CN110428888A - A kind of label management method and system - Google Patents
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Abstract
The present invention relates to data processing technique, a kind of label management method and system are provided.The signal that the electronic tag of this method elder generation the first preset quantity of timing acquisition issues, using the intensity data of the signal as characteristic value, and normalized is made to the characteristic value, signal strength data again based on each electronic tag, the first position coordinate of each electronic tag is calculated using triangle polyester fibre algorithm, based on the signal strength data after each electronic tag normalization, utilize preset location algorithm, the second position coordinate of each electronic tag is calculated, the coordinate value of the coordinate value of the first position coordinate and second position coordinate is inputted in predetermined formula, obtain each electronic tag final position coordinate.The accuracy of prediction electronic tag position can be improved in the present invention.
Description
Technical Field
The invention relates to the technical field of positioning, in particular to a label management method and a label management system.
Background
At present, large hospitals generally attach electronic tags to medical staff, patients in clinic, security personnel, protection personnel and other people in the form of industrial cards, bangles and the like. In addition, the electronic tags are attached to various mobile medical devices in hospitals, and it is very important to acquire accurate position information of each electronic tag.
At present, a triangular intensity algorithm is usually adopted to predict the indoor position of the electronic tag, but the triangular intensity algorithm has low precision and high positioning error rate and is difficult to meet the indoor positioning requirement.
Therefore, how to improve the accuracy of label management has become a technical problem to be solved urgently by those skilled in the art.
Disclosure of Invention
In view of the above, the present invention provides a tag management method and system, which aims to improve the accuracy of tag management.
In order to achieve the above object, the present invention provides a tag management method, including:
an acquisition step: the method comprises the steps of regularly acquiring signals sent by a first preset number of electronic tags, taking intensity data of the signals as characteristic values, and carrying out normalization processing on the characteristic values;
a first calculation step: calculating to obtain a first position coordinate of each electronic tag by utilizing a triangulation algorithm based on the signal intensity data of each electronic tag;
a second calculation step: calculating to obtain a second position coordinate of each electronic tag by using a preset positioning algorithm based on the normalized signal intensity data of each electronic tag; and
a prediction step: and inputting the coordinate value of the first position coordinate and the coordinate value of the second position coordinate into a predetermined formula to obtain the final position coordinate of each electronic tag.
Preferably, the preset positioning algorithm is obtained by training a gradient boosting decision tree model, and the specific training steps include:
acquiring electronic tag signal data detected by a second preset number of sampling points, and extracting signal intensity data of each sampling point from the electronic tag signal data as a characteristic value of the sampling point;
acquiring the position coordinates of each sampling point, and forming a sample set by the position coordinates of each sampling point and the characteristic values corresponding to each sampling point;
dividing the sample set into a first data set and a second data set according to a preset proportion, wherein the first data set is a training set, the second data set is a verification set, and the number of the training sets is larger than that of the verification sets;
training a gradient lifting decision tree model by using the training set to obtain the second preset algorithm; and
and verifying the accuracy of the second preset algorithm by using the verification set, and finishing training if the accuracy is greater than or equal to a preset threshold, or increasing the number of samples and re-executing the training step if the accuracy is less than the preset threshold.
Preferably, the electronic tag is an active electronic tag, and when the electric quantity of the electronic tag is detected to be lower than a preset threshold value, a warning message is sent out.
Preferably, the normalizing the characteristic values specifically includes normalizing the signal intensity data into a data set with a mean value of 0 and a standard deviation of 1, and the conversion formula is as follows:
，
wherein,represents a normalized value of the signal strength data,represents an average of the signal strength data,a standard deviation representing the signal strength data,a value representing each of said signal strength data.
Preferably, the predetermined formula is:
F(M) = a×+b×，
wherein F (M) represents the final predicted coordinates of the electronic tag,a coordinate value representing the coordinate of the first position,and a and b are predetermined weight values.
