A research management system for the rational allocation of research resources

Abstract

This invention discloses a research management system for the rational allocation of research resources, comprising a software functional layer. This layer includes a resource database, a dynamic resource sensing module, an intelligent matching and optimization allocation module, a cross-disciplinary collaborative resource pool module, a resource linkage control module, and a permission management and traceability module. The resource database enables real-time data interaction. The dynamic resource sensing module collects the status information of research resources in real time and synchronizes standardized data to the resource database. The intelligent matching and optimization allocation module generates resource allocation schemes based on resource database information and research project requirements using a multi-objective optimization algorithm. The cross-disciplinary collaborative resource pool module integrates idle research resources and constructs a shared pool. The resource linkage control module correlates project progress data with resource allocation status. The permission management and traceability module controls the resource operation permissions of different users.

Description

Technical Field

[0001] This invention relates to the field of management system technology, specifically a scientific research management system for the rational allocation of scientific research resources. Background Technology

[0002] Research resources are the core support for advancing scientific research, such as equipment, funding, manpower, and experimental materials. Especially in multidisciplinary research settings such as hospitals, the rational allocation of resources directly determines the efficiency and diversity of scientific innovation.

[0003] Current scientific research management systems have many shortcomings: First, resource perception is lagging and fragmented. Most systems only focus on the process management of project application and approval, lacking dynamic monitoring of the entire life cycle of resources. The usage status of large laboratory equipment relies on manual registration, with an information error rate of over 15%. This often leads to the contradiction that "some equipment is idle for 2-3 days a week, and some teams stagnate due to insufficient resources." Funding and human resources information are scattered in independent systems such as finance and human resources. Applying for resources for interdisciplinary projects requires contacting 5-8 departments, and the process takes more than 7 working days.

[0004] Secondly, the allocation method is subjective and inefficient. The current resource allocation is mainly based on the experience and judgment of managers, and there is obvious subjective bias: key teams often receive excessive resources (fund utilization rate is less than 60%), while the resource acquisition rate of young research teams and niche research directions is only 40% of that of key teams, which inhibits the diversity of scientific research innovation.

[0005] Third, there is a disconnect between progress and resources. The existing system has not established a linkage mechanism between project progress and resources: when the project progress is behind schedule, it is impossible to quickly locate the reasons for "insufficient resource supply" or "idle resources", resulting in about 20% of projects being delayed for more than 3 months due to resource mismatch, causing serious waste of scientific research resources.

[0006] In conclusion, there is an urgent need in the market for a scientific research management system that can monitor resource usage and allocate resources according to project progress. Summary of the Invention

[0007] The purpose of this invention is to provide a scientific research management system for the rational allocation of scientific research resources, aiming to improve the problems of lagging and fragmented resource perception, subjective and inefficient allocation, and disconnect between progress and resources in existing scientific research management systems.

[0008] This invention is implemented as follows: A research management system for the rational allocation of research resources includes a software functional layer, wherein the software functional layer includes: A resource database, which is used to enable real-time data interaction; The resource dynamic sensing module is used to collect the status information of scientific research resources in real time and synchronize standardized data to the resource database. The intelligent matching and optimization allocation module generates resource allocation schemes based on resource database information and scientific research project requirements through a multi-objective optimization algorithm. The interdisciplinary collaborative resource pool module is used to integrate idle scientific research resources and build a shared pool, which is open to interdisciplinary project applications. A resource linkage and control module is used to link project progress data with resource allocation status and dynamically adjust resource configuration. The permission management and traceability module is used to control the resource operation permissions of different users and record the entire process data of resource allocation.

[0009] Preferably, the resource dynamic sensing module includes an IoT sensing unit, a multi-system interface unit, and a data standardization unit. The IoT sensing unit and the multi-system interface unit collect different types of scientific research resource information in parallel. The data standardization unit is used to standardize the data. The IoT sensing unit includes a device operation status sensor and an experimental material inventory sensor. The device operation status sensor is used to collect information on the power-on status, continuous operating time, fault warning signals, and available time periods of the scientific research equipment. The scientific research equipment includes large-scale laboratory testing equipment, specifically at least one of a next-generation sequencer, a fluorescence quantitative PCR instrument, and a liquid chromatography-mass spectrometry instrument. The experimental material inventory sensor is used to collect information on the remaining quantity, expiration date, and temperature and humidity parameters of the storage environment for experimental reagents and consumables. The multi-system interface unit is used to interface with the institution's financial system, human resources system, and department-specific management system. The interface with the financial system synchronizes the current balance of research funds, detailed expenditures by project, and percentage of budget execution progress. The interface with the human resources system synchronizes the number of ongoing projects for researchers, weekly research work hours, professional skill tags, and professional title information. The interface with the department-specific management system synchronizes the ownership, access restrictions, and historical allocation records of the department's own research resources. The data standardization unit is used to convert heterogeneous data collected by IoT sensing units and multi-system docking units into structured data in a unified format; the structured data fields in the unified format include resource unique ID, resource type classification code, real-time status parameters, and data update timestamp.

[0010] Preferably, the intelligent matching and optimized allocation module includes a demand receiving unit, an algorithm calculation unit, and a scheme output unit. The demand receiving unit receives resource demand information submitted by researchers through the front end. The resource demand information includes resource type, quantity, expected usage period, project priority level, and team professional skill suitability requirements. The algorithm calculation unit uses a multi-objective optimization algorithm to construct an objective function for resource allocation. The weight coefficients of each sub-objective of the multi-objective optimization algorithm can be customized and adjusted through the institute-level management terminal, with the adjustment range of a single weight coefficient being 0.1 to 0.5. The first sub-objective of the objective function is to maximize resource utilization, specifically by minimizing the proportion of idle resources. The second sub-objective of the objective function is to maximize project priority matching, specifically by ensuring that the resource demand satisfaction rate of provincial and above key projects is not less than 90%. The third sub-objective of the objective function is to optimize team resource suitability, specifically by ensuring that the matching degree between the professional attributes of resources and team skill tags is not less than 80%. The scheme output unit generates an allocation scheme that includes a resource list, precise usage period, designated responsible person, and resource usage assessment indicators.

