Bluetooth positioning-driven factory personnel tracking management method and system

Abstract

The present invention discloses a Bluetooth positioning-driven factory personnel tracking and management method and system, relating to the field of personnel tracking technology. The method comprises: when a Bluetooth device worn by a factory worker is disconnected, recording the spatial location at the time of disconnection and automatically tracking the worker's movement trajectory; when the Bluetooth is reconnected, processing the movement data during the disconnection period and generating a coordinate sequence through the Bluetooth blind spot positioning network, thereby achieving accurate tracking and management of the worker's location during the disconnection period. This method solves the technical problem of existing Bluetooth-based factory personnel real-time positioning systems being unable to continuously and accurately track the worker's location during Bluetooth disconnection, achieving the technical effect of continuously and accurately tracking the worker's location during Bluetooth disconnection.

Description

Technical Field

[0001] The present application relates to the field of personnel tracking technology, and in particular to a factory personnel tracking management method and system driven by Bluetooth positioning. Background Art

[0002] As factories continue to expand and production processes become increasingly complex, the demand for real-time positioning and management of personnel is also increasing. In many manufacturing and industrial scenarios, by having factory employees wear Bluetooth positioning devices, their location and movement trajectory can be dynamically monitored, thereby improving the orderliness and safety of production. However, due to the complex internal structure of the factory, the large number of mechanical equipment, and severe signal obstruction, Bluetooth signal transmission often faces obstacles and interference, which makes the connection between Bluetooth and the server easily interrupted. During the period of disconnection, the specific location and movement path of employees are difficult to accurately capture, which has a potential impact on the factory's production scheduling, personnel deployment and safety management. The limitations of traditional positioning methods have become increasingly apparent in this context. How to maintain effective tracking of personnel locations during periods when Bluetooth signals are unstable or even disconnected has become a technical problem that needs to be solved urgently.

[0003] At present, there is a technical problem in the relevant technologies that the Bluetooth-based real-time positioning system for factory personnel cannot continue to accurately track the location of personnel during the period when the Bluetooth connection is disconnected. Summary of the Invention

[0004] This application solves the technical problem that the existing Bluetooth-based factory personnel real-time positioning system cannot continue to accurately track the location of personnel during the period when the Bluetooth connection is disconnected by providing a Bluetooth positioning-driven factory personnel tracking management method and system.

[0005] This application provides a Bluetooth positioning-driven factory personnel tracking and management method, including:

[0006] When the first employee positioning Bluetooth is disconnected, the spatial positioning at the time of disconnection is obtained, wherein the first employee positioning Bluetooth will automatically record the spatial movement trajectory with the spatial positioning at the time of disconnection as the origin after the disconnection; continuously request to reconnect with the first employee positioning Bluetooth, and when the reconnection is successful, obtain the Bluetooth blind spot movement trajectory and the spatial positioning at the time of successful reconnection from the first employee positioning Bluetooth; according to the factory workshop number, activate the Bluetooth blind spot positioning network to process the spatial positioning at the time of disconnection, the Bluetooth blind spot movement trajectory and the spatial positioning at the time of successful reconnection, and obtain a Bluetooth blind spot positioning coordinate sequence; according to the Bluetooth blind spot positioning coordinate sequence, perform blind spot coordinate compensation tracking management for the first employee.

[0007] This application also provides a Bluetooth positioning-driven factory personnel tracking management system, including:

[0008] A spatial positioning acquisition module, wherein the spatial positioning acquisition module is used to obtain the spatial positioning at the time of disconnection when the first employee positioning Bluetooth is disconnected, wherein the first employee positioning Bluetooth will automatically record the spatial movement trajectory with the spatial positioning at the time of disconnection as the origin after disconnection; a reconnection module, wherein the reconnection module is used to continuously request reconnection with the first employee positioning Bluetooth, and when the reconnection is successful, obtain the Bluetooth blind spot movement trajectory and the spatial positioning at the time of successful reconnection from the first employee positioning Bluetooth; a positioning coordinate sequence acquisition module, wherein the positioning coordinate sequence acquisition module is used to activate the Bluetooth blind spot positioning network according to the factory workshop number to process the spatial positioning at the time of disconnection, the Bluetooth blind spot movement trajectory and the spatial positioning at the time of successful reconnection, and obtain a Bluetooth blind spot positioning coordinate sequence; a coordinate compensation tracking management module, wherein the coordinate compensation tracking management module is used to perform first employee blind spot coordinate compensation tracking management according to the Bluetooth blind spot positioning coordinate sequence.

[0009] The Bluetooth positioning-driven factory personnel tracking and management method and system proposed in this application first records the spatial positioning at the time of disconnection and automatically tracks the movement trajectory when the Bluetooth device worn by the factory personnel is disconnected; when the Bluetooth is reconnected, the movement data during the disconnection period is processed, and a coordinate sequence is generated through the Bluetooth blind spot positioning network, thereby realizing accurate tracking and management of the personnel position during the disconnection period, achieving the technical effect of continuously and accurately tracking the position of factory personnel during the period when the Bluetooth connection is disconnected. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0011] Figure 1 A flowchart of a Bluetooth positioning-driven factory personnel tracking and management method provided in an embodiment of the present application;

[0012] Figure 2 This is a schematic diagram of the structure of the Bluetooth positioning-driven factory personnel tracking and management system provided in an embodiment of the present application.

[0013] Description of reference numerals: spatial positioning acquisition module 10 , reconnection module 20 , positioning coordinate sequence acquisition module 30 , coordinate compensation tracking management module 40 . DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0017] The present application embodiment provides a factory personnel tracking management method driven by Bluetooth positioning, such as Figure 1 As shown, the method includes:

[0018] Step S100, when the first employee positioning Bluetooth is disconnected, the spatial positioning at the time of disconnection is obtained, wherein the first employee positioning Bluetooth will automatically record the spatial movement trajectory with the spatial positioning at the time of disconnection as the origin after the disconnection. Specifically, when the first employee positioning Bluetooth is disconnected from the server, the system continues to receive its real-time positioning information until interruption, so as to accurately determine the spatial positioning at the time of disconnection, and this positioning becomes the key benchmark. Subsequently, the first employee positioning Bluetooth starts the automatic recording mechanism, with this positioning as the origin. Its acceleration sensor captures the acceleration changes in multi-directional motion and the speed information after integral calculation, and the gyroscope measures the three-dimensional space rotation angle and direction change. The two work together, and the Bluetooth internal positioning algorithm is comprehensively processed, using mathematical tools to decompose and synthesize the speed information, calculating the displacement components according to the time interval, and updating the coordinates with the origin accumulated displacement and connecting them into a complete spatial movement trajectory data, which is temporarily stored in the Bluetooth device and transmitted to the system after reconnection, providing a solid basis for restoring the employee's offline action path and ensuring the tracking accuracy and integrity.

[0019] Step S200, continuously requesting to reconnect with the first employee positioning Bluetooth, and when the reconnection is successful, obtaining the Bluetooth blind spot movement trajectory and the spatial positioning at the time of successful reconnection from the first employee positioning Bluetooth. Specifically, after the first employee positioning Bluetooth is disconnected from the server, the system starts the reconnection mechanism, and continuously sends a reconnection request data packet containing an identification code and a connection instruction to it at a preset time interval (such as every 1 second or depending on the system's real-time requirements and device performance). Once the reconnection is successful, the system immediately obtains the Bluetooth blind spot movement trajectory data recorded with the spatial positioning at the time of disconnection as the origin during the Bluetooth connection interruption, as well as the spatial positioning information at the time of successful reconnection from the Bluetooth device. This information helps to accurately connect the Bluetooth blind spot movement trajectory with the real-time positioning trajectory after reconnection, thereby constructing a complete action route map for employees and providing comprehensive and accurate data support for all aspects of factory management.

