A method of pedestrian navigation

Abstract

The present application relates to a kind of walking navigation method, based on the identification and positioning of user, guide equipment deployed in the environment around user is guided to user, in the navigation process, user can get rid of handheld terminal device;Further, there is no inherent defect of the technology itself of prior art, such as satellite navigation, navigation based on RSSI positioning, positioning based on the technical scheme of connected device, the performance influence of handheld terminal device related hardware, the precision and real-time problem caused by more environmental interference factors such as poor problem.The present application obtains the physical properties of user in passageway by the corresponding type of sensor deployed in passageway and covering passageway, identifies and locates user, can build pure intranet, can realize navigation, avoid the data security hidden danger existing in data interaction using public communication platform.The present application can be implemented in any scene where sensor can be deployed, and is highly versatile.

Description

Technical Field

[0001] This invention relates to the field of navigation technology, and more specifically, to a walking navigation method. Background Technology

[0002] Conventional navigation technologies, including satellite navigation, RSSI-based navigation, and device-connected navigation, are limited by inherent limitations, hardware performance, and environmental interference. These technologies are only suitable for specific conditions; otherwise, their accuracy and real-time performance are poor, lacking versatility. Correspondingly, existing technologies strongly link users to handheld devices, making it difficult for users to detach from them and resulting in insufficient ease of use.

[0003] Traditional navigation requires generating route information first, and then using that information as the basis for navigation. Because of this route information, there are inherent privacy risks due to data recording by navigation service providers or local security vulnerabilities. For navigation software that requires users to agree to privacy terms, user privacy is unnecessarily collected if the user ignores or compromises, a problem that is difficult to effectively address at present. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a walking navigation method that eliminates the dependence on handheld terminal devices during navigation; it has high versatility and can be adapted to various different environments; and it is implemented based on a peer-to-peer network, so there is no need to generate route information or store user perception information, thus ensuring user privacy and security.

[0005] The technical solution of the present invention is as follows:

[0006] A pedestrian navigation method involves obtaining a user's navigation needs and location information; calculating the user's next navigation steps based on the navigation needs and the user's real-time location information; and presenting the navigation information through a guidance device at the location corresponding to the user's real-time location information to guide the user.

[0007] As a preferred method, the system acquires the user's direction of travel, speed of travel, and posture data, and uses a prediction model pre-trained with preset conditions or through machine learning to predict the user's next action, including the user's direction of travel and speed of travel. The guidance device at the location that matches the predicted direction of travel and speed of the user's next step presents the user's next navigation information.

[0008] As a preferred option, if the prediction of the user's next action is inaccurate, the guidance device that presents the user's next navigation information is corrected based on the user's real-time travel direction data and travel speed data. The density of people around the user, environmental information, travel direction data, travel speed data, and posture data of people around the user are used as training samples and added to the sample library for further training and adjustment of the prediction model.

[0009] As a preferred method, the user's height, leg length, and cadence data are obtained to calculate the user's walking speed; if the prediction of the user's next action is inaccurate, the calculated walking speed is used instead of the predicted walking speed.

[0010] As a preferred approach, a personalized prediction model is established for different users. After identifying the user, the associated prediction model is used to predict the user's next action.

[0011] Preferably, the guidance device includes a visual information display device for displaying visual navigation information; the visual information display device acquires the user's real-time travel direction data and travel speed data, follows the user's travel direction and travel speed, and maintains a position at a certain distance from the user in the user's travel direction, and displays the user's next visual navigation information.

[0012] Alternatively, based on the user's navigation needs and real-time location information, a peer-to-peer network can be used for collaborative computation to obtain a visual information display device that matches the location where visual navigation information needs to be displayed for each user's next step; and a visual information display device that maintains a certain distance from the user relative to the user's real-time location information and real-time travel speed can then display the user's next step of visual navigation information.

[0013] As a preferred option, the distance between the visual information display device that displays visual navigation information and the user is calculated based on the user's height and stride length.

[0014] Preferably, the guidance device includes an audio playback device for playing audio navigation information; the audio playback device acquires the user's real-time location information and walking speed data, follows the user's position and walking speed, and maintains the sound field covering the user's position to play audio navigation information for the user's next step.

[0015] Alternatively, based on the user's navigation needs and real-time location information, a peer-to-peer network can be used for collaborative computation to obtain an audio playback device that matches the location where each user needs to play audio navigation information next; and an audio playback device that maintains sound field coverage of the user's location relative to the user's real-time location information and real-time walking speed can play the audio navigation information for the user's next step.

[0016] Preferably, the audio playback device is positioned above the channel and is equipped with a downward-facing sound-focusing shield, which is used to limit the sound field of the audio playback device to a certain range.

[0017] Alternatively, a gimbaled super-directional speaker can be used, adjusting the transmission angle according to the user's direction of travel and speed as needed to listen to audio navigation information.

[0018] Preferably, the physical attributes of users within the channel are obtained by deploying corresponding types of sensors that cover the channel, the navigation needs of users are obtained by human-computer interaction devices associated with users, and the navigation information for each user's next step is obtained through collaborative calculation via a peer-to-peer network.

[0019] Alternatively, navigation needs can be inferred from the user's trip information, which includes a confirmed or presumed destination, which may be the final destination or the next of multiple consecutive destinations.

[0020] As a preferred method, peer-to-peer networks are used to perform non-specific feature identification and location identification of users;

[0021] The peer-to-peer network consists of multiple node devices, and there is no master-slave relationship among all node devices. Each node device is equipped with a data acquisition device and a computing module. The data acquisition device includes at least one type of sensor for collecting different corresponding types of sensing data. Node devices set at different acquisition locations collect at least one point sample from the user, and the point sample is sensing data of the corresponding sensor type.

[0022] For a given node device, the collected sensing data is processed to obtain result data, which is then propagated to other node devices. Other node devices that receive the result data use it as one of the original data collected, and the result data influences the result data of other node devices. Based on this, without needing to obtain user identity information, multiple node devices in the peer-to-peer network perform collaborative computation to determine that each unique user is itself, achieving non-specific feature recognition and user location identification.

[0023] Preferably, the current node device receives the result data output by other node devices; for the current node device, the collected sensing data is combined with the result data from other node devices to calculate the result data of the current node device, and then sent to other node devices; the node devices in the peer-to-peer network perform collaborative calculations as sensing data is collected and result data is calculated.

[0024] Preferably, in a peer-to-peer network, for a specific point sample of a user, in the result data transmitted from the node device that collected the point sample to other node devices, the subsequent node devices adjust their perceptual attention based on the characteristics of the point sample, or report the characteristics of the point sample for subsequent node devices to adjust their perceptual attention; if the subsequent other node devices do not detect the characteristics of the point sample, but can determine from the characteristics of other point samples that the undetected characteristics of the point sample still belong to the user, then the undetected characteristics of the point sample are continued to be represented in the result data of the current node device and transmitted to other node devices.

[0025] As a preferred method, the method of reporting the features of the point sample for subsequent node devices to adjust the perceptual attention is as follows: based on the result data expressing the features of the point sample provided by the preceding node device, or the features of the point sample, the parameters of the data processing model of the subsequent node device are adjusted so that the subsequent node device can improve the computing power of the point sample to identify its features; or, the subsequent node device uses the perceptual attention model to match the features of the received point sample or the result data expressing the features of the point sample to adjust the computing power.

[0026] Preferably, when processing the output data of several preceding node devices, the node device, based on the data processing model, merges the point sample features and other information described by each node device into the same user when the user described by several preceding node devices can be identified as the same user through certain common point sample features.

[0027] Preferably, when the result data received by the node device indicates that the flag used by the current node device to identify the user before the current receipt of result data is different from the flag used by other node devices to identify the user, and the flags assigned to the user by other node devices have been updated, then the flag used by the current node device to identify the user before the current receipt of result data is converted.

[0028] As a preferred method, the method for converting the flag used by the current node device to identify the user before the current reception of result data is as follows:

[0029] Replace the flag used by the current node device to identify the user before the current reception of result data with the latest flag assigned to the user by other node devices;

[0030] Alternatively, record the conversion relationship between the flag used by the current node device to identify the user before the current reception of result data and the updated flag assigned to the user by other node devices, and perform the conversion when it is necessary to reference the result data received by the current node device in the current reception.

[0031] Alternatively, node devices can deploy conversion models to perform corresponding conversions on the identifiers of multiple users based on the input raw data or result data.

[0032] As a preferred option, for one or more point samples collected sequentially by node devices at different collection locations, if the feature values ​​of one or more point samples at different collection locations meet the preset similarity conditions or are determined by a specific model to have a correlation threshold, and are unique at each collection location, then it is determined that the point samples at different collection locations are correlated.

