A memory judgment training method and training device based on applied psychology

Abstract

The present invention relates to the technical field of applied psychology, and specifically relates to a memory judgment training method and training device based on applied psychology, including: analyzing the memory fragments of the test personnel to obtain memory representation values, analyzing training values based on the failed memory fragments, analyzing all memory failure fragments in all memory failure fragments based on the training signal to determine common values, determining the training times allocation ratio based on the common memory failure fragments, determining the allocated times of the common memory failure fragments based on the training times allocation ratio, training different types of common memory failure fragments based on the allocated times of the common memory failure fragments, determining the improvement rate, and allocating the training times corresponding to the improved memory training to the common memory failure fragments corresponding to the non-improved memory training. The present invention is beneficial to making the training resources tend to the common memory failure fragments with high occurrence frequency, and improving the utilization efficiency of training resources and the training effect.

Description

Technical Field

[0001] The present invention relates to the technical field of applied psychology, and particularly relates to a memory judgment training method and a training device based on applied psychology. Background Art

[0002] In today's era of information explosion, people are faced with a vast amount of information that needs to be memorized and processed. Good memory and accurate judgment ability are crucial for an individual's study, work, and life.

[0003] However, the existing memory training methods often lack scientificity and pertinence, and cannot be effectively adjusted according to the actual memory situation of individuals. Traditional memory training usually adopts a unified training mode. On the one hand, it does not take into account the differences in individuals' memory of different types of information, resulting in the inability to accurately determine which testers really need training, causing waste of resources, and the situation that those who really have needs cannot receive timely training, and the training pertinence is insufficient. On the other hand, the existing technology lacks an accurate assessment of the improvement degree of memory training, and cannot accurately mark memory training as improved or non-improved memory training based on training performance values, making it difficult to optimize the parts with poor training effects pertinently, and unable to achieve reasonable reallocation of training resources, and the phenomenon of resource waste may be relatively serious.

[0004] Therefore, we propose a memory judgment training method and a training device based on applied psychology. Summary of the Invention

[0005] The purpose of the present invention is to provide a memory judgment training method and a training device based on applied psychology to solve at least one of the above-mentioned problems of the existing technology.

[0006] In the first aspect, the present invention provides a memory judgment training method based on applied psychology, which specifically includes:

[0007] Step 1: Analyze the memory fragments of the tester to obtain a memory representation value. If the memory representation value is greater than the memory representation threshold, mark the corresponding memory fragment as a failed memory fragment;

[0008] Step 2: Based on the failed memory fragments, analyze to obtain a training value. If the training value is greater than the training threshold, generate a training signal;

[0009] Step 3: Based on the training signal, analyze all the memory failure fragments in all the memory failure fragments to determine a common value. If the common value is greater than or equal to the common threshold, mark the memory failure fragments of the corresponding type as memory failure common fragments;

[0010] Step 4: Based on the memory failure common fragments, determine the training times allocation ratio, and based on the training times allocation ratio, determine the allocation times of the memory common failure fragments;

[0011] Step Five: Based on the number of allocation times of the common fragments of memory failure, train different types of common fragments of memory failure to determine the improvement rate. Based on the improvement rate, evaluate the improvement degree of memory training, and mark the memory training as improved memory training and non-improved memory training according to the evaluation results;

[0012] Step Six: Allocate the allocated training times of the common fragments of memory failure corresponding to the improved memory training to the common fragments of memory failure corresponding to the non-improved memory training, and continue with the memory training.

[0013] In a second aspect, the present invention provides a memory judgment training device based on applied psychology, specifically including:

[0014] Memory representation analysis module: Analyze the memory fragments of the tester to obtain memory representation values. If the memory representation value is greater than the memory representation threshold, mark the corresponding memory fragment as a failed memory fragment;

[0015] Training requirement analysis module: Based on the failed memory fragments, analyze to obtain training values. If the training value is greater than the training threshold, generate a training signal;

[0016] Common memory failure analysis module: Based on the training signal, analyze all memory failure fragments in all memory failure fragments to determine a common value. If the common value is greater than or equal to the common threshold, mark the corresponding type of memory failure fragment as a common fragment of memory failure;

[0017] Training times allocation module: Based on the common fragments of memory failure, determine the training times allocation ratio, and based on the training times allocation ratio, determine the allocation times of the common fragments of memory failure;

[0018] Training performance evaluation module: Based on the number of allocation times of the common fragments of memory failure, train different types of common fragments of memory failure to determine the improvement rate. Based on the improvement rate, evaluate the improvement degree of memory training, and mark the memory training as improved memory training and non-improved memory training according to the evaluation results;

[0019] Training times reallocation module: Allocate the allocated training times of the common fragments of memory failure corresponding to the improved memory training to the common fragments of memory failure corresponding to the non-improved memory training, and continue with the memory training.

