Hospital intelligent financial management system based on multi-dimensional data fusion

Abstract

The invention provides a hospital intelligent financial management system based on multi-dimensional data fusion, and relates to the technical field of financial management, and the system carries out the data integration of multi-dimensional data obtained by a plurality of heterogeneous data sources, and obtains a financial related data warehouse; performing feature extraction on the financial related data warehouse to obtain a financial related feature matrix, determining a dimension link graph of each dimension in the financial related feature matrix, and constructing a flow link graph related to hospital finance through the dimension link graph of each dimension; extracting financial feature vectors of all dimensions from the financial related feature matrix, determining financial dimension entropies of all the dimensions according to the corresponding financial feature vectors, and screening out a financial core feature sequence based on all the financial dimension entropies and the process link atlas; and according to the financial core feature sequence, carrying out abnormal early warning on hospital finance. According to the method, the link relationship of the multi-source heterogeneous data can be mined, and the core features in the link relationship are extracted to perform anomaly detection, so that the early warning capability of a financial management system is improved.

Description

Technical Field

[0001] The present application relates to the technical field of financial management, and more specifically, to an intelligent hospital financial management system based on multi-dimensional data fusion. Background Art

[0002] In the financial management process of modern hospitals, there are many types of data involving multiple dimensions, such as financial data, patient charging information, medical insurance settlement, drug procurement, equipment purchase, department budget, doctor salary, operating costs, etc. Traditional financial management methods usually rely on single-dimensional data analysis, which makes it difficult to fully grasp the hospital's capital flow, operational efficiency and cost control. The hospital intelligent financial management system based on multi-dimensional data fusion aims to build an efficient and accurate financial decision-making system by integrating and analyzing multiple heterogeneous data sources, so as to improve the hospital's capital management capabilities and realize intelligent and transparent financial operations.

[0003] However, although the hospital financial management model of multidimensional data fusion has broad application prospects, in the existing technology, the financial data of the hospital involves multiple business systems, and the data format and storage method are different. For example, patient charge data may be stored in the HIS system, while procurement data comes from the ERP system, and medical insurance settlement data is provided by the Medical Insurance Bureau. These data sources are highly heterogeneous. And in the process of multidimensional data fusion, it is necessary to establish the relationship between various financial dimensions. For example, how does the budget execution of the department affect the overall financial health of the hospital? Is the expenditure on drug procurement reasonable? How does the collection cycle of medical insurance settlement affect cash flow? These problems need to be based on complex data modeling technology. Therefore, how to mine the link relationship of multi-source heterogeneous data and extract the core features for anomaly detection to improve the early warning ability of the financial management system is a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a hospital intelligent financial management system based on multi-dimensional data fusion, which can mine the link relationship of multi-source heterogeneous data and extract the core features therein for anomaly detection, so as to improve the early warning capability of the financial management system.

[0005] The present application provides a hospital intelligent financial management system based on multi-dimensional data fusion, the management system comprising: A data acquisition module is used to obtain multidimensional data related to hospital finance from multiple heterogeneous data sources, and integrate the multidimensional data to obtain a financial related data warehouse; A graph construction module is used to extract features from the finance-related data warehouse to obtain a finance-related feature matrix, determine a dimension link graph of each dimension in the finance-related feature matrix, and construct a process link graph related to hospital finance through the dimension link graphs of each dimension; A core screening module, used to extract financial feature vectors of each dimension from the financial-related feature matrix, determine the financial dimension entropy of each dimension according to the corresponding financial feature vector, and screen out a financial core feature sequence based on all financial dimension entropies and the process link map; The abnormal warning module is used to provide abnormal warning for hospital finances based on the financial core feature sequence.

[0006] In this embodiment, the multi-dimensional data related to hospital finance includes financial data, patient data, medical service data, employee salary data and drug purchase data.

[0007] In this embodiment, the multi-dimensional data is integrated to obtain a financial related data warehouse, which specifically includes: Preprocessing the multidimensional data to obtain preprocessed multidimensional data; Perform semantic mapping on the pre-processed multi-dimensional data to obtain financial-related data of each dimension; Build a financial-related data warehouse using financial-related data of all dimensions.

