College budget management system fusing new quality productivity theory

Abstract

The application relates to the technical field of data processing, and discloses a university budget management system fusing new quality productivity theory, which comprises the following steps: a state representation interface collects process representation data and maps the process representation data into a discrete innovation state bit vector; a differential efficiency mapping unit calculates an efficiency marginal increment in response to state bit jumping and generates an injection instruction; a physical bypass monitoring channel collects physical consumption time sequence data, and a closed-loop unit calculates metabolic entropy value representing resource consumption order from the physical consumption time sequence data; and a budget dynamic response control unit generates a state locking instruction when the state bit is constant and the metabolic entropy value meets a low-entropy convergence condition, wherein the application uses physical working condition entropy value correction logic state to maintain project resource state by identifying low-entropy resource consumption characteristics when there is no explicit output, so that the problem that discrete evaluation indexes cannot represent the blind area of continuous scientific research activities is solved.

Description

Technical Field

[0001] This invention relates to a university budget management system that integrates the theory of new quality productivity, and belongs to the field of data processing technology. Background Technology

[0002] Current university budget management systems generally adopt incremental models based on historical data or rigid annual quota models, assuming that funding needs are linearly or quasi-linearly distributed over time, and that output value is positively correlated with investment. In research scenarios involving the cultivation of new productivity, this assumption has a fundamental logical failure. Disruptive innovation outputs are random and sudden, while rigid budgets follow strict calendar annual cycles, leading to a mismatch between the calendar cycle and the innovation cycle. This mismatch results in the innovation boom period, such as when critical experimental breakthroughs require urgent equipment procurement, being in a budget freeze or depletion period. In non-critical periods, there is a phenomenon of rushed spending and idle resources. Traditional performance evaluation relies on financial indicators such as the funding execution rate. The low execution rate in the early stages of new productivity projects stems from cautious stagnation in the exploration of technical paths. Existing systems lack a mechanism to transform the verification of technical indicators into financial credit mapping through non-financial milestones, making it difficult for high-potential cold-start projects to obtain sustained support.

[0003] There are data processing difficulties between static resource allocation models and dynamic value creation processes. Existing technologies struggle to solve the time-series synchronization problem between the nonlinear characteristics of scientific research and innovation activities and the linear cyclical characteristics of administrative management systems. Simply maintaining the budgets of stagnant projects leads to resource waste, and abruptly cutting off supply stifles major projects poised for breakthroughs, creating a secondary contradiction between discrete evaluation indicators and continuous scientific research activities. For example, Chinese invention patent CN120611860A discloses a new spatial analysis method for quality productivity based on machine learning, constructing an indicator system including patent application density and nighttime light intensity dimensions, and utilizing XGBoost and AdaBoost machine learning methods. Learning models and SHAP value interpretation mechanisms enable quantitative analysis of factors influencing the spatial distribution of new productivity. This technical approach is a posterior analysis tool based on long-term statistical data, focusing on spatial planning and static stability assessment. While historical data regression analysis logic addresses the attribution problem of macro-factors, it struggles to capture micro-level discrete innovation events in scientific research activities, such as single experimental state transitions. It also lacks a mechanism to instantly convert unstructured process data into funding injection instructions. Such solutions only address the evaluation level, not the control level, and cannot achieve millisecond-level resource response at critical nodes in scientific research. The system remains passive and lagging when facing sudden innovation demands. Summary of the Invention

[0004] To address the problems mentioned in the background art, the technical solution of this invention is as follows: A university budget management system integrating the theory of new quality productivity, comprising:

[0005] The state characterization interface module connects to the scientific research project management database and is used to collect unstructured project process characterization data and map the process characterization data into discrete time series innovation state bit vectors. Each dimension of the innovation state bit vector uniquely corresponds to a preset technical indicator threshold state.

[0006] The differential performance mapping unit, connected to the state representation interface module, is used to monitor the temporal changes of the innovation state bit vector. When a state bit is detected to jump from zero to one, it calls the preset weighting rules to calculate the performance margin increment corresponding to the jump, and generates a quantitative injection instruction for the resource object repository based on the performance margin increment.

[0007] The physical bypass monitoring channel is independently connected to the log port of the project's associated hardware infrastructure and is used to collect physical consumption time-series data, including computing unit computing power occupancy and experimental material circulation frequency.

[0008] The entropy analysis closed-loop unit is connected to the physical bypass monitoring channel and is used to calculate the metabolic entropy value of physical consumption time series data within a preset sliding window. This metabolic entropy value represents the degree of orderliness of physical resource consumption.

[0009] The budget dynamic response control unit connects the differential performance mapping unit and the entropy analysis closed-loop unit. It has embedded life-sustaining locking logic. When it is detected that the innovation state bit vector remains constant within a preset period and the metabolic entropy value meets the preset low-entropy convergence condition, a state locking instruction is generated to shield the resource quota reclamation operation for the corresponding project and maintain the numerical state in the resource object repository.

[0010] Preferably, the state representation interface module includes semantic dimensionality reduction logic, which is used to parse the unstructured text description fields in the process representation data and map them into standardized binary status codes; the innovation state bit vector consists of multiple mutually orthogonal binary status codes, and the bit flipping action of each binary status code uniquely corresponds to the completion status confirmation of an independent scientific research milestone event; the differential performance mapping unit only triggers the performance marginal increment calculation process when a bit flipping of a binary status code is detected, thereby establishing a resource data update mechanism based on discrete event-driven mechanisms.

