A single-code-based position identification system, method, device and storage medium

Abstract

The present invention provides a position identification system, method, device and storage medium based on a single code channel, which relates to the technical field of position identification. The system includes: a transmitting module including at least four sending nodes, two adjacent sending nodes forming a sending node group, each sending node in the target sending node group and a sending node in the non-target sending node group forming a sending node pair; the single code channel module is used to fuse the initial signal data of each sending node in the sending node pair to obtain corresponding signal data; the receiving module is used to receive the signal data, identify and analyze each signal data to obtain analysis data; the calculation module is used to obtain the corresponding estimated distance according to the analysis data based on the path loss model; and then determine the position of the corresponding receiving module based on each analysis data. The present invention simplifies the complex multi-band configuration in the traditional positioning system. It makes the system design and implementation more efficient and reduces the hardware cost.

Description

Technical Field

[0001] The present invention relates to the technical field of position identification, and in particular to a position identification system, method, device and storage medium based on a single code channel. Background Art

[0002] Among current positioning technologies, positioning systems in indoor and complex environments face many challenges. Traditional positioning methods, such as GPS-based positioning, perform well in open environments, but have difficulty providing accurate location information in restricted environments such as indoors or in urban canyons. This is mainly due to the weakening of GPS signals and the influence of multipath effects, which significantly reduce positioning accuracy.

[0003] In addition, some related wireless positioning systems usually rely on complex configurations of multiple frequency bands and multiple antennas, which not only increases the cost and complexity of the system, but also makes system maintenance and management more difficult. In some application scenarios, especially in areas with high requirements for real-time and economic efficiency (such as smart homes, industrial automation, and logistics management), related technologies are difficult to meet user needs.

[0004] In terms of signal transmission, related technologies mostly use signal strength (RSSI) for distance estimation, but due to environmental changes (such as obstacles, personnel movement, etc.), fluctuations in signal strength can lead to inaccurate distance estimation. In addition, the lack of an effective signal quality assessment mechanism makes it difficult to implement signal priority processing and weighted calculation in a multi-node environment, further affecting the accuracy and reliability of positioning.

[0005] Therefore, there is an urgent need for a new type of positioning system that can provide high-precision location information in complex environments, while simplifying the system architecture, reducing costs, and improving the intelligence level of signal processing. Summary of the invention

[0006] The problem solved by the present invention is how to simplify the position identification system and improve the measurement accuracy.

[0007] In order to solve the above problems, the present invention provides a position identification system, method, device and storage medium based on a single code channel.

[0008] In a first aspect, the present invention provides a single code channel based position identification system, comprising: a transmitting module, a single code channel module, at least one receiving module and a calculating module;

[0009] The transmitting module includes at least four sending nodes, and two adjacent sending nodes constitute a sending node group, and the sending node groups all send signal data to each receiving module through a single code channel module, and any sending node in the target sending node group and any sending node in the non-target sending node group constitute a sending node pair; wherein the target sending node group is any of the sending node groups;

[0010] The transmitting module includes at least four sending nodes, each of which sends initial signal data to the receiving module through a single code channel module at a preset time interval, two adjacent sending nodes constitute a sending node group, and each sending node in the target sending node group and a sending node in the non-target sending node group respectively constitute a sending node pair; wherein the target sending node group is any of the sending node groups;

[0011] The single code channel module is used to fuse the initial signal data of each sending node in the sending node pair to obtain the signal data corresponding to the sending node pair;

[0012] The receiving module is used to receive the signal data of each sending node pair, and identify and analyze each signal data to obtain analysis data;

[0013] The calculation module is used to obtain a corresponding estimated distance according to each analysis data based on a path loss model; and determine the position of the corresponding receiving module according to each analysis data and the corresponding estimated distance.

[0014] Optionally, the initial signal data sent by each sending node is generated according to corresponding preset waveform parameters, and the preset waveform parameters corresponding to each sending node are different. The preset waveform parameters include one or more of amplitude, frequency and duration of the waveform signal.

[0015] Optionally, the signal data includes signal strength data of initial signal data of two sending nodes in the sending node pair, and the receiving module is specifically configured to:

[0016] Separating the signal data to obtain two temporary signal data and two signal strength data;

[0017] Matching the two temporary signal data with preset waveforms corresponding to different preset waveform parameters respectively, and determining the sending nodes respectively corresponding to the two temporary signal data;

[0018] The signal strengths of the two temporary signal data are compared, and the signal strength data corresponding to the sending node corresponding to the temporary signal data is determined according to the comparison result, wherein the analysis data includes the sending node corresponding to the temporary signal data and its signal strength data.

[0019] Optionally, the single-code channel-based location recognition system further includes an environmental sensing module;

[0020] The environment sensing module is used to collect environment change data, perform feature extraction on the environment change data to obtain environment feature data, and adjust parameters of the path loss model based on the environment feature data.

[0021] Optionally, the adjusting parameters of the path loss model based on the environmental characteristic data includes:

[0022] Determine, according to the environmental characteristic data, an ideal signal strength of the corresponding sending node received by the receiving module;

[0023] Determine corresponding error data according to the ideal signal strength of each sending node and the corresponding signal strength data;

[0024] Obtaining a preset threshold, and judging each of the error data based on the preset threshold;

[0025] When the error data does not meet the preset threshold, parameters of the path loss model are adjusted according to the current environmental characteristic data.

[0026] Optionally, the adjusting parameters of the path loss model based on the environmental characteristic data includes:

[0027] Classifying the environmental characteristic data to obtain first characteristic data and second characteristic data;

[0028] Determining whether the first characteristic data corresponds to a received signal strength less than a first preset value, or determining whether the second characteristic data corresponds to a received signal strength greater than a second preset value;

[0029] Adjusting parameters of the path loss model according to the first characteristic data and the second characteristic data;

[0030] The received signal strength is the ideal signal strength of the corresponding sending node received by the receiving module.

[0031] Optionally, the analysis data further includes position data of each sending node in the sending node pair; and determining the position of the corresponding receiving module according to each analysis data and the corresponding estimated distance further includes:

[0032] Determine corresponding signal-to-noise ratio data based on the signal strength data in the analysis data, and determine corresponding weight data of the sending node based on the signal strength data and the corresponding signal-to-noise ratio data;

[0033] The position of the receiving module is determined based on the weight data of each sending node, the corresponding position data and the estimated distance.

[0034] In a second aspect, the present invention further provides a single-code-based location recognition method, which is applied to the single-code-based location recognition system. The single-code-based location recognition method includes:

[0035] Control each sending node to send initial signal data according to a preset time interval;

[0036] The initial signal data of each sending node in the sending node pair are merged to obtain the signal data corresponding to the sending node pair; wherein the sending node pair is composed of each sending node in the target sending node group and one sending node in the non-target sending node group, and the target sending node group is composed of two adjacent sending nodes;

[0037] Identify and analyze each of the signal data to obtain analysis data;

[0038] Based on the path loss model, a corresponding estimated distance is obtained according to each of the analysis data; and the position of the corresponding receiving module is determined according to each of the analysis data and the corresponding estimated distance.

[0039] In a third aspect, the present invention further provides an electronic device, comprising a memory and a processor;

[0040] The memory is used to store computer programs;

[0041] The processor is used to implement the above-mentioned single code channel-based position identification method when executing the computer program.

[0042] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the single-code-channel-based position identification method as described above is implemented.