[0020] Step S300, according to the factory workshop number, activate the Bluetooth blind spot positioning network to process the spatial positioning at the time of disconnection, the movement trajectory of the Bluetooth blind spot and the spatial positioning at the time of successful reconnection, and obtain a Bluetooth blind spot positioning coordinate sequence. Specifically, according to the factory workshop number, the system activates the corresponding Bluetooth blind spot positioning network, which has previously obtained data such as the spatial positioning at the time of disconnection, the movement trajectory of the Bluetooth blind spot and the spatial positioning at the time of successful reconnection. After activation, the first to Nth Bluetooth blind spot positioning base networks receive data and analyze and calculate based on their own algorithm models that have been optimized and trained for specific areas or scenarios in the workshop. Taking the first Bluetooth blind spot positioning base network as an example, its algorithm model is constructed based on the precise mapping and digital modeling of the workshop space layout, dividing the workshop into many tiny sub-areas, and each sub-area is given specific spatial coding and signal propagation characteristic parameters. For obstacle distribution, the position, shape and size information of the obstacles are pre-acquired through laser scanning or other high-precision mapping means, and the information is integrated into the algorithm model. Regarding Bluetooth signal propagation characteristics, a signal strength attenuation model was constructed based on extensive field test data. A multipath propagation correction algorithm was developed to account for the reflection, refraction, and absorption effects of metal equipment, walls, and other components within the workshop. Upon receiving data, the system first determines the subregion where the Bluetooth blind spot trajectory's starting point resides based on spatial coding. By combining signal propagation characteristic parameters with the currently received Bluetooth signal strength information, the system preliminarily estimates the approximate location of the starting point. By analyzing the velocity changes within the Bluetooth blind spot trajectory, kinematic principles are used to calculate the displacement vectors between adjacent time points. For steering information, the trajectory's steering angle and direction are determined by combining gyroscope data (if available) or analyzing the directional changes of the displacement vectors at different time points. Based on the relative position of the trajectory with workshop equipment, such as when the trajectory approaches large metal equipment, the system uses the signal attenuation model and multipath propagation correction algorithm to compensate for signal anomalies caused by equipment interference. This allows for the precise calculation of the coordinates of each point on the trajectory, ultimately generating a corresponding positioning coordinate sequence. Similarly, the Nth Bluetooth blind spot positioning base network follows a similar process and its own unique algorithm model to generate the Nth Bluetooth blind spot positioning coordinate sequence. The fully connected network then evaluates the sequence distance parameters of these sequences pairwise, performs outlier factor analysis based on this, identifies abnormal sequences that differ greatly from other sequences, and finally extracts the positioning coordinate sequence corresponding to the minimum outlier factor as the Bluetooth blind spot positioning coordinate sequence, providing a reliable data basis for employee blind spot coordinate compensation tracking management.

[0021] In one possible implementation, based on the factory workshop number, the Bluetooth blind spot positioning network is activated to process the spatial positioning at the time of disconnection, the Bluetooth blind spot movement trajectory, and the spatial positioning at the time of successful reconnection to obtain a Bluetooth blind spot positioning coordinate sequence. Step S300 further includes step S310, where the Bluetooth blind spot positioning network includes a first Bluetooth blind spot positioning base network up to an Nth Bluetooth blind spot positioning base network, and a fully connected network. Specifically, the Bluetooth blind spot positioning network is composed of the first Bluetooth blind spot positioning base network up to the Nth Bluetooth blind spot positioning base network and the fully connected network. The first through Nth Bluetooth blind spot positioning base networks process positioning data for specific areas or scenarios within the factory floor. For example, the first Bluetooth blind spot positioning base network divides the open production area into grid cells, records coordinates and signal strength baselines, and after receiving data, estimates movement distance and direction based on the grid signal strength, fluctuations, and kinematic formulas at the trajectory starting point. This is combined with position corrections for large equipment and other facilities to generate a positioning coordinate sequence. The second Bluetooth blind spot positioning base network focuses on narrow corridors, constructing a one-dimensional coordinate system. It calculates speed and distance based on the signal propagation characteristics and intensity change rate in narrow spaces to generate a sequence. The other base networks operate similarly. The fully connected network then compares the positioning coordinate sequences output by each base network, quantifying differences using methods such as distance difference between coordinate points and trajectory morphology similarity. It then uses weighted averaging and data fitting to comprehensively adjust the results, weighting stable coordinate points with small differences and correcting or discarding those with large deviations. This approach produces more accurate and reliable Bluetooth blind spot positioning coordinate sequences, providing strong data support for personnel tracking and management.

[0022] In step S320, the spatial positioning at the time of disconnection, the Bluetooth blind spot movement trajectory, and the spatial positioning at the time of successful reconnection are processed based on the first Bluetooth blind spot positioning base network through the Nth Bluetooth blind spot positioning base network, respectively, to obtain a first Bluetooth blind spot positioning coordinate sequence through the Nth Bluetooth blind spot positioning coordinate sequence. Specifically, after receiving the spatial positioning at the time of disconnection, the Bluetooth blind spot movement trajectory, and the spatial positioning at the time of successful reconnection, the first Bluetooth blind spot positioning base network initiates a processing flow based on its internally constructed algorithm system. The algorithm system first accurately maps and digitally models the spatial geometry of the factory workshop, divides it into different area units and determines their spatial coordinate range and boundary conditions. At the same time, it constructs a detailed signal interference matrix through preliminary signal strength testing and propagation path simulation to consider the impact of internal equipment and obstacles on Bluetooth signal propagation. For example, for large metal equipment, the signal attenuation coefficient, reflection angle and multi-path propagation path set are calculated based on its material, shape, size and relative position with the positioning point. Based on a large amount of historical data and field observations, a speed range model for employees' normal movements in the workshop is established, taking into account factors such as the average, maximum and minimum speed limits of employees in different work tasks and regional environments, as well as a common path pattern library. Through cluster analysis of employees' daily movement trajectories, typical path patterns such as from raw material storage areas to production and processing areas are summarized. When receiving actual positioning data, the algorithm first determines the starting regional unit based on the spatial positioning at the time of disconnection. It then gradually infers the approximate direction of the trajectory within each regional unit by combining the signal strength change information and the signal interference matrix in the Bluetooth blind spot movement trajectory. It uses the speed range model to verify and correct the time interval and distance changes between adjacent points on the trajectory. It optimizes the trajectory path selection by referring to the common path pattern library. After comprehensive analysis and complex calculations, the input data is converted into a series of preliminary positioning coordinate points and connected in time or logical order to form the first Bluetooth blind spot positioning coordinate sequence. Similarly, the Nth Bluetooth blind spot positioning base network also processes the same input data according to a similar process and algorithm framework. However, due to differences in design and optimization direction, some focus on optimizing positioning accuracy in areas with dense complex equipment, while others focus on rapid response and positioning stability optimization in areas with high personnel flow. This leads to different focuses in data processing, thereby generating positioning coordinate sequences with unique characteristics.

