CN117842806A - User elevator taking control method and device based on artificial intelligence - Google Patents
User elevator taking control method and device based on artificial intelligence Download PDFInfo
- Publication number
- CN117842806A CN117842806A CN202410267513.2A CN202410267513A CN117842806A CN 117842806 A CN117842806 A CN 117842806A CN 202410267513 A CN202410267513 A CN 202410267513A CN 117842806 A CN117842806 A CN 117842806A
- Authority
- CN
- China
- Prior art keywords
- elevator
- model training
- user
- training set
- floor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 74
- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000012549 training Methods 0.000 claims abstract description 206
- 238000004590 computer program Methods 0.000 claims description 18
- 230000001815 facial effect Effects 0.000 claims description 18
- 238000003860 storage Methods 0.000 claims description 13
- 238000003825 pressing Methods 0.000 claims description 9
- 230000001960 triggered effect Effects 0.000 claims description 9
- 239000013598 vector Substances 0.000 claims description 7
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 3
- 230000010354 integration Effects 0.000 claims description 3
- 230000002349 favourable effect Effects 0.000 abstract 1
- 238000004891 communication Methods 0.000 description 16
- 230000006870 function Effects 0.000 description 15
- 238000010586 diagram Methods 0.000 description 10
- 238000012545 processing Methods 0.000 description 10
- 239000000872 buffer Substances 0.000 description 4
- 238000012795 verification Methods 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/0006—Monitoring devices or performance analysers
- B66B5/0012—Devices monitoring the users of the elevator system
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B1/00—Control systems of elevators in general
- B66B1/02—Control systems without regulation, i.e. without retroactive action
- B66B1/06—Control systems without regulation, i.e. without retroactive action electric
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B1/00—Control systems of elevators in general
- B66B1/34—Details, e.g. call counting devices, data transmission from car to control system, devices giving information to the control system
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B1/00—Control systems of elevators in general
- B66B1/34—Details, e.g. call counting devices, data transmission from car to control system, devices giving information to the control system
- B66B1/3415—Control system configuration and the data transmission or communication within the control system
- B66B1/3423—Control system configuration, i.e. lay-out
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B50/00—Energy efficient technologies in elevators, escalators and moving walkways, e.g. energy saving or recuperation technologies
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Software Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Computational Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Medical Informatics (AREA)
- Evolutionary Biology (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Human Computer Interaction (AREA)
- Multimedia (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Elevator Control (AREA)
Abstract
The embodiment of the application provides a user elevator taking control method and device based on artificial intelligence, wherein the method comprises the following steps: carrying out face recognition on all users of all floors of an office building through a camera of the office building, determining corresponding user identities, determining corresponding company floors of the users and scheduling weights corresponding to the company floors of the users according to the user identities and a preset office building database, and setting the user identities, the company floors of the users and the scheduling weights as a first model training set; setting historical people stream data and historical elevator taking data in a set time period of each floor of the office building as a second model training set; performing model training on a preset artificial intelligent prediction model according to the first model training set and the second model training set to obtain an elevator scheduling strategy after model training; the elevator dispatching method and the elevator dispatching device can effectively improve elevator dispatching efficiency and accuracy, and are favorable for elevator riding experience of users.
Description
Technical Field
The application relates to the field of artificial intelligence, in particular to a user elevator taking control method and device based on artificial intelligence.
Background
Because the intelligent device is more and more common at present, the safety in living houses or office areas is also more and more important, so that a plurality of intelligent elevators are applied and developed, the intelligent elevators are intelligent control elevators which are improved on the basis of traditional elevators, and the intelligent elevator has the characteristics of more intelligence and simplicity in the aspects of user safety, identification and manageability, and is widely applied to houses, banks, government departments, hotels, school dormitories, hotels and other places for ensuring the safety of users.
The inventor finds that the elevator in the prior art is still not intelligent enough in scheduling, the scheduling efficiency and accuracy are not high, and the elevator taking experience of users is not facilitated.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides a user elevator taking control method and device based on artificial intelligence, which can effectively improve the efficiency and accuracy of elevator dispatching and is beneficial to elevator taking experience of users.
In order to solve at least one of the above problems, the present application provides the following technical solutions:
in a first aspect, the present application provides an artificial intelligence based user boarding control method, including:
carrying out face recognition on all users of all floors of an office building through a camera of the office building, determining corresponding user identities, determining corresponding company floors of the users and scheduling weights corresponding to the company floors of the users according to the user identities and a preset office building database, and setting the user identities, the company floors of the users and the scheduling weights as a first model training set;
Setting historical people stream data and historical elevator taking data in a set time period of each floor of the office building as a second model training set;
model training is carried out on a preset artificial intelligent prediction model according to the first model training set and the second model training set to obtain an elevator dispatching strategy after model training, and corresponding elevator dispatching operation is executed through the elevator dispatching strategy when an elevator taking request sent by a user is received, wherein the elevator taking request is triggered by the user by pressing/touching an elevator button.
Further, the face recognition is performed on all users of the hall of each floor of the office building through the hall camera, and the corresponding user identity is determined, including:
facial image acquisition is carried out on all users of each floor of the office building through the elevator hall camera, and key feature extraction is carried out on the acquired facial images to obtain facial feature vectors;
and determining the corresponding user identity according to the matching operation of the facial feature vector and a preset face database.
Further, the determining, according to the user identity and a preset office building database, a corresponding company floor to which the user belongs and a scheduling weight corresponding to the company floor to which the user belongs, and setting the user identity, the company floor to which the user belongs and the scheduling weight as a first model training set includes:
Determining the corresponding company floor of the user according to the matching result of the user identity and a preset writing floor database;
and determining a scheduling weight corresponding to the floor of the company to which the user belongs according to the historical people stream data of the company to which the user belongs, and setting the user identity, the floor of the company to which the user belongs and the scheduling weight as a first model training set.
