleaning operation performed before the current moment. Historical cleaning index data may include historical oil stain change data, historical differential pressure recovery data, historical humidity change data, or historical usage frequency change data.

[0049] In one specific embodiment, differential pressure data before and after the most recent cleaning operation can be obtained; historical differential pressure recovery data can be determined based on the differential pressure data before and after the operation. Specifically, the difference between the differential pressure data before and after the operation is divided by the differential pressure data before cleaning to obtain the historical differential pressure recovery data.

[0050] In one specific embodiment, historical oil contamination change data can be used to indicate the changes in oil contamination before and after at least one pre-defined cleaning operation performed prior to the current moment. Specifically, historical oil contamination change data can be determined based on the difference between the predicted oil contamination index data corresponding to the historical cleaning operation and the corresponding historical differential pressure recovery data.

[0051] In a specific embodiment, the current oil pollution index data predicted at the time of the most recent cleaning operation (i.e., the current oil pollution index data when the cleaning operation was triggered) and the historical differential pressure recovery data corresponding to the most recent cleaning operation (the differential pressure recovery data after the most recent cleaning operation) can be obtained. The difference between the current oil pollution index data predicted at the time of the most recent cleaning operation and the historical differential pressure recovery data corresponding to the most recent cleaning operation can be used as historical oil pollution change data.

[0052] In one specific embodiment, historical humidity change data can be used to indicate humidity changes before and after at least one preset cleaning operation performed prior to the current moment. Specifically, the difference in humidity data before and after any cleaning operation can be used as the historical humidity change data corresponding to any given cleaning operation.

[0053] In one specific embodiment, historical usage frequency change data can be used to indicate the change in usage frequency before and after at least one preset cleaning operation performed prior to the current moment. Specifically, multiple cleaning time intervals can be determined based on the time of the cleaning operation. Correspondingly, the difference in the number of uses within the cleaning time interval before and after any cleaning operation can be used as the historical usage frequency change data corresponding to any given cleaning operation. Here, any cleaning time interval can refer to the time interval obtained by using the times of two adjacent cleaning operations as the start and end times, respectively.

[0054] In one specific embodiment, the current humidity adjustment factor can refer to the attenuation factor corresponding to the ambient humidity dimension. The current humidity adjustment factor can be used to characterize the degree of influence of humidity on the threshold.

[0055] In one specific embodiment, the current frequency adjustment factor can refer to the attenuation factor corresponding to the frequency dimension used. The current frequency adjustment factor can characterize the degree of influence of the used frequency on the threshold.

[0056] In a specific embodiment, when the historical cleaning index data includes historical oil contamination change data, historical differential pressure recovery data, historical humidity change data, and historical usage frequency change data, the above-mentioned determination of the current humidity adjustment coefficient and the current frequency adjustment coefficient based on the historical cleaning index data may include: Based on historical oil pollution change data, determine the first current learning weight; The current humidity adjustment coefficient is determined based on the first current learning weight, historical differential pressure recovery data, and historical humidity change data. Based on the first current learning weight, historical differential pressure recovery data, and historical usage frequency change data, the current frequency adjustment coefficient is determined.

[0057] In one specific embodiment, the first current learning weight can be used to indicate the magnitude of the coefficient update.

[0058] In one specific embodiment, the current first learning weight can be updated based on historical oil pollution change data to obtain the updated first learning weight. Specifically, the first learning weight can be obtained using the following formula:

[0059] in, λ （k） The updated first current learning weight; λ 0 represents the initial first current learning weight; α represents historical oil pollution change data; α is a preset adjustment parameter. Specifically, the preset adjustment parameter can be determined according to actual application needs, and the value range of the preset adjustment parameter can be 0.1~1.

[0060] In a specific embodiment, the change in the current humidity adjustment coefficient can be determined based on the first current learning weight, historical differential pressure recovery data, and historical humidity change data. Correspondingly, the current humidity adjustment coefficient can be updated based on this change. Specifically, the change in the current humidity adjustment coefficient can be obtained using the following formula:

[0061] in, This is the current humidity adjustment factor; The first current learning weight; Data was recovered from historical differential pressure. This is the preset differential pressure recovery data; This data represents historical humidity variations. Specifically, preset differential pressure recovery data can be used to indicate the desired differential pressure recovery rate; it can be set according to actual application needs. For example, the preset differential pressure recovery data could be 95%.

