Medical and financial integrated intelligent management system

Abstract

The invention discloses a medical and financial integrated intelligent management system. The system comprises a data collaboration module, an intelligent budget module, a resource overall planning module and an evaluation optimization module. The data collaboration module is used for acquiring financial data of all medical co-body member units through distributed nodes, and generating a standardized financial data set by using a data aggregation technology based on a main-branch account set subject mapping rule; the intelligent budgeting module generates a preliminary fund distribution scheme by using a multi-target zero-base budgeting model containing service quantity, function positioning and efficiency indexes based on a standardized financial data set; the resource overall planning module generates a dynamic fund allocation scheme based on a preset resource sharing mechanism by using a feature matching algorithm in combination with the preliminary fund allocation scheme; and the evaluation optimization module performs fund allocation efficiency and resource sharing efficiency evaluation on the dynamic fund allocation scheme by using an operational research optimization algorithm, and generates a scheme optimization suggestion in combination with a preset performance indicator. And medical coexistence financial data collaboration and fund intelligent management are realized.

Description

Technical Field

[0001] The present invention belongs to the field of medical community financial management, and in particular relates to an integrated intelligent management system for medical community finances. Background Art

[0002] With the development of medical information technology, digital management technology for medical communities has emerged. This technology, supported by information technology, aims to integrate regional medical resources and improve collaborative management efficiency. In the field of medical community financial management, traditional management methods mainly rely on decentralized data storage, manual budget preparation, and static resource allocation models. In traditional technologies, the financial data of each medical community member unit is usually stored independently in the local system, budget allocation is mostly based on historical data or fixed ratios, resource coordination relies on manual negotiation and administrative instructions, and the evaluation of fund utilization efficiency is achieved through post-audit and static indicator assessment. However, the current traditional financial management methods have significant problems: insufficient data collaboration: the financial data standards of member units are not unified, forming data islands, resulting in low efficiency and poor real-time performance of cross-institutional financial data integration, making it difficult to support global financial decision-making; lack of scientific budget allocation: lack of dynamic modeling capabilities based on service volume, functional positioning and performance indicators, budget preparation is often divorced from actual needs, and easily leads to imbalanced fund allocation or waste of resources; lagging resource coordination flexibility: static resource sharing mechanisms cannot respond to changes in medical service demand in real time, and fund allocation lacks the support of dynamic matching algorithms, resulting in prominent resource supply and demand mismatch problems; lagging evaluation and optimization systems: traditional performance evaluation relies on manual statistics and post-analysis, lacks real-time dynamic performance monitoring and intelligent optimization models, and is difficult to quickly discover problems in fund use and provide adjustment suggestions. Summary of the Invention

[0003] Based on this, it is necessary to provide an integrated intelligent financial management system for medical communities that can solve the above problems.

[0004] In the first aspect, this application provides a medical community financial integrated intelligent management system, including:

[0005] The data collaboration module is used to obtain the financial data of each medical community member unit through distributed nodes, and generate a standardized financial data set based on the preset main and sub-account mapping rules using data aggregation technology;

[0006] An intelligent budgeting module, which generates preliminary funding allocation plans based on standardized financial data sets and a zero-based budgeting model with multiple objectives including service volume, functional positioning, and performance indicators;

[0007] The resource coordination module is used to generate a dynamic fund allocation plan based on the preset resource sharing mechanism and the use of feature matching algorithms combined with the preliminary fund allocation plan;

[0008] The evaluation and optimization module is used to evaluate the fund allocation effectiveness and resource sharing efficiency of the dynamically adjusted fund allocation plan using operations research optimization algorithms, and generate plan optimization suggestions based on preset performance indicators.

[0009] In one embodiment, the smart budget module is further configured to:

[0010] Obtain medical service volume data of each medical community member unit and construct a resource demand assessment matrix;

[0011] Generate a dynamic adjustment coefficient based on the medical service volume deviation rate, and combine it with the resource demand assessment matrix to obtain the optimized resource demand assessment matrix;

[0012] A multi-objective zero-based budgeting model is constructed by combining the optimized resource demand assessment matrix, the functional positioning and performance indicators of each medical community member unit.

[0013] In one embodiment, the resource coordination module is further configured to generate a dynamic fund allocation plan based on a preset resource sharing mechanism according to the following calculation steps combined with the preliminary fund allocation plan:

[0014] The dynamic feature similarity is calculated using the following formula:

[0015]

[0016] Based on the dynamic feature similarity, the resource demand-supply matching model is constructed using the following formula:

[0017]

[0018] The dynamic capital allocation coefficient is calculated by combining the dynamic feature similarity and resource demand-supply matching model:

[0019]

[0020] The constraints of the calculation process are: and satisfy S ij (t) represents the feature similarity between member units i and j at time t, F i (t) represents the eigenvector of member unit i, λ k Represents the dynamic weight coefficient of the k-th dimension feature, Δ k (t) represents the real-time fluctuation value of the k-th dimension feature at time t, M ij (t) represents the resource demand-supply matching degree, S j (t) represents the resource supply capacity of member unit j at time t, D i (t) represents the resource demand value of member unit i at time t, Sim Fp(i, j) represents the similarity of the functional positioning of member units i and j, C i (t) represents the resource usage cost coefficient of unit i, α, β and γ represent the matching dimension weights, w ij (t) represents the dynamic capital allocation coefficient of member unit i to j, η represents the time sensitivity coefficient, T i and T j They represent the time when the demand is proposed and the time when resources are available, δ ij Indicates the resource sharing protocol identifier, B j (t) represents the budget amount applied for by unit member j at time t, and TotalBudget(t) represents the total budget of the medical community at time t.

[0021] In one embodiment, the resource coordination module is further configured to:

[0022] Obtain the capital flow characteristic data of each medical community member unit in the standardized financial data set and construct a capital flow characteristic analysis matrix;

[0023] Generate resource allocation priorities based on multi-dimensional evaluation results of resource contribution, technical collaboration, and service effectiveness, combined with resource sharing agreement identifiers;

[0024] Based on the preset resource sharing mechanism, combined with the preliminary funding allocation plan, the fund flow characteristic analysis matrix and the resource allocation priority, a feature matching algorithm is used to construct a medical fund pool balance model and generate a dynamic funding allocation plan.

