Multi-dimensional scientific and technological achievement and innovation resource matching method based on big data

Abstract

The invention discloses a multi-dimensional scientific and technological achievement and innovation resource matching method based on big data, and relates to the technical field of science and technology finance, and the method comprises the steps: obtaining enterprise working condition data and insurance and financial terms, inputting a dynamic classification model, outputting an enterprise type, and associating to an achievement transformation stage; the insurance scheme priority is rendered on the insurance interface, the corresponding insurance scheme is set according to the enterprise type, format contract terms are embedded into the insurance description, and insurance decision data are generated; and insuring decision data is fed back to the entropy risk assessment mechanism, and the weight coefficient and the trial period parameter of the difference factor are updated. According to the method, historical performance data, real-time trial period parameters and matching decisions of credit institutions are fused through an entropy risk assessment mechanism, migration risk values are dynamically generated, risk quantification of multi-source heterogeneous data is realized, and enterprise risk migration trajectories are accurately captured.

Description

Technical Field

[0001] The present invention relates to the field of science and technology finance technology, and in particular to a method for matching multi-dimensional scientific and technological achievements with innovative resources based on big data. Background Art

[0002] In recent years, insurance-based financial instruments have been gradually introduced into the technology transfer sector to mitigate corporate risk. Dynamic matching technologies based on big data have become a research hotspot. Existing technologies have achieved preliminary correlations between enterprise operating data and insurance terms, and have also established entropy risk assessment mechanisms based on historical performance data from credit institutions. Regarding the phases of technology transfer, dual-path mechanisms for free trials and paid conversions are gradually maturing, with some now supporting trial period parameter monitoring and technology pool matching decisions. Interface interaction technologies have also made progress, with dynamic rendering of insurance application interfaces and embedded delivery of contract terms becoming industry standards.

[0003] Currently, there are deficiencies in the real-time coordination of insurance solutions and dynamic risks. Insurance and financial terms are rigid, preventing dynamic prioritization based on a company's risk profile. The generated migration risk value only triggers a simple alert, failing to drive in-depth adaptation of insurance coverage and outcome buffer options. This results in high-risk companies being unable to immediately access high-protection solutions, and information overload on the interface exacerbates the decision-making burden. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a multi-dimensional scientific and technological achievements and innovation resource matching method based on big data to solve the problem of insufficient adaptability between insurance and financial terms and the dynamic risks of scientific and technological achievement transformation enterprises.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: The present invention provides a multi-dimensional scientific and technological achievements and innovation resource matching method based on big data, which includes: obtaining enterprise working condition data and insurance and financial terms, inputting a dynamic classification model, outputting enterprise types, and linking to the achievement transformation stage; If it is a free trial phase, call the preset agreement template to generate a free license agreement and monitor the trial period parameters. If it is a paid conversion phase, extract the achievement ID from the achievement pool, calculate the difference with the enterprise ID, generate a difference factor, and generate a matching decision based on the requirements list; The entropy risk assessment mechanism is used to integrate the historical performance data provided by the credit institutions, and the migration risk value is generated by combining the matching decision and the trial period parameters; When the migration risk value exceeds the preset risk threshold, the non-core clauses of the insurance and financial clauses are folded and the risk buffer achievement option is highlighted, triggering the interface rendering instruction and binding it to the insurance interface; Render the insurance plan priority on the insurance application interface, place the corresponding insurance plan at the top according to the enterprise type, and embed the formatted contract terms into the insurance instructions to generate insurance decision data; The insurance decision data is fed back to the entropy risk assessment mechanism to update the weight coefficient of the difference factor and the trial period parameters.

[0007] As a preferred solution of the multi-dimensional scientific and technological achievements and innovation resources matching method based on big data of the present invention, wherein: the enterprise operating condition data includes equipment operation logs, revenue scale ranges, demand lists and enterprise identification; The enterprise types include emergency failure type, sub-health type and normal operation type.

[0008] As a preferred solution of the method for matching multi-dimensional scientific and technological achievements with innovation resources based on big data described in the present invention, the specific steps of linking to the achievement transformation stage are as follows: Real-time collection of equipment operation logs, revenue ranges, demand lists, corporate logos, and insurance and financial terms; Obtain equipment failure rates based on equipment operation logs, convert revenue scale ranges into level labels, parse demand lists into demand density, and analyze insurance and financial terms to generate a liability intensity index. Integrate equipment failure rate, grade label, demand density and responsibility intensity index, build a multi-dimensional feature vector input dynamic classification model, output enterprise type and insurance adaptation identification, and link it to the results transformation stage.

[0009] As a preferred solution of the method for matching multi-dimensional scientific and technological achievements with innovative resources based on big data described in the present invention, the specific steps of monitoring trial period parameters are as follows: The preset agreement template is matched based on the enterprise type and insurance adaptability identifier, and insurance and financial terms constraints are injected. The trial period parameters and monitoring indicator set are generated based on the equipment failure rate and demand density, and a free license agreement is output. Through distributed nodes, device operation logs and results call behaviors are captured in real time, and abnormal fluctuations are identified by comparing the monitoring indicator set, triggering the compression of the trial period and the upgrade of insurance liability instructions.

[0010] As a preferred solution of the multi-dimensional scientific and technological achievements and innovation resources matching method based on big data of the present invention, the specific steps of generating a matching decision by combining the requirements list are as follows: Extract the achievement identifiers that have not been converted in recent years from the achievement pool, and filter out the achievement identifiers whose matching degree with the requirements list is lower than the preset matching degree threshold; The pre-screened achievement identification and enterprise identification are encoded into an achievement identification vector and an enterprise identification vector through a multimodal spatiotemporal encoder; The difference between the outcome identification vector and the enterprise identification vector is calculated through the Transformer cross-attention mechanism to generate a difference factor, which is then combined with the requirements list to generate a matching decision.