[0062] In one specific embodiment, the change in the current frequency adjustment coefficient can be determined based on the first current learning weight, historical differential pressure recovery data, and historical usage frequency change data. Correspondingly, the current frequency adjustment coefficient can be updated based on this change. Specifically, the change in the current frequency adjustment coefficient can be obtained using the following formula:

[0063] in, This is the current frequency adjustment coefficient; The first current learning weight; Data was recovered from historical differential pressure. This is the preset differential pressure recovery data; This data represents historical usage frequency changes. Specifically, preset differential pressure recovery data can be used to indicate the desired differential pressure recovery rate; it can be set according to actual application needs. For example, the preset differential pressure recovery data could be 95%.

[0064] In the above embodiments, a first current learning weight is determined based on historical oil stain change data. A current humidity adjustment coefficient is determined based on the first current learning weight, historical pressure difference recovery data, and historical humidity change data. The current humidity adjustment coefficient can be adaptively adjusted according to the cleaning effect. A current frequency adjustment coefficient is determined based on the first current learning weight, historical pressure difference recovery data, and historical usage frequency change data. The current frequency adjustment coefficient can be adaptively adjusted according to the cleaning effect. This enables accurate judgment of the cleaning needs of oil stains under different usage environments, improving cleaning effect, cleaning efficiency, and energy saving effect.

[0065] In one specific embodiment, the above method may further include: If the current ambient humidity data is detected to be greater than or equal to the preset ambient humidity data, the current threshold index data will be reduced.

[0066] In one specific embodiment, preset ambient humidity data can be used to determine whether the current ambient humidity is too high. Specifically, the preset ambient humidity data can be set according to actual application needs, and this disclosure does not impose any limitations.

[0067] In one specific embodiment, if the current ambient humidity data is detected to be greater than or equal to the preset ambient humidity data, it can be determined that the current humidity will cause the oil stains to become more viscous, and the oil stains will adhere more easily to the surface of the equipment. The current threshold index data can be appropriately reduced so that the preset cleaning operation can be started in advance.

[0068] In the above embodiments, by reducing the current threshold index data when the current ambient humidity data is detected to be greater than or equal to the preset ambient humidity data, the cleaning effect of oil stains in high humidity environments can be improved, and the cleaning efficiency and energy saving effect can be enhanced.

[0069] S203: Based on multimodal oil pollution sensing data, analyze the degree of oil pollution on the target equipment to obtain the target oil pollution index data corresponding to the target equipment.

[0070] In one specific embodiment, target oil contamination index data can be used to characterize the current oil contamination level of the target device.

[0071] In a specific embodiment, when the multimodal oil contamination sensing data includes current impeller vibration data, current duct pressure difference data, and current filter transmittance data, the above-mentioned analysis of the oil contamination level of the target equipment based on the multimodal oil contamination sensing data to obtain the target oil contamination index data corresponding to the target equipment may include: Based on the first preset detection weight and the current air duct pressure difference data, the first oil pollution index data is determined; Based on the second preset detection weight and the current impeller vibration data, the second oil pollution index data is determined; The third oil stain index data is determined based on the third preset detection weight and the current filter transmittance data; The first, second, and third oil pollution index data are overlaid to obtain the target oil pollution index data.

[0072] In one specific embodiment, the first preset detection weight can characterize the degree of influence of the current duct pressure difference data on the target oil pollution index data. Specifically, the first preset detection weight can be pre-set or updated according to actual application conditions.

[0073] In one specific embodiment, the first oil pollution index data can refer to the oil pollution index data determined based on the current duct pressure difference data. Specifically, the first oil pollution index data can be obtained by multiplying the first preset detection weight by the current duct pressure difference data.

[0074] In one specific embodiment, the second preset detection weight can characterize the degree of influence of the current impeller vibration data on the target oil pollution index data. Specifically, the second preset detection weight can be pre-set or updated according to actual application conditions.

[0075] In one specific embodiment, the second oil contamination index data can refer to the oil contamination index data determined based on the current impeller vibration data. Specifically, the second oil contamination index data can be obtained by multiplying the second preset detection weight by the current impeller vibration data.