[0008] In this embodiment, feature extraction is performed on the financial related data warehouse to obtain a financial related feature matrix, which specifically includes: Extracting features of the financial-related data of each dimension in the financial-related data warehouse respectively, and then obtaining financial feature vectors of each dimension; Construct a financial-related feature matrix based on the financial feature vectors of all dimensions.

[0009] In this embodiment, determining the dimension link diagram of each dimension in the financial-related feature matrix specifically includes: Selecting a dimension from all dimensions of the financial-related feature matrix, and obtaining a financial feature vector of the selected dimension; Determine the feature linkage degree between each financial-related feature in the financial feature vector of the selected dimension; A dimension link graph of the selected dimension is constructed according to the feature link degrees between the various financial-related features, thereby obtaining a dimension link graph of each dimension in the financial-related feature matrix.

[0010] In this embodiment, constructing a process link map related to hospital finance through the dimensional link map of each dimension specifically includes: Determine the key financial-related characteristics of each dimension, and then determine the characteristic linkage between each key financial-related characteristic; According to the characteristic linkage degree between each key financial-related characteristic, the dimension linkage diagrams of each dimension are connected to obtain the process linkage map related to hospital finance.

[0011] In this embodiment, determining the financial dimension entropy of each dimension according to the corresponding financial feature vector specifically includes: Get the scale factor for each dimension; For the financial feature vectors of each dimension, obtain the weight corresponding to each financial-related feature in the financial feature vector; The financial dimension entropy of the dimension corresponding to the financial feature vector is determined by the weight corresponding to each financial-related feature and the corresponding scale factor, and then the financial dimension entropy of each dimension is obtained.

[0012] In this embodiment, the financial core feature sequence is screened out based on all financial dimension entropies and the process link map, specifically including: For each financial-related feature in the financial-related feature matrix, determining the financial dimension entropy of the dimension where the financial-related feature is located; Extracting all feature link degrees corresponding to the financial-related features in the process link graph; Determine the financial core entropy of the financial related feature through the financial dimension entropy of the dimension where the financial related feature is located and all corresponding feature link degrees, and then obtain the financial core entropy of each financial related feature in the financial related feature matrix; All financial core features are screened out based on the financial core entropy of each financial-related feature; Construct a financial core feature sequence based on all financial core features.

[0013] In this embodiment, the abnormal warning of hospital finance is performed based on the financial core feature sequence, which is to input the financial core feature sequence into an abnormal detection model for detection, thereby obtaining an abnormal warning report of hospital finance.

[0014] In this embodiment, the anomaly detection model is a long short-term memory network model.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: The data acquisition module is used to obtain multidimensional data related to hospital finance from multiple heterogeneous data sources, and the multidimensional data is integrated to obtain a finance-related data warehouse; the graph construction module is used to extract features from the finance-related data warehouse to obtain a finance-related feature matrix, determine the dimension link graph of each dimension in the finance-related feature matrix, and construct a process link graph related to hospital finance through the dimension link graphs of each dimension; the core screening module is used to extract the financial feature vectors of each dimension in the finance-related feature matrix, determine the financial dimension entropy of each dimension according to the corresponding financial feature vector, and screen out the financial core feature sequence based on all the financial dimension entropies and the process link graph; the abnormal warning module performs abnormal warning of hospital finance according to the financial core feature sequence.

[0016] It can be seen that in this application, first, feature extraction is performed on the financial-related data warehouse to obtain a financial-related feature matrix, and a process link map related to hospital finance is constructed through the dimensional link map of each dimension, which can help to comprehensively and systematically identify and understand the key links and their interrelationships in hospital financial management; then, by extracting the financial feature vectors of each dimension in the financial-related feature matrix, and determining the financial dimension entropy of each dimension according to the corresponding financial feature vector, the amount of useful information in each dimension can be effectively quantified, and the financial core feature sequence is screened out based on the financial dimension entropy and the process link map, which can help focus on the features that have the greatest impact on the financial situation, eliminate redundant information, help accurately mine the link relationship in multi-source heterogeneous data, and extract the financial core features; finally, abnormal warning of hospital finances is performed based on the financial core feature sequence, which can effectively improve the intelligence and warning capabilities of the financial management system. By analyzing the financial core feature sequence, the system can identify abnormal fluctuations and potential risks in financial indicators in real time, thereby improving the warning capabilities of the financial management system.