Further, the setting the historical people stream data and the historical boarding data in the setting time period of each floor of the office building as the second model training set includes:
collecting historical people flow data and historical elevator taking data in a set time period of each floor of the write-in building and carrying out abnormal missing pretreatment;
and performing time dimension division on the historical people stream data and the historical elevator taking data subjected to the abnormal missing pretreatment according to a set time period, and setting the historical people stream data and the historical elevator taking data subjected to the time dimension division as a second model training set.
Further, the model training is performed on a preset artificial intelligent prediction model according to the first model training set and the second model training set to obtain an elevator scheduling strategy after model training, which comprises the following steps:
Data integration is carried out on the first model training set and the second model training set, and an integrated model training set is obtained;
and performing iterative training on a preset artificial intelligence prediction model according to the model training set, and determining the elevator scheduling strategy, wherein the elevator scheduling strategy comprises at least one of a shortest path strategy, a minimum waiting time strategy and a minimum running time strategy.
Further, the iterative training of the preset artificial intelligence prediction model according to the model training set, and determining the elevator dispatching strategy comprise:
performing iterative training on a preset artificial intelligent prediction model according to floor spacing characteristics and elevator running time characteristics in the model training set;
and outputting a shortest path strategy according to the dynamic shortest path in the iterative training result.
Further, the iterative training is performed on a preset artificial intelligence prediction model according to the model training set, and the elevator scheduling strategy is determined, which further includes:
performing iterative training on a preset artificial intelligent prediction model according to the user waiting time characteristics, the user current position characteristics and the user target floor characteristics in the model training set;
And outputting a minimum waiting time strategy according to the dynamic minimum waiting time in the iterative training result.
Further, the iterative training is performed on a preset artificial intelligence prediction model according to the model training set, and the elevator scheduling strategy is determined, which further includes:
performing iterative training on a preset artificial intelligent prediction model according to elevator running time characteristics in the model training set;
and outputting the shortest running time strategy according to the dynamic shortest running time in the iterative training result.
In a second aspect, the present application provides an artificial intelligence based user boarding control device, comprising:
the first model training set determining module is used for carrying out face recognition on all users of each floor of the office building through the elevator hall camera, determining corresponding user identities, determining corresponding company floors of the users and scheduling weights corresponding to the company floors of the users according to the user identities and a preset office building database, and setting the user identities, the company floors of the users and the scheduling weights as a first model training set;
the second model training set determining module is used for setting the historical people stream data and the historical elevator taking data in the setting time period of each floor of the office building as a second model training set;
The elevator scheduling strategy determining module is used for carrying out model training on a preset artificial intelligent prediction model according to the first model training set and the second model training set to obtain an elevator scheduling strategy after model training, and executing corresponding elevator scheduling operation through the elevator scheduling strategy when receiving an elevator taking request sent by a user, wherein the elevator taking request is triggered by the user by pressing/touching an elevator button.
In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the artificial intelligence based user boarding control method when the program is executed.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the artificial intelligence based user access control method.
In a fifth aspect, the present application provides a computer program product comprising computer programs/instructions which when executed by a processor implement the steps of the artificial intelligence based user access control method.
According to the technical scheme, the application provides a user elevator taking control method and device based on artificial intelligence, face recognition is carried out on all users in elevator waiting halls of all floors of an office building through elevator waiting hall cameras, corresponding user identities are determined, corresponding company floors of the users and scheduling weights corresponding to the company floors of the users are determined according to the user identities and a preset office building database, and the user identities, the company floors of the users and the scheduling weights are set as a first model training set; setting historical people stream data and historical elevator taking data in a set time period of each floor of the office building as a second model training set; model training is carried out on a preset artificial intelligent prediction model according to the first model training set and the second model training set, and an elevator dispatching strategy after model training is obtained, so that the efficiency and accuracy of elevator dispatching can be effectively improved, and elevator taking experience of a user is facilitated.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is one of flow diagrams of an artificial intelligence-based user boarding control method in an embodiment of the present application;
FIG. 2 is a second flow chart of an artificial intelligence based user boarding control method in an embodiment of the present application;
FIG. 3 is a third flow chart of an artificial intelligence based user boarding control method in an embodiment of the present application;
FIG. 4 is a schematic flow chart of a user elevator control method based on artificial intelligence in an embodiment of the present application;
FIG. 5 is a schematic flow chart of an artificial intelligence based user boarding control method in an embodiment of the present application;
FIG. 6 is a flowchart of a user boarding control method based on artificial intelligence according to an embodiment of the present application;
FIG. 7 is a flow chart of an artificial intelligence based user boarding control method according to an embodiment of the present application;
FIG. 8 is a flowchart of an artificial intelligence based user boarding control method according to an embodiment of the present application;
FIG. 9 is a block diagram of an artificial intelligence based user boarding control device in an embodiment of the present application;
fig. 10 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
The data acquisition, storage, use, processing and the like in the technical scheme meet the relevant regulations of national laws and regulations.
In consideration of the problems that an elevator in the prior art is still not intelligent enough in scheduling, scheduling efficiency and accuracy are not high, and user elevator taking experience is not facilitated, the application provides an artificial intelligence-based user elevator taking control method and device, face recognition is carried out on all users of elevator waiting halls of all floors of an office building through elevator waiting hall cameras, corresponding user identities are determined, corresponding company floors of the users and scheduling weights corresponding to the company floors of the users are determined according to the user identities and a preset office building database, and the user identities, the company floors of the users and the scheduling weights are set as a first model training set; setting historical people stream data and historical elevator taking data in a set time period of each floor of the office building as a second model training set; and carrying out model training on a preset artificial intelligent prediction model according to the first model training set and the second model training set to obtain an elevator dispatching strategy after model training, so that the efficiency and the accuracy of elevator dispatching can be effectively improved.
