Object Position Guidance Method and System

Abstract

The present application provides an object position guiding method and system, belonging to the field of Internet of Things technology. The method may include: receiving detection data from multiple object positions; inputting the detection data into a position guiding model in a local edge computing device to generate a target object position; and displaying the target object position to guide a user. When guiding the object position, the trained position guiding model can be used to reliably analyze the detection data from multiple object positions to obtain the target object position for user guidance. Moreover, the analysis process is carried out in the local edge computing device, and the calculation can be distributed to the detection devices close to the data source of the detection data for processing, with good real-time performance, high efficiency, and short delay. In addition, it can also provide computing power in the absence of network or poor network conditions. It effectively improves the guiding ability and guiding reliability of the object position.

Description

Technical Field

[0001] This application relates to the field of Internet of Things technology. Specifically, it relates to an object position guiding method and system. Background Art

[0002] Object position guiding is to intelligently guide the object position according to the state of the object, so as to provide the user with the optimal object position, enabling the user to find the most satisfactory object in the shortest time. Currently, when guiding the object position, there is a method of collecting the object state through wifi wireless technology or wired technology, uploading it to the cloud, and displaying the state through a display screen. In this way, there are problems that many data statistical capabilities during object position guiding are not effectively utilized, and there is no local processing ability. Therefore, there are problems of weak guiding ability and poor guiding reliability during object position guiding.

[0003] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0004] The purpose of the embodiments of this application is to provide an object position guiding method and system, which can effectively improve the guiding ability and guiding reliability of the object position.

[0005] According to an embodiment of this application, an object position guiding method may include: receiving detection data from multiple object positions; inputting the detection data into a position guiding model in a local edge computing device to generate a target object position; and displaying the target object position to guide the user.

[0006] According to an embodiment of this application, an object position guiding system may include: a receiving module, configured to receive detection data from multiple object positions; a sending module, configured to input the detection data into a position guiding model in a local edge computing device to generate a target object position; and a guiding module, configured to display the target object position to guide the user.

[0007] In some embodiments of this application, the object position guiding system is further configured to:

[0008] Analyze the detection data to obtain object occupancy information;

[0009] Synchronize the object occupancy information to a digital twin system to generate a digital twin of the object occupancy state;

[0010] Push the object occupancy state to a predetermined display position for display.

[0011] In some embodiments of the present application, the object position guiding system is further configured to:

[0012] Analyze the detection data to obtain object occupancy information;

[0013] Synchronize the object occupancy information to the cloud to update the object occupancy status in the cloud;

[0014] Push the object occupancy status to a second predetermined display position for display.

[0015] In some embodiments of the present application, the object position guiding system is further configured to:

[0016] Analyze the detection data to obtain object occupancy information;

[0017] Synchronize the object occupancy information to the rule engine so that the rule engine gives an alarm when the object reaches a predetermined condition according to the object occupancy information.

[0018] In some embodiments of the present application, it further includes:

[0019] Collect a sample training set of detection data in the cloud, and calibrate the target object position for each training sample in the sample training set;

[0020] Input the training samples in the sample training set into the guiding model in the cloud to train the guiding model;

[0021] Generate the guiding model after meeting the preset conditions.

[0022] In some embodiments of the present application, the receiving module is configured to:

[0023] Receive the detection data distributed by the LoRa core network in the local edge computing device through the Message Queuing Telemetry Transport Protocol.

[0024] In some embodiments of the present application, it further includes:

[0025] The LoRa core network in the local edge computing device receives the detection data transmitted by multiple detection terminals of the object positions through the LoRaWAN protocol.

[0026] In some embodiments of the present application, the object position guiding system is further configured to:

[0027] Obtain a detection terminal binding request;

[0028] Based on the binding request, register the detection terminal through the LoRa core network to obtain a registration result;

[0029] Return the registration result to the detection device so that the detection terminal can join the LoRa core network.

[0030] In some embodiments of the present application, based on the foregoing embodiments, the detection data includes:

[0031] Infrared detection data and electrothermal detection data.

[0032] According to another embodiment of the present application, an object position guiding terminal may include: a memory storing computer-readable instructions; a processor reading the computer-readable instructions stored in the memory to execute the method as described above.

[0033] According to another embodiment of the present application, a computer program medium stores computer-readable instructions, which, when executed by a processor of a computer, cause the computer to execute the method as described above.

[0034] According to the embodiments of the present application, it is possible to receive detection data from multiple object positions; input the detection data into a position guiding model in a local edge computing device to generate a target object position; and display the target object position to guide the user.

