Method, system, device, medium and chip for indoor signal fingerprint collection and positioning

Abstract

The present disclosure discloses a method, system, device, medium, and chip for indoor signal fingerprint collection and positioning. The indoor signal fingerprint collection and positioning method is based on a wireless network with flooding networking. The indoor signal fingerprint collection method includes: controlling a preselected node of the wireless network to move to a preset position and then sending a positioning data frame with itself as the sending target; the preselected node receives and parses the positioning data frame sent by a relay node of the wireless network; and storing the signal strength of the preset position and the positioning data frame sent by the relay node. This method does not require the installation of additional Bluetooth tags, but only requires the modification of the preselected nodes, using existing mesh nodes to collect signal strength and send it on demand, thereby reducing signal pollution and improving collection efficiency. By collecting multiple groups of signal strengths from all nodes to build a fingerprint database, the accuracy of the database is improved, the interference of signal fluctuations is reduced, and the accuracy of indoor positioning is thereby improved.

Description

Technical Field

[0001] The present disclosure relates to the field of indoor positioning technology, and in particular to a method, system, device, medium and chip for indoor signal fingerprint collection and positioning. Background Art

[0002] In indoor environments, using mature outdoor positioning technologies such as GPS (Global Positioning System) often fails to achieve the desired positioning accuracy. In recent years, the widespread adoption of WLAN (Wireless Local Area Networks) technology has provided new methods for indoor positioning. RSS (Received Signal Strength) fingerprint positioning is a commonly used indoor positioning method. Existing RSS-based location fingerprint matching methods involve two steps: establishing a fingerprint database and then using the fingerprint database to perform fingerprint matching on the target location.

[0003] The fingerprint database mainly collects fingerprint signals. The principle is to divide the target geographical area into several rectangular grids, collect signal data for a period of time at each grid point (for example, the collection time is 5 minutes, and the data is collected once per second), and then calculate an average RSS as the fingerprint, or calculate the sample distribution of RSS (such as variance) as the fingerprint. The coordinates of these grid points and the corresponding fingerprints form a fingerprint database, or a radio map.

[0004] Existing fingerprint data is based on Wi-Fi (wireless fidelity) signals and does not require the deployment of additional hardware. However, the fingerprint collection equipment can only passively receive Wi-Fi signals and cannot control the content and form of the received Wi-Fi signals, resulting in an inability to control the fingerprint signal collection process. Some solutions require the temporary deployment of some Zigbee (a wireless communication technology used for short distances and low rates) or UWB (Ultra Wide Band) hardware, which is costly and difficult to implement.

[0005] Public content

[0006] The technical problem to be solved by the present disclosure is to overcome the defects of the existing technology that can only passively receive Wi-Fi signals, resulting in the inability to control the fingerprint signal collection process or the need to deploy additional hardware, and to provide a method, system, device, medium and chip for indoor signal fingerprint collection and positioning.

[0007] The present disclosure solves the above technical problems through the following technical solutions:

[0008] The present disclosure provides an indoor signal fingerprint collection method, which is applied to a wireless network of flooding networking. The indoor signal fingerprint collection method includes:

[0009] Controlling a preselected node of the wireless network to move to a preset position and then sending a positioning data frame with itself as the sending target;

[0010] The preselected node receives and parses the positioning data frame sent by the relay node of the wireless network;

[0011] The preset position and the signal strength of the positioning data frame sent by the relay node are stored.

[0012] Preferably, the preselected node comprises a mobile node of the wireless network; and / or,

[0013] The preselected node is configured to send a wireless data frame to itself in response to the control instruction; and / or,

[0014] The preselected node is configured to read the RSSI of the received data frame through the network protocol stack, and obtain the RSSI of more distant node data frames by retaining the checksum error frame; and / or,

[0015] The preselected node includes at least one of a sweeping robot, a smart phone, a wearable device or other IoT nodes; and / or,

[0016] The relay node comprises an actively powered fixed node of the wireless network; and / or,

[0017] The wireless network includes a BLE Mesh (Bluetooth Wireless Mesh) network.

