Training tool for improving cognitive function of schizophrenia patient

Abstract

The invention discloses a training tool for improving the cognitive function of a schizophrenia patient, relates to the technical field of cognitive training of schizophrenia, and aims to solve the problem of poor cognitive training effect of schizophrenia. The device comprises an operator, a workbench, a bottom table and a monitoring system, the bottom end of the operator is connected with the upper end of the workbench, the bottom end of the workbench is connected with the upper end of the bottom table, the bottom table can ascend and descend and drives the workbench to ascend and descend after ascending and descending, and the operator conducts cognitive function training monitoring through the monitoring system. Accuracy and convenience of data acquisition of different training results are effectively improved through the monitoring system, microelectronic movement of a client is changed through the magnetic therapy piece, substance magnetism of biological tissue is formed according to the microelectronic movement, and therefore accuracy of brain wave data acquisition of a patient is enhanced, the magnetic therapy piece does not make direct contact with the skin of the patient, and the patient is prevented from being injured. And the use safety of the patient is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of schizophrenia cognitive training, and specifically provides a training tool for improving the cognitive function of schizophrenia patients. Background Technique

[0002] Schizophrenia is defined as a chronic mental disorder, including abnormalities in an individual's perception, emotion, and behavior.

[0003] Chinese Patent No. CN215995224U discloses a training tool that is convenient for improving the cognitive function of schizophrenia patients. It mainly uses an angle adjustment component and a support member. When in use, the patient can adjust the relative tilt angle of the display screen according to needs, so that the patient can learn and train at a comfortable angle, which is convenient for the patient to learn and train; the support member can support the protection cover during use to facilitate the patient to perform physical training and learning on the training area, and assist the patient in rehabilitative training. Although the above patent solves the problem of cognitive training, there are still the following problems in actual operation:

[0004] 1. When training patients, there is no more refined training classification and no electroencephalogram detection of patients, resulting in inaccurate final diagnosis results.

[0005] 2. Due to the different bodies of patients, the height of the training device cannot be adjusted according to the patient's body, resulting in poor comfort during training for patients.

[0006] 3. When training the cognitive function of schizophrenia patients, due to the single types and methods of training, the training effect of cognitive function is poor. Summary of the Invention

[0007] The purpose of the present invention is to provide a training tool for improving the cognitive function of schizophrenia patients. The monitoring system effectively improves the accuracy and convenience of obtaining different training result data. The magnetic therapy sheet changes the microelectronic movement of the customer, and according to the magnetic properties of the substances that make up biological tissues by the microelectronic movement, the accuracy of electroencephalogram data collection from patients is enhanced. The magnetic therapy sheet does not directly contact the patient's skin, ensuring the safety of patient use, and can solve the problems in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A training tool for improving the cognitive function of schizophrenia patients, including an operator, a workbench, a base, and a monitoring system. The bottom end of the operator is connected to the upper end of the workbench, the bottom end of the workbench is connected to the upper end of the base, the base can perform lifting motion, and after the base is lifted, it drives the workbench to perform lifting motion. The operator monitors the cognitive function training through the monitoring system;

[0010] The monitoring system is used for:

[0011] According to the patient's disease data obtained from the operator, classify the disease data according to the attributes of the disease. After the classification is completed, compare the obtained disease data with the standard disease data, judge the treatment score of the patient after training according to the comparison result, and classify the disease of the patient according to the treatment score.

[0012] Preferably, the operator includes a display, a printing port, an electrode patch interface, a speaker, an electrical connection hole, an electrical connection wire, and a handgrip. A display is provided on the front wall of the operator, a printing port and an electrode patch interface are provided below the display, a speaker and an electrical connection hole are provided on one side of the operator, one end of the electrical connection hole is connected to one end of the electrical connection wire, and the other end of the electrical connection wire is connected to one end of the handgrip.

[0013] Preferably, the display includes:

[0014] A target wavelength acquisition module for real-time acquisition of the target wavelength outside the display;

[0015] A first calculation module for calculating the current brightness outside the current display based on the acquired target wavelength;

[0016]

[0017] Among them, L represents the current brightness outside the current display; ω represents the reflection coefficient, and its value range is (0, 1); k represents the photosensitivity; λ represents the target wavelength; φ(λ) is the function of the light flux changing with the target wavelength; dφ(λ) represents the differential of the function of the light flux changing with the target wavelength; dλ represents the differential of the target wavelength; ζ(λ) represents the relative spectral sensitivity curve of the human eye; a represents the length of the display; b represents the width of the display;

[0018] A second calculation module for obtaining a preset brightness threshold and calculating the adjustable brightness value of the display based on the preset brightness threshold and the current brightness outside the display;

[0019] ΔL = |L 阈值 -L|;

[0020] Among them, ΔL represents the adjustable brightness value of the display; L 阈值 represents the preset brightness threshold;

[0021] A control and adjustment module is used to generate a brightness adjustment instruction based on an adjustable brightness value and control the display to adjust the brightness according to the brightness adjustment instruction.