In order to achieve the above object, the present invention also provides a tag management system, including: the device is characterized in that an automatic conference record generation program is stored in the memory and executed by the processor, and the automatic conference record generation program realizes the following steps:
an acquisition step: the method comprises the steps of regularly acquiring signals sent by a first preset number of electronic tags, taking intensity data of the signals as characteristic values, and carrying out normalization processing on the characteristic values;
a first calculation step: calculating to obtain a first position coordinate of each electronic tag by utilizing a triangulation algorithm based on the signal intensity data of each electronic tag;
a second calculation step: calculating to obtain a second position coordinate of each electronic tag by using a preset positioning algorithm based on the normalized signal intensity data of each electronic tag; and
a prediction step: and inputting the coordinate value of the first position coordinate and the coordinate value of the second position coordinate into a predetermined formula to obtain the final position coordinate of each electronic tag.
Preferably, the preset positioning algorithm is obtained by training a gradient boosting decision tree model, and the specific training steps include:
acquiring electronic tag signal data detected by a second preset number of sampling points, and extracting signal intensity data of each sampling point from the electronic tag signal data as a characteristic value of the sampling point;
acquiring the position coordinates of each sampling point, and forming a sample set by the position coordinates of each sampling point and the characteristic values corresponding to each sampling point;
dividing the sample set into a first data set and a second data set according to a preset proportion, wherein the first data set is a training set, the second data set is a verification set, and the number of the training sets is larger than that of the verification sets;
training a gradient lifting decision tree model by using the training set to obtain the second preset algorithm; and
and verifying the accuracy of the second preset algorithm by using the verification set, and finishing training if the accuracy is greater than or equal to a preset threshold, or increasing the number of samples and re-executing the training step if the accuracy is less than the preset threshold.
Preferably, the normalizing the characteristic values specifically includes normalizing the signal intensity data into a data set with a mean value of 0 and a standard deviation of 1, and the conversion formula is as follows:
，
wherein,represents a normalized value of the signal strength data,represents an average of the signal strength data,a standard deviation representing the signal strength data,a value representing each of said signal strength data.
Preferably, the predetermined formula is:
F(M) = a×+b×，
wherein F (M) represents the final predicted coordinates of the electronic tag,a coordinate value representing the coordinate of the first position,and a and b are predetermined weight values.
According to the label management method and system, signals sent by a first preset number of electronic labels are obtained at regular time, intensity data of the signals are used as characteristic values, normalization processing is conducted on the characteristic values, a first position coordinate of each electronic label is calculated by using a triangular positioning algorithm based on the signal intensity data of each electronic label, a second position coordinate of each electronic label is calculated by using a preset positioning algorithm based on the signal intensity data after normalization of each electronic label, the coordinate value of the first position coordinate and the coordinate value of the second position coordinate are input into a predetermined formula to obtain the final position coordinate of each electronic label, and therefore the accuracy of label management can be improved.
Drawings
FIG. 1 is a diagram of a preferred embodiment of a tag management system according to the present invention;
FIG. 2 is a block diagram illustrating a preferred embodiment of a process of the tag management method of FIG. 1;
FIG. 3 is a flow chart of a preferred embodiment of a tag management method of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a schematic diagram of a tag management system 1 according to a preferred embodiment of the present invention is shown.
The tag management system 1 includes but is not limited to: memory 11, processor 12, display 13, and network interface 14. The tag management system 1 is connected to a network through a network interface 14 to obtain original data. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a 4G network, a 5G network, Bluetooth (Bluetooth), Wi-Fi, or a communication network.
The memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In this embodiment, the memory 11 is generally used for storing an operating system and various application software installed in the tag management system 1, such as program codes of the tag management program 10. Further, the memory 11 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 12 is generally used for controlling the overall operation of the tag management system 1, such as performing data interaction or communication related control and processing. In this embodiment, the processor 12 is configured to run the program code stored in the memory 11 or process data, for example, run the program code of the tag management program 10.
The display 13 may be referred to as a display screen or display unit. The display 13 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and the like in some embodiments. The display 13 is used for displaying information processed in the tag management system 1 and for displaying a visual work interface, for example, displaying the results of data statistics.
The network interface 14 may optionally comprise a standard wired interface, a wireless interface (e.g. WI-FI interface), the network interface 14 typically being used for establishing a communication connection between the tag management system 1 and other electronic devices.
