CN119031440A - An information transmission system based on positioning system - Google Patents

Abstract

The present invention discloses an information transmission system based on a positioning system, which relates to the technical field of information transmission, and includes a collection and processing module for collecting real-time positioning data and processing the collected real-time positioning data; a path optimization module for real-time monitoring of network conditions and selecting an optimal data transmission path using a Dijkstra algorithm; an encryption and receiving module for encrypting and transmitting pre-processed positioning data, receiving the transmitted positioning data and verifying it; and a visualization and storage module for displaying the status of real-time positioning data in real time and storing all used positioning data. The present invention combines VLC and Bluetooth technology, uses Kalman filtering technology to fuse multi-source positioning data, predicts network conditions through an ARIMA model, and dynamically adjusts the path weight of the Dijkstra algorithm, which not only improves the efficiency and reliability of data transmission, but also can quickly adapt when the network status changes, and enhances the stability and reliability of positioning data.

Description

An information transmission system based on positioning system

Technical Field

The present invention relates to the technical field of information transmission, and in particular to an information transmission system based on a positioning system.

Background Art

In modern society, with the rapid development of information technology and communication technology, positioning systems are increasingly used in various fields. Positioning technology has gradually developed from the initial simple satellite positioning to today's multimodal positioning system, including the integrated application of multiple technologies such as GPS, Wi-Fi, Bluetooth and visible light communication VLC. The continuous evolution of these technologies has improved positioning accuracy and reliability, especially in indoor positioning. Various information transmission systems based on positioning systems have gradually been formed. These systems can not only realize the precise positioning of mobile devices, but also provide support for smart homes, smart cities, logistics management and other fields through real-time transmission and processing of positioning data. With the complexity of application scenarios and the diversification of needs, the existing positioning systems still face many challenges in terms of accuracy, real-time performance, data transmission security and network performance optimization.

Existing positioning systems mainly rely on a single technical means, such as Wi-Fi or Bluetooth for positioning. These systems are often subject to signal interference and obstruction in complex environments, resulting in low positioning accuracy and difficulty in meeting the needs of high-precision applications. Traditional information transmission systems usually ignore the impact of network conditions on data transmission performance and fail to achieve dynamic path optimization, resulting in low transmission efficiency and serious data packet loss and delay problems.

Summary of the invention

In view of the above problems existing in the existing information transmission system based on the positioning system, the present invention is proposed.

Therefore, the problem to be solved by the present invention is that the existing positioning systems mainly rely on a single technical means. These systems are often subject to signal interference and obstruction in complex environments, resulting in low positioning accuracy and difficulty in meeting the needs of high-precision applications. Traditional information transmission systems usually ignore the impact of network conditions on data transmission performance and fail to achieve dynamic path optimization, resulting in low transmission efficiency and serious problems of data packet loss and delay.

To solve the above technical problems, the present invention provides the following technical solutions: an information transmission system based on a positioning system, comprising: a collection and processing module, used to collect real-time positioning data and process the collected real-time positioning data; a path optimization module, used to monitor the network status in real time and use the Dijkstra algorithm to select the optimal data transmission path; an encryption and receiving module, used to encrypt and transmit the pre-processed positioning data, receive the transmitted positioning data and verify it; a visualization and storage module, used to display the status of the real-time positioning data in real time and store all the used positioning data.

As a preferred solution of the information transmission system based on the positioning system of the present invention, the collection of real-time positioning data refers to using a VLC-supported LED lamp and a mobile device VLC receiver, which are installed on the ceiling and wall of the room, setting the LED lamp to a visible light communication mode, transmitting the position information by modulating the optical power, and the mobile device VLC receiver receiving the optical signal to obtain the initial positioning data, and the formula is:

in is the received optical power, is the transmitted optical power, A is the effective area of the receiver, is the light efficiency, is the straight-line distance from the light source to the receiver;

Convert the optical signal received from the VLC device into a digital signal to obtain initial positioning data;

