CN117149782A - CRC networking management method and system based on big data analysis - Google Patents
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Abstract
The invention discloses a CRC networking management method and a CRC networking management system based on big data analysis, wherein the method comprises the following steps: CRC personnel information acquisition, CRC task scheduling, CRC networking management model information storage and CRC networking management model optimization. The invention belongs to the technical field of data processing, in particular to a CRC networking management method and system based on big data analysis, wherein the scheme adopts an informationized management means to comprehensively manage CRC working contents on a CRC networking management model; performing data hierarchical storage, applying quantum encryption to sensitive data encryption, adopting decentralization data storage, dispersing data in a plurality of nodes, and performing user intrusion detection through AWS; and (3) carrying out normalization and weighted summation on the target value by adopting an improved coarse granularity genetic algorithm, and carrying out multi-objective optimization and penalty factor evolution to obtain an optimal CRC networking management model.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a CRC networking management method and system based on big data analysis.
Background
CRC networking management is an information technology means, and a networking system is established to establish an online training system of CRC systematic expertise, so that the management level of research teams, SMO, CRO and sponsors on CRC is improved, the efficiency, quality and interactive communication of clinical research are improved, and a CRC professional platform is created. However, the existing CRC networking management has the technical problems that CRC training is not systematic, CRC personnel are messy to manage, and timely and effective management is difficult; the technical problems that the safety measures of the system are imperfect, so that information of CRC personnel is leaked, and the safety of clinical tests is endangered; the method has the technical problems that the research team, SMO, CRO and sponsor can comprehensively manage CRC work contents on the platform, the respective work targets are difficult to balance, multi-target optimization cannot be carried out, and the system work efficiency is low.
Disclosure of Invention
Aiming at the situation, in order to overcome the defects of the prior art, the invention provides a CRC networking management method and system based on big data analysis, aiming at the technical problems that CRC personnel are difficult to manage timely and effectively because of a system which is not formed by CRC training, an informatization management means is adopted to carry out online audit, statistics and management on CRC personnel, so that research teams, SMO, CRO and sponsors carry out CRC work content comprehensive management on a CRC networking management model; aiming at the technical problems that the security of clinical trial is endangered due to information leakage of CRC personnel caused by imperfect system security measures, data layered storage is adopted, quantum encryption is applied to sensitive data encryption, decentralization data storage is carried out, data are scattered on a plurality of nodes, user intrusion detection is carried out through AWS, and the security and reliability of the data are fully ensured; aiming at the technical problems that the research team, SMO, CRO and sponsor exist to comprehensively manage CRC work contents on the platform, the respective work targets are difficult to balance, multi-target optimization cannot be performed, and the system work efficiency is low, an improved coarse-granularity genetic algorithm is adopted to normalize and weight sum target values, multi-target optimization and penalty factor evolution are performed, the system work efficiency and CRC development environment are improved, the practical problem of multi-target optimization which cannot be processed by the traditional algorithm is processed, and the optimal CRC networking management model is obtained.
The technical scheme adopted by the invention is as follows: the invention provides a CRC networking management method based on big data analysis, which comprises the following steps:
step S1: the information acquisition of CRC personnel, in particular to acquisition of personal identity information, academic background, professional background and working experience of the CRC personnel;
step S2: CRC task scheduling, namely performing task analysis, task arrangement, progress tracking and task multiplexing on the CRC task;
step S3: the CRC networking management model adopts an informatization management means to carry out online audit, statistics and management on the CRC, so that research teams, SMO, CRO and sponsors can carry out CRC work content comprehensive management on the CRC networking management model, and the CRC is led to develop to a specialized direction;
step S4: CRC networking management model information storage, namely data layering storage, quantum encryption is applied to sensitive data encryption, decentralization data storage is adopted, data are scattered on a plurality of nodes, user intrusion detection is carried out through AWS, and safety and reliability of the data are fully guaranteed;
step S5: the CRC networking management model optimization is specifically that an improved coarse granularity genetic algorithm is used, target values are normalized and weighted summed, multi-objective optimization and penalty factor evolution are carried out, the system working efficiency and CRC development environment are improved, the actual problem of multi-objective optimization which cannot be processed by the traditional algorithm is solved, the problem that a local optimal value is trapped in the calculation process is avoided, and the optimal CRC networking management model is obtained.
Further, in step S1, the CRC personnel information acquisition includes the following steps:
step S11: collecting personal identity information, including CRC personnel name, gender, age and contact information;
step S12: the academic and professional background comprises the steps of collecting the academic, professional background and training experience of CRC personnel, and evaluating the qualification and the capability of the CRC personnel in a clinical trial;
step S13: working experience, recording the working experience of the CRC personnel, including the time of the clinical study, the study program and responsibilities involved, and evaluating the experience and expertise of the CRC personnel.
