Training method and training device based on application psychology memory judgment

Abstract

The invention relates to the technical field of applied psychology, in particular to a memory judgment training method and device based on applied psychology, and the method comprises the steps: analyzing a memory fragment of a tester to obtain a memory characterization value, analyzing to obtain a training value based on a failed memory fragment, and obtaining a memory characterization value based on a training signal; analyzing all the memory failure fragments in all the memory failure fragments, determining a generality value, determining a training frequency allocation ratio based on the memory failure generality fragments, determining the allocation frequency of the memory failure generality fragments based on the training frequency allocation ratio, and determining the memory failure generality fragments based on the allocation frequency of the memory failure generality fragments; according to the method, different types of memory failure common fragments are trained, the improvement rate is determined, and the training times corresponding to the improved memory training are allocated to the memory failure common fragments corresponding to the non-improved memory training. And the training resource utilization efficiency and the training effect are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of applied psychology, and in particular 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 massive amounts of information that need to be remembered and processed. Good memory and accurate judgment are essential for personal study, work and life.

[0003] However, existing memory training methods often lack scientificity and specificity, and cannot be effectively adjusted according to the individual's actual memory situation. Traditional memory training usually adopts a unified training model. On the one hand, it does not take into account the differences between individuals in memorizing different types of information, resulting in the inability to accurately determine which testers really need training, causing waste of resources. People who really need training cannot get timely training, and the training is not targeted enough. On the other hand, the existing technology lacks accurate evaluation of the degree of improvement 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 carry out targeted optimization of the parts with poor training effects, and cannot achieve reasonable redistribution of training resources. The waste of resources may be more serious.

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

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

[0006] In a first aspect, the present invention provides a memory judgment training method based on applied psychology, which specifically 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 fragment, a training value is analyzed and obtained. If the training value is greater than the training threshold, a training signal is generated; Step 3: Based on the training signal, all memory failure fragments in all memory failure fragments are analyzed to determine the commonality value. If the commonality value is greater than or equal to the commonality threshold, the corresponding type of memory failure fragment is marked as a memory failure commonality fragment; Step 4: Based on the common memory failure fragments, determine the training times allocation ratio, and based on the training times allocation ratio, determine the allocation times of the common memory failure fragments; Step 5: Based on the number of allocations of the common memory failure fragments, different types of common memory failure fragments are trained to determine the improvement rate, based on the improvement rate, the improvement degree of the memory training is evaluated, and the memory training is marked 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.

[0007] In a second aspect, the present invention provides a memory judgment training device based on applied psychology, specifically comprising: Memory representation analysis module: analyzes the memory fragments of the tester to obtain a memory representation value. If the memory representation value is greater than the memory representation threshold, the corresponding memory fragment is marked as a failed memory fragment; Training demand analysis module: Based on the failed memory fragment, the training value is analyzed and obtained. If the training value is greater than the training threshold, a training signal is generated; Memory failure commonality analysis module: Based on the training signal, all memory failure fragments in all memory failure segments are analyzed to determine the commonality value. If the commonality value is greater than or equal to the commonality threshold, the corresponding type of memory failure fragment is marked as a memory failure commonality fragment; Training times allocation module: based on the common memory failure fragments, determine the training times allocation ratio, and based on the training times allocation ratio, determine the allocation times of the common memory failure fragments; Training performance evaluation module: based on the number of allocations of common memory failure fragments, different types of common memory failure fragments are trained to determine the improvement rate, based on the improvement rate, the improvement degree of memory training is evaluated, and the memory training is marked as improved memory training and non-improved memory training according to the evaluation results; Training times redistribution module: allocates 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 continues the memory training.

[0008] Beneficial effects of the present invention: The present invention obtains a memory representation value by analyzing the memory fragments of the tester. 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 fragment, a training value is obtained through analysis. If the training value is greater than the training threshold, a training signal is generated. The present invention obtains a memory representation value by analyzing the fragments in the memory fragment, and then divides the successful and failed memory fragments. According to the failed memory fragment, it can be determined whether training is needed, thereby avoiding unnecessary training. At the same time, it is ensured that the testers who really need it can obtain training in time, thereby improving the pertinence and effectiveness of memory training.