[0011] Preferably, the differential performance mapping unit includes a hysteresis filtering module, which is used to start a verification time window of a preset duration after detecting a state jump in the innovation state bit vector. The hysteresis filtering module continuously monitors the numerical stability of the innovation state bit vector within the verification time window, and only allows the output of the quantization injection command when the innovation state bit vector maintains the state value after the jump without falling back within the entire verification time window, so as to filter out the interference of transient data fluctuations on the resource allocation logic.

[0012] Preferably, the entropy analysis closed-loop unit includes a time-series feature extractor, which is used to denoise the physical consumption time-series data and calculate its coefficient of variation within a preset sliding window. The entropy analysis closed-loop unit compares the coefficient of variation with a preset dispersion threshold. When the coefficient of variation is less than the dispersion threshold and the arithmetic mean of the physical consumption time-series data is greater than the preset baseline of the basic load, it is determined that the metabolic entropy value meets the low-entropy convergence condition, indicating that the project is in a high-intensity silent tackling state.

[0013] Preferably, the system also includes a global liquidity monitoring loop, used to read the total available capacity of the resource object repository in real time and calculate the remaining liquidity ratio; the differential performance mapping unit introduces a global damping coefficient generated by the global liquidity monitoring loop when calculating the marginal performance increment. As a dynamic adjustment factor, the global damping coefficient The calculation follows the formula below: ,in, The preset constant gain factor, The remaining liquidity ratio, The preset non-zero minimum value; global damping coefficient It is negatively correlated with the remaining liquidity ratio and is used to dynamically and non-linearly raise the threshold of the marginal increase in efficiency required to trigger a quantitative injection command.

[0014] Preferably, the system also includes a scarcity reverse calibration module, which is used to count the cumulative trigger frequency of a specific state bit in the innovation state bit vector across all projects in the entire system; the scarcity reverse calibration module generates a reverse weighting factor based on the cumulative trigger frequency, and the reverse weighting factor monotonically decreases as the cumulative trigger frequency increases; the differential performance mapping unit applies the reverse weighting factor to the corresponding state bit basic weight in real time, so that the marginal increment of performance corresponding to the same innovation state bit automatically decays as its frequency of occurrence in the system increases, thereby achieving a dynamic balance of resource allocation weights.

[0015] Preferably, the system also includes a common-mode obstruction diagnosis module, used to construct a project state obstruction matrix, which records the dwell time of all projects at each node of the innovation state position vector; the common-mode obstruction diagnosis module includes topological clustering analysis logic, used to identify whether there are obstructed state positions in the project state obstruction matrix whose dwell time exceeds a preset stagnation threshold and whose number of projects involved exceeds a preset common-mode threshold; when an obstructed state position is identified, an infrastructure construction suggestion instruction pointing to the public scientific research condition configuration port is generated, used to trigger the reinforcement operation of public resources.

[0016] Preferably, the computing power occupancy rate of the computing unit collected by the physical bypass monitoring channel comes from the job scheduling log of the high-performance computing cluster, and the frequency of experimental material circulation comes from the electronic tag scanning record of the dedicated experimental consumables; after the entropy analysis closed-loop unit normalizes the computing power occupancy rate of the computing unit and the frequency of experimental material circulation, it uses a weighted average algorithm to synthesize the metabolic entropy value to eliminate the dimensional differences of physical data in different dimensions.

[0017] Preferably, the quantitative injection instruction includes a value write opcode for a specific account in the resource object repository and the corresponding quota parameter; the differential performance mapping unit includes an instruction buffer queue. When the generated quota parameter exceeds the preset single injection limit, the excess quota is divided and stored in the instruction buffer queue. The value write opcode is executed in batches according to the preset time release curve to smooth the impact of resource injection on the system.

[0018] Preferably, the system is deployed on a hardware architecture consisting of distributed servers and dedicated storage arrays; the resource object repository is mapped to one or more resource status tables in the database, with each row corresponding to the current available resource value of a scientific research project; the differential performance mapping unit, the entropy analysis closed-loop unit, and the global liquidity monitoring loop run in parallel as independent logical processes in the computing nodes of the distributed server, and interact with each other through the internal message bus.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] 1. In the university budget based on the theory of new quality productivity, the multi-source heterogeneous unstructured scientific research process representation data is mapped into a standard discrete innovation state bit vector through the state representation interface module. The differential efficiency mapping unit monitors the time series changes of the vector. The budget instruction generation logic is triggered only when a state bit jump is detected and the calculated marginal efficiency increment exceeds the noise threshold. Based on the discrete signal edge trigger asynchronous processing architecture, a direct causal chain between resource injection actions and the occurrence time of innovation events is established. At the data processing level, the rigid dependence of the resource scheduling process on the preset financial calendar cycle is decoupled, so that the system can adapt to the suddenness of the Poisson distribution of scientific research and innovation activities and realize the real-time logic synchronization between resource control instructions and innovation output in the physical world.

[0021] 2. In the budget dynamic response control unit, an adaptive negative feedback adjustment loop based on global liquidity damping is constructed. The remaining liquidity ratio of the reserved fund pool is monitored in real time and the global damping coefficient, which is inversely correlated with it, is calculated. This coefficient is applied as a dynamic modulation factor to the differential efficiency calculation logic of all projects. When the system faces high-concurrency resource unlocking requests from multiple projects, which causes the fund pool level to drop, the mechanism nonlinearly raises the marginal incremental threshold of the efficiency required for budget injection. The dynamic pressure mechanism filters out low-efficiency requests and prioritizes the supply of resources to high-weight projects. This maintains the system load balance without manual intervention and eliminates the risk of the resource allocation system logic breaking down due to overloaded concurrent requests.

