Enterprise Innovation Cloud Assessment and Resource Matching System and Methodology

Abstract

This invention discloses a cloud-based enterprise innovation capability assessment and resource adaptation system and method, relating to the field of enterprise innovation management technology. The system includes the following steps: periodically acquiring industry lifecycle data, innovation characteristic data, and external data source data; performing a weighted average calculation on the industry lifecycle data, innovation characteristic data, and external data source data to obtain integrated innovation characteristic data; and applying sliding window processing to the integrated innovation characteristic data within the same period to output a trend curve of the integrated innovation characteristic data. This solution effectively eliminates interference from industry cycle fluctuations through dynamic alignment of the baseline curve and joint judgment using continuous windows. Simultaneously, by combining the trend curve to locate the start time of anomalies, it provides a precise time anchor point for subsequent causal analysis, accurately identifying persistent abnormal states in the integrated innovation characteristic data and avoiding misjudgments caused by single fluctuations.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of enterprise innovation management, in particular to an enterprise innovation cloud evaluation and resource adaptation system and method. BACKGROUND

[0002] With the rapid development of global technology and industry, enterprise innovation has become an important indicator to measure the core competitiveness and sustainable development ability of technology-based enterprises. Especially under the impetus of digitalization and intelligentization, enterprise innovation activities are becoming increasingly complex, and the innovation process involves multiple dimensions such as technology research and development, market transformation, organization coordination, and fund operation. At the same time, the industry life cycle, external policy environment, technological trends, and market changes have a more significant impact on innovation. Therefore, building a scientific, dynamic, and multi-source data-oriented innovation evaluation and resource adaptation system has become an important research direction in the field of enterprise management and technology services.

[0003] However, in the process of implementing the technical solutions of the present application, it is found that the above-mentioned technology at least has the following technical problems:

[0004] The prior art cannot effectively integrate industry life cycle, enterprise innovation signs, and external data source data, resulting in insufficient comprehensiveness and dynamics of innovation evaluation, lack of trend analysis of enterprise innovation sign data, difficulty in identifying abnormalities and outbreak nodes, and insufficient pertinence of subsequent innovation resource intervention schemes, which may lead to innovation resource allocation lag or overkill, and cannot accurately support enterprise innovation capability improvement. SUMMARY

[0005] The purpose of the present application is to provide an enterprise innovation cloud evaluation and resource adaptation system and method to solve the problems raised in the background art.

[0006] In order to achieve the above-mentioned purpose, the technical solutions of the present application are as follows:

[0007] In a first aspect, the present application discloses an enterprise innovation cloud evaluation and resource adaptation method, which is applied to the innovation cloud evaluation and resource adaptation of technology-based enterprises at various stages, comprising the following steps:

[0008] Periodically acquiring industry life cycle data, innovation sign data, and external data source data;

[0009] Weighted average calculation is performed on the industry life cycle data, innovation sign data, and external data source data to obtain fused innovation sign data;

[0010] The fused innovation sign data in the same cycle is subjected to sliding window processing, and a trend curve of the fused innovation sign data is outputted;

[0011] Call a preset reference curve, compare the trend curve with the preset reference curve, and obtain a deviation value;

[0012] Determine whether the deviation value is less than a preset threshold one in the continuous N sliding windows, and if yes, determine that the fusion innovative vital sign data corresponding to the deviation value is abnormal fusion innovative vital sign data, and further determine an abnormal time point in combination with the trend curve;

[0013] Determine whether the deviation value is greater than a preset threshold two in a single sliding window, and if yes, determine that the fusion innovative vital sign data corresponding to the deviation value is burst fusion innovative vital sign data, and further determine a burst time point in combination with the trend curve;

[0014] Serially connect the abnormal time point and the burst time point in the same period on a time line to generate an innovative event chain;

[0015] According to the innovative event chain, a preset knowledge reasoning and resource matching model is called to output an innovative resource intervention scheme.

[0016] In a second aspect, the present application discloses an enterprise innovation cloud evaluation and resource adaptation system, comprising:

[0017] A data acquisition module is configured to periodically acquire industry life cycle data, innovative vital sign data and external data source data;

[0018] A fusion innovative vital sign data calculation module is configured to perform weighted average calculation on the industry life cycle data, the innovative vital sign data and the external data source data to obtain fusion innovative vital sign data;

[0019] A deviation value calculation module is configured to perform sliding window processing on the fusion innovative vital sign data in the same period to output a trend curve of the fusion innovative vital sign data;

[0020] A preset reference curve is called, the trend curve is compared with the preset reference curve, and a deviation value is obtained;

[0021] A time point determination module is configured to determine whether the deviation value is less than a preset threshold one in the continuous N sliding windows, and if yes, determine that the fusion innovative vital sign data corresponding to the deviation value is abnormal fusion innovative vital sign data, and further determine an abnormal time point in combination with the trend curve;

[0022] Determine whether the deviation value is greater than a preset threshold two in a single sliding window, and if yes, determine that the fusion innovative vital sign data corresponding to the deviation value is burst fusion innovative vital sign data, and further determine a burst time point in combination with the trend curve;

[0023] An innovative resource intervention scheme output module is configured to serially connect the abnormal time point and the burst time point in the same period on a time line to generate an innovative event chain;

[0024] According to the innovation event chain, a preset knowledge reasoning and resource matching model is called to output an innovation resource intervention scheme.

[0025] Compared with the prior art, the present application has the following beneficial effects:

[0026] 1. The present application effectively eliminates the interference caused by industry cycle fluctuations through dynamic alignment of the reference curve and joint judgment of the continuous window, and combines the trend curve to locate the abnormal starting time, providing accurate time anchor points for subsequent causal analysis, accurately identifying the persistent abnormal state in the fusion innovation sign data, avoiding misjudgment caused by single fluctuation, focusing the subsequent causal chain analysis on specific time periods, and improving the efficiency of abnormal root cause analysis.

[0027] 2. The present application not only identifies external events related to the anomaly, but also quantifies the direction and strength of the effect, so that the abnormal attribution analysis changes from qualitative judgment to quantitative calculation, accurately identifies the external driving factors that cause the anomaly of the innovation sign data, avoids misjudgment caused by ignoring the influence of the external environment, provides data support for formulating accurate resource intervention schemes, and effectively solves the resource allocation deviation problem caused by the lack of external event correlation analysis in the prior art.