[0017] In summary, the technical solution adopted in this application can mine the link relationship of multi-source heterogeneous data and extract the core features therein for anomaly detection, so as to improve the early warning capability of the financial management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1 It is a module structure diagram of the hospital intelligent financial management system based on multidimensional data fusion provided by this application; Figure 2 is an exemplary flow chart for determining a dimension link diagram for each dimension in a financial-related feature matrix provided by the present application; Figure 3 is an exemplary flow chart for determining a financial core feature sequence provided by the present application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] The embodiment of the present application provides a hospital intelligent financial management system based on multidimensional data fusion, the core of which is to obtain multidimensional data related to hospital finance from multiple heterogeneous data sources through a data acquisition module, integrate the multidimensional data, and obtain a financial related data warehouse; extract features from the financial related data warehouse through a graph construction module to obtain a financial related feature matrix, determine the dimension link graph of each dimension in the financial related feature matrix, and construct a process link graph related to hospital finance through the dimension link graphs of each dimension; extract the financial feature vectors of each dimension in the financial related feature matrix through a core screening module, determine the financial dimension entropy of each dimension according to the corresponding financial feature vector, and screen out the financial core feature sequence based on all the financial dimension entropies and the process link graph; the abnormal warning module performs abnormal warning of hospital finance according to the financial core feature sequence. The above scheme can be used to mine the link relationship of multi-source heterogeneous data, and extract the core features therein for abnormal detection, so as to improve the early warning ability of the financial management system.

[0022] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 As shown, this figure is a module structure diagram of a hospital intelligent financial management system based on multidimensional data fusion according to this embodiment of the present application. The management system includes: a data acquisition module 100, a graph construction module 200, a core screening module 300 and an abnormal warning module 400, which are respectively described as follows: The data acquisition module 100 is used to obtain multi-dimensional data related to hospital finance from multiple heterogeneous data sources, and integrate the multi-dimensional data to obtain a finance-related data warehouse.

[0023] It should be noted that in this application, the multidimensional data related to hospital finance includes financial data, patient data, medical service data, employee salary data and drug procurement data; in specific implementation, the multidimensional data related to hospital finance can be obtained from multiple heterogeneous data sources. In this application, the heterogeneous data sources include the hospital's HIS (hospital information system), ERP (enterprise resource planning), LIS (laboratory information system), medical insurance settlement system, supply chain management system and labor cost system, etc.

[0024] In this embodiment, the multi-dimensional data is integrated to obtain a financial related data warehouse in the following manner, namely: Preprocessing the multidimensional data to obtain preprocessed multidimensional data; Perform semantic mapping on the pre-processed multi-dimensional data to obtain financial-related data of each dimension; Build a financial-related data warehouse using financial-related data of all dimensions.

[0025] In the specific implementation, first, the multidimensional data can be preprocessed, that is, the multidimensional data is cleaned and the data format is converted, so that the preprocessed multidimensional data can be obtained; then, the preprocessed multidimensional data can be semantically mapped. Through semantic mapping, the semantic relationship between the data of various heterogeneous data sources can be established, so that the data of different systems can be associated and aligned. For example, financial data → medical service data: through fields such as patient ID, department ID, diagnosis and treatment items, financial data and medical service data are associated (such as "CT examination fee" and "imaging department" are matched). Through the above method, financial related data of various dimensions can be obtained. The dimensions in this application include time dimension (financial analysis by day, week, month, quarter, and year), department dimension (income, expenditure, and cost structure of different departments), medical insurance dimension (medical insurance payment ratio vs. self-paid income), supply chain dimension (supplier procurement vs. drug use vs. expense expenditure) and labor cost dimension (doctor performance vs. labor cost vs. Revenue contribution), etc.; finally, a financial-related data warehouse can be built through financial-related data of all dimensions, that is, the financial-related data of all dimensions are stored in the database, so that a financial-related data warehouse can be obtained.