Further, in step S2, the CRC task scheduling includes the steps of:
step S21: task analysis, namely comprehensively analyzing the research project by CRC, and knowing the target, time requirement and resource requirement of the project;
step S22: task arrangement, namely distributing tasks to corresponding team members according to the emergency degree, the priority and the resource availability of the tasks;
step S23: progress tracking, CRC (cyclic redundancy check) periodically tracks the progress of tasks, including communication with team members, checking task states and submitting progress reports, and timely taking measures to solve the problems and delays;
step S24: and (3) task disc multiplexing, wherein when the task is completed, the CRC performs task summarization and disc multiplexing, and the execution condition of the task is evaluated.
Further, in step S3, the CRC networking management model includes the steps of:
step S31: CRC job entry audit, a support institution personnel checks the applicant job entry CRC record, checks the record, checks job entry CRC resume and project experience, distinguishes self-added and carefully identified projects in the project experience, and gives specific interview results for job entry CRC;
step S32: project auditing, which supports checking CRC application to add and create project records, auditing the records, and auditing project information;
step S33: certificate management, counting the certificate issuing conditions of the CRC personnel in the hospital, wherein the certificate issuing conditions comprise issued employee cards, remaining issued employee cards, expired employee cards and expired employee cards, and carrying out system prompt aiming at the certificate expiration conditions to prompt the personnel to reissue the employee cards;
step S34: counting the on-duty conditions of CRC personnel, distinguishing formal CRC and practical CRC, displaying the total number of the personnel in real time, counting the on-duty conditions of the CRC, including on-duty time, on-duty conditions, current projects and history projects, performing on-duty registration, providing the CRC personnel for checking and registering in a range of going to and leaving from the hospital, and filling in registration contents;
step S35: counting the leaving and delivering conditions of CRC personnel, checking all CRC initiating leave applications of a home, and simultaneously providing the project delivering conditions of the current CRC personnel under study, wherein the project delivering conditions comprise initiating CRC, accepting CRC, delivering state, initiating time and delivering report, and the institution manager confirms and examines the project delivering according to the project delivering conditions;
step S36: working report, counting the number of the work report participated in by the CRC personnel in the home, setting the maximum number of the work report participated in by the CRC according to dynamic configuration, checking the work report condition of the work report participated in the home on the week, month, season and year of the work report, and informing and reminding the personnel who do not fill the work report regularly;
step S37: work management, comprising the steps of:
step S371: study team management, maintenance of hospital clinical trial team personnel management, including account allocation, approval of joining applications;
step S372: the sponsor management checks the sponsor information of all the cooperation of the home, searches according to different keywords, and counts the progress of the sponsor project of the cooperation of the home, including the number of the research projects, the number of the non-started projects and the number of the junction projects;
step S373: CRO management, checking CRO information of all cooperations of the home, searching according to different keywords, and counting the progress of CRO projects of the cooperation of the home, wherein the progress comprises the number of the projects to be researched, the number of the projects to be started and the number of the projects to be ended;
step S374: SMO management, checking SMO information of all cooperations of the home, searching according to different keywords, and counting the progress of SMO projects and CRC personnel of the cooperations of the home.