[0022] 3. Introduce a resource metabolism entropy-based bypass activity verification mechanism. Collect time-series data on the physical consumption of computing resources or experimental material circulation frequency at the infrastructure level bound to the project. Calculate the metabolism entropy value within a sliding window to characterize the orderliness of resource consumption. The mechanism utilizes the objective accompanying relationship between physical resource consumption and real scientific research activities. During the long-term unchanged logical silence period of the innovation state bit vector, it automatically triggers the survival exemption logic by identifying low-entropy and continuous resource consumption characteristics. This distinguishes between the ineffective zombie stagnation state and the high-intensity long-cycle research state, solving the problem of system misjudgment and resource mis-recycling technical blind spots caused by the lack of process output signals under the single main channel evaluation mode. Attached Figure Description

[0023] Figure 1 This is a system logic architecture diagram of the present invention based on state transition and entropy correction;

[0024] Figure 2 This is a schematic diagram illustrating the differentiated configuration of metabolic entropy value weights for interdisciplinary projects according to the present invention.

[0025] Figure 3 This is a diagram of the physical deployment topology of the distributed cluster system of this invention. Detailed Implementation

[0026] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be noted that the accompanying drawings and the following embodiments are for illustrative purposes only and do not limit the scope of protection of the present invention in any way.

[0027] This invention discloses a university budget management system integrating the theory of new quality productivity. It includes a state representation interface module, a differential efficiency mapping unit, a physical bypass monitoring channel, an entropy analysis closed-loop unit, a budget dynamic response control unit, and a global liquidity monitoring loop. Each module interacts with data via an internal high-speed message bus. The differential efficiency mapping unit and the entropy analysis closed-loop unit, among others, run in parallel as independent logical processes within the computing nodes. In actual operation, the state representation interface module primarily handles analog-to-digital signal conversion. Given that research project processes are typically presented as unstructured text reports, which are difficult to directly use for automated quantitative calculations, this module connects to a research project management database and periodically retrieves process representation data. This data includes experimental logs, technical indicator test reports, or milestone acceptance documents. The module has a pre-built semantic dimensionality reduction logic containing a mapping table between preset technical keywords and binary state bits. When the semantic dimensionality reduction logic detects a definite technical breakthrough keyword in the process representation data, it flips the corresponding binary state code from logic 0 to logic 1. Multiple orthogonal binary state codes are arranged in a preset order to form a discrete time-series innovation state bit vector. Each dimension in this vector uniquely corresponds to the completion confirmation of a preset technical indicator threshold state or an independent scientific research milestone event. For example, the first dimension corresponds to the successful verification of the prototype, and the second dimension corresponds to the convergence of the core algorithm.

[0028] The differential performance mapping unit connects to the state representation interface module and performs the core tasks of event capture and value quantification. This unit focuses on capturing innovative state bit vectors. The system monitors temporal changes without calculating absolute state scores. Internally, each unit maintains a snapshot of the previous state. It monitors state bit transitions in real-time by performing an XOR operation with the current state vector. When a positive transition from zero to one is detected in any dimension of the state bit, the unit triggers a performance calculation process. This process calls pre-defined weighting rules, reads the base weight value corresponding to the transition bit, and calculates the marginal performance increment resulting from the transition. To eliminate data entry errors or short-term fluctuations, this unit integrates a hysteresis filter module, which activates a verification time window upon detecting a state change. The state bit remains constant only within this time window. Only then is the jump confirmed to be valid and the marginal performance increment output. The budget dynamic response control unit is based on marginal efficiency increments. Generate quantitative injection instructions for the resource object repository, which is mapped to a resource status table in the database. Each row records the current available funding amount for a research project. The quantitative injection instructions contain specific project accounts. The system calculates the required additional quota parameter. When the instruction is executed, the system directly adds the quota parameter to the available quota field of the corresponding account, completing the immediate injection of funds. This process is independent of the financial year calendar and is entirely triggered by innovation events, achieving time synchronization between resource supply and scientific research output. When the calculated quota parameter exceeds the preset single injection limit, the differential performance mapping unit stores the instruction in the instruction buffer queue and executes the value writing operation in batches according to the preset time release curve to smooth the impact of large-scale fund injection on the system's fund pool level. This control unit is equipped with resource quota recovery logic for periodically evaluating the period during which funds are in a dormant state. For long-term, stable projects, the recycling logic is based on the project's duration. and efficiency of fund utilization Fitting calculation of recovery threshold ,when Exceed and Below At that time, the credit limit recovery operation will be initiated to ensure the effective recycling of resources.

[0029] To prevent concurrent requests from multiple projects from depleting the system's liquidity pool, the system introduces a global liquidity monitoring loop and a global damping adjustment mechanism. The global liquidity monitoring loop reads the total available funds balance in the resource object repository in real time and divides it by the preset total capacity of the liquidity pool to calculate the remaining liquidity ratio. Differential performance mapping unit calculates marginal performance increment. At that time, a global damping coefficient generated by the global liquidity monitoring loop is introduced. As a dynamic adjustment factor, this global damping coefficient The calculation follows the formula: ,in The preset constant gain factor, The preset non-zero minimum value is used to prevent the denominator from being zero abnormally, and this mathematical relationship establishes the global damping coefficient. Ratio to residual liquidity The negative correlation between them means that when the level of funds in the pool decreases... When decreasing, The value increases non-linearly, and the system will reduce the original marginal efficiency increment. Divide by the current moment The value is adjusted to obtain an effective increment, which makes the innovative achievements required to trigger the same amount of budget injection for the project have a higher weight during periods of tight funding, thereby realizing automatic screening and pressure control based on resource scarcity.