[0022] Preferably, the workbench includes a table body, handgrip holes, a fixing bar, and a motor mounting box. Handgrip holes are respectively formed on both sides of the upper end of the table body. A fixing bar is formed between the handgrip holes. One end of the manipulator is embedded in the fixing bar. A motor mounting box is arranged on the front wall of the table body.

[0023] Preferably, the motor mounting box includes an inner cavity of the table body, a slide bar, an inner cavity of the slide bar, a top plate, a box body, an inner cavity of the box body, a handle, and a slider. An inner cavity of the table body is formed in the table body. Slide bars are respectively installed on both sides of the inner wall of the inner cavity of the table body. An inner cavity of the slide bar is formed in the slide bar.

[0024] Preferably, one end of the top plate is connected to one end of the box body. A handle is installed at the other end of the box body. An inner cavity of the box body is formed in the box body. Sliders are respectively installed on both sides of the outer wall of the box body. The sliders are embedded in the inner cavity of the slide bar formed in the slide bar, and the sliders can reciprocate in the inner cavity of the slide bar.

[0025] Preferably, the base includes a shock-absorbing plate, a bottom plate, fixing feet, lifting rods, and a lifting rod fixing platform. The shock-absorbing plate is bolted to the bottom end of the workbench. Fixing feet are respectively installed at the corners of the bottom end of the bottom plate. A lifting rod fixing platform is installed at the center of the upper end of the bottom plate. The bottom end of the lifting rod is connected to the upper end of the lifting rod fixing platform. The upper end of the lifting rod is connected to the upper end of the shock-absorbing plate, and the lifting rod can move up and down.

[0026] Preferably, the monitoring system includes:

[0027] A disease data attribute differentiation module is used for:

[0028] After the manipulator obtains the disease data of the patient, the disease data of the patient are training data and monitoring data;

[0029] Among them, the training data are first differentiated according to different training methods into different attributes;

[0030] The training data include attention training, memory training, calculation ability training, and movement training. For attention training, memory training, calculation ability training, and movement training, the attributes are respectively differentiated;

[0031] The monitoring data are electroencephalogram monitoring data. The electrode patches are attached to the corresponding positions of the patient, and the electroencephalogram data collected by the electrode patches are monitored and stored;

[0032] Among them, before the electrode patches are used, the magnetic therapy intensity and frequency of the electrode patches are first set to the lowest level;

[0033] After the setting is completed, place the magnetic detector on the electrode plate and check if the magnetic detector jumps. If it jumps, the electrode plate is in a qualified state and can be used normally. If the magnetic detector does not jump, the electrode plate is in an abnormal state and the staff will check the electrode plate.

[0034] Preferably, the monitoring system further includes:

[0035] An attribute data scoring module for:

[0036] Based on the training data and monitoring data obtained from the disease data attribute differentiation module, compare the training data and monitoring data with the standard training data and standard monitoring data respectively;

[0037] Among them, the standard training data and standard monitoring data are obtained from the database;

[0038] After comparing the training data and monitoring data with the standard training data and standard monitoring data, mark the data that is not within the standard range, and mark the specially marked data as abnormal data;

[0039] A disease level classification module for:

[0040] Based on the abnormal data obtained from the attribute data scoring module, count the threshold range by which the abnormal data exceeds the standard;

[0041] Classify the abnormal data according to the counted range threshold;

[0042] Among them, the higher the exceeded threshold, the greater the disease risk corresponding to the patient, and the lower the exceeded threshold, the smaller the disease risk corresponding to the patient.

[0043] Preferably, the monitoring system further includes:

[0044] A data division module for:

[0045] Retrieve the historical patient disease dataset stored in the database, divide the historical patient disease dataset, and divide the historical patient disease dataset into a historical training dataset and a historical monitoring dataset;

[0046] Obtain a preset variety of training types, and randomly select several first historical training datasets corresponding to the training types from the historical training dataset based on the variety of training types. At the same time, use the remaining historical training data to be classified in the historical training dataset as the second historical training dataset based on the selection result;

[0047] A data classification module for:

[0048] Read the first historical training dataset corresponding to each training type to determine the first training data features corresponding to each training type. At the same time, read the historical training data to be classified in the second historical training dataset to determine the second training data features corresponding to each historical training data to be classified;

[0049] Calculate the target similarity between the first training data features and the second training data features. At the same time, obtain the similarity threshold;

[0050] Compare the target similarity with the similarity threshold to determine whether the current historical training data to be classified belongs to the current training type;

[0051] When the target similarity is equal to or greater than the similarity threshold, it is determined that the current historical training data to be classified belongs to the current training type;

[0052] Otherwise, it is determined that the current historical training data to be classified does not belong to the current training type;

[0053] Classify the historical training data to be classified in the second historical training dataset based on the comparison result, and obtain the sub-historical training dataset corresponding to each training type based on the classification result;

[0054] Divide the historical monitoring dataset based on the training type to obtain the sub-historical monitoring dataset corresponding to each training type;

[0055] Diagnostic prediction model construction module, used for:

