Innovation project information processing method and system, computer equipment, readable storage medium and program product

Abstract

The invention relates to an innovation project information processing method and system, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring statistical information and planning resource supply information of an innovation project; according to the statistical information, constructing a resource allocation objective function for the innovation project; the resource allocation objective function comprises a resource use time function and a resource use cost function; constructing a resource allocation constraint function according to the planning resource supply information; determining a target resource allocation scheme for the innovation project according to the resource allocation target function and the resource allocation constraint function; and transmitting the target resource allocation scheme to an innovation project management platform for resource allocation of the innovation project. By adopting the method, the information processing efficiency of the innovation project can be improved, and better resource allocation of the innovation project is realized.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular, to a method, system, computer device, computer-readable storage medium, and computer program product for processing innovation project information. Background Art

[0002] Innovation projects require support of resources such as funds and human resources. Currently, innovation project information is mostly processed manually to determine the allocation of resources such as funds and human resources among various innovation projects, and the corresponding efficiency is not good, which affects the management of innovation projects. Summary of the Invention

[0003] Based on this, in view of the above technical problems, it is necessary to provide a method, system, computer device, computer-readable storage medium, and computer program product for processing innovation project information to improve the efficiency of processing innovation project information, thereby improving the efficiency of determining the resource allocation among various innovation projects, and further better serving the management of innovation projects.

[0004] In a first aspect, the present application provides a method for processing innovation project information, including:

[0005] Obtaining statistical information of an innovation project and planned resource supply information; wherein, the planned resource supply information includes human resource supply information and fund supply information planned for the innovation project; the statistical information includes resource usage information, and the resource usage information includes human resource information and fund resource information already used for the innovation project;

[0006] According to the statistical information, constructing a resource allocation objective function for the innovation project; the resource allocation objective function includes a resource usage time function and a resource usage cost function; the resource usage time function is used to determine the total usage time information corresponding to the resources used for the innovation project; the resource usage cost function is used to determine the total usage cost information corresponding to the resources used for the innovation project;

[0007] According to the planned resource supply information, constructing a resource allocation constraint function; the resource allocation constraint function represents the range of resources that can be allocated to the innovation project;

[0008] According to the resource allocation objective function and the resource allocation constraint function, determining a target resource allocation plan for the innovation project;

[0009] Transmitting the target resource allocation plan to an innovation project management platform for resource allocation of the innovation project.

[0010] In one of the embodiments, according to the resource allocation objective function and the resource allocation constraint function, determining a target resource allocation plan for the innovation project includes:

[0011] Construct a first Lagrangian function for the innovation project according to the resource usage time function, the resource allocation constraint function, and the Lagrange multiplier method;

[0012] Construct a second Lagrangian function for the innovation project according to the resource usage cost function, the resource allocation constraint function, and the Lagrange multiplier method;

[0013] Determine the target resource allocation plan for the innovation project according to the first Lagrangian function and the second Lagrangian function.

[0014] In one embodiment, determining the target resource allocation plan for the innovation project according to the Lagrangian function includes:

[0015] Determine a first resource allocation plan for the innovation project according to the first Lagrangian function; and determine the first weight corresponding to the first resource allocation plan;

[0016] Determine a second resource allocation plan for the innovation project according to the second Lagrangian function; and determine the second weight corresponding to the second resource allocation plan;

[0017] Determine the target resource allocation plan for the innovation project according to the first resource allocation plan, the first weight, the second resource allocation plan, and the second weight.

[0018] In one embodiment, determining the target resource allocation plan for the innovation project according to the resource allocation objective function and the resource allocation constraint function includes: determining the target resource allocation plan for the innovation project according to the resource allocation objective function, the resource allocation constraint function, and the Karush-Kuhn-Tucker conditions.

[0019] In one embodiment, the method further includes: displaying statistical information, resource supply information, and the target resource allocation plan based on a preset display device; receiving a modification instruction for the target resource allocation plan based on a preset instruction receiving device; and updating the target resource allocation plan according to the modification instruction.

[0020] In one embodiment, the method further includes: preprocessing the statistical information; the preprocessing includes at least one of denoising, formatting, and data cleaning; storing the preprocessed statistical information.

[0021] In a second aspect, the present application further provides an innovation project information processing system, including:

[0022] An acquisition module for acquiring statistical information of an innovation project and planning resource supply information; wherein, the planning resource supply information includes human resource supply information and financial resource supply information planned for the innovation project; the statistical information includes resource usage information, and the resource usage information includes human resource information and financial resource information already used for the innovation project.

[0023] A construction module for constructing a resource allocation objective function for the innovation project according to the statistical information; the resource allocation objective function includes a resource usage time function and a resource usage cost function; the resource usage time function represents the total usage time information corresponding to the resources used for the innovation project; the resource usage cost function represents the total usage cost information corresponding to the resources used for the innovation project; constructing a resource allocation constraint function according to the planning resource supply information; the resource allocation constraint function represents the range of resources that can be allocated to the innovation project.

[0024] An allocation module for determining a target resource allocation plan for the innovation project according to the resource allocation objective function and the resource allocation constraint function; transmitting the target resource allocation plan to an innovation project management platform for resource allocation of the innovation project.

[0025] In a third aspect, the present application further provides a computer device, including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the method described in the foregoing first aspect are implemented.

[0026] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in the foregoing first aspect are implemented.

[0027] In a fifth aspect, the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the method described in the foregoing first aspect are implemented.

[0028] The above innovation project information processing method, system, computer device, computer-readable storage medium and computer program product construct a resource allocation objective function for the innovation project according to statistical information, construct a resource allocation constraint function according to the planning resource supply information, and determine a target resource allocation plan for the innovation project according to the resource allocation objective function and the resource allocation constraint function; by transmitting the target resource allocation plan to the innovation project management platform for resource allocation of the innovation project. This realizes the automation of innovation project information processing, improves the efficiency of innovation project information processing, and at the same time, the determined target resource allocation plan can achieve better resource allocation for the innovation project while meeting the resource allocation constraints. Brief Description of the Drawings

[0029] To more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0030] Figure 1 It is a schematic flowchart of an innovative project information processing method in an embodiment;

[0031] Figure 2 It is another schematic flowchart of an innovative project information processing method in an embodiment;

[0032] Figure 3 It is a schematic structural diagram of an innovative project information processing system in an embodiment;

[0033] Figure 4 It is yet another schematic flowchart of an innovative project information processing method in an embodiment;

[0034] Figure 5 It is another schematic structural diagram of an innovative project information processing system in an embodiment;

[0035] Figure 6 It is an internal structural diagram of a computer device in an embodiment. Detailed Embodiments

[0036] In order to make the objectives, technical solutions, and advantages of the present application clearer, the following further details the present application in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0037] In one embodiment, as Figure 1 shown, an innovative project information processing method is provided. In this embodiment, it is exemplified that the method is applied to a server. It can be understood that the method can also be applied to a dedicated innovative project management system or platform, etc., and can also be applied to various devices including innovative project management functions. In this embodiment, the method includes steps S101 to S105:

[0038] Step S101: The server obtains the statistical information of the innovative project and the planned resource supply information; among them, the planned resource supply information includes the human resource supply information and the financial resource supply information planned for the innovative project; the statistical information includes the resource usage information, and the resource usage information includes the human resource information and the financial resource information already used for the innovative project.