[0011] Preferably, the interdisciplinary collaborative resource pool module includes an idle resource screening unit, a resource pool classification unit, a sharing application unit, and a sharing incentive unit. The idle resource screening unit is used to screen research resources in the resource database that meet preset idle conditions. The preset idle conditions include at least one of the following: equipment continuously idle for more than 24 hours, funding surplus remaining for more than 3 months, and researchers having more than 8 hours of idle time per week. The resource pool classification unit is used to divide idle resources into a basic medical resource pool, a clinical medical resource pool, and a bioinformatics resource pool according to disciplines. The sharing application unit is used to receive applications for the use of idle resources submitted by interdisciplinary project teams. The sharing incentive unit is used to include the actual sharing time of idle resources in the resource utilization efficiency assessment index of the original team. The resource utilization efficiency assessment index accounts for 15% to 20% of the priority weight of the original team's next resource application.

[0012] Preferably, the resource linkage control module includes a progress acquisition unit and a lag analysis unit. The progress acquisition unit is used to connect to the project progress management subsystem to collect the project's phase completion rate and key node achievement status. The acquisition frequency of the progress acquisition unit is consistent with the update frequency of the resource database, specifically, once every 30 minutes to 1 hour. The lag analysis unit is used to analyze the resource supply status of the corresponding project when the project progress lags behind the planned threshold. The project progress lag threshold can be customized through the management terminal, and the system default threshold is 5% of the planned progress. If the lag analysis unit determines that the lag is due to "insufficient resource supply," the resource linkage control module schedules and adapts resources from the cross-disciplinary collaborative resource pool to supplement the corresponding project. If the lag analysis unit determines that the lag is due to "resource idleness," the resource linkage control module pushes a resource idleness reminder to the project team and adjusts the idle resources to other projects with pending demand.

[0013] Preferably, the permission management and traceability module includes a permission hierarchy division unit and a traceability ledger generation unit. The permission hierarchy division unit divides user permissions into three levels: researcher-side permissions, department manager-side permissions, and institute-level manager-side permissions. The researcher-side permissions include submitting resource requests, viewing the resource allocation status of projects under their responsibility, and providing feedback on actual resource usage. The department manager-side permissions include reviewing resource allocation plans within their department and viewing monthly resource usage statistics for their department. The institute-level manager-side permissions include coordinating the allocation of research resources across the entire institution, adjusting the weight coefficients of multi-objective optimization algorithms, and exporting an annual report on the resource utilization efficiency of the entire institution. The traceability ledger generation unit is used to record the entire process of resource "application-review-scheduling-use-return". The record fields of the traceability ledger include operation execution time, operation responsible person ID, resource status change information, and unique identifier of associated project.

[0014] Preferably, it further includes a hardware layer, the hardware layer comprising: A data acquisition terminal is used in conjunction with a resource dynamic sensing module to collect resource status and project progress data in real time. Edge computing nodes, which are used to process low-latency data locally; The central server is used in conjunction with the resource database to handle data storage and core algorithm calculations.

[0015] Preferably, the data acquisition terminal includes an equipment status acquisition sub-terminal and a personnel project progress acquisition sub-terminal. The equipment status acquisition sub-terminal is deployed on the research equipment itself and is used to collect real-time data on equipment runtime, load rate, and fault status. The equipment status acquisition sub-terminal uses IoT sensors and supports data transmission to edge computing nodes at a frequency of 5 seconds per transmission. The personnel project progress acquisition sub-terminal is deployed on the research personnel's terminal devices and is used to collect data on project phase completion and actual resource usage records.

[0016] Preferably, the edge computing node is deployed on a department-level network node and is used to perform preliminary calculations on low-latency data such as real-time status of devices; the edge computing node is configured with a 128GB data cache unit to temporarily store the collected data when the network is interrupted and automatically synchronize it to the central server after the network is restored.

[0017] Preferably, the central server includes a distributed data storage unit and a core algorithm operation unit. The distributed data storage unit is used to store resource profile data, project process data, and scheduling operation record data. The core algorithm operation unit is used to run machine learning and deep learning algorithms to achieve resource matching and demand prediction functions.

[0018] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention achieves dynamic collection and standardized management of three core scientific research resources—equipment, funding, and human resources—through the collaboration of IoT sensing units and multi-system docking units. The resource information update frequency is increased to 30 minutes to 1 hour, completely solving the problems of lagging resource status and fragmented information in traditional systems.

[0019] 2. This invention replaces manual decision-making with algorithms, reducing subjective biases and significantly improving the resource acquisition rate of youth research projects; the collaborative resource pool reduces the resource matching cost of interdisciplinary projects.

[0020] 3. The linkage between project progress and resources in this invention avoids project delays caused by resource mismatch, and the full-process traceability facilitates assessment and optimization of resource allocation mechanisms.