[0020] Advantages of the present invention:

[0021] The present invention analyzes the memory fragments of testers to obtain memory representation values. If the memory representation value is greater than the memory representation threshold, the corresponding memory fragment is marked as a failed memory fragment. Based on the failed memory fragments, a training value is analyzed. If the training value is greater than the training threshold, a training signal is generated. The present invention analyzes the fragments in the memory fragments to obtain memory representation values, thereby dividing successful and failed memory fragments. Based on the failed memory fragments, it can be determined whether training is needed, avoiding unnecessary training, and at the same time ensuring that testers with real needs can obtain training in a timely manner, improving the pertinence and effectiveness of memory training.

[0022] Based on the training signal, analyze all the memory failure fragments in all the memory failure segments to determine a commonality value. If the commonality value is greater than or equal to the commonality threshold, mark the memory failure fragments of the corresponding type as memory failure common fragments. Based on the memory failure common fragments, determine the training times allocation ratio. Based on the training times allocation ratio, determine the allocation times of the memory failure common fragments. The present invention extracts the memory failure fragments in all the memory failure segments to determine the commonality value, and can accurately determine the memory failure common fragments. Combining with the preset training times, determine the allocated training times of each type of memory failure common fragment, and reasonably allocate training resources according to the frequency of occurrence of the fragments, so that the training resources tend to the memory failure common fragments with high occurrence frequency, improving the utilization efficiency of training resources and the training effect.

[0023] Based on the allocation times of the memory failure common fragments, train different types of memory failure common fragments to determine the improvement rate. Based on the improvement rate, evaluate the improvement degree of the memory training, and mark the memory training as improved memory training and non-improved memory training according to the evaluation results. Allocate the allocated training times of the memory failure common fragments corresponding to the improved memory training to the memory failure common fragments corresponding to the non-improved memory training, and continue the memory training. The present invention can accurately judge the training effect of each memory failure common fragment by evaluating the improvement degree of the memory training, providing a reliable basis for subsequent optimization. Reallocate the allocated training times of the memory failure common fragments corresponding to the improved memory training to the memory failure common fragments corresponding to the non-improved memory training, realizing the reasonable redistribution of training resources, helping to improve the overall memory training efficiency, avoiding resource waste, and concentrating the training resources on the parts with poor effects. Description of the Drawings

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 It is a flowchart of a memory judgment training method based on applied psychology according to an embodiment of the present invention;

[0026] Figure 2 It is a block diagram of a memory judgment training system based on applied psychology according to an embodiment of the present invention;

[0027] Figure 3 It is a schematic structural diagram of a memory judgment training device based on applied psychology according to an embodiment of the present invention;

[0028] Reference numerals in the attached drawings: 3, computer device; 301, processor; 302, memory; 303, computer program. Detailed implementation manners

[0029] In order to enable those skilled in the art of the present technology to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0030] Embodiment 1 Figure 1 It is a flowchart of a memory judgment training method based on applied psychology provided in Embodiment 1 of the present invention. A memory judgment training method based on applied psychology can be executed by a memory judgment training system based on applied psychology. A memory judgment training system based on applied psychology can be implemented by software and / or hardware. A memory judgment training system based on applied psychology can be configured in a memory judgment training device. Optionally, a memory judgment training device can be an electronic device, and the electronic device can be a notebook, a desktop computer, a smart tablet, etc., and the embodiments of the present invention do not limit this.