[0023] In step S330, the fully connected network fuses the first Bluetooth blind spot positioning coordinate sequence through the Nth Bluetooth blind spot positioning coordinate sequence to obtain the Bluetooth blind spot positioning coordinate sequence. Specifically, after the first Bluetooth blind spot positioning coordinate sequence through the Nth Bluetooth blind spot positioning coordinate sequence are generated, the fully connected network first performs pairwise sequence distance evaluation on the multiple positioning coordinate sequences. By calculating the spatial distances between corresponding coordinate points in different sequences, a series of sequence distance parameters are obtained. These parameters can intuitively reflect the degree of similarity and difference between the various positioning coordinate sequences. Based on the sequence distance parameters, the fully connected network performs outlier factor analysis. Sequences that differ significantly from other sequences in terms of coordinate point location, overall sequence trend, and other aspects may be deviated due to factors such as local environmental interference or data anomalies during processing by a base network. The fully connected network extracts the positioning coordinate sequence corresponding to the minimum outlier factor among the outlier factors of the first Bluetooth blind spot positioning coordinate sequence through the Nth Bluetooth blind spot positioning coordinate sequence and determines it as the final Bluetooth blind spot positioning coordinate sequence. The final sequence combines the advantages of each base network, eliminates possible anomalies, and can more accurately reflect the actual location change trajectory of employees during the Bluetooth blind area, providing a reliable positioning data basis for subsequent personnel tracking and management.

[0024] In one possible implementation, the Bluetooth blind spot positioning network includes a first Bluetooth blind spot positioning base network up to an Nth Bluetooth blind spot positioning base network, and a fully connected network. Step S310 further includes step S311, constructing a three-dimensional model of the factory workshop based on the factory workshop number, and constructing a three-dimensional grid model of the factory workshop three-dimensional model based on a preset spatial grid side length. Specifically, based on the factory workshop number, an accurate three-dimensional model of the factory workshop is constructed using the factory's architectural design drawings, field measurement data, and three-dimensional modeling technology. The model can truly reflect information such as the spatial structure, size, and layout of internal facilities of the workshop. Based on the preset spatial grid side length, the three-dimensional model of the factory workshop is further divided into a three-dimensional grid model of the factory workshop. For example, if the preset spatial grid side length is 1 meter, then every 1 cubic meter of space in the workshop will be divided into an independent grid unit. The grid units together constitute the three-dimensional grid model of the entire workshop, laying the basic framework for the subsequent construction of the neural network.

[0025] Step S312 configures each grid of the three-dimensional grid model of the factory workshop as both an input node and an output node, with the input information being 0 or 1, and the output information being 0 or 1, to obtain an initial graph neural network, wherein the grid input information and output information of the initial graph neural network are both 0 by default. Specifically, for the constructed three-dimensional grid model of the factory workshop, each grid therein is specially configured so that it has the functions of both an input node and an output node, and the input information and output information are limited to only 0 or 1, thereby obtaining an initial graph neural network. In the initial state, all grid input information and output information of the graph neural network are set to 0 by default. At this time, the neural network has not been trained and does not have the ability to effectively process employee location information.

[0026] Step S313 collects historical employee monitoring information for the three-dimensional grid model of the factory workshop. This information includes location record information at the time of disconnection, Bluetooth blind spot movement trajectory record information, reconnection location record information, and Bluetooth blind spot positioning coordinate sequence identification data. The Bluetooth blind spot movement trajectory record information is a movement trajectory recorded with the location record information at the time of disconnection as the origin. Specifically, the historical employee monitoring information corresponding to the three-dimensional grid model of the factory workshop is collected. This information covers several key aspects, including location record information at the time of Bluetooth disconnection, which accurately records the employee's location coordinates at the moment the Bluetooth connection was interrupted; Bluetooth blind spot movement trajectory record information, which uses the location record information at the time of disconnection as the origin and details the employee's movement path within the workshop during the Bluetooth connection interruption, such as the employee's movement distance in different directions and the direction of the turn; location record information at the time of reconnection, which clearly indicates the employee's location when Bluetooth is successfully reconnected; and Bluetooth blind spot positioning coordinate sequence identification data, which is used to verify and monitor the output results during subsequent neural network training.

[0027] Step S314 configures the 1-value input grid of the initial graph neural network based on the location record information at the time of disconnection, the Bluetooth blind spot movement trajectory record information, and the location record information at the time of reconnection. The 1-value output grid of the initial graph neural network is configured using the Bluetooth blind spot positioning coordinate sequence identification data as the 1-value output grid supervision data, and the initial graph neural network is trained to obtain the first Bluetooth blind spot positioning base network. Specifically, based on the collected location record information at the time of disconnection, the Bluetooth blind spot movement trajectory record information, and the location record information at the time of reconnection, a 1-value input grid is configured in the initial graph neural network. For example, if a Bluetooth disconnection event occurs for an employee within a specific grid area, the input information of the corresponding grid is set to 1 to indicate that the grid is associated with the employee's location. At the same time, the 1-value output grid of the initial graph neural network is configured using the Bluetooth blind spot positioning coordinate sequence identification data and used as the 1-value output grid supervision data. Then, the initial graph neural network is trained using a large number of samples of historical employee monitoring information through a machine learning algorithm, such as a backpropagation algorithm. During the training process, the neural network's weights and parameters were continuously adjusted so that the network's output gradually approached the Bluetooth blind spot positioning coordinate sequence identification data. After multiple rounds of training and optimization, when the network's output reached a certain level of accuracy and stability, the first Bluetooth blind spot positioning base network was successfully obtained.

[0028] In step S315, the steps for constructing the second Bluetooth blind spot positioning base network up to the Nth Bluetooth blind spot positioning base network are the same as the steps for constructing the first Bluetooth blind spot positioning base network. Specifically, the steps for constructing the second Bluetooth blind spot positioning base network up to the Nth Bluetooth blind spot positioning base network are exactly the same as the steps for constructing the first Bluetooth blind spot positioning base network. That is, first construct a three-dimensional grid model based on the factory workshop number and the preset spatial grid side length, initialize the graph neural network, collect employee historical monitoring information, and then configure the input and output grids according to the corresponding information and perform training, and finally obtain independent Bluetooth blind spot positioning base networks with similar construction processes and functions. Multiple Bluetooth blind spot positioning base networks will jointly provide multi-dimensional analysis and processing capabilities for the determination of subsequent Bluetooth blind spot positioning coordinate sequences, thereby improving the accuracy and reliability of the entire Bluetooth blind spot positioning system.

[0029] In one possible implementation, the 1-value input grid of the initial graph neural network is configured according to the positioning record information at the disconnection time, the Bluetooth blind spot movement trajectory record information, and the positioning record information at the reconnection time, and the 1-value output grid of the initial graph neural network is configured with the Bluetooth blind spot positioning coordinate sequence identification data as the 1-value output grid supervision data to train the initial graph neural network and obtain the first Bluetooth blind spot positioning base network. Step S314 further includes step S3141, constructing a blind spot positioning loss function: Among them, X1 represents the 1-value output grid supervision data of the initial graph neural network configured with the Bluetooth blind spot positioning coordinate sequence identification data, X 10 Characterize the 1-valued output grid of the initial graph neural network, n(X1∪X 10 ) represents X1 and X 10 The number of intersection 1 grids, n(X1∩X 10 ) represents X1 and X 10 The number of 1-valued grids of the union, e represents the natural constant, and LOSS represents the blind spot positioning loss value. Specifically, the blind spot positioning loss function is first constructed, and its expression is In this function, X1 represents the 1-valued output grid supervision data of the initial graph neural network configured by the Bluetooth blind spot positioning coordinate sequence identification data. It is the ideal result mode that we expect the neural network to output in the end. It is the grid identification information corresponding to the precise positioning coordinate sequence obtained based on a large amount of historical data and actual positioning conditions. 10 It represents the 1-value output grid actually generated by the initial graph neural network during the training process, that is, the current output result of the network. 10 ) is calculated based on X1 and X 10 The number of grids with a union value of 1, which reflects the total number of grids involved in the comparison; n(X1∩X 10 ) calculates the number of grid cells with 1-values ​​in the intersection of the two, representing the number of grid cells where the network output matches the ideal output. e is a natural constant. By constructing this function, the difference between the network output and the ideal output can be quantified as a loss value (LOSS). The closer the network output is to the ideal output, the higher the ratio of the number of grid cells with 1-values ​​in the intersection to the number of grid cells with 1-values ​​in the union, the larger the exponential value, and the smaller the loss value (LOSS). Conversely, the loss value increases, providing a clear optimization direction for subsequent neural network training.

