Intelligent toilet deployment scheme evaluation method, system and device based on artificial intelligence

Abstract

The application discloses an intelligent toilet deployment scheme evaluation method, system and equipment based on artificial intelligence, which comprises the following steps: obtaining a target position and a target configuration scheme of a target deployment scheme according to the target deployment scheme; obtaining a region label and a first intelligent toilet associated with the target position according to the target position; obtaining a first parameter according to the target position and the operation condition of the first intelligent toilet; obtaining a second parameter according to the target configuration scheme and the region label; and obtaining an evaluation result of the target deployment scheme according to the first parameter and the second parameter. At least the existing intelligent toilet deployment still depends on manual site selection and evaluation, lacks a systematic and effective evaluation method, and the selection of commercial operation space lacks basis, resulting in that the operation conditions of the commercial operation space are good or bad, and the comprehensive utilization rate is low.

Description

Technical Field

[0001] This application relates to the field of architectural planning technology, and in particular to an evaluation method, system and equipment for smart toilet deployment schemes based on artificial intelligence. Background Technology

[0002] A smart city utilizes information and communication technologies to sense, analyze, and integrate key information from the core systems of urban operation, thereby providing intelligent responses to various needs, including those related to people's livelihoods, environmental protection, public safety, urban services, and commercial activities. Its essence is to leverage advanced information technology to achieve intelligent management and operation of the city, ultimately creating a better life for its inhabitants and promoting harmonious and sustainable urban development. Smart toilets are one example of smart city construction.

[0003] Meanwhile, smart toilets also boldly innovate, leveraging toilet traffic to create a "1+N" new economic operation scenario, opening up new publicity windows and commercial space carriers for the city, and creating new commercial value. "1" refers to smart media, consisting of a 6-square-meter LED screen on the outside of the rest station and multiple smaller screens inside, creating new media placement scenarios suitable for different media combinations. "N" refers to commercial operation space, with 7-10 square meters of operating space, providing diverse commercial operation scenarios such as coffee stations, health stations, bus stations, sports event stations, media stations, Party and mass service stations, and sanitation worker rest stations, not only benefiting the public but also providing a platform for cross-industry cooperation for businesses.

[0004] The current deployment of smart toilets still relies on manual site selection and evaluation, lacking a systematic and effective evaluation method. The selection of commercial operating spaces also lacks a basis, resulting in inconsistent operating conditions and low overall utilization rates. Summary of the Invention

[0005] This invention provides an evaluation method, system, and equipment for smart toilet deployment schemes based on artificial intelligence. It provides a systematic evaluation method for smart toilet deployment schemes, which at least solves the problem that the current deployment of smart toilets still relies on manual site selection and evaluation, lacks a systematic and effective evaluation method, and the selection of commercial operating spaces also lacks a basis, resulting in inconsistent operating conditions of commercial operating spaces and low overall utilization rate.

[0006] This application provides an evaluation method for a smart toilet deployment scheme based on artificial intelligence, including:

[0007] Based on the target deployment plan, obtain the target location and target configuration plan of the target deployment plan;

[0008] Based on the target location, an area label and a first smart toilet associated with the target location are obtained. The area label is configured as a label to characterize user behavior and user characteristics in the area, and the first smart toilet is configured as a smart toilet whose distance from the target location is within a preset threshold range.

[0009] Based on the target location and the operation status of the first smart toilet, a first parameter is obtained. The first parameter is configured to be calculated based on the operation status of the first smart toilet and the distance between the target location and the first smart toilet.

[0010] Based on the target configuration scheme and the region label, a second parameter is obtained, wherein the second parameter is configured as the matching degree between the target configuration scheme and the region label;

[0011] The evaluation result of the target deployment scheme is obtained based on the first parameter and the second parameter.

[0012] Optionally, obtaining the target location and target configuration scheme of the target deployment scheme according to the target deployment scheme includes:

[0013] Based on the target deployment plan and preset keywords, the target deployment plan is parsed to obtain the target location and target configuration plan.

[0014] Optionally, obtaining the area tag associated with the target location and the first smart toilet based on the target location includes:

[0015] Based on the target location, using a GIS system, key entities and smart toilets whose navigation distance and time to the target location do not exceed a preset threshold are obtained;

[0016] Based on the key entities and smart toilets whose navigation distance and time from the target location do not exceed a preset threshold, obtain the area label and the first smart toilet associated with the target location.

