Enterprise standard system construction system based on dynamic classification and strategic adaptation

Abstract

The invention relates to the technical field of enterprise system construction, in particular to an enterprise standard system construction system based on dynamic classification and strategic adaptation, which comprises a dynamic classification module, a strategic adaptation verification module and a feedback optimization channel module, wherein strategic document semantic analysis and industry benchmark data are fused in real time through a dynamic classification module to generate a strategic weight coefficient, an environment sensitivity coefficient is calculated by combining compliance dynamics and a market risk model, and the fusion weight ratio of the strategic document semantic analysis and the industry benchmark data is dynamically adjusted based on threshold judgment, so that standard label weight distribution and hierarchical division are realized; the strategic adaptation verification module tracks the dynamic contribution degree of a standard node by using a node influence attenuation model of the association map, and detects strategic deviation by executing the deviation between an index and an expected value in real time; and after the feedback optimization channel module analyzes the deviation signal, the fusion weight is adjusted according to the deviation direction and amplitude, and standard level elastic lifting is triggered through sliding window monitoring, so that strategic suitability real-time verification and quantitative response to external environment change are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise system construction, and specifically to an enterprise standard system construction system based on dynamic classification and strategic adaptation. Background Art

[0002] Currently, enterprise standard system construction systems generally adopt static classification methods, hierarchically classifying standards based on fixed rules or manually preset tags. Such systems have some drawbacks: Firstly, the relevance between classification tags and enterprise strategic goals relies on subjective experience judgment, lacking dynamic quantitative analysis. For example, traditional methods extract keywords by manually interpreting strategic documents, but do not establish a mapping mechanism between semantic parsing and industry knowledge graphs, resulting in the classification weights being unable to accurately reflect changes in strategic priorities (such as when expanding emerging businesses, relevant standards are difficult to quickly upgrade to the core layer).

[0003] Secondly, the assessment of external environment sensitivity lags behind, making it difficult to respond in real time to compliance policy adjustments and market risk transmissions. Existing technologies usually adopt regular manual audits or simple rule matching (such as keyword monitoring), but do not integrate time decay models and supply chain fluctuation transmission algorithms, resulting in calculation deviations in compliance impact coefficients.

[0004] Finally, there is a lack of closed-loop feedback in strategic adaptation verification. Most systems rely on periodic manual audits or isolated KPI indicator comparisons, and do not construct a standard node influence decay model and a real-time contribution degree correlation map, resulting in rough detection granularity of strategic execution deviations (such as when the actual contribution degree of a certain classification tag drops by 30%, the system can only trigger a basic warning and cannot locate the abnormal dynamic decay rate of the associated node).

[0005] The above-mentioned drawbacks make it easy for enterprise standard systems to have problems such as fuzzy strategic orientation, rigid environmental response, and high resource misallocation rates. Especially in the context of digital transformation and VUCA (volatility, uncertainty, complexity, ambiguity) environments, the static architecture of traditional systems has become a key bottleneck restricting enterprise strategic agility. Summary of the Invention

[0006] The purpose of the present invention is to provide an enterprise standard system construction system based on dynamic classification and strategic adaptation to solve the problems raised in the above background art. Specifically, the technologies include how to achieve dynamic weight allocation of enterprise standard classification tags and real-time verification of strategic adaptability; and how to quantify the impact of external environment changes on the standard system and construct a closed-loop feedback optimization mechanism.

[0007] To achieve the above purpose, the present invention provides the following technical solutions: The enterprise standard system construction system based on dynamic classification and strategic adaptation includes a dynamic classification module, a strategic adaptation verification module, and a feedback optimization channel module, where: The dynamic classification module extracts verb-noun pairs in enterprise strategic documents through semantic role labeling technology, and generates initial strategic weight coefficients based on the decision-making intensity of verbs and the classification mapping of nouns in the industry knowledge graph. Further, by comparing the standard classification label distribution data of industry benchmark enterprises, it compensates and adjusts the classification labels in the initial coefficients that exceed the preset difference threshold, and finally outputs strategic weight coefficients to eliminate subjective biases and ensure the industry adaptability of strategic weights. The dynamic classification module extracts compliance keywords, effective time, and scope of application from industry compliance dynamic data, and calculates the compliance impact coefficient through a time decay model. At the same time, based on the supply chain fluctuations and competitor dynamics in market risk data, it calculates the market risk coefficient using a risk conduction model, and the weighted sum of the two generates the environmental sensitivity coefficient to quantify the conduction effect of external risks. The dynamic classification module dynamically adjusts the fusion weight ratio of strategic weight coefficients and environmental sensitivity coefficients according to whether the environmental sensitivity coefficient exceeds the preset safety threshold: if it exceeds the threshold, it adopts the environment-priority mode, and the weight of the environmental sensitivity coefficient is increased; if it does not exceed the threshold, it adopts the strategy-priority mode, and the weight of the strategic weight coefficient is increased. Based on the fused comprehensive weight value, it sorts the classification labels and divides them into the core standard layer, general standard layer, and observation standard layer, and outputs the classification weight threshold to achieve the dynamic reconstruction of the classification hierarchy driven by external risks.