[0043] The beneficial effects of the single-code-based position identification system, method, device and storage medium of the present invention are:

[0044] The transmitting module includes at least four transmitting nodes, which are grouped into a transmitting node group in pairs of two adjacent nodes. Each transmitting node sends signal data at a preset time interval to ensure the orderliness and stability of signal transmission, and any transmitting node in the target transmitting node group can form a transmitting node pair with any transmitting node in the non-target transmitting node group, thereby realizing diversified signal transmission and interaction.

[0045] The single code channel module fuses the initial signal data from the sending node pair. Two adjacent sending nodes form a sending node group, and the target sending node group is an arbitrarily selected sending node group. By fusing signal data, the reliability and accuracy of the signal can be improved.

[0046] The receiving module is used to receive signal data from each sending node, identify and analyze these signals, and extract useful information.

[0047] The calculation module calculates the estimated distance between the receiving module and the sending node based on the path loss model, and determines the specific location of the receiving module based on the analysis data and the estimated distance.

[0048] Therefore, the present invention combines multiple sending nodes and signal data fusion, and uses the path loss model to perform accurate distance estimation. This method can reduce the positioning error caused by single node signal interference or attenuation, thereby improving the accuracy of positioning, and the design of multiple sending nodes enables the system to effectively locate through other nodes when some nodes fail or the signal quality decreases, thereby enhancing the robustness of the system. And because the sending nodes in the same node group are adjacent, the environmental conditions (such as temperature, humidity, electromagnetic interference, etc.) they face are relatively similar, and they may experience similar signal attenuation. Therefore, the sending nodes of different node groups are constructed into sending node pairs, and by fusing the signals of these nodes, the overall strength of the signal can be improved, and the problem of weak single signals can be reduced, thereby improving the stability and reliability of the signal. And because the nodes in each sending node group form a sending node pair with nodes outside the group, this design can reduce the synchronous influence of the same environmental factors on all signals, thereby improving the anti-interference ability of the system.

[0049] At the same time, the single code channel module fuses the initial transmission signals of the transmitting nodes in the transmitting node pair to obtain a fused signal. By fusing the initial transmission signals, it can also effectively reduce noise and interference, enhance signal strength, and significantly improve signal quality. For example, when the signal of a node is weak or faulty, it can still rely on the information provided by other nodes to achieve higher system robustness. And the fused signal is more resistant to environmental noise and interference, improving availability and accuracy in complex environments. Signal fusion algorithm flexibility: Different signal fusion algorithms can be used, such as weighted average, Kalman filtering, etc., to adapt to different application requirements and channel conditions. This dynamic control mechanism can effectively reduce signal conflicts and interference and optimize the quality of signal transmission.

[0050] The system uses a combination of a single code channel module and multiple transmitting nodes, which simplifies the complex multi-band and multi-antenna configurations in traditional positioning systems. This simplification makes system design and implementation more efficient, reduces hardware costs and system maintenance difficulties, and makes the system easier to deploy and manage, making it suitable for a wide range of applications in different scenarios.

[0051] Finally, the receiving module can receive and analyze the signal data from each sending node pair in real time and quickly generate analysis data. This real-time performance enables the system to respond quickly to environmental changes and adapt to positioning needs in dynamic scenarios. And by analyzing the signal data of each sending node, the system can identify and filter out high-quality signals, thereby improving the accuracy of positioning. Combined with the path loss model, the system can better cope with the impact of environmental changes (such as obstacles, interference, etc.). It ensures stable positioning in complex environments and improves the overall reliability of the system.

[0052] In summary, the location recognition system based on a single code channel has significantly improved the performance and applicability of traditional positioning technology through multiple beneficial effects such as efficient positioning accuracy, simplified system architecture, flexible node configuration, dynamic signal control, real-time data analysis and enhanced signal quality processing. This technical solution not only meets the current market demand for efficient, economical and reliable positioning systems, but also provides a solid foundation for future intelligent positioning applications. The design of the system makes it suitable for a variety of application scenarios, including indoor positioning, industrial automation, smart home, logistics management, etc. Its flexibility and efficiency enable the system to meet the positioning needs of different industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is one of the example diagrams of a position recognition system based on a single code channel according to an embodiment of the present invention;

[0054] Figure 2 This is a second example diagram of a position recognition system based on a single code channel according to an embodiment of the present invention;

[0055] Figure 3 A flowchart of a position identification method based on a single code channel according to an embodiment of the present invention;

[0056] Figure 4 An exemplary diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be interpreted as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0058] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0059] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to"; the term "based on" means "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0060] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0061] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes, and are not used to limit the scope of these messages or information.

[0062] In the related art, the controller analyzes and processes the response information returned by the receiving device according to the received response signal, thereby determining the location of the receiving device. The response information is transmitted on the code channel, and the controller identifies the response information to determine the location of the receiving device. Although this technical solution determines the location of the signal, it has the problem that the installation of the receiving device is complicated, and due to the limitation of the transmission distance, it is impossible to judge the location identification information.

[0063] Based on the above problems, this embodiment provides a location identification system, method, device and storage medium based on a single code channel.

[0064] like Figure 1 As shown, an embodiment of the present invention provides a position identification system based on a single code channel, including: a transmitting module, a single code channel module, at least one receiving module and a calculating module.

[0065] The transmitting module includes at least four sending nodes, each of which sends initial signal data to the receiving module through the single code channel module at a preset time interval, two adjacent sending nodes constitute a sending node group, and each sending node in the target sending node group respectively constitutes a sending node pair with one of the sending nodes in the non-target sending node group; wherein the target sending node group is any of the sending node groups.

[0066] Specifically, the transmitting module includes at least four transmitting nodes. These nodes are distributed in a specific area. Each transmitting node is responsible for sending signals regularly so that the receiving module can obtain positioning information. Each transmitting node communicates with the receiving module through a single code channel module. The function of the single code channel module is to manage the sending and receiving of signals to ensure the effective transmission of signals. Each transmitting node sends initial signal data to the receiving module through the single code channel module at a preset time interval (for example, every few milliseconds). This timed sending mechanism ensures the continuity and real-time nature of the signal.

[0067] Two adjacent sending nodes form a sending node group. In this example, for example, sending nodes A, B, C, and D can form the following sending node groups: Sending node group 1: A and B (adjacent); Sending node group 2: D and C (adjacent). The target sending node group refers to the sending node group selected at any time, and the system can dynamically select it as needed. For example, you can select group 1 (A and B) as the target sending node group.

[0068] In the target sending node group, each sending node will form a sending node pair with a sending node in the non-target sending node group. For example, if the target sending node group is (A and B), the sending node pairs may be: A and C; B and D. This composition method enables each sending node to form a pair with other nodes, increasing the redundancy and reliability of signal transmission, and providing a basis for the subsequent fusion process. When the sending nodes in the same node group are adjacent, the environmental conditions they face (such as temperature, humidity, electromagnetic interference, etc.) are relatively similar, and they may experience similar signal attenuation. Therefore, the sending nodes of different node groups are constructed into sending node pairs. By fusing the signals of these nodes, the overall signal strength can be improved and the problem of weak individual signals can be reduced.

[0069] Moreover, the dynamic selection of the target sending node group enables the system to adjust the signal sending strategy according to real-time environmental changes (such as personnel movement, obstacle changes, etc.), thereby improving the adaptability of the system.

[0070] The single code channel module is used to fuse the initial signal data of each sending node in the sending node pair to obtain the signal data corresponding to the sending node pair.

