Intelligent toilet fault monitoring method, system and equipment

Abstract

The invention discloses an intelligent toilet fault monitoring method, system and equipment, and the method comprises the steps: obtaining the first use time sequence data of each toilet cubicle of a target intelligent toilet, and judging whether a first abnormal toilet cubicle exists or not according to the first use time sequence data and the use rate of each toilet cubicle; judging whether a second abnormal toilet cubicle exists or not according to the first use time sequence data and the average use time of each toilet cubicle; when the first abnormal toilet cubicle or the second abnormal toilet cubicle exists, acquiring first use time sequence data and historical maintenance data of the abnormal toilet cubicle; and obtaining a fault prediction result of the abnormal toilet cubicle through a fault prediction model according to the first use time sequence data and the historical maintenance data of the abnormal toilet cubicle. The intelligent toilet fault monitoring method and device at least solve the problem that fault monitoring of an existing intelligent toilet generally depends on evaluation of a user or routing inspection of a worker, fault monitoring has large delay, and the use efficiency of the intelligent toilet is low.

Description

Technical Field

[0001] The present application relates to the technical field of smart toilets, and in particular to a smart toilet fault monitoring method, system and equipment. Background Art

[0002] Smart cities use information and communication technologies to sense, analyze, and integrate key information from the core systems of urban operations, thereby responding intelligently to various needs including people's livelihood, environmental protection, public safety, urban services, and industrial and commercial activities. Its essence is to use advanced information technology to achieve smart urban management and operation, thereby creating a better life for people in the city and promoting the harmonious and sustainable growth of the city. Smart toilets are one of the constructions of smart cities.

[0003] At the same time, smart toilets are also boldly innovative. With the help of toilet traffic, they create a "1+N" new economic operation scenario, open up a new publicity window and commercial space carrier for the city, and create new commercial value. Among them, "1" refers to smart media, which consists of a 6-square-meter LED large screen outside the station and multiple small screens inside. It creates new media delivery scenarios and is suitable for different media delivery combinations. "N" refers to the commercial operation space. It has an operating space of 7-10 square meters and can provide a variety of commercial operation scenarios, such as coffee stations, health stations, bus stations, event stations, media stations, bus stations, sanitation rest stations, etc., which are convenient for the people and provide a platform for cross-border cooperation for enterprises.

[0004] The fault monitoring of existing smart toilets generally relies on user evaluations or staff inspections. However, many users do not have the habit of actively evaluating. If it relies on frequent inspections by staff, the labor cost will be high. Therefore, the fault monitoring of existing smart toilets has a large delay, resulting in low efficiency in the use of smart toilets. Summary of the invention

[0005] The present invention provides a smart toilet fault monitoring method, system and equipment, and provides a systematic smart toilet deployment plan evaluation method, which at least solves the problem that the existing smart toilet fault monitoring generally relies on user evaluation or staff inspection. However, many users do not have the habit of actively evaluating, and if it relies on frequent staff inspections, the labor cost is high. Therefore, the existing smart toilet fault monitoring has a large delay, resulting in low efficiency of smart toilet use.

[0006] The present application provides a smart toilet fault monitoring method, comprising: Acquire first usage time series data of each toilet seat in the target smart toilet, and determine whether there is a first abnormal toilet seat according to the first usage time series data and the usage rate of each toilet seat, wherein the first abnormal toilet seat is configured as a toilet seat whose usage rate and the average usage rate of all toilet seats have a difference exceeding a preset first threshold value; According to the first usage time series data, according to the average usage time of each toilet seat, it is determined whether there is a second abnormal toilet seat, wherein the second abnormal toilet seat is configured as a toilet seat whose average usage time is less than a preset second threshold; When there is a first abnormal toilet seat or a second abnormal toilet seat, obtaining first usage time series data and historical maintenance data of the abnormal toilet seat; According to the first usage time series data and historical maintenance data of the abnormal toilet seat, a fault prediction result of the abnormal toilet seat is obtained through a fault prediction model.

[0007] Optionally, before the step of obtaining the first usage time series data of each toilet seat of the target smart toilet and judging whether there is a first abnormal toilet seat according to the first usage time series data and the usage rate of each toilet seat, the step further includes: A usage evaluation of at least one smart toilet is obtained, and based on the usage evaluation of the at least one smart toilet, a smart toilet with an abnormal usage evaluation is obtained as a target smart toilet.

