Clinical test period prediction system and method based on virtual reality technology

Abstract

The invention discloses a clinical test period prediction system and method based on a virtual reality technology, and relates to the technical field of electric digital data processing. The clinical test period prediction system based on the virtual reality technology comprises a data acquisition module, a real-time evaluation module and an accuracy evaluation module. The collected test data is transmitted to the central processing unit to obtain the transmission data and the processing data, then real-time evaluation is carried out in combination with the interaction response time to obtain the data real-time evaluation value, and whether accuracy evaluation is carried out or not is judged based on the data real-time evaluation value and the preset real-time threshold value. And finally, according to the transmission data and the processing data, obtaining a data accurate evaluation value, and based on the data accurate evaluation value and a preset accurate threshold value, determining whether to carry out clinical test period prediction, thereby improving the accuracy and real-time performance of the data, further realizing improvement of the clinical test period prediction accuracy, and improving the clinical test period prediction accuracy. The problem of low accuracy of clinical test period prediction in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a clinical trial cycle prediction system and method based on virtual reality technology. Background Art

[0002] Before clinical trials are conducted, it is necessary to investigate various factors in clinical trials to infer the actual situation required for conducting clinical trials, so virtual clinical trials and simulation technologies are particularly important. However, using traditional methods to investigate various factors in clinical trials often only estimates the cycle and cost of clinical trials based on experience, which will lead to inaccurate evaluations, the risk of incomplete control of later trials, and increased actual response costs.

[0003] In the existing technology, virtual reality technology is used to create a virtual patient group, simulate the patient's behavior and response to treatment, conduct clinical trials in a virtual environment, and then model and predict various stages of the clinical trials by collecting data generated in the virtual environment.

[0004] For example, the system and method for demand and supply forecasting for clinical trials disclosed in the invention patent with announcement number: CN111357005B includes: a server receiving one or more electronic files containing parameters of the clinical trial from a user, the parameters including the number of patients, multiple sites, and one or more confidence values; the server calculating a corresponding demand profile for each of the multiple sites based on the number of patients; the server calculating a buffer quantity for each of the multiple sites based on one or more confidence values; the server calculating a supply plan for the clinical trial based on the demand profile and the buffer quantity; and the server sending the supply plan to the user.

[0005] For example, the clinical trial progress statistics and analysis tool based on big data announced by the invention patent with announcement number: CN117473241B includes: a data acquisition and verification module based on the original clinical trial data, using a data cleaning and verification algorithm to generate purified data; an adaptive threshold analysis module based on the purified data, using an adaptive threshold setting algorithm to generate threshold adjustment data; a dynamic data segmentation module based on the threshold adjustment data, using time series analysis and change point detection algorithms to generate data segmentation results; an intelligent data tracking module based on the data segmentation results, using streaming data analysis technology and event-driven models to generate tracking reports; a multi-dimensional statistical analysis module based on the tracking report, using cluster analysis, principal component analysis and time series analysis to generate comprehensive statistical analysis results; an AI predictive analysis module based on the comprehensive statistical analysis results, using machine learning technologies including neural networks and regression analysis to generate predictive analysis reports; a data visualization display module based on the predictive analysis report, using data visualization technology to generate visual display results; a decision support and optimization module based on the visual display results, combining clinical trial goals and data analysis to generate optimized decision plans.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: In the existing technology, virtual reality systems rely on multiple data sources to build virtual patient models, but patient data may have problems such as low accuracy and low real-time performance. These data problems will lead to inaccurate model input and low accuracy in predicting clinical trial cycles. Summary of the invention

[0007] The embodiments of the present application solve the problem of low accuracy in clinical trial cycle prediction in the prior art by providing a clinical trial cycle prediction system and method based on virtual reality technology, thereby improving the accuracy of clinical trial cycle prediction.

[0008] The embodiment of the present application provides a clinical trial cycle prediction system and method based on virtual reality technology, including: a data acquisition module, a real-time evaluation module and an accuracy evaluation module; wherein the data acquisition module is used to collect test data of a patient to be monitored through a data acquisition device, and collect an interactive response time, and dynamically adjust the collection frequency of the data acquisition device according to the collected test data of the patient to be monitored, wherein the test data includes physiological data and behavioral data, and the interactive response time represents the interactive response time of the patient to be monitored on the virtual reality platform; the real-time evaluation module is used to transmit the test data of the patient to be monitored to a central processor, and obtain the transmission data and the processing data, and evaluate the data real-time according to the interactive response time, the transmission data and the processing data to obtain a data real-time evaluation value, and judge whether to execute the function of the accuracy evaluation module based on the data real-time evaluation value and the preset real-time threshold, and the data real-time evaluation value is used to quantitatively evaluate the real-time of the data; the accuracy evaluation module is used to evaluate the data accuracy according to the transmission data and the processing data to obtain a data accuracy evaluation value, and judge whether to perform the clinical trial cycle prediction based on the data accuracy evaluation value and the preset accuracy threshold, and the data accuracy evaluation value is used to quantitatively evaluate the accuracy of the data.

[0009] Furthermore, the specific process of dynamically adjusting the collection frequency of the data collection device according to the collected test data of the patient to be monitored is as follows: A1, judging whether the physiological data in the initial test data is within the preset physiological data range. If the physiological data is not within the preset physiological data range, the collection frequency is adjusted to the first-level collection frequency and the test data of the patient to be monitored is re-collected, and A1 is re-executed; otherwise, A2 is executed, and the initial test data represents the data obtained by collecting data according to the preset collection frequency; A2, judging whether the physiological data in the initial test data is within the preset warning data range. If the physiological data is within the preset warning data range, the collection frequency is adjusted to the second-level collection frequency and the test data of the patient to be monitored is re-collected, and A1 is re-executed; otherwise, the collection frequency is kept unchanged.

[0010] Furthermore, the real-time evaluation module includes a first data transmission unit, a first data evaluation unit and a first data judgment unit; the first data transmission unit is used to encrypt and transmit the test data of the patient to be monitored to the central processing unit through the data transmission protocol, and cache the test data in the data acquisition device; the first data evaluation unit is used to determine whether the test data is successfully transmitted. If the test data is successfully transmitted, the real-time of the data is evaluated according to the interactive response time, transmission data and processing data to obtain a real-time evaluation value of the data, otherwise the cached test data is retransmitted to the central processing unit until the test data is successfully transmitted; the first data judgment unit is used to determine whether the real-time evaluation value of the data is greater than a preset real-time threshold. If the real-time evaluation value is not greater than the preset real-time threshold, the function of the accuracy evaluation module is executed, otherwise real-time adjustment is performed.