[0031] As Figure 1 shown, a memory judgment training method based on applied psychology provided in an embodiment of the present invention specifically includes:

[0032] Step 1: Analyze the memory fragments of the test personnel to obtain a memory representation value. If the memory representation value is greater than the memory representation threshold, mark the corresponding memory fragment as a failed memory fragment;

[0033] In some embodiments, by simulating a test scenario, recording during the simulation, and extracting the memory fragments of the test personnel from the recording, where the memory fragments include but are not limited to time fragments, location fragments, and character fragments;

[0034] Based on any memory fragment;

[0035] Mark the fragments in the memory segments where the tester fails to remember as memory failure fragments, count the number of memory failure fragments, and mark it as the number of failed memory fragments. Process the ratio of the number of failed memory fragments to all the fragments in the memory segment information to obtain a memory representation value;

[0036] Compare the memory representation value with a memory representation threshold:

[0037] If the memory representation value is less than or equal to the memory representation threshold, mark the corresponding memory segment as a successful memory segment;

[0038] If the memory representation value is greater than the memory representation threshold, mark the corresponding memory segment as a failed memory segment;

[0039] Step 2: Analyze the training value based on the failed memory segments. If the training value is greater than the training threshold, generate a training signal;

[0040] In some embodiments, count the number of failed memory segments within a monitoring period, sum the number of successful memory segments and the number of failed memory segments to obtain the total number of memory segments, process the ratio of the number of failed memory segments to the total number of memory segments to obtain the ratio of the number of failed memory segments;

[0041] Extract the memory representation value corresponding to the failed memory segments, perform a subtraction operation on the memory representation value of the failed memory segments and the memory representation threshold, and take the absolute value of the difference to obtain a memory deviation value. Sum and average the deviation degree values of all failed memory fragments to obtain a memory deviation average value. Process the ratio of the memory deviation average value to the memory representation value to obtain a failed memory degree ratio;

[0042] Sum the ratio of the number of failed memory segments and the failed memory degree ratio to obtain a training value;

[0043] Compare the training value with a training threshold:

[0044] If the training value is greater than the training threshold, it indicates that the current tester needs memory training and a training signal is generated;

[0045] If the training value is less than or equal to the training threshold, it indicates that the current tester does not need memory training and a non-training signal is generated;

[0046] The technical solution of this embodiment is as follows: Analyze the memory fragments of the testers to obtain memory representation values. If the memory representation value is greater than the memory representation threshold, mark the corresponding memory fragment as a failed memory fragment. Based on the failed memory fragments, analyze to obtain training values. If the training value is greater than the training threshold, generate a training signal. By analyzing the fragments in the memory fragments, the present invention obtains memory representation values, and then divides successful and failed memory fragments. Based on the failed memory fragments, it can be determined whether training is needed, avoiding unnecessary training, and at the same time ensuring that testers with real needs can obtain training in time, improving the pertinence and effectiveness of memory training.

[0047] Embodiment 2, as Figure 1 shown, a memory judgment training method based on applied psychology provided by an embodiment of the present invention specifically includes:

[0048] Step 3: Based on the training signal, analyze all the memory failure fragments in all the memory failure segments to determine a commonality value. If the commonality value is greater than or equal to the commonality threshold, mark the memory failure fragments of the corresponding type as memory failure common fragments;

[0049] In some embodiments, extract all the memory failure fragments in all the memory failure segments;

[0050] Based on any one memory failure segment, count the number of occurrences of each type of memory failure fragment in the memory failure segment, and perform a ratio process with the number of memory failure segments to obtain a commonality value;

[0051] Compare the commonality value with the commonality threshold:

[0052] If the commonality value is greater than or equal to the commonality threshold, mark the memory failure fragments of the corresponding type as memory failure common fragments;

[0053] If the commonality value is less than the commonality threshold, mark the memory failure fragments of the corresponding type as non-memory failure common fragments;

[0054] Step 4: Based on the memory failure common fragments, determine the training times allocation ratio, and based on the training times allocation ratio, determine the allocated times of the memory common failure fragments;

[0055] In some embodiments, based on any one type of memory failure common fragment;

[0056] Count the number of occurrences of the memory failure common fragments in all the memory failure segments, and mark the number of failure type times SX; Through the formula: Calculate to obtain the training times allocation ratio of the memory failure common fragments , where n represents the number of types of memory failure common fragments, i = 1, 2,..., n, Indicates the number of failure types corresponding to the common fragments of different types of memory failures; a preset number of training times is multiplied by the training times allocation ratio to obtain the allocated training times of the common fragments of memory failures;

