CN118612681A

CN118612681A - Remote monitoring method and system for wireless transmission of seat pressure signals

Info

Publication number: CN118612681A
Application number: CN202410618205.XA
Authority: CN
Inventors: 谢宇航; 王哲; 洪慧
Original assignee: Shaoxing Qingyan Micro Technology Co ltd
Current assignee: Shaoxing Qingyan Micro Technology Co ltd
Priority date: 2024-05-17
Filing date: 2024-05-17
Publication date: 2024-09-06

Abstract

The invention belongs to the technical field of remote monitoring, and particularly relates to a remote monitoring method and a remote monitoring system for wireless transmission of seat pressure signals, wherein the seat pressure changes are captured and analyzed in real time through a built-in pressure sensor and an embedded microprocessor, so that sitting posture changes and activity states of passengers are reflected in time, and health risks are effectively prevented; the wireless communication technology is applied to ensure that the pressure data is transmitted to the remote server in an instant encryption manner, so that the time from data acquisition to analysis is greatly shortened, the abnormal response speed is improved, the visual interface of the remote monitoring platform displays the real-time use condition and the historical pressure data of all seats on each aircraft, the real-time monitoring and the data analysis of a ground operation team are facilitated, the seat maintenance plan is optimized, the overall operation efficiency is improved, the intelligent level of the aviation seat is improved, powerful support is provided for the safety management of an airline company, the safety and comfort of passengers are ensured, and the overall efficiency of the aviation operation is improved.

Description

Remote monitoring method and system for wireless transmission of seat pressure signals

Technical Field

The invention belongs to the technical field of remote monitoring, and particularly relates to a remote monitoring method and a remote monitoring system for wireless transmission of seat pressure signals.

Background

In the modern aviation field, passenger safety and comfort are one of the key factors of interest to airlines. As an infrastructure for direct contact with passengers, aircraft seats are designed to meet not only stringent comfort requirements, but also to incorporate advanced safety monitoring techniques. Conventional airline seats, while continuously optimized in structure and materials, have limitations in intelligent monitoring. Particularly in long-range flight, the health condition, behavior dynamics and the use condition of the seat of the passenger are difficult to monitor in real time, which not only affects the experience of the passenger, but also forms a potential threat to the flight safety. For example, a passenger who is in an improper sitting position for a long period of time may cause deep vein thrombosis, and sudden seat pressure abnormalities may be a sign of sudden health problems for the passenger.

The main problems faced by current airline seat monitoring technology include:

Monitoring for loss in real time: there is a lack of effective technical means to continuously monitor the actual state of the passenger in the seat (e.g. sitting posture change, long time immobility, etc.) in real time.

Data processing delay: the data has long path from the seat sensor to the background process, low efficiency, and can not quickly reflect the state change of passengers, and delay the response of emergency.

Safety precaution is not enough: lack of an intelligent early warning system can not automatically identify and report potential occupant safety issues based on seat pressure changes.

The management is inconvenient: the ground operation team of the airline company is difficult to acquire the real-time use state of each seat in the flight in real time, and the resource scheduling and the maintenance plan making are affected.

Disclosure of Invention

The invention aims to provide a remote monitoring method and a remote monitoring system for wireless transmission of seat pressure signals, which can obviously improve the intelligentization level and safety of an aviation seat so as to solve the problems in the prior art in the background art.

In order to achieve the above purpose, the invention adopts the following technical scheme: a remote monitoring method for wireless transmission of seat pressure signals, comprising:

S1: the pressure sensor is arranged in the seat and used for continuously monitoring the pressure change of the surface of the seat, and when the pressure sensor detects that the pressure of the seat changes, the pressure change data are immediately collected;

S2: preprocessing and encoding the collected pressure change data through an embedded microprocessor, encrypting the encoded data through a wireless communication module, and converting the encrypted data into a wireless transmission data packet;

s3: the data packet is sent to a remote monitoring server through a wireless network, and after the remote server receives the data packet, the data packet is decrypted and analyzed to restore original pressure change data;

s4: analyzing the passenger state information according to the analyzed pressure change data, generating a monitoring report and early warning information in real time through the passenger state information, and automatically sending an alarm to a preset terminal when abnormal pressure change is detected;

S5: and displaying the service condition of the seat and the historical pressure change data on the remote monitoring platform for the manager to check and analyze.

