Joint disease data regularization method and system based on MRI combined with big data

Abstract

The invention relates to the technical field of data regularization, in particular to a joint disease data regularization method and system based on MRI in combination with big data, and the method comprises the steps: receiving a data acquisition instruction of joint disease data, starting a data processing unit, calculating a data storage index, obtaining joint extraction data through a data extraction unit and the data storage index, and storing the joint extraction data in a database; setting test time, performing data transmission test based on the test time and calculating a transmission equilibrium index, transmitting joint extraction data to a data integration unit and performing integration to obtain joint integration data, transmitting the joint integration data to a data storage unit and defining a joint disease body; and constructing a joint management structure, a joint evaluation structure and a joint evolution structure based on the joint disease ontology, designing a joint data storage rule, constructing a disease map database, and mapping joint disease data to the disease map database. The data processing efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data regularization, and in particular to a joint disease data regularization method and system based on MRI combined with big data. Background Art

[0002] With the continuous development of medical technology and the continuous advancement of medical imaging technology, MRI, as a non-invasive imaging method, is widely used in the detection of joint diseases. MRI can show the changes in joint soft tissue and bone structure in detail, which is of great significance for the early detection of joint injuries and lesions. However, with the rapid increase in the amount of data, data processing and analysis are facing unprecedented challenges, especially in the diagnosis and treatment of joint diseases. Massive medical data needs to be effectively collected, stored, transmitted and analyzed, and the standardization and regularization of data processing is particularly important.

[0003] Traditional methods of processing joint disease data often rely on manual input and manual processing, which is not only inefficient, but also prone to errors and lacks standardization. With the development of big data, the regularization of medical data has gradually become an important means to improve diagnostic accuracy and treatment effects, especially in the field of joint diseases. Through the regularization of patient data, accurate assessment of the patient's condition can be achieved. Therefore, how to improve data processing efficiency is an important issue that needs to be solved urgently. Summary of the invention

[0004] The present invention provides a method and system for regularizing joint disease data based on MRI combined with big data, the main purpose of which is to improve data processing efficiency.

[0005] To achieve the above object, the present invention provides a joint disease data regularization method based on MRI combined with big data, comprising:

[0006] Receiving a data acquisition instruction for joint disease data, and starting a pre-built data processing unit based on the data acquisition instruction, wherein the data processing unit includes: a data extraction unit, a data transmission unit, a data integration unit and a data storage unit, and the joint disease data includes: MRI data;

[0007] Acquire a data storage device set, calculate the data storage index of all data storage devices in the data storage device set, extract data using a data extraction unit and the data storage index, and obtain joint extraction data;

[0008] Setting a test time, performing a data transmission test based on the test time and calculating a transmission balance index, transmitting the joint extraction data to a data integration unit according to the transmission balance index and the data transmission unit and performing integration to obtain joint integration data;

[0009] transferring joint integration data to a data storage unit and defining a joint disease ontology;

[0010] Constructing a joint management structure, a joint evaluation structure and a joint evolution structure based on the joint disease ontology;

[0011] Designing joint data storage rules based on the joint management structure, the joint evaluation structure and the joint evolution structure;

[0012] A disease graph database is constructed, and joint disease data is mapped to the disease graph database based on the joint data storage rules to complete the regularization of joint disease data.

[0013] Optionally, calculating the data storage index of all data storage devices in the data storage device set includes:

[0014] Based on the data storage device set, sequential processing is performed on all data storage devices and the data storage devices are identified to obtain device serial numbers;

[0015] Detect current storage capacity, data access frequency and data update rate, and set access parameters, storage parameters and update parameters;

[0016] The maximum storage capacity, maximum access frequency, historical storage growth rate, maximum storage growth rate and maximum update rate are obtained, and the data storage index is calculated based on the device serial number, current storage capacity, data access frequency, data update rate, access parameters, storage parameters, update parameters, maximum storage capacity, maximum access frequency, historical storage growth rate, maximum storage growth rate and maximum update rate.

[0017] Optionally, the calculating of the data storage index based on the device serial number, current storage capacity, data access frequency, data update rate, access parameter, storage parameter, update parameter, maximum storage capacity, maximum access frequency, historical storage growth rate, maximum storage growth rate and maximum update rate includes:

[0018] The data storage index is calculated using the following formula:

[0019]

[0020] Among them, T α Refers to the data storage index of the data storage device with device serial number α, α refers to the device serial number, Y α Refers to the current storage capacity of the data storage device with device serial number α, U α Refers to the maximum storage capacity of the data storage device with device serial number α, I α Refers to the data access frequency of the data storage device with device serial number α, O αrefers to the maximum access frequency of the data storage device with device number α, β refers to the access parameter, e refers to the natural constant, P α ′ refers to the historical storage growth rate of the data storage device with device number α, P α It refers to the maximum storage growth rate of the data storage device with device serial number α, γ refers to the storage parameter, A α ′ refers to the data update rate of the data storage device with device serial number α, A α It refers to the maximum update rate of the data storage device with device number α, and δ refers to the update parameter.

[0021] Optionally, the extracting data using the data extraction unit and the data storage index to obtain the joint extraction data includes:

[0022] Generate a data response instruction, and send the data response instruction to all data storage devices in the data storage device set, and receive a data extraction instruction returned by the data storage device based on a preset waiting time;

[0023] Marking the data storage device that returns the data extraction command as a responded storage device, and marking the data storage device that does not return the data extraction command as a non-responded storage device;

[0024] sorting the data storage devices according to the data storage index to obtain an extraction order;

[0025] Detect the unresponsive device number of the unresponsive storage device, detect the management device of the unresponsive storage device based on the unresponsive device number, transmit the unresponsive device number to the management device and extract data based on the data extraction instruction, the responded storage device and the extraction order to obtain joint extraction data.