[0017] Optionally, obtaining the area tag and the first smart toilet associated with the target location based on the key entities and smart toilets whose navigation distance and time from the target location do not exceed a preset threshold includes:

[0018] Based on the key entities whose navigation distance and time to the target location do not exceed a preset threshold, obtain the key user profile corresponding to the key entities;

[0019] Based on the key user profile and the key entity, obtain the area label associated with the target location.

[0020] Optionally, obtaining the first parameter based on the target location and the operational status of the first smart toilet includes:

[0021] Based on the target location and the location of the first smart toilet, obtain the navigation distance and time for the target location and the first smart toilet;

[0022] The first parameter is obtained based on at least one of the following: navigation distance and time, average daily usage of the first smart toilet, average daily queuing time of the first smart toilet, and average daily vacancy time of the first smart toilet.

[0023] Optionally, obtaining the second parameter based on the target configuration scheme and the region label includes:

[0024] Based on the region labels, regional demand data is obtained through a pre-trained artificial intelligence model;

[0025] The second parameter is obtained based on the target configuration scheme and the regional demand data.

[0026] Optionally, obtaining the second parameter based on the target configuration scheme and the regional demand data includes:

[0027] Based on the regional demand data and the first smart toilet, demand gap data is obtained;

[0028] The second parameter is obtained based on the demand gap data and the target configuration scheme.

[0029] On another front, an AI-based smart toilet deployment scheme evaluation system is characterized by including a GIS system and an evaluation platform:

[0030] The GIS system is configured as follows:

[0031] Provide geographic information for the assessment platform;

[0032] The evaluation platform is configured as follows:

[0033] Based on the target deployment plan, obtain the target location and target configuration plan of the target deployment plan;

[0034] Based on the target location, an area label and a first smart toilet associated with the target location are obtained. The area label is configured as a label to characterize user behavior and user characteristics in the area, and the first smart toilet is configured as a smart toilet whose distance from the target location is within a preset threshold range.

[0035] Based on the target location and the operation status of the first smart toilet, a first parameter is obtained. The first parameter is configured to be calculated based on the operation status of the first smart toilet and the distance between the target location and the first smart toilet.

[0036] Based on the target configuration scheme and the region label, a second parameter is obtained, wherein the second parameter is configured as the matching degree between the target configuration scheme and the region label;

[0037] The evaluation result of the target deployment scheme is obtained based on the first parameter and the second parameter.

[0038] Optionally, obtaining the area tag associated with the target location and the first smart toilet based on the target location includes:

[0039] Based on the target location, using a GIS system, key entities and smart toilets whose navigation distance and time to the target location do not exceed a preset threshold are obtained;

[0040] Based on the key entities and smart toilets whose navigation distance and time from the target location do not exceed a preset threshold, obtain the area label and the first smart toilet associated with the target location.

[0041] In another aspect, embodiments of this application also provide 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-described method.

[0042] In another aspect, embodiments of this application also provide a computer-readable storage medium storing a computer program, wherein a processor executes the computer program to implement the above-described method.

[0043] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0044] This invention discloses an AI-based method, system, and device for evaluating smart toilet deployment schemes, comprising: obtaining a target location and a target configuration scheme based on a target deployment scheme; obtaining a region label associated with the target location and a first smart toilet based on the target location, wherein the region label is configured to characterize user behavior and user characteristics in the region, and the first smart toilet is configured to be a smart toilet located within a preset threshold range from the target location; obtaining a first parameter based on the target location and the operational status of the first smart toilet, wherein the first parameter is configured to be calculated based on the operational status of the first smart toilet and the distance between the target location and the first smart toilet; obtaining a second parameter based on the target configuration scheme and the region label, wherein the second parameter is configured to be the matching degree between the target configuration scheme and the region label; and obtaining an evaluation result of the target deployment scheme based on the first parameter and the second parameter. This invention addresses at least the problems of existing smart toilet deployments relying on manual site selection and evaluation, lacking a systematic and effective evaluation method, and the lack of a basis for selecting commercial operating spaces, resulting in inconsistent operational status and low overall utilization of commercial operating spaces. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0046] Figure 1 This is a flowchart illustrating an evaluation method for a smart toilet deployment scheme based on artificial intelligence, as described in this application.

[0047] Figure 2 This is a schematic diagram of the structure of a computer device according to this application.