[0035] When guiding the object position, the detection data from multiple object positions can be reliably analyzed through a trained position guiding model to obtain the target object position for user guidance. Moreover, the analysis process is carried out in the local edge computing device, and the calculation can be distributed to the detection devices close to the data source of the detection data for processing, without having to transmit all the data back to the cloud for processing. It has characteristics such as good real-time performance, high efficiency, and short latency, and can also provide computing power in the absence or poor network conditions. Furthermore, it effectively improves the guiding ability and guiding reliability of the object position.

[0036] Other features and advantages of the present application will become apparent through the following detailed description in conjunction with the drawings, or will be partially learned through the practice of the present application.

[0037] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and are not intended to limit the present application. Description of the Drawings

[0038] Figure 1 Shows a schematic diagram of a system to which embodiments of the present application can be applied.

[0039] Figure 2 Shows a flowchart of an object position guiding method according to an embodiment of the present application.

[0040] Figure 3Shows a schematic diagram of the architecture of an object position guidance system in an application scenario according to an embodiment of the present application.

[0041] Figure 4 Shows based on Figure 3 The flowchart of binding a detection terminal to a local edge computing device in an application scenario according to an embodiment of the present application based on the system architecture shown.

[0042] Figure 5 Shows based on Figure 4 The flowchart of object position guidance in an application scenario according to an embodiment of the present application based on the embodiment shown.

[0043] Figure 6 Shows based on Figure 4 The flowchart of object position guidance in another application scenario according to an embodiment of the present application based on the embodiment shown.

[0044] Figure 7 Shows based on Figure 4 The flowchart of object position guidance in another application scenario according to an embodiment of the present application based on the embodiment shown.

[0045] Figure 8 Shows a block diagram of an object position guidance system according to an embodiment of the present application.

[0046] Figure 9 Shows a block diagram of an electronic device according to an embodiment of the present application. Detailed implementation manners

[0047] Now, example embodiments will be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art.

[0048] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present application. However, those skilled in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present application.

[0049] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0050] The flowcharts shown in the accompanying drawings are only exemplary illustrations, not necessarily including all content and operations / steps, nor necessarily executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0051] Figure 1 A schematic diagram of a system 100 to which embodiments of the present application can be applied is shown.

[0052] As Figure 1 shown, the system 100 can include a local edge computing device 101, a network 102, and a detection terminal 103. The local edge computing device 101 and the detection terminal 103 can communicate through the network 102, and the network 102 can be a wired network, a wireless network, etc. In the implementation manner of this example, communication can be based on LoRa wireless technology.

[0053] It should be understood that Figure 1 the numbers of local edge computing devices, networks, etc. in

[0054] The local edge computing device 101 can interact with the detection terminal 103 based on the LoRa wireless technology through the network 102 to receive the detection data sent by the detection terminal 103 from multiple object locations. The local edge computing device 101 can be various devices with computing and processing capabilities, including but not limited to servers, personal computers, mobile phones, etc. In the implementation mode of this example, the local edge computing device 101 is a LoRa gateway based on edge computing, and the architecture of this gateway can include: (1) Physical hardware: The physical hardware of the edge computing server can include relevant hardware for functions such as computing, storage, and networking, such as hardware like CPU, memory, hard disk, network card, etc.; (2) Virtualization module: Used for virtualization of capabilities such as computing, networking, and storage, providing virtualization services for the computing platform; (3) Basic platform: Can include basic services such as databases, application services, security, management and monitoring, etc.; (4) Edge computing platform: Can include edge computing services such as LoRa core network, local keep-alive (digital twin system), function computing (rule engine), guidance model (artificial intelligence model), blockchain, etc.; (5) Guidance system: Can provide local services for the application side based on the data and models provided by the LoRa core network, local keep-alive, rule engine, and artificial intelligence model inference, and at the same time transmit the relevant data to the cloud through the API method. This LoRa gateway based on edge computing, compared with ordinary LoRa gateways, supports distributed deployment and has the following three capabilities: collecting edge data (for example, collecting the detection data of the detection terminal based on the LoRa core network), the ability of intelligent operation (for example, intelligent analysis based on the guidance model), and executable decision feedback (for example, decision-making based on the rule engine). At the same time, the biggest difference between edge computing and cloud computing lies in that edge computing adopts a distributed computing architecture, dispersing the computing to the proximal devices close to the data source for processing, rather than sending all data back to the cloud for processing, with characteristics such as good real-time performance, high efficiency, and short latency, and it can also provide computing capabilities in the situation of no network or poor network.

[0055] In an embodiment of the present application, the local edge computing device 101 can receive detection data from multiple object locations; input the detection data into the location guidance model in the local edge computing device to generate the target object location; and display the target object location to guide the user.

