A method and system for evaluating the health of an enterprise based on nested isomorphism, and a computer readable storage medium

Abstract

The present application relates to the technical field of business operation analysis and risk assessment, and particularly relates to a business operation health degree evaluation method and system based on nested isomorphism and a computer readable storage medium; the method comprises the following steps: obtaining the scores of brand power B, product power P, channel power C and organization power S after standardization; taking the minimum value of P, C and S as operation capacity P e ; adopting a proportional acceptance method to force brand power to be constrained by the lowest acceptance rate to obtain effective brand power B e ; taking the geometric mean of B e and P e as the basic health degree, introducing an isomorphism factor I for calibration, and outputting the final operation health degree and diagnosis results; the present application automatically constrains the effective value of a brand to be below the shortest board of an operation system, and fundamentally solves the evaluation deviation of 'high score and low ability' caused by the fact that a linear weighting model cannot reflect the short board locking effect; the present application is suitable for business health degree evaluation, crisis early warning and resource allocation optimization.

Description

Technical Field

[0001] This invention relates to the field of business analysis and risk assessment technology, and in particular to a method, system and computer-readable storage medium for assessing business health based on nested isomorphism. Background Technology

[0002] Assessing a company's operational health is a core issue in corporate strategic management and risk warning. Currently, mainstream computer-based assessment methods fall into two main categories: the first is a financially oriented approach, represented by the Altman Z-score model, which calculates bankruptcy risk through linear weighting of multiple financial indicators; the second is a brand-oriented approach, represented by the Keller CBBE model and the Aaker five-star model, which focuses on brand equity in consumers' minds. However, a common characteristic of these methods is their use of linear weighting calculation logic. Their core assumption is that the contributions of each assessment dimension are independent and easily additive, meaning that an improvement in any dimension linearly improves the overall health score. However, the actual operational patterns of businesses fundamentally contradict this assumption: when a company has a significant weakness in a particular operational dimension, that weakness becomes a "locked-in" bottleneck to its overall operational capabilities. For example, a company with weak product manufacturing capabilities, even with extremely high brand awareness, will have its actual operational health severely constrained by its manufacturing shortcomings. Existing linear weighted models cannot capture this bottleneck effect, and may give systematically higher evaluation results to companies that are "outstanding in one dimension but have fatal weaknesses", resulting in the evaluation paradox of "high scores but low capabilities". Therefore, there is an urgent need for a computer-based method that can overcome the shortcomings of linear weighting and automatically constrain health assessment to the weakest link in the operating system. Summary of the Invention

[0003] The purpose of this invention is to provide a method, system, and computer-readable storage medium for assessing the health of business operations based on nested isomorphism, which addresses the shortcomings of existing technologies and can overcome the defects of linear weighting and automatically constrain the health assessment to the weakest link in the business system.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: On the one hand, the present invention provides a method for assessing the operational health of enterprises based on nested isomorphism, comprising the following steps: S1: Collect brand data, product data, channel data, and organizational operation data of the target company from public databases or third-party industry databases via computer networks, and standardize the raw data to obtain brand power score B, product power score P, channel power score C, and organizational power score S. S2: The minimum value among product strength score (P), channel strength score (C), and organizational strength score (S) is used as the operational acceptance capability. : ; S3: The brand power score (B) is then used to determine the brand's commitment to its promises based on product power, channel power, and organizational power. A minimum commitment ratio is used as a mandatory constraint to obtain the effective brand power score. : ; S4: Effective Brand Power With operating acceptance capacity The geometric mean is used to obtain the basic nested health score. : ; S5: Introduce an isomorphism factor to the basic nested health score for multi-level calibration to obtain the final operational health score. Among them, the isomorphism factor Coefficient of variation based on B, P, C, S Construction: , in These are the calibration coefficients determined through sample regression; S6: Based on the final operational health score Output the enterprise operation diagnosis results.

[0005] A further improvement to the above scheme is that, in step S5, the multi-layer calibration also includes the introduction of an industry-based calibration factor R, an industry brand sensitivity factor K, and an enterprise product strength factor. At least one of them, the calibration formula is: .