[0025] In one embodiment, the evaluation and optimization module is further configured to:

[0026] Based on the preset performance indicators, the operations research optimization algorithm is used to evaluate the fund allocation effectiveness and resource sharing efficiency of the dynamic fund allocation plan and generate a difference evaluation matrix;

[0027] Based on the difference assessment matrix, the preset dynamic compensation model of medical resources is used to generate an optimized adjustment path;

[0028] Combine the optimization adjustment path with the preset performance indicators to generate solution optimization suggestions.

[0029] In one embodiment, the evaluation and optimization module is further configured to:

[0030] Based on preset anomaly detection rules, the company conducts multi-dimensional analysis of abnormal financial data in the dynamic fund allocation plan execution data, generating an audit traceability request that includes the anomaly type, occurrence node, and risk level.

[0031] Based on audit traceability requests, a medical financial causal analysis model is constructed and a problem root cause map is generated;

[0032] Based on the problem root cause map and combined with the optimization adjustment path, intelligent decision-making algorithms are used to generate solution optimization suggestions.

[0033] In one embodiment, the system further includes a data verification module for:

[0034] Use preset data verification rules to verify the accuracy and completeness of the financial data of each medical community member unit, and generate a verification report containing the location, type and degree of deviation of abnormal data;

[0035] Based on the verification report, a multi-dimensional analysis of the type, frequency, and impact of abnormal data is performed to generate analysis results. The analysis is used to determine the abnormality level and root cause.

[0036] The root cause determined based on the analysis results refers to automatic labeling of abnormal data and triggering of a preset graded warning mechanism based on the abnormality level of the labeled data.

[0037] Secondly, this application also provides a method for intelligent management of medical community financial integration, including:

[0038] Obtain the financial data of each medical community member unit through distributed nodes, and use data aggregation technology to generate a standardized financial data set based on the preset main and sub-account mapping rules;

[0039] Based on standardized financial data sets, a preliminary funding allocation plan is generated using a zero-based budgeting model based on multiple objectives including service volume, functional positioning and effectiveness indicators;

[0040] Based on the preset resource sharing mechanism, a dynamic fund allocation plan is generated by combining the feature matching algorithm with the preliminary fund allocation plan;

[0041] Operations research optimization algorithms are used to evaluate the fund allocation effectiveness and resource sharing efficiency of the dynamically adjusted fund allocation plan, and plan optimization suggestions are generated based on preset performance indicators.

[0042] On the third aspect, the present application also provides a computer device including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps corresponding to the functions of the above-mentioned medical community financial integrated intelligent management system.

[0043] Fourthly, the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the steps corresponding to the functions of an integrated financial intelligent management system for a medical community.

[0044] The above-mentioned intelligent management system, method, computer equipment and storage medium for integrated financial management of medical community obtains financial data through distributed nodes through data collaboration module and generates standardized financial data sets based on main and sub-account mapping rules and data aggregation technology, so as to solve the problems of inconsistent financial data standards of member units and low efficiency and poor real-time performance of cross-institutional data integration caused by data silos in traditional methods, and realize global collaboration and standardization of financial data; the intelligent budget module builds a multi-objective zero-based budget model including service volume, functional positioning and efficiency indicators based on standardized data sets to generate preliminary fund allocation plans, breaking through the limitations of traditional budgets that rely on historical data or fixed ratios, and improving the matching degree and scientificity of budget allocation with actual needs; the resource coordination module relies on preset resource sharing mechanism and feature matching algorithm, combined with preliminary plans to generate dynamic The dynamic funding allocation plan changes the lag of the traditional static resource allocation model, and realizes real-time and accurate matching of funds and resources through dynamic feature similarity and resource demand-supply matching model, solving the problem of resource supply and demand mismatch; the evaluation and optimization module uses operations research optimization algorithm to evaluate the funding allocation efficiency and resource sharing efficiency of the dynamic plan and generates optimization suggestions based on performance indicators, overcoming the shortcomings of traditional post-audit and static assessment, and building a real-time dynamic evaluation and optimization system that can quickly identify problems and provide adjustment paths; the overall technical solution solves the problems of insufficient data collaboration, lack of scientific budget allocation, lagging resource coordination flexibility and lagging evaluation and optimization system in traditional medical community financial management through the synergy of various modules, and improves the intelligence level of medical community financial integrated management, resource allocation efficiency and collaborative sharing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 This is a structural diagram of a medical community financial integrated intelligent management system of the present invention;

[0047] Figure 2 This is a flow chart of the medical community financial integrated intelligent management method of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0049] The present invention is based on a distributed server cluster, a cloud computing platform, and a database system. It includes a front-end data acquisition terminal deployed in each medical community member unit, an intermediate server cluster for data processing and algorithm operation, and a distributed database for storing standardized financial data. When the member units of the medical community need to collaboratively manage financial data and dynamically allocate financial resources, the front-end terminal collects financial data and uploads it to the distributed nodes. The intermediate server generates a plan after processing through data aggregation, budget model, and feature matching algorithm, interacts with the database to store data, and finally feedbacks dynamic fund allocation and optimization suggestions to each unit, realizing intelligent management of the entire process.

[0050] In one embodiment, Figure 1 As shown, an integrated intelligent management system for the financial affairs of a medical community is provided. This embodiment is described by taking the system deployed on a hardware architecture consisting of a distributed server cluster, a cloud computing platform and a distributed database as an example. The architecture includes a front-end data acquisition terminal deployed in each member unit of the medical community, an intermediate server cluster for data processing and algorithm operation, and a distributed database for storing standardized financial data. It is understandable that the system can also be applied to a system architecture that includes terminal data acquisition, server algorithm processing and database storage interaction. Financial data is collected through the terminal and uploaded to the distributed node. The middle layer performs data aggregation, budget model calculation and feature matching algorithm generation scheme, and synchronously interacts with the database to complete data storage and call, and finally feedbacks dynamic fund allocation results and optimization suggestions to each member unit. In this embodiment, the system includes:

[0051] The data collaboration module 11 is used to obtain the financial data of each medical community member unit through distributed nodes, and generate a standardized financial data set based on the preset main and sub-account mapping relationship using data aggregation technology.