[0056] In an embodiment of the present application, the local edge computing device 101 can parse the detection data to obtain object occupancy information; synchronize the object occupancy information to the digital twin system to generate a digital twin of the object occupancy state; and push the object occupancy state to a predetermined display location for display.

[0057] In one embodiment of the present application, the local edge computing device 101 may parse the detection data to obtain object occupancy information; synchronize the object occupancy information to the cloud to update the object occupancy status in the cloud; and push the object occupancy status to a second predetermined display position for display.

[0058] In one embodiment of the present application, the local edge computing device 101 may parse the detection data to obtain object occupancy information; synchronize the object occupancy information to the rule engine so that the rule engine issues a warning when the object reaches a predetermined condition according to the object occupancy information.

[0059] In one embodiment of the present application, the local edge computing device 101 may collect a sample training set of detection data in the cloud, and each training sample in the sample training set calibrates the target object position; input the training samples in the sample training set into the guidance model in the cloud to train the guidance model; and generate the guidance model after meeting the preset conditions.

[0060] In one embodiment of the present application, the local edge computing device 101 may distribute detection data through the LoRa core network in the local edge computing device based on the Message Queuing Telemetry Transport protocol.

[0061] In one embodiment of the present application, the local edge computing device 101 may receive detection data transmitted by detection terminals at multiple object positions through the LoRaWAN protocol via the LoRa core network.

[0062] In one embodiment of the present application, the local edge computing device 101 may obtain a detection terminal binding request; based on the binding request, register the detection terminal through the LoRa core network to obtain a registration result; and return the registration result to the detection device so that the detection terminal joins the LoRa core network.

[0063] Figure 2 Schematically shows a flowchart of an object position guidance method according to an embodiment of the present application. The execution subject of the object position guidance method may be an electronic device with computing and processing capabilities, such as Figure 1 the local edge computing device 101 shown in Figure 2 As shown in

[0064] Step S210, receiving detection data from multiple object positions;

[0065] Step S220: Input the detection data into the position guidance model in the local edge computing device to generate the target object position.

[0066] Step S230: Display the target object position to guide the user.

[0067] The following describes the specific processes of each step when guiding the object position.

[0068] In step S210, receive detection data from multiple object positions.

[0069] The object can be an object such as a toilet pit or a seat; the detection data can be data related to the occupancy status of the object detected by the detection terminal. The dual detection technology combining infrared and electrothermal can be used for detection to obtain the detection data and ensure the detection accuracy.

[0070] Receiving detection data from multiple object positions can be receiving data related to whether there is occupancy detected by the detection terminal transmitted by the detection terminal, and the transmission method can be through LoRa wireless technology.

[0071] By receiving detection data from multiple object positions, it can be used for analyzing the object status.

[0072] In one embodiment, it further includes that the LoRa core network in the local edge computing device receives the detection data transmitted by the detection terminals at multiple object positions through the LoRaWAN protocol.

[0073] The LoRa core network can be responsible for the authentication, binding, and unbinding of LoRa node devices (detection terminals), the setting and modification of the working modes of node devices, the encryption and decryption of node device data, etc. The detection terminal can transmit the detected detection data to the LoRa core network through the LoRaWAN protocol. The LoRa core network is placed in the local edge computing device, which can utilize the characteristics and capabilities of LoRa with low cost and wide coverage. By installing a single local edge computing device (Lora indoor gateway), it can cover the detection terminals at relatively wide object positions at low cost. In one example, the object is the toilet pits in a building. There are multiple toilets in the building, and detection terminals are respectively deployed at each toilet pit. Through the LoRa core network in the local edge computing device, it can cover the detection terminals set at the toilet pits in the three-story space of the building, and then receive the detection data of each object position.

[0074] In one embodiment, based on the foregoing embodiment, it further includes:

[0075] Obtain the detection terminal binding request;

[0076] Based on the binding request, register the detection terminal through the LoRa core network to obtain the registration result.

[0077] Return the registration result to the detection device so that the detection terminal can join the LoRa core network.

[0078] Obtaining the detection terminal binding request can be receiving the binding request sent by the registration requester. In one example, after the applet scans the QR code on the detection terminal, it obtains identifiers such as the device serial number (SN number) and transmits it to the cloud device management, so that the cloud device management sends the binding request of the detection terminal based on the identifier. Then, according to the received binding request, the registration of the detection terminal can be completed through the LoRa core network in the local edge computing device, and then the local edge computing device can manage the detection terminal based on the LoRa core network. Furthermore, returning the registration result to the detection device can inform whether the registration is successful. If successful, the detection terminal can request the LoRa core network and then communicate with the local edge computing device based on the LoRa core network. In one example, returning the registration result to the detection device so that the detection terminal can join the LoRa core network can be returning the registration result to the cloud device management. The cloud device management notifies the applet that the registration is successful and the UI displays it; then, the detection device is powered on and requests to join the LoRa core network. The LoRa core network detects that the detection terminal has been registered and then passes the network access request.