[0039] Among them, the innovation project can be a project that requires resources such as manpower and funds, and it can produce innovation results, such as new technologies, new products, and so on.

[0040] Among them, the planned resource supply information can be the resource information that can be supplied planned for the innovation project. Exemplarily, for innovation project A, the planned resource supply information can include the amount of funds that can be supplied or the maximum amount of funds, etc. Resources can be resources used to support innovation projects, such as human resources, financial resources, etc.

[0041] In some embodiments, the server can perform normalization processing on the planned supply information corresponding to different types of resources to obtain the planned resource supply information. Similarly, normalization processing can also be performed on the resource usage information corresponding to different types of resources.

[0042] In some embodiments, the server can obtain the statistical information and planned resource supply information of the innovation project by a specific period or in real time. Exemplarily, the statistical information and planned resources of the innovation project can be regularly reported to the management system of the innovation project manually and stored, and the server can directly obtain the statistical information and planned resource supply information of the innovation project from this system.

[0043] Step S102: The server constructs a resource allocation objective function for the innovation project according to the statistical information; the resource allocation objective function includes a resource usage time function and a resource usage cost function; the resource usage time function is used to determine the total usage time information corresponding to the resources used for the innovation project; the resource usage cost function is used to determine the total usage cost information corresponding to the resources used for the innovation project.

[0044] In some embodiments, the server can construct different resource allocation objective functions according to different categories of resources. Exemplarily, it can be determined that for resources in the human category, a resource usage time function is constructed to determine the total usage time information of the innovation project for human resources. For example, the human resources already used for the innovation project include 10 technicians, and the usage duration corresponding to each technician is 100 hours. Therefore, the total usage time information can be obtained as 10×100 = 1000 hours.

[0045] In some embodiments, when the resources allocated to the innovation project are determined, the server can determine the total usage time information and / or total usage cost information of the innovation project for the allocated resources through the resource allocation objective function.

[0046] Step S103: The server constructs a resource allocation constraint function according to the planned resource supply information; the resource allocation constraint function represents the range of resources that can be allocated to the innovation project.

[0047] In some embodiments, the planned resource supply information may change dynamically. Therefore, the resource allocation constraint function can be updated according to the change in the planned resource supply information.

[0048] In some embodiments, the resource allocation constraint function may be a function in the form of an inequality, and the range of resources that can be allocated to the innovation project characterized by it may be a range with an upper limit and / or a lower limit; of course, the resource allocation constraint function may also be a function in the form of an equation, and the range of resources that can be allocated to the innovation project characterized by it may be a specific value. For example, when the planned resource supply information represents that the funds supplied to the innovation project are a fixed amount, the resource allocation constraint function may be a function in the form of an equation.

[0049] Step S104: The server determines a target resource allocation plan for the innovation project according to the resource allocation objective function and the resource allocation constraint function.

[0050] In some embodiments, the server may separately determine the corresponding resource allocation plans for different resource types, and then determine the target resource allocation plan according to the resource allocation plans corresponding to each type of resource.

[0051] Step S105: The server transmits the target resource allocation plan to the innovation project management platform for resource allocation of the innovation project.

[0052] The above technical solution constructs a resource allocation objective function for the innovation project according to the statistical information, constructs a resource allocation constraint function according to the planned resource supply information, determines a target resource allocation plan for the innovation project according to the resource allocation objective function and the resource allocation constraint function; and transmits the target resource allocation plan to the innovation project management platform for resource allocation of the innovation project. This realizes the automation of innovation project information processing, improves the efficiency of innovation project information processing, and at the same time, the determined target resource allocation plan can achieve better resource allocation for the innovation project while meeting the resource allocation constraints.

[0053] In one of the embodiments, as Figure 2 shown, the aforementioned "determine a target resource allocation plan for the innovation project according to the resource allocation objective function and the resource allocation constraint function" may include steps S201 to S203:

[0054] Step S201: The server constructs a first Lagrangian function for the innovation project according to the resource usage time function, the resource allocation constraint function, and the Lagrange multiplier method.

[0055] In some embodiments, by using a resource usage time function, a resource allocation constraint function, and the Lagrange multiplier method, a first Lagrange function for an innovation project is constructed, and the optimal resource usage time function under the constraints corresponding to the resource allocation constraint function can be determined, that is, the minimum resource usage time under the premise of meeting resource supply is determined.

[0056] Step S202: The server constructs a second Lagrange function for the innovation project according to a resource usage cost function, a resource allocation constraint function, and the Lagrange multiplier method.

[0057] In some embodiments, by using a resource usage cost function, a resource allocation constraint function, and the Lagrange multiplier method, a second Lagrange function for an innovation project is constructed, and the optimal resource usage cost function under the constraints corresponding to the resource allocation constraint function can be determined, that is, the minimum resource usage cost under the premise of meeting resource supply is determined.

[0058] Step S203: The server determines a target resource allocation plan for the innovation project according to the first Lagrange function and the second Lagrange function.

[0059] In some embodiments, the server can determine a target resource allocation plan for the innovation project according to the actual management requirements for the innovation project, based on the first Lagrange function and the second Lagrange function. Exemplarily, in the case where the actual management requirements for the innovation project indicate a greater concern for resource usage cost, the target resource allocation plan can be determined according to the first Lagrange function, and the determined target resource allocation plan can be appropriately adjusted and corrected according to the second Lagrange function.

[0060] In the above technical solution, the corresponding Lagrange functions are respectively constructed from two aspects of resource usage cost and resource usage time to obtain the first Lagrange function and the second Lagrange function, and then the target resource allocation plan for the innovation project is determined according to the first Lagrange function and the second Lagrange function, which enables the determined target resource allocation plan to take into account both resource usage cost and resource usage time and better meet the actual requirements of the innovation project for resource allocation.

[0061] In one embodiment, the aforementioned "determining a target resource allocation plan for an innovation project according to the Lagrangian function" may include: determining a first resource allocation plan for the innovation project according to the first Lagrangian function; and determining a first weight corresponding to the first resource allocation plan. Determining a second resource allocation plan for the innovation project according to the second Lagrangian function; and determining a second weight corresponding to the second resource allocation plan. Determining a target resource allocation plan for the innovation project according to the first resource allocation plan, the first weight, the second resource allocation plan, and the second weight.

[0062] In some embodiments, the server may determine the first weight and the second weight according to the actual management requirements for the innovation project. Exemplarily, in the case where the actual management requirements for the innovation project indicate a greater concern for resource usage costs, it may be determined that the first weight is less than the second weight.

[0063] In some embodiments, the server may obtain an allocation preference index for the resource allocation of the innovation project. For example, the allocation preference index for resource usage time is 3, and the allocation preference for resource usage cost is 7, and the total value of the allocation preference index is 10. Thus, it can be determined that compared with shortening the resource usage time, more preference is given to reducing the usage cost. According to the allocation preference, the corresponding first weight and second weight can be determined, which are 30% and 70% respectively.

[0064] In some embodiments, a relationship comparison table between the allocation preference index and the planned resource supply information may be pre-constructed, so that the server can directly obtain the above-mentioned allocation preference index according to the planned resource supply information.

[0065] In some embodiments, the first weight and the second weight may be the same or different.