[0018] Preferably, the indoor signal fingerprint collection method further includes:

[0019] The preselected node is controlled to receive the positioning data frames sent by all relay nodes of the wireless network and report the positioning data frames to the link layer for parsing.

[0020] Preferably, the step of reporting the positioning data frame to the link layer for parsing includes:

[0021] Determining whether a positioning data frame with the same node identifier has sampled signal strength; the node identifier is used to identify the relay node that sends the positioning data frame;

[0022] If not, the RSSI (Received Signal Strength Indicator) field of the positioning data frame is parsed and saved to a local or network database.

[0023] Preferably, before the step of storing the preset position and the signal strength of the positioning data frame sent by the relay node, the indoor signal fingerprint collection method further includes:

[0024] A plurality of vector arrays of signal strengths of the positioning data frames are collected at the same preset position, and means and / or variances of the plurality of vector arrays are calculated.

[0025] The present disclosure also provides an indoor positioning method, the indoor positioning method comprising:

[0026] Obtain the indoor fingerprint of the node to be located;

[0027] According to the indoor fingerprint of the node to be located and the fingerprint library, the location information of the node to be located is calculated using a positioning algorithm;

[0028] The fingerprint library is constructed using the indoor signal fingerprint collection method described above.

[0029] The present disclosure further provides an indoor signal fingerprint collection system, which is applied to a wireless network of flooding networking. The indoor signal fingerprint collection system includes:

[0030] A preselected node control module, configured to control a preselected node of the wireless network to move to a preset position and then send a positioning data frame with itself as the sending target;

[0031] The preselected node receives and parses the positioning data frame sent by the relay node of the wireless network;

[0032] The signal strength storage module is used to store the preset position and the signal strength of the positioning data frame sent by the relay node.

[0033] Preferably, the preselected node comprises a mobile node of the wireless network; and / or,

[0034] The preselected node is configured to send a wireless data frame to itself in response to the control instruction; and / or,

[0035] The preselected node is configured to read the RSSI of the received data frame through the network protocol stack, and obtain the RSSI of more distant node data frames by retaining the checksum error frame; and / or,

[0036] The preselected node includes at least one of a sweeping robot, a smart phone, a wearable device or other IoT nodes; and / or,

[0037] The relay node comprises an actively powered fixed node of the wireless network; and / or,

[0038] The wireless network includes a BLE Mesh network.

[0039] Preferably, the indoor signal fingerprint collection system further includes:

[0040] The signal analysis control module is used to control the pre-selected node to receive the positioning data frames sent by all relay nodes of the wireless network and report the positioning data frames to the link layer for analysis.

[0041] Preferably, the signal analysis control module is specifically used to determine whether the positioning data frame with the same node identifier has sampled the signal strength; the node identifier is used to identify the relay node that sends the positioning data frame;

[0042] The signal analysis control module is specifically used to analyze the RSSI field of the positioning data frame when it is determined that the positioning data frame with the same node identifier has not sampled the signal strength and save it to a local or network database.

[0043] Preferably, the indoor signal fingerprint collection system further includes:

[0044] The signal acquisition control module is used to acquire a plurality of vector arrays of signal strengths of the positioning data frames at the same preset position and calculate the mean and / or variance of the plurality of vector arrays.

[0045] The present disclosure further provides an indoor positioning system, comprising:

[0046] Fingerprint acquisition module, used to obtain the indoor fingerprint of the node to be located;

[0047] A location calculation module is used to calculate the location information of the node to be located using a positioning algorithm based on the indoor fingerprint of the node to be located and a fingerprint library;

[0048] The fingerprint database is constructed using the indoor signal fingerprint acquisition system described above.