[0057] It should be noted that the allocated training times of the common fragments of memory failures represent: the number of training times required for the memory type corresponding to the common fragments of memory failures. For example, if the preset number of training times is 100 times and the training times allocation ratio of the common fragments of character memory failures is 0.8, then 80 memory training sessions need to be conducted for the testers in terms of character types;

[0058] The technical solution of this embodiment is: based on the training signal, analyze all memory failure fragments in all memory failure segments to determine the commonality value. If the commonality value is greater than or equal to the commonality threshold, mark the memory failure fragments of the corresponding type as the common fragments of memory failures. Based on the common fragments of memory failures, determine the training times allocation ratio, and based on the training times allocation ratio, determine the allocated times of the common fragments of memory failures. By extracting the memory failure fragments in all memory failure segments and determining the commonality value, the present invention can accurately determine the common fragments of memory failures, and in combination with the preset number of training times, determine the allocated training times of the common fragments of different types of memory failures, and reasonably allocate training resources according to the frequency of occurrence of the fragments, so that the training resources tend to the common fragments of memory failures with high occurrence frequencies, improving the utilization efficiency of training resources and the training effect.

[0059] Embodiment Three, as Figure 1 shown, a memory judgment training method based on applied psychology provided by an embodiment of the present invention specifically includes:

[0060] Step Five: Based on the allocated times of the common fragments of memory failures, train the common fragments of different types of memory failures to determine the improvement rate. Based on the improvement rate, evaluate the improvement degree of the memory training, and mark the memory training as improved memory training and non-improved memory training according to the evaluation results;

[0061] In some embodiments, count the number of times the common fragments of memory failures appear within the monitoring period after training and mark it as the training type times XL; through the formula: , calculate the improvement rate of the common fragments of memory failures , where represents the training type times corresponding to the i-th type of common fragments of memory failures, represents the allocated training times of the i-th type of common fragments of memory failures, represents the number of memory segments in which the common fragments of memory failure of the i-th type were in before training; Exemplarily, before training, the common fragments of memory failure of a person appeared 10 times (SX) in the memory failure segments, and the number of memory segments was 100. After training, the common fragments of memory failure of the person appeared 4 times (XL) in the memory failure segments, and the number of allocated training times for the common fragments of memory failure was 80. Then the training improvement rate of the common fragments of memory failure of the person ; Compare the improvement rate of the memory fragments with the improvement rate threshold;

[0062] If the improvement rate of the memory fragments is greater than or equal to the improvement rate threshold, then mark the memory training of the common fragments of memory failure of the person as improved memory training;

[0063] If the improvement rate of the memory fragments is less than the improvement rate threshold, then mark the memory training of the common fragments of memory failure as non-improved memory training;

[0064] Step Six: Allocate the number of allocated training times of the common fragments of memory failure corresponding to the improved memory training to the common fragments of memory failure corresponding to the non-improved memory training, and continue the memory training;

[0065] Specifically, sum up the number of allocated training times of all the common fragments of memory failure corresponding to the improved memory training to obtain the total number of allocable training times, and mark it as ; Among all the non-improved memory trainings, based on any one of the common fragments of memory failure corresponding to the non-improved memory training;

[0066] Through the formula: Calculate the current number of allocated training times of the common fragments of memory failure corresponding to the non-improved memory training where, represents the initial number of allocated training times of the common fragments of memory failure corresponding to the non-improved memory training, represents the improvement rate of the common fragments of memory failure corresponding to the r-th non-improved memory training;

[0067] According to the current number of allocated training times of the common fragments of memory failure corresponding to the non-improved memory training Perform memory training again; the technical solution of this embodiment is as follows: Based on the allocation times of the common fragments of memory failure, train different types of common fragments of memory failure to determine the improvement rate. Based on the improvement rate, evaluate the improvement degree of memory training, and mark the memory training as improved memory training and non-improved memory training according to the evaluation results. Allocate the allocated training times of the common fragments of memory failure corresponding to the improved memory training to the common fragments of memory failure corresponding to the non-improved memory training, and continue with the memory training. By evaluating the improvement degree of memory training, the present invention can accurately judge the training effect of each common fragment of memory failure, provide a reliable basis for subsequent optimization, reallocate the allocated training times of the common fragments of memory failure corresponding to the improved memory training to the common fragments of memory failure corresponding to the non-improved memory training, realize the reasonable reallocation of training resources, help improve the overall memory training efficiency, avoid waste of resources, and concentrate the training resources on the parts with poor effects.