[0033] Preferably, for one or more point samples collected simultaneously by node devices at different collection locations, if the node devices at different collection locations collect data from the same spatial field, and there is only one user in the spatial field, or the collected point sample can correctly point to one of the multiple users, then for a certain user, one or more point samples collected by node devices at different collection locations are correlated.

[0034] Preferably, the data acquisition device of the node device includes one or more of the following: an image acquisition device, an electromagnetic induction device, a temperature measurement device, a vibration frequency sensing device, and a lidar. The data acquired by the above devices and the three-dimensional point cloud acquired by the lidar, or the point cloud generated from images acquired by multiple image acquisition devices, are jointly calculated to obtain three-dimensional points with data. The image color, contour, lines, reflectivity, motion trend, electromagnetic characteristics, temperature, temperature change trend, vibration frequency, and vibration frequency change trend based on two-dimensional perception are used as additional attributes of the corresponding three-dimensional points to form an attributed three-dimensional point cloud. Combining electromagnetic induction, temperature law, vibration frequency change characteristics, motion correlation, and reflectivity, the correspondence between each region of the attributed three-dimensional point cloud and each part or related part of the user's 3D appearance is determined.

[0035] Preferably, when it is necessary to obtain the user's identity information, an identity information acquisition command is triggered. The identity information acquisition command is used as one of the inputs to participate in the calculation of the result data of the node device. By driving the node device in the peer-to-peer network that is connected to the barrier-free data collection conditions that can obtain the user's identity information to respond with the corresponding result data, the user's identity information can be obtained.

[0036] As a preferred approach, the peer-to-peer network verifies the authenticity of the user's identity information to determine their permissions. In this approach, the node device in the peer-to-peer network that can obtain identity information does not provide the identity information itself, but only expresses the verification result in the result data of the node device based on the verification requirements for the authenticity of the identity information in the received result data.

[0037] Preferably, in a peer-to-peer network, the node device capable of obtaining identity information does not provide identity information. Instead, the information source device that drives the provision of identity information establishes an encrypted information transmission channel with the node device input terminal that needs to obtain identity information, or establishes an encrypted information transmission channel using other network communication modes, and uses the identity information as one of the inputs to the node device.

[0038] Preferably, the data acquisition device includes one or more of the following: image acquisition device, audio acquisition device, temperature measurement device, vibration frequency sensing device, lidar, chemical sensor, and electromagnetic induction device.

[0039] Preferably, the human-computer interaction device associated with the user is connected to the node device as an access device and submits navigation requests to the peer-to-peer network; each guidance device joins the peer-to-peer network through one or more node devices; if it is determined based on collaborative computing that the guidance device needs to present the navigation information based on the user's next step, the current node device will send instructions to the guidance device connected to the current node device according to the calculated result data, and control the guidance device to complete the presentation of navigation information.

[0040] Preferably, the user's next navigation information is represented in the result data; the guidance device receives the result data output by the connected node device. If a specific element in the result data indicates that the guidance device needs to present navigation information, or if the result data is used as one of the inputs to the node device's data processing model to calculate and determine that the corresponding guidance device needs to present navigation information, then the guidance device presents the corresponding navigation information.

[0041] Preferably, when the guidance device needs to present navigation information, the guidance device combines the result data received from other node devices to calculate its own result data, and controls the guidance device to present the corresponding navigation information based on the obtained result data.

[0042] Preferably, for the guidance device, the calculated result data includes the optimal solution for all situations obtained at the current moment based on the collaborative calculation of all users in the channel and the external environment; the navigation information for the user's next step is presented through the guidance device.

[0043] Preferably, the guiding device receives the result data output by other node devices. The principle is as follows: when the corresponding guiding device needs to present navigation information, if the result data calculated by one or more node devices can determine the guiding device that needs to present navigation information, then the corresponding guiding device is added to the node list for transmitting the current result data, and the one or more node devices directly transmit the result data to the guiding device or the node device connected to the guiding device; or, the guiding device receives the result data output by other node devices in a layer-by-layer transmission manner.

[0044] As a preferred option, based on preset conditions or algorithm output and model output, the corresponding guiding device is added to the node list for transmitting result data.

[0045] Preferably, the guiding device is a node device with execution components configured with specific functions. The execution feedback information of the execution components of the guiding device is fed back to the guiding device and participates in the calculation of the subsequent result data of the guiding device.

[0046] Preferably, when the collaborative computing results determine that the travel direction and speed of multiple users are consistent, the distance between users meets the travel distance standard, and among the multiple users, only one user submits a navigation request or multiple users submit the same navigation request, then the multiple users are grouped into a navigation user group.

[0047] As a preferred option, among the users in the navigation user group, based on the results of collaborative computing, if it is determined that the vision and / or hearing of some users are interfered with by other users, then visual navigation information and / or audio navigation information will be displayed to the user who submitted the navigation request or the user at the forefront whose next navigation information has the correct direction of travel; if the vision and hearing of the user who submitted the navigation request are both interfered with by other users, then visual navigation information or audio navigation information will be displayed to other users whose vision or hearing is not interfered with.

[0048] If all users' vision and hearing are not disturbed by other users, then visual navigation information and audio navigation information are displayed to each user.

[0049] The beneficial effects of this invention are as follows:

[0050] The pedestrian navigation method described in this invention is based on user identification and positioning. It guides the user through guidance devices deployed in the user's surrounding environment. During the navigation process, the user can get rid of the handheld terminal device. Therefore, it does not have the problems of poor accuracy and real-time performance caused by the inherent defects of existing technologies such as satellite navigation, RSSI-based navigation, and navigation based on connection devices, the performance impact of handheld terminal device hardware, and many environmental interference factors.

[0051] This invention utilizes sensors of corresponding types deployed in and covering a channel to acquire the physical attributes of users within the channel, enabling user identification and location. It can construct a pure intranet for navigation, avoiding the data security risks associated with data interaction using public communication platforms. This invention is applicable to any scenario where sensors can be deployed, demonstrating strong versatility.

[0052] This invention utilizes a peer-to-peer network for collaborative computing, performing non-specific feature identification and location identification on all users within the channel to complete identity recognition and positioning. In the peer-to-peer network, there is no primary or secondary relationship between all node devices, and no fixed connection path between node devices. Node devices only receive the calculation results of other node devices and send out their own calculation result data. The detection of events and / or the response of corresponding guidance devices do not rely on the identification and control of a single node device, but are jointly confirmed through collaborative computing by multiple node devices in the peer-to-peer network. Furthermore, this invention can distribute computing functions across the entire network without relying on single-point user identification, reducing the hardware and software requirements of single-point computing, resulting in high execution efficiency and significantly improved anti-attack capabilities. The relatively symmetrical information among node devices prevents illegal data tampering. Even if a single node device is physically compromised and its transmitted data is altered, the network-wide computing involves highly redundant and complex calculations and multi-dimensional verification. Therefore, the alteration of data transmitted by a single node device does not affect the overall network computing results. Moreover, it allows for rapid location of faulty and tampered node devices, ensuring the reliability of the overall network computing results. This, in turn, resolves the conflict between data sharing and information security between departments.

[0053] This invention can identify each unique user without requiring specific features or identity information, achieving non-specific feature recognition. This invention uses non-specific feature recognition for user identification, identity verification, or event monitoring, resulting in high accuracy and precise location identification. This invention can identify and verify users and protect privacy while addressing issues related to transportation, education, healthcare, epidemic prevention, public services, emergency response, public security, counter-terrorism, community management and services, market behavior, workplace safety, and civilized behavior.

[0054] This invention employs non-specific feature recognition, effectively preventing risks caused by theft or counterfeiting of specific features, thus significantly enhancing security. It uses a non-contact, passive method for seamless user identification, greatly improving ease of execution. Based on the aforementioned peer-to-peer network, this invention can be easily deployed over coverage areas ranging from hundreds of meters to hundreds of kilometers, making it suitable for various geographical areas.

[0055] In this invention, the peer-to-peer network does not directly control the guidance device as a single machine; the guidance device's response execution (i.e., presentation of navigation information) is based on the calculation results obtained through collaborative computing, resulting in high response efficiency and avoiding illegal responses such as false execution or failure to execute when required due to network attacks. To prevent hijacking, this invention can also use multiple node devices to collaboratively control the guidance device, further improving its immunity to hijacking attacks. Detailed Implementation

[0056] The present invention will be further described in detail below with reference to the embodiments.