[0009] Based on the training signal, all memory failure fragments in all memory failure segments are analyzed to determine the commonality value. If the commonality value is greater than or equal to the commonality threshold, the corresponding type of memory failure fragment is marked as a memory failure common fragment. Based on the memory failure common fragment, the training number allocation ratio is determined. Based on the training number allocation ratio, the allocation number of memory failure common fragments is determined. The present invention extracts the memory failure fragments in all memory failure segments to determine the commonality value, and can accurately determine the memory failure common fragments. Combined with the preset training number, the allocation training number of each type of memory failure common fragment is determined. According to the frequency of occurrence of the fragments, the training resources are reasonably allocated, so that the training resources tend to be memory failure common fragments with high frequency, thereby improving the utilization efficiency of training resources and the training effect.

[0010] Based on the number of allocations of common fragments of memory failure, different types of common fragments of memory failure are trained to determine the improvement rate, based on the improvement rate, the improvement degree of memory training is evaluated, and the memory training is marked as improved memory training and non-improved memory training according to the evaluation result, the allocated training times of the common fragments of memory failure corresponding to the improved memory training are allocated to the common fragments of memory failure corresponding to the non-improved memory training, and the memory training is continued. 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, and 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, thereby realizing the reasonable redistribution of training resources, which helps to improve the overall memory training efficiency, avoid waste of resources, and concentrate training resources on the parts with poor effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 This is a flow chart of a memory judgment training method based on applied psychology according to an embodiment of the present invention; Figure 2 This is a block diagram of a memory judgment training system based on applied psychology according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a memory judgment training device based on applied psychology according to an embodiment of the present invention; Figure numbers: 3, computer device; 301, processor; 302, memory; 303, computer program. DETAILED DESCRIPTION

[0013] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0014] Embodiment 1, Figure 1 A flowchart of a memory judgment training method based on applied psychology is provided for the first embodiment 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 based on applied psychology. Optionally, a memory judgment training device based on applied psychology can be an electronic device, which can be a notebook, a desktop computer, a smart tablet, etc., and the embodiment of the present invention does not limit this.

[0015] like Figure 1 As shown, an embodiment of the present invention provides a memory judgment training method based on applied psychology, which specifically 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; In some embodiments, by simulating a test scenario, recording the simulation process, and extracting memory fragments of the tester from the recording, wherein the memory fragments include but are not limited to time fragments, location fragments, and character fragments; Based on any memory fragment; The fragments in the memory fragments that the tester fails to remember are marked as memory failure fragments, the number of memory failure fragments is counted and marked as the number of failed memory fragments, and the number of failed memory fragments is compared with the number of all fragments in the memory fragment information to obtain the memory representation value; Compare the memory representation value to the memory representation threshold: If the memory representation value is less than or equal to the memory representation threshold, the corresponding memory segment is marked as a successful memory segment; If the memory representation value is greater than the memory representation threshold, the corresponding memory fragment is marked as a failed memory fragment; Step 2: Based on the failed memory fragment, a training value is analyzed and obtained. If the training value is greater than the training threshold, a training signal is generated; In some embodiments, the number of failed memory segments in a monitoring period is counted, the number of successful memory segments is summed with the number of failed memory segments to obtain the total number of memory segments, and the number of failed memory segments is ratioed with the total number of memory segments to obtain a ratio of the number of failed memory segments; Extract the memory representation value corresponding to the failed memory fragment, perform subtraction processing on the memory representation value of the failed memory fragment 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 memory deviation mean, perform ratio processing on the memory deviation mean and the memory representation value to obtain the failed memory degree ratio; The training value is obtained by summing the ratio of the number of failed memory segments and the ratio of the degree of failed memory; Compare the training value to the training threshold: If the training value is greater than the training threshold, it means that the current tester needs to undergo memory training and generate a training signal; If the training value is less than or equal to the training threshold, it means that the current tester does not need to perform memory training, and a non-training signal is generated; The technical solution of this embodiment is: 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. Based on the failed memory fragment, analyze to obtain a training value. If the training value is greater than the training threshold, generate a training signal. The present invention analyzes the fragments in the memory fragment to obtain a memory representation value, and then divides the successful and failed memory fragments. According to the failed memory fragment, it can be determined whether training is needed to avoid unnecessary training. At the same time, it ensures that testers who really need it can get training in time, thereby improving the pertinence and effectiveness of memory training.