[0079] In one embodiment, receiving detection data from multiple object locations includes:

[0080] Receiving the detection data distributed by the LoRa core network in the local edge computing device through the Message Queuing Telemetry Transport Protocol.

[0081] The Message Queuing Telemetry Transport Protocol (MQTT) is a message protocol based on the publish / subscribe programming model of binary messages. It is an instant messaging protocol with low overhead and low bandwidth occupancy. It can provide real-time and reliable message services for connecting remote devices with very little code and bandwidth, and can work reliably in remote devices with low hardware performance and environments with poor network conditions.

[0082] In one embodiment, the detection data includes:

[0083] Infrared detection data and electrothermal detection data.

[0084] The infrared detection data and the electrothermal detection data can be information related to the occupancy status of the position of the detection object obtained through the combination of infrared and electrothermal methods, which can improve the accuracy of object status detection. The electrothermal plus infrared detection scheme has higher accuracy compared to the pure infrared scheme. Because in the infrared scheme, static objects will be misjudged. If the user in the pit does not move, infrared will consider it as an unoccupied state, which obviously does not conform to the scenario application. Therefore, electrothermal is used to sense the human heat map to assist infrared in detecting whether there are moving objects, achieving a combination of static and dynamic sensing, with a larger sensing range and higher detection accuracy. In one embodiment, through verification, the detection method selects the Grid-EYE infrared array detection scheme with higher detection accuracy and wider range. In one embodiment, the infrared detection data and the electrothermal detection data are obtained by performing quadratic fitting on the original environmental data collected during the data collection process to preprocess the noise points existing in the information collected by the hardware, ensuring the accuracy of analysis. In one embodiment, a status change buffer needs to be set for the detection terminal. For example, the status change buffer can be set according to the rule that if a person is detected within 20 seconds, it is considered that there is someone, and if no one is detected within 40 seconds, it is considered that there is no one.

[0085] In step S220, the detection data is input into the position guidance model in the local edge computing device to generate the target object position.

[0086] The position guidance model is a model trained based on cloud big data analysis and artificial intelligence learning. It quickly infers the occupancy data information of the object to obtain the optimal object position guidance information, that is, the target object position. For example, the model trained through cloud big data analysis and artificial intelligence models can, according to the object occupancy status data such as at different time periods, different positions, and the time length of object occupancy, when the object is idle, intelligently recommend the nearest route; when there is no idle object, intelligently calculate the possible object.

[0087] When guiding the object position, the trained position guidance model can reliably analyze the detection data from multiple object positions to obtain the target object position for user guidance. Moreover, the analysis process is carried out in the local edge computing device, and the calculation can be distributed to the detection devices close to the data source of the detection data for processing, rather than having to transmit all the data back to the cloud for processing. It has characteristics such as good real-time performance, high efficiency, and short delay, and can also provide computing power in the situation of no network or poor network. Furthermore, it effectively improves the object position guidance ability and guidance reliability.

[0088] In one embodiment, it further includes:

[0089] Collecting a sample training set of detection data in the cloud, and calibrating the target object position for each training sample in the sample training set;

[0090] Input the training samples in the sample training set into the guiding model in the cloud to train the guiding model;

[0091] After meeting the preset conditions, generate the guiding model.

[0092] The samples of the detection data are the detection data samples of different objects collected, and each sample is calibrated with the corresponding optimal recommended target object position. Store, abstract, and organize the relevant data in the cloud, and use the powerful computing power of the cloud to analyze and train the data until the guiding model meets the preset conditions (for example, the recommendation accuracy rate is higher than the predetermined threshold), and the required guiding model can be obtained.

[0093] In step S230, display the target object position to guide the user.

[0094] Displaying the target object position can be on positions such as mini-programs, Weibo, web, official accounts, and display screens. The function of application-side display can be implemented by calling the interfaces provided by the cloud or the edge computing platform, meeting the ability of intelligent recommendation and guidance of object positions, and guiding the user effectively and flexibly.

[0095] In one embodiment, after receiving the detection data from multiple object positions, the object position guiding method further includes:

[0096] Analyze the detection data to obtain object occupancy information;

[0097] Synchronize the object occupancy information to the digital twin system to generate a digital twin of the object occupancy state;

[0098] Push the object occupancy state to a predetermined display position for display.