[0011] As a preferred solution of the multi-dimensional scientific and technological achievements and innovation resource matching method based on big data described in the present invention, the difference factor refers to a dynamic quantitative indicator that quantifies the matching degree between scientific and technological achievements and enterprise needs.

[0012] As a preferred solution of the multi-dimensional scientific and technological achievements and innovation resources matching method based on big data of the present invention, wherein: the migration risk value is generated by combining the matching decision and the trial period parameter, the specific steps are as follows: Call the credit institution interface to obtain historical performance data, synchronously combine the trial period parameters and difference factors, clean abnormal data through dynamic fluctuation filtering algorithm, and output a standardized data matrix; The standardized data matrix is ​​input into the feature fusion layer, and the influence weights of historical performance data, trial period parameters, and difference factors are dynamically adjusted to generate a multi-dimensional fusion feature tensor; Based on the multi-dimensional fusion feature tensor, the migration risk value is calculated through the entropy risk assessment mechanism.

[0013] As a preferred solution of the method for matching multi-dimensional scientific and technological achievements with innovative resources based on big data described in the present invention, the triggering interface rendering instruction is bound to the insurance interface, and the specific steps are as follows: Parse insurance and financial terms, identify core and non-core terms through natural language analysis, and automatically collapse non-core terms into expandable grayscale panels when the migration risk value exceeds the preset risk threshold, generating a collapsed term set. Extract matching buffer results from the results pool, and generate a highlight result set based on the enterprise working condition data and the matching buffer results; The collapsed clause set and the highlighted result set are input into the neural rendering engine, and the rendering priority is dynamically modulated by migrating the risk value to generate a visualization instruction set. The visualization instruction set is pushed to the insurance interface node through a low-latency communication protocol to update the visualization component.

[0014] As a preferred solution of the method for matching multi-dimensional scientific and technological achievements with innovative resources based on big data described in the present invention, the specific steps of generating insurance decision data are as follows: Based on the enterprise type and migration risk value, the time series trend of equipment failure rate and the topological relationship of equipment cluster are integrated to generate a risk profile vector through the spatiotemporal graph convolutional network; The risk profile vector is input into the neural symbolic joint engine, combined with the enterprise type and migration risk value, and the risk pattern is analyzed through the neural network component. The symbolic rule component is superimposed to output the insurance plan priority score; Dynamically adjust the display order of insurance and financial terms based on the insurance plan priority score and output interactive terms components; Render insurance plans in descending order of their priority scores, embed interactive clause components, capture user selection behavior in real time, and generate insurance decision data.

[0015] As a preferred solution of the multi-dimensional scientific and technological achievements and innovation resources matching method based on big data of the present invention, the weight coefficient of the updating difference factor and the trial period parameter are specifically updated as follows: Analyze user behavior characteristics in insurance decision-making data, including risk sensitivity, clause attention, and decision hesitation index; Based on risk sensitivity and clause attention, the weight coefficient of the difference factor is adjusted, and the trial period parameters are adjusted in combination with the decision hesitation index and risk sensitivity; The updated weight coefficient of the difference factor and the trial period parameter are fed back to the entropy risk assessment mechanism to reconstruct the feature fusion layer and optimize the risk assessment accuracy.

[0016] The beneficial effects of the present invention are: through the entropy risk assessment mechanism, the historical performance data of credit institutions, real-time trial period parameters and matching decisions are integrated to dynamically generate migration risk values, realize risk quantification of multi-source heterogeneous data, and accurately capture the enterprise risk migration trajectory; when the risk value exceeds the threshold, it automatically triggers the insurance clause folding and result highlighting, and feeds back to the insurance interface in seconds to generate visual decision support; at the same time, through the closed-loop feedback of insurance decision data, the difference factor weights and trial period parameters are dynamically calibrated, so that the entropy risk assessment mechanism can be continuously self-optimized to improve the risk prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is a flow chart of the multi-dimensional scientific and technological achievements and innovation resources matching method based on big data.

[0019] Figure 2 A flowchart that associates enterprise type classification with the stages of achievement transformation.

[0020] Figure 3 Flowchart of entropy risk assessment mechanism.

[0021] Figure 4 Flowchart generated for insurance decision data. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0023] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0025] Reference Figure 1 , is an embodiment of the present invention, which provides a multi-dimensional scientific and technological achievements and innovation resource matching method based on big data, including the following steps: S1: Obtain enterprise working condition data and insurance and financial terms, input dynamic classification model, output enterprise type, and link to the achievement transformation stage. For details, please refer to Figure 2 .

[0026] S1.1: Enterprise operating data includes equipment operation logs, revenue scale ranges, demand lists and enterprise identification.

[0027] It should be noted that the device operation log records the device operation status, fault events and timestamps.

[0028] The revenue scale interval represents the annual revenue range of the enterprise and is stored in the form of a numerical interval.

[0029] The requirements list lists the company's current technical requirements, each of which includes a description of the requirement and a priority.

[0030] A corporate ID is a unique code used to identify a corporate entity.

[0031] S1.2: Enterprise types include emergency failure type, sub-health type and normal operation type.

[0032] It should be noted that the enterprise type classification standards are as follows: emergency failure type indicates high equipment failure rate and high demand density, sub-health type indicates medium equipment failure rate or unstable revenue, and normal operation type indicates low equipment failure rate and stable revenue.

