A clothes drying rack sonar detection and processing method based on artificial intelligence

Abstract

The present invention provides a clothes drying rack sonar detection processing method and system based on artificial intelligence, which belongs to the field of artificial intelligence technology, and specifically comprises: dividing the disinfection working period into different working periods according to the disinfection treatment time, and determining the optimal working period in the working period according to the distribution of the disinfectable divided time periods in different working periods and the probability of personnel appearing in different divided time periods, disinfecting the clothes drying in the optimal working period, and according to the analysis results of the sonar detection processing data of the current working period and the probability of personnel appearing in the current working period, using the artificial intelligence-based prediction model to determine the working continuity prediction value of the clothes drying rack, and determining whether the disinfection treatment needs to be suspended in the current working period based on the work continuity prediction value, thereby ensuring the continuity of the disinfection treatment and the accuracy of personnel identification.

Description

Technical Field

[0001] The present invention belongs to the technical field of artificial intelligence, and in particular relates to a clothes drying rack sonar detection and processing method based on artificial intelligence. Background Art

[0002] Many of today's smart clothes drying racks have added the function of automatically disinfecting clothes. In order to avoid causing harm to people walking under the clothes drying rack, there is a millimeter-wave radar technology solution: embedded in the ceiling-mounted clothes drying rack, it will give feedback after recognizing the approach of a human body. However, the use of millimeter-wave radar will lead to an increase in the overall manufacturing cost. Therefore, how to realize the recognition of people walking under the clothes drying rack has become a technical problem that needs to be solved urgently.

[0003] In order to solve the above technical problems, the present application realizes the recognition of moving personnel within a preset area through a sonar module integrated in a voice recognition module, and controls the automatic disinfection module according to the recognition and analysis results. Specifically, the present invention provides a clothes drying rack sonar detection and processing method based on artificial intelligence. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0005] According to one aspect of the present invention, an artificial intelligence-based clothes drying rack sonar detection and processing method is provided.

[0006] A clothes drying rack sonar detection and processing method based on artificial intelligence, characterized by specifically comprising:

[0007] S1 determines the weight of the clothes on the clothes drying rack and the disinfection treatment time of the clothes according to the weight sensing module of the clothes drying rack;

[0008] S2 determines the disinfection working period of the clothes drying rack according to the current time and the preset disinfection processing time, determines the historical detection data of personnel and the probability of personnel appearance in different divided time periods of the disinfection working period according to the sonar detection processing data integrated in the voice recognition module of the clothes drying rack, and determines the disinfection processing divided time period in the divided time period according to the probability of personnel appearance;

[0009] S3 divides the disinfection working period into different working periods according to the duration of disinfection treatment, and determines the optimal working period in the working period according to the distribution of the disinfection treatment divided periods in different working periods and the probability of personnel appearing in different divided periods;

[0010] S4 disinfects the clothes drying during the optimal working period, and uses an artificial intelligence-based prediction model to determine the working continuity prediction value of the clothes drying rack according to the analysis results of the sonar detection processing data of the current working period and the probability of people appearing in the current working period, and determines whether the disinfection process needs to be suspended during the current working period based on the working continuity prediction value.

[0011] The beneficial effects of the present invention are:

[0012] 1. The optimal working period is determined according to the distribution of the disinfectable time periods in different working periods and the probability of personnel appearing in different time periods. Not only the difference in the reliability of continuous disinfection work caused by the number of disinfectable time periods and the temporal continuity in different working periods is taken into account, but also the probability of personnel appearing in different time periods is considered comprehensively to realize the probability of interruption of disinfection, thereby realizing the screening of the optimal working period with higher work continuity and lower interruption probability.

[0013] 2. According to the analysis results of the sonar detection processing data in the current working period and the probability of personnel appearance in the current working period, an artificial intelligence-based prediction model is used to determine the working continuity prediction value of the clothes drying rack, thereby avoiding the occurrence of technical problems with a high probability of interruption of the disinfection process due to too many people in the current working period, and ensuring the continuity and reliability of the disinfection process.

