Hemodialysis management system and method

Abstract

The invention discloses a hemodialysis management system and method, and relates to the technical field of data management, the system comprises an information management analysis module, a storage analysis management module and a compression storage analysis module, and solves the problems that retrieval and analysis are difficult due to lack of effective classification management, and the efficiency is high. In order to solve the technical problems that hemodialysis information is classified in the same type according to a hemodialysis scheme, coding processing and index generation are combined, data can be quickly and accurately retrieved and managed, and the data read-write efficiency is influenced, so that the hemodialysis information can be quickly and accurately retrieved and managed. According to the method, the data searching and calling efficiency is improved, the distributed storage nodes are divided into the high-performance nodes and the low-performance nodes according to the performance of the distributed storage nodes, then the high-frequency access information and the high-performance nodes as well as the low-frequency access information and the low-performance nodes are reasonably stored and planned, the data reading and writing speed is increased, and the data access waiting time is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and particularly to a hemodialysis management system and method. Background Art

[0002] In the medical field, hemodialysis, as an important means of treating diseases such as renal failure, generates a large amount of patient data.

[0003] There are many deficiencies in the traditional hemodialysis management method. For example, patient information is scattered and difficult to integrate, and the data lacks effective classification management, resulting in difficulties in retrieval and analysis; in terms of data storage, reasonable planning is not carried out according to the data access frequency and the performance of storage nodes, affecting the read and write efficiency of data; for data processing, including redundancy elimination, format conversion, etc., there is no systematic method, so that the quality and usability of data are affected. With the development of medical informatization, the demand for an efficient and intelligent hemodialysis management system is becoming increasingly urgent. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a hemodialysis management system and method, which solves the problems of lack of effective classification management, resulting in difficulties in retrieval and analysis, and at the same time, reasonable planning is not carried out according to the data access frequency and the performance of storage nodes, affecting the read and write efficiency of data.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A hemodialysis management system, comprising: An information management and analysis module, configured to classify the same type of hemodialysis information to obtain classification information, perform encoding processing to generate index information, bundle the two to generate retrieval management information, and transmit it to the storage analysis and management module; A storage analysis and management module, configured to calculate the corresponding access frequency of the retrieval management information according to the historical data, classify the retrieval management information by comparing with a comparison standard to obtain high-frequency access and low-frequency access information, classify the distributed storage nodes to obtain high-performance nodes and low-performance nodes, and simultaneously perform storage planning on the high-frequency access and low-frequency access information respectively to generate a storage planning signal; Analyze the storage planning signal, partition all high-frequency and low-frequency access information according to time information, evenly divide the storage space of the distributed storage nodes, and store the two at the same time to generate a storage analysis signal and transmit it to the storage compression analysis module; The storage compression analysis module performs redundancy elimination processing on the obtained partition information, obtains the duplicate data therein, replaces it according to the replacement template to obtain duplicate replacement data. Similarly, it replaces the non-duplicate data to obtain non-duplicate replacement data, combines the duplicate replacement data and the non-duplicate replacement data to generate combined data, stores it to generate compressed storage information, and simultaneously transmits it to the management information output module.

[0006] As a further solution of the present invention, it further includes a patient information collection module and a management information output module; The patient information collection module is used to collect the hemodialysis information of the patient and transmit it to the information management analysis module; The management information output module is used to perform storage management on the hemodialysis information according to the obtained compressed storage information.

[0007] As a further solution of the present invention, the specific manner in which the information management analysis module generates retrieval management information is as follows: Obtain the patient's hemodialysis information, classify it according to the dialysis plan to obtain classification information, labeled as i, and i = a, b,..., n, where n is the number of classification types. Obtain the hemodialysis information corresponding to classification i, and then label it as k, and k = 1, 2,..., m, where m is the number of information under this classification; Encode the hemodialysis information k in the classification information i to generate corresponding index information, and combine and bundle the obtained index information with the hemodialysis information k to generate retrieval management information.

[0008] As a further solution of the present invention, the specific manner in which the storage analysis management module obtains high-frequency access and low-frequency access information is as follows: Obtain the historical access records of the retrieval management information k, count the number of accesses Ck within the time t, calculate the access frequency Pk, and calculate the average value of all Pk to obtain the comparison standard P0; If Pk > P 0 , mark the information k as high-frequency access information, and conversely if Pk ≤ P 0 , mark it as low-frequency access information.

[0009] As a further solution of the present invention, the specific manner in which the storage analysis management module generates a storage planning signal is as follows: Obtain all distributed storage nodes, classify them into high-performance and low-performance nodes according to node performance, and make storage plans for high-frequency access information and high-performance nodes, and low-frequency access information and low-performance nodes respectively to generate a storage planning signal.