[0033] S1.3: Real-time collection of equipment operation logs, revenue scale ranges, demand lists, corporate logos, and insurance and financial terms.

[0034] Deploy a data collection agent to read device operation logs from enterprise device sensors in real time through the API interface. The device operation logs contain real-time device status data.

[0035] Subscribe to real-time revenue range updates via the company's financial data stream.

[0036] Call the demand management platform API to capture the demand list in real time.

[0037] Call the enterprise registration database to query the enterprise ID in real time, and the enterprise ID is used as the unique key value.

[0038] Call the insurance database interface to obtain insurance financial terms in real time. Insurance financial terms are text data that describe the scope of insurance liability.

[0039] S1.4: Obtain equipment failure rate based on equipment operation logs, convert revenue scale ranges into grade labels, parse demand lists into demand density, and parse insurance and financial terms to generate a liability intensity index.

[0040] Specifically, the ratio of the number of failure events in the equipment operation log to the total operation time is counted as the equipment failure rate.

[0041] Map the revenue scale range to a level label. For example, a revenue scale range of 0-1 million yuan is labeled as a low level, 1 million-5 million yuan is labeled as a medium level, and 5 million yuan or more is labeled as a high level.

[0042] Analyze the number and urgency of requirements in the requirement list and output the requirement density.

[0043] Use natural language analysis to analyze insurance and financial clause texts, extract liability clause keywords (such as "compensation scope" and "exemption clause"), and obtain a liability intensity index.

[0044] S1.5: Integrate equipment failure rate, grade label, demand density and responsibility intensity index, build a multi-dimensional feature vector input dynamic classification model, output enterprise type and insurance adaptation identification, and link it to the results transformation stage.

[0045] It should be noted that the pre-training process of the dynamic classification model is as follows: collect historical enterprise operating condition data and insurance and financial terms, and combine historical equipment failure rate, historical grade label, historical demand density and historical liability intensity index into a historical multidimensional feature vector, and at the same time obtain the corresponding historical enterprise type and historical insurance adaptation identification; use the historical multidimensional feature vector as input feature, historical enterprise type and historical insurance adaptation identification as supervision labels, and use the supervised learning algorithm to train the dynamic classification model; minimize the enterprise type classification error rate and insurance adaptation identification prediction error during the training process; save the dynamic classification model parameters after training is completed to obtain the dynamic classification model.

[0046] Specifically, the numerical values ​​of the four indicators, namely, equipment failure rate, grade label, demand density and responsibility intensity index, are combined into a multidimensional feature vector. The dimension of the multidimensional feature vector is fixed to four dimensions, and each dimension corresponds to an indicator value.

[0047] The multidimensional feature vector is input into the dynamic classification model. The dynamic classification model classifies the multidimensional feature vector according to historical training data and preset classification rules, and outputs the enterprise type and insurance adaptation identifier. The insurance adaptation identifier is a binary value (0 or 1).

[0048] Based on the enterprise type and insurance adaptation identifier output by the dynamic classification model, it is associated with the achievement transformation stage through preset mapping rules: Free trial period applicable conditions: When the enterprise type is emergency failure type (high equipment failure rate and high demand density), it is mandatory to associate with the free trial phase.

[0049] Applicable conditions for the paid conversion stage: When the enterprise type is normal operation (equipment failure rate is less than 5% and revenue is stable), it is directly associated with the paid conversion stage.

[0050] If the enterprise type is sub-healthy, it needs to be combined with the insurance adaptation mark: If the insurance adaptation flag is 1 (insurance adapted), the payment conversion stage is associated; If the insurance adaptation flag is 0 (insurance not applicable), then the readjustment requirement list is returned.

[0051] It should be noted that the preset classification rules refer to the decision rules formed by the dynamic classification model through the supervised learning algorithm during the training process, which are used to output the enterprise type and insurance adaptation identification based on the multi-dimensional feature vector.

[0052] The preset mapping rules refer to the pre-defined key-value pair logic, with the enterprise type and insurance adaptation identifier as the "key" mapped to the "value" of the results transformation stage or operation, and are used to associate the enterprise type and insurance adaptation identifier output by the dynamic classification model with the results transformation stage.

[0053] S2: If it is a free trial phase, call the preset agreement template to generate a free license agreement and monitor the trial period parameters. If it is a paid conversion phase, extract the achievement ID from the achievement pool, calculate the difference with the enterprise ID, generate a difference factor, and generate a matching decision based on the requirements list.

[0054] S2.1: Match the preset agreement template according to the enterprise type and insurance adaptation identifier, inject insurance and financial terms constraints, combine the equipment failure rate and demand density, generate trial period parameters and monitoring indicator sets, and output a free license agreement.

[0055] It should be noted that the preset agreement template refers to a document containing a basic terms framework pre-stored in the template library. After selecting a specific template based on the enterprise type and insurance adaptation identifier, the insurance and financial terms constraints are dynamically injected, and the trial period parameters are filled in based on the equipment failure rate and demand density to generate a free license agreement.

[0056] Specifically, according to the enterprise type and insurance adaptation identifier, select the corresponding free license agreement template from the preset agreement templates.

[0057] Analyze insurance and financial terms, extract constraints on liability scope, exemption clauses, compensation limits, premium calculation rules, claim time limit, and dispute resolution methods, and inject them into the selected agreement template.

[0058] According to the equipment failure rate and demand density, the equipment failure rate is converted into a risk level, which includes high risk level, medium risk level and low risk level. The demand density is converted into an urgency level, which includes high urgency level, medium urgency level and low urgency level. The risk level and urgency level are combined to generate trial period parameters, which include trial duration and the upper limit of the number of results calls.