[0049] The present disclosure further provides an indoor positioning system, which includes a user-side positioning subsystem and a network-side positioning subsystem:

[0050] The user-side positioning subsystem responds to a request from the user to determine its own location, and obtains and sends the user's location information using the indoor positioning method described above;

[0051] The network-side positioning subsystem responds to the request initiated by the network to determine the user's location, sends a positioning instruction to the target user node, uses the user-side positioning subsystem to perform user-side positioning, and sends the obtained location information to the requesting node in the network.

[0052] The present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the indoor signal fingerprint collection method as described above when executing the computer program.

[0053] The present disclosure also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the indoor signal fingerprint collection method as described above is implemented.

[0054] The present disclosure also provides a chip, which executes the indoor signal fingerprint collection method as described above.

[0055] The positive progress of this disclosure is:

[0056] The indoor signal fingerprint collection method provided by the present invention is applied to wireless networks of flooding networking. In a home environment, Bluetooth mesh nodes and networks are already available, so there is no need to install additional Bluetooth tags. For example, the light bulbs and switches in each room of the home environment are equipped with mesh chips and are plugged in. Only the pre-selected nodes need to be modified, and the pre-selected nodes can send positioning data frames with themselves as the target as needed. The signal strength is collected by the existing mesh nodes and sent on demand, which reduces signal pollution, improves collection efficiency, improves the accuracy of indoor positioning, reduces the interference of signal fluctuations, and greatly improves the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0058] Figure 1 This is the first flow chart of the indoor signal fingerprint collection method in Example 1 of the present disclosure.

[0059] Figure 2 This is the second flow chart of the indoor signal fingerprint collection method in Example 1 of the present disclosure.

[0060] Figure 3 This is a two-dimensional plane diagram of a three-dimensional space in Example 1 of the present disclosure.

[0061] Figure 4 This is a flowchart of the indoor positioning method in Example 2 of the present disclosure.

[0062] Figure 5 This is a structural diagram of the indoor signal fingerprint collection system in Example 3 of the present disclosure.

[0063] Figure 6 This is a structural diagram of the indoor positioning system in Example 4 of the present disclosure.

[0064] Figure 7 This is a schematic diagram of the structure of the electronic device in Example 5 of the present disclosure. DETAILED DESCRIPTION

[0065] The present disclosure is further illustrated below by way of examples, but the present disclosure is not limited to the scope of the examples.

[0066] It should be noted that the execution entity of the indoor signal fingerprint collection method and indoor positioning method provided in the present disclosure can be a separate chip, chip module or UE, or a chip or chip module integrated in the UE.

[0067] The indoor signal fingerprint collection system and indoor positioning system described in the embodiments can be a separate chip, chip module, or UE, or a chip or chip module integrated into a UE. The various modules / units included in the indoor signal fingerprint collection system can be software modules / units, hardware modules / units, or partially software modules / units and partially hardware modules / units.

[0068] For example, for each device or product applied to or integrated into a chip, each module / unit contained therein may be implemented in the form of hardware such as circuits, or at least some of the modules / units may be implemented in the form of software programs, which run on a processor integrated inside the chip, and the remaining modules / units may be implemented in the form of hardware such as circuits; for each device or product applied to or integrated into a chip module, each module / unit contained therein may be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (such as a chip, circuit module, etc.) or different components of the chip module, or at least some of the modules / units may be implemented in the form of software programs. / Unit can be implemented in the form of a software program, which runs on a processor integrated inside the chip module, and the remaining modules / units can be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated in the UE, the various modules / units contained therein can be implemented in the form of hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or different components in the terminal, or, at least some modules / units can be implemented in the form of a software program, which runs on a processor integrated inside the UE, and the remaining modules / units can be implemented in the form of hardware such as circuits.

[0069] Example 1

[0070] Please refer to Figure 1 , which is the first flow chart of the indoor signal fingerprint collection method in this embodiment. Specifically, Figure 1 As shown, the indoor signal fingerprint collection method is applied to a wireless network of flooding networking, and the indoor signal fingerprint collection method includes:

[0071] S101, controlling a preselected node of a wireless network to move to a preset position and then sending a positioning data frame with itself as the sending target;

[0072] The preselected node receives and parses the positioning data frame sent by the relay node of the wireless network;

[0073] S102: Store the preset position and the signal strength of the positioning data frame sent by the relay node.