Further, in step S4, the CRC networking management model information storage includes the steps of:
step S41: after receiving a data storage request of a user, carrying out data layering storage on the data according to the requirements of the user, dividing the data into three levels of common, important and sensitive, storing the data of different levels by adopting different encryption modes, encrypting the common data only for data abstracts, transmitting the content in a plaintext form, carrying out random encryption on the important data by adopting a symmetric encryption algorithm, and fully encrypting the sensitive data by adopting quantum encryption;
step S42: the method comprises the steps of removing centralized data storage, realizing the decentralized data storage through a blockchain technology, enabling each cloud service provider to serve as a node in a block, obtaining data storage weight through a random number election mechanism, randomly dividing complete data into different data fragments, storing each different data fragment in different nodes, enabling the cloud server to obtain access rights when a node part breaks down, enabling the data to still reliably exist on other nodes, enabling the cloud service provider to provide corresponding data backup and save a data recovery path, improving data disaster recovery capability, enabling each node to generate a block, enabling a block header to record a hash value, a time stamp and a random number of a previous block, enabling a block body to record a hash value, a data storage address, data backup address, node information and a searchable encryption index of stored data, enabling the cloud server to obtain access rights and then needing backup storage, and reserving a path in the block body;
step S43: calculating block mapping, wherein a data cloud center of the decentralised data storage is responsible for data partitioning and recording the block mapping, so that the data can be efficiently stored and accessed in the whole decentralised network, the decentralised data storage encrypts the block mapping, and meanwhile, hash operation is carried out on the whole file data, a corresponding hash value is stored, when the data is acquired from a data storage cloud end next time, the hash value is recalculated, and if the hash values are the same, the data is complete; if the hash values are different, the data is attacked, and the block mapping is calculated according to the following formula:
；
wherein Q is block mapping, data is stored Data, P is actual stored node, n is total actual stored node number, A is storage medium, f, s and t are the number of storage medium corresponding to actual stored node;
step S44: calculating the probability of complete attack of the data, wherein the data stored by different cloud service providers are random, and when the total number of cloud service providers and the number of copies divided by the decentralized identity authentication of the data are large enough, the data security can be ensured, and the probability of complete attack of the data is calculated by using the following formula:
；
where ρ is the probability that the data is fully attacked, M is the selected cloud server provider, M is the total number of cloud server providers, N is the number of copies the data is split into by the off-center avatar authentication, |! Is a mathematical factorial symbol;
step S45: performing user intrusion detection, when the off-center identity authentication receives a service request of a user, AWS (wireless subscriber identity module) detects whether the user is an illegal user or not by analyzing the user identity, the user authority, the last login time, the current state, the login position and the login IP (Internet protocol), and if the user is the illegal user, relevant user information is added into a threat update table; otherwise, the request of the user is accepted and the service is provided for the user.
Further, in step S5, the CRC networking management model optimization includes the steps of:
step S51: normalizing the target value, and defining the maximum value of the target k to be expressed as maxf by adopting a modified coarse-grained genetic algorithm k The minimum value is expressed as minf k Normalized interval is [ a, b]The normalization function is calculated using the following formula:
；
in phi k (X) is a normalization function, a, b are the upper and lower limits of the normalization interval, X is an independent variable parameter, f k (X) is an objective function value, k represents an objective, k=1, 2, … …, r;
step S52: the weighted sum, each target of the multiple objective function is multiplied by a coefficient w, w e [0,1], the sum of all coefficients is 1, the formula used is as follows:
；
wherein w is k Is the coefficient of the corresponding objective function, r is the total number of the objective k;
step S53: calculating an optimized CRC networking management model, adding all objective functions with coefficients to obtain a single objective function, wherein the optimal point of the improved single objective optimization problem is a non-inferior optimal front point, and the non-inferior optimal front point is a set formed by all non-inferior solutions in the multi-objective optimization problem and is used for providing a plurality of optional optimal models, and the optimized CRC networking management model is calculated by the following formula:
；
where F (X) is a single objective function, minimum refers to the process of finding the minimum of the single objective function in the optimization problem;
step S54: the punishment factor evolves, a small floating is created according to the change range by using the lower bound of the punishment factor and the current value of the punishment factor, and random mutation is carried out to complete the evolution of the punishment factor;
step S55: and obtaining an optimal CRC networking management model, namely obtaining a local optimal solution by gradually evolving a combination of strategy evolution penalty factors and kernel function parameters, and selecting a combination with the highest classification precision from all the local optimal solutions to obtain the optimal CRC networking management model.
The invention provides a CRC networking management system based on big data analysis, which comprises a CRC personnel information acquisition module, a CRC task scheduling module, a CRC networking management model information storage module and a CRC networking management model optimization module;
the CRC personnel information acquisition module is used for acquiring basic personal information, academic background, professional background and working experience of CRC personnel;
the CRC task scheduling module is used for carrying out task analysis, task arrangement, progress tracking and task multiplexing on the CRC task;
the CRC networking management model module adopts an informatization management means to carry out online audit, statistics and management on CRC, so that research teams, SMO, CRO and sponsors can carry out CRC work content comprehensive management on the CRC networking management model;
the CRC networking management model information storage module is used for carrying out data layering storage, applying quantum encryption to sensitive data encryption, adopting decentralization data storage, dispersing data in a plurality of nodes, and carrying out user intrusion detection through AWS;
the CRC networking management model optimization module is used for carrying out normalization and weighted summation on target values, carrying out multi-objective optimization and penalty factor evolution, improving the working efficiency of the system and CRC development environment, and processing the multi-objective optimization actual problem which cannot be processed by the traditional algorithm to obtain the optimal CRC networking management model.