In order to effectively improve the efficiency and accuracy of elevator dispatching and facilitate elevator taking experience of users, the application provides an embodiment of an artificial intelligence-based elevator taking control method for users, referring to fig. 1, wherein the artificial intelligence-based elevator taking control method specifically comprises the following contents:
step S101: carrying out face recognition on all users of all floors of an office building through a camera of the office building, determining corresponding user identities, determining corresponding company floors of the users and scheduling weights corresponding to the company floors of the users according to the user identities and a preset office building database, and setting the user identities, the company floors of the users and the scheduling weights as a first model training set;
optionally, in this embodiment, the camera may be disposed in the hall of each floor of the office building, so as to ensure that the camera can comprehensively capture users in the hall. The camera settings, including angle, resolution, etc., are adjusted to ensure that a clear face image is obtained. The system of this embodiment may be a piece of software integrating the face detection and recognition functions. The system is ensured to have an efficient face detection algorithm, and users in a hall can be identified quickly and accurately.
Optionally, in this embodiment, face recognition may be performed through a camera of the hall, so as to obtain identity information of the user. This may include identification of the user's name, employee ID, etc. After identifying the user identity, the system accesses a preset office building database in which the detailed information of all registered users, including company floor, employee information, etc., is stored. And inquiring corresponding information in the database according to the user identity, and determining the company floor to which the user belongs. This step is performed based on existing registration information to ensure that the relationship of the user to the company floor is accurately matched. And extracting scheduling weight information corresponding to the company floor from the database according to the company floor to which the user belongs. These weights may be based on historical data of floors, traffic predictions, company employee size, etc. to affect weight allocation for subsequent elevator schedules. The user identity, the floor of the company to which the user belongs and the obtained scheduling weights are set as part of the first model training set. This training set will be used to train the artificial intelligence model to learn the associations between user identities, corporate floors and scheduling weights.
Through the steps, the system can acquire the related company floors and scheduling weights according to the identity information of the user, and the information is organized into a first model training set. This helps to build a model that enables it to better predict the weight allocation of different users in elevator dispatch.
Step S102: setting historical people stream data and historical elevator taking data in a set time period of each floor of the office building as a second model training set;
optionally, in this embodiment, for each floor of the write building, historical people stream data and historical elevator taking data may be collected. The historical traffic data includes the traffic of people on each floor during different time periods, and the historical elevator taking data records the use of the elevator on each floor. And meanwhile, the collected historical people stream data and the historical elevator taking data are preprocessed, so that the data quality and consistency are ensured. This may involve operations to remove outliers, fill missing data, normalize the data, etc., to make the data suitable for model training.
Then, the present embodiment can divide the historical people stream data and the historical boarding data according to the set time period. This may be divided by hour, by day, by weekday, etc. dimensions to obtain a more accurate training set. And combining the divided historical people stream data and the historical elevator taking data into a training set of the second model. Each sample may include information of the traffic of people at a floor, the use of the elevator, etc. within a certain time period. The diversity of the data sets is ensured, and different floors and time periods are covered.
Step S103: model training is carried out on a preset artificial intelligent prediction model according to the first model training set and the second model training set to obtain an elevator dispatching strategy after model training, and corresponding elevator dispatching operation is executed through the elevator dispatching strategy when an elevator taking request sent by a user is received, wherein the elevator taking request is triggered by the user by pressing/touching an elevator button.
Alternatively, the present embodiment may integrate the first model training set (user identity, company floor, scheduling weight) and the second model training set (historical traffic data, historical boarding data) into one comprehensive training set. It is ensured that the data set contains diversity and sufficient information. And performing feature engineering on the integrated training set, and extracting the features related to elevator dispatching. This may include the scheduling weight of the user, historical traffic data for the floor, historical use of the elevator, etc. Ensuring that the selected features have relevance to model predictive targets. And verifying the trained model by using the verification set, and evaluating the performance of the model. And (3) adjusting and optimizing the model according to the verification result so as to improve the generalization performance of the model.
Through the steps, the preset artificial intelligence prediction model is trained to obtain an optimized elevator dispatching strategy. This strategy will take into account many aspects based on the user's identity information, corporate floors, historical people stream data, and historical elevator taking data, etc., to achieve a more intelligent, personalized elevator dispatch.
As can be seen from the above description, the user taking control method based on artificial intelligence provided in the embodiments of the present application can perform face recognition on all users in each floor hall of an office building through a hall camera, determine a corresponding user identity, determine a corresponding company floor to which the user belongs and a scheduling weight corresponding to the company floor to which the user belongs according to the user identity and a preset office building database, and set the user identity, the company floor to which the user belongs and the scheduling weight as a first model training set; setting historical people stream data and historical elevator taking data in a set time period of each floor of the office building as a second model training set; and carrying out model training on a preset artificial intelligent prediction model according to the first model training set and the second model training set to obtain an elevator dispatching strategy after model training, so that the efficiency and the accuracy of elevator dispatching can be effectively improved.
In an embodiment of the artificial intelligence-based user boarding control method of the present application, referring to fig. 2, the following may be further specifically included:
step S201: facial image acquisition is carried out on all users of each floor of the office building through the elevator hall camera, and key feature extraction is carried out on the acquired facial images to obtain facial feature vectors;
step S202: and determining the corresponding user identity according to the matching operation of the facial feature vector and a preset face database.
Specifically, in this embodiment, when a user enters the hall and stands in the field of view of the camera, the system starts face collection. This includes capturing facial images of the user and extracting key features to create unique facial feature vectors. And extracting the characteristics of the collected facial images through a face recognition system, and matching the facial images with a facial characteristic library of registered users. This may be a database stored in a preset office building database containing facial feature information of registered users. If the match is successful, the system confirms the identity of the user. Identity information (e.g., company floor, user name, etc.) of the registered user is associated with the currently identified user. This step requires ensuring the accuracy and robustness of the matching algorithm to avoid misrecognition.