[0074] Specifically, wireless networks with flooding networking use a "network flooding" approach to publish and relay messages. This means that messages are not routed through a specific process or transmitted along a specific path formed by a series of specific devices. Instead, all devices within the transmission range receive the message, and the relay device forwards the message to all other devices within its transmission range. The advantage of flooding networking is that it eliminates the need for dedicated devices to act as centralized routers. A failure of a centralized router could cause the entire network to cease operation, thus impacting the reliability of network communications. Flooding networking also means that messages can generally reach their destination via multiple paths. The failure of one or more nodes along a path does not affect message delivery on other paths, creating a highly reliable network. Wireless networks include BLE mesh networks. The preselected nodes include mobile nodes of the wireless network; the preselected nodes are configured to send wireless data frames to themselves in response to control instructions; the preselected nodes are configured to read the RSSI of the received data frames through the network protocol stack, and obtain the RSSI of more distant node data frames by retaining the checksum error frame; the preselected nodes include at least one of a sweeping robot, a smart phone, a wearable device or other IoT nodes; specifically, the mobile node can be a sweeping robot with Bluetooth function in the BLE Mesh network, which can move freely in a two-dimensional plane, and can use its own gyroscope, accelerometer, magnetometer (compass) and other sensors to calculate its own displacement and draw its own movement trajectory in the two-dimensional plane; the relay node includes an actively powered fixed node of the wireless network; specifically, indoor BLE or BLEMESH devices are connected to the common BLE Mesh network and become nodes in the network, including nodes at fixed locations, such as lights, smart switches, etc. It is best to have 2 to 3 fixed-location Mesh nodes in each room. Here, the fixed-location nodes refer to actively powered nodes, which can be configured as proxy (agent / relay) nodes.

[0075] Please refer to Figure 2 , which is the second flow chart of the indoor signal fingerprint collection method in this embodiment. Specifically, Figure 2 As shown, in this embodiment, the indoor signal fingerprint collection method further includes:

[0076] S201: Control the preselected node to receive positioning data frames sent by all relay nodes in the wireless network and report the positioning data frames to the link layer for analysis. Specifically, the preselected node is a modified node. Typically, nodes in a BLE Mesh network do not receive weak signals with low signal strength. To ensure that the preselected node receives signals from all Mesh nodes as much as possible, the preselected node is designed to report all signals received by the antenna to the link layer, including weak signals with an RSSI of -95dBm (a unit of wireless signal strength) or below.

[0077] In an optional implementation, the indoor signal fingerprint collection method further includes:

[0078] S202: Determine whether the positioning data frame with the same node identifier has sampled the signal strength; the node identifier is used to identify the relay node that sends the positioning data frame;

[0079] S203: If not, parse the RSSI field of the positioning data frame and save it to a local or network database.

[0080] Specifically, existing BLE Mesh network nodes only receive and store the first received data frame when receiving the same data frame, discarding subsequent duplicate frames. The preselected nodes are modified so that, rather than discarding data frames based on duplicates, they instead determine whether the frames sent by a particular node have been sampled for signal strength. For example, assuming the first received positioning data frame is forwarded from node Node1, since it is not a duplicate and will not be discarded, the link layer of preselected node X resolves the destination address to itself and resolves the RSSI of the positioning data frame, saving it locally as rssi1. Upon receiving the data frame a second time or thereafter, the node identifier of the forwarding confirmation node (e.g., Node2) is first resolved. To ensure these frames are not discarded, the link layer data frame verification function of preselected node X is designed to determine whether the RSSI of a signal with the same node identifier has been sampled. If so, it is discarded. If not, but a duplicate frame has been received previously, it is not discarded and continues to resolve the RSSI field and saves it locally as rssi2. The RSSI values corresponding to some nodes may be 0, because the pre-selected node X may not receive the RSSI of some nodes in the end.

