Data processing method, system and equipment in medical data platform, medium and program product

Abstract

The invention provides a data processing method, system and device in a medical data platform, a medium and a program product. The method comprises the following steps: receiving a plurality of medical data analysis requests in the medical data platform; whether the current medical data analysis request is repeated or not is judged, if yes, part of the repeated medical data analysis requests are intercepted, and the remaining repeated medical data analysis requests serve as target medical data analysis requests; if not, taking the current medical data analysis request as a target medical data analysis request; and responding to the target medical data analysis request, and clearing the target medical data analysis request which fails in response. The method comprises the following steps: intercepting repeated medical data analysis requests to obtain a target medical data analysis request; responding to the target medical data analysis request, and clearing the target medical data analysis request which fails in response; repeated requests are reduced, unnecessary network load and server processing are effectively reduced, and the response speed is increased.

Description

Technical Field

[0001] The present disclosure relates to the technical field of medical image processing, and in particular to a data processing method, system, device, medium and program product in a medical data platform. Background Art

[0002] Existing medical image processing systems typically need to process very large datasets, including high-resolution images and complex medical records. These systems often face inefficient data transmission and precision issues when processing large integers (such as patient IDs and medical record numbers). Furthermore, traditional medical image processing systems often face network congestion and excessive server load when handling large numbers of concurrent requests. Repeated data requests can lead to wasted system resources, delayed responses, or even processing failures, compromising the speed and accuracy of medical decision-making. Summary of the Invention

[0003] The technical problem to be solved by the present disclosure is to overcome the defects of the medical image processing system in the prior art, which may cause waste of system resources and response delays or processing failures due to repeated data requests, thereby affecting the speed and accuracy of medical decision-making, and to provide a data processing method, system, device, medium and program product in a medical data platform.

[0004] The present disclosure solves the above technical problems through the following technical solutions:

[0005] A first aspect of the present disclosure provides a data processing method in a medical data platform, the data processing method comprising:

[0006] receiving a plurality of medical data analysis requests from the medical data platform;

[0007] Determine whether the current medical data analysis request is repeated. If so, intercept some of the repeated medical data analysis requests and use the remaining repeated medical data analysis requests as target medical data analysis requests. If not, use the current medical data analysis request as the target medical data analysis request.

[0008] Respond to the target medical data analysis request and clear the target medical data analysis request to which the response fails.

[0009] Preferably, the step of responding to the target medical data analysis request includes:

[0010] Search a preset database for a medical image corresponding to the target medical data analysis request.

[0011] Preferably, the step of responding to the target medical data analysis request further comprises:

[0012] Parsing the target medical data analysis request;

[0013] Determine whether the target medical data analysis request is parsed successfully. If so, display the first medical image corresponding to the parsed target medical data analysis request in the medical data platform; if not, display the second medical image corresponding to the original target medical data analysis request in the medical data platform.

[0014] Preferably, the step of determining whether the current medical data analysis request is repeated includes:

[0015] Determine whether several medical data analysis requests are identical. If so, retain any one of the medical data analysis requests as a target medical data analysis request and delete the remaining identical medical data analysis requests; if not, use the current medical data analysis request as the target medical data analysis request.

[0016] Preferably, the step of responding to the target medical data analysis request includes:

[0017] Determine whether the target medical data analysis request is responded successfully. If not, clear the target medical data analysis request that failed to respond; if so, store the target medical data analysis request in the request pool in the medical data platform.

[0018] Preferably, the step of parsing the target medical data analysis request includes:

[0019] Converting the target medical data analysis request into a character string;

[0020] and / or,

[0021] The medical data analysis request is a medical image analysis request.