[0076] In one specific embodiment, the third preset detection weight can characterize the degree of influence of the current filter transmittance data on the target oil stain index data. Specifically, the third preset detection weight can be pre-set or updated according to actual application conditions.

[0077] In one specific embodiment, the third oil contamination index data can refer to the oil contamination index data determined based on the current filter transmittance data. Specifically, the third oil contamination index data can be obtained by multiplying the third preset detection weight by the current filter transmittance data.

[0078] In a specific embodiment, the target oil pollution index data can be obtained using the following formula:

[0079] in, For target oil pollution index data; The first preset detection weight; This is the current duct pressure differential data; This is the second preset detection weight; This is the current impeller vibration data; The third preset detection weight; This is the current light transmittance data for the filter.

[0080] In the above embodiments, a first oil contamination index is determined based on a first preset detection weight and current duct pressure difference data; a second oil contamination index is determined based on a second preset detection weight and current impeller vibration data; and a third oil contamination index is determined based on a third preset detection weight and current filter light transmittance data. The first, second, and third oil contamination index data are then superimposed to obtain the target oil contamination index data. This improves the prediction accuracy of the target oil contamination index data for the target equipment, thereby enabling accurate judgment of the cleaning needs for oil contamination levels under different usage environments, and improving cleaning effect, cleaning efficiency, and energy saving effect.

[0081] In one specific embodiment, the above method may further include: Obtain historical cleaning metrics data; Based on historical cleanliness index data, current impeller vibration data, current duct pressure difference data, and current filter transmittance data, update the first preset detection weight, the second preset detection weight, and the third preset detection weight.

[0082] In one specific embodiment, historical cleaning index data can characterize the cleaning effect after at least one execution of a preset cleaning operation before the current moment.

[0083] In a specific embodiment, where the historical cleanliness index data includes historical oil stain change data, the above-mentioned updating of the first preset detection weight, the second preset detection weight, and the third preset detection weight based on the historical cleanliness index data, current impeller vibration data, current duct pressure difference data, and current filter transmittance data may include: Based on historical oil pollution change data, determine the second current learning weight; Based on the second current learning weight, historical oil pollution change data, and current duct pressure difference data, update the first preset detection weight; The second preset detection weight is updated based on the second current learning weight, historical oil pollution change data, and current impeller vibration data. The third preset detection weight is updated based on the second current learning weight, historical oil stain change data, and current filter transmittance data.

[0084] In one specific embodiment, the second current learning weight can be used to indicate the update strength of the coefficients. Specifically, the update process of the second current learning weight can refer to the update process of the first current learning weight described above, and will not be repeated here.

[0085] In a specific embodiment, the updated first preset detection weight can be obtained by the following formula:

[0086] in, The updated first preset detection weight; The first preset detection weight before the update; This refers to historical oil pollution change data; The second current learning weight; This is the current duct pressure difference data.

[0087] In a specific embodiment, the updated second preset detection weight can be obtained by the following formula:

[0088] in, The updated second preset detection weights; The second preset detection weight before the update; The second current learning weight; This refers to historical oil pollution change data; This is the current impeller vibration data.

[0089] In a specific embodiment, the updated third preset detection weight can be obtained by the following formula:

[0090] in, The updated third preset detection weights; The third preset detection weight before the update; The second current learning weight; This refers to historical oil pollution change data; This is the current light transmittance data for the filter.

[0091] In the above embodiments, by determining a second current learning weight based on historical oil stain change data, updating a first preset detection weight based on the second current learning weight, historical oil stain change data, and current duct pressure difference data, updating a second preset detection weight based on the second current learning weight, historical oil stain change data, and current impeller vibration data, and updating a third preset detection weight based on the second current learning weight, historical oil stain change data, and current filter light transmittance data, adaptive adjustment of the first, second, and third preset detection weights can be achieved in combination with the cleaning effect, thereby further improving the prediction accuracy of the target oil stain index data of the target equipment.

[0092] S205: Compare the target oil pollution index data with the current threshold index data to obtain the target comparison result.

[0093] In one specific embodiment, the target comparison result can be used to indicate the magnitude relationship between the target oil pollution index data and the current threshold index data. The target comparison result may include a first comparison result or a second comparison result. The first comparison result can be used to indicate that the target oil pollution index data is greater than or equal to the current threshold index data; the second comparison result can be used to indicate that the target oil pollution index data is less than the current threshold index data.