[0081] In another optional implementation, before step S102, the indoor signal fingerprint collection method further includes:

[0082] S204: Collect multiple sets of signal strength vector arrays of positioning data frames at the same preset location and calculate the mean and / or variance of the multiple sets of vector arrays. By collecting multiple sets of signal strengths and calculating the mean or variance, interference from signal fluctuations can be reduced.

[0083] The present embodiment is further described below by taking examples. Figure 3 is a two-dimensional plane diagram of a three-dimensional space, such as Figure 3As shown in the figure, there are nine fixed nodes with proxy relay functionality, labeled Node1, Node2, Node3, Node4, Node5, Node6, Node7, Node8, and Node9. The pre-selected node is labeled X. These ten nodes are connected via BLE in a mesh network. The process of modifying X to establish a fingerprint database is as follows:

[0084] 1. X moves to position 1 (manual calibration, existing methods can be used). Node X is designed to send a data frame to itself. All proxy-supported nodes within the radio range of X in the mesh network will relay this data frame in turn. Due to the flooding characteristics of the BLE Mesh network, node X will continue to receive relayed data frames. At this time:

[0085] a) To ensure that X receives signals from all Mesh nodes as much as possible, X is designed to report all signals received by the antenna to the link layer, including weak signals with an RSSI below -95dBm;

[0086] b) Assuming that the first received data frame is forwarded from Node1 (this frame is not a duplicate frame and is not discarded), X's link layer resolves the destination address to itself, then parses the RSSI and saves it locally, recording it as rssi1;

[0087] c) When receiving the data frame for the second time and thereafter, the node ID of the data frame that confirms the forwarding is first parsed. For example, if it is Node2, to ensure that these frame data are not discarded, the link layer data frame check function of X is designed to determine whether the signal with the same node ID has been sampled by RSSI. If so, it is discarded. If not, but a duplicate frame has been received before, it is not discarded and the RSSI field is continued to be parsed and saved locally, recorded as rssi2. Some RSSI values may be 0 because X may not receive the RSSI of some nodes.

[0088] d) Repeat the above steps until all nine nodes forward the data frame to X, parse the rssi values, and save the records as [position 1; rssi1, rssi2, rssi3, rssi4, rssi5, rssi6, rssi7, rssi8, rssi9];

[0089] 2. X moves to position 2 and repeats the above steps. The fingerprint information collected at position 2 is [position 2; rssi1, rssi2, rssi3, rssi4, rssi5, rssi6, rssi7, rssi8, rssi9];

[0090] 3. X moves to position 3, 4, 5, ..., and repeats the above steps to collect fingerprints, ultimately obtaining signal fingerprint information at different positions on a two-dimensional plane;

[0091] 4. Multiple signal fingerprint vector arrays can be collected at the same location to calculate the average or variance to reduce the interference of signal fluctuations.

[0092] The indoor signal fingerprint collection method provided by the present invention is applied to wireless networks of flooding networking. In a home environment, Bluetooth mesh nodes and networks are already available, so there is no need to install additional Bluetooth tags. For example, mesh chips are installed on the light bulbs and switches in each room of the home environment and are plugged in. It is only necessary to modify the pre-selected nodes so that the pre-selected nodes receive all signals, including strong signals and weak signals, and save or discard the data frames based on whether the positioning data frames with the same node identifier have sampled the signal strength. The pre-selected nodes can send positioning data frames with themselves as the target as needed, use existing mesh nodes to collect signal strength, send it on demand, reduce signal pollution, and improve collection efficiency. By collecting multiple groups of signal strengths of all nodes to build a fingerprint database, the accuracy of the database is improved, the interference of signal fluctuations is reduced, and the accuracy of indoor positioning is improved, which greatly improves the user experience.

[0093] Example 2

[0094] Please refer to Figure 4 , which is a flow chart of the indoor positioning method in this embodiment. Specifically, Figure 4 As shown, the indoor positioning method includes:

[0095] S301, obtaining the indoor fingerprint of the node to be located;

[0096] S302: Calculate the location information of the node to be located using a positioning algorithm based on the indoor fingerprint of the node to be located and a fingerprint library; the fingerprint library is constructed using the indoor signal fingerprint collection method of Example 1.

