CN113821503A - Medical data processing method and device and edge server - Google Patents
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Abstract
The invention provides a method and a device for processing medical data and an edge server, and the method comprises the steps of firstly, acquiring the medical data in a server of a specified intranet system; then cleaning the medical data based on a preset data cleaning mode to obtain cleaned medical data; and then, performing association processing on the cleaned medical data based on preset association information to obtain at least one group of data groups with relevant association information, and finally sending the data groups to a cloud server. According to the invention, the medical data is cleaned and associated by the edge server, so that the data processing efficiency of the system is improved, and the stability of the system is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a medical data processing method and device and an edge server.
Background
In the related art, a front-end processor is generally used to acquire a medical image file and a corresponding medical report from an image archiving and communication system and an information management system in a hospital intranet, and send the acquired data to a cloud server, the cloud server performs data verification on the medical image file and the medical report, performs data association and other processing on the medical image file and the corresponding medical image file, and stores the associated data. However, in this method, the cloud server has a large computation amount and a large consumption of computing resources in the process of processing the data, which causes system instability and easily causes failure.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for processing medical data, and an edge server, so as to improve the processing efficiency of the medical data and improve the stability of the system.
In a first aspect, an embodiment of the present invention provides a method for processing medical data, where the method is applied to an edge server, the edge server is connected to a server of a specified intranet system, and the edge server is further connected to a cloud server; the method comprises the following steps: acquiring medical data in a server of a designated intranet system; cleaning the medical data based on a preset data cleaning mode to obtain cleaned medical data; performing association processing on the cleaned medical data based on preset association information to obtain at least one group of data groups; wherein, the data in the same group of data groups have related associated information; and sending the data set to a cloud server.
With reference to the first aspect, an embodiment of the present invention provides a first possible implementation manner of the first aspect, where the medical data includes a plurality of sub-data; the data cleaning mode comprises data deduplication processing; the method comprises the steps of cleaning medical data based on a preset data cleaning mode, wherein the cleaning process comprises the following steps; searching whether the medical data comprises a plurality of same subdata; if yes, data deduplication processing is carried out on the same multiple sub data.
With reference to the first aspect, an embodiment of the present invention provides a second possible implementation manner of the first aspect, where the medical data includes a plurality of sub-data; the subdata comprises fields corresponding to a plurality of entries; the data cleaning mode comprises the steps of processing subdata which does not meet a first preset condition based on a preset mode; the method comprises the steps of cleaning medical data based on a preset data cleaning mode, wherein the cleaning process comprises the following steps; for each subdata, searching fields corresponding to preset entries of the subdata from fields corresponding to a plurality of entries of the subdata; if the subdata is not found, determining that the subdata does not meet a first preset condition, and processing the subdata based on a preset mode; if the field is found, judging whether the format of the field corresponding to the preset item of the subdata meets the preset format; and if the format of the field corresponding to the preset entry of the subdata does not meet the preset format, determining that the subdata does not meet a first preset condition, and processing the subdata based on a preset mode.
With reference to the first aspect, an embodiment of the present invention provides a third possible implementation manner of the first aspect, where the medical data includes a plurality of sub-data; the subdata comprises fields corresponding to a plurality of entries; the association information includes an association entry; the method comprises the following steps of performing association processing on cleaned medical data based on preset association information to obtain at least one group of data sets, wherein the steps comprise: according to the cleaned medical data, searching characters corresponding to the associated items from fields corresponding to the multiple items of the subdata; and determining the sub data with the same characters corresponding to the associated entry as a data group.
With reference to the first aspect, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect, wherein the medical data includes a plurality of medical images and a plurality of medical reports; the medical image comprises fields corresponding to the patient number, the registration number and the examination case number; the medical report comprises fields corresponding to the patient number, the registration number and the examination example number; the associated information comprises a patient number, a registration number and an examination case number; the method comprises the following steps of performing association processing on cleaned medical data based on preset association information to obtain at least one group of data sets, wherein the steps comprise: for the cleaned medical data, the medical image and the medical report having the same field corresponding to the patient number, the registration number, and the examination case number are determined as a data group.
With reference to the first aspect, an embodiment of the present invention provides a fifth possible implementation manner of the first aspect, where the method further includes: receiving configuration information sent by a cloud server; the configuration information includes data cleansing means, association information, and compensation entries.
With reference to the fifth possible implementation manner of the first aspect, an embodiment of the present invention provides a sixth possible implementation manner of the first aspect, where the medical data includes a plurality of sub-data; the subdata comprises fields corresponding to a plurality of entries; the method further comprises the following steps: for the subdata which does not form a data group, searching fields corresponding to compensation entries from the fields corresponding to a plurality of entries of the subdata; and sending the fields corresponding to the searched compensation items to a server of the designated intranet system, so that the server of the designated intranet system searches medical data which have relevant associated information and do not form subdata of the data group based on the fields corresponding to the compensation items.
With reference to the first aspect, an embodiment of the present invention provides a seventh possible implementation manner of the first aspect, where the step of acquiring medical data in a server of a specified intranet system includes: sending a data reading instruction to a server of a specified intranet system through a preset network card; and receiving medical data fed back by a server of the designated intranet system according to the data reading instruction.
With reference to the first aspect, an embodiment of the present invention provides an eighth possible implementation manner of the first aspect, where the method further includes: adding time information for medical data when the medical data sent by a server of a specified intranet system is received; the time information indicates a time when the medical data arrives at the current device; and when the time difference between the real-time of the current equipment and the time information is larger than a preset time threshold, deleting the medical data corresponding to the time information.
With reference to the first aspect, an embodiment of the present invention provides a ninth possible implementation manner of the first aspect, where the method further includes: and if the heartbeat data sent by the server of the specified intranet system is not received within the set time, generating alarm information.