[0021] 4. This invention makes all scientific research resources transparent and comprehensive. Through the management system, they can be checked with one click, and responsibilities can be assigned to individuals, improving work efficiency, strengthening cooperation between disciplines, and enabling scientific research managers to clearly grasp the progress and achieve overall allocation. Attached Figure Description

[0022] Figure 1 This is a structural block diagram of the management system of the present invention; Figure 2 This is a structural block diagram of the resource dynamic sensing module of the present invention; Figure 3This is a structural block diagram of the intelligent matching and optimized allocation module of the present invention; Figure 4 This is a structural block diagram of the interdisciplinary collaborative resource pool module of the present invention; Figure 5 This is a structural block diagram of the resource linkage control module of the present invention; Figure 6 This is a structural block diagram of the permission management and traceability module of the present invention; Figure 7 This is a structural block diagram of the data acquisition terminal of the present invention; Figure 8 This is a structural block diagram of the central server of this invention. Detailed Implementation

[0023] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0024] The following description, in conjunction with the accompanying drawings and specific embodiments, provides further details: Example 1

[0025] like Figure 1 As shown, a research management system for the rational allocation of research resources includes a software functional layer. This layer comprises a resource database, a dynamic resource sensing module, an intelligent matching and optimization allocation module, a cross-disciplinary collaborative resource pool module, a resource linkage control module, and a permission management and traceability module. The resource database enables real-time data interaction. The dynamic resource sensing module collects real-time status information of research resources and synchronizes standardized data to the resource database. The intelligent matching and optimization allocation module generates resource allocation schemes based on resource database information and research project requirements using a multi-objective optimization algorithm. The cross-disciplinary collaborative resource pool module integrates idle research resources and constructs a shared pool, open to cross-disciplinary project applications. The resource linkage control module correlates project progress data with resource allocation status and dynamically adjusts resource configuration. The permission management and traceability module controls the resource operation permissions of different users and records data throughout the entire resource allocation process.

[0026] like Figure 2As shown, the resource dynamic sensing module includes an IoT sensing unit, a multi-system interface unit, and a data standardization unit. The IoT sensing unit and the multi-system interface unit collect different types of research resource information in parallel. The data standardization unit is used to standardize the data. The IoT sensing unit includes equipment operation status sensors and experimental material inventory sensors. The equipment operation status sensors collect information on the power-on status, continuous operating time, fault warning signals, and available time periods of research equipment. Research equipment includes large-scale laboratory testing equipment, specifically at least one of next-generation sequencers, quantitative PCR instruments, and liquid chromatography-mass spectrometry. The experimental material inventory sensors collect information on the remaining quantity of experimental reagents and consumables, their expiration dates, and the temperature and humidity parameters of the storage environment. The multi-system interface unit interfaces with the institution's financial system, personnel system, and departmental management system. The interface with the institution's financial system synchronizes the current balance of research funds, detailed expenditures by project, and the percentage of budget execution progress. The interface with the institution's personnel system synchronizes the number of ongoing projects for researchers, weekly research work hours, professional skill tags, and professional title information. The multi-system integration unit interfaces with the department's dedicated management system to synchronize the ownership, access restrictions, and historical allocation records of the department's own research resources. The data standardization unit converts heterogeneous data collected by the IoT sensing unit and the multi-system integration unit into structured data in a unified format. The unified format structured data fields include a unique resource ID, resource type classification code, real-time status parameters, and data update timestamp.

[0027] like Figure 3 As shown, the intelligent matching and optimized allocation module includes a demand receiving unit, an algorithm calculation unit, and a solution output unit. The demand receiving unit receives resource demand information submitted by researchers through the front end. Resource demand information includes resource type, quantity, expected usage period, project priority level, and team professional skill suitability requirements. The algorithm calculation unit uses a multi-objective optimization algorithm to construct the objective function for resource allocation. The weight coefficients of each sub-objective of the multi-objective optimization algorithm can be customized through the institute-level management interface, with the adjustment range for a single weight coefficient being 0.1 to 0.5. The first sub-objective of the objective function is to maximize resource utilization, specifically by minimizing the proportion of idle resources. The second sub-objective of the objective function is to maximize project priority matching, specifically by ensuring that the resource demand fulfillment rate for provincial and above key projects is no less than 90%. The third sub-objective of the objective function is to optimize team resource suitability, specifically by ensuring that the matching degree between the professional attributes of resources and team skill tags is no less than 80%. The solution output unit generates an allocation plan that includes a resource list, precise usage period, designated responsible person, and resource usage assessment indicators. The objective function is a dynamic weighted multi-objective optimization objective function, which includes resource constraints. The specific formula is as follows: Constraints: 1. 2. 3. Symbol definition: The overall optimization score at time t (the higher the score, the better the allocation scheme). Dynamic weights at time t As the initial weights, For the remaining time of the project, (Based on the project cycle) Resource utilization rate, project priority matching degree, and team fit at time t; Project i requires resource type j; The total available resources of resource type j at time t; The time period during which project i uses exclusive device k.

[0028] Function: Introduces dynamic weights: the shorter the remaining time of the project... smaller Priority weight The higher the resource utilization rate, the higher the weight. The lower the value, the better it is suited for resource allocation in emergency projects; the addition of total resource constraints and exclusive equipment time period conflict constraints avoids allocation schemes that are "theoretically optimal but practically infeasible".

[0029] Results: The "actual executability rate" of the allocation plan increased from 72% to 98%; resource response time for emergency projects was reduced by 60%. Reduced from 48 hours to 19 hours .

[0030] The resource utilization rate is calculated using a time-weighted multi-resource integration utilization rate formula, as follows: Symbol definition: Corresponding equipment resources, funding resources, and human resources; Resource type weight (e.g., equipment) Funding Human resources ); project Resources actually used at all times quantity; resource exist "Time Period Value Coefficient" (Peak Hour) Peak Low peak ).

[0031] Purpose: 1. To integrate the three core research resources of "equipment, funding, and human resources," avoiding the problem of high utilization rates of individual resources but overall low efficiency; 2. To introduce a "time-period value coefficient." Distinguishing the use value of resources at different times For example, laboratory equipment is more valuable during working hours than at night. .

[0032] Result: The "value utilization rate" of scientific research resources Rather than simply quantity utilization Increased by 25%; • Avoided the waste of "occupying high-value resources during low-value periods", and increased the effective utilization rate of equipment during peak periods from 65% to 90%.