[0094] S207: Based on the target comparison results, perform preset cleaning operations for the target equipment.

[0095] In one specific embodiment, if the target comparison result is the first comparison result, a preset cleaning operation can be performed on the target device.

[0096] In one specific embodiment, if the target comparison result is the second comparison result, that is, the preset cleaning operation for the target device is not performed, but the current multimodal oil stain perception data can continue to be acquired so that the cleaning operation can be accurately performed as the oil stain level gradually increases to meet the current threshold index data.

[0097] In one specific embodiment, the above method may further include: If the current usage frequency is detected to be less than or equal to the preset usage frequency, the current threshold indicator data will be increased. If the current cycle usage frequency is less than or equal to the preset usage frequency, and the operation control command corresponding to the preset cleaning operation is detected, the preset oil softening operation is executed. If the preset oil softening operation is detected to have finished, the preset cleaning operation will be executed.

[0098] In one specific embodiment, the preset usage frequency can be set according to the actual application needs, and this disclosure does not limit it.

[0099] In one specific embodiment, taking the preset oil softening operation as a preheating operation as an example, the preheating time can be set in combination with the usage frequency, and can be determined by the following formula:

[0100] in, This refers to the duration of this pre-event promotion; These are preset coefficients; This refers to the frequency data used by the equipment.

[0101] In one specific embodiment, the preset oil softening operation refers to softening the oil in the target device. Specifically, the preset oil softening operation can be executed by activating the oil softening module. It is understood that under low-frequency use, the oil may have hardened, making cleaning difficult; softening the oil through short-term heating or vibration can make it easier to clean.

[0102] In the above embodiments, by acquiring the current multimodal oil stain perception data of the target device and the current threshold index data of the target device, the oil stain degree of the target device is analyzed based on the multimodal oil stain perception data to obtain the target oil stain index data corresponding to the target device. This enables the analysis of the oil stain degree of the target device. Then, the target oil stain index data and the current threshold index data are compared to obtain the target comparison result, which enables the determination of the current cleaning needs of the target device. Next, based on the target comparison result, a preset cleaning operation is executed for the target device, which enables the target device to improve the cleaning effect in different usage environments. It makes accurate cleaning decisions based on the real-time state of oil stains, thereby achieving efficient, accurate and energy-saving oil stain cleaning.

[0103] Figure 3 This is a block diagram illustrating an oil stain cleaning device according to an exemplary embodiment. Specifically, as shown... Figure 3 As shown, the device may include: The first data acquisition module 310 is used to acquire the current multimodal oil pollution sensing data of the target device, as well as the current threshold index data corresponding to the target device. The oil contamination level analysis module 320 is used to perform oil contamination level analysis on the target device based on the multimodal oil contamination sensing data, and obtain the target oil contamination index data corresponding to the target device; The comparison processing module 330 is used to compare the target oil pollution index data and the current threshold index data to obtain the target comparison result; The first execution module 340 is used to perform a preset cleaning operation on the target device based on the target comparison result.

[0104] In one specific embodiment, the device may further include: The second data acquisition module is used to acquire the device usage frequency data and the current ambient humidity data corresponding to the target device. The first coefficient determination module is used to determine the current adjustment coefficient based on the device usage frequency data and the current ambient humidity data; The threshold update module is used to update the current threshold index data based on the current adjustment coefficient.

[0105] In one specific embodiment, the device may further include: The third data acquisition module is used to acquire historical cleaning index data; the historical cleaning index data represents the cleaning effect after at least one preset cleaning operation performed before the current moment. The second coefficient determination module is used to determine the current humidity adjustment coefficient and the current frequency adjustment coefficient based on the historical cleaning index data. Accordingly, the first coefficient determination module may include: The third coefficient determination module is used to determine the current adjustment coefficient based on the current humidity adjustment coefficient, the current frequency adjustment coefficient, the device usage frequency data, and the current ambient humidity data.