[0057] To address the shortcomings of existing technologies, such as poor versatility, accuracy, and real-time performance, and reliance on handheld devices, this invention provides a pedestrian navigation method. Based on user identification and positioning, it guides users through guidance devices deployed in the user's surrounding environment. During navigation, users can ditch their handheld devices, thus achieving high accuracy and real-time performance. This invention utilizes sensors of corresponding types deployed in and covering a passageway to acquire the physical attributes of users within the passageway for identification and positioning. It can construct a pure intranet for navigation, avoiding the data security risks associated with data interaction using public communication platforms. This invention can be implemented in any scenario where sensors can be deployed, demonstrating strong versatility.

[0058] The pedestrian navigation method described in this invention identifies (or locks onto) and locates users. Based on the user's navigation needs (which may include starting position, multiple transit points, and route requirements such as fewer people, shortest path, fewer turns, etc.), without calculating and generating route information, it calculates and outputs the user's next action one by one based on the situation of all users within the collection range, while meeting the navigation needs, thus gradually guiding the user to the destination. When using this invention for navigation, the user's navigation needs are first obtained, and the user's location information is obtained in real time. Then, based on the navigation needs and the user's real-time location information, the user's next navigation information is calculated. The navigation information is presented through a guidance device at the location corresponding to the user's real-time location information to guide the user. This invention guides the user in stages. Typically, navigation information is presented to the user at locations where route selection is required (not a complete selection of route information; in fact, this invention does not generate complete route information, but rather refers to situations where the user needs to choose one of the roads at intersections), to guide the user to choose the appropriate direction of travel; this process continues until navigation is complete.

[0059] In this embodiment, the guidance device can be implemented as a visual information display device, an audio playback device, or a combination of both. The specific implementation can be chosen based on the implementation scenario and performance requirements. The visual information display device can be a projection device (projected onto the ground or wall), a dedicated display device (set on the ground or wall), a multi-purpose display device (such as billboards, light boxes, signs, outdoor displays, etc.), or a combination thereof. The audio playback device can be a dedicated playback device, a multi-purpose playback device (such as an advertising terminal with sound playback function, a background music playback device, an information broadcasting device, etc.), or a combination thereof. When there are multiple users and navigation information needs to be presented to multiple users simultaneously, different types of guidance devices can choose a presentation method suitable for their own functional characteristics. For example, a projection device can simultaneously display visual navigation information for each user, while an audio playback device can play audio navigation information for each user one by one.

[0060] To enhance user experience and provide pre-guided navigation without interruption, this invention also acquires user data on direction of travel, speed of travel, and posture. Using preset conditions (such as preset judgment criteria) or a pre-trained prediction model based on machine learning, it predicts the user's next action, including direction of travel and speed. For example, a user whose posture data indicates "looking around" will typically slow down or stop walking; a user whose posture data indicates "looking straight ahead" and whose speed data indicates "walking quickly" will typically maintain or potentially increase their speed. Furthermore, the guidance device, based on the predicted direction and speed of the user's next movement, presents the user's next navigation information. For example, if the predicted speed is to slow down or stop, the presentation of the user's next navigation information can be delayed or not displayed until the user moves to the location where navigation information needs to be presented. The gap time can be used to present other content, including navigation information for other users or other types of content. If the predicted speed is to remain constant or increase, the presentation of the user's next navigation information can be maintained or brought forward accordingly. Therefore, the presentation of navigation information is dynamically and proactively adjusted, rather than solely relying on the user's location.

[0061] In addition, the prediction of the user's walking speed can be made through auxiliary means. Specifically, the user's height data, leg length data, and cadence data can be obtained to calculate the user's walking speed. If the prediction of the user's next action is inaccurate, the calculated walking speed will be used instead of the predicted walking speed.

[0062] If the prediction of the user's next action is inaccurate, the navigation device presenting the user's next step is corrected based on the user's real-time travel direction and speed data. The density of people around the user, environmental information, and the travel direction, speed, and posture data of people in the user's vicinity are used as training samples and added to a sample library for further training and adjustment of the prediction model to improve its accuracy. To further improve the accuracy of the prediction model, this invention can establish individual prediction models for different users. After user identification, the associated prediction model is used to predict the user's next action.

[0063] When the aforementioned guidance device is implemented as a visual information display device, it is used to display visual navigation information. In order to improve the user's experience in obtaining visual navigation information, in this invention, a visual information display device that acquires the user's real-time travel direction data and travel speed data, follows the user's travel direction and travel speed, and maintains a position at a certain distance from the user in the user's travel direction displays the user's next visual navigation information; that is, the display of visual navigation information follows the user's travel direction and travel speed and is displayed in front of the user at a position convenient for the user to view, and the display effect is that it moves and displays in the same direction and at the same speed as the user.

[0064] Alternatively, based on the user's navigation needs and real-time location information, the peer-to-peer network provided by this invention performs collaborative computation to obtain a visual information display device that matches the location where visual navigation information needs to be displayed for each user's next step. This visual information display device, positioned at a certain distance from the user's real-time location and speed, then displays the user's next visual navigation information. The peer-to-peer network provided by this invention, based on collaborative computation, can perform non-specific feature recognition of users and determine their real-time location, direction of travel, and speed. Furthermore, based on the results of collaborative computation, the visual information display device that needs to display visual navigation information can then display the corresponding visual navigation information.

[0065] The distance between the visual information display device that displays visual navigation information and the user is calculated based on the user's height and stride length.

[0066] When the guidance device is implemented as an audio playback device for playing audio navigation information; in order to improve the user's experience in obtaining audio navigation information, in this invention, the audio playback device acquires the user's real-time location information and walking speed data, follows the user's position and walking speed, and keeps the sound field covering the user's position to play the audio navigation information for the user's next step; that is, the playback of audio navigation information follows the user's walking direction and walking speed, and is played in the sound field aligned with and covering the user's position, with the playback effect being that it moves and plays in the same direction and at the same speed as the user.

[0067] Alternatively, based on the user's navigation needs and real-time location information, the peer-to-peer network provided by this invention can be used for collaborative computation to obtain an audio playback device that matches the location where each user needs to play audio navigation information next. This audio playback device, relative to the user's real-time location and speed, maintains sound field coverage of the user's location and plays the user's next audio navigation information. The peer-to-peer network provided by this invention, based on collaborative computation, can perform non-specific feature recognition of users and determine their real-time location, direction of travel, and speed. Simultaneously, based on the results of collaborative computation, the audio playback device that needs to play audio navigation information can play the corresponding audio navigation information.

[0068] The audio playback device is positioned above the passageway and is equipped with a downward-facing sound-focusing shield. The sound-focusing shield is used to limit the sound field of the audio playback device to a certain range, such as ensuring that the sound can be clearly transmitted to the space required for one person to walk below, and minimizing its diffusion to the surrounding area; or, a super-directional speaker with a pan-tilt unit is used, and the transmission angle is adjusted according to the direction and speed of the user who needs to listen to the audio navigation information, so as to ensure that the super-directional speaker is aimed at the user.

[0069] To avoid the influence of satellite positioning, electronic maps, and other communication devices, and to eliminate reliance on handheld terminal devices, this invention uses sensors of corresponding types deployed in and covering the channel to acquire the physical attributes (physical space, sound, appearance, body temperature, smell, etc.) of users within the channel. It then uses a human-computer interaction device associated with the user (which can be a fixed or mobile device, only needed for submitting navigation requests) to obtain the user's navigation needs, and performs collaborative calculations through the peer-to-peer network provided by this invention to obtain navigation information for each user's next step.

[0070] In practical implementation, the human-computer interaction device can be omitted, and navigation needs can be inferred from the user-authorized itinerary information. This itinerary information includes a confirmed or presumed destination, which may be the final destination or the next of multiple consecutive destinations. The itinerary information can be implemented as scheduled events or other needs (e.g., a booked restaurant, an agreed-upon meeting person, the next item in a medical checkup, etc.). In the implementation of this invention, when the peer-to-peer network covers the user's navigation needs, the user leaves their home, i.e., departs from their starting point, and the peer-to-peer network immediately performs real-time calculations of various states and needs. These needs include navigation requirements. Through various calculations performed according to laws, regulations, and user agreements, the peer-to-peer network can guide the user step-by-step from the road to indoors, and then step-by-step from indoors to their destination (or the next of multiple consecutive destinations).