[0033] The project priority matching degree calculation formula adopts a multi-dimensional dynamic priority formula, and the specific calculation formula is as follows: Symbol definition: Basic priority score for project i (national-level project) ,provincial School level ); Impact coefficient of project i (expected top journal papers) ordinary journals No expected results ); : Remaining deadline for project i (in days); Resource requirement fulfillment / requirement for project i.

[0034] Function: Breaking through the limitations of the traditional "prioritizing only by project level", it incorporates the dimensions of impact of results and time urgency, making priority matching more in line with scientific research value and time constraints.

[0035] Results: Resource requirement fulfillment rate for high-impact emergency projects increased from 80% to 98%; "Low-value projects occupying high-priority resources" was avoided. The "value concentration" of scientific research resources increased by 30%.

[0036] The team resource fit is calculated using a skills-performance weighted fit formula, as follows: Symbol definition: The total number of skills required for the resource; Team i's proficiency level with skill k (levels 1-5, level 5 being mastery); : The matching degree between skill k and resources (0~1, 1 is a perfect match); : Historical success rate (0~1) of team i using resources containing skill k; : The highest proficiency level of skill k (default 5).

[0037] Purpose: To upgrade from "skill availability" to a multi-dimensional assessment of "skill proficiency + historical performance", avoiding wasting resources for teams that "have skills but are not proficient / have low success rates".

[0038] Results: The "efficiency rate" of resource utilization Such as the proportion of valid data produced by the equipment Increased from 60% to 85% The team reduced equipment failures / budget overruns caused by unfamiliarity with resource operation by 40%.

[0039] like Figure 4 As shown, the interdisciplinary collaborative resource pool module includes an idle resource screening unit, a resource pool classification unit, a sharing application unit, and a sharing incentive unit. The idle resource screening unit filters research resources in the resource database that meet preset idle conditions. These preset idle conditions include at least one of the following: equipment continuously idle for more than 24 hours, funding surplus remaining for more than 3 months, and researchers having more than 8 hours of idle time per week. The resource pool classification unit categorizes idle resources by discipline into basic medical resources, clinical medical resources, and bioinformatics resources. The sharing application unit receives applications from interdisciplinary project teams to use idle resources. The sharing incentive unit includes the actual sharing time of idle resources in the resource utilization efficiency assessment index of the original team. The resource utilization efficiency assessment index accounts for 15% to 20% of the priority weight of the original team's next resource application. The interdisciplinary collaborative resource pool module needs to calculate the net sharing efficiency, and the formula for net sharing efficiency (after deducting sharing costs) is... Symbol definition: Interdisciplinary sharing time of resources / original idle time; The unit time value of resource k (e.g., sequencer 100 yuan / hour). Cross-disciplinary shared communication and coordination costs (e.g., meeting duration × staff hourly wage); Costs of transferring / allocating resources (such as equipment calibration fees and material transportation costs).

[0040] Purpose: To upgrade from "simple sharing of time" to a net efficiency assessment of "sharing benefits - sharing costs", avoiding ineffective sharing where "sharing benefits are lower than costs".

[0041] Results: "Net benefit achievement rate" of interdisciplinary sharing Net efficiency 280% The percentage increased from 5596 to 90%; resource waste caused by excessively high sharing costs was reduced by 60%.

[0042] like Figure 5 As shown, the resource linkage and control module includes a progress acquisition unit and a lag analysis unit. The progress acquisition unit connects to the project progress management subsystem to collect data on project stage completion rates and key milestone achievement status. The acquisition frequency of the progress acquisition unit is consistent with the update frequency of the resource database, specifically every 30 minutes to 1 hour. The lag analysis unit analyzes the resource supply status of the corresponding project when the project progress lags behind the planned threshold. The project progress lag threshold can be customized through the management interface; the system default threshold is 5% of the planned progress. If the lag analysis unit determines that the lag is due to "insufficient resource supply," the resource linkage and control module schedules suitable resources from the cross-disciplinary collaborative resource pool to supplement the corresponding project. If the lag analysis unit determines that the lag is due to "resource idleness," the resource linkage and control module pushes a resource idleness reminder to the project team and allocates idle resources to other projects with pending needs. The resource linkage and control module adjusts the resource room using the resource-progress coupling coefficient formula. With adjustment costs The specific calculation formula is as follows: Symbol definition: project Resource supply adequacy ratio; project Resource utilization efficiency The project output at time t, such as the number of experiments completed. ; project Resource adjustment cost coefficient For example, the time spent on equipment relocation / the time spent on the re-approval process for funding. .

[0043] Purpose: To link "resource supply" with "resource utilization rate" and incorporate it into adjustment costs, thereby avoiding the problem of "blindly adjusting resources to catch up on progress, leading to higher losses". Effect: The "net benefit ratio" of resource adjustments Costs of schedule improvement / adjustment Improved from 1.2 to 2.5; the rate of additional project delays due to frequent resource adjustments decreased by 50%.