[0106] In one specific embodiment, the historical cleaning index data includes historical oil stain change data, historical differential pressure recovery data, historical humidity change data, and historical usage frequency change data; the second coefficient determination module may include: The first weight determination module is used to determine the first current learning weight based on the historical oil pollution change data; The fourth coefficient determination module is used to determine the current humidity adjustment coefficient based on the first current learning weight, the historical differential pressure recovery data, and the historical humidity change data. The fifth coefficient determination module is used to determine the current frequency adjustment coefficient based on the first current learning weight, the historical pressure difference recovery data, and the historical usage frequency change data.

[0107] In one specific embodiment, the multimodal oil contamination sensing data includes current impeller vibration data, current duct pressure difference data, and current filter transmittance data; the oil contamination level analysis module 320 may include: The first indicator determination module is used to determine the first oil pollution indicator data based on the first preset detection weight and the current air duct pressure difference data; The second indicator determination module is used to determine the second oil pollution indicator data based on the second preset detection weight and the current impeller vibration data; The third indicator determination module is used to determine the third oil stain indicator data based on the third preset detection weight and the current filter transmittance data; The overlay processing module is used to overlay the first oil pollution index data, the second oil pollution index data, and the third oil pollution index data to obtain the target oil pollution index data.

[0108] In one specific embodiment, the device may further include: The fourth data acquisition module is used to acquire historical cleaning index data; the historical cleaning index data represents the cleaning effect after at least one preset cleaning operation performed before the current moment; The weight update module is used to update the first preset detection weight, the second preset detection weight, and the third preset detection weight based on the historical cleaning index data, the current impeller vibration data, the current duct pressure difference data, and the current filter light transmittance data.

[0109] In one specific embodiment, the historical cleaning index data includes historical oil pollution change data; the weight update module includes: The second weight determination module is used to determine the second current learning weight based on the historical oil pollution change data; The first weight update module is used to update the first preset detection weight based on the second current learning weight, the historical oil pollution change data, and the current air duct pressure difference data; The second weight update module is used to update the second preset detection weight based on the second current learning weight, the historical oil pollution change data, and the current impeller vibration data; The third weight update module is used to update the third preset detection weight based on the second current learning weight, the historical oil stain change data, and the current filter transmittance data.

[0110] In one specific embodiment, the device may further include: The second execution module is used to reduce the current threshold index data when the current ambient humidity data is detected to be greater than or equal to the preset ambient humidity data.

[0111] In one specific embodiment, the device may further include: The third execution module is used to add the current threshold index data when the current cycle usage frequency is detected to be less than or equal to the preset usage frequency. The fourth execution module is used to execute a preset oil softening operation when it is detected that the current cycle usage frequency is less than or equal to the preset usage frequency and the operation control command corresponding to the preset cleaning operation is detected. The fifth execution module is used to execute the preset cleaning operation when the preset oil softening operation is detected to have ended.

[0112] Regarding the apparatus in the above embodiments, the specific manner in which each module and unit performs its operations has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0113] Figure 4 This is a block diagram illustrating an electronic device for cleaning oil stains on a target device according to an exemplary embodiment. The electronic device may be a server, and its internal structure diagram may be as follows: Figure 4As shown, the electronic device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements an oil stain cleaning method.

[0114] Figure 5 This is a block diagram illustrating another electronic device for cleaning oil stains on a target device according to an exemplary embodiment. The electronic device may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown, the electronic device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements an oil stain cleaning method. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0115] Those skilled in the art will understand that Figure 4 or Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present disclosure and does not constitute a limitation on the electronic device to which the present disclosure is applied. A specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0116] In an exemplary embodiment, an electronic device is also provided, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the oil stain cleaning method as described in the embodiments of this disclosure.

[0117] In an exemplary embodiment, a computer-readable storage medium is also provided, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the oil stain cleaning method of the present disclosure embodiments.

[0118] In an exemplary embodiment, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to perform the oil stain cleaning method of the present disclosure embodiments.

[0119] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0120] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0121] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for cleaning oil stains, characterized in that, The method includes: Acquire the current multimodal oil pollution sensing data of the target device, as well as the current threshold index data corresponding to the target device; Based on the multimodal oil pollution sensing data, the oil pollution level of the target device is analyzed to obtain the target oil pollution index data corresponding to the target device; The target oil pollution index data and the current threshold index data are compared to obtain the target comparison result; Based on the target comparison results, a preset cleaning operation is performed on the target device.