The beneficial results obtained by adopting the scheme of the invention are as follows:
(1) Aiming at the technical problems that CRC training is not in a system, CRC personnel management is messy and difficult to manage timely and effectively, an informationized management means is adopted to carry out online audit, statistics and management on CRC, so that research teams, SMO, CRO and sponsors carry out CRC work content comprehensive management on a CRC networking management model;
(2) Aiming at the technical problems that the security of clinical trials is endangered due to information leakage of CRC personnel caused by imperfect system security measures, data hierarchical storage is adopted, quantum encryption is applied to sensitive data encryption, decentralised data storage is adopted, data are scattered on a plurality of nodes, user intrusion detection is carried out through AWS, and the security and reliability of the data are fully ensured;
(3) Aiming at the technical problems that the research team, SMO, CRO and sponsor exist to comprehensively manage CRC work contents on the platform, the respective work targets are difficult to balance, multi-target optimization cannot be performed, and the system work efficiency is low, an improved coarse-granularity genetic algorithm is adopted to normalize and weight sum target values, multi-target optimization and penalty factor evolution are performed, the system work efficiency and CRC development environment are improved, the practical problem of multi-target optimization which cannot be processed by the traditional algorithm is processed, and the optimal CRC networking management model is obtained.
Drawings
FIG. 1 is a flow chart of a CRC networking management method based on big data analysis provided by the invention;
FIG. 2 is a schematic diagram of a CRC networking management system based on big data analysis provided by the invention;
FIG. 3 is a flow chart of step S3;
fig. 4 is a flow chart of step S4;
fig. 5 is a flow chart of step S5.
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention; all other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "top," "bottom," "inner," "outer," and the like indicate orientation or positional relationships based on those shown in the drawings, merely to facilitate description of the invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the invention.
Referring to fig. 1, the CRC networking management method based on big data analysis provided by the present invention includes the following steps:
step S1: the information acquisition of CRC personnel, in particular to acquisition of personal identity information, academic background, professional background and working experience of the CRC personnel;
step S2: CRC task scheduling, namely performing task analysis, task arrangement, progress tracking and task multiplexing on the CRC task;
step S3: the CRC networking management model adopts an informatization management means to carry out online audit, statistics and management on the CRC, so that research teams, SMO, CRO and sponsors can carry out CRC work content comprehensive management on the CRC networking management model, the CRC is led to develop towards the specialized direction, and the high-quality development of clinical research is assisted;
step S4: CRC networking management model information storage, namely data layering storage, quantum encryption is applied to sensitive data encryption, decentralization data storage is adopted, data are scattered on a plurality of nodes, user intrusion detection is carried out through AWS, and safety and reliability of the data are fully guaranteed;
step S5: the CRC networking management model optimization is specifically that an improved coarse granularity genetic algorithm is used, target values are normalized and weighted summed, multi-objective optimization and penalty factor evolution are carried out, the system working efficiency and CRC development environment are improved, the actual problem of multi-objective optimization which cannot be processed by the traditional algorithm is solved, the problem that a local optimal value is trapped in the calculation process is avoided, and the optimal CRC networking management model is obtained.
In a second embodiment, referring to fig. 1, the embodiment is based on the above embodiment, and in step S1, the CRC personnel information acquisition includes the following steps:
step S11: collecting personal identity information, including CRC personnel name, gender, age and contact information;
step S12: the academic and professional background comprises the steps of collecting the academic, professional background and related training experiences of CRC personnel, and evaluating the qualification and the capability of the CRC personnel in a clinical trial;
step S13: working experience, recording the working experience of the CRC personnel, including the time of the clinical study, the study program and responsibilities involved, and evaluating the experience and expertise of the CRC personnel.
Embodiment three, referring to fig. 1, the embodiment is based on the above embodiment, and in step S2, the CRC task scheduling includes the following steps:
step S21: task analysis, namely comprehensively analyzing the research project by CRC, and knowing the target, time requirement and resource requirement of the project;
step S22: task arrangement, namely distributing tasks to corresponding team members according to the emergency degree, the priority and the resource availability of the tasks;
step S23: progress tracking, CRC (cyclic redundancy check) periodically tracks the progress of tasks, including communication with team members, checking task states and submitting progress reports, and timely taking measures to solve the problems and delays;
step S24: and (3) task disc multiplexing, wherein when the task is completed, the CRC performs task summarization and disc multiplexing, and the execution condition of the task is evaluated.