[0022] A second aspect of the present disclosure provides a data processing system in a medical data platform, the data processing system comprising:

[0023] A receiving module, configured to receive a medical data analysis request;

[0024] The judgment module is used to judge whether the current medical data analysis request is repeated. If so, the request interception module is called; if not, the determination module is called;

[0025] The request interception module is used to intercept some repeated medical data analysis requests and use the remaining repeated medical data analysis requests as target medical data analysis requests;

[0026] The determining module is configured to use the current medical data analysis request as a target medical data analysis request;

[0027] The response module is used to respond to the target medical data analysis request and clear the target medical data analysis request that fails to respond.

[0028] Preferably, the response module includes:

[0029] The search unit is configured to search a preset database for a medical image corresponding to a target medical data analysis request.

[0030] Preferably, the response module further includes:

[0031] A parsing unit, configured to parse a target medical data analysis request;

[0032] The first judgment unit is used to judge whether the target medical data analysis request is parsed successfully, and if so, call the first display unit; if not, call the second display unit;

[0033] A first display unit, configured to display a first medical image corresponding to the parsed target medical data analysis request on the medical data platform;

[0034] The second display unit is used to display the second medical image corresponding to the original target medical data analysis request in the medical data platform.

[0035] Preferably, the request interception module includes:

[0036] a second judging unit, configured to judge whether the plurality of medical data analysis requests are the same, and if so, call the deleting unit; if not, call the determining unit;

[0037] The deleting unit is configured to retain any one medical data analysis request as a target medical data analysis request and delete the remaining identical medical data analysis requests;

[0038] The determining unit is configured to use the current medical data analysis request as the target medical data analysis request.

[0039] Preferably, the response module includes:

[0040] a third judging unit, configured to judge whether the target medical data analysis request is responded to successfully, and if not, call the clearing unit; if so, call the storage unit;

[0041] The clearing unit is configured to clear target medical data analysis requests to which responses have failed;

[0042] The storage unit is used to store the target medical data analysis request in a request pool in the medical data platform.

[0043] Preferably, the parsing unit is used to convert the target medical data analysis request into a character string;

[0044] and / or,

[0045] The medical data analysis request is a medical image analysis request.

[0046] A third aspect of the present disclosure provides a medical data platform, including the data processing system in the second aspect.

[0047] The fourth aspect of the present disclosure provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and used to run on the processor, wherein when the processor executes the computer program, the data processing method in the medical data platform described in the first aspect is implemented.

[0048] A fifth aspect of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the data processing method in the medical data platform described in the first aspect.

[0049] A sixth aspect of the present disclosure provides a computer program product, comprising a computer program, which, when executed by a processor, implements the data processing method in the medical data platform as described in the first aspect.

[0050] On the basis of conforming to the common sense in this field, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present disclosure.

[0051] The positive progress of this disclosure is:

[0052] The present disclosure intercepts repeated medical data analysis requests to obtain target medical data analysis requests; responds to target medical data analysis requests, and clears target medical data analysis requests that fail to respond; reduces repeated requests, effectively reduces unnecessary network load and server processing, and speeds up response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flowchart of the data processing method in the medical data platform provided in Example 1 of the present disclosure.

[0054] Figure 2 A schematic diagram of the modules of the data processing system in the medical data platform provided in Example 2 of the present disclosure.

[0055] Figure 3 This is a structural diagram of an electronic device for implementing a data processing method in a medical data platform according to embodiment 4 of the present disclosure. DETAILED DESCRIPTION

[0056] The present disclosure is further illustrated below by way of examples, but the present disclosure is not limited to the scope of the examples.

[0057] In the embodiments of the present disclosure, prefixes such as "first" and "second" are used only to distinguish different description objects, and have no limiting effect on the position, order, priority, quantity or content of the described objects. In the embodiments of the present disclosure, the use of prefixes such as ordinal numbers to distinguish description objects does not constitute a limitation on the described objects. For the statement of the described objects, please refer to the description in the context of the claims or embodiments, and no unnecessary limitations should be constituted due to the use of such prefixes. In addition, in the description of this embodiment, unless otherwise specified, the meaning of "plurality" is two or more.