Preferably, in S2, the preprocessing includes filtering to remove noise, and the normalization processing is performed on the raw data using formula (1):

wherein X is original pressure data, and X _norm is normalized data.

Preferably, in S2, the data security is enhanced by hashing the data packet according to formula (2):

wherein, data is a data packet to be encrypted, n is the length of the data packet, m is a big prime number, data [ i ] is the value of the element or byte at the i-th position in the data packet, 31 ^n-i-1 is a weight factor, and decreases along with the increment of i.

Preferably, in S4, the occupant behavior is predicted according to the current pressure change using a machine learning algorithm, and the model expression is formula (3): y=f (x; θ);

Wherein y is a prediction result, x is an input feature, and θ is a model parameter.

Preferably, in S4, the early warning information includes a seat number, an early warning level, and a suggested action, and the calculation of the early warning level is according to formula (4):

Wherein Δp is the pressure variation, and Pth is a preset pressure variation threshold.

Preferably, in S2, the wireless communication module supports a plurality of communication protocols including, but not limited to Wi-Fi, bluetooth, and LTE.

Preferably, in S5, the remote monitoring platform includes a graphical user interface to enable a manager to observe the seat pressure profile and differentiate the pressure levels by color.

Preferably, at S2, the checksum of the data packet is calculated by equation (5) prior to transmission of the data packet:

Wherein, data is the data packet to be sent, and n is the data packet length.

Preferably, the method further comprises: the self-diagnosis and maintenance are carried out regularly to ensure the normal operation of the sensor, the wireless communication module and the server, and the process is realized by executing a maintenance script, which comprises the following steps:

Making a maintenance plan: setting a schedule for periodic maintenance according to the running characteristics and the requirements of the system;

writing a maintenance script: script is written according to different maintenance requirements of the sensor, the wireless communication module and the server;

integrated automation tool: the maintenance script is scheduled and executed using an operation and maintenance automation tool, and the maintenance tasks are automatically triggered according to a predetermined schedule.

In another aspect, the present invention provides a remote monitoring system for wireless transmission of a seat pressure signal, comprising:

The pressure signal acquisition module is used for installing a pressure sensor in the seat and continuously monitoring the pressure change of the surface of the seat, and immediately acquiring pressure change data when the pressure sensor detects that the pressure of the seat is changed;

the data packet acquisition module is used for preprocessing and encoding the acquired pressure change data through the embedded microprocessor, encrypting the encoded data through the wireless communication module and converting the encrypted data into a wireless transmission data packet;

The data packet decompression module is used for transmitting the data packet to the remote monitoring server through the wireless network, and after the remote server receives the data packet, the data packet is decrypted and analyzed to restore original pressure change data;

the early warning information monitoring module is used for analyzing the passenger state information according to the analyzed pressure change data, generating a monitoring report and early warning information in real time through the passenger state information, and automatically sending an alarm to a preset terminal when abnormal pressure change is detected;

And the remote monitoring module is used for displaying the service condition of the seat and the historical pressure change data on the remote monitoring platform for the manager to check and analyze.

The invention has the technical effects and advantages that: compared with the prior art, the remote monitoring method and the system for wireless transmission of the seat pressure signals have the following advantages:

According to the invention, the pressure change of the seat is captured and analyzed in real time through the built-in pressure sensor and the embedded microprocessor, so that the sitting posture change and the activity state of the passenger are reflected in time, and the health risk is effectively prevented; the wireless communication technology is applied to ensure that the pressure data is transmitted to the remote server in an instant encryption manner, so that the time from data acquisition to analysis is greatly shortened, the abnormal response speed is improved, the visual interface of the remote monitoring platform displays the real-time use condition and the historical pressure data of all seats on each aircraft, the real-time monitoring and the data analysis of a ground operation team are facilitated, the seat maintenance plan is optimized, the overall operation efficiency is improved, the intelligent level of the aviation seat is improved, powerful support is provided for the safety management of an airline company, the safety and comfort of passengers are ensured, and the overall efficiency of the aviation operation is improved.