[0008] Optionally, the acquiring of first usage time series data of each toilet seat of the target smart toilet, and judging whether there is a first abnormal toilet seat according to the first usage time series data and the usage rate of each toilet seat, includes: Acquire first usage time series data of each toilet seat in the target smart toilet, and calculate the usage rate of each toilet seat in the time window through at least one time window; According to the utilization rate of each toilet seat, determine whether there is a toilet seat whose utilization rate and the average utilization rate of all toilet seats differ by more than a preset first threshold value; If it exists, then this toilet will be regarded as the first abnormal toilet.

[0009] Optionally, judging whether there is a second abnormal toilet according to the first usage time series data and the average usage time of each toilet seat includes: Obtain first usage time series data of each toilet seat in the target smart toilet, and calculate the average usage time of each toilet seat in the time window through at least one time window; According to the average usage time of each toilet seat, determine whether there is a toilet seat whose average usage time is less than a preset second threshold; If it exists, this toilet will be taken as the second abnormal toilet.

[0010] Optionally, before the step of obtaining the fault prediction result of the abnormal toilet seat through a fault prediction model according to the first usage time series data and historical maintenance data of the abnormal toilet seat, the step further includes: Establishing an initial fault prediction model, wherein the initial fault prediction model is configured to use time series data as input and toilet seat failure possibility as output; According to the historical maintenance data, third time series data and toilet seat failure data corresponding to the historical maintenance data are obtained; The fault prediction model is obtained by training the initial fault prediction model according to the third time series data and the toilet seat failure result.

[0011] Optionally, obtaining the fault prediction result of the abnormal toilet seat through a fault prediction model according to the first usage time series data and historical maintenance data of the abnormal toilet seat includes: According to the first usage time series data of the abnormal toilet seat, the first usage time series data is input into the fault prediction model to obtain a first possible fault of the abnormal toilet seat; Acquire first evaluation data that matches the target smart toilet with the first usage time series data; Extracting first key information from the first evaluation data according to the first evaluation data; According to the first key information and the first fault possibility, a fault prediction result of the abnormal toilet seat is obtained.

[0012] Optionally, obtaining the fault prediction result of the abnormal toilet seat according to the first key information and the first fault possibility includes: According to the first key information, a second fault possibility is obtained through a preset fault mapping; According to the first fault possibility, a first weight set is obtained, and according to the second fault possibility, a second weight set is obtained; Obtaining a third weight set according to the first weight set and the second weight set; According to the third weight set, a fault prediction result of the abnormal toilet seat is obtained.

[0013] Optionally, after the step of obtaining the fault prediction result of the abnormal toilet seat through a fault prediction model according to the first usage time series data and historical maintenance data of the abnormal toilet seat, the method further includes: According to the fault prediction result of the abnormal toilet seat, a corresponding maintenance work order is generated and the maintenance work order is dispatched to the corresponding maintenance personnel.

[0014] On the other hand, a smart toilet fault monitoring system includes a fault monitoring platform and a smart toilet management platform: The smart toilet management platform is configured as follows: Obtaining usage status of each toilet seat in the target smart toilet, and obtaining first usage time series data of each toilet seat according to the usage status; The fault monitoring platform is configured as follows: Acquire first usage time series data of each toilet seat in the target smart toilet through the smart toilet management platform, and determine whether there is a first abnormal toilet seat according to the first usage time series data and the usage rate of each toilet seat, wherein the first abnormal toilet seat is configured as a toilet seat whose usage rate and the average usage rate of all toilet seats exceed a preset first threshold; According to the first usage time series data, according to the average usage time of each toilet seat, it is determined whether there is a second abnormal toilet seat, wherein the second abnormal toilet seat is configured as a toilet seat whose average usage time is less than a preset second threshold; When there is a first abnormal toilet seat or a second abnormal toilet seat, obtaining first usage time series data and historical maintenance data of the abnormal toilet seat; According to the first usage time series data and historical maintenance data of the abnormal toilet seat, a fault prediction result of the abnormal toilet seat is obtained through a fault prediction model.

[0015] On the other hand, an embodiment of the present application further provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above method.

[0016] On the other hand, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and a processor executes the computer program to implement the above method.