[0006] A further improvement to the above scheme is that step S6 also includes calculating the brand excess limit. : , And based on the final operational health Brand over-quota Combined with the coefficient of variation The system divides enterprises into multiple preset diagnostic segments and outputs corresponding diagnostic labels.

[0007] A further improvement to the above scheme is that step S6 also includes a pricing capability diagnosis: Compare the brand premium capability score with the product pricing support score; If the brand premium capability score is lower than the product pricing support score, a "pricing inadequate" diagnostic signal will be output. If the brand premium capability score is higher than the product pricing support score, a "pricing is too high" diagnostic signal will be output.

[0008] A further improvement to the above scheme includes the following steps: S7: Identify the dimension with the lowest score among B, P, C, and S as the shortest constraint board; generate a resource allocation scheme that prioritizes investing resources in the shortest constraint board based on the preset marginal revenue calculation rules.

[0009] A further improvement to the above scheme includes the following steps: S81: Real-time collection of product public opinion scores via internet public opinion data interface. Corporate public opinion score ; S82: Weighted composite of public opinion scores to determine the accelerator net effect :

[0010] in, and The weighting coefficients are determined through regression analysis of the sample data, and the intensity of accelerator events is calculated. :

[0011] when The accelerator effect is triggered when the preset threshold is exceeded; S83: Accelerator net effect Incorporate into the dynamic performance forecast formula to calculate the projected future sustainable revenue growth rate. :

[0012] in, For industry dynamic calibration coefficients, This serves as a dynamic capacity multiplier factor. S84: When the dynamic prediction result meets the preset deterioration conditions, output a dynamic deterioration warning signal. The priority of this warning signal is higher than the warning level generated only based on the static health status.

[0013] A further improvement to the above scheme is to calculate the four-dimensional scores and basic health scores independently for each segment of a diversified group across industries, and then perform a weighted summation based on the revenue share of each segment.

[0014] A further improvement to the above scheme is that, for B2B industrial enterprises, in the brand power score B: the perception layer adopts industry awareness and the coverage of the bidding list; The cognitive layer assesses the level of understanding of the technical solutions and their compliance with industry standards. The trust layer is based on the number of successful cases and the renewal rate of major clients; The implementation level is assessed based on contract fulfillment rate and after-sales service response speed.

[0015] On the other hand, the present invention provides a business health assessment system based on nested isomorphism, comprising: The data acquisition module is used to collect brand data, product data, channel data, and organizational operation data of the target company through a computer network; The data preprocessing module is used to standardize the collected raw data to obtain brand power score B, product power score P, channel power score C, and organizational power score S. The health assessment module is used to calculate the business's acceptance capacity. Calculate effective brand power And the calculation of the underlying health ; The diagnostic output module is used to output business diagnostic results and crisis warning signals based on the final business health and brand excess. On the other hand, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements all the steps of any of the methods described above.

[0016] The beneficial effects of this invention are as follows: By using the minimum value among product strength, channel strength, and organizational strength as the operational acceptance capability, and by employing a minimum acceptance ratio to forcibly constrain brand strength, this invention fundamentally overcomes the technical defect of existing linear weighted models that cannot reflect the "weakest link lock-in" effect. When any operational dimension has a fatal weakness, that weakness automatically locks in the effective conversion of brand strength through a proportional acceptance mechanism. This ensures that the evaluation results truly reflect the actual health level of enterprises that are "outstanding in a single dimension but have a fatal weakness," eliminating the evaluation paradox of "high scores but low capabilities." The geometric average calculation of effective brand strength and operational acceptance capability further ensures that when any one aspect approaches zero, the overall health level simultaneously returns to zero, conforming to the mathematical representation of a company in a true state of collapse. Attached Figure Description

[0017] Figure 1 The present invention provides an overall flowchart of a method for assessing the health of business operations based on nested isomorphism.