[0048] The diagram is labeled as follows: 101-Processor, 102-Communication bus, 103-Network interface, 104-User interface, 105-Memory.

[0049] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0050] To enable those skilled in the art to better understand the present disclosure, the technical solutions of the present disclosure will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present disclosure, and not all embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present disclosure.

[0051] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0052] Example 1

[0053] like Figure 1 As shown, an evaluation method for a smart toilet deployment scheme based on artificial intelligence includes:

[0054] S1. Based on the target deployment plan, obtain the target location and target configuration plan of the target deployment plan.

[0055] Optionally, the target deployment plan can be a text file such as a project plan, or it can be structured data from which key information has been extracted.

[0056] Optionally, the target deployment scheme may include one or more target locations and one or more target configuration schemes.

[0057] Optionally, the target configuration scheme includes a basic configuration scheme and an extended configuration scheme. The basic configuration scheme is configured as the basic configuration of the smart toilet, including but not limited to the number and configuration of toilet stalls. The extended configuration scheme is configured as the extended configuration of the smart toilet, including but not limited to sanitation worker rest rooms, retail spaces, and bus stops. The extended configuration scheme may also include only specific equipment or functions, such as vending machines and shared power banks.

[0058] S2. Based on the target location, obtain the area label and the first smart toilet associated with the target location.

[0059] Specifically, the area label is configured as a label to characterize the user behavior and user characteristics of the area, and the first smart toilet is configured as a smart toilet whose distance from the target location is within a preset threshold range.

[0060] Optionally, based on the target location, at least one area tag associated with the target location and at least one first smart toilet can be obtained.

[0061] Optionally, the region label associated with the target location can be a pre-set label or a label generated based on the characteristics of the region where the target location is located.

[0062] S3. Obtain the first parameter based on the target location and the operation status of the first smart toilet.

[0063] Specifically, the first parameter is configured to be calculated based on the operation status of the first smart toilet and the distance between the target location and the first smart toilet.

[0064] Optionally, the busier the first smart toilet is located closer to the target location, the more favorable the value of the first parameter will be to the final evaluation result.

[0065] S4. Obtain the second parameter based on the target configuration scheme and region label.

[0066] Specifically, the second parameter is configured as the degree of matching between the target configuration scheme and the region label.

[0067] Optionally, the higher the compatibility between the target configuration scheme and the regional label, the more beneficial the value of the second parameter will be to the final evaluation result.

[0068] S5. Based on the first and second parameters, obtain the evaluation results of the target deployment plan.

[0069] By adopting the above method, based on the operation status of other smart toilets adjacent to the smart toilet in the target deployment plan and the user tags in the area, the deployment plan is comprehensively evaluated. This solves at least the problem that the current deployment of smart toilets still relies on manual site selection and evaluation, lacks a systematic and effective evaluation method, and the selection of commercial operation space also lacks a basis, resulting in inconsistent operation status of commercial operation space and low overall utilization rate.

[0070] Example 2

[0071] This embodiment, based on Embodiment 1, presents an evaluation method for a smart toilet deployment scheme based on artificial intelligence, including:

[0072] S1. Based on the target deployment plan, obtain the target location and target configuration plan of the target deployment plan.

[0073] Optionally, the target deployment plan can be a text file such as a project plan, or it can be structured data from which key information has been extracted.

[0074] Optionally, the target deployment scheme may include one or more target locations and one or more target configuration schemes.

[0075] Optionally, the target configuration scheme includes a basic configuration scheme and an extended configuration scheme. The basic configuration scheme is configured as the basic configuration of the smart toilet, including but not limited to the number and configuration of toilet stalls. The extended configuration scheme is configured as the extended configuration of the smart toilet, including but not limited to sanitation worker rest rooms, retail spaces, and bus stops. The extended configuration scheme may also include only specific equipment or functions, such as vending machines and shared power banks.

[0076] Optionally, based on the target deployment plan, obtain the target location and target configuration plan of the target deployment plan, including:

[0077] Based on the target deployment plan and preset keywords, the target deployment plan is parsed to obtain the target location and target configuration.

[0078] Optionally, based on the target deployment plan, obtain the target location and target configuration plan of the target deployment plan, including:

[0079] The input target deployment plan text is preprocessed to remove noise, punctuation marks, etc., and then segmented and tagged with words for subsequent analysis.