[0030] To address the issue of long-term research projects being misjudged as stalled due to a lack of explicit output, the system constructs a life-sustaining locking mechanism based on a physical bypass monitoring channel and an entropy analysis closed-loop unit. The physical bypass monitoring channel is independently connected to the log ports of the project's associated hardware infrastructure, collecting physical consumption time-series data that does not rely on manual input. This data includes specific jobs in the high-performance computing cluster. The computing power occupancy rate of the computing unit and the circulation frequency of specific electronic tags in the dedicated experimental consumables management system are used to normalize the above data by the entropy analysis closed-loop unit. The unit then calculates the statistical coefficient of variation within a preset sliding window as a representation of metabolic entropy. This metabolic entropy is used to measure the orderliness of resource consumption. When the computing power occupancy rate of the computing unit remains consistently high with minimal fluctuations, and the coefficient of variation is less than a preset dispersion threshold, the calculation is successful. When the metabolic entropy value satisfies the low-entropy convergence condition, the budget dynamic response control unit is embedded with life-sustaining locking logic. When the innovative state bit vector of a certain project is detected... If no change occurs within the preset period, but the associated metabolic entropy value meets the low entropy convergence condition, a state lock command is generated. This command blocks budget recovery or quota freezing operations triggered by no output for the project, maintains the current resource level, and ensures resource supply during the silent attack and defense period.

[0031] In addition, the system includes a scarcity inverse calibration module and a common-mode hindrance diagnosis module to optimize resource allocation structure. The scarcity inverse calibration module statistically analyzes the innovation state bit vector. The cumulative trigger frequency of a specific status bit across all projects in the entire system. The module is based on the formula The weight of this state bit is dynamically adjusted, where Based on weights, and As a constant, this logic automatically decays the budget weight of a certain technical indicator as its popularity increases, suppressing the resource consumption of repetitive, low-level research. The common-mode blockage diagnosis module constructs a project state blockage matrix, recording the dwell time of all projects at each state position. The module includes topological clustering analysis logic, which identifies the dwell time at a certain state position. Number of projects exceeding the preset stagnation threshold Exceeding the preset common mode threshold At that time, the node was determined to be a system-level common technical bottleneck. At this point, the module generates an infrastructure construction suggestion instruction pointing to the public scientific research conditions configuration port, which is associated with the list of blocked projects and the total budget potential, and is used to trigger reinforcement operations such as expanding the public computing power platform or purchasing dedicated testing equipment.

[0032] Example 1: In a major research project focusing on disruptive artificial intelligence algorithms, the system faces a temporal disharmony between the nonlinear, sudden characteristics of innovation activities and the linear calendar cycle of the budget management system. During a two-year and eight-month silent incubation period, the project's budget execution rate was extremely low. Traditional rigid budget models had already generated early warning signals for fund recovery, but the R&D team was actually in a state of high-intensity computing power development, with the fiscal year about to end... Month, Project Continuous Tian did not submit any bit vectors that could trigger innovation status to the scientific research management system. The technical milestone document, which jumps through time, would traditionally be considered an inefficient and stalled project, automatically generating a budget recovery instruction. To avoid resource misjudgment caused by secondary conflicts between discrete evaluation indicators and continuous scientific research activities, the system activates a physical bypass monitoring channel and an entropy analysis closed-loop unit. The bypass monitoring channel continuously collects job scheduling logs from the high-performance computing cluster, extracts time-series data on the computing power occupancy of computing units associated with the project, and simultaneously collects electronic tag scanning records of dedicated experimental consumables. This physical consumption time-series data is input into the entropy analysis closed-loop unit, which calculates the computing power occupancy data within the timeframe. The coefficient of variation within the sliding window, used to characterize the temporal orderliness of resource consumption behavior, was maintained at a stable daily average during this critical period. The above values, and the coefficient of variation is lower than the preset dispersion threshold. The metabolic entropy value is determined to meet the low-entropy convergence condition. The budget dynamic response control unit receives the judgment signal of constant state bit and the bypass verification signal of low-entropy challenge state, triggers the life-sustaining locking logic, generates the water level locking command, maintains the current budget water level of the project, and shields the budget recovery operation triggered by no state transition.

[0033] And enter the core algorithm convergence report into the scientific research management system to trigger the innovation state bit vector. The Position jump to A positive state transition is captured by the differential performance mapping unit, which then initiates... After verifying the hourly lag filter and confirming the state stability, the marginal efficiency increment corresponding to the jump was calculated. This increment far exceeds the activation threshold. This coincided with several school-level projects making breakthroughs at the end of the year, simultaneously issuing requests for substantial budget injections, leading to a decrease in the remaining liquidity ratio of the school-level virtual reservoir. sharp drop to The global liquidity monitoring loop detected At a low position, according to the inverse proportional function Real-time calculation of high-strength global damping coefficient and will The value is broadcast to the differential performance mapping unit, and The value is applied as a multiplier factor in the generation logic of the budget elasticity coefficient, dynamically raising the efficiency marginal increment threshold required to trigger fund unlocking; despite overall fund shortages, this algorithm breaks through the corresponding efficiency marginal increment. The revised effective increment is still significant enough to break through the raised dynamic threshold. Based on the revised high elasticity coefficient, the budget dynamic response control unit unlocks the corresponding amount of funds from the reservoir, generates a quantitative injection instruction, and transfers funds to the project execution account in real time. Meanwhile, another project, which was launched concurrently, also broke through its corresponding marginal efficiency increment. Smaller, under high damping coefficient After correction, the dynamic threshold cannot be breached, and the request is automatically suppressed by the system. This process reflects the problem redefinition logic, and the system introduces a global liquidity-based approach. The factor transforms the arbitration problem of concurrent requests into a dynamic optimization problem of the marginal contribution rate of unit funds, naturally selecting the projects with the most innovative value at the current moment, realizing the automatic aggregation of resource allocation, and finally achieving microsecond-level logical synchronization between the funding supply curve and the nonlinear burst curve of scientific research activities. This ensures that projects with the potential to contribute high-quality productivity receive resource support at critical moments, while the system maintains extremely strong liquidity and stability.