[0008] The association graph construction unit in the strategic adaptation verification module assigns the strategic weight coefficient as the initial influence value of the standard classification node, and generates the attenuation rate by weighting the environmental sensitivity coefficient (such as 0.6) and the preset basic attenuation factor. It periodically updates the node influence value through an exponential decay function (such as node real-time influence = initial influence × e^(-attenuation rate × number of cycles)) to simulate the continuous weakening effect of external environmental changes on the node influence. The adaptation deviation signal calculation unit in the strategic adaptation verification module extracts the real-time execution indicators of each classification label, normalizes them into contribution values, and then generates the real-time execution contribution by allocating index weights through the strategic weight coefficient. At the same time, it calculates the expected contribution based on the node real-time influence value and the strategic goal completion coefficient. By comparing the relative deviation between the real-time execution contribution and the expected contribution, it generates an adaptation deviation signal to accurately locate the label and deviation amplitude of the strategic execution deviation.

[0009] The feedback optimization channel module analyzes the deviation direction (positive / negative) and deviation amplitude of the adaptation deviation signal, and maps them to deviation levels. If the environmental sensitivity coefficient exceeds the environmental safety threshold and there is a negative deviation, it increases the fusion weight ratio of the environmental sensitivity coefficient; if it does not exceed the threshold and there is a negative deviation, it increases the weight of the strategic weight coefficient to dynamically switch the optimization strategy according to the deviation root cause. The feedback optimization channel module performs a sliding window monitoring on the classification weight threshold, where: if the labels in the core standard layer continuously show negative deviations and the amplitude exceeds the preset threshold (e.g., >30%), it will be downgraded to the general standard layer; if the environmental sensitivity coefficient of the observed standard layer label mutates (e.g., the single-cycle increase amplitude >50%), an emergency upgrade to the core standard layer will be triggered, which is used to achieve the elastic rise and fall of the standard level and the risk emergency response.

[0010] Compared with the prior art, the beneficial effects of the present invention are: Through the dynamic classification module, the strategic orientation and the quantitative analysis of the external environment are fused in real time, the strategic weight coefficient is generated based on semantic role annotation and industry benchmark data, the environmental sensitivity coefficient is calculated by combining the time decay model and the risk conduction model, and the fusion weight ratio of the two coefficients is dynamically adjusted through threshold judgment, so as to realize the dynamic allocation of the classification label weight and the risk-driven reconstruction of the core level; The strategic adaptation verification module accurately locates the labels and amplitudes of the strategic execution deviation through the node influence attenuation model of the association graph and the calculation of the real-time / expected contribution degree deviation; The feedback optimization channel module triggers the adjustment of the weight ratio based on the deviation direction and amplitude, and realizes the elastic rise and fall of the standard level through the sliding window monitoring mechanism, and finally forms a closed-loop feedback optimization mechanism of "dynamic classification - deviation detection - weight adjustment - level reconstruction", which not only ensures the dynamic adaptability of the standard system and the strategic goal, but also improves the response speed of the system to external mutations and the accuracy of resource allocation through the quantitative environmental risk conduction effect and the real-time verification mechanism. Brief Description of the Drawings

[0011] Figure 1 It is a schematic diagram of the overall module of the present invention; Figure 2 It is a schematic diagram of the unit of the strategic adaptation verification module of the present invention.