[0030] Step S3142, based on the blind spot positioning loss function, configure the 1-value input grid of the initial graph neural network according to the positioning record information at the time of disconnection, the Bluetooth blind spot movement trajectory record information, and the positioning record information at the time of reconnection, and configure the 1-value output grid of the initial graph neural network with the Bluetooth blind spot positioning coordinate sequence identification data as the 1-value output grid supervision data, train the initial graph neural network, and obtain the first Bluetooth blind spot positioning base network. Specifically, the training of the initial graph neural network is carried out based on the constructed blind spot positioning loss function. According to the collected positioning record information at the time of disconnection, the grid position of the employee at the moment of Bluetooth disconnection is determined, and the input information of the grid in the initial graph neural network is configured as 1 to mark this position as the starting point of the employee's movement trajectory. According to the Bluetooth blind spot movement trajectory record information, the grid path passed by the employee during the Bluetooth loss of connection is analyzed, and the grids involved in the path are configured as 1-value input grids in sequence according to their logical relationship in the trajectory to simulate the stimulation of the employee's movement process in the workshop on the neural network input layer. Then, based on the location record information at the time of reconnection, the grid where the employee was located when he reconnected to Bluetooth is determined, and the corresponding configuration is also performed in the input layer. The 1-value output grid of the initial graph neural network is configured with the Bluetooth blind spot positioning coordinate sequence identification data as the supervision data. During the training process, the actual output of the network and the supervision data are substituted into the blind spot positioning loss function to calculate the loss value, and then the back propagation algorithm is used to adjust the weights and connection parameters of the neural network according to the loss value, so that the loss value gradually decreases. After multiple iterative trainings, the parameters of the network are continuously optimized until the output result of the network can stably approach the Bluetooth blind spot positioning coordinate sequence identification data. At this time, the first Bluetooth blind spot positioning base network is successfully obtained. The network has the ability to accurately predict the Bluetooth blind spot positioning coordinate sequence based on the input information related to the disconnection and reconnection of the employee's Bluetooth connection, providing reliable positioning basic services for the subsequent actual factory personnel positioning and tracking scenarios.

[0031] In one possible implementation, the first Bluetooth blind spot positioning coordinate sequence up to the Nth Bluetooth blind spot positioning coordinate sequence is fused through the fully connected network to obtain the Bluetooth blind spot positioning coordinate sequence. Step S330 further includes step S331, configuring a fully connected rule factor to construct the fully connected network, wherein the fully connected rule factor comprises: performing pairwise sequence distance evaluation on the first Bluetooth blind spot positioning coordinate sequence up to the Nth Bluetooth blind spot positioning coordinate sequence to obtain a number of sequence distance parameters. Specifically, the fully connected rule factor is configured to construct the fully connected network. The core operation of the fully connected rule factor is to perform pairwise sequence distance evaluation on the first Bluetooth blind spot positioning coordinate sequence up to the Nth Bluetooth blind spot positioning coordinate sequence. For each two different Bluetooth blind spot positioning coordinate sequences, the distance difference between the corresponding coordinate points therebetween is calculated. For example, for a coordinate point in the first Bluetooth blind spot positioning coordinate sequence and a corresponding coordinate point in the second Bluetooth blind spot positioning coordinate sequence (the correspondence here can be based on a matching of a temporal sequence or a spatial logical sequence), the distance value between them is calculated using a specific distance calculation formula, such as the Euclidean distance formula. After calculating all these pairwise combinations, we can obtain several sequence distance parameters. These parameters form the basic data for subsequent analysis and can intuitively reflect the similarities and differences between different Bluetooth blind spot positioning coordinate sequences. Sequences with high similarity have relatively small distance parameter values, while sequences with low similarity have relatively large distance parameter values.

[0032] Step S332: Based on the multiple sequence distance parameters, the first Bluetooth blind spot positioning coordinate sequence is traversed until the Nth Bluetooth blind spot positioning coordinate sequence to perform outlier factor analysis, and the outlier factors of the first Bluetooth blind spot positioning coordinate sequence are obtained until the Nth Bluetooth blind spot positioning coordinate sequence is obtained. Specifically, based on the multiple sequence distance parameters obtained above, the first Bluetooth blind spot positioning coordinate sequence is traversed until the Nth Bluetooth blind spot positioning coordinate sequence to perform outlier factor analysis. For each Bluetooth blind spot positioning coordinate sequence, the distance relationship between it and other sequences is analyzed. The calculation of the outlier factor will comprehensively consider the distance parameters of the sequence and all other sequences. For example, if the distance parameters of a Bluetooth blind spot positioning coordinate sequence are significantly larger than those of most other sequences, that is, it has low consistency with other sequences in spatial position or trajectory morphology, then its outlier factor will be higher; conversely, if the distance parameters of a sequence with other sequences are relatively balanced and smaller, its outlier factor will be lower. Through the analysis process, the outlier factors of the first Bluetooth blind spot positioning coordinate sequence to the Nth Bluetooth blind spot positioning coordinate sequence can be obtained. The outlier factor can quantify the abnormality degree of each sequence in the entire sequence set.

[0033] Step S333 extracts the positioning coordinate sequence from the outlier factor of the first Bluetooth blind spot positioning coordinate sequence to the minimum value of the outlier factor of the Nth Bluetooth blind spot positioning coordinate sequence, and outputs it as the Bluetooth blind spot positioning coordinate sequence. Specifically, after obtaining the outlier factor of the first Bluetooth blind spot positioning coordinate sequence to the outlier factor of the Nth Bluetooth blind spot positioning coordinate sequence, extract the positioning coordinate sequence corresponding to the minimum value. The sequence corresponding to the minimum value has the smallest difference from all other sequences, that is, it best conforms to the overall trend and most sequence characteristics. It is output as the final Bluetooth blind spot positioning coordinate sequence. The sequence has been integrated and optimized by the fully connected network, and can eliminate abnormal positioning information caused by factors such as local environmental interference or errors in individual Bluetooth blind spot positioning base networks to the greatest extent. This provides a more reliable and accurate positioning coordinate data basis for subsequent employee blind spot coordinate compensation tracking management, ensuring that the actual movement trajectory of employees during the Bluetooth blind spot period can be accurately restored in the factory personnel tracking management system, thereby improving the positioning accuracy and stability of the entire system.

[0034] In one possible implementation, based on the blind spot positioning loss function, the 1-value input grid of the initial graph neural network is configured according to the positioning record information at the time of disconnection, the Bluetooth blind spot movement trajectory record information, and the positioning record information at the time of reconnection, and the 1-value output grid of the initial graph neural network is configured with the Bluetooth blind spot positioning coordinate sequence identification data, as the 1-value output grid supervision data, the initial graph neural network is trained to obtain the first Bluetooth blind spot positioning base network, and step S3142 further includes step S31421, based on the preset trajectory length, the Bluetooth blind spot movement trajectory record information is cropped to obtain Bluetooth blind spot movement trajectory pruning information, wherein the Bluetooth blind spot movement trajectory pruning information is a non-continuous trajectory. Specifically, the Bluetooth blind spot movement trajectory record information is cropped according to the preset trajectory length. The preset trajectory length is determined based on a combination of factors such as the actual size of the factory workshop, the effective transmission range of the Bluetooth signal, and the normal movement speed range of employees in the workshop. During the pruning process, key trajectory points are selected from the original Bluetooth blind spot trajectory records according to certain time or spatial interval standards, so that the resulting Bluetooth blind spot trajectory pruned information becomes a discontinuous trajectory. For example, if the preset trajectory length is to select a trajectory point every 3 meters, the original continuous Bluetooth blind spot trajectory will be simplified into a discontinuous trajectory consisting of a series of discrete trajectory points spaced 3 meters apart. This is done to reduce the amount of data, highlight the key features of the trajectory, reduce noise interference in the data, and improve the efficiency and accuracy of subsequent neural network training.