[0017] Compared with the prior art, the present invention has the following advantages and beneficial effects: The present invention provides a method, system and device for monitoring faults of smart toilets, including: obtaining first time-series data of each toilet seat of a target smart toilet, judging whether there is a first abnormal toilet seat according to the first time-series data and the usage rate of each toilet seat, wherein the first abnormal toilet seat is configured as a toilet seat whose usage rate and the average usage rate of all toilet seats exceed a preset first threshold value; judging whether there is a second abnormal toilet seat according to the first time-series data and the average usage time of each toilet seat, wherein the second abnormal toilet seat is configured as a toilet seat whose average usage time is less than a preset second threshold value; when there is a first abnormal toilet seat or a second abnormal toilet seat, obtaining first time-series data and historical maintenance data of the abnormal toilet seat; obtaining the fault prediction result of the abnormal toilet seat according to the first time-series data and historical maintenance data of the abnormal toilet seat through a fault prediction model. At least the existing fault monitoring of smart toilets generally relies on user evaluation or staff inspection, but many users do not have the habit of actively evaluating, and if it relies on frequent staff inspections, the labor cost is high, so the existing fault monitoring of smart toilets has a large delay, resulting in low efficiency of smart toilets. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific implementation of the present application or the technical solution in the prior art, the following is a brief introduction to the drawings required for the specific implementation or the prior art description. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to the actual scale.

[0019] Figure 1 A flowchart of a smart toilet fault monitoring method in this application; Figure 2 This is a schematic diagram of the structure of a computer device in this application.

[0020] Markings in the figure: 101 - processor, 102 - communication bus, 103 - network interface, 104 - user interface, 105 - memory.

[0021] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the scheme of the present disclosure, the technical scheme in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present disclosure.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0024] Example 1 like Figure 1 As shown, a smart toilet fault monitoring method includes: S1. Obtain the first usage time series data of each toilet seat in the target smart toilet, and determine whether there is a first abnormal toilet seat based on the first usage time series data and the usage rate of each toilet seat.

[0025] Specifically, the first abnormal toilet seat is configured as a toilet seat whose usage rate difference with the average usage rate of all toilet seats exceeds a preset first threshold.

[0026] Optionally, the first usage time series data refers to the usage time series data of the latest fixed time window. The length of the time window can be set by the manager of the smart toilet, generally 3-12 hours. Specifically, if the time window is 6 hours and the current time is 12:00, then the first usage time series data refers to the usage time series data of the corresponding toilet seat during the period of 6:00-12:00. Optionally, the length of the time window can be related to the usage frequency of the smart toilet. The more frequently used the smart toilet is, the shorter the time window can be.

[0027] Optionally, the calculation method for the utilization rate of toilet seats is: utilization rate = t 1 / t 2 , where t 1 is configured as the cumulative time that the toilet seat is used in the first usage time series data, t 2The length of the time window corresponding to the first usage time series data is configured.

[0028] Optionally, the method for determining whether there is a first abnormal toilet seat includes: calculating the average usage rate of all toilet seats, and determining whether there is a toilet seat whose usage rate is lower than the average usage rate and the difference with the average usage rate exceeds a preset threshold; if so, the toilet seat is the first abnormal toilet seat.

[0029] S2. According to the first usage time series data and the average usage time of each toilet seat, determine whether there is a second abnormal toilet seat.

[0030] Specifically, the second abnormal toilet seat is configured as a toilet seat whose average usage time is less than a preset second threshold.

[0031] Optionally, the second threshold value can be a preset fixed value, such as 30 seconds, 1 minute, etc. The specific value can be set according to different toilet types, or it can be obtained by calculating the average of the average usage time of each toilet. That is, the second threshold value can be the average of the average usage time of each toilet minus a fixed value or multiplied by a coefficient.

[0032] S3. When there is a first abnormal toilet seat or a second abnormal toilet seat, obtain the first usage time series data and historical maintenance data of the abnormal toilet seat.

[0033] Specifically, obtaining the first usage time series data and historical maintenance data of an abnormal toilet seat means obtaining the first usage time series data and historical maintenance data of the first abnormal toilet seat or the second abnormal toilet seat, and analyzing the cause of the failure of the first abnormal toilet seat or the second abnormal toilet seat through the first usage time series data and historical maintenance data.

[0034] S4. According to the first usage time series data and historical maintenance data of the abnormal toilet seat, a fault prediction result of the abnormal toilet seat is obtained through a fault prediction model.

[0035] Specifically, by analyzing the first usage time series data and historical maintenance data of the abnormal toilet, the fault prediction model is used to analyze the changes in the usage time series data of the abnormal toilet before and after the fault, and then combined with the changes in the usage time series data before and after the fault corresponding to the historical maintenance data of the abnormal toilet, the fault prediction result of the abnormal toilet is obtained.

[0036] By adopting the above method, the toilet usage data of the smart toilet is analyzed to quickly identify the faulty toilet seat, and the cause of the fault is analyzed through the AI ​​model to ensure that the faulty toilet seat can be maintained in time. At least the problem that the fault monitoring of existing smart toilets generally relies on user evaluation or staff inspection is solved. However, many users do not have the habit of actively evaluating, and if it relies on frequent inspections by staff, the labor cost is high. Therefore, there is a large delay in the fault monitoring of existing smart toilets, resulting in low efficiency of smart toilet use.