Fourth embodiment, referring to fig. 1 and 3, the embodiment is based on the above embodiment, and in step S3, the CRC networking management model includes the following steps:
step S31: CRC job entry audit, a support institution personnel checks the applicant job entry CRC record, checks the record, checks job entry CRC resume and project experience, distinguishes self-added and carefully identified projects in the project experience, and gives specific interview results for job entry CRC;
step S32: project auditing, which supports checking CRC application to add and create project records, and auditing the records, wherein the auditing project information comprises project names, project types, handover times, test stages, contract cases, departments, CRC information, sponsors, CRO and SMO;
step S33: certificate management, counting the certificate issuing conditions of the CRC personnel in the hospital, wherein the certificate issuing conditions comprise issued employee cards, remaining issued employee cards, expired employee cards and expired employee cards, and carrying out system prompt aiming at the certificate expiration conditions to prompt the personnel to reissue the employee cards;
step S34: counting the on-duty conditions of CRC personnel, distinguishing formal CRC and practical CRC, displaying the total number of the personnel in real time, counting the on-duty conditions of the CRC, including on-duty time, on-duty conditions, current projects and history projects, performing on-duty registration, providing the CRC personnel for checking and registering in a range of going to and leaving from the hospital, and filling in registration contents;
step S35: counting the leaving and delivering conditions of CRC personnel, checking all CRC initiating leave applications of a home, and simultaneously providing the project delivering conditions of the current CRC personnel under study, wherein the project delivering conditions comprise initiating CRC, accepting CRC, delivering state, initiating time and delivering report, and the institution manager confirms and examines the project delivering according to the project delivering conditions;
step S36: working report, counting the number of the work report participated in by the CRC personnel in the home, setting the maximum number of the work report participated in by the CRC according to dynamic configuration, checking the work report condition of the work report participated in the home on the week, month, season and year of the work report, and informing and reminding the personnel who do not fill the work report regularly;
step S37: work management, comprising the steps of:
step S371: study team management, maintenance of hospital clinical trial team personnel management, including account allocation, approval of joining applications;
step S372: the sponsor management checks the sponsor information of all the cooperation of the home, searches according to different keywords, and counts the progress of the sponsor project of the cooperation of the home, including the number of the research projects, the number of the non-started projects and the number of the junction projects;
step S373: CRO management, checking CRO information of all cooperations of the home, searching according to different keywords, and counting the progress of CRO projects of the cooperation of the home, wherein the progress comprises the number of the projects to be researched, the number of the projects to be started and the number of the projects to be ended;
step S374: SMO management, checking SMO information of all cooperations of the home, searching according to different keywords, and counting the progress of SMO projects and CRC personnel of the cooperations of the home.
Through executing the operation, the information management means is adopted to carry out online audit, statistics and management on the CRC, so that research teams, SMO, CRO and sponsors can carry out comprehensive management on CRC work contents on a CRC networking management model, and the technical problems that CRC training is not systematic, CRC personnel management is messy and management is difficult to carry out timely and effectively are solved.
Fifth embodiment, referring to fig. 1 and 4, the embodiment is based on the above embodiment, and in step S4, the CRC networking management model information storage includes the following steps:
step S41: after receiving a data storage request of a user, carrying out data layering storage on the data according to the requirements of the user, dividing the data into three levels of common, important and sensitive, storing the data of different levels by adopting different encryption modes, encrypting the common data only for data abstracts, transmitting the content in a plaintext form, carrying out random encryption on the important data by adopting a symmetric encryption algorithm, and fully encrypting the sensitive data by adopting quantum encryption;
step S42: the method comprises the steps of removing centralized data storage, realizing the decentralized data storage through a blockchain technology, enabling each cloud service provider to serve as a node in a block, obtaining data storage weight through a random number election mechanism, randomly dividing complete data into different data fragments, storing each different data fragment in different nodes, enabling the cloud server to obtain access rights when a node part breaks down, enabling the data to still reliably exist on other nodes, enabling the cloud service provider to provide corresponding data backup and save a data recovery path, improving data disaster recovery capability, enabling each node to generate a block, enabling a block header to record a hash value, a time stamp and a random number of a previous block, enabling a block body to record a hash value, a data storage address, data backup address, node information and a searchable encryption index of stored data, enabling the cloud server to obtain access rights and then needing backup storage, and reserving a path in the block body;
step S43: calculating block mapping, wherein a data cloud center of the decentralised data storage is responsible for data partitioning and recording the block mapping, so that the data can be efficiently stored and accessed in the whole decentralised network, the decentralised data storage encrypts the block mapping, and meanwhile, hash operation is carried out on the whole file data, a corresponding hash value is stored, when the data is acquired from a data storage cloud end next time, the hash value is recalculated, and if the hash values are the same, the data is complete; if the hash values are different, the data is attacked, and the block mapping is calculated according to the following formula:
；
wherein Q is block mapping, data is stored Data, P is actual stored node, n is total actual stored node number, A is storage medium, f, s and t are the number of storage medium corresponding to actual stored node;
step S44: calculating the probability of complete attack of the data, wherein the data stored by different cloud service providers are random, and when the total number of cloud service providers and the number of copies divided by the decentralized identity authentication of the data are large enough, the data security can be ensured, and the probability of complete attack of the data is calculated by using the following formula:
；
where ρ is the probability that the data is fully attacked, M is the selected cloud server provider, M is the total number of cloud server providers, N is the number of copies the data is split into by the off-center avatar authentication, |! Is a mathematical factorial symbol;
step S45: performing user intrusion detection, when the off-center identity authentication receives a service request of a user, AWS (wireless subscriber identity module) detects whether the user is an illegal user or not by analyzing the user identity, the user authority, the last login time, the current state, the login position and the login IP (Internet protocol), and if the user is the illegal user, relevant user information is added into a threat update table; otherwise, the request of the user is accepted and the service is provided for the user.