After obtaining the initial positioning data, place Bluetooth beacons indoors, scan the RSSI value of the Bluetooth beacon through the Bluetooth module on the mobile device, and use the RSSI three-sided positioning formula to calculate the distance between the mobile device and the Bluetooth beacon to obtain accurate positioning data. The formula is:

in is the distance between the mobile device and the beacon, is the transmission power, is the received power, n is the path loss factor;

Add timestamps to each data point received by VLC and Bluetooth beacons and sort them in chronological order;

Align the VLC and Bluetooth beacon data according to the timestamp to form a data vector ;

The data vector Use Kalman filtering for fusion, the formula is:

in is the fused positioning data, K is the Kalman gain, and M is the observation model matrix;

Use the network monitoring tool SNMP to collect network indicator data in real time on mobile devices, including bandwidth usage, latency, and packet loss rate.

As a preferred solution of the information transmission system based on the positioning system of the present invention, wherein: the processing of the collected real-time positioning data refers to checking the integrity of the positioning data and using the mean to fill in the missing data;

Set the standard deviation and use the Z-score standard score method to identify and delete data points with numerical anomalies in the positioning data;

According to the time sequence of the processed positioning data.

As a preferred solution of the information transmission system based on the positioning system of the present invention, wherein: the real-time monitoring of the network status and the use of the Dijkstra algorithm to select the optimal data transmission path refers to constructing a training set using historical data;

Remove missing and duplicate values from the network indicator data collected in real time by the network monitoring tool SNMP on mobile devices, and perform normalization processing;

Arrange the collected network performance data in chronological order, use the statistical software Python to draw the autocorrelation function ACF and partial autocorrelation function PACF graphs for the collected network performance data, and obtain the parameters of the ARIMA model (p, the order of the time series autoregressive term, q, the order of the moving average term, and d, the number of differences);

Use historical data sets to train the ARIMA model and use cross-validation methods to optimize model parameters and minimize prediction errors;

Use the network index data collected in real time to input the trained ARIMA model for prediction, and output the predicted bandwidth BW, network delay LT and packet loss rate PLR;

The network performance data predicted by the model is compared with the actual network performance data of historical data, and the predicted mean square error MSE is calculated to evaluate the accuracy of the model. The formula is:

in is the actual value, is the predicted value, U is the number of historical data points;

Use the network indicator data collected in real time to input into the trained ARIMA model to predict future network conditions;

Define the routers and switches in the network as nodes in the graph, define the edges between nodes based on physical connections and network protocols, and dynamically update the weight of each edge based on the latest predicted network performance indicator data;

Update the weight of each edge in the Dijkstra algorithm according to the prediction results. The weight update formula is:

Where Q(e) is the weight of edge e, BW(e) is the bandwidth of edge e, LT is the network delay of edge e, PLR is the packet loss rate of edge e, , and The weight coefficients of bandwidth, delay and packet loss rate respectively;

determining a source node and a destination node of a data transmission request in a network;

Use Dijkstra's algorithm to calculate the shortest path cost starting from the source node to all other nodes:

Set the shortest path estimates of all nodes to infinity and the source node to 0;

Set all nodes as unvisited, select the currently unvisited minimum cost node, and update the path cost of its adjacent nodes;

Repeat the previous step until all nodes are visited;

Choose the path with the lowest cost for data transmission.

As a preferred solution of the information transmission system based on the positioning system of the present invention, the encrypted transmission of the pre-processed positioning data refers to using the SHA_256 hash function to generate a hash value H(P) from the real-time position l and time data t in the processed positioning data d, and the formula is:

;

Generate an encryption key k using a secure random number generator;

The processed positioning data d is encrypted using the AES-256 algorithm, and the timestamp and positioning data of the data point are recorded. The encryption operation uses the following formula:

Where C is the encrypted data, and AES_Encrypt is the encryption operation using the secret key k;

The hash value H(P) of the data is transmitted together with the encrypted data;

Store the encrypted keys in a key management system.