[0034] Example 2: This experiment aims to verify, through quantitative data, the ability of the entropy analysis closed-loop unit in this invention to identify the silent research progress state, and the resource arbitration efficiency of the global liquidity damping mechanism under multi-project concurrent request conditions. The experimental platform is built on a distributed server cluster to simulate a high-performance computing environment in a university, with a capacity of [missing data - likely referring to a specific parameter or value] per second. The system boasts sub-floating-point arithmetic capabilities for data processing; it integrates a simulated scientific research project management database for injecting unstructured text data and simulating fund flows in a resource object repository. Data acquisition precision is set as follows: computing power occupancy rate and acquisition frequency. precision Fund flow data update frequency Preset sliding window for entropy analysis closed-loop unit Set as This aims to ensure that the system has sufficient data sample size when determining project activity, and the dispersion threshold of metabolic entropy values. Set as This value is calculated by statistically analyzing historical computing power consumption data from a large number of high-intensity silent research projects to determine the upper limit of the coefficient of variation. This embodiment demonstrates the effectiveness of the solution by comparing the differences in resource scheduling between the traditional model, the model with missing features, and the complete solution.

[0035] Trial run Monitor the resource status of project P1. This project is in the [number]th [year]. to During this period, the research and development was conducted in silence, and in the... A critical state bit transition occurs; to During the quiet period, the innovation state bit vector of project P1 The daily coefficient of variation of computing power utilization recorded by the physical bypass monitoring channel remained constant. Around [time period], this data phenomenon indicates that project P1 is in a high-intensity, silent development phase; control group A uses the traditional annual quota model, and in the [number]th [year]... That is, because the fund execution rate is lower than Triggering a fund freeze, available resources are reclaimed. Control group B only uses state bit transition logic and lacks an entropy analysis mechanism. Because there is no state bit transition to reclaim some resources, the sample group of this invention in the first... The life-sustaining locking logic of the budget dynamic response control unit is triggered. Since the low-entropy convergence condition of metabolic entropy is met, the system generates a state locking command, which effectively prevents budget recovery operations. This result verifies that the entropy analysis closed-loop unit can successfully distinguish between ineffective stagnation and high-intensity tackling of problems using physical operating data, thus solving the problem of the blind spot in the characterization of continuous scientific research activities by discrete evaluation indicators.

[0036] Table 1: Comparison of Resource Status During Quiet Period

[0037]

[0038] The maintenance of resource status during the silent period by the sample group of this invention directly confirms the effectiveness of the life-sustaining locking mechanism; in the first... Project P1 experiences a state transition; the marginal efficiency increment is calculated. The system simulation received four concurrent requests, causing the global liquidity ratio to drop. Down to At this point, the global liquidity monitoring loop calculates the global damping coefficient. ,set up ,but Differential performance mapping unit will The value is applied to the budget flexibility coefficient generation logic, dynamically raising the efficiency marginal increment threshold required to trigger fund unlocking. .

[0039] Table 2: Effectiveness Arbitration Data under High Damping Coefficient

[0040]

[0041] exist Under conditions of low liquidity, the four requests that could have been fully injected were, after The effective increment after value correction and In contrast, the injection of inefficient request P4 was suppressed by the system. The mechanism of this invention automatically directs resources to projects with higher unit contribution rates by dynamically raising the activation threshold, verifying the system's ability to automatically arbitrate and control risks for concurrent requests from multiple projects under total resource constraints. The budget dynamic response control unit of this invention can accurately identify and lock the resource level of projects when there is no explicit output. When the system faces the boundary condition of resource scarcity, the global damping adjustment loop prioritizes the funding needs of high marginal efficiency projects P1, P2, and P3 by dynamically raising the injection threshold, while suppressing inefficient request P4 to maintain the stability of system funds. This result confirms that the solution of this invention effectively decouples the rigid temporal dependence of resource allocation at the data processing level, providing a dynamic resource allocation mechanism that adapts to the nonlinear characteristics of scientific research and innovation activities.

[0042] Example 3: This example combines Figures 1 to 3 An explanation of the university budget management system that integrates the theory of new quality productivity, such as... Figure 1 As shown, the process begins with the scientific research project management database inputting unstructured process representation data into the state representation interface module, which is then mapped into discrete innovation state bit vectors through semantic dimensionality reduction processing. The change in this vector triggers the differential performance mapping unit to monitor state transitions, and after verification by hysteresis filtering, calculates the marginal performance increment. Simultaneously, the global liquidity monitoring loop calculates the global damping coefficient D based on the real-time reading of the total available capacity and remaining liquidity ratio of the resource object repository and feeds it back to the differential performance mapping unit as a dynamic adjustment factor. In parallel, the physical bypass monitoring channel independently collects and preprocesses physical consumption time-series data, including computing power and material flow, and inputs it into the entropy analysis closed-loop unit to calculate the metabolic entropy value, characterizing the orderliness of resource consumption, and determine whether the low-entropy convergence condition is met. Finally, the budget dynamic response control unit, based on the D-corrected value… or combination The constant state signal and the low-entropy convergence signal generate quantization injection instructions or state locking instructions, which can be used to perform write or lock operations on scientific research projects in the resource object repository using the resource value state table.

[0043] like Figure 2 As shown, the system employs a differentiated weighting strategy when calculating metabolic entropy values. For basic physics projects, the computing power weight... For chemical engineering projects, the material weight β is set to a dominant value close to 0.9, while the computing power weight β remains at a low level. This weight reversal mechanism based on project type ensures that the system can accurately synthesize metabolic entropy values ​​according to the physical characteristics of specific disciplines.