[0012] In the figure: 100, dynamic classification module; 200, strategic adaptation verification module; 201, association graph construction unit; 202, adaptation deviation signal calculation unit; 300, feedback optimization channel module. Detailed Embodiments

[0013] Next, the present invention provides a technical solution: an enterprise standard system construction system based on dynamic classification and strategic adaptation, such as

[0014] Next, the present invention provides a technical solution: an enterprise standard system construction system based on dynamic classification and strategic adaptation, such asFigure 1 - Figure 2 As shown in the figure, it includes a dynamic classification module 100, a strategic adaptation verification module 200, and a feedback optimization channel module 300.

[0015] The dynamic classification module 100 collects enterprise strategic documents, industry compliance dynamic data, and market risk data in real time, where: The dynamic classification module 100 accesses the enterprise internal strategic management system in real time, captures the latest version of the strategic planning document, extracts the text content through a document parsing engine, and identifies strategic goal paragraphs (usually including chapter titles such as "Strategic Focus", "Three-Year Plan", "Core Measures"); and performs semantic cleaning on the text content, removing non-critical descriptive statements (such as background introductions, general principles), and retaining statements containing specific action instructions (such as "Reduce the supply chain cost by 20%" and "Increase customer satisfaction to 90%") as enterprise strategic documents. The dynamic classification module 100 accesses the public database or compliance warning platform released by industry regulatory agencies to obtain updated industry standards, certification requirements, and violation case notifications in real time; performs structured processing on the data, extracts compliance keywords (such as "carbon emission restrictions", "data security level", "product quality certification") and associated metadata such as the effective time and scope of application as industry compliance dynamic data. The dynamic classification module 100 integrates financial market data interfaces (such as stock volatility index, exchange rate changes), supply chain public opinion monitoring systems (such as supplier production capacity anomaly warnings, logistics delay alerts), and competitor dynamic databases; performs sentiment analysis and event type annotation on unstructured data (such as public opinion text) to generate risk level indicators (such as "high risk", "medium risk", "low risk") as market risk data.

[0016] The dynamic classification module 100 generates strategic weight coefficients based on the semantic parsing results of enterprise strategic documents to quantify the association strength between each classification label and the enterprise strategic goal, specifically including: According to the cleaned strategic document text (retaining statements containing specific instructions such as "reduce costs" and "increase market share"), that is, the enterprise strategic document; Use semantic role labeling technology to extract verb-noun pairs (such as "optimize - process", "reconstruct - supply chain"); According to the decision-making intensity of the verb (for example, "optimize" = 0.7, "reconstruct" = 0.9) and the classification mapping result of the noun in the industry knowledge graph (such as "supply chain" corresponding to the "supply chain management standard" label), generate the initial strategic weight coefficient, and obtain the initial strategic weight coefficient list of each classification label. Obtain the distribution data of standard classification labels of industry benchmark enterprises from the public database, and compare the differences between the initial strategic weight coefficient list of each classification label and the distribution of standard classification labels of industry benchmark enterprises; if the difference exceeds the preset difference threshold, compensate and increase the initial strategic weight coefficient of the classification label that does not meet the safety threshold according to the industry benchmark ratio, and output the strategic weight coefficient of each classification label to quantify the association strength between each classification label and the enterprise strategic goal.

[0017] The dynamic classification module 100 generates an environmental sensitivity coefficient based on industry compliance dynamic data and market risk data to quantify the response intensity of each classification label to external environmental changes, specifically including According to the industry compliance dynamic data (such as "the newly revised carbon emission standard will come into effect in 6 months"), calculate the semantic similarity between the compliance keyword (such as "carbon emission") and the classification label (such as "environmental management standard") (output 0.85 through the word vector model); According to the urgency of compliance effective time (6 months = medium sensitivity) and the severity of violation penalties (proportion of fine amount), generate a weighted compliance impact coefficient (for example, 0.7) by weighting, and output the compliance dynamic sensitivity coefficient of each classification label; According to the supply chain fluctuation data (such as "the delivery delay rate of a certain supplier has increased to 25%"), competitor dynamics (such as "competitive products launch low-price alternative solutions"); calculate the supply chain fluctuation coefficient (for example, 0.8) through the time decay model (the weight of data in the recent 1 month accounts for 70%); according to the risk conduction model (the proportion of production losses caused by supply chain interruption), output the market risk coefficient (for example, 0.6), and output the market risk sensitivity coefficient of each classification label; Perform weighted summation on the compliance dynamic sensitivity coefficient of each classification label and the market risk sensitivity coefficient of each classification label to obtain the environmental sensitivity coefficient of each classification label to quantify the response intensity of each classification label to external environmental changes.