[0071] In practical implementation, traditional single-point identification methods can be used to identify users at designated locations to confirm their identities and associate location information. Alternatively, the peer-to-peer network-based collaborative computing provided by this invention can be used for non-specific feature-based identity recognition. The peer-to-peer network of this invention is based on collaborative computing, does not rely on single-point identification, and distributes computational functions across the entire network, reducing the hardware and software requirements of single-point computation, resulting in high execution efficiency and significantly improved anti-attack capabilities. The relatively symmetrical information among node devices prevents illegal data tampering. Even if a single node device is physically compromised and its transmitted data is altered, the network-wide computation is a highly redundant and complex calculation with numerous multi-dimensional verifications. Therefore, the alteration of data transmitted by a single node device does not affect the overall network computation results. Furthermore, it allows for rapid location of faulty and tampered node devices, ensuring the reliability of the overall network computation results. This, in turn, resolves the conflict between data sharing and information security between departments.

[0072] The result data transmitted between node devices can be the processing result of information rather than the information itself. Therefore, the raw data collected (i.e., the perceived data) does not need to be stored. Node devices only receive the calculation results output by other node devices and send out their own calculation results. The amount of information contained in a single calculation result is insufficient to reconstruct any event or target information. A definite result can only be obtained by joint calculation of the calculation results of the entire peer-to-peer network, multi-dimensional data matrix elements, and physical space and facility correspondence. The collaborative calculation has less dependence on the information transmitted by a few node devices, which can fundamentally change the nature of traditional information technology's single-point security sensitivity.

[0073] In this invention, user identity information and location information can be obtained through collaborative computation via a peer-to-peer network provided by this invention. Specifically, the peer-to-peer network is used to perform non-specific feature recognition and location recognition on users. The term "non-specific feature recognition" differs from the common understanding of "recognition" in a strict conceptual definition. Commonly, "recognition" refers to identifying a user's concrete form or specific identity information, such as who they are (including their name and other specific information indicating their identity) or what they are (e.g., a car, a person). However, the "recognition" in this invention refers to identifying each unique user (i.e., the user or other fixed or moving objects besides the user) as itself; that is, for a given object to be identified, its existence is unique. After implementing "non-specific feature recognition," this invention determines that the object to be identified (i.e., the user who has not been identified or whose identity has not been confirmed) is itself, and not other objects to be identified. The result of "non-specific feature recognition" does not require determining the specific characteristics of the object to be identified, nor does it require determining the object's identity information or concrete form. For example, if a person is considered object A to be verified, and an object is considered object B to be verified, then after implementing "non-specific feature recognition," it is not necessary to identify whether object A is a person or what their specific identity is, nor is it necessary to identify whether object B is an object or what kind of object it is; rather, it is necessary to determine that object A is object A itself, and object B is object B itself. Then, corresponding services or controls can be provided for object A or object B.

[0074] The peer-to-peer network comprises multiple node devices, all without a hierarchy, forming a decentralized network and computing architecture. Unlike traditional single-point aggregation computing models, the data transmission direction between node devices in this invention does not have a fixed, preset path relationship. In the peer-to-peer network described in this invention, a particular node device processes the collected raw data to obtain result data, and then propagates the result data to other node devices. Other node devices that receive the result data use it as one of their collected raw data, thus influencing the result data of other node devices. For ease of description, the aforementioned "particular node device" is referred to as the "current node device," and the "other node devices" are referred to as "subsequent node devices." One aspect of this influence is that the result data calculated by subsequent node devices is not entirely determined by the raw data they themselves collected, but rather jointly determined by the result data output by the current node device. Specifically, the result data output by the current node device may change the data processing model and parameters used by subsequent node devices to calculate the result data, thereby affecting the result data of subsequent node devices. For example, if the output data of the current node device is correlated with the raw data collected by subsequent node devices, it is necessary to consider the impact of the output data of the current node device on the accuracy of the output data of the subsequent node devices. Specifically, for the perception of a specific user, if the result data is calculated based solely on the raw data collected by subsequent node devices, it can only reflect the real-time (including real-time location and time) single-point result judgment of that user within the perception range of the subsequent node devices. However, the output data of the current node device reflects the direct perception data and result judgments of that user at other locations and at other times, or other indirectly related perception data and result judgments, which helps to improve the accuracy and comprehensiveness of the result data of the subsequent node devices, including superimposed calculations of the same dimension and correlation references of different dimensions.

[0075] Because there is no master-slave relationship between nodes in a peer-to-peer network, point-to-point transmission is possible. Therefore, for a given user's perceived data reflected in the output data of one node, the information is relatively symmetrical among the other nodes receiving that data. These other nodes use the received data as input, combining it with their own sensor data to calculate their own results. These results naturally encompass both the received data and the information from their own sensors, and are then transmitted to the next layer of nodes. Thus, for a given user's perceived data, information is relatively symmetrical across all nodes. This prevents the impact of tampering or falsification of the calculation process and results of a single node on the overall data. It also serves as a means to detect faulty, tampered, or non-compliant nodes, fundamentally solving the inherent vulnerabilities of traditional information technology: information asymmetry leading to false, forged, or erroneous information, which becomes a point of entry for fraud and cyberattacks. Furthermore, it addresses issues such as poor accuracy, excessive processing time, low reliability, and poor responsiveness in complex applications. Ultimately, it can truly become the information infrastructure for comprehensive management of large areas and the infrastructure for the digital economy. Unlike blockchain technology, which relies on independent computation by each node to determine the result and emphasizes the preservation of original data, this invention focuses on peer-to-peer collaborative computation among node devices. Through this collaborative computation, each node device can adjust its own data processing model (i.e., the algorithm for calculating the result data) and parameters when processing data. This adjustment is a feedback mechanism from all node devices, transforming the computation of all node devices into a unified whole. Instead of individual nodes performing calculations independently, all node devices collaboratively complete the computation. The adjustments to the node device's data processing model are objectively real and will impact subsequent data processing iterations.

[0076] Node devices are equipped with data acquisition devices (in specific implementations, these may include one or more of the following: image acquisition devices, audio acquisition devices, temperature measurement devices, vibration frequency sensing devices, lidar, chemical sensors, and electromagnetic induction devices) and a computing module. The data acquisition devices include at least one type of sensor for collecting sensing data of different corresponding types. The computing module calculates the resulting data based on a data processing model. Node devices located at different acquisition positions (i.e., at different physical installation locations) collect at least one point sample from the user; the point sample is sensing data corresponding to the sensor type. Based on this, without needing to obtain user identity information, multiple node devices in the peer-to-peer network perform collaborative computation to determine that each unique user is themselves, achieving non-specific feature recognition and user location identification.

[0077] Specifically, taking a given node device as the current node device, and considering the data transmission between its preceding and subsequent node devices (in this invention, preceding and subsequent node devices are only used to describe their sequential relationship with the current node device in the current calculation and data transmission process, and do not imply any necessary sequential or priority relationship between them), the current node device receives the result data output by other node devices (including preceding node devices), and subsequent node devices receive the result data output by other node devices (including the current node device). For the current node device, the collected sensing data is combined with the result data from other node devices (including preceding node devices) to calculate the result data of the current node device, and this result data is sent to other node devices (including subsequent node devices). Similarly, the working process of subsequent node devices is the same as that of the current node device, and preceding node devices also receive the result data from the preceding node devices of their predecessors and perform the same working process as the current node device; that is, the node devices in the peer-to-peer network perform the same working process. Furthermore, the node devices in the peer-to-peer network perform collaborative calculations as sensing data is collected and result data is calculated. In this process, the output data of a certain node device is only received and used as input by the subsequent layer of node devices, and the output data of the subsequent layer of node devices will cover the output data of the preceding layer of node devices (including the aforementioned node device).

[0078] In a peer-to-peer network, all events are processed synchronously, and it is not necessarily necessary to explicitly produce phased outputs such as what event was discovered or what the specific content of the event is. In a peer-to-peer network, only the sensor's perception and the corresponding execution device (in this invention, the guiding device) respond are explicit. All other intermediate processes are processed simultaneously through collaborative computing. That is, during the operation of this invention, the intermediate process of event discovery is imperceptible. As collaborative computing proceeds and the node device obtains the result data, the corresponding execution device automatically responds and executes.

[0079] To further ensure the trustworthiness of the data source and computation process, in this invention, all node devices encrypt their computational results based on an encrypted consensus mechanism, obtaining encrypted results, which are then sent to other node devices. The encrypted consensus mechanism includes one or more consensus mechanisms, with different mechanisms corresponding to changes in the encryption algorithm structure and parameters of the node devices.