[0044] like Figure 3 As shown, the system's physical deployment architecture uses a distributed server cluster as the core of intelligent computing. Internally, it runs a differential performance mapping engine and an entropy analysis closed-loop engine, which interact through an internal high-speed message bus. The cluster receives time-series data streams of high-performance computing cluster logs and experimental material flow records collected from the physical condition access terminal. At the same time, it accepts the adjustment of the damping coefficient D calculated by the global liquidity monitoring loop in the global resource control node. The resource injection instructions generated by the computing core are sent to the resource object repository in the underlying dedicated storage array to update the fund status table and form a liquidity feedback closed loop pointing to the global control node.

[0045] Example 4: This example addresses the black box of algorithm logic and the black box of parameter thresholds by constructing a reproducible process. In the initial stage of a research project, the system faces the challenge of converting unstructured text descriptions entered by the project leader, such as the design of a new algorithm framework, into discrete state signals that the system can judge. To solve this challenge, the state representation interface module incorporates semantic dimensionality reduction logic, adopting the following operation flow: Input: Specific project The corresponding latest unstructured text description The processing logic involves invoking a semantic parser, which uses a pre-defined keyword-state bit mapping dictionary to match specific combinations of nouns, adjectives, and verbs to pre-defined standardized binary status codes. For example, matching the algorithm framework and the completed combination, Set as Output: The standardized binary status code of the successful match. Assigned to the innovation state bit vector In the corresponding dimension, each bit-flipping action of a standardized binary status code uniquely corresponds to the completion status confirmation of an independent scientific research milestone event. The differential performance mapping unit only detects the completion status of a milestone event. Performance margin increment triggered when bit flip occurs The calculation process.

[0046] The system is pre-configured with an adaptive update procedure based on a word vector space dictionary. It establishes a corpus of unstructured texts containing over 10,000 historical research projects, and uses a continuous bag-of-words model to generate a high-dimensional word vector space. The centroid coordinates of the core seed word vector space corresponding to the standard state position are calculated. During system operation, the state representation interface module executes the logic for counting the frequency of missed words, monitoring and extracting candidate nouns not covered by the pre-configured dictionary and whose frequency exceeds a preset frequency threshold. The cosine similarity between the candidate noun vector and the centroid coordinates of the state position is calculated. If the calculated cosine similarity is greater than a preset semantic judgment threshold... At that time, a temporary mapping relationship between candidate nouns and status bits is automatically established and written into the mapping dictionary; semantic determination threshold. Following a standardized calibration process, the full similarity of synonym pairs belonging to the same state position in historical data was calculated, and the 10th percentile of the statistical similarity distribution was taken as the similarity score. The benchmark was set to 0.75 in the data calibration of the National Natural Science Foundation of China project; large models were automatically captured through vector computation, and non-standard quantum computing terms were accurately mapped to corresponding binary status codes. To eliminate logical misjudgments caused by missing words in static dictionaries, a context-window-based disambiguation procedure is implemented to address the polysemy of terms in interdisciplinary projects. When extracting keywords, five-unit context feature vectors are simultaneously extracted from the words before and after them. These vectors are then input into a pre-trained linear classifier for logistic regression. A state position flip is confirmed only when the confidence score exceeds 0.85. The linear classifier's weight parameters are obtained through gradient descent training on historical polysemous word samples, transforming simple keyword matching into probability-based judgments in a high-dimensional feature space, thus ensuring the integrity of the innovative state position vector. Handling uniqueness and stability in complex semantic scenarios.

[0047] The scarcity reverse calibration module is used to count the cumulative trigger frequency of a specific state bit across all items in the entire system. This mechanism generates a reverse weighting factor to achieve a dynamic balance of resource allocation weights. The constant in this mechanism... and The settings are not empirical values, but determined through a systematic calibration procedure. The calibration procedure involves analyzing data from the university-level innovation funds over the past five years. Retrospective analysis was conducted on historical data from completed projects to determine the final economic benefit index of the projects. The cumulative trigger frequency of its corresponding status bit The fitting and calibration goals are to find a set of... Value, making exist In When it is less than this time, it is close to , and achieve When the number of times exceeds a certain threshold, the weight decays to a certain value. of Numerical deduction: Through fitting analysis, the constant Set as ,constant Set as When the cumulative trigger frequency hour, ,but At this point, the weights remain essentially unchanged, providing a high incentive for cold-start innovation. When the cumulative trigger frequency... hour, ,but At this point, the weight has decreased by nearly half, effectively curbing technological inflation.

[0048] During system operation, to identify common technical bottlenecks hindering the progress of multiple independent projects, the common-mode hindrance diagnostic module executes topological clustering analysis logic, employing a variant of the density-based clustering algorithm to analyze the project state hindrance matrix. Analyze the columns, where the elements Record Project In the state of innovation Continuous stay time Processing logic: Identify dwell time (Preset stagnation threshold, set to) The set of blocked projects Then for each state bit Calculate the number of blocked projects Finally if (Preset common mode threshold, set to) If there are multiple items, then determine the status bit. bottlenecks in public technology Output: Generates an infrastructure construction suggestion instruction pointing to the public research conditions configuration port, which is associated with the blocked status bit. The nature of the project and the total budget involved are used to trigger reinforcement operations of public resources.