[0026] The graph construction module 200 is used to extract features from the finance-related data warehouse to obtain a finance-related feature matrix, determine the dimensional link graph of each dimension in the finance-related feature matrix, and construct a hospital finance-related process link graph through the dimensional link graphs of each dimension.

[0027] In this embodiment, the feature extraction of the financial related data warehouse to obtain the financial related feature matrix may be performed in the following manner, namely: Extracting features of the financial-related data of each dimension in the financial-related data warehouse respectively, and then obtaining financial feature vectors of each dimension; Construct a financial-related feature matrix based on the financial feature vectors of all dimensions.

[0028] In the specific implementation, first, feature extraction can be performed on the financial-related data of each dimension in the financial-related data warehouse, that is, feature extraction can be performed on the financial-related data of each dimension in the financial-related data warehouse through time series analysis and statistical methods, so that each dimension can obtain multiple financial-related features, so that the vector composed of all financial-related features of the corresponding dimension can be used as the financial feature vector. For example, the feature vector of the department dimension may include financial-related features such as income proportion, labor cost proportion, bed occupancy rate and equipment maintenance cost. The financial feature vectors of each dimension can be obtained by the above method; then, a financial-related feature matrix can be constructed based on the financial feature vectors of all dimensions, that is, the dimensions are taken as rows of the matrix, the corresponding financial feature vectors are filled into the matrix, and the final matrix is ​​taken as the financial-related feature matrix.

[0029] Preferably, in this embodiment, reference Figure 2 As shown in FIG. 1 , this figure is an exemplary flow chart of determining the dimension link diagram of each dimension in the financial-related feature matrix in an embodiment of the present application. In this embodiment, determining the dimension link diagram of each dimension in the financial-related feature matrix can be implemented by the following steps: In step S21, one dimension is selected from all dimensions of the financial-related feature matrix, and a financial feature vector of the selected dimension is obtained; In step S22, the feature linking degree between each financial-related feature in the financial feature vector of the selected dimension is determined; In step S23, a dimension link graph of the selected dimension is constructed according to the feature link degrees between the various finance-related features, thereby obtaining a dimension link graph of each dimension in the finance-related feature matrix.

[0030] In specific implementation, first, a dimension can be selected from all dimensions of the financial-related feature matrix, and the financial feature vector corresponding to the selected dimension can be obtained; then, the feature linkage degree between each financial-related feature in the financial feature vector of the selected dimension can be determined, wherein the feature linkage degree represents the degree of correlation linkage between the financial-related features, the Pearson correlation coefficient between the financial-related features can be calculated, and the calculation result is used as the feature linkage degree between the financial-related features, and the feature linkage degree between each financial-related feature in the financial feature vector can be obtained in the above manner; finally, a dimension linkage graph of the selected dimension can be constructed according to the feature linkage degree between each financial-related feature, that is, each financial-related feature in the financial feature vector of the selected dimension is connected, and the corresponding feature linkage degree is used as the value of the edge, so that the dimension linkage graph of the selected dimension can be obtained, and the dimension linkage graph of each dimension in the financial-related feature matrix can be obtained in the above manner.

[0031] In this embodiment, the following method can be used to construct a process link map related to hospital finance through the dimensional link map of each dimension, namely: Determine the key financial-related characteristics of each dimension, and then determine the characteristic linkage between each key financial-related characteristic; According to the characteristic linkage degree between each key financial-related characteristic, the dimension linkage diagrams of each dimension are connected to obtain the process linkage map related to hospital finance.

[0032] In the specific implementation, first, for the financial feature vectors of each dimension, the sum of the feature linkage degrees corresponding to each financial-related feature in the financial feature vector is calculated, and the financial-related feature with the largest sum of feature linkage degrees is used as the key financial-related feature of the corresponding dimension; then, the feature linkage degrees between each key financial-related feature can be calculated by the above-mentioned feature linkage degree calculation method; finally, the dimensional link graphs of each dimension can be connected according to the feature linkage degrees between each key financial-related feature, that is, each key financial-related feature is connected, and the corresponding feature linkage degrees are used as the values ​​of the edges, so that a process link graph related to hospital finance can be obtained.