In a second aspect, an embodiment of the present invention further provides a device for processing medical data, where the device is disposed in an edge server, the edge server is connected to a server of a specified intranet system, and the edge server is further connected to a cloud server; the device includes: the data acquisition module is used for acquiring medical data in a server of a specified intranet system; the data cleaning module is used for cleaning the medical data based on a preset data cleaning mode to obtain the cleaned medical data; the data association module is used for associating the cleaned medical data based on preset association information to obtain at least one group of data sets; wherein, the data in the same group of data groups have related associated information; and the data sending module is used for sending the data group to the cloud server.
In a third aspect, an embodiment of the present invention further provides a medical data processing system, which includes a server of an instruction intranet system, an edge server, and a cloud server, which are connected in sequence, and the method is applied to the edge server.
In a fourth aspect, embodiments of the present invention also provide a machine-readable storage medium storing machine-executable instructions, which when invoked and executed by a processor, cause the processor to implement the above-mentioned method for processing medical data.
According to the medical data processing method, the medical data processing device and the edge server, firstly, medical data in a server of a designated intranet system are obtained; then cleaning the medical data based on a preset data cleaning mode to obtain cleaned medical data; and then, performing association processing on the cleaned medical data based on preset association information to obtain at least one group of data groups with relevant association information, and finally sending the data groups to a cloud server. According to the method, the medical data are cleaned and associated by the edge server, so that the data processing efficiency of the system is improved, and the stability of the system is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic structural diagram of a medical data acquisition system in related art according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for processing medical data according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a medical data processing system according to an embodiment of the present invention;
FIG. 4 is a flow chart of another method for processing medical data according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a medical data processing system according to an embodiment of the present invention;
FIG. 6 is a flow chart of another method for processing medical data according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a medical data processing apparatus according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an edge server according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In hospitals, a large amount of medical data is generated every day, and the medical data generally includes basic information of patients, image data generated by applying various medical devices to the patients, and diagnosis results made by doctors based on the image data.
Among them, various image data are usually stored in PACS (Picture Archiving and Communication Systems, meaning image Archiving and Communication Systems) which is a system applied to a hospital image department, and the main task is to generate various medical images in daily life. The basic Information of the patient and the diagnosis result based on the image data made by the doctor can form a medical report, and the contents are usually stored in an Information management System, for example, the contents related to the Radiology department are stored in a Radiology Information management System (RIS); the system is a management system for registration, triage, image diagnosis report of radiology department, and inquiry and statistics of various information of radiology department. For safety, the information management system and the image archiving and communication system are both disposed in the hospital intranet.
In the related art, a system based on a server (such as a server of an RIS system and a server of a PACS system) of a designated intranet system, a front-end processor, and a medical image cloud is generally used to systematically manage medical data of each hospital, and fig. 1 illustrates this application scenario for easy understanding. The server of a designated intranet system is provided with at least one front-end processor, and the front-end processors are connected with the medical image cloud.
The medical image cloud is realized through the cloud server, the cloud storage of medical image information is used as a data base, the medical image cloud computing application service is used as a core, the virtualization and big data technology is used as a support, and an online cloud service mode based on medical images in various forms is provided for medical institutions, medical insurance departments and individuals of examinees in a cloud transmission mode. The front-end processor is generally an intermediate device used in a hospital field, and is used for communicating data exchange among different networks, such as a private medical insurance network and a hospital intranet, a private medical network and a hospital intranet, a public network and a hospital intranet, and the like.
In the system, a front-end processor respectively obtains a medical image file and a medical report stored in a medical intranet environment from an image filing and communication system in an intranet system server and an information management system, and sends the medical image file and the medical report to a cloud server. The cloud server performs data verification on the medical image file and the medical report, performs data association and other processing on the medical image file and the corresponding medical image file, and stores the associated data.
However, in the above manner, the cloud server has a large computation amount when processing the data, and the computing resource consumption is high, which causes system instability and easily causes failure.
Based on this, the method and the device for processing medical data and the edge server provided by the embodiment of the invention can be applied to the processing process of various medical data generated by a medical system.
Referring first to fig. 2, a flowchart of a method for processing medical data is shown, where the method is applied to an edge server, the edge server is connected to a server of a designated intranet system, and the edge server is further connected to a cloud server. In general, a server belonging to a designated intranet system of a hospital is connected to an edge server or an edge server cluster, and a plurality of edge servers are connected to a cloud server. In fig. 3, three edge servers are taken as an example, and each edge server is connected to a server of a designated intranet system.
The method comprises the following steps:
step S200, acquiring medical data in a server of a designated intranet system.
The medical data may include medical reports, medical images, and other data related to patient visits. These data are usually stored in a system in the hospital intranet, such as the RIS system and PACS system, as described above, in relation to the data type. The designated intranet system can comprise a RIS system and a PACS system.
The mode of acquiring the medical data in the server of the designated intranet system can be that the server of the designated intranet system sends the medical data to the edge server according to the preset time or frequency; the edge server may send a data reading request to a server of a designated intranet system according to a set frequency, and then receive medical data returned by the server of the designated intranet system. The specific mode can be determined according to the protocol between the edge server and the server of the designated intranet system. When the edge server sends a data reading request, the data reading request usually needs to carry a user name, a password, and other identifiers that are verified by a designated intranet system in advance, so that the designated intranet system verifies the validity of the data reading request.
And S202, cleaning the medical data based on a preset data cleaning mode to obtain the cleaned medical data.
The data cleaning process is a process of removing data meeting preset conditions in the medical data, and the data is usually incomplete data or repeated data. The preset condition may be multiple types, such as repeated data, and if the received medical data includes the same data, only one of the medical data is retained, and the other data is deleted. The preset conditions can also be that the data is incomplete, and the medical data which needs to be stored for a long time usually must include the identification number or medical insurance card number of the patient, the diagnosis information of the doctor and the like; for the image data, it is necessary to include the number or registration number information of the image data, and the number or registration number information is generally important information for associating the image data with the corresponding medical report.