[0035] Step S31422: Based on the blind spot positioning loss function, the initial graph neural network is configured with a 1-value input grid according to the location record information at the time of disconnection, the Bluetooth blind spot movement trajectory pruning information, and the location record information at the time of reconnection. The initial graph neural network is configured with a 1-value output grid using the Bluetooth blind spot positioning coordinate sequence identifier data as 1-value output grid supervision data to train the initial graph neural network and obtain the first Bluetooth blind spot positioning base network. Specifically, the initial graph neural network is trained based on the previously constructed blind spot positioning loss function. Based on the location record information at the time of disconnection, the grid corresponding to the employee's location at the moment of Bluetooth disconnection is set as a 1-value input grid in the input layer of the initial graph neural network to mark the starting point of the trajectory. Using the Bluetooth blind spot movement trajectory pruning information, the grids corresponding to each discrete trajectory point are sequentially set as 1-value input grids according to their logical order in the trajectory, simulating the stimulation of the neural network by the employee's movement path during the Bluetooth blind spot. Based on the location record information at the time of reconnection, the grid corresponding to the employee's location when Bluetooth reconnected is also set as a 1-value input grid. At the same time, the Bluetooth blind spot positioning coordinate sequence identification data is used to configure the initial graph neural network's 1-value output grid, which serves as the 1-value output grid supervision data. During the training process, the network's actual output and supervision data are substituted into the blind spot positioning loss function to calculate the loss value. The backpropagation algorithm is then used to adjust the neural network's weights and connection parameters based on the loss value. Through multiple iterative training, the network parameters are continuously optimized until the network output results can stably approach the Bluetooth blind spot positioning coordinate sequence identification data. At this point, the first Bluetooth blind spot positioning base network is successfully trained. This network can relatively accurately predict the Bluetooth blind spot positioning coordinate sequence based on the input relevant positioning information, providing strong support for factory personnel's positioning and tracking in Bluetooth blind spots.

[0036] In one possible implementation, based on the factory workshop number, the Bluetooth blind spot positioning network is activated to process the spatial positioning at the time of disconnection, the Bluetooth blind spot movement trajectory, and the spatial positioning at the time of successful reconnection to obtain a Bluetooth blind spot positioning coordinate sequence. Step S300 further includes step S340 to obtain the trajectory start positioning and trajectory end positioning of the Bluetooth blind spot movement trajectory. Specifically, the trajectory start positioning and trajectory end positioning of the Bluetooth blind spot movement trajectory are obtained. The two positioning information are key elements for judging the integrity and accuracy of the Bluetooth blind spot movement trajectory. By extracting and parsing the relevant data recorded by the employee positioning Bluetooth when the connection is disconnected and reconnected successfully, the specific coordinate values ​​of the trajectory start positioning and the trajectory end positioning are determined.

[0037] Step S350, when the starting position of the trajectory is the same as the spatial positioning at the time of disconnection, and the ending position of the trajectory is the same as the spatial positioning at the time of successful reconnection, and the Bluetooth blind spot movement trajectory is a continuous trajectory, the Bluetooth blind spot movement trajectory is positioned based on the spatial positioning at the time of disconnection and the spatial positioning at the time of successful reconnection, to obtain the Bluetooth blind spot movement trajectory. Specifically, when the starting position of the trajectory is exactly the same as the spatial positioning at the time of disconnection obtained previously, and the ending position of the trajectory is also consistent with the spatial positioning at the time of successful reconnection, and the Bluetooth blind spot movement trajectory appears as a continuous trajectory, it indicates that the Bluetooth blind spot movement trajectory has good consistency and accuracy in time and space. At this time, it is relatively simple and direct to perform positioning processing on the Bluetooth blind spot movement trajectory based on the spatial positioning at the time of disconnection and the spatial positioning at the time of successful reconnection. Since the starting and ending points of the trajectory are clear and continuous, it can be regarded as a complete and reliable movement path. The Bluetooth blind spot movement trajectory can be directly determined to be the actual movement trajectory of the employee during the Bluetooth connection interruption. Subsequently, relevant personnel tracking management analysis can be performed based on this trajectory, such as calculating the employee's movement speed during the Bluetooth blind spot, the area passed through, and other information, providing data support for the factory's production management, safety monitoring, etc.

[0038] Otherwise, in step S360, the Bluetooth blind spot positioning network is activated based on the factory workshop number to process the spatial positioning at the time of disconnection, the Bluetooth blind spot movement trajectory, and the spatial positioning at the time of successful reconnection, thereby obtaining the Bluetooth blind spot positioning coordinate sequence. Specifically, if the above ideal conditions are not met, that is, the trajectory start location does not match the spatial location at the time of disconnection, or the trajectory end location is inconsistent with the spatial location at the time of successful reconnection, or the Bluetooth blind spot movement trajectory is discontinuous, a more complex Bluetooth blind spot positioning network is required. Based on the factory workshop number, a pre-built Bluetooth blind spot positioning network is activated, and the spatial location at the time of disconnection, the problematic Bluetooth blind spot movement trajectory, and the spatial location at the time of successful reconnection are input into the network. Each component in the Bluetooth blind spot positioning network, such as the first Bluetooth blind spot positioning base network to the Nth Bluetooth blind spot positioning base network and the fully connected network, will analyze and integrate the data according to their respective algorithms and processing flows. Through multi-dimensional data processing, such as preliminary positioning calculation of the base network and sequence optimization of the fully connected network, a Bluetooth blind spot positioning coordinate sequence is ultimately obtained. The coordinate sequence can restore the actual location change trajectory of employees during the Bluetooth blind area as accurately as possible under complex Bluetooth positioning anomalies, ensuring that the factory personnel tracking management system can maintain high positioning accuracy and reliability under various circumstances, providing solid technical support for the efficient operation and management of the factory.

[0039] Step S400, performs blind spot coordinate compensation tracking management for the first employee based on the Bluetooth blind spot positioning coordinate sequence. Specifically, based on the Bluetooth blind spot positioning coordinate sequence, in the background data processing module of the factory personnel tracking management system, the fitted discrete coordinate points are connected in time or logical order, and the spline interpolation method is used to restore the employee's Bluetooth blind spot movement trajectory and integrate it with the positioning trajectory during normal connection to form a complete movement route data set. Based on this, the employee's Bluetooth blind spot movement characteristics are analyzed, and information such as production processes and equipment layout are associated to evaluate work efficiency and abnormal behavior, provide support for management decisions, and provide timely warnings in the event of an employee's abnormal stay in key equipment or dangerous areas. At the same time, this management can achieve real-time tracking of employees, and managers can view their locations and trajectories at any time. In case of emergency, they can determine the distribution location and formulate plans, and the trajectory is updated in real time with the employee's activities. The analysis results and management strategies are dynamically adjusted to ensure efficient and safe factory operations.