[0010] As a further solution of the present invention, the specific manner in which the storage analysis management module analyzes the storage planning signal is as follows: Select a group of high-performance nodes for analysis, obtain the corresponding high-frequency access information and information time, partition the high-frequency access information according to the information time, and generate partition information; At the same time, the node storage space is evenly divided, the partition information is stored in chronological order, and a storage analysis signal is generated. The same process is performed on all high-performance and low-performance nodes, and a storage analysis signal is generated and transmitted to the storage compression analysis module.

[0011] As a further solution of the present invention, the specific manner in which the storage compression analysis module generates compressed storage information is: Obtain all partition information, remove blanks and useless redundant content, extract duplicate data in the partition information after redundant removal, generate a replacement template, replace the duplicate data according to the template to obtain duplicate replacement data, obtain the remaining data, replace it according to its properties and corresponding rules, and generate non-duplicate replacement data; The repeated replacement data and the non-repeated replacement data are combined to obtain combined data, and the combined data is stored according to the obtained combined data to generate corresponding compressed storage information.

[0012] As a further solution of the present invention, the specific manner in which the storage compression analysis module generates non-repetitive replacement data is: For text data, use the search and replace function in the template or create a code and name mapping table to replace the code with the full name; For numerical data, according to the dialysis data requirements, use formulas or functions to complete the calculation and display it in the template in percentage form; For date data, analyze the date in the database and convert it with the help of date formatting function to get the conversion number.

[0013] A hemodialysis management method, the method specifically comprising the following steps: Step 1: classify the acquired hemodialysis information to obtain classification information, perform coding processing to generate index information, and bundle the two to generate retrieval management information; Step 2: Calculate the corresponding access frequency of the retrieval management information according to its historical data, and classify the retrieval management information by comparing it with the comparison standard to obtain high-frequency access and low-frequency access information; Step 3: Classify the distributed storage nodes to obtain high-performance nodes and low-performance nodes, and perform storage planning for high-frequency access and low-frequency access information respectively to generate storage planning signals; Step 4: partition all high-frequency and low-frequency access information according to time information, and evenly divide the storage space of the distributed storage nodes, store both at the same time, and generate a storage analysis signal; Step 5. Perform redundancy elimination processing on the obtained partition information, obtain the duplicate data therein, perform replacement according to the replacement template to obtain duplicate replacement data, and similarly perform replacement on the non-duplicate data to obtain non-duplicate replacement data; Step 6. Combine the duplicate replacement data and the non-duplicate replacement data to generate combined data, and store it to generate compressed storage information.

[0014] The present invention provides a hemodialysis management system and method. Compared with the prior art, it has the following beneficial effects: By classifying hemodialysis information of the same type according to the dialysis plan, and combining encoding processing and index generation, the present invention can quickly and accurately retrieve and manage data, improve the efficiency of data search and call. According to the performance of distributed storage nodes, they are divided into high-performance and low-performance nodes, and then the high-frequency access information is reasonably stored with high-performance nodes, and the low-frequency access information is reasonably stored with low-performance nodes, which improves the read and write speed of data and reduces the data access waiting time.

[0015] In the storage compression analysis module of the present invention, redundancy elimination processing is performed on the partition information to remove blank and useless information. At the same time, reasonable replacement processing is performed on duplicate data and non-duplicate data respectively, which reduces the waste of data storage space. By formatting numerical data, converting the date data format and other operations, the readability and availability of data are improved, providing a more reliable data basis for subsequent data analysis and medical decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is the system principle block diagram of the present invention; Figure 2 is the step method diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. Embodiment 1

[0018] Please refer to Figure 1 , the present application provides a hemodialysis management system, including a patient information collection module, an information management and analysis module, a storage analysis and management module, a compression storage analysis module, and a management information output module, and in combination with Figure 1 it can be known that the above functional modules are unidirectionally electrically connected.

[0019] Patient information collection module, which is used to collect the hemodialysis information of patients and transmit it to the information management and analysis module. The hemodialysis information includes the personal information of patients and the corresponding dialysis information. Specifically, the personal information includes name, age, gender, medical history, and allergy history, and the dialysis information includes dialysis plan, dialysis record, and complication situation.