[0033] It should be noted that extracting features from the financial-related data warehouse to obtain a financial-related feature matrix and constructing a hospital financial-related process link map through the dimension link map of each dimension can help comprehensively and systematically identify and understand the key links and their interrelationships in hospital financial management. By revealing the link relationship in multi-source heterogeneous data, the core associations between various financial-related features can be extracted, and then dynamic monitoring can be performed through the process link map.

[0034] The core screening module 300 is used to extract the financial feature vectors of each dimension from the financial-related feature matrix, determine the financial dimension entropy of each dimension according to the corresponding financial feature vector, and screen out the financial core feature sequence based on all the financial dimension entropies and the process link map.

[0035] In specific implementation, the financial feature vectors of each dimension can be extracted from the financial related feature matrix by traversal.

[0036] In this embodiment, the financial dimension entropy of each dimension may be determined according to the corresponding financial feature vector in the following manner, namely: Get the scale factor for each dimension; For the financial feature vectors of each dimension, obtain the weight corresponding to each financial-related feature in the financial feature vector; The financial dimension entropy of the dimension corresponding to the financial feature vector is determined by the weight corresponding to each financial-related feature and the corresponding scale factor, and then the financial dimension entropy of each dimension is obtained.

[0037] In specific implementation, first, the scale factor of each dimension can be obtained. The scale factor is a coefficient for normalizing different financial-related features, which is used to ensure that different financial-related features can be compared at the same scale. The scale factor of each dimension can be set through expert knowledge or analysis of historical data; then, for the financial feature vectors of each dimension, the weights corresponding to each financial-related feature in the financial feature vector can be obtained, and the corresponding weights can be preset through historical experience; finally, the financial dimension entropy of the corresponding dimension of the financial feature vector can be determined through the weights corresponding to each financial-related feature and the corresponding scale factors, wherein the financial dimension entropy represents the financial-related information content contained in the corresponding dimension, and each financial-related feature can be multiplied by the corresponding weight, and then the scale factors of each multiplication result can be multiplied, so that the sum of all the results obtained is used as the financial dimension entropy of the corresponding dimension of the financial feature vector. The financial dimension entropy of each dimension can be obtained in the above manner.

[0038] Preferably, in this embodiment, reference Figure 3 As shown, this figure is an exemplary flow chart of determining a financial core feature sequence in an embodiment of the present application. In this embodiment, the financial core feature sequence is screened out based on all financial dimension entropies and the process link map, which can be implemented by the following steps: In step S31, for each financial-related feature in the financial-related feature matrix, the financial dimension entropy of the dimension in which the financial-related feature is located is determined; In step S32, all feature link degrees corresponding to the financial-related features are extracted from the process link graph; In step S33, the financial core entropy of the financial related feature is determined by the financial dimension entropy of the dimension where the financial related feature is located and all corresponding feature link degrees, and then the financial core entropy of each financial related feature in the financial related feature matrix is ​​obtained; In step S34, all financial core features are screened out according to the financial core entropy of each financial-related feature; In step S35, a financial core feature sequence is constructed based on all the financial core features.

[0039] In specific implementation, first, for each financial-related feature in the financial-related feature matrix, the financial dimension entropy of the dimension where the financial-related feature is located can be determined by traversal; then, all feature link degrees corresponding to the financial-related feature can be extracted in the process link graph by traversal, that is, the values ​​of all edges connected to the financial-related feature; further, the financial core entropy of the financial-related feature can be determined by the financial dimension entropy of the dimension where the financial-related feature is located and all corresponding feature link degrees, wherein the financial core entropy represents the core degree of the financial-related feature in financial management, and the product of the sum of all feature link degrees corresponding to the financial-related feature and the financial dimension entropy of the dimension where the financial-related feature is located can be used as the financial core entropy of the financial-related feature. The financial core entropy of each financial-related feature in the financial-related feature matrix can be obtained by the above method.

