WO2024098982A1

WO2024098982A1 - Right brain training assistance apparatus and training evaluation algorithm

Info

Publication number: WO2024098982A1
Application number: PCT/CN2023/121094
Authority: WO
Inventors: 郭志刚
Original assignee: 郭志刚
Priority date: 2022-11-12
Filing date: 2023-09-25
Publication date: 2024-05-16
Also published as: CN116107485A

Abstract

The present invention relates to a right brain training assistance apparatus and a training evaluation algorithm. The invention is characterized in that: a processing machine comprises a processor and a cache unit; a memory comprises a training module and a training effect evaluation module; a playback device, a training input terminal, and a control button group are connected to the processing machine; the memory is connected to a system setting key group, the processing machine, a network interface, and a display screen, and the network interface is connected to a cloud server; and the cloud server comprises a memory parameter and coefficient operation module and a training terminal database. The present invention improves focus and can guide trainees to develop a right-brain photographic memory method; moreover, a memory formula in a right brain training effect evaluation module scores and ranks training effects, providing those participating in training with an extremely intuitive understanding of their own memory improvement, the rankings of this objective and scientific memory evaluation method likewise urging the trainee to work hard during training so as to continuously improve their focus and memory levels, thereby integrating training and competition.

Description

A right brain training auxiliary device and training evaluation algorithm

Technical Field

The present invention belongs to the technical field of attention and right brain memory development training auxiliary devices, and in particular relates to a right brain training auxiliary device and a training evaluation algorithm.

Background technique

Memory affects a person's life. I have seen teachers who can recite the text fluently and tell which row and column has what content and what symbol like turning pages of a book. I have also tried this kind of memory effect. This kind of memory is called photographic memory. It belongs to right brain memory. Any learning memory is extremely important. Knowledge learning needs to spend at least half of the time on it. A good memory will greatly improve learning efficiency.

The left brain is responsible for abstract thinking, such as language, reasoning, judgment, calculation, reading, etc.; the right brain is responsible for figurative thinking, such as perception, graphics, color, melody, imagination, etc. Photographic memory utilizes the image processing ability of the right brain, and its working principle is to use the unique image memory of the right brain to achieve cognitive purposes. In the development of the right brain potential, the most effective way to reflect the development of the right brain is memory. In terms of the memory capacity of the left and right brains of the human brain, the image memory function of the right brain is 1 million times that of the left brain. Scientists point out that throughout their lives, most people only use 3%-4% of their brains, and the remaining 97% is hidden in the subconscious of the right brain;

Right brain memory methods include connection memory method, classification memory method, story association method, homophonic memory method, etc. These memory methods are all skills, and image memory is the core and the most basic part of these memory methods; memory methods are moves, and actual memory is power. The photographic memory of the right brain is 1 million times that of the logical memory of the left brain. Developing the right brain memory will improve the learning efficiency of all learners and activate a series of right brain potentials such as space design and artistic imagination;

Good concentration can ensure focus, which leads to efficient memory, even Students can learn by rote and concentrate on memorizing. Diligent students can often master a lot of knowledge by rote memorization, because they improve their memory through repeated memorization, but this method is inefficient, and because this kind of memory is logical memory, the content is not very detailed. Right brain development training can effectively improve concentration, because people must concentrate when memorizing images.

At present, the training methods of photographic memory include the four-order Rubik's Cube color training method and the card memory method. The four-order Rubik's Cube color training method is a memory training method that restores the color of the Rubik's Cube blocks that are shuffled in order. It has a good effect on improving students' rapid memory ability; the card training method is to arrange the cards in a certain order, requiring students to stare at the cards for a certain period of time, memorize the color and pattern on the surface of each card, and then shuffle the order of the cards, requiring students to place the cards in the original order. This method can activate the right brain and train photographic memory. Compared with the four-order Rubik's Cube color training method, the training difficulty is increased and the training effect is better; there are also many other methods such as yellow cards, three-color cards, and mandalas to train the right brain to improve memory. Simply put, it is to convert different text information into image information, and then use other scientific methods to remember it in the mind. This process uses the image memory function of the right brain. If you observe carefully, you will find that it is easy for you to remember an image, and you can remember it easily and unintentionally. If you use this phenomenon to associate the text information that needs to be remembered with a picture you are familiar with, you will find that memory is so simple;

The current situation is that right brain training is short-term training provided by training institutions. The training time is short and the training methods are complicated. Proficiently mastering and applying right brain memory methods is simply not achievable within the training cycle, and a lot of time is still needed to consolidate the learned methods in the later stages. Therefore, the embarrassing situation of learning and using them in the end often occurs. In addition, students have heavy learning tasks, and it is time-consuming and laborious to participate in long-term training to train photographic memory, and the training effect cannot be quantified in the short term.

For most people, there is a need for a convenient and easy-to-use product to carry out photographic memory training from easy to difficult. Based on this, a device is developed to train the image memory method, and through long-term It is very important to provide effective and convenient training to enable users to fully master this photographic memory method.

The forward listening and reverse narration training method can not only train photographic memory but also allow participating students to maintain their attention. First, you need to input the data string you heard in reverse order into the memory training auxiliary device. You must maintain sufficient concentration, otherwise you will not be able to input the content of the data string you heard in reverse order into the training auxiliary device, which can effectively train your attention; in addition, when training to input a long data string in reverse order, when the length exceeds 8 digits, if you want to input this long data string in reverse order into the auxiliary device, you must remember this data string in the same way as an image, because you cannot quickly recite the data string in reverse order through reverse reasoning and input it into the auxiliary training device. In this way, you can effectively train your right brain image memory.

Through the auxiliary device's forward-listening and reverse-order input method, the participants in the training can deeply understand the key points of the photographic memory method from the training, and then be trained in some techniques and methods of photographic memory, so that the photographic memory method can become a learning habit, and finally thoroughly master and apply the photographic memory method.

There is no such training device on the market at present, and there is no such mobile phone APP training software for forward listening and reverse order. Most of them are mainly shorthand positive memory training.

Currently, the effect of right brain training on the market cannot be quantified. Boring training cannot show one's own progress in the short term, and teachers and parents cannot effectively understand students' training situation and training results. Visualization effect is very important to improve training enthusiasm. The memory auxiliary training device takes this situation into consideration. Through scientific memory formula calculation, participants in photographic memory training can see their own progress and the comparison between themselves and other trainees, which motivates trainees to train to improve their level.

The online mode of the auxiliary training device can provide online training method prompts based on the training scores of the trainees, and provide online information push guidance at different stages and levels to consolidate and improve the training effect.

Currently, the listening and reciting mode is trained by manually writing out several groups of numbers, with one person saying and the other reciting. This is time-consuming and laborious. As a long-term training, it is a lot of work for both parties. In addition, the effect of right brain training is improved faster when a person is quiet.

The training auxiliary device provides multiple modes of training, which can stimulate the right brain memory training effect in many aspects, so that the training effect can be improved faster. Everyone can also adjust the training difficulty according to their own situation.

The auxiliary training device is easy to carry and provides more training time. You can train anytime and anywhere by putting on headphones without worrying about being disturbed and affecting your training.