[0080] Named Entity Recognition (NER) is used to extract target locations, such as cities, buildings, coordinates, etc.

[0081] Identify key attributes of specific configuration schemes, such as device type, quantity, and configuration parameters.

[0082] Obtain the target location and target configuration of the target deployment plan.

[0083] The above solution is based on Natural Language Processing (NLP) technology. The core of NLP is converting natural language into a computer-readable form, and then using various algorithms and models to perform semantic understanding, information extraction, and text generation. Its goal is to enable computers to understand and process human language, thereby achieving tasks such as human-computer interaction, information extraction, and semantic analysis.

[0084] S2. Based on the target location, obtain the area label and the first smart toilet associated with the target location.

[0085] Specifically, the area label is configured as a label to characterize the user behavior and user characteristics of the area, and the first smart toilet is configured as a smart toilet whose distance from the target location is within a preset threshold range.

[0086] Optionally, based on the target location, at least one area tag associated with the target location and at least one first smart toilet can be obtained.

[0087] Optionally, the region label associated with the target location can be a pre-set label or a label generated based on the characteristics of the region where the target location is located.

[0088] Optionally, based on the target location, obtain the area tag and the first smart toilet associated with the target location, including:

[0089] Based on the target location and using the GIS system, key entities and smart toilets whose navigation distance and time from the target location do not exceed preset thresholds are obtained;

[0090] Based on key entities and smart toilets whose navigation distance and time from the target location do not exceed a preset threshold, obtain the area label and the first smart toilet associated with the target location.

[0091] Optionally, the GIS system can be integrated into the smart toilet management platform, and the system must record the location of each smart toilet.

[0092] Optionally, based on key entities and smart toilets whose navigation distance and time from the target location do not exceed a preset threshold, obtain the area tag and first smart toilet associated with the target location, including:

[0093] Based on key entities whose navigation distance and time from the target location do not exceed a preset threshold, obtain key user profiles corresponding to the key entities;

[0094] Based on key user profiles and key entities, obtain regional labels associated with the target location. These regional labels are used to characterize the corresponding user groups and their main activities.

[0095] Specifically, the above functionality can be achieved using the following pseudocode:

[0096] import data_library # Data processing library

[0097] import ai_model # AI model library

[0098] def fetch_location_data(target_location):

[0099] # Get data of the region associated with the target location

[0100] location_data = data_library.get_data_by_location(target_location)

[0101] return location_data

[0102] def generate_user_profiles(location_data):

[0103] # Get user profiles

[0104] user_profiles = []

[0105] for user in location_data['users']:

[0106] profile = {

[0107] 'user_id': user['id'],

[0108] 'age': user['age'],

[0109] 'gender': user['gender'],

[0110] 'interests': user['interests'],

[0111] 'activities': user['activities']

[0112] }

[0113] user_profiles.append(profile)

[0114] return user_profiles

[0115] def extract_key_buildings(location_data):

[0116] # Extract key entity information

[0117] key_buildings = []

[0118] for building in location_data['buildings']:

[0119] key_buildings.append(building['name'])

[0120] return key_buildings

[0121] def generate_user_labels(user_profiles, ai_model):

[0122] # Generating region labels based on AI models

[0123] user_labels = {}

[0124] for profile in user_profiles:

[0125] # Using AI models to infer user profiles

[0126] label = ai_model.predict_label(profile)

[0127] if label is not in user_labels:

[0128] user_labels[label] = []

[0129] user_labels[label].append(profile['user_id'])

[0130] return user_labels

[0131] def main(target_location):

[0132] # Main Process

[0133] location_data = fetch_location_data(target_location)

[0134] user_profiles = generate_user_profiles(location_data)

[0135] key_buildings = extract_key_buildings(location_data)

[0136] user_labels = generate_user_labels(user_profiles, ai_model)

[0137] # Output Results

[0138] return {

[0139] 'key_buildings': key_buildings,

[0140] 'user_labels': user_labels,

[0141] 'user_profiles': user_profiles

[0142] }

[0143] Optionally, key entity information can be building information, institutional information, business information, etc. associated with the target location. The association with the target location can be that the distance does not exceed a preset threshold, or that they belong to the same administrative region (such as a street) or the same business district.

[0144] Optionally, region tags can be configured as user profiles plus user behavior, such as "30-50 year old users, shopping", "male users, dining", "500-1000 people, working", etc.