[0028] 3. The present application integrates the knowledge reasoning engine and the rule engine, combines the time sequence relationship in the event chain with external causal factors, realizes dynamic optimization of resource matching, and through abnormal-external event causal chain analysis, can predict resource demand and adjust intervention timing in advance, improve the pertinence and timeliness of the innovation resource intervention scheme, and solve the resource allocation lag problem caused by the lack of causal analysis and dynamic matching in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0029] The disclosure of the present application will be described with reference to the accompanying drawings. It should be understood that the drawings are only for illustrative purposes, and are not intended to limit the scope of protection of the present application. In the drawings, the same reference numerals are used to refer to the same parts. Among them:

[0030] Figure 1 The step flow chart of the enterprise innovation power cloud evaluation and resource adaptation method of the present application;

[0031] Figure 2 The flowchart for generating a trend curve provided by the present application;

[0032] Figure 3 The flowchart for generating an innovation event chain provided by the present application;

[0033] Figure 4 The flowchart for outputting an innovation resource intervention scheme provided by the present application;

[0034] Figure 5 The module function schematic diagram of the enterprise innovation cloud evaluation and resource adaptation system provided by the application is shown in the figure. DETAILED DESCRIPTION

[0035] It is easy to understand that, according to the technical scheme of the application, those skilled in the art can propose a plurality of structure modes and implementation modes which can be replaced with each other without changing the essential spirit of the application. Therefore, the following specific embodiments and drawings are only exemplary descriptions of the technical scheme of the application, and should not be regarded as the whole or as a limitation or restriction on the technical scheme of the application.

[0036] SUMMARY

[0037] In the prior art, the innovation of science and technology enterprises is mainly evaluated by relying on static index system and single dimension data, which is difficult to dynamically reflect the influence of industry cycle change and external environment on enterprise innovation; the traditional method usually adopts fixed time interval data collection method, which lacks the fusion processing ability of multi-source heterogeneous data, resulting in that the evaluation result lags behind the actual innovation process, for example, a certain intelligent hardware enterprise in the product research and development stage, due to the influence of supply chain fluctuation and policy adjustment on research and development investment, leading to the disconnection between innovation resource allocation and market demand, resulting in the extension of research and development cycle and the waste of resources.

[0038] In order to solve the above problems, it is found that the existing technology has the core defects of insufficient data integration ability, lack of trend analysis and lagging resource adaptation; by analyzing the correlation between enterprise innovation and industry life cycle and external events, a dynamic data fusion mechanism is constructed, combining industry stage characteristics with real-time data; further research shows that the use of sliding window processing can effectively capture the continuous change trend of innovation signs, and the deviation detection based on the reference curve can identify abnormal fluctuations and explosive growth nodes; finally, through the innovation event chain connected with key time points, combined with knowledge reasoning and resource matching model, accurate intervention scheme is generated.

[0039] After introducing the basic idea of the application, the embodiments of the application will be specifically introduced with reference to the drawings.

[0040] Embodiment one:

[0041] Please refer to Figures 1-4 , the enterprise innovation cloud evaluation and resource adaptation method is applied to the innovation cloud evaluation and resource adaptation of science and technology enterprises at each stage, including the following steps:

[0042] Periodically acquire industry life cycle data, innovation sign data and external data source data;

[0043] perform weighted average calculation on the industry life cycle data, the innovation sign data and the external data source data to obtain fused innovation sign data;

[0044] perform sliding window processing on the fused innovation sign data in the same period to output a trend curve of the fused innovation sign data;

[0045] call a preset reference curve, compare the trend curve with the preset reference curve, and obtain a deviation value;

[0046] determine whether the deviation value is less than a preset threshold one in the continuous N sliding windows, and if yes, determine that the fused innovation sign data corresponding to the deviation value is abnormal fused innovation sign data, and further determine an abnormal time point in combination with the trend curve;

[0047] determine whether the deviation value is greater than a preset threshold two in a single sliding window, and if yes, determine that the fused innovation sign data corresponding to the deviation value is burst fused innovation sign data, and further determine a burst time point in combination with the trend curve;

[0048] concatenate the abnormal time point and the burst time point in the same period in a time line to generate an innovation event chain;

[0049] according to the innovation event chain, call a preset knowledge reasoning and resource matching model, and output an innovation resource intervention scheme.

[0050] The industry life cycle data refers to structured data containing the stage of the industry and the corresponding weight parameter obtained through an industry database interface, and can be specifically realized by calling a standardized data package through an API interface, and is used to reflect the constraint condition of the overall development trend of the industry on the innovation activity.

[0051] The innovation sign data covers operation data in the dimensions of technical research and development, market conversion, etc., and can be specifically collected in real time through an enterprise ERP system, and is used to quantify the core indicators of the innovation capability of the enterprise.

[0052] The external data source data includes policy files, market intelligence and other heterogeneous data, and can be specifically captured by a network crawler to grab public information sources, and is used to capture the influence of the external environment on the innovation of the enterprise.

[0053] The sliding window processing refers to calculating the mean value of the time series data according to a fixed time length, and can be specifically realized by sliding calculation with a 30-day window length, and is used to eliminate data noise and extract trend characteristics.

[0054] The preset reference curve refers to an innovation sign standard change curve generated based on historical data training, and can be specifically generated by modeling the data of industry benchmark enterprises through a machine learning model, and is used to provide a dynamic comparison reference.

[0055] In the implementation process, the enterprise periodically collects three types of data: industry life cycle data, innovation sign data, and external data source data, uses weighted average calculation to obtain fused innovation sign data;

[0056] Subsequently, a sliding window method (such as a window length of 3 months and a step length of 1 month) is used to analyze the trend of the fused innovation sign data, and the same type of data as the preset reference curve is compared to calculate the deviation value;

[0057] If the deviation values of the continuous N (such as 4) windows are all lower than the preset threshold one (such as -8), the abnormal fused innovation sign data is identified, and the abnormal time point is determined by searching the position of the abnormal fused innovation sign data in the trend curve; if the deviation value in a single sliding window is higher than the preset threshold two (such as +10), the burst fused innovation sign data is identified, and the burst time point is determined by searching the position of the burst fused innovation sign data in the trend curve;

[0058] The abnormal and burst time points are concatenated into an innovation event chain, and based on a knowledge reasoning and resource matching model, customized innovation resource intervention suggestions (such as special fund investment, industry-university-research cooperation, external expert introduction, etc.) are output.

[0059] Beneficial effects: The application effectively solves the problem of multi-source data integration and realizes dynamic correlation analysis of industry characteristics and enterprise innovation. Through the deviation detection mechanism, the abnormal stagnation and sudden growth nodes in the innovation process are accurately identified to provide accurate time windows for resource allocation; the knowledge reasoning and resource matching model based on the event chain can establish a mapping relationship between innovation fluctuations and resource demand, avoid the blindness and lag of resource allocation, and improve the innovation efficiency of technology-based enterprises.