[0014] A further technical solution is that the duration of the disinfection treatment of the clothes to be dried is determined based on a matching result of the weight of the clothes to be dried on the clothes drying rack.

[0015] A further technical solution is that the preset disinfection treatment time is determined according to the optimal disinfection treatment cycle for the clothes drying.

[0016] A further technical solution is that the analysis results of the sonar detection processing data of the current working period include the number of detection personnel, the time intervals and durations of appearance of different detection personnel.

[0017] A further technical solution is to use the divided time period corresponding to the current moment as the current working time period.

[0018] A further technical solution is that the method for determining the predicted value of the working continuity of the clothes drying rack is:

[0019] Determine the number of detection personnel in the current working period according to the analysis results of the sonar detection processing data in the current working period, and determine the discontinuity evaluation amount of the clothes drying rack in the current working period in combination with the appearance time intervals and appearance durations of different detection personnel;

[0020] By taking the discontinuity evaluation amount of the clothes drying rack in the current working period and the probability of personnel appearance as input quantities, an artificial intelligence-based prediction model is adopted to determine the working continuity prediction value of the clothes drying rack.

[0021] A further technical solution is to determine whether the disinfection process needs to be suspended during the current working period based on the work continuity prediction value, specifically including:

[0022] When the predicted value of the working continuity of the current working period does not meet the requirement, it is determined that the disinfection process of the current working period needs to be suspended.

[0023] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0025] Figure 1 It is a flow chart of a clothes drying rack sonar detection processing method based on artificial intelligence;

[0026] Figure 2 It is a flow chart of a method for determining the probability of occurrence of personnel within a divided time period;

[0027] Figure 3 The present invention is a flow chart of a method for determining an optimal working period among working periods. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0029] The sonar detection technology based on intelligent voice recognition clothes drying rack belongs to the technical field of ultrasonic detection. It consists of a voice module, a microphone, and a speaker. The speaker emits a low-frequency ultrasonic signal, and the microphone processes the received echo signal. On the basis of a voice recognition module, it implements ultrasonic detection by implanting an algorithm in the voice chip on the voice module. In addition to the traditional voice recognition function, it can also run a low-frequency ultrasonic signal system to process and analyze the received echo signal to achieve perception of objects in the environment.

[0030] The functions that this clothes drying rack can accomplish include: first, the smart clothes drying rack can be awakened by offline voice recognition, and the wake-up word and command word can be customized. The wake-up word is a technology based on intelligent voice recognition technology. The voice assistant on the device can be awakened by recognizing a specific wake-up word, so that the device can receive the user's instructions and complete the corresponding action; the command word is the action that the user needs the device to complete. The wake-up word and command word can be customized according to the user's needs to improve the user's experience. ② Another part of the function that can be completed is ranging. By implanting an algorithm in the voice chip of the voice module, the frequency of transmitting and receiving sound waves of the general voice module is increased from the general 8k to 24k, ensuring that the frequency will not generate noise to the human body, and high anti-interference, high precision (millimeter level), and high response (millimeter level) can be achieved. The number of speakers and microphones can be increased, and different environmental parameters and human body status parameters can be output according to different scene requirements. ③ In order to meet the intelligent needs of clothes drying racks, many styles of smart clothes drying racks are now equipped with automatic disinfection function. The specific implementation method is: the clothes drying rack is installed on the balcony or in a place with enough space. When you need to dry clothes, you can lower the clothes drying rack through the remote control, or you can lower the clothes drying rack through the voice command word "lower the clothes drying rack". After the clothes are hung, the clothes drying rack starts working and turns on the automatic disinfection function. The purpose of sonar detection is to prevent the disinfection function from causing harm to people approaching the clothes drying rack when someone approaches the clothes drying rack in the environment; the specific detection distance can be modified in advance through the algorithm program. In order to meet the use needs of clothes drying racks in most scenarios, we set the detection distance to be centered on the clothes drying rack and identify a circular area with a radius of 1m downward. When the ultrasonic wave detects that the human body passes through the edge range, it responds quickly to turn off the disinfection function to avoid harm to the human body.

