Auxiliary system for communication of disabled people

Abstract

The invention discloses an auxiliary system for communication of disabled people, and relates to the technical field of intelligent communication. The system comprises a signal capturing and cloud uploading unit, a signal screening and verification unit, a data fusion and storage unit, a personal archive database construction unit, a personalized communication auxiliary database establishment unit, a mapping rule table making unit and a mapping rule display unit. According to the invention, the communication signal of the disabled is captured through the sensor technology, and through cloud screening and verification, the accuracy and effectiveness of the communication signal are ensured, and the barrier-free communication between the disabled and others is greatly enhanced, so that the disabled can express themselves more smoothly, the communication barrier is reduced, and the barrier-free communication of information is promoted.

Description

Technical Field

[0001] The present invention relates to intelligent communication technology, and in particular to an auxiliary system for communication of disabled persons. Background Art

[0002] For the deaf-mute, visually impaired and physically disabled, communication is not only a bridge for them to perceive the world, express their emotions and thoughts, but also a key way to integrate into society and realize their personal values. However, the public generally lacks understanding and recognition of the unique communication methods of people with disabilities. This invisible language barrier has built a high wall of communication, hindering their pace in daily interactions, social integration and personal growth, reducing their quality of life and psychological security, and greatly limiting their scope of activities and potential.

[0003] Therefore, it is particularly urgent and meaningful to develop an intelligent auxiliary communication system that can help people with disabilities express themselves freely and reduce obstacles in life. With the rapid development of science and technology, cutting-edge technologies such as wearable devices, AI projection, high-precision intelligent recognition and advanced voice control are gradually being integrated into daily life and work, showing strong application potential and transformative power. The integration and innovation of these technologies have laid a solid technical foundation for building an efficient, easy-to-use and adaptable communication assistance system for people with disabilities.

[0004] However, due to the difficulty in assisting data synchronization between different sensor devices, the technical implementation needs to rely on powerful computing power and high-end equipment, resulting in rising costs; at the same time, complex functional design may increase the user's operating burden, and insufficient recognition accuracy may directly affect the communication effect.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0006] In view of the problems in the related art, the present invention proposes an auxiliary system for communication for disabled persons to overcome the above technical problems existing in the existing related art.

[0007] To this end, the specific technical solution adopted by the present invention is as follows:

[0008] An auxiliary system for communication of disabled persons, the system comprising: a signal capture and cloud upload unit, a signal screening and verification unit, a data fusion and storage unit, a personal profile database construction unit, a personalized communication auxiliary database establishment unit, a mapping rule table formulation unit and a mapping rule display unit;

[0009] A signal capture and cloud upload unit, which is used to capture the communication signals of the disabled person using sensor technology and upload the communication signals to a cloud server;

[0010] A signal screening and verification unit is used to screen and verify communication signals based on a cloud server to obtain communication data;

[0011] A data fusion and storage unit, used for fusion processing of the communication data, and storing the fusion processed communication data to obtain a communication database;

[0012] A personal profile database construction unit, used to collect personal information of persons with disabilities and construct a personal profile database;

[0013] A personalized communication auxiliary database establishment unit, used to construct a personalized communication model based on the personal information in the personal file database using a machine learning algorithm, generate personalized communication data, and establish a personalized communication auxiliary database;

[0014] A mapping rule table formulation unit is used to determine the mapping relationship between the communication database and the personalized communication auxiliary database and establish a mapping rule table;

[0015] The mapping rule display unit is used to send the mapping rule table to a preset communication assistance terminal to display corresponding communication assistance information.

[0016] Furthermore, the communication signals include: gesture signals, eye movement signals, sound signals, tactile signals and brain-computer interface signals.

[0017] Furthermore, based on the cloud server, the communication signals are screened and tested, and the communication data obtained include:

[0018] The cloud server receives the communication signal through the network interface and encodes the communication signal into a unified data format;

[0019] The encoded communication signals are inspected according to preset inspection rules to check whether there are omissions and anomalies;

[0020] If there is an omission, the cloud server sends a command to the sensor device to collect the missing communication signal, and if there is an abnormality, the abnormal communication signal is eliminated;

[0021] Based on the inspection results, communication data is obtained.