By executing the operations, data layered storage is adopted, quantum encryption is applied to sensitive data encryption, decentralization data storage is carried out, data are scattered on a plurality of nodes, user intrusion detection is carried out through AWS, the safety and reliability of the data are fully ensured, the technical problem that information leakage of CRC personnel is caused due to imperfect system safety measures, and therefore the safety of clinical tests is endangered is solved.
Embodiment six, referring to fig. 1 and 5, based on the above embodiment, in step S5, the CRC networking management model optimization includes the following steps:
step S51: normalizing the target value, and defining the maximum value of the target k to be expressed as maxf by adopting a modified coarse-grained genetic algorithm k The minimum value is expressed as minf k Normalized interval is [ a, b]The normalization function is calculated using the following formula:
；
in phi k (X) is a normalization function, a, b are the upper and lower limits of the normalization interval, X is an independent variable parameter, f k (X) is an objective function value, k represents an objective, k=1, 2, … …, r;
step S52: the weighted sum, each target of the multiple objective function is multiplied by a coefficient w, w e [0,1], the sum of all coefficients is 1, the formula used is as follows:
；
wherein w is k Is the coefficient of the corresponding objective function, r is the total number of the objective k;
step S53: calculating an optimized CRC networking management model, adding all objective functions with coefficients to obtain a single objective function, wherein the optimal point of the improved single objective optimization problem is a non-inferior optimal front point, and the non-inferior optimal front point is a set formed by all non-inferior solutions in the multi-objective optimization problem and is used for providing a plurality of optional optimal models, and the optimized CRC networking management model is calculated by the following formula:
；
where F (X) is a single objective function, minimum refers to the process of finding the minimum of the single objective function in the optimization problem;
step S54: the punishment factor evolves, a small floating is created according to the change range by using the lower bound of the punishment factor and the current value of the punishment factor, and random mutation is carried out to complete the evolution of the punishment factor;
step S55: and obtaining an optimal CRC networking management model, namely obtaining a local optimal solution by gradually evolving a combination of strategy evolution penalty factors and kernel function parameters, and selecting a combination with the highest classification precision from all the local optimal solutions to obtain the optimal CRC networking management model.
By executing the above operation, the improved coarse-granularity genetic algorithm is adopted to normalize and weight and sum the target values, and the multi-objective optimization and penalty factor evolution are carried out, so that the system working efficiency and CRC development environment are improved, the multi-objective optimization practical problem which cannot be processed by the traditional algorithm is processed, the optimal CRC networking management model is obtained, and the technical problems that the research team, SMO, CRO and sponsor carry out comprehensive management of CRC working contents on the platform, the respective working targets are difficult to balance, the multi-objective optimization cannot be carried out, and the system working efficiency is low are solved.
An embodiment seven, referring to fig. 2, based on the foregoing embodiment, the CRC networking management system based on big data analysis provided by the present invention includes a CRC personnel information acquisition module, a CRC task scheduling module, a CRC networking management model information storage module, and a CRC networking management model optimization module;
the CRC personnel information acquisition module is used for acquiring basic personal information, academic background, professional background and working experience of CRC personnel;
the CRC task scheduling module is used for carrying out task analysis, task arrangement, progress tracking and task multiplexing on the CRC task;
the CRC networking management model module adopts an informatization management means to carry out online audit, statistics and management on CRC, so that research teams, SMO, CRO and sponsors can carry out CRC work content comprehensive management on the CRC networking management model;
the CRC networking management model information storage module is used for carrying out data layering storage, applying quantum encryption to sensitive data encryption, adopting decentralization data storage, dispersing data in a plurality of nodes, and carrying out user intrusion detection through AWS;
the CRC networking management model optimization module is used for carrying out normalization and weighted summation on target values, carrying out multi-objective optimization and penalty factor evolution, improving the working efficiency of the system and CRC development environment, and processing the multi-objective optimization actual problem which cannot be processed by the traditional algorithm to obtain the optimal CRC networking management model.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
The invention and its embodiments have been described above with no limitation, and the actual construction is not limited to the embodiments of the invention as shown in the drawings. In summary, if one of ordinary skill in the art is informed by this disclosure, a structural manner and an embodiment similar to the technical solution should not be creatively devised without departing from the gist of the present invention.