[0011] Furthermore, the specific process of encrypting and transmitting the test data of the patient to be monitored to the central processing unit through the data transmission protocol is as follows: encrypting the collected test data and adding a digital signature to the encrypted test data; integrating the test data with the digital signature to obtain a data packet, wherein the integration means integrating different types of data and adding metadata to the obtained data packet; establishing a connection with the central processing unit through the TCP protocol, and sending the data packet with metadata added through an encrypted channel.

[0012] Furthermore, the specific steps of evaluating the real-time performance of the data according to the interactive response time, the transmission data and the processing data to obtain the real-time evaluation value of the data are as follows: obtaining the transmission time and the processing time according to the recorded timestamp, the transmission time represents the difference between the transmission completion time and the collection completion time, and the processing time represents the difference between the processing completion time and the transmission completion time; obtaining the preset evaluation data from the preset database, the preset evaluation data including the preset transmission time, the preset transmission rate, the preset processing time and the preset interactive response time; performing a ratio operation on the transmission time and the preset transmission time to obtain the transmission time proportion; performing a ratio operation on the transmission data volume and the transmission time to obtain the transmission rate; performing a ratio operation on the transmission rate and the preset transmission rate to obtain the transmission rate proportion; performing a ratio operation on the processing time and the preset processing time to obtain the processing time proportion; performing a ratio operation on the interactive response time and the preset interactive response time to obtain the interactive response time proportion; obtaining the data real-time evaluation value based on the obtained transmission time proportion, transmission rate proportion, processing time proportion and interactive response time proportion; the specific limiting expression of the data real-time evaluation value is: ; ; In the formula, i is the number of the patient to be monitored, , N is the total number of patients to be monitored, is the number of times the test data of the i-th patient to be monitored is collected, , is the total number of times the test data of the i-th patient to be monitored is collected, represents the number of patients to be monitored Real-time evaluation value of the data collected, represents the number of patients to be monitored The transmission time of the acquisition, represents the number of patients to be monitored The amount of data transmitted per acquisition, represents the number of patients to be monitored The transfer rate of the acquisition, represents the number of patients to be monitored The processing time of each acquisition, represents the number of patients to be monitored The interactive response time of the acquisition, Indicates the preset transmission time. Indicates the preset transmission rate. Indicates the preset processing time, Indicates the preset interactive response time.

[0013] Furthermore, the specific process of the real-time adjustment is as follows: B1, determine whether the transmission time is less than the preset transmission time. If the transmission time is less than the preset transmission time, execute B2, otherwise the data packet is split level by level and retransmitted until the transmission time is less than the preset transmission time; B2, determine whether the ratio of the transmission data volume to the transmission time is less than the preset transmission rate. If the ratio of the transmission data volume to the transmission time is less than the preset transmission rate, execute B3, otherwise increase the network bandwidth level by level and retransmit until the ratio of the transmission data volume to the transmission time is less than the preset transmission rate; B3, determine whether the processing time is less than the preset processing time. If the processing time is less than the preset processing time, re-acquire the real-time evaluation value of the data, otherwise decompose the data processing task into a preset number of subtasks and evenly distribute them to a preset number of nodes for data processing until the processing time is less than the preset processing time.

[0014] Furthermore, the accuracy assessment module includes a data identification unit, a second data assessment unit and a second data judgment unit; the data identification unit is used to identify and mark abnormal data and missing data through a data processing tool, and to obtain the amount of abnormal data and the amount of missing data by performing statistics on the abnormal data and the amount of missing data, wherein the abnormal data represents data that deviates from other data points in the data, and the missing data represents null values ​​in the data; the second data assessment unit is used to obtain the extreme value of the collected data according to the collected test data, obtain the extreme value of the transmitted data according to the received test data, and perform ratio calculation on the amount of abnormal data and the amount of missing data in the processed data with the amount of transmitted data in the transmitted data to obtain the abnormal data. According to the proportion of missing data, the data accuracy is evaluated based on the obtained extreme values ​​of collected data, the extreme values ​​of transmitted data, the proportion of abnormal data and the proportion of missing data to obtain a data accuracy evaluation value, wherein the extreme values ​​of collected data include the maximum and minimum values ​​in the collected test data, and the extreme values ​​of transmitted data include the maximum and minimum values ​​in the transmitted test data; the second data judgment unit is used to judge whether the data accuracy evaluation value is greater than a preset accuracy threshold value, if the accuracy evaluation value is not greater than the preset accuracy threshold value, then a clinical trial cycle prediction is performed, otherwise the accuracy is adjusted and the function of the data evaluation unit is re-executed until the accuracy evaluation value does not exceed the preset accuracy threshold value.

[0015] Furthermore, the specific process of the accuracy adjustment is as follows: C1, determine whether the proportion of abnormal data is less than the preset abnormal threshold. If the proportion of abnormal data is less than the preset abnormal threshold, execute C2; ​​C2, determine whether the proportion of missing data is less than the preset missing threshold. If the proportion of missing data is less than the preset missing threshold, re-obtain the data accuracy assessment value.

[0016] Furthermore, the accuracy adjustment also includes abnormal data processing and missing data processing: The specific process of abnormal data processing is as follows: D1, determine whether the proportion of abnormal data is less than the preset first-level abnormal threshold. If the proportion of abnormal data is less than the preset first-level abnormal threshold, delete the abnormal data, otherwise execute D2; D2, determine whether the proportion of abnormal data is less than the preset abnormal threshold. If the proportion of abnormal data is less than the preset abnormal threshold, replace the abnormal data with the corresponding median, otherwise re-collect test data; The specific process of missing data processing is as follows: E1, determine whether the proportion of missing data is less than the preset first-level missing threshold. If the proportion of missing data is less than the preset first-level missing threshold, delete the record containing missing data, otherwise execute E2; E2, determine whether the proportion of missing data is less than the preset missing threshold. If the proportion of missing data is less than the preset missing threshold, fill the missing data by linear interpolation method, otherwise re-collect test data.

[0017] Furthermore, an embodiment of the present application provides a clinical trial cycle prediction method based on virtual reality technology, which is characterized by comprising the following steps: S1, collecting test data of a patient to be monitored through a data acquisition device, and collecting interactive response time, and dynamically adjusting the collection frequency of the data acquisition device according to the collected test data of the patient to be monitored, wherein the test data includes physiological data and behavioral data, and the interactive response time represents the interactive response time of the patient to be monitored on the virtual reality platform; S2, transmitting the test data of the patient to be monitored to a central processor, and obtaining transmission data and processing data, evaluating the real-time nature of the data according to the interactive response time, the transmission data and the processing data to obtain a real-time data evaluation value, judging whether to execute the function of the accuracy evaluation module based on the real-time data evaluation value and a preset real-time threshold, and the real-time data evaluation value is used to quantitatively evaluate the real-time nature of the data; S3, evaluating the accuracy of the data according to the transmission data and the processing data to obtain a data accuracy evaluation value, judging whether to perform a clinical trial cycle prediction based on the data accuracy evaluation value and a preset accuracy threshold, and the data accuracy evaluation value is used to quantitatively evaluate the accuracy of the data.