In an embodiment of the artificial intelligence-based user boarding control method of the present application, referring to fig. 3, the following may be further specifically included:
step S301: determining the corresponding company floor of the user according to the matching result of the user identity and a preset writing floor database;
step S302: and determining a scheduling weight corresponding to the floor of the company to which the user belongs according to the historical people stream data of the company to which the user belongs, and setting the user identity, the floor of the company to which the user belongs and the scheduling weight as a first model training set.
Optionally, in this embodiment, face recognition may be performed through a camera of the hall, so as to obtain identity information of the user. This may include identification of the user's name, employee ID, etc. After identifying the user identity, the system accesses a preset office building database in which the detailed information of all registered users, including company floor, employee information, etc., is stored. And inquiring corresponding information in the database according to the user identity, and determining the company floor to which the user belongs. This step is performed based on existing registration information to ensure that the relationship of the user to the company floor is accurately matched. And extracting scheduling weight information corresponding to the company floor from the database according to the company floor to which the user belongs. These weights may be based on historical data of floors, traffic predictions, company employee size, etc. to affect weight allocation for subsequent elevator schedules. The user identity, the floor of the company to which the user belongs and the obtained scheduling weights are set as part of the first model training set. This training set will be used to train the artificial intelligence model to learn the associations between user identities, corporate floors and scheduling weights.
In an embodiment of the artificial intelligence-based user boarding control method of the present application, referring to fig. 4, the following may be further specifically included:
step S401: collecting historical people flow data and historical elevator taking data in a set time period of each floor of the write-in building and carrying out abnormal missing pretreatment;
step S402: and performing time dimension division on the historical people stream data and the historical elevator taking data subjected to the abnormal missing pretreatment according to a set time period, and setting the historical people stream data and the historical elevator taking data subjected to the time dimension division as a second model training set.
Optionally, in this embodiment, for each floor of the write building, historical people stream data and historical elevator taking data may be collected. The historical traffic data includes the traffic of people on each floor during different time periods, and the historical elevator taking data records the use of the elevator on each floor. And meanwhile, the collected historical people stream data and the historical elevator taking data are preprocessed, so that the data quality and consistency are ensured. This may involve operations to remove outliers, fill missing data, normalize the data, etc., to make the data suitable for model training.
Then, the present embodiment can divide the historical people stream data and the historical boarding data according to the set time period. This may be divided by hour, by day, by weekday, etc. dimensions to obtain a more accurate training set. And combining the divided historical people stream data and the historical elevator taking data into a training set of the second model. Each sample may include information of the traffic of people at a floor, the use of the elevator, etc. within a certain time period. The diversity of the data sets is ensured, and different floors and time periods are covered.
In an embodiment of the artificial intelligence-based user boarding control method of the present application, referring to fig. 5, the following may be further specifically included:
step S501: data integration is carried out on the first model training set and the second model training set, and an integrated model training set is obtained;
step S502: and performing iterative training on a preset artificial intelligence prediction model according to the model training set, and determining the elevator scheduling strategy, wherein the elevator scheduling strategy comprises at least one of a shortest path strategy, a minimum waiting time strategy and a minimum running time strategy.
Alternatively, the present embodiment may integrate the first model training set (user identity, company floor, scheduling weight) and the second model training set (historical traffic data, historical boarding data) into one comprehensive training set. It is ensured that the data set contains diversity and sufficient information. And performing feature engineering on the integrated training set, and extracting the features related to elevator dispatching. This may include the scheduling weight of the user, historical traffic data for the floor, historical use of the elevator, etc. Ensuring that the selected features have relevance to model predictive targets. And verifying the trained model by using the verification set, and evaluating the performance of the model. And (3) adjusting and optimizing the model according to the verification result so as to improve the generalization performance of the model.
Through the steps, the preset artificial intelligence prediction model is trained to obtain an optimized elevator dispatching strategy. This strategy will take into account many aspects based on the user's identity information, corporate floors, historical people stream data, and historical elevator taking data, etc., to achieve a more intelligent, personalized elevator dispatch.
In an embodiment of the artificial intelligence-based user boarding control method of the present application, referring to fig. 6, the following may be further specifically included:
step S601: performing iterative training on a preset artificial intelligent prediction model according to floor spacing characteristics and elevator running time characteristics in the model training set;
step S602: and outputting a shortest path strategy according to the dynamic shortest path in the iterative training result.
Alternatively, the shortest path algorithm in this embodiment aims to find the shortest path for elevator operation to reach the destination floor as soon as possible. Common algorithms include Dijkstra algorithm and a algorithm.
Specifically, a map between floors is constructed, the current floor of the elevator is taken as a starting point, and the target floor is taken as an ending point. Initializing a distance table, recording the distance from the starting point to each floor, and setting the initial distance to be infinity. From the start point, the adjacent floor with the shortest distance is selected, and the distance table is updated. And repeating the steps until reaching the target floor, and obtaining the shortest path.
It follows that in this algorithm, a machine learning model can be used to predict the distance between each floor. This predictive model may be trained using inter-floor distance, elevator run time, etc. features in a historical dataset (second model training set). After model training, the shortest path can be calculated dynamically by monitoring the situation between the elevator and the floor in real time.
In an embodiment of the artificial intelligence-based user boarding control method of the present application, referring to fig. 7, the following may be further specifically included:
step S701: performing iterative training on a preset artificial intelligent prediction model according to the user waiting time characteristics, the user current position characteristics and the user target floor characteristics in the model training set;
step S702: and outputting a minimum waiting time strategy according to the dynamic minimum waiting time in the iterative training result.
Optionally, the minimum waiting time algorithm in this embodiment considers the current elevator operating state and passenger waiting conditions to minimize passenger waiting time.
Specifically, waiting passengers for each floor are counted. When the elevator is idle or the running direction coincides with the direction of the most waiting, the floor to which the most waiting is going is selected. If the elevator has a destination floor, keeping running in the current direction; otherwise, the direction is selected according to the waiting passengers.