[0056] Correspond the sub-historical training dataset and the sub-historical monitoring dataset corresponding to the training type, determine multiple different sub-historical monitoring data corresponding to each sub-historical training data, sort the multiple different sub-historical monitoring data in descending order, and divide the sorted sub-historical monitoring data according to the preset interval to obtain the division result. At the same time, match the corresponding diagnostic result based on the division result;

[0057] Construct a diagnostic prediction model based on the sub-historical training dataset, the sub-historical monitoring dataset, the division result, and the diagnostic result corresponding to the division result;

[0058] Prediction module, used to input the patient's disease data into the diagnostic prediction model for evaluation when the patient's disease data is obtained in the operator, and determine the diagnostic prediction result of the patient's disease data in the corresponding diagnostic type.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0060] 1. A training tool for improving the cognitive function of schizophrenia patients. According to the handgrip, the patient can perform targeted exercise training. According to the exercise training, the patient's condition can be stabilized. At the same time, it is also more helpful to improve the patient's immunity and promote the patient's recovery. The height of the lifting rod is adjusted according to the patient's height, which further improves the comfort of the patient when operating the manipulator.

[0061] 2. A training tool for improving the cognitive function of schizophrenia patients. Through the monitoring system, the accuracy and convenience of obtaining different training result data are effectively improved. By changing the microelectronic movement of the customer through the magnetic therapy sheet and based on the magnetic properties of the substances that make up biological tissues according to the microelectronic movement, the accuracy of collecting the patient's brain wave data is strengthened. The magnetic therapy sheet does not come into direct contact with the patient's skin, ensuring the safety of the patient's use.

[0062] 3. By collecting the target wavelength in the external environment in real time, the current brightness outside the display can be effectively calculated. Then, by calculating the adjustable brightness value of the display, a brightness adjustment instruction is effectively generated, and the brightness of the display is automatically adjusted adaptively according to the brightness adjustment instruction, improving the intelligence of the display brightness control, thereby improving the user experience and achieving the purpose of energy conservation.

[0063] 4. By retrieving the historical patient disease data set in the database, the effective analysis of the historical patient disease data set (historical training data set and historical monitoring data set) is effectively realized. Then, the effective classification of the historical training data set and historical monitoring data set is realized, and the construction of the diagnostic prediction model is achieved, which is beneficial to providing a diagnostic basis for patient diagnosis and thus ensuring the effectiveness and accuracy of the diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 is the overall structural schematic diagram of the present invention;

[0065] Figure 2 is the structural schematic diagram of the manipulator of the present invention;

[0066] Figure 3 is the structural schematic diagram of the workbench of the present invention;

[0067] Figure 4 is the structural schematic diagram of the motor installation box of the present invention;

[0068] Figure 5 is the structural schematic diagram of the base of the present invention;

[0069] Figure 6 is the schematic diagram of the monitoring system module of the present invention.

[0070] In the figure: 1. Operator; 11. Display; 12. Printing port; 13. Electrode sheet interface; 14. Speaker; 15. Electrical connection hole; 16. Electrical connection line; 17. Handgrip; 2. Workbench; 21. Table body; 22. Handgrip hole; 23. Fixed strip; 24. Motor mounting box; 241. Inner cavity of the table body; 242. Slide bar; 243. Inner cavity of the slide bar; 244. Top plate; 245. Box body; 246. Inner cavity of the box body; 247. Handle; 248. Slide block; 3. Bottom base; 31. Shock-absorbing plate; 32. Bottom plate; 33. Fixed feet; 34. Lifting rod; 35. Lifting rod fixing platform. Detailed implementation manners

[0071] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to 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 creative efforts shall fall within the protection scope of the present invention.

[0072] In order to solve the problems in the prior art that during the training of patients, there is no more refined training classification and no electroencephalogram detection of patients, resulting in inaccurate final diagnosis results, please refer to Figures 1 - 4 , the following technical solutions are provided in this embodiment:

[0073] A training tool for improving the cognitive function of schizophrenia patients, including an operator 1, a workbench 2, a bottom base 3 and a monitoring system. The bottom end of the operator 1 is connected to the upper end of the workbench 2, the bottom end of the workbench 2 is connected to the upper end of the bottom base 3, the bottom base 3 can perform lifting movement, and after the bottom base 3 is lifted, it drives the workbench 2 to perform lifting movement. The operator 1 monitors the cognitive function training through the monitoring system;

[0074] The monitoring system is used for:

[0075] According to the patient's disease data obtained from the operator 1, the disease data is classified according to the attributes of the disease. After the classification is completed, the obtained disease data is compared with the standard disease data, and the treatment score of the patient after training is judged according to the comparison result, and the disease of the patient is graded according to the treatment score.

[0076] Specifically, according to the handgrip 17, the patient can perform targeted exercise training, which can stabilize the patient's condition, and at the same time, it is more helpful to improve the patient's immunity and promote the patient's recovery. The height of the lifting rod 34 is adjusted according to the patient's height, which further improves the comfort of the patient when operating the manipulator 1. The monitoring system effectively improves the accuracy and convenience of obtaining different training result data. The magnetic therapy patch changes the microelectronic movement of the patient, and according to the microelectronic movement, it constitutes the magnetic properties of the substances of biological tissues, thereby strengthening the accuracy of collecting the patient's brain wave data. The magnetic therapy patch does not directly contact the patient's skin, ensuring the safety of the patient's use.