[0058] In the embodiments of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0059] Example 1

[0060] Figure 1 This is a flow chart of a data processing method in a medical data platform provided in Example 1 of the present disclosure, such as Figure 1 As shown, the processing method includes:

[0061] S1. Receive a medical data analysis request;

[0062] In this embodiment, the medical data analysis request is a medical image analysis request.

[0063] S2. Determine whether the current medical data analysis request is repeated. If so, execute step S3; if not, execute step S4;

[0064] S3. intercepting some of the repeated medical data analysis requests and using the remaining repeated medical data analysis requests as target medical data analysis requests;

[0065] S4. Using the current medical data analysis request as the target medical data analysis request;

[0066] S5. Respond to the target medical data analysis request and clear the target medical data analysis request to which the response fails.

[0067] In this embodiment, by introducing request interceptors and response interceptors, the EventEmitter class is used to prevent the same medical data analysis request from being sent repeatedly. This is particularly important when processing multiple image analysis tasks in parallel, and can effectively reduce unnecessary network load and server processing. Specifically, a request interceptor is added to the Axios (a promise-based network request library) instance to check and prevent duplicate medical data analysis requests. A response interceptor is added to clear the recorded request after receiving the response, or to handle the situation where the request fails. The request results are uniformly processed by the response interceptor to ensure that the result of each request can be accurately fed back to the relevant image processing task, thereby improving the reliability of data processing and the response speed of the system.

[0068] This embodiment intercepts repeated medical data analysis requests to obtain target medical data analysis requests; responds to target medical data analysis requests and clears target medical data analysis requests that fail to respond; reduces repeated requests, effectively reduces unnecessary network load and server processing, and speeds up response speed.

[0069] In an optional embodiment, S5 includes:

[0070] Search a preset database for a medical image corresponding to the target medical data analysis request.

[0071] In this embodiment, the preset database may be a JSONBig database.

[0072] In this embodiment, the JSONBig library is integrated into the medical image processing platform. Specifically, on the server side of the medical image processing platform, the JSONBig library is installed through npm to ensure that large integer data (such as medical record numbers, test numbers, etc.) in all medical data analysis requests can be safely transmitted and parsed to avoid loss of accuracy.

[0073] In this embodiment, when processing medical image data, the data involved is large and contains large integers (such as patient IDs and test numbers). It is necessary to ensure the integrity and accuracy of this data during network transmission. To this end, it is necessary to integrate the JSONBig library to process these large integer data.

[0074] In an optional embodiment, S5 further includes:

[0075] S501, parsing the target medical data analysis request;

[0076] S502: Determine whether the target medical data analysis request is parsed successfully. If yes, execute step S503; if not, execute step S504.

[0077] S503: Displaying the first medical image corresponding to the parsed target medical data analysis request on the medical data platform;

[0078] S504: Display the second medical image corresponding to the original target medical data analysis request on the medical data platform.

[0079] In this example, a JSON (Lightweight Data Interchange Format) parser is configured: the data processing module of the medical image processing platform is modified to introduce JSONBig as the default JSON parser. This ensures that all JSON data received from external systems is parsed using JSONBig. Specifically, when creating or configuring an Axios instance, the `transformResponse` option is set to use the JSONBig library to parse the returned JSON data.

[0080] It should be noted that the first image is an image corresponding to the target medical data analysis request after successful parsing; the second image is an image corresponding to the target medical data analysis request before parsing after failed parsing.

[0081] In an optional embodiment, S2 includes:

[0082] S21, determining whether the plurality of medical data analysis requests are the same, if so, executing step S22; if not, executing step S23;

[0083] S22. Retain any medical data analysis request as a target medical data analysis request, and delete the remaining identical medical data analysis requests;

[0084] S23. Use the current medical data analysis request as the target medical data analysis request.