Claims (8)
1. The CRC networking management method based on big data analysis is characterized by comprising the following steps of: the method comprises the following steps:
step S1: the method comprises the steps of collecting CRC personnel information, specifically collecting personal information of CRC personnel;
step S2: CRC task scheduling, in particular to analysis, arrangement, tracking and multiplexing of CRC tasks;
step S3: the CRC networking management model is used for comprehensively managing CRC working contents on the CRC networking management model by adopting an informationized management means;
step S4: CRC networking management model information storage, namely data layering storage, quantum encryption is applied to sensitive data encryption, decentralization data storage is adopted, data are scattered on a plurality of nodes, and user intrusion detection is carried out through AWS;
step S5: and optimizing the CRC networking management model, namely carrying out normalization and weighted summation on target values by adopting an improved coarse granularity genetic algorithm, and carrying out multi-objective optimization and penalty factor evolution to obtain the optimal CRC networking management model.
2. The big data analysis based CRC networking management method of claim 1, characterized in that: in step S4, the CRC networking management model information storage includes the following steps:
step S41: after receiving a data storage request of a user, carrying out data layering storage on the data according to the requirements of the user, dividing the data into three levels of common, important and sensitive, storing the data of different levels by adopting different encryption modes, encrypting the common data only for data abstracts, transmitting the content in a plaintext form, carrying out random encryption on the important data by adopting a symmetric encryption algorithm, and fully encrypting the sensitive data by adopting quantum encryption;
step S42: the method comprises the steps of decentralizing data storage, realizing decentralizing data storage through a block chain technology, enabling each cloud service provider to serve as a node in a block, obtaining data storage weight through a random number election mechanism, enabling complete data to be randomly divided into different data fragments, enabling each different data fragment to be stored in different nodes, enabling each node to generate a block, enabling a block head to record a hash value, a timestamp and a random number of a previous block, enabling a block body to record the hash value, a data storage address, a data backup address, node information and a searchable encryption index of stored data, enabling a cloud server to obtain access rights, and enabling each block to record identity information of the cloud server;
step S43: calculating block mapping, wherein a data cloud center of the decentralised data storage is responsible for data partitioning and recording the block mapping, the decentralised data storage encrypts the block mapping, meanwhile, the decentralised data storage carries out hash operation on the whole file data, a corresponding hash value is stored, the hash value is recalculated when the data is acquired from a data storage cloud next time, and if the hash values are the same, the data is complete; if the hash values are different, the data is attacked, and the block mapping is calculated according to the following formula:
；
wherein Q is block mapping, data is stored Data, P is actual stored node, n is total actual stored node number, A is storage medium, f, s and t are the number of storage medium corresponding to actual stored node;
step S44: calculating the probability of the data being completely attacked, wherein the data stored by different cloud service providers are random, and the probability of the data being completely attacked is calculated according to the following formula:
；
wherein ρ is the probability of the data being fully attacked, M is the selected cloud server provider, M is the total number of cloud server providers, N is the number of copies of the data divided by the de-centralized avatar authentication, and | is a mathematical factorial symbol;
step S45: performing user intrusion detection, when the off-center identity authentication receives a service request of a user, AWS (wireless subscriber identity module) detects whether the user is an illegal user or not by analyzing the user identity, the user authority, the last login time, the current state, the login position and the login IP (Internet protocol), and if the user is the illegal user, relevant user information is added into a threat update table; otherwise, the request of the user is accepted and the service is provided for the user.
3. The big data analysis based CRC networking management method of claim 1, characterized in that: in step S5, the CRC networking management model optimization includes the steps of:
step S51: normalizing the target value, and defining the maximum value of the target k to be expressed as maxf by adopting a modified coarse-grained genetic algorithm k The minimum value is expressed as minf k Normalized interval is [ a, b]The normalization function is calculated using the following formula:
；
in phi k (X) is a normalization function, a, b are the upper and lower limits of the normalization interval, X is an independent variable parameter, f k (X) is an objective function value, k represents an objective, k=1, 2, … …, r;
step S52: the weighted sum, each target of the multiple objective function is multiplied by a coefficient w, w e [0,1], the sum of all coefficients is 1, the formula used is as follows:
；
wherein w is k Is the coefficient of the corresponding objective function, r is the total number of the objective k;
step S53: calculating an optimized CRC networking management model, adding all objective functions with coefficients to obtain a single objective function, wherein the optimal point of the improved single objective optimization problem is a non-inferior optimal front point, and calculating the optimized CRC networking management model by using the following formula:
；
where F (X) is a single objective function, minimum refers to the process of finding the minimum of the single objective function in the optimization problem;
step S54: the punishment factor evolves, a small floating is created according to the change range by using the lower bound of the punishment factor and the current value of the punishment factor, and random mutation is carried out to complete the evolution of the punishment factor;
step S55: and obtaining an optimal CRC networking management model, namely obtaining a local optimal solution by gradually evolving a combination of strategy evolution penalty factors and kernel function parameters, and selecting a combination with the highest classification precision from all the local optimal solutions to obtain the optimal CRC networking management model.