[0077] The manipulator 1 includes a display 11, a printing port 12, an electrode patch interface 13, a speaker 14, an electrical connection hole 15, an electrical connection wire 16, and a handgrip 17. The front wall of the manipulator 1 is provided with the display 11. The printing port 12 and the electrode patch interface 13 are provided below the display 11. The speaker 14 and the electrical connection hole 15 are arranged on one side of the manipulator 1. One end of the electrical connection hole 15 is connected to one end of the electrical connection wire 16, and the other end of the electrical connection wire 16 is connected to one end of the handgrip 17.

[0078] Specifically, the display 11 can display the patient's training results and the detection results of the brain waves, improving the portability for the patient and medical staff to view. At the same time, through the printing port 12, the training results and the detection results of the brain waves can be printed. The speaker 14 can play sounds. According to the handgrip 17, the patient can perform targeted exercise training, which can stabilize the patient's condition, and at the same time, it is more helpful to improve the patient's immunity and promote the patient's recovery. When the patient holds the handgrip 17, the patient performs synchronous operations according to the prompts on the display 11. Medical staff can set the targeted exercise intensity according to the conditions of different patients, further improving the effectiveness of the patient's training.

[0079] The workbench 2 includes a table body 21, a handgrip hole 22, a fixing bar 23, and a motor mounting box 24. On both sides of the upper end of the table body 21, handgrip holes 22 are respectively opened. A fixing bar 23 is opened between the handgrip holes 22. One end of the operator 1 is embedded in the fixing bar 23. The front wall of the table body 21 is provided with a motor mounting box 24. The motor mounting box 24 includes an inner cavity 241 of the table body, a slide bar 242, an inner cavity 243 of the slide bar, a top plate 244, a box body 245, an inner cavity 246 of the box body, a handle 247, and a slider 248. An inner cavity 241 of the table body is opened in the table body 21. On both sides of the inner wall of the inner cavity 241 of the table body, slide bars 242 are respectively installed. An inner cavity 243 of the slide bar is opened in the slide bar 242. One end of the top plate 244 is connected to one end of the box body 245. A handle 247 is installed at the other end of the box body 245. An inner cavity 246 of the box body is opened in the box body 245. Sliders 248 are respectively installed on both sides of the outer wall of the box body 245. The sliders 248 are embedded in the inner cavity 243 of the slide bar opened in the slide bar 242. The sliders 248 can perform reciprocating motions in the inner cavity 243 of the slide bar.

[0080] Specifically, the bottom end of the display 11 is embedded in the fixing bar 23, effectively improving the stability of the display 11. When the handgrip 17 is not in use, place the handgrip 17 in the handgrip hole 22. Electrode sheets can be placed in the inner cavity 246 of the box body. One end of the electrode sheet is used to monitor the brain waves of the patient, and the other end of the electrode sheet is connected to the electrode sheet interface 13. The monitoring data of the patient's brain waves can be viewed on the display 11. When the patient needs to monitor the brain waves, first pull the handle 247. After the handle 247 is pulled, the slider 248 can be pulled out in the inner cavity 243 of the slide bar. After being pulled out, the electrode sheet in the inner cavity 246 of the box body can be used, effectively improving the convenience of using the electrode sheet.

[0081] To solve the problem in the prior art that due to the different bodies of patients, the height of the training device cannot be adjusted according to the patient's body, resulting in poor comfort during training for the patient, please refer to Figure 5 , this embodiment provides the following technical solutions:

[0082] The bottom platform 3 includes a shock-absorbing plate 31, a bottom plate 32, fixing feet 33, lifting rods 34, and a lifting rod fixing platform 35. The shock-absorbing plate 31 is bolted to the bottom end of the workbench 2. Fixing feet 33 are respectively installed at the corners of the bottom end of the bottom plate 32. A lifting rod fixing platform 35 is installed at the center of the upper end of the bottom plate 32. The upper end of the lifting rod fixing platform 35 is connected to the bottom end of the lifting rod 34. The upper end of the lifting rod 34 is connected to the upper end of the shock-absorbing plate 31. The lifting rod 34 can perform lifting motions.

[0083] Specifically, the inside of the shock-absorbing plate 31 is made of shock-absorbing rubber, which further improves the stability of the workbench 2 during lifting. The lifting rod 34 is activated by an external control device. After the lifting rod 34 is activated, it can be lifted or lowered. After the lifting rod 34 is lifted or lowered, the workbench 2 can be raised or lowered. The height of the lifting rod 34 is adjusted according to the height of the patient, which further improves the comfort of the patient when operating on the operator 1.