[0018] The dynamic classification module 100 fuses the strategic weight coefficient and the environmental sensitivity coefficient to output the classification weight threshold, specifically including: When the environmental sensitivity coefficient exceeds the preset environmental safety threshold (for example, >0.7), it is determined that the external environmental fluctuation is significant, and the environmental priority mode is adopted to increase the weight ratio of the environmental sensitivity coefficient in the fusion formula (for example, from 50% to 70%); When the environmental sensitivity coefficient does not exceed the preset environmental safety threshold (for example, >0.7), the strategic priority mode is adopted to increase the weight ratio of the strategic weight coefficient in the fusion formula; Take the combined strategic weight coefficient and the environmental sensitivity coefficient as the comprehensive weight value, sort all classification labels from high to low according to the comprehensive weight value, and conduct hierarchical division as the classification weight threshold, where it is divided into the core standard layer: the top 20% of the labels (with the highest comprehensive weight value), which are compulsorily included in the priority implementation scope of the enterprise standard system; the general standard layer: the middle 50% of the labels, which are used as the regular implementation standards; the observation standard layer: the bottom 30% of the labels, which are included in the monitoring and not enforced for the time being, and are only triggered to be upgraded when the environmental sensitivity coefficient mutates.

[0019] The association graph construction unit 201 in the strategic adaptation verification module 200 takes the strategic weight coefficient as the initial influence value of the standard classification node, combines the environmental sensitivity coefficient to calculate the attenuation rate of the node influence, and constructs the association graph between the standard system and the strategic goal, specifically including: Directly assign the strategic weight coefficient of each classification label output by the dynamic classification module 100 as the initial influence value of the associated standard classification node (such as the initial influence of the "supply chain management standard" node = 0.9), so as to quantify the basic association strength between the node and the enterprise strategic goal, where the standard classification node is used to uniformly represent the classification labels in the enterprise standard system and serves as an entity node in the association graph to carry data and calculation logic; Based on the environmental sensitivity coefficient generated by the dynamic classification module 100 (reflecting the response intensity of the external environment to the classification label), calculate the attenuation rate of the standard classification node. The formula is: Attenuation rate = basic attenuation factor × (1 + environmental sensitivity coefficient), where the basic attenuation factor is obtained by fitting industry historical data (such as 0.05 / month). The higher the environmental sensitivity coefficient (>0.7), the faster the attenuation rate; Example: If the environmental sensitivity coefficient of a certain classification label is 0.8, then its node attenuation rate = 0.05 × (1 + 0.8) = 0.09 / month, indicating that the influence of this node decays by 9% per month over time; Taking the standard classification node as the vertex, construct the edge weight based on the semantic association between labels in the industry knowledge graph (such as the semantic similarity between "carbon emission limit" and "environmental management standard") to form the association graph between the standard system and the strategic goal, where the initial influence value of the node is adjusted according to the attenuation rate according to the time period (such as monthly) to generate the real-time influence value of the node. The formula is an exponential decay function, specifically as follows: Node real-time influence value = initial influence value × e^(-dynamic attenuation rate × time period). Example: For a node with an initial influence of 0.9, the influence after 1 month = 0.9 × e^(-0.09 × 1) ≈ 0.82.

[0020] The adaptation deviation signal calculation unit 202 in the strategic adaptation verification module 200 generates an adaptation deviation signal reflecting the degree of deviation from the strategic goal by comparing the real-time execution contribution degree of the standard classification node with the expected contribution degree based on the initial influence value, specifically including: Access the enterprise standard system execution system (such as compliance management system, project progress platform), extract the real-time execution indicators of each classification label (such as "carbon emission compliance rate", "supply chain cost reduction rate"), and normalize them into contribution degree values between 0 and 1; Generate the real-time execution contribution degree through linear weighting (the index weights are assigned by the strategic weight coefficients), and calculate the expected contribution degree based on the initial influence value and the decay model: expected contribution degree = node real-time influence value × strategic goal completion coefficient (set according to the strategic stage progress, such as when the annual goal is 50% completed, the coefficient = 0.5); Generate an adaptation deviation signal reflecting the degree of deviation from the strategic goal by comparing the real-time execution contribution degree of the standard classification node with the expected contribution degree based on the initial influence value. The formula is: Adaptation deviation signal = |real-time execution contribution degree - expected contribution degree| / expected contribution degree.