[0022] Furthermore, the data fusion and storage unit includes: a time registration module, an initial fusion sequence acquisition module, a final fusion sequence acquisition module and a communication database establishment module;

[0023] The time registration module is used to extract time series data from the communication data and perform spatiotemporal registration on the time series data;

[0024] The initial fusion sequence acquisition module is used to apply the weighted fusion algorithm to the time series data after spatiotemporal registration to obtain the initial fusion sequence of various communication data at different times;

[0025] The final fusion sequence acquisition module is used to input the initial fusion sequence into the pre-built communication data fusion model to obtain the final fusion sequence of various communication data at different times;

[0026] The communication database establishment module is used to take the final fusion sequence as the final communication data, store the final communication data, and establish a communication database.

[0027] Furthermore, a weighted fusion algorithm is applied to the time series data after spatiotemporal registration to obtain the initial fusion sequence of various communication data at different times, including:

[0028] S11, extracting the communication data at each time point based on the time series data after spatiotemporal registration, and obtaining the average value by calculating the maximum and minimum values ​​of the communication data at each time point;

[0029] S12, adding the average value as a reference value to the spatiotemporal series data at any time, and comparing the communication data with the reference value;

[0030] S13, respectively calculating the mathematical expectations of the communication data that are greater than the reference value and less than the reference value, obtaining the expected median, and using the expected median as a new reference value;

[0031] S14, repeating steps S12 to S13 until the difference between two consecutive reference values ​​is less than a preset threshold, and obtaining a true value;

[0032] S15. Merge the true values ​​at each moment to obtain an initial fusion sequence.

[0033] Furthermore, the calculation formula for the expected median is:

[0034]

[0035] In the formula, K represents the expected median;

[0036] E(N) represents the mathematical expectation of communication data greater than the reference value;

[0037] E(M) represents the mathematical expectation of communication data that is less than the reference value.

[0038] Furthermore, the initial fusion sequence is input into the pre-built communication data fusion model to obtain the final fusion sequence of various communication data at different times, including:

[0039] S21. Based on the initial fusion sequence, set the fading factor of the communication data, and initialize the state variables, noise model and covariance matrix;

[0040] S22, inputting the initial fusion sequence into the pre-built communication data fusion model, combining the state transfer equation of the communication data fusion model with the set fading factor, predicting the state value at the current moment, and adjusting the covariance matrix;

[0041] S23, comparing the communication data with the predicted state value, calculating the prediction error, and correcting the state value according to the Kalman gain, and further adjusting the covariance matrix;

[0042] S24, repeating steps S22 to S23 to obtain the final fusion sequence at different times. Further, the calculation formula of the state value is:

[0043] Di=ADi-1+Bivi+pi

[0044] Where D i Indicates the status value of the i-th communication data;

[0045] A represents the state transition equation from the i-1th communication data to the i-th communication data;

[0046] D i-1 Indicates the status value of the i-1th communication data;

[0047] B i represents the covariance matrix of the i-th communication data;

[0048] v i represents the fading factor of the i-th communication data;

[0049] p i Represents the noise model of the i-th communication data.

[0050] Furthermore, based on the personal information in the personal profile database, a personalized communication model is constructed using a machine learning algorithm to generate personalized communication data, and a personalized communication auxiliary database is established, including:

[0051] Preprocess the personal information in the personal archive database, extract feature data from the preprocessed personal information, and establish a feature data set;

[0052] Combine communication assistance needs with machine learning algorithms to build a personalized communication model;

[0053] Using the feature data set to train and evaluate the personalized communication model, and obtaining the evaluated personalized communication model;

[0054] Input personal information into the evaluated personalized communication model and output personalized communication data;

[0055] Integrate personalized communication data with personal information to establish a personalized communication assistance database.

[0056] Furthermore, the characteristic data includes: personal characteristic data, historical communication records, text content characteristics, behavior pattern characteristics, and preference and interest characteristics.