It follows that this algorithm focuses on the waiting times of passengers and that the first training set of models may contain information about the waiting times of each passenger. By training the model, the optimal elevator dispatching decision can be predicted according to the characteristics of the current passenger, the target floor, the elevator state and the like.
In an embodiment of the artificial intelligence-based user boarding control method of the present application, referring to fig. 8, the following may be further specifically included:
step S801: performing iterative training on a preset artificial intelligent prediction model according to elevator running time characteristics in the model training set;
step S802: and outputting the shortest running time strategy according to the dynamic shortest running time in the iterative training result.
Alternatively, the minimum run time algorithm in this embodiment aims to minimize the total run time of the elevator, taking into account the requests of existing destination floors and current passengers.
Specifically, the floor requested by the current passenger is counted and used as the target floor. And selecting the nearest target floor according to the current floor and the direction of the elevator. If there is no destination floor currently, the nearest passenger request floor is selected. And after updating the target floor, determining a driving path according to a shortest path algorithm.
It follows that in this algorithm, the first model training set may contain information about the elevator run time taking into account the total run time of the elevator. By means of the historical dataset, a model can be trained, predicting which destination floor is chosen in different situations, resulting in the least total running time.
In summary, the first model training set and the second model training set are patterns in the historical data learned by machine learning techniques to better optimize the elevator dispatching algorithm. These models can be updated continuously by monitoring real-time data to adapt to changing elevator usage.
In order to effectively improve the efficiency and accuracy of elevator dispatching and facilitate the elevator taking experience of users, the application provides an embodiment of an artificial intelligence-based user elevator taking control device for realizing all or part of the content of the artificial intelligence-based user elevator taking control method, see fig. 9, wherein the artificial intelligence-based user elevator taking control device specifically comprises the following contents:
the first model training set determining module 10 is configured to perform face recognition on all users in each floor hall of an office building through a hall camera, determine corresponding user identities, determine corresponding company floors to which the users belong and scheduling weights corresponding to the company floors to which the users belong according to the user identities and a preset office building database, and set the user identities, the company floors to which the users belong and the scheduling weights as a first model training set;
A second model training set determining module 20, configured to set, as a second model training set, historical people stream data and historical boarding data in a set time period of each floor of the office building;
the elevator scheduling policy determining module 30 is configured to perform model training on a preset artificial intelligence prediction model according to the first model training set and the second model training set, obtain an elevator scheduling policy after the model training, and execute a corresponding elevator scheduling job according to the elevator scheduling policy when receiving an elevator taking request sent by a user, where the elevator taking request is triggered by the user by pressing/touching an elevator button.
As can be seen from the above description, the user taking control device based on artificial intelligence provided in the embodiments of the present application can perform face recognition on all users in each floor hall of an office building through a hall camera, determine a corresponding user identity, determine a corresponding company floor to which the user belongs and a scheduling weight corresponding to the company floor to which the user belongs according to the user identity and a preset office building database, and set the user identity, the company floor to which the user belongs and the scheduling weight as a first model training set; setting historical people stream data and historical elevator taking data in a set time period of each floor of the office building as a second model training set; and carrying out model training on a preset artificial intelligent prediction model according to the first model training set and the second model training set to obtain an elevator dispatching strategy after model training, so that the efficiency and the accuracy of elevator dispatching can be effectively improved.
In order to effectively improve the efficiency and accuracy of elevator dispatching and facilitate elevator riding experience of users, from the aspect of hardware, the application provides an embodiment of electronic equipment for realizing all or part of contents in the elevator riding control method based on artificial intelligence, wherein the electronic equipment specifically comprises the following contents:
a processor (processor), a memory (memory), a communication interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete communication with each other through the bus; the communication interface is used for realizing information transmission between the user elevator taking control device based on artificial intelligence and related equipment such as a core service system, a user terminal, a related database and the like; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, etc., and the embodiment is not limited thereto. In this embodiment, the logic controller may refer to an embodiment of the user boarding control method based on artificial intelligence in the embodiment and an embodiment of the user boarding control device based on artificial intelligence, and the contents thereof are incorporated herein and are not repeated here.
It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Wherein, intelligent wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..
In practical application, part of the user elevator taking control method based on artificial intelligence can be executed on the electronic equipment side as described in the above description, or all operations can be completed in the client equipment. Specifically, the selection may be made according to the processing capability of the client device, and restrictions of the use scenario of the user. The present application is not limited in this regard. If all operations are performed in the client device, the client device may further include a processor.
The client device may have a communication module (i.e. a communication unit) and may be connected to a remote server in a communication manner, so as to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementations may include a server of an intermediate platform, such as a server of a third party server platform having a communication link with the task scheduling center server. The server may include a single computer device, a server cluster formed by a plurality of servers, or a server structure of a distributed device.
Fig. 10 is a schematic block diagram of a system configuration of an electronic device 9600 of an embodiment of the present application. As shown in fig. 10, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this fig. 10 is exemplary; other types of structures may also be used in addition to or in place of the structures to implement telecommunications functions or other functions.
In one embodiment, the artificial intelligence based user access control method functions may be integrated into the central processor 9100. The central processor 9100 may be configured to perform the following control:
step S101: carrying out face recognition on all users of all floors of an office building through a camera of the office building, determining corresponding user identities, determining corresponding company floors of the users and scheduling weights corresponding to the company floors of the users according to the user identities and a preset office building database, and setting the user identities, the company floors of the users and the scheduling weights as a first model training set;
step S102: setting historical people stream data and historical elevator taking data in a set time period of each floor of the office building as a second model training set;
Step S103: model training is carried out on a preset artificial intelligent prediction model according to the first model training set and the second model training set to obtain an elevator dispatching strategy after model training, and corresponding elevator dispatching operation is executed through the elevator dispatching strategy when an elevator taking request sent by a user is received, wherein the elevator taking request is triggered by the user by pressing/touching an elevator button.