Currently, there is no universal memory assessment training device on the market that can evaluate a person's memory level, so most people do not know what their memory level is.

Evaluating memory level is not simply about the length of time. There is a simple algorithm, which takes a group of memory time data sets of training personnel data strings as an example, subtracts the difference between the longest memory time of the trainee and the memory time required for training, and then divides it by the difference between the longest memory time and the shortest memory time in the training personnel data set to obtain your memory level in this training data set. However, if this method results in one or several longer memory times, then all memory level assessments with shorter times will appear at an excellent level of more than 90 points, which cannot objectively evaluate your actual situation. In addition, this evaluation algorithm cannot convert your memory level between different data string lengths and different memory modes, nor can it objectively and truthfully evaluate the true situation of the training personnel's memory level.

The memory algorithm formula established by the auxiliary device can motivate trainees to challenge training in more difficult modes with longer data strings. Since most people can easily achieve a faster speed based on the algorithm reasoning in a monotonous mode with a relatively short data string, the upper limit score value is limited.

Only by mastering more knowledge can we integrate more knowledge content; the ultimate goal of memory aids is to make all students involved in learning no longer worry about memory.

Summary of the invention

In view of the problems existing in the prior art, the purpose of the present invention is to provide a technical solution of a right brain training auxiliary device and a training evaluation algorithm.

The right brain training auxiliary device is characterized by comprising a processor, a memory, a player, a display The player, the training input terminal, the control button group are connected to the processor, the memory is connected to the system setting button group, the processor, the network interface, the display screen, and the network interface is connected to the cloud server; the cloud server includes a memory parameter and coefficient calculation module, and a training terminal database.

The right brain training auxiliary device is characterized by:

The training input terminal is connected to the processor of the processing machine. The training input terminal is a key input or a touch screen software keyboard. The keyboard layout setting includes a keyboard with pure numbers 0 to 9, a pure letter keyboard, a mixed keyboard of numbers and letters, a text keyboard, a keyboard with twelve zodiac images and text groups, a color keyboard, a musical note keyboard, or a mixed keyboard with any combination of the above modes. The training input terminal is used for the keyboard trainer to input the content heard from the player in reverse order.

The cache unit of the processor is connected to the memory. The cache unit obtains the system parameters stored in the memory and then initializes the training module. The cache unit is used to store the temporary results generated by the processor and the data generated by the right brain training effect evaluation module. The processor randomly generates a data string in a specified format according to the set data string length and training mode, converts the data string into a voice format and plays it through a player. After completing a round of training, the training effect evaluation module calculates and gives a training score, and at the same time stores the training mode, memory duration, and training data string length in the memory, and uploads the data to the cloud server through a network interface.

The player is a built-in speaker or an external headset, which plays relevant content according to the requirements of the processor, including: training content, error prompts, training scores, and teaching encouragement voice;

The display screen displays the following contents: training mode, training ranking, training score, system error prompt, training mode bonus coefficient, and system setting menu;

The system setting button group includes the menu button, up button, down button, and confirm button. According to user needs, enter the system setting menu to realize the number of training times, data string length, training mode, and network interface connection. Setting, resetting terminal performance records, entering training personnel information, training methods, memory formula parameter settings, mode coefficient settings, after setting is completed, saving the parameters to the memory and reinitializing the operating parameters of the training module in the processor;

The network interface uploads and updates the memory formula parameters and coefficients, uploads training data, updates the current training mode data content, updates the training system program on the memory, obtains the training ranking, and obtains the information from the cloud server to the auxiliary training device by connecting to the network;

The training module of the memory is completed by the user inputting the content broadcast by the player in reverse order through the training input terminal;

The training effect evaluation module of the memory calculates the score of the training data according to the memory formula on the memory after completing a set of training and displays the score on the display screen. At the same time, the training data of the auxiliary training device is uploaded to the cloud server through the network interface, including: training mode, number of training times, memory duration, length of training data string, training score, and the latest memory formula parameters and mode coefficients on the memory and cache unit are updated according to the content sent back by the cloud server;

The training terminal database of the cloud server stores the basic information of each auxiliary training device: device number, latest score, training ranking, training mode, memory duration, training data string length, memory parameter and coefficient data, memory parameter and coefficient operation module;

The memory parameter and coefficient calculation module of the cloud server starts the memory parameter and coefficient calculation module according to the module operation time interval or the number of terminal data updates of the auxiliary training device, and stores the adjusted memory parameter and mode coefficient data in the terminal database of the cloud server;

The control button group includes a power switch, a replay button, and an Enter button, which are used in conjunction with the training input terminal. The right brain training auxiliary device and training evaluation algorithm are characterized in that the training module has two training methods:

Scoring method: Generate a data string of a certain length and pattern based on the parameters stored in the memory, and play it in positive order. Play the content and wait for the user to input the comparison training in reverse order through the training input terminal. After reaching the set number of training times, the training data is stored in the storage and the data is interacted with the cloud server through the network interface;

Non-scoring method: Generate a data string of a certain length and pattern based on the parameters on the memory, or store idioms, sentences, poems or a combination of multiple contents that meet the parameter setting requirements on the memory, and play or pause the training content according to the set playback time interval or the replay button of the control button group. The trainee recalls the voice content continuously played by the auxiliary training device in reverse order to perform non-scoring memory training.

The right brain training auxiliary device and training evaluation algorithm are characterized in that the memory training steps of the scoring method are as follows:

(a) Receiving input content step: the trainee inputs the data string voice heard from the player in reverse order at the training input terminal, and presses the Enter button to end the input;

(b) Internal operation steps of the auxiliary training device: the processor generates a data string according to the training mode and the parameters set by the system setting button group and puts it into the cache, and compares the reverse data string generated by the data string with the input content of the training input terminal;

1) Correct comparison: The processor generates another data string according to the data string parameter requirements stored in the cache unit, and converts the data string into a voice format and plays it out through the player; the processor waits for the input of the training input terminal to complete and then clicks the Enter button to proceed to the next comparison process;

2) Comparison error: replay the data string voice in the buffer through the player, waiting for the user to continue to input the correct content through the training input terminal; the data string voice in the buffer can also be repeatedly played through the replay button of the control button group;

3) The processor completes the required number of training times in the cache and prompts the end of training through the player; the memory duration, training mode, data string length, and number of training times are stored in the memory and the memory training score is displayed on the display.

The right brain training auxiliary device and training evaluation algorithm are characterized in that the memory training steps of the non-scoring method are as follows:

Press the start button of the control button group, the player plays the training content as required by the parameter settings, the trainees follow the content for reverse narration training, the player continues to play the training content according to the set time interval parameters, and the replay button of the control button group can pause or continue the training content playback.