[0057] The beneficial effects of the present invention are:

[0058] 1. The present invention captures the communication signals of disabled persons through sensor technology, and screens and verifies them through the cloud, thereby ensuring the accuracy and effectiveness of the communication signals, greatly enhancing the barrier-free communication between disabled persons and others, enabling disabled persons to express themselves more smoothly, reducing communication barriers, and promoting barrier-free exchange of information.

[0059] 2. The present invention constructs a communication database by integrating communication data, which not only lays a solid foundation for subsequent in-depth data analysis, but also greatly accelerates the processing and response speed of communication information, and improves the overall communication efficiency; in addition, through machine learning algorithms, it is possible to mine feature data from the personal profile database, construct a personalized communication model that meets the unique needs of each disabled person, generate highly customized communication auxiliary data, and realize personalized customization of communication services, thereby improving communication efficiency.

[0060] 3. The present invention ensures the effective mapping between the communication database and the personalized communication assistance database through the collaborative work of the mapping rule table formulation unit and the mapping rule display unit, and displays the mapping results in real time on the preset communication assistance terminal, providing intuitive and easy-to-understand communication assistance information for disabled people, further improving the communication experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 It is a principle block diagram of an auxiliary system for communication for disabled persons according to an embodiment of the present invention.

[0063] In the figure:

[0064] 1. Signal capture and cloud upload unit; 2. Signal screening and verification unit; 3. Data fusion and storage unit; 4. Personal profile database construction unit; 5. Personalized communication auxiliary database establishment unit; 6. Mapping rule table formulation unit; 7. Mapping rule display unit. DETAILED DESCRIPTION

[0065] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments. They can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention.

[0066] According to an embodiment of the present invention, an auxiliary system for communication of disabled persons is provided.

[0067] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to an embodiment of the present invention, the auxiliary system for communication of disabled persons includes: a signal capture and cloud upload unit 1, a signal screening and verification unit 2, a data fusion and storage unit 3, a personal profile database construction unit 4, a personalized communication auxiliary database establishment unit 5, a mapping rule table formulation unit 6 and a mapping rule display unit 7;

[0068] The signal capturing and cloud uploading unit 1 is used to capture the communication signals of the disabled person using sensor technology and upload the communication signals to the cloud server.

[0069] Specifically, communication signals include: gesture signals, eye movement signals, sound signals, tactile signals and brain-computer interface signals.

[0070] It should be noted that sensor technology is a device or apparatus that can sense the measured object (such as gestures, eye movements, sounds, touch and brain electrical activity) and convert it into a usable output signal (such as an electrical signal), including:

[0071] Gesture recognition sensors capture gesture movements through cameras and use image processing technology to identify specific gestures. They can be integrated into wearable devices or smart home systems to capture gesture signals from deaf and mute people.

[0072] Eye tracking sensors capture the movement of eyeballs through infrared rays or cameras, thereby analyzing the gaze direction and eye movement information of visually impaired people. They are widely used in assisted reading, interface navigation and other fields.

[0073] Voice recognition sensors (for sound signals in non-traditional sense). For the deaf and mute, voice recognition sensors may be used more to capture the sound signals they send through specific devices (such as speech synthesizers), or to recognize sounds in the environment for reminders or navigation.

[0074] Tactile sensors can sense tactile information such as pressure and vibration of objects. For people with physical disabilities, such sensors can be integrated into prostheses, wheelchairs or other assistive devices to capture and respond to tactile signals.

[0075] Brain-computer interface (BCI) sensors are a technology that directly connects brain signals to external devices. They capture the brain's electrical activity (such as brain waves) through electrodes or implanted sensors and convert them into recognizable signals. This technology has great potential in assisting communication, controlling external devices, etc., especially for people with disabilities who have lost traditional communication abilities due to nerve damage.

[0076] The signal screening and verification unit 2 is used to screen and verify the communication signal based on the cloud server to obtain the communication data.