[0084] In order to solve the problem in the prior art that during the cognitive function training of schizophrenia patients, due to the overly single types and methods of training, the training effect of cognitive function is poor. Please refer to Figure 6 , the following technical solutions are provided in this embodiment:

[0085] A monitoring system, including:

[0086] A disease data attribute differentiation module, for:

[0087] After the operator 1 obtains the disease data of the patient, the disease data of the patient is training data and monitoring data;

[0088] Among them, the training data is first differentiated according to different training methods into different attributes;

[0089] The training data includes attention training, memory training, calculation ability training, and motor training. For attention training, memory training, calculation ability training, and motor training, the attributes are differentiated respectively;

[0090] The monitoring data is electroencephalogram monitoring data. The electrode patches are attached to the corresponding positions of the patient, and according to the electroencephalogram data collected by the electrode patches, the electroencephalogram data is monitored and stored;

[0091] Among them, before the electrode patches are used, the magnetic therapy intensity and frequency of the electrode patches are set to the lowest level;

[0092] After the setting is completed, the magnetic detector is placed on the electrode patch to detect whether the magnetic detector jumps. If it jumps, the electrode patch is in a qualified state and can be used normally. If the magnetic detector does not jump, the electrode patch is in an abnormal state, and the staff will check the electrode patch.

[0093] An attribute data scoring module, for:

[0094] Based on the training data and monitoring data obtained from the disease data attribute differentiation module, the training data and monitoring data are respectively compared with the standard training data and standard monitoring data;

[0095] Among them, the standard training data and standard monitoring data are obtained from the database;

[0096] After comparing the training data and monitoring data with the standard training data and standard monitoring data, the data outside the standard range is specially marked, and the specially marked data is marked as abnormal data;

[0097] The disease severity classification module is used for:

[0098] Based on the abnormal data obtained from the attribute data scoring module, the threshold range by which the abnormal data exceeds the standard is statistically counted;

[0099] Classify the abnormal data according to the statistically counted range threshold;

[0100] Among them, the higher the exceeded threshold, the greater the disease risk corresponding to the patient, and the lower the exceeded threshold, the smaller the disease risk corresponding to the patient.

[0101] Specifically, patients can perform attention training, memory training, calculation ability training, and motor training on the display 11. Among them, attention training includes reaction time training and training on the stability, selectivity, transferability, and distributability of attention. For example, a cancellation test can be conducted on the patient on the display 11, where a set of irregular numbers is given to the patient, and they are required to cross out a specific number in a specific order. Attention should be paid to grading the training intensity during attention training, and the training environment and tasks should be adjusted in a timely manner according to the patient's condition. Memory training includes rehearsal, semantic elaboration, mnemonic devices such as the method of loci, etc. Based on the display 11, patients can perform photo memory, map memory, rehearsing short stories, etc. At the same time, during the training process, appropriate prompts can be given to the patient according to the speaker 14. Calculation ability training mainly includes number concepts, arithmetic rules, estimation, financial management ability, etc. Calculation problems, shopping scenario simulations, etc. can be carried out on the display 11. Motor training mainly involves the patient holding the handgrip 17 with their hand and then performing motor training according to the prompts on the display 11. Whether the patient's movement posture is consistent with that on the display 11 is judged. Electroencephalogram monitoring can be performed on the patient according to the electrode patches. First, the magnetic therapy patch is placed on the electrode patch, and then the magnetic therapy patch is attached to the patient's brain. The magnetic therapy patch changes the microelectronic movement of the patient, and based on the microelectronic movement, the magnetic properties of the substances that make up biological tissues are formed, thereby enhancing the accuracy of electroencephalogram data acquisition for the patient. The magnetic therapy patch does not come into direct contact with the patient's skin, ensuring the safety of patient use. According to the disease data attribute differentiation module, attention training data, memory training data, calculation ability training data, motor training data, and electroencephalogram monitoring data can be obtained respectively, and the attention training data, memory training data, calculation ability training data, motor training data, and electroencephalogram monitoring data are differentiated respectively. The disease data attribute differentiation module effectively improves the accuracy and convenience of obtaining different training result data. Through the attribute data scoring module, the attention training data, memory training data, calculation ability training data, motor training data, and electroencephalogram monitoring data can be compared with the standard data. According to the comparison results, it is judged whether there is abnormal data in the attention training data, memory training data, calculation ability training data, motor training data, and electroencephalogram monitoring data, effectively improving the judgment of cognitive function training data and enabling more rapid acquisition of abnormal data of the patient. Through the disease level classification module, the abnormal data in the attention training data, memory training data, calculation ability training data, motor training data, and electroencephalogram monitoring data can be classified by level. The higher the threshold exceeded, the greater the corresponding disease risk for the patient, and the lower the threshold exceeded, the smaller the corresponding disease risk for the patient, effectively enhancing the convenience for patients and medical staff to view cognitive function abnormal diseases.