[0021] The feedback optimization channel module 300 receives the adaptation deviation signal and analyzes its deviation direction and deviation amplitude. According to the deviation direction and deviation amplitude, it triggers the differential update of the classification weight threshold in the dynamic classification module 100, specifically including: Determine the deviation direction according to the sign attribute of the adaptation deviation signal calculation value (|real-time execution contribution degree - expected contribution degree| / expected contribution degree), including positive deviation and negative deviation. Among them, positive deviation means that the real-time execution contribution degree > expected contribution degree (actual execution exceeds the strategic expectation); negative deviation means that the real-time execution contribution degree < expected contribution degree (actual execution fails to meet the strategic expectation); Map the absolute value of the adaptation deviation signal (|real-time execution contribution degree - expected contribution degree| / expected contribution degree) to the deviation level, including low-amplitude deviation, medium-amplitude deviation, and high-amplitude deviation. Among them, low-amplitude deviation means that the absolute value of the adaptation deviation signal ≤ 15% (allowable fluctuation within the threshold); medium-amplitude deviation means that the absolute value of the adaptation deviation signal is between 15% and 30% (weight fine-tuning needs to be triggered); high-amplitude deviation means that the absolute value of the adaptation deviation signal > 30% (weight reconstruction needs to be triggered); If it is a negative deviation and the environmental sensitivity coefficient > the preset environmental safety threshold (significant external environmental fluctuations), increase the weight ratio of the environmental sensitivity coefficient in the fusion formula, and the adjustment amplitude is positively correlated with the deviation amplitude; Example: The original environmental weight is 70%, medium-amplitude deviation (20%) triggers the weight to be increased to 75%; high-amplitude deviation (35%) triggers the weight to be increased to 85%; If it is a positive deviation and the environmental sensitivity coefficient > the preset environmental safety threshold, maintain or reduce the environmental weight ratio to avoid overresponding to short-term fluctuations; When the environmental sensitivity coefficient ≤ the preset environmental safety threshold (stable external environment), adjust the strategic weight coefficient according to the deviation direction. When the deviation direction is a negative deviation, increase the strategic weight ratio (e.g., from 50% → 60%) to strengthen the alignment of strategic goals; when the deviation direction is a positive deviation, maintain the dominant position of the strategic weight coefficient and only fine-tune the hierarchical classification label division. The feedback optimization channel module 300 performs a sliding window monitoring on the comprehensive weight values of the top 20% labels. If a label shows a negative deviation for two consecutive cycles and the deviation amplitude > 20% (deviation threshold), it will be downgraded to the general standard layer; if the environmental sensitivity coefficient of the observed standard layer label mutates (e.g., monthly increase > 50%), an emergency upgrade to the core layer will be triggered.

[0022] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An enterprise standard system construction system based on dynamic classification and strategic adaptation, characterized by: It includes a dynamic classification module (100), a strategic adaptation verification module (200) and a feedback optimization channel module (300), wherein: The dynamic classification module (100) collects enterprise strategy documents, industry compliance dynamic data and market risk data in real time, generates a strategic weight coefficient based on the semantic analysis results of the enterprise strategy documents to quantify the correlation strength between each classification label and the enterprise strategic goal, and generates an environmental sensitivity coefficient based on the industry compliance dynamic data and market risk data to quantify the response strength of each classification label to changes in the external environment, and integrates the strategic weight coefficient and the environmental sensitivity coefficient to output a classification weight threshold; The strategic adaptation verification module (200) uses the strategic weight coefficient as the initial influence value of the standard classification node, calculates the dynamic decay rate of the standard classification node in combination with the environmental sensitivity coefficient, and constructs a correlation map; and generates an adaptation deviation signal reflecting the degree of deviation from the strategic goal by comparing the real-time execution contribution of the standard classification node with the expected contribution based on the initial influence value; The feedback optimization channel module (300) receives the adaptation deviation signal and analyzes the deviation direction and deviation amplitude thereof, adjusts the fusion weight ratio of the strategic weight coefficient and the environmental sensitivity coefficient according to the deviation direction and deviation amplitude, and triggers the differentiated update of the classification weight threshold in the dynamic classification module (100).