[0020] Information management and analysis module, which is used to classify and manage the obtained hemodialysis information. The specific classification and management method is as follows: Obtain the hemodialysis information of patients, classify the hemodialysis information of the same type according to the dialysis plan to obtain classification information, and at the same time label it as i, and i = a, b,..., n, where n represents the types of classification information, and obtain the corresponding hemodialysis information in classification information i, and at the same time label it as k, and k = 1, 2,..., m, where m represents the number of hemodialysis information; Then, encode the hemodialysis information k in classification information i. For example, assign a unique ID code to each patient, using a combination of letters and numbers, such as "PAT0001", where "PAT" represents the patient, and the following numbers are sequential numbers; for dialysis treatment items, digital coding can be used, such as "01" representing conventional hemodialysis, "02" representing hemofiltration, etc. At the same time, generate the corresponding index information, combine and bundle the obtained index information with the hemodialysis information k to generate retrieval management information, and transmit it to the storage analysis management module.

[0021] Storage analysis management module, which is used to store and analyze the obtained retrieval management information. First, accurately obtain the corresponding historical access records according to the retrieval management information. In this process, set a specific acquisition time range t, and carefully count the corresponding access times Ck within the time t for each item of retrieval management information k; Then, use the formula to calculate the access frequency Pk corresponding to the retrieval management information k. After calculating the access frequencies Pk of all retrieval management information k, according to the formula calculate the mean value of all access frequencies Pk and denote it as the comparison standard P 0 ; Subsequently, carefully compare the access frequency P1 corresponding to each item of retrieval management information k with the comparison standard P 0 If the access frequency Pk is greater than the comparison standard P 0 , then clearly mark the retrieval management information k as high-frequency access information. On the contrary, if the access frequency Pk is less than the comparison standard P 0 , then mark the corresponding retrieval management information k as low-frequency access information.

[0022] For example, assume there are 5 pieces of retrieval management information, denoted as k1, k2, k3, k4, and k5 respectively. Set the acquisition time t to one week (7 days). After statistics, within this week, the access count C of k1 k1 = 10 times. Then its access frequency P k1 is 1.43 times per day; The access count C of k2 k2 = 5 times. Then its access frequency P k2 is 0.71 times per day; The access count C of k3 k3 = 15 times. Then its access frequency P k1 is 2.14 times per day; The access count C of k4 k4 = 3 times. Then its access frequency P k4 is 0.43 times per day; The access count C of k5 k5 = 8 times. Then its access frequency P k5 is 1.14 times per day; Then calculate the mean value P corresponding to the access frequency 0 to obtain P 0 , = 1.17 times per day. Then compare it with the corresponding access frequencies respectively. After comparison, k1 is marked as high-frequency access information, k2 is marked as low-frequency access information, k3 is marked as high-frequency access information, k4 is marked as low-frequency access information, and k5 is marked as low-frequency access information.

[0023] Next, obtain all distributed storage nodes, and classify them into high-performance nodes and low-performance nodes according to the corresponding node performance of the distributed storage nodes. High-performance nodes have faster read / write speeds, larger storage capacities, and shorter response times; low-performance nodes have relatively weaker performance. Then perform storage planning for high-frequency access information with high-performance nodes and low-frequency access information with low-performance nodes to generate corresponding storage planning signals. Here, evenly divide the high-frequency access information and low-frequency access information according to the number of high-performance nodes and low-performance nodes, and store the obtained evenly divided quantities into the corresponding performance nodes respectively; Process the generated storage planning signals. Take any group of high-performance nodes as the analysis object and process the generated storage planning signals. First, obtain the high-frequency access information corresponding to this high-performance node. At the same time, extract the information time (i.e., the time when the information is generated or last modified) corresponding to these high-frequency access information, and perform partition processing on all high-frequency access information according to the information time. For example, it can be divided according to time intervals (such as by day, week, month, etc.) to generate partition information. At the same time, evenly divide the internal storage space of the high-performance node to obtain several evenly divided storage spaces.

[0024] Next, store the partition information into the evenly divided storage space in chronological order of the information to generate a storage analysis signal. Similarly, the same processing is performed on all high-performance nodes and low-performance nodes. For low-performance nodes, obtain their corresponding low-frequency access information, perform partition processing according to the information time, evenly divide the internal storage space of the nodes, and then store the partition information into the evenly divided storage space in chronological order to generate the corresponding storage analysis signal.

[0025] Similarly, the same processing is performed on all high-performance nodes and low-performance nodes to generate storage analysis signals and transmit them to the storage compression analysis module.