[0145] S3. Obtain the first parameter based on the target location and the operation status of the first smart toilet.

[0146] Specifically, the first parameter is configured to be calculated based on the operation status of the first smart toilet and the distance between the target location and the first smart toilet.

[0147] Optionally, the busier the first smart toilet is located closer to the target location, the more favorable the value of the first parameter will be to the final evaluation result.

[0148] Optionally, based on the target location and the operational status of the first smart toilet, the first parameters are obtained, including:

[0149] Based on the target location and the location of the first smart toilet, obtain the navigation distance and time for the target location and the first smart toilet;

[0150] The first parameter is obtained based on at least one of the following: navigation distance and time, average daily usage of the first smart toilet, average daily queuing time of the first smart toilet, and average daily vacancy time of the first smart toilet.

[0151] Specifically, a third parameter is obtained based on the target location and the navigation distance and time of the first smart toilet;

[0152] The fourth parameter was obtained based on the average daily usage of the first smart toilet;

[0153] The fifth parameter was obtained based on the average daily queuing time of the first smart toilet;

[0154] The sixth parameter was obtained based on the average daily vacancy time of the first smart toilet;

[0155] The first parameter is obtained based on at least one of the third, fourth, fifth, and sixth parameters.

[0156] Among them, the larger the first parameter, the better it is for the final evaluation result. When the third, fourth, fifth, and sixth parameters are positively correlated with the first parameter, the shorter the navigation distance and time between the target location and the first smart toilet, the larger the third parameter, the more times the first smart toilet is used per day, the larger the fourth parameter, the longer the average daily queuing time of the first smart toilet, the larger the fifth parameter, the shorter the average daily vacancy time of the first smart toilet, and the larger the sixth parameter.

[0157] Specifically, the first parameter can be calculated as A1 = A3 * A4 * A5 * A6, where A1 is the first parameter, A3 is the average navigation distance between the target location and the first smart toilet * average navigation time, A4 is the average daily usage of the first smart toilet, A5 is the average daily queuing time of the first smart toilet, and A6 is 1 - the average daily vacancy rate of the first smart toilet. The average daily vacancy rate of the first smart toilet = average daily vacancy time / average daily operating hours.

[0158] S4. Obtain the second parameter based on the target configuration scheme and region label.

[0159] Specifically, the second parameter is configured as the degree of matching between the target configuration scheme and the region label.

[0160] Optionally, the higher the compatibility between the target configuration scheme and the regional label, the more beneficial the value of the second parameter will be to the final evaluation result.

[0161] Optionally, based on the target configuration scheme and region label, a second parameter is obtained, including:

[0162] Based on the region label, regional demand data is obtained through a pre-trained artificial intelligence model. The regional demand data is configured to reflect the region's demand for basic and extended configurations corresponding to that region label.

[0163] The second parameter is obtained based on the target configuration scheme and regional demand data.

[0164] Optionally, before the step of obtaining regional demand data based on regional labels using a pre-trained artificial intelligence model, the following may also be included:

[0165] An initial model for demand data prediction is established, which is configured to use demand data in the target area with basic and extended configurations as output parameters, and regional label data and utilization rates of basic and extended configurations as input parameters.

[0166] Obtain the utilization rates of the basic and extended configurations of existing smart toilets, as well as the area label data of existing smart toilets. Perform data augmentation on the above data to obtain the training set, test set, and validation set for the initial model of demand data prediction.

[0167] Based on the training set, test set, and validation set of the initial model, a demand data prediction model is obtained.

[0168] Specifically, the above functionality can be achieved using the following code:

[0169] import numpy as np

[0170] import pandas as pd

[0171] from sklearn.model_selection import train_test_split

[0172] from sklearn.linear_model import LinearRegression

[0173] from sklearn.metrics import mean_squared_error

[0174] # Step 1: Define the initial model for demand data forecasting

[0175] class DemandPredictionModel:

[0176] def __init__(self):

[0177] self.model = LinearRegression()

[0178] def train(self, X, y):

[0179] self.model.fit(X, y)

[0180] def predict(self, X):

[0181] return self.model.predict(X)

[0182] # Step 2: Obtain data from existing smart toilets

[0183] def get_existing_data():

[0184] # Assuming the data is already stored in a CSV file

[0185] data = pd.read_csv('smart_toilets_data.csv')

[0186] utilization_basic = data['basic_utilization']

[0187] utilization_extended = data['extended_utilization']

[0188] area_labels = data['area_labels']

[0189] return utilization_basic, utilization_extended, area_labels

[0190] # Step 3: Data Augmentation

[0191] def data_augmentation(utilization_basic, utilization_extended, area_labels):

[0192] # The example data has been augmented; actual implementation should be based on specific needs.