[0060] The application further proposes that the innovation sign data is periodically obtained through the enterprise internal management system, and the innovation sign data includes technical research and development data (such as research and development investment amount, research and development project quantity, patent application and authorization quantity, etc.), market conversion data (such as patent conversion rate, product market share, new product market quantity, market feedback score, etc.), organization coordination data (such as team collaboration frequency, number of inter-departmental coordination projects, key position talent flow rate, internal training frequency, etc.), and fund resilience data (such as cash flow, fund chain breakage risk, credit limit utilization rate, etc.);

[0061] The industry life cycle data is periodically obtained through the application program interface of the industry database, and the industry life cycle data includes the current life cycle stage of the industry (such as budding, growth, maturity, and decline) and the preset weight parameter of the innovation sign data corresponding to each stage (such as the weight parameter of the technical research and development data in the growth stage is 0.3, and the weight parameter of the fund resilience data in the decline stage is 0.5);

[0062] Periodically acquire external data source data through web crawlers, including structured data related to enterprises and their industries (such as industry public financial reports, market statistical reports), semi-structured data (such as news information, policy announcements, public opinion monitoring results), and unstructured data (such as expert advice, network comments, forum discussion content, etc.).

[0063] Among them, the enterprise internal management system refers to the digital platform used by enterprises for daily operation and management, which can be realized by adopting ERP system or customized data acquisition system, and is used to integrate real-time data generated by internal innovation activities such as technology research and development and market conversion;

[0064] The application program interface of the industry database refers to the programming interface that provides standardized data access, which can be realized by adopting RESTful API or GraphQL interface, and is used to dynamically obtain industry life cycle stages and their corresponding weight parameters;

[0065] Web crawler refers to a program that automatically collects Internet public data, which can be realized by adopting Python-based Scrapy framework or distributed crawler system, and is used to crawl policy files, market reports, social media and other external data sources to expand data coverage.

[0066] In the specific implementation process, the enterprise internal management system extracts core innovation sign data such as technology research and development data and market conversion data at a fixed cycle, ensuring the real-time and completeness of internal data;

[0067] The application program interface of the industry database obtains industry life cycle stage information at a fixed cycle, such as start-up, growth, maturity or decline, and synchronizes the corresponding weight parameters of the stage, for example, technology research and development data may be given higher weight in the growth period;

[0068] The web crawler configures differential collection strategies for different external data sources, such as using database direct connection for structured data and using natural language processing technology for semantic analysis for unstructured data, and finally forms a unified format of external data source data set. Through the cooperation of the above three data acquisition methods, multi-dimensional information of enterprise internal innovation capability, industry dynamics and external environment can be covered, providing comprehensive input for subsequent fusion calculation.

[0069] By the technical solution, the application solves the problems of single data source and weight update lag in the prior art, realizes dynamic integration of multi-dimensional data, for example, when an industry enters a mature period, the weight of technical research and development data is automatically reduced, and the weight of market conversion data is increased, so that the fusion innovation sign data is more in line with the innovation characteristics of enterprises at this stage; at the same time, the introduction of external data sources can capture the influence of policy changes or market emergencies on innovation, avoid the deviation of evaluation results from the actual situation due to external environment mutation, and thus provide a reliable data basis for subsequent trend analysis, anomaly detection and resource adaptation.

[0070] The application further proposes linear calculation of the industry life cycle data, the innovation sign data and the external data source data to obtain the fusion innovation sign data, which specifically includes:

[0071] According to the preset weight parameter of the innovation sign data, the innovation sign data is weighted and calculated, and weighted innovation sign data is output;

[0072] The external data source data is filtered and processed;

[0073] The weighted innovation sign data and the filtered external data source data are normalized;

[0074] The weighted innovation sign data and the filtered external data source data are normalized.

[0075] The weighted innovation sign data and the filtered external data source data are normalized.

[0076] The filtering process refers to quality filtering of the external data source, which can be realized by confidence calculation combined with a set value, for example, after feature extraction and normalization processing, data with a confidence lower than a set value is removed, so as to exclude the interference of noise or low confidence data on the fusion result.

[0077] The normalization process refers to converting data of different dimensions or orders of magnitude to a unified scale, which can be realized by maximum-minimum value standardization or Z-score standardization method, for example, the weighted innovation sign data and the external data source data are mapped to the [0, 1] interval, so as to eliminate the influence of data distribution difference on weighted average calculation.

[0078] The weighted average calculation refers to calculating the mean value after weighting different sources of data, which can be realized by using fixed weight or dynamic weight allocation strategy, for example, the normalized weighted innovation sign data and the screened external data source data are weighted and summed according to a preset proportion to form a comprehensive evaluation index.

[0079] The specific implementation process is as follows:

[0080] First, the weight parameters of the preset innovation sign data are obtained, for example: technical research and development data 0.35, market conversion data 0.3, organizational synergy data 0.2, and financial resilience data 0.15; the innovation sign data is weighted and calculated by using the preset weight parameters of the innovation sign data to obtain the weighted innovation sign score, so as to ensure that the evaluation system can fully highlight the outstanding performance of enterprises in technology and market, and take into account the stability of organization and funds;

[0081] Subsequently, the external data source data is screened;

[0082] In the data normalization stage, the range normalization method is used to normalize the weighted innovation sign data and the screened external data source data to the interval [0, 1] to eliminate the dimensional and scale differences and ensure the fairness of the subsequent weighted average process;

[0083] After normalization, the fusion weight of the weighted innovation sign data and the screened external data source data (such as weighted innovation sign data 0.7 and screened external data source data 0.3) is set, and the weighted average is performed to finally form the fusion innovation sign data reflecting the innovation power of the enterprise. The fusion data can not only dynamically respond to industry policies and market trends, but also can make up for the limitations of single internal evaluation, and realize the improvement of objectivity and forward-looking of innovation power evaluation.

[0084] Through the above technical solution, the present application solves the problem of fusion result deviation caused by complex data sources and uneven quality in the prior art. Through phased weighting, screening and normalization processing, the accuracy and consistency of multi-source data fusion are improved, providing a reliable data foundation for subsequent trend analysis.

[0085] The present application further proposes that the screening processing of the external data source data specifically includes:

[0086] The external data source data is subjected to data formatting, deduplication, and content normalization processing;

[0087] The processed external data source data is subjected to feature extraction, the extracted features are subjected to normalization processing, and linear weighting calculation is performed thereon to obtain the confidence of the external data source data;

[0088] determining whether the confidence is lower than a set value, and if so, eliminating the external data source data corresponding to the confidence.

[0089] Among them, data formatting refers to converting structured, semi-structured and unstructured data from different sources into a unified format, which can be realized by using JSON or XML standard format conversion tools, and is used to eliminate data heterogeneity;

[0090] De-duplication refers to identifying and deleting duplicate data entries through hash algorithm or similarity matching algorithm, such as using SimHash algorithm to calculate text similarity, which is used to eliminate redundant data interference;

[0091] Content normalization processing refers to word segmentation, part-of-speech tagging and entity recognition of unstructured text, such as using NLP toolkit, which is used to extract standardized semantic features;

[0092] Feature extraction refers to extracting key indicators related to innovation signs from normalized data, such as extracting keyword weights through TF-IDF algorithm, which is used to quantify the influence factors of external data;

[0093] Confidence refers to calculating the reliability of feature indicators through linear weighting, such as using AHP to determine the weight coefficients of each feature, which is used to evaluate data reliability;

[0094] Set value refers to the pre-defined confidence threshold, such as the critical value obtained by training historical data, which is used to dynamically filter valid data.

[0095] The specific process is as follows:

[0096] First, the external data source data is unified in format, and the repeated data is automatically identified and eliminated through hash algorithm and intelligent text comparison technology. For data content, the descriptive text is normalized by natural language processing (NLP) module, such as unified terminology expression, elimination of irrelevant information and standardized time and unit expression;

[0097] After the preliminary cleaning, the data features are extracted, and the influence of different dimensions or dimensions is eliminated by normalization method. The normalized feature data is linearly weighted according to historical experience and expert knowledge, so as to obtain the confidence of each external data source data;

[0098] The confidence of all external data source data is determined by set value. If the confidence of a certain external data source data is lower than the set value (such as 0.6), the corresponding data is automatically eliminated, ensuring that the data basis of subsequent innovation evaluation has high accuracy and reliability.

[0099] Beneficial effects: the present application solves the technical problem that the fusion calculation is distorted due to the uneven quality of external data sources; by constructing a complete processing link including data cleaning, feature quantization and dynamic screening, invalid data and noise interference are effectively eliminated, for example, industry report data with contradictions are avoided from being included in analysis, ensuring that the data input into the weighted average calculation link has reliability and consistency, providing a high-quality data foundation for subsequent trend analysis and resource matching.

[0100] The present application further proposes that the fusion innovative sign data in the same period is subjected to sliding window processing, and the trend curve of the fusion innovative sign data specifically includes:

[0101] The fusion innovative sign data of each type in the same period is sorted according to the timestamp;

[0102] From the starting point of the period, the fusion innovative sign data is intercepted with a set sliding window length as the fusion innovative sign data in the sliding window;

[0103] The mean value of the fusion innovative sign data in each sliding window is calculated;

[0104] The center time of the sliding window is taken as the horizontal coordinate, and the mean value of the fusion innovative sign data in each sliding window is taken as the vertical coordinate, and all sliding windows are sequentially spliced to form the trend curve of each type of fusion innovative sign data.

[0105] Wherein, the sliding window length refers to the length of the time range for intercepting data, which can be realized by fixed time length such as 30 days or dynamically adjusted window, and its function is to balance the data fluctuation sensitivity and trend stability;

[0106] The center time of the sliding window refers to the midpoint time of the window coverage time period, which can be calculated by the arithmetic average of the window start and end time, and its function is to eliminate the deviation of the window edge time point to the trend positioning;

[0107] The trend curve splicing refers to connecting the mean values corresponding to the center points of each window in time sequence, which can be realized by linear interpolation or piecewise function, and its function is to form a continuous and analyzable data change trajectory.

[0108] Specifically, first, sort the fusion innovation sign data of each type in the same period according to the timestamp; set the sliding window length to 3 months, and the window step to 1 month; from January, sequentially perform window interception on the fusion innovation sign data of each type; in each sliding window, calculate the mean value of the fusion innovation sign data of each type, for example, 1-3 months, 2-4 months, 3-5 months, and 4-6 months; in order to enhance the fineness of the analysis, set the window center time (for example, February, March, April, May) as the horizontal coordinate point of the trend curve, and the mean value as the vertical coordinate, and draw the trend curve of the fusion innovation sign data of each type.

[0109] Through the above technical solution, the present application effectively solves the problems of coarse trend analysis granularity and phase misalignment in the prior art; through the combination of the sliding window and the center time positioning, the short-period fluctuation characteristics in the fusion data can be accurately captured, providing a high-precision trend reference for subsequent anomaly detection and outbreak point identification; for example, when detecting market conversion data anomalies, the trend curve generated by the method can clearly present the continuous downward trend of the conversion rate in a specific 3-day window, while the traditional monthly statistics cannot timely capture such short-term anomaly signals.

[0110] The present application further proposes to judge whether the deviation value in the continuous N sliding windows is less than the preset threshold, and if yes, determine that the fusion innovation sign data corresponding to the deviation value is abnormal fusion innovation sign data, and then determine the abnormal time point in combination with the trend curve, specifically comprising:

[0111] Call the preset reference curve corresponding to each type of fusion innovation sign data;

[0112] Align the preset reference curve with the trend curve of the same type of fusion innovation sign data in the time period;

[0113] For each time point, respectively take the trend curve value and the reference curve value;

[0114] Calculate the deviation value according to the trend curve value and the reference curve value, and the specific calculation formula is as follows:

[0115]

[0116] In the formula, denotes the deviation value, denotes the trend curve value, denotes the reference curve value;

[0117] Detect the deviation value in time sequence for all sliding windows;

[0118] determine that the fusion innovative sign data corresponding to the deviation value is abnormal fusion innovative sign data if the deviation value is less than a preset threshold one, yes; the preset threshold one is set according to the type of the fusion innovative sign data;

[0119] Further, the starting and ending sliding windows corresponding to the abnormal fusion innovative sign data are located in combination with the trend curve, and the starting window time point of the continuous N sliding windows is taken as the abnormal time point.

[0120] Wherein, the deviation value refers to the difference between the trend curve value and the reference curve value, which can be calculated by using Euclidean distance or standardized mean square error, and is used to objectively reflect whether the data fluctuation exceeds the reasonable range.

[0121] The specific steps are as follows:

[0122] The reference curves of various types of innovative sign data are fitted by using the data of the historical best innovative performance period of the enterprise as a comparative reference; the trend curve of the innovative sign data of the same type in the current period is aligned with the reference curve on the time axis, so that the innovative activities corresponding to each time point are comparable;

[0123] The trend curve value and the reference curve value are extracted respectively, and the deviation value of the two is calculated;

[0124] It is judged whether the deviation value in the continuous N (such as 4) sliding windows is less than the preset threshold one (such as-8), if it is satisfied, the innovative sign data corresponding to the window is determined as abnormal fusion innovative sign data, further combined with the trend curve, the abnormal starting and ending windows are located, and the starting window time point of the abnormal interval is taken as the abnormal time point, which is convenient for subsequent resource intervention and strategy adjustment.

[0125] Through the above technical scheme, the present application can accurately identify the persistent abnormal state in the fusion innovative sign data, avoid misjudgment caused by single fluctuation, effectively distinguish between incidental events and systematic abnormalities through the sliding window continuous detection mechanism, provide reliable time sequence positioning basis for innovation resource intervention; The accurate determination of the abnormal time point makes the subsequent causal chain analysis focus on the external events in a specific period, and improves the efficiency of abnormal root cause analysis.

[0126] The present application further proposes that after determining the abnormal time point in combination with the trend curve, the correlation between the abnormal fusion innovative sign data and the external data source data of the abnormal time point is calculated, which specifically includes:

[0127] The correlation between each type of abnormal fusion innovative sign data and the external data source data of the abnormal time point is calculated, and the specific calculation formula is as follows:

[0128]

[0129] In the formula, represents the correlation calculation result of the abnormal fusion innovative sign data and the external data source data of the abnormal time point, represents the abnormal time point, represents the center time point of the sliding window, represents the time range of the sliding window, represents the value of the abnormal fusion innovative sign data at time , represents the value of the external data source data at time , represents the mean value of the abnormal fusion innovative sign data within the sliding window, represents the mean value of the external data source data within the sliding window;

[0130] The total correlation score is calculated by weighted fusion of each correlation calculation result;

[0131] It is judged whether the total correlation score is greater than a preset correlation value. If yes, the external data source data of the abnormal time point is input into the pre-constructed Bayesian network, and the influence probability difference value of the abnormal fusion innovative sign data is output;

[0132] According to the influence probability difference value, the influence direction and influence strength of the external data source data of the abnormal time point on the abnormal fusion innovative sign data are determined, and an abnormal-external event causal chain is generated;

[0133] The influence direction is determined according to the positive and negative of the influence probability difference value, and the influence strength is determined according to the absolute value of the influence probability difference value;

[0134] The Bayesian network takes each type of abnormal fusion innovative sign data and external data source data of the abnormal time point as a node variable, and obtains the dependency relationship between nodes through deep learning inference.

[0135] The correlation calculation refers to quantifying the correlation degree between abnormal data and external events by statistical methods, which can be realized by Pearson correlation coefficient or grey correlation degree algorithm, and is used for screening external events that have potential causal relationship with abnormal data fluctuations;

[0136] The weighted fusion calculation refers to comprehensive evaluation of multiple correlation indicators, which can use entropy weight method or analytic hierarchy process to determine the weight parameter, and is used to eliminate the deviation of a single correlation indicator;

[0137] The Bayesian network refers to a causal relationship inference tool based on a probabilistic graphical model, which can be realized by training the conditional probability table between nodes through historical data, and is used for quantitative analysis of the dynamic influence of external events on abnormal data;

[0138] The influence probability difference refers to the difference in the probability of abnormal data before and after the introduction of the external event by the Bayesian network, which can be calculated by the difference between the posterior probability and the prior probability, and is used to represent the driving strength of the external event on the abnormal data.

[0139] The specific implementation process is: when the abnormal time point is detected, first, multi-dimensional correlation analysis is performed on the abnormal fusion innovative sign data and the external data source data of the abnormal time point, for example, if the enterprise technology research and development data deviates from the benchmark curve for a long time, the external data source data such as policy changes and market fluctuations in this period needs to be extracted, and the correlation scores of each type of abnormal fusion innovative sign data and the external data source data of the abnormal time point are calculated, and the total correlation score is calculated by weighted fusion of each correlation score;

[0140] Further, when the total correlation score exceeds the preset correlation value, the external data source data of the corresponding abnormal time point is input into the pre-constructed Bayesian network, which constructs the probability dependence relationship between each type of abnormal fusion innovative sign data and external data source data based on historical data, and can output the influence probability difference of the external data source data leading to the abnormal fusion innovative sign data;

[0141] According to the positive and negative signs of the influence probability difference, it can be judged whether the external data source data suppresses or intensifies the abnormal fusion innovative sign data, and the absolute value size reflects the strength of the effect, and the finally generated causal chain clearly marks the correlation mode between the external data source data and the abnormal fusion innovative sign data, providing traceable decision basis for subsequent resource adaptation.

[0142] Through the above technical solution, the application can accurately identify the external driving factors leading to the abnormal innovative sign data, avoid misjudgment caused by ignoring the influence of the external environment, for example, when the fund resilience data is detected to be abnormal, the same period bank credit policy adjustment event can be quickly associated, and the specific value of the policy that makes the fund abnormal probability increase is calculated. The quantitative analysis of such causal relationship provides data support for formulating accurate resource intervention schemes, and effectively solves the resource allocation deviation problem caused by the lack of external event correlation analysis in the prior art.

[0143] The application further proposes to connect the abnormal time points and the outbreak time points in the same period in series on the timeline to generate an innovative event chain, which specifically includes:

[0144] Labeling each abnormal fusion innovative sign data and outbreak fusion innovative sign data detected in the same period as a specific type of fusion innovative sign data;

[0145] Recording the occurrence sliding window and the duration of each abnormal fusion innovative sign data and outbreak fusion innovative sign data;

[0146] According to the recording results, the relationship between the adjacent abnormal fusion innovation sign data and the burst fusion innovation sign data of the same type is analyzed from the start point of the cycle, and the data chain of different data types is obtained, for example:

[0147] If the burst fusion innovation sign data occurs before the abnormal fusion innovation sign data, it is determined as a "burst and fall back" data chain;

[0148] If the abnormal fusion innovation sign data appears first, and then the burst fusion innovation sign data appears, it is determined as an "innovation repair" data chain;

[0149] If two burst fusion innovation sign data appear continuously, it is determined as a "continuous burst" data chain;

[0150] If two abnormal fusion innovation sign data appear continuously, it is determined as an "innovation exhaustion" data chain;

[0151] From the start point of the cycle, the data chain of different data types is numbered according to the same way according to the timeline;

[0152] For the same data chain number, the correlation of the data chain of different types of fusion innovation sign data is calculated;

[0153] For example, when the data chain number is 5, the technology research and development data is a "continuous burst" data chain, and the market conversion data is an "innovation exhaustion" data chain, a "research and development-transformation" cross-type event chain is constructed, and it is speculated that the research results cannot be effectively transformed, resulting in a decline in market performance;

[0154] Determine whether the correlation calculation result is greater than the preset correlation value. If yes, the data chain of the corresponding type fusion innovation sign data is merged into an innovation event chain.

[0155] Among them, the type annotation refers to marking each abnormal or burst data with the innovation sign category it belongs to, such as technology research and development or financial resilience. Specific implementation can be achieved by using label matching algorithm, which is used to distinguish innovation events in different dimensions;

[0156] The occurrence of the sliding window and its duration refers to the starting and ending time range of recording data anomalies or bursts, which can be realized by time stamp tracking and window boundary calculation, and is used to quantify the duration characteristics of events;

[0157] The correlation of the data chain refers to the degree of interaction of different types of data on the timeline, which can be calculated by using Pearson correlation coefficient or event co-occurrence probability, and is used to identify the potential connection of cross-dimension innovation events.

[0158] The specific implementation process is as follows:

[0159] For each abnormal fusion innovation sign data and burst fusion innovation sign data, automatically label the data type (technology research and development data, market conversion data, organization coordination data, and fund resilience data), and record the sliding window time and duration of its occurrence. Arrange all abnormal and burst fusion innovation sign data in the same period in chronological order. By analyzing abnormal fusion innovation sign data and burst fusion innovation sign data with the same data type and adjacent to each other, a data chain is automatically constructed.

[0160] Each data chain is numbered (such as T1, T2, T3, etc.). For the same data chain number, further correlation analysis method (such as Pearson correlation coefficient) is used to calculate the correlation of different types of data chains (such as "innovation repair" data chain of technology research and development data and "sustained burst" data chain of market conversion data). If the correlation calculation result of two data chains is greater than a preset correlation value (such as 0.8), they are merged into an innovation event chain.

[0161] Through the above technical solution, the present application solves the problem of innovation event recognition fragmentation in the prior art, realizes the serial analysis of cross-dimension abnormal and burst events, for example, when there is a strong correlation between technology research and development stagnation and fund resilience fluctuation, the system can accurately identify the impact of their synergistic effect on the overall innovation force, thereby providing integrated intervention basis for resource allocation and avoiding scheme deviation caused by single-dimension resource allocation.

[0162] The present application further proposes that according to the innovation event chain, a preset knowledge reasoning and resource matching model is called to output an innovation resource intervention scheme, which specifically includes:

[0163] A preset knowledge reasoning and resource matching model is called. The knowledge reasoning and resource matching model includes a knowledge reasoning engine, a knowledge graph, a resource library, and a rule engine.

[0164] The innovation event chain and the abnormal-external event causal chain are taken as the input of the knowledge reasoning and resource matching model. The knowledge reasoning engine analyzes the type, time sequence structure, and causal relationship of the innovation event chain and the abnormal-external event causal chain, and outputs a determination result.

[0165] Through the knowledge graph, the determination result is matched with the available innovation elements in the resource library, and the optimal resource combination and intervention path are reasoned in combination with expert rules.

[0166] Through the rule engine, multi-level intervention suggestions are generated for the determination result, and then the abnormal time point and the burst time point are taken as the intervention opportunity of each intervention suggestion.

[0167] The optimal resource combination, intervention path, intervention suggestion, and intervention opportunity are integrated to output an innovation resource intervention scheme.

[0168] The knowledge reasoning engine refers to a computing module for analyzing the logical relationship of the event chain and the cause-effect chain, and can be specifically implemented by using an inference algorithm based on semantic analysis. The key nodes in the innovation event chain and their relevance are identified.

[0169] The knowledge graph refers to a structured database for storing innovation elements and their associated relationships, and can be specifically implemented by using a graph database technology. The innovation resources are dynamically matched with the requirements in the event chain.

[0170] The resource library refers to a data set containing innovation elements such as technology, funds, and talents, and can be specifically implemented by using a distributed storage system. The resource library provides a resource base that can be called.

[0171] The rule engine refers to a program module for generating intervention suggestions according to preset logic, and can be specifically implemented by using a rule system based on a decision tree. The rule engine converts complex cause-effect relationships into executable intervention strategies.

[0172] Specifically, after the innovation event chain and the abnormal-external event cause-effect chain are input into the knowledge reasoning and resource matching model, the knowledge reasoning engine first analyzes the time sequence association and the cause-effect relationship in the innovation event chain and the abnormal-external event cause-effect chain. Then, the knowledge graph filters the matched innovation elements from the resource library based on the analysis results. For example, for the problem of technology research and development lag, the patent database and the research and development team resources are matched. The rule engine generates hierarchical intervention suggestions according to the priority rules set by experts. For example, the research and development investment ratio is adjusted first, and the fund injection is triggered at the abnormal time point. Finally, the system integrates the resource matching results, the intervention path, and the triggering time into an executable scheme.

[0173] In some specific embodiments, the knowledge reasoning engine can use natural language processing technology to analyze the cause-effect relationship in unstructured data, such as extracting external factors affecting technology research and development from policy documents. The knowledge graph can dynamically update industry technology trend data, such as real-time access to patent disclosure data to supplement the resource library. The rule engine can configure multiple sets of expert rule libraries, such as setting different intervention priorities for different industry life cycle stages.

[0174] Through the above technical solutions, the application can improve the pertinence and timeliness of the innovation resource intervention scheme, and solve the resource allocation lag problem caused by the lack of cause-effect analysis and dynamic matching in the prior art. Through the cooperation of the knowledge graph and the rule engine, the innovation elements are accurately called, and the problem of excessive investment or mismatch of resources is avoided, thereby improving the overall efficiency of enterprise innovation capability evaluation and resource adaptation.

[0175] In order to better understand the above embodiments, an application scenario example is given as follows:

[0176] Take a certain technology company (mainly intelligent sensor modules and application integration) as an example, it will enter the transition period of industry growth and maturity in 2024;

[0177] Periodically collect three types of data:

[0178] Industry database interface to obtain industry life cycle data (growth-maturity period, technology weight 0.28, market weight 0.32, synergy weight 0.22, and capital weight 0.18);

[0179] Enterprise ERP system automatically aggregates innovation sign data such as technology research and development, market conversion, organizational synergy, and capital resilience (such as R&D investment, patent quantity, product listing number, market share, team collaboration, and cash flow);

[0180] Web crawler to capture external data sources such as policy documents, market reports, and public opinion trends (including structured and unstructured data, automatic formatting, deduplication, NLP feature extraction and confidence filtering, with a set value of 0.65).

[0181] Each data is weighted (internal innovation signs 0.7, external data sources 0.3), normalized (range method mapping [0,1]), and processed with a sliding window (window 3 months, step 1 month) to generate a fusion innovation sign data trend curve. For example, the mean of each sliding window for technology research and development data from January 2024 to June 2024 is [0.78, 0.75, 0.70, 0.65, 0.67, 0.73]. Compared with the baseline curve [0.80, 0.79, 0.77, 0.76, 0.75, 0.74], the deviation value is found to be less than -0.08 (preset threshold one is -0.08) for the last four consecutive sliding windows from March 2024 to June 2024. The fusion innovation sign data corresponding to the deviation value is determined to be abnormal fusion innovation sign data, and the abnormal time point is March 2024.

[0182] Further, the external data source data associated with the abnormal time point is found to have "core component import restriction" policy news from February 2024 to March 2024. The correlation analysis (Pearson coefficient 0.82, Bayesian network influence probability difference -0.37) determines that the policy is the main driving factor, generating a "technology innovation anomaly-policy external causal chain." At the same time, the market conversion score explodes in a single window in May 2024 (deviation value +0.12> preset threshold two +0.10), corresponding to "product new function listing" news, with a correlation of 0.74, determining it as a "market explosion-external innovation event chain."

[0183] Finally, the innovation event chain links the "technology innovation anomaly - policy impact - synergistic optimization - market explosion" compound chain, calls the knowledge reasoning and resource matching model, inputs the event chain and causal chain, the knowledge reasoning engine analyzes "policy impact leads to continuous technology innovation anomalies, market explosion benefits from synergistic optimization", the knowledge graph matches the resource library (technology cooperation, university think tank, fund guidance, market promotion, etc.), and the rule engine outputs the following intervention suggestions according to the analysis results:

[0184] Intervention timing: 2024.03 (technology anomaly), 2024.05 (market explosion);

[0185] Resource combination: introduce university think tank joint research + policy consulting service + special fund support + market promotion team;

[0186] Intervention path: technology bottleneck → patent research project start; supply chain risk → policy consultant intervention; market explosion → increase promotion investment

[0187] Multi-level suggestions: complete university cooperation docking in short term (1 month), promote core component domestic substitution in medium term (2-3 months), and establish normal cooperation and public opinion monitoring mechanism in long term (6 months).

[0188] Example two:

[0189] Please refer to Figure 5 Enterprise Innovation Cloud Assessment and Resource Adaptation System, including:

[0190] Data acquisition module, used for periodically acquiring industry life cycle data, innovation sign data and external data source data;

[0191] Fusion innovation sign data calculation module, used for weighted average calculation of the industry life cycle data, innovation sign data and external data source data, to obtain fusion innovation sign data;

[0192] Deviation value calculation module, used for sliding window processing of the fusion innovation sign data in the same period, to output a trend curve of the fusion innovation sign data;

[0193] Call the preset reference curve, compare the trend curve with the preset reference curve, and obtain the deviation value;

[0194] Time point determination module, used for judging whether the deviation value is less than a preset threshold in the continuous N sliding windows. If yes, it is determined that the fusion innovation sign data corresponding to the deviation value is abnormal fusion innovation sign data, and then the abnormal time point is determined in combination with the trend curve;

[0195] If the deviation value within a single sliding window is greater than a preset threshold of 2, then the fusion innovation characteristic data corresponding to the deviation value is determined as the burst fusion innovation characteristic data, and then the burst time point is determined by combining the trend curve.

[0196] The innovation resource intervention scheme output module is used to connect abnormal time points and outbreak time points within the same period on the timeline to generate an innovation event chain.

[0197] Based on the innovation event chain, a preset knowledge reasoning and resource matching model is invoked to output an innovation resource intervention plan.

[0198] This embodiment has the same technical effects as Embodiment 1.

[0199] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0200] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A cloud evaluation and resource adaptation method for enterprise innovation, applied to the cloud evaluation and resource adaptation of innovative enterprises at various stages, characterized in that, The method comprises the following steps: Periodically acquiring industry life cycle data, innovation sign data and external data source data; Performing weighted average calculation on the industry life cycle data, innovation sign data and external data source data to obtain fused innovation sign data; Performing sliding window processing on the fused innovation sign data in the same period to output a trend curve of the fused innovation sign data; Calling a preset reference curve, comparing the trend curve with the preset reference curve to obtain a deviation value; Judging whether the deviation value in the continuous N sliding windows is less than a preset threshold one, and if yes, determining that the fused innovation sign data corresponding to the deviation value is abnormal fused innovation sign data, and further determining an abnormal time point in combination with the trend curve; Judging whether the deviation value in a single sliding window is greater than a preset threshold two, and if yes, determining that the fused innovation sign data corresponding to the deviation value is burst fused innovation sign data, and further determining a burst time point in combination with the trend curve; Serially connecting the abnormal time point and the burst time point in the same period on a time line to generate an innovation event chain; According to the innovation event chain, calling a preset knowledge reasoning and resource matching model to output an innovation resource intervention scheme; Calling a preset knowledge reasoning and resource matching model; The knowledge reasoning and resource matching model comprises a knowledge reasoning engine, a knowledge graph, a resource library and a rule engine; Taking the innovation event chain and an abnormal-external event causal chain as inputs of the knowledge reasoning and resource matching model, analyzing the type, time sequence structure and causal relationship of the innovation event chain and the abnormal-external event causal chain by the knowledge reasoning engine to output a determination result; Matching the determination result with available innovation elements in the resource library by the knowledge graph, and reasoning an optimal resource combination and an intervention path in combination with expert rules; Generating multi-level intervention suggestions for the determination result by the rule engine, and further taking the abnormal time point and the burst time point as intervention time of each intervention suggestion; Integrating the optimal resource combination, the intervention path, the intervention suggestion and the intervention time to output the innovation resource intervention scheme. 2.The enterprise innovation capability cloud evaluation and resource adaptation method according to claim 1, characterized in that: Periodically acquiring industry life cycle data, innovation sign data and external data source data specifically comprises: Periodically acquiring innovation sign data through an enterprise internal management system; the innovation sign data comprises technical research and development data, market conversion data, organization coordination data and fund resilience data; Periodically acquiring industry life cycle data through an application program interface connected with an industry database; the industry life cycle data comprises a current life cycle stage of the industry and a preset weight parameter of innovation sign data corresponding to each stage; Periodically acquiring external data source data through a web crawler; the external data source data comprises structured data, semi-structured data and unstructured data related to the enterprise and the industry thereof. 3.The enterprise innovation capability cloud evaluation and resource adaptation method according to claim 2, characterized in that: Performing linear calculation on the industry life cycle data, innovation sign data and external data source data to obtain fused innovation sign data specifically comprises: Performing weighted calculation on the innovation sign data according to a preset weight parameter of the innovation sign data to output weighted innovation sign data; Performing screening processing on the external data source data; normalizing the weighted innovative sign data and the screened external data source data; performing weighted average calculation on the normalized weighted innovative sign data and the screened external data source data to obtain fused innovative sign data.

4. The enterprise innovation cloud assessment and resource adaptation method of claim 3, wherein: The screening processing of the external data source data specifically includes: performing data formatting, deduplication and content normalization processing on the external data source data; extracting features from the processed external data source data, normalizing the extracted features, and performing linear weighting calculation thereon to obtain a confidence level of the external data source data; determining whether the confidence level is lower than a set value, and if so, eliminating the external data source data corresponding to the confidence level.

5. The enterprise innovation cloud assessment and resource adaptation method of claim 1, wherein: The sliding window processing of the fused innovative sign data in the same period and the output of the trend curve of the fused innovative sign data specifically include: sorting each type of fused innovative sign data in the same period according to the time stamp; starting from the beginning of the period, cutting the fused innovative sign data with a set sliding window length as the fused innovative sign data in the sliding window; calculating the mean value of the fused innovative sign data in each sliding window; taking the center time of the sliding window as the horizontal coordinate and the mean value of the fused innovative sign data in each sliding window as the vertical coordinate, sequentially splicing all the sliding windows to form the trend curve of each type of fused innovative sign data.

6. The enterprise innovation cloud assessment and resource adaptation method of claim 1, wherein: The determination of whether the deviation value in the continuous N sliding windows is less than a preset threshold one, and the determination of the abnormal fused innovative sign data corresponding to the deviation value, and the determination of the abnormal time point in combination with the trend curve specifically include: calling a preset reference curve corresponding to each type of fused innovative sign data; aligning the preset reference curve with the trend curve of the same type of fused innovative sign data in the time period; for each time point, respectively taking the trend curve value and the reference curve value; calculating the deviation value according to the trend curve value and the reference curve value; detecting the deviation value in time sequence for all sliding windows; determining whether the deviation value in the continuous N sliding windows is less than a preset threshold one, and if so, determining that the fused innovative sign data corresponding to the deviation value is abnormal fused innovative sign data; the preset threshold one is set according to the type of the fused innovative sign data; further combining the trend curve to locate the starting and ending sliding windows corresponding to the abnormal fused innovative sign data, and taking the starting window time point of the continuous N sliding windows as the abnormal time point.

7. The enterprise innovation cloud assessment and resource adaptation method of claim 1, wherein: After determining the abnormal time point in combination with the trend curve, the correlation between the abnormal fused innovative sign data and the external data source data of the abnormal time point is calculated, specifically including: calculating the correlation between each type of abnormal fused innovative sign data and the external data source data of the abnormal time point; performing weighted fusion calculation on each correlation calculation result to obtain a total correlation score; determining whether the total correlation score is greater than a preset correlation value, and if so, inputting the external data source data of the abnormal time point into a pre-constructed Bayesian network to output an influence probability difference value of the abnormal fused innovative sign data. According to the influence probability difference, the influence direction and the influence strength of the abnormal fusion innovative sign data of the external data source data at the abnormal time point are determined, and an abnormal-external event causal chain is generated; The influence direction is determined according to the positive and negative of the influence probability difference; and the influence strength is determined according to the absolute value of the influence probability difference. The Bayesian network takes each type of abnormal fusion innovative sign data and the external data source data at the abnormal time point as a node variable, and obtains the dependency relationship between the nodes through deep learning inference.

8. The enterprise innovation cloud assessment and resource adaptation method of claim 7, wherein: The abnormal time point and the outbreak time point in the same period are connected in a time line to generate an innovative event chain, which specifically includes: Each abnormal fusion innovative sign data and outbreak fusion innovative sign data detected in the same period are labeled as a specific type of fusion innovative sign data; The occurrence sliding window and the duration of each abnormal fusion innovative sign data and outbreak fusion innovative sign data are recorded; According to the recording results, the relationship between the same type of adjacent abnormal fusion innovative sign data and outbreak fusion innovative sign data is analyzed from the start of the period, and a data chain of different data types is obtained; The data chain of different data types is numbered in the same way according to the time line from the start of the period; The relevance of the data chain of different types of fusion innovative sign data is calculated for the same data chain number; If the relevance calculation result is greater than a preset relevance value, the data chain of the corresponding type of fusion innovative sign data is merged into an innovative event chain.

9. A cloud-based enterprise innovation capability assessment and resource adaptation system, characterized in that: It includes: A data acquisition module is configured to periodically acquire industry life cycle data, innovative sign data, and external data source data; A fusion innovative sign data calculation module is configured to calculate the industry life cycle data, innovative sign data, and external data source data by weighted average to obtain fusion innovative sign data; A deviation value calculation module is configured to perform sliding window processing on the fusion innovative sign data in the same period to output a trend curve of the fusion innovative sign data; A preset reference curve is called to compare the trend curve with the preset reference curve to obtain a deviation value; A time point determination module is configured to determine whether the deviation value is less than a preset threshold one in the continuous N sliding windows. If yes, the fusion innovative sign data corresponding to the deviation value is determined as abnormal fusion innovative sign data, and then the abnormal time point is determined in combination with the trend curve; It is determined whether the deviation value is greater than a preset threshold two in a single sliding window. If yes, the fusion innovative sign data corresponding to the deviation value is determined as outbreak fusion innovative sign data, and then the outbreak time point is determined in combination with the trend curve; An innovative resource intervention scheme output module is configured to connect the abnormal time point and the outbreak time point in the same period in a time line to generate an innovative event chain; According to the innovative event chain, a preset knowledge reasoning and resource matching model is called to output an innovative resource intervention scheme; A preset knowledge reasoning and resource matching model is called; The knowledge reasoning and resource matching model includes a knowledge reasoning engine, a knowledge graph, a resource library, and a rule engine. The innovation event chain and the abnormal-external event causal chain are taken as inputs of a knowledge reasoning and resource matching model, the type, time sequence structure and causal relationship of the innovation event chain and the abnormal-external event causal chain are analyzed by a knowledge reasoning engine, and a determination result is output; Through a knowledge graph, the determination result is matched with available innovation elements in a resource library, and an optimal resource combination and intervention path are reasoned in combination with expert rules; Through a rule engine, multi-level intervention suggestions are generated for the determination result, and then the abnormal time point and the outbreak time point are taken as intervention opportunities of each intervention suggestion; An innovation resource intervention scheme is output by comprehensively combining the optimal resource combination, the intervention path, the intervention suggestion and the intervention opportunity.

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