2. The method according to claim 1, characterized in that, The method further includes: Obtain the device usage frequency data and the current ambient humidity data corresponding to the target device; Based on the device usage frequency data and the current ambient humidity data, determine the current adjustment coefficient; Update the current threshold index data based on the current adjustment coefficient.

3. The method according to claim 2, characterized in that, The method further includes: Acquire historical cleaning index data; the historical cleaning index data represents the cleaning effect after at least one preset cleaning operation performed before the current moment. Based on the historical cleaning index data, determine the current humidity adjustment coefficient and the current frequency adjustment coefficient; The step of determining the current adjustment coefficient based on the device usage frequency data and the current ambient humidity data includes: The current adjustment coefficient is determined based on the current humidity adjustment coefficient, the current frequency adjustment coefficient, the device usage frequency data, and the current ambient humidity data.

4. The method according to claim 3, characterized in that, The historical cleaning index data includes historical oil stain change data, historical pressure difference recovery data, historical humidity change data, and historical usage frequency change data. The step of determining the current humidity adjustment coefficient and the current frequency adjustment coefficient based on the historical cleaning index data includes: Based on the historical oil pollution change data, a first current learning weight is determined; The current humidity adjustment coefficient is determined based on the first current learning weight, the historical differential pressure recovery data, and the historical humidity change data; The current frequency adjustment coefficient is determined based on the first current learning weight, the historical differential pressure recovery data, and the historical usage frequency change data.

5. The method according to claim 1, characterized in that, The multimodal oil contamination sensing data includes current impeller vibration data, current duct pressure difference data, and current filter transmittance data; based on the multimodal oil contamination sensing data, the oil contamination level analysis of the target equipment is performed to obtain target oil contamination index data corresponding to the target equipment, including: Based on the first preset detection weight and the current air duct pressure difference data, the first oil pollution index data is determined; The second oil contamination index data is determined based on the second preset detection weight and the current impeller vibration data; Based on the third preset detection weight and the current filter transmittance data, the third oil stain index data is determined; The first oil pollution index data, the second oil pollution index data, and the third oil pollution index data are overlaid to obtain the target oil pollution index data.

6. The method according to claim 5, characterized in that, The method further includes: Acquire historical cleaning index data; the historical cleaning index data represents the cleaning effect after at least one preset cleaning operation performed before the current moment; Based on the historical cleaning index data, the current impeller vibration data, the current duct pressure difference data, and the current filter light transmittance data, the first preset detection weight, the second preset detection weight, and the third preset detection weight are updated.

7. The method according to claim 6, characterized in that, The historical cleanliness index data includes historical oil stain change data; the step of updating the first preset detection weight, the second preset detection weight, and the third preset detection weight based on the historical cleanliness index data, the current impeller vibration data, the current duct pressure difference data, and the current filter transmittance data includes: Based on the historical oil pollution change data, a second current learning weight is determined; The first preset detection weight is updated based on the second current learning weight, the historical oil pollution change data, and the current air duct pressure difference data; The second preset detection weight is updated based on the second current learning weight, the historical oil pollution change data, and the current impeller vibration data; The third preset detection weight is updated based on the second current learning weight, the historical oil stain change data, and the current filter transmittance data.

8. The method according to claim 1, characterized in that, The method further includes: If the current ambient humidity data is detected to be greater than or equal to the preset ambient humidity data, the current threshold index data is reduced.

9. The method according to any one of claims 1-8, characterized in that, The method further includes: If the current period usage frequency is detected to be less than or equal to the preset usage frequency, the current threshold indicator data is increased; If the current cycle usage frequency is less than or equal to the preset usage frequency, and the operation control command corresponding to the preset cleaning operation is detected, the preset oil softening operation is executed. If the preset oil softening operation is detected to have ended, the preset cleaning operation is executed.

10. An oil stain cleaning device, characterized in that, The device includes: The first data acquisition module is used to acquire the current multimodal oil pollution sensing data of the target device, as well as the current threshold index data corresponding to the target device. The oil contamination level analysis module is used to analyze the oil contamination level of the target device based on the multimodal oil contamination sensing data, and obtain the target oil contamination index data corresponding to the target device; The comparison processing module is used to compare the target oil pollution index data and the current threshold index data to obtain the target comparison result; The first execution module is used to perform a preset cleaning operation on the target device based on the target comparison result.

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