[0052] Among them, the financial data of each unit (such as income and expenditure records, budget applications, asset data, etc.) can be collected in real time through distributed nodes deployed in each member unit of the medical community (such as front-end data collection terminals); the preset main and sub-account account mapping rules include the account mapping relationship with the personalized sub-account sets of each member unit, and the financial accounts of different member units with different formats and standards are uniformly converted into globally consistent main account accounts to solve the problem of inconsistent data standards. Data aggregation technology (such as ETL cleaning and data integration algorithms) is used to clean, deduplicate, verify and integrate the mapped financial data to generate a globally unified standardized financial data set; this data set covers key information such as the overall financial status of the medical community and the capital flow of each unit, providing a standardized data foundation for subsequent budget allocation and resource coordination.

[0053] The intelligent budget module 12 is used to generate a preliminary funding allocation plan based on a standardized financial data set using a zero-based budgeting model based on multiple objectives including service volume, functional positioning and performance indicators.

[0054] Zero-Based Budgeting (ZBB) abandons traditional budgeting methods based on historical data or fixed ratios. Starting from zero, it reassesses the rationality of each funding request. Through multi-objective modeling, it integrates the following key dimensions: service volume, which quantifies the medical service supply capacity of each member unit (such as outpatient volume, inpatient bed days, and surgical volume) to reflect actual resource needs; functional positioning, which distinguishes the roles of member units (such as lead hospitals, primary care institutions, and specialized hospitals) and aligns them with their regional medical functions (such as diagnosis and treatment of difficult diseases and basic public health services); and performance indicators, which introduce efficiency and quality evaluation parameters (such as bed turnover rate, patient satisfaction, and cost-benefit ratio) to ensure that funds are allocated to high-performing units. Through multi-objective collaborative optimization, a multi-objective optimization model is constructed to solve the funding allocation weights of each member unit within the constraints of the total budget, achieving a dynamic match between funding allocation and actual medical service needs, institutional functional positioning, and performance.

[0055] The resource coordination module 13 is used to generate a dynamic fund allocation plan based on a preset resource sharing mechanism by utilizing a feature matching algorithm in combination with a preliminary fund allocation plan.

[0056] Among them, the preset resource sharing mechanism clarifies the sharing scope, priority and constraints (such as agreement constraints and authority control) of resources (such as funds, equipment, and manpower) among member units. This mechanism provides a rule basis for dynamic fund allocation to ensure that the flow of resources is in line with the overall strategic goals of the medical community (such as improving primary medical capabilities and collaborative development of disciplines). The feature matching algorithm extracts real-time feature data of each member unit through multi-dimensional feature extraction: based on a standardized financial data set, it extracts the following: demand characteristics: resource demand type (such as infrastructure, equipment procurement, personnel training), demand urgency, service coverage population, etc.; supply characteristics: the amount of sharable resources (such as idle funds, redundant equipment), technical capabilities (such as specialist diagnosis and treatment level), historical collaboration records, etc.; association characteristics: geographical distance between units, functional complementarity (such as the collaboration needs between general hospitals and specialist hospitals), etc. Dynamic matching calculation quantifies the demand-supply matching between member units through similarity calculation (such as cosine similarity, weighted matching algorithm) or graph network model, identifies the optimal combination of resource sharing, and provides data support for fund redistribution. Based on the preliminary funding allocation plan generated by the intelligent budget module, the real-time resource supply and demand status of each unit is analyzed through a feature matching algorithm, and the preliminary plan is dynamically revised: for units with urgent needs and insufficient supply, funds are allocated from units with redundant resources (such as allocating idle funds from central hospitals to grassroots health centers); for unit combinations with clear sharing agreements (such as the pairing of lead hospitals and community health service centers), priority is given to ensuring the funding allocation of collaborative projects.

[0057] The evaluation and optimization module 14 is used to evaluate the fund allocation effectiveness and resource sharing efficiency of the dynamically adjusted fund allocation plan using an operations research optimization algorithm, and generate plan optimization suggestions based on preset performance indicators.

[0058] Operational research algorithms (such as linear programming, data envelopment analysis (DEA), and dynamic programming) are used to quantitatively evaluate the following core dimensions of dynamic funding allocation plans: funding allocation effectiveness: analyzing the alignment of funding flows with the volume of medical services and functional positioning (e.g., the growth rate of outpatient visits per 10,000 yuan of funding), and the efficiency of funding use (e.g., project budget execution rate and cost-benefit ratio); resource sharing efficiency: assessing the actual effectiveness of resource sharing among member units (e.g., equipment sharing rate, cross-institutional collaborative project funding output ratio), and the implementation costs of the sharing mechanism (e.g., coordination time and transaction costs). A multidimensional evaluation model is constructed through algorithms to generate quantitative evaluation results (e.g., effectiveness scores and efficiency indices), identifying inefficient links in funding allocation (e.g., units with high idle fund rates and combinations with insufficient sharing and coordination). Pre-set performance indicators may include policy indicators such as the proportion of primary care investment and the rate of public health service funding compliance; efficiency indicators such as the per capita medical expenditure growth rate and the rate of improvement in bed utilization; and effectiveness indicators such as patient satisfaction and the completion rate of two-way referrals. Compare and analyze the evaluation results of dynamic plans with pre-set performance indicators to determine the degree of deviation between actual performance and target values ​​(e.g., a unit's fund utilization rate is 5% below the indicator threshold). Through analysis of the evaluation results and differences, optimization recommendations are generated using a rule engine or heuristic algorithm (such as a genetic algorithm). This technical approach of algorithmic evaluation, indicator guidance, and intelligent optimization fills the gaps in traditional management, where the evaluation system is lagging behind and optimization methods are lacking, enabling the upgrade of medical community fund management from extensive allocation to precise governance.

[0059] The above-mentioned intelligent management system for the integrated finance of the medical community uses distributed nodes to obtain financial data through the data collaboration module and generates a standardized financial data set based on the mapping relationship between the main and sub-account sets and data aggregation technology, so as to solve the problems of inconsistent financial data standards of member units and low efficiency and poor real-time performance of cross-institutional data integration caused by data silos, and realize the global collaboration and standardization of financial data; the intelligent budget module constructs a multi-objective zero-based budget model including service volume, functional positioning and efficiency indicators based on the standardized data set to generate a preliminary fund allocation plan, breaking through the limitations of relying on historical data or fixed ratios, and improving the matching degree and scientificity of budget allocation with actual needs; the resource coordination module relies on the preset resource sharing mechanism and feature matching algorithm, combined with the preliminary plan to generate dynamic The funding allocation plan changes the lag of the traditional static resource allocation model, realizes real-time and accurate matching of funds and resources through dynamic feature matching, and solves the problem of resource supply and demand mismatch; the evaluation and optimization module uses operations research optimization algorithms to evaluate the funding allocation efficiency and resource sharing efficiency of dynamic plans and generates optimization suggestions based on performance indicators, overcoming the shortcomings of traditional post-audits and static assessments, building a real-time dynamic evaluation and optimization system, quickly discovering problems and providing adjustment paths; through the overall synergy of various modules, it solves the problems of insufficient data collaboration, lack of scientific budget allocation, lagging resource coordination flexibility and lagging evaluation and optimization system in traditional medical community financial management, and improves the intelligence level of medical community financial integrated management, resource allocation efficiency and collaborative sharing efficiency.