[0097] The indoor positioning method of this embodiment can be initiated by the user side or the network side. The positioning implementation scheme initiated by the user side is as follows:

[0098] For the node to be located, it moves to the unknown position X and sends a data frame to itself, recording the signal strength data as [position X; rssi1', rssi2', rssi3', rssi4', rssi5', rssi6', rssi7', rssi8', rssi9']. According to the signal strength vector array, the positioning algorithm is used to calculate the position X.

[0099] The implementation scheme for network-side positioning is as follows:

[0100] The node P that needs to be located in the Mesh network collects signal fingerprints and sends them to the node with the fingerprint library. The fingerprint library and positioning algorithm on the node calculate the position P.

[0101] The indoor positioning method provided by the present invention is applied to a wireless network of flooding networking. The indoor signal fingerprint collection method in Example 1 is used to build a fingerprint library, which improves the controllability of the fingerprint library establishment process, reduces signal pollution, and improves collection efficiency. By collecting multiple groups of signal strengths of all nodes to build a fingerprint database, the accuracy of the database is improved, the interference of signal fluctuations is reduced, and the accuracy of indoor positioning is improved, thereby greatly improving the user experience. The signal fingerprint database used for positioning can also be located on a pre-selected node, without the need for a background node, and the entire system does not need to be connected to the Internet, and everything is completed within the self-organizing network.

[0102] Example 3

[0103] Please refer to Figure 5 , which is a structural diagram of the indoor signal fingerprint collection system in this embodiment. Specifically, Figure 5 As shown, the indoor signal fingerprint collection system is applied to a wireless network of flooding networking, and the indoor signal fingerprint collection system includes:

[0104] The pre-selected node control module 1 is used to control the pre-selected node of the wireless network to move to a preset position and then send a positioning data frame with itself as the sending target;

[0105] The preselected node receives and parses the positioning data frame sent by the relay node of the wireless network;

[0106] The signal strength storage module 2 is used to store the preset position and the signal strength of the positioning data frame sent by the relay node.

[0107] Specifically, the preselected nodes include movable nodes of the wireless network; the preselected nodes are configured to send wireless data frames to themselves in response to control instructions; the preselected nodes are configured to read the RSSI of the received data frames through the network protocol stack, and obtain the RSSI of more distant node data frames by retaining the checksum error frames; the preselected nodes include at least one of a sweeping robot, a smart phone, a wearable device or other Internet of Things nodes; the relay nodes include actively powered fixed nodes of the wireless network; the wireless network includes a BLE Mesh (Bluetooth wireless mesh) network.

[0108] In an optional embodiment, the indoor signal fingerprint collection system further includes:

[0109] The signal analysis control module is used to control the pre-selected node to receive the positioning data frames sent by all relay nodes in the wireless network and report the positioning data frames to the link layer for analysis.

[0110] In this embodiment, the signal analysis control module is specifically used to determine whether the positioning data frame with the same node identifier has sampled the signal strength; the node identifier is used to identify the relay node that sends the positioning data frame;

[0111] The signal analysis control module is specifically used to analyze the RSSI field of the positioning data frame when determining that the positioning data frame with the same node identifier has not sampled the signal strength and save it to a local or network database.

[0112] In another optional embodiment, the indoor signal fingerprint collection system further includes:

[0113] The signal acquisition control module is used to collect vector arrays of signal strengths of multiple groups of positioning data frames at the same preset position and calculate the mean and / or variance of the multiple groups of vector arrays.

[0114] As for the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can be referred to the partial description of the method embodiment. The system embodiment described above is only illustrative, wherein the modules described as separate components may or may not be physically separated, and the modules displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. Ordinary technicians in this field can understand and implement it without expending creative work.