[0080] Node devices communicate using standard-sized data packets (i.e., result data or calculation results). In this invention, the node devices in the peer-to-peer network are similar to human neurons. Just as each neuron does not transmit specific data directly describing external events, the node devices do not output raw data. Instead, they process the raw data acquired by connected sensors and data acquisition devices into standard-sized data packets (i.e., result data or calculation results, similar to nerve impulses in neurons) based on their own data processing model (similar to the biological characteristics of nerve cells). The information contained in a single data packet is insufficient to reconstruct any event or target information. A definite result can only be obtained through collaborative computation involving the calculation results across the entire peer-to-peer network, multi-dimensional data matrix elements, and the correspondence between physical space and facilities. Collaborative computation has little dependence on the data output by a few node devices, and it simultaneously processes all requests received or initiated by all node devices. It is a collaborative verification computation of highly multi-dimensional related information, thereby fundamentally changing the traditional single-point security sensitivity of information systems.

[0081] To ensure data integrity and the effective execution of collaborative computing, this invention deploys a QoS mechanism in the peer-to-peer network, which prioritizes the transmission quality of result data between node devices.

[0082] In practical implementation, the peer-to-peer network can be configured using one or a combination of 4G, 5G, or MESH modes to suit different application scenarios. The optimal solution is achieved by considering factors such as feasibility and cost. The MESH mode is based on the LTE standard, communicating at the LTE physical layer. Data is carried by a customized frame structure, and interaction is performed using a dedicated wireless communication protocol. Customizing the frame structure for peer-to-peer network computing and employing a proprietary wireless communication protocol developed for urban cluster peer-to-peer network computing further enhances its security and reliability. Furthermore, the wireless algorithm is fully adaptable to the multipath channel environment controlled by a consensus mechanism required for peer-to-peer network computing. Communication distances range from 100 meters to 10 kilometers within cities, and up to 120 kilometers in the field using omnidirectional antennas. In this embodiment, the Mesh network communication distance is 50-150 meters between indoor nodes and 50 meters to 120 kilometers between outdoor nodes, with each node capable of connecting to 65,535 nodes. In addition, when networking in 4G and 5G modes, there is no limit to the communication distance, and the number of node devices that can be connected depends on the computing power of the computing chip and the communication latency.

[0083] In a peer-to-peer network, for a specific point sample of an object to be identified, the resulting data transmitted from the node that collected the point sample to other nodes allows subsequent nodes to adjust their perceptual attention based on the features of that point sample (it's not necessary for the feature of the point sample to be included in the resulting data; rather, the feature of the point sample participates in the computation of the preceding node, so that the resulting data of the preceding node can be used as input to the data processing model of the subsequent node, allowing the subsequent node's data processing model to adjust the perceptual attention during computation); or, the features of the point sample can be reported for subsequent nodes to adjust their perceptual attention (the feature of the point sample is directly described in the resulting data). If other subsequent nodes do not detect the feature of the point sample, but can determine from the features of other point samples that the undetected feature of the point sample still belongs to the object to be identified, then the undetected feature of the point sample is continued to be described in the resulting data of the current node and transmitted to other nodes. For example, if a preceding node device senses the color of an object A to be identified, but the current node device does not sense the color of the object A to be identified, but it can be determined from the sensing data of other node devices that there is another object A to be identified besides other objects to be identified, then the color of the object A to be identified that has not been sensed will still be represented in the result data of the current node device.

[0084] In this embodiment, the method for reporting the features of the point sample for subsequent node devices to adjust the perceptual attention is as follows: adjusting the parameters of the data processing model of the subsequent node device based on the features of the point sample provided by the preceding node device, so that the subsequent node device can improve the computing power of the point sample to identify its features; or, the subsequent node device uses the perceptual attention model to match the features of the received point sample to adjust the computing power.

[0085] The “feature” mentioned above has a different meaning from the “feature recognition” in the prior art. The “feature recognition” in the prior art usually refers to information that can determine the identity of a user, while the “feature” in this invention represents a kind of perceived data belonging to the object to be identified, such as coordinates, colors belonging to the object to be identified, etc. The “non-specific feature recognition” of the object to be identified cannot be directly completed by the “feature” perceived by a single point.

[0086] In this embodiment, the method for reporting the features of the point sample for subsequent node devices to adjust the perceptual attention is as follows: based on the result data expressing the features of the point sample provided by the preceding node device (in this invention, the features of the point sample are usually not provided themselves, but expressed in the result data), or the features of the point sample (i.e. the features of the point sample itself), the parameters of the data processing model of the subsequent node device are adjusted so that the subsequent node device can improve the computing power of the point sample to identify its features; or, the subsequent node device uses the perceptual attention model to match the features of the received point sample or the result data expressing the features of the point sample to adjust the computing power.

[0087] When a node device processes the output data from several preceding node devices, based on the data processing model, if the objects to be identified described by several preceding node devices can be determined to be the same user through certain common point sample features, the point sample features and other information described by each node device are merged into the same user. For example, point sample features in physical space that almost completely overlap at the same time can be used to determine that they belong to the same user.

[0088] When the result data received by a node device indicates that the flag used by the current node device to identify the object to be identified before the current reception of result data is different from the flags used by other node devices to identify the object to be identified, and the flags assigned to the object by other node devices have been updated, then the flag used by the current node device to identify the object to be identified before the current reception of result data is converted. Specifically, the method for converting the flag used by the current node device to identify the object to be identified before the current reception of result data is as follows:

[0089] The flag used by the current node device to identify the object to be identified before the current reception of result data is replaced with the latest flag assigned to the object by other node devices; this is a simpler implementation of the present invention.

[0090] Alternatively, the conversion relationship between the flag used by the current node device to identify the object to be identified before the current receiving result data and the updated flags assigned to the object by other node devices can be recorded, and the conversion can be performed when the current node device's current receiving result data needs to be referenced; this is a relatively complex implementation method provided by the present invention.

[0091] Alternatively, the node device can deploy a conversion model to perform corresponding conversions on the labels of multiple objects to be identified based on the input raw data or result data; this is a more complex implementation provided by the present invention.

[0092] In this invention, in order to improve the effectiveness of "non-specific feature recognition", for one or more point samples collected successively by node devices at different collection locations, if the feature values ​​of one or more point samples at different collection locations meet the preset similarity conditions or are determined by a specific model to have a correlation threshold, and are unique at each collection location, then it is determined that the point samples at different collection locations are correlated.

[0093] On the other hand, for one or more point samples collected simultaneously by node devices at different collection locations, if the node devices at different collection locations collect data on the same spatial field, and there is only one object to be identified in the spatial field, or the collected point sample can correctly point to one of the multiple objects to be identified, then for a certain object to be identified, one or more point samples collected by node devices at different collection locations are correlated.

[0094] In this invention, the data acquisition device of the node device includes one or more combinations of an image acquisition device, an electromagnetic induction device, a temperature measurement device, and a vibration frequency sensing device, and a lidar. The data acquired by the aforementioned devices (i.e., one or more combinations of the image acquisition device, electromagnetic induction device, temperature measurement device, and vibration frequency sensing device) and the three-dimensional point cloud acquired by the lidar, or the point cloud generated from images acquired by multiple image acquisition devices, are jointly calculated to obtain three-dimensional points with data. The image color, contour, lines, reflectivity, motion trend, electromagnetic characteristics, temperature, temperature change trend, vibration frequency, and vibration frequency change trend based on two-dimensional perception are used as additional attributes of the corresponding three-dimensional points to constitute an attributed three-dimensional point cloud. Combining electromagnetic induction, temperature patterns, vibration frequency change characteristics, motion correlation (different motion correlations exhibited by different materials such as ropes and fabrics), and reflectivity, the correspondence between each region of the attributed three-dimensional point cloud and each part or related part of the 3D appearance of the object to be identified is determined. This embodiment utilizes the attributes and correlations of attributed 3D point clouds to determine the relationships between points, the correspondence between the regions to which each related point belongs and each part or related part of the 3D appearance of the object to be identified, and can more accurately determine the point sample features belonging to the object to be identified, thereby improving the efficiency and accuracy of "non-specific feature recognition".

[0095] In the process of "non-specific feature recognition," this invention can also acquire the identity information of the object to be identified when necessary. Specifically, when it is determined that the identity information of the object to be identified needs to be acquired, an identity information acquisition command is triggered. This command is used as one of the inputs in the calculation of the result data of the node device. By driving the node device connected to the peer-to-peer network with barrier-free data collection conditions capable of acquiring the identity information of the object to be identified to respond with the corresponding result data, the identity information of the object to be identified is acquired. The acquisition of identity information is also the result of collaborative calculation; that is, the determination that identity information needs to be acquired triggers the acquisition of identity information, rather than being additionally triggered by a specific request command. Based on this invention, if permission calculation is triggered by a request command, in most cases, it can be completed without acquiring identity information. Only in a few cases, when it is found that permission calculation cannot be completed without acquiring identity information, is the determination that identity information needs to be acquired generated according to implementation requirements. For example, if collaborative computing reveals that a person's identity information exists in several location-specific QR code registration systems, package pickup registration systems, or consumer registration systems, and prior authorization from the person or legal access to these systems is obtained, then the peer-to-peer network can drive node devices connected to these systems via barrier-free data collection. The obtained information is then transmitted to the peer-to-peer network through each node device for information comparison and to provide accurate identity information. Based on this, the present invention can also minimize the possibility of identity tampering with a system.

