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

Abstract

The invention relates to an innovation achievement 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 of innovation achievements; the statistical information comprises technical maturity index information of the innovation achievements; according to the statistical information, a non-linear model of technology maturity index information changing along with time is constructed; determining life cycle prediction information of the innovation achievement according to the nonlinear model; determining resource demand information of the innovation achievement in different life cycles according to the life cycle prediction information; the resource demand information comprises fund demand information and manpower demand information; determining a first resource allocation scheme according to the resource demand information; based on the innovation subject information, constructing a game model to determine a second resource allocation scheme; and determining a target resource allocation scheme according to the first resource allocation scheme and the second resource allocation scheme. The method can improve the innovation achievement information processing efficiency.

Description

Technical Field

[0001] This application relates to the field of information technology, and particularly to a method, system, computer device, computer-readable storage medium, and computer program product for processing innovation achievement information. Background Art

[0002] In the stages from research and development, testing, promotion, maturity to withdrawal of innovation achievements, resources such as funds and manpower are required. Currently, innovation achievement information is mostly processed manually to determine the allocation of resources such as funds and manpower among various innovation achievements, and the corresponding efficiency is not good. 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 achievement information to improve the efficiency of processing innovation achievement information, so as to more efficiently determine the resource allocation among various innovation achievements.

[0004] In a first aspect, this application provides a method for processing innovation achievement information, including:

[0005] Obtain the statistical information of innovation achievements; the statistical information includes the information of the technology maturity index of innovation achievements;

[0006] According to the statistical information, construct a non-linear model of the change of the technology maturity index information over time; and according to the non-linear model, determine the life cycle prediction information of innovation achievements;

[0007] According to the life cycle prediction information, determine the resource demand information of innovation achievements in different life cycles; the resource demand information includes the fund demand information and the manpower demand information;

[0008] According to the resource demand information, determine the first resource allocation plan;

[0009] Obtain the innovation entity information corresponding to each innovation achievement; based on the innovation entity information, construct a game model to determine the second resource allocation plan;

[0010] According to the first resource allocation plan and the second resource allocation plan, determine the target resource allocation plan.

[0011] In one embodiment, the statistical information further includes the already allocated resource information corresponding to the innovation achievements; according to the statistical information, constructing a non-linear model of the change of the technology maturity index information over time includes:

[0012]

[0013] Among them, x(t) represents the technology maturity corresponding to the technology maturity index information at time t, and the range is [0, 1];

[0014] u(t) represents the allocated resource information at time t;

[0015] α is the acceleration coefficient of the allocated resource information on the technology progress;

[0016] β is the inhibition coefficient of the technology maturity on the progress;

[0017] η(t) is an external disturbance term, representing uncertain factors such as market changes and technology bottlenecks.

[0018] In one embodiment, obtaining the innovation entity information corresponding to each innovation result; based on the innovation entity information, constructing a game model to determine the second resource allocation plan, including:

[0019] Obtaining the innovation entities corresponding to each innovation result and the resource supply information; wherein, the resource supply information includes the supply capacity information of the innovation entity for the resource demand information;

[0020] Taking the innovation entity as the game subject and constructing a game model based on the resource supply information to determine the second resource allocation plan.

[0021] In one embodiment, obtaining the statistical information of the innovation result, including:

[0022] Obtaining the statistical information of the innovation result;

[0023] Preprocessing the statistical information; the preprocessing includes at least one of denoising, formatting, and data cleaning;

[0024] Storing the preprocessed statistical information.

[0025] In one embodiment, the method further includes:

[0026] Based on a preset display device, displaying the life cycle prediction information, resource demand information, innovation entity information, and target resource allocation plan;

[0027] Based on a preset instruction receiving device, receiving a modification instruction for the target resource allocation plan; and updating the target resource allocation plan according to the modification instruction.

[0028] In one embodiment, the method further includes:

[0029] Based on a preset period, obtaining the revenue index information of the innovation result corresponding to the allocated resource in the target resource allocation plan; and,

[0030] Updating the target resource allocation plan according to the revenue index information.

[0031] In a second aspect, the present application further provides an innovative achievement information processing system, including:

[0032] An acquisition module, configured to acquire statistical information of innovative achievements; the statistical information includes technical maturity index information of the innovative achievements;

[0033] A first determination module, configured to construct a non-linear model of the change of the technical maturity index information over time according to the statistical information; and determine life cycle prediction information of the innovative achievements according to the non-linear model; determine resource requirement information of the innovative achievements in different life cycles according to the life cycle prediction information; the resource requirement information includes capital requirement information and manpower requirement information; determine a first resource allocation plan according to the resource requirement information;

[0034] A second determination module, configured to acquire innovation entity information corresponding to each of the innovative achievements; construct a game model based on the innovation entity information to determine a second resource allocation plan;

[0035] A third determination module, configured to determine a target resource allocation plan according to the first resource allocation plan and the second resource allocation plan.

[0036] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0037] 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 following steps are implemented:

[0038] 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 following steps are implemented:

[0039] For the above-mentioned innovative achievement information processing method, system, computer device, computer-readable storage medium and computer program product, on the one hand, by predicting the life cycle of the innovative achievements, the resource requirement information corresponding to the innovative achievements in different life cycles is determined, and thus based on the resource requirement information, a first resource allocation plan is obtained; on the other hand, from the perspective of the innovation entities corresponding to each innovative achievement, a game model is constructed to determine a second resource allocation plan. By integrating the respective corresponding plans of the above two aspects, namely the first resource allocation plan and the second resource allocation plan, a target resource allocation plan is determined, which realizes the automation of innovative achievement information processing, helps to improve efficiency, and also helps to improve the accuracy of the determined target resource allocation plan. Description of the Drawings

[0040] To more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the accompanying drawings required for the description of the embodiments of the present application or related technologies. Obviously, the accompanying 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.

[0041] Figure 1 It is the first process schematic diagram of the innovative achievement information processing method in an embodiment;

[0042] Figure 2 It is the second process schematic diagram of the innovative achievement information processing method in an embodiment;

[0043] Figure 3 It is the third process schematic diagram of the innovative achievement information processing method in an embodiment;

[0044] Figure 4 It is the first structural schematic diagram of the innovative achievement information processing system in an embodiment;

[0045] Figure 5 It is the second structural schematic diagram of the innovative achievement information processing system in an embodiment;

[0046] Figure 6 It is the logical principle schematic diagram of the innovative achievement information processing system in an embodiment;

[0047] Figure 7 It is the structural block diagram of the innovative achievement information processing system in an embodiment;

[0048] Figure 8 It is the internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0049] 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 accompanying 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.

[0050] In the process of managing innovative achievements, how to efficiently and accurately track and optimize resource allocation has become a key issue. With the continuous progress of technology, the process from the birth to maturity of innovative achievements has become more and more complex, involving a wide variety of resource types, uncertain technical development paths, and traditional methods often unable to adapt to this dynamic change, resulting in problems such as resource waste and decision-making lag.

[0051] In the prior art, most systems rely on manual input or simple data collection methods, using fixed interfaces or static models for data acquisition and analysis. Most of the life cycle prediction methods are based on linear regression or other simplified mathematical models, focusing on the statistical analysis of historical data and failing to fully consider the impact of technological changes and market dynamics on innovation achievements. In terms of resource allocation, many traditional methods use static configuration methods, adjusting based on certain experience or previous data, and lacking dynamic game analysis of multi-party competition and cooperation. Although these methods provide data support and preliminary resource allocation in some application scenarios, they cannot effectively cope with innovation projects with rapid technological progress and large demand changes.

[0052] The deficiencies of the prior art lie in its inability to accurately cope with the complexity and uncertainty of the innovation achievement life cycle. Firstly, traditional data collection methods tend to ignore the multi-dimensionality and timeliness of information, resulting in poor data accuracy and thus affecting subsequent analysis and decision-making. Secondly, most life cycle predictions rely on simplified linear models and cannot truly reflect the non-linear changes in the technological progress of innovation achievements, leading to inaccurate prediction results. Thirdly, resource allocation methods are usually too rigid, failing to consider the complex situations of multi-party games, resulting in uneven resource allocation and unable to stimulate the maximum potential of all parties. Finally, existing decision support systems rely on static reports and cannot update data in real time, resulting in lagging decisions.

[0053] Based on the above analysis, the present application provides an innovation achievement information processing method, which will be described by way of embodiments as follows:

[0054] In one embodiment, as Figure 1 shown, an innovation achievement information processing method is provided. This embodiment takes the application of this method to a server as an example for illustration. It can be understood that this method can also be applied to a system including a terminal and a server. Of course, this method can be applied to a dedicated innovation achievement management platform or system, or to other systems or platforms involving innovation achievement management, such as a scientific research project management system, etc. This method includes steps S101 to step S106:

[0055] Step S101: The server obtains the statistical information of the innovation achievement; the statistical information includes the information of the technology maturity index of the innovation achievement.

[0056] Among them, the innovation achievements can be those in the field of science and technology, and the field of science and technology is not restricted. For example, the innovation achievements can include innovation achievements with actual physical forms such as new materials and new drugs, and can also include innovation achievements without actual physical forms such as those related to intelligent driving control and improving the accuracy of face recognition. The innovation achievements can be complete innovation achievements, such as a new drug that has been launched on the market, or incomplete innovation achievements, such as those still in the R & D and improvement stages. For example, the corresponding actual product is a new energy vehicle in the experimental stage.

[0057] Among them, the statistical information can be the information related to each life cycle stage of the innovation achievement, including but not limited to the used information of the innovation achievement about resources such as funds and manpower, and the R & D time consumed by the innovation achievement, etc. In some embodiments, the life cycle of the innovation achievement can be divided into at least two stages.

[0058] Among them, the technology maturity index information can be pre - calibrated and statistically calculated. For example, it can be determined according to the R & D time consumed and the output quantity of relevant innovation achievements, etc.

[0059] Step S102: The server constructs a non - linear model of the change of the technology maturity index information over time according to the statistical information; and determines the life cycle prediction information of the innovation achievement according to the non - linear model.

[0060] In some embodiments, the change of the technology maturity index information corresponding to the innovation achievement over time can be non - linear. Therefore, a corresponding non - linear model can be constructed to reflect the development process of the innovation achievement and determine which stage of its life cycle the innovation achievement is currently in, that is, the life cycle prediction information.

[0061] In some embodiments, the similarity between different innovation achievements can be determined, and the innovation achievements corresponding to the similarity higher than the preset value are determined as similar innovation achievements. The prediction similarity between the life cycle prediction information corresponding to the similar innovation achievements is determined. When the prediction similarity is within the preset range, the resource demand information of the innovation achievement in different life cycles is determined according to the life cycle prediction information.

[0062] In some embodiments, historical innovation achievements with a similarity to the innovation achievement reaching the preset value and the historical statistical information of the historical innovation achievements can be obtained. The historical statistical information includes the historical cycle information of the historical innovation achievements. Thus, the life cycle prediction information of the innovation achievement can be comprehensively determined according to the non - linear model and the historical cycle information.

[0063] Step S103: The server determines the resource demand information of the innovation achievement in different life cycles according to the life cycle prediction information; the resource demand information includes the fund demand information and the manpower demand information.

[0064] Among them, the resource requirement information may characterize the requirements of the innovation achievement for resources such as human resources and funds. In some embodiments, the resource requirement information may vary depending on the different innovation achievements or the different life cycles of the innovation achievements.

[0065] Among them, the fund requirement information may characterize the requirements of the innovation achievement for funds. For example, the funds required for the output of the innovation achievement; the human resource requirement information may characterize the requirements of the innovation achievement for human resources. For example, the situation of the technical personnel required for the output of the innovation achievement.

[0066] Step S104: The server determines a first resource allocation plan according to the resource requirement information.

[0067] In some embodiments, the server may determine the first resource allocation plan according to the resource requirement information and in combination with the foregoing historical resource allocation situation corresponding to the historical innovation achievements.

[0068] Step S105: The server obtains the innovation entity information corresponding to each innovation achievement; based on the innovation entity information, a game model is constructed to determine a second resource allocation plan.

[0069] Among them, the innovation entity information may characterize the entity information responsible for each stage such as the output and use of the innovation achievement. For example, the innovation entity information corresponding to innovation achievement a includes Company A.

[0070] In some embodiments, one innovation achievement may correspond to multiple pieces of innovation entity information. For example, innovation achievement a is jointly responsible by Company A1 and Company A2.

[0071] In some embodiments, one piece of innovation entity information may correspond to multiple innovation achievements. For example, Company F is responsible for innovation achievements F1, F2, F3, etc.

[0072] In some embodiments, the server may, for the resource allocation problem among the innovation achievements, construct a game model based on game theory principles such as Nash equilibrium, so as to determine the second resource allocation plan.

[0073] Step S106: The server determines a target resource allocation plan according to the first resource allocation plan and the second resource allocation plan.

[0074] In some embodiments, the server may generate a corresponding visualization file according to the target resource allocation plan and send it to a display terminal, such as a monitoring screen for innovation achievement management, so as to provide a decision-making basis for innovation achievement management personnel.

[0075] In the above technical solution, on the one hand, by predicting the life cycle of the innovation achievement, the resource requirement information corresponding to the innovation achievement in different life cycles is determined, and based on the resource requirement information, the first resource allocation plan is obtained; on the other hand, from the perspective of the innovation entity corresponding to each innovation achievement, a game model is constructed to determine the second resource allocation plan. By comprehensively considering the above two corresponding plans, namely the first resource allocation plan and the second resource allocation plan, the target resource allocation plan is determined, which realizes the automation of innovation achievement information processing, helps to improve efficiency, and also helps to improve the accuracy of the determined target resource allocation plan.

[0076] In one embodiment, the foregoing "the statistical information further includes the allocated resource information corresponding to the innovation achievement; according to the statistical information, a non-linear model of the change of the technology maturity index information with time is constructed" may include:

[0077]

[0078] Where x(t) represents the technology maturity corresponding to the technology maturity index information at time t, and the range is [0, 1];

[0079] u(t) represents the allocated resource information at time t;

[0080] α is the acceleration coefficient of the allocated resource information on the technology progress;

[0081] β is the inhibition coefficient of the technology maturity on the progress;

[0082] η(t) is an external disturbance term, representing uncertain factors such as market changes and technology bottlenecks.

[0083] In some embodiments, the above acceleration coefficient, inhibition coefficient and external disturbance term can be calibrated. For example, the historical data of a large number of innovation achievements can be analyzed, and different acceleration coefficients, inhibition coefficients and external disturbance terms can be calibrated for different categories of innovation achievements.

[0084] In some embodiments, considering that the change of the technology maturity index information with time is non-linear, the server can construct a corresponding Logistic model. The Logistic model is a non-linear regression model with the form of a Logistic function.

[0085] The server constructs a non-linear model of the change of the technology maturity index information with time through the above formula, which helps to provide a more accurate reference basis for the determination of the life cycle prediction information.

[0086] In one embodiment, as Figure 2As shown, the aforementioned "obtaining the innovation entity information corresponding to each innovation achievement; based on the innovation entity information, constructing a game model to determine the second resource allocation plan" may include steps S201 to S202:

[0087] Step S201: The server obtains the innovation entities corresponding to each innovation achievement and the resource supply information; wherein, the resource supply information includes the supply capacity information of the innovation entity for the resource demand information.

[0088] In some embodiments, the server may obtain the resource supply budget information of each innovation entity for the innovation achievement. For example, the capital budget invested by Company A in the innovation achievement, the available human resources, etc. Based on the resource supply budget information, the supply capacity information of the innovation entity for the resource demand information can be determined.

[0089] Step S202: The server takes the innovation entity as the game entity and constructs a game model based on the resource supply information to determine the second resource allocation plan.

[0090] The above technical solution starts from the perspective of the innovation entity corresponding to the innovation achievement, considers the game between different innovation entities, takes the innovation entity as the game entity, and constructs a game model based on the resource supply information, thereby determining the second resource allocation plan. This enables the second resource allocation plan to reflect the game of the innovation entity in resource allocation for the innovation achievement, helps to improve the benefits generated by resource allocation, that is, determines a more accurate resource allocation plan.

[0091] In one embodiment, the aforementioned "obtaining the statistical information of the innovation achievement" may include: obtaining the statistical information of the innovation achievement; preprocessing the statistical information; the preprocessing includes at least one of denoising, formatting, and data cleaning; storing the preprocessed statistical information.

[0092] By preprocessing the statistical information, the quality of the statistical information can be improved, which helps to improve the processing efficiency of the innovation achievement information.

[0093] In one embodiment, the aforementioned innovation achievement information processing method may further include: displaying the life cycle prediction information, resource demand information, innovation entity information, and 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.

[0094] The server displays information such as lifecycle prediction information, resource requirement information, innovation entity information, and target resource allocation plan, etc., so as to provide a basis for the management of innovation achievements, etc. At the same time, it supports the modification of the target resource allocation plan, improving the flexibility of innovation achievement information processing and the convenience of innovation achievement management.

[0095] In one embodiment, as Figure 3 shown, the aforementioned innovation achievement information processing method may further include steps S301 to S302:

[0096] Step S301: Based on a preset period, obtain the benefit index information of the innovation achievements corresponding to the resources already allocated in the target resource allocation plan.

[0097] In some embodiments, according to actual needs, the benefit index information can be custom-built in advance and reported to the server regularly in a manual manner.

[0098] Step S302: Update the target resource allocation plan according to the benefit index information.

[0099] In some embodiments, the server can analyze the benefit index information to determine the marginal benefits of different types of resources. For example, the change in the benefit index information brought about by allocating 10,000 yuan of funds each time, and adjust the target resource allocation plan according to the level of marginal benefits.

[0100] By updating the target resource allocation plan periodically, this helps to ensure the accuracy of the target allocation plan.

[0101] In an exemplary embodiment, an innovation achievement data processing system is provided, including: a data collection and integration module, a lifecycle prediction module, a resource requirement evaluation and optimization module, a game theory and resource optimization module, a multi-stage control and scheduling module, and a visualization and reporting module. As Figure 4 and Figure 5 shown, the structural schematic of the system is given, and as shown in 6, the logical principle schematic of the system is given. The following will explain each module of the system separately:

[0102] First, the data collection and integration module:

[0103] It is used to collect and integrate data (i.e., information) related to innovation achievements from multiple data sources. This module is responsible for obtaining data from various sources and standardizing the data to ensure its integrity and consistency. This module is closely related to subsequent modules such as lifecycle prediction and resource optimization, ensuring the high efficiency of the entire system and the scientific nature driven by data.

[0104] In some embodiments, the data acquisition and integration module obtains information from multiple data sources in an automated manner, including but not limited to patent databases, scientific research literature databases, market research databases, enterprise innovation records, etc. Due to the significant differences in data formats, storage structures, and update mechanisms among different data sources, this module adopts a set of standardized data processing strategies to ensure the comparability and reliability of the collected data in terms of format, content, and time dimension. In addition, this module also provides data quality control and integrity detection functions to reduce redundant data, identify abnormal data, and optimize storage and access efficiency, thereby ensuring the accuracy of subsequent analysis and prediction.

[0105] The data acquisition and integration module may include a data acquisition unit, a data processing unit, and a data storage unit. Regarding the data acquisition unit, the following is an explanation: It is responsible for obtaining data from different sources, mainly including the following methods: Generally, the API interface method is preferred to obtain data. For example, for a patent database, the system retrieves relevant information on innovation achievements according to the time dimension or keywords through a standardized data query interface and stores it in a structured data format. In addition, the API method can ensure real-time data synchronization to ensure that the obtained data is the latest version.

[0106] As an option, for data that cannot be directly obtained through the API, web scraping technology can be used. This method is mainly used for obtaining unstructured data. For example, for market research reports, industry trend analysis documents, etc., the system uses text parsing algorithms to automatically extract web page or document data and convert it into a usable format. For example, for a PDF-format market research report, the system first performs optical character recognition on it, and then automatically identifies and extracts key content through a text classification model.

[0107] In some embodiments, for the data within the system, such as the innovation achievement records in the project management system, the database direct connection method can be used for data synchronization. For example, the system regularly connects to the innovation achievement management database and automatically extracts newly entered data to ensure data integrity and consistency.

[0108] In some embodiments, to ensure the quality of the collected data, the system provides an automatic data verification mechanism, including but not limited to: 1. Data field integrity check to ensure that all key data fields have values and avoid data missing; 2. Timestamp consistency check to ensure the correct update sequence of data and avoid information errors caused by data synchronization lag; 3. Duplicate removal mechanism to avoid duplicate storage of the same data and improve storage efficiency.

[0109] Regarding the data processing unit, the description is as follows: After the data collection is completed, the data processing unit is responsible for cleaning, transforming, and standardizing the format of the raw data to ensure the consistency and applicability of the data. This unit is used for: 1. Data format conversion: Since the data formats from different sources are inconsistent, this system adopts a unified data structure standard. For example, for the time format, the system will convert all data into the ISO8601 standard format to ensure the comparability across data sources. 2. Data cleaning: This system adopts data integrity detection rules to automatically eliminate redundant data and complete missing data. Specifically: For predictable missing data (such as some fields missing in certain market data), the system uses a regression model to estimate reasonable values. 3. For unpredictable missing data (such as descriptive fields in certain patent information), the system adopts a data imputation algorithm for filling, or marks the data as "not applicable" for subsequent processing. 4. Outlier detection: This system adopts a method based on statistical analysis to identify abnormal data. For example, the system will calculate the mean and standard deviation of the data. If a data point deviates too much from the mean, it will be marked as an outlier and further reviewed manually or automatically eliminated.

[0110] In some embodiments, the data processing unit can also perform data consistency checks, including: 1. Field consistency check to ensure that the data formats of the same type in different data sources are consistent; 2. Logical consistency check to ensure that numerical data is within a reasonable range. For example, the patent application date of an innovation achievement should not be later than its marketization date; 3. Time series rationality check to ensure that the timestamps of the data are logical. For example, the experimental data of a research achievement cannot be earlier than the start time of the research.

[0111] Regarding the data storage unit, the description is as follows: After the data processing is completed, the data storage unit stores the data in a distributed storage system to ensure the efficient access and update of the data. This system adopts an incremental update mechanism, that is, only the newly added or modified data is stored to reduce the storage burden. For example, when a new round of data collection is completed, the system will automatically compare the current data with the existing data in the database and only store the changed part to improve the storage efficiency. The functions of the data storage unit include: data deduplication to ensure that the data collected in different batches is not stored repeatedly; index optimization to improve the data retrieval efficiency. For example, an inverted index is adopted to accelerate the query of text data; version management to retain historical data and support time series analysis.

[0112] In some embodiments, the system can regularly perform data cleaning to delete useless or expired data to keep the storage system running efficiently. For example, for market research data that is more than five years old and has not been called by the recent analysis module, it can be archived to reduce the storage space occupation.

[0113] In some embodiments, the data collection and integration module may have a data quality control mechanism to ensure that the collected and processed data meet the quality standards of the system. Specifically, a multi-level quality detection strategy is adopted, including: 1. Automated data detection: Regularly perform data integrity check tasks to ensure that all necessary fields are correctly filled. For example, if a certain innovation achievement lacks a technology classification label in the data, it will be automatically marked and a completion suggestion will be generated. 2. Data consistency comparison: This system supports comparison and analysis across data sources. For example, compare the information of the same innovation achievement between the patent database and market data. If inconsistencies are found, the system will automatically generate correction suggestions. 3. Manual review mechanism: For abnormal data, a manual review function is supported. Users can view the abnormal data marked by the system and manually confirm whether it is reasonable, and at the same time record the review results for optimizing future data cleaning strategies.

[0114] Second, the life cycle prediction module:

[0115] It is used to analyze the technological development trend of innovation achievements based on the data provided by the data collection and integration module and generate life cycle prediction results; this module is used to realize the dynamic analysis and trend prediction of innovation achievements in different life cycle stages from birth, development to maturity and decline. This module relies on the multi-source heterogeneous data collected, cleaned and standardized by the aforementioned module one to provide a basis for subsequent resource optimization allocation and strategic decision-making.

[0116] The life cycle prediction module comprehensively reflects the multi-dimensional characteristics of innovation achievements in different development stages by constructing a mathematical model of the dynamic changes in the life cycle of innovation achievements and combining historical development data, market feedback data, industrial chain-related data, etc., and conducts dynamic prediction. This module particularly considers the multi-stage and multi-factor interactive effects of innovation achievements, uses the method of non-linear dynamic systems to describe them, and systematically solves the key factors affecting the life cycle of innovation achievements by optimizing the calculation method, in order to obtain information such as the future development trend and potential value change of innovation achievements.

[0117] The life cycle prediction module may include a non - linear dynamic system modeling unit and an optimization calculation unit. Regarding the non - linear dynamic system modeling unit, the following is described: It constructs a mathematical model that can reflect the development dynamics for the characteristic evolution process of innovative achievements at different life cycle stages. The model adopted takes into account the complex non - linear relationships among different influencing factors within the life cycle of innovative achievements, including market acceptance, technological maturity, industrial chain supporting degree, intellectual property protection intensity, etc. Specifically, to reflect the development state of the life cycle of innovative achievements, the system first sets a group of life cycle state variables, which are used to describe the state information of innovative achievements at any moment and include characteristic data in multiple aspects such as technology, market, and economy. For example, the life cycle state variables include but are not limited to: technological maturity index; market penetration rate index; patent and intellectual property value index; input - output ratio index; policy support intensity index; industrial chain dependence index.

[0118] During the operation of the system, the life cycle state variables change dynamically over time, reflecting the whole process of innovative achievements from initial research and development, experimental demonstration, market promotion, mature application to withdrawal from the market. The changes of the state variables are described by a group of non - linear dynamic relationships, which reflect the typical characteristics of different stages of the life cycle, including the evolution trends of different stages such as slow growth, rapid expansion, mature saturation, and decline.

[0119] In some embodiments, the non - linear dynamic system modeling unit uses a non - linear dynamics model to simulate the life cycle of innovative achievements. The technological maturity of innovative achievements does not change linearly but shows the characteristic of accelerating growth. It develops slowly in the initial stage and enters the rapid growth stage with technological breakthroughs and finally tends to saturation. Therefore, the process of technological maturity changing over time can be described by the Logistic growth model, and the formula is as follows:

[0120] Where: represents the rate of change of technological maturity over time t; x(t) represents the technological maturity at time t, with a range of [0, 1]; u(t) represents the resource input at time t; α and β are respectively the acceleration coefficient of resource input on technological progress and the inhibition coefficient of technological maturity on progress; η(t) is an external perturbation term, representing uncertain factors such as market changes and technological bottlenecks. In this model, the change of x(t) is not only affected by the resource input u(t) but also restricted by the maturity of the technology itself. That is, as the technology approaches saturation, its progress speed will gradually slow down. By fitting historical data, the system can determine the specific values of parameters such as α, β, and η(t) for accurate technology prediction.

[0121] In some embodiments, for the formula corresponding to the above Logistic growth model, the parameters other than time can be determined by means of a preset algorithm or manual calibration. Exemplarily, the sample values corresponding to the parameters can be determined by manual calibration first, so as to obtain sample data, which includes the sample values of the foregoing manually calibrated parameters and the sample statistical data of the corresponding innovation achievements. Then, the sample data is used to train an artificial intelligence model, so that the artificial intelligence model can learn the mapping relationship between the sample statistics and the sample values. Finally, the trained artificial intelligence model can be used to determine the values of the corresponding parameters from the statistical information (data) of the innovation achievements.

[0122] As an implementation manner, to depict the dynamic evolution of the innovation achievement life cycle, the system sets that the rate of life cycle change is jointly determined by factors such as the current state, external influencing factors, and time. Among them, the external influencing factors include but are not limited to: policy changes; market competition; capital investment level; industry development trends; changes in user needs.

[0123] In some embodiments, to more scientifically describe the development characteristics of the life cycle, the system will introduce dynamic parameters (such as growth rate, saturation level, environmental sensitivity, etc.), which are all calculated based on a comprehensive calculation of historical data and real-time data and can dynamically reflect the changes in the environment where the innovation achievement is located. For example, the innovation achievement grows slowly in the initial stage of market promotion, accelerates in the growth stage, and slows down or even declines in the maturity stage. The changes in these different stages are all expressed through a dynamic system model.

[0124] As a possible way, the system will establish corresponding classification models for different types of innovation achievements based on historical life cycle case data, and model the life cycle dynamics respectively to ensure that the development paths of innovation achievements with different characteristics can be described in line with the actual situation.

[0125] Regarding the optimization calculation unit, the following is described: After the establishment of the life cycle dynamic model, the optimization calculation unit is responsible for performing the optimization solution of influencing factors based on the life cycle change process reflected by the model, and obtaining the optimal control strategy within the life cycle of the innovation result. The optimization calculation unit first calculates the objective function values at different stages of the life cycle based on the historical evolution trajectory of the life cycle state variables and in combination with the current market and technological environment. For example: in the growth stage, the goal is to accelerate market penetration; in the mature stage, the goal is to maximize economic benefits; in the decline stage, the goal is to withdraw smoothly or find new application scenarios. The optimization calculation unit comprehensively considers the regulatory effect of external control factors on the life cycle path and finally forms a set of dynamic control strategies. The external control factors considered include but are not limited to: policy intervention means (such as financial subsidies, technical standard formulation, etc.); capital investment (such as R & D funds, market promotion expenses, etc.); upstream and downstream cooperation mechanisms in the industrial chain; technology upgrade or substitution paths; market promotion rhythm control.

[0126] In some embodiments, to reasonably design the control strategy, the input external control variables can be modeled and analyzed through a dynamic control model to clarify the influence direction and action intensity of different control factors on the life cycle change.

[0127] In some embodiments, the following constraint conditions will be considered during the optimization process: 1. Life cycle benefit constraint, that is, within the life cycle of the innovation result, maximize the comprehensive economic, social and technological value; 2. Resource input constraint, that is, balance the benefits and inputs under the premise of limited resources; 3. Time constraint, that is, complete the goal of the innovation result from birth to maturity within the predetermined life cycle length.

[0128] In some embodiments, to ensure the scientificity and adaptability of the optimization solution, the optimization calculation unit supports a real-time adjustment mechanism based on data feedback, that is, dynamically optimize the formulated life cycle control plan according to the actual development status of the innovation result to ensure its consistency with changes in the market, technology and other environments.

[0129] In this embodiment, the life cycle prediction module is closely connected to the data collection and integration module. The former relies on the high-quality and multi-dimensional data provided by the latter as input, including historical innovation result life cycle data, real-time market dynamic data, policy and regulation data, technology development trend data, etc.

[0130] In some embodiments, the system first completes data aggregation and standardization processing by the data collection and integration module, and then the life cycle prediction module calls these data to perform life cycle dynamic modeling. After the model is built in the non-linear dynamic system modeling unit, it is handed over to the optimization calculation unit for parameter solution and control path analysis.

[0131] In some embodiments, to ensure the timeliness and accuracy of the life cycle prediction results, automatic periodic updates can be performed. The system can automatically update the life cycle prediction model parameters according to the new data obtained in real time by the data acquisition module, dynamically correct the life cycle path prediction, and improve the model's ability to adapt to changes in the market and technological environment.

[0132] Third, the resource demand assessment and optimization module:

[0133] It is used to analyze the resource demands of the innovation achievements at different stages according to the prediction results of the life cycle prediction module and perform optimized allocation. On the basis of the system's completion of the dynamic modeling and trend prediction of the entire life cycle of the innovation achievements, to ensure that the innovation achievements obtain appropriate resource support at different life cycle stages, the system further sets up a resource optimization allocation module. In the overall operation of the system, this module is responsible for conducting resource demand analysis based on the results of life cycle state changes, and realizing the optimized allocation of dynamic resources on the premise of limited resources, ensuring that the innovation achievements can obtain scientific and reasonable resource support from the stages of research and development, experimentation, promotion, maturity to withdrawal. The resource optimization allocation module is closely connected with the data acquisition and integration module and the life cycle prediction module. Relying on standardized and multi-dimensional data, as well as life cycle dynamic evolution information, it dynamically adjusts the investment of various resources required by the innovation achievements, thereby ensuring the continuous progress and value realization of the innovation achievements.

[0134] In some embodiments, the resource demand assessment and optimization module includes a resource demand assessment unit and a resource optimization unit, which are respectively used to realize the dynamic quantification of resource demands and the solution of the optimized allocation goal, and guide the actual resource scheduling through clear allocation results.

[0135] In some embodiments, the resource demand assessment unit first quantitatively assesses the resource demands of the innovation achievements at different life cycle stages according to the life cycle dynamic information output by the life cycle prediction module, and makes reasonable corrections in combination with the actual situation, and finally forms a resource demand list as the basis for subsequent optimization calculations.

[0136] In some embodiments, the types of resources required by the innovation achievements at different life cycle stages are different. To meet the specific development needs of different stages, the resource demands can be classified by category, including but not limited to: financial resources, such as R & D investment, market promotion funds, industrialization subsidies, etc.; human resources, such as scientific research personnel, management personnel, marketing personnel; physical resources, such as experimental equipment, production facilities, testing equipment; policy resources, such as standard support, patent layout, administrative license; industrial chain resources, such as upstream and downstream partners, supplier resources, etc.

[0137] In some embodiments, based on the dynamic change process of the life cycle state variables, relying on the development data of historical similar innovation achievements, the quantity, type, and time distribution of resources required in different stages of the life cycle can be determined through methods such as comparative analysis and regression models. For example, in the R & D stage of innovation achievements, the system identifies high-intensity capital and scientific research manpower requirements, while in the market promotion stage, it focuses on resource investment related to market channel construction and user services.

[0138] In some embodiments, the system sets up a correlation function between various resources and life cycle state variables to clarify the corresponding relationship between the life cycle state and resource requirements. During the resource requirement assessment process, the system automatically calculates the total amount of resource requirements and phased requirements within a certain period in the future based on the current life cycle state and predicted path of the innovation achievement, forming a resource requirement data set with a clear time distribution and distinct categories.

[0139] In some embodiments, the system can correct resource requirements by combining external influencing factors such as policies and market environments. For example, when the policy support intensity is high, the proportion of external capital support for the same innovation achievement increases, thereby reducing the demand for internally self-raised funds. The system automatically identifies changes in the external policy environment during the assessment process and adjusts the resource requirement results accordingly.

[0140] In some embodiments, to avoid unreasonable resource requirements, insufficient investment, or resource waste, after the resource requirement assessment is completed, the system can execute an automatic verification mechanism to check the reasonableness of resource requirements based on historical data and expert rules, ensuring that the resource requirement assessment results are practical and scientific.

[0141] In some embodiments, based on the resource requirement list formed by the resource requirement assessment unit, the resource optimization unit combines the actual available resources to optimize the resource allocation under constraints, forming an optimal resource allocation plan to guide the resource investment decisions in each stage of the innovation achievement.

[0142] In some embodiments, the resource optimization unit first dynamically identifies the total amount of currently available resources in the system, including: various types of funds that can be actually disposed of currently; personnel and equipment resources that can be called; actual policy and industrial chain support resources that can be obtained, etc.

[0143] To ensure the rationality of resource allocation, when the system performs optimization, the following basic constraints need to be met: 1. Total resource constraint: The total amount of actually allocated resources does not exceed the total available amount of the system; 2. Minimum resource guarantee constraint: In the key stages of the innovation achievement life cycle (such as the trial demonstration period and the initial stage of market promotion), the minimum resource investment standard set by the system needs to be met to ensure the smooth progress of the achievement; 3. Phased ratio constraint: For different stages of the life cycle, the system sets different resource ratios according to the development focus to ensure that resources are concentrated in key stages.

[0144] In some embodiments, during the resource allocation process, the resource optimization unit measures the value of resource allocation according to the resource benefit function, which is used to describe the marginal benefits of different types of resources in enhancing the value of innovation achievements at different stages. For example, the promotion effect of capital investment on the innovation achievement from R & D to market entry, and the impact of market promotion investment on market share.

[0145] In some embodiments, the system makes a prediction for the future stage based on the change trend of the life cycle state variable, and preferentially guarantees the resource input requirements at key nodes such as market promotion and industrialization, so as to maximize the value of the achievement through concentrated resource investment.

[0146] In some embodiments, to ensure the executability and balance of resource optimization, the system supports multi-objective optimization, taking into account the maximization of economic benefits and resource utilization efficiency. For example, the system can optimize two objectives of "enhancing the market value of innovation achievements" and "minimizing resource costs" simultaneously, and balance them by reasonably setting weight factors.

[0147] During the resource optimization process, the system also supports real-time dynamic adjustment. When the new stage state data output by the life cycle prediction module indicates that the actual development of the innovation achievement deviates from the original prediction, the system will automatically trigger the recalculation process of the resource optimization module, timely adjust the resource allocation plan, and ensure that the resource allocation is synchronized with the development trend of the innovation achievement.

[0148] In this embodiment, the resource optimization and allocation module realizes the dynamic interaction of real-time data and analysis results with the aforementioned data collection and integration module and life cycle prediction module.

[0149] In some embodiments, the system periodically receives external data such as market, technology, policy, and competitive environment from the data collection and integration module as the basis for the external environment of resource optimization, dynamically updates the resource supply and external available resource information. At the same time, the system calls the life cycle development path generated by the life cycle prediction module as the basis for resource allocation optimization to ensure that the resource allocation conforms to the current development state and future trend of the innovation achievement.

[0150] In some embodiments, the output result of the resource optimization and allocation module finally forms a clear resource allocation list, including information such as resource category, allocation quantity, allocation time sequence, and allocation target, which is used to guide the actual resource scheduling and project promotion process.

[0151] Fourth, game theory and resource optimization module:

[0152] It is used to optimize resource allocation based on the resource requirements of innovation achievements, combined with competition and cooperation relationships. Specifically, after the system completes the data standardization processing, life cycle dynamic prediction, resource requirement assessment, and basic resource allocation of innovation achievements, in order to further solve the problems of multi-agent interest coordination and resource dynamic adjustment in actual operations, the system sets up a game analysis and resource allocation optimization module. This module specifically conducts dynamic game analysis on the complex relationships among multiple resource participants involved in innovation achievements, clarifies the resource investment, revenue expectations, and constraints of all parties, and on this basis, realizes the dynamic optimization and adjustment of the resource allocation plan.

[0153] In some embodiments, the life cycle development process of innovation achievements usually involves multiple entities such as government authorities, enterprises, scientific research institutions, and upstream and downstream enterprises in the industrial chain. There are game relationships among these entities in terms of resource investment, revenue distribution, and risk bearing for innovation achievements. Therefore, it is necessary to reasonably evaluate the behavioral strategies through a specially designed game analysis method, and then combine the total amount of resources and distribution rules to conduct the final resource optimization allocation. The setting of this module ensures that the system transitions from a single resource allocation mechanism to a dynamic equilibrium allocation mechanism facing multiple entities, guaranteeing the system coordination in the process of promoting innovation achievements and the scientific nature of resource allocation.

[0154] The game theory and resource optimization module includes a game analysis unit and a resource allocation optimization unit. Regarding the game analysis unit, mainly for multiple interest entities involved in innovation achievements, a game model between resource investment and revenue distribution is established, clarifying the roles, resource investment expectations, and possible revenues of different entities in the development process of innovation achievements, and dynamically modeling and analyzing their interactive behaviors.

[0155] In some embodiments, the system first identifies all parties with close relationships to innovation achievements and takes them as game entities, respectively assigning entity numbers, denoted as the first entity, the second entity to the kth entity, where k is the total number of participating entities. The resource investment of each entity is defined by the system as a specific variable, representing the total amount of resources provided by the entity for promoting innovation achievements, including but not limited to funds, human resources, technology, market channels, policy support, etc. Specifically, the system sets resource investment constraints for each entity, representing the maximum amount of resources that each entity can invest and the minimum resource guarantee requirements to ensure the basic conditions required for the development of innovation achievements. In the game analysis unit, the system systematically describes the entity's resource investment and the possible final returns by establishing a revenue function, clarifying the revenue expectations of the entity at different resource investment levels. The calculation of the entity's revenue will comprehensively consider multiple aspects such as direct economic revenue (such as technology transformation income), policy revenue (such as financial subsidies, tax incentives), market revenue (such as market share), and social benefits (such as brand influence).

[0156] In some embodiments, based on the Nash equilibrium theory in game theory, the system analyzes the optimal resource investment strategies of different entities when facing the established resource investments of other entities, so as to obtain the resource investment balance solutions for all entities. The system dynamically simulates the resource game behaviors of each entity under the expected benefits and investment constraints at different stages of the innovation result life cycle by setting a multi-entity behavior response matrix.

[0157] In some embodiments, the system simultaneously models the cooperation and competition relationships between entities, considers the joint investment, sharing mechanism, and revenue sharing mechanism in the process of resource investment, and further analyzes the behavior strategies of each entity under specific cooperation-competition relationships, and finally obtains a resource investment plan that conforms to the multi-party interest balance.

[0158] In some embodiments, to ensure the dynamic adaptability of the model, the game analysis unit can identify the current life cycle stage of the innovation result by combining the dynamic data output by the life cycle prediction module, and dynamically adjust the behavior strategies between entities. For example, in the growth stage of the life cycle, the system will appropriately increase the weight of market entities, while in the technology research stage, it focuses on the analysis of the resource investment strategies of scientific research institutions and technology units.

[0159] Regarding the resource allocation optimization unit, based on the multi-entity resource investment strategy balance obtained by the game analysis unit, it combines the overall resource volume of the system and the characteristics of the life cycle stage to perform the final resource allocation optimization to ensure the fairness, rationality, and dynamic adaptability of resource allocation.

[0160] In some embodiments, the system first determines the resource investment intention of each entity based on the balance solution obtained by the game analysis unit as the preliminary allocation basis, and at the same time collects the total disposable resources at the system level, including the existing internal resources and the external resources that can be obtained, and adjusts the allocation quota of each entity according to the actual resource supply. Specifically, in the process of allocation optimization, the system sets the following constraints: 1. Total resource constraint, that is, the sum of all resources available for allocation in the system does not exceed the preset total resource supply; 2. Minimum resource guarantee constraint, that is, for the resource categories necessary to ensure the progress of the innovation result, ensure that it is not lower than the minimum demand standard proposed by the life cycle prediction module; 3. Interest balance constraint, that is, ensure that all participating entities reasonably obtain the corresponding resource shares according to their resource investment ratios or expected revenues, and avoid the withdrawal of cooperation caused by the imbalance of allocation for a certain party; 4. Time series constraint, that is, dynamically adjust the resource allocation ratio at different life cycle stages of the innovation result to ensure the priority guarantee of the resources required in different stages.

[0161] In some embodiments, the resource allocation optimization unit incorporates all constraints into a unified resource optimization model for solution, and assigns different weight coefficients to different entities. The weight coefficients are determined comprehensively based on factors such as the key role of the entity in promoting innovation achievements, resource contribution, and policy preference, so as to guide the final resource allocation ratio.

[0162] In some embodiments, the system performs dynamic programming on the resource allocation situations at different time points to ensure that, under the condition of limited total resources, the resource investment at the key nodes of the life cycle is preferentially guaranteed. For example, at the initial stage of market promotion, the system preferentially allocates market resources, and at the stage of technical research and development, the system preferentially guarantees R & D resources.

[0163] In some embodiments, the system can perform dynamic feedback correction according to the actual resource usage situation. Combining the actual resource input and the development of achievements obtained from the data collection and integration module, it automatically adjusts the resource allocation for the next stage to form a dynamic closed-loop optimization mechanism for resource allocation.

[0164] In this embodiment, the game analysis and resource allocation optimization module forms a tight coupling in terms of data and decision-making with the foregoing modules to ensure the integrated operation of the entire system logic.

[0165] In some embodiments, the system first obtains the basic data related to innovation achievements through the data collection and integration module, including information such as the industrial chain, market, policy, funds, and human resources. Subsequently, the life cycle prediction module predicts the development path of the innovation achievements and outputs the development requirements at each stage, providing a dynamic benchmark for resource requirements and allocation. Based on the life cycle prediction results, the resource requirement assessment and optimization module forms a preliminary resource requirement plan. However, due to the interest distribution among multiple entities, the final resource allocation needs to be subjected to game analysis and balance optimization through this module, and finally outputs a resource allocation plan that is evenly recognized by multiple entities.

[0166] In some embodiments, after forming the final resource allocation plan, the system outputs the plan to the resource execution management module as the specific resource allocation basis during the process of promoting innovation achievements, and continuously updates and adjusts it dynamically during the operation of the system.

[0167] Fifth, the multi-stage control and scheduling module:

[0168] It is used to dynamically adjust the resource allocation strategies at each stage based on the optimization results of the game theory and resource optimization module, and evaluate the scheduling benefits. Specifically, on the basis of completing the foregoing data collection and integration module, life cycle prediction module, resource optimization configuration module, and game analysis and resource allocation optimization module, in order to realize the dynamic scheduling of resources at each stage of the life cycle of innovation achievements and evaluate the resource investment effect in real time, the system further sets up a phased resource scheduling and benefit evaluation module.

[0169] In some embodiments, the life cycle of an innovation result spans multiple stages, and the types of resource requirements, input intensities, and actual effectiveness in each stage all undergo dynamic changes. Without a dynamic and precise scheduling mechanism and benefit evaluation means, it is easy to cause a disconnect between resource allocation and use, affecting the smooth progress of the innovation result. Therefore, as the end - execution and feedback link of system resource management, this module ensures that resources can be released as planned and by stage, and adjusts in a timely manner based on the actual usage effect, providing a basis for feedback and adjustment to the previous modules (such as the resource allocation optimization module), and ensuring the closed - loop and dynamic optimization of the system resource operation.

[0170] The multi - stage control and scheduling module may include a phased resource scheduling unit and a scheduling benefit evaluation unit. Regarding the phased resource scheduling unit, the following is an explanation: Based on the allocation results output by the resource allocation optimization module, it formulates and executes a phased resource scheduling plan according to the life cycle stage of the innovation result, the priority of resource use, and the resource supply situation, ensuring that resources are supplied to the executing unit on time and as needed.

[0171] In some embodiments, the system first determines the types, quantities, input times, and priorities of resources required in this stage based on the current development state of the innovation result output by the life cycle prediction module. The system divides different types of resources (including funds, personnel, equipment, policy support, etc.) into scheduling batches with clear time nodes, forming a phased resource scheduling list.

[0172] In some embodiments, the system details the resource scheduling in each stage according to the following elements: resource category, such as R & D funds, marketing expenses, technical personnel, experimental equipment; scheduling time, specifying the start and end times or cycles of the specific scheduling; scheduling quantity, the specific input quantity or amount of resources; responsible entity, the responsible unit for receiving and using resources; scheduling priority, determining the urgency and priority of resources according to the key nodes of the life cycle.

[0173] In some embodiments, to ensure the rationality of scheduling, the system audits the resource - using ability and previous usage situation of the receiving unit. If a certain unit has irregular or inefficient previous resource usage, the system will automatically adjust its resource scheduling quota or suspend the allocation to other responsible units to ensure the effective flow of resources.

[0174] In some embodiments, the system sets up a dynamic monitoring mechanism to track the whole process of all scheduled resources, including information such as scheduling issuance, receipt confirmation, actual release, and usage situation, automatically generating a resource scheduling log to ensure the transparency and traceability of the scheduling process and forming a complete resource scheduling file.

[0175] In some embodiments, for some resources with obvious dynamic changes (such as marketing funds and equipment procurement), the system supports a real-time scheduling adjustment mechanism, which dynamically adjusts the scheduling quantity and time according to real-time monitoring data to ensure synchronization with actual needs.

[0176] Regarding the scheduling benefit evaluation unit, the description is as follows: It is used to quantitatively evaluate the actual usage effect of resources after the execution of stage-based resource scheduling, covering multiple dimensions such as the promotion effect of innovation achievements, resource usage efficiency, and the completion degree of stage goals.

[0177] In some embodiments, the scheduling benefit evaluation unit conducts a comprehensive effect evaluation on the resources scheduled in the previous stage according to the evaluation cycles set by the system (such as quarterly, semi-annually, or according to project nodes). The evaluation includes the following aspects: 1. The actual usage of resources, such as whether the resources are invested on time, whether the investment amount is sufficient, and whether the resource usage process conforms to the intended purpose; the promotion effect of innovation achievements, such as whether stage-based technical problems are solved, whether market validation is up to standard, and whether key achievements are delivered; 2. Resource usage efficiency, such as the input-output ratio and the technical or market improvement effect brought by unit resource input; 3. The completion degree of stage goals, such as the actual achievement of system preset goals (such as R & D completion degree, market development ratio, etc.).

[0178] In some embodiments, the system sets clear quantitative criteria for each of the above evaluation dimensions. For example, for the usage efficiency of capital investment, the system uses the actually completed technology development, patent applications, and market development achievements as output items, and compares them with the total capital investment in the same period to form benefit indicators such as the capital investment-output ratio; for human resources, the system evaluates efficiency based on man-day consumption and achievement contribution.

[0179] In some embodiments, the system quantitatively evaluates the resource input benefit through the following formula: E = O / I, where: E represents the resource input benefit; O represents the actual output brought by the resource input (such as technical achievements, market sales, patent authorizations, etc.); I represents the total resource input (such as total capital, total manpower, etc.).

[0180] In some embodiments, the system compares the actual evaluation results with the expected benefits in the initial resource allocation optimization plan to identify actual deviations and analyze the reasons for the deviations, such as deviations caused by changes in the external market environment, policy adjustments, or improper internal resource use and ineffective implementation.

[0181] In some embodiments, to achieve dynamic feedback, the system automatically pushes the evaluation results to the resource optimization configuration module and the game analysis module, which serves as an important basis for resource allocation and optimization adjustment in the next cycle. If it is found that the resource benefits do not meet expectations, the system supports automatic generation of resource adjustment suggestions, including: increasing or decreasing the input of specific categories of resources; adjusting the resource delivery time or cycle; replacing the resource responsible entity; enhancing the cooperation of certain resources (such as increasing technical support, etc.).

[0182] In some embodiments, the system forms a comprehensive report on all stage evaluation results, including detailed resource usage, benefit evaluation conclusions, existing problems and cause analysis, subsequent adjustment suggestions, etc., for reference by system managers or decision-makers.

[0183] In this embodiment, the stage resource scheduling and benefit evaluation module is closely linked with modules such as the data collection and integration module, the life cycle prediction module, the resource optimization configuration module, and the game analysis and resource allocation optimization module to ensure the whole process from data collection, prediction, optimization to scheduling and evaluation is connected.

[0184] In some embodiments, the system obtains external data such as market, technology, and policies in real time through the data collection and integration module, periodically triggers life cycle prediction updates, which in turn affects the resource optimization and game analysis results. The stage resource scheduling and benefit evaluation module performs scheduling, recording, and feedback of evaluation results after the actual resource delivery, providing a quantitative basis for subsequent resource dynamic adjustment and forming a dynamic closed-loop of the system.

[0185] Sixth, the visualization and reporting module:

[0186] It is used to display the technological development, resource configuration status, and related analysis results of innovation achievements to support the decision-making process. Specifically, to achieve the transparent display of multi-dimensional data, resource configuration, and dynamic execution results involved in innovation achievements throughout the life cycle by the system, and at the same time form a standardized, standard, and traceable management report, the system sets up a data display and report generation module. Based on the output data of the aforementioned data collection and integration module, life cycle prediction module, resource optimization configuration module, game analysis and resource allocation optimization module, and stage resource scheduling and benefit evaluation module, this module undertakes the functions of data display and result summary in the system. Through this module, the system can centrally process the data and analysis results scattered in different units, so that managers can comprehensively grasp the progress of innovation achievements and timely adjust resource allocation and strategic deployment.

[0187] The visualization and reporting module includes a data display unit and a report generation unit. The data display unit is explained as follows: It is used to dynamically and visually present key data involved in the entire life cycle of innovation results, ensuring that managers can intuitively grasp the key information such as the status of each stage of innovation results, resource input, resource utilization effect and future development trends.

[0188] In some embodiments, the system first extracts the life cycle status variables of the innovative results from the life cycle prediction module, including but not limited to technology maturity, market acceptance, industrial chain scalability and other aspects, and dynamically generates a life cycle progress chart according to the time series.

[0189] In some embodiments, the system displays the status changes of innovation results at different life cycle stages in the form of line graphs, bar graphs, etc., including: the current technical level; the degree of marketization process; the gap compared with industry standards or similar products. Specifically, the system compares the resource allocation data generated by the resource optimization configuration module with the actual phased resource scheduling data, and dynamically forms a resource allocation and execution progress chart, including but not limited to: resource category distribution chart (such as the proportion of different types of resources such as funds, manpower, equipment, etc.); resource allocation comparison chart between the allocation subject and the execution subject; phased resource scheduling plan and actual execution deviation chart.

[0190] In some embodiments, the system also forms a multi-agent participation relationship diagram based on the results of the game analysis module, dynamically displaying the distribution of resource input and profit distribution of each participating entity, including the resource game balance state and change trend between the entities.

[0191] In some embodiments, the system can dynamically display the key benefit indicators output by the phased resource scheduling and benefit evaluation module, including input-output ratio, resource utilization efficiency, phase target achievement rate, etc., to assist users in quickly evaluating the current innovation achievement promotion effect.

[0192] As an option, the system supports users to customize viewing dimensions, such as: detailed progress and resource status of a single innovative achievement; allocation and execution of a certain entity or a certain type of resources; horizontal comparison of multiple innovative achievements in the same batch or the same industrial chain.

[0193] In some embodiments, the system provides functions such as filtering, sorting, and associated views. Users can filter data based on conditions such as resource category, innovation achievement stage, and execution effect, and display related analysis results in a linked manner.

[0194] Regarding the report generation unit, the following is described: Based on the core data summarized by the data display unit, it automatically generates a structured and standardized comprehensive report, covering the life cycle status of innovation achievements, resource input-output, income distribution, and implementation effects, forming an official document to support strategic decision-making and process supervision.

[0195] In some embodiments, the report generation unit automatically summarizes data from the following aspects according to a preset template: 1. Life cycle stage report, including the current stage of technological development status, market promotion situation, patent and intellectual property layout, expected development goals for the next stage, etc.; 2. Resource allocation and utilization report, which details the resource allocation plan, actual scheduling situation, execution deviation and reasons for each stage; 3. Multi-agent game analysis report, including the rights and obligations, participation relationships, and dynamic adjustment records of all participating agents in resource input and income distribution; 4. Benefit evaluation report, covering the comparison of actual resource input and output, completion of phased goals, resource utilization efficiency, income and risk analysis, etc.; 5. Problems and suggestions, aiming at problems such as lagging resource execution and bottlenecks in achievement promotion, the system automatically generates targeted adjustment suggestions.

[0196] In some embodiments, the system details the relationship between resource input and actual income through quantitative data tables and trend charts. For example, for financial resources, the system will compare the planned capital investment amount, actual received amount, and actual used amount, and clarify the excess or insufficient part, and mark the reasons (such as incomplete approval, market factor changes, etc.).

[0197] In some embodiments, the report generation unit supports templated reports, including fixed formats and dynamically filled parts. The fixed formats include titles, chapter sequences, basic descriptions, etc., and the dynamically filled parts include real-time data and charts automatically extracted from the system.

[0198] In some embodiments, to meet the needs of different reporting objects, the system supports generating different types of report versions, including but not limited to: project execution report, for project execution units, focusing on resource availability and execution issues; management decision-making report, for superior management departments, focusing on overall progress, resource benefits, strategic adjustment suggestions; policy compliance report, for regulatory agencies, focusing on policy implementation, input-output compliance, etc.

[0199] In some embodiments, the report generation unit supports generating multiple file formats, such as PDF, Word, HTML, etc., and supports compliance functions such as electronic signatures and data traces, which are convenient for submission, archiving, and public release.

[0200] In some embodiments, the system also supports automatically generating a report summary, extracting key information, including the current status of the project, main achievements, existing problems, urgent decision-making suggestions, etc., for senior managers to quickly browse and make decisions.

[0201] In some embodiments, the data display and report generation module maintains real-time dynamic linkage with other modules of the system to ensure the timeliness and accuracy of the displayed data and report content. The data display unit extracts basic data in real time from the data collection and integration module, dynamically obtains innovation achievement stage data from the life cycle prediction module, obtains resource allocation results from the resource optimization configuration module, obtains multi-agent strategies from the game analysis module, and obtains execution and evaluation results from the resource scheduling and benefit evaluation module, and dynamically updates the display content. At the same time, the report generation unit automatically generates global or special reports according to the time periods set by the system (such as monthly, quarterly) or trigger conditions (such as completion of phased goals, adjustment of resource allocation), and provides hierarchical reports for different roles such as management, project responsible units, and regulatory agencies to ensure the closed-loop system management.

[0202] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, 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 moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0203] Based on the same inventive concept, the embodiments of the present application also provide an innovation achievement information processing system for implementing the innovation achievement information processing method involved above. The implementation solutions provided by this system to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the innovation achievement information processing system provided below can refer to the limitations on the innovation achievement information processing method in the above text, and will not be repeated here.

[0204] In an exemplary embodiment, as Figure 7 shown, an innovation achievement information processing system 700 is provided, including:

[0205] An acquisition module 701, configured to acquire statistical information of innovation achievements; the statistical information includes technical maturity index information of the innovation achievements;

[0206] The first determination module 702 is configured to construct a non-linear model of the change of the technology maturity index information over time according to the statistical information; and determine the life cycle prediction information of the innovation achievement according to the non-linear model; determine the resource requirement information of the innovation achievement in different life cycles according to the life cycle prediction information; the resource requirement information includes capital requirement information and manpower requirement information; determine the first resource allocation plan according to the resource requirement information;

[0207] The second determination module 703 is configured to obtain the innovation entity information corresponding to each innovation achievement; construct a game model based on the innovation entity information to determine the second resource allocation plan;

[0208] The third determination module 704 is configured to determine the target resource allocation plan according to the first resource allocation plan and the second resource allocation plan.

[0209] In one embodiment, the first determination module 702 is further configured to the statistical information further includes the allocated resource information corresponding to the innovation achievement; construct a non-linear model of the change of the technology maturity index information over time according to the statistical information, including:

[0210]

[0211] wherein, x(t) represents the technology maturity corresponding to the technology maturity index information at time t, and the range is [0, 1];

[0212] u(t) represents the allocated resource information at time t;

[0213] α is the acceleration coefficient of the allocated resource information on the technology progress;

[0214] β is the inhibition coefficient of the technology maturity on the progress;

[0215] η(t) is an external disturbance term, representing uncertain factors such as market changes and technology bottlenecks.

[0216] In one embodiment, the second determination module 703 is further configured to obtain the innovation entity information corresponding to each innovation achievement; construct a game model based on the innovation entity information to determine the second resource allocation plan, including: obtaining the innovation entity corresponding to each innovation achievement and the resource supply information; wherein, the resource supply information includes the supply capacity information of the innovation entity for the resource requirement information; taking the innovation entity as the game subject, and constructing a game model based on the resource supply information to determine the second resource allocation plan.

[0217] In one embodiment, the obtaining module 701 is further configured to obtain the statistical information of the innovation achievements, including: obtaining the statistical information of the innovation achievements; preprocessing the statistical information; the preprocessing includes at least one of denoising, formatting, and data cleaning; storing the preprocessed statistical information.

[0218] In one embodiment, the third determination module 704 is further configured to display the life cycle prediction information, the resource requirement information, the innovation entity 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.

[0219] In one embodiment, the third determination module 704 is further configured to obtain the benefit index information of the innovation achievements corresponding to the allocated resources in the target resource allocation plan based on a preset period; and update the target resource allocation plan according to the benefit index information.

[0220] Each module in the above innovation achievement information processing system can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0221] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 8 shown. 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 the data required for the innovation achievement information processing method, such as the statistical information of the innovation achievements. 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. The computer program, when executed by the processor, implements an innovation achievement information processing method.

[0222] Those skilled in the art can understand, Figure 8The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0223] 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.

[0224] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0225] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0226] 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 the present 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 RandomAccess Memory (ReRAM), MagnetoresistiveRandomAccess Memory (MRAM), Ferroelectric RandomAccess 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 RandomAccess Memory (SRAM) or Dynamic RandomAccess Memory (DRAM), etc. The databases involved in the embodiments provided in the present 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 the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, Artificial Intelligence (AI) processors, etc., without limitation.

[0227] 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 the present application.

[0228] 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 information of innovative achievements, characterized in that: The method comprises: Obtaining statistical information of the innovation achievements; the statistical information includes technical maturity index information of the innovation achievements; Based on the statistical information, a nonlinear model of the technology maturity index information changing over time is constructed; and based on the nonlinear model, life cycle prediction information of the innovation achievement is determined; Determine resource demand information of the innovation achievement in different life cycles according to the life cycle prediction information; the resource demand information includes capital demand information and manpower demand information; Determining a first resource allocation scheme according to the resource demand information; Acquire the innovation subject information corresponding to each of the innovation achievements; construct a game model based on the innovation subject information to determine the second resource allocation plan; A target resource allocation scheme is determined according to the first resource allocation scheme and the second resource allocation scheme.

2. The method according to claim 1, characterized in that: The statistical information also includes information about allocated resources corresponding to the innovation achievements; and constructing a nonlinear model of the technology maturity index information changing over time based on the statistical information includes: Wherein, x(t) represents the technology maturity corresponding to the technology maturity index information at time t, and the range is [0, 1]; u(t) represents the allocated resource information at time t; α is the acceleration coefficient of the allocated resource information on technological progress; β is the inhibition coefficient of the maturity of the technology on progress; η(t) is an external disturbance term, which represents the uncertainty factors of market changes and technical bottlenecks.

3. The method according to claim 1, characterized in that The obtaining of the innovation subject information corresponding to each of the innovation achievements; Based on the innovation subject information, a game model is constructed to determine a second resource allocation scheme, including: Acquire the innovation subject and resource supply information corresponding to each of the innovation achievements; wherein the resource supply information includes the supply capacity information of the innovation subject for the resource demand information; The innovation subject is used as a game subject, and a game model is constructed based on the resource supply information to determine a second resource allocation plan.

4. The method according to claim 1, characterized in that: The statistical information on innovation achievements includes: Obtain statistical information on innovation results; Preprocessing the statistical information; the preprocessing includes at least one of denoising, formatting and data cleaning; The statistical information after the preprocessing is stored.

5. The method according to claim 1, characterized in that The method further comprises: Based on a preset display device, display the life cycle prediction information, the resource demand information, the innovation subject information and the target resource allocation plan; 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 any one of claims 1 to 5, characterized in that The method further comprises: Based on a preset period, obtaining the income indicator information of the allocated resources in the target resource allocation plan corresponding to the innovation achievement; and The target resource allocation plan is updated according to the revenue indicator information.

7. An innovative achievement information processing system, characterized in that: The system comprises: An acquisition module, used to acquire statistical information of the innovation achievements; the statistical information includes technical maturity index information of the innovation achievements; A first determination module is used to construct a nonlinear model of the technology maturity index information changing over time based on the statistical information; and determine the life cycle prediction information of the innovation achievement based on the nonlinear model; determine the resource demand information of the innovation achievement in different life cycles based on the life cycle prediction information; the resource demand information includes capital demand information and manpower demand information; and determine a first resource allocation plan based on the resource demand information; A second determination module is used to obtain the innovation subject information corresponding to each of the innovation achievements; based on the innovation subject information, a game model is constructed to determine a second resource allocation plan; The third determining module is used to determine a target resource allocation scheme according to the first resource allocation scheme and the second resource allocation scheme.

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.

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