As a preferred solution of the information transmission system based on the positioning system of the present invention, wherein: the receiving and verifying of the transmitted positioning data refers to retrieving the encryption key k from the key management system;

Use the same AES-256 decryption algorithm to decrypt the received encrypted data C. The formula is:

in is the decrypted positioning data, C is the received encrypted data, k is the decryption key, It is the decryption function of AES-256;

Use the SHA-256 hash function to decrypt the positioning data Position in and time Recalculate the hash value , the formula is:

The hash value that will be recalculated Compare with the hash value H(P) received during transmission. If = H(P), then the verification is successful, if ≠H(P), it means that an error occurred during data transmission and the data needs to be resent.

As a preferred solution of the information transmission system based on the positioning system of the present invention, wherein: the real-time display of the status of the real-time positioning data refers to the function of defining a visual interface, including displaying the real-time positioning data transmission speed, transmission status and data size;

Design interfaces using UX, build dashboards using CSS and Chart.js, and create dynamic visualizations of real-time positioning data.

As a preferred solution of the information transmission system based on the positioning system described in the present invention, the storage of all used positioning data refers to storing the collected and analyzed positioning data, encrypted and decrypted positioning data and corresponding timestamps in a database, setting security access measures, regularly performing integrity checks on stored data and backup data, and synchronously storing the test results.

A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of the above-mentioned information transmission system based on the positioning system when executing the computer program.

A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned information transmission system based on a positioning system.

The beneficial effects of the present invention are as follows: the present invention combines VLC and Bluetooth technologies, uses Kalman filtering technology to fuse multi-source positioning data, predicts network conditions through an ARIMA model, and dynamically adjusts the path weight of the Dijkstra algorithm, which not only improves the efficiency and reliability of data transmission, but also can quickly adapt when the network status changes, and enhances the stability and reliability of positioning data.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

FIG. 1 is a schematic diagram of the structure of an information transmission system based on a positioning system.

FIG. 2 is a schematic diagram of a flow chart of an information transmission system based on a positioning system.

DETAILED DESCRIPTION

In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

Example 1

1 and 2, which are the first embodiment of the present invention, the embodiment provides an information transmission system based on a positioning system, the information transmission system based on a positioning system includes:

S1, a collection and processing module, used to collect real-time positioning data and process the collected real-time positioning data;

Specifically, collecting real-time positioning data means using VLC-supported LED lights and mobile device VLC receivers, installed on the ceiling and walls of the room, setting the LED lights to visible light communication mode, transmitting location information by modulating optical power, and the mobile device VLC receiver receiving the optical signal to obtain initial positioning data. The formula is:

in is the received optical power, is the transmitted optical power, A is the effective area of the receiver, The optical efficiency is the efficiency of the receiver in converting the received optical signal into an electrical signal. is the straight-line distance from the light source to the receiver;

Convert the optical signal received from the VLC device into a digital signal to obtain initial positioning data;

After obtaining the initial positioning data, place Bluetooth beacons indoors, scan the RSSI value of the Bluetooth beacon through the Bluetooth module on the mobile device, and use the RSSI three-sided positioning formula to calculate the distance between the mobile device and the Bluetooth beacon to obtain accurate positioning data. The formula is:

in is the distance between the mobile device and the beacon, is the transmission power, is the received power, n is the path loss factor;

Add timestamps to each data point received by VLC and Bluetooth beacons and sort them in chronological order;

Align the VLC and Bluetooth beacon data according to the timestamp to form a data vector ;

The data vector Use Kalman filter for fusion, the formula is:

in is the fused positioning data, K is the Kalman gain, and M is the observation model matrix;

Use the network monitoring tool SNMP to collect network indicator data in real time on mobile devices, including bandwidth usage, latency, and packet loss rate.

By installing VLC-supported LED lights on indoor ceilings and walls and setting them to visible light communication mode, modulating light intensity to transmit location information, the mobile device VLC receiver receives the light signal and obtains initial positioning data. This process utilizes the high bandwidth and low interference characteristics of VLC technology to ensure high accuracy and reliability of positioning data. Bluetooth beacons are arranged indoors, and the RSSI value of the Bluetooth beacon is scanned by the Bluetooth module on the mobile device. The RSSI three-sided positioning formula is used to calculate the distance between the mobile device and the tag to obtain precise positioning data. The positioning data obtained by VLC and Bluetooth are merged into a measurement vector. The Kalman filter algorithm is used to fuse the positioning data obtained by VLC and Bluetooth to improve the accuracy and stability of the data. SNMP is used to collect network indicator data in real time on mobile devices, including bandwidth utilization, latency, and packet loss rate. Real-time network monitoring can provide instant feedback on network performance, help dynamically adjust data transmission paths, and optimize transmission efficiency.