[0066] The above technical solution assigns the first weight and the second weight to the first resource allocation plan and the second resource allocation plan respectively, and then fuses the first resource allocation plan and the second resource allocation plan according to the first weight and the second weight, and further determines the target resource allocation plan, which enables the target resource allocation plan to take into account both aspects of resource usage cost and resource usage time and better meet the actual allocation requirements.

[0067] In one embodiment, the aforementioned "determining a target resource allocation plan for an innovation project according to the resource allocation objective function and the resource allocation constraint function" may include: determining a target resource allocation plan for the innovation project according to the resource allocation objective function, the resource allocation constraint function, and the Karush-Kuhn-Tucker conditions.

[0068] Among them, the Karush-Kuhn-Tucker conditions, that is, the Karush-Kuhn-Tucker conditions, the KKT conditions.

[0069] In some embodiments, as described above, the resource allocation constraint function can be a constraint function in the form of an inequality. Therefore, the corresponding optimization problem to be solved is a constrained non-linear optimization problem. Based on this, the KKT conditions can be used for solution.

[0070] For a resource allocation constraint function that can be a constraint function in the form of an equation, the corresponding Lagrangian function can be constructed through Lagrange multipliers for solution, so as to determine the target resource allocation scheme. For a resource allocation constraint function that can be a constraint function in the form of an inequality, the KKT conditions can be used for solution to determine the target resource allocation scheme. This expands the applicable scope of the above method and ensures the accuracy of the determined target resource allocation scheme.

[0071] In one embodiment, the foregoing method may further include: presenting statistical information, resource supply information, and the target resource allocation scheme based on a preset display device; receiving a modification instruction for the target resource allocation scheme based on a preset instruction receiving device; and updating the target resource allocation scheme according to the modification instruction.

[0072] In some embodiments, the server may further present the above-mentioned first weight and second weight based on a preset display device, receive a modification instruction for the first weight and second weight based on a preset instruction receiving device; and modify the first weight and second weight according to the modification instruction and update the target resource allocation scheme.

[0073] By presenting information such as statistical information, resource supply information, and the target resource allocation scheme, the server can provide a basis for the management of innovation projects, etc., and at the same time support the modification of the target resource allocation scheme, improving the flexibility of innovation project information processing and the convenience of innovation achievement management.

[0074] In one embodiment, the foregoing method may further include: preprocessing the statistical information; the preprocessing includes at least one of denoising, formatting, and data cleaning; storing the preprocessed statistical information.

[0075] By preprocessing the statistical information, the quality of the statistical information can be improved, which helps to improve the processing efficiency of innovation project information.

[0076] Existing big data achievement management and evaluation systems usually rely on data collection, storage, and analysis platforms. By real-time monitoring the resource usage, research task progress, equipment operation status, etc. corresponding to innovation projects, they help innovation project managers make more accurate decisions. These systems obtain real-time data from innovation projects, clean, analyze, and optimize the data. Subsequently, the systems can provide real-time decision support for managers, including aspects such as resource scheduling optimization, budget control, and progress monitoring.

[0077] However, existing systems rely on static models for resource scheduling, lack adaptability to real-time changes, and fail to consider the dynamic changes of innovation projects. Moreover, most of the optimization algorithms in the existing technologies focus on single objectives, such as minimizing cost or time, and fail to effectively balance the relationships between multiple objectives, resulting in the inability to comprehensively consider the mutual influences of multiple factors during the optimization process. The feedback mechanism of existing systems is insufficient. Innovation project managers usually rely on periodic reports rather than real-time monitoring, which makes it difficult for managers to make timely adjustments when problems occur in the progress of innovation projects.

[0078] Based on the above analysis, in one embodiment, an innovation project information processing system is provided, such as Figure 3 , which can include a data collection and preprocessing module, a resource scheduling and optimization module, a real-time data stream and feedback module, a decision support and visualization module, an optimization solution and algorithm module, and a performance evaluation and monitoring module. Through the coordinated action of these modules, the system can perform efficient and flexible resource allocation for innovation projects, timely identify and respond to changes in innovation projects, and thus improve the accuracy, efficiency, and controllability of innovation project management.

[0079] (1) Data collection and preprocessing module

[0080] This module real-time collects various data required in innovation project management through multiple interfaces and sensor systems. These data may come from all aspects of innovation projects, including the project progress, equipment usage status, personnel workload, etc. corresponding to innovation projects. For these data, the system will perform real-time cleaning and standardization processing to ensure the consistency and accuracy of the data, and further provide a reliable basis for subsequent resource scheduling, optimization, and decision-making.

[0081] Data collection is the first step of the entire module. Various data in the innovation project are obtained through a real-time monitoring system. For example, the system can be connected to the innovation project management system, equipment management system, personnel working hours recording system, etc., to collect the following types of data in real time: the progress data of the innovation project, such as the completion status of tasks, remaining working hours, etc.; the usage status of equipment, including equipment on / off time, equipment failure records, etc.; the workload data of personnel, such as the working hours of each employee, the amount of tasks completed, etc. These data may come from different sources, such as sensors, API interfaces, or manual input, etc. Therefore, in this embodiment, multiple methods are used for data collection to ensure the comprehensiveness and real-time nature of the data.

[0082] Since there may be various abnormal data during the data collection process, such as noise, missing values, or inconsistent formats, data cleaning must be carried out. In this embodiment, the main tasks of data cleaning include: removing outliers: removing values that significantly deviate from the normal range through statistical methods or predefined rules to ensure the validity of the data; filling missing values: dealing with missing data by interpolation, mean filling, or other methods; deduplication: removing duplicate data records to avoid redundant information in subsequent processing; format unification: ensuring the consistency of data formats, and converting data from different sources into a unified format for subsequent processing.

[0083] For example, the usage time data of a certain device may contain invalid negative values or overly abnormal time spans, and such data will be cleaned or filled with a reasonable algorithm.

[0084] Data standardization is to convert data of different types and sources into a unified standard format and convert data values into a unified dimension. The purpose of data standardization is to eliminate the differences brought by different units and dimensions, so that different types of data can be compared and processed on the same platform. The methods of data standardization include: for numerical data, through linear transformation or normalization methods, converting data with different dimensions into a unified dimension. For example, the progress percentage of an innovation project can be normalized to the range of 0 to 1; for categorical data, one-hot encoding or other appropriate encoding methods can be used to convert discrete categorical data into a format that can be processed by a computer. For example, in innovation project management, the work progress of different departments may be recorded according to different standards, and the system will perform standardization operations to unify the progress of each department into a standard range for subsequent comprehensive evaluation.

[0085] In some embodiments, to ensure the effectiveness of data processing, the following mathematical models are used in the system for data cleaning and standardization:

[0086] Data normalization formula: Among them, X′ is the normalized data, X is the original data, X min and X max are the minimum and maximum values in the dataset respectively. Through this formula, the original data can be mapped between 0 and 1 for subsequent processing.

[0087] Missing value filling formula: Among them, X filled represents the data value after filling, X missing represents the missing value, is the sum of the existing data, and n is the number of existing data. This method maintains the integrity of the data by filling in the missing values and avoids biases in subsequent calculations.

[0088] (II) Resource Scheduling and Optimization Module

[0089] For the resource scheduling and optimization module, the resources corresponding to the innovation project (including personnel, equipment, funds, etc.) are affected by various constraint factors. Therefore, the resource scheduling and optimization module must find a balance among multiple goals. By optimizing the objective function, this module can determine how to maximize the benefits and efficiency of the innovation project with limited resources, or achieve an optimal balance in terms of time and cost.