[0068] Embodiment 4, as Figure 2 shown, a memory judgment training device based on applied psychology provided by an embodiment of the present invention specifically includes:

[0069] Memory representation analysis module: Analyze the memory fragments of the tester to obtain the memory representation value. If the memory representation value is greater than the memory representation threshold, mark the corresponding memory fragment as a failed memory fragment;

[0070] Training requirement analysis module: Based on the failed memory fragments, analyze to obtain the training value. If the training value is greater than the training threshold, generate a training signal;

[0071] Common memory failure analysis module: Based on the training signal, analyze all the memory failure fragments in all the memory failure fragments to determine the common value. If the common value is greater than or equal to the common threshold, mark the memory failure fragments of the corresponding type as common fragments of memory failure;

[0072] Training times allocation module: Based on the common fragments of memory failure, determine the training times allocation ratio, and based on the training times allocation ratio, determine the allocated times of the common fragments of memory failure;

[0073] Training performance evaluation module: Based on the allocated times of the common fragments of memory failure, train different types of common fragments of memory failure to determine the improvement rate. Based on the improvement rate, evaluate the improvement degree of memory training, and mark the memory training as improved memory training and non-improved memory training according to the evaluation results;

[0074] Training times reallocation module: Allocate the allocated training times of the common fragments of memory failure corresponding to the improved memory training to the common fragments of memory failure corresponding to the non-improved memory training, and continue with the memory training.

[0075] Example 5, refer to Figure 3 , the embodiment of the present invention further provides a computer device 3, including: a memory 302, a processor 301, and a computer program 303 stored on the memory 302. When the computer program 303 is executed on the processor 301, it implements a training method for memory judgment based on applied psychology as described in any one of the above methods.

[0076] The computer device 3 can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art can understand that Figure 3 merely an example of the computer device 3, which does not constitute a limitation on the computer device 3, and may include more or fewer components than shown in the figure, or combine some components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0077] The so-called processor 301 may be a central processing unit (CPU), and the processor 301 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The memory 302 may be an internal storage unit of the computer device 3 in some embodiments, such as the hard disk or memory of the computer device 3. The memory 302 may also be an external storage device of the computer device 3 in other embodiments, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 3. Further, the memory 302 may also include both the internal storage unit and the external storage device of the computer device 3. The memory 302 is used to store an operating system, application programs, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 302 may also be used to temporarily store data that has been output or will be output.

[0078] Embodiment 6. The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it implements a method for training memory judgment based on applied psychology as described in any one of the above methods.

[0079] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above embodiment methods of the present application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the photographing device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0080] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0081] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0082] In the embodiments disclosed in the present application, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0083] One point is that the couplings or direct couplings or communication connections shown or discussed among each other can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0084] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0086] The above has described a detailed description of an embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the application of the present invention shall still fall within the scope covered by the patent of the present invention.