As can be seen from the above description, the electronic device provided in the embodiment of the present application performs face recognition on all users in each floor hall of an office building through a hall camera, determines a corresponding user identity, determines a corresponding company floor to which the user belongs and a scheduling weight corresponding to the company floor to which the user belongs according to the user identity and a preset office building database, and sets the user identity, the company floor to which the user belongs and the scheduling weight as a first model training set; setting historical people stream data and historical elevator taking data in a set time period of each floor of the office building as a second model training set; and carrying out model training on a preset artificial intelligent prediction model according to the first model training set and the second model training set to obtain an elevator dispatching strategy after model training, so that the efficiency and the accuracy of elevator dispatching can be effectively improved.
In another embodiment, the user boarding control device based on artificial intelligence may be configured separately from the central processing unit 9100, for example, the user boarding control device based on artificial intelligence may be configured as a chip connected to the central processing unit 9100, and the function of the user boarding control method based on artificial intelligence is realized through the control of the central processing unit.
As shown in fig. 10, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 need not include all of the components shown in fig. 10; in addition, the electronic device 9600 may further include components not shown in fig. 10, and reference may be made to the related art.
As shown in fig. 10, the central processor 9100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, which central processor 9100 receives inputs and controls the operation of the various components of the electronic device 9600.
The memory 9140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information about failure may be stored, and a program for executing the information may be stored. And the central processor 9100 can execute the program stored in the memory 9140 to realize information storage or processing, and the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, but not limited to, an LCD display.
The memory 9140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), SIM card, etc. But also a memory which holds information even when powered down, can be selectively erased and provided with further data, an example of which is sometimes referred to as EPROM or the like. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 storing application programs and function programs or a flow for executing operations of the electronic device 9600 by the central processor 9100.
The memory 9140 may also include a data store 9143, the data store 9143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, address book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. A communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, as in the case of conventional mobile communication terminals.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, etc., may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and to receive audio input from the microphone 9132 to implement usual telecommunications functions. The audio processor 9130 can include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100 so that sound can be recorded locally through the microphone 9132 and sound stored locally can be played through the speaker 9131.
The embodiments of the present application further provide a computer readable storage medium capable of implementing all steps in the artificial intelligence based user boarding control method in which the execution subject in the above embodiments is a server or a client, the computer readable storage medium having stored thereon a computer program that when executed by a processor implements all steps in the artificial intelligence based user boarding control method in which the execution subject in the above embodiments is a server or a client, for example, the processor implements the following steps when executing the computer program:
Step S101: carrying out face recognition on all users of all floors of an office building through a camera of the office building, determining corresponding user identities, determining corresponding company floors of the users and scheduling weights corresponding to the company floors of the users according to the user identities and a preset office building database, and setting the user identities, the company floors of the users and the scheduling weights as a first model training set;
step S102: setting historical people stream data and historical elevator taking data in a set time period of each floor of the office building as a second model training set;
step S103: model training is carried out on a preset artificial intelligent prediction model according to the first model training set and the second model training set to obtain an elevator dispatching strategy after model training, and corresponding elevator dispatching operation is executed through the elevator dispatching strategy when an elevator taking request sent by a user is received, wherein the elevator taking request is triggered by the user by pressing/touching an elevator button.
As can be seen from the above description, the computer readable storage medium provided in the embodiments of the present application performs face recognition on all users in each floor hall of an office building through a hall camera, determines a corresponding user identity, determines a corresponding company floor to which the user belongs and a scheduling weight corresponding to the company floor to which the user belongs according to the user identity and a preset office building database, and sets the user identity, the company floor to which the user belongs and the scheduling weight as a first model training set; setting historical people stream data and historical elevator taking data in a set time period of each floor of the office building as a second model training set; and carrying out model training on a preset artificial intelligent prediction model according to the first model training set and the second model training set to obtain an elevator dispatching strategy after model training, so that the efficiency and the accuracy of elevator dispatching can be effectively improved.
Embodiments of the present application further provide a computer program product capable of implementing all the steps in the artificial intelligence based user boarding control method in which the execution subject in the above embodiments is a server or a client, and the computer program/instructions implement the steps of the artificial intelligence based user boarding control method when executed by a processor, for example, the computer program/instructions implement the steps of:
step S101: carrying out face recognition on all users of all floors of an office building through a camera of the office building, determining corresponding user identities, determining corresponding company floors of the users and scheduling weights corresponding to the company floors of the users according to the user identities and a preset office building database, and setting the user identities, the company floors of the users and the scheduling weights as a first model training set;
step S102: setting historical people stream data and historical elevator taking data in a set time period of each floor of the office building as a second model training set;
step S103: model training is carried out on a preset artificial intelligent prediction model according to the first model training set and the second model training set to obtain an elevator dispatching strategy after model training, and corresponding elevator dispatching operation is executed through the elevator dispatching strategy when an elevator taking request sent by a user is received, wherein the elevator taking request is triggered by the user by pressing/touching an elevator button.
As can be seen from the above description, the computer program product provided in the embodiments of the present application performs face recognition on all users in each floor hall of an office building through a hall camera, determines a corresponding user identity, determines a corresponding company floor to which the user belongs and a scheduling weight corresponding to the company floor to which the user belongs according to the user identity and a preset office building database, and sets the user identity, the company floor to which the user belongs and the scheduling weight as a first model training set; setting historical people stream data and historical elevator taking data in a set time period of each floor of the office building as a second model training set; and carrying out model training on a preset artificial intelligent prediction model according to the first model training set and the second model training set to obtain an elevator dispatching strategy after model training, so that the efficiency and the accuracy of elevator dispatching can be effectively improved.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (10)
1. The user elevator taking control method based on artificial intelligence is characterized by comprising the following steps:
carrying out face recognition on all users of all floors of an office building through a camera of the office building, determining corresponding user identities, determining corresponding company floors of the users and scheduling weights corresponding to the company floors of the users according to the user identities and a preset office building database, and setting the user identities, the company floors of the users and the scheduling weights as a first model training set;
Setting historical people stream data and historical elevator taking data in a set time period of each floor of the office building as a second model training set;
model training is carried out on a preset artificial intelligent prediction model according to the first model training set and the second model training set to obtain an elevator dispatching strategy after model training, and corresponding elevator dispatching operation is executed through the elevator dispatching strategy when an elevator taking request sent by a user is received, wherein the elevator taking request is triggered by the user by pressing/touching an elevator button.