[0060] In one embodiment, the smart budget module 12 is further configured to:

[0061] Obtain medical service volume data of each medical community member unit and construct a resource demand assessment matrix;

[0062] Generate a dynamic adjustment coefficient based on the medical service volume deviation rate, and combine it with the resource demand assessment matrix to obtain the optimized resource demand assessment matrix;

[0063] A multi-objective zero-based budgeting model is constructed by combining the optimized resource demand assessment matrix, the functional positioning and performance indicators of each medical community member unit.

[0064] Specifically, medical data can be structured and categorized by department, disease type, and service level. Dynamic weighting parameters can be set based on the resource consumption characteristics of different service categories (e.g., manpower, equipment, and consumables), generating a quantitative assessment matrix reflecting the actual resource needs of each member unit. Actual service volume is compared with historical data and regional standards to calculate the deviation rate. When the deviation rate is within a threshold, a linear adjustment mode is used. When it exceeds the threshold, a nonlinear compensation mechanism is activated to generate a dynamic adjustment coefficient. The demand assessment results are then revised in real time to produce an optimized resource demand assessment matrix. Weights can be assigned based on the role of each member unit of the medical community: for example, lead hospitals (e.g., tertiary hospitals) can focus on the diagnosis and treatment of difficult diseases and technological leadership, with budgets tilted toward specialty development and scientific research innovation; for grassroots institutions (e.g., community hospitals), a focus on public health and basic medical care can be achieved, with budgets prioritizing services such as family doctor contracts and chronic disease management. Effectiveness indicators can be used to assess the effectiveness of each unit's funding by introducing efficiency metrics (e.g., bed occupancy rate, patient turnover rate) and quality metrics (e.g., patient satisfaction, misdiagnosis rate). For example, units with high bed occupancy rates and meeting patient satisfaction standards will receive additional budgets. Abandoning the traditional model of increasing or decreasing budgets based on historical budget proportions, we will start from scratch and calculate a reasonable budget for each unit, taking into account service volume demand, functional positioning weights, and efficiency scores. Through budget guidance, member units will be incentivized to optimize their service structure: grassroots institutions will increase public health service coverage, while major hospitals will reduce hospitalization rates for common diseases and focus on difficult-to-treat conditions. This will support the goal of tiered diagnosis and treatment, with minor illnesses treated at the grassroots level, major illnesses treated in hospitals, and rehabilitation returned to the community, thereby reducing resource waste.

[0065] In one embodiment, the resource coordination module 13 is further configured to generate a dynamic fund allocation plan based on a preset resource sharing mechanism according to the following calculation steps combined with the preliminary fund allocation plan:

[0066] The dynamic feature similarity is calculated using the following formula:

[0067]

[0068] Based on the dynamic feature similarity, the resource demand-supply matching model is constructed using the following formula:

[0069]

[0070] The dynamic capital allocation coefficient is calculated by combining the dynamic feature similarity and resource demand-supply matching model:

[0071]

[0072] The constraints of the calculation process are: and satisfy S ij(t) represents the feature similarity between member units i and j at time t, F i (t) represents the eigenvector of member unit i, λ k Represents the dynamic weight coefficient of the k-th dimension feature, Δ k (t) represents the real-time fluctuation value of the k-th dimension feature at time t, M ij (t) represents the resource demand-supply matching degree, S j (t) represents the resource supply capacity of member unit j at time t, D i (t) represents the resource demand value of member unit i at time t, Sim Fp (i, j) represents the similarity of the functional positioning of member units i and j, C i (t) represents the resource usage cost coefficient of unit i, α, β and γ represent the matching dimension weights, w ij (t) represents the dynamic capital allocation coefficient of member unit i to j, η represents the time sensitivity coefficient, T i and T j They represent the time when the demand is proposed and the time when resources are available, δ ij Indicates the resource sharing protocol identifier, B j (t) represents the budget amount applied for by unit member j at time t, and TotalBudget(t) represents the total budget of the medical community at time t.

[0073] For example, F i (t) represents the feature vector of member unit i at time t, which includes multi-dimensional features such as resource demand type (such as equipment procurement, human resource training), service coverage population, and geographical location; The basic matching degree based on cosine similarity reflects the directional consistency of the feature vector; the dynamic weight coefficient λ of the k-th dimension feature k It can be adjusted according to the strategic goals of the medical community (such as the improvement of primary medical capacity, the weight of public health service characteristics is increased); the real-time fluctuation value Δ of the k-th dimension feature at time t k (t) (such as the increase in demand for emergency supplies caused by public health emergencies) can be obtained through the real-time data interface. Through dynamic weights and real-time fluctuation factors, the similarity calculation can adapt to the resource allocation orientation of the medical community at different development stages, avoiding the limitations of traditional static matching. ij (t) represents the resource demand-supply matching degree, and the resource supply capacity S of member unit j at time t j (t) includes the amount of shared resources such as idle funds, redundant equipment capacity, and human resources reserves; the resource demand value D of member unit i at time t i (t) can be calculated based on the budget application amount, service volume gap and other data in the standardized financial data set; the similarity of the functional positioning of member units i and j Sim Fp(i, j) (such as the complementarity score between general hospitals and specialized hospitals) its MaxSim FP is the global maximum similarity benchmark, and the resource usage cost coefficient C of unit i i (t) can be equipment operation and maintenance costs and labor cost rates, etc. The matching dimension weights α, β and γ can be determined by the analytic hierarchy process (AHP), reflecting the medical community's differentiated considerations on demand priority, functional synergy, and cost efficiency. Avoid the one-sidedness of single-dimensional matching, such as considering the urgency of demand at the same time. Collaborative Value Sim Fp , ensuring that funds flow to highly coordinated and efficient resource combinations. exp(-η·|T i -T j |) is a time-sensitive factor to ensure that recent demands are matched first; the resource sharing protocol identifier δ ij (1 can be used to indicate the existence of an agreement, and 0 to indicate no agreement). This forces the allocation of funds to occur only between units with cooperative relationships to ensure compliance. Ensure that the total amount of funds allocated to all units does not exceed the total budget of the medical community to avoid the risk of overspending. Use time-sensitive factors to achieve timely matching of demand and supply (such as giving priority to emergency equipment demand over regular procurement), and use agreement identification to ensure that resource flow is in line with the strategic collaboration framework of the medical community.