The right brain training evaluation algorithm is characterized in that: after a round of testing is completed at the training input end, the memory score of the current time is calculated by the processor according to the memory formula, and the data of the current training is uploaded to the cloud server through the network interface: the number of training times, memory time, data string length, training mode, auxiliary device machine ID number, and training user information. After receiving the uploaded data, the cloud server retrieves the data corresponding to the auxiliary device machine ID number in the training terminal database. If the current training auxiliary device machine ID number does not exist in the database, a new data record with the training auxiliary device machine ID number as the keyword is created in the training terminal database, and then the received data: number of training times, memory time, data string length, training mode, auxiliary device machine ID, and training user information are written into the database; if the corresponding auxiliary device machine ID number is retrieved in the cloud server, the corresponding data of the memory time and memory score in the database are updated according to the current training mode and data string length. The update must be that the latest memory score is higher than the score in the database corresponding to the existing training mode and data string length, otherwise it will not be updated.

The right brain training evaluation algorithm is characterized in that: in the digital training mode, the derivation process of the memory formula is as follows:

When the total number of sample data of the digital training mode in the cloud server database exceeds 400, and the number of memory duration data records corresponding to at least three data string lengths exceeds 20, the digital training mode memory formula can be deduced. The steps are as follows:

a) First, based on the Ebbinghaus memory experiment data, it is inferred that the length of the training data string l and the memory time t are a parabolic curve;

b) Taking the same data string length l ₀ as the reference, read the field data corresponding to the time length t of the digital training mode from the database. Here, the time field data is calculated by arithmetic average to obtain the average value t ₀ , and the weighted average method can also be used to obtain the average value t ₀ , and obtain a set of coordinate points (l ₀ , t ₀ ); according to the above method, take two data string lengths corresponding to the time field and calculate the arithmetic average to obtain another two sets of coordinate points (l ₁ , t ₁ ), (l ₂ , t ₂ ); use the parabola quadratic equation for these three coordinate points to obtain three equations:
t ₀ = a*l ₀ ² + b*l ₀ + c
t ₁ = a*l ₁ ² + b*l ₁ + c
t ₂ =a*l ₂ ² +b*l ₂ +c

By solving the equation, we get the parameters of a, b, and c, so we get the relationship between the data string length l and the memory time t as t _number = a _number * l _number ² + b _number * l _number + c _number ; each coordinate on the parabola equation represents the relationship between the length of the training data string of all people and the average time required, and each data string length vertically corresponds to the average time required for all personnel to train with this data string length; of course, the shorter the training time, the higher the memory score. The most important indicator for memory formula evaluation is the time required for memory training. The scoring standard is that the less time required for training, the higher the score. We use the time when the data string length is mapped to the parabola divided by the actual training time of the trainee at the same data string length as the memory formula, and thus the memory formula is:

100 is the magnification factor used to amplify the memory score value.

The right brain training evaluation algorithm is characterized in that: when the stand-alone machine is not connected to the Internet and does not have the latest memory formula parameters or the database data of the cloud server is not enough, the system will set an initialized memory formula to give the right brain training memory score. The formula should conform to the Ebbinghaus memory experiment. The relationship between the length of the data string and the memory duration is a parabolic equation. The relationship between the length and the memory duration is: t =al ² +b l+c, the parameters are used to deduce an initial memory formula based on the data of internal experimental training. It is not important whether the formula completely conforms to the actual situation. As long as the data of the trainer reaches a certain amount, the memory parameter and coefficient calculation module on the cloud server will calculate the parameters more accurately.

The right brain training evaluation algorithm is characterized by:

There is a coefficient relationship between the digital training mode and the letter training mode. In the letter training mode, the derivation process of the memory formula is as follows:

If the database data of each training mode is insufficient in the cloud server data, the coefficient of each mode and the pure digital memory formula is set as a fixed parameter: l is the length of the training data string, t is the average time to remember a single character of the training data string, and n is the conversion coefficient of the training mode; according to the difficulty, the digital training mode is initially set as the benchmark. When the sample data of the digital training mode and the letter training mode are both more than 400, and there are more than 20 data records of the memory time field corresponding to at least three data string lengths, the relationship between the data string length and the memory time of the digital training mode and the letter training mode can be derived;

According to the memory formula of the digital training mode, the parabolic relationship between the length of the training data string l _letters and the memory time t letters of _{the letter} training mode can be deduced: t _letters = a _letters * l _letters ² + b _letters * l _letters + c _letters

The database of the cloud server randomly selects three data string lengths that meet the requirements, namely, l _a , l _b , and l _c , and substitutes them into the relationship formula _of the digital training mode, t _number = a _number * l _number ² + b _number * l _number + c _number , and the relationship formula of the letter training mode, t _letter = a _letter * l _letter ² + b _letter * l letter + c _letter . The two formulas respectively obtain three results, namely, the digital training mode t ₁ , t ₂ , t ₃ , and the letter mode t _a , t _b , t _c . The training time is different under the same data string length. The same training group has different memory time just because of different training difficulty, so the same scoring result should be given between the two. Therefore, the memory time t ₁ and t _a are equal under the two modes under the same data string length, t ₂ and t _b are equal, and t ₃ and t _c are equal. The conversion is as follows:
t ₁ = _ta * S _{coefficient of digital mode and letter mode}
t ₂ = t _b *S _{coefficient of digital mode and letter mode}
t ₃ = t _c *S _{coefficient of digital mode and letter mode}

Therefore, the mode coefficients between the two training modes are: The relationship between the length of the same training data string and the pattern coefficient between the two training patterns results in three coordinate sets Using three coordinate sets, we can get the equations for the two training pattern lengths and the conversion coefficients:

Based on the above equation of relationship coefficient, the memory formula for converting the letter mode memory score into the number mode parameter formula is:

Similarly, the conversion coefficients between letters, colors, musical notes, animals, and zodiac signs and the digital training mode can also be obtained according to the above method. The coefficients are started in a timely manner according to the amount of data update of the auxiliary training device or the memory formula parameters and the time interval conditions of the coefficient calculation module. The memory formula parameters and coefficients are adjusted accurately through the background calculation of the database data of the cloud server, so that the memory formula can better meet the actual scoring standards.

After the calculation of the memory formula parameters and coefficients is completed, the results are stored in the parameter coefficient table of the cloud server so that the training auxiliary device can obtain the latest memory formula parameters and coefficients after uploading the data.

The cloud server updates the client's memory formula parameters and coefficients, and its operation process is as follows: the parameter coefficients of the memory formula are constantly updated with the enrichment of training data in the cloud server database, making the memory formula more scientific and accurate, and more accurately reflecting the changes in the trainee's memory level. While the auxiliary training terminal uploads the training data, it obtains the latest parameter coefficient updates from the parameter and coefficient table in the cloud server and gives the trainee appropriate training suggestions and supporting methods based on the trainee's training score. The suggestions and methods are sent back to the storage device of the auxiliary training device, and the trainee can read the message content when setting the menu; scientific parameters allow trainees to adjust their own Have a more realistic understanding of the right brain memory level.