[0044] like Figure 6 As shown, the access control and traceability module includes an access control hierarchy unit and a traceability ledger generation unit. The access control hierarchy unit divides user permissions into three levels: researcher access, department manager access, and institute-level manager access. Researcher access includes submitting resource requests, viewing the resource allocation status of projects under their responsibility, and providing feedback on actual resource usage. Department manager access includes reviewing resource allocation plans within their department and viewing monthly resource usage statistics for their department. Institute-level manager access includes coordinating the allocation of research resources across the entire institute, adjusting the weight coefficients of multi-objective optimization algorithms, and exporting an annual report on the resource utilization efficiency of the entire institute. The traceability ledger generation unit records the entire process of resource "application-review-scheduling-use-return". The record fields in the traceability ledger include operation execution time, operation responsible person ID, resource status change information, and unique identifier of the associated project. Example 2

[0045] like Figure 1 As shown, a research management system for the rational allocation of research resources includes a software functional layer. This layer comprises a resource database, a dynamic resource sensing module, an intelligent matching and optimization allocation module, a cross-disciplinary collaborative resource pool module, a resource linkage control module, and a permission management and traceability module. The resource database enables real-time data interaction. The dynamic resource sensing module collects real-time status information of research resources and synchronizes standardized data to the resource database. The intelligent matching and optimization allocation module generates resource allocation schemes based on resource database information and research project requirements using a multi-objective optimization algorithm. The cross-disciplinary collaborative resource pool module integrates idle research resources and constructs a shared pool, open to cross-disciplinary project applications. The resource linkage control module correlates project progress data with resource allocation status and dynamically adjusts resource configuration. The permission management and traceability module controls the resource operation permissions of different users and records data throughout the entire resource allocation process.

[0046] like Figure 2As shown, the resource dynamic sensing module includes an IoT sensing unit, a multi-system interface unit, and a data standardization unit. The IoT sensing unit and the multi-system interface unit collect different types of research resource information in parallel. The data standardization unit is used to standardize the data. The IoT sensing unit includes equipment operation status sensors and experimental material inventory sensors. The equipment operation status sensors collect information on the power-on status, continuous operating time, fault warning signals, and available time periods of research equipment. Research equipment includes large-scale laboratory testing equipment, specifically at least one of next-generation sequencers, quantitative PCR instruments, and liquid chromatography-mass spectrometry. The experimental material inventory sensors collect information on the remaining quantity of experimental reagents and consumables, their expiration dates, and the temperature and humidity parameters of the storage environment. The multi-system interface unit interfaces with the institution's financial system, personnel system, and departmental management system. The interface with the institution's financial system synchronizes the current balance of research funds, detailed expenditures by project, and the percentage of budget execution progress. The interface with the institution's personnel system synchronizes the number of ongoing projects for researchers, weekly research work hours, professional skill tags, and professional title information. The multi-system integration unit interfaces with the department's dedicated management system to synchronize the ownership, access restrictions, and historical allocation records of the department's own research resources. The data standardization unit converts heterogeneous data collected by the IoT sensing unit and the multi-system integration unit into structured data in a unified format. The unified format structured data fields include a unique resource ID, resource type classification code, real-time status parameters, and data update timestamp.

[0047] like Figure 3 As shown, the intelligent matching and optimized allocation module includes a demand receiving unit, an algorithm calculation unit, and a solution output unit. The demand receiving unit receives resource demand information submitted by researchers through the front end. Resource demand information includes resource type, quantity, expected usage period, project priority level, and team professional skill suitability requirements. The algorithm calculation unit uses a multi-objective optimization algorithm to construct the objective function for resource allocation. The weight coefficients of each sub-objective of the multi-objective optimization algorithm can be customized through the institute-level management interface, with the adjustment range for a single weight coefficient being 0.1 to 0.5. The first sub-objective of the objective function is to maximize resource utilization, specifically by minimizing the proportion of idle resources. The second sub-objective of the objective function is to maximize project priority matching, specifically by ensuring that the resource demand fulfillment rate for provincial and above key projects is no less than 90%. The third sub-objective of the objective function is to optimize team resource suitability, specifically by ensuring that the matching degree between the professional attributes of resources and team skill tags is no less than 80%. The solution output unit generates an allocation plan that includes a resource list, precise usage period, designated responsible person, and resource usage assessment indicators. The objective function is a dynamic weighted multi-objective optimization objective function, which includes resource constraints. The specific formula is as follows: Constraints: 1. 2. 3. Symbol definition: The overall optimization score at time t (the higher the score, the better the allocation scheme). Dynamic weights at time t As the initial weights, For the remaining time of the project, (Based on the project cycle) Resource utilization rate, project priority matching degree, and team fit at time t; Project i requires resource type j; The total available resources of resource type j at time t; The time period during which project i uses exclusive device k.

[0048] Function: Introduces dynamic weights: the shorter the remaining time of the project... smaller Priority weight The higher the resource utilization rate, the higher the weight. The lower the value, the better it is suited for resource allocation in emergency projects; the addition of total resource constraints and exclusive equipment time period conflict constraints avoids allocation schemes that are "theoretically optimal but practically infeasible".

[0049] Results: The "actual executability rate" of the allocation plan increased from 72% to 98%; resource response time for emergency projects was reduced by 60%. Reduced from 48 hours to 19 hours .

[0050] The resource utilization rate is calculated using a time-weighted multi-resource integration utilization rate formula, as follows: Symbol definition: Corresponding equipment resources, funding resources, and human resources; Resource type weight (e.g., equipment) Funding Human resources ); project Resources actually used at all times quantity; resource exist "Time Period Value Coefficient" (Peak Hour) Peak Low peak ).

[0051] Purpose: 1. To integrate the three core research resources of "equipment, funding, and human resources," avoiding the problem of high utilization rates of individual resources but overall low efficiency; 2. To introduce a "time-period value coefficient." Distinguishing the use value of resources at different times For example, laboratory equipment is more valuable during working hours than at night. .

[0052] Result: The "value utilization rate" of scientific research resources Rather than simply quantity utilization Increased by 25%; • Avoided the waste of "occupying high-value resources during low-value periods", and increased the effective utilization rate of equipment during peak periods from 65% to 90%.

[0053] The project priority matching degree calculation formula adopts a multi-dimensional dynamic priority formula, and the specific calculation formula is as follows: Symbol definition: Basic priority score for project i (national-level project) ,provincial School level ); Impact coefficient of project i (expected top journal papers) ordinary journals No expected results ); : Remaining deadline for project i (in days); Resource requirement fulfillment / requirement for project i.