[0077] Specifically, based on the cloud server, the communication signals are screened and tested, and the communication data obtained includes:

[0078] The cloud server receives the communication signal through the network interface and encodes the communication signal into a unified data format;

[0079] The encoded communication signals are inspected according to preset inspection rules to check whether there are omissions and anomalies;

[0080] If there is an omission, the cloud server sends a command to the sensor device to collect the missing communication signal, and if there is an abnormality, the abnormal communication signal is eliminated;

[0081] Based on the inspection results, communication data is obtained.

[0082] The data fusion and storage unit 3 is used to fuse the communication data and store the fused communication data to obtain a communication database.

[0083] Specifically, the data fusion and storage unit 3 includes: a time registration module, an initial fusion sequence acquisition module, a final fusion sequence acquisition module and a communication database establishment module;

[0084] The time registration module is used to extract time series data from communication data and perform spatiotemporal registration on the time series data.

[0085] It should be noted that extracting time series data from communication data and performing spatiotemporal registration of the time series data include:

[0086] According to the timestamp in the communication data, the time series information of each communication data is extracted to ensure the correct time sequence. The communication data can be grouped or aggregated according to different time intervals (such as seconds, minutes, hours) to form a unified time series format; for data with different time intervals, downsampling (if the communication data is too dense) or interpolation technology (if the communication data is sparse) can be used to ensure the continuity and consistency of the time series; if the communication data contains the user's geographic location data (such as GPS information, IP address, etc.), it is necessary to extract this spatial information from the communication record and convert the spatial information into standard latitude and longitude coordinates to facilitate subsequent spatiotemporal alignment processing.

[0087] Based on the time series and spatial data of communication data, a spatiotemporal registration model is established. The goal of this model is to align data at different times and spaces to ensure that communication data at different time points can be compared and analyzed in a unified time and space dimension; time alignment, based on timestamp information, aligns time series data from different sources. Common methods include alignment based on fixed time intervals, event-based time alignment, etc.; spatial alignment, using spatial position coordinates, position correction is performed on communication data from different locations. If there are errors or offsets in spatial data, a registration algorithm (such as the ICP algorithm) can be used for position matching and correction; spatiotemporal fusion, the data that has been aligned in time and space is fused to generate a unified spatiotemporal sequence, which can reflect communication activities occurring at different times and places, facilitating subsequent spatiotemporal analysis.

[0088] The initial fusion sequence acquisition module is used to apply a weighted fusion algorithm to the time series data after spatiotemporal registration to obtain the initial fusion sequences of various communication data at different times.

[0089] Specifically, a weighted fusion algorithm is applied to the time series data after spatiotemporal registration to obtain the initial fusion sequence of various communication data at different times, including:

[0090] S11. Based on the time series data after spatiotemporal registration, the communication data at each time point is extracted, and the average value is obtained by calculating the maximum and minimum values ​​of the communication data at each time point.

[0091] S12. Add the average value as a reference value to the spatiotemporal series data at any time, and compare the communication data with the reference value.

[0092] S13, respectively calculating the mathematical expectations of the communication data that are greater than the reference value and less than the reference value, obtaining the expected median, and using the expected median as a new reference value.

[0093] Specifically, the calculation formula for the expected median is:

[0094]

[0095] In the formula, K represents the expected median;

[0096] E(N) represents the mathematical expectation of communication data greater than the reference value;

[0097] E(M) represents the mathematical expectation of communication data that is less than the reference value.

[0098] S14, repeating steps S12 to S13 until the difference between two consecutive reference values ​​is less than a preset threshold, thereby obtaining a true value.

[0099] S15. Merge the true values ​​at each moment to obtain an initial fusion sequence.

[0100] The final fusion sequence acquisition module is used to input the initial fusion sequence into the pre-built communication data fusion model to obtain the final fusion sequence of various communication data at different times.

[0101] Specifically, the initial fusion sequence is input into the pre-built communication data fusion model to obtain the final fusion sequence of various communication data at different times, including:

[0102] S21. Based on the initial fusion sequence, set the fading factor of the communication data, and initialize the state variables, noise model and covariance matrix.