[0031] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a clothes drying rack sonar detection and processing method based on artificial intelligence is provided, which is characterized by specifically comprising:

[0032] S1 determines the weight of the clothes on the clothes drying rack and the disinfection treatment time of the clothes according to the weight sensing module of the clothes drying rack;

[0033] Specifically, the duration of the disinfection treatment of the clothes drying is determined according to a matching result of the weight of the clothes drying on the clothes drying rack.

[0034] Furthermore, the preset disinfection treatment time is determined according to the optimal disinfection treatment cycle for the clothes drying.

[0035] S2 determines the disinfection working period of the clothes drying rack according to the current time and the preset disinfection processing time, determines the historical detection data of personnel and the probability of personnel appearance in different divided time periods of the disinfection working period according to the sonar detection processing data integrated in the voice recognition module of the clothes drying rack, and determines the disinfection processing divided time period in the divided time period according to the probability of personnel appearance;

[0036] Specifically, Figure 2 As shown, the method for determining the probability of personnel appearing in the divided time periods is:

[0037] Determine the number of historically detected personnel in the divided time periods of different dates and the time intervals between different historically detected personnel through the historical detection data of personnel in the divided time periods, and determine the frequency of personnel appearance in the divided time periods of different dates based on the number of historically detected personnel in the divided time periods of different dates and the time intervals between different historically detected personnel;

[0038] The probability of a person appearing in a divided time period is determined according to the frequency of the person appearing in the divided time periods on different dates.

[0039] Furthermore, when the probability of the presence of personnel in the divided time period is less than a preset probability, the divided time period is determined to be a divided time period that can be disinfected.

[0040] In another embodiment, the method for determining the probability of occurrence of personnel within the divided time periods is:

[0041] Determine the number of historically detected personnel in the divided time periods of different dates through the historical detection data of personnel in the divided time periods, and take the date when the number of historically detected personnel is greater than the preset number of personnel as the unmatched date, and judge whether the number of unmatched dates meets the requirements, if so, proceed to the next step, if not, determine the probability of personnel appearance in the divided time periods based on the number of unmatched dates;

[0042] Obtain the appearance time intervals between different historical detected persons in different divided time periods on different dates, and determine the frequent appearance durations in different divided time periods on different dates based on the appearance time intervals, and judge whether the number of dates whose frequent appearance durations do not meet the requirements meets the requirements, if so, proceed to the next step, if not, determine the appearance probability of the persons in the divided time periods based on the number of dates whose frequent appearance durations do not meet the requirements;

[0043] Determine the frequency of personnel appearance in the divided time periods of different dates according to the number of historically detected personnel in the divided time periods of different dates and the time intervals between the appearances of different historically detected personnel, and judge whether the number of dates in the divided time periods whose frequency of personnel appearance does not meet the requirement meets the requirement, if so, proceed to the next step, if not, determine the probability of personnel appearance in the divided time periods based on the number of dates in the divided time periods whose frequency of personnel appearance does not meet the requirement;

[0044] The probability of a person appearing in a divided time period is determined according to the frequency of the person appearing in the divided time periods on different dates.

[0045] In another embodiment, the method for determining the probability of occurrence of personnel within the divided time periods is:

[0046] Determine the number of historically detected personnel in the divided time periods of different dates through the historical personnel detection data in the divided time periods, and judge whether the total number of historically detected personnel in the divided time periods meets the requirements. If so, proceed to the next step; if not, determine the probability of personnel appearance in the divided time periods based on the total number of historically detected personnel in the divided time periods;

[0047] The date when the number of historically detected personnel is greater than the preset number of personnel is regarded as the unmatched date, and the date evaluation amount of the unmatched date is determined according to the number of historically detected personnel and the number of unmatched dates on different unmatched dates, and it is judged whether the date evaluation amount of the unmatched date meets the requirements, if so, proceed to the next step, if not, determine the probability of personnel appearance in the divided time period based on the date evaluation amount of the unmatched date;