[0049] Example 5: In a university application scenario, the system is deployed in an environment that manages two types of scientific research projects: chemical engineering and basic physics. The resource consumption characteristics of the two types of projects have cross-domain differences. This requires the system to be pre-calibrated and baseline calibrated before on-site deployment. If the weights are not adjusted, the traditional system may lead to a decrease in the accuracy of the entropy analysis closed-loop unit for the activity analysis of chemical engineering projects (which mainly consume experimental materials), and an over-reliance on the evaluation results of basic physics projects (which mainly consume computing power). To ensure the system's adaptability to different project types, a standardized on-site deployment pre-calibration / debugging procedure needs to be performed during on-site deployment.

[0050] During on-site deployment, the system invokes the physical bypass monitoring channel, selecting one for each of the two types of projects. A sample project that is recognized as being in a state of high-intensity research and development, collecting... Based on the physical consumption time-series data, the system initiates the initial weight calibration module and performs the following steps: Assess the collected computing unit computing power occupancy rate... and frequency of experimental material turnover Perform separately -Score normalization yields and To eliminate the dimensional differences in physical data across different dimensions, an iterative optimization algorithm is then used to search for a set of weighted average coefficients. and ,in Used for metabolic entropy values ,Right now The optimization objective is to minimize The discrepancy between this project being marked as a high-intensity research project by field experts and the determination of the optimal outcome for fundamental physics projects is significant. , For chemical engineering projects, determine , The system will optimize the two sets of weighted coefficients. and The data is written to the local memory of the budget dynamic response control unit as personalized parameters for each project type. During the actual operation of the project, the entropy analysis closed-loop unit automatically selects the corresponding weight to metabolize the entropy value based on the project type. The system simultaneously retrieves historical data and calculates and writes the low-entropy convergence thresholds corresponding to different project types. For example, the basic physics project is set as Chemical engineering projects are set as This enables the life-sustaining lock-in mechanism to precisely adapt to the resource consumption characteristics of different projects.

[0051] Example 6: In large-scale university-level research fund management institutions, the system needs to perform an initial offline calibration of the performance-liquidity matrix for all research projects at the beginning of a new fiscal year. This provides an accurate parameter baseline for the online global damping adjustment loop and differential performance mapping unit, addressing the risks of missing parameter setting basis and insufficient quantification of logical judgment conditions. The system retrieves all... Offline analysis was conducted on historical research data and funding records of each on-campus project to determine performance-based weighting. Calibration: The system employs a historical weighted regression analysis method based on expert review to fit each innovation state position. The contribution of the fitting results to the final scientific research outcome conversion rate will be taken as... The matrix is ​​initialized, and then the global damping coefficient is calculated. Parameter optimization: system simulation Record the liquidity of the fund pool in each round of historical concurrent requests. The changes were fitted using the least squares method. With system arbitration success rate The functional relationship between them Defined as high The goal of optimizing the project's funding success rate is to ensure... Under the premise of determining the optimal constant This increases the success rate of arbitration. Maximize, and finally determine the constant. .

[0052] To eliminate the interference of transient data fluctuations on resource allocation logic, the system needs to quantify and determine the verification time window required by the hysteresis filtering module of the differential performance mapping unit. and the resource recovery threshold required by the budget dynamic response control unit hysteresis filter time window The determination was based on statistical analysis of the unstructured text input delays corresponding to all historical milestone events, and calculations were performed. The average settling time from the state bit transition to final document confirmation for milestone events ,Will Set as of To ensure data stability and resource quota recovery thresholds, [the system] will be adjusted accordingly. The setting is based on the project (Duration of stay) and efficiency of fund utilization Relationship fitting, recovery threshold The calculation follows the following logic: when Exceed (Preset) ), and the efficiency of fund utilization ,in The calculation method is as follows lower than (Minimum threshold) When the system initiates a recycling operation, it performs a multi-node self-check and synchronization procedure upon startup. This requires all logical processes on the distributed servers, including the differential performance mapping unit and the entropy analysis closed-loop unit, to synchronize their states via the internal message bus, ensuring that each process maintains its own innovation state bit vector. Snapshots and Global Liquidity Numerical consistency, the numerical difference at any node exceeds When the threshold is reached, an error alarm is triggered, and all nodes pull authoritative data again from the resource object repository.

[0053] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A university budget management system integrating new quality productivity theory, characterized in that, include: The state characterization interface module connects to the scientific research project management database and is used to collect unstructured project process characterization data and map the process characterization data into discrete time series innovation state bit vectors. Each dimension of the innovation state bit vector uniquely corresponds to a preset technical indicator threshold state. The differential performance mapping unit, connected to the state representation interface module, is used to monitor the temporal changes of the innovation state bit vector. When a state bit is detected to jump from zero to one, the basic weight value corresponding to the jump bit is read and the preset weighting rule is called to calculate the performance marginal increment corresponding to the jump. Based on the performance marginal increment, a quantitative injection instruction for the resource object repository is generated. The physical bypass monitoring channel is independently connected to the log port of the project's associated hardware infrastructure and is used to collect physical consumption time-series data, including computing unit computing power occupancy and experimental material circulation frequency. The entropy analysis closed-loop unit is connected to the physical bypass monitoring channel and is used to calculate the metabolic entropy value of physical consumption time series data within a preset sliding window. This metabolic entropy value represents the degree of orderliness of physical resource consumption. The budget dynamic response control unit connects the differential performance mapping unit and the entropy analysis closed-loop unit. It has embedded life-sustaining locking logic. When it is detected that the innovation state bit vector remains constant within a preset period and the metabolic entropy value meets the preset low entropy convergence condition, a state locking instruction is generated to shield the resource quota reclamation operation for the corresponding project and maintain the numerical state in the resource object repository. Furthermore, the state representation interface module includes semantic dimensionality reduction logic, which is used to parse unstructured text description fields in process representation data and map them into standardized binary status codes; the innovation state bit vector consists of multiple mutually orthogonal binary status codes, and the bit flipping action of each binary status code uniquely corresponds to the completion status confirmation of an independent scientific research milestone event; the differential performance mapping unit only triggers the calculation process of performance marginal increment when a bit flipping of a binary status code is detected, thereby establishing a resource data update mechanism based on discrete event-driven operation. The differential performance mapping unit connects to the state representation interface module and performs the core tasks of event capture and value quantification. This unit focuses on capturing innovative state bit vectors. The system monitors temporal changes without calculating absolute state scores. Internally, each unit maintains a snapshot of the previous state. It monitors state bit transitions in real-time by performing an XOR operation with the current state vector. When a positive transition from zero to one is detected in any dimension of the state bit, the unit triggers a performance calculation process. This process calls pre-defined weighting rules, reads the base weight value corresponding to the transition bit, and calculates the marginal performance increment resulting from the transition. The resource object repository is mapped to a resource status table in the database. Each row records the current available funding amount for a research project, and the quantitative injection instruction includes a specific project account. The system will add the required amount parameter to the available amount field of the corresponding account when the instruction is executed, thus completing the immediate injection of funds.