Optionally, the label management system 1 may further include a user interface, the user interface may include a Display (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface may further include a standard wired interface and a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or the like.
The tag management system 1 may further include a Radio Frequency (RF) circuit, a sensor, an audio circuit, and the like, which will not be described herein.
In the above embodiment, the processor 12 may implement the following steps when executing the program for obtaining the electronic tag location 10 stored in the memory 11:
an acquisition step: the method comprises the steps of regularly acquiring signals sent by a first preset number of electronic tags, taking intensity data of the signals as characteristic values, and carrying out normalization processing on the characteristic values;
a first calculation step: calculating to obtain a first position coordinate of each electronic tag by utilizing a triangulation algorithm based on the signal intensity data of each electronic tag;
a second calculation step: calculating to obtain a second position coordinate of each electronic tag by using a preset positioning algorithm based on the normalized signal intensity data of each electronic tag; and
a prediction step: and inputting the coordinate value of the first position coordinate and the coordinate value of the second position coordinate into a predetermined formula to obtain the final position coordinate of each electronic tag.
For a detailed description of the above steps, please refer to the following description of fig. 2 regarding a program module diagram of an embodiment of the tag management program 10, and fig. 3 regarding a flowchart of an embodiment of a tag management method.
In other embodiments, the tag management program 10 may be divided into a plurality of modules, which are stored in the memory 12 and executed by the processor 13 to accomplish the present invention. The modules referred to herein are referred to as a series of computer program instruction segments capable of performing specified functions.
Referring to fig. 2, a block diagram of an embodiment of the tag management program 10 of fig. 1 is shown. In this embodiment, the tag management program 10 may be divided into: an acquisition module 110, a first calculation module 120, a second module 130, and a prediction module 140.
The receiving module 110 is configured to obtain signals sent by a first preset number of electronic tags at regular time, use the intensity data of the signals as a feature value, and perform normalization processing on the feature value.
In this embodiment, signals sent by a preset number (e.g., 500) of electronic tags are acquired at preset time intervals (e.g., 1 second), and the acquired signal strength data of each electronic tag is used as a characteristic value of the electronic tag, where the acquired information includes identification information that can uniquely identify the electronic tag, such as the signal strength data of a plurality of electronic tags, and MAC addresses or Service Set Identifiers (SSIDs) of corresponding electronic tags.
Then, normalization processing is performed on the characteristic values, and the data of the signal intensity can be normalized into a data set with a mean value of 0 and a standard deviation of 1, wherein the conversion formula is as follows:
，
wherein,represents a normalized value of the signal strength data,represents the average of the signal strength data,represents the standard deviation of the signal strength data,values representing respective signal strength data.
In an embodiment, all data of each feature value can be linearly converted into the [0, 1] interval according to the maximum value of the signal intensity data and the minimum value of the signal intensity data in the feature values.
The first calculating module 120 is configured to calculate, by using a triangulation algorithm, a first position coordinate of each electronic tag based on the signal strength data of each electronic tag.
The position coordinates of the electronic tags can be calculated by adopting a triangulation algorithm, when the position coordinates of three or more electronic tags are determined, and the distances from other electronic tags to the known electronic tags can be determined according to the detected signal intensity of the electronic tags, the first position coordinates of the electronic tags can be calculated according to the information.
The second calculating module 130 is configured to calculate, based on the normalized signal intensity data of each electronic tag, a second position coordinate of each electronic tag by using a preset positioning algorithm.
And inputting the characteristic value after the normalization processing into a preset positioning algorithm, and calculating to obtain a second position coordinate of the electronic tag corresponding to the characteristic value.