2. The enterprise standard system construction system based on dynamic classification and strategic adaptation according to claim 1 is characterized in that: The dynamic classification module (100) generates a strategic weight coefficient including: The verb and noun pairs in the enterprise strategy documents are extracted through semantic role labeling technology, and the initial strategic weight coefficient is generated according to the decision-making strength of the verbs and the classification mapping of the nouns in the industry knowledge graph; Obtain the standard classification label distribution data of industry benchmark companies, compare the initial strategic weight coefficient with the industry benchmark distribution difference, make coefficient compensation adjustments for classification labels that exceed the preset difference threshold, and output the strategic weight coefficient.

3. The enterprise standard system construction system based on dynamic classification and strategic adaptation according to claim 1 is characterized in that: The dynamic classification module (100) generates an environmental sensitivity coefficient including: Extract compliance keywords and their effective time and applicable scope based on industry compliance dynamic data, and calculate compliance impact coefficients; According to the supply chain fluctuations and competitor dynamics in the market risk data, the market risk coefficient is calculated by combining the time decay model and the risk transmission model; The compliance impact coefficient and the market risk coefficient are weighted and summed to generate the environmental sensitivity coefficient.

4. The enterprise standard system construction system based on dynamic classification and strategic adaptation according to claim 1 is characterized in that: The process of the dynamic classification module (100) outputting the classification weight threshold specifically includes: When the environmental sensitivity coefficient exceeds the preset environmental safety threshold, the fusion weight ratio of the environmental sensitivity coefficient is increased; When the environmental sensitivity coefficient does not exceed the preset environmental safety threshold, the fusion weight ratio of the strategic weight coefficient is increased; The classification labels are sorted and hierarchically divided according to the fused comprehensive weight values, and the classification weight threshold is output.

5. The enterprise standard system construction system based on dynamic classification and strategic adaptation according to claim 1 is characterized in that: The strategic adaptation verification module (200) comprises an association map construction unit (201). When the association map construction unit (201) constructs the association map, the strategic weight coefficient is assigned as the initial influence value of the standard classification node, and the dynamic attenuation rate of the node is calculated based on the environmental sensitivity coefficient. The dynamic attenuation rate is generated by weighting the basic attenuation factor and the environmental sensitivity coefficient.

6. The enterprise standard system construction system based on dynamic classification and strategic adaptation according to claim 5 is characterized in that: The association graph construction unit (201) adjusts the initial influence value of the node according to the time period and the dynamic decay rate to generate the real-time influence value of the node, and the formula is an exponential decay function.

7. The enterprise standard system construction system based on dynamic classification and strategic adaptation according to claim 1 is characterized in that: The strategic adaptation verification module (200) comprises an adaptation deviation signal calculation unit (202), and the process of generating the adaptation deviation signal by the adaptation deviation signal calculation unit (202) specifically comprises: Extract the real-time execution indicators of each classification label and normalize them into contribution values. Allocate indicator weights through strategic weight coefficients to generate real-time execution contribution. Calculate the expected contribution based on the node's real-time influence value and the strategic goal completion coefficient; An adaptation deviation signal is generated by comparing the relative deviation between the real-time execution contribution and the expected contribution.

8. The enterprise standard system construction system based on dynamic classification and strategic adaptation according to claim 1 is characterized in that: When the feedback optimization channel module (300) analyzes the adaptation deviation signal, it maps the deviation level according to the absolute value of the deviation, and adjusts the fusion weight ratio according to the threshold relationship between the deviation direction and the environmental sensitivity coefficient.

9. The enterprise standard system construction system based on dynamic classification and strategic adaptation according to claim 8 is characterized in that: The feedback optimization channel module (300) increases the fusion weight ratio of the environmental sensitivity coefficient when the environmental sensitivity coefficient exceeds a preset environmental safety threshold and a negative deviation occurs; When the environmental sensitivity coefficient does not exceed the preset environmental safety threshold and a negative deviation occurs, the fusion weight ratio of the strategic weight coefficient is increased.

10. The enterprise standard system construction system based on dynamic classification and strategic adaptation according to claim 1 is characterized in that: The feedback optimization channel module (300) performs sliding window monitoring on the classification weight threshold, and when the core standard layer label continuously deviates in a negative direction and the deviation exceeds a preset deviation threshold, it is downgraded to the general standard layer; When the environmental sensitivity coefficient of the standard layer tag changes suddenly and exceeds the preset increase, an emergency upgrade to the core standard layer is triggered.

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