[0040] The embodiment of the present application adopts a method of recording the spatial positioning at the time of disconnection and automatically tracking the movement trajectory of the Bluetooth device worn by the factory personnel when the connection is disconnected; when the Bluetooth is reconnected, the movement data during the disconnection period is processed, and a coordinate sequence is generated through the Bluetooth blind spot positioning network, thereby realizing accurate tracking and management of the personnel position during the disconnection period, achieving the technical effect of realizing continuous and accurate tracking of the factory personnel position during the period when the Bluetooth connection is disconnected.

[0041] In the above, refer to Figure 1 The factory personnel tracking management method driven by Bluetooth positioning according to an embodiment of the present invention is described in detail. Figure 2 The following describes a factory personnel tracking and management system driven by Bluetooth positioning according to an embodiment of the present invention.

[0042] The Bluetooth positioning-driven factory personnel tracking and management system according to an embodiment of the present invention solves the technical problem of existing Bluetooth-based real-time factory personnel location systems being unable to continuously and accurately track personnel locations during Bluetooth disconnections. This system achieves the technical effect of continuously and accurately tracking factory personnel locations during Bluetooth disconnections. The Bluetooth positioning-driven factory personnel tracking and management system includes a spatial positioning acquisition module 10, a reconnection module 20, a positioning coordinate sequence acquisition module 30, and a coordinate compensation tracking and management module 40.

[0043] The spatial positioning acquisition module 10 is used to obtain the spatial positioning at the time of disconnection when the first employee positioning Bluetooth is disconnected, wherein the first employee positioning Bluetooth will automatically record the spatial movement trajectory with the spatial positioning at the time of disconnection as the origin after disconnection.

[0044] The reconnection module 20 is used to continuously request reconnection with the first employee positioning Bluetooth. When the reconnection is successful, the reconnection module 20 obtains the Bluetooth blind area movement trajectory and the spatial positioning at the time of successful reconnection from the first employee positioning Bluetooth.

[0045] The positioning coordinate sequence acquisition module 30 is used to activate the Bluetooth blind spot positioning network according to the factory workshop number to process the spatial positioning at the disconnection time, the Bluetooth blind spot movement trajectory and the spatial positioning at the successful reconnection time to obtain the Bluetooth blind spot positioning coordinate sequence.

[0046] The coordinate compensation tracking management module 40 is used to perform the first employee's blind spot coordinate compensation tracking management according to the Bluetooth blind spot positioning coordinate sequence.

[0047] The specific configuration of the positioning coordinate sequence acquisition module 30 will be described in detail below. As described above, based on the factory workshop number, the Bluetooth blind spot positioning network is activated to process the spatial positioning at the time of disconnection, the Bluetooth blind spot movement trajectory, and the spatial positioning at the time of successful reconnection to obtain a Bluetooth blind spot positioning coordinate sequence. The positioning coordinate sequence acquisition module 30 further includes: a Bluetooth blind spot positioning network component unit, the Bluetooth blind spot positioning network component unit being used for the Bluetooth blind spot positioning network, including a first Bluetooth blind spot positioning base network through an Nth Bluetooth blind spot positioning base network, and a fully connected network; a spatial positioning processing unit, the spatial positioning processing unit being used to process the spatial positioning at the time of disconnection, the Bluetooth blind spot movement trajectory, and the spatial positioning at the time of successful reconnection based on the first Bluetooth blind spot positioning base network through the Nth Bluetooth blind spot positioning base network, respectively, to obtain a first Bluetooth blind spot positioning coordinate sequence through an Nth Bluetooth blind spot positioning coordinate sequence; and a coordinate sequence fusion unit, the coordinate sequence fusion unit being used to fuse the first Bluetooth blind spot positioning coordinate sequence through the Nth Bluetooth blind spot positioning coordinate sequence via the fully connected network to obtain the Bluetooth blind spot positioning coordinate sequence.

[0048] Among them, the Bluetooth blind spot positioning network includes the first Bluetooth blind spot positioning base network to the Nth Bluetooth blind spot positioning base network, and a fully connected network. The Bluetooth blind spot positioning network component unit further includes: a factory workshop three-dimensional model construction subunit, the factory workshop three-dimensional model construction subunit is used to construct a factory workshop three-dimensional model based on the factory workshop number, and based on the preset spatial grid side length, a factory workshop three-dimensional grid model of the factory workshop three-dimensional model; a three-dimensional grid model configuration subunit, the three-dimensional grid model configuration subunit is used to configure each grid of the factory workshop three-dimensional grid model as an input node and an output node at the same time, the input information is 0 or 1, and the output information is 0 or 1, to obtain an initial graph neural network, wherein the grid input information and output information of the initial graph neural network are both 0 by default; a historical monitoring information collection subunit, the historical monitoring information collection subunit is used to collect employee historical monitoring information of the factory workshop three-dimensional grid model, wherein the employee historical monitoring information includes the disconnection time setting Position recording information, Bluetooth blind spot movement trajectory recording information, reconnection time positioning recording information and Bluetooth blind spot positioning coordinate sequence identification data, the Bluetooth blind spot movement trajectory recording information is a movement trajectory recorded with the disconnection time positioning recording information as the origin; an initial graph neural network training subunit, the initial graph neural network training subunit is used to configure the 1-value input grid of the initial graph neural network according to the disconnection time positioning recording information, the Bluetooth blind spot movement trajectory recording information, and the reconnection time positioning recording information, and configure the 1-value output grid of the initial graph neural network with the Bluetooth blind spot positioning coordinate sequence identification data as the 1-value output grid supervision data to train the initial graph neural network to obtain the first Bluetooth blind spot positioning base network; a second Bluetooth blind spot positioning base network construction subunit, the second Bluetooth blind spot positioning base network construction subunit is used for the second Bluetooth blind spot positioning base network until the Nth Bluetooth blind spot positioning base network, and the construction steps are the same as the first Bluetooth blind spot positioning base network construction steps.

[0049] Among them, the 1-value input grid of the initial graph neural network is configured according to the positioning record information at the time of disconnection, the Bluetooth blind spot movement trajectory record information, and the positioning record information at the time of reconnection, and the 1-value output grid of the initial graph neural network is configured with the Bluetooth blind spot positioning coordinate sequence identification data. As the 1-value output grid supervision data, the initial graph neural network is trained to obtain the first Bluetooth blind spot positioning base network. The initial graph neural network training subunit further includes: a blind spot positioning loss function construction micro unit, and the blind spot positioning loss function construction micro unit is used to construct a blind spot positioning loss function: Among them, X1 represents the 1-value output grid supervision data of the initial graph neural network configured with the Bluetooth blind spot positioning coordinate sequence identification data, X 10Characterize the 1-valued output grid of the initial graph neural network, n(X1∪X 10 ) represents X1 and X 10 The number of intersection 1 grids, n(X1∩X 10 ) represents X1 and X 10 The number of 1-value grids of the union, e represents the natural constant, and LOSS represents the blind spot positioning loss value. Specifically, the first Bluetooth blind spot positioning base network obtains a micro unit, and the first Bluetooth blind spot positioning base network obtains a micro unit for configuring the 1-value input grid of the initial graph neural network based on the blind spot positioning loss function, according to the positioning record information at the time of disconnection, the Bluetooth blind spot movement trajectory record information, and the positioning record information at the time of reconnection, and configuring the 1-value output grid of the initial graph neural network with the Bluetooth blind spot positioning coordinate sequence identification data as the 1-value output grid supervision data, training the initial graph neural network, and obtaining the first Bluetooth blind spot positioning base network.