The preset positioning algorithm is obtained by training a gradient boosting decision tree model, and the specific training steps comprise:
acquiring electronic tag signal data detected by a preset number (for example, 10000) of sampling points in a specified area (for example, a hospital), and extracting signal intensity data of each sampling point from the electronic tag signal data as a characteristic value of the sampling point;
determining and acquiring a position coordinate of each sampling point, wherein a determination reference point of the position coordinate may be a certain point in a school (for example, a hospital portal), and the position coordinate of each sampling point and a feature value corresponding to each sampling point are combined into a sample set;
dividing the sample set into a first data set and a second data set according to a preset proportion, wherein the first data set is a training set, the second data set is a verification set, and the number of samples in the training set is greater than that in the verification set, for example: randomly extracting (for example, 70%) positions of sampling points and feature values corresponding to the sampling points from the sample set as a training set, and randomly extracting (for example, 30%) positions of the sampling points and feature values corresponding to the sampling points from the sample set as a verification set;
training the gradient lifting decision tree model by using 70% of sample data, determining model parameters of the position positioning model, and obtaining a relation between position coordinates of sampling points and electronic tag signal intensity data corresponding to the sampling points;
and verifying the accuracy of the position positioning model by using 30% of sample data, and finishing the training if the accuracy is greater than or equal to a preset accuracy (for example, 95%), or increasing the number of samples and re-executing the training step if the accuracy is less than the preset accuracy (for example, 95%).
And the prediction module 140 is configured to input the coordinate value of the first position coordinate and the coordinate value of the second position coordinate into a predetermined formula, so as to obtain a final position coordinate of each electronic tag.
In this embodiment, the coordinate value of the first position coordinate and the coordinate value of the second position coordinate of each electronic tag obtained by the above calculation are input into a predetermined formula to obtain the final position coordinate of the electronic tag, specifically, corresponding weight values are given to the first position coordinate and the second position coordinate, and the predetermined formula is:
F(M) = a×+b×，
wherein F (M) represents the final predicted coordinates of the electronic tag,a coordinate value representing the coordinate of the first position,and a and b are predetermined weight values. For example: the value of a is 40% and the value of b is 60%.
In another embodiment, the electronic tag is an active electronic tag, and when the power of the electronic tag is detected to be lower than a preset threshold, a warning message is sent out. Furthermore, a corresponding electronic fence can be set for each electronic tag, and when the moving range of the electronic tag is detected to exceed the corresponding electronic fence, warning information is sent out.
In addition, the invention also provides a label management method. Referring to fig. 3, a method flow diagram of an embodiment of the method for label management according to the present invention is shown. The following steps of the method of implementing label management when the processor 12 of the label management system 1 executes the label management program 10 stored in the memory 11:
step S10: the method comprises the steps of obtaining signals sent by a first preset number of electronic tags at regular time, using intensity data of the signals as characteristic values, and carrying out normalization processing on the characteristic values.
In this embodiment, signals sent by a preset number (e.g., 500) of electronic tags are acquired at preset time intervals (e.g., 1 second), and the acquired signal strength data of each electronic tag is used as a characteristic value of the electronic tag, where the acquired information includes identification information that can uniquely identify the electronic tag, such as the signal strength data of a plurality of electronic tags, and MAC addresses or Service Set Identifiers (SSIDs) of corresponding electronic tags.
Then, normalization processing is performed on the characteristic values, and the data of the signal intensity can be normalized into a data set with a mean value of 0 and a standard deviation of 1, wherein the conversion formula is as follows:
，
wherein,represents a normalized value of the signal strength data,represents the average of the signal strength data,represents the standard deviation of the signal strength data,values representing respective signal strength data.
Step S20: and calculating to obtain a first position coordinate of each electronic tag by utilizing a triangulation algorithm based on the signal intensity data of each electronic tag.
In this embodiment, a triangulation algorithm may be used to calculate the position coordinates of the electronic tags, and when the position coordinates of three or more electronic tags are determined, and the distances from other electronic tags to the known electronic tags can be determined according to the detected signal strength of the electronic tags, the first position coordinates of the electronic tags can be calculated according to the above information.
Step S30: and calculating to obtain a second position coordinate of each electronic tag by using a preset positioning algorithm based on the normalized signal intensity data of each electronic tag.
And inputting the characteristic value after the normalization processing into a preset positioning algorithm, and calculating to obtain a second position coordinate of the electronic tag corresponding to the characteristic value.