[0059] Generate a monitoring indicator set based on the equipment failure rate. The monitoring indicator set includes equipment failure thresholds and achievement call frequency thresholds. Integrate the agreement template, insurance constraints, trial period parameters and monitoring indicator set to generate a free license agreement.

[0060] It should be noted that the equipment failure threshold is set based on the risk level (high / medium / low) converted from the equipment failure rate. A high risk level corresponds to a high failure threshold. The example value is: the high-risk level equipment failure threshold is set to an upper limit of 10 failures per day; the result call frequency threshold is set based on the urgency (high / medium / low) converted from the demand density. A high urgency level corresponds to a high call frequency threshold; the example value is: the high-urgency result call frequency threshold is set to a lower limit of 5 calls per day.

[0061] S2.2: Capture device operation logs and achievement call behaviors in real time through distributed nodes, compare monitoring indicator sets to identify abnormal fluctuations, and trigger trial period compression and insurance liability upgrade instructions.

[0062] Specifically, a lightweight data collection node is deployed on the enterprise server to capture device operation logs and results call behaviors in real time. The results call behaviors include API call records and function usage times.

[0063] Compare the real-time captured device operation logs and result call behaviors with the monitoring indicator set: When the number of equipment failures in a single day exceeds the high failure threshold, it is marked as abnormal fluctuation; When the achievement call frequency is lower than the achievement call high call frequency threshold, it is marked as abnormal fluctuation; Instructions are triggered based on the type of abnormal fluctuation. If the equipment failure rate fluctuates abnormally beyond the standard for several consecutive days, an instruction to compress the trial period will be triggered. If the achievement call frequency is lower than the achievement call frequency threshold and the equipment failure rate is high, an insurance liability upgrade instruction will be triggered.

[0064] S2.3: Extract the achievement identifiers that have not been transformed in recent years from the achievement pool, and filter out the achievement identifiers whose matching degree with the requirements list is lower than the preset matching degree threshold.

[0065] Specifically, the achievement identifiers that have not been converted in the past three years are screened from the achievement pool, and the semantic similarity of each achievement identifier is compared with the requirements list. The achievement identifiers with semantic similarity lower than the preset matching threshold are removed, and the pre-screened achievement identifiers are retained.

[0066] It should be noted that the preset matching threshold is set based on the demand density generated by the analysis of the demand list. The higher the demand density, the stricter the matching threshold. For example, when the demand density is high (greater than 8 items / month), the preset matching threshold is set to 0.6.

[0067] S2.4: Encode the pre-screened achievement identifiers and enterprise identifiers into an achievement identifier vector and an enterprise identifier vector through a multimodal spatiotemporal encoder; Specifically, the pre-screened achievement identification and enterprise identification are input into the multimodal spatiotemporal encoder, and the technical field classification code, R&D timestamp and registration region code associated with the achievement identification are parsed. The technical field classification code is mapped into a field embedding vector, the R&D timestamp is converted into a time feature vector through a time encoder, and the registration region code is generated into a spatial feature vector through a geocoder. The field embedding vector, time feature vector and spatial feature vector are fused to generate the achievement identification vector.

[0068] Based on the three characteristics of equipment failure rate, level label and demand density, the equipment failure rate is converted into a failure risk vector, the level label is mapped into a level vector, and the demand density is converted into a demand intensity vector. The failure risk vector, level vector and demand intensity vector are integrated to generate an enterprise identification vector.

[0069] S2.5: Calculate the difference between the outcome identification vector and the enterprise identification vector through the Transformer cross-attention mechanism, generate a difference factor, and combine it with the requirements list to generate a matching decision.

[0070] Specifically, combined with the priority ranking of the requirements list, the achievement identifiers are arranged in descending order according to the difference factor to generate a matching decision.

[0071] The difference between the achievement identification vector and the enterprise identification vector is calculated through the Transformer cross attention mechanism to generate a difference factor, which is expressed as: ; Where, represents the difference factor, represents the normalization function, represents the result identification vector, Represents the corporate logo vector, represents the vector dimension, Represents the requirement list vector.

[0072] It should be noted that is dimensionless, is dimensionless, is dimensionless, is dimensionless, It is dimensionless and ultimately maintains dimensional unity.

[0073] It should be noted that the difference factor refers to a dynamic quantitative indicator that quantifies the degree of match between scientific and technological achievements and corporate needs. Its value lies in resolving information barriers, improving conversion efficiency and dynamically adapting to needs.

[0074] S3: Call the entropy risk assessment mechanism to integrate the historical performance data provided by the credit agency, combine the matching decision and the trial period parameters to generate the migration risk value. Figure 3 .

[0075] S3.1: Call the credit institution interface to obtain historical performance data, simultaneously combine the trial period parameters and difference factors, clean abnormal data through the dynamic fluctuation filtering algorithm, and output a standardized data matrix.

[0076] Specifically, historical performance data is obtained through the credit agency API. The historical performance data includes the company's historical insurance claims, compensation amounts and default records, and is simultaneously combined with trial period parameters and difference factors.

[0077] Calculate the average value and fluctuation range of the sliding window of historical performance data, and eliminate data points that deviate from the average value by more than the preset fluctuation range.

[0078] The retained historical performance data, trial period parameters and difference factors are standardized, the overall average value and dispersion of the historical performance data, trial period parameters and difference factors are calculated, the data distribution is adjusted according to a unified dimension, and a standardized data matrix is ​​generated with the number of rows being the amount of valid data and the number of columns being three, where the three columns correspond to the historical performance data, trial period parameters and difference factors respectively.