[0048] Obtain the appearance time intervals between different historical detected persons in different divided time periods of different dates, and determine the frequent appearance durations in different divided time periods of different dates based on the appearance time intervals, determine the date frequency evaluation amount according to the number of dates whose frequent appearance durations do not meet the requirements and the frequent appearance durations of dates whose frequent appearance durations do not meet the requirements, and judge whether the date frequency evaluation amount meets the requirements. If so, proceed to the next step; if not, determine the appearance probability of the persons in the divided time periods based on the date frequency evaluation amount;

[0049] The frequency of personnel appearance in the divided time periods of different dates is determined based on the number of historically detected personnel in the divided time periods of different dates and the time intervals between the appearances of different historically detected personnel, and the probability of personnel appearance in the divided time periods is determined by combining the date frequency evaluation amount and the date evaluation amount of the mismatched dates.

[0050] S3 divides the disinfection working period into different working periods according to the duration of disinfection treatment, and determines the optimal working period in the working period according to the distribution of the disinfection treatment divided periods in different working periods and the probability of personnel appearing in different divided periods;

[0051] Furthermore, the disinfection working period is divided into different working periods according to the duration of the disinfection treatment, specifically including:

[0052] The disinfection working period is divided into different continuous time periods according to the duration of the disinfection treatment, and different continuous time periods are used as different working periods.

[0053] Specifically, Figure 3 As shown, the method for determining the optimal working period in the working period is:

[0054] Determine the personnel frequent appearance time period in the working period according to the personnel appearance probabilities in different divided time periods of the working period, and determine the time period work abnormality amount of the working period according to the number of personnel frequent appearance time periods and the personnel appearance probabilities in different personnel frequent appearance time periods;

[0055] The stable working time is determined by the distribution of the disinfectable divided time periods of the working period, and the working reliability of the working period is determined by combining the frequency of personnel appearance in different disinfectable divided time periods and the number of disinfectable divided time periods;

[0056] The comprehensive working reliability of the working period is determined based on the working reliability of the working period and the working abnormality of the period, and the optimal working period in the working period is determined through the comprehensive working reliability.

[0057] Further, determining the optimal working period in the working period according to the comprehensive working reliability specifically includes:

[0058] The matching working period with the greatest comprehensive working reliability is taken as the optimal working period.

[0059] In another embodiment, the method for determining the optimal working period in the working period is:

[0060] Determine the personnel frequent appearance time period in the working period according to the personnel appearance probability in different divided time periods of the working period, judge whether the number of personnel frequent appearance time periods in the working period meets the requirement, if so, proceed to the next step, if not, determine that the working period does not belong to the optimal working period;

[0061] Determine the abnormal amount of work in the working period according to the number of personnel frequent appearance periods and the probability of personnel appearance in different personnel frequent appearance periods, and judge whether the abnormal amount of work in the working period meets the requirements. If yes, proceed to the next step; if not, determine that the working period does not belong to the optimal working period;

[0062] Based on the number of sterilizable divided time periods in the working period, determine whether there are other working periods whose deviation from the number of sterilizable divided time periods in the working period is within a preset range, if so, proceed to the next step, if not, determine that the working period is the optimal working period;

[0063] The stable working time is determined by the distribution of the disinfectable divided time periods of the working period, and the working reliability of the working period is determined by combining the frequency of personnel appearance in different disinfectable divided time periods and the number of disinfectable divided time periods;

[0064] The comprehensive working reliability of the working period is determined based on the working reliability of the working period and the working abnormality of the period, and the optimal working period in the working period is determined through the comprehensive working reliability.