2. The artificial intelligence based user taking control method according to claim 1, wherein the step of identifying all users of each floor hall of the office building by the hall camera to determine the corresponding user identity comprises the steps of:
facial image acquisition is carried out on all users of each floor of the office building through the elevator hall camera, and key feature extraction is carried out on the acquired facial images to obtain facial feature vectors;
and determining the corresponding user identity according to the matching operation of the facial feature vector and a preset face database.
3. The artificial intelligence based user boarding control method of claim 1, wherein the determining, according to the user identity and a preset office building database, a corresponding user belonging company floor and a scheduling weight corresponding to the user belonging company floor, and setting the user identity, the user belonging company floor and the scheduling weight as a first model training set, comprises:
Determining the corresponding company floor of the user according to the matching result of the user identity and a preset writing floor database;
and determining a scheduling weight corresponding to the floor of the company to which the user belongs according to the historical people stream data of the company to which the user belongs, and setting the user identity, the floor of the company to which the user belongs and the scheduling weight as a first model training set.
4. The artificial intelligence based user boarding control method of claim 1, wherein the setting of the historical people stream data and the historical boarding data for each floor of the office building for a set period of time as a second model training set comprises:
collecting historical people flow data and historical elevator taking data in a set time period of each floor of the write-in building and carrying out abnormal missing pretreatment;
and performing time dimension division on the historical people stream data and the historical elevator taking data subjected to the abnormal missing pretreatment according to a set time period, and setting the historical people stream data and the historical elevator taking data subjected to the time dimension division as a second model training set.
5. The method for controlling elevator taking by a user based on artificial intelligence according to claim 1, wherein the performing model training on a preset artificial intelligence prediction model according to the first model training set and the second model training set to obtain an elevator scheduling strategy after the model training comprises:
Data integration is carried out on the first model training set and the second model training set, and an integrated model training set is obtained;
and performing iterative training on a preset artificial intelligence prediction model according to the model training set, and determining the elevator scheduling strategy, wherein the elevator scheduling strategy comprises at least one of a shortest path strategy, a minimum waiting time strategy and a minimum running time strategy.
6. The artificial intelligence based user boarding control method of claim 5, wherein the iteratively training a preset artificial intelligence prediction model according to the model training set, determining the elevator scheduling policy, comprises:
performing iterative training on a preset artificial intelligent prediction model according to floor spacing characteristics and elevator running time characteristics in the model training set;
and outputting a shortest path strategy according to the dynamic shortest path in the iterative training result.
7. The artificial intelligence based user boarding control method of claim 5, wherein the iterative training of a preset artificial intelligence prediction model according to the model training set, determining the elevator scheduling policy, further comprises:
Performing iterative training on a preset artificial intelligent prediction model according to the user waiting time characteristics, the user current position characteristics and the user target floor characteristics in the model training set;
and outputting a minimum waiting time strategy according to the dynamic minimum waiting time in the iterative training result.
8. The artificial intelligence based user boarding control method of claim 5, wherein the iterative training of a preset artificial intelligence prediction model according to the model training set, determining the elevator scheduling policy, further comprises:
performing iterative training on a preset artificial intelligent prediction model according to elevator running time characteristics in the model training set;
and outputting the shortest running time strategy according to the dynamic shortest running time in the iterative training result.
9. An artificial intelligence based user boarding control device, the device comprising:
the first model training set determining module is used for carrying out face recognition on all users of each floor of the office building through the elevator hall camera, determining corresponding user identities, determining corresponding company floors of the users and scheduling weights corresponding to the company floors of the users according to the user identities and a preset office building database, and setting the user identities, the company floors of the users and the scheduling weights as a first model training set;
The second model training set determining module is used for setting the historical people stream data and the historical elevator taking data in the setting time period of each floor of the office building as a second model training set;
the elevator scheduling strategy determining module is used for carrying out model training on a preset artificial intelligent prediction model according to the first model training set and the second model training set to obtain an elevator scheduling strategy after model training, and executing corresponding elevator scheduling operation through the elevator scheduling strategy when receiving an elevator taking request sent by a user, wherein the elevator taking request is triggered by the user by pressing/touching an elevator button.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the artificial intelligence based user access control method of any of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410267513.2A CN117842806B (en) | 2024-03-08 | 2024-03-08 | User elevator taking control method and device based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410267513.2A CN117842806B (en) | 2024-03-08 | 2024-03-08 | User elevator taking control method and device based on artificial intelligence |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117842806A true CN117842806A (en) | 2024-04-09 |