[0102] Specifically, the display 11 includes:

[0103] A target wavelength acquisition module, which is used to acquire the target wavelength outside the display 11 in real time;

[0104] A first calculation module, which is used to calculate the current brightness outside the current display based on the acquired target wavelength;

[0105]

[0106] Wherein, L represents the current brightness outside the current display; ω represents the reflection coefficient, and its value range is (0, 1); k represents the photosensitivity; λ represents the target wavelength; φ(λ) is the variation function of the luminous flux with respect to the target wavelength; dφ(λ) represents the differential of the variation function of the luminous flux with respect to the target wavelength; dλ represents the differential of the target wavelength; ζ(λ) represents the relative spectral sensitivity curve of the human eye; a represents the length of the display 11; b represents the width of the display 11;

[0107] A second calculation module, which is used to obtain a preset brightness threshold and calculate the adjustable brightness value of the display 11 based on the preset brightness threshold and the current brightness outside the display 11;

[0108] ΔL = |L 阈值 - L|;

[0109] Wherein, ΔL represents the adjustable brightness value of the display 11; L 阈值 represents the preset brightness threshold;

[0110] A control and adjustment module, which is used to generate a brightness adjustment instruction based on the adjustable brightness value and control the display 11 to perform brightness adjustment based on the brightness adjustment instruction.

[0111] In this embodiment, the preset brightness threshold can be set in advance and is set based on the visible brightness of the human eye.

[0112] In this embodiment, the brightness adjustment instruction can be determined based on the adjustable brightness value and is an instruction used to automatically adjust the brightness of the display 11.

[0113] The working principle and beneficial effects of the above technical solution are: by acquiring the target wavelength outside in real time, the current brightness outside the display can be effectively calculated, and then by calculating the adjustable brightness value of the display, a brightness adjustment instruction can be effectively generated, and the brightness of the display can be automatically adjusted adaptively according to the brightness adjustment instruction, improving the intelligence of the display brightness control, thereby improving the user experience and achieving the purpose of energy saving.

[0114] Specifically, the monitoring system further includes:

[0115] A data partitioning module, which is used for:

[0116] Retrieve the historical patient disease dataset stored in the database, and partition the historical patient disease dataset into a historical training dataset and a historical monitoring dataset;

[0117] Obtain multiple preset training types, and randomly select several first historical training datasets corresponding to the training types from the historical training dataset. At the same time, based on the selection result, regard the remaining historical training data to be classified in the historical training dataset as the second historical training dataset;

[0118] The data classification module is used for:

[0119] Read the first historical training dataset corresponding to each training type to determine the first training data features corresponding to each training type. At the same time, read the historical training data to be classified in the second historical training dataset to determine the second training data features corresponding to each historical training data to be classified;

[0120] Calculate the target similarity between the first training data features and the second training data features, and at the same time, obtain the similarity threshold;

[0121] Compare the target similarity with the similarity threshold to determine whether the current historical training data to be classified belongs to the current training type;

[0122] When the target similarity is equal to or greater than the similarity threshold, it is determined that the current historical training data to be classified belongs to the current training type;

[0123] Otherwise, it is determined that the current historical training data to be classified does not belong to the current training type;

[0124] Classify the historical data to be classified in the second historical training dataset based on the comparison result, and obtain the sub-historical training dataset corresponding to each training type based on the classification result;

[0125] Partition the historical monitoring dataset based on the training type to obtain the sub-historical monitoring dataset corresponding to each training type;

[0126] The diagnostic prediction model construction module is used for:

[0127] Correspond the sub-historical training dataset and the sub-historical monitoring dataset corresponding to the training type, determine multiple different sub-historical monitoring data corresponding to each sub-historical training data, sort the multiple different sub-historical monitoring data in descending order, and segment the sorted sub-historical monitoring data according to the preset interval to obtain the segmentation result. At the same time, match the corresponding diagnostic results based on the segmentation result;

[0128] Construct a diagnostic prediction model based on the sub-history training dataset, the sub-history monitoring dataset, the segmentation result, and the diagnostic result corresponding to the segmentation result;

[0129] An estimation module, when patient disease data is obtained in the operator 1, is configured to input the patient disease data into the diagnostic prediction model for evaluation, and determine the diagnostic prediction result of the patient disease data in the corresponding diagnostic type.

[0130] In this embodiment, the database can be pre-set to store the historical patient disease data obtained after monitoring the patient.

[0131] In this embodiment, the historical training dataset may include: historical training datasets corresponding to attention training, memory training, computing ability training, and motor training.

[0132] In this embodiment, the historical monitoring dataset may include historical electroencephalogram monitoring data of all previous patients.

[0133] In this embodiment, the training types may include: attention training, memory training, computing ability training, and motor training.

[0134] In this embodiment, the first historical training dataset may be several datasets corresponding to the training type in the historical training set.

[0135] In this embodiment, the first training data feature may be a training data feature determined based on the training type and the size, range, and data attributes of the data in the first historical training dataset.

[0136] In this embodiment, the second training data feature may be a training data feature determined based on the size, range, and data attributes of the data in the second historical training dataset.

[0137] In this embodiment, the similarity threshold can be pre-set to classify the historical training data in the second historical training dataset, where the historical training data in the second historical training dataset is classified into the corresponding first historical training dataset.

[0138] In this embodiment, determine multiple different sub-history monitoring data corresponding to each sub-history training data. That is, for example, when the training type is: attention training, the sub-history training data under the training type can be time, such as one hour. That is: the attention training result in the first hour under the training type of attention training (where the attention training result is the corresponding sub-history monitoring data (specifically, it can be electroencephalogram data).