Furthermore, the processing of the collected real-time positioning data refers to setting a standard deviation, and the processing of the collected real-time positioning data refers to checking the integrity of the positioning data and using the mean to fill in the missing data;

Set the standard deviation and use the Z-score standard score method to identify and delete data points with numerical anomalies in the positioning data;

According to the time sequence of the processed positioning data.

In the present invention, by setting the standard deviation and using the Z-score standard score method to process real-time positioning data, data points with abnormal values can be effectively identified and removed. This method improves data quality and reduces the impact of errors, especially in indoor environments, where data outliers are more common due to multipath effects and signal reflections. Effectively processing these outliers can significantly improve the accuracy of location information and the overall performance of the system.

S2, path optimization module, is used to monitor the network status in real time and select the optimal data transmission path using the Dijkstra algorithm;

Specifically, real-time monitoring of network conditions and using the Dijkstra algorithm to select the optimal data transmission path refers to building a training set using historical data;

Remove missing and duplicate values from the network indicator data collected in real time by the network monitoring tool SNMP on mobile devices, and perform normalization processing;

Arrange the collected network performance data in chronological order, use the statistical software Python to draw the autocorrelation function ACF and partial autocorrelation function PACF graphs for the collected network performance data, and obtain the parameters of the ARIMA model (p, the order of the time series autoregressive term, q, the order of the moving average term, and d, the number of differences);

Use historical data sets to train the ARIMA model and use cross-validation methods to optimize model parameters and minimize prediction errors;

Use the network index data collected in real time to input the trained ARIMA model for prediction, and output the predicted bandwidth BW, network delay LT and packet loss rate PLR;

The network performance data predicted by the model is compared with the actual network performance data of historical data, and the predicted mean square error MSE is calculated to evaluate the accuracy of the model. The formula is:

in is the actual value, is the predicted value, U is the number of historical data points;

Use the network indicator data collected in real time to input into the trained ARIMA model to predict future network conditions;

Define the routers and switches in the network as nodes in the graph, define the edges between nodes based on physical connections and network protocols, and dynamically update the weight of each edge based on the latest predicted network performance indicator data;

Update the weight of each edge in the Dijkstra algorithm according to the prediction results. The weight update formula is:

Where Q(e) is the weight of edge e, BW(e) is the bandwidth of edge e, LT is the network delay of edge e, PLR is the packet loss rate of edge e, , and The weight coefficients of bandwidth, delay and packet loss rate respectively;

determining a source node and a destination node of a data transmission request in a network;

Use Dijkstra's algorithm to calculate the shortest path cost starting from the source node to all other nodes:

Set the shortest path estimates of all nodes to infinity and the source node to 0;

Set all nodes as unvisited, select the currently unvisited minimum cost node, and update the path cost of its adjacent nodes;

Repeat the previous step until all nodes are visited;

Choose the path with the lowest cost for data transmission.

By removing missing and duplicate values and performing normalization, the accuracy and consistency of the data are ensured. This step provides high-quality input data for subsequent data analysis and prediction, thereby improving the accuracy of the prediction. Python statistical software is used to plot the autocorrelation function ACF and the partial autocorrelation function PACF to determine the parameters of the ARIMA model. This analysis helps identify the time series characteristics in the data and lays the foundation for establishing an effective prediction model. The SARIMA model is trained based on historical data, and the model parameters are optimized using the cross-validation method. Real-time data is input into the model for prediction, and the predicted mean square error MSE is calculated to evaluate the accuracy of the model. This method can dynamically predict network conditions, adjust network management strategies in a timely manner, and minimize the impact of network performance fluctuations. According to the prediction results of the ARIMA model, the weight of each edge in the graph is dynamically adjusted, and the Dijkstra algorithm is used to calculate the optimal path. This method can adjust the data transmission path according to real-time changes in network conditions, optimize the use of network resources, and improve the efficiency and reliability of data transmission.