[0074] In one embodiment, the resource coordination module 13 is further configured to:

[0075] Obtain the capital flow characteristic data of each medical community member unit in the standardized financial data set and construct a capital flow characteristic analysis matrix;

[0076] Generate resource allocation priorities based on multi-dimensional evaluation results of resource contribution, technical collaboration, and service effectiveness, combined with resource sharing agreement identifiers;

[0077] Based on the preset resource sharing mechanism, combined with the preliminary funding allocation plan, the fund flow characteristic analysis matrix and the resource allocation priority, a feature matching algorithm is used to construct a medical fund pool balance model and generate a dynamic funding allocation plan.

[0078] Specifically, the capital flow characteristic data of each unit is extracted from the standardized financial data set, including but not limited to the scale of capital inflow / outflow, revenue and expenditure structure (such as the proportion of personnel salaries, equipment procurement frequency), capital turnover cycle, etc. From the dimensions of time, purpose and collaboration, the capital flow frequency and peak period can be counted by day / week / month respectively; classified into medical service expenditure, public health investment, scientific research and teaching funds, etc.; cross-institutional capital transaction records (such as referral settlement, equipment sharing expenses), etc., with member units as rows and characteristic dimensions as columns, to construct a capital flow characteristic analysis matrix. The multi-dimensional evaluation system can be divided into: measuring the contribution of units to the overall resource pool of the medical community based on resource contribution (such as the length of time idle equipment is shared and the number of technical outputs); evaluating the frequency and effectiveness of cross-institutional technical cooperation based on technical collaboration (such as the number of joint diagnosis and treatment projects and the sharing rate of scientific research results); and calculating scores for service effectiveness based on efficiency indicators in standardized financial data (such as outpatient volume growth rate and patient satisfaction). Combined with the resource sharing agreement identifier, a quantifiable comprehensive score can be obtained. Member units can be sorted in descending order according to the comprehensive score to generate a resource allocation priority queue. The medical fund pool balance model considers: a preliminary fund allocation plan (derived from the initial allocation results of the intelligent budgeting module); a fund flow analysis matrix reflecting each unit's fund usage preferences and collaborative potential; and a resource allocation priority queue to determine the order and weight of fund redistribution. A priority-based greedy algorithm or a graph network model can be used to traverse the priority queue. For each unit, within the scope of the resource sharing agreement, it matches units with strong fund supply capabilities and complementary flow characteristics. This generates a dynamic fund allocation plan, enabling the transfer of funds from low-efficiency, low-collaboration units to high-efficiency, high-collaboration units.

[0079] In one embodiment, the evaluation and optimization module 14 is further configured to:

[0080] Based on the preset performance indicators, the operations research optimization algorithm is used to evaluate the fund allocation effectiveness and resource sharing efficiency of the dynamic fund allocation plan and generate a difference evaluation matrix;

[0081] Based on the difference assessment matrix, the preset dynamic compensation model of medical resources is used to generate an optimized adjustment path;

[0082] Combine the optimization adjustment path with the preset performance indicators to generate solution optimization suggestions.

[0083] For example, the performance indicator system may include: policy indicators such as the proportion of primary medical investment and the rate of public health funding compliance; efficiency indicators such as the per capita medical expenditure growth rate and the bed utilization rate improvement rate; effectiveness indicators such as patient satisfaction and two-way referral completion rate; operations research optimization algorithms such as data envelopment analysis (DEA) can be used to evaluate the effectiveness of fund allocation, calculate the technical efficiency and scale efficiency values ​​of each member unit, and identify inefficient units using funds (such as DEA ineffective units); use dynamic programming (DP) models to analyze resource sharing efficiency, quantify the input-output ratio of cross-institutional collaboration (such as the service volume growth driven by every 10,000 yuan of shared funds), generate a difference evaluation matrix, and intuitively display the degree of deviation of each unit on different indicators. Based on the evaluation differences, the dynamic compensation model R = Δ·W·C can be used to generate specific strategies and paths for resource reallocation, where W is the indicator weight matrix (reflecting the strategic priority of the medical community), C is the compensation coefficient matrix (including the reduction / reward ratio), and R is the resource reallocation matrix. The amount of capital adjustment for each unit is clarified: for example, for DEA ineffective units (inefficient use of funds), their excess funds are reduced according to the difference rate (for example, when the difference rate is greater than 20%, the budget is reduced by 10%-15%); for units with high resource sharing efficiency (such as cross-institutional collaborative project output ratio greater than the industry average), the budget is reduced by 10%-15%. value), and compensation rewards are given according to the degree of contribution (such as a 5% additional budget reward for every 10,000 yuan of shared funds); an adjustment path from the current allocation state to the target state is constructed through a graph theory shortest path algorithm (such as the Dijkstra algorithm), ensuring that the capital flow cost during the adjustment process is minimized (such as cross-institutional transfer fees and coordination time); an example path: grassroots hospital A needs to cut 500,000 yuan due to insufficient utilization of public health funds. 300,000 yuan of this will be transferred to grassroots hospital B, which has a close collaborative relationship and meets patient satisfaction standards, and 200,000 yuan will be supplemented to regional emergency center C. Combining the assessment results with the adjustment path, we can provide actionable optimization recommendations from the following dimensions: For fund reallocation, clarify the source, destination, and amount of fund transfers (e.g., transferring 200,000 yuan from unit X's equipment procurement budget to unit Y's talent training budget); for resource-sharing strategies, recommend signing new collaboration agreements or adjusting sharing parameters (e.g., lowering the equipment sharing threshold and optimizing the revenue sharing ratio) for units with low sharing efficiency; and for management improvements: for units that fail to meet performance indicators, provide specific improvement measures (e.g., "Unit Z needs to increase its bed turnover rate to above 85% within three months; it is recommended to introduce an intelligent scheduling system") to generate optimization recommendations. Through a combination of quantitative evaluation using operations research algorithms, precise adjustment of dynamic compensation models, and closed-loop management of intelligent recommendations, we have established an intelligent optimization system for fund allocation within the medical community.