[0018] Figure 2 This is a schematic diagram of the module structure of a nested isomorphic enterprise health assessment system provided in an embodiment of the present invention. Detailed Implementation

[0019] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0020] Example 1: As Figure 1-2 As shown, this invention provides a method for assessing the operational health of enterprises based on nested isomorphism, comprising the following steps: S1: Collect brand data, product data, channel data, and organizational operation data of the target company from publicly available enterprise databases or third-party industry databases via computer networks. Standardize the raw data to obtain brand power score (B), product power score (P), channel power score (C), and organizational power score (S). Specifically, collect brand data, product data, channel data, and organizational operation data of the target company from publicly available enterprise databases (including enterprise annual report databases, capital market databases, brand index databases, search engine databases, and e-commerce platform data interfaces) via computer networks. Clean and standardize the collected raw data, using the industry-standard min-max standardization method to uniformly map the values ​​of all dimensions to the 0-100 score range to obtain the brand power score (B), product power score (P), channel power score (C), and organizational power score (S). The brand power score B is calculated by weighting the scores of four sub-dimensions: perception, cognition, trust, and implementation. The weights of each sub-dimension can be adjusted according to industry characteristics. The perception layer measures the breadth of brand awareness in the market, including but not limited to indicators such as brand recognition and search popularity. The cognition layer measures the depth of brand awareness in the market, including but not limited to indicators such as the clarity of brand positioning and the clarity of differentiated value proposition. The trust layer measures the level of market trust in the brand, including but not limited to indicators such as user satisfaction and net promoter score. The implementation layer measures the degree of alignment between brand promises and actual deliverables, including but not limited to indicators such as consistency of marketing actions and the completeness of the after-sales service system. The product strength score P is calculated by weighting the scores of four sub-dimensions: R&D technology strength, manufacturing supply chain strength, product quality strength, and product structure strength. R&D technology strength measures a company's technological innovation capabilities, including but not limited to indicators such as the number of patents and core technology barriers; manufacturing supply chain strength measures a company's production and supply capabilities, including but not limited to indicators such as production capacity and supply chain stability; product quality strength measures a company's product quality level, including but not limited to indicators such as product pass rate and user complaint rate; and product structure strength measures the rationality of a company's product layout, including but not limited to indicators such as product iteration speed and product line completeness. The channel strength score C is calculated by weighting the scores of three sub-dimensions: channel coverage breadth, channel operational efficiency, and channel digitalization level. Channel coverage breadth measures the company's market coverage, including but not limited to indicators such as the number of offline outlets and online platform coverage; channel operational efficiency measures the company's channel operation level, including but not limited to indicators such as single-store output and channel turnover rate; and channel digitalization level measures the company's level of digital transformation of its channels, including but not limited to indicators such as online sales ratio and digital tool penetration rate. The organizational capability score S is calculated by weighting the scores of three sub-dimensions: organizational efficiency, financial security, and execution capability. Organizational efficiency measures the efficiency of an organization's operations, including but not limited to indicators such as revenue per employee and profit per employee; financial security measures the financial health of an organization, including but not limited to indicators such as debt-to-equity ratio and cash flow from operating activities; and execution capability measures the organization's ability to execute its strategies, including but not limited to indicators such as the speed of strategy implementation and project completion rate. As those skilled in the art will understand, the specific indicators for the above sub-dimensions can be selected and adjusted according to the characteristics of different industries and different companies, as long as they can objectively reflect the capability level of the corresponding dimension. S2: The minimum value among product strength score (P), channel strength score (C), and organizational strength score (S) is used as the operational acceptance capability. : , The meaning is: the weakest link among the three dimensions of operation determines the overall upper limit of the company's operating system's fulfillment of its brand promise; S3: The brand power score (B) is then used to determine the brand's commitment to its promises based on product power, channel power, and organizational power. A minimum commitment ratio is used as a mandatory constraint to obtain the effective brand power score. :