[0040] In addition, in the specific implementation, all financial core features can be screened out based on the financial core entropy of each financial related feature; first, a threshold can be set based on historical experience and data analysis; then, the financial core entropy of each financial related feature can be compared with the set threshold. If the financial core entropy of the financial related feature exceeds the set threshold, it means that the financial related feature has a greater impact on the financial system and should usually be regarded as a core feature, that is, a financial core feature. All financial core features can be screened out through the comparison process; finally, a financial core feature sequence can be constructed based on all financial core features, that is, a sequence composed of all financial core features is used as a financial core feature sequence. The financial core feature sequence represents the most important feature set for the hospital financial management system, which can effectively reflect the financial status and operation of the hospital, and the financial core feature sequence is an important basis for subsequent anomaly detection, early warning system and decision support system.

[0041] It should be noted that by extracting the financial feature vectors of each dimension from the financial-related feature matrix and determining the financial dimension entropy of each dimension based on the corresponding financial feature vector, the amount of useful information in each dimension can be effectively quantified. Screening out the financial core feature sequence based on the financial dimension entropy and process link map can help focus on the features that have the greatest impact on the financial status, exclude redundant information, help accurately mine the link relationship in multi-source heterogeneous data, and extract the financial core features.

[0042] The abnormal warning module 400 is used to provide abnormal warning of hospital finances based on the financial core feature sequence.

[0043] In this embodiment, the abnormal warning of hospital finance is performed based on the financial core feature sequence, which is to input the financial core feature sequence into an abnormal detection model for detection, thereby obtaining an abnormal warning report of hospital finance. It should be noted that in this application, the abnormal detection model is a long short-term memory network model.

[0044] In specific implementation, the extracted financial core feature sequence can be input into the anomaly detection model, namely the LSTM model. The LSTM model will automatically learn the long-term dependencies and rules in the financial core feature sequence, including the trend of financial indicators changing over time, cyclical fluctuations and abnormal patterns. The LSTM memory unit can capture these time dependencies and make predictions about future financial situations. By learning from historical financial data, LSTM can identify potential anomalies in future data and generate anomaly warning reports. For example, the anomaly warning report will point out the anomalies of financial data, possible causes, and possible risks. The report can also provide targeted suggestions to help the hospital financial management team make timely adjustments or take necessary countermeasures.

[0045] It should be noted that abnormal early warning of hospital finances based on the financial core feature sequence can effectively improve the intelligence and early warning capabilities of the financial management system. By analyzing the financial core feature sequence, the system can identify abnormal fluctuations and potential risks in financial indicators in real time, thereby improving the early warning capabilities of the financial management system.

[0046] It can be seen that in this application, first, feature extraction is performed on the financial-related data warehouse to obtain a financial-related feature matrix, and a process link map related to hospital finance is constructed through the dimensional link map of each dimension, which can help to comprehensively and systematically identify and understand the key links and their interrelationships in hospital financial management; then, by extracting the financial feature vectors of each dimension in the financial-related feature matrix, and determining the financial dimension entropy of each dimension according to the corresponding financial feature vector, the amount of useful information in each dimension can be effectively quantified, and the financial core feature sequence is screened out based on the financial dimension entropy and the process link map, which can help focus on the features that have the greatest impact on the financial situation, eliminate redundant information, help accurately mine the link relationship in multi-source heterogeneous data, and extract the financial core features; finally, abnormal warning of hospital finances is performed based on the financial core feature sequence, which can effectively improve the intelligence and warning capabilities of the financial management system. By analyzing the financial core feature sequence, the system can identify abnormal fluctuations and potential risks in financial indicators in real time, thereby improving the warning capabilities of the financial management system.

[0047] In summary, the technical solution adopted in this application can mine the link relationship of multi-source heterogeneous data and extract the core features therein for anomaly detection, so as to improve the early warning capability of the financial management system.

[0048] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0049] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, the storage medium including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically-erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0050] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. A hospital intelligent financial management system based on multidimensional data fusion, characterized in that: The management system comprises: A data acquisition module is used to obtain multidimensional data related to hospital finance from multiple heterogeneous data sources, and integrate the multidimensional data to obtain a financial related data warehouse; A graph construction module is used to extract features from the finance-related data warehouse to obtain a finance-related feature matrix, determine a dimension link graph of each dimension in the finance-related feature matrix, and construct a hospital finance-related process link graph through the dimension link graphs of each dimension; A core screening module, used to extract financial feature vectors of each dimension from the financial-related feature matrix, determine the financial dimension entropy of each dimension according to the corresponding financial feature vector, and screen out a financial core feature sequence based on all financial dimension entropies and the process link map; The abnormal warning module is used to provide abnormal warning for hospital finances based on the financial core feature sequence.