[0037] Example 2 This embodiment is based on Example 1, and provides a smart toilet fault monitoring method, including: S1. Obtain the first usage time series data of each toilet seat in the target smart toilet, and determine whether there is a first abnormal toilet seat based on the first usage time series data and the usage rate of each toilet seat.

[0038] Specifically, the first abnormal toilet seat is configured as a toilet seat whose usage rate difference with the average usage rate of all toilet seats exceeds a preset first threshold.

[0039] Optionally, the first usage time series data refers to the usage time series data of the latest fixed time window. The length of the time window can be set by the manager of the smart toilet, generally 3-12 hours. Specifically, if the time window is 6 hours and the current time is 12:00, then the first usage time series data refers to the usage time series data of the corresponding toilet seat during the period of 6:00-12:00. Optionally, the length of the time window can be related to the usage frequency of the smart toilet. The more frequently used the smart toilet is, the shorter the time window can be.

[0040] Optionally, the calculation method for the utilization rate of toilet seats is: utilization rate = t 1 / t 2 , where t 1 is configured as the cumulative time that the toilet seat is used in the first usage time series data, t 2 The length of the time window corresponding to the first usage time series data is configured.

[0041] Optionally, the method for determining whether there is a first abnormal toilet seat includes: calculating the average usage rate of all toilet seats, and determining whether there is a toilet seat whose usage rate is lower than the average usage rate and the difference with the average usage rate exceeds a preset threshold; if so, the toilet seat is the first abnormal toilet seat.

[0042] Optionally, before the step of obtaining the first usage time series data of each toilet seat of the target smart toilet and judging whether there is a first abnormal toilet seat according to the first usage time series data and the usage rate of each toilet seat, the step further includes: Obtain the usage evaluation of at least one smart toilet, and according to the usage evaluation of at least one smart toilet, obtain the smart toilet with abnormal usage evaluation as the target smart toilet. The abnormal usage evaluation includes that the average evaluation of the smart toilet in a preset time period is lower than the average evaluation of the smart toilet, lower than the average evaluation of all toilets, or lower than a preset evaluation threshold. This solution can quickly determine the target smart toilet that may have a faulty toilet seat through the above method, without real-time monitoring of all smart toilets, reducing hardware requirements. At the same time, the usage evaluation can be simply satisfied or dissatisfied, and most users can quickly evaluate without the need for users to feedback specific dissatisfaction or faults. Specifically, users can evaluate through mobile phone APP, mini-programs, or evaluation panels set in smart toilets.

[0043] Optionally, obtaining first usage time series data of each toilet seat of the target smart toilet, and judging whether there is a first abnormal toilet seat according to the first usage time series data and the usage rate of each toilet seat, including: Obtain the first usage time series data of each toilet seat in the target smart toilet, and calculate the usage rate of each toilet seat in the time window through at least one time window; According to the utilization rate of each toilet seat, determine whether there is a toilet seat whose utilization rate and the average utilization rate of all toilet seats differ by more than a preset first threshold value; If it exists, then this toilet will be regarded as the first abnormal toilet.

[0044] S2. According to the first usage time series data and the average usage time of each toilet seat, determine whether there is a second abnormal toilet seat.

[0045] Specifically, the second abnormal toilet seat is configured as a toilet seat whose average usage time is less than a preset second threshold.

[0046] Optionally, the second threshold value can be a preset fixed value, such as 30 seconds, 1 minute, etc. The specific value can be set according to different toilet types, or it can be obtained by calculating the average of the average usage time of each toilet. That is, the second threshold value can be the average of the average usage time of each toilet minus a fixed value or multiplied by a coefficient.

[0047] Optionally, judging whether there is a second abnormal toilet according to the first usage time series data and the average usage time of each toilet seat includes: Obtain the first usage time series data of each toilet seat in the target smart toilet, and calculate the average usage time of each toilet seat in the time window through at least one time window; According to the average usage time of each toilet seat, determine whether there is a toilet seat whose average usage time is less than a preset second threshold; If it exists, this toilet will be taken as the second abnormal toilet.

[0048] S3. When there is a first abnormal toilet seat or a second abnormal toilet seat, obtain the first usage time series data and historical maintenance data of the abnormal toilet seat.

[0049] Specifically, obtaining the first usage time series data and historical maintenance data of an abnormal toilet seat means obtaining the first usage time series data and historical maintenance data of the first abnormal toilet seat or the second abnormal toilet seat, and analyzing the cause of the failure of the first abnormal toilet seat or the second abnormal toilet seat through the first usage time series data and historical maintenance data.