The preset positioning algorithm is obtained by training a gradient boosting decision tree model, and the specific training steps comprise:
acquiring electronic tag signal data detected by a preset number (for example, 10000) of sampling points in a specified area (for example, a hospital), and extracting signal intensity data of each sampling point from the electronic tag signal data as a characteristic value of the sampling point;
determining and acquiring a position coordinate of each sampling point, wherein a determination reference point of the position coordinate may be a certain point in a school (for example, a hospital portal), and the position coordinate of each sampling point and a feature value corresponding to each sampling point are combined into a sample set;
dividing the sample set into a first data set and a second data set according to a preset proportion, wherein the first data set is a training set, the second data set is a verification set, and the number of samples in the training set is greater than that in the verification set, for example: randomly extracting (for example, 70%) positions of sampling points and feature values corresponding to the sampling points from the sample set as a training set, and randomly extracting (for example, 30%) positions of the sampling points and feature values corresponding to the sampling points from the sample set as a verification set;
training the gradient lifting decision tree model by using 70% of sample data, determining model parameters of the position positioning model, and obtaining a relation between position coordinates of sampling points and electronic tag signal intensity data corresponding to the sampling points;
and verifying the accuracy of the position positioning model by using 30% of sample data, and finishing the training if the accuracy is greater than or equal to a preset accuracy (for example, 95%), or increasing the number of samples and re-executing the training step if the accuracy is less than the preset accuracy (for example, 95%).
Step S40: and inputting the coordinate value of the first position coordinate and the coordinate value of the second position coordinate into a predetermined formula to obtain the final position coordinate of each electronic tag.
In this embodiment, the coordinate value of the first position coordinate and the coordinate value of the second position coordinate of each electronic tag obtained by the above calculation are input into a predetermined formula to obtain the final position coordinate of the electronic tag, specifically, corresponding weight values are given to the first position coordinate and the second position coordinate, and the predetermined formula is:
F(M) = a×+b×，
wherein F (M) represents the final predicted coordinates of the electronic tag,a coordinate value representing the coordinate of the first position,and a and b are predetermined weight values. For example: the value of a is 40% and the value of b is 60%.
In another embodiment, the electronic tag is an active electronic tag, and when the power of the electronic tag is detected to be lower than a preset threshold, a warning message is sent out. Furthermore, a corresponding electronic fence can be set for each electronic tag, and when the moving range of the electronic tag is detected to exceed the corresponding electronic fence, warning information is sent out.
Furthermore, the embodiment of the present invention also provides a computer-readable storage medium, which may be any one or any combination of a hard disk, a multimedia card, an SD card, a flash memory card, an SMC, a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), a portable compact disc read only memory (CD-ROM), a USB memory, and the like. The computer readable storage medium includes a tag management program 10, and when executed by a processor, the tag management program 10 implements the following operations:
an acquisition step: the method comprises the steps of regularly acquiring signals sent by a first preset number of electronic tags, taking intensity data of the signals as characteristic values, and carrying out normalization processing on the characteristic values;
a first calculation step: calculating to obtain a first position coordinate of each electronic tag by utilizing a triangulation algorithm based on the signal intensity data of each electronic tag;
a second calculation step: calculating to obtain a second position coordinate of each electronic tag by using a preset positioning algorithm based on the normalized signal intensity data of each electronic tag; and
a prediction step: and inputting the coordinate value of the first position coordinate and the coordinate value of the second position coordinate into a predetermined formula to obtain the final position coordinate of each electronic tag.
The specific implementation of the computer-readable storage medium of the present invention is substantially the same as the specific implementation of the above-mentioned label management method, and will not be described herein again.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a tag management system, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. A label management method is applied to a label management system and is characterized by comprising the following steps:
an acquisition step: the method comprises the steps of regularly acquiring signals sent by a first preset number of electronic tags, taking intensity data of the signals as characteristic values, and carrying out normalization processing on the characteristic values;
a first calculation step: calculating to obtain a first position coordinate of each electronic tag by utilizing a triangulation algorithm based on the signal intensity data of each electronic tag;
a second calculation step: calculating to obtain a second position coordinate of each electronic tag by using a preset positioning algorithm based on the normalized signal intensity data of each electronic tag; and
a prediction step: and inputting the coordinate value of the first position coordinate and the coordinate value of the second position coordinate into a predetermined formula to obtain the final position coordinate of each electronic tag.