Drawings

FIG. 1 is a flow chart of a method for remotely monitoring a wireless transmission of a seat pressure signal according to the present invention;

FIG. 2 is a block diagram of a remote monitoring system for wireless transmission of seat pressure signals in accordance with the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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 made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The invention provides a remote monitoring method for wireless transmission of a seat pressure signal, which can be used for automobile seats, bus seats, aviation seats, chairs of a dance theater, office chairs and the like. The invention is used for distance description in the aspect of the aeronautical seat, aims to optimize the seat maintenance plan, improves the overall operation efficiency, not only improves the intelligent level of the aeronautical seat, but also provides powerful support for the safety management of the aeronautical company, ensures the safety and comfort of passengers, and simultaneously improves the overall efficiency of aeronautical operation.

As shown in fig. 1, the above-mentioned remote monitoring method for wireless transmission of a seat pressure signal includes the following steps:

Specifically, a pressure Sensor (Sensor Module): a high-precision low-power-consumption semiconductor piezoresistive sensor, such as the MPX series of Freescale, is arranged in a key bearing area of a seat, such as the contact points of a seat cushion and a backrest. These sensors are able to accurately sense even slight pressure changes and convert them into electrical signals.

Embedded microprocessor (Microcontroller Unit, MCU): and a microcontroller of ARM Cortex-M series, such as STM32F4, is used for data acquisition and preliminary processing. The MCU is connected with the pressure sensor through an I2C or SPI interface, reads the pressure change value in real time, and performs necessary signal amplification, filtering and analog-to-digital conversion (ADC).

Wireless communication module (WIRELESS TRANSCEIVER): a Wi-Fi or Bluetooth Low Energy (BLE) module, such as ESP32 or nRF52840, is integrated for wireless transmission of the processed data. The modules support wireless communication in the 2.4GHz frequency band, have good penetrability and low power consumption characteristics, and are suitable for equipment operated for a long time.

Power management unit (Power Management Unit, PMU): in consideration of the non-contact power supply convenience of the seat, a small lithium polymer battery is matched with an energy collection technology (such as a piezoelectric effect or a solar panel) to supply power to the whole system, so that the long-term stable operation of the system is ensured.

Principle of implementation

And a data acquisition stage: when a passenger sits, the pressure sensor generates resistance change due to stress, and the change is converted into a voltage signal. The microprocessor periodically reads these voltage values and converts them to digital signals using a built-in ADC.

Data processing and encryption: the MCU performs preliminary analysis, such as denoising and smoothing, on the acquired digital signals, and encodes effective data according to a preset protocol. In order to ensure the security of data in the wireless transmission process, the coded data is encrypted, and an AES-128 bit encryption standard is generally adopted.

Wireless data transmission: the encrypted data packet is transmitted out through the wireless communication module, is connected to a Wi-Fi router in the cabin or is directly in butt joint with a satellite communication system, and is further transmitted to a remote monitoring server on the ground. In the transmission process, the system can automatically find the optimal communication path to ensure stable data transmission.

Remote monitoring and analysis: after receiving the data, the remote server decrypts and parses the pressure data to convert the pressure data into an intuitive pressure profile or passenger behavior pattern. By algorithmically analyzing these data, it can be determined whether the passenger remains in the same posture for a long time, leaves the seat, etc., and once an abnormal situation is found, the system will immediately trigger an alarm and notify the crew or ground control center via a short message, email or APP. According to the method, through integrated hardware and intelligent processing flow, accurate monitoring of the use state of the aviation seat is achieved, and the flight safety and the passenger care level are improved.

further, the preprocessing includes filtering to remove noise, and normalization processing is performed on the raw data by using formula (1): wherein X is original pressure data, and X _norm is normalized data.