[0071] Specifically, as a key component of the location identification system, the single code channel module is responsible for processing the signal data from the sending node pair. Its main task is to perform signal fusion processing to improve the reliability and accuracy of the signal.

[0072] Each sending node in the sending node pair (for example, node A and node B) sends initial signal data at a preset time interval. The single code channel module receives signal data from these sending nodes. These signals may include different strength, phase, frequency and other information.

[0073] The single code channel module uses specific algorithms (such as weighted averaging, filtering, etc.) to fuse the signal data from the same transmitting node pair. This process can include: Denoising: removing the noise caused by multipath propagation to make the signal clearer. Weighted fusion: assigning different weights to different signals according to the quality of the signal (such as signal strength) to ensure that more reliable signal data is better considered. Compensation and correction: considering the path loss and other environmental factors in signal propagation, the signal is compensated.

[0074] After fusion processing, the single code channel module generates corresponding fusion signal data. These data reflect the comprehensive signal information of the sending node pair, and usually have higher signal-to-noise ratio and reliability.

[0075] Finally, the fused signal data is transmitted to the receiving module for subsequent analysis and positioning calculation. In order to avoid changes in the signal strength of the separated signal obtained after fusion and separation, the signal strength of the initial signal data of each initiating node pair in the sending node pair can be represented by an identifier during the signal fusion process, and combined together and added to the fused signal data.

[0076] By fusing signals from different sending nodes, the single code channel module can effectively reduce the uncertainty caused by signal interference and attenuation, thereby improving the overall reliability of the signal. The fused signal can more accurately reflect the information of the real location, thereby improving the accuracy of the location estimation, especially when the environment is complex or the signal is weak. The single code channel module can enhance the system's ability to resist noise and interference, such as signal interference caused by other electronic devices or environmental factors, by fusing data from multiple signals.

[0077] By fusing multiple signals, the fluctuations and instability caused by a single signal are reduced, ensuring the continuous and effective operation of the entire system in a changing environment. The fused signal data is often smaller in size, which can reduce bandwidth requirements during transmission, thereby improving the data transmission efficiency of the entire system.

[0078] The receiving module is used to receive the signal data of each sending node pair, and identify and analyze each signal data to obtain analysis data;

[0079] Specifically, the receiving module receives signal data from a plurality of sending node pairs (such as a sending node pair consisting of A and B, and a sending node pair consisting of C and D) according to a preset communication protocol.

[0080] This signal data is obtained by fusion based on the initial transmission signals of the included transmission nodes. For example: transmission nodes A and C, the fusion formula can be expressed as:

[0081] ;

[0082] in, represents the environmental noise, is the signal data, is the initial transmission signal of the sending node A, is the initial sending signal of sending node C.

[0083] By fusing the initial transmitted signals, noise and interference can be effectively reduced, signal strength can be enhanced, and signal quality can be significantly improved; for example, when the signal of a node is weak or faulty, it can still rely on the information provided by other nodes to achieve higher system robustness. The fused signal is more resistant to environmental noise and interference, improving availability and accuracy in complex environments.

[0084] The initial signal data may include information such as node ID, transmission time, signal strength, etc. The receiving module temporarily stores the received signal data in the cache for subsequent processing. The timestamp of each received data packet is also recorded to ensure that the time series information of the signal can be analyzed.

[0085] The receiving module uses a signal processing algorithm to identify each received signal data. This may involve: first, separating and identifying each signal data. For example: Signal strength analysis: Analyze the strength and quality of the signal and remove low-quality signal data. Signal denoising: Use filtering technology to reduce the impact of background noise and improve signal clarity. Signal decoding: Extract valid information from the signal, such as generating location coordinates, unique identification of the sending node, etc. The receiving module performs analysis based on the identified signal data and generates analysis data.

[0086] Therefore, the receiving module can effectively process and analyze the signal data of multiple sending nodes, improving the overall recognition efficiency of the system for signals. When the receiving module can analyze and filter out low-quality or interfering signals, the anti-interference ability of the system is enhanced. This ensures that the positioning system can provide stable services even in an environment with multiple signal sources and interference factors. And the receiving module can integrate the data of multiple sending nodes, so that the system can make comprehensive judgments based on information from multiple sources. By comparing and analyzing multi-node information, more comprehensive services can be provided to users.

[0087] At the same time, real-time reception and analysis of signals enables the system to quickly respond to environmental changes, such as user movement and changes in signal strength, ensuring instant and accurate positioning and navigation services.

[0088] The calculation module is used to obtain a corresponding estimated distance according to each analysis data based on a path loss model; and determine the position of the corresponding receiving module according to each analysis data and the corresponding estimated distance.

[0089] Specifically, the calculation module receives signal analysis data from the receiving module, and the data generally includes signal strength information of each sending node and location information of the node (such as the coordinates of the node).

[0090] The path loss model is used to describe the law of signal strength changing with distance during signal propagation. Commonly used path loss models include free space model, log-normal fading model and real-scene environment model.

[0091] Select the appropriate path loss model based on the application scenario. For example, a log-normal fading model may be used in indoor environments, while a free space model may be used in open areas. The calculation module uses the received signal strength (such as RSSI) data and the formula of the path loss model to calculate the estimated distance between the receiving module and each transmitting node. The estimated distance information and the known location of the transmitting node are then used to determine the specific location of the receiving module using triangulation or least squares. This process requires distance information from at least three (ideally up to three) transmitting nodes to ensure the accuracy and reliability of its positioning. The calculation module transmits the final estimated location result back to other parts of the system (such as the user interface or decision support system) for appropriate operations or feedback.

[0092] For example, to record the signal strength information of each signal, the path loss model can be expressed as:

[0093] ;

[0094] in, is the loss value at unit distance (usually 1 meter); n is the path loss index, which reflects the impact of the environment and usually depends on the propagation environment (such as open areas, cities, indoors, etc.); d is the distance between the transmitting node and the receiving module (in meters); X is other influencing factors, such as multipath effects or environmental interference.

[0095] According to the received signal strength and path loss model, the following relationship can be set:

[0096] ;

[0097] in, is the received signal strength (dBm), is the transmit power of the transmitting node (dBm), is the path loss at a given distance d.

[0098] By rearranging the path loss equation, we can solve for the distance d:

[0099] .

[0100] By using the path loss model and signal strength data, the calculation module can efficiently estimate the distance of the receiving module, thereby improving the positioning accuracy of the entire positioning system. Under different environmental conditions, the appropriate path loss model can be selected according to actual needs, so that the system can flexibly adapt to a variety of scenarios (such as indoors, outdoors, and complex environments) and provide stable positioning services. In addition, by combining the distance information from multiple sending nodes and calculating the position through a comprehensive algorithm, the impact of random fluctuations in the signal of a single node can be effectively suppressed, thereby enhancing the system's anti-interference ability.

[0101] The computing module can quickly process the received data and update the location in real time, ensuring that the location information of the user or device is up to date, thereby improving the system response speed and adapting to the needs of rapidly changing scenarios. The accurate positioning capability enables the system to be widely used in various scenarios such as navigation, asset tracking, personnel positioning, environmental monitoring, etc., promoting the development of intelligent applications.