[0018] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By transmitting the collected test data to the central processing unit, the transmission data and the processing data are obtained, and a real-time evaluation is performed based on the data to obtain a real-time evaluation value of the data. Then, based on the real-time evaluation value of the data and the preset real-time threshold, it is determined whether to perform an accuracy evaluation. Finally, a data accuracy evaluation value is obtained based on the transmission data and the processing data. Based on the data accuracy evaluation value and the preset accuracy threshold, it is determined whether to perform a clinical trial cycle prediction, thereby improving the accuracy and real-time performance of the data, and further achieving an improvement in the accuracy of clinical trial cycle prediction, effectively solving the problem of low accuracy of clinical trial cycle prediction in the prior art.

[0019] 2. By encrypting the collected test data and adding a digital signature to the encrypted test data, the test data with the digital signature are integrated to obtain a data packet. Finally, a connection is established with the central processor through the TCP protocol, and the data packet with metadata added is sent through an encrypted channel, thereby ensuring that the test data will not be tampered with or leaked during transmission, thereby improving the security and credibility of data transmission.

[0020] 3. The transmission time and processing time are obtained through the recorded timestamp, and then based on the transmission time and the preset transmission time, it is determined whether to split the data packet step by step. Then, based on the ratio of the amount of transmitted data to the transmission time and the preset transmission rate, it is determined whether to increase the network bandwidth step by step. Finally, based on the processing time and less than the preset processing time, it is determined whether to reallocate the data processing task, thereby improving the real-time and accuracy of data transmission, and further improving the stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of the structure of a clinical trial cycle prediction system based on virtual reality technology provided in an embodiment of the present application; Figure 2 A schematic diagram of the change of the real-time evaluation value of the data provided in the embodiment of the present application; Figure 3 A schematic diagram of the change of the real-time evaluation value of the data provided in the embodiment of the present application; Figure 4 A flowchart of a clinical trial cycle prediction method based on virtual reality technology provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The embodiments of the present application solve the problem of low accuracy of clinical trial cycle prediction in the prior art by providing a clinical trial cycle prediction system and method based on virtual reality technology. The collection frequency is dynamically adjusted through the collected test data, and then the test data of the patient to be monitored is transmitted to the central processor, and the transmission data and processing data are obtained. The interactive response time, transmission data and processing data are evaluated in real time, and compared with the preset real-time threshold to determine whether to perform accuracy evaluation. Finally, the data accuracy is evaluated according to the transmission data and the processing data to obtain a data accuracy evaluation value, and compared with the preset accuracy threshold to determine whether to perform clinical trial cycle prediction, thereby achieving improved accuracy of clinical trial cycle prediction.

[0023] The technical solution in the embodiment of the present application is to solve the problem of low accuracy in predicting the clinical trial cycle mentioned above, and the overall idea is as follows: A real-time evaluation is performed by acquiring the interactive response time, transmission data, and processing data to obtain a real-time evaluation value of the data. Then, based on the real-time evaluation value of the data and the preset real-time threshold, it is determined whether to perform an accuracy evaluation. Finally, an accuracy evaluation is performed based on the transmission data and the processing data to obtain a data accuracy evaluation value. Based on the data accuracy evaluation value and the preset accuracy threshold, it is determined whether to perform a clinical trial cycle prediction, thereby achieving the effect of improving the accuracy of clinical trial cycle prediction.

[0024] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0025] like Figure 1 As shown, it is a structural schematic diagram of a clinical trial cycle prediction system based on virtual reality technology provided by an embodiment of the present application. The clinical trial cycle prediction system based on virtual reality technology provided by an embodiment of the present application includes: a data acquisition module, a real-time evaluation module and an accuracy evaluation module; wherein the data acquisition module is used to collect the test data of the patient to be monitored through a data acquisition device, and collect the interactive response time, and dynamically adjust the collection frequency of the data acquisition device according to the collected test data of the patient to be monitored, the test data includes physiological data and behavioral data, and the interactive response time represents the interactive response time of the patient to be monitored on the virtual reality platform; the real-time evaluation module is used to transmit the test data of the patient to be monitored to a central processor, and obtain the transmission data and the processing data, evaluate the real-time of the data according to the interactive response time, the transmission data and the processing data to obtain a real-time evaluation value of the data, and judge whether to execute the function of the accuracy evaluation module based on the real-time evaluation value of the data and the preset real-time threshold, and the real-time evaluation value of the data is used to quantitatively evaluate the real-time of the data; the accuracy evaluation module is used to evaluate the accuracy of the data according to the transmission data and the processing data to obtain a data accuracy evaluation value, and judge whether to conduct a virtual clinical trial based on the data accuracy evaluation value and the preset accuracy threshold, and the data accuracy evaluation value is used to quantitatively evaluate the accuracy of the data.

[0026] In this embodiment, the data acquisition equipment includes a heart rate monitor, a blood pressure monitor, a motion capture device and an infrared sensor; the behavioral data includes sleep duration and reaction time; the heart rate has different characteristics in deep sleep, light sleep and rapid eye movement, so the sleep duration is obtained based on the heart rate and timestamp measured by the heart rate monitor; in the virtual reality device, the infrared sensor tracks the movement of the hand or controller of the patient to be monitored, and measures the time it takes for the patient to be monitored to respond; the interactive response time is used to reflect the response speed of the system; the central processing unit is used to process the data received from the data acquisition equipment for further analysis and decision-making; the virtual clinical trial creates a digital copy of the patient to be monitored through the trial data of the patient to be monitored, simulates the development trend of past data, simulates the living geographical environment, mobility, and frequency of visits to the hospital of the patient to be monitored, and simulates the number of clinical trials of researchers, the clinical trial cycle, and the expenditure situation, and constructs a multi-scale model to comprehensively simulate the future real clinical trial environment, and finally gives feedback on future clinical trials based on various aspects of the simulated clinical trials. The actual situation can be predicted, and the simulation results can be viewed in an overall and intuitive manner; the data copy represents the digital twin, which is used to feedback the individual's physiological and pathological characteristics, and is used to simulate drug reactions and disease processes; the construction of multi-scale models includes patient response simulation, trial cycle prediction, resource consumption prediction and environmental impact analysis; patient response simulation means simulating the response of each patient under different treatment plans based on the digital copy, and predicting their performance in the trial and potential adverse reactions; trial cycle prediction means predicting the duration of each stage by simulating different intervention frequencies and operating procedures in clinical trials, so as to give an estimate of the entire trial cycle; resource consumption prediction means predicting the resource consumption of the entire trial process based on the investment of equipment, personnel, funds and other resources required in the trial, so as to help optimize funding and resource allocation; environmental impact analysis means simulating the potential impact of different external environments on the trial cycle; the external environment includes urban medical resources, transportation, and social economy; through the above steps, the real-time and accuracy of the data are ensured, thereby achieving the improvement of the accuracy of clinical trial cycle prediction.