Specifically, pretreatment and normalization (equation 1)

Filtering to remove noise: first, high frequency noise is removed by a digital low pass filter (e.g., a first order butterworth filter) to preserve the effective signal.

And (3) standardization treatment: equation 1 is used to map the original pressure data X into the [0,1] interval, and data normalization is achieved. When the processing is conducive to the subsequent algorithm processing, the data of different sensors or different time points can be directly compared, and the influence caused by the difference of data magnitude is reduced.

Further, the data security is enhanced by performing hash operation on the data packet according to the formula (2): wherein, data is a data packet to be encrypted, n is the length of the data packet, m is a big prime number, data [ i ] is the value of the element or byte at the i-th position in the data packet, 31 ^n-i-1 is a weight factor, and decreases along with the increment of i.

Specifically, the hash operation enhances data security (formula 2), where data is a data packet to be encrypted, n is a data packet length, and m is a large prime number. The formula generates a hash value H (data) with a fixed length by hash operation through modulo operation of a large prime number m and a decreasing weight factor of 31 ^n-i-1. The calculation mode can effectively mix and arrange the data packet contents, even small changes in the data packet can cause huge differences in hash values, and therefore safety in the data transmission process is enhanced.

In the formula (2), the meaning of each parameter is as follows:

H (data) represents a hash value obtained by hash operation, which is a fixed-length output used for verifying the integrity and uniqueness of data.

The data is a data packet to be encrypted, namely the original data information which needs to be subjected to hash operation, and particularly relates to a data packet to be transmitted, which is formed by preprocessing and encoding seat pressure change data.

I is an iteration variable used to traverse each element or byte in the packet. It starts from 0 up to the last element of the packet, i.e. 31 ^n-i-1.

Data i represents the value of the element or byte at the i-th position in the packet.

N is the length of the data packet, i.e. the total number of elements or bytes in the data packet. This value determines the upper limit of the loop, ensuring that every part of the packet is incorporated into the hash operation.

31 ^n-i-1 This is a weight factor that decreases with increasing i. It makes the preceding element in the data packet more contributing to the final hash value than the following element, which may be designed to increase the non-linear nature of the hash operation, improving security.

Mod m represents a modulo operation, where m is a preselected large prime number. The function of the modulo operation is to ensure that the range of hash values is limited, so that the hash values are convenient to store and compare, the irreversibility and the collision resistance of the hash function are increased, and the selection of m is critical to the safety of the hash function. By modulo m operation, the resulting hash value will fall within the range of 0 to 31 ^n-i-1.

Further, before the packet is transmitted, the checksum of the packet is calculated by equation (5): Wherein, data is the data packet to be sent, and n is the data packet length. Equation (5) is used to calculate the checksum of the data packet, ensuring data integrity. Modulo 2 ¹⁶ is employed here to limit the checksum to a 16-bit range, is easy to handle and is sufficient to cover common errors. By comparing the checksums before and after transmission, whether the data has errors in the transmission process can be rapidly judged.

In addition, the wireless communication module supports a variety of communication protocols including, but not limited to Wi-Fi, bluetooth, and LTE.

Specifically, in the remote monitoring method for wireless transmission of the seat pressure signal, the design and implementation of the wireless communication module are key components, and aim to ensure the high efficiency, compatibility and reliability of data transmission. The following is an overview of the hardware selection and operating principles of the wireless communication module in a specific embodiment:

Hardware selection

1. Multiprotocol wireless communication chip: a chip integrated with various communication protocols such as Wi-Fi, bluetooth (including classical bluetooth and bluetooth low energy BLE), thread/Zigbee and the like is selected and used, for example, KW41Z of NXP or high-pass QCA 4020. The chips provide a highly integrated solution, different wireless communication modes can be flexibly switched on a single module, and an optimal transmission mode is selected according to the application scene and the requirement of a network environment.

2. Antenna design: internal or external antennas are used, depending on the structure and environment of use of the seat. For seats with limited internal space, a miniaturized, high-gain built-in PCB antenna is used, ensuring signal coverage and transmission quality. For the occasion with open environment, the external antenna can be considered to obtain better signal penetration force and transmission distance.