[0026] Optionally, the calculating the transmission balance index includes:

[0027] Marking is performed based on the data transmission unit to obtain a marked transmission unit, wherein the data transmission unit includes: a main line transmission unit, a first auxiliary line unit and a second auxiliary line unit, and the marked transmission unit includes: a No. 1 transmission unit, a No. 2 transmission unit and a No. 3 transmission unit;

[0028] Obtain the data transmission rate, the maximum transmission rate, the weight parameter and the marking index, and calculate the transmission balance index based on the marking transmission unit, the data transmission rate, the maximum transmission rate, the weight parameter and the marking index:

[0029]

[0030] Among them, Q refers to the transmission balance index, i refers to the marking index, and E i refers to the current data transmission rate of the tag transmission unit with tag index i, Wi Denote the maximum transmission rate of the tag transmission unit with tag index i as ε i Denote the weight parameter of the tag transmission unit with tag index i

[0031] Optionally, the step of transmitting the joint extraction data to the data integration unit according to the transmission balance index and the data transmission unit and performing integration to obtain the joint integration data includes:

[0032] Set high-load intervals, medium-load intervals, low-load intervals, and interval allocation rules;

[0033] The interval allocation rules are as follows:

[0034] Low-load interval: 0 ≤ Q ≤ 0.5, and the main line transmission unit transmits the joint extraction data alone;

[0035] Medium-load interval: 0.5 < Q ≤ 0.7, and the joint extraction data is transmitted in the ratio of 70% for the main line transmission unit and 30% for the first auxiliary line unit;

[0036] High-load interval: 1 ≥ Q > 0.7, and the joint extraction data is transmitted in the ratio of 50% for the main line transmission unit, 25% for the first auxiliary line unit, and 25% for the second auxiliary line unit;

[0037] Based on the interval allocation rules, determine the allocation ratio for the transmission balance index, and based on the allocation ratio and the data transmission unit, transmit the joint extraction data to the data integration unit and perform integration to obtain the joint integration data.

[0038] Optionally, the step of defining the joint disease ontology includes:

[0039] Define joint parts, management personnel, detection categories, and health events;

[0040] Based on the joint parts, management personnel, detection categories, and health events, define the joint disease ontology.

[0041] Optionally, the step of constructing the joint management structure, joint evaluation structure, and joint evolution structure includes:

[0042] Define the joint management relationship between joint parts and management personnel, the joint detection relationship between joint parts and detection categories, and the joint event relationship between joint parts and health events;

[0043] Based on the joint management relationship, joint detection relationship, and joint event relationship, construct the joint management structure;

[0044] Set the functional integrity, joint health status, biochemical indicators and imaging assessment contents, and build the joint assessment structure based on the functional integrity, joint health status, biochemical indicators and imaging assessment contents;

[0045] Define the change process and time relationship, and build the joint evolution structure based on the joint health status, change process and time relationship.

[0046] Optionally, the design of joint data storage rules includes:

[0047] Uniquely identify the joint to obtain the joint identifier, set the joint ID based on the joint identifier, obtain the patient ID, and construct a joint node based on the joint part, the joint ID and the patient ID;

[0048] Based on the identification of the management personnel, a management ID is obtained, and the management name and management department are obtained, and a management node is constructed based on the management ID, management name and management department;

[0049] Obtaining a test date and a test result, and constructing a test node based on the test category, the test date and the test result;

[0050] Get the event date and event severity, and construct an event node based on the health event, event date and event severity

[0051] Constructing node storage rules based on the joint nodes, management nodes, detection nodes and event nodes, and obtaining joint node attributes, management node attributes, detection node attributes and event node attributes;

[0052] Constructing attribute storage rules based on the joint node attributes, management node attributes, detection node attributes and event node attributes;

[0053] The joint management structure is used to construct relationship storage rules, and joint data storage rules are designed according to the node storage rules, attribute storage rules and relationship storage rules.

[0054] To achieve the above object, the present invention also provides a joint disease data regularization system based on MRI combined with big data, comprising:

[0055] A joint data extraction module, for receiving a detection instruction of a diamond wire, using the detection instruction to start a pre-built diamond wire detection unit and set a cutting speed and a feeding speed of the diamond wire, wherein the diamond wire detection unit comprises: a vibration detection unit and an image acquisition unit, wherein the vibration detection unit is placed directly below the diamond wire and the image acquisition unit is placed directly above the diamond wire; cutting is performed based on the cutting speed and the feeding speed and the torsional deformation of the diamond wire is calculated in real time;

[0056] The extraction data integration module is used to determine whether the torsional deformation of the diamond wire is within a preset deformation range; if the torsional deformation of the diamond wire is within the deformation range, then return to the above-mentioned step of performing diamond wire cutting based on the cutting speed and the feed speed and calculating the torsional deformation of the diamond wire in real time; if the torsional deformation of the diamond wire is not within the deformation range, then based on the vibration detection unit, vibration detection is performed on the diamond wire to obtain a diamond wire vibration signal, and the diamond wire vibration signal is converted into a frequency domain to obtain a diamond wire frequency domain signal;

[0057] A storage rule design module is used to perform energy calculation based on the diamond wire frequency domain signal to obtain a signal energy spectrum; use the image acquisition unit to perform continuous image acquisition on the diamond wire to obtain an acquired image set, and perform image optimization on the acquired image set to obtain an optimal image;

[0058] The disease data mapping module is used to obtain the initial texture entropy value of the diamond wire, and calculate the degree of wear based on the initial texture entropy value and the optimal image; perform signal decomposition on the frequency domain signal of the diamond wire based on the signal energy spectrum to obtain a signal component set, and calculate the energy value of each signal component in the signal component set; calculate based on the wear degree and the energy value to obtain the fracture risk value, and complete real-time data regularization.

[0059] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0060] A memory storing at least one instruction;

[0061] A processor executes the instructions stored in the memory to implement the above-mentioned method for regularizing joint disease data based on MRI combined with big data.

[0062] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned joint disease data regularization method based on MRI combined with big data.