[0050] Optionally, before the step of obtaining the fault prediction result of the abnormal toilet seat by using the fault prediction model according to the first usage time series data and the historical maintenance data of the abnormal toilet seat, the step further includes: Establishing an initial fault prediction model, wherein the initial fault prediction model is configured to use time series data as input and toilet seat failure possibility as output; According to the historical maintenance data, third time series data and toilet seat failure data corresponding to the historical maintenance data are obtained; The fault prediction model is obtained after the initial fault prediction model is trained according to the third time series data and the toilet seat failure results.

[0051] Specifically, the above functions can be implemented through the following pseudo code: # Step 1: Initialize the initial fault prediction model function initialize_fault_prediction_model(): model = create_model(input_type="time_series", output_type="toilet_fault") return model # Step 2: Get historical data function get_historical_data(): historical_maintenance_data = load_data("historical_maintenance_data") third_time_series_data = extract_time_series_data(historical_maintenance_data) toilet_fault_results = extract_fault_results(historical_maintenance_data) return third_time_series_data, toilet_fault_results # Step 3: Train the fault prediction model function train_fault_prediction_model(model, third_time_series_data, toilet_fault_results): model.train(input_data=third_time_series_data, output_data=toilet_fault_results) return model # Main process function main(): # Initialize the fault prediction model model = initialize_fault_prediction_model() # Get historical data third_time_series_data, toilet_fault_results = get_historical_data() # Train the model trained_model = train_fault_prediction_model(model, third_time_series_data, toilet_fault_results) return trained_model.

[0052] Optionally, according to the first use time series data and historical maintenance data of the abnormal toilet seat, a fault prediction result of the abnormal toilet seat is obtained through a fault prediction model, including: According to the first time series data of use of the abnormal toilet seat, the first time series data of use is input into the fault prediction model to obtain the first fault possibility of the abnormal toilet seat; Acquire first evaluation data that matches the target smart toilet with the first usage time series data; Extracting first key information from the first evaluation data according to the first evaluation data; According to the first key information and the first fault possibility, a fault prediction result of the abnormal toilet seat is obtained.

[0053] Specifically, the first key information in the first evaluation data is extracted through NLP technology. The first key information may include the toilet number, the description of the equipment status, the description of the environmental status, etc. For example, the door of the first toilet cannot be closed, the toilet has a strong odor, etc. The above method can be combined with the user's fuzzy comments to improve the accuracy of the fault prediction results. For example, when the user comments that the second toilet has a strong odor, it may be that the excrement is not discharged to the non-designated area and needs to be cleaned, or it may be that the air purification equipment is faulty, etc. However, in the case where the excrement is not discharged to the non-designated area and needs to be cleaned, the average use time of the faulty toilet will be significantly shorter than that of the air purification equipment failure, because most users will leave immediately when they see this situation. At this time, combining the first fault may obtain a more accurate fault prediction result.

[0054] Optionally, obtaining a fault prediction result of the abnormal toilet seat according to the first key information and the first fault possibility includes: According to the first key information, a second fault possibility is obtained through a preset fault mapping; According to the first fault possibility, a first weight set is obtained, and according to the second fault possibility, a second weight set is obtained; Obtaining a third weight set according to the first weight set and the second weight set; According to the third weight set, a fault prediction result of the abnormal toilet seat is obtained.

[0055] Optionally, before obtaining the possible step of the second fault according to the first key information through a preset fault mapping, it also includes establishing a fault mapping, wherein the fault mapping is configured as the probability of the first key information corresponding to different faults, such as the probability of a heavy odor corresponding to an air purification device failure is 70%, and the probability of the corresponding excrement not being discharged to a non-designated area is 30%. The above probability is obtained through the correspondence between the key information of the evaluation of all smart toilets and the final inspection results. For example, the above probability can be that out of 10 times of key information with a heavy odor, 7 times are air purification device failures, and 3 times are excrement not being discharged to a non-designated area.