[0021] in, These are respectively called product fulfillment ratio, channel fulfillment ratio, and organizational fulfillment ratio. Their physical meaning is the ability of product strength, channel strength, and organizational strength to fulfill every aspect of the brand promise. The minimum fulfillment ratio determines the proportion of the brand promise actually fulfilled by the operating system. At that time, all acceptance ratios were not less than 1. The brand promise was fully honored by the operating system. In other words, when the score for a certain operational dimension is lower than the brand score, the acceptance rate for that dimension is less than 1. Discounts are applied based on the minimum acceptance rate; any amount exceeding this rate becomes a trust liability and does not generate effective value. S4: Effective Brand Power With operating acceptance capacity The geometric mean is used to obtain the basic nested health score. : , Using a geometric mean instead of an arithmetic mean is more sensitive to weaknesses. If any value is close to zero, the overall health is also close to zero. This corresponds to the real collapse of a company when it is "brand idle" or "operationally paralyzed" in real business. An arithmetic mean will mask the severity of a unilateral collapse. S5: Introduce an isomorphism factor to the basic nested health score for multi-level calibration to obtain the final operational health score. Among them, the isomorphism factor Coefficient of variation based on B, P, C, S Construction:

[0022] in These are calibration coefficients determined through sample regression, with values ​​ranging from 0.5 to 0.7. Reflecting the tolerance of the operating system to differences between dimensions—when completely isomorphic = 1, the higher the degree of non-isomorphism, The lower the value, the greater the penalty to the synergy of health. S6: Based on the final operational health score Output the enterprise operation diagnosis results.

[0023] This invention overcomes the technical deficiency of existing linear weighted models that fail to reflect the "weakest link" effect by using the minimum value among product strength, channel strength, and organizational strength as the operational acceptance capability, and by imposing a minimum acceptance ratio on brand strength. When any operational dimension has a fatal weakness, that weakness automatically locks in the effective conversion of brand strength through a proportional acceptance mechanism. This ensures that the evaluation results truly reflect the actual health level of enterprises that are "outstanding in a single dimension but have a fatal weakness," eliminating the evaluation paradox of "high scores but low capabilities." The geometric average calculation of effective brand strength and operational acceptance capability further ensures that when any one aspect approaches zero, the overall health also reaches zero, conforming to the mathematical representation of a company in a true state of collapse.

[0024] In step S5 of this invention, the multi-layer calibration further includes introducing an industry-based calibration factor R, an industry brand sensitivity factor K, and an enterprise product strength factor. At least one of them, the calibration formula is: , Wherein, R is the industry basic calibration factor, which is used to adapt to the basic operating characteristics of different industries and is determined through industry-specific regression; K is the industry brand sensitivity factor, which is used to measure the influence weight of brands on consumer decisions. The product strength factor is used to calibrate the irreplaceable barriers of a company's products; the industry-based calibration factor enables the model to adapt to the inherent differences in the operating characteristics of different industries; the industry brand sensitivity factor quantifies the industry heterogeneity of the brand's influence on consumer decisions; and the company's product strength factor calibrates the buffering effect of the product's irreplaceable barriers on health. This multi-layered calibration mechanism ensures that the evaluation results are statistically comparable and accurate when compared across industries and companies.

[0025] Step S6 of the present invention further includes calculating the brand excess limit. : , And based on the final operational health Brand over-quota Combined with the coefficient of variation The system divides enterprises into multiple pre-defined diagnostic segments and outputs corresponding diagnostic labels. It quantifies the proportion of brand promises exceeding operational capacity, when When the value is greater than 0, the brand commitment exceeds the company's ability to fulfill its obligations, and the excess becomes a trust liability. By combining the final business health score with the coefficient of variation, the computer can automatically classify the company into different diagnostic segments and output corresponding diagnostic labels and four-level crisis warning signals, realizing the automated output from numerical calculation to structured diagnosis.

[0026] Step S6 of the present invention also includes pricing capability diagnosis: Compare the brand premium capability score with the product pricing support score; If the brand premium capability score is lower than the product pricing support score, a "pricing inadequate" diagnostic signal will be output. If the brand premium capability score is higher than the product pricing support score, a "pricing is too high" diagnostic signal will be output.