[0193] augmented_data = np.concatenate((utilization_basic.values.reshape(-1,1),

[0194] utilization_extended.values.reshape(-1, 1),

[0195] area_labels.values.reshape(-1, 1)), axis=1)

[0196] X = augmented_data

[0197] y = np.random.rand(len(X)) # Example of generating target requirement data

[0198] return train_test_split(X, y, test_size=0.2, random_state=42)

[0199] # Step 4: Training the initial model for predicting required data

[0200] def main():

[0201] utilization_basic, utilization_extended, area_labels = get_existing_data()

[0202] X_train, X_test, y_train, y_test = data_augmentation(utilization_basic, utilization_extended, area_labels)

[0203] # Instantiate the model

[0204] demand_model = DemandPredictionModel()

[0205] # Training Model

[0206] demand_model.train(X_train, y_train)

[0207] # Step 5: Test the demand data prediction model

[0208] predictions = demand_model.predict(X_test)

[0209] mse = mean_squared_error(y_test, predictions)

[0210] print(f'Mean Squared Error: {mse}')

[0211] # Step 6: Output the demand data prediction model

[0212] return demand_model

[0213] if __name__ == "__main__":

[0214] model = main().

[0215] Optionally, based on regional labels, regional demand data can be obtained through a pre-trained artificial intelligence model. This can be:

[0216] Based on the utilization rates of the basic and extended configurations set in the regional labels and target configuration schemes, the demand data of the basic and extended configurations in the target region is obtained as output parameters through the demand data prediction model.

[0217] Optionally, a second parameter can be obtained based on the target configuration scheme and regional demand data, including:

[0218] Based on regional demand data and the first smart toilet, demand gap data is obtained. The demand gap data is configured to show the gap between the basic configuration and the extended configuration in the region corresponding to the regional label.

[0219] The second parameter is obtained based on the demand gap data and the target configuration scheme.

[0220] S5. Based on the first and second parameters, obtain the evaluation results of the target deployment plan.

[0221] Optionally, when the first parameter and the second parameter are positively correlated with the evaluation result of the target deployment scheme, the evaluation result = the first parameter × the second parameter.

[0222] Optionally, if the evaluation result of the target deployment plan is not less than the preset first threshold, the output result is "evaluation passed"; if the evaluation result of the target deployment plan is less than the preset first threshold but not less than the preset second threshold, the output result is "pending" or "needs further evaluation"; if the evaluation result of the target deployment plan is less than the preset second threshold, the output result is "evaluation failed".

[0223] Optionally, the method for determining the first threshold and the second threshold is to evaluate and rank the existing smart toilets according to the evaluation method of smart toilet deployment scheme based on artificial intelligence, and take the 50th percentile (which can also be adjusted according to the actual situation) as the first threshold and the 25th percentile (which can also be adjusted according to the actual situation) as the second threshold.

[0224] Optionally, the first and second thresholds can be recalculated and adjusted according to preset conditions or periods.

[0225] By adopting the above method, based on the operation status of other smart toilets adjacent to the smart toilet in the target deployment plan and the user tags in the area, the deployment plan is comprehensively evaluated. This solves at least the problem that the current deployment of smart toilets still relies on manual site selection and evaluation, lacks a systematic and effective evaluation method, and the selection of commercial operation space also lacks a basis, resulting in inconsistent operation status of commercial operation space and low overall utilization rate.

[0226] Example 3

[0227] An AI-based smart toilet deployment scheme evaluation system, characterized by including a GIS system and an evaluation platform:

[0228] The GIS system is configured as follows:

[0229] Provide geographic information for the assessment platform;

[0230] The evaluation platform is configured as follows:

[0231] Based on the target deployment plan, obtain the target location and target configuration plan of the target deployment plan;

[0232] Based on the target location, obtain the area label and the first smart toilet associated with the target location. The area label is configured as a label to characterize the user behavior and user characteristics in the area, and the first smart toilet is configured as a smart toilet whose distance from the target location is within a preset threshold range.