[0065] In another embodiment, the method for determining the optimal working period in the working period is:

[0066] S31 determines the frequent appearance period of personnel in the working period according to the appearance probability of personnel in different divided time periods of the working period, and determines whether there is a frequent appearance period of personnel in the working period. If yes, proceed to the next step, if not, proceed to step S33;

[0067] S32 determines the abnormal amount of work in the working period according to the number of personnel frequent appearance periods and the probability of personnel appearance in different personnel frequent appearance periods, and judges whether the abnormal amount of work in the working period is greater than the preset abnormal threshold. If so, proceed to step S34, if not, proceed to the next step;

[0068] S33: determining whether there are other working periods whose deviation from the number of sterilizable divided time periods in the working period is within a preset range based on the number of divided time periods in the working period that can be sterilized; if so, proceeding to the next step; if not, determining that the working period is the optimal working period;

[0069] S34 determines the stable working time according to the distribution of the disinfectable divided time periods of the working period, and determines the working reliability of the working period in combination with the frequency of personnel appearance in different disinfectable divided time periods and the number of disinfectable divided time periods;

[0070] S35 determines the comprehensive working reliability of the working period based on the working reliability of the working period and the working abnormality amount of the period, and determines the optimal working period among the working periods through the comprehensive working reliability.

[0071] S4 disinfects the clothes drying during the optimal working period, and uses an artificial intelligence-based prediction model to determine the working continuity prediction value of the clothes drying rack according to the analysis results of the sonar detection processing data of the current working period and the probability of people appearing in the current working period, and determines whether the disinfection process needs to be suspended during the current working period based on the working continuity prediction value.

[0072] Specifically, the analysis results of the sonar detection processing data of the current working period include the number of detection personnel, the appearance time intervals and appearance durations of different detection personnel.

[0073] Furthermore, the divided time period corresponding to the current moment is used as the current working time period.

[0074] It should be noted that the method for determining the predicted value of the working continuity of the clothes drying rack is:

[0075] Determine the number of detection personnel in the current working period according to the analysis results of the sonar detection processing data in the current working period, and determine the discontinuity evaluation amount of the clothes drying rack in the current working period in combination with the appearance time intervals and appearance durations of different detection personnel;

[0076] By taking the discontinuity evaluation amount of the clothes drying rack in the current working period and the probability of personnel appearance as input quantities, an artificial intelligence-based prediction model is adopted to determine the working continuity prediction value of the clothes drying rack.

[0077] Further, determining whether the disinfection process needs to be suspended during the current working period based on the work continuity prediction value specifically includes:

[0078] When the predicted value of the working continuity of the current working period does not meet the requirement, it is determined that the disinfection process of the current working period needs to be suspended.

[0079] Through the above embodiments, the present invention achieves the following beneficial effects:

[0080] 1. The optimal working period is determined according to the distribution of the disinfectable time periods in different working periods and the probability of personnel appearing in different time periods. Not only the difference in the reliability of continuous disinfection work caused by the number of disinfectable time periods and the temporal continuity in different working periods is taken into account, but also the probability of personnel appearing in different time periods is considered comprehensively to realize the probability of interruption of disinfection, thereby realizing the screening of the optimal working period with higher work continuity and lower interruption probability.

[0081] 2. According to the analysis results of the sonar detection processing data in the current working period and the probability of personnel appearance in the current working period, an artificial intelligence-based prediction model is used to determine the working continuity prediction value of the clothes drying rack, thereby avoiding the occurrence of technical problems with a high probability of interruption of the disinfection process due to too many people in the current working period, and ensuring the continuity and reliability of the disinfection process.