| CN117842806B CN117842806B (en) | 2024-05-03 |
Family
ID=90542194
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410267513.2A Active CN117842806B (en) | 2024-03-08 | 2024-03-08 | User elevator taking control method and device based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117842806B (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5616896A (en) * | 1993-11-11 | 1997-04-01 | Kone Oy | Procedure for controlling an elevator group |
| JP2003095562A (en) * | 2001-07-18 | 2003-04-03 | Mitsubishi Electric Building Techno Service Co Ltd | Control device of door of elevator |
| TW201504130A (en) * | 2013-07-17 | 2015-02-01 | Hon Hai Prec Ind Co Ltd | Control system and method for elevator |
| CN104340779A (en) * | 2013-07-23 | 2015-02-11 | 鸿富锦精密工业（深圳）有限公司 | Control system and method of elevator |
| CN109179101A (en) * | 2018-09-07 | 2019-01-11 | 平安科技（深圳）有限公司 | Elevator control method, device, computer equipment and computer readable storage medium |
| CN110980456A (en) * | 2019-12-17 | 2020-04-10 | 南京理工大学 | Elevator group control dispatching method based on traffic flow and adaptive neural fuzzy inference |
| CN113682908A (en) * | 2021-08-31 | 2021-11-23 | 电子科技大学 | Intelligent scheduling method based on deep learning |
| CN114229637A (en) * | 2021-12-03 | 2022-03-25 | 北京声智科技有限公司 | Elevator floor determining method, device, equipment and computer readable storage medium |
| CN114634071A (en) * | 2022-01-30 | 2022-06-17 | 别克派利尔电梯(扬州)有限公司 | Artificial intelligence optimization operating system based on elevator dispatching neural network training |
| CN115057308A (en) * | 2022-07-20 | 2022-09-16 | 上海三菱电梯有限公司 | Elevator allocation method |
| CN116663748A (en) * | 2023-07-26 | 2023-08-29 | 常熟理工学院 | Elevator dispatching decision-making method and system based on cyclic neural network |
-
2024
- 2024-03-08 CN CN202410267513.2A patent/CN117842806B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5616896A (en) * | 1993-11-11 | 1997-04-01 | Kone Oy | Procedure for controlling an elevator group |
| JP2003095562A (en) * | 2001-07-18 | 2003-04-03 | Mitsubishi Electric Building Techno Service Co Ltd | Control device of door of elevator |
| TW201504130A (en) * | 2013-07-17 | 2015-02-01 | Hon Hai Prec Ind Co Ltd | Control system and method for elevator |
| CN104340779A (en) * | 2013-07-23 | 2015-02-11 | 鸿富锦精密工业（深圳）有限公司 | Control system and method of elevator |
| CN109179101A (en) * | 2018-09-07 | 2019-01-11 | 平安科技（深圳）有限公司 | Elevator control method, device, computer equipment and computer readable storage medium |
| CN110980456A (en) * | 2019-12-17 | 2020-04-10 | 南京理工大学 | Elevator group control dispatching method based on traffic flow and adaptive neural fuzzy inference |
| CN113682908A (en) * | 2021-08-31 | 2021-11-23 | 电子科技大学 | Intelligent scheduling method based on deep learning |
| CN114229637A (en) * | 2021-12-03 | 2022-03-25 | 北京声智科技有限公司 | Elevator floor determining method, device, equipment and computer readable storage medium |
| CN114634071A (en) * | 2022-01-30 | 2022-06-17 | 别克派利尔电梯(扬州)有限公司 | Artificial intelligence optimization operating system based on elevator dispatching neural network training |
| CN115057308A (en) * | 2022-07-20 | 2022-09-16 | 上海三菱电梯有限公司 | Elevator allocation method |
| CN116663748A (en) * | 2023-07-26 | 2023-08-29 | 常熟理工学院 | Elevator dispatching decision-making method and system based on cyclic neural network |
Non-Patent Citations (1)
| Title |
|---|
| 乔元健;: "基于PLC和神经网络的电梯群控最优化方法设计", 智慧工厂, no. 05, 15 May 2020 (2020-05-15), pages 47 - 49 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117842806B (en) | 2024-05-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103918247B (en) | Intelligent mobile phone sensor logic based on background environment | |
| CN102782733B (en) | Adopt the method and the allocation plan that are equipped with the smart phone of sensor | |
| CN102907077B (en) | For the system and method for the intelligent audio record of mobile device | |
| CN111114554B (en) | Method, device, terminal and storage medium for predicting travel track | |
| CN113064700B (en) | Container deployment method and device | |
| US20220277752A1 (en) | Voice interaction method and related apparatus | |
| CN109887268B (en) | Vehicle scheduling method, device and storage medium | |
| CN110471735A (en) | Operational view interface method for pushing and device | |
| CN111833509A (en) | Intelligent queuing management method and device | |
| CN112766978A (en) | Subway face brushing payment method and device | |
| CN112017670B (en) | Target account audio identification method, device, equipment and medium | |
| KR102492333B1 (en) | Method and apparatus for providing information about a medical appointment schedule to a terminal based on parking-related information using a neural network | |
| CN107368998A (en) | Agenda managing method and Related product | |
| CN117842806B (en) | User elevator taking control method and device based on artificial intelligence | |
| CN113065879A (en) | Data stream quality inspection method and system | |
| CN113727272A (en) | Distributed intelligent interaction method and device, electronic equipment and storage medium | |
| CN112562688A (en) | Voice transcription method, device, recording pen and storage medium | |
| CN112948763A (en) | Method and device for predicting quantity of component, electronic equipment and storage medium | |
| KR102339494B1 (en) | Electronic device for managnign entering or leaving vehicle and method for operating thereof | |
| CN112820298B (en) | Voiceprint recognition method and device | |
| CN112750448B (en) | Sound scene recognition method, device, equipment and storage medium | |
| CN115118820A (en) | Call processing method and device, computer equipment and storage medium | |
| CN113450745A (en) | Immersive vehicle-mounted KTV point singing control method, system, terminal and storage medium | |
| CN111089581A (en) | Traffic guidance method, terminal and robot | |
| CN117455558A (en) | Data processing method and device based on combined model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address | ||
| CP03 | Change of name, title or address |
Address after: 310000, 24th, 25th, and 26th floors of Building 3, Fashion Wantong City, Cangqian Street, Yuhang District, Hangzhou City, Zhejiang Province Patentee after: Hangzhou Yufan Intelligent Technology Co.,Ltd. Country or region after: China Address before: 310000, 24th, 25th, and 26th floors of Building 3, Fashion Wantong City, Cangqian Street, Yuhang District, Hangzhou City, Zhejiang Province Patentee before: UNIVERSAL UBIQUITOUS TECHNOLOGY Co.,Ltd. Country or region before: China |