[0026] A storage compression analysis module, which is used to perform storage management on blood pressure dialysis information according to the obtained storage analysis signals, and the specific storage management method is as follows: Obtain all partition information and perform redundancy elimination processing on the partition information. Here, the redundancy elimination is to eliminate the blank and useless information in the partition information. At the same time, obtain the duplicate data in the partition information after the redundancy elimination processing and generate the corresponding replacement template. Here, the corresponding capital letters are used in the replacement template. For example, replace all "names" with A, replace "age" with B, and so on, and perform the corresponding replacement processing. Replace the duplicate data with the replacement template to generate duplicate replacement data. At the same time, obtain the remaining data and perform the corresponding replacement according to the nature of the remaining data and the corresponding replacement rules to generate non-duplicate replacement data. The specific replacement method is as follows: For text data, determine how to perform replacement in the template. For example, if the patient's diagnosis information is represented in code form in the original data (such as "DM" represents diabetic nephropathy), a replacement rule can be set in the template to replace the code with the complete diagnosis name ("diabetic nephropathy"). The find-and-replace function or a mapping table of codes and names can be used to achieve this; For numerical data, perform calculations and formatting as needed. For example, in dialysis data, it may be necessary to calculate the dehydration rate based on the patient's weight and the amount of water removed and display the result in percentage form in the template. Formulas or functions can be used to achieve the calculation and formatting of numerical values.

[0027] For date data, determine how to display and process it in the template. For example, convert the dialysis date from the date format in the database (such as "2025-04-16") to a more readable format (such as "April 16, 2025"). Date formatting functions can be used to achieve this; Generate corresponding non-duplicate replacement data based on the above replacement rules, combine the duplicate replacement data and the non-duplicate replacement data to obtain combined data, and here the combined data is combined for a single type of duplicate replacement data and non-duplicate replacement data. For example, for replacement data A of "name", obtain the corresponding name and replace the two, and finally get A: ZH.

[0028] And store according to the obtained combined data to generate corresponding compressed storage information, and at the same time transmit it to the management information output module.

[0029] The management information output module is used to store hemodialysis information according to the obtained compressed storage information. Embodiment 2

[0030] Please refer to Figure 2 , as Embodiment 2 of the present invention, provides a hemodialysis management method, which specifically includes the following steps: Step 1: Classify the obtained hemodialysis information to obtain classification information, perform coding processing to generate index information, and at the same time bundle the two to generate retrieval management information, and the specific processing method is the same as that of the information management analysis module in Embodiment 1; Step 2: Calculate the corresponding access frequency according to the historical data of the retrieval management information, and classify the retrieval management information by comparing with the comparison standard to obtain high-frequency access and low-frequency access information, and the specific processing method is the same as that of the storage analysis management module in Embodiment 1; Step 3: Classify the distributed storage nodes into high-performance nodes and low-performance nodes, and at the same time respectively perform storage planning on high-frequency access and low-frequency access information to generate storage planning signals, and the specific processing method is the same as that of the storage analysis management module in Embodiment 1; Step 4: Partition all high-frequency and low-frequency access information according to time information, evenly divide the storage space of the distributed storage nodes, and at the same time store the two to generate storage analysis signals, and the specific processing method is the same as that of the storage analysis management module in Embodiment 1; Step 5: Perform redundancy elimination processing on the obtained partition information, obtain the duplicate data among them, replace according to the replacement template to obtain duplicate replacement data, and similarly replace the non-duplicate data to obtain non-duplicate replacement data, and the specific processing method is the same as that of the storage compression analysis module in Embodiment 1; Step 6: Combine the duplicate replacement data and the non-duplicate replacement data to generate combined data, and store it to generate compressed storage information, and the specific processing method is the same as that of the storage compression analysis module in Embodiment 1.

[0031] For some of the data in the above formula, only their numerical values are taken for calculation, and the parameter units are not substituted for calculation. At the same time, the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0032] The above embodiments are only used to illustrate the technical method 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 of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A hemodialysis management system, characterized in that: include: The information management and analysis module is used to classify the hemodialysis information of the same type, obtain the classification information, perform coding processing to generate index information, and bundle the two to generate retrieval management information, and transmit it to the storage analysis management module; The storage analysis management module is used to calculate the corresponding access frequency of the retrieval management information according to its historical data, and classify the retrieval management information by comparing it with the comparison standard to obtain high-frequency access and low-frequency access information, and classify the distributed storage nodes to obtain high-performance nodes and low-performance nodes, and respectively perform storage planning for high-frequency access and low-frequency access information to generate storage planning signals; Analyze the storage planning signal, partition all high-frequency and low-frequency access information according to time information, and evenly divide the storage space of distributed storage nodes, store both at the same time, generate storage analysis signals, and transmit them to the storage compression analysis module; The storage compression analysis module performs redundancy elimination processing on the obtained partition information, obtains the duplicate data therein, replaces it according to the replacement template, obtains duplicate replacement data, and similarly replaces the non-duplicate data to obtain non-duplicate replacement data. The duplicate replacement data and the non-duplicate replacement data are combined to generate combined data, and the combined data is stored to generate compressed storage information, which is simultaneously transmitted to the management information output module.