[0050] The first Bluetooth blind spot positioning coordinate sequence to the Nth Bluetooth blind spot positioning coordinate sequence are fused through the fully connected network to obtain the Bluetooth blind spot positioning coordinate sequence. The coordinate sequence fusion unit further includes: a fully connected rule factor configuration subunit, the fully connected rule factor configuration subunit is used to configure the fully connected rule factor and construct the fully connected network, wherein the fully connected rule factor is: performing pairwise sequence distance evaluation on the first Bluetooth blind spot positioning coordinate sequence to the Nth Bluetooth blind spot positioning coordinate sequence to obtain a plurality of sequence distance parameters; an outlier factor analysis subunit, the outlier factor analysis subunit is used to traverse the first Bluetooth blind spot positioning coordinate sequence to the Nth Bluetooth blind spot positioning coordinate sequence based on the plurality of sequence distance parameters to perform outlier factor analysis to obtain the outlier factor of the first Bluetooth blind spot positioning coordinate sequence to the Nth Bluetooth blind spot positioning coordinate sequence; a positioning coordinate sequence extraction subunit, the positioning coordinate sequence extraction subunit is used to extract a positioning coordinate sequence with the minimum value of the outlier factor of the first Bluetooth blind spot positioning coordinate sequence to the Nth Bluetooth blind spot positioning coordinate sequence, and output it as the Bluetooth blind spot positioning coordinate sequence.

[0051] Among them, based on the blind spot positioning loss function, the 1-value input grid of the initial graph neural network is configured according to the positioning record information at the time of disconnection, the Bluetooth blind spot movement trajectory record information, and the positioning record information at the time of reconnection, and the 1-value output grid of the initial graph neural network is configured with the Bluetooth blind spot positioning coordinate sequence identification data, as the 1-value output grid supervision data, the initial graph neural network is trained to obtain the first Bluetooth blind spot positioning base network, and the first Bluetooth blind spot positioning base network acquisition micro unit further includes: a record information clipping nano unit, and the record information clipping nano unit is used to clip the Bluetooth blind spot movement trajectory record information based on a preset trajectory length. , obtaining Bluetooth blind spot movement trajectory pruning information, wherein the Bluetooth blind spot movement trajectory pruning information is a discontinuous trajectory; a first Bluetooth blind spot positioning base network obtains a nano unit, and the first Bluetooth blind spot positioning base network obtains a nano unit for configuring a 1-value input grid of the initial graph neural network based on the blind spot positioning loss function, according to the positioning record information at the disconnection moment, the Bluetooth blind spot movement trajectory pruning information, and the positioning record information at the reconnection moment, and configuring a 1-value output grid of the initial graph neural network with the Bluetooth blind spot positioning coordinate sequence identification data as the 1-value output grid supervision data, training the initial graph neural network, and obtaining the first Bluetooth blind spot positioning base network.

[0052] Among them, the positioning coordinate sequence acquisition module 30 further includes: a trajectory positioning unit, the trajectory positioning unit is used to obtain the trajectory starting positioning and trajectory ending positioning of the Bluetooth blind spot movement trajectory; a Bluetooth blind spot movement trajectory acquisition unit, the Bluetooth blind spot movement trajectory acquisition unit is used to, when the trajectory starting positioning is the same as the spatial positioning at the disconnection moment, and the trajectory ending positioning is the same as the spatial positioning at the reconnection success moment, and the Bluetooth blind spot movement trajectory is a continuous trajectory, locate the Bluetooth blind spot movement trajectory based on the spatial positioning at the disconnection moment and the spatial positioning at the reconnection success moment, and obtain the Bluetooth blind spot movement trajectory; a Bluetooth blind spot positioning coordinate sequence acquisition unit, the Bluetooth blind spot positioning coordinate sequence acquisition unit is used to, otherwise, activate the Bluetooth blind spot positioning network according to the factory workshop number to process the spatial positioning at the disconnection moment, the Bluetooth blind spot movement trajectory and the spatial positioning at the reconnection success moment, and obtain the Bluetooth blind spot positioning coordinate sequence.

[0053] The Bluetooth positioning-driven factory personnel tracking and management system provided in the embodiment of the present invention can execute the Bluetooth positioning-driven factory personnel tracking and management method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0054] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0055] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A factory personnel tracking and management method driven by Bluetooth positioning, characterized in that: include: When the first employee positioning Bluetooth is disconnected, the spatial location at the time of disconnection is obtained, wherein the first employee positioning Bluetooth automatically records the spatial movement trajectory with the spatial location at the time of disconnection as the origin after the disconnection; Continuously requesting to reconnect with the first employee positioning Bluetooth, and when the reconnection is successful, obtaining the Bluetooth blind area movement trajectory and the spatial location at the time of successful reconnection from the first employee positioning Bluetooth; According to the factory workshop number, the Bluetooth blind spot positioning network is activated to process the spatial positioning at the time of disconnection, the Bluetooth blind spot movement trajectory, and the spatial positioning at the time of successful reconnection to obtain a Bluetooth blind spot positioning coordinate sequence; Performing blind spot coordinate compensation tracking management for the first employee according to the Bluetooth blind spot positioning coordinate sequence; According to the factory workshop number, the Bluetooth blind spot positioning network is activated to process the spatial positioning at the disconnection time, the Bluetooth blind spot movement trajectory, and the spatial positioning at the successful reconnection time to obtain a Bluetooth blind spot positioning coordinate sequence, including: The Bluetooth blind area positioning network includes the first Bluetooth blind area positioning base network to the Nth Bluetooth blind area positioning base network, and a fully connected network; According to the first Bluetooth blind spot positioning base network to the Nth Bluetooth blind spot positioning base network, respectively processing the spatial positioning at the disconnection time, the Bluetooth blind spot movement trajectory, and the spatial positioning at the time of successful reconnection, to obtain a first Bluetooth blind spot positioning coordinate sequence to an Nth Bluetooth blind spot positioning coordinate sequence; fusing the first Bluetooth blind spot positioning coordinate sequence to the Nth Bluetooth blind spot positioning coordinate sequence through the fully connected network to obtain the Bluetooth blind spot positioning coordinate sequence; The method of fusing the first Bluetooth blind spot positioning coordinate sequence to the Nth Bluetooth blind spot positioning coordinate sequence through the fully connected network to obtain the Bluetooth blind spot positioning coordinate sequence includes: Configure a full connectivity rule factor to construct the fully connected network, wherein the full connectivity rule factor is: Perform pairwise sequence distance evaluation on the first Bluetooth blind spot positioning coordinate sequence to the Nth Bluetooth blind spot positioning coordinate sequence to obtain a plurality of sequence distance parameters; Based on the plurality of sequence distance parameters, traverse the first Bluetooth blind spot positioning coordinate sequence until the Nth Bluetooth blind spot positioning coordinate sequence to perform outlier factor analysis, and obtain outlier factors of the first Bluetooth blind spot positioning coordinate sequence until the Nth Bluetooth blind spot positioning coordinate sequence; A positioning coordinate sequence having a minimum value of the outlier factor of the first Bluetooth blind spot positioning coordinate sequence up to the outlier factor of the Nth Bluetooth blind spot positioning coordinate sequence is extracted, and outputted as the Bluetooth blind spot positioning coordinate sequence.