On the basis of determining the integrity of the data, the preset condition may further include that the setting information in the data does not meet a preset standard. When the data is composed of a plurality of fields, the setting information is represented by the setting field. Some information has a data format specified by a certain law or regulation; for example, the field digits representing the identity card need to meet the identity card standard, whether the format of the mobile phone number of the patient needs the mobile phone number standard or not and the like, and the process of determining whether the data format of the information meets the preset standard or not is to check the legality of the data. For medical data, such as the names of certain diseases, diagnostic terms, etc., it is necessary to check their data authoritativeness, i.e. to comply with their medical properties.
The edge server may also upload two case data one after the other in case of receiving updated medical data, i.e. the relevant content of the medical data that was received, e.g. one patient in one surgical treatment. The two cases are data of the diagnosis and treatment process, and the data about the identity information of the patient is usually the same, such as the ID of the patient, the registration number of the doctor, and the like. However, when the data before and after the update is different in addition to the update content, the data also needs to be cleaned.
When the data in the medical data meet the preset conditions, the data can be directly deleted, and the data can also be sent to the server of the specified intranet system, so that the server of the specified intranet system can completely supplement the data and then send the data to the edge server for reprocessing. Step S204, performing association processing on the cleaned medical data based on preset association information to obtain at least one group of data sets; wherein data in the same group of data groups have related associated information.
The related information is generally important information, or information that needs to be noticed when storing the medical data, and is generally information that can determine a medical examination event. For example, when the visit data of the same patient needs to be stored together, i.e. a group of data arrays is formed, the associated information may be information capable of indicating the identity of the patient, such as a patient unique number (also called "patient unique ID" or "patient ID"), typically the identification number or social security number of the patient, etc. At this time, the identification number or the medical insurance card number in one piece of data can be read from the cleaned medical data, then the identification number or the social insurance card number of the patient is searched from the cleaned medical data, and other medical data including the identification number or the social insurance card number of the patient are used as a group of data arrays.
Further, if it is desired to store the data of a visit of the same patient, the associated information may be information indicating the identity of the patient and information indicating a single visit of the patient, such as a patient ID, a visit registration number, a visit instance ID, and the like. In this case, the patient ID, the visit registration number, and the date of the visit in one piece of data may be read from the cleaned medical data, and then the other pieces of medical data including the read identification number, the visit registration number, and the examination case ID may be searched for in the cleaned medical data, and these pieces of data may be used as one set of data.
Step S206, sending the data set to the cloud server.
Specifically, the data set can be sent to the cloud server through communication between a preset network card and the cloud server. In general, the medical data needs to be stored in a cloud server in a set format, and the edge server can also perform standardization processing on the medical data and then send the medical data to the cloud server. At the moment, the cloud server is mainly used for storing the data group sent by the edge server, so that the resource occupation of the cloud server is greatly reduced, and the stability of the system is improved.
The medical data processing method comprises the steps of firstly, acquiring medical data in a server of a designated intranet system; then cleaning the medical data based on a preset data cleaning mode to obtain cleaned medical data; and then, performing association processing on the cleaned medical data based on preset association information to obtain at least one group of data groups with relevant association information, and finally sending the data groups to a cloud server. According to the method, the edge server is used for cleaning and correlating the medical data, so that the data processing efficiency of the system is improved, and the stability of the system is improved.
The embodiment of the invention also provides another medical data processing method, which is realized on the basis of the method in the embodiment; the method mainly describes a specific implementation process for acquiring medical data in a server of a specified intranet system (see steps S400-S402), a specific implementation process for cleaning the medical data based on a preset data cleaning mode (see steps S404-S414), and a specific implementation process for associating the cleaned medical data based on preset associated information (see steps S416-S418); as shown in fig. 4, the method includes the steps of:
and step S400, sending a data reading instruction to a server of the specified intranet system through a preset network card.
Generally, each network card has a unique MAC (Media Access Control Address) Address; the MAC address of the network card set in the edge server may pass the verification of the designated intranet system in advance, so that the edge server and the server of the designated intranet system may implement communication.
The data reading instruction may include a user identifier verified by a specified intranet system, a time range of data to be acquired, a system for storing the data to be acquired, and the like.
Step S402, receiving medical data fed back by a server of the specified intranet system according to the data reading instruction. Wherein, the medical data generally comprises a plurality of subdata; the sub data may be various types of data, such as document data, image data, and the like.
Step S404, searching whether the medical data comprises a plurality of same subdata; if yes, go to step S406; if not, step S408 is performed.
Each subdata in the medical data may include fields corresponding to a plurality of entries; the entry may refer to a data type corresponding to the content expressed by the character, for example, the entry may be an identification number, or a doctor diagnosis result. When the two sub data are the same, the entries included in the two sub data and the fields corresponding to the entries are the same.
Step S406, carrying out data deduplication processing on the same plurality of subdata; step S408 is performed. The process is a data cleaning mode, namely data deduplication processing.
Step S408, aiming at each subdata, searching fields corresponding to preset items of the subdata from fields corresponding to a plurality of items of the subdata; if not, executing step S410; if so, go to step S412.
The sub-data may be sub-data in the medical data subjected to the data deduplication processing. The preset entry is usually an indispensable entry in medical data, such as an identification number or a medical insurance card number of a patient, and if the information is lacked, the data cannot be associated and processed.
Step S410, determining that the subdata does not meet a first preset condition, and processing the subdata based on a preset mode; step S416 is performed. The first preset condition may include a field corresponding to a preset entry. The preset entries may be set to one or more based on the requirement.
The preset mode may be to perform format conversion on data lacking a corresponding field or not meeting a standard, to supplement the corresponding field with a set field or convert a current format into a standard format, to perform uniform formatting storage, and then to label the data, so as to facilitate subsequent searching and supplementing from historical data of a relevant patient, for example, to search the previous visit record data of the patient according to the identification card information of the patient, then to search the mobile phone number of the patient from the previous visit record data, to supplement the mobile phone number to the current medical data of the patient, and to send the supplemented information to a server of a corresponding designated intranet, so as to enable relevant personnel to confirm. The preset mode can also be used for sending data which lacks the corresponding field or does not meet the standard to a corresponding server of a designated intranet, deleting the data, and storing the data after the server provides the complete field.