[0139] In this embodiment, the preset interval range can be set in advance, which is the range for dividing the size of the sub-historical monitoring data, and then the sub-historical monitoring data is divided into different value ranges. Different value ranges can represent the importance degree of the monitoring data size for the patient's diagnostic symptoms.

[0140] In this embodiment, the diagnostic result can be the result obtained after diagnosing the historical patient disease dataset, which is based on the actual situation combined by doctors and the like.

[0141] In this embodiment, the diagnostic prediction result can be the evaluation of the patient's schizophrenia cognitive status based on the change situation of the monitoring data (brain wave data) determined under the training data (diagnostic type).

[0142] The working principle and beneficial effects of the above technical solution are as follows: By retrieving the historical patient disease dataset from the database, the effective analysis of the historical patient disease dataset (historical training dataset and historical monitoring dataset) can be effectively realized, and then the effective classification of the historical training dataset and historical monitoring dataset can be realized, so as to realize the construction of the diagnostic prediction model, which is beneficial to providing a diagnostic basis for patient diagnosis, and then ensuring the effectiveness and accuracy of the diagnosis.

[0143] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0144] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A training tool for improving the cognitive function of schizophrenia patients, comprising an operator (1), a workbench (2), a base (3) and a monitoring system, characterized in that: The bottom end of the manipulator (1) is connected to the upper end of the workbench (2), the bottom end of the workbench (2) is connected to the upper end of the base (3), the base (3) can move up and down, and after the base (3) moves up and down, it drives the workbench (2) to move up and down. The manipulator (1) monitors cognitive function training through a monitoring system; The monitoring system is used for: According to the patient's disease data obtained from the manipulator (1), classify the disease data according to the attributes of the disease. After classification, compare the obtained disease data with the standard disease data, judge the treatment score of the patient after training according to the comparison result, and divide the level of the patient's disease according to the treatment score.

2. The training tool for improving the cognitive function of schizophrenia patients according to claim 1, characterized in that: The manipulator (1) includes a display (11), a printing port (12), an electrode patch interface (13), a speaker (14), an electrical connection hole (15), an electrical connection wire (16) and a handgrip (17). The display (11) is provided on the front wall of the manipulator (1), the printing port (12) and the electrode patch interface (13) are provided below the display (11), the speaker (14) and the electrical connection hole (15) are provided on one side of the manipulator (1), one end of the electrical connection hole (15) is connected to one end of the electrical connection wire (16), and the other end of the electrical connection wire (16) is connected to one end of the handgrip (17).

3. The training tool for improving the cognitive function of schizophrenia patients according to claim 2, characterized in that, The display (11) includes: A target wavelength acquisition module for real-time acquisition of the target wavelength outside the display (11); A first calculation module for calculating the current brightness outside the current display based on the acquired target wavelength; Where, L represents the current brightness outside the current display; ω represents the reflection coefficient, and its value range is (0, 1); k represents the photosensitivity; λ represents the target wavelength; φ(λ) is the variation function of the luminous flux with the target wavelength; dφ(λ) represents the differential of the variation function of the luminous flux with the target wavelength; dλ represents the differential of the target wavelength; ζ(λ) represents the relative spectral sensitivity curve of the human eye; a represents the length of the display (11); b represents the width of the display (11); A second calculation module for obtaining a preset brightness threshold and calculating the adjustable brightness value of the display (11) based on the preset brightness threshold and the current brightness outside the display (11); ΔL = |L 阈值 - L|; where ΔL represents the adjustable brightness value of the display (11); L 阈值 represents a preset brightness threshold; A control adjustment module for generating a brightness adjustment instruction based on the adjustable brightness value and controlling the display (11) to adjust the brightness based on the brightness adjustment instruction.

4. A training tool for improving the cognitive function of schizophrenia patients according to claim 1, characterized in that: The workbench (2) includes a table body (21), handgrip holes (22), a fixing strip (23) and a motor mounting box (24). Handgrip holes (22) are respectively provided on both sides of the upper end of the table body (21), a fixing strip (23) is provided between the handgrip holes (22), one end of the manipulator (1) is embedded in the fixing strip (23), and the motor mounting box (24) is provided on the front wall of the table body (21).

5. A training tool for improving the cognitive function of schizophrenia patients according to claim 4, characterized in that: The motor installation box (24) includes a table inner cavity (241), a slide bar (242), a slide bar inner cavity (243), a top plate (244), a box body (245), a box body inner cavity (246), a handle (247), and a slider (248). A table inner cavity (241) is formed in the table body (21). Slide bars (242) are respectively installed on both sides of the inner wall of the table inner cavity (241). A slide bar inner cavity (243) is formed in the slide bar (242).

6. The training tool for improving the cognitive function of schizophrenia patients according to claim 5, characterized in that: One end of the top plate (244) is connected to one end of the box body (245). A handle (247) is installed at the other end of the box body (245). A box body inner cavity (246) is formed in the box body (245). Sliders (248) are respectively installed on both sides of the outer wall of the box body (245). The sliders (248) are embedded in the slide bar inner cavity (243) formed in the slide bar (242), and the sliders (248) can reciprocate in the slide bar inner cavity (243).

