Machine room equipment management method and system based on cloud computing

Abstract

The invention relates to a computer room equipment management method and system based on cloud computing, and the method achieves the dynamic monitoring and risk assessment of a construction process through constructing a computer room component model of a real object size and integrating a design value and actual measurement data. Firstly, a system calculates a direct risk factor (reflecting actual construction deviation) and an indirect risk factor (quantifying influences of associated components) of each component, and a comprehensive checking list is established; secondly, importance parameter correction risk factors are introduced, external influence coefficients are calculated in combination with environmental data, and an elastic construction period model is constructed to achieve dynamic adjustment; when component auditing fails, the system automatically marks the associated component and calculates a risk increment, and an auditing list is updated through iteration until a complete component path is formed; according to the method, a physical connection relationship, a risk conduction mechanism and environmental factors are innovatively incorporated into a unified analysis framework, and the problems of risk prevention and control lagging, resource allocation imbalance and the like in a traditional management mode are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer room equipment management, and in particular to a computer room equipment management method and system based on cloud computing. Background Art

[0002] Traditional computer room equipment management often faces numerous challenges. Locally deployed management systems, limited by hardware performance, struggle to cope with the data processing and storage requirements of large-scale equipment, resulting in inefficient management. Equipment status monitoring is untimely, leading to delayed fault detection and impacting normal computer room operations. Furthermore, the lack of standardized management interfaces and standards for different brands and models of equipment increases management complexity. Cloud computing technology, with its powerful computing and storage capabilities, high scalability, and flexible resource allocation, can overcome the limitations of traditional management and provide a more efficient and intelligent solution for computer room equipment management. Based on this, a cloud computing-based computer room equipment management method is proposed.

[0003] The Chinese invention patent application number 202310370713.6 discloses a computer room equipment adaptive management system and method based on cloud computing, including an assembled module, a BIM component management module, a computer room engineering processing module, an audit port module, and an adaptive feedback adjustment module. The assembled module, the BIM component management module, the computer room engineering processing module, the audit port module, and the adaptive feedback adjustment module are connected in sequence. The present invention proposes a prefabricated assembled computer room equipment installation system based on cloud computing, which can solve the various drawbacks brought about by traditional installation and improve the installation level from multiple directions such as energy saving and environmental protection, construction period management, cost control, construction technology quality, and safe production. At the same time, based on the BIM technology management of the project display board during the installation of prefabricated assembled computer room equipment, data analysis, warnings, and intelligent processing between components are provided.

[0004] Existing methods typically assess the risks of individual components in isolation, ignoring the interdependencies between components and leading to blind spots in risk prevention and control. Furthermore, construction schedules lack flexibility, failing to respond in real time to environmental changes and construction anomalies, and resource allocation is often imprecise. This extensive management model can easily lead to the accumulation of construction deviations and the spread of risk, severely impacting the quality and progress of computer room construction. The introduction of intelligent dynamic management technologies is urgently needed to achieve refined control. Summary of the Invention

[0005] The present invention aims to solve the technical problems existing in the prior art and provides a novel method for the manufacture of a novel nanostructured carbon foam.

[0006] The present invention solves the above technical problems with the following technical solution: a computer room equipment management method based on cloud computing, the method comprising: S1, build a computer room component model based on actual size; S2, obtain component design values and actual measurement values; S3, calculate the direct and indirect risk factors of each component based on S2 and establish a component review list; S4, obtain the importance parameters of each component and modify the direct and indirect risk factors; S5, based on the revised direct and indirect risk factors, calculates the feasibility of the component path and determines the audit checklist results; collects environmental data of the computer room, calculates the external coefficient of its impact on the computer room component path, and constructs a flexible construction period model to adjust the component path in real time; S6, if a component fails the review, the system automatically marks its associated components and calculates the increment of its indirect risk factor; S7, re-establish the component review list based on the increment of indirect risk factors until the complete computer room component path is achieved.

[0007] Preferably, said S4, modifying direct and indirect risk factors, comprises: The modified direct risk factor formula is:

[0008] α is the correction coefficient, is the importance parameter of all components in the current stage; the direct risk factor calculation formula is: , , , is the dynamic weight coefficient, is the design value, is the actual value; The modified indirect risk factor formula is:

[0009] β is the correction coefficient, is the maximum number of associated indicators of components in the project; the calculation formula for the indirect risk factor is: , is the dynamic weight coefficient, , , is the number of associated indicators of the current component, is the maximum number of related indicators of the project, .