[0096] Specifically, the peer-to-peer network determines the permissions of the target by verifying the authenticity of the identity information. Nodes in the peer-to-peer network capable of acquiring identity information may choose not to provide the identity information (or may provide it depending on implementation requirements), but instead express the verification result in their own result data based solely on the verification requirements for the identity information's authenticity found in the received result data. In other words, in this invention, even when a node capable of acquiring identity information does not provide it, the verification result is expressed in its own result data based solely on the verification requirements for the identity information's authenticity found in the received result data.

[0097] When a node device in a peer-to-peer network that can obtain identity information does not provide identity information, the information source device that drives the provision of identity information establishes an encrypted file transmission channel with the input terminal of the node device that needs to obtain identity information, or establishes an encrypted information transmission channel using other network communication modes; and uses the identity information as one of the inputs of the node device.

[0098] When necessary, in order to meet the needs of other traditional computing modes for raw data, such as the need for evidence preservation in traditional evidence presentation, in this embodiment, the node settings can be equipped with a data storage device for storing the raw data sensed by the sensor.

[0099] In practical implementation, the node device can also be equipped with leakage protection and other functions in its power supply. The node device can also provide various communication interfaces, including fiber optic interfaces and wireless communication interfaces; it can also provide a data interface for connecting external storage devices. The node device can be powered by solar energy or mains power. When implemented outdoors, the node device can be installed on poles such as streetlights (without crossarms, mounted on the main pole, or integrated into the lampshade); in pole-less areas, if implemented indoors, it can be wall-mounted or integrated into the ceiling.

[0100] When this invention is implemented indoors and outdoors, the node devices, as artificial intelligence facilities installed in public spaces, can serve as digital economy infrastructure for urban clusters, providing 24 / 7 seamless coverage. Through collaborative computing across node devices, vehicle identification at any location within the coverage area can achieve near 100% accuracy, with location identification accuracy related to sensor accuracy.

[0101] In this invention, the architecture of a peer-to-peer computing network consists of nodes of the same type and function. Each node dynamically adjusts its data processing model in real time according to the network's consensus mechanism. The raw data collected by the data acquisition devices (including sensors, cameras, etc.) connected to each node is processed and encrypted by the node according to its own data processing model, generating byte-level processing and encryption results (i.e., result data). This result data is then sent to other node devices (the computational and encryption results received by the current node from other node devices are also considered part of the raw data collected by the current node). Therefore, the effect of the raw data sensed by each sensor will propagate exponentially among a massive number of peer-to-peer node devices. If each node sends its result data to 100 surrounding node devices, after four units of time, hundreds of millions of node devices will be affected by the event sensed by that sensor. In this computing model, information is relatively symmetrical and immune to tampering and forgery. It fundamentally solves the inherent hidden dangers of traditional information technology, namely, the false, forged, and erroneous information caused by information asymmetry, which in turn become entry points for fraud and cyberattacks, as well as the problems of long cycles, poor accuracy, and poor adaptability in complex and integrated applications. In turn, it truly becomes an information infrastructure for comprehensive management of large areas and a digital economy infrastructure.

[0102] This invention utilizes collaborative computing in a peer-to-peer network. When the results of this collaborative computing can identify an event, the event discovery is complete. In this embodiment, the discovery of an event by the peer-to-peer network includes the content of the event, the location of the event, and the corresponding response. In a peer-to-peer network, all events are processed synchronously; it is not necessarily necessary to explicitly produce staged outputs such as what event was discovered or what its specific content is. In a peer-to-peer network, only the sensor's perception and the corresponding execution device's response are explicitly defined. All other intermediate processes are processed simultaneously through collaborative computing. That is, during the operation of this invention, the intermediate process of event discovery is imperceptible; it is as the collaborative computing progresses, the node devices obtain the result data, and the corresponding execution devices automatically respond and execute.

[0103] In this invention, each guidance device joins a peer-to-peer network through one or more node devices. To prevent hijacking, this invention can use multiple node devices to collaboratively control the guidance device, further improving immunity to hijacking attacks. A human-computer interaction device associated with the user connects to the node devices as an access device and submits navigation requests to the peer-to-peer network. In this invention, a navigation request can be considered a request command, i.e., requesting the user to drive autonomously from one location to another. Responses to the request command include various scenarios such as "request-execution," "request-response," or others. When the result data calculated by one or more node devices in the peer-to-peer network matches the request command, the result corresponding to the request command is represented in the result data output by one or more node devices, according to preset conditions, a pre-deployed program, or a data processing model deployed on the node device. If, based on collaborative computation, it is determined that the current node device needs to respond to the request command, the current node device will send instructions to the execution device connected to the current node device according to the calculated result data, controlling the execution device to complete the response action; this is the "request-execution" scenario. In this invention, if the guidance device needs to present the navigation information based on the user's next step, the current node device will send an instruction to the guidance device connected to the current node device according to the calculated result data, and control the guidance device to complete the presentation of navigation information.

[0104] Based on peer-to-peer collaborative computing, the execution device can act as one of the node devices. As collaborative computing progresses, when the result data obtained by the execution device can correspond to the request command and perform the relevant operation, the execution device completes the response to the request command. In this invention, the user's next navigation information is represented in the result data; the guidance device receives the result data output by the connected node device. If a specific element in the result data indicates that the guidance device needs to present navigation information, or if the result data is used as one of the inputs to the node device's data processing model, and the calculation determines that the corresponding guidance device needs to present navigation information, then the guidance device presents the corresponding navigation information.

[0105] When the guidance device needs to present navigation information, it combines the result data received from other node devices to calculate its own result data. Based on this result data, the guidance device controls the presentation of the corresponding navigation information. In this invention, the guidance device does not need to first determine whether it needs to present navigation information. Instead, it combines the result data received from other node devices with the perception data collected by its own sensors, inputs this data into its own data processing model, and the output result data determines whether the guidance device should present navigation information and what content of the navigation information should be presented.

[0106] In this invention, the calculated result data includes the optimal solution for all scenarios obtained through collaborative calculations between all users within the channel and the external environment at the current moment; the user's next navigation information is presented through the guidance device. In this invention, the results of calculations for various types of information in the peer-to-peer network are all represented as result data. All guidance devices, as node devices, contribute the optimal solution for all scenarios when participating in the collaborative calculations of the peer-to-peer network. Furthermore, the control commands of all guidance devices are the optimal solution commands output by the node devices connected to them after collaborative calculations. This invention eliminates the traditional generation and transmission of commands to avoid security vulnerabilities that could make the guidance device a risk point.

[0107] In this embodiment, the guiding device is a node device that connects to the execution component of a specific function. The execution feedback information of the execution component of the guiding device is fed back to the guiding device and participates in the calculation of the subsequent result data of the guiding device.

[0108] In this invention, since the guiding device can act as one of the node devices, its response execution is based on the calculation results obtained through collaborative computing, resulting in high response efficiency and avoiding illegal responses such as false execution or failure to execute when required due to network attacks. To prevent hijacking, this invention can also use multiple node devices to collaboratively control the guiding device, further enhancing its immunity to hijacking attacks.

[0109] In a peer-to-peer network, the result data calculated and output by node devices can be implemented as a representation of the state corresponding to the perceived data (i.e., the raw data). This state value can be used for representation, thus eliminating the need for node devices to store and transmit the raw data. In this embodiment, the data or elements in the multidimensional matrix are related to the installation location, attributes, etc., of each node device. Therefore, when transmitting the result data, what is actually transmitted is the transcoded result after transcoding multiple sets of parameters. A multidimensional matrix is ​​actually a combination of multiple sets of parameters. For example, if a user's path is from abcd, and the physical location of the abcd node device is fixed, then the sequence abcd can be expressed by a single character or a similar concept during the multi-parameter transcoding transmission.

[0110] Based on the technical characteristics of peer-to-peer networks, they can be applied to various use cases that provide targeted services or controls for specific users or events. Since the data transmitted between node devices is the result of information processing, rather than the information itself, the raw data collected (i.e., perceived data) does not need to be stored. Node devices only receive the calculation results output by other node devices and send out their own calculation results. The information contained in a single calculation result is insufficient to reconstruct any event or target information; a definite result can only be obtained through collaborative calculation using the calculation results across the entire peer-to-peer network, multi-dimensional data matrix elements, and the correspondence between physical space and facilities. Collaborative calculation has less dependence on the information transmitted by a few node devices, thus fundamentally changing the traditional single-point security sensitivity of information systems.