Step S412, determining whether the format of the field corresponding to the preset entry of the sub-data satisfies the preset format. If yes, go to step S414; if not, step S410 is performed. The first preset condition may further include that a field corresponding to the preset entry conforms to a preset format, for example, the id number conforms to the id standard.
Step S414, for the cleaned medical data, searching for characters corresponding to the associated entries from fields corresponding to the multiple entries of the sub-data.
The association entry is preset association information. The associated information can be obtained from configuration information sent by the cloud server; in addition, the configuration information may also include a data cleansing method. The association entry may include one or more entries, and is usually set to a plurality of entries, such as a patient ID, a visit registration number, and a test case ID, in order to ensure the accuracy of association.
For example, when the medical data includes a plurality of medical images and a plurality of medical reports, the medical images include fields corresponding to a patient number, a registration number, and an examination case number; when the medical report includes fields corresponding to the patient number, the registration number and the examination case number, the associated information can be set as the patient number, the registration number and the examination case number; when the medical image and the medical report are related, the medical image and the medical report having the same field corresponding to the patient number, the registration number, and the examination case number may be determined as the data set for the cleaned medical data.
In step S416, the sub data having the same character corresponding to the associated entry is determined as a data group.
Step S418, sending the data set to the cloud server.
In addition, for the subdata which does not form the data group, the field corresponding to the compensation entry can be searched from the fields corresponding to the plurality of entries of the subdata; and sending the fields corresponding to the searched compensation items to a server of the designated intranet system, so that the server of the designated intranet system searches medical data which have relevant associated information and do not form subdata of the data group based on the fields corresponding to the compensation items. The compensation entry can be determined from configuration information sent by the cloud server. The compensation entry may include a plurality of entries, such as a patient number, a registration number, and a test case number.
Since the medical data may not be uploaded timely, for example, the initial diagnosis record data of the doctor is generated first, and the corresponding medical image may take a long time to be generated and uploaded to the server of the designated intranet system, sub-data that does not form a data group may appear in the data pulled once. In the above situation, if the server of the designated intranet system does not find the medical data corresponding to the compensation entry, the generated medical data may be pulled from the server of the designated intranet system again after the set time, and the medical data having the associated information related to the sub data that does not form the data group is found in the medical data, and then the data group is formed, and is uploaded to the server or stored.
In some cases, the edge server may also assume the function of storing medical data. However, since the amount of medical data is large, it is necessary to release the medical data after a certain time of receiving the medical data since the storage space of the edge server is limited. When medical data sent by a server of a designated intranet system is received, time information can be added to the medical data; the time information indicates a time when the medical data arrives at the current device; the time information may be implemented in the form of a timestamp; and when the time difference between the real-time of the current equipment and the time information is larger than a preset time threshold, deleting the medical data corresponding to the time information. The time threshold may be a time set according to the demand, such as one month, three months, or half a year.
Since the amount of medical data is huge and the generation time is not fixed, the edge server and the server of the designated intranet system need to perform instant communication, and in order to keep the communication network smooth, the server of the designated intranet system may agree to send a set field, also referred to as heartbeat data, to the edge server at a set frequency. If the edge server does not receive heartbeat data sent by the server of the designated intranet system within the set time, the server of the designated intranet system is considered to have a fault or the communication network has a fault, so that alarm information is generated, and related personnel can recover communication as soon as possible.
According to the medical data processing method, the edge server is used for carrying out data duplication elimination, set field verification and association processing on the medical data, so that the resource occupation of the cloud server is reduced, and the system stability is improved.
The embodiment of the invention also provides another medical data processing method which is realized on the basis of the method in the embodiment. According to the method, a cloud side end synergistic thought is utilized, a front-end computer is used as a server for edge calculation, data collection is achieved, the calculation amount of hospital data correlation verification, data cleaning and the like can be borne, cloud computing pressure is reduced, bandwidth consumption is reduced, and timeliness of data processing is improved.
With the development of cloud storage technology, hospitals have strong requirements for storage and backup of case data: according to the provisions of the fifty-third clause of the medical institution's management, the retention period of the outpatient medical records of the medical institution must be no less than fifteen years, and the retention period of the hospitalized medical records must be no less than thirty years. In hospital data, more than 90% of the data are image data, the cost for establishing a data center of the data center to store mass image data is extremely high, and the maintenance cost is higher, so that the data backup requirement provided by each hospital on the image cloud is very strong.
However, the network environment of a hospital is complicated. For the sake of patient data security, each system in the hospital is deployed and communicates data in the intranet environment of the hospital. With the development of medical informatization, the demand of remote medical treatment is strong, and the national health committee establishes a medical private network and a medical insurance private network, so that various networks exist in a hospital and data among network segments cannot be intercommunicated. Because each hospital has its own medical information system and is deployed in the hospital intranet, the cross-hospital data retrieval of each hospital is difficult.
Also, different types of medical data may be stored in different intranet systems; for example, medical image reports and medical image files are managed in different systems: the image reports are managed in the RIS system of the hospital and the image files are managed in the PACS system of the hospital, which makes it difficult to perform a complete image examination data query on the patient.
The PACS system is Picture Archiving and Communication Systems throughout, meaning image Archiving and Communication Systems. The system is applied to a hospital image department, and mainly aims to store various daily medical images (including images generated by equipment such as nuclear magnetism, electronic Computer Tomography (CT), ultrasound, various X-ray machines, various infrared instruments, microscopes and the like) in a digital manner through various interfaces (such as an analog interface, a medical digital imaging and communication DICOM interface and a network interface). It has important roles in transmitting data and organizing and storing data among various image devices.