[0056] Optionally, the first possible fault and the second possible fault include a fault type and a fault probability. Specifically, the higher the fault probability, the higher the weight value of the fault type. For example, the second possible fault is "air purification equipment failure, 70%; excrement is not discharged into a non-designated area, 30%", then the second weight set can be "air purification equipment failure, 0.7; excrement is not discharged into a non-designated area, 0.3", and the corresponding first possible fault is "excrement is not discharged into a non-designated area, 60%, toilet door lock is damaged, 30%, air purification equipment failure, 10%", the first weight set can be "excrement is not excreted into non-designated areas, 0.6, toilet door lock is damaged, 0.3, air purification equipment is faulty, 0.1", the third weight set can be "excrement is not excreted into non-designated areas, 0.18, toilet door lock is damaged, 0, air purification equipment is faulty, 0.07", according to the third weight set, the fault prediction result of the abnormal toilet is that excrement is not excreted into non-designated areas, or the fault prediction result of the abnormal toilet is that excrement is not excreted into non-designated areas or air purification equipment is faulty. Optionally, according to the third weight set, the method for obtaining the fault prediction result of the abnormal toilet may be to select one or more fault types with the highest weight in the third weight set as the fault prediction result, or to select a fault type with a weight exceeding a preset threshold in the third weight set as the fault prediction result.

[0057] Optionally, when the fault type with a larger weight in the third weight set is more likely to be the fault prediction result, the higher the fault probability corresponding to the fault type in the first fault possibility and the second fault possibility, the larger the weight of the fault possibility in the first weight set and the second weight set, and the first weight set and the second weight set are positively correlated with the third weight set.

[0058] S4. According to the first usage time series data and historical maintenance data of the abnormal toilet seat, a fault prediction result of the abnormal toilet seat is obtained through a fault prediction model.

[0059] Specifically, by analyzing the first usage time series data and historical maintenance data of the abnormal toilet, the fault prediction model is used to analyze the changes in the usage time series data of the abnormal toilet before and after the fault, and then combined with the changes in the usage time series data before and after the fault corresponding to the historical maintenance data of the abnormal toilet, the fault prediction result of the abnormal toilet is obtained.

[0060] Optionally, after the step of obtaining the fault prediction result of the abnormal toilet seat by using the fault prediction model according to the first usage time series data and the historical maintenance data of the abnormal toilet seat, the step further includes: According to the fault prediction results of abnormal toilet seats, a corresponding maintenance work order is generated and sent to the corresponding maintenance personnel. The maintenance personnel can select appropriate maintenance equipment to repair the faulty toilet seats on site according to the fault prediction results on the maintenance work order.

[0061] Example 3 A smart toilet fault monitoring system, including a fault monitoring platform and a smart toilet management platform: The smart toilet management platform is configured as: Obtain the usage status of each toilet seat in the target smart toilet, and obtain the first usage time series data of each toilet seat according to the usage status; The fault monitoring platform is configured as: Obtain the first usage time series data of each toilet seat in the target smart toilet through the smart toilet management platform, and determine whether there is a first abnormal toilet seat according to the first usage time series data and the usage rate of each toilet seat, wherein the first abnormal toilet seat is configured as a toilet seat whose usage rate and the average usage rate of all toilet seats exceed a preset first threshold value; According to the first usage time series data, according to the average usage time of each toilet seat, it is determined whether there is a second abnormal toilet seat, and the second abnormal toilet seat is configured as a toilet seat whose average usage time is less than a preset second threshold; When there is a first abnormal toilet seat or a second abnormal toilet seat, obtaining first usage time series data and historical maintenance data of the abnormal toilet seat; According to the first usage time series data and historical maintenance data of the abnormal toilet seat, the fault prediction result of the abnormal toilet seat is obtained through the fault prediction model.

[0062] Optionally, before the step of obtaining the first usage time series data of each toilet seat of the target smart toilet through the smart toilet management platform and judging whether there is a first abnormal toilet seat according to the first usage time series data and the usage rate of each toilet seat, the step further includes: Obtain a usage evaluation of at least one smart toilet, and according to the usage evaluation of at least one smart toilet, obtain a smart toilet with an abnormal usage evaluation as a target smart toilet.

[0063] Optionally, the first usage time series data of each toilet seat in the target smart toilet is obtained through the smart toilet management platform, and according to the first usage time series data and the usage rate of each toilet seat, it is determined whether there is a first abnormal toilet seat, including: Obtain the first usage time series data of each toilet seat in the target smart toilet through the smart toilet management platform, and calculate the usage rate of each toilet seat in the time window through at least one time window; According to the utilization rate of each toilet seat, determine whether there is a toilet seat whose utilization rate and the average utilization rate of all toilet seats differ by more than a preset first threshold value; If it exists, then this toilet will be regarded as the first abnormal toilet.

[0064] Optionally, judging whether there is a second abnormal toilet according to the first usage time series data and the average usage time of each toilet seat includes: Obtain the first usage time series data of each toilet seat in the target smart toilet, and calculate the average usage time of each toilet seat in the time window through at least one time window; According to the average usage time of each toilet seat, determine whether there is a toilet seat whose average usage time is less than a preset second threshold; If it exists, this toilet will be taken as the second abnormal toilet.