[0084] In one embodiment, the evaluation and optimization module 14 is further configured to:

[0085] Based on preset anomaly detection rules, the company conducts multi-dimensional analysis of abnormal financial data in the dynamic fund allocation plan execution data, generating an audit traceability request that includes the anomaly type, occurrence node, and risk level.

[0086] Based on audit traceability requests, a medical financial causal analysis model is constructed and a problem root cause map is generated;

[0087] Based on the problem root cause map and combined with the optimization adjustment path, intelligent decision-making algorithms are used to generate solution optimization suggestions.

[0088] Specifically, the preset anomaly detection rules can be: threshold type, such as a single fund transfer exceeding 5% of the total budget of the medical community, a year-on-year increase of >100% in the budget for equipment procurement of grassroots institutions, etc.; compliance, cross-institutional fund flows that violate the resource sharing agreement (such as large transfers between units without an agreement), budget adjustments approved beyond authority, etc.; trend type, a decrease in fund turnover rate of >20% for three consecutive cycles, abnormal fluctuations in the proportion of certain service expenditures (such as public health funds being 30% lower than the regional average for two consecutive months). Analysis from multiple dimensions such as: time dimension: abnormal occurrence period such as emergency transfers during non-working hours, periodic patterns such as large-scale expenditures concentrated at the end of each month); spatial dimension: occurrence nodes (member units, departments, accounts), cross-institutional links (such as funds flowing from Hospital A to Enterprise C via Platform B); attribute dimension: fund use (equipment procurement, staff salaries, emergency reserves), risk type (compliance risk, efficiency risk, integrity risk); for abnormal data in fund allocation, an audit traceability request containing the abnormality type, occurrence node and risk level is generated. For the constructed medical financial causal analysis model, its data association mining can use the Apriori algorithm to analyze the association rules between abnormal data and historical financial records and business events (for example, the support rate of "overspending on equipment procurement in primary hospitals" and "fluctuations in regional medical equipment bidding prices" is greater than 80%); Bayesian networks are used to model multi-factor causal relationships, quantify the contribution of each factor to abnormal events (for example, "budget preparation errors" increase the posterior probability of "overspending" by 45%), generate a root cause map, and construct a directed acyclic graph (DAG) with abnormal events as central nodes and related factors as child nodes. The edge weights represent the causal strength (for example, the weight of "policy changes → increased equipment procurement costs" is 0.7); the map visualizes the key paths (for example, "reduction in fiscal appropriations → budget compression → illegal use of special funds by primary hospitals") and supports drilling down to query detailed data at each node. Based on the problem root cause map, combined with optimized adjustment paths and using intelligent decision-making algorithms, corresponding strategies can be automatically triggered based on risk levels (such as forcing funds to be frozen and special audits to be initiated for high-risk anomalies). Through historical optimization case training, personalized recommendations can be generated (such as the recommendation of a combination of "renegotiating the procurement agreement and applying for special subsidies" for the problem of "overspending on primary equipment"). Through the organic combination of multi-dimensional anomaly analysis, causal model construction, and intelligent decision-making algorithms, a full-process intelligent system for financial risk management and control of medical communities has been established.

[0089] In one embodiment, the system further includes a data verification module for:

[0090] Use preset data verification rules to verify the accuracy and completeness of the financial data of each medical community member unit, and generate a verification report containing the location, type and degree of deviation of abnormal data;

[0091] Based on the verification report, a multi-dimensional analysis of the type, frequency, and impact of abnormal data is performed to generate analysis results. The analysis is used to determine the abnormality level and root cause.

[0092] The root cause determined based on the analysis results refers to automatic labeling of abnormal data and triggering of a preset graded warning mechanism based on the abnormality level of the labeled data.

[0093] For example, the preset data verification rules can verify the data from the following dimensions: format verification verifies the format of the data field (such as the date format is YYYY-MM-DD, the amount field is numeric), and rejects unstructured data (such as text data mixed into the amount field); value domain verification checks the data value range (such as outpatient volume ≥ 0, drug purchase price ≤ 120% of the historical average price), and marks abnormal values ​​that exceed the reasonable value range; logical verification: verifies cross-field logical relationships (such as "total income = outpatient income + hospitalization income + other income"), and identifies data with contradictory cross-references; integrity verification: detects whether required fields (such as unit name, transaction time, and fund purpose) are empty to ensure that no data items are missed. Generate a verification report containing the location, type, and degree of deviation of the abnormal data, with the abnormal type being (format error / value range exceeded / logical contradiction / field missing). The multi-dimensional framework of the analysis can be: for the time dimension, count the frequency of abnormalities (such as the value range of a unit exceeds the limit for 5 consecutive months) and periodic patterns (such as the concentrated occurrence of format errors at the end of each month); for the type dimension, classify and count the proportion of various types of abnormalities (such as logical contradictions account for 40%, and field missing accounts for 30%), and identify the main problem types; for the impact dimension: evaluate the impact of abnormal data on budget allocation and resource coordination (such as the missing income data of a grassroots hospital resulting in a budget under-allocation of 2 million yuan); for the unit dimension: compare the abnormality rate of each member unit (such as the abnormality rate of tertiary hospitals is less than 5%, and the abnormality rate of grassroots institutions is 15%), and identify the weak links in data management; establish a three-tier rating system: low risk (L), no impact on core business (such as format errors in non-mandatory fields); medium risk (M), which may lead to Local decision-making deviation (such as a deviation of the amount of a single item <10%); high risk (H), seriously affecting global management (such as missing income / expenditure data, logical contradictions leading to budget deviation >20%); automatic labeling technology such as natural language processing can be used to add semantic labels to abnormal data fields (such as "suspected inflated expenses" and "inter-period income adjustment"), and trigger according to the risk level: Low risk (L): Automatically generate a "Data Correction Reminder" and push it to the data reporting personnel through system messages, requiring correction within 3 working days; Medium risk (M): Trigger an email alert to the unit's financial director, with details of the abnormality and impact analysis, and require a rectification report to be submitted within 1 week; High risk (H): Initiate a text message alert to the medical community's financial management center, and at the same time freeze the budget application authority corresponding to the relevant data until the problem is resolved. Through preset rule-driven automated verification, multi-dimensional data analysis and intelligent hierarchical alerts.