[0085] During the specific implementation process, at least two medical data analysis requests are transmitted to the request interceptor, which determines whether the at least two medical data analysis requests are the same. If so, any one of the medical data analysis requests is retained, the remaining identical medical data analysis requests are deleted, and any one of the retained medical data analysis requests is transmitted to the response interceptor; if not, the current medical data analysis request is transmitted to the response interceptor.

[0086] In an optional embodiment, S5 includes:

[0087] S51, determine whether the target medical data analysis request is responded to successfully, if not, execute step S52; if so, execute step S53;

[0088] S52. Clearing the target medical data analysis request to which the response failed;

[0089] S53. Store the target medical data analysis request in a request pool in the medical data platform.

[0090] In this embodiment, the response interceptor is used to determine whether the received target medical data analysis request is successfully responded to. If it fails, the target medical data analysis request that failed to respond is cleared. If it succeeds, the target medical data analysis request is stored in the request pool in the medical data platform. Furthermore, the responded requests and failed requests are cleared from the request pool.

[0091] In an optional embodiment, S501 includes:

[0092] Convert the target medical data analysis request to a string.

[0093] In this embodiment, when parsing data, the JSONBig library is used to convert all large integer values into string form, avoiding the precision problem of JavaScript native Number type.

[0094] During the specific implementation process, after the medical data analysis request is processed by the request interceptor and the response interceptor, the processed medical data analysis request is sent to the JSONBig library. The medical image corresponding to the processed medical data analysis request (i.e., the target medical data analysis request) is searched in the JSONBig library and the medical image is parsed through the JSONBig library. If the parsing is successful, the medical image corresponding to the parsed target medical data analysis request is displayed in the medical data platform. If the parsing fails, the medical image corresponding to the original target medical data analysis request is displayed in the medical data platform.

[0095] This embodiment improves medical image processing efficiency through a throttling mechanism and is particularly suitable for managing data requests in high-load environments. Improvements are made to network communication and data processing within the medical image processing platform. This technology is primarily used to process and analyze large data sets from medical imaging devices, enhancing the efficiency and accuracy of medical diagnoses. Specifically, the performance of the medical image processing platform is optimized through specialized request processing and secure large integer handling. Patient identification data from hospital information systems is processed using JSONBig to handle large integers such as medical record numbers. When sending medical data analysis requests, a request interceptor is used to prevent duplicate analysis requests for data within the same data platform, thereby optimizing resource utilization and improving response speed. Secure processing of large integers ensures data transmission and processing accuracy, making it suitable for medical applications requiring high precision. The processing of medical data analysis requests through request and response interceptors reduces duplicate requests, reduces server burden, and accelerates response speed, which is particularly critical for real-time or near-real-time medical image analysis. Effective network request management reduces resource consumption and improves the operational efficiency of the entire medical image processing system. This embodiment not only enhances the data processing capacity and efficiency of the medical image processing platform but also ensures data processing accuracy and system reliability.

[0096] Example 2

[0097] Corresponding to the aforementioned embodiment of a data processing method in a medical data platform, the present disclosure also provides an embodiment of a data processing system in a medical data platform.

[0098] Figure 2 This is a module diagram of a data processing system in a medical data platform provided in Example 2 of the present disclosure, such as Figure 2 As shown, the processing system includes: a receiving module 21, a judging module 22, a request intercepting module 23, a determining module 24, and a responding module 25;

[0099] Receiving module 21, for receiving a medical data analysis request;

[0100] In this embodiment, the medical data analysis request is a medical image analysis request.

[0101] The judging module 22 is used to judge whether the current medical data analysis request is repeated. If so, the request intercepting module 23 is called; if not, the determining module 24 is called;

[0102] a request interception module 23, configured to intercept some of the repeated medical data analysis requests and use the remaining repeated medical data analysis requests as target medical data analysis requests;

[0103] a determination module 24, configured to use the current medical data analysis request as a target medical data analysis request;

[0104] The response module 25 is configured to respond to the target medical data analysis request and clear the target medical data analysis request to which the response fails.