[0065] Optionally, before the step of obtaining the fault prediction result of the abnormal toilet seat by using the fault prediction model according to the first usage time series data and the historical maintenance data of the abnormal toilet seat, the step further includes: Establishing an initial fault prediction model, wherein the initial fault prediction model is configured to use time series data as input and toilet seat failure possibility as output; According to the historical maintenance data, third time series data and toilet seat failure data corresponding to the historical maintenance data are obtained; The fault prediction model is obtained after the initial fault prediction model is trained according to the third time series data and the toilet seat failure results.

[0066] Optionally, according to the first use time series data and historical maintenance data of the abnormal toilet seat, a fault prediction result of the abnormal toilet seat is obtained through a fault prediction model, including: According to the first time series data of use of the abnormal toilet seat, the first time series data of use is input into the fault prediction model to obtain the first fault possibility of the abnormal toilet seat; Acquire first evaluation data that matches the target smart toilet with the first usage time series data; Extracting first key information from the first evaluation data according to the first evaluation data; According to the first key information and the first fault possibility, a fault prediction result of the abnormal toilet seat is obtained.

[0067] Optionally, obtaining a fault prediction result of the abnormal toilet seat according to the first key information and the first fault possibility includes: According to the first key information, a second fault possibility is obtained through a preset fault mapping; According to the first fault possibility, a first weight set is obtained, and according to the second fault possibility, a second weight set is obtained; Obtaining a third weight set according to the first weight set and the second weight set; According to the third weight set, a fault prediction result of the abnormal toilet seat is obtained.

[0068] Optionally, after the step of obtaining the fault prediction result of the abnormal toilet seat by using the fault prediction model according to the first usage time series data and the historical maintenance data of the abnormal toilet seat, the step further includes: According to the fault prediction results of the abnormal toilet seats, a corresponding maintenance work order is generated and dispatched to the corresponding maintenance personnel.

[0069] Example 4 This embodiment provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement any of the above methods.

[0070] Specifically, Figure 2 As shown, Figure 2 This is a schematic diagram of the computer device structure of the hardware operating environment involved in the embodiment of the present application. The computer device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. Among them, the communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the user interface 104 may also include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 105 may optionally be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) memory, or it may be a stable non-volatile memory (NVM), such as at least one disk storage. The processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component.

[0071] Those skilled in the art will understand that Figure 2 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0072] like Figure 2 As shown, the memory 105 as a storage medium may include an operating system, a network communication module, a user interface module, and an application program for implementing a smart toilet fault monitoring method.

[0073] exist Figure 2 In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in the present application can be set in the electronic device, and the electronic device calls the application stored in the memory 105 for implementing a smart toilet fault monitoring method through the processor 101 to implement the above method.

[0074] Example 5 This embodiment provides a computer-readable storage medium, on which a computer program is stored, and a processor executes the computer program to implement any of the above methods.

[0075] In some embodiments, the computer readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or various devices including one or any combination of the above memories. The computer may be various computing devices including intelligent terminals and servers.

[0076] In the above embodiments of the present disclosure, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0078] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0079] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0080] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a non-volatile storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present disclosure. The aforementioned non-volatile storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0081] The above are only preferred embodiments of the present disclosure. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present disclosure. These improvements and modifications should also be regarded as the protection scope of the present disclosure.

Claims

1. A smart toilet fault monitoring method, characterized in that: include: Acquire first usage time series data of each toilet seat in the target smart toilet, and determine whether there is a first abnormal toilet seat according to the first usage time series data and the usage rate of each toilet seat, wherein the first abnormal toilet seat is configured as a toilet seat whose usage rate and the average usage rate of all toilet seats exceed a preset first threshold value; According to the first usage time series data, according to the average usage time of each toilet seat, determine whether there is a second abnormal toilet seat, where the second abnormal toilet seat is configured as a toilet seat whose average usage time is less than a preset second threshold; When there is a first abnormal toilet seat or a second abnormal toilet seat, obtaining first usage time series data and historical maintenance data of the abnormal toilet seat; According to the first usage time series data and historical maintenance data of the abnormal toilet seat, a fault prediction result of the abnormal toilet seat is obtained through a fault prediction model.

2. A smart toilet fault monitoring method according to claim 1, characterized in that: Before the step of obtaining the first time series data of each toilet seat in the target smart toilet and judging whether there is a first abnormal toilet seat according to the first time series data and the usage rate of each toilet seat, the step further includes: A usage evaluation of at least one smart toilet is obtained, and based on the usage evaluation of the at least one smart toilet, a smart toilet with an abnormal usage evaluation is obtained as a target smart toilet.