The DICOM (Digital Imaging and Communications in Medicine) format described above is an international standard for medical images and related information (ISO 12052). It defines a medical image format that can be used for data exchange with a quality that meets clinical needs. The files in the PACS system comprise DICOM files and non-DICOM files; the DICOM file is a file for storing medical images in a DICOM format. The Non-DICOM file is also called Non-DICOM file. In the medical image files, the files of other formats except the DICOM file are non-DICOM files, and the formats are usually JPG, PDF, and the like.
The RIS system is a radiology department information management system, which is a management system for registration, triage, image diagnosis report of radiology department and various information inquiry, statistics and other works of radiology department, and is closely connected with the PACS system to form a solution for managing digital medical equipment, images and reports in hospitals.
The regional medical image cloud is established, massive data backup of hospitals can be facilitated, medical image reports and image quality of medical institutions are improved, and meanwhile the requirement of cross-hospital data retrieval is met. But also needs to solve the problems of cross-network data transmission and cross-system data association of hospital internal networks and medical private networks.
At present, the scheme adopted is to adopt a front-end processor as a gateway communication center of a hospital intranet and a private medical network. The main functions of the front-end processor, in addition to connecting the two network segments, are to collect data of each system in the hospital, such as CT data, MRI (magnetic resonance imaging) data, DR (Computed Radiography) data, DR (Digital Radiography), ultrasound data, DSA (Digital subtraction angiography) data, and medical reports and registration information corresponding to the data, which are stored in the RIS system, are collected in the front-end processor, and then sent to the image cloud (i.e., the "medical image cloud").
Because the image reports and the inspection data volume that the hospital produced every day are big, adopt the front-end processor to upload the data set of many hospitals to the high in the clouds, the mode that carries out data cleaning and data check by the high in the clouds server computation volume is big, consumes resources, easily breaks down, causes great pressure to the high in the clouds server, and the requirement is very high to server performance, leads to system cost height, and the maintenance degree of difficulty is big, and the maintenance cost is high.
In addition, the effectiveness of the above-described data processing is poor; the uncleaned data are transmitted to the cloud, so that the requirement on bandwidth is higher; in the method, the cloud architecture and the service logic are complex, the development difficulty is high, the operation and maintenance cost is high, and the delivery difficulty is high.
The method provided by the embodiment of the invention is based on the basic idea of edge computing, redefines the boundaries of all modules by utilizing cloud side cooperation (cloud computing and edge computing cooperation), and finishes partial cloud computing by advancing to a front-end computer, thereby greatly reducing the cloud pressure. In the method, the front-end processor is replaced by the edge server, the data collection function of the front-end processor is still reserved, and the functions of data cleaning, data verification and the like are completed by the edge server, and the working principle diagram of the system is shown in fig. 5. As shown in fig. 6, the method includes the following steps:
1. configuration information sent by the cloud server to the edge server: the edge server is generally equipped with two network cards (or more network cards), one of which is connected to an intranet in a hospital and is mainly responsible for receiving or pulling data generated by each system in the hospital, and the other network card is connected to a private medical network and is mainly used for interacting with a cloud service, namely transmitting the processed medical data to the cloud server and receiving configuration information about data acquisition sent by the cloud. The configuration information may include communication information of the hospital end system, such as an address, a port number, a user name, a password, and the like of a database view of the RIS system, an AETitle, an IP, a port number, a data acquisition mode of the PACS system, an AETitle compensated by Q/R, an IP, a port number, and the like; may further include an associated field (corresponding to the above-mentioned "associated entry") and a compensation field (corresponding to the above-mentioned "compensation entry"); it may also include information about the task of data collection: such as the period of acquisition, the type of data acquired, the start time of the data acquired, etc.
2. The edge server sends a request to establish communication to a server of the in-home system to establish communication.
3. The server of the in-hospital system (corresponding to the "server of the designated intranet system") transmits the RIS report and the image data (DICOM file and non-DICOM file) to the edge server: the RIS data of each hospital and DICOM and non-DICOM data on PACS are collected to the edge server for collection and storage in respective modes.
4. And the edge server performs data cleaning on the collected data: ways of data cleansing include, but are not limited to: data field integrity clean (delete the data if some fields do not exist); unique cleaning of data fields (i.e., data deduplication); data validity cleaning (if the universal field does not conform to the standard format, the data is deleted); authoritative cleaning of data (e.g., deleting data if certain fields do not conform to the medical data format); data consistency cleaning (generally, deleting data before and after updating when the data is different except the updated content).
5. The edge server performs data association on the cleaned RIS report data and the image file: the RIS data and the image file belonging to the same medical examination are associated to form a complete examination data.
6. And the edge server sends the associated data to the cloud server.
7. And the cloud server stores the associated data into a database and a corresponding file storage directory.
8. The edge server sends the unassociated data to the servers of the in-home system: data that is not related (there is RIS data or there is no video file or there is a video file or there is no RIS data) is considered to be abnormal data, and it is necessary to perform compensation pull on missing data at regular time. If the missing data cannot be completely supplemented after a reasonable period of time (such as 2 days), the process is changed into manual processing, and the missing data needs to be manually checked.
In general, after the cloud server pulls the processed medical data, the data can still be stored in the edge server for a period of time, so that the hospital can browse and query the related data conveniently. A sufficient time (e.g. 3 months) can be set as the upper limit of the data storage time of the edge server, and when the time limit is reached, the data can be automatically cleared, so as to release sufficient storage space for storing new data, and also avoid the large storage space allocated by the edge server and increase the purchase cost of the hospital.
In addition, the edge server is also responsible for monitoring the health state of the data transmission node and giving an alarm in time when a fault occurs.