[0090] In some embodiments, the objective function of resource scheduling optimization involves multiple factors such as time, cost, and quality. To comprehensively optimize these goals, it can be achieved based on the following calculation formulas:

[0091] The calculation formula involving time is:

[0092] T = ∑ i t i x i ; where t i represents the usage time per unit of resource i, and X i represents the usage amount of resource i.

[0093] The cost function is used to minimize the total cost of the innovation project, set as:

[0094] C = ∑ i c i x i ; where C i is the cost per unit of resource i, and X i is still the usage amount of resource i.

[0095] Quality optimization usually maximizes a certain quality index, set as:

[0096] Q = ∑ i q i x i ; where qi Represents the degree of contribution of resource i to quality, X i is the usage amount of the resource.

[0097] Resources can be equipment, personnel, funds, etc. For each resource, its usage amount X needs to be defined i , such as the number of hours of equipment use, the man-hours of personnel, the funds consumed, etc. These quantified resource data will be used as variables in the optimization process. For example, if the equipment resource is X i , then X i can represent the running time of the equipment or the resource consumption.

[0098] In some embodiments, the conversion of resource amounts to time, cost, and quality usually depends on specific definitions of resources and objectives. For example: The equipment usage amount X i can be converted to time (such as the number of hours of equipment operation), converted to cost (such as the rental cost of the equipment), or converted to quality (such as the production quality during equipment operation). Again, for example, the man-hours of personnel X i can be converted to time (i.e., the number of working hours), cost (such as salary), and quality (such as the efficiency and quality of task completion).

[0099] In some embodiments, the resource scheduling and optimization module can adopt a multi-objective optimization method. By weighted summation of multiple objective functions, the overall effect of resource scheduling is optimized. For example, in an innovation project, the objective functions may include: the time minimization objective, used to ensure that the innovation project can be completed within the specified time; the cost minimization objective, used to control the budget of the innovation project so that the expenditure of the innovation project remains within a reasonable range; the quality maximization objective, used to ensure that the quality of the innovation project results is not lower than the expectation; the risk minimization objective, used to reduce the risks that may occur during the implementation of the innovation project. In practical applications, the above objective functions need to be weighted and balanced according to the actual situation. Different innovation projects may have different requirements for objectives such as time, cost, and quality. The system can flexibly adapt to different business needs by adjusting the weights of the objective functions.

[0100] During the process of resource scheduling, certain constraint conditions must be met. These constraint conditions include: time constraint, the completion time of the innovation project is limited, and it is necessary to ensure that the task is completed within the specified time; budget constraint, the total budget of the innovation project is limited, and it is necessary to ensure that the expenditure of the innovation project is within the budget; resource availability constraint, the resources (such as personnel, equipment, etc.) of the innovation project are limited, and these resources must be reasonably allocated.

[0101] In some embodiments, constraints can be constructed based on the following function: g(x) ≤ x; where g(x) is the constraint function representing the usage of resources, b is the constraint value. For example, for time constraints, the constraint function may represent the maximum completion time of tasks in an innovation project; for resource constraints, the constraint function may represent the maximum usage of a certain resource. By introducing these constraint conditions, the system can optimize resource scheduling while ensuring that the innovation project does not exceed the predetermined resource limits during actual execution.

[0102] In some embodiments, the resource scheduling and optimization module uses a variety of optimization algorithms for solving, including but not limited to linear programming (LP), integer programming (IP), and heuristic algorithms, etc. For specific resource scheduling problems, the system can select an appropriate optimization method to improve the solving efficiency. Specifically, the system constructs a Lagrangian function to handle the constrained optimization problem. The form of the Lagrangian function is:

[0103] L = f(x) + λ · (g(x) - b); where L represents the Lagrangian function; f(x) is the objective function representing the objective to be minimized or maximized (such as time, cost, etc.); g(x) is the constraint function representing various constraint conditions in resource scheduling; λ is the Lagrange multiplier used to handle the constraint problem; b is the constraint value. By solving this Lagrangian function, the system can optimize the objective function under the given constraint conditions and find the optimal resource scheduling plan.

[0104] (III) Real-time Data Stream and Feedback Module

[0105] For the real-time data stream and feedback module, data is collected in real time from various sensors, device status monitoring systems, and personnel work management systems to form a data stream. After being processed, the data stream is fed back to the resource scheduling and optimization module of the system so as to dynamically adjust the resource scheduling plan according to the real-time feedback. This dynamic adjustment mechanism is a significant difference between this system and traditional static scheduling schemes, ensuring the efficient utilization of resources in actual applications and avoiding resource waste.

[0106] In some embodiments, the real-time data acquisition unit is a core component of the real-time data stream and feedback module. Through technical means such as sensors and API interfaces, the data acquisition unit monitors the usage of various resources in the innovation project in real time. These data include, but are not limited to: device status data, such as the start / stop status, operation efficiency, and fault records of devices; personnel work data, such as workload, task completion, and working hour records; resource consumption data, such as energy usage, material consumption, and budget consumption. By obtaining these data in real time, the system can comprehensively and accurately reflect the current resource usage situation of the innovation project. Generally, the real-time data acquisition unit is connected to the device management system, personnel management system, etc. to ensure the comprehensiveness and real-time nature of the data source.

[0107] In some embodiments, data stream processing is a key step in this module. Due to the large amount of data collected in real time and the diverse data types and structures involved, the data stream processing unit needs to effectively process the data. To ensure fast data calculation and response, the system adopts big data stream processing technologies, such as platforms like Apache Kafka and Apache Flink, for real-time calculation and analysis. Specifically, the data stream processing unit includes the following steps: data filtering: preliminarily screening the original data to remove irrelevant or redundant data; data conversion: uniformly converting the original data from different sources into a standard format for subsequent processing; data aggregation: aggregating the data from multiple data sources, for example, merging the usage data from different devices to generate a unified resource consumption report.

[0108] In some embodiments, data stream calculation is not just a simple calculation of resource consumption and device usage, but is directly combined with the optimized management of the innovation project results. Through real-time data stream feedback, the system not only adjusts resource scheduling but also directly optimizes the management process of the results to ensure the quality, progress, and cost control of the final results.

[0109] In the actual result management scenario, the following factors need to be considered: The resource consumption situation R(t) includes the real-time consumption status of resources such as manpower, equipment, and funds; the device usage situation E(t): monitoring the operation status, load condition, and fault information of the device; the result output D(t): evaluating the actual results of the current stage, comparing them with the expected results, and optimizing the final results by adjusting the resource and device status. To ensure the efficiency of data stream processing, the following mathematical model is adopted to optimize the calculation and processing of the data stream. The data stream calculation formula is:

[0110] D(t) = f(R(t), E(t), P(t)); where D(t) is the output at time t, representing the actual result evaluation of the current stage, t is the result management calculation function used to evaluate the impact of resources and equipment status on the result and make optimization adjustments, R(t) is the resource consumption at time t, including the usage of manpower, equipment, and materials, E(t) is the equipment usage at time t, monitoring the operation efficiency and health status of the equipment, and P(t) is the result deviation at time t, that is, the gap between the current result and the expected goal. When the progress of a certain R & D task lags behind, the system calculates the result deviation according to P(t) and automatically increases the manpower input R(t) or optimizes the experimental equipment E(t) to ensure the timely delivery of the innovation project. The system monitors the output D(t) of the production line in real time and adjusts the equipment operation status E(t) and raw material supply R(t) to improve product quality and production efficiency.