[0111] In this invention, since the output data of each node device reflects the state evolution of the output data of the preceding node devices, the user's behavior, attributes, state, or events at the time of being perceived by the preceding node devices can be inferred based on the output data received by the current node device. For example, when it is necessary to find the location of user a 15 minutes ago, the location corresponding to the node device that user a was perceived at the current moment can be obtained, and the location of user a can be inferred. Then, based on the transmission path of the output data, it can be inferred back to 15 minutes ago, and the location of user a 15 minutes ago can be estimated (determined by the node device that user a was perceived). Furthermore, the node device does not need to store the original data about user a. That is, based on this invention, it is not necessary to identify the original data to find user a, but rather to first infer the node device that user a was perceived, and if necessary, obtain the original data about user a at the time when it needs to be found from the storage device connected to the node device.

[0112] In this invention, the guiding device receives result data output by other node devices. The principle is as follows: when navigation information needs to be presented by a corresponding guiding device, if the result data calculated by one or more node devices can determine the guiding device that needs to present navigation information, then the corresponding guiding device is added to the node list for transmitting the current result data. The one or more node devices directly transmit the result data to the guiding device or the node devices connected to the guiding device. This is done based on preset conditions, algorithm output, or model output, adding the corresponding guiding device to the node list for transmitting result data. Alternatively, the guiding device receives result data output by other node devices in a layer-by-layer transmission manner. During the collaborative computing process of the peer-to-peer network, each node device also calculates the node list for receiving result data during each result data calculation. Based on the current result data, it clearly knows which one or more guiding devices need to be added and will be added to the node list. The guiding device or the node devices connected to the guiding device are directly used as subsequent node devices in the next layer to directly receive the current result data, achieving normal layer-by-layer transmission and transforming the peer-to-peer network into a three-dimensional architecture. For example, if the result data from the current node device clearly indicates that it needs to be submitted to the public security bureau as evidence, then according to the normal layer-by-layer transmission method, the result data from the current node device would require at least one or more layers of transmission to reach the corresponding node device in the public security bureau. However, if the node device in the public security bureau's network is added to the node list, then the corresponding node device in the public security bureau can directly receive the result data from the current node device when it is transmitted to the next layer, thereby greatly shortening the processing time and improving responsiveness. This invention uses a peer-to-peer network; therefore, this temporary construction is precisely the advantage of this invention. The traditional layer-by-layer aggregation architecture of information systems cannot withstand the complex computing requirements brought about by this temporary network construction.

[0113] To conserve resources and improve the operational efficiency of navigation devices, enabling them to meet the needs of more users, this invention establishes a navigation user group when the collaborative calculation results determine that multiple users share the same direction and speed of travel, the distance between users meets the travel distance standard, and only one user submits a navigation request, or multiple users submit identical navigation requests. Therefore, when providing guidance, only one or a few users within the navigation user group need to be guided to complete the guidance for the entire group.

[0114] In this invention, the selection of users for guidance within a navigation user group specifically involves, based on the results of collaborative computation, if it is determined that the vision and / or hearing of some users are interfered with by other users, then visual navigation information and / or audio navigation information are prioritized for the user who submitted the navigation request or the user at the forefront whose next navigation information has the correct direction of travel. If both the vision and hearing of the user who submitted the navigation request are interfered with by other users, then visual navigation information or audio navigation information is displayed to other users whose vision or hearing is not interfered with. For example, two users walking side by side do not interfere with each other visually, but they do interfere with each other auditorily; when four users walk in pairs, the users in the front row cause visual interference to the users in the back row, but do not interfere with each other auditorily, while users in the same row cause auditory interference. In specific implementation, the type and presentation method of navigation information can be selected according to the results of collaborative computation.

[0115] If all users' vision and hearing are not disturbed by other users, then visual navigation information and audio navigation information are displayed to each user to meet each user's needs.

[0116] The above embodiments are merely illustrative of the present invention and are not intended to limit the invention. Any changes or modifications to the above embodiments based on the technical essence of the present invention will fall within the scope of the claims of the present invention.

Claims

1. A pedestrian navigation method, characterized in that, The system obtains the user's navigation needs and location information; based on the navigation needs and the user's real-time location information, it calculates the user's next navigation information; the navigation information is presented through a guidance device at the location corresponding to the user's real-time location information to guide the user. The guidance device includes a visual information display device for displaying visual navigation information; A visual information display device that acquires real-time data on the user's direction of travel and speed, follows the user's direction of travel and speed, and maintains a certain distance from the user in the direction of travel, and displays visual navigation information for the user's next step. The distance between the visual information display device that displays visual navigation information and the user is calculated based on the user's height and stride length.

2. The pedestrian navigation method according to claim 1, characterized in that, The system acquires the user's direction of travel, speed of travel, and posture data. Using preset conditions or a prediction model pre-trained through machine learning, it predicts the user's next action, including the user's direction of travel and speed. The guidance device at the location that matches the predicted direction of travel and speed of the user's next step presents the user's next navigation information.

3. The pedestrian navigation method according to claim 2, characterized in that, If the prediction of the user's next action is inaccurate, the guidance device that presents the user's next navigation information will be corrected based on the user's real-time travel direction data and travel speed data. The density of people around the user, environmental information, travel direction data, travel speed data, and posture data of people around the user will be used as training samples and added to the sample library for further training and adjustment of the prediction model.

4. The pedestrian navigation method according to claim 2, characterized in that, The system acquires the user's height, leg length, and cadence data to calculate the user's walking speed. If the prediction of the user's next move is inaccurate, the calculated walking speed is used instead of the predicted walking speed.

5. The pedestrian navigation method according to claim 2, characterized in that, We establish personalized prediction models for different users. After identifying users, we use the associated prediction models to predict the user's next action.

6. The pedestrian navigation method according to claim 1, characterized in that, Alternatively, another method for displaying visual navigation information to the user by maintaining a certain distance from the user is as follows: based on the user's navigation needs and real-time location information, a peer-to-peer network is used for collaborative calculation to obtain a visual navigation display device that matches the location where visual navigation information needs to be displayed to each user next; and the visual navigation information is displayed to the user by maintaining a certain distance from the user relative to the user's real-time location information and real-time travel speed.

7. The pedestrian navigation method according to claim 1, characterized in that, The guidance device includes an audio playback device for playing audio navigation information; an audio playback device that acquires the user's real-time location information and speed data, follows the user's position and speed, maintains sound field coverage of the user's position, and plays audio navigation information for the user's next step. Alternatively, based on the user's navigation needs and the user's real-time location information, a peer-to-peer network can be used for collaborative computation to obtain an audio playback device that matches the location where each user needs to play audio navigation information next. And relative to the user's real-time location information and real-time travel speed, the audio playback device maintains sound field coverage of the user's location and plays audio navigation information for the user's next step.

8. The pedestrian navigation method according to claim 7, characterized in that, The audio playback device is positioned above the channel and is equipped with a downward-facing acoustic shield, which is used to limit the sound field of the audio playback device to a certain range. Alternatively, a gimbaled super-directional speaker can be used, adjusting the transmission angle according to the user's direction of travel and speed as needed to listen to audio navigation information.

9. The walking navigation method according to claim 1, 6, or 7, characterized in that, By deploying corresponding types of sensors in and covering the channel, the physical attributes of users within the channel are obtained. The navigation needs of users are obtained through human-computer interaction devices associated with users. And through collaborative computing via a peer-to-peer network, navigation information for each user's next step is obtained.

10. The pedestrian navigation method according to claim 9, characterized in that, Alternatively, navigation needs can be inferred from the user's trip information, which includes a definite or presumed destination, which may be the final destination or the next of multiple consecutive destinations.

11. The pedestrian navigation method according to claim 9, characterized in that, Utilize peer-to-peer networks to perform non-specific feature identification and location identification for users; The peer-to-peer network consists of multiple node devices, and there is no master-slave relationship among all node devices. Each node device is equipped with a data acquisition device and a computing module. The data acquisition device includes at least one type of sensor for collecting different corresponding types of sensing data. Node devices set at different acquisition locations collect at least one point sample from the user, and the point sample is sensing data of the corresponding sensor type. For a given node device, the collected sensing data is processed to obtain result data, which is then propagated to other node devices. Other node devices that receive the result data use it as one of the original data collected, and the result data influences the result data of other node devices. Based on this, without needing to obtain user identity information, multiple node devices in the peer-to-peer network perform collaborative computation to determine that each unique user is itself, achieving non-specific feature recognition and user location identification.