[0099] The object occupancy information may include whether someone is occupying, etc. The digital twin system is a system based on digital twin technology. Digital twin is a simulation process that fully utilizes data such as physical models, sensor updates, and operation history, integrates multiple disciplines, multiple physical quantities, multiple scales, and multiple probabilities, and completes mapping in the virtual space to reflect the entire life cycle process of the corresponding physical equipment. A digital twin is a virtual model that is completely corresponding and consistent with the physical entity in the real world, which can simulate its behavior and performance in the real environment in real time, and is also called a digital twin model. The digital twin (which can also be called local keep-alive) can save the real-time state of object occupancy based on the object occupancy information, and can be synchronized and updated with the cloud. When the cloud connection is disconnected, it can also operate normally at the location where the digital twin is located, providing real-time data changes and updates for object position guidance. Through the digital twin, the object occupancy state can be pushed to a predetermined display position such as a local display screen for display.

[0100] The digital twin system is located in the local edge computing device and can generate digital twins of the storage object status in real time locally. In one example, the detection device continuously detects data at the object location and uploads the detected data to the LoRa core network of the local edge computing device. The LoRa core network distributes the subscribed data to the object location guiding system of the local edge computing device for processing via the mqtt method. The object location guiding system parses the data and then synchronizes whether there is someone in the digital twin pit. Finally, the digital twin sends the current pit status to the display screen for UI display to guide users. By implanting edge computing and processing capabilities in the local edge computing device to preprocess and locally store the object occupancy data, regional autonomy can be achieved.

[0101] In one embodiment, after receiving detection data from multiple object locations, the object location guiding method further includes:

[0102] Parsing the detection data to obtain object occupancy information;

[0103] Synchronizing the object occupancy information to the cloud to update the object occupancy status in the cloud;

[0104] Pushing the object occupancy status to a second predetermined display location for display.

[0105] For example, the detection device continuously detects data and uploads the data to the LoRa core network. The LoRa core network distributes the subscribed data to the guiding system for processing via the mqtt method. The guiding system parses the data and then reports the object occupancy information of the cloud device management. The cloud device management sends the current pit status to the applet for UI display.

[0106] In one embodiment, after receiving detection data from multiple object locations, the object location guiding method further includes:

[0107] Parsing the detection data to obtain object occupancy information;

[0108] Synchronizing the object occupancy information to the rule engine so that the rule engine can give an alarm when the object reaches a predetermined condition according to the object occupancy information.

[0109] Rule engine: The linkage relationship between the detection device and the scenario can be set, and a preset action will be executed when the trigger condition is reached. For example, the rule is set that there is someone detected at the object location for 1 hour continuously, and it can be notified whether there is an abnormality in the device or personnel. Furthermore, synchronizing the object occupancy information to the rule engine can enable the rule engine to give an alarm when the object reaches a predetermined condition according to the object occupancy information.

[0110] Figure 3The figure shows a schematic architecture diagram of an object location guidance system in an application scenario according to an embodiment of the present application.

[0111] As shown in Figure 3 the figure, the architecture of the object location guidance system includes a device side, a local edge computing terminal, a cloud side, and an application side.

[0112] Device side: There are multiple toilets in a building, and detection terminals are respectively deployed in each toilet cubicle. The detection terminal can detect detection data such as whether there is someone in the toilet cubicle through a combination of infrared and electrothermal methods, and can transmit the collected data to the local edge computing device through the LoRaWAN protocol.

[0113] Local edge computing terminal: It includes an edge computing platform and a guidance system.

[0114] The edge computing platform includes edge computing services such as a LoRa core network, local keep-alive (digital twin system), function computing (rule engine), and guidance model (artificial intelligence model); LoRa core network: responsible for the authentication, binding, and unbinding of LoRa node devices (toilet cubicle detection terminals), setting and modifying the working mode of node devices, and encrypting and decrypting the data of node devices. Local keep-alive: It can also be called a digital twin, which stores the real-time state of the toilet cubicle detection terminal, can be synchronized and updated with the cloud side, and can still operate normally locally when the cloud connection is disconnected, providing real-time data changes and updates for the guidance system. Rule engine: Set the linkage relationship between the toilet cubicle detection terminal and the scenario, and when the trigger condition is reached, a preset action will be executed. For example, set the rule that if someone is detected in the cubicle for 1 hour continuously, it can notify whether there is an abnormal phenomenon of the device or personnel. Guidance model: A model trained based on cloud big data analysis and artificial intelligence models, which can quickly infer the local cubicle occupancy data information to obtain the optimal cubicle guidance information. For example, the model trained through cloud big data analysis and artificial intelligence models is for different time periods, different locations, and the occupancy time lengths of different cubicles. When there is an idle cubicle, it can intelligently recommend the nearest route; when there is no idle cubicle, it can intelligently calculate possible cubicles.