[0103] S22, inputting the initial fusion sequence into the pre-built communication data fusion model, combining the state transfer equation of the communication data fusion model with the set fading factor, predicting the state value at the current moment, and adjusting the covariance matrix.

[0104] Specifically, the calculation formula of the status value is:

[0105] Di=ADi-1+Bivi+ p i

[0106] Where D i Indicates the status value of the i-th communication data;

[0107] A represents the state transition equation from the i-1th communication data to the i-th communication data;

[0108] D i-1 Indicates the status value of the i-1th communication data;

[0109] B i represents the covariance matrix of the i-th communication data;

[0110] v i represents the fading factor of the i-th communication data;

[0111] p i Represents the noise model of the i-th communication data.

[0112] S23. Compare the communication data with the predicted state value, calculate the prediction error, and correct the state value according to the Kalman gain to further adjust the covariance matrix.

[0113] S24. Repeat steps S22 to S23 to obtain final fusion sequences at different times.

[0114] The communication database establishment module is used to take the final fusion sequence as the final communication data, store the final communication data, and establish a communication database.

[0115] The personal profile database building unit 4 is used to collect personal information of the disabled person and build a personal profile database.

[0116] It should be noted that personal information includes identity information (name, gender, date of birth, contact information, ID number or equivalent unique identification code, etc.), health status information (disability category: such as visual disability, hearing disability, speech disability, limb disability, intellectual disability, mental disability, etc.; disability level: disability level assessed according to standards, such as level one, level two, level three, level four, etc.; medical history; treatment status, etc.), communication ability assessment (assessment results of the current communication ability of persons with disabilities, including the effectiveness, proficiency and limitations of communication methods such as gestures, eye movements, touch, sound or brain-computer interfaces), personal preferences and habits (personal preferences of persons with disabilities in communication, such as preferred communication methods, commonly used vocabulary or phrases, interest in specific topics, etc., as well as their daily habits and living environment. This information helps to provide communication assistance that is more tailored to individual needs).

[0117] The personalized communication auxiliary database establishing unit 5 is used to construct a personalized communication model based on the personal information in the personal profile database using a machine learning algorithm, generate personalized communication data, and establish a personalized communication auxiliary database.

[0118] Specifically, based on the personal information in the personal profile database, a personalized communication model is constructed using a machine learning algorithm to generate personalized communication data, and a personalized communication auxiliary database is established, including:

[0119] The personal information in the personal archive database is preprocessed, and feature data is extracted from the preprocessed personal information to establish a feature data set.

[0120] Specifically, characteristic data include: personal characteristic data, historical communication records, text content characteristics, behavior pattern characteristics, and preference and interest characteristics.

[0121] Combine communication assistance needs with machine learning algorithms to build a personalized communication model.

[0122] The personalized communication model is trained and evaluated using the feature data set to obtain the evaluated personalized communication model.

[0123] Input personal information into the evaluated personalized communication model and output personalized communication data.

[0124] Integrate personalized communication data with personal information to establish a personalized communication assistance database.

[0125] The mapping rule table formulation unit 6 is used to determine the mapping relationship between the communication database and the personalized communication auxiliary database and establish a mapping rule table.

[0126] It should be noted that determining the mapping relationship between the communication database and the personalized communication auxiliary database and establishing the mapping rule table include:

[0127] Identify which data elements in the communication database (such as signal type, keywords, emotional tags, etc.) may be associated with the information in the personalized communication auxiliary database; evaluate the importance and relevance of these association points, and determine which factors are most critical for establishing the mapping relationship; based on the analysis results, design a series of mapping rules that define how to map the data in the communication database to the corresponding entries or models in the personalized communication auxiliary database. The mapping rules include conditional judgment (such as "if the signal type is gesture and the gesture matches X, then call personalized communication model Y"), data conversion (such as converting the original signal data into a format recognizable by the model), etc.; organize the designed mapping rules into a table form, namely the mapping rule table, which should clearly list the input data items in the communication database, the mapping logic, the output targets in the personalized communication auxiliary database, and other information.