2. A hemodialysis management system according to claim 1, characterized in that: It also includes a patient information collection module and a management information output module; The patient information collection module is used to collect the patient's hemodialysis information and transmit it to the information management and analysis module; The management information output module is used to store and manage the hemodialysis information according to the obtained compressed storage information.

3. A hemodialysis management system according to claim 1, characterized in that: The specific method for the information management analysis module to generate the retrieval management information is: Obtain the patient's hemodialysis information, classify it according to the dialysis plan, and obtain the classified information, labeled as i, where i=a, b, ..., n, n is the number of classification types, obtain the hemodialysis information corresponding to classification i, and then label it as k, where k=1, 2, ..., m, m is the number of information under this classification; The hemodialysis information k in the classification information i is coded to generate corresponding index information, and the obtained index information is combined and bundled with the hemodialysis information k to generate retrieval management information.

4. A hemodialysis management system according to claim 1, characterized in that: The specific method for the storage analysis management module to obtain the high-frequency access and low-frequency access information is as follows: Get the historical access records of the retrieval management information k, count the number of accesses Ck within time t, calculate the access frequency Pk, and calculate the comparison standard P0 for all Pk; If Pk>P0, the information k is marked as high-frequency access information, otherwise if Pk≤P0, it is marked as low-frequency access information.

5. A hemodialysis management system according to claim 1, characterized in that: The specific method in which the storage analysis management module generates the storage planning signal is: All distributed storage nodes are obtained, and they are divided into high-performance nodes and low-performance nodes according to node performance. High-frequency access information and high-performance nodes are stored separately, and low-frequency access information and low-performance nodes are stored separately to generate storage planning signals.

6. A hemodialysis management system according to claim 1, characterized in that: The specific method in which the storage analysis management module analyzes the storage planning signal is as follows: Select a group of high-performance nodes for analysis, obtain the corresponding high-frequency access information and information time, partition the high-frequency access information according to the information time, and generate partition information; At the same time, the node storage space is evenly divided, the partition information is stored in chronological order, and a storage analysis signal is generated. The same process is performed on all high-performance and low-performance nodes, and a storage analysis signal is generated and transmitted to the storage compression analysis module.

7. A hemodialysis management system according to claim 1, characterized in that: The specific method of generating compressed storage information by the storage compression analysis module is as follows: Obtain all partition information, remove blanks and useless redundant content, extract duplicate data in the partition information after redundant removal, generate a replacement template, replace the duplicate data according to the template to obtain duplicate replacement data, obtain the remaining data, replace it according to its properties and corresponding rules, and generate non-duplicate replacement data; The repeated replacement data and the non-repeated replacement data are combined to obtain combined data, and the combined data is stored according to the obtained combined data to generate corresponding compressed storage information.

8. A hemodialysis management system according to claim 7, characterized in that: The specific method of generating non-repetitive replacement data by the storage compression analysis module is as follows: For text data, use the search and replace function in the template or create a code and name mapping table to replace the code with the full name; For numerical data, according to the dialysis data requirements, use formulas or functions to complete the calculation and display it in the template in percentage form; For date data, analyze the date in the database and convert it with the help of date formatting function to get the conversion number.

9. A hemodialysis management method, executed by a hemodialysis management system according to any one of claims 1 to 8, characterized in that: The method specifically comprises the following steps: Step 1: classify the acquired hemodialysis information to obtain classification information, perform coding processing to generate index information, and bundle the two to generate retrieval management information; Step 2: Calculate the corresponding access frequency of the retrieval management information according to its historical data, and classify the retrieval management information by comparing it with the comparison standard to obtain high-frequency access and low-frequency access information; Step 3: Classify the distributed storage nodes to obtain high-performance nodes and low-performance nodes, and perform storage planning for high-frequency access and low-frequency access information respectively to generate storage planning signals; Step 4: partition all high-frequency and low-frequency access information according to time information, and evenly divide the storage space of the distributed storage nodes, store both at the same time, and generate a storage analysis signal; Step 5: perform redundancy elimination processing on the obtained partition information, obtain duplicate data therein, replace it according to the replacement template, and obtain duplicate replacement data. Similarly, replace the non-duplicate data to obtain non-duplicate replacement data; Step 6: Combine the repeated replacement data and the non-repeated replacement data to generate combined data, store the combined data, and generate compressed storage information.

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