[0027] Furthermore, the specific process of dynamically adjusting the collection frequency of the data collection device according to the collected test data of the patient to be monitored is as follows: A1, judging whether the physiological data in the initial test data is within the preset physiological data range. If the physiological data is not within the preset physiological data range, the collection frequency is adjusted to the first-level collection frequency and the test data of the patient to be monitored is re-collected, and A1 is re-executed; otherwise, A2 is executed. The initial test data represents the data obtained by collecting data according to the preset collection frequency. The first-level collection frequency is used to trigger the collection frequency when the physiological data of the patient to be monitored is not within the preset physiological data range, so as to prevent the condition of the patient to be monitored from deteriorating; A2, judging whether the physiological data in the initial test data is within the preset warning data range. If the physiological data is within the preset warning data range, the collection frequency is adjusted to the second-level collection frequency and the test data of the patient to be monitored is re-collected, and A1 is re-executed; otherwise, the collection frequency is kept unchanged. The second-level collection frequency is used to trigger the collection frequency when the physiological data of the patient to be monitored is within the preset warning range, so as to discover possible abnormal conditions of the patient to be monitored.

[0028] In this embodiment, physiological data refers to data reflecting the physiological state of the human body, such as heart rate, blood pressure, blood oxygen saturation, body temperature, etc.; the preset physiological data range is obtained from the preset database and is a normal physiological range set according to medical standards. For example, the heart rate in a resting state is 60-100 times / minute, and the normal physiological range of blood oxygen saturation is 95%-100%; the primary acquisition frequency refers to the data acquisition frequency set to prevent the situation from further deteriorating when the physiological data is not within the preset physiological data range; the primary acquisition frequency is obtained from the preset database and is a collection frequency set according to medical standards. For example, if the heart rate is abnormal, the primary acquisition frequency is set to 1 time / minute; the preset Assume that the warning data range is obtained from a preset database, represented by the first 20% of the preset physiological data range and the last 20% of the preset physiological data range, for example, if the preset physiological data range is 1-10, then the preset warning data range is 1-2 or 9-10; the secondary collection frequency represents the data collection frequency set when the physiological data is within the preset warning data range; the secondary collection frequency is obtained from the preset database and is the collection frequency set according to medical standards, for example, if the heart rate is within the preset warning range, the collection frequency is set to 15 times / minute; the secondary collection frequency is less than the primary collection frequency and greater than the preset collection frequency; through the above steps, the efficiency and accuracy of monitoring are improved.

[0029] Furthermore, the real-time evaluation module includes a first data transmission unit, a first data evaluation unit and a first data judgment unit; the first data transmission unit is used to encrypt and transmit the test data of the patient to be monitored to the central processing unit through the data transmission protocol, and cache the test data in the data acquisition device; the first data evaluation unit is used to determine whether the test data is successfully transmitted. If the test data is successfully transmitted, the real-time of the data is evaluated according to the interactive response time, transmission data and processing data to obtain a real-time evaluation value of the data, otherwise the cached test data is retransmitted to the central processing unit until the test data is successfully transmitted; the first data judgment unit is used to determine whether the real-time evaluation value of the data is greater than a preset real-time threshold. If the real-time evaluation value is not greater than the preset real-time threshold, the function of the accuracy evaluation module is executed, otherwise real-time adjustment is performed.

[0030] In this embodiment, the test data is used to analyze the health status of the patient to be monitored; the central processing unit is responsible for receiving and processing the test data transmitted from the data acquisition device; the preset real-time threshold is obtained from the preset database and is represented by the average value of the real-time evaluation value of the data in the historical time period; the historical time period is set from the beginning of last month to the beginning of this month, for example, if it is currently August 11, the historical time period is set from July 1 to August 1; common data transmission protocols include Transmission Control Protocol (TCP), User Datagram Protocol (UDP), etc. In this embodiment, the TCP protocol is selected; through the above steps, the reliability and real-time performance of data transmission are improved, thereby realizing the evaluation of data real-time performance.

[0031] Furthermore, the specific process of encrypting and transmitting the test data of the patient to be monitored to the central processing unit through the data transmission protocol is as follows: encrypting the collected test data and adding a digital signature to the encrypted test data; integrating the test data with the digital signature to obtain a data packet, where integration means integrating different types of data and adding metadata to the obtained data packet, where the metadata includes data timestamp, sender identity, encryption method, and transmission data volume; establishing a connection with the central processing unit through the TCP protocol, and sending the data packet with metadata added through an encrypted channel.

[0032] In this embodiment, common encryption algorithms include symmetric encryption (Advanced Encryption Standard, AES) and asymmetric encryption (Rivest-Shamir-Adleman, RSA). In this embodiment, the AES encryption algorithm is selected to ensure that the data will not be cracked or tampered with during transmission. In order to verify the integrity and source of the data, a digital signature can be added to the data packet after encryption. A data summary is generated by a hash algorithm, and then the summary is encrypted with a private key to generate a digital signature, which is attached to the data packet. The receiver can use the sender's public key to verify the authenticity of the signature and the integrity of the data. Hash algorithms include Message Digest Algorithm 5 (MD5), Secure Hash Algorithm 1 (SHA-1) and Secure Hash Algorithm 256 (SHA-256). 256, SHA-256), in this embodiment, the SHA-256 algorithm is selected to generate a 256-bit hash value, which is a commonly used secure hash algorithm and is widely used in blockchain, digital signature and other fields; metadata represents the information attached to the data packet, including timestamp, identity, encryption method, etc., to help the receiver understand the background of the data; through the above steps, it is ensured that the test data is safe and reliable during the transmission process, will not be tampered with or leaked, and the source and integrity of the data can be verified, thereby achieving the improvement of the security and credibility of data transmission.