[0102] For example, this location identification system is deployed in a large shopping mall. The layout of the shopping mall is as follows: Transmitting node setting: Four transmitting nodes are set in the four areas of the shopping mall (entrance, cashier counter, merchandise area and rest area), marked as A, B, C and D. Every two adjacent transmitting nodes form a group of transmitting node groups: Group 1: A and B; Group 2: B and C; Group 3: C and D; Group 4: A and D (forming a ring layout); The transmitting module controls each transmitting node (A, B, C and D) to send signal data at a preset time interval (for example, every 100 milliseconds). The data sent by each transmitting node includes information such as node ID, timestamp and signal strength. The receiving module receives the signal data from each transmitting node in real time, and identifies and analyzes these signals. At the same time, the receiving module records the signal strength of each node, performs denoising, and retains the valid signal. For example, the signal strength data received by the receiving module are: Node A: 40dBm; Node B: 45dBm; Node C: 50dBm; Node D: 55dBm; the calculation module uses the path loss model to estimate the distance between the receiving module and each sending node based on the received signal strength data.

[0103] The selected path loss model calculates the estimated distance of each sending node; the recalculation module uses the estimated distance and the known location of the sending node to determine the specific location of the receiving module through triangulation or least squares method.

[0104] In this embodiment, by combining multiple sending nodes and signal data fusion, and using the path loss model to perform accurate distance estimation. This method can reduce the positioning error caused by single node signal interference or attenuation, thereby improving the accuracy of positioning, and the design of multiple sending nodes enables the system to effectively locate through other nodes when some nodes fail or the signal quality decreases, thereby enhancing the robustness of the system. And when the sending nodes in the same node group are adjacent, the environmental conditions (such as temperature, humidity, electromagnetic interference, etc.) they face are relatively similar, and they may experience similar signal attenuation. Therefore, the sending nodes of different node groups are constructed into sending node pairs, and by fusing the signals of these nodes, the overall strength of the signal can be improved, and the problem of weak single signals can be reduced, thereby improving the stability and reliability of the signal. And because the nodes in each sending node pair form pairs with nodes outside the group, this design can reduce the synchronous influence of the same environmental factors on all signals, thereby improving the anti-interference ability of the system.

[0105] At the same time, the single code channel module fuses the initial transmission signals of the transmitting nodes in the transmitting node pair to obtain a fused signal. By fusing the initial transmission signals, it can also effectively reduce noise and interference, enhance signal strength, and significantly improve signal quality. For example, when the signal of a node is weak or faulty, it can still rely on the information provided by other nodes to achieve higher system robustness. And the fused signal is more resistant to environmental noise and interference, improving availability and accuracy in complex environments. Signal fusion algorithm flexibility: Different signal fusion algorithms can be used, such as weighted average, Kalman filtering, etc., to adapt to different application requirements and channel conditions. This dynamic control mechanism can effectively reduce signal conflicts and interference and optimize the quality of signal transmission.

[0106] The system uses a combination of a single code channel module and multiple transmitting nodes, which simplifies the complex multi-band and multi-antenna configurations in traditional positioning systems. This simplification makes system design and implementation more efficient, reduces hardware costs and system maintenance difficulties, and makes the system easier to deploy and manage, making it suitable for a wide range of applications in different scenarios.

[0107] Finally, the receiving module can receive and analyze the signal data from each sending node pair in real time and quickly generate analysis data. This real-time performance enables the system to respond quickly to environmental changes and adapt to positioning needs in dynamic scenarios. And by analyzing the signal data of each sending node, the system can identify and filter out high-quality signals, thereby improving the accuracy of positioning. Combined with the path loss model, the system can better cope with the impact of environmental changes (such as obstacles, interference, etc.). It ensures stable positioning in complex environments and improves the overall reliability of the system.

[0108] In summary, the location recognition system based on a single code channel has significantly improved the performance and applicability of traditional positioning technology through multiple beneficial effects such as efficient positioning accuracy, simplified system architecture, flexible node configuration, dynamic signal control, real-time data analysis and enhanced signal quality processing. This technical solution not only meets the current market demand for efficient, economical and reliable positioning systems, but also provides a solid foundation for future intelligent positioning applications. The design of the system makes it suitable for a variety of application scenarios, including indoor positioning, industrial automation, smart home, logistics management, etc. Its flexibility and efficiency enable the system to meet the positioning needs of different industries.

[0109] Optionally, the initial signal data sent by each sending node is generated according to corresponding preset waveform parameters, and the preset waveform parameters corresponding to each sending node are different. The preset waveform parameters include one or more of amplitude, frequency and duration of the waveform signal.

[0110] Optionally, the signal data includes signal strength data of initial signal data of two sending nodes in the sending node pair, and the receiving module is specifically configured to:

[0111] Separating the signal data to obtain two temporary signal data and two signal strength data;

[0112] Matching the two temporary signal data with preset waveforms corresponding to different preset waveform parameters respectively, and determining the sending nodes respectively corresponding to the two temporary signal data;

[0113] The signal strengths of the two temporary signal data are compared, and the signal strength data corresponding to the sending node corresponding to the temporary signal data is determined according to the comparison result, wherein the analysis data includes the sending node corresponding to the temporary signal data and its signal strength data.

[0114] Specifically, each sending node generates initial signal data according to the corresponding preset waveform parameters. These parameters include one or more of the amplitude, frequency, and duration of the waveform signal, and the parameters of each sending node may be different. For example: the preset waveform parameters of sending node A are: amplitude of 10V, frequency of 2kHz, and duration of 100ms. The preset waveform parameters of sending node B are: amplitude of 8V, frequency of 1kHz, and duration of 150ms.

[0115] The receiving module receives signal data from a pair of sending nodes (for example, sending nodes A and B), and separates the received signal data to obtain two temporary signal data and two corresponding signal strength data. The separation process takes into account that signals sent by different nodes may be mixed due to signal overlap.

[0116] The two temporary signal data obtained are matched with the preset waveforms corresponding to different preset waveform parameters. During the matching process, the receiving module compares the shape, amplitude and frequency of the temporary signal data with the preset waveform. Determine the sending node corresponding to each temporary signal data. For example, if the temporary signal data matches the preset waveform parameters corresponding to node A, it can be considered that the signal belongs to sending node A.

[0117] The signal strengths of the two temporary signal data are compared. The receiving module will analyze the two signal strength data, find the difference, and determine the final value of the sending node signal strength data corresponding to the temporary signal data based on the comparison result. For example, if the signal strength of one temporary signal data is greater than the signal strength of another temporary signal data, the larger signal strength data of the two signal strength data is used. Thus, the analysis data includes the corresponding signal strength data of each sending node and its corresponding temporary signal data.

[0118] The final output of the analysis data includes the identification of each sending node (temporary signal data) and its corresponding signal strength data for subsequent calculation and positioning.

[0119] By using different preset waveform parameters, signals from different sending nodes can be effectively distinguished from multiple signals, which improves the independence of signal data and the accuracy of analysis, thereby improving the positioning accuracy of the system.

[0120] The signal matching process effectively reduces the interference caused by aliased signals and enhances the system's adaptability to complex signal environments. In signal superposition or interference environments, the system can still reliably identify the signals of each sending node. The preset waveform parameters of different sending nodes allow the system to be flexibly configured and can be adjusted according to specific application scenarios and user needs to optimize signal transmission and recognition performance.

[0121] Since the fused signal data is obtained by fusing the initial signal data of the two sending nodes in the sending node pair, compared with the two sending nodes in the same sending node group, the distance between the two sending nodes in the sending node pair is farther, so the environmental conditions they face will be more different, which will lead to a greater difference in the initial signal data of the two sending nodes in the sending node pair, which is more conducive to separation, resulting in higher separation accuracy, and thus higher positioning accuracy of the subsequent receiving module.