Claims

1. A memory judgment training device based on applied psychology, characterized in that Including: Memory representation analysis module: Analyze the memory fragments of the tester, extract the memory fragments of the tester, mark the number of fragments where the tester fails to remember in the memory fragments as the number of failed memory fragments, perform a ratio process on the number of failed memory fragments and all the fragments in the memory fragment information to obtain a memory representation value. If the memory representation value is greater than the memory representation threshold, mark the corresponding memory fragment as a failed memory fragment; Training requirement analysis module: Based on the failed memory fragments, perform a ratio process on the number of failed memory fragments and the total number of memory fragments to obtain the ratio of the number of failed memory fragments. Take the absolute value of the difference between the memory representation value of the failed memory fragment and the memory representation threshold to obtain a memory deviation value. Sum and average the deviation degree values of all failed memory fragments to obtain a memory deviation average value. Perform a ratio process on the memory deviation average value and the memory representation value to obtain the ratio of the degree of failed memory. Sum the ratio of the number of failed memory fragments and the ratio of the degree of failed memory to obtain a training value. If the training value is greater than the training threshold, generate a training signal; Commonality analysis module for memory failure: Based on the training signal, analyze all the failed memory fragments in all the memory failure fragments. Perform a ratio process on the number of occurrences of each type of failed memory fragment in the memory failure fragment and the number of memory failure fragments to obtain a commonality value. If the commonality value is greater than or equal to the commonality threshold, mark the corresponding type of failed memory fragment as a common failed memory fragment; Training times allocation module: Based on the common failed memory fragments, count the number of times the common failed memory fragments appear in all the memory failure fragments; Through the formula: Calculate the training times allocation ratio of the common fragments of memory failure , where n represents the number of types of the common fragments of memory failure, i = 1, 2, …, n, represents the number of failure types corresponding to different types of the common fragments of memory failure. Based on the training times allocation ratio, determine the allocated times of the common fragments of memory failure; Training performance evaluation module: Multiply the preset training times by the training times allocation ratio to obtain the allocated training times for the common failed memory fragments. Train different types of common failed memory fragments, determine the improvement rate, evaluate the improvement degree of the memory training based on the improvement rate, and mark the memory training as improved memory training and non-improved memory training according to the evaluation results; Training times reallocation module: Allocate the allocated training times of the common failed memory fragments corresponding to the improved memory training to the common failed memory fragments corresponding to the non-improved memory training, and continue the memory training.

2. The training device for memory judgment based on applied psychology according to claim 1, wherein The way to obtain the improvement rate is: Count the number of times the common failed memory fragments appear in the monitoring period after training and mark it as the number of training types; Through the formula: The improvement rate of the common fragments of memory failure is calculated , where represents the number of training types corresponding to the common fragments of memory failure of the i-th type, represents the allocated training times of the common fragments of memory failure of the i-th type, represents the number of memory segments in which the common fragments of memory failure of the i-th type are in before training.

3. The a memory judgment training device based on applied psychology according to claim 2, wherein The process of determining the non-improved memory training is: If the improvement rate of the common fragments of memory failure is less than the improvement rate threshold, the memory training of the common fragments of memory failure is marked as non-improved memory training.

4. The training device for memory judgment based on applied psychology according to claim 1, wherein The process of allocating the allocated training times of the common failed memory fragments corresponding to the improved memory training to the common failed memory fragments corresponding to the non-improved memory training is: Sum the allocated training times for all the common fragments of memory failures corresponding to the memory improvement training to obtain the total allocable training times, and mark them as ; In all non-memory improvement training, based on any common fragment of memory failure corresponding to a non-memory improvement training; Through the formula: Calculate the current allocated training times of the common fragments of memory failure corresponding to the non-improving memory training where represents the initial allocated training times of the common fragments of memory failure corresponding to the non-improving memory training, represents the improvement rate of the r-th common fragment of memory failure corresponding to the non-improving memory training; according to the current allocated training times of the common fragments of memory failure corresponding to the non-improving memory training Conduct memory training again.

5. A memory judgment training method based on applied psychology, which is executed by the device described in any one of the above claims 1-4, characterized in that, This method includes: Step 1: Analyze the memory fragments of the tester to obtain a memory representation value. If the memory representation value is greater than the memory representation threshold, mark the corresponding memory fragment as a failed memory fragment; Step 2: Based on the failed memory fragments, analyze to obtain a training value. If the training value is greater than the training threshold, generate a training signal; Step 3: Based on the training signal, analyze all memory failure fragments in all memory failure segments to determine the commonality value. If the commonality value is greater than or equal to the commonality threshold, mark the memory failure fragments of the corresponding type as memory failure common fragments; Step 4: Based on the memory failure common fragments, determine the training times allocation ratio. Based on the training times allocation ratio, determine the allocated times of the memory common failure fragments; Step 5: Based on the allocated times of the memory failure common fragments, train different types of memory failure common fragments to determine the improvement rate. Based on the improvement rate, evaluate the improvement degree of the memory training, and mark the memory training as improved memory training and non-improved memory training according to the evaluation results; Step 6: Allocate the allocated training times of the memory failure common fragments corresponding to the improved memory training to the memory failure common fragments corresponding to the non-improved memory training, and continue the memory training.