Principle of operation

Data encapsulation and protocol conversion: the microprocessor encapsulates the processed pressure signal data according to the requirements of the selected communication protocol, including adding header information, check bits, etc., so that the receiving end can correctly interpret. The encapsulation format may be different for different protocols, e.g. Wi-Fi may employ a TCP/IP protocol stack, while bluetooth BLE may use the GATT protocol.

Communication protocol selection and handover: the wireless communication module automatically selects an optimal communication protocol according to the configuration of the remote monitoring system and the currently available network environment. For example, wi-Fi may be used preferentially to take advantage of its high-speed transmission capabilities in airport lounges; on a moving vehicle, it is possible to switch to LTE or bluetooth to accommodate dynamically changing network connection conditions.

Encryption and security: no matter which communication protocol is adopted, the data is encrypted by a hardware accelerator or a security module arranged in a microprocessor before transmission, for example, AES (advanced encryption standard) is used for encrypting the data packet, so that the security of the data in the air transmission process is ensured, and illegal interception or tampering is prevented.

And (3) energy management: considering the energy demand of wireless transmission, the module design may integrate a low power mode, such as entering a sleep state when there is no data transmission, to save battery power. Particularly, for low-power consumption protocols such as Bluetooth LE and Zigbee, the endurance time of the seat monitoring system can be obviously prolonged.

Through the comprehensive application of the hardware and the principle, the wireless communication module can reliably and efficiently transmit the seat pressure signal in various network environments, and a solid foundation is provided for a remote monitoring system.

In the embodiment, key links such as wireless transmission of data packets, receiving, decryption and analysis of a server side are covered in detail, so that pressure change data acquired from a seat sensor can be accurately transmitted to a remote monitoring server and effectively utilized. The following is a detailed description of the hardware configuration and operation principle of this process:

Hardware configuration

Wireless network infrastructure: the data packets can be ensured to be smoothly transmitted from the wireless communication module at the seat to the remote monitoring server by using the existing Wi-Fi network (802.11 ac/n/G/b) or the cellular network (such as 4G/5G LTE) at an airport, an airplane or other places. In a mobile scenario, such as an aircraft, a dedicated satellite communication system (e.g., inaarsat or Iridium) may also be provided to maintain a continuous network connection.

Remote monitoring server: the high-performance server cluster is adopted, and a multi-core CPU (such as Intel Xeon series), a high-capacity RAM and high-speed SSD storage are provided, so that the high efficiency of data processing and storage is ensured. The server operating system may be Linux because it is open-source, stable, and supports a wide range of data processing tools.

Network equipment: the method comprises the steps of a router, a firewall, a load balancer and the like, and ensures stable transmission and network security of data packets. For example, enterprise level routers are responsible for routing data packets, firewalls implement security policies that prevent unauthorized access, and load balancers ensure stable operation of servers at high traffic.

Principle of operation

Packaging and transmitting data packets: at the seat, the microprocessor encapsulates the processed and encrypted data packet through the wireless communication module according to a preset protocol (such as TCP/IP), including a source address, a destination address, a port number, verification information, and the like, and then transmits the data packet through the wireless network.

Network transmission: the data packet passes through the wireless network, possibly through the forwarding of a plurality of intermediate nodes (such as routers and switches), and finally reaches the network entrance where the remote monitoring server is located. In cellular or satellite networks, data may also need to be relayed through a base station or satellite.

The server receives: a Network Interface Card (NIC) of the remote monitoring server receives the data packets, and an operating system network stack processes network layer and transport layer protocols to ensure that the data is correctly transferred to an application program layer.

Decryption and parsing: server side software (e.g., customized backend services, possibly based on frames of node. Js, python Flask/Django, etc.) receives the data packets, and first decrypts the data packets using a corresponding decryption algorithm (e.g., AES decryption, corresponding to the S2 encryption algorithm). The original pressure change data is then parsed from the data packet format, possibly involving reading, type conversion, etc. in the field order specified by the protocol.