[0054] Function: Breaking through the limitations of the traditional "prioritizing only by project level", it incorporates the dimensions of impact of results and time urgency, making priority matching more in line with scientific research value and time constraints.

[0055] Results: Resource requirement fulfillment rate for high-impact emergency projects increased from 80% to 98%; "Low-value projects occupying high-priority resources" was avoided. The "value concentration" of scientific research resources increased by 30%.

[0056] The team resource fit is calculated using a skills-performance weighted fit formula, as follows: Symbol definition: The total number of skills required for the resource; Team i's proficiency level with skill k (levels 1-5, level 5 being mastery); : The matching degree between skill k and resources (0~1, 1 is a perfect match); : Historical success rate (0~1) of team i using resources containing skill k; : The highest proficiency level of skill k (default 5).

[0057] Purpose: To upgrade from "skill availability" to a multi-dimensional assessment of "skill proficiency + historical performance", avoiding wasting resources for teams that "have skills but are not proficient / have low success rates".

[0058] Results: The "efficiency rate" of resource utilization Such as the proportion of valid data produced by the equipment Increased from 60% to 85% The team reduced equipment failures / budget overruns caused by unfamiliarity with resource operation by 40%.

[0059] like Figure 4 As shown, the interdisciplinary collaborative resource pool module includes an idle resource screening unit, a resource pool classification unit, a sharing application unit, and a sharing incentive unit. The idle resource screening unit filters research resources in the resource database that meet preset idle conditions. These preset idle conditions include at least one of the following: equipment continuously idle for more than 24 hours, funding surplus remaining for more than 3 months, and researchers having more than 8 hours of idle time per week. The resource pool classification unit categorizes idle resources by discipline into basic medical resources, clinical medical resources, and bioinformatics resources. The sharing application unit receives applications from interdisciplinary project teams to use idle resources. The sharing incentive unit includes the actual sharing time of idle resources in the resource utilization efficiency assessment index of the original team. The resource utilization efficiency assessment index accounts for 15% to 20% of the priority weight of the original team's next resource application. The interdisciplinary collaborative resource pool module needs to calculate the net sharing efficiency, and the formula for net sharing efficiency (after deducting sharing costs) is... Symbol definition: Interdisciplinary sharing time of resources / original idle time; The unit time value of resource k (e.g., sequencer 100 yuan / hour). Cross-disciplinary shared communication and coordination costs (e.g., meeting duration × staff hourly wage); Costs of transferring / allocating resources (such as equipment calibration fees and material transportation costs).

[0060] Purpose: To upgrade from "simple sharing of time" to a net efficiency assessment of "sharing benefits - sharing costs", avoiding ineffective sharing where "sharing benefits are lower than costs".

[0061] Results: "Net benefit achievement rate" of interdisciplinary sharing Net efficiency 280% The percentage increased from 5596 to 90%; resource waste caused by excessively high sharing costs was reduced by 60%.

[0062] like Figure 5 As shown, the resource linkage and control module includes a progress acquisition unit and a lag analysis unit. The progress acquisition unit connects to the project progress management subsystem to collect data on project stage completion rates and key milestone achievement status. The acquisition frequency of the progress acquisition unit is consistent with the update frequency of the resource database, specifically every 30 minutes to 1 hour. The lag analysis unit analyzes the resource supply status of the corresponding project when the project progress lags behind the planned threshold. The project progress lag threshold can be customized through the management interface; the system default threshold is 5% of the planned progress. If the lag analysis unit determines that the lag is due to "insufficient resource supply," the resource linkage and control module schedules suitable resources from the cross-disciplinary collaborative resource pool to supplement the corresponding project. If the lag analysis unit determines that the lag is due to "resource idleness," the resource linkage and control module pushes a resource idleness reminder to the project team and allocates idle resources to other projects with pending needs. The resource linkage and control module adjusts the resource room using the resource-progress coupling coefficient formula. With adjustment costs The specific calculation formula is as follows: Symbol definition: project Resource supply adequacy ratio; project Resource utilization efficiency The project output at time t, such as the number of experiments completed. ; project Resource adjustment cost coefficient For example, the time spent on equipment relocation / the time spent on the re-approval process for funding. .

[0063] Purpose: To link "resource supply" with "resource utilization rate" and incorporate it into adjustment costs, thereby avoiding the problem of "blindly adjusting resources to catch up on progress, leading to higher losses". Effect: The "net benefit ratio" of resource adjustments Costs of schedule improvement / adjustment Improved from 1.2 to 2.5; the rate of additional project delays due to frequent resource adjustments decreased by 50%.

[0064] like Figure 6 As shown, the access control and traceability module includes an access control hierarchy unit and a traceability ledger generation unit. The access control hierarchy unit divides user permissions into three levels: researcher access, department manager access, and institute-level manager access. Researcher access includes submitting resource requests, viewing the resource allocation status of projects under their responsibility, and providing feedback on actual resource usage. Department manager access includes reviewing resource allocation plans within their department and viewing monthly resource usage statistics for their department. Institute-level manager access includes coordinating the allocation of research resources across the entire institute, adjusting the weight coefficients of multi-objective optimization algorithms, and exporting an annual report on the resource utilization efficiency of the entire institute. The traceability ledger generation unit records the entire process of resource "application-review-scheduling-use-return". The record fields in the traceability ledger include operation execution time, operation responsible person ID, resource status change information, and unique identifier of the associated project.

[0065] like Figure 1 As shown, it also includes a hardware layer, which comprises data acquisition terminals, edge computing nodes, and a central server. The data acquisition terminals are used in conjunction with the resource dynamic perception module to collect resource status and project progress data in real time. Edge computing nodes are used to process low-latency data locally. The central server is used in conjunction with the resource database to handle data storage and core algorithm calculations.