[0079] It should be noted that the preset fluctuation range refers to the three times standard deviation interval formed by the historical performance data in the sliding window calculation, which is used by the dynamic fluctuation filtering algorithm to eliminate abnormal data points that deviate from the average value by more than this interval.

[0080] S3.2: Input the standardized data matrix into the feature fusion layer, dynamically adjust the influence weights of historical performance data, trial period parameters and difference factors, and generate a multi-dimensional fusion feature tensor.

[0081] Specifically, the weight ratio is allocated according to the numerical value of the difference factor: the higher the difference factor, the greater the weight ratio (for example, when the difference factor is 90, the weight ratio is 60%).

[0082] The weights of historical performance data and trial period parameters are adjusted inversely (for example, when the weight of the difference factor increases, the weights of historical performance data and trial period parameters decrease accordingly).

[0083] The three values ​​in each row of the standardized data matrix are weighted and summed according to the weight ratio (for example, historical performance data accounts for 20%, trial period parameters account for 20%, and difference factors account for 60%), and the multi-dimensional fusion feature tensor is output.

[0084] S3.3: Specifically, based on the multi-dimensional fusion feature tensor, the migration risk value is calculated through the entropy risk assessment mechanism, and the expression is: ; ; ; Where, represents the migration risk value, represents the Sigmoid function, represents the second weight matrix, represents the rectified linear unit function, represents the first weight matrix, represents the tensor vectorization operator, represents the multi-dimensional fusion feature tensor, Represents a multi-dimensional fusion feature tensor vector, represents the first bias vector, Represents the second bias scalar.

[0085] It should be noted that is dimensionless, is dimensionless, is dimensionless, is dimensionless, is dimensionless, It is dimensionless and ultimately maintains dimensional unity.

[0086] It should be noted that the first weight matrix refers to the pre-trained parameter matrix, which is used to linearly transform the vectorized multi-dimensional fusion feature tensor into the hidden layer space. Its dimension is dynamically determined according to the number of hidden layer nodes (for example, when the input vector is 128-dimensional, the dimension of the first weight matrix is ​​256×128).

[0087] The second weight matrix refers to the pre-trained parameter matrix, which is used to linearly map the hidden layer output to the risk probability space. Its dimension is fixed to 1×the number of hidden layer nodes (for example, when the hidden layer has 256 nodes, the dimension of the second weight matrix is ​​1×256).

[0088] The first bias vector refers to the pre-trained bias parameter vector of the hidden layer. Its dimension is consistent with the number of hidden layer nodes (for example, when there are 256 nodes, the first bias vector is a 256-dimensional vector). It is used to adjust the output offset of the hidden layer activation function.

[0089] The second bias scalar refers to the pre-trained bias constant scalar (single value) of the output layer, which is used to adjust the baseline offset of the final migration risk value (for example, the baseline value adjustment constant is -0.3).

[0090] The entropy risk assessment mechanism optimally quantifies multi-dimensional risk migration trajectories in real time by dynamically integrating three heterogeneous data sources: historical performance data from credit institutions, real-time matching decisions, and trial period parameters. Compared to the limitations of traditional credit scorecard models that rely solely on static historical data, the use of trial period parameters allows migration risk values ​​to respond instantly to changes in the company's operating status. Furthermore, by incorporating the technology transfer adaptability indicator into the differential factors in the matching decisions, innovation risk is incorporated into the assessment framework. This real-time fusion of heterogeneous data from multiple sources improves the accuracy of migration risk values ​​and effectively addresses the lag in insurance solutions caused by data fragmentation.

[0091] S4: When the migration risk value exceeds the preset risk threshold, the non-core terms of the insurance and financial terms are folded and the risk buffer result option is highlighted, triggering the interface rendering instruction and binding it to the insurance interface.

[0092] S4.1: Parse insurance and financial terms, identify core and non-core terms through natural language, and automatically collapse non-core terms into expandable grayscale panels when the migration risk value exceeds the preset risk threshold to generate a collapsed term set.

[0093] It should be noted that the preset risk threshold is set based on historical risk assessment experience and is used to demarcate the high-risk and low-risk decision boundaries. Example: The migration risk threshold is set to 60.

[0094] Specifically, a dependency syntax analysis algorithm is used to identify core and non-core clauses in insurance and financial terms. Core clauses include the scope of liability and compensation limit, while non-core clauses include dispute resolution methods and premium calculation details. The core clause determination rule is an independent sentence containing the keywords "compensation", "liability" and "limit".

[0095] When the migration risk value exceeds the preset risk threshold: non-core clauses are converted to expandable grayscale panels (folded by default, with the background color set to light gray), core clauses maintain their original display style, are structured as a JSON array, and a folded clause set is output.

[0096] S4.2: Extract matching buffer results from the result pool, and generate a highlighted result set based on the enterprise working condition data and the matching buffer results.

[0097] Specifically, access the achievement pool to filter the achievement identifiers marked as "optional conversion" in the matching decision. For each achievement identifier, extract the associated achievement fault compatibility coefficient and achievement demand coverage coefficient, combine the equipment failure rate and demand density in the enterprise working condition data, generate the buffer achievement adaptability, filter the achievement identifiers whose buffer achievement adaptability reaches the preset highlight threshold, and generate a highlighted achievement set.

[0098] It should be noted that the preset highlight threshold is set based on the dynamic distribution of the buffer result adaptation. For example, eighty percent of the highest buffer result adaptation is taken as the benchmark value. When the highest buffer result adaptation is 5.0, the preset highlight threshold is set to 4.0.