[0105] In this embodiment, by introducing request interceptors and response interceptors, the EventEmitter class is used to prevent the same medical data analysis request from being sent repeatedly. This is particularly important when processing multiple image analysis tasks in parallel, and can effectively reduce unnecessary network load and server processing. Specifically, a request interceptor is added to the Axios (a promise-based network request library) instance to check and prevent duplicate medical data analysis requests. A response interceptor is added to clear the recorded request after receiving the response, or to handle the situation where the request fails. The request results are uniformly processed by the response interceptor to ensure that the result of each request can be accurately fed back to the relevant image processing task, thereby improving the reliability of data processing and the response speed of the system.

[0106] This embodiment intercepts repeated medical data analysis requests to obtain target medical data analysis requests; responds to target medical data analysis requests and clears target medical data analysis requests that fail to respond; reduces repeated requests, effectively reduces unnecessary network load and server processing, and speeds up response speed.

[0107] In an optional embodiment, the response module 25 includes:

[0108] The search unit is configured to search a preset database for a medical image corresponding to a target medical data analysis request.

[0109] In this embodiment, the preset database may be a JSONBig database.

[0110] In this embodiment, the JSONBig library is integrated into the medical image processing platform. Specifically, on the server side of the medical image processing platform, the JSONBig library is installed through npm to ensure that large integer data (such as medical record numbers, test numbers, etc.) in all medical data analysis requests can be safely transmitted and parsed to avoid loss of accuracy.

[0111] In this embodiment, when processing medical image data, the data involved is large and contains large integers (such as patient IDs and test numbers). It is necessary to ensure the integrity and accuracy of this data during network transmission. To this end, it is necessary to integrate the JSONBig library to process these large integer data.

[0112] In an optional embodiment, the response module 25 further includes:

[0113] A parsing unit, configured to parse a target medical data analysis request;

[0114] The first judgment unit is used to judge whether the target medical data analysis request is parsed successfully, and if so, call the first display unit; if not, call the second display unit;

[0115] A first display unit, configured to display a first medical image corresponding to the parsed target medical data analysis request on the medical data platform;

[0116] The second display unit is used to display the second medical image corresponding to the original target medical data analysis request in the medical data platform.

[0117] In this example, a JSON (Lightweight Data Interchange Format) parser is configured: the data processing module of the medical image processing platform is modified to introduce JSONBig as the default JSON parser. This ensures that all JSON data received from external systems is parsed using JSONBig. Specifically, when creating or configuring an Axios instance, the `transformResponse` option is set to use the JSONBig library to parse the returned JSON data.

[0118] It should be noted that the first image is an image corresponding to the target medical data analysis request after successful parsing; the second image is an image corresponding to the target medical data analysis request before parsing after failed parsing.

[0119] In an optional embodiment, the request interception module includes:

[0120] A second judgment unit is configured to judge whether the plurality of medical data analysis requests are the same, and if so, call the deletion unit; if not, call the determination unit;

[0121] a deleting unit, configured to retain any medical data analysis request as a target medical data analysis request and delete remaining identical medical data analysis requests;

[0122] The determination unit is configured to use the current medical data analysis request as the target medical data analysis request.

[0123] During the specific implementation process, at least two medical data analysis requests are transmitted to the request interceptor, and the request interceptor determines whether the at least two medical data analysis requests are the same. If so, any one of the medical data analysis requests is retained, the remaining medical data analysis requests are deleted, and any one of the retained medical data analysis requests is transmitted to the response interceptor; if not, the current medical data analysis request is transmitted to the response interceptor.

[0124] In an optional embodiment, the response module 25 includes:

[0125] a third judging unit, configured to judge whether the target medical data analysis request is responded to successfully, and if not, to call the clearing unit; if so, to call the storage unit;

[0126] A clearing unit, configured to clear target medical data analysis requests to which responses have failed;

[0127] The storage unit is used to store the target medical data analysis request in a request pool in the medical data platform.