[0016] Embodiment 2, as Figure 1 As shown, an embodiment of the present invention provides a memory judgment training method based on applied psychology, which specifically includes: Step 3: Based on the training signal, all memory failure fragments in all memory failure fragments are analyzed to determine the commonality value. If the commonality value is greater than or equal to the commonality threshold, the corresponding type of memory failure fragment is marked as a memory failure commonality fragment; In some embodiments, all memory failure fragments in all memory failure fragments are extracted; Based on any memory failure fragment, count the number of occurrences of each memory failure fragment in the memory failure fragment, and perform ratio processing with the number of memory failure fragments to obtain the commonality value; Compare the commonality value to the commonality threshold: If the commonality value is greater than or equal to the commonality threshold, the corresponding type of memory failure fragment is marked as a memory failure commonality fragment; If the commonality value is less than the commonality threshold, the corresponding type of memory failure fragment is marked as a non-memory failure commonality fragment; Step 4: Based on the common memory failure fragments, determine the training times allocation ratio, and based on the training times allocation ratio, determine the allocation times of the common memory failure fragments; In some embodiments, based on any memory failure commonality fragment; Count the number of times common memory failure fragments appear in all memory failure fragments, and mark the number of failure types SX; by formula: Calculate the training times distribution ratio of common fragments of memory failure , where n represents the number of types of common fragments of memory failure, i=1, 2, ..., n, Indicates the number of failure types corresponding to different types of memory failure common fragments; preset training times, multiplying the preset training times by the training times allocation ratio to obtain the allocated training times for the memory failure common fragments; It should be noted that the number of allocated training times for the common memory failure fragments represents the number of training times required for the memory type corresponding to the common memory failure fragments. For example, if the preset number of training times is 100 times and the allocation ratio of the number of training times for the common memory failure fragments of characters is 0.8, then the tester needs to be trained 80 times in terms of the character type. The technical solution of this embodiment is: based on the training signal, all memory failure fragments in all memory failure segments are analyzed to determine the commonality value; if the commonality value is greater than or equal to the commonality threshold, the corresponding type of memory failure fragments are marked as memory failure common fragments; based on the memory failure common fragments, the training times allocation ratio is determined; based on the training times allocation ratio, the allocation times of the memory failure common fragments are determined; the present invention extracts the memory failure fragments in all memory failure segments to determine the commonality value, and can accurately determine the memory failure common fragments; the allocation times of training for each type of memory failure common fragments are determined in combination with a preset training times; training resources are reasonably allocated according to the frequency of occurrence of the fragments, so that training resources tend to be allocated to memory failure common fragments with high frequency, thereby improving the utilization efficiency of training resources and the training effect.

[0017] Embodiment three, as Figure 1 As shown, an embodiment of the present invention provides a memory judgment training method based on applied psychology, which specifically includes: Step 5: Based on the number of allocations of the common memory failure fragments, different types of common memory failure fragments are trained to determine the improvement rate, based on the improvement rate, the improvement degree of the memory training is evaluated, and the memory training is marked as improved memory training and non-improved memory training according to the evaluation results; In some embodiments, the number of occurrences of common fragments of memory failure after training in the monitoring period is counted and marked as the number of training type times XL; by the formula: , calculate the improvement rate of common fragments of memory failure ,in, The number of training types corresponding to the common fragments of memory failure of the i-th type is represented by, It represents the number of times the allocation training of the common fragments of the i-th type of memory failure is performed. It represents the number of memory segments that the i-th type of common memory failure fragments are in before training; for example, before training, the common memory failure fragments of the characters appeared 10 times (SX) in the memory failure segments, and there were 100 memory segments. After training, the common memory failure fragments of the characters appeared 4 times (XL) in the memory failure segments, and the number of allocated training times for the common memory failure fragments was 80 times. Then, the training improvement rate of the common memory failure fragments of the characters is ; Improvement rate of memory fragments comparing with the improvement rate threshold; If the improvement rate of memory fragments If it is greater than or equal to the improvement rate threshold, the memory training of the common fragments of character memory failure is marked as improved memory training; If the improvement rate of memory fragments If it is less than the improvement rate threshold, the memory training of the common fragments of memory failure is marked as non-improvement memory training; Step 6: Allocate the allocated training times of the memory failure commonality fragments corresponding to the improved memory training to the memory failure commonality fragments corresponding to the non-improved memory training, and continue the memory training; Specifically, the number of allocated training times for all memory failure common fragments corresponding to all memory improvement trainings is summed up to obtain the total number of allocated training times, which is marked as ; In all non-improvement memory trainings, the common fragments of memory failure corresponding to any non-improvement memory training; By formula: Calculate the current number of allocated training times for the common fragments of memory failure corresponding to the non-improved memory training ,in, It represents the initial allocation training times of the common fragments of memory failure corresponding to the non-improvement memory training. represents the improvement rate of the common fragments of memory failure corresponding to the rth non-improvement memory training; According to the current distribution training times of the common fragments of memory failure corresponding to the non-improvement memory training Memory training is performed again; the technical solution of this embodiment is: based on the number of allocations of memory failure common fragments, different types of memory failure common fragments are trained to determine the improvement rate, based on the improvement rate, the improvement degree of memory training is evaluated, and the memory training is marked as improved memory training and non-improved memory training according to the evaluation result, the allocated training times of memory failure common fragments corresponding to the improved memory training are allocated to the memory failure common fragments corresponding to the non-improved memory training, and the memory training is continued. By evaluating the improvement degree of memory training, the present invention can accurately judge the training effect of each memory failure common fragment, provide a reliable basis for subsequent optimization, and reallocate the allocated training times of memory failure common fragments corresponding to the improved memory training to the memory failure common fragments corresponding to the non-improved memory training, so as to realize the reasonable redistribution of training resources, which helps to improve the overall memory training efficiency, avoid waste of resources, and concentrate training resources on the parts with poor effects.