[0033] Furthermore, the specific steps of evaluating the real-time performance of the data according to the interactive response time, the transmission data and the processing data to obtain the real-time evaluation value of the data are as follows: obtain the transmission time and the processing time according to the recorded timestamp, the transmission time represents the difference between the transmission completion time and the collection completion time, and the processing time represents the difference between the processing completion time and the transmission completion time; obtain the preset evaluation data from the preset database, the preset evaluation data includes the preset transmission time, the preset transmission rate, the preset processing time and the preset interactive response time; perform a ratio operation on the transmission time and the preset transmission time to obtain the transmission time ratio; perform a ratio operation on the transmission data volume and the transmission time to obtain the transmission rate; perform a ratio operation on the transmission rate and the preset transmission rate to obtain the transmission rate ratio; perform a ratio operation on the processing time and the preset processing time to obtain the processing time ratio; perform a ratio operation on the interactive response time and the preset interactive response time to obtain the interactive response time ratio; obtain the data real-time evaluation value based on the obtained transmission time ratio, transmission rate ratio, processing time ratio and interactive response time ratio; the specific limiting expression of the data real-time evaluation value is: ; ; In the formula, i is the number of the patient to be monitored, , N is the total number of patients to be monitored, is the number of times the test data of the i-th patient to be monitored is collected, , is the total number of times the test data of the i-th patient to be monitored is collected, represents the number of patients to be monitored Real-time evaluation value of the data collected, represents the number of patients to be monitored The transmission time of the acquisition, represents the number of patients to be monitored The amount of data transmitted per acquisition, represents the number of patients to be monitored The transfer rate of the acquisition, represents the number of patients to be monitored The processing time of each acquisition, represents the number of patients to be monitored The interactive response time of the acquisition, Indicates the preset transmission time. Indicates the preset transmission rate. Indicates the preset processing time, Indicates the preset interactive response time.

[0034] In this embodiment, the preset evaluation data represents the standard value used to evaluate the real-time performance of the data; the preset evaluation data is obtained from a preset database, the preset transmission time is represented by the average value of the historical transmission time, the preset transmission rate is represented by the average value of the historical transmission rate, the preset processing time is represented by the average value of the historical processing time, and the preset interactive response time is represented by the average value of the historical interactive response time; through the above steps, a comprehensive quantitative evaluation of the real-time performance of the data in each link such as transmission, processing and interactive response is achieved.

[0035] For example, the preset transmission time is 1 millisecond, the preset transmission rate is 1 byte / millisecond, the preset processing time is 1 millisecond, and the preset interactive response time is 1 millisecond. Figure 2 and Figure 3As shown, it is a schematic diagram of the change of the real-time evaluation value of data provided by the embodiment of the present application. From the figure, it can be seen that when the transmission time, processing time, interactive response time and transmission rate are all 1, the more the acquisition times, the smaller the real-time evaluation value of data; when the acquisition times, processing time, interactive response time and transmission rate are all 1, the longer the transmission time, the smaller the real-time evaluation value of data; when the acquisition times, transmission time, interactive response time and transmission rate are all 1, the longer the processing time, the smaller the real-time evaluation value of data; when the acquisition times, transmission time, processing time and transmission rate are all 1, the longer the interactive response time, the smaller the real-time evaluation value of data; when the acquisition times, transmission time, processing time and transmission rate are all 1, the longer the interactive response time, the smaller the real-time evaluation value of data; when the acquisition times, transmission time, processing time and interactive response time are all 1, the greater the transmission rate, the greater the real-time evaluation value of data; in summary, the real-time evaluation value of data is negatively correlated with the acquisition times, transmission time, processing time and interactive response time, and the real-time evaluation value of data is positively correlated with the transmission rate.

[0036] Specifically, the algorithm of real-time data evaluation value is combined with the number of times the test data of the i-th patient to be monitored is collected. ”, “The i-th patient to be monitored Transfer time of acquisition ”, “The i-th patient to be monitored Transfer rate of acquisition ”, “The i-th patient to be monitored Processing time of each acquisition ” and “the i-th patient to be monitored Interactive response time of acquisition ”, comprehensive analysis is used to obtain “the number of patients to be monitored Real-time evaluation value of collected data ", and each parameter is related to each other, not independent, it is a whole, for example, in this formula, "the number of times the test data of the i-th patient to be monitored is collected is numbered " may lead to the increase of "the i-th patient to be monitored Real-time evaluation value of collected data " decreases, does not necessarily mean that "the i-th patient to be monitored is Real-time evaluation value of collected data " is small, and at this time, we also need to consider "the number of patients to be monitored Transfer time of acquisition ”, “The i-th patient to be monitored Transfer rate of acquisition ”, “The i-th patient to be monitored Processing time of each acquisition ” and “the i-th patient to be monitored Interactive response time of acquisition " can also determine the real-time evaluation value of the data at the next moment. It takes into account the number of times the test data of the i-th patient to be monitored is collected in a quantitative way. ”, “The i-th patient to be monitored Transfer time of acquisition ”, “The i-th patient to be monitored Transfer rate of acquisition ”, “The i-th patient to be monitored Processing time of each acquisition ”, “The i-th patient to be monitored Interactive response time of acquisition ” and “the i-th patient to be monitored Real-time evaluation value of collected data "The correlation and mutual influence between them. Generally speaking, an increase in the number of data collection times may lead to an increase in transmission time and processing time, which in turn leads to a decrease in the real-time evaluation value of the data; an increase in the transmission rate can shorten the transmission time, thereby increasing the real-time evaluation value; the shorter the processing time, the faster the data can be processed and fed back, and the greater the real-time evaluation value; the shorter the interactive response time, the shorter the time to collect data, and the greater the real-time evaluation value. The algorithm obtained by comprehensive analysis can accurately quantify the real-time analysis of the data, indicating the effect of accurately evaluating the real-time evaluation value of the data. Therefore, precise analysis is carried out by establishing mathematical forms.

[0037] Furthermore, the specific process of real-time adjustment is as follows: B1, determine whether the transmission time is less than the preset transmission time. If the transmission time is less than the preset transmission time, execute B2, otherwise the data packet is split level by level and retransmitted until the transmission time is less than the preset transmission time; B2, determine whether the ratio of the transmission data volume to the transmission time is less than the preset transmission rate. If the ratio of the transmission data volume to the transmission time is less than the preset transmission rate, execute B3, otherwise increase the network bandwidth level by level and retransmit until the ratio of the transmission data volume to the transmission time is less than the preset transmission rate; B3, determine whether the processing time is less than the preset processing time. If the processing time is less than the preset processing time, re-acquire the real-time evaluation value of the data, otherwise decompose the data processing task into a preset number of subtasks and evenly distribute them to a preset number of nodes for data processing until the processing time is less than the preset processing time.

[0038] In this embodiment, timestamps are often used to record information about key time points in data collection, transmission, and processing; gradually increasing network bandwidth specifically refers to increasing the network bandwidth of the network card and increasing the number of ports, for example, replacing a 100M network card with a 1000M network card; increasing the number of ports by adding switch devices, for example, increasing 1 switch to 2 switches; switches are used to connect multiple devices in a local area network so that the devices can communicate with each other; nodes represent servers used to process data; through the above steps, the real-time and accuracy of data transmission are improved, the performance degradation caused by transmission delays or processing bottlenecks is reduced, and the stability and reliability of the system are improved.