[0128] In this embodiment, the response interceptor is used to determine whether the received target medical data analysis request is successfully responded to. If it fails, the target medical data analysis request that failed to respond is cleared. If it succeeds, the target medical data analysis request is stored in the request pool in the medical data platform. Furthermore, the responded requests and failed requests are cleared from the request pool.

[0129] In an optional embodiment, the parsing unit is configured to convert the target medical data analysis request into a character string.

[0130] In this embodiment, when parsing data, the JSONBig library is used to convert all large integer values into string form, avoiding the precision problem of JavaScript native Number type.

[0131] During the specific implementation process, after the medical data analysis request is processed by the request interceptor and the response interceptor, the processed medical data analysis request is sent to the JSONBig library. The medical image corresponding to the processed medical data analysis request (i.e., the target medical data analysis request) is searched in the JSONBig library and the medical image is parsed through the JSONBig library. If the parsing is successful, the medical image corresponding to the parsed target medical data analysis request is displayed in the medical data platform. If the parsing fails, the medical image corresponding to the original target medical data analysis request is displayed in the medical data platform.

[0132] This embodiment improves medical image processing efficiency through a throttling mechanism and is particularly suitable for managing data requests in high-load environments. Improvements are made to network communication and data processing within the medical image processing platform. This technology is primarily used to process and analyze large data sets from medical imaging devices, enhancing the efficiency and accuracy of medical diagnoses. Specifically, the performance of the medical image processing platform is optimized through specialized request processing and secure large integer handling. Patient identification data from hospital information systems is processed using JSONBig to handle large integers such as medical record numbers. When sending medical data analysis requests, a request interceptor is used to prevent duplicate analysis requests for data within the same data platform, thereby optimizing resource utilization and improving response speed. Secure processing of large integers ensures data transmission and processing accuracy, making it suitable for medical applications requiring high precision. The processing of medical data analysis requests through request and response interceptors reduces duplicate requests, reduces server burden, and accelerates response speed, which is particularly critical for real-time or near-real-time medical image analysis. Effective network request management reduces resource consumption and improves the operational efficiency of the entire medical image processing system. This embodiment not only enhances the data processing capacity and efficiency of the medical image processing platform but also ensures data processing accuracy and system reliability.

[0133] Since the system embodiments generally correspond to the method embodiments, reference will be made to the description of the method embodiments for relevant details. The system embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components of the units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the disclosed solution.

[0134] Example 3

[0135] The present disclosure provides a medical data platform, including the data processing system in Example 2.

[0136] In this embodiment, the data processing system is integrated into the medical data platform.

[0137] Example 4

[0138] Figure 3 This is a structural diagram of an electronic device shown in Example 4 of the present disclosure. The electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor. When the processor executes the computer program, it implements the data processing method in the medical data platform described in any of the above embodiments. Figure 3The electronic device 90 shown is only an example and should not limit the functionality and scope of use of the embodiments of the present disclosure.

[0139] like Figure 3 As shown, the electronic device 90 may be a general-purpose computing device, such as a server device. Components of the electronic device 90 may include, but are not limited to, the at least one processor 91, the at least one memory 92, and a bus 93 connecting different system components (including the memory 92 and the processor 91).

[0140] The bus 93 includes a data bus, an address bus, and a control bus.

[0141] The memory 92 may include a volatile memory, such as a random access memory (RAM) 921 and / or a cache memory 922 , and may further include a read-only memory (ROM) 923 .

[0142] The memory 92 may also include a program tool 925 (or utility) having a set (at least one) of program modules 924, such program modules 924 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0143] The processor 91 executes various functional applications and data processing by running the computer program stored in the memory 92, such as the data processing method in the medical data platform provided in any of the above embodiments.