Data processing and storage: the analyzed raw data is further processed, possibly including data cleaning, outlier detection, data conversion, etc., and then stored in a database (e.g., mySQL, mongoDB) or a large data storage system (e.g., hadoop HDFS) to provide a basis for subsequent monitoring, analysis, and early warning.

By tightly matching the hardware configuration and the working principle, seamless transmission and processing of data from the seat to a remote monitoring server are realized, and a solid technical support is provided for real-time monitoring of the pressure signal of the seat.

further, a machine learning algorithm is adopted to predict the passenger behavior (such as leaving the seat, changing the sitting posture and the like) according to the current pressure change, and the model expression is represented by a formula (3): y=f (x; θ);

y is the predicted outcome, such as a classification signature (representing different occupant behavior states) or a regression value (e.g., predicted pressure trend). x is an input feature vector containing a series of pressure change data obtained from the seat sensor at the current time and possibly other related information such as time stamps, historical pressure patterns, etc. θ is a model parameter including weights and bias terms, learned by training data, and the optimization objective is typically to maximize prediction accuracy or minimize prediction error. In a specific implementation, models such as logistic regression, support vector machines, neural networks and the like can be adopted, and the models are selected according to the complexity and data characteristics of the actual problems.

Further, the early warning information includes a seat number, an early warning level and a suggested action, and the calculation of the early warning level is according to formula (4): Wherein Δp is the pressure variation, and P _th is a preset pressure variation threshold.

The calculation of the early warning level aims to quickly identify potential abnormal conditions according to the comparison of the absolute value (delta P) of the pressure variation and a preset threshold value (P _th). The level division may be: early warning level 1 may represent a slight concern, requiring attention but not immediate response; level 2 indicates that attention is required, possibly requiring personnel inspection; level 3 indicates an emergency situation and immediate action is required.

In a specific embodiment, assuming that a certain flight seat monitoring system detects a sudden drop in the pressure of the seat A3 at a certain moment, the machine learning model of formula (3) predicts that the occupant is likely to be about to stand up. The model inputs pressure change data per second over the past 5 minutes as a time series feature, and learns a typical pressure pattern before the occupant leaves the seat.

Next, the system calculates the pressure change amount to Δp=80n, and the preset normal pressure change threshold P _th =50n, using α=0.8. According to the formula (4), the calculated early warning level is 3, which indicates that the emergency situation is caused, and the system immediately sends alarm information comprising 'seat A3, early warning level 3, possibly with the emergency departure of the passenger, please check rapidly' to the handheld terminal of the flight attendant through a wireless network, so as to ensure rapid response to potential safety problems.

In summary, by combining advanced data processing technology, machine learning prediction model and flexible early warning mechanism, the system can effectively improve monitoring efficiency and safety of passenger state.

S5: and displaying the service condition of the seat and the historical pressure change data on the remote monitoring platform for the manager to check and analyze. Further, the remote monitoring platform comprises a graphical user interface, so that a manager can observe the seat pressure distribution map and distinguish the pressure by color.

Specifically, the S5 stage relates to the development of a remote monitoring platform with comprehensive functions for displaying and analyzing the service condition of the seat and historical pressure change data in real time. The following is a detailed description of how this concept may be transformed into specific embodiments:

remote monitoring platform architecture

Data acquisition and transmission

Data acquisition layer: the pressure sensor built-in each seat continuously monitors pressure change, and data is transmitted to a local data collection node or directly uploaded to a cloud server in a wired or wireless mode (such as Wi-Fi, bluetooth or LoRa).

Data transmission protocol: and MQTT (Message Queuing Telemetry Transport) or other low-power-consumption wide area network communication protocols are adopted, so that data can be efficiently and safely transmitted to a remote monitoring platform.

Data storage and processing

Cloud database: the collected data is stored in a cloud database, such as Amazon DynamoDB or Google Firestore, which facilitates large-scale data management and quick interrogation.

Data processing and analysis: and (3) cleaning and aggregating the real-time data flow by using a big data processing frame (such as APACHE SPARK), analyzing historical data, and extracting key indexes such as pressure change trend, frequency and the like.