[0122] In the case of external interference or signal attenuation, the position can still be accurately located by comparing and analyzing the signal strength, making the entire system more stable. The combination of signal separation and matching reduces the need for complexity in signal processing algorithms, while still being able to effectively process multiple signals, improving the efficiency and speed of data processing. By timely analyzing and comparing signal strength and temporary signal data, the receiving module can quickly provide location information and adapt to application scenarios that require real-time feedback (such as autonomous driving, drone navigation, etc.).

[0123] This signal data analysis process can effectively distinguish and identify signals from different sources by combining the functions of the receiving module with the characteristics of the sending node, significantly improving the performance and reliability of the system. It is flexible and adaptable, providing strong technical support for various information systems that require precise positioning.

[0124] Optionally, the single-code channel-based location recognition system further includes an environmental sensing module;

[0125] The environment sensing module is used to collect environment change data, perform feature extraction on the environment change data to obtain environment feature data, and adjust parameters of the path loss model based on the environment feature data.

[0126] Specifically, Figure 2 As shown in the figure, in the single-code-based location recognition system, the introduction of the environmental sensing module can significantly improve the accuracy and adaptability of the system. The environmental sensing module is responsible for collecting real-time environmental change data, extracting features based on these data, and finally generating environmental feature data.

[0127] The environmental sensing module monitors changes in the surrounding environment and collects environmental data such as temperature, humidity, light intensity, object occlusion, electromagnetic interference, etc. It also analyzes the collected data and extracts features related to environmental changes. These features may include information such as environmental noise levels, the presence of dynamic obstacles, and weather changes.

[0128] The environmental sensing module obtains data from different sensors. For example, the temperature sensor measures the current temperature as 25°C, the humidity sensor measures the humidity as 60%, and the light sensor measures the light intensity as 300 lux. The raw environmental data is converted into feature data that can be used for further analysis. For example, based on the temperature and humidity, the "dry" or "humid" state is extracted, which may affect signal propagation.

[0129] Form a dataset containing environmental features that describes the current state of the environment. This can be a vector, for example: ;

[0130] Based on the extracted environmental feature data, adjust the parameters of the path loss model. Traditional path loss models (such as the Hata model or the Okumura model) may need to be optimized according to actual environmental conditions. Here, the loss coefficient of signal propagation can be adjusted by introducing environmental feature factors.

[0131] Assume that in high humidity conditions, signal loss increases by 10%, while in good lighting conditions (such as under direct sunlight), signal loss may decrease by 5%. During the analysis, if the current environmental state is "humid", the adjusted path loss model may be as follows: ;

[0132] By dynamically adjusting the path loss model, signal propagation can be more accurately predicted based on environmental changes, improving the accuracy of distance estimation; that is, the system can more accurately reflect the impact of the environment, thereby improving the accuracy of the receiving module's position estimation.

[0133] In complex environments (such as shopping malls, exhibitions, or areas blocked by mechanical objects), environmental features can reflect dynamic changes in a timely manner to ensure that the system can work properly under various conditions. In addition, real-time feedback of environmental change data enables the system to adjust accordingly and perform intelligent environmental adaptation. This intelligent improvement can not only optimize positioning, but also improve user experience. At the same time, the collected environmental feature data can be used to train machine learning models to further improve the system's adaptive and learning capabilities, allowing it to continuously optimize under different environmental conditions.

[0134] In summary, the value of the environmental sensing module to the single-channel location recognition system not only improves the accuracy and reliability, but also increases the intelligence and adaptability of the system, making it more practical in a changing environment.

[0135] Optionally, the adjusting parameters of the path loss model based on the environmental characteristic data includes:

[0136] Determine, according to the environmental characteristic data, an ideal signal strength of the corresponding sending node received by the receiving module;

[0137] Determine corresponding error data according to the ideal signal strength of each sending node and the corresponding signal strength data;

[0138] Obtaining a preset threshold, and judging each of the error data based on the preset threshold;

[0139] When the error data does not meet the preset threshold, parameters of the path loss model are adjusted according to the current environmental characteristic data.

[0140] Specifically, before adjusting the path loss model parameters, it is necessary to first collect relevant environmental characteristic data. These data may include geographical information of the environment, distribution of objects (such as buildings, trees, etc.), signal propagation characteristics (such as frequency, reflection, diffraction, etc.) and meteorological conditions (such as wind speed, rainfall, etc.).

[0141] Based on the collected environmental characteristic data, the receiving module calculates the ideal signal strength of the sending node under specific environmental conditions. The ideal signal strength refers to the theoretical strength that the signal can reach without any interference and loss. The received signal strength data is compared with the corresponding ideal signal strength to determine the error data. The error data can be calculated using the following formula:

[0142] Error data = ideal signal strength Received signal strength data;

[0143] The calculation of error data is to facilitate the evaluation of the loss suffered by the signal during the propagation process.

[0144] The system sets a preset threshold value, which is used to determine whether the error data is within an acceptable range. The preset threshold is usually formulated based on experience or previous actual measurement results, with the purpose of identifying the degree of signal quality degradation. Each calculated error data is judged to determine whether it meets the preset threshold. If the error data is within the preset threshold range, it means that the signal strength is relatively stable and in line with expectations; if there is an error data outside the range, it means that there is a significant path loss problem.

[0145] When it is found that the error data does not meet the preset threshold, the receiving module will adjust the parameters of the path loss model according to the current environmental characteristic data. The adjustment may include: adjusting the signal attenuation coefficient; adjusting the impact of environmental factors (such as the reflection characteristics of obstacles) on the signal. The purpose of this adjustment is to optimize the model so that it can more accurately reflect the actual signal propagation situation, thereby improving positioning accuracy.

[0146] By adjusting the path loss model in real time, the system can more accurately reflect the signal propagation characteristics in the actual environment, thereby significantly improving positioning accuracy. This process enables the system to dynamically adapt to different environmental conditions (such as urban and rural areas, daytime climate changes, etc.), improving performance in various complex environments. At the same time, the path loss model can be monitored and adjusted in real time, which is particularly important for applications that require instant feedback and high availability (such as autonomous driving and intelligent transportation).

[0147] When a signal strength deviation is identified, timely adjustment of model parameters can reduce the risk of misjudgment and mispositioning, and improve the efficiency and accuracy of signal processing. Environmental factors are taken into account when making decisions, and the power of signal transmission and the sensitivity of reception can be more reasonably allocated, thereby optimizing the use of system resources and reducing energy consumption. That is, through continuous environmental monitoring and dynamic adjustment of model parameters, the system can continuously self-learn and optimize, forming a feedback mechanism for sustainable improvement.

[0148] This process can significantly improve the positioning accuracy and adaptability of the system by adjusting the parameters of the path loss model based on environmental feature data. The dynamic identification and real-time update of environmental factors enable the system to better cope with different working environments, thereby providing more reliable positioning services for various application scenarios.

[0149] Optionally, the adjusting parameters of the path loss model based on the environmental characteristic data includes:

[0150] Classifying the environmental characteristic data to obtain first characteristic data and second characteristic data;

[0151] Determining whether the first characteristic data corresponds to a received signal strength less than a first preset value, or determining whether the second characteristic data corresponds to a received signal strength greater than a second preset value;

[0152] Adjusting parameters of the path loss model according to the first characteristic data and the second characteristic data;

[0153] The received signal strength is the ideal signal strength of the corresponding sending node received by the receiving module.