2. The hospital intelligent financial management system based on multidimensional data fusion as claimed in claim 1, characterized in that: The multi-dimensional data related to hospital finance includes financial data, patient data, medical service data, employee salary data and drug purchase data.

3. The hospital intelligent financial management system based on multidimensional data fusion as claimed in claim 1, characterized in that: The multi-dimensional data is integrated to obtain a financial related data warehouse, which specifically includes: Preprocessing the multidimensional data to obtain preprocessed multidimensional data; Perform semantic mapping on the pre-processed multi-dimensional data to obtain financial-related data of each dimension; Build a financial-related data warehouse using financial-related data of all dimensions.

4. The hospital intelligent financial management system based on multidimensional data fusion as claimed in claim 1, characterized in that: The feature extraction is performed on the financial related data warehouse to obtain the financial related feature matrix, which specifically includes: Extracting features of the financial-related data of each dimension in the financial-related data warehouse respectively, and then obtaining financial feature vectors of each dimension; Construct a financial-related feature matrix based on the financial feature vectors of all dimensions.

5. The hospital intelligent financial management system based on multidimensional data fusion as claimed in claim 1, characterized in that: Determining the dimension link diagram of each dimension in the financial-related feature matrix specifically includes: Selecting a dimension from all dimensions of the financial-related feature matrix, and obtaining a financial feature vector of the selected dimension; Determine the feature linkage degree between each financial-related feature in the financial feature vector of the selected dimension; A dimension link graph of the selected dimension is constructed according to the feature link degrees between the various financial-related features, thereby obtaining a dimension link graph of each dimension in the financial-related feature matrix.

6. The hospital intelligent financial management system based on multidimensional data fusion as claimed in claim 1, characterized in that: The process link map related to hospital finance is constructed through the dimension link map of each dimension, including: Determine the key financial-related characteristics of each dimension, and then determine the characteristic linkage between each key financial-related characteristic; According to the characteristic linkage degree between each key financial-related characteristic, the dimension linkage diagrams of each dimension are connected to obtain the process linkage map related to hospital finance.

7. The hospital intelligent financial management system based on multidimensional data fusion as claimed in claim 1, characterized in that: The financial dimension entropy of each dimension is determined according to the corresponding financial feature vector, including: Get the scale factor for each dimension; For the financial feature vectors of each dimension, obtain the weight corresponding to each financial-related feature in the financial feature vector; The financial dimension entropy of the dimension corresponding to the financial feature vector is determined by the weight corresponding to each financial-related feature and the corresponding scale factor, and then the financial dimension entropy of each dimension is obtained.

8. The hospital intelligent financial management system based on multi-dimensional data fusion as claimed in claim 1, characterized in that: Based on all financial dimension entropies and the process link map, the financial core feature sequences are screened out, including: For each financial-related feature in the financial-related feature matrix, determining the financial dimension entropy of the dimension where the financial-related feature is located; Extracting all feature link degrees corresponding to the financial-related features in the process link graph; Determine the financial core entropy of the financial related feature through the financial dimension entropy of the dimension where the financial related feature is located and all corresponding feature link degrees, and then obtain the financial core entropy of each financial related feature in the financial related feature matrix; All financial core features are screened out based on the financial core entropy of each financial-related feature; Construct a financial core feature sequence based on all financial core features.

9. The hospital intelligent financial management system based on multi-dimensional data fusion as claimed in claim 1, characterized in that: The abnormal early warning of hospital finance based on the financial core feature sequence is to input the financial core feature sequence into an abnormal detection model for detection, and then obtain an abnormal early warning report of hospital finance.

10. The hospital intelligent financial management system based on multi-dimensional data fusion as claimed in claim 9, characterized in that: The anomaly detection model is a long short-term memory network model.

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