The beneficial effects of the present invention are: on the one hand, the right brain training auxiliary device can improve the concentration of trainees, and on the other hand, through long-term training, trainees can master the right brain photographic memory method under the training mode designed by the auxiliary training device and continuously improve their memory level through training; and in order to quantify the effect of training, a memory formula for evaluating the effect of right brain training is designed, the meaning of the formula can allow trainees to see the progress of their training level through scoring, and can also know the ranking and level difference with other trainees through scoring, and can also let teachers and parents know the memory training situation of students participating in the training in real time; the cloud server can provide online memory methods and related methods training message push according to the memory score of the trainees, so that trainees at different levels can improve their memory level to a new level through more in-depth method learning; the improvement of the right brain training level not only improves the memory level and doubles the efficiency of knowledge learning, but also this simple and effective training mode has a good effect on preventing Alzheimer's disease and children's attention disorder, and doctors can also use the score as an indicator to evaluate the situation of trainees.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a structural diagram of the device of the present invention;

Fig. 2 is a plan view of the right brain training auxiliary device of the present invention;

FIG3 is a plan view of a simplified version of the right brain training auxiliary device of the present invention;

FIG4 is a flow chart of the right brain training auxiliary device of the present invention;

FIG5 is a diagram showing the parameters and coefficients of the memory formula of the present invention;

FIG6 is a graph showing a memory formula of the present invention;

FIG7 is an illustration of conversion coefficient curves between different modes of the present invention;

FIG8 is a table showing experimental data collection of the right brain digital training mode of the present invention.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings:

A right brain training auxiliary device comprises a training input terminal 6, a processor 1, a memory 2, a player 3 (built-in speaker or external headset), a display screen 4, a system setting button group 5, a network interface 7, a cloud server 8, and a control button group 9. The training input terminal 6 is connected to the processor 1; the processor 1 comprises a processor and a cache unit; the memory 2 is connected to the system setting button group 5, the processor 1, the network interface 7, and the display screen 4; the player 3 (built-in speaker or external headset) is connected to the processor 1, the display screen 4 is connected to the processor 1, the system setting button group 5 is connected to the memory 2, and the control button group 9 is connected to the processor 1; the memory 2 comprises two module groups, which are composed of a training module and a training effect evaluation module; the network interface 7 is connected to the memory and the cloud server 8; the cloud server 8 comprises a memory parameter and coefficient calculation module, and a training terminal database.

The training input terminal 6 is connected to the processor of the processing machine 1, as shown in FIG2 , and the training input terminal 6 includes a zodiac input terminal s22, a numeric keyboard or a musical note or a color input terminal s23, and a letter input terminal s24. Each key of the numeric keyboard or a musical note or a color input terminal s23 consists of 10 colors, providing a color training mode; the zodiac input terminal s22 can be a single training of the abbreviations of the zodiac, or a training of the abbreviations of the zodiac animal names, or a training combined with other input terminals; the numeric keyboard or a musical note or a color input terminal s23 can be a pure digital training, a musical note training, or a training of different colors on the input terminal, or a training of this group of input terminals combined with other input terminals Training; letter input terminal s24 can be pure letter training or combined training with other input terminals; training mode types: pure numbers, pure musical notes, pure colors, pure zodiac abbreviations, pure zodiac animal abbreviations, pure letters, combination of numbers and letters, combination of colors and letters, combination of numbers and zodiac signs, etc., all combination types under non-conflicting input terminals; as shown in Figure 3, a simplified version of the memory auxiliary training device, the training modes can be selected: pure numbers, musical notes, and colors. Three training modes, each key consists of 10 colors, can provide color training mode; the input terminal can also combine various input modes according to needs to provide pattern training; it can also be a touch screen software keyboard of the above keyboard combination.

The cache unit of processor 1 is connected to memory 2, and the cache unit obtains the system parameters stored in memory 2. Initialize the training module program, the cache unit is used to store the temporary results generated by the processor and the data generated by the right brain training effect evaluation algorithm.

Processor 1 randomly generates a data string in a specified format according to the data string length and training mode set by the system, converts the data string into a voice format and plays it through a player; after completing a round of training, the training effect evaluation module calculates and gives a training score, and stores the training mode, memory duration, training data string length and other data in the memory, and uploads the data to the cloud server through the network interface.

The player 3 (built-in speaker or external headset) plays relevant content as required by the processor, including: training content, error prompts, training scores, teaching encouragement voice, etc.; as shown in Figures 2 and 3, components s27 and s35 are volume adjustment buttons and speakers, and the auxiliary device can also be connected to Bluetooth headsets through settings.

As shown in FIG. 2 and FIG. 3 , component s25 and component s33 are both display screens, and the display contents of the display screens include: training mode, training ranking, training score, system error prompt, training mode bonus coefficient, system setting menu and the like.

The components s21 and s31 as shown in Figures 2 and 3 are both system setting button groups, and the setting content is dynamically adjusted according to the product type; the system setting button group is a group of buttons, including a menu button, an up button, a down button, and an OK button. According to user requirements, the system setting menu is entered: number of training times, training data string length, training mode, network interface connection settings, terminal performance record reset, training personnel information entry, training method. After the settings are completed, the parameters are saved to the memory and the running parameters of the training module program in the processor are reinitialized.

The network interface 7 uploads and updates the following by connecting to the Internet: memory formula parameters and coefficients, uploading training data, updating the current training mode data content, updating the training system program on the memory, obtaining training rankings, and obtaining information on the auxiliary training device of the cloud server.

There are two training methods for the training module:

Scoring method: Generate a data string of specific length and pattern according to the parameter rules on the memory, and play it in positive order. Play the content and then wait for the training input end to input the comparison training in reverse order. After reaching the set number of training times, the training data is stored in the memory and the data is exchanged with the cloud server 8 through the network interface 7;

Non-scoring method: Generate a data string of a specific length and pattern according to the parameter rules on the memory or store idioms, sentences, poems or any content combination that meets the parameter setting requirements on the memory, and play or pause the training content according to the set playback time interval or the replay button of the control button group. The trainee recalls the voice content continuously played by the auxiliary training device in reverse order to perform non-scoring memory training; in this method, the auxiliary training device is similar to a voice playback device for rapid memory practice. . The training effect evaluation module is to calculate the score of the training data according to the memory formula on the memory after the auxiliary training device completes a set of training and display the score on the display screen, and upload the training data of the auxiliary training device to the cloud server through the network interface, including: training mode, number of training times, memory duration, length of training data string, training score, and update the latest memory formula parameters and mode coefficients on the memory and cache unit according to the content received from the cloud server.

The training terminal database of the cloud server stores the basic information of each auxiliary training device: equipment number, latest score, training ranking, training mode, memory duration, training data string length, memory parameters and coefficient data, memory parameters and coefficient calculation module.

The memory parameter and coefficient calculation module of the cloud server starts the memory parameter and coefficient calculation module program according to the module operation time interval or the number of terminal data updates of the auxiliary training device, and stores the adjusted memory parameters and mode coefficient data in the terminal database on the cloud server.

As shown in Figures 2 and 3, components s28 and s36 are power switches, components s29 and s37 are replay buttons, and components s30 and s38 are Enter buttons; the control button group consists of a power switch, a replay button, an Enter button, and a volume control button.

The steps of memory training of the scoring method are as follows:

(a) Step of receiving input content: The trainee listens to the data string voice heard from the player 3 and inputs it during training. Input the data in reverse order at terminal 6 and press the Enter button to end the input.