[0111] In some embodiments, the system evaluates the deviation between the current result and the expected goal based on P(t) and optimizes the project progress and quality by adjusting the construction personnel R(t) and mechanical equipment E(t). The feedback adjustment formula:

[0112] where Δx(t) represents the scheduling adjustment amount at time t, D(t) is the current actual data flow result, is the expected result of the system, α is the adjustment coefficient controlling the adjustment amplitude of the feedback. Through this formula, the system can adjust the resource scheduling strategy according to the deviation between the real-time data and the expected result to ensure the reasonable allocation of resources for the innovation project.

[0113] (IV) Decision Support and Visualization Module

[0114] For the decision support and visualization module, by presenting the resource allocation, scheduling process, key performance indicators (KPIs), and other relevant information in the innovation project, it provides a decision-making basis for innovation project managers. These information are usually presented in the form of charts, dashboards, and dynamic reports to ensure that managers can quickly understand the progress of the innovation project and the resource usage status.

[0115] Generally, innovation project managers need to view the key indicators of the innovation project through simple and intuitive graphics. This requires presenting a large amount of real-time data in the form of charts, curve graphs, pie charts, etc. In this embodiment, the core function of the decision support and visualization module is to visualize the data so that managers can clearly see the resource usage, the progress of the innovation project, and other important decision-making indicators at a glance.

[0116] Specifically, this module generates dynamic visualization graphs based on the real-time data obtained from the real-time data stream and the feedback module. For example, it can generate: a task progress bar that shows the completion progress of each task to help managers understand the implementation status of current tasks; a pie chart of resource usage that displays the consumption of resources, such as equipment utilization rate and personnel workload; a line chart of budget and cost to help managers understand the budget usage and cost change trends of innovation projects; a device failure trend chart that shows the occurrence frequency of device failures and their impact on the progress of innovation projects.

[0117] Through these graphical displays, managers can clearly see the allocation of various resources and potential risk points in the innovation project. For example, when the progress of a certain task is slow, the display of the progress bar will immediately attract the attention of managers, prompting them to take measures to adjust resources.

[0118] In some embodiments, considering the large volume and complexity of data, managers usually cannot directly obtain useful information from the raw data. Therefore, information filtering and refinement is another important function of the decision support and visualization module. Specifically, the system filters and refines the data to be displayed by setting conditions and thresholds. For example, the system can set thresholds for certain innovation project indicators (such as resource utilization rate, budget overrun, etc.). When an indicator reaches the set threshold, the system will automatically highlight it to remind managers to pay attention. Exemplarily, if the usage time of a certain device exceeds the predetermined range, the system will automatically display a warning signal on the dashboard to remind managers to adjust the device usage arrangement in a timely manner. If the budget consumption of the innovation project exceeds the expectation, the system will also guide managers to take necessary control measures through corresponding charts or warning prompts.

[0119] In some embodiments, the decision support and visualization module not only provides static chart displays, but also can provide decision support for managers through dynamic calculations and multi-dimensional analysis. The system analyzes historical data and real-time data to help managers consider problems from multiple perspectives and provide the best decision-making solutions. For example, the system calculates the effectiveness of the current scheduling plan based on the real-time resource usage and task progress. If it is found that the usage efficiency of a certain resource is low, the system can automatically generate a recommended plan, such as adjusting the priority of resources, optimizing task scheduling, etc., so as to improve the resource usage efficiency and the overall progress of the innovation project.

[0120] In some embodiments, during the process of multi-dimensional decision support, the system comprehensively considers multiple key performance indicators (KPIs). For example, the following aspects can be considered: Time efficiency: The system calculates the reasons for time delays by comparing the actual progress with the planned progress and provides optimization solutions; Cost control: The system calculates the risk of budget overrun based on the current budget consumption and provides suggestions for budget adjustment; Quality management: The system analyzes the quality indicators of the innovation project results through real-time data feedback and provides improvement measures based on quality problems.

[0121] In some embodiments, the decision support and visualization module not only helps managers understand the existing situation, but also provides optimization and adjustment strategies. Based on the decision support information provided by the system, managers can quickly identify problems such as unreasonable resource allocation, lagging progress, or budget overrun, and take corresponding adjustment measures. For example, the system can calculate the optimal resource scheduling plan according to the optimization algorithm to help managers make decisions. If the current task lags behind due to insufficient resources, the system will recommend increasing the corresponding resource investment or adjusting the priorities of other tasks to ensure that the innovation project can be completed on time.

[0122] In some embodiments, to achieve multi-dimensional decision support and optimization strategies, the system performs calculations based on data analysis models. For example, a multi-objective optimization model is used to optimize multiple objectives simultaneously (such as time, cost, quality, etc.). The system performs multi-objective optimization through the following formula: Z = w1·f1(x) + w2·f2(x) + … + w n ·f n (x); where, f i (x) represents the i-th objective function, w i represents the weight of the i-th objective function, x is the decision variable. Through this model, the system can balance multiple objectives and provide comprehensive decision support. n is the total number of objective functions.

[0123] (V) Optimization Solving and Algorithm Module

[0124] This module takes into account that the resources in the achievement management may change at any time. For example, personnel changes, budget adjustments, etc. By combining the dynamic optimization algorithm and the Lagrange multiplier method, it can adjust the resource allocation plan in real time. This method can ensure continuous optimization of resource utilization in a dynamic environment. Specifically, to consider the changes in resources, a dynamic optimization algorithm can be introduced. Assume that in a dynamic environment, it is necessary to adjust the resource allocation, and the objective function is updated as follows:

[0125] where, T′ is the total time or total cost, t i is the time consumption of the i-th task, x i(t) is the resource allocation variable for the i-th task at time t, which can be dynamically adjusted according to actual changes, and n is the total number of tasks. To achieve this dynamic optimization, a dynamic programming algorithm is used to determine the optimal resource allocation under the time series t, with the goal of continuously updating x according to dynamic constraints (such as resource changes). i such that the overall objective function T′ is minimized.

[0126] In some embodiments, by combining the Lagrange multiplier method with machine learning methods, the changes in task constraints can be identified and the optimization strategy can be automatically adjusted. The optimized objective function can be expressed as:

[0127] where C′ is the total cost or expense, c i is the cost of the i-th task, q i is the resource usage, Q max is the maximum limit of resource usage, n is the total number of tasks, and λ i is the Lagrange multiplier, which reflects the degree of relaxation of the constraint. By optimizing at each time step, combining the prediction results of machine learning and real-time data analysis, the constraint conditions and resource allocation strategies are automatically adjusted to ensure that the constraints in actual work are intelligently identified and dynamically optimized.

[0128] In some embodiments, to adapt to the changing constraint conditions in the system, by comprehensively using dynamic programming and machine learning, an optimization objective function can be introduced: where Q′ is the total resource usage, q i is the resource usage of the i-th task, f(t i ) is the dynamic factor adjusted based on time, which reflects the timeliness of resource usage, t i is the time required for the i-th task, n is the total number of tasks, and α is an adjustable coefficient that affects the sensitivity of system adjustment. This optimization objective function combines timeliness and dynamic factors, which helps to achieve rapid adaptation of resources in a dynamic environment and improve the efficiency of the system.