According to the mode, the data of each hospital are cleaned and associated independently through the edge server, the data of each hospital are prevented from being mixed and traversed, the edge end pressure is controllable, and the cloud computing pressure is suddenly reduced. Because the edge server undertakes processing such as data cleaning and data association, the calculation amount of the cloud server is reduced, the resource consumption is reduced, the server cost is reduced, the effectiveness of data processing is improved to a certain extent, and the data transmission bandwidth is reduced. The edge server is deployed by adopting standardized matching and standard modularized service, the delivery difficulty is low, the cloud framework is simplified to a certain extent, the development difficulty is reduced, the operation and maintenance cost is reduced, and the delivery difficulty is reduced.
Corresponding to the embodiment of the medical data processing method, the embodiment of the invention also provides a medical data processing device, which is arranged on an edge server, wherein the edge server is connected with a server of a specified intranet system, and is also connected with a cloud server; as shown in fig. 7, the apparatus includes:
a data acquisition module 600, configured to acquire medical data in a server of a designated intranet system;
the data cleaning module 602 is configured to perform cleaning processing on the medical data based on a preset data cleaning manner, so as to obtain cleaned medical data;
a data association module 604, configured to perform association processing on the cleaned medical data based on preset association information to obtain at least one group of data sets; wherein, the data in the same group of data groups have related associated information;
the data sending module 606 is configured to send the data set to the cloud server.
The medical data processing device firstly acquires the medical data in a server of a designated intranet system; then cleaning the medical data based on a preset data cleaning mode to obtain cleaned medical data; and then, performing association processing on the cleaned medical data based on preset association information to obtain at least one group of data groups with relevant association information, and finally sending the data groups to a cloud server. According to the method, the medical data are cleaned and associated by the edge server, so that the data processing efficiency of the system is improved, and the stability of the system is improved.
Further, the data obtaining module is further configured to: sending a data reading instruction to a server of a specified intranet system through a preset network card; and receiving medical data fed back by a server of the designated intranet system according to the data reading instruction.
Further, the medical data comprises a plurality of subdata; the data cleaning mode comprises data deduplication processing; the data cleaning module is further configured to: searching whether the medical data comprises a plurality of same subdata; if yes, data deduplication processing is carried out on the same multiple sub data.
Further, the data cleaning mode comprises processing the subdata which does not meet the first preset condition based on a preset mode; the method comprises the steps of cleaning medical data based on a preset data cleaning mode, wherein the cleaning process comprises the following steps; for each subdata, searching fields corresponding to preset entries of the subdata from fields corresponding to a plurality of entries of the subdata; if the subdata is not found, determining that the subdata does not meet the first preset condition, and processing the subdata based on a preset mode; if the field is found, judging whether the format of the field corresponding to the preset item of the subdata meets the preset format; and if not, determining that the subdata does not meet the first preset condition, and processing the subdata based on a preset mode.
Further, the medical data comprises a plurality of subdata; the subdata comprises fields corresponding to a plurality of entries; the association information includes an association entry; the data association module is further configured to: according to the cleaned medical data, searching characters corresponding to the associated items from fields corresponding to the multiple items of the subdata; and determining the sub data with the same characters corresponding to the associated entry as a data group.
Further, the medical data includes a plurality of medical images and a plurality of medical reports; the medical image comprises fields corresponding to the patient number, the registration number and the examination case number; the medical report comprises fields corresponding to the patient number, the registration number and the examination example number; the associated information comprises a patient number, a registration number and an examination case number; the data association module is further configured to: for the cleaned medical data, the medical image and the medical report having the same field corresponding to the patient number, the registration number, and the examination case number are determined as a data group.
Further, the apparatus further includes a configuration information receiving module, configured to: receiving configuration information sent by a cloud server; the configuration information includes data cleansing means, association information, and compensation entries.
Further, the medical data comprises a plurality of subdata; the subdata comprises fields corresponding to a plurality of entries; the above-mentioned device still includes: the compensation field searching module is used for searching fields corresponding to compensation items from fields corresponding to a plurality of items of the subdata aiming at the subdata which does not form a data group; and the compensation field sending module is used for sending the field corresponding to the searched compensation entry to a server of the designated intranet system so that the server of the designated intranet system searches the medical data which has the relevant associated information and does not form the subdata of the data group based on the field corresponding to the compensation entry.
Further, the above apparatus further comprises: the time information adding module is used for adding time information to the medical data when the medical data sent by the server of the designated intranet system is received; the time information indicates a time when the medical data arrives at the current device; and the data deleting module is used for deleting the medical data corresponding to the time information when the time difference between the real-time of the current equipment and the time information is greater than a preset time threshold.
Further, the above apparatus further comprises: and the alarm module is used for generating alarm information if the heartbeat data sent by the server of the specified intranet system is not received within the set time.
The implementation principle and the generated technical effect of the medical data processing device provided by the embodiment of the invention are the same as those of the medical data processing method embodiment, and for the sake of brief description, the corresponding contents in the medical data processing method embodiment can be referred to where the embodiment of the medical data processing device is not mentioned.
An embodiment of the present invention further provides a medical data processing system, where the system includes a server of an instruction intranet system, an edge server, and a cloud server, which are connected in sequence, and may be specifically represented by fig. 3. The method is applied to the edge server.
An embodiment of the present invention further provides an edge server, as shown in fig. 8, the edge server includes a processor 130 and a memory 131, the memory 131 stores machine executable instructions capable of being executed by the processor 130, and the processor 130 executes the machine executable instructions to implement the processing method of the medical data.
Further, the edge server shown in fig. 8 further includes a bus 132 and a communication interface 133, and the processor 130, the communication interface 133 and the memory 131 are connected through the bus 132.
The Memory 131 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 133 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used. The bus 132 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 8, but that does not indicate only one bus or one type of bus.
The processor 130 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 130. The Processor 130 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 131, and the processor 130 reads the information in the memory 131 and completes the steps of the method of the foregoing embodiment in combination with the hardware thereof.
The embodiment of the present invention further provides a machine-readable storage medium, where the machine-readable storage medium stores machine-executable instructions, and when the machine-executable instructions are called and executed by a processor, the machine-executable instructions cause the processor to implement the method for processing medical data, and specific implementation may refer to method embodiments, and is not described herein again.