[0082] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0083] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0084] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A clothes drying rack sonar detection and processing method based on artificial intelligence, characterized in that: Specifically include: Determine the weight of the clothes on the clothes drying rack and the disinfection treatment time of the clothes on the clothes drying rack according to the weight sensing module of the clothes drying rack; Determine the disinfection working period of the clothes drying rack according to the current time and the preset disinfection processing time, determine the historical detection data of personnel and the probability of personnel appearance in different divided time periods of the disinfection working period according to the sonar detection processing data integrated in the voice recognition module of the clothes drying rack, and determine the disinfection processing divided time period in the divided time period according to the probability of personnel appearance; Dividing the disinfection working period into different working periods according to the duration of disinfection treatment, and determining the optimal working period in the working period according to the distribution of the disinfection treatment divided periods in different working periods and the probability of personnel appearing in different divided periods; The clothes drying rack are disinfected during the optimal working period, and according to the analysis results of the sonar detection processing data of the current working period and the probability of the presence of personnel in the current working period, a prediction model based on artificial intelligence is used to determine the working continuity prediction value of the clothes drying rack, and based on the working continuity prediction value, it is determined whether the disinfection process needs to be suspended during the current working period; The method for determining the optimal working period in the working period is: Determine the personnel frequent appearance time period in the working period according to the personnel appearance probabilities in different divided time periods of the working period, and determine the time period work abnormality amount of the working period according to the number of personnel frequent appearance time periods and the personnel appearance probabilities in different personnel frequent appearance time periods; The stable working time is determined by the distribution of the disinfectable divided time periods of the working period, and the working reliability of the working period is determined by combining the frequency of personnel appearance in different disinfectable divided time periods and the number of disinfectable divided time periods; The comprehensive working reliability of the working period is determined based on the working reliability of the working period and the working abnormality of the period, and the optimal working period in the working period is determined through the comprehensive working reliability.

2. The clothes drying rack sonar detection and processing method based on artificial intelligence as claimed in claim 1, characterized in that: The duration of the disinfection treatment of the clothes drying is determined according to the matching result of the weight of the clothes drying on the clothes drying rack.

3. The clothes drying rack sonar detection and processing method based on artificial intelligence as claimed in claim 1 is characterized in that: The preset disinfection treatment time is determined according to the optimal disinfection treatment cycle of the clothes drying.

4. The clothes drying rack sonar detection and processing method based on artificial intelligence as claimed in claim 1, characterized in that: The method for determining the probability of occurrence of personnel within the divided time periods is: Determine the number of historically detected personnel in the divided time periods of different dates and the time intervals between different historically detected personnel through the historical detection data of personnel in the divided time periods, and determine the frequency of personnel appearance in the divided time periods of different dates based on the number of historically detected personnel in the divided time periods of different dates and the time intervals between different historically detected personnel; The probability of a person appearing in a divided time period is determined according to the frequency of the person appearing in the divided time periods on different dates.

5. The clothes drying rack sonar detection and processing method based on artificial intelligence as claimed in claim 1, characterized in that: When the probability of the presence of a person in the divided time period is less than a preset probability, the divided time period is determined to be a divided time period that can be disinfected.

6. The clothes drying rack sonar detection and processing method based on artificial intelligence as claimed in claim 1, characterized in that: The disinfection working period is divided into different working periods according to the disinfection treatment time, specifically including: The disinfection working period is divided into different continuous time periods according to the duration of the disinfection treatment, and different continuous time periods are used as different working periods.

7. The clothes drying rack sonar detection and processing method based on artificial intelligence as claimed in claim 1, characterized in that: Determining the optimal working period in the working period by using the comprehensive working reliability specifically includes: The matching working period with the greatest comprehensive working reliability is taken as the optimal working period.

8. The clothes drying rack sonar detection and processing method based on artificial intelligence as claimed in claim 1, characterized in that: The method for determining the predicted value of the working continuity of the clothes drying rack is: Determine the number of detection personnel in the current working period according to the analysis results of the sonar detection processing data in the current working period, and determine the discontinuity evaluation amount of the clothes drying rack in the current working period in combination with the appearance time intervals and appearance durations of different detection personnel; By taking the discontinuity evaluation amount of the clothes drying rack in the current working period and the probability of personnel appearance as input quantities, an artificial intelligence-based prediction model is adopted to determine the working continuity prediction value of the clothes drying rack.

9. The clothes drying rack sonar detection and processing method based on artificial intelligence as claimed in claim 1, characterized in that: Determining whether the disinfection process needs to be suspended during the current working period based on the work continuity prediction value specifically includes: When the predicted value of the working continuity of the current working period does not meet the requirement, it is determined that the disinfection process of the current working period needs to be suspended.

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