2. The university budget management system integrating the theory of new quality productivity as described in claim 1, characterized in that, The differential performance mapping unit includes a hysteresis filtering module, which is used to start a verification time window of a preset duration after detecting a state jump in the innovation state bit vector. The hysteresis filtering module continuously monitors the numerical stability of the innovation state bit vector within the verification time window, and only allows the output of the quantization injection command when the innovation state bit vector maintains the state value after the jump without falling back within the entire verification time window, so as to filter out the interference of transient data fluctuations on the resource allocation logic.

3. A university budget management system integrating new quality productivity theory as described in claim 1, characterized in that, The entropy analysis closed-loop unit includes a time-series feature extractor, which is used to denoise the physical consumption time-series data and calculate its coefficient of variation within a preset sliding window. The entropy analysis closed-loop unit compares the coefficient of variation with a preset dispersion threshold. When the coefficient of variation is less than the dispersion threshold and the arithmetic mean of the physical consumption time-series data is greater than the preset baseline of the basic load, it is determined that the metabolic entropy value meets the low-entropy convergence condition, indicating that the project is in a high-intensity silent tackling state.

4. A university budget management system integrating new quality productivity theory as described in claim 1, characterized in that, The system also includes a global liquidity monitoring loop, used to read the total available capacity of the resource object repository in real time and calculate the remaining liquidity ratio; the differential performance mapping unit introduces a global damping coefficient generated by the global liquidity monitoring loop when calculating the marginal performance increment. As a dynamic adjustment factor, the global damping coefficient The calculation follows the formula below: ,in, The preset constant gain factor, The remaining liquidity ratio, The preset non-zero minimum value; global damping coefficient It is negatively correlated with the remaining liquidity ratio and is used to dynamically and non-linearly raise the threshold of the marginal increase in efficiency required to trigger a quantitative injection command.

5. A university budget management system integrating new quality productivity theory according to claim 1, characterized in that, The system also includes a scarcity inverse calibration module, which is used to count the cumulative trigger frequency of a specific state bit in the innovation state bit vector across all projects in the entire system; the scarcity inverse calibration module generates an inverse weighting factor based on the cumulative trigger frequency, and the inverse weighting factor monotonically decreases as the cumulative trigger frequency increases; The differential performance mapping unit applies the inverse weighting factor to the corresponding state position base weight in real time, so that the marginal performance increment corresponding to the same innovative state position automatically decays as its frequency of occurrence in the system increases, thereby achieving a dynamic balance of resource allocation weights.

6. A university budget management system integrating new quality productivity theory according to claim 1, characterized in that, The system also includes a common-mode stagnation diagnosis module, which is used to construct a project state stagnation matrix. This matrix records the dwell time of all projects at each node of the innovation state position vector. The common-mode stagnation diagnosis module contains topological clustering analysis logic, which is used to identify whether there are any blocked state positions in the project state stagnation matrix whose dwell time exceeds a preset stagnation threshold and whose number of projects involved exceeds a preset common-mode threshold. When a blocked state position is identified, an infrastructure construction suggestion instruction is generated pointing to the public scientific research condition configuration port, which is used to trigger the reinforcement operation of public resources.

7. A university budget management system integrating new quality productivity theory according to claim 1, characterized in that, The computing power occupancy rate of the computing unit collected by the physical bypass monitoring channel comes from the job scheduling log of the high-performance computing cluster, and the frequency of experimental material circulation comes from the electronic tag scanning records of experimental consumables. After the entropy analysis closed-loop unit normalizes the computing power occupancy rate of the computing unit and the frequency of experimental material circulation, it uses a weighted average algorithm to synthesize the metabolic entropy value to eliminate the dimensional differences of physical data in different dimensions.

8. A university budget management system integrating new quality productivity theory according to claim 1, characterized in that, The quantitative injection instruction includes a value write opcode for a specific account in the resource object repository and the corresponding quota parameter; the differential performance mapping unit includes an instruction buffer queue. When the generated quota parameter exceeds the preset single injection limit, the excess quota is divided and stored in the instruction buffer queue. The value write opcode is executed in batches according to the preset time release curve to smooth the impact of resource injection on the system.

9. A university budget management system integrating new quality productivity theory according to claim 1, characterized in that, The system is deployed on a hardware architecture consisting of distributed servers and storage arrays; The resource object repository is stored in a storage array and mapped to one or more resource status tables in the database. Each row corresponds to the current available resource value of a scientific research project. The differential performance mapping unit, the entropy analysis closed-loop unit, and the global liquidity monitoring loop run in parallel as independent logical processes on the computing nodes of the distributed server and interact with each other through an internal message bus.

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