(b) Internal operation steps of the auxiliary training device: the processor 1 generates a data string according to the training mode and the parameters set by the system setting button group 5 and puts it into the cache, and compares the reverse data string generated by the data string with the input content of the training input terminal 6;

1) The comparison is correct: the processor 1 generates another data string according to the data string parameter requirements stored in the cache unit, and converts the data string into a voice format and plays it out through the player 3; the processor 1 waits for the input of the training input terminal 6 to complete and then clicks the Enter button to proceed to the next comparison process;

2) Comparison error: replay the data string voice in the buffer through the player 3, waiting for the user to continue to input the correct content through the training input terminal 6; the data string voice in the buffer can also be repeatedly played through the replay button of the control button group 9;

3) The processor 1 completes the required number of training sessions in the cache and prompts the end of training via the player 3; the memory duration, training mode, data string length, and number of training sessions are stored in the memory 2 and the memory training score is displayed on the display screen 4.

4 is a flow chart of the right brain training auxiliary device, and the scoring method flow includes the following steps:

Step s0, start: power on the auxiliary training device;

Step s1, initialization of the auxiliary training device: the system is initialized or the system is reset to a standby state after completing a round of training;

Step s2, training starts: the trainer clicks the power switch (start button) s26 and s34 in Figure 2 or Figure 3 to start a round of memory training, the player plays the start prompt voice, and the display screen will also show a countdown prompt so that the trainer can pay more attention to the training content. If the training start button is clicked during the training or after a round of training, a new round of training will be restarted;

Step s3, generating training content: the system generates a random training data string according to the parameters set in the initialization: training mode, training data string length, and the system starts counting the number of training times or accumulating the number of training times;

Step s4, playing the training content: converting the data string in the buffer into voice and playing it through the player, starting or continuing the timing;

Step s5, input training results: input the content you listened to in reverse order as required, press the Enter button to end the result input, and pause the timing;

Step s6, training result verification: compare the data string input from the training input terminal with the data string randomly generated by the processor in reverse order; if the comparison is correct, proceed to step s7, if the comparison is wrong, proceed to step s4;

Step s7: if the set number of training times is completed, the training timer stops, and the system training count completes the set number of training times and goes to step s8; if the number of training times is less than the set number of training times, it goes to step s3;

Step s8, memory score: according to the total training time, number of trainings, training mode, and data string length, the memory formula calculates the memory score of this training;

Step s9, end prompt tone: the memory score and training time of step s8 are displayed on the display screen, and the end of the training is notified by voice;

Step s10, cloud server connection: initiate an access request to the cloud service. If the connection fails, proceed to step s2. If the connection is successful, proceed to step s11.

Step s11, cloud server interaction data: upload the current training mode, data string length, memory time, number of trainings, auxiliary device machine ID number, and training user information to the cloud server. After receiving the uploaded data, the cloud server retrieves the data corresponding to the auxiliary device machine ID number in the training terminal database. If the current training auxiliary device machine ID number does not exist in the database, a new data record with the training auxiliary device machine ID number as the keyword is created in the training terminal database, and then the received data: number of trainings, memory time, data string length, training mode, auxiliary device machine ID, and training user information are written into the database. If the corresponding auxiliary device machine ID number is retrieved in the cloud server, the corresponding data of memory time and memory score in the database are updated according to the current training mode and data string length. The update must be that the latest memory score is higher than the score in the database corresponding to the existing training mode and data string length, otherwise it will not be updated; and Request the latest memory formula parameters and coefficients;

Step s12, memory parameter coefficient saving: the memory parameters and coefficients obtained from the cloud server and the current training ranking are saved in the memory of the auxiliary training device, and the parameter coefficients in the processor cache are updated in real time, and the training ranking is displayed on the display screen.

The training steps of the non-scoring method are as follows: a data string of a specific length and pattern is generated according to the parameters set by the auxiliary training device, or an idiom, sentence, poem or any content combination that meets the parameter setting requirements is stored in the memory, and the training content is played or paused according to the set playback time interval or the replay button of the control button group. The trainee recalls the voice content continuously played by the auxiliary training device in reverse order to perform non-scoring memory training; in this method, the auxiliary training device is similar to a voice playback device for rapid memory practice.

See Figure 5, which is a flow chart of the cloud server memory parameter and coefficient calculation module, the process includes:

Step s51 and step s52 are prerequisites for starting the memory formula parameter and coefficient calculation module. As long as one of them is met, it can be started. When the data update amount of the calculation module reaches the set number of times or the time interval from the last start of the calculation module reaches the set time, the memory parameter and coefficient calculation module of step s53 is started;

Step s54, parameters and coefficients are saved to the database: the parameters and coefficients calculated by the memory parameter and coefficient operation module are updated to the parameter coefficient table of the cloud server database so that the auxiliary training device client can obtain them.

The steps of the right brain training effect evaluation method are as follows: the training input terminal completes a round of testing, the processor calculates the memory score of the current time according to the memory formula, and uploads the data of the current training to the cloud server through the network interface: the number of trainings, memory duration, data string length, training mode, auxiliary device machine ID number, and training user information. After receiving the uploaded data, the cloud server retrieves the data corresponding to the auxiliary device machine ID number in the training terminal database. If the database does not have the current training auxiliary device machine ID number, the cloud server will retrieve the data corresponding to the auxiliary device machine ID number in the training terminal database. A new data record with the training auxiliary device machine ID number as the keyword is created in the database, and then the received data: number of training times, memory time, data string length, training mode, auxiliary device machine ID, and training user information are written into the database. If the corresponding auxiliary device machine ID number is retrieved in the cloud server, the corresponding data of memory time and memory score in the database are updated according to the current training mode and data string length. The update must be that the latest memory score is higher than the score in the database corresponding to the existing training mode and data string length, otherwise it will not be updated.

The source of the idea and deduction process of the memory formula: German psychologist Ebbinghaus devoted his life to experimental psychology research on memory and proposed the famous "Ebbinghaus Forgetting Curve"; in Ebbinghaus' memory experiment: the relationship between the length of the learning material and the learning rate is that when the length of the syllable group increases, the number of times required to recite correctly increases sharply. For example, it takes 16.6 recitations to memorize 12 syllables, while it takes 55 recitations to memorize 36 syllables; the memory formula is constructed based on the theory;

The construction of memory formula under the same training mode and the conversion process of memory formula scoring rules between different training modes are as follows:

i. Each recitation time is t, the recitation time of 12 syllables is 16.6t, and the recitation time of 36 syllables is 55t; the average recording time of each syllable when reciting 12 syllables is: 16.6t/12=1.3833t, the average memory time of each syllable when reciting 36 syllables is: 55t/36=1.5277t; 1.5277t>1.3833; According to this experiment, it can be inferred that the average time spent on reciting more syllables will also take more time; the average memory time required for the characters in the following memory data string is expressed in terms of memory duration;

ii. The designed memory formula scoring method must have an objective evaluation standard for all people participating in memory training, that is, no matter what training mode or data string length you use, the adaptability of the target memory calculation formula must meet these conditions: 1. Memory training with different data string lengths and different training modes will be given standardized scores through the memory formula; you will not be given a lower score because the length of the data string you train is short. As long as your input speed is fast enough, you can still get a higher score; also You will not be given a lower score just because the length of your training data string is too long and the average time required is long. As long as your speed is shorter than the average time required by the comprehensive standard of the memory formula, you can still get a relatively high score. 2. The formula is adaptive and will dynamically adjust the parameters of the scoring formula according to the training data results of all auxiliary training terminals to make the parameters suitable for the comprehensive standards of all participants in the training. For example, if the length of the training data string is short, the system will automatically optimize the memory formula parameters according to the adjustment of the training data of all participants in the training, requiring a shorter and faster training time to get a high score.

iii. Taking the pure digital training mode as an example, when the total number of sample data of the digital training mode in the cloud server database exceeds 400, and there are at least three data string lengths corresponding to the memory duration data records exceeding 20, the digital training mode memory formula can be deduced.