[0027] When brand premium capability is lower than product pricing support, it indicates that the company's product has a pricing basis but the brand has failed to translate into premium capability, indicating a lag in brand building; conversely, it indicates that brand premium is detached from the actual supporting capability of the product, and there is a risk of consumer expectations being disappointed, thus filling the gap in the existing technology for assessing the rationality of pricing.

[0028] The present invention also includes the following steps: S7: Identify the dimension with the lowest score among B, P, C, and S as the shortest constraint board; based on the preset marginal revenue calculation rules, generate a resource allocation plan that prioritizes resource allocation to the shortest constraint board. The generation rule for the resource allocation plan is: prioritize allocating total resources to the shortest constraint board until that dimension is no longer a weakness, and then move to the next shortest constraint board. For enterprises identified as being in a weakness zone, the output resource allocation suggestion is to allocate all of the total resources to the shortest constraint board, with zero investment in the other dimensions. For enterprises identified as being in a brand weakness zone, the output resource allocation suggestion is to allocate a higher proportion of brand building than the sum of the shares of all other dimensions.

[0029] The present invention also includes the following steps: S81: Real-time collection of product public opinion scores via internet public opinion data interface. Corporate public opinion score ; S82: Weighted composite of public opinion scores to determine the accelerator net effect :

[0030] in, and The weighting coefficients, determined through regression analysis of sample data, are preferably 0.6 and 0.4 in this embodiment. The value range is [-0.5, 1.0], and the intensity of the accelerator event is calculated. :

[0031] when The accelerator effect is triggered when the preset threshold is exceeded. , , After empirical calibration using historical samples, the preferred values ​​in this embodiment are determined to be 0.4, 0.3, and 0.3, respectively. Min-max normalization to the [0,1] interval. Empirical calibration confirms: forward accelerator. The cognitive leap rule is triggered at 0.6; a negative accelerator. The multiplier deduction rule is triggered when the value is > 0.5; S83: Accelerator net effect Incorporate into the dynamic performance forecast formula to calculate the projected future sustainable revenue growth rate. :

[0032] in, This is an industry-wide dynamic calibration coefficient, empirically calibrated based on the previous year's industry-wide sample data. The capacity dynamic multiplier factor is calculated using the following formula: ,in This represents the company's annual capacity change rate. The overall support elasticity coefficient is calculated by weighting the scores from four dimensions: B, P, C, and S. S84: When the dynamic prediction result meets the preset deterioration conditions, output a dynamic deterioration warning signal. The priority of this warning signal is higher than the warning level generated only based on the static health status.

[0033] By collecting product and corporate public opinion data in real time and weighting them to synthesize the accelerator net effect, the previously unquantifiable impact of external events is transformed into calculable risk factors. The calculation of accelerator event intensity further distinguishes the impact, persistence, and brand relevance of different events, making the quantification of impact severity more precise. After incorporating the accelerator net effect into the dynamic performance forecasting formula, the computer can, even when the static health status indicates safety, proactively detect the systemic erosion trend of external shocks on the company's operating status and issue higher-priority dynamic deterioration warning signals, significantly improving the foresight and sensitivity of crisis early warning.

[0034] Taking Gree Electric Appliances' 2024 data as an example: Step 1: The computer collects the original data of various evaluation indicators of Gree Electric Appliances in 2024 from public data sources such as Wind Database, CSMAR Database, and Chnbrand Brand Index Database through the data interface module. After the industry-standard min-max standardization, the following scores are obtained: Brand Power Score B=65.8, Product Power Score P=86.5, Channel Power Score C=70.2, and Organizational Power Score S=82.4.

[0035] Step 2: The computer performs the following calculations according to a preset algorithm:

[0036]

[0037]

[0038] Since B (65.8) is not greater than any of the values ​​of P, C, and S, all acceptance ratios are not less than 1, and the brand commitment is fully accepted by the operating system.