[0144] The electronic device 90 can also communicate with one or more external devices 94 (e.g., keyboards, pointing devices, etc.). Such communication can be performed through an input / output (I / O) interface 95. In addition, the electronic device 90 can also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 96. Figure 3 As shown, the network adapter 96 communicates with other modules of the electronic device 90 via the bus 93. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 90, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, RAID (RAID) systems, tape drives, and data backup storage systems.

[0145] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0146] Example 5

[0147] Embodiment 5 of the present disclosure further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the data processing method in the medical data platform provided in any of the above embodiments is implemented.

[0148] The readable storage medium may include, but is not limited to, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0149] Example 6

[0150] Embodiment 6 of the present disclosure further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the data processing method in the medical data platform described in any one of the above items.

[0151] The program code for executing the computer program product of the present disclosure may be written in any combination of one or more programming languages, and the program code may be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on the remote device.

[0152] While specific embodiments of the present disclosure have been described above, those skilled in the art will appreciate that these are merely illustrative and that the scope of protection of the present disclosure is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present disclosure, and such changes and modifications are intended to fall within the scope of protection of the present disclosure.

Claims

1. A data processing method in a medical data platform, characterized in that: The data processing method includes: receiving a plurality of medical data analysis requests from the medical data platform; Determine whether the current medical data analysis request is repeated. If so, intercept some of the repeated medical data analysis requests and use the remaining repeated medical data analysis requests as target medical data analysis requests. If not, use the current medical data analysis request as the target medical data analysis request. Respond to the target medical data analysis request and clear the target medical data analysis request to which the response fails.

2. The data processing method in the medical data platform according to claim 1, characterized in that: The step of responding to the target medical data analysis request includes: Search a preset database for a medical image corresponding to the target medical data analysis request.

3. The data processing method in the medical data platform according to claim 1, characterized in that: The step of responding to the target medical data analysis request further includes: Parsing the target medical data analysis request; Determine whether the target medical data analysis request is parsed successfully. If so, display the first medical image corresponding to the parsed target medical data analysis request in the medical data platform; if not, display the second medical image corresponding to the original target medical data analysis request in the medical data platform.

4. The data processing method in the medical data platform according to claim 1, characterized in that: The step of determining whether the current medical data analysis request is repeated includes: Determine whether several medical data analysis requests are identical. If so, retain any one of the medical data analysis requests as a target medical data analysis request and delete the remaining identical medical data analysis requests; if not, use the current medical data analysis request as the target medical data analysis request.

5. The data processing method in the medical data platform according to claim 1, characterized in that: The step of responding to the target medical data analysis request includes: Determine whether the target medical data analysis request is responded successfully. If not, clear the target medical data analysis request that failed to respond; if so, store the target medical data analysis request in the request pool in the medical data platform.

6. The data processing method in the medical data platform according to claim 3, characterized in that: The step of parsing the target medical data analysis request includes: Converting the target medical data analysis request into a character string; and / or, The medical data analysis request is a medical image analysis request.

7. A data processing system in a medical data platform, characterized in that: The data processing system comprises: A receiving module, configured to receive a medical data analysis request; The judgment module is used to judge whether the current medical data analysis request is repeated. If so, the request interception module is called; if not, the determination module is called; The request interception module is used to intercept some repeated medical data analysis requests and use the remaining repeated medical data analysis requests as target medical data analysis requests; The determining module is configured to use the current medical data analysis request as a target medical data analysis request; The response module is used to respond to the target medical data analysis request and clear the target medical data analysis request that fails to respond.

8. A medical data platform, characterized in that: A data processing system comprising the method of claim 7.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and configured to run on the processor, wherein: When the processor executes the computer program, the data processing method in the medical data platform according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the data processing method in the medical data platform according to any one of claims 1 to 6 is implemented.

11. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the data processing method in the medical data platform according to any one of claims 1 to 6 is implemented.

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