[0129] In some embodiments, the constraint conditions are not only constants but may also change with time, task progress, or external factors. By combining the Lagrange multiplier method with machine learning algorithms, the changes in constraints can be identified in real time and the optimization strategy can be automatically adjusted, thereby enhancing the intelligence level of the system. For example, in the resource scheduling problem, if the goal is to minimize the total cost of an innovation project and there is a budget constraint, the Lagrange function will find the optimal resource allocation plan by adjusting the Lagrange multiplier, which can not only minimize the cost but also meet the budget constraint.

[0130] As an option, the KKT conditions play an important role in solving constrained optimization problems. The KKT conditions are an extension of the Lagrangian optimization method and are applicable to optimizing problems with inequality constraints. The optimization solution and algorithm module further refine the optimization solution process in resource scheduling by solving the KKT conditions. Specifically, the basic form of solving the KKT conditions is as follows:

[0131] where is the gradient of the objective function, representing the rate of change of the objective function at the current solution point, is the gradient of the constraint function, and λ is the Lagrange multiplier. By adjusting this multiplier, the constraint conditions and the objective function can simultaneously satisfy the optimal solution.

[0132] The objective function f(x) represents the objective to be optimized in performance management, such as resource utilization rate, cost, time, etc. To perform optimization, the gradient of the objective function needs to be calculated first This gradient vector represents the direction in which the objective function changes fastest at the current solution point x, usually obtained by performing partial derivative operations on each decision variable.

[0133] In performance management, the objective function may be related to factors such as task progress, resource allocation, cost optimization, etc. For example, assume the objective function is the minimization of the total cost f(x), and the gradient will represent the optimal direction for each resource allocation decision, that is, how to adjust each resource allocation to reduce the total cost.

[0134] The constraint function g(x) represents the limitations in aspects such as resources, time, and cost in performance management. For example, there may be budget constraints, personnel quantity constraints, time constraints, etc., which can all be expressed as constraint conditions g(x) ≤ 0.

[0135] In practical applications, the gradient of the constraint function is used to describe the direction of change of the constraint conditions near the current solution point. It provides the optimization algorithm with which direction of adjustment will lead to violation or satisfaction of the constraint conditions, thus guiding the algorithm to find the optimal solution.

[0136] In the performance management scenario, through the KKT conditions, we can combine the objective function and the constraint function to obtain the optimal solution of a constrained optimization problem. The KKT conditions require the following two parts:

[0137] The gradient of the objective function and the gradient of the constraint function must satisfy a certain linear combination relationship, that is The Lagrange multiplier λ is used to adjust the influence degree of the constraint conditions. λ reflects the influence on the objective function when the constraint is violated.

[0138] Regarding the gradient of the objective function, the following explanations are provided: First, determine the objectives in performance management (such as minimizing cost, time, or maximizing output value, etc.), and then obtain the gradient by performing partial derivative operations on the objective function. For example, if the objective is the total time The gradient is which reflects the contribution of each task time to the total time.

[0139] Regarding the gradient of the constraint function, the following explanations are provided: According to specific constraint conditions (such as budget constraints, resource constraints, etc.), obtain the gradient of the constraint by performing partial derivative operations on the constraint function g(x). For example, if the constraint condition is (The total resource usage does not exceed the maximum resource amount), the gradient of the constraint function is which represents the impact of each resource usage amount on the total resource consumption.

[0140] In the resource scheduling problem, the KKT conditions can obtain a more accurate optimal solution by solving the constrained optimization problem. For example, when two objectives of minimizing time and minimizing cost need to be considered simultaneously, the KKT conditions can give specific strategies on how to adjust resources to achieve a balance between the two.

[0141] In some embodiments, the optimal control theory is used for real-time adjustment of resource scheduling in dynamic systems. Specifically, when certain tasks in an innovation project change, the system needs to quickly adjust resources, and the optimal control theory can provide theoretical support for these adjustments. The optimal control theory is applied to real-time adjust the resource scheduling strategy to ensure the continuous optimal operation of the innovation project. The basic formula of optimal control is:

[0142] where represents the rate of change of the state variable, L is the Lagrangian function, represents the derivative with respect to time, is the partial derivative of the Lagrangian function L with respect to the rate of change of the state variable. Through this formula, the system can adjust the allocation of resources in real time when the task progress changes, ensuring that the innovation project can continue along the optimal path. For example, when a device fails, the system can adjust the task allocation of the device through the optimal control theory, transfer the task to other devices, and ensure the timely completion of the innovation project.

[0143] In some embodiments, the optimization solving and algorithm module can also select different optimization algorithms according to the complexity of the specific problem. In this embodiment, the system can select different optimization methods according to the characteristics of the resource scheduling problem: integer programming: applicable to the case where the usage amount of resources is a discrete value in the resource scheduling problem; genetic algorithm: applicable to solving large-scale optimization problems and capable of finding the optimal solution by simulating the natural selection process; simulated annealing algorithm: applicable to dealing with complex constraint conditions and capable of jumping out of the local optimal solution with a certain probability to find the global optimal solution. Through these optimization algorithms, the system can select the most suitable solving method in different situations to ensure the efficiency and accuracy of the solving process.

[0144] (VI) Performance Evaluation and Monitoring Module

[0145] For the performance evaluation and monitoring module, by analyzing various performance indicators of the system (such as computing time, resource consumption, response time, etc.), the operation efficiency and optimization effect of the system are quantitatively evaluated. These data are of great reference value to the managers of innovation projects and can help them discover potential problems in resource allocation, task scheduling, or system operation, and then make adjustments and optimizations.

[0146] In some embodiments, performance monitoring is one of the core functions of the performance evaluation and monitoring module. To ensure the efficient operation of the system, the system needs to monitor the performance of each link in real time. Specifically, the objects of performance monitoring include: computing time: measuring the time required by the system in the resource scheduling and optimization process and monitoring the efficiency of task execution; resource consumption: tracking the amount of resources consumed by the system during execution, such as computing resources, storage resources, etc.; response time: referring to the response speed of the system to user requests or data changes to ensure that the system can adjust resource allocation in a timely manner; task completion time: monitoring the completion status of each task to ensure the timely progress of innovation projects.

[0147] In some embodiments, to achieve these performance monitoring functions, the system collects this data in real time through means such as sensors and loggers, and feeds it back to the performance evaluation module for processing. The system can also use professional monitoring tools to capture various performance data in real time. Specifically, the system not only monitors performance, but can also generate detailed performance evaluation reports based on the collected performance data. These evaluation reports help innovation project managers understand the performance during the execution of innovation projects and provide a basis for subsequent optimization decisions. The content of the performance evaluation report may include: Resource utilization efficiency: The report will describe in detail the utilization efficiency of each resource (such as personnel, equipment, etc.) and give relevant optimization suggestions; Time efficiency: The report will reflect the time consumed by the system during execution and compare the difference between the expected time and the actual time; Budget consumption: The report will calculate the budget consumption of the innovation project during implementation and evaluate the risk of overspending; Task delay analysis: The report will also analyze the delay situation of tasks in the innovation project and give corresponding adjustment plans.