[0039] Step 3: The computer calculates the coefficient of variation of the four-dimensional scores. isomorphism factor After further incorporating industry calibration factors and brand sensitivity factors, the final operational health score is obtained. .

[0040] Step 4: Computer calculates brand excess credit. Based on the preset judgment rules, the computer outputs the judgment of the "brand deficiency area" segment, and the health level is marked as Grade A.

[0041] Step 5: Among the scores of each dimension, B (65.8) is the lowest and is identified as the shortest constraint board. The computer outputs a suggestion to prioritize the investment of resources in channel construction.

[0042] Step 6: The computer collects public opinion data in real time. If there is no major negative impact, Acc≈0, and the predicted SGrowth is consistent with the static conclusion. If continuous negative public opinion is detected, causing Acc to become significantly negative, even if the static health is in the safe zone, the system will still issue a dynamic deterioration warning, indicating that brand premium and channel capabilities may be further eroded by external shocks.

[0043] This embodiment also provides a business health assessment system based on nested isomorphism, including: The data acquisition module is used to collect brand data, product data, channel data, and organizational operation data of the target company through a computer network; The data preprocessing module is used to standardize the collected raw data to obtain brand power score B, product power score P, channel power score C, and organizational power score S. The health assessment module is used to calculate the business's acceptance capacity. Calculate effective brand power And the calculation of the underlying health ; The diagnostic output module is used to output business diagnostic results and crisis warning signals based on the final business health and brand excess. This embodiment also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements all the steps of any of the methods described above. Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented using hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. Example

[0044] For diversified groups across industries, this embodiment calculates the four-dimensional scores and basic health scores independently for each segment, and then performs a weighted summation based on the revenue share of each segment. This avoids confusion of segment characteristics and distortion of constraint relationships caused by forcibly applying a single four-dimensional score to diversified groups.

[0045] Taking a certain communication equipment manufacturer as an example: When the computer calculates its brand power score B, it adopts a B2B indicator system: the perception layer uses industry awareness and bidding list coverage as data sources, the cognition layer uses the understanding of technical solutions and the cognition of industry standard compliance, the trust layer uses the number of successful cases and the renewal rate of major customers, and the implementation layer uses the contract fulfillment rate and after-sales service response speed.

[0046] The remaining steps are the same as in the first embodiment, and will not be repeated here. Example

[0047] In this embodiment, for B2B industrial enterprises, the brand power score B includes: the perception layer uses industry awareness and the coverage of the bidding list. The cognitive layer assesses the level of understanding of the technical solutions and their compliance with industry standards. The trust layer is based on the number of successful cases and the renewal rate of major clients; The implementation layer uses contract fulfillment rate and after-sales service response speed; it can more accurately reflect the brand value composition of B2B industrial enterprises with technology trust and delivery reliability as the core, and ensure that the nested isomorphic evaluation method can maintain evaluation validity under different business models.

[0048] Take a technology group spanning five major sectors: ICT infrastructure, terminals, cloud computing, digital energy, and intelligent vehicles, as an example. The computer first calculates the four-dimensional score and basic health of each business segment independently. Then, it weights and summarizes the results of each segment based on the revenue share of each segment, and generates a comprehensive diagnosis at the group level through the computer.

[0049] The remaining steps are the same as in the first embodiment, and will not be repeated here.

[0050] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0051] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0052] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0053] Of course, the above are only preferred embodiments of the present invention. Therefore, all equivalent changes or modifications made in accordance with the structure, features and principles of the present invention patent application are included in the scope of the present invention patent application.