[0063] In order to solve the problems described in the background technology, the present invention realizes the regularized processing of joint disease data by receiving the acquisition instructions of joint disease data and constructing a comprehensive data processing flow covering multiple steps such as data storage, transmission test, data integration and joint disease ontology construction, which not only ensures the accuracy and consistency of joint disease data during collection and storage, but also improves the efficiency and precision of data processing through efficient data transmission and integration technology. In addition, by constructing storage rules and disease graph databases, multi-source data are managed in a standardized manner, which significantly improves the support capabilities for the diagnosis, evaluation and treatment of joint diseases, and meets the needs of modern medicine for accurate and efficient data processing; first, by automatically receiving the acquisition instructions of joint disease data, the timeliness and accuracy of joint disease data collection are ensured. This automated process reduces manual intervention, improves the efficiency of data collection, and also avoids human errors, providing a solid foundation for subsequent data processing and analysis; secondly, the data processing unit is started to effectively coordinate and manage various data processing tasks, including data extraction, integration, storage, etc., which improves work efficiency, reduces delays in each link, and through automated operations The complexity of manual operation is reduced, and the operation efficiency of the system is improved; afterwards, the data storage index of the data storage device is calculated. By calculating the data storage index of the data storage device, the performance and load of the storage device can be effectively evaluated, the efficiency of the data storage process is ensured, the allocation of storage resources is optimized, the appearance of data storage bottlenecks is avoided, and the reliability and efficiency of data storage are improved; then, the data transmission test is performed based on the test time and the transmission balance index is calculated. By calculating the transmission balance index, the load of the data transmission unit can be known. According to the load of the data transmission unit, the data to be transmitted can be allocated, and the data transmission delay caused by excessive load is avoided, and the efficiency and stability of data transmission are improved; afterwards, the joint management structure, the joint evaluation structure and the joint evolution structure are constructed through the joint disease ontology, and the various characteristics of joint diseases are described and analyzed more comprehensively, and a clear data management framework is established; finally, the joint data storage rules are designed to ensure the standardization and consistency of data storage, reduce data redundancy and confusion, improve the query and access efficiency of data, promote the circulation and sharing of data between different links and systems, and provide support for large-scale data analysis. Therefore, the present invention can improve data processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A schematic diagram of a flow chart of a joint disease data regularization method based on MRI combined with big data provided by an embodiment of the present invention;

[0065] Figure 2 A functional module diagram of a joint disease data regularization system based on MRI combined with big data provided by an embodiment of the present invention;

[0066] Figure 3 A schematic diagram of the structure of an electronic device for implementing the method for regularizing joint disease data based on MRI combined with big data provided in one embodiment of the present invention.

[0067] Description of reference numerals:

[0068] 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0069] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0070] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0071] The embodiment of the present application provides a method for regularizing joint disease data based on MRI combined with big data. The execution subject of the method for regularizing joint disease data based on MRI combined with big data includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for regularizing joint disease data based on MRI combined with big data can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0072] Reference Figure 1 FIG. 1 is a flow chart of a joint disease data regularization method based on MRI combined with big data provided by an embodiment of the present invention. In this embodiment, the joint disease data regularization method based on MRI combined with big data includes:

[0073] S1. Receive a data acquisition instruction for joint disease data, and start a pre-built data processing unit based on the data acquisition instruction, wherein the data processing unit includes: a data extraction unit, a data transmission unit, a data integration unit and a data storage unit, and the joint disease data includes: MRI data.

[0074] It can be explained that joint disease data refers to data on joint diseases of patients, data acquisition instructions refer to instructions issued manually for obtaining joint disease data from data storage devices, data storage devices refer to devices for temporarily storing joint disease data, and when joint disease data are just generated, the just generated joint disease data are temporarily stored in the data storage device, data processing unit refers to a unit for collecting, transmitting and storing joint disease data, which is started by the data acquisition instruction and consists of a data extraction unit, a data transmission unit and a data storage unit, data extraction unit refers to a unit for extracting joint disease data from the data storage device, data transmission unit refers to a unit for transmitting joint disease data, data integration unit refers to a unit for integrating data extracted from multiple data storage devices, data storage unit refers to a unit for storing joint disease data, MRI data refers to medical imaging data obtained using magnetic resonance imaging technology, which is often used to diagnose various diseases, especially in the nervous system, muscle joints and soft tissues, due to its advantages of being non-invasive and providing high-resolution images.

[0075] For example, Xiao Zhang is a staff member of a hospital. One day, Xiao Zhang needs to obtain joint disease data, so Xiao Zhang issues a data acquisition instruction, and starts a data processing unit according to the data acquisition instruction, and obtains the joint disease data through the data processing unit.

[0076] S2. Obtain a data storage device set, calculate the data storage index of all data storage devices in the data storage device set, and use the data extraction unit and the data storage index to extract data to obtain joint extraction data.

[0077] It can be explained that the data storage device set refers to the set formed by the data storage devices, the data storage index refers to the data reflecting the data storage status of the data storage device, the larger the data storage index is, the higher the load of the data storage device is, and the joint extraction data refers to the joint disease data extracted from the data storage device using the data extraction unit.

[0078] In detail, the step of calculating the data storage index of all data storage devices in the data storage device set includes:

[0079] Based on the data storage device set, sequential processing is performed on all data storage devices and the data storage devices are identified to obtain device serial numbers;

[0080] Detect current storage capacity, data access frequency and data update rate, and set access parameters, storage parameters and update parameters;

[0081] The maximum storage capacity, maximum access frequency, historical storage growth rate, maximum storage growth rate and maximum update rate are obtained, and the data storage index is calculated based on the device serial number, current storage capacity, data access frequency, data update rate, access parameters, storage parameters, update parameters, maximum storage capacity, maximum access frequency, historical storage growth rate, maximum storage growth rate and maximum update rate.

[0082] It can be explained that based on the data storage device set, all data storage devices are sequentially processed and the data storage devices are identified. Obtaining the device serial number means sorting all the data storage devices in the data storage device set and uniquely identifying the sorted data storage devices for identifying the data storage devices. The unique identification means that the identification is unique, and each data storage device has a unique identification. The device serial number refers to the serial number obtained after sorting and uniquely identifying the data storage devices, which is used to identify the data storage device. The current storage capacity refers to the amount of data stored in the data storage device. The data access rate refers to the frequency with which the data storage device is accessed. A high data access frequency means that the data storage device needs to process more accesses in a short period of time, which will increase the load. The data update rate refers to the data storage The rate at which data is updated in the device. The access parameter refers to the degree of influence of the data access frequency on the data storage index. The storage parameter refers to the degree of influence of the historical storage growth rate on the data storage index. The update parameter refers to the degree of influence of the data update rate on the data storage index. The maximum storage capacity refers to the maximum amount of data that the data storage device can store. The maximum access frequency refers to the upper limit of the data access frequency of the data storage device set in advance. Exceeding the maximum access frequency will cause the data storage device to be overloaded. The historical storage growth rate refers to the speed at which data has grown in the data storage device over a certain period of time in the past. For example, the historical storage growth rate is 100GB / month. The maximum storage growth rate refers to the maximum speed at which data grows in the data storage device over a certain period of time. The maximum update rate refers to the maximum rate at which data is updated in the data storage device.