S3, encryption and receiving module, used for encrypting and transmitting the pre-processed positioning data, receiving the transmitted positioning data and verifying it;

Furthermore, encrypting and transmitting the pre-processed positioning data means using the SHA_256 hash function to generate a hash value H(P) from the real-time position l in the processed positioning data d including the longitude L, the latitude B and the time data t, and the formula is:

;

Generate an encryption key k using a secure random number generator;

The processed positioning data d is encrypted using the AES-256 algorithm, and the timestamp and positioning data of the data point are recorded. The encryption operation uses the following formula:

Where C is the encrypted data, and AES_Encrypt is the encryption operation using the secret key k;

Transmit the hash value H(P) of the data and the encrypted data C;

Encrypted keys are stored in a key management system so that only authorized systems and individuals can access them.

In the present invention, the SHA-256 hash function is first used to process the pre-processed positioning data including longitude L, dimension B and time data t to generate an encryption key k. The advantage of this step is that the hash function provides data consistency and irreversibility, ensures the uniqueness and security of the key, and is suitable for key generation in a dynamic data environment. The generated key k is used to encrypt the positioning data through the AES-256 algorithm. In this process, all key data points, including timestamps, device IDs and location data, are securely encrypted to ensure the confidentiality of the data. The use of AES-256 ensures that the security of the data will not be compromised even in extreme cases, thereby greatly enhancing the security protection of data transmission. The encrypted keys are stored in a dedicated key management system to ensure that only authorized systems and individuals can access these keys. The introduction of this step adds an extra security layer to prevent key leakage and ensure the security integrity of the entire system.

Furthermore, receiving the transmitted positioning data and verifying it refers to retrieving the key k used for encryption from the key management system;

Use the same AES-256 decryption algorithm to decrypt the received encrypted data C. The formula is:

in is the decrypted positioning data, C is the received encrypted data, k is the decryption key, It is the decryption function of AES-256;

Use the SHA-256 hash function to decrypt the positioning data Position in and time Recalculate the hash value , the formula is:

The hash value that will be recalculated Compare with the hash value H(P) received during transmission. If = H(P), then the verification is successful, if ≠H(P), it means that an error occurred during data transmission and the data needs to be resent.

The present invention first retrieves the encryption key k from the key management system, and then uses the same AES-256 algorithm to decrypt the received encrypted data C. This step ensures that even if the data is intercepted during transmission, a third party without the key cannot interpret the data content. The correct execution of the decryption step is the key to ensuring data integrity and confidentiality. After decryption, the present invention uses the SHA-256 hash function to recalculate the hash value for the location and time information. By comparing this newly calculated hash value with the original hash value H received during transmission, it can be verified whether the data has been tampered with during transmission. This verification mechanism provides a strong data integrity guarantee to ensure that the received data is the original data when it was sent.

S4, visualization and storage module, used to display the status of real-time positioning data in real time and store all used positioning data;

Specifically, real-time display of the status of real-time positioning data refers to defining the function of a visualization interface, including displaying the real-time positioning data transmission speed, transmission status, and data size;

Design interfaces using UX, build dashboards using CSS and Chart.js, and create dynamic visualizations of real-time positioning data.

The present invention defines a feature-rich visualization interface to achieve intuitive display of real-time positioning data including transmission speed, status, data size and priority. The core of this step is to provide real-time feedback and detailed data views to help users understand the real-time status of data flow, so as to monitor and manage the system more effectively. By adopting the user experience design principle, the present invention optimizes the interface for user interaction with data, ensures the readability of information and the intuitiveness of operation. Good UX design can reduce the user's operating difficulty and improve user satisfaction, while reducing the risk of misoperation caused by unfriendly interface. The dashboard built with CSS and Chart.js is not only beautiful but also powerful. It can dynamically display the changes of real-time data, show the changes of data transmission speed through real-time updated charts, or display the priority of data through labels of different colors. The implementation of this dynamic visualization makes the interpretation of complex data more direct and simple.