Graphical user interface design

Real-time monitoring interface

Seat layout view: the front page of the monitoring platform displays a seat layout diagram of the whole area (such as an airplane cabin, an office area and the like), each seat is represented in an icon form, a detailed information window pops up when a mouse is hovered or clicked, and the current pressure value and the seat occupation state are displayed.

Pressure profile: color coding is used to indicate the amount of pressure on different seats, e.g. green indicates a seat in the normal pressure range, yellow alerts a slight pressure anomaly, red indicates an abnormally high pressure or an abrupt change, requiring immediate attention. This is accomplished using thermodynamic techniques such as the D3.js or ECharts chart store.

Historical data analysis tool

Timeline slide: the top of the interface provides a timeline control, which allows an administrator to select a specific time period and dynamically check the pressure change trend of all seats in the time period.

Data visualization: and various chart options such as a line graph, a histogram or a scatter graph are provided, and pressure change curves of single or multiple seats along with time are displayed, so that the comparison analysis and the anomaly detection are facilitated.

Advanced analytical function: the integrated machine learning model interface allows a user to perform predictive analysis based on historical data, such as predicting seat usage peaks for a period of time in the future, or identifying a particular behavior pattern.

Security and rights management

Data encryption: security in the data transmission and storage process is ensured, SSL/TLS encryption communication is adopted, and data is stored in a database in an encrypted manner.

Access control: the platform realizes multi-level authority management, and ensures that management personnel (such as a first-line operator, a data analyzer and an advanced management personnel) with different roles can only access data and functions matched with the responsibilities of the management personnel.

Through the design, the remote monitoring platform not only can present the service condition and pressure change of the seat in real time, but also provides a powerful data analysis tool for management staff, thereby improving the operation efficiency, ensuring the safety of passengers and optimizing the space resource allocation.

In some other embodiments, the above-mentioned remote monitoring method for wireless transmission of a seat pressure signal further includes: the self-diagnosis and maintenance are carried out regularly to ensure the normal operation of the sensor, the wireless communication module and the server, and the process is realized by executing a maintenance script, which comprises the following steps:

In addition, a data compression step is added after preprocessing and encoding, and Huffman encoding is adopted for lossless data compression. The specific principle of the steps is as follows:

a. frequency analysis: firstly, counting the occurrence frequency of different values in the collected seat pressure change data, and determining the occurrence probability of each value.

B. Constructing a Huffman tree: and constructing a Huffman tree according to the result of the frequency analysis. In this process, the two values (or characters) with the lowest frequency of occurrence are combined into a new node, the frequency of which is the sum of the two, and then the process is repeated until all the values are combined into a single tree structure. In this tree, the path length from the root to the leaf node represents the encoded length of the corresponding value.

C. generating an encoding table: by traversing the Huffman tree, a unique binary code is generated for each value. In this coding scheme, high frequency values will be assigned shorter codes, while low frequency values correspond to longer codes, thereby achieving efficient compression of data.

D. Encoding data: and converting the original pressure change data into a data stream after Huffman coding according to the generated coding table. This process essentially replaces each value in the original data with its corresponding binary codeword.

Through the steps, the Huffman coding can obviously reduce redundancy in data, especially for the condition that a large amount of repeated or approximately repeated data exists, so that the data volume is effectively reduced on the premise of not losing any information, and the efficiency and the speed of wireless transmission are improved.

In another aspect, the present invention proposes a remote monitoring system for wireless transmission of a seat pressure signal, as shown in fig. 2, comprising: the system comprises a pressure signal acquisition module, a data packet decompression module, an early warning information monitoring module and a remote monitoring module.