[0083] In detail, the data storage index is calculated based on the device serial number, current storage capacity, data access frequency, data update rate, access parameter, storage parameter, update parameter, maximum storage capacity, maximum access frequency, historical storage growth rate, maximum storage growth rate and maximum update rate, including:

[0084] The data storage index is calculated using the following formula:

[0085]

[0086] Among them, T α Refers to the data storage index of the data storage device with device serial number α, α refers to the device serial number, Y α Refers to the current storage capacity of the data storage device with device serial number α, Uα Refers to the maximum storage capacity of the data storage device with device serial number α, I α Refers to the data access frequency of the data storage device with device serial number α, O α refers to the maximum access frequency of the data storage device with device number α, β refers to the access parameter, e refers to the natural constant, P α ′ refers to the historical storage growth rate of the data storage device with device number α, P α It refers to the maximum storage growth rate of the data storage device with device serial number α, γ refers to the storage parameter, A α ′ refers to the data update rate of the data storage device with device serial number α, A α It refers to the maximum update rate of the data storage device with device number α, and δ refers to the update parameter.

[0087] In detail, the method of extracting data using a data extraction unit and a data storage index to obtain joint extraction data includes:

[0088] Generate a data response instruction, and send the data response instruction to all data storage devices in the data storage device set, and receive a data extraction instruction returned by the data storage device based on a preset waiting time;

[0089] Marking the data storage device that returns the data extraction command as a responded storage device, and marking the data storage device that does not return the data extraction command as a non-responded storage device;

[0090] sorting the data storage devices according to the data storage index to obtain an extraction order;

[0091] Detect the unresponsive device number of the unresponsive storage device, detect the management device of the unresponsive storage device based on the unresponsive device number, transmit the unresponsive device number to the management device and extract data based on the data extraction instruction, the responded storage device and the extraction order to obtain joint extraction data.

[0092] It can be explained that the data response instruction refers to an instruction for detecting whether the data storage device is capable of data extraction, the waiting time refers to the pre-set time for waiting for the data storage device to return the data extraction instruction. Exceeding the waiting time indicates that there is a problem with the data storage device and data extraction cannot be performed in time. The data extraction instruction refers to an instruction received by the data storage device and returned within the waiting time. The instruction indicates that the data storage device functions normally and data extraction can be performed. The responded storage device refers to the data storage device that has returned the data extraction instruction within the waiting time. The unresponsive storage device refers to the data storage device that has not returned the data extraction instruction within the waiting time. The extraction order refers to the order for data extraction obtained by sorting from large to small according to the data storage index of each data storage device. The unresponsive device number refers to the number of the unresponsive storage device, which is unique. The management device that manages the unresponsive storage device can be identified through this unresponsive device number. The management device refers to a computer that manages the data storage device. The management device can identify the corresponding unresponsive storage device according to the received unresponsive device number and adjust the identified unresponsive storage device.

[0093] For example, assume that there are four data storage devices in a data storage device collection, namely device No. 1, device No. 2, device No. 3 and device No. 4, generate a data response instruction, send the data response instruction to the data storage device, and after a waiting time, receive data extraction instructions sent back by device No. 1, device No. 2 and device No. 3, mark device No. 1, device No. 2 and device No. 3 as responded storage devices, mark device No. 4 as unresponsive storage device, the data storage index of device No. 1 is 0.9, the data storage index of device No. 2 is 0.5, and the data storage index of device No. 3 is 0.8, then sort them, and the extraction order is: device No. 1, device No. 3, device No. 2, and data extraction is performed on device No. 1, device No. 3 and device No. 2 based on the data extraction instruction and the extraction order to obtain joint extraction data.

[0094] S3. Set the test time, perform data transmission test based on the test time and calculate the transmission balance index, transmit the joint extraction data to the data integration unit according to the transmission balance index and the data transmission unit and perform integration to obtain joint integration data.

[0095] To explain, test time refers to the time for data transmission test, data transmission test refers to the test of the data transmission rate of the data transmission unit, data transmission rate refers to the rate at which the data transmission unit transmits data, transmission balance index refers to the parameter for evaluating the load of the data transmission unit, data integration unit refers to the unit for integrating joint extracted data, and joint integrated data refers to the data obtained after integrating the joint extracted data.

[0096] In detail, the calculation of the transmission balance index includes:

[0097] Marking is performed based on the data transmission unit to obtain a marked transmission unit, wherein the data transmission unit includes: a main line transmission unit, a first auxiliary line unit and a second auxiliary line unit, and the marked transmission unit includes: a No. 1 transmission unit, a No. 2 transmission unit and a No. 3 transmission unit;

[0098] Obtain the data transmission rate, the maximum transmission rate, the weight parameter and the marking index, and calculate the transmission balance index based on the marking transmission unit, the data transmission rate, the maximum transmission rate, the weight parameter and the marking index:

[0099]

[0100] Among them, Q refers to the transmission balance index, i refers to the marking index, and E i refers to the current data transmission rate of the tag transmission unit with tag index i, W i refers to the maximum transmission rate of the tag transmission unit with tag index i, ε i Refers to the weight parameter of the tag transmission unit with tag index i.

[0101] It can be explained that the marked transmission unit refers to the unit obtained after marking the data transmission unit, the main line transmission unit refers to the unit that mainly performs data transmission, and the main line transmission unit is used for data transmission preferentially. The first auxiliary line unit and the second auxiliary line unit refer to the units that assist the main line transmission unit in data transmission. The task of data transmission is shared with the main line transmission unit according to the transmission balance index. Transmission unit No. 1 refers to the main line transmission unit after marking, transmission unit No. 2 refers to the first auxiliary line unit after marking, and transmission unit No. 3 refers to the second auxiliary line unit after marking. The maximum transmission rate refers to the maximum rate at which the data transmission unit transmits data. The weight parameter refers to the influence of the marked transmission unit on the transmission balance index. The marked index refers to the index for matching the marked transmission unit. When i=1, the main line transmission unit is matched, when i=2, the first auxiliary line unit is matched, and when i=3, the second auxiliary line unit is matched.