As shown in Figure 6, it is a relationship diagram between the time required for memory and the length of the memory data string. The steps of memory formula deduction are as follows: According to the Ebbinghaus memory experiment data, it is inferred that the training data string length l and the memory time t are more in line with the parabolic curve; in the case of insufficient auxiliary training end data samples, according to the existing experimental conditions, the relationship between the training data string length l and the memory time t is more in line with the regular parabolic curve, and the relationship diagram is drawn with rectangular coordinates: the horizontal axis is the data string length l, and the vertical axis is the time t;

As long as the cloud server has enough memory sample data of the auxiliary training device terminal, it can analyze the sample data rules, whether the sample data is a regular sample or an irregular sample, adjust the memory formula through the neural network learning method, and use a suitable curve analysis method to accurately calculate the memory formula; the auxiliary training device updates the client's memory formula by accessing the cloud server;

According to the regular parabola equation, a quadratic parabola equation can be customized if three plane coordinate points are known;

Take the digital training mode as an example: taking the same data string length l ₀ as the reference, read the field data corresponding to the time length t of the digital training mode from the database. The vertical line s61 corresponding to l ₀ in Figure 6 is the memory time distribution point of the training terminal in the digital training mode. Here, the time field data is calculated by arithmetic average to obtain the average value t ₀ , and the weighted average method can also be used to obtain the average value t ₀ , and a set of coordinate points (l ₀ , t ₀ ) is obtained; according to the above method, The other two data string lengths corresponding to the time fields are also calculated by arithmetic mean to obtain another two sets of coordinate points (l ₁ , t ₁ ) and (l ₂ , t ₂ ); the parabola quadratic equation is used for these three coordinate points to obtain three equations
t ₀ = a*l ₀ ² + b*l ₀ + c
t ₁ = a*l ₁ ² + b*l ₁ + c,
_t2 = a*l ₂ ² + b*l ₂ + c,

By solving the equation, we get the parameters of a, b, and c, so we get the relationship between the data string length l and the memory time t as t _number = a _number * l _number ² + b _number * l _number + c _number ; each coordinate on the parabola equation represents the relationship between the length of the training data string for all people and the average time required, and each data string length vertically corresponds to the average time required for all people to train with this data string length; of course, the shorter the training time, the higher the memory level, and the most important indicator for memory formula evaluation is the time required for memory training, and the scoring standard is that the less time required for training, the higher the score; we use the time when the data string length is mapped to the parabola divided by the actual training time used by the trainee at the same data string length as the memory formula, and thus the memory formula is:

100 is the magnification factor used to amplify the memory score value.

When the stand-alone machine is not connected to the Internet and does not have the latest memory formula parameters or the cloud server database data is not enough, the system will set an initial memory formula to give the right brain training memory score. The formula should conform to the Ebbinghaus memory experiment. The relationship between the data string length and the memory time is a parabolic equation. The relationship between the length and the time is t= ^al2 +b l+c. The parameters are used to deduce an initial memory formula based on the data of the internal experimental training. It is not important whether the formula is completely consistent with the actual situation. As long as the data of the trainer reaches a certain amount, the memory parameter and coefficient calculation module on the cloud server 8 will calculate the parameters more accurately.

As shown in Figure 8, the figure shows the actual training of 6 groups of trainees with different data string lengths in the digital training mode. Test data, according to the data in the figure, the following three equations are obtained:
0.38333＝3 ² a+3b+c,
0.66667＝6 ² a+6b+c,
0.9525＝8 ² a+8b+c,

Solving the equation yields a = 0.00692, b = 0.03213, c = 0.22477; the equation of the parabola is:

t＝0.00692l ² +0.03213l+0.22477, so the initial memory formula is set to

The memory parameter and coefficient calculation module has more accurate calculation steps for the coefficients between different training modes: the difficulty of different training modes is different. The difficulty of pure digital training is different from that of mixed digital and alphabetic training or training with other text, musical notes, colors, etc. The parabola derived between the length l of the data string of each training mode and the required time t is different, so we need to add a coefficient to run through the relationship between each training mode, and make the two parabolas overlap into one parabola by multiplying by a dynamic coefficient, so that the training scoring standards of trainees in different modes will be combined into one; each trainee can get the same scoring standard no matter which training mode is used, even if the overall level of the trainee is improved, the relationship between the training modes will be dynamically adjusted to make the training scoring more objective and standard.

FIG. 7 is a graph showing the relationship between the memory duration and the data string length for the digital training mode and the letter training mode. According to the graph shown in the figure, the conversion coefficient deduction process between the two modes is as follows:

When the database data of each training mode is insufficient in the cloud server data, the coefficient of each mode and the pure digital memory formula is set as a fixed parameter: l is the length of the training data string, t is the average time to remember a single character of the training data string, and n is the conversion coefficient of the training mode; according to the difficulty, the digital training mode is initially set as the benchmark, the coefficient of the letter training mode to the digital training mode is 0.8, the coefficient of the mixed digital and letter training mode to the digital training mode is 0.7, and the coefficient of the musical note training mode to the digital training mode is 0.6; When the sample data of the digit training mode and the letter training mode exceed 400, and the memory duration field data records corresponding to at least three data string lengths exceed 20, the relationship between the data string length and memory duration of the digit training mode and the letter training mode can be derived.

The memory formula can be deduced to deduce the parabolic relationship between the length of the training data string of the letter training mode l _letters and the memory time t _letters t _letters = a _letters * l _letters ² + b _letters * l _letters + c _letters . Generally speaking, the difficulty of the letter training mode is higher than that of the digital training mode. It is tentatively assumed that s71 in Figure 7 is a graph showing the relationship between the letter mode memory time and the data string length curve, and s72 is a graph showing the relationship between the digital mode memory time and the data string length curve. If it is inconsistent with the assumption, the subsequent mode coefficient calculation will automatically correct this assumption.