[0154] Specifically, the receiving module first acquires and collects environmental characteristic data. These data may include different types of signal interference sources, types and layouts of obstacles, characteristics of geographical locations, etc. These environmental characteristic data are classified to obtain first characteristic data and second characteristic data. For example, the first characteristic data may correspond to a specific type of obstacle (such as a tall building), which affects the signal strength. The second characteristic data may correspond to environmental conditions (such as weather changes, etc.), which may affect the signal propagation state.

[0155] The system determines whether the first characteristic data corresponds to a received signal strength less than a first preset value. If the signal strength is lower than the value, it may indicate that the signal is severely attenuated within the influence range of the characteristic data.

[0156] At the same time, it is determined whether the second characteristic data corresponds to a received signal strength greater than a second preset value. If the signal strength is higher than the value, it may indicate that the signal propagates well within the influence range of the characteristic data.

[0157] According to the above judgment results, the parameters of the path loss model are adjusted:

[0158] If the signal strength corresponding to the first characteristic data is lower than the first preset value, it may be necessary to increase the signal attenuation coefficient or adjust the environmental parameters in the model to better adapt to the current environmental characteristics.

[0159] If the signal strength corresponding to the second characteristic data is higher than the second preset value, it may be necessary to correspondingly reduce the signal attenuation parameter in the path loss model, or to strengthen the consideration of certain propagation conditions.

[0160] The received signal strength here refers to the ideal signal strength of the corresponding sending node received by the receiving module, which is the strength that the signal should be able to achieve in the absence of other interference.

[0161] By classifying and analyzing environmental feature data, the parameters of the path loss model can be adjusted more accurately to better reflect the actual signal propagation conditions, thereby improving the accuracy of the entire system. At the same time, the system can dynamically adjust the path loss model in real time according to environmental changes (such as different obstacles, weather conditions, etc.), thereby improving the system's adaptability. And by identifying and processing situations where the signal strength is lower than the first preset value or higher than the second preset value, the system can actively optimize the signal propagation route to ensure that the signal between each sending node is more reliable and stable.

[0162] In high-demand environments (such as indoor positioning, wireless communications, etc.), effective path loss model adjustment can provide better services and reduce user connection problems or positioning errors during use. Since the system can autonomously adjust the path loss model in real time, it reduces the need for manual intervention and equipment maintenance, thereby reducing overall maintenance costs. At the same time, through continuous parameter adjustment and environmental response, the system includes a self-learning module that can gradually improve its performance under different environmental conditions and form a more intelligent response mechanism.

[0163] This process improves the actual application performance of the path loss model by classifying environmental feature data and judging signal strength. This method can not only optimize signal propagation and positioning effects, but also adapt to different operating environments and improve the overall performance of the system.

[0164] This solution allows the system to respond to dynamic and static factors in real time. By classifying environmental feature data, it can adjust model parameters more flexibly to ensure signal stability and accuracy. And by dynamically adjusting the parameters of the path loss model, it can effectively reduce signal attenuation caused by environmental changes, improve signal reception quality and positioning accuracy, and reduce the probability of misjudgment and failure. At the same time, it ensures that the system uses limited signal resources more effectively under different conditions, reduces unnecessary energy consumption and interference, and improves the overall efficiency of equipment and systems. For end users, it provides more reliable signals and accurate positioning services, and improves the experience of using smart devices (such as navigation systems), especially in complex and dynamic environments such as shopping malls, blocks or airports.

[0165] In some embodiments, suppose we are testing wireless signal transmission in a city, and the goal is to optimize the signal strength of the receiving module to improve the overall communication performance. We will use a path loss model to evaluate the propagation of signals in an urban environment.

[0166] During the test, we collected the following environmental characteristics:

[0167] The first characteristic data: the height, location and distribution of multiple high-rise buildings in the city.

[0168] The second characteristic data: weather information (such as cloudy days, rainy days) and time information (morning rush hour, evening rush hour, etc.).

[0169] The receiving module receives signals from multiple sending nodes at a specific location. Assume that at a certain test time, the received signal strength is as follows:

[0170] The received signal strength of the transmitting node A is 70 dBm (the ideal signal strength should be 85 dBm).

[0171] The received signal strength of the transmitting Node B is 90 dBm (the ideal signal strength should be 80 dBm).

[0172] Set the following preset values: First preset value: 75 dBm; Second preset value: 85 dBm.

[0173] For the first characteristic data (the impact of high-rise buildings): the received signal strength of the sending node A is 70 dBm, which is less than the first preset value (75 dBm). Therefore, the signal strength corresponding to the first characteristic data is lower than the preset value.

[0174] For the second characteristic data (weather influence): the received signal strength of the transmitting node B is 90 dBm, which is greater than the second preset value (85 dBm). Therefore, the signal strength corresponding to the second characteristic data is higher than the preset value.

[0175] According to the above judgment results, the parameters of the path loss model are adjusted:

[0176] For sending node A: Since its signal strength is lower than the first preset value, it indicates that this location is greatly affected by the high-rise building. We can increase the signal attenuation coefficient and adjust the relevant parameters in the path loss model, such as the height or number of high-rise buildings considered in the model, to better reflect the attenuation of the signal in the high-rise building environment.

[0177] For the transmitting node B: Since its signal strength is higher than the second preset value, it means that the signal propagation condition in this area is good and may face the risk of over-prediction. Therefore, we can appropriately reduce the signal attenuation parameter so that the path loss model can more accurately represent the signal propagation condition in this environment.

[0178] After the parameters are adjusted, monitoring and testing will continue; the updated path loss model will be applied in subsequent data analysis to ensure that the signal strength assessment in this environment is more accurate and provide support for subsequent communications and positioning.

[0179] Optionally, when the first characteristic data corresponding to the sending node is less than the first preset value and the second characteristic data is greater than the second preset value, the path loss model is also adjusted based on the two characteristic data; the adjustment of the path loss model needs to comprehensively consider the influence of the two characteristic data, so as to accurately reflect the changes in the environment and optimize the positioning accuracy. By dynamically adjusting the path loss model, the impact of environmental changes on signal propagation can be effectively dealt with. Such flexibility not only improves the positioning accuracy of the system, but also enhances the adaptability of the system.

[0180] Optionally, the analysis data further includes position data of each sending node in the sending node pair; and determining the position of the corresponding receiving module according to each analysis data and the corresponding estimated distance further includes:

[0181] Determine corresponding signal-to-noise ratio data based on the signal strength data in the analysis data, and determine corresponding weight data of the sending node based on the signal strength data and the corresponding signal-to-noise ratio data;

[0182] The position of the corresponding receiving module is determined based on the weight data of each sending node, the corresponding position data and the estimated distance.

[0183] Specifically, the system obtains real-time signal strength information (analysis data) through the receiving module, which usually comes from multiple sending nodes (such as base stations, routers, etc.). Based on the preliminary measurement of the signal strength data, the estimated distance from the sending node to the receiving module is obtained.

[0184] By analyzing the received signal strength data, the corresponding signal-to-noise ratio is calculated. This data is a key indicator for evaluating signal quality. A high signal-to-noise ratio usually means better signal quality and higher transmission reliability.