2. The label management method of claim 1, wherein the predetermined positioning algorithm is obtained by training a gradient boosting decision tree model, and the specific training steps include:
acquiring electronic tag signal data detected by a second preset number of sampling points, and extracting signal intensity data of each sampling point from the electronic tag signal data as a characteristic value of the sampling point;
acquiring the position coordinates of each sampling point, and forming a sample set by the position coordinates of each sampling point and the characteristic values corresponding to each sampling point;
dividing the sample set into a first data set and a second data set according to a preset proportion, wherein the first data set is a training set, the second data set is a verification set, and the number of the training sets is larger than that of the verification sets;
training a gradient lifting decision tree model by using the training set to obtain the second preset algorithm; and
and verifying the accuracy of the second preset algorithm by using the verification set, and finishing training if the accuracy is greater than or equal to a preset threshold, or increasing the number of samples and re-executing the training step if the accuracy is less than the preset threshold.
3. The tag management method according to claim 1, wherein the electronic tag is an active electronic tag, and when it is detected that the power of the electronic tag is lower than a preset threshold, a warning message is issued.
4. The tag management method according to claim 1, wherein the normalization processing of the feature values specifically includes normalizing each signal intensity data into a data set having a mean value of 0 and a standard deviation of 1, and a conversion formula is as follows:
，
wherein,represents a normalized value of the signal strength data,representing an average of said signal strength dataThe value of the one or more of,a standard deviation representing the signal strength data,a value representing each of said signal strength data.
5. The label management method of claim 1, wherein the predetermined formula is:
F(M) = a×+b×，
wherein F (M) represents the final predicted coordinates of the electronic tag,a coordinate value representing the coordinate of the first position,and a and b are predetermined weight values.
6. A tag management system, comprising a memory and a processor, wherein the memory stores a tag management program, and the tag management program is executed by the processor, and the following steps are implemented:
an acquisition step: the method comprises the steps of regularly acquiring signals sent by a first preset number of electronic tags, taking intensity data of the signals as characteristic values, and carrying out normalization processing on the characteristic values;
a first calculation step: calculating to obtain a first position coordinate of each electronic tag by utilizing a triangulation algorithm based on the signal intensity data of each electronic tag;
a second calculation step: calculating to obtain a second position coordinate of each electronic tag by using a preset positioning algorithm based on the normalized signal intensity data of each electronic tag; and
a prediction step: and inputting the coordinate value of the first position coordinate and the coordinate value of the second position coordinate into a predetermined formula to obtain the final position coordinate of each electronic tag.
7. The label management system of claim 6, wherein the predetermined positioning algorithm is obtained by training a gradient boosting decision tree model, and the specific training steps include:
acquiring electronic tag signal data detected by a second preset number of sampling points, and extracting signal intensity data of each sampling point from the electronic tag signal data as a characteristic value of the sampling point;
acquiring the position coordinates of each sampling point, and forming a sample set by the position coordinates of each sampling point and the characteristic values corresponding to each sampling point;
dividing the sample set into a first data set and a second data set according to a preset proportion, wherein the first data set is a training set, the second data set is a verification set, and the number of the training sets is larger than that of the verification sets;
training a gradient lifting decision tree model by using the training set to obtain the second preset algorithm; and
and verifying the accuracy of the second preset algorithm by using the verification set, and finishing training if the accuracy is greater than or equal to a preset threshold, or increasing the number of samples and re-executing the training step if the accuracy is less than the preset threshold.
8. The tag management system of claim 6, wherein the normalization of the eigenvalues specifically comprises normalizing each signal strength data to a data set with a mean value of 0 and a standard deviation of 1, the conversion formula being as follows:
，
wherein,represents a normalized value of the signal strength data,represents an average of the signal strength data,a standard deviation representing the signal strength data,a value representing each of said signal strength data.
9. The label management system of claim 6, wherein the predetermined formula is:
F(M) = a×+b×，
wherein F (M) represents the final predicted coordinates of the electronic tag,a coordinate value representing the coordinate of the first position,and a and b are predetermined weight values.
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