[0128] The mapping rule display unit 7 is used to send the mapping rule table to a preset communication assistance terminal to display corresponding communication assistance information.

[0129] It should be noted that the mapping rule table data is packaged into a format suitable for transmission, such as JSON, XML or binary file, to ensure the integrity and readability of the data; according to the network environment and terminal characteristics, a suitable data transmission method is selected, such as HTTP request, WebSocket connection, FTP transmission, etc.; through the selected transmission method, the packaged mapping rule table data is sent to the preset communication assistance terminal, which can be automatically completed by the server-side program; when the communication assistance terminal receives communication signals from the disabled (such as gestures, eye movements, sounds, etc.), the application can parse these signals in real time; according to the parsed communication signals, the corresponding mapping rules are queried in the mapping rule table stored locally; according to the queried mapping rules, combined with the information in the personalized communication assistance database, the corresponding communication assistance information is generated, and the generated communication assistance information is displayed in a visual manner on the screen of the communication assistance terminal, such as text prompts, pictures, animations or voice outputs.

[0130] In summary, with the help of the above technical solution of the present invention, the communication signals of disabled people are captured by sensor technology, and after cloud screening and verification, the accuracy and effectiveness of the communication signals are ensured, which greatly enhances the barrier-free communication between disabled people and others, enables disabled people to express themselves more smoothly, reduces communication barriers, and promotes barrier-free communication of information. By integrating communication data, a communication database is constructed, which not only lays a solid foundation for subsequent in-depth data analysis, but also greatly accelerates the processing and response speed of communication information, and improves the overall communication efficiency; in addition, through machine learning algorithms, feature data can be mined from the personal archive database, and a personalized communication model that meets the unique needs of each disabled person can be constructed, and highly customized communication auxiliary data can be generated, realizing personalized customization of communication services, thereby improving communication efficiency. Through the collaborative work of the mapping rule table formulation unit and the mapping rule display unit, the effective mapping between the communication database and the personalized communication auxiliary database is ensured, and the mapping results are displayed in real time on the preset communication auxiliary terminal, providing intuitive and easy-to-understand communication auxiliary information for disabled people, further improving the communication experience.

[0131] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An auxiliary system for communication for disabled persons, characterized in that: The system includes: a signal capture and cloud upload unit, a signal screening and verification unit, a data fusion and storage unit, a personal profile database construction unit, a personalized communication auxiliary database establishment unit, a mapping rule table formulation unit and a mapping rule display unit; The signal capture and cloud upload unit is used to capture the communication signals of the disabled person using sensor technology and upload the communication signals to the cloud server; The signal screening and verification unit is used to screen and verify the communication signal based on the cloud server to obtain communication data; The data fusion and storage unit is used to fuse the communication data and store the fused communication data to obtain a communication database; The personal profile database building unit is used to collect personal information of the disabled and build a personal profile database; The personalized communication auxiliary database establishment unit is used to construct a personalized communication model based on the personal information in the personal profile database using a machine learning algorithm, generate personalized communication data, and establish a personalized communication auxiliary database; The mapping rule table formulation unit is used to determine the mapping relationship between the communication database and the personalized communication auxiliary database and establish a mapping rule table; The mapping rule display unit is used to send the mapping rule table to a preset communication assistance terminal to display corresponding communication assistance information.

2. The auxiliary system for communication for disabled persons according to claim 1, characterized in that: The communication signals include: gesture signals, eye movement signals, sound signals, tactile signals and brain-computer interface signals.

3. The auxiliary system for communication for disabled persons according to claim 1, characterized in that: The communication signal is screened and tested based on the cloud server to obtain the communication data, including: The cloud server receives the communication signal through the network interface and encodes the communication signal into a unified data format; The encoded communication signals are inspected according to preset inspection rules to check whether there are omissions and anomalies; If there is an omission, the cloud server sends a command to the sensor device to collect the missing communication signal, and if there is an abnormality, the abnormal communication signal is eliminated; Based on the inspection results, communication data is obtained.