2. The method according to claim 1, wherein The first Bluetooth blind area positioning base network construction step includes: Based on the factory workshop number, construct a three-dimensional model of the factory workshop, and based on a preset spatial grid side length, construct a three-dimensional grid model of the factory workshop three-dimensional model; Configuring each grid of the three-dimensional grid model of the factory workshop as both an input node and an output node, with input information being 0 or 1 and output information being 0 or 1, to obtain an initial graph neural network, wherein the grid input information and output information of the initial graph neural network are both 0 by default; Collecting historical employee monitoring information of the three-dimensional grid model of the factory workshop, wherein the historical employee monitoring information includes location record information at the time of disconnection, Bluetooth blind spot movement trajectory record information, location record information at the time of reconnection, and Bluetooth blind spot positioning coordinate sequence identification data, wherein the Bluetooth blind spot movement trajectory record information is a movement trajectory recorded with the location record information at the time of disconnection as the origin; According to the positioning record information at the time of disconnection, the Bluetooth blind spot movement trajectory record information, and the positioning record information at the time of reconnection, a 1-value input grid of the initial graph neural network is configured; the 1-value output grid of the initial graph neural network is configured with the Bluetooth blind spot positioning coordinate sequence identification data as the 1-value output grid supervision data, and the initial graph neural network is trained to obtain the first Bluetooth blind spot positioning base network; The steps for constructing the second Bluetooth blind area positioning base network up to the Nth Bluetooth blind area positioning base network are the same as the steps for constructing the first Bluetooth blind area positioning base network.

3. The method according to claim 2, wherein The 1-value input grid of the initial graph neural network is configured according to the positioning record information at the time of disconnection, the Bluetooth blind spot movement trajectory record information, and the positioning record information at the time of reconnection. The 1-value output grid of the initial graph neural network is configured with the Bluetooth blind spot positioning coordinate sequence identification data as the 1-value output grid supervision data, and the initial graph neural network is trained to obtain the first Bluetooth blind spot positioning base network, including: Construct blind spot positioning loss function: Among them, X1 represents the 1-value output grid supervision data of the initial graph neural network configured with the Bluetooth blind spot positioning coordinate sequence identification data, X 10 Characterize the 1-valued output grid of the initial graph neural network, n(X1∪X 10 ) represents X1 and X 10 The number of intersection 1 grids, n(X1∩X 10 ) represents X1 and X 10 The number of grids with a union value of 1, e represents the natural constant, and LOSS represents the blind area positioning loss value; Based on the blind spot positioning loss function, the 1-value input grid of the initial graph neural network is configured according to the positioning record information at the time of disconnection, the Bluetooth blind spot movement trajectory record information, and the positioning record information at the time of reconnection. The 1-value output grid of the initial graph neural network is configured with the Bluetooth blind spot positioning coordinate sequence identification data as the 1-value output grid supervision data, and the initial graph neural network is trained to obtain the first Bluetooth blind spot positioning base network.

4. The method according to claim 3, wherein Based on the blind spot positioning loss function, configuring the 1-value input grid of the initial graph neural network according to the positioning record information at the disconnection time, the Bluetooth blind spot movement trajectory record information, and the positioning record information at the reconnection time, configuring the 1-value output grid of the initial graph neural network with the Bluetooth blind spot positioning coordinate sequence identification data as the 1-value output grid supervision data, training the initial graph neural network, and obtaining the first Bluetooth blind spot positioning base network, further comprising: Based on a preset trajectory length, the Bluetooth blind area movement trajectory record information is trimmed to obtain Bluetooth blind area movement trajectory pruning information, wherein the Bluetooth blind area movement trajectory pruning information is a discontinuous trajectory; Based on the blind spot positioning loss function, the 1-value input grid of the initial graph neural network is configured according to the positioning record information at the time of disconnection, the Bluetooth blind spot movement trajectory pruning information, and the positioning record information at the time of reconnection. The 1-value output grid of the initial graph neural network is configured with the Bluetooth blind spot positioning coordinate sequence identification data as the 1-value output grid supervision data, and the initial graph neural network is trained to obtain the first Bluetooth blind spot positioning base network.

5. The method according to claim 1, wherein According to the factory workshop number, the Bluetooth blind spot positioning network is activated to process the spatial positioning at the time of disconnection, the Bluetooth blind spot movement trajectory, and the spatial positioning at the time of successful reconnection to obtain a Bluetooth blind spot positioning coordinate sequence, which includes: Obtaining the trajectory start location and trajectory end location of the Bluetooth blind area movement trajectory; When the starting location of the trajectory is the same as the spatial location at the time of disconnection, and the ending location of the trajectory is the same as the spatial location at the time of successful reconnection, and the Bluetooth blind spot movement trajectory is a continuous trajectory, the Bluetooth blind spot movement trajectory is positioned based on the spatial location at the time of disconnection and the spatial location at the time of successful reconnection to obtain the Bluetooth blind spot movement trajectory; Otherwise, according to the factory workshop number, the Bluetooth blind spot positioning network is activated to process the spatial positioning at the disconnection time, the Bluetooth blind spot movement trajectory and the spatial positioning at the successful reconnection time to obtain the Bluetooth blind spot positioning coordinate sequence.

6. Bluetooth positioning driven factory personnel tracking management system, characterized by: The system comprises: A spatial positioning acquisition module, configured to obtain the spatial location at the time of disconnection when the first employee positioning Bluetooth is disconnected, wherein the first employee positioning Bluetooth automatically records a spatial movement trajectory with the spatial location at the time of disconnection as the origin after the disconnection; a reconnection module, the reconnection module being configured to continuously request reconnection with the first employee positioning Bluetooth, and when reconnection is successful, obtaining a movement trajectory of the Bluetooth blind area and a spatial location at the time of successful reconnection from the first employee positioning Bluetooth; A positioning coordinate sequence acquisition module is used to activate the Bluetooth blind spot positioning network according to the factory workshop number to process the spatial positioning at the disconnection time, the Bluetooth blind spot movement trajectory, and the spatial positioning at the time of successful reconnection to obtain a Bluetooth blind spot positioning coordinate sequence; A coordinate compensation tracking management module, configured to perform coordinate compensation tracking management for the first employee's blind spot according to the Bluetooth blind spot positioning coordinate sequence; According to the factory workshop number, the Bluetooth blind spot positioning network is activated to process the spatial positioning at the disconnection time, the Bluetooth blind spot movement trajectory, and the spatial positioning at the successful reconnection time to obtain a Bluetooth blind spot positioning coordinate sequence, including: The Bluetooth blind area positioning network includes the first Bluetooth blind area positioning base network to the Nth Bluetooth blind area positioning base network, and a fully connected network; According to the first Bluetooth blind spot positioning base network to the Nth Bluetooth blind spot positioning base network, respectively processing the spatial positioning at the disconnection time, the Bluetooth blind spot movement trajectory, and the spatial positioning at the time of successful reconnection, to obtain a first Bluetooth blind spot positioning coordinate sequence to an Nth Bluetooth blind spot positioning coordinate sequence; fusing the first Bluetooth blind spot positioning coordinate sequence to the Nth Bluetooth blind spot positioning coordinate sequence through the fully connected network to obtain the Bluetooth blind spot positioning coordinate sequence; The method of fusing the first Bluetooth blind spot positioning coordinate sequence to the Nth Bluetooth blind spot positioning coordinate sequence through the fully connected network to obtain the Bluetooth blind spot positioning coordinate sequence includes: Configure a full connectivity rule factor to construct the fully connected network, wherein the full connectivity rule factor is: Perform pairwise sequence distance evaluation on the first Bluetooth blind spot positioning coordinate sequence to the Nth Bluetooth blind spot positioning coordinate sequence to obtain a plurality of sequence distance parameters; Based on the plurality of sequence distance parameters, traverse the first Bluetooth blind spot positioning coordinate sequence until the Nth Bluetooth blind spot positioning coordinate sequence to perform outlier factor analysis, and obtain outlier factors of the first Bluetooth blind spot positioning coordinate sequence until the Nth Bluetooth blind spot positioning coordinate sequence; A positioning coordinate sequence having a minimum value of the outlier factor of the first Bluetooth blind spot positioning coordinate sequence up to the outlier factor of the Nth Bluetooth blind spot positioning coordinate sequence is extracted, and outputted as the Bluetooth blind spot positioning coordinate sequence.

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