[0115] Guidance system: It can provide local services for the application side based on the data and models provided by the LoRa core network, local keep-alive, rule engine, and artificial intelligence model inference, and at the same time transmit relevant data to the cloud side through the APi interface. A single local edge computing terminal can cover a three-story building space based on the LoRa core network, and data preprocessing, rule routing, intelligent analysis, and other edge calculations can be performed at the local edge end.

[0116] Cloud: It includes cloud device management, big data analysis, and artificial intelligence model training modules. Cloud device management: Responsible for functions such as adding, deleting, and modifying local edge computing devices; responsible for communication, data synchronization, and interaction between local edge computing devices; providing corresponding services and capabilities for applications such as mini-programs and web on the application side. Big data analysis and artificial intelligence model training: Collect all the detection data uploaded by toilet cubicle detection terminals, store, abstract, and organize the relevant detection data, and use the powerful computing power of the cloud to analyze and train the detection data to obtain the required toilet cubicle guidance model.

[0117] Application side: It mainly includes four display forms, namely mini-programs, Weibo, web, official accounts, and display screens. The functions of the application are realized by calling the interface APIs provided by the cloud or the edge computing platform, meeting the functions and capabilities of intelligent toilet cubicle recommendation and guidance.

[0118] Figure 4 shows a Figure 3 flowchart of binding a detection terminal to a local edge computing device according to an embodiment of the present application in an application scenario based on the shown system architecture.

[0119] Refer to Figure 4 As shown, when binding a toilet cubicle detection terminal to a local edge computing device, it includes:

[0120] Step S410, the mini-program scans the QR code on the toilet cubicle detection terminal, obtains the device serial number SN, and transmits it to the cloud device management;

[0121] Step S420, the cloud device management calls the guidance system based on the SN to request binding of the toilet cubicle detection terminal;

[0122] Step S430, the guidance system completes the registration of the toilet cubicle detection terminal through the LoRa core network interface;

[0123] Step S440, the guidance system returns the result of successful registration to the cloud device management;

[0124] Step S450, the cloud device management notifies the mini-program of successful registration, and the UI is displayed;

[0125] Step S460, the detection device is powered on and requests to join the LoRa core network;

[0126] Step S470, the LoRa core network detects that the detection device has been registered, and through the network access request, completes the binding of the toilet cubicle detection terminal to the local edge computing device.

[0127] Figure 5 shows a Figure 4Flowchart of object position guidance according to an embodiment of the present application in an application scenario of the illustrated embodiment.

[0128] Refer to Figure 5 As shown, when performing object position guidance, specifically, when guiding the toilet cubicle, it includes:

[0129] Step S510, the toilet cubicle detection terminal continuously detects data in the cubicle and uploads the data to the LoRa core network;

[0130] Step S520, the LoRa core network subscribes and distributes the data to the guidance system through the mqtt method;

[0131] Step S530, the guidance system parses the data to obtain the cubicle occupancy information, and then reports the current cubicle status of whether there is someone in the cubicle to the cloud device management;

[0132] Step S540, the cloud device management sends the current cubicle status to the mini program for UI display to perform user guidance.

[0133] Figure 6 Shows based on Figure 4 Flowchart of object position guidance according to an embodiment of the present application in another application scenario of the illustrated embodiment.

[0134] Refer to Figure 6 As shown, when performing object position guidance, specifically, when guiding the toilet cubicle, it includes:

[0135] Step S610, the toilet cubicle detection terminal continuously detects data in the cubicle and uploads the data to the LoRa core network;

[0136] Step S620, the LoRa core network subscribes and distributes the data to the guidance system through the mqtt method;

[0137] Step S630, the guidance system parses the data to obtain the cubicle occupancy information, and then synchronizes the current cubicle status of whether there is someone in the cubicle for local keep-alive;

[0138] Step S640, the local keep-alive sends the current cubicle status to the display screen for UI display to perform user guidance.

[0139] Figure 7 Shows based on Figure 4 Flowchart of object position guidance according to an embodiment of the present application in another application scenario of the illustrated embodiment.

[0140] Refer to Figure 7 As shown, when performing object position guidance, specifically, when guiding the toilet cubicle, it includes:

[0141] Step S710, collect data in cloud big data, use artificial intelligence to train the guidance model, and after obtaining the guidance model that meets the predetermined conditions, the cloud pushes the guidance model to the artificial intelligence module of the edge computing platform.