[0102] In detail, the transmission balance index and the data transmission unit transmit the joint extraction data to the data integration unit and perform integration to obtain the joint integration data, including:

[0103] Set high load interval, medium load interval, low load interval and interval allocation rules;

[0104] The interval allocation rule is:

[0105] Low load range: 0≤Q≤0.5, the main line transmission unit transmits joint extraction data alone;

[0106] Medium load range: 0.5 < Q ≤ 0.7. The joint-extracted data is transmitted in the ratio of 70% to the main transmission unit and 30% to the first auxiliary line unit.

[0107] High load range: 1 ≥ Q > 0.7. The joint-extracted data is transmitted in the ratio of 50% to the main transmission unit, 25% to the first auxiliary line unit, and 25% to the second auxiliary line unit.

[0108] Based on the interval allocation rule, the transmission balance index is determined to obtain the allocation ratio. Based on the allocation ratio and the data transmission unit, the joint-extracted data is transmitted to the data integration unit and integrated to obtain the joint integrated data.

[0109] Interpretably, the high load range, medium load range, and low load range are intervals set according to the transmission balance index, which reflects the load of the data transmission unit. The interval allocation rule refers to the rule for task allocation to the data transmission unit according to the high load range, medium load range, and low load range. Task allocation refers to the allocation of the task volume to the main transmission unit, the first auxiliary line unit, and the second auxiliary line unit. The task volume refers to the amount of data transmitted by the data transmission unit. The allocation ratio refers to the ratio specified by the interval allocation rule.

[0110] S4. Transmit the joint integrated data to the data storage unit and define the joint disease ontology.

[0111] Interpretably, the joint disease ontology refers to the structured expression of the joint integrated data. It connects the joint integrated data through logical relationships and stores it in a graph database. The structured expression refers to the organization, classification, and description in a standardized manner, enabling the joint integrated data to have a clear hierarchy and logical relationships, so as to be efficiently stored, queried, and analyzed.

[0112] Specifically, the definition of the joint disease ontology includes:

[0113] Define the joint part, management personnel, detection category, and health event;

[0114] Based on the joint part, management personnel, detection category, and health event, define the joint disease ontology.

[0115] Interpretably, the joint part refers to joints in different parts, such as the knee joint and hip joint. The management personnel refer to doctors. The detection category refers to the examination methods, such as MRI and X-ray. The health event refers to events related to joints, such as diagnosis and surgery.

[0116] S5. Based on the joint disease ontology, construct a joint management structure, a joint evaluation structure, and a joint evolution structure.

[0117] Interpretably, the joint management structure refers to the structure that manages the relationship between joint parts, managers, detection categories and health events; the joint evaluation structure refers to the structure that evaluates the health status of the joints; and the joint evolution structure refers to the structure that describes the changing process of the joints at different stages.

[0118] In detail, the construction of the joint management structure, the joint evaluation structure and the joint evolution structure includes:

[0119] Define the joint management relationship between joint parts and management personnel, define the joint detection relationship between joint parts and detection categories, and define the joint event relationship between joint parts and health events;

[0120] Building a joint management structure based on the joint management relationship, joint detection relationship and joint event relationship;

[0121] Set the functional integrity, joint health status, biochemical indicators and imaging assessment contents, and build the joint assessment structure based on the functional integrity, joint health status, biochemical indicators and imaging assessment contents;

[0122] Define the change process and time relationship, and build the joint evolution structure based on the joint health status, change process and time relationship.

[0123] Interpretable, joint management relationship refers to the relationship between doctors and joint parts, for example, a doctor is responsible for treating a patient's knee disease, joint detection relationship refers to the relationship between joint parts and detection categories, indicating the examination method accepted by the joint part, for example, the knee joint underwent MRI examination, and joint event relationship refers to the relationship between joint parts and health events, for example, a patient's knee joint underwent knee surgery.

[0124] For example, the joint management relationship: Dr. Li, an arthrologist, is responsible for the right knee joint of patient A, the joint detection relationship: patient A's right knee joint underwent an X-ray examination, and the joint event relationship: patient A underwent knee surgery.

[0125] Understandably, functional integrity refers to the basic functions of the joints, such as the range of motion of the joints, muscle strength, etc., joint health refers to the health status of the joints, such as whether there is inflammation, effusion, cartilage damage, etc. in the joints, biochemical indicators refer to indicators that reflect the health of the joints through synovial fluid, such as an increase in white blood cells in the synovial fluid and the presence of inflammation, and imaging assessment content refers to the content of evaluating joint damage through imaging examinations.

[0126] For example, functional integrity: the range of motion of the right knee joint is 90 degrees, joint health status: there is cartilage damage in the right knee joint, biochemical indicators: synovial fluid analysis shows leukocytosis and mild inflammation, imaging evaluation content: MRI shows cartilage damage in the right knee joint and fluid accumulation in the joint cavity.

[0127] Interpretable, the change process refers to the change process of the health status of the joint, for example, injury-inflammation-recovery, and the time relationship refers to the time sequence of recording changes in the health status of the joint, for example, January 2022: healthy, March 2022: mild inflammation.

[0128] S6. Design joint data storage rules based on the joint management structure, joint evaluation structure and joint evolution structure.

[0129] Interpretably, joint data storage rules refer to the rules designed to ensure the integrity, accuracy and efficiency of joint disease data during the storage process of joint disease data.

[0130] In detail, the design joint data storage rules include:

[0131] Uniquely identify the joint to obtain the joint identifier, set the joint ID based on the joint identifier, obtain the patient ID, and construct a joint node based on the joint part, the joint ID and the patient ID;

[0132] Based on the identification of the management personnel, a management ID is obtained, and the management name and management department are obtained, and a management node is constructed based on the management ID, management name and management department;

[0133] Obtaining a test date and a test result, and constructing a test node based on the test category, the test date and the test result;

[0134] Get the event date and event severity, and construct an event node based on the health event, event date and event severity

[0135] Constructing node storage rules based on the joint nodes, management nodes, detection nodes and event nodes, and obtaining joint node attributes, management node attributes, detection node attributes and event node attributes;

[0136] Constructing attribute storage rules based on the joint node attributes, management node attributes, detection node attributes and event node attributes;

[0137] The joint management structure is used to construct relationship storage rules, and joint data storage rules are designed according to the node storage rules, attribute storage rules and relationship storage rules.