[0115] Example 4

[0116] Please refer to Figure 6 , which is a schematic diagram of the structure of the indoor positioning system in this embodiment. Specifically, Figure 6 As shown, the indoor positioning system includes:

[0117] Fingerprint acquisition module 3, used to obtain the indoor fingerprint of the node to be located;

[0118] The location calculation module 4 is used to calculate the location information of the node to be located using a positioning algorithm according to the indoor fingerprint of the node to be located and a fingerprint library; the fingerprint library is constructed using the indoor signal fingerprint acquisition system of Example 3.

[0119] This embodiment further provides an indoor positioning system, which includes a user-side positioning subsystem and a network-side positioning subsystem:

[0120] The user-side positioning subsystem responds to the user's request to determine its own location by using the indoor positioning method of Example 2 to obtain and send the user's location information;

[0121] The network-side positioning subsystem responds to the request for determining the user's location initiated by the network, sends a positioning instruction to the target user node, uses the above-mentioned user-side positioning subsystem to perform user-side positioning, and sends the obtained location information to the requesting node in the network. For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can be referred to the partial description of the method embodiment. The system embodiment described above is merely schematic, wherein the modules described as separate components may or may not be physically separated, and the modules displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0122] Example 5

[0123] Figure 7 This is a schematic diagram of the structure of an electronic device provided in Example 5 of the present disclosure. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the indoor signal fingerprint collection method of Example 1 or the indoor positioning method of Example 2 is implemented. Figure 7 The electronic device 30 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0124] like Figure 7 As shown, the electronic device 30 may be a general-purpose computing device, such as a server device. Components of the electronic device 30 may include, but are not limited to, the at least one processor 31, the at least one memory 32, and a bus 33 connecting different system components (including the memory 32 and the processor 31).

[0125] The bus 33 includes a data bus, an address bus, and a control bus.

[0126] The memory 32 may include a volatile memory, such as a random access memory (RAM) 321 and / or a cache memory 322 , and may further include a read-only memory (ROM) 323 .

[0127] The memory 32 may also include a program / utility 325 having a set (at least one) of program modules 324, such program modules 324 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0128] The processor 31 executes various functional applications and data processing by running computer programs stored in the memory 32, such as the indoor signal fingerprint collection method of embodiment 1 or the indoor positioning method of embodiment 2 of the present disclosure.

[0129] The electronic device 30 can also communicate with one or more external devices 34 (e.g., a keyboard, pointing device, etc.). This communication can occur via an input / output (I / O) interface 35. Furthermore, the model-generating device 30 can also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 36. As shown, the network adapter 36 communicates with other modules of the model-generating device 30 via a bus 33. It should be understood that, although not shown, other hardware and / or software modules can be used in conjunction with the model-generating device 30, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, RAID (RAID) systems, tape drives, and data backup storage systems.

[0130] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0131] Example 6

[0132] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the indoor signal fingerprint collection method of embodiment 1 or the indoor positioning method of embodiment 2 is implemented.

[0133] The readable storage medium may include, but is not limited to, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0134] In a possible implementation, the present disclosure can also be implemented in the form of a program product, which includes program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the indoor signal fingerprint collection method of Example 1 or the indoor positioning method of Example 2.

[0135] The program code for executing the present disclosure may be written in any combination of one or more programming languages, and may be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on the remote device.

[0136] Example 7

[0137] This embodiment provides a chip, which executes the indoor signal fingerprint collection method in embodiment 1 or the indoor positioning method in embodiment 2.

[0138] The chip provided in this embodiment reduces signal pollution, improves collection efficiency, improves database accuracy, and thereby improves indoor positioning accuracy by executing the above-mentioned indoor signal fingerprint collection method or indoor positioning method.

[0139] While specific embodiments of the present disclosure have been described above, those skilled in the art will appreciate that these are merely illustrative and that the scope of protection of the present disclosure is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present disclosure, and such changes and modifications are intended to fall within the scope of protection of the present disclosure.