3. A smart toilet fault monitoring method according to claim 1, characterized in that: The obtaining of first usage time series data of each toilet seat in the target smart toilet, and judging whether there is a first abnormal toilet seat according to the first usage time series data and the usage rate of each toilet seat, includes: Acquire first usage time series data of each toilet seat in the target smart toilet, and calculate the usage rate of each toilet seat in the time window through at least one time window; According to the utilization rate of each toilet seat, determine whether there is a toilet seat whose utilization rate and the average utilization rate of all toilet seats differ by more than a preset first threshold value; If it exists, then this toilet will be regarded as the first abnormal toilet.

4. A smart toilet fault monitoring method according to claim 1, characterized in that: The determining, based on the first usage time series data and the average usage time of each toilet seat, whether there is a second abnormal toilet seat includes: Obtain first usage time series data of each toilet seat in the target smart toilet, and calculate the average usage time of each toilet seat in the time window through at least one time window; According to the average usage time of each toilet seat, determine whether there is a toilet seat whose average usage time is less than a preset second threshold; If it exists, this toilet will be taken as the second abnormal toilet.

5. A smart toilet fault monitoring method according to claim 1, characterized in that: Before the step of obtaining the fault prediction result of the abnormal toilet seat by using a fault prediction model according to the first usage time series data and the historical maintenance data of the abnormal toilet seat, the method further includes: Establishing an initial fault prediction model, wherein the initial fault prediction model is configured to use time series data as input and toilet seat failure possibility as output; According to the historical maintenance data, third time series data and toilet seat failure data corresponding to the historical maintenance data are obtained; The fault prediction model is obtained by training the initial fault prediction model according to the third time series data and the toilet seat failure result.

6. A smart toilet fault monitoring method according to claim 5, characterized in that: The method of obtaining the fault prediction result of the abnormal toilet seat by using a fault prediction model according to the first usage time series data and the historical maintenance data of the abnormal toilet seat includes: According to the first usage time series data of the abnormal toilet seat, the first usage time series data is input into the fault prediction model to obtain a first possible fault of the abnormal toilet seat; Acquire first evaluation data that matches the target smart toilet with the first usage time series data; Extracting first key information from the first evaluation data according to the first evaluation data; According to the first key information and the first fault possibility, a fault prediction result of the abnormal toilet seat is obtained.

7. A smart toilet fault monitoring method according to claim 1, characterized in that: The obtaining, according to the first key information and the first possibility of failure, a failure prediction result of the abnormal toilet seat includes: According to the first key information, a second fault possibility is obtained through a preset fault mapping; According to the first fault possibility, a first weight set is obtained, and according to the second fault possibility, a second weight set is obtained; Obtaining a third weight set according to the first weight set and the second weight set; According to the third weight set, a fault prediction result of the abnormal toilet seat is obtained.

8. A smart toilet fault monitoring method according to claim 1, characterized in that: After the step of obtaining the fault prediction result of the abnormal toilet seat through a fault prediction model according to the first usage time series data and the historical maintenance data of the abnormal toilet seat, the method further includes: According to the fault prediction result of the abnormal toilet seat, a corresponding maintenance work order is generated and the maintenance work order is dispatched to the corresponding maintenance personnel.

9. A smart toilet fault monitoring system, characterized in that: Including fault monitoring platform and smart toilet management platform: The smart toilet management platform is configured as follows: Obtaining usage status of each toilet seat in the target smart toilet, and obtaining first usage time series data of each toilet seat according to the usage status; The fault monitoring platform is configured as follows: Acquire first usage time series data of each toilet seat in the target smart toilet through the smart toilet management platform, and determine whether there is a first abnormal toilet seat according to the first usage time series data and the usage rate of each toilet seat, wherein the first abnormal toilet seat is configured as a toilet seat whose usage rate and the average usage rate of all toilet seats exceed a preset first threshold; According to the first usage time series data, according to the average usage time of each toilet seat, determine whether there is a second abnormal toilet seat, where the second abnormal toilet seat is configured as a toilet seat whose average usage time is less than a preset second threshold; When there is a first abnormal toilet seat or a second abnormal toilet seat, obtaining first usage time series data and historical maintenance data of the abnormal toilet seat; According to the first usage time series data and historical maintenance data of the abnormal toilet seat, a fault prediction result of the abnormal toilet seat is obtained through a fault prediction model.

10. A computer device, characterized in that: The computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1-8.

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