7. A training tool for improving the cognitive function of schizophrenia patients according to claim 1, characterized in that: The bottom table (3) includes a shock-absorbing plate (31), a bottom plate (32), fixed feet (33), a lifting rod (34), and a lifting rod fixing platform (35). The shock-absorbing plate (31) is bolted to the bottom end of the workbench (2). Fixed feet (33) are respectively installed at the bottom corners of the bottom plate (32). A lifting rod fixing platform (35) is installed at the center of the upper end of the bottom plate (32). The upper end of the lifting rod fixing platform (35) is connected to the bottom end of the lifting rod (34). The upper end of the lifting rod (34) is connected to the upper end of the shock-absorbing plate (31), and the lifting rod (34) can perform lifting motion.

8. A training tool for improving the cognitive function of schizophrenia patients according to claim 1, characterized in that: The monitoring system includes: A disease data attribute differentiation module, which is used for: After the operator (1) obtains the disease data of the patient, the disease data of the patient is training data and monitoring data; Among them, the training data is first differentiated according to different training methods into different attributes; The training data includes attention training, memory training, calculation ability training, and motor training. For attention training, memory training, calculation ability training, and motor training, the attributes are differentiated respectively; The monitoring data is electroencephalogram monitoring data. The electrode patches are attached to the corresponding positions of the patient, and the electroencephalogram data collected by the electrode patches is monitored and stored; Among them, before the electrode patches are used, the magnetic therapy intensity and frequency of the electrode patches are first set to the lowest gear; After the setting is completed, a magnetic detector is placed on the electrode patch to detect whether the magnetic detector jumps. If it jumps, the electrode patch is in a qualified state and can be used normally. If the magnetic detector does not jump, the electrode patch is in an abnormal state, and the staff will check the electrode patch.

9. A training tool for improving the cognitive function of schizophrenia patients according to claim 8, characterized in that: The monitoring system further includes: An attribute data scoring module, which is used for: Based on the training data and monitoring data obtained in the disease data attribute differentiation module, the training data and monitoring data are respectively compared with the standard training data and standard monitoring data; Among them, the standard training data and standard monitoring data are obtained from the database; After the training data and monitoring data are compared with the standard training data and standard monitoring data, the data that is not within the standard range is specially marked, and the specially marked data is marked as abnormal data; A disease level classification module, which is used for: Based on the abnormal data obtained from the attribute data scoring module, the threshold range where the abnormal data exceeds the standard is statistically analyzed; The abnormal data is classified into levels according to the statistically analyzed range threshold; Among them, the higher the exceeded threshold, the greater the disease risk corresponding to the patient, and the lower the exceeded threshold, the smaller the disease risk corresponding to the patient.

10. A training tool for improving the cognitive function of schizophrenia patients according to claim 1, characterized in that, The monitoring system further includes: A data partitioning module for: Retrieving the historical patient disease data set stored in the database, partitioning the historical patient disease data set, and partitioning the historical patient disease data set into a historical training data set and a historical monitoring data set; Obtaining a variety of preset training types, randomly selecting a number of first historical training data sets corresponding to the training types from the historical training data set based on the variety of training types, and at the same time, taking the remaining historical training data to be classified in the historical training data set as the second historical training data set based on the selection result; A data classification module for: Reading the first historical training data set corresponding to each training type to determine the first training data features corresponding to each training type, and at the same time, reading the historical training data to be classified in the second historical training data set to determine the second training data features corresponding to each historical training data to be classified; Calculating the target similarity between the first training data features and the second training data features, and at the same time, obtaining the similarity threshold; Comparing the target similarity with the similarity threshold to determine whether the current historical training data to be classified belongs to the current training type; When the target similarity is equal to or greater than the similarity threshold, it is determined that the current historical training data to be classified belongs to the current training type; Otherwise, it is determined that the current historical training data to be classified does not belong to the current training type; Classifying the historical data to be classified in the second historical training data set based on the comparison result, and obtaining a sub-historical training data set corresponding to each training type based on the classification result; Partitioning the historical monitoring data set based on the training type to obtain a sub-historical monitoring data set corresponding to each training type; A diagnostic prediction model construction module for: Corresponding the sub-historical training data set and the sub-historical monitoring data set corresponding to the training type, determining multiple different sub-historical monitoring data corresponding to each sub-historical training data, sorting the multiple different sub-historical monitoring data in descending order, and segmenting the sorted sub-historical monitoring data according to a preset interval to obtain a segmentation result. At the same time, matching the corresponding diagnostic results based on the segmentation result; Constructing a diagnostic prediction model based on the sub-historical training data set, the sub-historical monitoring data set, the segmentation result, and the diagnostic results corresponding to the segmentation result; An estimation module for, when patient disease data is obtained in the operator (1), inputting the patient disease data into the diagnostic prediction model for evaluation to determine the diagnostic prediction result of the patient disease data in the corresponding diagnostic type.

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