[0094] The above-mentioned intelligent financial integrated management system for medical communities generates standardized financial data sets through the data collaboration module using distributed nodes and main and sub-account mapping rules, solves the problems of inconsistent financial data standards and data islands among member units, and improves the efficiency and real-time performance of cross-institutional data integration; the intelligent budget module constructs a multi-objective zero-based budget model based on the standardized data set, which includes service volume, functional positioning and performance indicators, breaks through the limitations of traditional budgets that rely on historical data, and improves the matching degree and scientificity of budget allocation with actual needs; the resource coordination module relies on the preset resource sharing mechanism and dynamic feature similarity and resource demand-supply matching model, combines the preliminary plan to generate a dynamic fund allocation plan, changes the lag of the static resource allocation model, realizes real-time and accurate matching of funds and resources, and solves the problem of resource supply and demand mismatch; the evaluation and optimization module uses operations research optimization algorithms, medical financial causal analysis models, etc. to evaluate the performance of dynamic plans, analyze anomalies and trace the root causes, generates optimization suggestions based on performance indicators, builds a real-time dynamic evaluation and optimization system, quickly discovers problems and provides adjustment paths; the data verification module verifies the accuracy and completeness of data through preset rules, analyzes abnormal data in multiple dimensions and triggers graded warnings to ensure data quality. The overall technical solution solves problems such as insufficient data collaboration, lack of scientific budget allocation, lagging resource coordination flexibility, lagging evaluation and optimization system, and weak data quality control in traditional medical community financial management through the collaboration of various modules, and improves the intelligence level of medical community financial integrated management, resource allocation efficiency, collaborative sharing efficiency, and risk prevention and control capabilities.

[0095] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0096] Based on the same inventive concept, the embodiment of the present application also provides a method for realizing the above-mentioned medical community financial integrated intelligent management system. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method. Therefore, the specific limitations in one or more embodiments of the medical community financial integrated intelligent management method provided below can be found in the above-mentioned limitations on the medical community financial integrated intelligent management system, and will not be repeated here.

[0097] In an exemplary embodiment, Figure 2 As shown, a method for intelligent management of medical community financial integration is provided, including:

[0098] S101, obtain the financial data of each medical community member unit through distributed nodes, and generate a standardized financial data set using data aggregation technology based on the preset main and sub-account mapping rules;

[0099] S102, based on the standardized financial data set, generates a preliminary funding allocation plan using a zero-based budgeting model based on multiple objectives including service volume, functional positioning and effectiveness indicators;

[0100] S103, based on the preset resource sharing mechanism, a dynamic fund allocation plan is generated by combining the feature matching algorithm with the preliminary fund allocation plan;

[0101] S104: Use operations research optimization algorithms to evaluate the fund allocation effectiveness and resource sharing efficiency of the dynamically adjusted fund allocation plan, and generate plan optimization suggestions based on preset performance indicators.

[0102] In one embodiment, the method further comprises:

[0103] S201, obtain the medical service volume data of each medical community member unit and construct a resource demand assessment matrix;

[0104] S202, generating a dynamic adjustment coefficient based on the medical service volume deviation rate, and combining it with the resource demand assessment matrix to obtain an optimized resource demand assessment matrix;

[0105] S203, build a multi-objective zero-based budgeting model by combining the optimized resource demand assessment matrix, the functional positioning and performance indicators of each medical community member unit.

[0106] In one embodiment, the method further includes S301, generating a dynamic fund allocation plan based on a preset resource sharing mechanism according to the following calculation steps combined with the preliminary fund allocation plan:

[0107] The dynamic feature similarity is calculated using the following formula:

[0108]

[0109] Based on the dynamic feature similarity, the resource demand-supply matching model is constructed using the following formula:

[0110]

[0111] The dynamic capital allocation coefficient is calculated by combining the dynamic feature similarity and resource demand-supply matching model:

[0112]

[0113] The constraints of the calculation process are: and satisfy S ij (t) represents the feature similarity between member units i and j at time t, F i (t) represents the eigenvector of member unit i, λ k Represents the dynamic weight coefficient of the k-th dimension feature, Δ k (t) represents the real-time fluctuation value of the k-th dimension feature at time t, M ij (t) represents the resource demand-supply matching degree, S j (t) represents the resource supply capacity of member unit j at time t, D i (t) represents the resource demand value of member unit i at time t, Sim Fp (i, j) represents the similarity of the functional positioning of member units i and j, C i (t) represents the resource usage cost coefficient of unit i, α, β and γ represent the matching dimension weights, w ij (t) represents the dynamic capital allocation coefficient of member unit i to j, η represents the time sensitivity coefficient, T i and T j They represent the time when the demand is proposed and the time when resources are available, δ ij Indicates the resource sharing protocol identifier, B j (t) represents the budget amount applied for by unit member j at time t, and TotalBudget(t) represents the total budget of the medical community at time t.

[0114] In one embodiment, the method further comprises:

[0115] S401, obtaining the capital flow characteristic data of each medical community member unit in the standardized financial data set, and constructing a capital flow characteristic analysis matrix;

[0116] S402, generating a resource allocation priority based on the multi-dimensional evaluation results of resource contribution, technical collaboration, and service effectiveness, combined with the resource sharing agreement identifier;

[0117] S403, based on the preset resource sharing mechanism, combined with the preliminary fund allocation plan, fund flow feature analysis matrix and resource allocation priority, a feature matching algorithm is used to build a medical fund pool balance model and generate a dynamic fund allocation plan.

[0118] In one embodiment, the method further comprises:

[0119] S501, based on the preset performance indicators, using the operations research optimization algorithm to evaluate the dynamic fund allocation scheme's fund allocation effectiveness and resource sharing efficiency, and generate a difference evaluation matrix;

[0120] S502, based on the difference evaluation matrix, using the preset medical resource dynamic compensation model, generating an optimized adjustment path;

[0121] S503: Generate solution optimization suggestions by combining the optimization adjustment path with the preset performance indicators.

[0122] In one embodiment, the method further comprises:

[0123] S601: Based on preset anomaly detection rules, perform multi-dimensional analysis on abnormal financial data in the dynamic fund allocation plan execution data, and generate an audit traceability request including the anomaly type, occurrence node, and risk level;

[0124] S602: Based on the audit traceability request, a medical financial causal analysis model is constructed and a problem root cause map is generated;

[0125] S603: Based on the problem root cause map and combined with the optimization adjustment path, an intelligent decision-making algorithm is used to generate solution optimization suggestions.