4. The big data analysis based CRC networking management method of claim 1, characterized in that: in step S3, the CRC networking management model includes the steps of:
step S31: CRC job entry audit, supporting organization personnel check the applicant job entry CRC record, and approval is carried out on the record;
step S32: project auditing, which supports checking CRC application to add and create project records, and auditing the records;
step S33: certificate management, counting certificate issuing conditions of CRC personnel in a hospital, and carrying out system prompt aiming at certificate expiration conditions to prompt personnel to reissue employee cards;
step S34: counting the working conditions of CRC personnel, distinguishing formal CRC and practice CRC, displaying the total number of personnel in real time, counting the working conditions of CRC in a hospital, and performing on-duty registration;
step S35: counting the off-job handover situation of CRC personnel, checking all CRC initiated off-job applications of the home, and simultaneously providing the project handover situation of the current CRC personnel under study, and confirming and checking the project handover by the institution management personnel according to the project handover situation;
step S36: working report, counting the number of the project participated by the personnel with the CRC at home, setting the maximum project number received by the CRC according to dynamic configuration, checking the working report condition of the personnel with the CRC on the project, and informing and reminding the personnel who do not fill the working report regularly;
step S37: work management, comprising the steps of:
step S371: study team management, maintenance of hospital clinical trial team personnel management;
step S372: the sponsor manages, checks the sponsor information of all the cooperation of the home, searches according to different keywords, and counts the progress of the cooperation sponsor project of the home;
step S373: CRO management, checking CRO information of all cooperations of the home, searching according to different keywords, and counting the progress of the cooperation CRO project of the home;
step S374: SMO management, checking SMO information of all cooperations of the home, searching according to different keywords, and counting the progress of SMO projects and CRC personnel of the cooperations of the home.
5. The big data analysis based CRC networking management method of claim 1, characterized in that: in step S2, the CRC task scheduling includes the steps of:
step S21: task analysis, CRC (cyclic redundancy check) comprehensively analyzes the research project;
step S22: task arrangement, namely distributing tasks to corresponding team members according to the emergency degree, the priority and the resource availability of the tasks;
step S23: progress tracking, CRC (cyclic redundancy check) periodically tracks the progress of tasks, and measures are taken in time to solve the problems and delays;
step S24: and (3) task disc multiplexing, wherein when the task is completed, the CRC performs task summarization and disc multiplexing, and the execution condition of the task is evaluated.
6. The big data analysis based CRC networking management method of claim 1, characterized in that: in step S1, the CRC personnel information acquisition includes the following steps:
step S11: collecting personal identity information, including CRC personnel name, gender, age and contact information;
step S12: the academic and professional contexts, including collecting the academic, professional contexts, and related training experiences of CRC personnel;
step S13: working experience, recording the working experience of the CRC personnel, including the time of the clinical study, the study program and responsibilities involved.
7. CRC networking management system based on big data analysis, for implementing CRC networking management method based on big data analysis according to any of claims 1-6, characterized in that: the system comprises a CRC personnel information acquisition module, a CRC task scheduling module, a CRC networking management model information storage module and a CRC networking management model optimization module.
8. The big data analysis based CRC networking management system of claim 7, characterized in that: the CRC personnel information acquisition module is used for acquiring basic personal information, academic background, professional background and working experience of CRC personnel;
the CRC task scheduling module is used for carrying out task analysis, task arrangement, progress tracking and task multiplexing on the CRC task;
the CRC networking management model module adopts an informatization management means to carry out online audit, statistics and management on CRC, so that research teams, SMO, CRO and sponsors can carry out CRC work content comprehensive management on the CRC networking management model;
the CRC networking management model information storage module is used for carrying out data layering storage, applying quantum encryption to sensitive data encryption, adopting decentralization data storage, dispersing data in a plurality of nodes, and carrying out user intrusion detection through AWS;
the CRC networking management model optimization module specifically adopts an improved coarse granularity genetic algorithm to normalize and weight sum target values, and performs multi-objective optimization and penalty factor evolution to obtain an optimal CRC networking management model.
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