The medical data processing method and apparatus and the computer program product of the edge server provided in the embodiments of the present invention include a computer readable storage medium storing a program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiments, and specific implementation may refer to the method embodiments, and will not be described herein again.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (13)
1. The method for processing the medical data is characterized in that the method is applied to an edge server, the edge server is connected with a server of a designated intranet system, and the edge server is also connected with a cloud server; the method comprises the following steps:
acquiring medical data in a server of the specified intranet system;
cleaning the medical data based on a preset data cleaning mode to obtain the cleaned medical data;
performing association processing on the cleaned medical data based on preset association information to obtain at least one group of data groups; wherein, the data in the same group of data groups have related associated information;
and sending the data set to the cloud server.
2. The method of claim 1, wherein the medical data comprises a plurality of subdata; the data cleaning mode comprises data deduplication processing;
the method comprises the steps of cleaning the medical data based on a preset data cleaning mode, wherein the cleaning process comprises the following steps;
searching whether the medical data comprises a plurality of same subdata;
if yes, data deduplication processing is carried out on the same multiple sub data.
3. The method of claim 1, wherein the medical data comprises a plurality of subdata; the subdata comprises fields corresponding to a plurality of entries; the data cleaning mode comprises the step of processing subdata which does not meet a first preset condition based on a preset mode;
the method comprises the steps of cleaning the medical data based on a preset data cleaning mode, wherein the cleaning process comprises the following steps;
for each subdata, searching fields corresponding to preset entries of the subdata from fields corresponding to a plurality of entries of the subdata;
if the subdata is not found, determining that the subdata does not meet a first preset condition, and processing the subdata based on a preset mode;
if the field format is found, judging whether the format of the field corresponding to the preset entry of the subdata meets the preset format;
and if the format of the field corresponding to the preset entry of the subdata does not meet the preset format, determining that the subdata does not meet a first preset condition, and processing the subdata based on a preset mode.
4. The method of claim 1, wherein the medical data comprises a plurality of subdata; the subdata comprises fields corresponding to a plurality of entries; the association information comprises an association entry;
the step of performing association processing on the cleaned medical data based on preset association information to obtain at least one group of data sets comprises the following steps:
searching characters corresponding to the associated entries from fields corresponding to a plurality of entries of the subdata aiming at the cleaned medical data;
and determining the subdata with the same characters corresponding to the associated entry as a data group.
5. The method of claim 1, wherein the medical data comprises a plurality of medical images and a plurality of medical reports; the medical image comprises fields corresponding to a patient number, a registration number and an examination case number; the medical report comprises fields corresponding to a patient number, a registration number and an examination case number; the related information comprises a patient number, a registration number and an examination case number;
the step of performing association processing on the cleaned medical data based on preset association information to obtain at least one group of data sets comprises the following steps:
and determining medical images and medical reports with the same fields corresponding to the patient number, the registration number and the examination example number as a data set according to the cleaned medical data.
6. The method of claim 1, further comprising:
receiving configuration information sent by the cloud server; the configuration information includes data cleaning mode, associated information and compensation items.
7. The method of claim 6, wherein the medical data comprises a plurality of subdata; the subdata comprises fields corresponding to a plurality of entries; the method further comprises the following steps:
for the subdata which does not form a data group, searching fields corresponding to the compensation entries from fields corresponding to a plurality of entries of the subdata;
and sending the searched field corresponding to the compensation entry to the server of the specified intranet system, so that the server of the specified intranet system searches the medical data which are not formed into the subdata of the data group and have the relevant associated information based on the field corresponding to the compensation entry.
8. The method according to claim 1, wherein the step of obtaining medical data in the server of the specified intranet system comprises:
sending a data reading instruction to a server of the specified intranet system through a preset network card;
and receiving the medical data fed back by the server of the specified intranet system according to the data reading instruction.
9. The method of claim 1, further comprising:
adding time information to the medical data when the medical data sent by the server of the specified intranet system is received; the time information indicates a time when the medical data arrives at a current device;
and when the time difference between the real-time of the current equipment and the time information is greater than a preset time threshold value, deleting the medical data corresponding to the time information.
10. The method of claim 1, further comprising:
and if the heartbeat data sent by the server of the specified intranet system is not received within the set time, generating alarm information.
11. The device for processing the medical data is characterized in that the device is arranged on an edge server, the edge server is connected with a server of a designated intranet system, and the edge server is further connected with a cloud server; the device comprises:
the data acquisition module is used for acquiring medical data in the server of the specified intranet system;
the data cleaning module is used for cleaning the medical data based on a preset data cleaning mode to obtain the cleaned medical data;
the data association module is used for associating the cleaned medical data based on preset association information to obtain at least one group of data sets; wherein, the data in the same group of data groups have related associated information;
and the data sending module is used for sending the data group to the cloud server.
12. A medical data processing system, comprising a server of an instruction intranet system, an edge server and a cloud server which are connected in sequence, wherein the method of any one of claims 1 to 10 is applied to the edge server.
13. A machine-readable storage medium having stored thereon machine-executable instructions which, when invoked and executed by a processor, cause the processor to carry out a method of processing medical data as claimed in any one of claims 1 to 10.