[0126] In one embodiment, the method further comprises:

[0127] S701: Use the preset data verification rules to verify the accuracy and completeness of the financial data of each medical community member unit, and generate a verification report containing the location, type and degree of deviation of abnormal data;

[0128] S702: Based on the verification report, a multi-dimensional analysis is performed on the type, frequency, and impact of the abnormal data to generate analysis results. The analysis is used to determine the abnormality level and root cause.

[0129] S703 , automatically marking the abnormal data according to the root cause determined by the analysis result, and triggering a preset graded warning mechanism according to the abnormality level of the marked data.

[0130] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps corresponding to the functions of the aforementioned medical community financial integrated intelligent management system.

[0131] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0132] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0133] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. A medical community financial integrated intelligent management system, characterized by: The system comprises: The data collaboration module is used to obtain the financial data of each medical community member unit through distributed nodes, and generate a standardized financial data set based on the preset main and sub-account mapping rules using data aggregation technology; an intelligent budgeting module for generating a preliminary funding allocation plan based on the standardized financial data set using a zero-based budgeting model based on multiple objectives including service volume, functional positioning, and performance indicators; A resource coordination module is used to generate a dynamic fund allocation plan based on a preset resource sharing mechanism and by utilizing a feature matching algorithm in combination with the preliminary fund allocation plan; The evaluation and optimization module is used to evaluate the fund allocation effectiveness and resource sharing efficiency of the dynamically adjusted fund allocation plan using an operations research optimization algorithm, and generate plan optimization suggestions based on preset performance indicators.

2. The system according to claim 1, wherein: The smart budget module is also used to: Obtain the medical service volume data of each medical community member unit and construct a resource demand assessment matrix; Generate a dynamic adjustment coefficient based on the medical service volume deviation rate, and combine it with the resource demand assessment matrix to obtain an optimized resource demand assessment matrix; The multi-objective zero-based budgeting model is constructed by combining the optimized resource demand assessment matrix, the functional positioning and performance indicators of each medical community member unit.

3. The system according to claim 1, wherein: The resource coordination module is further configured to generate a dynamic fund allocation plan based on a preset resource sharing mechanism according to the following calculation steps combined with the preliminary fund allocation plan: The dynamic feature similarity is calculated using the following formula: Based on the dynamic feature similarity, the resource demand-supply matching model is constructed using the following formula: The dynamic capital allocation coefficient is calculated by combining the dynamic feature similarity and resource demand-supply matching model: The constraints of the calculation process are: and satisfy S ij (t) represents the feature similarity between member units i and j at time t, F i (t) represents the eigenvector of member unit i, λ k Represents the dynamic weight coefficient of the k-th dimension feature, Δ k (t) represents the real-time fluctuation value of the k-th dimension feature at time t, M ij (t) represents the resource demand-supply matching degree, S j (t) represents the resource supply capacity of member unit j at time t, D i (t) represents the resource demand value of member unit i at time t, Sim Fp (i, j) represents the similarity of the functional positioning of member units i and j, C i (t) represents the resource usage cost coefficient of unit i, α, β and γ represent the matching dimension weights, w ij (t) represents the dynamic capital allocation coefficient of member unit i to j, η represents the time sensitivity coefficient, T i and T j They represent the time when the demand is proposed and the time when resources are available, δ ij Indicates the resource sharing protocol identifier, B j (t) represents the budget amount applied for by unit member j at time t, and TotalBudget(t) represents the total budget of the medical community at time t.

4. The system according to claim 1, wherein: The resource coordination module is also used to: Obtain the capital flow characteristic data of each medical community member unit in the standardized financial data set and construct a capital flow characteristic analysis matrix; Generate resource allocation priorities based on multi-dimensional evaluation results of resource contribution, technical collaboration, and service effectiveness, combined with resource sharing agreement identifiers; Based on the preset resource sharing mechanism, combined with the preliminary fund allocation plan, fund flow feature analysis matrix and resource allocation priority, a feature matching algorithm is used to construct a medical fund pool balance model to generate the dynamic fund allocation plan.

5. The system according to claim 1, wherein: The evaluation and optimization module is also used to: Based on the preset performance indicators, the operations research optimization algorithm is used to evaluate the fund allocation effectiveness and resource sharing efficiency of the dynamic fund allocation plan to generate a difference evaluation matrix; Based on the difference evaluation matrix, an optimized adjustment path is generated using a preset dynamic compensation model for medical resources; Combining the optimization adjustment path with the preset performance indicators, the solution optimization suggestion is generated.

6. The system according to claim 5, characterized in that The evaluation and optimization module is also used to: Based on preset anomaly detection rules, perform multi-dimensional analysis on abnormal financial data in the dynamic fund allocation plan execution data, and generate an audit traceability request including the anomaly type, occurrence node, and risk level; Based on the audit traceability request, a medical financial causal analysis model is constructed and a problem root cause map is generated; Based on the problem root cause map and combined with the optimization adjustment path, an intelligent decision-making algorithm is used to generate the solution optimization suggestion.

7. The system according to claim 1, wherein: The system also includes a data verification module for: Use the preset data verification rules to verify the accuracy and completeness of the financial data of each member unit of the medical community, and generate a verification report containing the location, type and degree of deviation of abnormal data; Perform a multi-dimensional analysis of the type, frequency, and impact of abnormal data based on the verification report to generate an analysis result, wherein the analysis is used to determine the abnormality level and root cause; The root cause determined according to the analysis result refers to automatically marking the abnormal data and triggering a preset graded warning mechanism according to the abnormality level of the marked data.

8. A medical community financial integrated intelligent management method, characterized by: The method comprises: Obtain the financial data of each medical community member unit through distributed nodes, and use data aggregation technology to generate a standardized financial data set based on the preset main and sub-account mapping rules; Based on the standardized financial data set, a preliminary funding allocation plan is generated using a zero-based budgeting model based on multiple objectives including service volume, functional positioning and performance indicators; Based on the preset resource sharing mechanism, a dynamic fund allocation plan is generated by combining the preliminary fund allocation plan with a feature matching algorithm; The operations research optimization algorithm is used to evaluate the fund allocation effectiveness and resource sharing efficiency of the dynamically adjusted fund allocation plan, and the plan optimization suggestions are generated in combination with the preset performance indicators.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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