Furthermore, storing all used positioning data means storing the collected and analyzed positioning data, encrypted and decrypted positioning data and corresponding timestamps in a database, setting security access measures, regularly performing integrity checks on stored data and backup data, and synchronously storing the test results.

All generated positioning data, including original data, encrypted and decrypted data, and corresponding timestamps, are systematically stored in a dedicated database. This centralized storage not only facilitates data management and access, but also reduces the risk of data redundancy and inconsistency through unified management. To ensure data security, the present invention implements strict security access measures on the database, which include but are not limited to the use of identity authentication, permission control, and encrypted access technologies to prevent unauthorized access and data leakage. These measures improve the security of the data storage system, ensure that only authorized users can access sensitive data, perform integrity checks on stored data and its backups, and ensure that the data has not been tampered with or damaged during the storage process. Through regular testing, data problems can be discovered and corrected in a timely manner, thereby ensuring the accuracy and reliability of the data. This process is crucial to maintaining data integrity, especially in the face of external threats and system failures. In order to further enhance the transparency and traceability of data management, the present invention stores the results of the integrity test together with the original data. This measure not only makes data management more transparent, but also facilitates the audit of historical data and problem tracking.

Example 2

If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

The logic and/or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims (10)

1. An information transmission system based on a positioning system, which is characterized in that: comprising the steps of (a) a step of,

The collecting and processing module is used for collecting real-time positioning data and processing the collected real-time positioning data;

the path optimization module is used for monitoring network conditions in real time and selecting an optimal data transmission path by using a Dijkstra algorithm;

The encryption and receiving module is used for carrying out encryption transmission on the preprocessed positioning data, receiving the transmitted positioning data and carrying out verification;

and the visualization and storage module is used for displaying the state of the real-time positioning data in real time and storing all the used positioning data.

2. The positioning system-based information transmission system according to claim 1, wherein: the collection of real-time positioning data refers to the use of an LED lamp supporting VLC and a VLC receiver of a mobile device, the VLC receiver is installed on an indoor ceiling and a wall, the LED lamp is set into a visible light communication mode, the VLC receiver of the mobile device receives light signals through modulating light power transmission position information, initial positioning data is obtained, and the formula is as follows:

Wherein the method comprises the steps of In order to receive the optical power of the light,For the emitted optical power, a is the effective area of the receiver,In order for the light to be of a light efficiency,Is the linear distance from the light source to the receiver;

Converting the light signal received from the VLC device into a digital signal to obtain initial positioning data;

after initial positioning data is obtained, a Bluetooth beacon is arranged at an indoor position, the RSSI value of the Bluetooth beacon is scanned through a Bluetooth module on mobile equipment, the distance between the mobile equipment and the Bluetooth beacon is calculated by using an RSSI trilateral positioning formula, and accurate positioning data is obtained, wherein the formula is as follows:

Wherein the method comprises the steps of For the distance between the mobile device and the beacon,For the transmit power of the signal to be transmitted,For received power, n is a path loss factor;

Adding a time stamp to each data point received by the VLC and Bluetooth beacons, and sequencing according to a time sequence;

Aligning the VLC and Bluetooth beacon data according to the time stamp to form a data vector ；

Vector dataFusion is carried out by using Kalman filtering, and the formula is as follows:

Wherein the method comprises the steps of K is Kalman gain, M is an observation model matrix;

Network monitoring tool SNMP is used to collect network metrics data in real time at the mobile device, including bandwidth usage, delay and packet loss rate.

3. The positioning system-based information transmission system according to claim 2, wherein: the processing of the collected real-time positioning data means checking the integrity of the positioning data, and filling the missing data by using the average value;

setting standard deviation, identifying data points with abnormal values in the positioning data by using a Z-score standard score method, and deleting the data points;

According to the time sequence of the processed positioning data.