[0018] Embodiment 4, as Figure 2 As shown, an embodiment of the present invention provides a memory judgment training device based on applied psychology, which specifically includes: Memory representation analysis module: analyzes the memory fragments of the tester to obtain a memory representation value. If the memory representation value is greater than the memory representation threshold, the corresponding memory fragment is marked as a failed memory fragment; Training demand analysis module: Based on the failed memory fragment, the training value is analyzed and obtained. If the training value is greater than the training threshold, a training signal is generated; Memory failure commonality analysis module: Based on the training signal, all memory failure fragments in all memory failure segments are analyzed to determine the commonality value. If the commonality value is greater than or equal to the commonality threshold, the corresponding type of memory failure fragment is marked as a memory failure commonality fragment; Training times allocation module: based on the common memory failure fragments, determine the training times allocation ratio, and based on the training times allocation ratio, determine the allocation times of the common memory failure fragments; Training performance evaluation module: based on the number of allocations of common memory failure fragments, different types of common memory failure fragments are trained to determine the improvement rate, based on the improvement rate, the improvement degree of memory training is evaluated, and the memory training is marked as improved memory training and non-improved memory training according to the evaluation results; Training times redistribution module: allocates 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 continues the memory training.

[0019] Example 5, refer to Figure 3The embodiment of the present invention further provides a computer device 3, comprising: a memory 302 and a processor 301 and a computer program 303 stored in the memory 302. When the computer program 303 is executed on the processor 301, a memory judgment training method based on applied psychology as described in any one of the above methods is implemented.

[0020] The computer device 3 may be a computing device such as a desktop computer, a notebook, a PDA, or 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 will appreciate that Figure 3 It is only an example of computer device 3 and does not constitute a limitation on computer device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, etc.

[0021] The processor 301 may be a central processing unit (CPU), and the processor 301 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A 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 a 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 memory card (SmartMediaCard, SMC), a secure digital (Secure Digital, SD) card, a flash card, etc. equipped on the computer device 3. Further, the memory 302 may also include both an internal storage unit of the computer device 3 and an external storage device. The memory 302 is used to store an operating system, an application program, a boot loader, data, and other programs, such as program codes of the computer program, etc. The memory 302 can also be used to temporarily store data that has been output or is to be output.

[0022] Embodiment 6. The embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements a memory judgment training method based on applied psychology as described in any one of the above methods.

[0023] 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 this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, USB flash drive, mobile hard disk, disk or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0024] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0025] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0026] In the embodiments disclosed in the present application, it should be understood that the disclosed apparatus / terminal equipment and method can be implemented in other ways. For example, the apparatus / terminal equipment embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. One point, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, which may be electrical, mechanical or other forms.

[0027] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0028] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0029] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A memory judgment training method based on applied psychology, characterized in that: include: 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 fragment, a training value is analyzed and obtained. If the training value is greater than the training threshold, a training signal is generated; Step 3: Based on the training signal, all memory failure fragments in all memory failure fragments are analyzed to determine the commonality value. If the commonality value is greater than or equal to the commonality threshold, the corresponding type of memory failure fragment is marked as a memory failure commonality fragment; Step 4: Based on the common memory failure fragments, determine the training times allocation ratio, and based on the training times allocation ratio, determine the allocation times of the common memory failure fragments; Step 5: Based on the number of allocations of the common memory failure fragments, different types of common memory failure fragments are trained to determine the improvement rate, based on the improvement rate, the improvement degree of the memory training is evaluated, and the memory training is marked 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.

2. The memory judgment training method based on applied psychology according to claim 1 is characterized in that: The memory representation value is obtained in the following manner: Extract the memory fragment of the tester, based on any memory fragment; The fragments in the memory fragments that the tester fails to remember are marked as memory failure fragments, the number of memory failure fragments is counted and marked as the number of failed memory fragments, and the number of failed memory fragments is ratioed with the number of all fragments in the memory fragment information to obtain the memory representation value.