Claims

1. A method for collecting indoor signal fingerprints, characterized in that: The indoor signal fingerprint collection method is applied to a wireless network of flooding networking, and the indoor signal fingerprint collection method includes: Controlling a preselected node of the wireless network to move to a preset position and then sending a positioning data frame with itself as the sending target; The preselected node receives and parses the positioning data frame sent by the relay node of the wireless network; The preset position and the signal strength of the positioning data frame sent by the relay node are stored.

2. The indoor signal fingerprint collection method according to claim 1, characterized in that: The preselected node comprises a mobile node of the wireless network; and / or, The preselected node is configured to send a wireless data frame to itself in response to the control instruction; And / or, the preselected node is configured to read the RSSI of the received data frame through the network protocol stack, and obtain the RSSI of more distant node data frames by retaining the checksum error frame; and / or, The preselected node includes at least one of a sweeping robot, a smart phone, a wearable device or other IoT nodes; and / or, The relay node comprises an actively powered fixed node of the wireless network; and / or, The wireless network includes a BLE Mesh network.

3. The indoor signal fingerprint collection method according to any one of claims 1 or 2, characterized in that: The indoor signal fingerprint collection method further includes: The preselected node is controlled to receive the positioning data frames sent by all relay nodes of the wireless network and report the positioning data frames to the link layer for parsing.

4. The indoor signal fingerprint collection method according to claim 3, characterized in that: The step of reporting the positioning data frame to the link layer for parsing includes: Determining whether a positioning data frame with the same node identifier has sampled signal strength; the node identifier is used to identify the relay node that sends the positioning data frame; If not, the RSSI field of the positioning data frame is parsed and saved to a local or network database.

5. The indoor signal fingerprint collection method according to any one of claims 1 to 3, characterized in that: Before the step of storing the preset position and the signal strength of the positioning data frame sent by the relay node, the indoor signal fingerprint collection method further includes: A plurality of vector arrays of signal strengths of the positioning data frames are collected at the same preset position, and means and / or variances of the plurality of vector arrays are calculated.

6. An indoor positioning method, characterized in that: The indoor positioning method comprises: Obtain the indoor fingerprint of the node to be located; According to the indoor fingerprint of the node to be located and the fingerprint library, the location information of the node to be located is calculated using a positioning algorithm; The fingerprint database is constructed using the indoor signal fingerprint collection method according to any one of claims 1 to 5.

7. An indoor signal fingerprint acquisition system, characterized in that: The indoor signal fingerprint collection system is applied to a wireless network of flooding networking, and the indoor signal fingerprint collection system includes: A preselected node control module, configured to control a preselected node of the wireless network to move to a preset position and then send a positioning data frame with itself as the sending target; The preselected node receives and parses the positioning data frame sent by the relay node of the wireless network; The signal strength storage module is used to store the preset position and the signal strength of the positioning data frame sent by the relay node.

8. An indoor positioning system, characterized in that: The indoor positioning system comprises: Fingerprint acquisition module, used to obtain the indoor fingerprint of the node to be located; A location calculation module is used to calculate the location information of the node to be located using a positioning algorithm based on the indoor fingerprint of the node to be located and a fingerprint library; The fingerprint database is constructed using the indoor signal fingerprint acquisition system according to claim 7.

9. An indoor positioning system, characterized in that: The indoor positioning system includes a user-side positioning subsystem and a network-side positioning subsystem: The user-side positioning subsystem obtains and sends the user's location information using the indoor positioning method according to claim 6 in response to a request initiated by the user to determine its own location; The network-side positioning subsystem responds to the request initiated by the network to determine the user's location, sends a positioning instruction to the target user node, uses the user-side positioning subsystem to perform user-side positioning, and sends the obtained location information to the requesting node in the network.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the indoor signal fingerprint collection method according to any one of claims 1 to 5 is implemented.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the indoor signal fingerprint collection method according to any one of claims 1 to 5 is implemented.

12. A chip, characterized in that: The chip executes the indoor signal fingerprint collection method according to any one of claims 1 to 5.

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