[0138] Explainable, joint identification refers to a mark, which marks the human joints to confirm the joint position, joint ID refers to the number representing the joint, each joint ID represents a joint, patient ID refers to the number representing the patient, joint node refers to the node storing the basic information of the joint, the basic information of the joint refers to the joint ID, the joint position and the patient ID, management ID refers to the number representing the management personnel, each management personnel has only one management ID, and the management ID is unique, management name refers to the name of the management personnel, management department refers to the department where the management personnel is located, manager node refers to the node storing the basic information of the manager, the basic information of the manager refers to the management ID, management name and management department, test date refers to the date of the test, test result refers to the result of the test, test node refers to the node storing the test information, the test information refers to the test category, test date and test Results: event date refers to the time when the event was sent, event severity refers to the severity of the event, event node refers to the node that stores basic event information, event basic information refers to event date, event severity and health events, node storage rules refer to the rules for storing nodes in the graph database, joint node attributes refer to data used to describe joint nodes, management node attributes refer to data used to describe management nodes, detection node attributes refer to data used to describe detection nodes, event node attributes refer to data used to describe event nodes, attribute storage rules refer to the rules for storing joint node attributes, management node attributes, detection node attributes and event node attributes in the graph database, and relationship storage rules refer to using joint management relationships to connect joint parts and management personnel, using joint detection relationships to connect joint parts and detection categories, and using joint event relationships to connect joint parts and health events.

[0139] For example, assuming that on January 20, 2022, a patient's knee joint needs to receive an MRI examination due to a moderate sprain and is diagnosed as soft tissue injury by a doctor on January 21, 2022, a joint node is established, and the joint node includes: joint ID, i.e., the ID of the knee joint, joint part, i.e., the knee joint, and patient ID; a management node is established, and the management node includes: management ID, i.e., the ID of the doctor in charge of the patient, management name, i.e., the name of the doctor in charge of the patient, management department, i.e., the department where the doctor in charge of the patient is located; a detection node is established, and the detection node includes: detection category, i.e., MRI, detection date, i.e., January 21, 2022, detection result, i.e., soft tissue injury; an event node is established, and the event node includes: health event The event is sprain, the event date is January 20, 2022, and the event severity is moderate sprain. A joint assessment structure is established, which includes: functional integrity, joint health status, biochemical indicators and imaging evaluation content. The functional integrity is that the patient's knee joint range of motion is 70 to 120 degrees, and the joint health status is soft tissue injury. The biochemical indicators are that the white blood cells in the synovial fluid are higher than the normal level, and there is mild inflammation. The imaging evaluation content is MRI showing soft tissue injury. A joint evolution structure is established, which includes: joint health status, change process and time relationship. The joint health status is soft tissue injury, the change process is health-soft tissue injury, and the time relationship is January 21, 2022, soft tissue injury.

[0140] S7. Construct a disease graph database, map the joint disease data to the disease graph database based on the joint data storage rules, and complete the regularization of the joint disease data.

[0141] It can be explained that the disease graph database refers to a graph database used to store joint disease data. The graph database is Neo4j, which is a prior art and will not be described in detail here.

[0142] The present invention is to solve the problems described in the background technology. The present invention realizes the regularized processing of joint disease data by combining the acquisition instructions of receiving joint disease data and constructing a comprehensive data processing flow covering multiple steps such as data storage, transmission test, data integration and joint disease ontology construction. It not only ensures the accuracy and consistency of joint disease data during collection and storage, but also improves the efficiency and accuracy of data processing through efficient data transmission and integration technology. In addition, by constructing storage rules and disease graph databases, multi-source data are managed in a standardized manner, which significantly improves the support capabilities for the diagnosis, evaluation and treatment of joint diseases, and meets the needs of modern medicine for accurate and efficient data processing. First, by automatically receiving the acquisition instructions of joint disease data, the timeliness and accuracy of joint disease data collection are ensured. This automated process reduces manual intervention, improves the efficiency of data collection, and also avoids human errors, providing a solid foundation for subsequent data processing and analysis. Secondly, the data processing unit is started to effectively coordinate and manage various data processing tasks, including data extraction, integration, storage, etc., which improves work efficiency, reduces delays in each link, and automatically The operation reduces the complexity of manual operation and improves the operation efficiency of the system; afterwards, the data storage index of the data storage device is calculated. By calculating the data storage index of the data storage device, the performance and load of the storage device can be effectively evaluated, the efficiency of the data storage process is ensured, the allocation of storage resources is optimized, the appearance of data storage bottlenecks is avoided, and the reliability and efficiency of data storage are improved; then, the data transmission test is performed based on the test time and the transmission balance index is calculated. By calculating the transmission balance index, the load of the data transmission unit can be known. According to the load of the data transmission unit, the data to be transmitted can be allocated, avoiding data transmission delays due to excessive load, and improving the efficiency and stability of data transmission; afterwards, the joint management structure, joint evaluation structure and joint evolution structure are constructed through the joint disease ontology, and the various characteristics of joint diseases are described and analyzed more comprehensively, and a clear data management framework is established; finally, the joint data storage rules are designed to ensure the standardization and consistency of data storage, reduce data redundancy and confusion, improve the query and access efficiency of data, promote the circulation and sharing of data between different links and systems, and provide support for large-scale data analysis. Therefore, the present invention can improve data processing efficiency.

[0143] like Figure 2 The figure shows a functional module diagram of a joint disease data regularization system based on MRI combined with big data provided by one embodiment of the present invention.

[0144] The joint disease data regularization system 100 based on MRI combined with big data of the present invention can be installed in an electronic device. According to the functions to be implemented, the joint disease data regularization system 100 based on MRI combined with big data can include a joint data extraction module 101, an extraction data integration module 102, a storage rule design module 103 and a disease data mapping module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0145] The joint data extraction module 101 is used to receive a data acquisition instruction for joint disease data, and start a pre-built data processing unit based on the data acquisition instruction, wherein the data processing unit includes: a data extraction unit, a data transmission unit, a data integration unit and a data storage unit, and the joint disease data includes: MRI data; obtain a data storage device set, calculate the data storage index of all data storage devices in the data storage device set, and use the data extraction unit and the data storage index to extract data to obtain joint extraction data;

[0146] The extracted data integration module 102 is used to set the test time, perform data transmission test based on the test time and calculate the transmission balance index, transmit the joint extracted data to the data integration unit according to the transmission balance index and the data transmission unit and perform integration to obtain joint integration data;

[0147] The storage rule design module 103 is used to transfer the joint integrated data to the data storage unit and define the joint disease ontology; construct a joint management structure, a joint evaluation structure and a joint evolution structure based on the joint disease ontology; and design a joint data storage rule based on the joint management structure, the joint evaluation structure and the joint evolution structure;

[0148] The disease data mapping module 104 is used to construct a disease map database, map the joint disease data to the disease map database based on the joint data storage rules, and complete the regularization of the joint disease data.