6. The method for training memory judgment based on applied psychology according to claim 5, wherein The acquisition method of the memory representation value is as follows: Extract the memory segments of the tester, based on any one memory segment; Mark the fragments where the tester has memory failures in the memory segment as memory failure fragments, count the number of memory failure fragments, and mark it as the number of failed memory fragments. Perform a ratio process on the number of failed memory fragments and all the fragment numbers in the memory segment information to obtain the memory representation value.

7. A memory judgment training method based on applied psychology according to claim 5, characterized in that The acquisition method of the training value is as follows: Count the number of failed memory segments within the monitoring period, sum the number of successful memory segments and the number of failed memory segments to obtain the total number of memory segments. Perform a ratio process on the number of failed memory segments and the total number of memory segments to obtain the ratio of the number of failed memory segments; Extract the memory representation value corresponding to the failed memory segment, perform a subtraction process on the memory representation value of the failed memory segment and the memory representation threshold, and take the absolute value of the difference to obtain the memory deviation value. Sum and average the deviation degree values of all failed memory fragments to obtain the average memory deviation. Perform a ratio process on the average memory deviation and the memory representation value to obtain the ratio of the failed memory degree; Sum the ratio of the number of failed memory segments and the ratio of the failed memory degree to obtain the training value.

8. A memory judgment training method based on applied psychology according to claim 5, characterized in that The acquisition method of the commonality value is as follows: Extract all the memory failure fragments in all memory failure segments; Based on any one memory failure segment, count the number of occurrences of each type of memory failure fragment in the memory failure segment, and perform a ratio process with the number of memory failure segments to obtain the commonality value.

9. A memory judgment training method based on applied psychology according to claim 5, characterized in that The acquisition method of the training times allocation ratio is as follows: Based on any common fragment of memory failure, count the number of times the common fragment of memory failure appears in all memory failure segments, and mark the number of times of the failure type; through the formula: The training times allocation ratio of the common fragment of memory failure is calculated where n represents the number of types of common fragments of memory failure, i = 1, 2, …, n, represents the number of times of the failure type corresponding to different types of common fragments of memory failure.

10. A method for training the memory judgment based on applied psychology according to claim 9, characterized in that The acquisition method of the allocated times of the memory common failure fragments is as follows: Preset the training times, perform a multiplication process on the preset training times and the training times allocation ratio to obtain the allocated training times of the memory failure common fragments.

11. A method for training memory judgment based on applied psychology according to claim 5, characterized in that, The acquisition method of the improvement rate is as follows: Count the number of occurrences of the memory failure common fragments within the monitoring period after training, and mark it as the number of training type occurrences; Through the formula: , the improvement rate of the common fragments of memory failure is calculated , where represents the number of training type times corresponding to the common fragments of the i-th type of memory failure, represents the allocated training times of the common fragments of the i-th type of memory failure, represents the number of memory segments in which the common fragments of the i-th type of memory failure are in before training.

12. A method for training memory judgment based on applied psychology according to claim 11, characterized in that, The determination process of the non-improved memory training is as follows: If the improvement rate of memory fragments is less than the improvement rate threshold, the memory training of the common fragments of memory failure is marked as non-improved memory training.

13. A memory judgment training method based on applied psychology according to claim 5, characterized in that, The process of allocating the allocated training times of the memory failure common fragments corresponding to the improved memory training to the memory failure common fragments corresponding to the non-improved memory training is as follows: Sum the allocated training times for all the common fragments of memory failures corresponding to the memory improvement training to obtain the total allocable training times, which are marked as ; In all non-memory improvement training, based on any common fragment of memory failure corresponding to a non-memory improvement training; Through the formula: Calculate the current allocated training times of the common fragments of memory failure corresponding to the non-improving memory training , where represents the initial allocated training times of the common fragments of memory failure corresponding to the non-improving memory training, represents the improvement rate of the r-th common fragment of memory failure corresponding to the non-improving memory training; According to the current allocated training times of the common memory failure fragments corresponding to non-improving memory training Conduct memory training again.

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