[0099] S4.3: Input the collapsed clause set and the highlighted result set into the neural rendering engine, dynamically modulate the rendering priority by migrating the risk value, and generate a visualization instruction set.

[0100] Specifically, the structured data of the collapsed clause set and the structured data of the highlighted achievement set are read. The structured data of the collapsed clause set includes the clause ID, text content, panel status and style attributes, and the structured data of the highlighted achievement set includes the achievement ID and the reason for highlighting.

[0101] The rendering weight factor is dynamically generated by combining the migration risk value with the preset rendering weight rule, and the rendering weight factor is adjusted proportionally based on the migration risk value.

[0102] The rendering weight factor is input into the neural rendering engine, the spatial relationship between the folded state distribution of the folded clause set and the highlighted result set is analyzed, and the layer layout parameters are output. The layer layout parameters include element coordinates, size, color transparency and interaction event binding rules, which are integrated into a visualization instruction set.

[0103] It should be noted that the preset rendering weight rule refers to the basic rendering weight constant and additional rendering weight constant pre-configured by the neural rendering engine, and the parameter combination of the rendering weight factor is dynamically calculated by the proportion of the migration risk value in the upper limit of the risk scale.

[0104] S4.4: Push the visualization instruction set to the insurance interface node via a low-latency communication protocol to update the visualization component.

[0105] Specifically, the WebSocket protocol is used to push the visualization instruction set to the insurance interface in real time.

[0106] The insurance interface parses the visual instruction set and dynamically updates three components: Insurance clause display area: fold the non-core clause panel; Achievement recommendation area: highlight achievement collections are displayed at the top; Risk warning column: embeds the migration risk value label.

[0107] A better approach is to establish a risk-driven dynamic interface reconstruction mechanism. Compared to traditional methods that only statically display all clauses, which leads to information overload and the submergence of key risk warnings for high-risk enterprises, this method uses natural language processing to intelligently identify and collapse non-core clauses, solving the problem of information redundancy. Highly adaptable buffering results are matched in real time based on enterprise operating data, and risk response guidance is enhanced with visual highlighting. A neural rendering engine dynamically modulates rendering priority based on migration risk values, ensuring that key information is pinned to the top within seconds. This effectively solves the disconnect between insurance plans and dynamic risks caused by conventional interface rigidity.

[0108] S5: Render the insurance plan priority on the insurance interface, place the corresponding insurance plan at the top according to the enterprise type, and embed the formatted contract terms into the insurance instructions to generate insurance decision data. For details, please refer to Figure 4 .

[0109] S5.1: Based on the enterprise type and migration risk value, the temporal trend of equipment failure rate and the topological relationship of equipment cluster are integrated to generate a risk profile vector through the spatiotemporal graph convolutional network.

[0110] It should be noted that the pre-training process of the spatiotemporal graph convolutional network is as follows: historical enterprise types, historical migration risk values, historical equipment failure rate time series trends, and historical equipment cluster topology relationships are collected to form a historical training data set. The historical equipment cluster topology relationships are input into the graph convolutional network branch to extract the spatial feature vector, and the historical equipment failure rate time series trends are input into the time series convolutional network branch to extract the temporal feature vector. The spatial feature vector and the temporal feature vector are concatenated to generate spatiotemporal fusion features. At the same time, the historical enterprise types are encoded as classification vectors, and the historical migration risk values ​​are normalized to scalars and concatenated to the spatiotemporal fusion features to form a complete input feature vector. The complete input feature vector is input into the fully connected layer to output the predicted risk profile vector. The difference between the predicted risk profile vector and the true risk profile vector is calculated using the mean square error loss function. The parameters of the graph convolutional network, the time series convolutional network, and the fully connected layer are updated through backpropagation until convergence, thus obtaining the spatiotemporal graph convolutional network.

[0111] Specifically, the enterprise type is encoded as a three-dimensional classification vector, and the migration risk value is normalized into a scalar between zero and one. At the same time, the failure rate fluctuation curve of the equipment failure rate in recent months is extracted to generate the time series trend of the equipment failure rate. The equipment connection matrix in the equipment operation log is extracted to generate the equipment cluster topology relationship.

[0112] The topological relationship of the equipment cluster is input into the graph convolutional network to generate spatial topological association, and the temporal trend of the equipment failure rate is input into the time series convolutional network to generate time fluctuation characteristics.

[0113] The output of the graph convolutional network is fused with the output of the temporal convolutional network to generate spatial-temporal fusion features, and the three-dimensional classification vector, normalized migration risk value and spatial-temporal fusion features are spliced ​​and compressed into a risk portrait vector through a fully connected layer.

[0114] S5.2: Input the risk profile vector into the neural symbolic joint engine, combine the enterprise type and migration risk value, analyze the risk pattern through the neural network component, superimpose the symbolic rule component, and output the insurance plan priority score.

[0115] Specifically, the risk profile vector is input into the three fully connected layers of the neural network component, each layer is activated by the rectified linear unit function, and the risk pattern probability distribution value is output.

[0116] At the same time, the enterprise type and migration risk value are input into the symbol rule component, and the symbol rule is executed to obtain the basic score correction value.

[0117] The risk pattern probability distribution value is converted into a benchmark score, and the basic score correction value output by the symbol rule component is superimposed to generate the insurance plan priority score.

[0118] S5.3: Dynamically adjust the display order of insurance and financial terms based on the insurance plan priority score and output interactive terms components.

[0119] Specifically, the insurance and financial terms are read, and a mapping relationship is established by associating the unique identifier of each term with the insurance plan priority score.