[0039] Furthermore, the accuracy evaluation module includes a data identification unit, a second data evaluation unit and a second data judgment unit; the data identification unit is used to identify and mark abnormal data and missing data through a data processing tool, and to obtain the amount of abnormal data and the amount of missing data by statistics, wherein the abnormal data indicates data that deviates from other data points in the data, and the missing data indicates a null value in the data; the second data evaluation unit is used to obtain the extreme value of the collected data according to the collected test data in ascending order, obtain the extreme value of the transmission data according to the received test data in ascending order, perform ratio calculation on the amount of abnormal data and the amount of missing data in the processed data with the amount of transmission data in the transmission data to obtain the proportion of abnormal data and the proportion of missing data, and evaluate the data accuracy based on the obtained extreme value of the collected data, the extreme value of the transmission data, the proportion of abnormal data and the proportion of missing data to obtain the data accuracy evaluation value, wherein the extreme value of the collected data includes the maximum value and the minimum value in the collected test data, and the extreme value of the transmission data includes the maximum value and the minimum value in the transmitted test data; the specific limiting expression of the data accuracy evaluation value is: ; In the formula, i is the number of the patient to be monitored, , N is the total number of patients to be monitored, is the number of times the test data of the i-th patient to be monitored is collected, , is the total number of times the test data of the i-th patient to be monitored is collected, represents the number of patients to be monitored The accurate evaluation value of the data collected represents the number of patients to be monitored The amount of abnormal data collected, represents the number of patients to be monitored The amount of data transmitted per acquisition, represents the number of patients to be monitored The amount of missing data collected, represents the number of patients to be monitored The minimum value of the collected data collected in times, represents the number of patients to be monitored The minimum value of the transmission data collected in each acquisition. represents the number of patients to be monitored The maximum value of the collected data collected in times, represents the number of patients to be monitored The maximum value of the transmission data collected the second time; the second data judgment unit: is used to judge whether the data accuracy evaluation value is greater than the preset accuracy threshold. If the accuracy evaluation value is not greater than the preset accuracy threshold, the clinical trial cycle prediction is performed, otherwise the accuracy is adjusted and the function of the data evaluation unit is re-executed until the accuracy evaluation value does not exceed the preset accuracy threshold.

[0040] In this embodiment, the preset accuracy threshold is obtained from a preset database and is represented by the sum of the average value and twice the variance of the data accuracy evaluation value in the historical time period; definition ,in, is the i-th patient to be monitored The proportion of abnormal data collected, defined as ,in, is the i-th patient to be monitored The percentage of missing data collected, defined as ,in, is the i-th patient to be monitored The minimum deviation of the acquisition, defined as ,in, is the i-th patient to be monitored The maximum deviation of the acquisition is For example, if the total number of patients to be monitored is 1.5, the total number of patients to be monitored N is 1, and the total number of acquisitions T is 5, the change of the data accuracy evaluation value is shown in Table 1: Table 1 Statistics of changes in data accuracy assessment values The proportion of abnormal data The proportion of missing data Minimum deviation Maximum deviation Data Accurate Assessment Value 0.259 2.011 0.233 1.064 0.294 0.259 2.011 0.233 0.325 0.360 0.259 2.011 1.232 0.325 0.288 0.259 0.456 1.232 0.325 0.485 1.789 0.456 1.232 0.325 0.237 It can be seen from the table that when the proportion of abnormal data, the proportion of missing data and the minimum deviation remain unchanged, the larger the maximum deviation, the smaller the data accuracy assessment value; when the proportion of abnormal data, the proportion of missing data and the maximum deviation remain unchanged, the larger the minimum deviation, the smaller the data accuracy assessment value; when the proportion of abnormal data, the minimum deviation and the maximum deviation remain unchanged, the larger the proportion of missing data, the smaller the data accuracy assessment value; when the proportion of missing data, the minimum deviation and the maximum deviation remain unchanged, the larger the proportion of abnormal data, the smaller the data accuracy assessment value; in summary, the data accuracy assessment value is negatively correlated with the proportion of abnormal data, the proportion of missing data and the minimum deviation. Furthermore, the specific process of accuracy adjustment is as follows: C1, determine whether the proportion of abnormal data is less than the preset abnormal threshold. If the proportion of abnormal data is less than the preset abnormal threshold, execute C2; ​​C2, determine whether the proportion of missing data is less than the preset missing threshold. If the proportion of missing data is less than the preset missing threshold, re-obtain the data accuracy assessment value.

[0041] In this embodiment, the preset abnormality threshold is obtained from a preset database and is represented by the sum of the average value of the abnormal data proportion in the historical time period and twice the variance; the preset missing threshold is obtained from a preset database and is represented by the sum of the average value of the missing data proportion in the historical time period and twice the variance; through the above steps, the accuracy and reliability of the data are improved.

[0042] Furthermore, the accuracy is adjusted, and then it also includes abnormal data processing and missing data processing: the specific process of abnormal data processing is as follows: D1, determine whether the proportion of abnormal data is less than the preset first-level abnormal threshold. If the proportion of abnormal data is less than the preset first-level abnormal threshold, delete the abnormal data, otherwise execute D2; D2, determine whether the proportion of abnormal data is less than the preset abnormal threshold. If the proportion of abnormal data is less than the preset abnormal threshold, replace the abnormal data with the corresponding median, otherwise re-collect test data; the specific process of missing data processing is as follows: E1, determine whether the proportion of missing data is less than the preset first-level missing threshold. If the proportion of missing data is less than the preset first-level missing threshold, delete the records containing missing data, otherwise execute E2; E2, determine whether the proportion of missing data is less than the preset missing threshold. If the proportion of missing data is less than the preset missing threshold, fill the missing data through linear interpolation method, otherwise re-collect test data.

[0043] In this embodiment, the preset first-level abnormality threshold is obtained from a preset database and is represented by the difference between the average value of the abnormal data proportion in the historical time period and twice the variance; abnormal data represents data points in the data that do not conform to the expected distribution or logical law, which may be caused by measurement errors, recording errors or extreme situations; the median represents the number in the middle position after a group of values ​​are arranged in ascending order. If the value is an odd number, the median is the number in the middle; if it is an even number, the median is the average of the two middle numbers; missing data means that the values ​​of some records or fields in the data are empty; the preset first-level missing threshold is obtained from a preset database and is represented by the difference between the average value of the missing data proportion in the historical time period and twice the variance; linear interpolation represents a simple data filling method, which estimates the value of the missing data point through a linear relationship based on the known data points before and after the missing data point; through the above steps, data integrity and accuracy are improved.