[0148] In some embodiments, the evaluation report is generated dynamically. As the innovation project progresses, the system will generate reports regularly for innovation project managers to refer to. Specifically, the process of generating the report includes the following steps: Data collection: Collect various performance data of the system operation from the performance monitoring module; Data analysis: Analyze the collected data and calculate various performance indicators; Report generation: Display the analysis results in the form of charts, tables, etc. to generate the final evaluation report.

[0149] As an option, the performance evaluation and monitoring module also includes a feedback mechanism for providing feedback to other modules of the system (such as the resource scheduling and optimization module) according to the evaluation results. When the operating efficiency of the system is lower than expected, the evaluation report will automatically generate feedback information and transmit it to the optimization module. The optimization module adjusts the resource scheduling strategy according to the feedback information to ensure that the project corresponding to the innovation project can quickly return to the best operating state. For example, if the system monitors that the consumption of a certain resource exceeds the predetermined budget, the evaluation report will remind the manager to check the budget consumption and adjust the resource allocation if necessary. This feedback mechanism ensures that the entire system can be dynamically adjusted according to the evaluation results, improving the execution efficiency of the innovation project.

[0150] In some embodiments, the system evaluates performance by introducing a mathematical model to ensure the accuracy of the evaluation results. For example, in calculating time and resource consumption, the following formulas can be used:

[0151] Calculation time evaluation formula: where, T total represents the total calculation time, T iDenote the execution time of the i-th task. n is the total number of tasks. Through this formula, the system can calculate the total computing time of the innovation project and compare it with the scheduled time.

[0152] Resource consumption evaluation formula: Among them, R total Denotes the total resource consumption, R i Denotes the consumption of the i-th resource. m is the total number of resources. Through this formula, the system can monitor the consumption of various resources during the execution of the innovation project and generate relevant reports.

[0153] Budget consumption evaluation formula: Among them, B total Denotes the total budget consumption, B i Denotes the budget consumption of the i-th innovation project stage. k is the number of innovation project stages. This formula can help managers understand whether the budget of the innovation project is overspent and provide a reference for subsequent adjustments.

[0154] In some embodiments, please refer to the appendix Figure 4 , and provide a result management evaluation method based on big data, including the following steps S401 to step S405:

[0155] Step S401: Collect innovation project progress, resource consumption, equipment status, and personnel configuration data through sensors and API interfaces, and clean and standardize the collected data to ensure data consistency;

[0156] Step S402: According to the multi-objective optimization algorithm, perform an optimal solution for resource scheduling through Lagrangian duality, KKT conditions, and optimal control theory to obtain the optimal resource allocation plan;

[0157] Step S403: Monitor the real-time data stream through the real-time data stream platform, calculate the real-time resource usage, and adjust the resource scheduling strategy through the feedback mechanism;

[0158] Step S404: Generate charts and dashboards through the visualization platform based on the real-time data and optimization results to display resource usage, scheduling plans, and optimization effect information;

[0159] Step S405: Monitor the real-time performance of the system and generate a performance evaluation report to evaluate the effect of resource scheduling and the system operation efficiency.

[0160] For step S401, through technical means such as sensors and API interfaces, the system collects various types of data related to innovation projects in real time, such as the progress of innovation projects, resource consumption, equipment status, and personnel allocation. These data usually come from different devices and systems. Through data cleaning and standardization processing, outliers are removed, missing data is filled, and the data format is unified, thus ensuring the consistency and accuracy of the data. The key purpose of data preprocessing is to convert the raw data from multiple data sources into a standardized data format that the system can use, providing high-quality input data for subsequent optimization algorithms.

[0161] For step S402, after obtaining the cleaned and standardized data, the system uses a multi-objective optimization algorithm to perform an optimal solution for resource scheduling. The system comprehensively considers different objectives in innovation projects, such as time minimization, cost minimization, quality maximization, and risk minimization. Through Lagrangian duality, KKT conditions, and optimal control theory, the system solves the resource scheduling problem. Through these optimization methods, the system can obtain the optimal resource allocation plan on the premise of meeting the constraint conditions. The optimization process of the objective function ensures the optimal allocation of different resources in the innovation project, thus maximizing the benefits of the innovation project.

[0162] For step S403, the system continuously monitors the usage of innovation project resources through a real-time data stream platform to ensure real-time adjustment of resource scheduling during the execution of innovation projects. The monitoring of real-time data streams not only reflects the current state of resources but also calculates the resource utilization efficiency and adjusts the resource scheduling strategy according to real-time feedback information. For example, if the utilization efficiency of a certain resource is lower than expected, the system will automatically make dynamic adjustments based on the real-time data stream feedback to ensure that the innovation project can continue as planned.

[0163] For step S404, based on real-time data and optimization results, the system displays various key indicators through a visualization platform, such as resource usage, scheduling plans, and optimization effects. Through forms such as charts and dashboards, managers can intuitively understand the resource allocation, progress, and optimization effects of innovation projects, helping them make more scientific and timely decisions. This visualization support converts the complex data of the system into easily understandable display results, enhancing the transparency and operability of decision-making.

[0164] For step S405, the system monitors the real-time performance and generates an evaluation report. These reports include evaluation of the effectiveness of resource scheduling, analysis of system operation efficiency, and feedback on various key performance indicators (KPIs). Evaluation reports help identify problems in the execution of innovation projects and provide feedback to managers so that timely adjustments can be made. For example, if the efficiency of a resource is low, the report will provide optimization suggestions; if the progress of the innovation project lags behind, the system will also provide solutions for adjusting resource allocation. Through this feedback mechanism, the system can continuously optimize resource scheduling to ensure that the innovation project can be successfully completed according to the predetermined goals.

[0165] The above technical solution has the following beneficial effects: 1. Through data collection, preprocessing, optimization solution and real-time feedback, the resource scheduling efficiency and optimization accuracy are improved. Compared with the static resource scheduling method in the prior art, this application ensures that the innovative project can flexibly respond to changes by dynamically adjusting the resource allocation, avoiding the waste of resources and progress delays caused by data lag or neglect of real-time factors in the traditional method. 2. Introducing a multi-objective optimization algorithm, combining Lagrangian duality, KKT conditions and optimal control theory, optimizing the resource scheduling scheme, and achieving the technical effect of global optimization by comprehensively considering multiple goals such as time, cost, and quality. Compared with the technical solution of single-objective optimization in the prior art, this application can achieve a balance between multiple goals, avoiding the shortcomings of traditional methods that can only optimize a certain goal and ignore other goals. 3. Continuously monitor the usage of corresponding project resources of innovative projects through a real-time data stream platform, and dynamically adjust the resource scheduling scheme according to real-time feedback, achieving the technical effect of real-time response and optimization of resource allocation. Compared with the fixed resource allocation strategy in traditional technology, this application can be flexibly adjusted according to real-time data, and potential problems can be discovered and adjusted in a timely manner, which significantly improves the efficiency of resource utilization of innovation projects. 4. The use of a visual platform to display resource usage, scheduling plans and optimization effects provides managers with an intuitive decision-making support tool. Compared with the existing technology that is difficult to grasp the progress of innovation projects in real time, this application uses clear and easy-to-understand charts and dashboards to enable managers to obtain key information in a short time and make scientific decisions quickly, thereby improving the efficiency and accuracy of innovation project management.