Specifically, the pressure signal acquisition module is used for installing a pressure sensor in the seat and is used for continuously monitoring the pressure change of the surface of the seat, and when the pressure sensor detects that the pressure of the seat changes, the pressure change data are immediately acquired;

Specifically, the data packet acquisition module is used for preprocessing and encoding the acquired pressure change data through the embedded microprocessor, encrypting the encoded data through the wireless communication module and converting the encrypted data into a data packet for wireless transmission;

specifically, the data packet decompression module is used for transmitting the data packet to the remote monitoring server through the wireless network, and after the remote server receives the data packet, the data packet is decrypted and parsed to restore original pressure change data;

Specifically, the early warning information monitoring module is used for analyzing the passenger state information according to the analyzed pressure change data, generating a monitoring report and early warning information in real time through the passenger state information, and automatically sending an alarm to a preset terminal when abnormal pressure change is detected;

specifically, the remote monitoring module is used for displaying the service condition of the seat and the historical pressure change data on the remote monitoring platform for the manager to check and analyze.

In addition, the pressure signal acquisition module, the data packet decompression module, the early warning information monitoring module and the remote monitoring module are further used for implementing other steps of the wireless transmission seat pressure signal remote monitoring method when the pressure signal acquisition module, the data packet decompression module, the early warning information monitoring module and the remote monitoring module are executed, and are not described in detail herein.

In addition, the invention also provides a terminal device, and the remote monitoring method of the wireless transmission seat pressure signal in the embodiment is mainly applied to the terminal device, wherein the terminal device can be a PC, a portable computer, a mobile terminal and other devices with display and processing functions.

In particular, the terminal device may include a processor (e.g., CPU), a communication bus, a user interface, a network interface, and a memory. Wherein the communication bus is used for realizing connection communication among the components; the user interface may include a Display screen (Display), an input unit such as a Keyboard (Keyboard); the network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface); the memory may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory, or alternatively may be a storage device independent of the aforementioned processor.

The memory stores a readable storage medium, the readable storage medium stores a remote monitoring program, and the processor can call the remote monitoring program stored in the memory and execute the remote monitoring method for wireless transmission of the seat pressure signal provided by the embodiment of the invention.

It will be appreciated that the readable storage medium may be a tangible device that can hold and store instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.

The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.

The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as SMALLTALK, C ++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.

Finally, it should be noted that: the foregoing description is only illustrative of the preferred embodiments of the present invention, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements or changes may be made without departing from the spirit and principles of the present invention.

Claims

1. A method for remotely monitoring a wireless transmission of a seat pressure signal, comprising:

2. The method for remote monitoring of a wireless transmission of a seat pressure signal according to claim 1, wherein in S2, the preprocessing includes filtering to remove noise, and the normalization processing is performed on the raw data using formula (1):

wherein X is original pressure data, and X _norm is normalized data.

3. The method for remotely monitoring a wireless transmission of a seat pressure signal according to claim 1, wherein in S2, the data security is enhanced by hashing the data packet according to formula (2):

4. The method for remotely monitoring a wireless transmission of a seat pressure signal according to claim 1, wherein in S4, a machine learning algorithm is used to predict the occupant behavior according to the current pressure change, and the model expression is formula (3): y=f (x; θ);

5. The method for remotely monitoring a wireless transmission of a seat pressure signal according to claim 1, wherein in S4, the early warning information includes a seat number, an early warning level and a recommended action, and the early warning level is calculated according to formula (4):

6. A method of remotely monitoring a wireless transmission of a seat pressure signal according to claim 1, wherein in S2 the wireless communication module supports a plurality of communication protocols including but not limited to Wi-Fi, bluetooth and LTE.

7. The method of claim 1, wherein in S5, the remote monitoring platform includes a graphical user interface to allow a manager to observe the seat pressure profile and distinguish the pressure levels by color.

8. The method of claim 1, wherein at S2, the checksum of the data packet is calculated by equation (5) prior to the transmission of the data packet:

Wherein, data is the data packet to be sent, and n is the data packet length.

9. A method of remotely monitoring a wireless transmission of a seat pressure signal as recited in claim 1, characterized by further comprising: the self-diagnosis and maintenance are carried out regularly to ensure the normal operation of the sensor, the wireless communication module and the server, and the process is realized by executing a maintenance script, which comprises the following steps:

10. A remote monitoring system for wirelessly transmitting a seat pressure signal, comprising: