Dynamic matching system and method for entrepreneurship resources

Abstract

The invention relates to the technical field of entrepreneurship resource management, and discloses an entrepreneurship resource dynamic matching system and method. The system comprises a multi-source data integration module which collects and fuses project requirements, market environment and industry policy data; the demand feature modeling module is used for extracting multidimensional features of entrepreneurship demands; the resource portrait construction module is used for constructing a resource dynamic portrait; the dynamic matching optimization module is used for generating a resource matching priority matrix; and the collaborative network updating module outputs a dynamic collaborative resource matching network. The method sequentially executes corresponding operations based on each module of the system. The method solves the problems of difficulty in data integration, simple and static matching mode, lack of dynamic update cooperation and the like in startup resource matching, can accurately analyze demand and resource characteristics, integrates policies and risks to perform matching optimization, realizes resource dynamic adaptation and collaborative development, effectively improves startup resource matching efficiency and precision, and has good application prospects. And development of entrepreneurship projects is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of entrepreneurial resource management, and in particular to a dynamic matching system and method for entrepreneurial resources. Background Art

[0002] In today's entrepreneurial environment, the success of entrepreneurial projects often depends on the efficient acquisition and rational allocation of various resources. However, existing entrepreneurial resource matching methods have many flaws and are unable to meet the actual needs of entrepreneurs.

[0003] On the one hand, data acquisition and integration are fraught with difficulties. Entrepreneurial project needs are diverse, encompassing funding, technology, talent, and venues. This demand data is scattered across various platforms and channels, with varying formats and standards, making it difficult to effectively integrate. Market environment data is equally complex, encompassing resource availability, price fluctuations, and industry competition, making comprehensive and accurate data acquisition challenging. The acquisition of industry policy data also suffers from lags and inconsistent interpretation, making it difficult to provide timely and effective guidance for matching entrepreneurial resources. This difficulty in data acquisition and integration leaves entrepreneurial resource matching lacking a comprehensive and accurate information foundation, severely impacting both its accuracy and efficiency.

[0004] On the other hand, existing matching methods are overly simplistic and static. Traditional entrepreneurial resource matching is mostly based on manual experience or simple keyword matching, failing to deeply analyze the inherent characteristics of entrepreneurial needs and resources. For example, when considering entrepreneurial projects' demand for technical talent, they focus solely on matching professional skills with keywords, ignoring key factors such as talent's accumulated experience and teamwork capabilities, and failing to consider the impact of technological development trends on talent demand. Furthermore, these methods are unable to track changes in the market environment and industry policies in real time, making it impossible to adjust matching strategies in a timely manner. In a rapidly changing market environment and frequently adjusted industry policies, this static matching approach leads to long-term mismatches between entrepreneurial resources and project needs, resulting in severe waste of resources, hindering the advancement of entrepreneurial projects, and missed development opportunities.

[0005] Furthermore, existing matching systems lack dynamic updating and collaborative optimization mechanisms. The needs of entrepreneurial projects change at different stages of development, and the market environment and industry policies are constantly evolving. However, existing systems struggle to timely update matching results based on these dynamic changes. Furthermore, there is a lack of effective coordination mechanisms between various entrepreneurial resources and between them and entrepreneurial projects, preventing the formation of an organic ecosystem. This hinders the full utilization of entrepreneurial resources, making it difficult for entrepreneurial projects to receive comprehensive and multi-level support, and thus restricting their growth and development. Existing methods for matching entrepreneurial resources are no longer able to adapt to the evolving needs of the entrepreneurial market. Therefore, an innovative dynamic matching system and method are urgently needed to address these issues. Summary of the Invention

[0006] The purpose of the present invention is to provide a system and method for dynamically matching entrepreneurial resources to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: a dynamic matching system for entrepreneurial resources, the system comprising:

[0008] Multi-source data integration module: used to collect and integrate multi-dimensional entrepreneurial resource data sets; the multi-dimensional data includes project demand data, market environment data and industry policy data;

[0009] Demand feature modeling module: Based on the project demand data, the demand feature decomposition algorithm is used to extract the multi-dimensional features of entrepreneurial demand, including demand urgency, resource adaptability, and industry trend correlation;

[0010] Resource profile building module: used to build a dynamic resource profile based on the market environment data through a resource profile generation algorithm, wherein the dynamic profile includes resource availability indicators, cost fluctuation patterns, and supply and demand response coefficients;

[0011] Dynamic matching optimization module: performs multi-dimensional constraint analysis on the industry policy data to generate a resource matching priority matrix; the multi-dimensional constraint analysis includes policy compliance verification and market risk quantification;

[0012] Collaborative network update module: Through the collaborative network incremental algorithm, the multi-dimensional characteristics of entrepreneurial needs, resource dynamic profiles and resource matching priority matrix are integrated to output a dynamic collaborative resource matching network.

[0013] Preferably, the demand feature decomposition algorithm includes:

[0014] Perform semantic analysis on the project demand data and remove redundant noise fields;

[0015] Divide demand type subsets based on demand clustering algorithm and calculate the correlation density between each subset;

[0016] Generate demand priority weights based on correlation closeness and preset industry trend thresholds;

[0017] Combine the demand priority weight with resource adaptability to form a multi-dimensional demand feature vector.

[0018] Preferably, the resource portrait generation algorithm includes:

[0019] Classifying the market environment data by resource attributes and extracting the time series change trend of each attribute;

[0020] Use trend fitting algorithms to analyze the cyclical characteristics of cost fluctuation patterns, generate cost dynamic coefficients, and combine supply and demand response coefficients with cost dynamic coefficients for multi-dimensional fusion to form a dynamic resource portrait;

[0021] The portrait parameters are normalized through the standardization layer to output a resource portrait with unified dimensions.

[0022] Preferably, the multidimensional constraint analysis process includes:

[0023] Based on the compliance clauses of industry policy data, a rule engine is used to divide policy constraint levels;

[0024] Based on the market risk quantification model, the policy constraint level is associated with the risk indicator to generate a risk weight matrix;

[0025] The policy constraint level and risk weight matrix are integrated through the priority calculation formula to generate a resource matching priority matrix.

[0026] Preferably, the collaborative network incremental algorithm includes:

[0027] Perform sliding window difference calculation on the multi-dimensional characteristics of the entrepreneurial demand to extract the demand change gradient;

[0028] Dynamically adjust the weight of the resource dynamic portrait according to its timeliness coefficient;

[0029] The demand change gradient is superimposed and integrated with the adjusted portrait weight to generate incremental matching features;

[0030] The incremental matching features are mapped to the historical collaborative network through the network update strategy, and a dynamic collaborative resource matching network is output.

[0031] Preferably, the parameter optimization method of the demand clustering algorithm includes:

[0032] Calculate the initial clustering granularity and minimum association density based on the historical demand data distribution;

[0033] Traverse different parameter combinations through the compactness evaluation index and select the clustering result with the highest compactness;

[0034] Dynamically adjust the demand subset division granularity and minimum association density based on the clustering results to optimize the calculation accuracy of demand priority weights.

[0035] Preferably, the implementation steps of the trend fitting algorithm include:

[0036] Perform outlier correction on resource attribute time series to generate a smooth trend curve;

[0037] The piecewise linear fitting algorithm is used to extract the fluctuation cycle parameters of different time intervals. The length and amplitude of the main cost fluctuation cycle are calculated based on the cycle parameters to generate the cost dynamic coefficient.

[0038] The time-varying trend of the main cycle length is statistically analyzed through a sliding window to enhance the dynamic representation capability of resource profiling.

[0039] Preferably, the method for constructing the priority calculation formula includes:

[0040] Define the nonlinear mapping relationship between policy constraint levels and market risk weights;

[0041] Based on the standardized priority benchmark value of the industry policy impact range, the mapping result is mapped to the preset priority interval through the constraint function to generate the standardized priority value;

[0042] The standardized priority values are associated with resource matching rules to form a priority matrix with multi-dimensional constraints.

[0043] Preferably, the network update strategy includes: generating a version identifier based on the time tag of the incremental matching feature, identifying the difference nodes between the current incremental feature and the historical network through a difference matching algorithm, and dynamically updating the weights of the difference nodes according to the timeliness coefficient.

[0044] Preferably, the present invention further includes a method for dynamic matching of entrepreneurial resources, which is applied to the above-mentioned dynamic matching system for entrepreneurial resources, and the method includes the following steps:

[0045] Step S1: Collect and integrate project demand data, market environment data, and industry policy data through a multi-source data integration module to form a multi-dimensional entrepreneurial resource data set;

[0046] Step S2: Based on the project demand data, using the demand feature decomposition algorithm, extract the multi-dimensional characteristics of entrepreneurial demand including demand urgency, resource adaptability and industry trend correlation in the demand feature modeling module;

[0047] Step S3: Based on the market environment data, a resource profile generation algorithm is used to construct a resource dynamic profile in a resource profile construction module, which includes resource availability indicators, cost fluctuation patterns, and supply and demand response coefficients;

[0048] Step S4: performing multi-dimensional constraint analysis on the industry policy data, including policy compliance verification and market risk quantification, and generating a resource matching priority matrix in the dynamic matching optimization module;

[0049] Step S5: The collaborative network incremental algorithm is used to integrate the multi-dimensional characteristics of entrepreneurial needs, the dynamic resource profile, and the resource matching priority matrix in the collaborative network update module to output a dynamic collaborative resource matching network.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] In terms of data processing and integration, the multi-source data integration module collects and integrates project demand data, market environment data, and industry policy data to form a comprehensive, multi-dimensional data set of entrepreneurial resources. This approach solves the problem of fragmented and inconsistent entrepreneurial data formats, laying a solid data foundation for subsequent precise matching. Based on this integrated data, entrepreneurs can gain a more comprehensive understanding of the market and their own needs, avoiding resource mismatches caused by information gaps.

[0052] The Demand Feature Modeling module and the Resource Profile Construction module, respectively, deeply explore the inherent characteristics of entrepreneurial needs and resources. The Demand Feature Decomposition algorithm extracts multidimensional characteristics of entrepreneurial needs, encompassing demand urgency, resource compatibility, and correlation with industry trends, enabling entrepreneurs to clearly grasp the priorities and key points of their needs. The Resource Profile Generation algorithm constructs a dynamic resource profile, encompassing resource availability indicators, cost fluctuation patterns, and supply-demand response coefficients, enabling entrepreneurs to intuitively understand the dynamic characteristics of various resources. These two modules work together to significantly improve the accuracy of matching resources with needs, reducing resource waste and ineffective investment.

[0053] The dynamic matching optimization module generates a resource matching priority matrix through multi-dimensional constraint analysis and processing of industry policy data. Policy compliance verification ensures that entrepreneurial activities are conducted within a legal and compliant framework, avoiding potential policy risks. Market risk quantification allows entrepreneurs to understand potential risks in resource matching in advance, enabling them to make more informed decisions. This matching optimization mechanism, based on policy and risk considerations, ensures the stable development of entrepreneurial projects and increases their success rate.

[0054] The collaborative network update module's collaborative network increment algorithm integrates the multi-dimensional characteristics of entrepreneurial demand, dynamic resource profiles, and a resource matching priority matrix to output a dynamic collaborative resource matching network. This network can be updated in real time based on market changes, maintaining a dynamic fit between resources and demand. Furthermore, this collaborative network fosters collaboration among entrepreneurial resources, fostering a positive entrepreneurial ecosystem. For example, financial, technical, and human resources can be efficiently connected and developed collaboratively within this network, providing comprehensive and continuous support for entrepreneurial projects and promoting their rapid growth.

[0055] In general, the dynamic entrepreneurial resource matching system and method of the present invention provides entrepreneurs with an efficient, accurate and sustainable entrepreneurial resource matching solution through innovations in multiple aspects such as optimizing data processing, improving matching accuracy, controlling risks and promoting collaborative development, which is of great significance to promoting the prosperity and development of the entrepreneurial market. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a working principle diagram of the entrepreneurial resource dynamic matching system of the present invention;

[0057] Figure 2 Algorithm graph generated for resource profiling;

[0058] Figure 3 Algorithm diagram for collaborative network increment;

[0059] Figure 4 Diagram of the method built for the priority calculation formula. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0061] See also Figures 1-4 The present invention provides a dynamic matching system for entrepreneurial resources, aiming to achieve efficient and accurate matching of entrepreneurial resources to meet the needs of entrepreneurial projects in a complex and changing environment. The specific implementation process is as follows:

[0062] Multi-source data integration module: This module is responsible for collecting and integrating multi-dimensional entrepreneurial resource data sets, covering project demand data, market environment data, and industry policy data. Project demand data comes from demand documents submitted by entrepreneurs, online questionnaires, etc., including detailed information such as the type and quantity of required resources, expected input and output; market environment data is obtained through market research institutions, industry databases, web crawlers and other channels, and includes market supply conditions, price fluctuation data, industry competition situation, etc. for various resources; industry policy data is collected from government official websites and policy and regulations databases, covering industrial support policies, regulatory provisions, and other content. Through pre-processing operations such as data cleaning and format conversion, these multi-source data are integrated into a unified data format to provide comprehensive and accurate data support for subsequent modules.

[0063] Demand Feature Modeling Module: Based on the integrated project demand data, a demand feature decomposition algorithm is used to extract the multidimensional characteristics of entrepreneurial demand. This algorithm first performs semantic analysis on the project demand data, applying natural language processing techniques to remove redundant noise fields, such as irrelevant modifiers and repeated expressions, to obtain key demand information. Next, a demand clustering algorithm is used to divide demand types into subsets. For example, entrepreneurial demand can be divided into subsets such as funding, technology, and human resources. The degree of interdependence between different demand types is analyzed by calculating the correlation between each subset. Next, demand priority weights are generated based on the correlation and preset industry trend thresholds to determine the importance of each demand type. Finally, the demand priority weights are combined with resource adaptability to form a multidimensional demand feature vector that includes demand urgency, resource adaptability, and industry trend correlation, providing a clear demand orientation for subsequent resource matching.

[0064] Resource Profile Construction Module: Based on market environment data, a resource profile generation algorithm is used to construct a dynamic resource profile. The algorithm first classifies resource attributes based on market environment data, such as financial resources, technical resources, and human resources. It then extracts the time series trend of each attribute and analyzes the supply and supply variation patterns of the resources. A trend fitting algorithm is then used to analyze the cyclical characteristics of cost fluctuation patterns, generate cost dynamic coefficients, and combine these with the supply and demand response coefficients for a multi-dimensional fusion to form a dynamic resource profile that includes resource availability indicators, cost fluctuation patterns, and supply and demand response coefficients. Finally, a standardization layer normalizes the profile parameters, converting parameters of different dimensions to a unified dimension. This outputs a resource profile that is easy to compare and analyze, allowing the characteristics of various resource types to be evaluated under the same standards.

[0065] Dynamic Matching Optimization Module: This module applies multi-dimensional constraint analysis to industry policy data to generate a resource matching priority matrix. This process first uses a rules engine to categorize policy constraints based on the compliance clauses in the industry policy data, determining the policy compliance level of each resource. Next, based on a market risk quantification model, it correlates policy constraint levels with risk indicators to generate a risk weight matrix, assessing potential risks in the resource matching process. Finally, a priority calculation formula integrates the policy constraint levels and the risk weight matrix to generate a resource matching priority matrix that comprehensively considers policy compliance and market risk, providing a reasonable prioritization for resource matching.

[0066] Collaborative Network Update Module: This module uses a collaborative network incremental algorithm to integrate the multidimensional characteristics of entrepreneurial demand, dynamic resource profiles, and a resource matching priority matrix to output a dynamic collaborative resource matching network. The algorithm first performs a sliding window difference calculation on the multidimensional characteristics of entrepreneurial demand, extracts the demand change gradient, and analyzes the demand trend over time. Next, it dynamically adjusts the resource profile weights based on the timeliness coefficient of the dynamic resource profile, highlighting the importance of recent data. The demand change gradient is then superimposed and fused with the adjusted profile weights to generate incremental matching features. Finally, a network update strategy is used to map the incremental matching features to the historical collaborative network, identifying and updating the differential nodes in the network. This output creates a dynamic collaborative resource matching network that reflects market changes in real time, enabling dynamic and accurate matching of entrepreneurial resources.

[0067] Embodiment 1:

[0068] In this embodiment, the specific implementation details of the demand feature decomposition algorithm are further described. The demand feature decomposition algorithm is the core algorithm of the demand feature modeling module, which performs semantic parsing, clustering analysis, weight calculation and other operations on project demand data to generate accurate multi-dimensional demand feature vectors.

[0069] When semantically analyzing project requirement data, lexical analysis and syntactic analysis techniques from natural language processing are used. Lexical analysis breaks down the requirement text into individual lexical units. For example, the sentence "A technical team with experience in AI algorithm development is needed, with a budget of less than 5 million yuan" can be broken down into words such as "need," "AI algorithm development experience," "technical team," "budget," and "less than 5 million yuan." Syntactic analysis analyzes the grammatical structure between words, determining the subject, predicate, object, attributive, adverbial, and complement components of a sentence, thereby clarifying the core content and key limiting conditions of the requirement. This method effectively eliminates redundant noise fields such as "hope" and "probably" while retaining key requirement information.

[0070] Demand type subsets are divided based on a demand clustering algorithm, using the K-Means clustering algorithm. Before applying the K-Means clustering algorithm, the initial cluster granularity and minimum correlation density must be calculated based on the distribution of historical demand data. Assuming that the historical demand data contains a variety of entrepreneurial needs, such as funding, technology, and site requirements, the initial number of clusters, K, is determined by analyzing the characteristic distribution of this data. For example, based on the data dispersion and business experience, the initial value of K is set to 3, resulting in a preliminary division into three demand subsets: funding, technology, and site. The minimum correlation density measures the closeness of clusters and is determined by calculating the distance between data points (e.g., Euclidean distance). When calculating the closeness of correlation between subsets, taking the funding and technology demand subsets as an example, the average distance between the demand data points in the two subsets is calculated. The smaller the distance, the higher the closeness of correlation, indicating a stronger interdependence between funding and technology needs.

[0071] Demand priority weights are generated based on the correlation strength and the preset industry trend threshold. Let the correlation strength be C and the preset industry trend threshold be T. If C > T, then the priority weight for this type of demand is higher. For example, under current industry trends, the demand for AI technology R&D is closely correlated with the demand for capital investment, and the correlation strength is greater than the preset threshold. Therefore, when generating demand priority weights, the weights of these two types of demands will be increased accordingly.

[0072] Combine the demand priority weight with the resource adaptability to form a multi-dimensional demand feature vector. Assume that the demand priority weight vector is W = [w1,w2,…,w n ], the resource adaptation vector is A=[a1,a2,…,a n ], where w i represents the priority weight of the i-th category demand, a i represents the resource adaptability corresponding to the i-th type of demand, and n is the total number of demand types. The calculation method of the multi-dimensional demand feature vector F is F = [w1×a1,w2×a2,…,w n ×a n ] In this way, the importance of demand and the adaptability of resources are comprehensively considered, providing a more accurate description of demand characteristics for subsequent resource matching.

[0073] Example 2:

[0074] This embodiment focuses on the detailed implementation of the resource portrait generation algorithm. The resource portrait generation algorithm is the key algorithm of the resource portrait construction module. By analyzing and processing market environment data, a comprehensive and dynamic resource portrait is constructed.

[0075] When classifying resource attributes within market environment data, we use technology resources as an example. We break down these attributes into categories such as technology type (e.g., software technology, hardware technology), technology maturity, and technology update cycle. For each attribute, we extract its time series trend. For example, for the technology update cycle attribute of a certain type of software technology, we collect data on the version updates of that technology over the past few years to form a time series.

[0076] The trend fitting algorithm is used to analyze the periodic characteristics of the cost fluctuation pattern. First, the resource attribute time series is processed for outlier correction. Assuming that the resource attribute time series data is x1, x2,…, x n , identify outliers through statistical methods (such as the 3σ criterion). i satisfy in If is the mean of the data and σ is the standard deviation, the data point is regarded as an outlier and is corrected by linear interpolation or median filling method to generate a smooth trend curve.

[0077] Then, a piecewise linear fitting algorithm is used to extract the fluctuation cycle parameters of different time intervals. The time series is divided into multiple time periods, and the data of each time period is linearly fitted to obtain parameters such as the slope and intercept of the fitting line. These parameters are used to calculate the main cycle length and fluctuation amplitude of the cost fluctuation and generate the cost dynamic coefficient. Suppose the fitting line equation of a resource cost time series in a time period is y=kx+b. By analyzing the fitting lines of multiple time periods, the main cycle length T and fluctuation amplitude A of the cost fluctuation are determined. The cost dynamic coefficient D can be expressed as D=f(T,A), where f is a functional relationship determined according to business needs, for example It represents the ratio of cost fluctuation to the length of the main cycle.

[0078] By using a sliding window to count the time-varying trend of the main cycle length, the dynamic characterization capability of resource profiling is enhanced. Set a sliding window size of m, slide the window in sequence on the time series, and count the changes in the main cycle length in each window. For example, if the window slides from time point t1 to t2, calculate the difference in the main cycle lengths between the two windows. By analyzing the changing trend of ΔT, we can timely capture the dynamic changes of the resource cost fluctuation cycle, thereby more accurately reflecting the cost fluctuation pattern of resources and optimizing the resource portrait.

[0079] Finally, the normalization layer is used to normalize the image parameters. Assume that a parameter in the resource image is P, and its value range is [P min ,P max ], the calculation formula of the normalized parameter P′ is After normalizing all portrait parameters, a resource portrait with unified dimensions is output to facilitate comparison and analysis of different resources.

[0080] Example 3:

[0081] In this embodiment, the specific implementation of the multi-dimensional constraint analysis process is described in detail. The multi-dimensional constraint analysis process is the core operation of the dynamic matching optimization module, which generates a resource matching priority matrix through analysis and calculation of industry policy data.

[0082] A rules engine is used to categorize policy constraints based on the compliance clauses within industry policy data. For example, if the use or acquisition of a resource fully complies with all policy terms, the resource's policy constraint level is low. If it partially complies but presents some potential risks, the resource's policy constraint level is medium. If it clearly violates the policy terms, the resource's policy constraint level is high. The rules engine pre-defines the judgment rules for various policy terms and determines the policy constraint level for each resource type.

[0083] Based on the market risk quantification model, the policy constraint level is associated with the risk index to generate a risk weight matrix. The market risk quantification model comprehensively considers multiple factors, such as market demand fluctuations, competitor behavior, and technology substitution risks. Let the policy constraint level be L, with values of 1 (low), 2 (medium), and 3 (high), and the risk indexes are R1, R2, ..., R m , through a certain calculation method (such as weighted summation), the policy constraint level is associated with the risk indicator. Assume that the risk weight matrix is M, where the element M ij It represents the weight of the i-th type of resource under the j-th risk indicator, and is calculated as M ij =g(L,R j ), g is a function determined according to market conditions and business experience, such as M ij =L×R j , which means that the higher the policy constraint level and the larger the risk index value, the greater the corresponding risk weight.

[0084] The policy constraint level and risk weight matrix are integrated through the priority calculation formula to generate the resource matching priority matrix. Define the nonlinear mapping relationship between the policy constraint level and the market risk weight. Let the policy constraint level be L, the market risk weight be W, and the mapping relationship be h(L, W). For example, h(L, W) = L 2 ×W, represents the product of the square of the policy constraint level and the market risk weight. According to the standardized priority benchmark value of the industry policy impact range, the priority benchmark value is set to B, and the mapping result is mapped to the preset priority interval [P min ,P max], generate the standardized priority value P, the calculation formula is:

[0085]

[0086] where h min and h max are the minimum and maximum values of h(L, W), respectively. By associating the standardized priority values with the resource matching rules, a multi-dimensionally constrained priority matrix is formed, providing a scientific and reasonable basis for prioritization of resource matching.

[0087] Example 4:

[0088] This embodiment focuses on the specific implementation steps of the collaborative network incremental algorithm. The collaborative network incremental algorithm is the core algorithm of the collaborative network update module, which updates the collaborative resource matching network through dynamic analysis of entrepreneurial needs and resource profiles.

[0089] Perform sliding window difference calculation on the multi-dimensional characteristics of entrepreneurial demand to extract the demand change gradient. Assume that the multi-dimensional characteristic vector of entrepreneurial demand is F = [f1,f2,…,f n ], the time series is t1, t2,…, t k , the sliding window size is m. At time point t i , the demand feature vector in the window is The calculation method of demand change gradient G is: Where Δt is the time interval, for example, Δt = t i -t i-1 ,By calculating the demand change gradient, it can reflect the rate of change and trend of demand over time.

[0090] Dynamically adjust the weight of the resource dynamic portrait according to the timeliness coefficient of the resource dynamic portrait. Let the resource dynamic portrait weight vector be W = [w1,w2,…,w n ], the timeliness coefficient is α, the value range is [0,1], and it gradually decreases with time. The calculation method of the adjusted portrait weight vector W′ is W′=[α×w1,α×w2,…,α×w n ] In this way, the importance of recent resource portrait data can be highlighted, making resource matching more in line with current market conditions.

[0091] The demand change gradient is superimposed and fused with the adjusted portrait weight to generate incremental matching features. Let the incremental matching feature vector be I, then Through this superposition and fusion method, the changes in demand and the timeliness of resource portraits are comprehensively considered to generate more targeted incremental matching features.

[0092] The incremental matching features are mapped to the historical collaborative network through the network update strategy. The network update strategy generates a version identifier based on the time tag of the incremental matching features, for example, the time tag is t i , the version identifier is The difference matching algorithm is used to identify the difference nodes between the current incremental features and the historical network. The difference matching algorithm compares the incremental matching feature vector with the feature vector of the node in the historical network and calculates the difference between the two. If the difference is greater than a certain threshold, the node is considered a difference node. The weight of the difference node is dynamically updated according to the timeliness coefficient. Let the original weight of the difference node be W node , the updated weight is W′ node , the update formula is W′ node =α×W node +(1-α)×I node , where I node In this way, the eigenvalues of the corresponding nodes in the incremental matching feature vector are dynamically updated to achieve the dynamic update of the historical collaborative network and output a dynamic collaborative resource matching network that can reflect market changes in real time.

[0093] Example 5:

[0094] This embodiment comprehensively describes the parameter optimization method of the demand clustering algorithm, the implementation steps of the trend fitting algorithm, the construction method of the priority calculation formula, and the network update strategy.

[0095] Regarding parameter optimization for the demand clustering algorithm, the initial cluster size and minimum correlation density are calculated based on the distribution of historical demand data. Historical demand data contains various demand information from past entrepreneurial projects. Statistical analysis of this data, such as calculating the standard deviation and variance, determines the data dispersion and thus sets the initial cluster size. For example, if the data dispersion is high, the number of initial clusters is appropriately increased to better segment demand type subsets. The minimum correlation density is determined by calculating the distance between data points. Data points with a distance less than a certain threshold are considered to have a high degree of correlation. A closeness evaluation metric is used to iterate through different parameter combinations, and the cluster with the highest closeness is selected. Examples of closeness evaluation metrics include the silhouette coefficient and the Calinski-Harabasz index. For example, the closer the silhouette coefficient is to 1, the better the clustering effect, indicating high similarity between samples within a cluster and low similarity with samples from other clusters. As different parameter combinations are iterated, parameters such as the number of clusters and distance measurement are continuously adjusted. The silhouette coefficient is calculated for each combination, and the parameter combination with the highest silhouette coefficient is selected as the optimal result. Based on the clustering results, the granularity of demand subset division and the minimum correlation density are dynamically adjusted to optimize the accuracy of demand priority weight calculation. If the data points within a certain demand subset are still relatively scattered, the subset can be further subdivided. If the correlation between certain subsets is too close, the minimum correlation density can be appropriately adjusted and the demand priority weights can be recalculated to make the weights more accurately reflect the relationship between the demands.

[0096] In terms of implementing trend fitting algorithms, when correcting outliers in resource attribute time series, in addition to using the 3σ criterion, machine learning-based outlier detection algorithms, such as the isolation forest algorithm, can also be utilized. The isolation forest algorithm constructs a binary tree to isolate data points; the more isolated a data point is, the more likely it is an outlier. When using a piecewise linear fitting algorithm to extract fluctuation cycle parameters for different time intervals, in addition to calculating the slope and intercept, Fourier transforms can be used to analyze the frequency components of the time series and identify the primary fluctuation cycle. When using a sliding window to calculate the time-varying trend of the primary cycle length, the exponentially weighted moving average (EWMA) can be used to smooth the primary cycle length, making the trend more stable and better reflecting long-term trends.

[0097] Regarding the construction of the priority calculation formula, a neural network model can be used to define the nonlinear mapping relationship between policy constraint levels and market risk weights. This neural network model is trained using extensive historical data to learn the complex relationship between policy constraint levels and market risk weights. When standardizing priority benchmark values based on the scope of industry policy impact, the varying degrees of impact of different policies on different resource types can be considered, with different standardized parameters set for different resource types. When associating standardized priority values with resource matching rules, factors such as resource scarcity and substitutability can be incorporated to further optimize the priority matrix.

[0098] In terms of network update strategies, when generating version identifiers based on the time tags of incremental matching features, a combination of timestamps and hash values can be used to improve the uniqueness and security of version identifiers. When using a difference matching algorithm to identify nodes that differ between the current incremental features and the historical network, more precise similarity metrics such as cosine similarity can be used to more accurately determine node differences. When dynamically updating the weights of differential nodes based on timeliness coefficients, an adaptive learning rate approach can be used to automatically adjust the learning rate based on network updates, making weight updates more efficient and stable.

[0099] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0100] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A dynamic matching system for entrepreneurial resources, characterized by: include: Multi-source data integration module: used to collect and integrate multi-dimensional entrepreneurial resource data sets; The multi-dimensional data includes project demand data, market environment data and industry policy data; Demand feature modeling module: Based on the project demand data, the demand feature decomposition algorithm is used to extract the multi-dimensional features of entrepreneurial demand, including demand urgency, resource adaptability, and industry trend correlation; Resource profile building module: used to build a resource dynamic profile based on the market environment data through a resource profile generation algorithm, wherein the resource dynamic profile includes resource availability indicators, cost fluctuation patterns, and supply and demand response coefficients; Dynamic matching optimization module: performs multi-dimensional constraint analysis on the industry policy data to generate a resource matching priority matrix; the multi-dimensional constraint analysis includes policy compliance verification and market risk quantification; Collaborative network update module: Through the collaborative network incremental algorithm, the multi-dimensional characteristics of entrepreneurial needs, resource dynamic profiles and resource matching priority matrix are integrated to output a dynamic collaborative resource matching network.

2. The entrepreneurial resource dynamic matching system according to claim 1, characterized in that: The demand feature decomposition algorithm includes: Perform semantic analysis on the project demand data and remove redundant noise fields; Divide demand type subsets based on demand clustering algorithm and calculate the correlation density between each subset; Generate demand priority weights based on correlation closeness and preset industry trend thresholds; Combine the demand priority weight with resource adaptability to form a multi-dimensional demand feature vector.

3. The entrepreneurial resource dynamic matching system according to claim 1, characterized in that: The resource portrait generation algorithm includes: Classifying the market environment data by resource attributes and extracting the time series change trend of each attribute; Use trend fitting algorithms to analyze the cyclical characteristics of cost fluctuation patterns, generate cost dynamic coefficients, and combine supply and demand response coefficients with cost dynamic coefficients for multi-dimensional fusion to form a dynamic resource portrait; The portrait parameters are normalized through the standardization layer to output a resource portrait with unified dimensions.

4. The entrepreneurial resource dynamic matching system according to claim 1, characterized in that: The multidimensional constraint analysis process includes: Based on the compliance clauses of industry policy data, a rule engine is used to divide policy constraint levels; Based on the market risk quantification model, the policy constraint level is associated with the risk indicator to generate a risk weight matrix; The policy constraint level and risk weight matrix are integrated through the priority calculation formula to generate a resource matching priority matrix.

5. The entrepreneurial resource dynamic matching system according to claim 1, characterized in that: The collaborative network incremental algorithm includes: Perform sliding window difference calculation on the multi-dimensional characteristics of the entrepreneurial demand to extract the demand change gradient; Dynamically adjust the weight of the resource dynamic portrait according to its timeliness coefficient; The demand change gradient is superimposed and integrated with the adjusted portrait weight to generate incremental matching features; The incremental matching features are mapped to the historical collaborative network through the network update strategy, and a dynamic collaborative resource matching network is output.

6. The entrepreneurial resource dynamic matching system according to claim 2, characterized in that: The parameter optimization method of the demand clustering algorithm includes: Calculate the initial clustering granularity and minimum association density based on the historical demand data distribution; Traverse different parameter combinations through the compactness evaluation index and select the clustering result with the highest compactness; Dynamically adjust the demand subset division granularity and minimum association density based on the clustering results to optimize the calculation accuracy of demand priority weights.

7. The entrepreneurial resource dynamic matching system according to claim 3, characterized in that: The implementation steps of the trend fitting algorithm include: Perform outlier correction on resource attribute time series to generate a smooth trend curve; The piecewise linear fitting algorithm is used to extract the fluctuation cycle parameters of different time intervals. The length and amplitude of the main cost fluctuation cycle are calculated based on the cycle parameters to generate the cost dynamic coefficient. The time-varying trend of the main cycle length is statistically analyzed through a sliding window to enhance the dynamic representation capability of resource profiling.

8. The entrepreneurial resource dynamic matching system according to claim 4, characterized in that: The method for constructing the priority calculation formula includes: Define the nonlinear mapping relationship between policy constraint levels and market risk weights; Based on the standardized priority benchmark value of the industry policy impact range, the mapping result is mapped to the preset priority interval through the constraint function to generate the standardized priority value; The standardized priority values are associated with resource matching rules to form a priority matrix with multi-dimensional constraints.

9. The entrepreneurial resource dynamic matching system according to claim 5, characterized in that: The network update strategy includes: generating a version identifier based on the time tag of the incremental matching feature, identifying the difference nodes between the current incremental feature and the historical network through a difference matching algorithm, and dynamically updating the weights of the difference nodes according to the timeliness coefficient.

10. A method for dynamically matching entrepreneurial resources, applied to the dynamic matching system for entrepreneurial resources according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step S1: Collect and integrate project demand data, market environment data, and industry policy data through a multi-source data integration module to form a multi-dimensional entrepreneurial resource data set; Step S2: Based on the project demand data, using the demand feature decomposition algorithm, extract the multi-dimensional characteristics of entrepreneurial demand including demand urgency, resource adaptability and industry trend correlation in the demand feature modeling module; Step S3: Based on the market environment data, a resource profile generation algorithm is used to construct a resource dynamic profile in a resource profile construction module, which includes resource availability indicators, cost fluctuation patterns, and supply and demand response coefficients; Step S4: performing multi-dimensional constraint analysis on the industry policy data, including policy compliance verification and market risk quantification, and generating a resource matching priority matrix in the dynamic matching optimization module; Step S5: The collaborative network incremental algorithm is used to integrate the multi-dimensional characteristics of entrepreneurial needs, the dynamic resource profile, and the resource matching priority matrix in the collaborative network update module to output a dynamic collaborative resource matching network.

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