[0120] Arrange the clause set in descending order according to the insurance plan priority score, generate a sorted clause sequence, and configure an interactive event handler for each clause in the sorted clause sequence, including an event binding for expanding the clause details when the mouse hovers over the clause and an event binding for recording the user operation timestamp when the mouse clicks.

[0121] Encapsulate the clause identification, sorting sequence number and interaction event configuration into an interactive clause component.

[0122] S5.4: Render insurance plans in descending order of their priority scores, embed interactive clause components, capture user selection behavior in real time, and generate insurance decision data.

[0123] Specifically, an insurance plan display container is created in the insurance interface area, and the insurance plan list is sorted from high to low according to the insurance plan priority score. The plan with the highest score is displayed at the top with full details, and the remaining plans are displayed in a folded state.

[0124] Embed interactive clause components for each insurance plan and bind mouse hover event handlers and click event handlers.

[0125] Event listeners are used to capture the user's mouse hovering time, click selection actions, and check confirmation operations in real time. When the user submits an insurance application, the current company type, the user's final selected insurance plan identifier, the risk confirmation check status, and the operation timestamp are integrated to generate insurance decision data.

[0126] S6: Feedback the insurance decision data to the entropy risk assessment mechanism to update the weight coefficient of the difference factor and the trial period parameter.

[0127] S6.1: Analyze user behavior characteristics in insurance decision-making data, including risk sensitivity, clause attention, and decision hesitation index; Specifically, the risk confirmation mark, clause expansion timestamp sequence, first operation timestamp and final decision timestamp are extracted from the insurance decision data.

[0128] Based on the cumulative number of times the risk confirmation mark is true, the risk sensitivity is calculated, and the difference between adjacent time points in the clause expansion timestamp sequence is summed to generate the total clause attention time. Based on the difference between the first operation timestamp and the final decision timestamp, the actual decision time is generated.

[0129] Risk sensitivity is equal to the proportion of the number of times the risk confirmation mark is true in the total insured events, clause attention is equal to the ratio of the total clause attention time to the actual decision time, and the decision hesitation index is equal to the ratio of the actual decision time to the benchmark decision time.

[0130] S6.2: Based on risk sensitivity and clause attention, adjust the weight coefficient of the difference factor, and adjust the trial period parameters based on the decision hesitation index and risk sensitivity.

[0131] Specifically, the weight coefficient of the difference factor is adjusted according to the proportional scaling rule based on the risk sensitivity percentage value. The higher the risk sensitivity, the greater the increase in the weight coefficient.

[0132] At the same time, the weight coefficient increase is added according to the auxiliary proportion rule based on the percentage value of the clause attention. The higher the clause attention, the greater the additional increase.

[0133] Combining the decision hesitation index percentage value and the risk sensitivity percentage value, the trial duration compression ratio is obtained through a linear adjustment rule. The higher the decision hesitation index and the higher the risk sensitivity, the greater the compression ratio.

[0134] The percentage increase in the upper limit of the number of achievement calls is generated according to the risk sensitivity percentage value and the step-by-step adjustment rules. The higher the risk sensitivity, the greater the increase in the upper limit of the number of calls.

[0135] S6.3: Feedback the updated weight coefficient of the difference factor and the trial period parameter to the entropy risk assessment mechanism, reconstruct the feature fusion layer, and optimize the risk assessment accuracy.

[0136] Specifically, the weight coefficient of the updated difference factor is written into the feature fusion layer of the entropy risk assessment mechanism, replacing the original weight coefficient, and the updated trial period parameter is synchronized to the entropy risk assessment mechanism, replacing the original trial period parameter.

[0137] In the feature fusion layer, the weighted fusion ratio rules of historical performance data, trial period parameters and difference factors are reconfigured according to the new weight coefficients.

[0138] The error rate between the predicted migration risk value and the actual migration risk value is recalculated using the updated feature fusion layer parameters. When the error rate decreases by more than the preset optimization threshold, the risk assessment accuracy optimization is confirmed to be complete.

[0139] It should be noted that the preset optimization threshold is set based on the historical risk assessment error fluctuation range and is used to determine the effectiveness of parameter updates. For example, the error rate reduction threshold is set at 5%.

[0140] In summary, the present invention integrates the historical performance data of credit institutions, real-time trial period parameters and matching decisions through the entropy risk assessment mechanism, dynamically generates migration risk values, realizes risk quantification of multi-source heterogeneous data, and accurately captures the enterprise risk migration trajectory; when the risk value exceeds the threshold, it automatically triggers the insurance clause folding and result highlighting, and feeds back to the insurance interface in seconds to generate visual decision support; at the same time, through the closed-loop feedback of insurance decision data, the difference factor weights and trial period parameters are dynamically calibrated, so that the entropy risk assessment mechanism can be continuously self-optimized to improve the risk prediction accuracy.

[0141] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-dimensional scientific and technological achievements and innovation resource matching method based on big data, characterized by: include: Obtain enterprise operating data and insurance and financial terms, input into a dynamic classification model, output enterprise type, and link to the achievement transformation stage; If it is a free trial phase, call the preset agreement template to generate a free license agreement and monitor the trial period parameters. If it is a paid conversion phase, extract the achievement ID from the achievement pool, calculate the difference with the enterprise ID, generate a difference factor, and generate a matching decision based on the requirements list; The entropy risk assessment mechanism is used to integrate the historical performance data provided by the credit institutions, and the migration risk value is generated by combining the matching decision and the trial period parameters; When the migration risk value exceeds the preset risk threshold, the non-core clauses of the insurance and financial clauses are folded and the risk buffer achievement option is highlighted, triggering the interface rendering instruction and binding it to the insurance interface; Render the insurance plan priority on the insurance application interface, place the corresponding insurance plan at the top according to the enterprise type, and embed the formatted contract terms into the insurance instructions to generate insurance decision data; The insurance decision data is fed back to the entropy risk assessment mechanism to update the weight coefficient of the difference factor and the trial period parameters.

2. The method for matching multi-dimensional scientific and technological achievements with innovative resources based on big data according to claim 1, characterized in that: The enterprise operating condition data includes equipment operation logs, revenue scale ranges, demand lists and enterprise identification; The enterprise types include emergency failure type, sub-health type and normal operation type.

3. The method for matching multi-dimensional scientific and technological achievements with innovative resources based on big data according to claim 1, characterized in that: The specific steps for linking to the achievement transformation stage are as follows: Real-time collection of equipment operation logs, revenue ranges, demand lists, corporate logos, and insurance and financial terms; Obtain equipment failure rates based on equipment operation logs, convert revenue scale ranges into level labels, parse demand lists into demand density, and analyze insurance and financial terms to generate a liability intensity index. Integrate equipment failure rate, grade label, demand density and responsibility intensity index, build a multi-dimensional feature vector input dynamic classification model, output enterprise type and insurance adaptation identification, and link it to the results transformation stage.

4. The method for matching multi-dimensional scientific and technological achievements with innovative resources based on big data according to claim 3, characterized in that: The specific steps for monitoring trial period parameters are as follows: The preset agreement template is matched based on the enterprise type and insurance adaptability identifier, and insurance and financial terms constraints are injected. The trial period parameters and monitoring indicator set are generated based on the equipment failure rate and demand density, and a free license agreement is output. Through distributed nodes, device operation logs and results call behaviors are captured in real time, and abnormal fluctuations are identified by comparing the monitoring indicator set, triggering the compression of the trial period and the upgrade of insurance liability instructions.

5. The method for matching multi-dimensional scientific and technological achievements with innovative resources based on big data according to claim 4, characterized in that: The specific steps for generating a matching decision by combining the requirements list are as follows: Extract the achievement identifiers that have not been converted in recent years from the achievement pool, and filter out the achievement identifiers whose matching degree with the requirements list is lower than the preset matching degree threshold; The pre-screened achievement identification and enterprise identification are encoded into an achievement identification vector and an enterprise identification vector through a multimodal spatiotemporal encoder; The difference between the outcome identification vector and the enterprise identification vector is calculated through the Transformer cross-attention mechanism to generate a difference factor, which is then combined with the requirements list to generate a matching decision.

6. The method for matching multi-dimensional scientific and technological achievements with innovative resources based on big data according to claim 5, characterized in that: The difference factor refers to a dynamic quantitative indicator that quantifies the degree of matching between scientific and technological achievements and enterprise needs.

7. The method for matching multi-dimensional scientific and technological achievements with innovative resources based on big data according to claim 1, characterized in that: The specific steps for generating the migration risk value by combining the matching decision and the trial period parameters are as follows: Call the credit institution interface to obtain historical performance data, synchronously combine the trial period parameters and difference factors, clean abnormal data through dynamic fluctuation filtering algorithm, and output a standardized data matrix; The standardized data matrix is ​​input into the feature fusion layer, and the influence weights of historical performance data, trial period parameters, and difference factors are dynamically adjusted to generate a multi-dimensional fusion feature tensor; Based on the multi-dimensional fusion feature tensor, the migration risk value is calculated through the entropy risk assessment mechanism.

8. The method for matching multi-dimensional scientific and technological achievements with innovative resources based on big data according to claim 7, characterized in that: The trigger interface rendering instruction is bound to the insurance interface. The specific steps are as follows: Parse insurance and financial terms, identify core and non-core terms through natural language analysis, and automatically collapse non-core terms into expandable grayscale panels when the migration risk value exceeds the preset risk threshold, generating a collapsed term set. Extract matching buffer results from the results pool, and generate a highlight result set based on the enterprise working condition data and the matching buffer results; The collapsed clause set and the highlighted result set are input into the neural rendering engine, and the rendering priority is dynamically modulated by migrating the risk value to generate a visualization instruction set. The visualization instruction set is pushed to the insurance interface node through a low-latency communication protocol to update the visualization component.

9. The method for matching multi-dimensional scientific and technological achievements with innovative resources based on big data according to claim 8, characterized in that: The specific steps of generating insurance decision data are as follows: Based on the enterprise type and migration risk value, the time series trend of equipment failure rate and the topological relationship of equipment cluster are integrated to generate a risk profile vector through the spatiotemporal graph convolutional network; The risk profile vector is input into the neural symbolic joint engine, combined with the enterprise type and migration risk value, and the risk pattern is analyzed through the neural network component. The symbolic rule component is superimposed to output the insurance plan priority score; Dynamically adjust the display order of insurance and financial terms based on the insurance plan priority score and output interactive terms components; Render insurance plans in descending order of their priority scores, embed interactive clause components, capture user selection behavior in real time, and generate insurance decision data.

10. The method for matching multi-dimensional scientific and technological achievements with innovation resources based on big data according to claim 9, characterized in that: The specific steps for updating the weight coefficient of the difference factor and the trial period parameter are as follows: Analyze user behavior characteristics in insurance decision-making data, including risk sensitivity, clause attention, and decision hesitation index; Based on risk sensitivity and clause attention, the weight coefficient of the difference factor is adjusted, and the trial period parameters are adjusted in combination with the decision hesitation index and risk sensitivity; The updated weight coefficient of the difference factor and the trial period parameter are fed back to the entropy risk assessment mechanism to reconstruct the feature fusion layer and optimize the risk assessment accuracy.

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