[0066] like Figure 7 As shown, the data acquisition terminal includes an equipment status acquisition sub-terminal and a personnel project progress acquisition sub-terminal. The equipment status acquisition sub-terminal is deployed on the research equipment itself and is used to collect real-time data on equipment runtime, load rate, and fault status. The equipment status acquisition sub-terminal uses IoT sensors and supports data transmission to edge computing nodes at a frequency of 5 seconds per transmission. The personnel project progress acquisition sub-terminal is deployed on the terminal devices of researchers and is used to collect data on project phase completion and actual resource usage records.

[0067] like Figure 1As shown, edge computing nodes are deployed at the departmental network level to perform preliminary calculations on low-latency data related to real-time device status. Each edge computing node is equipped with a 128GB data cache unit to temporarily store collected data when the network is interrupted, and automatically synchronize it to the central server after the network is restored.

[0068] like Figure 8 As shown, the central server includes a distributed data storage unit and a core algorithm processing unit. The distributed data storage unit stores resource profile data, project workflow data, and scheduling operation records. The core algorithm processing unit runs machine learning and deep learning algorithms to achieve resource matching and demand prediction functions.

[0069] Working Principle: This scientific research management system revolves around the efficient allocation and full-process control of scientific research resources. It achieves rational resource allocation through the collaborative operation of the hardware and software functional layers: The hardware layer's data acquisition terminals collect real-time data on the operating status of scientific research equipment, experimental material inventory, and project stage completion. This data is then processed locally by edge computing nodes to achieve low latency (data can be temporarily stored during network interruptions). This data, along with heterogeneous data such as financial, personnel, and departmental resources obtained from multi-system interface units, is converted into structured data in a unified format by a data standardization unit and synchronized to the resource database. In the software functional layer, the intelligent matching and optimized allocation module receives resource requests submitted by researchers. Based on the resource database information, it generates an allocation scheme containing resource lists and usage periods through a multi-objective optimization algorithm with customizable weights. The interdisciplinary collaborative resource pool module filters... Resources that meet the idle conditions are selected and categorized by discipline to build a shared pool, supporting interdisciplinary project applications. The sharing time is also included in the original team's performance evaluation to incentivize sharing. The resource linkage and control module collects project progress every 30 minutes to 1 hour. When the progress lags behind the preset threshold, the resource supply status is analyzed. If the resource is insufficient, it is supplemented from the shared pool. If the resource is idle, a reminder is pushed and the resource is adjusted to other projects with needs. The permission management and traceability module divides the permissions into three levels: researchers, department managers, and institute-level managers, corresponding to operations such as resource request submission, departmental resource review, and overall institutional resource coordination. At the same time, a traceability ledger is generated to record the entire process of resource "application-review-scheduling-use-return". The central server is responsible for core data storage and machine learning and deep learning algorithm operations to ensure the overall efficient and stable operation of the system.

[0070] In summary, compared with existing technologies, this application achieves dynamic collection and standardized management of three core research resources—equipment, funding, and human resources—through the collaboration of IoT sensing units and multi-system interface units. The resource information update frequency is increased to 30 minutes to 1 hour per update, completely resolving the problems of lagging resource status and fragmented information in traditional systems. By replacing manual decision-making with algorithms, subjective biases are reduced, significantly improving the resource acquisition rate for youth research projects; the collaborative resource pool reduces the resource integration costs for interdisciplinary projects. The linkage between project progress and resources avoids project delays caused by resource mismatch, and full-process traceability facilitates the assessment and optimization of resource allocation mechanisms.

[0071] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A scientific research management system for scientific research resource allocation, comprising a software function layer, characterized in that, The software functional layer includes: A resource database, which is used to enable real-time data interaction; The resource dynamic sensing module is used to collect the status information of scientific research resources in real time and synchronize standardized data to the resource database. The intelligent matching and optimization allocation module generates resource allocation schemes based on resource database information and scientific research project requirements through a multi-objective optimization algorithm. The interdisciplinary collaborative resource pool module is used to integrate idle scientific research resources and build a shared pool, which is open to interdisciplinary project applications. A resource linkage and control module is used to link project progress data with resource allocation status and dynamically adjust resource configuration. The permission management and traceability module is used to control the resource operation permissions of different users and record the entire process data of resource allocation. 2.The scientific research management system for scientific research resource allocation of claim 1, wherein, The resource dynamic sensing module includes an IoT sensing unit, a multi-system interface unit, and a data standardization unit. The IoT sensing unit and the multi-system interface unit collect different types of scientific research resource information in parallel. The data standardization unit is used to standardize the data. The IoT sensing unit includes a device operation status sensor and an experimental material inventory sensor. The device operation status sensor is used to collect information on the power-on status, continuous operating time, fault warning signals, and available time periods of the scientific research equipment. The scientific research equipment includes large-scale laboratory testing equipment, specifically at least one of a next-generation sequencer, a fluorescence quantitative PCR instrument, and a liquid chromatography-mass spectrometry instrument. The experimental material inventory sensor is used to collect information on the remaining quantity, expiration date, and temperature and humidity parameters of the storage environment for experimental reagents and consumables. The multi-system interface unit is used to interface with the institution's financial system, human resources system, and department-specific management system. The interface with the financial system synchronizes the current balance of research funds, detailed expenditures by project, and percentage of budget execution progress. The interface with the human resources system synchronizes the number of ongoing projects for researchers, weekly research work hours, professional skill tags, and professional title information. The interface with the department-specific management system synchronizes the ownership, access restrictions, and historical allocation records of the department's own research resources. The data standardization unit is used to convert heterogeneous data collected by IoT sensing units and multi-system docking units into structured data in a unified format; the structured data fields in the unified format include resource unique ID, resource type classification code, real-time status parameters, and data update timestamp. 3.The scientific research management system for scientific research resource allocation of claim 1, wherein, The intelligent matching and optimization allocation module includes a demand receiving unit, an algorithm calculation unit, and a scheme output unit. The demand receiving unit receives resource demand information submitted by researchers through the front end. The resource demand information includes resource type, quantity, expected usage period, project priority level, and team professional skill suitability requirements. The algorithm calculation unit uses a multi-objective optimization algorithm to construct an objective function for resource allocation. The weight coefficients of each sub-objective of the multi-objective optimization algorithm can be customized and adjusted through the institute-level management terminal, with the adjustment range of a single weight coefficient being 0.1 to 0.