4. The auxiliary system for communication for disabled persons according to claim 1, characterized in that: The data fusion and storage unit includes: a time registration module, an initial fusion sequence acquisition module, a final fusion sequence acquisition module and a communication database establishment module; The time registration module is used to extract time series data from the communication data and perform time-space registration on the time series data; The initial fusion sequence acquisition module is used to apply a weighted fusion algorithm to the time series data after spatiotemporal registration to obtain the initial fusion sequences of various communication data at different times; The final fusion sequence acquisition module is used to input the initial fusion sequence into the pre-built communication data fusion model to obtain the final fusion sequence of various communication data at different times; The communication database establishing module is used to take the final fusion sequence as the final communication data, store the final communication data, and establish a communication database.

5. The auxiliary system for communication for disabled persons according to claim 4, characterized in that: The weighted fusion algorithm is applied to the time series data after the spatiotemporal registration to obtain the initial fusion sequence of various communication data at different times, including: S11, extracting the communication data at each time point based on the time series data after spatiotemporal registration, and obtaining the average value by calculating the maximum and minimum values ​​of the communication data at each time point; S12, adding the average value as a reference value to the spatiotemporal series data at any time, and comparing the communication data with the reference value; S13, respectively calculating the mathematical expectations of the communication data that are greater than the reference value and less than the reference value, obtaining the expected median, and using the expected median as a new reference value; S14, repeating steps S12 to S13 until the difference between two consecutive reference values ​​is less than a preset threshold, and obtaining a true value; S15. Merge the true values ​​at each moment to obtain an initial fusion sequence.

6. The auxiliary system for communication for disabled persons according to claim 5, characterized in that: The calculation formula of the expected median is: In the formula, K represents the expected median; E(N) represents the mathematical expectation of communication data greater than the reference value; E(M) represents the mathematical expectation of communication data that is less than the reference value.

7. The auxiliary system for communication for disabled persons according to claim 4, characterized in that: The initial fusion sequence is input into the pre-built communication data fusion model to obtain the final fusion sequence of various communication data at different times, including: S21. Based on the initial fusion sequence, set the fading factor of the communication data, and initialize the state variables, noise model and covariance matrix; S22, inputting the initial fusion sequence into the pre-built communication data fusion model, combining the state transfer equation of the communication data fusion model with the set fading factor, predicting the state value at the current moment, and adjusting the covariance matrix; S23, comparing the communication data with the predicted state value, calculating the prediction error, and correcting the state value according to the Kalman gain, and further adjusting the covariance matrix; S24. Repeat steps S22 to S23 to obtain final fusion sequences at different times.

8. The auxiliary system for communication for disabled persons according to claim 7, characterized in that: The calculation formula of the state value is: Of=ADi-1+Bivi+ p the Where D i Indicates the status value of the i-th communication data; A represents the state transition equation from the i-1th communication data to the i-th communication data; D i-1 Indicates the status value of the i-1th communication data; B i represents the covariance matrix of the i-th communication data; v i represents the fading factor of the i-th communication data; p i Represents the noise model of the i-th communication data.

9. The auxiliary system for communication for disabled persons according to claim 1, characterized in that: The method of constructing a personalized communication model based on the personal information in the personal profile database using a machine learning algorithm, generating personalized communication data, and establishing a personalized communication auxiliary database includes: Preprocess the personal information in the personal archive database, extract feature data from the preprocessed personal information, and establish a feature data set; Combine communication assistance needs with machine learning algorithms to build a personalized communication model; Using the feature data set to train and evaluate the personalized communication model, and obtaining the evaluated personalized communication model; Input personal information into the evaluated personalized communication model and output personalized communication data; Integrate personalized communication data with personal information to establish a personalized communication assistance database.

10. The auxiliary system for communication for disabled persons according to claim 9, characterized in that: The characteristic data includes: personal characteristic data, historical communication records, text content characteristics, behavior pattern characteristics, and preference and interest characteristics.

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