The cloud server database randomly selects three data strings of length l _a , l _b , l _c that meet the requirements, and substitutes them into the digital training mode relationship formula t _number = a _number * l _number ² + b _number * l _number + c _number and

The relationship formula of letter training mode is t _letter = a _letter * l _letter ² + b _letter * l _letter + c _letter . The two formulas respectively obtain three results, namely, digital training mode t ₁ , t ₂ , t ₃ , letter mode _ta , t _b , t _c . The training time is different under the same data string length. The same training group only has different memory time due to different training difficulties, so the same scoring result should be given between the two. Therefore, under the same data string length, the memory time t ₁ is equal to _ta , t ₂ is equal to t _b , and t ₃ is equal to t _c in the two modes. The conversion is as follows:
t ₁ = _ta * S _{coefficient of digital mode and letter mode}
t ₂ = t _b *S _{coefficient of digital mode and letter mode}
t ₃ = t _c *S _{coefficient of digital mode and letter mode}

Therefore, the mode coefficients between the two training modes are: The relationship between the length of the same training data string and the pattern coefficient between the two training patterns results in three coordinate sets Using three coordinate sets according to (iv) we can get two equations for the training pattern length and the conversion coefficient:

The equation for the relationship coefficient, which converts the letter mode memory score into the number mode parameter formula, is:

Similarly, the conversion coefficients between letters, colors, musical notes, animals, zodiac signs and digital training modes can also be obtained according to the above method. The coefficients are started in time according to the amount of data update of the auxiliary training device or the memory formula parameters and the time interval conditions of the coefficient calculation module. The memory formula parameters and coefficients are adjusted accurately through the background calculation of the database data of the cloud server, so that the memory formula can better meet the actual scoring standards.

The cloud server updates the client's memory formula parameters and coefficients. The operation process is as follows:

As the training data in the cloud server database is enriched, the parameter coefficients of the memory formula are continuously adjusted, and the memory formula parameters and training mode coefficients are continuously adjusted, which can more accurately reflect the changes in the trainee's memory level. When the training terminal uploads the training data, it obtains the latest parameter coefficient updates from the parameter and coefficient table in the cloud server and gives the trainee appropriate training suggestions and supporting methods based on the trainee's training scores. The suggestions and methods are transmitted back to the storage device of the auxiliary training device, and the trainee can read the message content when setting the menu. Scientific parameters can allow trainees to have a more realistic understanding of their right brain memory level.

The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

A right brain training auxiliary device, characterized in that it comprises a processor (1), a memory (2), a player (3), a display screen (4), a system setting button group (5), a training input terminal (6), a network interface (7), a cloud server (8) and a control button group (9), wherein the processor (1) comprises a processor and a cache unit, the memory (2) comprises a training module and a training effect evaluation module, the player (3), the training input terminal (6) and the control button group (9) are connected to the processor (1), the memory (2) is connected to the system setting button group (5), the processor (1), the network interface (7) and the display screen (4), and the network interface (7) is connected to the cloud server (8); the cloud server (8) comprises a memory parameter and coefficient calculation module and a training terminal database.
The right brain training auxiliary device according to claim 1 is characterized in that:

The training input terminal (6) is connected to the processor of the processing machine (1). The training input terminal (6) is a key input or a touch screen software keyboard. The keyboard layout setting includes a keyboard with pure numbers 0 to 9, a pure letter keyboard, a mixed keyboard of numbers and letters, a text keyboard, a keyboard with twelve zodiac signs and text, a color keyboard, a musical note keyboard, or a mixed keyboard with any combination of the above modes. The training input terminal (6) is used by the keyboard trainer to input the content heard from the player (3) in reverse order.

The cache unit of the processor (1) is connected to the memory (2), the cache unit obtains the system parameters stored in the memory (2) and then initializes the training module, and the cache unit is used to store temporary results generated by the processor and data generated by the right brain training effect evaluation module; the processor (1) randomly generates a data string in a specified format according to the set data string length and training mode, converts the data string into a voice format and plays it through a player (3); after completing a round of training, the training effect evaluation module calculates and gives a training score, and at the same time stores the training mode, memory duration, and training data string length in the memory (2), and uploads the data to a cloud server (8) through a network interface (7);

The player (3) is a built-in speaker or an external headset, and plays relevant content according to the requirements of the processor (1), including: training content, error prompts, training scores, and teaching encouragement voice;

The display screen (4) displays the following contents: training mode, training ranking, training score, system error prompt, training mode bonus coefficient, and system setting menu;

The system setting button group (5) includes a menu button, an up button, a down button, and an OK button. The system setting menu is entered according to the user's needs to realize the number of training times, data string length, training mode, network interface connection settings, terminal performance record reset, training personnel information entry, training method, memory formula parameter settings, and mode coefficient settings. After the settings are completed, the parameters are saved to the memory (2) and the operating parameters of the training module in the processor (1) are reinitialized;

The network interface (7) uploads and updates the memory formula parameters and coefficients, uploads training data, updates the current training mode data content, updates the training system program on the memory (2), obtains the training ranking, and obtains the information from the cloud server (8) to the auxiliary training device by connecting to the network;

The training module of the memory (2) is completed by the user inputting the content broadcast by the player in reverse order through the training input terminal (6);

The training effect evaluation module of the memory (2) calculates the score of the training data according to the memory formula on the memory (2) after completing a set of training and displays the score on the display screen (4). At the same time, the training data of the auxiliary training device is uploaded to the cloud server (8) through the network interface (7), including: training mode, number of training times, memory duration, length of training data string, and training score. The latest memory formula parameters and mode coefficients on the memory (2) and the cache unit are updated according to the content returned by the cloud server (8);

The training terminal database of the cloud server (8) stores basic information of each auxiliary training device: device number, latest score, training ranking, training mode, memory duration, training data string length, memory parameter and coefficient data, memory parameter and coefficient calculation module;

The memory parameter and coefficient calculation module of the cloud server (8) starts the memory parameter and coefficient calculation module according to the module operation time interval or the number of terminal data updates of the auxiliary training device, and stores the adjusted memory parameter and mode coefficient data in the terminal database of the cloud server (8);

The control button group (9) includes a power switch, a replay button, and an Enter button, and is used in conjunction with the training input terminal (6).
According to the right brain training auxiliary device and training evaluation algorithm of claim 1, it is characterized in that the training module has two training methods:

Scoring method: Generate a data string of a certain length and pattern based on the parameters stored in the memory (2), play the content in forward order and then wait for the user to input the comparison training in reverse order through the training input terminal (6), and after reaching the set number of training times, store the training data in the memory (2) and exchange data with the cloud server (8) through the network interface (7);

The non-scoring method is as follows: a data string of a certain length and pattern is generated according to the parameters on the memory (2), or an idiom, sentence, poem or a combination of multiple contents that meets the parameter setting requirements is stored on the memory, and the training content is played or paused according to the set playback time interval or the replay button of the control button group (9). The trainee recalls the voice content continuously played by the auxiliary training device in reverse order, thereby performing non-scoring memory training.
According to claim 3, a right brain training auxiliary device and training evaluation algorithm is characterized in that the memory training steps of the scoring method are as follows:

(a) Step of receiving input content: the trainee inputs the data string voice heard from the player (3) in reverse order at the training input terminal (6), and presses the Enter button to end the input;

(b) Internal operation steps of the auxiliary training device: the processor (1) generates a data string according to the training mode and the parameters set by the system setting button group (5) and puts it into the cache, and compares the reverse data string generated by the data string with the input content of the training input terminal (6);

1) The comparison is correct: the processor (1) generates another data string according to the data string parameter requirements stored in the cache unit, and converts the data string into a voice format and plays it out through the player (3); the processor (1) waits for the training input terminal (6) to complete the input and then clicks the Enter button to proceed to the next comparison process;