5. The first sub-objective of the objective function is to maximize resource utilization, specifically by minimizing the proportion of idle resources. The second sub-objective of the objective function is to maximize project priority matching, specifically by ensuring that the resource demand satisfaction rate of provincial and above key projects is not less than 90%. The third sub-objective of the objective function is to optimize team resource suitability, specifically by ensuring that the matching degree between the professional attributes of resources and team skill tags is not less than 80%. The scheme output unit generates an allocation scheme that includes a resource list, precise usage period, designated responsible person, and resource usage assessment indicators. 4.The scientific research management system for scientific research resource allocation of claim 1, wherein, The interdisciplinary collaborative resource pool module includes an idle resource screening unit, a resource pool classification unit, a sharing application unit, and a sharing incentive unit. The idle resource screening unit is used to screen research resources in the resource database that meet preset idle conditions. The preset idle conditions include at least one of the following: equipment idle time exceeding 24 hours continuously, funding surplus remaining time exceeding 3 months, and researchers idle time exceeding 8 hours per week. The resource pool classification unit is used to divide idle resources into basic medical resource pool, clinical medical resource pool, and bioinformatics resource pool according to discipline. The sharing application unit is used to receive applications for the use of idle resources submitted by interdisciplinary project teams. The sharing incentive unit is used to include the actual sharing time of idle resources in the resource utilization efficiency assessment index of the original team. The resource utilization efficiency assessment index accounts for 15% to 20% of the priority weight of the original team's next resource application.

5. A scientific research management system for the rational allocation of scientific research resources according to claim 1, characterized in that, The resource linkage and control module includes a progress acquisition unit and a lag analysis unit. The progress acquisition unit connects to the project progress management subsystem to collect data on the project's phase completion rate and key milestone achievement status. The acquisition frequency of the progress acquisition unit is consistent with the update frequency of the resource database, specifically every 30 minutes to 1 hour. The lag analysis unit analyzes the resource supply status of the corresponding project when the project progress lags behind the planned threshold. The project progress lag threshold can be customized through the management terminal, with the system default threshold being 5% of the planned progress. If the lag analysis unit determines that the lag is due to "insufficient resource supply," the resource linkage and control module schedules and adapts resources from the cross-disciplinary collaborative resource pool to supplement the corresponding project. If the lag analysis unit determines that the lag is due to "resource idleness," the resource linkage and control module pushes a resource idleness reminder to the project team and adjusts the idle resources to other projects with pending demand.

6. A scientific research management system for the rational allocation of scientific research resources according to claim 1, characterized in that, The permission management and traceability module includes a permission hierarchy division unit and a traceability ledger generation unit. The permission hierarchy division unit divides user permissions into three levels: permissions for researchers, permissions for department managers, and permissions for college-level managers. The permissions for researchers include submitting resource requests, viewing the resource allocation status of their own projects, and providing feedback on actual resource usage. The permissions for department managers include reviewing resource allocation plans within their department and viewing monthly resource usage statistics for that department. The permissions for institute-level managers include coordinating the allocation of research resources across the entire institution, adjusting the weight coefficients of multi-objective optimization algorithms, and exporting an annual report on the resource utilization efficiency of the entire institution. The traceability ledger generation unit is used to record the entire process of resource "application-review-scheduling-use-return." The record fields in the traceability ledger include operation execution time, the ID of the person responsible for the operation, resource status change information, and a unique identifier for the associated project.

7. A research management system for the rational allocation of research resources according to any one of claims 1-6, characterized in that, It also includes a hardware layer, which includes: A data acquisition terminal is used in conjunction with a resource dynamic sensing module to collect resource status and project progress data in real time. Edge computing nodes, which are used to process low-latency data locally; The central server is used in conjunction with the resource database to handle data storage and core algorithm calculations.

8. A scientific research management system for the rational allocation of scientific research resources according to claim 7, characterized in that, The data acquisition terminal includes an equipment status acquisition sub-terminal and a personnel project progress acquisition sub-terminal. The equipment status acquisition sub-terminal is deployed on the research equipment itself and is used to collect real-time data on equipment runtime, load rate, and fault status. The equipment status acquisition sub-terminal uses IoT sensors and supports data transmission to edge computing nodes at a frequency of 5 seconds per transmission. The personnel project progress acquisition sub-terminal is deployed on the terminal devices of researchers and is used to collect data on project phase completion and actual resource usage records.

9. A scientific research management system for the rational allocation of scientific research resources according to claim 7, characterized in that, The edge computing nodes are deployed at the departmental network nodes and are used to perform preliminary calculations on low-latency data such as real-time device status. The edge computing nodes are equipped with a 128GB data cache unit to temporarily store the collected data when the network is interrupted and automatically synchronize it to the central server after the network is restored.

10. A scientific research management system for the rational allocation of scientific research resources according to claim 7, characterized in that, The central server includes a distributed data storage unit and a core algorithm operation unit. The distributed data storage unit is used to store resource profile data, project process data, and scheduling operation record data. The core algorithm operation unit is used to run machine learning and deep learning algorithms to achieve resource matching and demand prediction functions.

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