[0149] In detail, each module in the joint disease data regularization system 100 based on MRI combined with big data in the embodiment of the present invention is used in the same manner as described above. Figure 1 The same technical means are used as the method for regularizing joint disease data based on MRI combined with big data described in the text, and can produce the same technical effects, so I will not go into details here.

[0150] like Figure 3 , is a schematic diagram of the structure of an electronic device for implementing a method for regularizing joint disease data based on MRI combined with big data, provided by one embodiment of the present invention.

[0151] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a joint disease data regularization method program based on MRI combined with big data.

[0152] Wherein, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the joint disease data regularization method program based on MRI combined with big data, but also can be used to temporarily store data that has been output or is to be output.

[0153] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, and executes or executes the programs or modules stored in the memory 11 (for example, a joint disease data regularization method program based on MRI combined with big data, etc.), and calls the data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0154] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0155] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0156] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0157] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0158] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0159] The program of the joint disease data regularization method based on MRI combined with big data stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0160] Receiving a data acquisition instruction for joint disease data, and starting a pre-built data processing unit based on the data acquisition instruction, wherein the data processing unit includes: a data extraction unit, a data transmission unit, a data integration unit and a data storage unit, and the joint disease data includes: MRI data;

[0161] Acquire a data storage device set, calculate the data storage index of all data storage devices in the data storage device set, extract data using a data extraction unit and the data storage index, and obtain joint extraction data;

[0162] Setting a test time, performing a data transmission test based on the test time and calculating a transmission balance index, transmitting the joint extraction data to a data integration unit according to the transmission balance index and the data transmission unit and performing integration to obtain joint integration data;

[0163] transferring joint integration data to a data storage unit and defining a joint disease ontology;

[0164] Constructing a joint management structure, a joint evaluation structure and a joint evolution structure based on the joint disease ontology;

[0165] Designing joint data storage rules based on the joint management structure, the joint evaluation structure and the joint evolution structure;

[0166] A disease graph database is constructed, and joint disease data is mapped to the disease graph database based on the joint data storage rules to complete the regularization of joint disease data.

[0167] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0168] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0169] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0170] Receiving a data acquisition instruction for joint disease data, and starting a pre-built data processing unit based on the data acquisition instruction, wherein the data processing unit includes: a data extraction unit, a data transmission unit, a data integration unit and a data storage unit, and the joint disease data includes: MRI data;

[0171] Acquire a data storage device set, calculate the data storage index of all data storage devices in the data storage device set, extract data using a data extraction unit and the data storage index, and obtain joint extraction data;

[0172] Setting a test time, performing a data transmission test based on the test time and calculating a transmission balance index, transmitting the joint extraction data to a data integration unit according to the transmission balance index and the data transmission unit and performing integration to obtain joint integration data;

[0173] transferring joint integration data to a data storage unit and defining a joint disease ontology;

[0174] Constructing a joint management structure, a joint evaluation structure and a joint evolution structure based on the joint disease ontology;

[0175] Designing joint data storage rules based on the joint management structure, the joint evaluation structure and the joint evolution structure;

[0176] A disease graph database is constructed, and joint disease data is mapped to the disease graph database based on the joint data storage rules to complete the regularization of joint disease data.

[0177] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0178] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0179] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0180] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A joint disease data regularization method based on MRI combined with big data, characterized in that: The method comprises: Receiving a data acquisition instruction for joint disease data, and starting a pre-built data processing unit based on the data acquisition instruction, wherein the data processing unit includes: a data extraction unit, a data transmission unit, a data integration unit and a data storage unit, and the joint disease data includes: MRI data; Acquire a data storage device set, calculate the data storage index of all data storage devices in the data storage device set, extract data using a data extraction unit and the data storage index, and obtain joint extraction data; Setting a test time, performing a data transmission test based on the test time and calculating a transmission balance index, transmitting the joint extraction data to a data integration unit according to the transmission balance index and the data transmission unit and performing integration to obtain joint integration data; transferring joint integration data to a data storage unit and defining a joint disease ontology; Constructing a joint management structure, a joint evaluation structure and a joint evolution structure based on the joint disease ontology; Designing joint data storage rules based on the joint management structure, the joint evaluation structure and the joint evolution structure; A disease graph database is constructed, and joint disease data is mapped to the disease graph database based on the joint data storage rules to complete the regularization of joint disease data.

2. The joint disease data regularization method based on MRI combined with big data as claimed in claim 1, characterized in that: The step of calculating the data storage index of all data storage devices in the data storage device set includes: Based on the data storage device set, sequential processing is performed on all data storage devices and the data storage devices are identified to obtain device serial numbers; Detect current storage capacity, data access frequency and data update rate, and set access parameters, storage parameters and update parameters; The maximum storage capacity, maximum access frequency, historical storage growth rate, maximum storage growth rate and maximum update rate are obtained, and the data storage index is calculated based on the device serial number, current storage capacity, data access frequency, data update rate, access parameters, storage parameters, update parameters, maximum storage capacity, maximum access frequency, historical storage growth rate, maximum storage growth rate and maximum update rate.

3. The joint disease data regularization method based on MRI combined with big data as claimed in claim 2, characterized in that: The data storage index is calculated based on the device serial number, current storage capacity, data access frequency, data update rate, access parameter, storage parameter, update parameter, maximum storage capacity, maximum access frequency, historical storage growth rate, maximum storage growth rate and maximum update rate, including: The data storage index is calculated using the following formula: Among them, T α Refers to the data storage index of the data storage device with device serial number α, α refers to the device serial number, Y α Refers to the current storage capacity of the data storage device with device serial number α, U α Refers to the maximum storage capacity of the data storage device with device serial number α, I α Refers to the data access frequency of the data storage device with device serial number α, O α refers to the maximum access frequency of the data storage device with device number α, β refers to the access parameter, e refers to the natural constant, P α ′ refers to the historical storage growth rate of the data storage device with device number α, P α It refers to the maximum storage growth rate of the data storage device with device serial number α, γ refers to the storage parameter, A α ′ refers to the data update rate of the data storage device with device serial number α, A α It refers to the maximum update rate of the data storage device with device number α, and δ refers to the update parameter.