[0185] Then, the system calculates weight data for each sending node based on the signal strength data and signal-to-noise ratio data. The weight calculation can be based on the following principle: the stronger the signal strength and the higher the signal-to-noise ratio, the greater the weight of the sending node in the location estimation. Conversely, the weight decreases. This weight mechanism can reflect the influence of each sending node on the location determination and improve the accuracy of the evaluation.

[0186] According to the weight data and estimated distance of each sending node, the actual position of the receiving module is calculated using weighted average and other methods:

[0187] Such as: Position calculation formula example:

[0188] ;

[0189] Among them, P RX is the position of the receiving module, P i is the location data of the i-th sending node, W i is the weight data of the i-th sending node.

[0190] By introducing signal-to-noise ratio and weights, the system can better evaluate the contribution of each sending node to the position estimation, especially in the presence of interference or signal attenuation. And using signal-to-noise ratio and signal strength data for weighting can effectively improve the accuracy of position estimation and reduce the position deviation caused by interference or signal attenuation. The weight-based data processing method reduces the impact of a remote node or a node with poor signal quality on the final result, and improves the stability and reliability of position estimation. At the same time, signal strength and signal-to-noise ratio are real-time available data. The scheme can provide the position of the receiving module instantly through fast calculation, which is very critical for real-time applications (such as navigation, real-time monitoring, etc.). By combining the use of signal-to-noise ratio and signal strength, wireless communication resources can be more effectively managed and optimized to ensure that the best positioning service can be obtained within a limited frequency band. At the same time, through a specific weight calculation method, the system can simplify the complexity of multi-node calculations, reduce the consumption of computing resources, and improve the overall system performance. And this scheme has strong adaptability to environmental changes. In an environment with weak signals or interference, the calculation of the signal-to-noise ratio can automatically adjust the weight of the sending node to make the position estimation more robust.

[0191] That is, the solution constructs a weight-based data processing method to determine the location of the receiving module by analyzing the signal strength data, signal-to-noise ratio data, and the corresponding estimated distance. This not only improves the accuracy and robustness of positioning, but also enhances the system's adaptability in complex environments, thus providing strong support for practical applications.

[0192] In one embodiment, a single-code channel-based location recognition system is described, which is mainly used for indoor positioning services and is suitable for large shopping malls, exhibition halls or other complex environments. The system consists of a transmitting module, a single-code channel module, a receiving module, a computing module and an environmental sensing module.

[0193] Transmitter module: includes four transmitting nodes (node ​​A, node B, node C, node D), and every two adjacent nodes form a transmitting node group: transmitting node group 1: node A and node B; transmitting node group 2: node C and node D. Each transmitting node sends signal data at a preset time interval (such as once per second), and the signal data uses specific preset waveform parameters (such as an amplitude of 1V, a frequency of 2.4GHz, and a waveform signal duration of 100ms). Transmitting node pair 1: node A and node C, transmitting node pair 1: node B and node D.

[0194] Single code channel module: the initial signal data of each sending node in the sending node pair are merged to obtain the signal data corresponding to the sending node pair.

[0195] Receiving module: It is configured at a specific location indoors and can receive signal data from the sending node pair. It separates the signal data to obtain two temporary signal data and two signal strength data. It matches and identifies the temporary signal data through the preset waveform, determines the corresponding sending node, and analyzes and obtains the signal strength data.

[0196] Calculation module: Based on the signal strength data and path loss model obtained by the receiving module, the estimated distance from the receiving module to each sending node is calculated. Combined with the signal strength and signal-to-noise ratio data, the final position of the receiving module is determined.

[0197] Environmental sensing module: collects environmental change data through sensors (such as temperature and humidity sensors) and extracts features to obtain environmental feature data. According to the environmental feature data, the path loss model parameters are adjusted to improve the accuracy of position estimation.

[0198] Implementation steps: Signal transmission: Each sending node sends signal data at a preset time interval. Node A sends signal data, followed by nodes C, B, and D in turn, and the initial signal data of each sending node in the sending node pair are fused through the single code channel module to obtain the signal data corresponding to the sending node pair.

[0199] The receiving module receives the signal data from each transmitting node pair and identifies the source of the signal according to the preset waveform parameters. By analyzing the signal data, the receiving module obtains the corresponding signal strength data (for example, the signal strength received from node A is 55dBm).

[0200] The calculation module calculates the signal-to-noise ratio based on the received signal strength data (for example, the signal-to-noise ratio is 15dB). The weight data of each sending node is calculated based on the signal strength and signal-to-noise ratio (for example, the weight of node A is 0.6, the weight of node B is 0.3, and the weight of node C is 0.1). Then, using the path loss model, the estimated distance between the receiving module and each sending node is calculated based on the signal strength data (for example, the estimated distance of node A is 10 meters, node B is 15 meters, and node C is 20 meters).

[0201] Finally, the position of the receiving module is calculated by the weighted average method based on the weight of each sending node and the estimated distance:

[0202] ;

[0203] At the same time, the environmental sensing module collects environmental change data (such as signal attenuation caused by temperature changes) and extracts characteristic data. According to the characteristic data, the parameters of the path loss model are adjusted (for example, when the signal strength is less than 60dBm, the attenuation coefficient in the model is adjusted).

[0204] This embodiment can effectively improve the position estimation accuracy of the receiving module through comprehensive calculation of signal strength, signal-to-noise ratio and weight. At the same time, the addition of the environmental sensing module enables the system to adjust the path loss model in real time, adapt to environmental changes, and ensure the reliability of positioning.

[0205] The use of single-channel modules simplifies the coordination of signal transmission, reduces signal interference, and improves the overall efficiency of the system.

[0206] This embodiment demonstrates a location recognition system based on a single code channel. Through signal transmission and reception of multiple sending nodes, analysis of signal strength and signal-to-noise ratio, and real-time monitoring and model adjustment of environmental data, it can achieve high-precision positioning services in complex environments and has broad application potential.

[0207] like Figure 3 As shown, a single-code-based location recognition method provided by an embodiment of the present invention is applied to the single-code-based location recognition system. The single-code-based location recognition method includes:

[0208] Step S100, controlling each sending node to send initial signal data according to a preset time interval;

[0209] Step S200, fusing the initial signal data of each sending node in the sending node pair to obtain signal data corresponding to the sending node pair; wherein the sending node pair is composed of each sending node in the target sending node group and one sending node in the non-target sending node group, and the target sending node group is composed of two adjacent sending nodes;

[0210] Step S300, identifying and analyzing each of the signal data to obtain analysis data;

[0211] Step S400, based on the path loss model, obtain a corresponding estimated distance according to each of the analysis data; and determine the position of the corresponding receiving module according to each of the analysis data and the corresponding estimated distance.

[0212] Specifically, according to a preset time interval (for example, once per second), each sending node (such as node A, node B, node C, node D) is controlled to send signal data through a single code channel module. When sending a signal, each sending node uses specific preset waveform parameters (such as amplitude, frequency, and waveform duration) to ensure the identifiability of the signal at the receiving module. The initial signal data of each sending node in the sending node pair is fused to obtain the signal data corresponding to the sending node pair;

[0213] The signal data is identified and analyzed. The identification process includes comparing the waveform, amplitude and other characteristics of the signal to determine the source sending node of the signal.

[0214] Based on the path loss model, the received signal strength data is used to calculate the estimated distance between the receiving module and each transmitting node. The path loss model takes into account the attenuation characteristics of the signal during propagation, which usually includes the impact of free space loss and environmental factors (such as obstacles, interference, etc.) on the signal.