[0044] like Figure 4 As shown, it is a flow chart of a clinical trial cycle prediction method based on virtual reality technology provided in an embodiment of the present application. The clinical trial cycle prediction method based on virtual reality technology provided in an embodiment of the present application includes the following steps: S1, collecting test data of the patient to be monitored through a data acquisition device, and collecting interactive response time, dynamically adjusting the collection frequency of the data acquisition device according to the collected test data of the patient to be monitored, the test data includes physiological data and behavioral data, and the interactive response time represents the interactive response time of the patient to be monitored on the virtual reality platform; S2, transmitting the test data of the patient to be monitored to a central processor, and obtaining transmission data and processing data, evaluating the real-time performance of the data according to the interactive response time, the transmission data and the processing data to obtain a real-time evaluation value of the data, judging whether to execute the function of the accuracy evaluation module based on the real-time evaluation value of the data and the preset real-time threshold, and the real-time evaluation value of the data is used to quantitatively evaluate the real-time performance of the data; S3, evaluating the accuracy of the data according to the transmission data and the processing data to obtain a data accuracy evaluation value, judging whether to perform clinical trial cycle prediction based on the data accuracy evaluation value and the preset accuracy threshold, and the data accuracy evaluation value is used to quantitatively evaluate the accuracy of the data.

[0045] In this embodiment, virtual reality technology (VR) is a technology that allows users to feel as if they are in a real situation by simulating environments, perceptions, natural skills, and sensor devices, and to interact with objects in the virtual world, thereby obtaining an immersive experience. Virtual reality provides an interactive virtual environment for the patient to be monitored, allowing the patient to perform various test tasks or simulate daily life behaviors in a virtual scene. The virtual reality interactive environment can not only enhance the patient's sense of participation and compliance, but also test the patient's behavior, reaction, and cognitive ability in a virtual scene. Through the virtual reality interactive platform, the patient's behavior data and interactive response time can be accurately collected to evaluate the patient's health status and reaction ability. Based on the test data in the virtual reality interactive platform, the patient's clinical trial cycle can be better predicted. In addition, virtual reality technology can simulate different test scenarios to ensure the wide coverage and effectiveness of data, thereby improving the reliability of clinical trial data. Clinical trials are processes used to evaluate the safety and effectiveness of new drugs, new therapies, or medical devices. Clinical trial cycle prediction is based on the collected data, and a certain algorithm or model is used to predict the time required to complete the entire clinical trial. By collecting data in real time and accurately, the scientificity and accuracy of clinical trial design and decision-making are improved, thereby shortening the clinical trial cycle.

[0046] To summarize, the embodiments of the present application transmit the collected test data to a central processing unit, obtain the transmission data and the processing data, perform a real-time evaluation based on the data to obtain a real-time evaluation value of the data, and then determine whether to perform an accuracy evaluation based on the real-time evaluation value of the data and a preset real-time threshold. Finally, a data accuracy evaluation value is obtained based on the transmission data and the processing data, and a judgment is made whether to perform a clinical trial cycle prediction based on the data accuracy evaluation value and the preset accuracy threshold, thereby improving the accuracy and real-time performance of the data, and further achieving an improvement in the accuracy of clinical trial cycle prediction, effectively solving the problem of low accuracy of clinical trial cycle prediction in the prior art.

[0047] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0048] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0049] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0050] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0051] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0052] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A clinical trial cycle prediction system based on virtual reality technology, characterized in that: It includes data acquisition module, real-time evaluation module and accuracy evaluation module; The data acquisition module is used to collect the test data of the patient to be monitored through the data acquisition device, and collect the interactive response time, and dynamically adjust the collection frequency of the data acquisition device according to the collected test data of the patient to be monitored, wherein the test data includes physiological data and behavioral data, and the interactive response time represents the interactive response time of the patient to be monitored on the virtual reality platform; The real-time evaluation module is used to transmit the test data of the patient to be monitored to the central processor, and obtain the transmission data and the processing data, evaluate the real-time of the data according to the interactive response time, the transmission data and the processing data to obtain a real-time evaluation value of the data, and determine whether to execute the function of the accuracy evaluation module based on the real-time evaluation value of the data and the preset real-time threshold value, and the real-time evaluation value of the data is used to quantitatively evaluate the real-time of the data; The accuracy assessment module is used to evaluate the data accuracy based on the transmitted data and processed data to obtain a data accuracy assessment value, and to determine whether to perform a clinical trial cycle prediction based on the data accuracy assessment value and a preset accuracy threshold. The data accuracy assessment value is used to quantify the accuracy of the assessment data.

2. The clinical trial cycle prediction system based on virtual reality technology as claimed in claim 1, characterized in that: The specific process of dynamically adjusting the collection frequency of the data collection device according to the collected test data of the patient to be monitored is as follows: A1, judging whether the physiological data in the initial test data is within the preset physiological data range, if the physiological data is not within the preset physiological data range, adjusting the acquisition frequency to the first-level acquisition frequency and re-acquiring the test data of the patient to be monitored, and re-execute A1, otherwise execute A2, the initial test data represents the data acquired by data acquisition according to the preset acquisition frequency; A2, determine whether the physiological data in the initial test data is within the preset warning data range. If the physiological data is within the preset warning data range, adjust the collection frequency to the secondary collection frequency and re-collect the test data of the patient to be monitored, and re-execute A1, otherwise keep the collection frequency unchanged.

3. The clinical trial cycle prediction system based on virtual reality technology as claimed in claim 1, characterized in that: The real-time evaluation module includes a first data transmission unit, a first data evaluation unit and a first data judgment unit; The first data transmission unit is used to encrypt and transmit the test data of the patient to be monitored to the central processor through the data transmission protocol, and cache the test data in the data acquisition device; The first data evaluation unit is used to determine whether the test data is successfully transmitted. If the test data is successfully transmitted, the data real-time performance is evaluated according to the interactive response time, the transmission data and the processing data to obtain a data real-time evaluation value. Otherwise, the cached test data is retransmitted to the central processing unit until the test data is successfully transmitted. The first data judgment unit is used to judge whether the real-time evaluation value of the data is greater than the preset real-time threshold. If the real-time evaluation value is not greater than the preset real-time threshold, the function of the accuracy evaluation module is executed, otherwise real-time adjustment is performed.

4. The clinical trial cycle prediction system based on virtual reality technology as claimed in claim 3, characterized in that: The specific process of encrypting and transmitting the test data of the patient to be monitored to the central processor through the data transmission protocol is as follows: Encrypt the collected test data and add a digital signature to the encrypted test data; Integrate the digitally signed test data to obtain a data package, and add metadata to the obtained data package; A connection is established with the central processor via the TCP protocol, and the data packets with metadata added are sent through an encrypted channel.