Claims

1. A method for assessing the operational health of enterprises based on nested isomorphism, characterized in that: Includes the following steps: S1: Collect brand data, product data, channel data, and organizational operation data of the target company from public databases or third-party industry databases via computer networks, and standardize the raw data to obtain brand power score B, product power score P, channel power score C, and organizational power score S. S2: The minimum value among product strength score (P), channel strength score (C), and organizational strength score (S) is used as the operational acceptance capability. : ， S3: The brand power score (B) is then used to determine the brand's commitment to its promises based on product power, channel power, and organizational power. A minimum commitment ratio is used as a mandatory constraint to obtain the effective brand power score. : ， S4: Effective Brand Power With operating acceptance capacity The geometric mean is used to obtain the basic nested health score. : ， S5: Introduce an isomorphism factor to the basic nested health score for multi-level calibration to obtain the final operational health score. Among them, the isomorphism factor Coefficient of variation based on B, P, C, S Construction: ,in These are the calibration coefficients determined through sample regression; S6: Based on the final operational health score Output the enterprise operation diagnosis results.

2. The enterprise operational health assessment method based on nested isomorphism according to claim 1, characterized in that: In step S5, multi-layer calibration also includes introducing an industry-based calibration factor R, an industry brand sensitivity factor K, and an enterprise product strength factor. At least one of them, the calibration formula is: 。 3. The enterprise operational health assessment method based on nested isomorphism according to claim 1, characterized in that: Step S6 also includes calculating the brand excess limit. : ， And based on the final operational health Brand over-quota Combined with the coefficient of variation The system divides enterprises into multiple preset diagnostic segments and outputs corresponding diagnostic labels.

4. A method for assessing enterprise operational health based on nested isomorphism according to claim 3, characterized in that: Step S6 also includes a pricing capability diagnosis: Compare the brand premium capability score with the product pricing support score; If the brand premium capability score is lower than the product pricing support score, a "pricing inadequate" diagnostic signal will be output. If the brand premium capability score is higher than the product pricing support score, a "pricing is too high" diagnostic signal will be output.

5. A method for assessing enterprise operational health based on nested isomorphism according to claim 1, characterized in that: It also includes the following steps: S7: Identify the dimension with the lowest score among B, P, C, and S as the shortest constraint board; generate a resource allocation scheme that prioritizes investing resources in the shortest constraint board based on the preset marginal revenue calculation rules.

6. A method for assessing enterprise operational health based on nested isomorphism according to claim 1, characterized in that: It also includes the following steps: S81: Real-time collection of product public opinion scores via internet public opinion data interface. Corporate public opinion score ; S82: Weighted composite of public opinion scores to determine the accelerator net effect : ,in, and The weighting coefficients are determined through regression analysis of the sample data, and the intensity of accelerator events is calculated. : ， when The accelerator effect is triggered when the preset threshold is exceeded. S83: Accelerator net effect Incorporate into the dynamic performance forecast formula to calculate the projected future sustainable revenue growth rate. : ， in, For industry dynamic calibration coefficients, This serves as a dynamic capacity multiplier factor. S84: When the dynamic prediction result meets the preset deterioration conditions, output a dynamic deterioration warning signal. The priority of this warning signal is higher than the warning level generated only based on the static health status.

7. A method for assessing enterprise operational health based on nested isomorphism according to claim 1, characterized in that: For diversified groups across industries, the four-dimensional scores and basic health scores are calculated independently for each segment, and then weighted and aggregated based on the revenue share of each segment.

8. A method for assessing enterprise operational health based on nested isomorphism according to claim 1, characterized in that: For B2B industrial enterprises, in the brand power score B: the perception layer uses industry awareness and the coverage of bidding lists; The cognitive layer assesses the level of understanding of the technical solutions and compliance with industry standards. The trust layer is based on the number of successful cases and the renewal rate of major clients; The implementation level is assessed based on contract fulfillment rate and after-sales service response speed.

9. A business health assessment system based on nested isomorphism, characterized in that: include: The data acquisition module is used to collect brand data, product data, channel data, and organizational operation data of the target company through a computer network; The data preprocessing module is used to standardize the collected raw data to obtain brand power score B, product power score P, channel power score C, and organizational power score S. The health assessment module is used to calculate the business's acceptance capacity. Calculate effective brands And calculate the underlying health ; The diagnostic output module is used to output business diagnostic results and crisis warning signals based on the final business health and brand excess.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When a computer program is executed by a processor, it implements all the steps of any one of the methods described in claims 1-8.

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