[0142] Step S720, the toilet cubicle detection terminal continuously detects data in the cubicle and uploads the data to the LoRa core network;

[0143] Step S730, the LoRa core network subscribes and distributes the data to the guidance system through the mqtt method;

[0144] Step S740, the guidance system pushes the data to the artificial intelligence module for inference of the guidance model to obtain the optimal cubicle recommendation;

[0145] Step S750, the guidance system obtains the optimal cubicle recommendation;

[0146] Step S760, the guidance system pushes the recommendation result to the applet and the display screen for display to guide the user.

[0147] Through the embodiments of the present application, when guiding the toilet cubicle, the characteristics and capabilities of the LoRa technology of low cost and wide coverage are utilized. By installing a single local edge computing device, the three floors of the building can be covered. The toilet cubicle detection terminal uses a dual detection technology combining infrared and electrothermal to effectively improve the detection accuracy of the toilet cubicle. In the local edge computing device, the edge computing and processing capabilities are implanted to preprocess and locally store the toilet cubicle occupancy data to achieve regional autonomy. Information such as the toilet cubicle occupancy time is calculated and analyzed by big data and artificial intelligence in the cloud to obtain the guidance model in different time periods and different scenarios, and the model is sent to the local edge computing device to realize real-time dynamic intelligent recommendation and user guidance for the toilet cubicle.

[0148] Figure 8 The block diagram of an object position guidance system according to an embodiment of the present application is shown.

[0149] As Figure 8 shown, the object position guidance system 800 may include a receiving module 810, a sending module 820, and a guidance module 830. The receiving module 810 may be configured to receive detection data from multiple object positions; the sending module 820 may be configured to input the detection data into a position guidance model in a local edge computing device to generate a target object position; the guidance module 830 may be configured to display the target object position to guide the user.

[0150] In some embodiments of the present application, the object position guidance system is further configured to:

[0151] Parse the detection data to obtain object occupancy information;

[0152] Synchronize the object occupancy information to the digital twin system to generate a digital twin of the object occupancy status;

[0153] Push the object occupancy status to a predetermined display position for display.

[0154] In some embodiments of the present application, the object position guiding system is further configured to:

[0155] Analyze the detection data to obtain object occupancy information;

[0156] Synchronize the object occupancy information to the cloud to update the object occupancy status in the cloud;

[0157] Push the object occupancy status to a second predetermined display position for display.

[0158] In some embodiments of the present application, the object position guiding system is further configured to:

[0159] Analyze the detection data to obtain object occupancy information;

[0160] Synchronize the object occupancy information to the rule engine so that the rule engine issues a warning when the object reaches a predetermined condition according to the object occupancy information.

[0161] In some embodiments of the present application, it further includes:

[0162] Collect a sample training set of detection data in the cloud, and calibrate the target object position for each training sample in the sample training set;

[0163] Input the training samples in the sample training set into the guiding model in the cloud to train the guiding model;

[0164] Generate the guiding model after meeting the preset conditions.

[0165] In some embodiments of the present application, the receiving module is configured to:

[0166] Receive the detection data distributed by the LoRa core network in the local edge computing device through the Message Queuing Telemetry Transport Protocol.

[0167] In some embodiments of the present application, it further includes:

[0168] The LoRa core network in the local edge computing device receives the detection data transmitted by multiple detection terminals of the object positions through the LoRaWAN protocol.

[0169] In some embodiments of the present application, the object position guiding system is further configured to:

[0170] Obtain a detection terminal binding request;

[0171] Based on the binding request, register the detection terminal through the LoRa core network to obtain a registration result;

[0172] Return the registration result to the detection device so that the detection terminal joins the LoRa core network.

[0173] In some embodiments of the present application, based on the foregoing embodiments, the detection data includes:

[0174] Infrared detection data and electrothermal detection data.

[0175] It should be noted that although several modules or units of the device for action execution are mentioned in the foregoing detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0176] Figure 9 A block diagram of an electronic device according to an embodiment of the present application is schematically shown.

[0177] It should be noted that Figure 9 The illustrated electronic device 900 is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0178] As Figure 9 shown, the electronic device 900 includes a central processing unit (CPU) 901, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 902 or the program loaded from the storage section 908 into the random access memory (RAM) 903. In the RAM 903, various programs and data required for system operation are also stored. The CPU 901, ROM 902, and RAM 903 are connected to each other through a bus 904. The input / output (I / O) interface 905 is also connected to the bus 904.