[0215] Based on the analysis data of each sending node and the corresponding estimated distance, a weighted algorithm (such as weighted average method) is used to calculate the actual position of the receiving module. The specific calculation process can be combined with the signal-to-noise ratio data to assign a weight to each sending node, thereby improving the accuracy of the position estimation.

[0216] By combining the signal strength and path loss model, the distance between the receiving module and the sending node can be estimated more accurately, thereby improving the positioning accuracy. And this method can adapt to different environmental changes. Through the dynamic adjustment of the path loss model, the system can maintain good positioning performance in different indoor environments. The signal sending mechanism with a preset time interval ensures that the system can obtain the latest signal data in real time, respond quickly to environmental changes, and provide instant positioning information. This method is suitable for a variety of application scenarios, such as indoor navigation, asset tracking, smart home, etc., and has great market potential.

[0217] The single-code channel location recognition method of this embodiment forms an efficient and accurate positioning mechanism by controlling the signal transmission of the sending node, the recognition and analysis of the signal data, the calculation of the estimated distance, and the determination of the position of the receiving module. The implementation of this method will significantly improve the application effect of the location recognition system in complex environments and provide more reliable technical support for related fields.

[0218] like Figure 4 As shown, an electronic device 400 provided by an embodiment of the present invention includes a memory 410 and a processor 420; the memory 410 is used to store a computer program; the processor 420 is used to implement the single code channel position measurement method as described above when executing the computer program.

[0219] In other words, an electronic device 400 includes a memory 410 and a processor 420 coupled to the memory 410; the memory 410 is configured to store a computer program; and the processor 420 is configured to perform the following operations when executing the computer program:

[0220] Control each sending node to send initial signal data according to a preset time interval;

[0221] The initial signal data of each sending node in the sending node pair are merged to obtain the signal data corresponding to the sending node pair; wherein the sending node pair is composed of each sending node in the target sending node group and one sending node in the non-target sending node group, and the target sending node group is composed of two adjacent sending nodes;

[0222] Identify and analyze each of the signal data to obtain analysis data;

[0223] Based on the path loss model, a corresponding estimated distance is obtained according to each of the analysis data; and the position of the corresponding receiving module is determined according to each of the analysis data and the corresponding estimated distance.

[0224] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the single-code channel position measurement method as described above is implemented.

[0225] In other words, a non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor performs the following operations:

[0226] Control each sending node to send initial signal data according to a preset time interval;

[0227] The initial signal data of each sending node in the sending node pair are merged to obtain the signal data corresponding to the sending node pair; wherein the sending node pair is composed of each sending node in the target sending node group and one sending node in the non-target sending node group, and the target sending node group is composed of two adjacent sending nodes;

[0228] Identify and analyze each of the signal data to obtain analysis data;

[0229] Based on the path loss model, a corresponding estimated distance is obtained according to each of the analysis data; and the position of the corresponding receiving module is determined according to each of the analysis data and the corresponding estimated distance.

[0230] An electronic device 400 that can be used as a server or client of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device 400 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 400 can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0231] The electronic device 400 includes a computing unit, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The computing unit, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0232] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc. In the present application, the unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present invention. In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0233] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A position recognition system based on a single code channel, characterized in that: include: A transmitting module, a single code channel module, at least one receiving module, a computing module and an environmental sensing module; The transmitting module includes at least four sending nodes, each of which sends initial signal data to the receiving module through the single code channel module at a preset time interval, two adjacent sending nodes constitute a sending node group, and each sending node in the target sending node group and a sending node in the non-target sending node group respectively constitute a sending node pair; wherein the target sending node group is any of the sending node groups; The single code channel module is used to fuse the initial signal data of each sending node in the sending node pair to obtain the signal data corresponding to the sending node pair; the signal data includes signal strength data of the initial signal data of two sending nodes in the sending node pair; The receiving module is used to receive the signal data of each sending node pair, and identify and analyze each signal data to obtain analysis data; The calculation module is used to obtain a corresponding estimated distance according to each of the analysis data based on a path loss model; and determine the position of the corresponding receiving module according to each of the analysis data and the corresponding estimated distance; The environment sensing module is used to collect environment change data, perform feature extraction on the environment change data to obtain environment feature data, and perform parameter adjustment on the path loss model based on the environment feature data; The step of adjusting parameters of the path loss model based on the environmental characteristic data includes: Determine, according to the environmental characteristic data, an ideal signal strength of the corresponding sending node received by the receiving module; Determine corresponding error data according to the ideal signal strength of each sending node and the corresponding signal strength data; Obtaining a preset threshold, and judging each of the error data based on the preset threshold; When the error data does not meet the preset threshold, parameters of the path loss model are adjusted according to the current environmental characteristic data.

2. The single-code-based position recognition system according to claim 1, characterized in that: The initial signal data sent by each sending node is generated according to corresponding preset waveform parameters, and the preset waveform parameters corresponding to each sending node are different. The preset waveform parameters include one or more of amplitude, frequency and duration of the waveform signal.

3. The single-code-based position recognition system according to claim 2, characterized in that: The receiving module is specifically used for: Separating the signal data to obtain two temporary signal data and two signal strength data; Matching the two temporary signal data with preset waveforms corresponding to different preset waveform parameters respectively, and determining the sending nodes respectively corresponding to the two temporary signal data; The signal strengths of the two temporary signal data are compared, and the signal strength data corresponding to the sending node corresponding to the temporary signal data is determined according to the comparison result, wherein the analysis data includes the sending node corresponding to the temporary signal data and its signal strength data.

4. The single-code-based position recognition system according to claim 1, characterized in that: The adjusting parameters of the path loss model based on the environmental characteristic data further includes: Classifying the environmental characteristic data to obtain first characteristic data and second characteristic data; Determining whether the first characteristic data corresponds to a received signal strength less than a first preset value, or determining whether the second characteristic data corresponds to a received signal strength greater than a second preset value; Adjusting parameters of the path loss model according to the first characteristic data and the second characteristic data; The received signal strength is the ideal signal strength of the corresponding sending node received by the receiving module.

5. The single-code-based position recognition system according to claim 3, characterized in that: The analysis data also includes position data of each of the sending nodes in the sending node pair; The determining the position of the corresponding receiving module according to each of the analysis data and the corresponding estimated distance further comprises: Determine corresponding signal-to-noise ratio data based on the signal strength data in the analysis data, and determine corresponding weight data of the sending node based on the signal strength data and the corresponding signal-to-noise ratio data; The position of the receiving module is determined based on the weight data of each sending node, the corresponding position data and the estimated distance.

6. A location recognition method based on a single code channel, characterized in that: Applied to the single-code-channel based position identification system according to any one of claims 1 to 5, the single-code-channel based position identification method comprises: Control each sending node to send initial signal data according to a preset time interval; The initial signal data of each sending node in the sending node pair are merged to obtain the signal data corresponding to the sending node pair; wherein the sending node pair is composed of each sending node in the target sending node group and one sending node in the non-target sending node group, and the target sending node group is composed of two adjacent sending nodes; Identify and analyze each of the signal data to obtain analysis data; Based on the path loss model, a corresponding estimated distance is obtained according to each of the analysis data; and the position of the corresponding receiving module is determined according to each of the analysis data and the corresponding estimated distance.

7. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to implement the single code channel based position identification method as claimed in claim 6 when executing the computer program.

8. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the single-code-channel-based position recognition method as claimed in claim 6 is implemented.

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