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Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103473375A (en) * | 2013-09-29 | 2013-12-25 | 方正国际软件有限公司 | Data cleaning method and data cleaning system |
| CN104346102A (en) * | 2013-08-06 | 2015-02-11 | 上海联影医疗科技有限公司 | Automatic data deleting method based on DICOM |
| CN104462527A (en) * | 2014-12-22 | 2015-03-25 | 龙信数据（北京）有限公司 | Data deduplication method and device |
| CN106778021A (en) * | 2016-12-31 | 2017-05-31 | 深圳市前海康启源科技有限公司 | Medical diagnosis information management system and method |
| CN106815336A (en) * | 2016-12-31 | 2017-06-09 | 深圳市易特科信息技术有限公司 | Medical data inquiry system and method based on big data associated storage |
| CN108319605A (en) * | 2017-01-16 | 2018-07-24 | 医渡云（北京）技术有限公司 | The structuring processing method and system of medical examination data |
| CN108563789A (en) * | 2018-04-28 | 2018-09-21 | 成都致云科技有限公司 | Data cleaning method based on Spark frames and device |
| CN109446191A (en) * | 2018-11-09 | 2019-03-08 | 医渡云（北京）技术有限公司 | Medical treatment data processing system and method, storage medium and electronic equipment |
| CN109522344A (en) * | 2018-10-30 | 2019-03-26 | 平安医疗健康管理股份有限公司 | A kind of data processing method and equipment based on data visualization |
| CN109597801A (en) * | 2018-11-14 | 2019-04-09 | 金色熊猫有限公司 | Medical data standardized management method and system, electronic equipment, storage medium |
| CN110473623A (en) * | 2019-07-16 | 2019-11-19 | 武汉联影医疗科技有限公司 | Medical service system |
| CN111916167A (en) * | 2019-05-09 | 2020-11-10 | 中国信息通信研究院 | Medical big data processing method based on front-end processor system and front-end processor system |
| CN112349404A (en) * | 2020-11-03 | 2021-02-09 | 中国人民解放军总医院 | Multi-center medical equipment big data cloud platform based on cloud-edge-end architecture |
| CN112653703A (en) * | 2020-12-25 | 2021-04-13 | 杭州中科先进技术研究院有限公司 | Multi-medical-protocol conversion analysis method and system based on edge calculation |
| CN112768058A (en) * | 2021-01-22 | 2021-05-07 | 武汉大学 | Method and device for processing medical data of metering information type |
| CN112863626A (en) * | 2021-03-08 | 2021-05-28 | 北京冠新医卫软件科技有限公司 | Multi-platform similar medical data removing method, device and equipment |
| CN112948369A (en) * | 2021-04-19 | 2021-06-11 | 北京明略昭辉科技有限公司 | Data cleaning method, system and storage medium based on configuration rule |
| CN112988710A (en) * | 2021-03-18 | 2021-06-18 | 成都青云之上信息科技有限公司 | Big data processing method and system |
| CN113190543A (en) * | 2021-05-24 | 2021-07-30 | 全球能源互联网研究院有限公司 | Data cleaning method and device, electronic equipment and storage medium |
| CN113314203A (en) * | 2020-02-27 | 2021-08-27 | 中移(成都)信息通信科技有限公司 | Medical image data storage system, method, device, equipment and medium |
-
2021
- 2021-09-23 CN CN202111116762.4A patent/CN113821503A/en active Pending
Patent Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104346102A (en) * | 2013-08-06 | 2015-02-11 | 上海联影医疗科技有限公司 | Automatic data deleting method based on DICOM |
| CN103473375A (en) * | 2013-09-29 | 2013-12-25 | 方正国际软件有限公司 | Data cleaning method and data cleaning system |
| CN104462527A (en) * | 2014-12-22 | 2015-03-25 | 龙信数据（北京）有限公司 | Data deduplication method and device |
| CN106778021A (en) * | 2016-12-31 | 2017-05-31 | 深圳市前海康启源科技有限公司 | Medical diagnosis information management system and method |
| CN106815336A (en) * | 2016-12-31 | 2017-06-09 | 深圳市易特科信息技术有限公司 | Medical data inquiry system and method based on big data associated storage |
| CN108319605A (en) * | 2017-01-16 | 2018-07-24 | 医渡云（北京）技术有限公司 | The structuring processing method and system of medical examination data |
| CN108563789A (en) * | 2018-04-28 | 2018-09-21 | 成都致云科技有限公司 | Data cleaning method based on Spark frames and device |
| CN109522344A (en) * | 2018-10-30 | 2019-03-26 | 平安医疗健康管理股份有限公司 | A kind of data processing method and equipment based on data visualization |
| CN109446191A (en) * | 2018-11-09 | 2019-03-08 | 医渡云（北京）技术有限公司 | Medical treatment data processing system and method, storage medium and electronic equipment |
| CN109597801A (en) * | 2018-11-14 | 2019-04-09 | 金色熊猫有限公司 | Medical data standardized management method and system, electronic equipment, storage medium |
| CN111916167A (en) * | 2019-05-09 | 2020-11-10 | 中国信息通信研究院 | Medical big data processing method based on front-end processor system and front-end processor system |
| CN110473623A (en) * | 2019-07-16 | 2019-11-19 | 武汉联影医疗科技有限公司 | Medical service system |
| CN113314203A (en) * | 2020-02-27 | 2021-08-27 | 中移(成都)信息通信科技有限公司 | Medical image data storage system, method, device, equipment and medium |
| CN112349404A (en) * | 2020-11-03 | 2021-02-09 | 中国人民解放军总医院 | Multi-center medical equipment big data cloud platform based on cloud-edge-end architecture |
| CN112653703A (en) * | 2020-12-25 | 2021-04-13 | 杭州中科先进技术研究院有限公司 | Multi-medical-protocol conversion analysis method and system based on edge calculation |
| CN112768058A (en) * | 2021-01-22 | 2021-05-07 | 武汉大学 | Method and device for processing medical data of metering information type |
| CN112863626A (en) * | 2021-03-08 | 2021-05-28 | 北京冠新医卫软件科技有限公司 | Multi-platform similar medical data removing method, device and equipment |
| CN112988710A (en) * | 2021-03-18 | 2021-06-18 | 成都青云之上信息科技有限公司 | Big data processing method and system |
| CN112948369A (en) * | 2021-04-19 | 2021-06-11 | 北京明略昭辉科技有限公司 | Data cleaning method, system and storage medium based on configuration rule |
| CN113190543A (en) * | 2021-05-24 | 2021-07-30 | 全球能源互联网研究院有限公司 | Data cleaning method and device, electronic equipment and storage medium |
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