4. The joint disease data regularization method based on MRI combined with big data as claimed in claim 3, characterized in that: The method of extracting data using a data extraction unit and a data storage index to obtain joint extraction data includes: Generate a data response instruction, and send the data response instruction to all data storage devices in the data storage device set, and receive a data extraction instruction returned by the data storage device based on a preset waiting time; Marking the data storage device that returns the data extraction command as a responded storage device, and marking the data storage device that does not return the data extraction command as a non-responded storage device; sorting the data storage devices according to the data storage index to obtain an extraction order; Detect the unresponsive device number of the unresponsive storage device, detect the management device of the unresponsive storage device based on the unresponsive device number, transmit the unresponsive device number to the management device, and perform data extraction based on the data extraction instruction, the responsive storage device, and the extraction order to obtain joint extraction data.

5. The joint disease data regularization method based on MRI combined with big data as claimed in claim 4, characterized in that: The calculating the transmission balance index includes: Perform marking based on the data transmission unit to obtain a marked transmission unit. The data transmission unit includes: a main line transmission unit, a first auxiliary line unit, and a second auxiliary line unit. The marked transmission unit includes: a first transmission unit, a second transmission unit, and a third transmission unit; Obtain the data transmission rate, the maximum transmission rate, the weight parameter, and the marking index, and calculate the transmission balance index based on the marked transmission unit, the data transmission rate, the maximum transmission rate, the weight parameter, and the marking index: Among them, Q refers to the transmission balance index, i refers to the marking index, and E i refers to the current data transmission rate of the tag transmission unit with tag index i, W i refers to the maximum transmission rate of the tag transmission unit with tag index i, ε i Refers to the weight parameter of the tag transmission unit with tag index i.

6. The joint disease data regularization method based on MRI combined with big data as claimed in claim 5, characterized in that: The transmitting the joint extraction data to the data integration unit according to the transmission balance index and the data transmission unit and performing integration to obtain joint integration data includes: Set a high-load interval, a medium-load interval, a low-load interval, and an interval allocation rule; The interval allocation rule is: Low-load interval: 0≤Q≤0.5, the main line transmission unit transmits the joint extraction data alone; Medium-load interval: 0.5<Q≤0.7, the joint extraction data is transmitted in a ratio of 70% for the main line transmission unit and 30% for the first auxiliary line unit; High-load interval: 1≥Q>0.7, the joint extraction data is transmitted in a ratio of 50% for the main line transmission unit, 25% for the first auxiliary line unit, and 25% for the second auxiliary line unit; Judge the transmission balance index based on the interval allocation rule to obtain an allocation ratio, and transmit the joint extraction data to the data integration unit based on the allocation ratio and the data transmission unit and perform integration to obtain joint integration data.

7. The joint disease data regularization method based on MRI combined with big data as claimed in claim 6, characterized in that: The defining the joint disease ontology includes: Define joint parts, management personnel, detection categories, and health events; Define the joint disease ontology based on the joint parts, management personnel, detection categories, and health events.

8. The joint disease data regularization method based on MRI combined with big data as claimed in claim 7, characterized in that: The constructing the joint management structure, the joint evaluation structure, and the joint evolution structure includes: Define the joint management relationship between the joint part and the management personnel, define the joint detection relationship between the joint part and the detection category, and define the joint event relationship between the joint part and the health event; Construct the joint management structure based on the joint management relationship, the joint detection relationship, and the joint event relationship; Set the functional integrity, the joint health status, the biochemical index, and the imaging evaluation content, and construct the joint evaluation structure based on the functional integrity, the joint health status, the biochemical index, and the imaging evaluation content; Define the change process and the time relationship, and construct the joint evolution structure based on the joint health status, the change process, and the time relationship.

9. The joint disease data regularization method based on MRI combined with big data as claimed in claim 8, characterized in that: The designing the joint data storage rule includes: Uniquely identify the joint to obtain a joint identifier, set the joint ID based on the joint identifier, obtain the patient ID, and construct a joint node based on the joint part, the joint ID, and the patient ID; Identify based on the management personnel to obtain a management ID, and obtain the management name and management department, and construct a management node based on the management ID, the management name, and the management department; Obtaining a test date and a test result, and constructing a test node based on the test category, the test date and the test result; Get the event date and event severity, and construct an event node based on the health event, event date and event severity Constructing node storage rules based on the joint nodes, management nodes, detection nodes and event nodes, and obtaining joint node attributes, management node attributes, detection node attributes and event node attributes; Constructing attribute storage rules based on the joint node attributes, management node attributes, detection node attributes and event node attributes; The joint management structure is used to construct relationship storage rules, and joint data storage rules are designed according to the node storage rules, attribute storage rules and relationship storage rules.

10. A joint disease data regularization system based on MRI combined with big data, characterized in that: The system comprises: A joint data extraction module is used to receive a data acquisition instruction for joint disease data, and start a pre-built data processing unit based on the data acquisition instruction, wherein the data processing unit includes: a data extraction unit, a data transmission unit, a data integration unit and a data storage unit, and the joint disease data includes: MRI data; obtain a data storage device set, calculate the data storage index of all data storage devices in the data storage device set, and use the data extraction unit and the data storage index to extract data to obtain joint extraction data; The extraction data integration module is used to set the test time, perform data transmission test based on the test time and calculate the transmission balance index, transmit the joint extraction data to the data integration unit according to the transmission balance index and the data transmission unit, and perform integration to obtain joint integration data; A storage rule design module is used to transfer joint integrated data to a data storage unit and define a joint disease ontology; construct a joint management structure, a joint evaluation structure and a joint evolution structure based on the joint disease ontology; and design joint data storage rules based on the joint management structure, the joint evaluation structure and the joint evolution structure; The disease data mapping module is used to construct a disease graph database, map the joint disease data to the disease graph database based on the joint data storage rules, and complete the regularization of the joint disease data.

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