[0233] Based on the target location and the operation status of the first smart toilet, a first parameter is obtained. The first parameter is configured to be calculated based on the operation status of the first smart toilet and the distance between the target location and the first smart toilet.

[0234] Based on the target configuration scheme and the region label, the second parameter is obtained, which is configured as the matching degree between the target configuration scheme and the region label;

[0235] The evaluation results of the target deployment scheme are obtained based on the first and second parameters.

[0236] Optionally, based on the target deployment plan, obtain the target location and target configuration plan of the target deployment plan, including:

[0237] Based on the target deployment plan and preset keywords, the target deployment plan is parsed to obtain the target location and target configuration.

[0238] Optionally, based on the target location, obtain the area tag and the first smart toilet associated with the target location, including:

[0239] Based on the target location and using the GIS system, key entities and smart toilets whose navigation distance and time from the target location do not exceed preset thresholds are obtained;

[0240] Based on key entities and smart toilets whose navigation distance and time from the target location do not exceed a preset threshold, obtain the area label and the first smart toilet associated with the target location.

[0241] Optionally, based on key entities and smart toilets whose navigation distance and time from the target location do not exceed a preset threshold, obtain the area tag and first smart toilet associated with the target location, including:

[0242] Based on key entities whose navigation distance and time from the target location do not exceed a preset threshold, obtain key user profiles corresponding to the key entities;

[0243] Based on key user profiles and key entities, obtain regional labels associated with the target location.

[0244] Optionally, based on the target location and the operational status of the first smart toilet, the first parameters are obtained, including:

[0245] Based on the target location and the location of the first smart toilet, obtain the navigation distance and time for the target location and the first smart toilet;

[0246] The first parameter is obtained based on at least one of the following: navigation distance and time, average daily usage of the first smart toilet, average daily queuing time of the first smart toilet, and average daily vacancy time of the first smart toilet.

[0247] Optionally, based on the target configuration scheme and region label, a second parameter is obtained, including:

[0248] Based on regional labels, regional demand data is obtained through a pre-trained artificial intelligence model;

[0249] The second parameter is obtained based on the target configuration scheme and regional demand data.

[0250] Optionally, a second parameter can be obtained based on the target configuration scheme and regional demand data, including:

[0251] Based on regional demand data and the first smart toilet, we obtained demand gap data;

[0252] The second parameter is obtained based on the demand gap data and the target configuration scheme.

[0253] Example 4

[0254] 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 methods described above.

[0255] Specifically, such as Figure 2 As shown, Figure 2 This is a schematic diagram of the computer device structure of the hardware operating environment involved in the embodiments of this 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. The communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 104 may also include a standard wired interface or a wireless interface. The network interface 103 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 105 may be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as at least one disk storage device. 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, discrete gate or transistor logic device, or discrete hardware component.

[0256] Those skilled in the art will understand that Figure 2 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0257] like Figure 2 As shown, the memory 105, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and an application for implementing an evaluation method for an artificial intelligence-based smart toilet deployment scheme.

[0258] 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 this application can be set in the electronic device, and the electronic device can call the application stored in the memory 105 through the processor 101 to implement an evaluation method for a smart toilet deployment scheme based on artificial intelligence to implement the above method.

[0259] Example 5

[0260] 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 methods described above.

[0261] 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 it may be a device including one or any combination of the above-mentioned memories. The computer may be a variety of computing devices, including smart terminals and servers.

[0262] In the above embodiments of this disclosure, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0263] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0264] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0265] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0266] If the integrated unit is implemented as 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 this disclosure, in essence, 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. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this disclosure. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0267] The above are merely preferred embodiments of this disclosure. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this disclosure, and these improvements and modifications should also be considered within the scope of protection of this disclosure.