2) Comparison error: replay the data string voice in the buffer through the player (3), and wait for the user to continue to input the correct content through the training input terminal (6); or the replay button of the control button group (9) can be used to repeatedly play the data string voice in the buffer;

3) The processor (1) completes the number of training times required in the cache and prompts the end of training through the player (3); stores the memory duration, training mode, data string length, and number of training times in the memory (2) and displays the memory training score on the display screen (4).
According to claim 3, a right brain training auxiliary device and training evaluation algorithm is characterized in that the memory training steps of the non-scoring method are as follows:

When the start button of the control button group (9) is pressed, the player (3) plays the training content according to the parameter setting requirements, and the trainee follows the content to perform reverse narration training. The player (3) continues to play the training content according to the set time interval parameters, and the replay button of the control button group (9) can pause or continue the playing of the training content.
A right brain training evaluation algorithm, characterized in that: a training input terminal (6) ends a round of testing, a processor (1) calculates the memory score of the current time according to a memory formula, and uploads the data of the current training to a cloud server (8) through a network interface (7): training times, memory duration, data string length, training mode, auxiliary device machine ID number, and training user information. After receiving the uploaded data, the cloud server (8) retrieves the data corresponding to the auxiliary device machine ID number in the training terminal database. If the current training auxiliary device machine ID number does not exist in the database, a new data record with the training auxiliary device machine ID number as a keyword is created in the training terminal database, and then the received data: training times, memory duration, data string length, training mode, auxiliary device machine ID, and training user information are written into the database; if the corresponding auxiliary device machine ID number is retrieved in the cloud server (8), the corresponding data of memory duration and memory score in the database are updated according to the current training mode and data string length. The update must be that the latest memory score is higher than the score in the database corresponding to the existing training mode and data string length, otherwise it is not updated.
A right brain training evaluation algorithm according to claim 6, characterized in that: the digital training mode The derivation process of the memory formula is as follows:

When the total number of sample data of the digital training mode in the database of the cloud server exceeds 400, and the number of memory duration data records corresponding to at least three data string lengths exceeds 20, the digital training mode memory formula can be deduced. The steps are as follows:

a) First, based on the Ebbinghaus memory experiment data, it is inferred that the length of the training data string l and the memory time t are a parabolic curve;

b) Taking the same data string length l 0 as the reference, read the field data corresponding to the time length t of the digital training mode from the database. Here, the time field data is calculated by arithmetic average to obtain the average value t 0 , and the weighted average method can also be used to obtain the average value t 0 , and obtain a set of coordinate points (l 0 , t 0 ); according to the above method, take two data string lengths corresponding to the time field and calculate the arithmetic average to obtain another two sets of coordinate points (l 1 , t 1 ), (l 2 , t 2 ); use the parabola quadratic equation for these three coordinate points to obtain three equations:
t 0 = a*l 0 2 + b*l 0 + c
t 1 = a*l 1 2 + b*l 1 + c
t 2 =a*l 2 2 +b*l 2 +c

By solving the equation, we get the parameters of a, b, and c, so we get the relationship between the data string length l and the memory time t as t number = a number * l number 2 + b number * l number + c number ; each coordinate on the parabola equation represents the relationship between the length of the training data string of all people and the average time required, and each data string length vertically corresponds to the average time required for all people to train with this data string length; of course, the shorter the training time, the higher the memory score. The most important indicator for memory formula evaluation is the time required for memory training. The scoring standard is that the less time required for training, the higher the score. We use the time when the data string length is mapped to the parabola divided by the actual training time of the trainee at the same data string length as the memory formula, and thus the memory formula is: 100 is the magnification factor used to amplify the memory score value.
According to claim 7, a right brain training evaluation algorithm is characterized in that: when a single machine is not connected to the Internet and does not have the latest memory formula parameters or the database data of the cloud server is not enough, the system will set an initialized memory formula to give the right brain training a memory score, and the formula should conform to the Ebbinghaus memory experiment. The relationship between the length of the data string and the memory duration is a parabolic equation, and the relationship between the length and the memory duration is: t= al2 +b l+c. The parameters are used to deduce an initial memory formula based on the data of internal experimental training. It is not important whether the formula is completely consistent with the actual situation. As long as the data of the trainee reaches a certain amount, the memory parameter and coefficient calculation module on the cloud server will calculate the parameters more accurately.
A right brain training evaluation algorithm according to claim 7, characterized in that:

There is a coefficient relationship between the digital training mode and the letter training mode. In the letter training mode, the derivation process of the memory formula is as follows:

If the database data of each training mode is insufficient in the cloud server data, the coefficient of each mode and the pure digital memory formula is set as a fixed parameter: l is the length of the training data string, t is the average time to remember a single character of the training data string, and n is the conversion coefficient of the training mode; according to the difficulty, the digital training mode is initially set as the benchmark. When the sample data of the digital training mode and the letter training mode are both more than 400, and there are more than 20 data records of the memory time field corresponding to at least three data string lengths, the relationship between the data string length and the memory time of the digital training mode and the letter training mode can be derived;

According to the memory formula of the digital training mode, the parabolic relationship between the length of the training data string l letters and the memory time t letters of the letter training mode can be deduced: t letters = a letters * l letters 2 + b letters * l letters + c letters

The cloud server database randomly selects three data strings of length l a , l b , l c that meet the requirements and substitutes them into the digital training pattern relationship formula t number = a number * l number 2 + b number * l number + c number and letter training pattern relationship formula t letter = a letter * l letter 2 + b letter * l letter + c letter . The two formulas get three results respectively, digital training mode t 1 , t 2 , t 3 , letter mode ta , t b , t c . The training time is different under the same data string length. The same training group only has different memory time due to different training difficulties, so the same scoring result should be given between the two. Therefore, under the same data string length, the memory time t 1 is equal to ta , t 2 is equal to t b , and t 3 is equal to t c . The conversion is as follows:
t 1 = ta * S coefficient of digital mode and letter mode
t 2 = t b *S coefficient of digital mode and letter mode
t 3 = t c *S coefficient of digital mode and letter mode

Therefore, the mode coefficients between the two training modes are: The relationship between the length of the same training data string and the pattern coefficient between the two training patterns results in three coordinate sets Using three coordinate sets, we can get the equations for the two training pattern lengths and the conversion coefficients:

Based on the above equation of relationship coefficient, the memory formula for converting the letter mode memory score into the number mode parameter formula is:

Similarly, the conversion coefficients between letters, colors, musical notes, animals, and zodiac signs and the digital training mode can also be obtained according to the above method. The coefficients are started in a timely manner according to the amount of data update of the auxiliary training device or the memory formula parameters and the time interval conditions of the coefficient calculation module. The memory formula parameters and coefficients are adjusted accurately through the background calculation of the database data of the cloud server, so that the memory formula can better meet the actual scoring standards.

After the calculation of the memory formula parameters and coefficients is completed, the results are stored in the parameter coefficient table of the cloud server so that the training auxiliary device can obtain the latest memory formula parameters and coefficients after uploading the data.