[0166] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0167] Based on the same inventive concept, an embodiment of the present application further provides an innovative project information processing system for implementing the innovative project information processing method involved above. The implementation solutions provided by this system to solve problems are similar to the implementation solutions recorded in the above method. Therefore, the specific limitations in one or more embodiments of the innovative project information processing system provided below can refer to the limitations on the innovative project information processing method in the above text, and will not be repeated here.

[0168] In an exemplary embodiment, as Figure 5 shown, an innovative project information processing system 500 is provided, including:

[0169] An acquisition module 501, configured to acquire statistical information of an innovative project and planned resource supply information; wherein, the planned resource supply information includes human resource supply information and financial resource supply information planned for the innovative project; the statistical information includes resource usage information, and the resource usage information includes human resource information and financial resource information already used for the innovative project;

[0170] A construction module 502, configured to construct a resource allocation objective function for the innovative project according to the statistical information; the resource allocation objective function includes a resource usage time function and a resource usage cost function; the resource usage time function represents the total usage time information corresponding to the resources used for the innovative project; the resource usage cost function represents the total usage cost information corresponding to the resources used for the innovative project; construct a resource allocation constraint function according to the planned resource supply information; the resource allocation constraint function represents the range of resources that can be allocated to the innovative project;

[0171] An allocation module 503, configured to determine a target resource allocation plan for the innovative project according to the resource allocation objective function and the resource allocation constraint function; and transmit the target resource allocation plan to an innovative project management platform for resource allocation of the innovative project.

[0172] In one embodiment, the allocation module 503 is further configured to determine a target resource allocation plan for the innovation project according to the resource allocation objective function and the resource allocation constraint function, including: constructing a first Lagrangian function for the innovation project according to the resource usage time function, the resource allocation constraint function, and the Lagrange multiplier method; constructing a second Lagrangian function for the innovation project according to the resource usage cost function, the resource allocation constraint function, and the Lagrange multiplier method; and determining a target resource allocation plan for the innovation project according to the first Lagrangian function and the second Lagrangian function.

[0173] In one embodiment, the allocation module 503 is further configured to determine a target resource allocation plan for the innovation project according to the Lagrangian function, including: determining a first resource allocation plan for the innovation project according to the first Lagrangian function; and determining a first weight corresponding to the first resource allocation plan; determining a second resource allocation plan for the innovation project according to the second Lagrangian function; and determining a second weight corresponding to the second resource allocation plan; and determining a target resource allocation plan for the innovation project according to the first resource allocation plan, the first weight, the second resource allocation plan, and the second weight.

[0174] In one embodiment, the allocation module 503 is further configured to determine a target resource allocation plan for the innovation project according to the resource allocation objective function and the resource allocation constraint function, including: determining a target resource allocation plan for the innovation project according to the resource allocation objective function, the resource allocation constraint function, and the Karush-Kuhn-Tucker conditions.

[0175] In one embodiment, the allocation module 503 is further configured to display statistical information, resource supply information, and the target resource allocation plan based on a preset display device; receive a modification instruction for the target resource allocation plan based on a preset instruction receiving device; and update the target resource allocation plan according to the modification instruction.

[0176] In one embodiment, the acquisition module 501 is configured to preprocess the statistical information; the preprocessing includes at least one of denoising, formatting, and data cleaning; and store the preprocessed statistical information.

[0177] Each module in the above innovation project information processing system can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above respective modules.

[0178] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structural diagram may be as shown in Figure 6 . The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data required for the innovative project information processing method, such as statistical information, etc. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements an innovative project information processing method.

[0179] Those skilled in the art can understand that Figure 6 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0180] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0181] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0182] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0183] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0184] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this application.

[0185] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for processing innovative project information, characterized in that: The method comprises: Obtaining statistical information and planned resource supply information of the innovation project; wherein the planned resource supply information includes human resource supply information and financial supply information planned for the innovation project; the statistical information includes resource use information, and the resource use information includes human resource information and financial resource information used for the innovation project; According to the statistical information, a resource allocation objective function for the innovation project is constructed; the resource allocation objective function includes a resource usage time function and a resource usage cost function; the resource usage time function is used to determine the total usage time information corresponding to the resources used for the innovation project; the resource usage cost function is used to determine the total usage cost information corresponding to the resources used for the innovation project; Constructing a resource allocation constraint function according to the planned resource supply information; the resource allocation constraint function represents the range of resources that can be allocated to the innovation project; Determining a target resource allocation scheme for the innovation project according to the resource allocation objective function and the resource allocation constraint function; The target resource allocation plan is transmitted to the innovation project management platform for resource allocation of the innovation project.

2. The method according to claim 1, characterized in that Determining a target resource allocation scheme for the innovation project according to the resource allocation objective function and the resource allocation constraint function includes: Constructing a first Lagrangian function for the innovation project according to the resource usage time function, the resource allocation constraint function and the Lagrangian multiplier method; Constructing a second Lagrangian function for the innovation project according to the resource usage cost function, the resource allocation constraint function and the Lagrangian multiplier method; A target resource allocation scheme for the innovation project is determined according to the first Lagrangian function and the second Lagrangian function.

3. The method according to claim 2, characterized in that Determining a target resource allocation scheme for the innovation project according to the Lagrangian function includes: Determine a first resource allocation scheme for the innovation project according to the first Lagrangian function; and determine a first weight corresponding to the first resource allocation scheme; Determine a second resource allocation scheme for the innovation project according to the second Lagrangian function; and determine a second weight corresponding to the second resource allocation scheme; A target resource allocation scheme for the innovation project is determined based on the first resource allocation scheme, the first weight, the second resource allocation scheme, and the second weight.

4. The method according to claim 1, characterized in that Determining a target resource allocation scheme for the innovation project according to the resource allocation objective function and the resource allocation constraint function includes: According to the resource allocation objective function, the resource allocation constraint function and the Carlo-Kuhn-Tucker condition, a target resource allocation plan for the innovation project is determined.

5. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: Based on a preset display device, display the statistical information, the resource supply information and the target resource allocation scheme; Based on a preset instruction receiving device, a modification instruction for the target resource allocation scheme is received; and according to the modification instruction, the target resource allocation scheme is updated.

6. The method according to claim 1, characterized in that The method further comprises: Preprocessing the statistical information; the preprocessing includes at least one of denoising, formatting and data cleaning; The statistical information after the preprocessing is stored.

7. An innovative project information processing system, characterized in that: The system comprises: An acquisition module, used to acquire statistical information and planned resource supply information of the innovation project; wherein the planned resource supply information includes human resource supply information and financial supply information planned for the innovation project; the statistical information includes resource use information, and the resource use information includes human resource information and financial resource information used for the innovation project; A construction module, used to construct a resource allocation objective function for the innovation project based on the statistical information; the resource allocation objective function includes a resource usage time function and a resource usage cost function; the resource usage time function is used to determine the total usage time information corresponding to the resources used for the innovation project; the resource usage cost function is used to determine the total usage cost information corresponding to the resources used for the innovation project; a resource allocation constraint function is constructed based on the planned resource supply information; the resource allocation constraint function represents the range of resources that can be allocated to the innovation project; The allocation module is used to determine the target resource allocation plan for the innovation project according to the resource allocation objective function and the resource allocation constraint function; and transmit the target resource allocation plan to the innovation project management platform for resource allocation of the innovation project.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images