Claims

1. A method for evaluating smart toilet deployment schemes based on artificial intelligence, characterized in that, include: According to the target deployment plan, the target location and target configuration plan of the target deployment plan are obtained. The target configuration plan includes a basic configuration plan and an extended configuration plan. The basic configuration plan is configured as the basic configuration of the smart toilet, including at least one of the number of toilet stalls and the configuration of toilet stalls. The extended configuration plan is configured as the extended configuration of the smart toilet, including at least one of the sanitation worker rest room, retail space, and bus station. Based on the target location, an area label and a first smart toilet associated with the target location are obtained. The area label is configured as a label to characterize user behavior and user characteristics in the area, and the first smart toilet is configured as a smart toilet whose distance from the target location is within a preset threshold range. Based on the target location and the operation status of the first smart toilet, a first parameter is obtained. The first parameter is configured to be calculated based on the operation status of the first smart toilet and the distance between the target location and the first smart toilet. Based on the region labels, regional demand data is obtained through a pre-trained artificial intelligence model; Based on the regional demand data and the first smart toilet, demand gap data is obtained; Based on the demand gap data and the target configuration scheme, a second parameter is obtained, which is configured as the matching degree between the target configuration scheme and the regional label; The evaluation result of the target deployment scheme is obtained based on the first parameter and the second parameter.

2. The method for evaluating a smart toilet deployment scheme based on artificial intelligence according to claim 1, characterized in that, The step of obtaining the target location and target configuration scheme of the target deployment scheme according to the target deployment scheme includes: Based on the target deployment plan and preset keywords, the target deployment plan is parsed to obtain the target location and target configuration plan.

3. The method for evaluating a smart toilet deployment scheme based on artificial intelligence according to claim 1, characterized in that, The step of obtaining the area tag associated with the target location and the first smart toilet based on the target location includes: Based on the target location, using a GIS system, key entities and smart toilets whose navigation distance and time to the target location do not exceed a preset threshold are obtained; Based on the key entities and smart toilets whose navigation distance and time from the target location do not exceed a preset threshold, obtain the area label and the first smart toilet associated with the target location.

4. The evaluation method for a smart toilet deployment scheme based on artificial intelligence according to claim 1, characterized in that, The step of obtaining a region tag and a first smart toilet associated with the target location based on key entities and smart toilets whose navigation distance and time from the target location do not exceed a preset threshold includes: Based on the key entities whose navigation distance and time to the target location do not exceed a preset threshold, obtain the key user profile corresponding to the key entities; Based on the key user profile and the key entity, obtain the area label associated with the target location.

5. The method for evaluating a smart toilet deployment scheme based on artificial intelligence according to claim 1, characterized in that, The step of obtaining the first parameter based on the target location and the operational status of the first smart toilet includes: Based on the target location and the location of the first smart toilet, obtain the navigation distance and time for the target location and the first smart toilet; The first parameter is obtained based on at least one of the following: navigation distance and time, average daily usage of the first smart toilet, average daily queuing time of the first smart toilet, and average daily vacancy time of the first smart toilet.

6. An evaluation system for smart toilet deployment schemes based on artificial intelligence, characterized in that, Including GIS systems and assessment platforms: The GIS system is configured as follows: Provide geographic information for the assessment platform; The evaluation platform is configured as follows: According to the target deployment plan, the target location and target configuration plan of the target deployment plan are obtained. The target configuration plan includes a basic configuration plan and an extended configuration plan. The basic configuration plan is configured as the basic configuration of the smart toilet, including at least one of the number of toilet stalls and the configuration of toilet stalls. The extended configuration plan is configured as the extended configuration of the smart toilet, including at least one of the sanitation worker rest room, retail space, and bus station. Based on the target location, an area label and a first smart toilet associated with the target location are obtained. The area label is configured as a label to characterize user behavior and user characteristics in the area, and the first smart toilet is configured as a smart toilet whose distance from the target location is within a preset threshold range. Based on the target location and the operation status of the first smart toilet, a first parameter is obtained. The first parameter is configured to be calculated based on the operation status of the first smart toilet and the distance between the target location and the first smart toilet. Based on the region labels, regional demand data is obtained through a pre-trained artificial intelligence model; Based on the regional demand data and the first smart toilet, demand gap data is obtained; Based on the demand gap data and the target configuration scheme, a second parameter is obtained, which is configured as the matching degree between the target configuration scheme and the regional label; The evaluation result of the target deployment scheme is obtained based on the first parameter and the second parameter.

7. The AI-based smart toilet deployment scheme evaluation system according to claim 6, characterized in that, The step of obtaining the area tag associated with the target location and the first smart toilet based on the target location includes: Based on the target location, using a GIS system, key entities and smart toilets whose navigation distance and time to the target location do not exceed a preset threshold are obtained; Based on the key entities and smart toilets whose navigation distance and time from the target location do not exceed a preset threshold, obtain the area label and the first smart toilet associated with the target location.

8. A computer device, characterized in that, The computer device includes 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 1-5.

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