[0179] The following components are connected to the I / O interface 905: an input section 906 including a keyboard, a mouse, etc.; an output section 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as required. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is installed on the drive 910 as required so that a computer program read therefrom is installed into the storage section 908 as required.

[0180] Specifically, according to an embodiment of the present application, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present application includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes program codes for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from the removable medium 911. When the computer program is executed by a central processing unit (CPU) 901, various functions defined in the system of the present application are performed.

[0181] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. And in this application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical fiber cable, RF (radio frequency), etc., or any suitable combination of the above.

[0182] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram can represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in a block may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and the combination of blocks in a block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0183] The units involved in the embodiments described in this application can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not, in some cases, constitute a limitation on the units themselves.

[0184] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or may exist alone without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the methods described in the above embodiments.

[0185] It should be noted that although several modules or units of the devices for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0186] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software or by software in combination with necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, a server, a touch terminal, or a network device, etc.) to execute the methods according to the embodiments of the present application.

[0187] After considering the specification and practicing the disclosed embodiments herein, those skilled in the art will readily conceive of other embodiments of the present application. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include known common knowledge or conventional technical means in the technical field not disclosed in the present application.

[0188] It should be understood that the present application is not limited to the embodiments described above and shown in the drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. An object position guiding method, characterized in that, include: receiving detection data from a plurality of detection terminals, each detection terminal being deployed at an object location; Inputting the detection data into a location guidance model in a local edge computing device to generate a target object location; the local edge computing device includes a location guidance model and a LoRa core network; the LoRa core network is responsible for authentication, binding and unbinding of the detection terminal; The location guidance model is a model trained based on cloud big data analysis and artificial intelligence models. The location guidance model is used to intelligently infer possible idle objects based on object occupancy status data when there are no idle objects. The object occupancy status data includes the length of time an object occupies at different time periods and different locations. The target object position is displayed to guide the user.

2. The method according to claim 1, wherein Also includes: parsing the detection data to obtain object occupancy information; Synchronizing the object occupancy information to the digital twin system to generate a digital twin of the object occupancy state; The object occupancy status is pushed to a predetermined display location for display.

3. The method according to claim 1, wherein Also includes: parsing the detection data to obtain object occupancy information; synchronizing the object occupancy information to the cloud to update the object occupancy status in the cloud; The object occupancy status is pushed to a second predetermined display location for display.

4. The method according to claim 1, wherein Also includes: parsing the detection data to obtain object occupancy information; The object occupancy information is synchronized to a rule engine, so that the rule engine issues an early warning when determining that the object meets a predetermined condition according to the object occupancy information.

5. The method according to claim 1, wherein Also includes: Collecting a sample training set of detection data in the cloud, wherein each training sample in the sample training set calibrates the position of the target object; Inputting the training samples in the sample training set into the guidance model in the cloud to train the guidance model; After the preset conditions are met, the guidance model is generated.

6. The method according to claim 1, wherein The receiving detection data from a plurality of detection terminals comprises: Receive the detection data distributed by the LoRa core network in the local edge computing device through the message queue telemetry transmission protocol.

7. The method according to claim 6, characterized in that, Also includes: The LoRa core network in the local edge computing device receives detection data transmitted by the detection terminals at the locations of the multiple objects through the LoRaWAN protocol.

8. The method according to claim 7, wherein Also includes: Get the detection terminal binding request; Based on the binding request, register the detection terminal through the LoRa core network to obtain a registration result; The registration result is returned to the detection terminal so that the detection terminal joins the LoRa core network.

9. The method according to any one of claims 1-8, characterized in that, The detection data includes: Infrared detection data and electrothermal detection data.

10. An object position guiding system, characterized in that, include: A receiving module, used for receiving detection data from a plurality of detection terminals, each detection terminal being deployed at an object location; A sending module, used for inputting the detection data into a location guidance model in a local edge computing device to generate a target object location; the local edge computing device includes a location guidance model and a LoRa core network; the LoRa core network is responsible for the authentication, binding and unbinding of the detection terminal; The position guidance model is a model trained based on cloud big data analysis and artificial intelligence models. The position guidance model is used to intelligently calculate possible idle objects according to object occupancy status data when there are no idle objects. The object occupancy status data includes the time lengths of object occupancy at different times and different positions. A guidance module, configured to display the target object position to guide the user.

11. An object position guiding terminal, characterized in that, Comprising: A memory storing computer-readable instructions; A processor, reading the computer-readable instructions stored in the memory to execute the method according to any one of claims 1-9.

12. A computer program medium, characterized in that, Computer-readable instructions are stored thereon. When the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the method according to any one of claims 1-9.

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