4. A positioning system based information transfer system as claimed in claim 3, wherein: the real-time monitoring network condition uses Dijkstra algorithm to select optimal data transmission path to use historical data to construct training set;

Removing missing values and repeated values of a network monitoring tool SNMP in the mobile equipment, collecting network index data in real time, and carrying out normalization processing;

The collected network performance data are arranged in time sequence, a statistical software Python is used for drawing an auto-correlation function ACF and a partial auto-correlation function PACF graph on the collected network performance data, and the order of a parameter p time sequence auto-regression term, the order of a q moving average term and d differential times of an ARIMA model are obtained;

training an ARIMA model by using a historical data set, optimizing model parameters by using a cross-validation method, and minimizing a prediction error;

Inputting network index data collected in real time into a trained ARIMA model for prediction, and outputting a predicted broadband BW, network delay LT and packet loss rate PLR;

Comparing the model predicted network performance data with the historical data actual network performance data, and calculating the accuracy of a predicted mean square error MSE evaluation model, wherein the formula is as follows:

Wherein the method comprises the steps of As a result of the fact that the value,U is the number of historical data points for the predicted value;

Inputting network index data collected in real time into a trained ARIMA model to predict future network conditions;

Defining routers and switches in a network as nodes in a graph, defining edges between the nodes according to physical connection and a network protocol, and dynamically updating the weight of each edge according to the latest predicted network performance index data;

And updating the weight of each side in the Dijkstra algorithm according to the prediction result, wherein a weight updating formula is as follows:

where Q (e) is the weight of edge e, BW (e) is the wideband of edge e, LT is the network delay of edge e, PLR is the packet loss rate of edge e, 、AndWeight coefficients of bandwidth, delay and packet loss rate respectively;

determining a source node and a target node of a data transmission request in a network;

The Dijkstra algorithm is used to calculate the shortest path cost to all other nodes starting from the source node:

setting the shortest path estimated value of all nodes to infinity, and setting the source node to 0;

setting all nodes as unaccessed, selecting the least cost node which is not accessed currently, and updating the path cost of the adjacent nodes;

Repeating the operation of the previous step until all nodes are accessed;

and selecting a path with the lowest cost for data transmission.

5. The positioning system-based information transmission system according to claim 4, wherein: the encryption transmission of the preprocessed positioning data means that a hash value H (P) is generated by using a SHA_256 hash function of a real-time position l and time data t in the processed positioning data d, and the formula is as follows:

generating an encryption key k using a secure random number generator;

Encrypting the processed positioning data d by using an AES-256 algorithm, recording the time stamp of the data point and the positioning data, wherein the encryption operation adopts the following formula:

wherein C is encrypted data, and AES_encrypt is encrypted by using a key k;

transmitting the hash value H (P) of the data with the encrypted data C;

the encrypted key is stored in a key management system.

6. The positioning system-based information transmission system of claim 5, wherein: said receiving and verifying transmitted positioning data means retrieving a key k for encryption from a key management system;

decrypting the received encrypted data C using the same AES-256 decryption algorithm, given by

Wherein the method comprises the steps ofFor decrypted positioning data, C is the received encrypted data, k is the decrypted key,A decryption function for AES-256;

decrypted positioning data using SHA-256 hash function Is a position in (a)And time ofRecalculating hash valuesThe formula is:

hash value to be recalculated Comparing with the hash value H (P) received in transmission, if=H (P), then the verification is successful, ifNot equal to H (P), the data is shown to be erroneous during transmission and retransmitted.

7. The positioning system-based information transmission system of claim 6, wherein: the condition of displaying the real-time positioning data in real time refers to the function of defining a visual interface, and the function comprises displaying the transmission speed, the transmission state and the data size of the real-time positioning data;

And constructing a dashboard by using an UX design interface and using CSS and Chart. Js, and dynamically visualizing the real-time positioning data.

8. The positioning system-based information transfer system of claim 7, wherein: storing all used positioning data means storing the positioning data generated by collection and analysis, encrypted and decrypted positioning data and corresponding time stamps into a database, setting security access measures, regularly detecting the integrity of the stored data and the backup data, and synchronously storing detection results.

9. A computer device, comprising: a memory and a processor; the memory stores a computer program characterized in that: the processor, when executing the computer program, implements the steps of the positioning system based information transmission system of any one of claims 1 to 8.

10. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements the steps of the positioning system based information transmission system of any of claims 1 to 8.