12. The pedestrian navigation method according to claim 11, characterized in that, The current node device receives the result data output by other node devices; for the current node device, it combines the collected sensing data with the result data from other node devices to calculate the result data of the current node device, and then sends it to other node devices; In a peer-to-peer network, node devices perform collaborative computation as they collect sensing data and calculate result data.

13. The pedestrian navigation method according to claim 11, characterized in that, In a peer-to-peer network, for a specific point sample of a user, the resulting data transmitted from the node device that collected the point sample to other node devices allows subsequent node devices to adjust their perceptual attention based on the features of that point sample, or report the features of that point sample for subsequent node devices to adjust their perceptual attention. If other subsequent node devices do not detect the features of that point sample, but can determine from the features of other point samples that the undetected features still belong to that user, then the undetected features of that point sample are continued to be represented in the result data of the current node device and transmitted to other node devices.

14. The pedestrian navigation method according to claim 13, characterized in that, The method for reporting the features of the point sample to subsequent node devices for adjusting the perceptual attention is as follows: based on the result data expressing the features of the point sample provided by the preceding node device, or the features of the point sample, adjust the parameters of the data processing model of the subsequent node device so that the subsequent node device can improve the computing power of the subsequent node device to identify the features of the point sample; or, the subsequent node device uses the perceptual attention model to match the features of the received point sample or the result data expressing the features of the point sample to adjust the computing power.

15. The pedestrian navigation method according to claim 14, characterized in that, When a node device processes the output data of several preceding node devices, based on the data processing model, if the users described by several preceding node devices can be identified as the same user through certain common point sample features, the point sample features and other information described by each node device will be merged into the same user.

16. The pedestrian navigation method according to claim 15, characterized in that, If the result data received by a node device indicates that the flag used by the current node device to identify the user before the current receipt of result data is different from the flag used by other node devices to identify the user, and the flags assigned to the user by other node devices have been updated, then the flag used by the current node device to identify the user before the current receipt of result data is converted.

17. The pedestrian navigation method according to claim 15, characterized in that, The method for converting the flag used to identify the user by the current node device before the current reception of result data is as follows: Replace the flag used by the current node device to identify the user before the current reception of result data with the latest flag assigned to the user by other node devices; Alternatively, record the conversion relationship between the flag used by the current node device to identify the user before the current reception of result data and the updated flag assigned to the user by other node devices, and perform the conversion when it is necessary to reference the result data received by the current node device in the current reception. Alternatively, node devices can deploy conversion models to perform corresponding conversions on the identifiers of multiple users based on the input raw data or result data.

18. The pedestrian navigation method according to claim 13, characterized in that, For one or more point samples collected sequentially by node devices at different collection locations, if the feature values ​​of one or more point samples at different collection locations meet the preset similarity conditions or are determined by a specific model to have a correlation threshold, and are unique at each collection location, then it is determined that the point samples at different collection locations are correlated.

19. The pedestrian navigation method according to claim 13, characterized in that, If node devices at different acquisition locations collect one or more point samples simultaneously, and if the node devices at different acquisition locations collect samples from the same spatial field, and there is only one user in the spatial field, or the collected point sample can correctly point to one of the multiple users to which it belongs, then for a certain user, the one or more point samples collected by node devices at different acquisition locations are correlated.

20. The pedestrian navigation method according to claim 19, characterized in that, The data acquisition device of the node equipment includes one or more of the following: image acquisition device, electromagnetic induction device, temperature measurement device, vibration frequency sensing device, and lidar. It performs joint calculations on the data acquired by the above devices and the 3D point cloud acquired by the lidar, or on the point cloud generated from images acquired by multiple image acquisition devices, to obtain 3D points with data. It uses image color, contour, lines, reflectivity, motion trend, electromagnetic characteristics, temperature, temperature change trend, vibration frequency, and vibration frequency change trend based on 2D perception as additional attributes of the corresponding 3D points, forming an attributed 3D point cloud. Combining electromagnetic induction, temperature patterns, vibration frequency change characteristics, motion correlation, and reflectivity, it determines the correspondence between each region of the attributed 3D point cloud and each or related part of the user's 3D appearance.

21. The pedestrian navigation method according to claim 11, characterized in that, When it is necessary to obtain the user's identity information, an identity information acquisition command is triggered. The identity information acquisition command is used as one of the inputs to participate in the calculation of the result data of the node device. By driving the node device in the peer-to-peer network that is connected to the barrier-free data collection conditions that can obtain the user's identity information, the corresponding result data is responded to, thereby realizing the acquisition of the user's identity information.

22. The pedestrian navigation method according to claim 21, characterized in that, Peer-to-peer networks determine user permissions by verifying the authenticity of user identity information. In a peer-to-peer network, the node device that can obtain identity information does not provide the identity information itself, but only expresses the verification result in the result data of the node device based on the verification requirements for the authenticity of identity information in the received result data.

23. The pedestrian navigation method according to claim 22, characterized in that, In a peer-to-peer network, the node device capable of obtaining identity information does not provide the identity information itself. Instead, the information source device that drives the provision of identity information establishes an encrypted information transmission channel with the node device input terminal that needs to obtain the identity information, or establishes an encrypted information transmission channel using other network communication modes, and uses the identity information as one of the inputs to the node device.

24. The pedestrian navigation method according to claim 11, characterized in that, The data acquisition device includes one or more of the following: image acquisition device, audio acquisition device, temperature measurement device, vibration frequency sensing device, lidar, chemical sensor, and electromagnetic induction device.

25. The pedestrian navigation method according to claim 11, characterized in that, The human-computer interaction device associated with the user connects to the node device as an access device and submits navigation requests to the peer-to-peer network; each guidance device joins the peer-to-peer network through one or more node devices; If the collaborative computing determines that the guidance device needs to present the navigation information based on the user's next step, the current node device will send instructions to the guidance device connected to the current node device according to the calculated result data, and control the guidance device to complete the presentation of navigation information.

26. The pedestrian navigation method according to claim 25, characterized in that, The user's next navigation information is represented in the result data; the guiding device receives the result data output by the connected node device. If a specific element in the result data indicates that the guiding device needs to present navigation information, or if the result data is used as one of the inputs to the node device's data processing model to calculate and determine that the corresponding guiding device needs to present navigation information, then the guiding device presents the corresponding navigation information.

27. The pedestrian navigation method according to claim 26, characterized in that, When the guidance device needs to present navigation information, it combines the result data received from other node devices to calculate its own result data, and controls the guidance device to present the corresponding navigation information based on the obtained result data.

28. The pedestrian navigation method according to claim 27, characterized in that, For the guidance device, the calculated result data includes the optimal solution for all situations obtained at the current moment based on the collaborative calculation of all users in the channel and the external environment; the navigation information for the user's next step is presented through the guidance device.

29. The pedestrian navigation method according to claim 27, characterized in that, The guiding device receives the result data output by other node devices. The principle is as follows: when the corresponding guiding device needs to present navigation information, if the result data calculated by one or more node devices can determine the guiding device that needs to present navigation information, then the corresponding guiding device is added to the node list that transmits the current result data. The one or more node devices directly transmit the result data to the guiding device or the node device connected to the guiding device. Alternatively, the device can be guided to receive the output data from other node devices in a layer-by-layer transmission manner.

30. The pedestrian navigation method according to claim 29, characterized in that, Based on preset conditions or algorithm output and model output, the corresponding guiding devices are added to the node list for transmitting result data.

31. The pedestrian navigation method according to claim 26, characterized in that, The guiding device is a node device with execution components configured with specific functions. The execution feedback information of the execution components of the guiding device is fed back to the guiding device and participates in the calculation of the subsequent result data of the guiding device.

32. The pedestrian navigation method according to claim 11, characterized in that, If the collaborative computing results determine that multiple users have consistent travel directions and speeds, and the distance between users meets the travel distance standard, and if only one user submits a navigation request or multiple users submit the same navigation request, then the multiple users will be grouped into a navigation user group.

33. The pedestrian navigation method according to claim 32, characterized in that, Among the users in the navigation user group, based on the results of collaborative computing, if it is determined that the vision and / or hearing of some users are interfered with by other users, visual navigation information and / or audio navigation information will be displayed and / or played to the user who submitted the navigation request or the user whose next navigation information has the correct direction of travel. If the user submitting the navigation request is visually and aurally disturbed by other users, then other users whose vision or hearing is not disturbed will be shown visual navigation information or played audio navigation information. If all users' vision and hearing are not disturbed by other users, then visual navigation information and audio navigation information are displayed to each user.