5. The clinical trial cycle prediction system based on virtual reality technology as claimed in claim 3, characterized in that: The specific steps of evaluating the data real-time performance according to the interactive response time, the transmission data and the processing data to obtain the data real-time evaluation value are as follows: The transmission time and processing time are obtained according to the recorded timestamps. The transmission time represents the difference between the transmission completion time and the acquisition completion time, and the processing time represents the difference between the processing completion time and the transmission completion time. Acquire preset evaluation data from a preset database, wherein the preset evaluation data includes a preset transmission time, a preset transmission rate, a preset processing time, and a preset interactive response time; The transmission time is ratioed to the preset transmission time to obtain the transmission time ratio; The transmission rate is obtained by performing a ratio operation on the amount of transmitted data and the transmission time; The transmission rate is calculated by comparing the transmission rate with the preset transmission rate to obtain the transmission rate ratio; The processing time is ratioed to the preset processing time to obtain the processing time ratio; The interactive response time is ratioed to the preset interactive response time to obtain the interactive response time ratio; The real-time evaluation value of the data is obtained based on the obtained transmission time proportion, transmission rate proportion, processing time proportion and interactive response time proportion; The specific limiting expression of the real-time evaluation value of the data is: ； ； In the formula, i is the number of the patient to be monitored, , N is the total number of patients to be monitored, is the number of times the test data of the i-th patient to be monitored is collected, , is the total number of times the test data of the i-th patient to be monitored is collected, represents the number of patients to be monitored Real-time evaluation value of the data collected, represents the number of patients to be monitored The transmission time of the acquisition, represents the number of patients to be monitored The amount of data transmitted per acquisition, represents the number of patients to be monitored The transfer rate of the acquisition, represents the number of patients to be monitored The processing time of each acquisition, represents the number of patients to be monitored The interactive response time of the acquisition, Indicates the preset transmission time. Indicates the preset transmission rate. Indicates the preset processing time, Indicates the preset interactive response time.

6. The clinical trial cycle prediction system based on virtual reality technology as claimed in claim 3, characterized in that: The specific process of the real-time adjustment is as follows: B1, determine whether the transmission time is less than the preset transmission time. If the transmission time is less than the preset transmission time, execute B2, otherwise the data packet is split level by level and retransmitted until the transmission time is less than the preset transmission time; B2, determine whether the ratio of the amount of data transmitted to the transmission time is less than the preset transmission rate. If the ratio of the amount of data transmitted to the transmission time is less than the preset transmission rate, execute B3, otherwise increase the network bandwidth step by step and retransmit until the ratio of the amount of data transmitted to the transmission time is less than the preset transmission rate; B3, determine whether the processing time is less than the preset processing time. If the processing time is less than the preset processing time, re-acquire the real-time evaluation value of the data. Otherwise, decompose the data processing task into a preset number of subtasks and evenly distribute them to a preset number of nodes for data processing until the processing time is less than the preset processing time.

7. The clinical trial cycle prediction system based on virtual reality technology as claimed in claim 1, characterized in that: The accuracy assessment module includes a data identification unit, a second data assessment unit, and a second data judgment unit; The data identification unit is used to identify and mark abnormal data and missing data through a data processing tool, and to perform statistics on the abnormal data and missing data to obtain the amount of abnormal data and the amount of missing data; The second data evaluation unit is used to obtain a collection data extreme value according to the collected test data, obtain a transmission data extreme value according to the received test data, perform ratio calculations on the amount of abnormal data and the amount of missing data in the processed data and the amount of transmission data in the transmission data to obtain an abnormal data ratio and a missing data ratio, and evaluate the data accuracy based on the obtained collection data extreme value, transmission data extreme value, abnormal data ratio and missing data ratio to obtain a data accuracy evaluation value; The second data judgment unit is used to judge whether the data accuracy evaluation value is greater than the preset accuracy threshold. If the accuracy evaluation value is not greater than the preset accuracy threshold, the clinical trial cycle prediction is performed, otherwise the accuracy is adjusted and the function of the second data evaluation unit is re-executed until the accuracy evaluation value does not exceed the preset accuracy threshold.

8. The clinical trial cycle prediction system based on virtual reality technology as claimed in claim 7, characterized in that: The specific process of the accuracy adjustment is as follows: C1, determine whether the proportion of abnormal data is less than the preset abnormal threshold. If the proportion of abnormal data is less than the preset abnormal threshold, execute C2; C2, determine whether the missing data ratio is less than the preset missing threshold. If the missing data ratio is less than the preset missing threshold, re-obtain the accurate evaluation value of the data.

9. The clinical trial cycle prediction system based on virtual reality technology as claimed in claim 7, characterized in that: The accuracy adjustment also includes abnormal data processing and missing data processing: The specific process of abnormal data processing is as follows: D1, determine whether the proportion of abnormal data is less than the preset first-level abnormal threshold. If the proportion of abnormal data is less than the preset first-level abnormal threshold, delete the abnormal data, otherwise execute D2; D2, determine whether the proportion of abnormal data is less than the preset abnormal threshold. If the proportion of abnormal data is less than the preset abnormal threshold, replace the abnormal data with the corresponding median, otherwise re-collect the test data; The specific process of missing data processing is as follows: E1, determine whether the missing data ratio is less than the preset first-level missing threshold. If the missing data ratio is less than the preset first-level missing threshold, delete the records containing missing data, otherwise execute E2; E2, determine whether the missing data ratio is less than the preset missing threshold. If the missing data ratio is less than the preset missing threshold, fill the missing data by linear interpolation method, otherwise re-collect the test data.

10. A clinical trial cycle prediction method based on virtual reality technology, characterized in that: The following steps are involved: S1, collecting test data of the patient to be monitored through a data acquisition device, and collecting interactive response time, and dynamically adjusting the collection frequency of the data acquisition device according to the collected test data of the patient to be monitored, wherein the test data includes physiological data and behavioral data, and the interactive response time represents the interactive response time of the patient to be monitored on the virtual reality platform; S2, transmitting the test data of the patient to be monitored to the central processor, and obtaining the transmission data and the processing data, evaluating the real-time performance of the data according to the interactive response time, the transmission data and the processing data to obtain a real-time evaluation value of the data, and judging whether to execute the function of the accuracy evaluation module based on the real-time evaluation value of the data and a preset real-time threshold, wherein the real-time evaluation value of the data is used to quantitatively evaluate the real-time performance of the data; S3, evaluating the data accuracy based on the transmitted data and the processed data to obtain a data accuracy evaluation value, and judging whether to perform a clinical trial cycle prediction based on the data accuracy evaluation value and a preset accuracy threshold, wherein the data accuracy evaluation value is used to quantify the accuracy of the evaluation data.

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