3. The memory judgment training method based on applied psychology according to claim 1, characterized in that: The training value is obtained in the following way: Count the number of failed memory fragments within the monitoring period, sum the number of successful memory fragments and the number of failed memory fragments to obtain the total number of memory fragments, and perform ratio processing 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; Extract the memory representation value corresponding to the failed memory fragment, perform subtraction processing on the memory representation value of the failed memory fragment 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 memory deviation mean, perform ratio processing on the memory deviation mean and the memory representation value to obtain the failed memory degree ratio; The training value is obtained by summing the ratio of the number of failed memory segments and the ratio of the degree of failed memory.

4. The memory judgment training method based on applied psychology according to claim 1, characterized in that: The commonality value is obtained as follows: Extract all memory failure fragments from all memory failure fragments; Based on any memory failure fragment, the number of occurrences of each memory failure fragment in the memory failure fragment is counted, and the ratio is processed with the number of memory failure fragments to obtain the commonality value.

5. The memory judgment training method based on applied psychology according to claim 1, characterized in that: The training times allocation ratio is obtained as follows: Based on any common memory failure fragment, count the number of times the common memory failure fragment appears in all memory failure fragments, and mark the number of failure types SX; By formula: Calculate the training times distribution ratio of common fragments of memory failure , where n represents the number of types of common fragments of memory failure, i=1, 2, ..., n, Indicates the number of failure types corresponding to common fragments of different types of memory failures.

6. The memory judgment training method based on applied psychology according to claim 5, characterized in that: The method for obtaining the allocation times of the common memory failure fragments is as follows: The preset number of training times is multiplied by the training times allocation ratio to obtain the allocated training times for the common fragments of memory failure.

7. The memory judgment training method based on applied psychology according to claim 1, characterized in that: The improvement rate is obtained as follows: Count the number of times common fragments of memory failure appear during the monitoring period after training, and mark them as training type number XL; By formula: , calculate the improvement rate of common fragments of memory failure ,in, The number of training types corresponding to the common fragments of memory failure of the i-th type is represented by, It represents the number of times the allocation training of the common fragments of the i-th type of memory failure is performed. It represents the number of memory fragments that the common fragments of the i-th type of memory failure were in before training.

8. The memory judgment training method based on applied psychology according to claim 7, characterized in that: The non-improvement memory training determination process is: If the improvement rate of memory fragments If it is less than the improvement rate threshold, the memory training of the common fragments of memory failure will be marked as non-improvement memory training.

9. The memory judgment training method based on applied psychology according to claim 1, 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: summing up the allocated training times of the memory failure common fragments corresponding to all the improved memory training to obtain the total number of allocated training times, and marking it as ; In all non-improvement memory trainings, the common fragments of memory failure corresponding to any non-improvement memory training; By formula: Calculate the current number of allocated training times for the common fragments of memory failure corresponding to the non-improved memory training ,in, It represents the initial allocation training times of the common fragments of memory failure corresponding to the non-improvement memory training. represents the improvement rate of the common fragments of memory failure corresponding to the rth non-improvement memory training; According to the current distribution training times of the common fragments of memory failure corresponding to the non-improvement memory training Do the memory training again.

10. A memory judgment training device based on applied psychology, the device realizing a memory judgment training method based on applied psychology as claimed in any one of claims 1 to 9, characterized in that: include: Memory representation analysis module: analyzes the memory fragments of the tester to obtain a memory representation value. If the memory representation value is greater than the memory representation threshold, the corresponding memory fragment is marked as a failed memory fragment; Training demand analysis module: Based on the failed memory fragment, the training value is analyzed and obtained. If the training value is greater than the training threshold, a training signal is generated; Memory failure commonality analysis module: Based on the training signal, all memory failure fragments in all memory failure segments are analyzed to determine the commonality value. If the commonality value is greater than or equal to the commonality threshold, the corresponding type of memory failure fragment is marked as a memory failure commonality fragment; Training times allocation module: based on the common memory failure fragments, determine the training times allocation ratio, and based on the training times allocation ratio, determine the allocation times of the common memory failure fragments; Training performance evaluation module: based on the number of allocations of common memory failure fragments, different types of common memory failure fragments are trained to determine the improvement rate, based on the improvement rate, the improvement degree of memory training is evaluated, and the memory training is marked as improved memory training and non-improved memory training according to the evaluation results; Training times redistribution module: allocates 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 continues the memory training.

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