[0010] Preferably, the increment of the indirect risk factor in S6 includes: if the achievable rate of a component does not reach the achievable rate threshold, modifying the status of the component to a failed state on the audit list, recording it as an audit failure, finding the associated components directly connected to the audit failed component, marking the identified associated components in the system, and calculating the increment of the indirect risk factor of the audit failed component; wherein the calculation formula for the increment of the indirect risk factor is:

[0011] The physical meaning of the correlation index number The larger the value, the lower the probability of component failure itself; the diffusion coefficient γ controls the intensity of risk propagation. Used for normalization.

[0012] Preferably, the calculation of the external coefficient of its influence on the component path of the computer room includes: if there is a component that reaches or exceeds the achievable rate threshold, the corresponding component is planned into the component path, the external coefficient is calculated for the component path, and the achievable rate is corrected; wherein the calculation formula of the external coefficient is:

[0013] For environmental interference, is the external risk level, and is the weight coefficient, Score the plan's completeness; , is the weight of the i-th evaluation indicator, is the score of the i-th evaluation indicator, and n is the number of evaluation indicators.

[0014] Preferably, the correcting the achievable rate includes: correcting the achievable rate using an external coefficient Yes, for the corrected Re-evaluate the audit checklist results; revise the achievable rate calculation formula as follows: , in, .

[0015] Preferably, the S5, constructing a flexible construction period model, includes: collecting actual project progress data in real time, and if the construction period needs to be updated, determining the component path contribution value of each component based on the location of the construction period update time; quantifying the contribution rate of each component to the overall risk propagation path, and locating high-risk components.

[0016] Preferably, the S5, constructing a flexible duration model, includes: collecting actual project progress data in real time; if the duration needs to be updated, determining the component path contribution value of each component based on the location of the duration update time; quantifying the contribution rate of each component to the overall risk propagation path, and locating high-risk components; and revising the project duration based on the high-risk components; wherein the revised project duration calculation formula is as follows:

[0017] λ is the elasticity coefficient, k is the number of high-risk components, is the contribution rate of a component on the risk transmission path, New is the new construction period, , Original refers to the original construction period.

[0018] Preferably, the contribution rate includes: based on the actual and planned duration data, taking the new duration calculation point as the benchmark, reversely tracing the successfully audited dependent components to form a risk propagation path, and calculating the path contribution value of each component on the risk propagation path , . and is a dynamic adjustment parameter; then the contribution rate is obtained, the contribution rates are sorted in descending order, and a high-risk threshold is defined. Components exceeding this threshold are considered high-risk components; the contribution rate calculation formula is: , is the sum of the contribution values of all components on the current risk propagation path, It is the contribution value of a component on the risk propagation path.

[0019] Preferably, the quantification of the contribution rate of each component to the overall risk propagation path includes: identifying core components whose contribution rates exceed a threshold, generating their impact areas and calculating physical distances; wherein, core components are defined as components whose contribution rates are greater than a contribution value threshold; analyzing the functional associations between core components and constructing an association network model; wherein, the functional associations include whether there are upstream and downstream dependencies and whether a specific function is completed together; setting the maximum number of hops for contribution rate propagation, starting from the highest risk component, propagating along the network to determine the scope of the risk diffusion area, and quantifying the multi-level risk impact.

[0020] This application also provides a computer room equipment management system based on cloud computing, the system comprising: The modeling module is used to build a BIM model of the project based on the actual size according to the computer room design drawings and actual needs, and update the physical connection relationship of the components in real time; The calculation module is used to calculate the direct risk factor and indirect risk factor of the component and generate the correction factor in combination with the importance parameter; The management module is used to calculate the component realizability rate based on the revised risk factors, dynamically update the audit status, mark the associated components when the component audit fails, and calculate the indirect risk factor increment; The adjustment module is used to calculate the external coefficient according to environmental interference factors, external risk level and completeness of emergency plan, dynamically modify the construction period model, and adjust the achievable rate threshold of the component path.

[0021] The beneficial effects of the present invention are: Based on the computer room design drawings and actual requirements, a BIM model is constructed to accurately capture component information. By calculating direct and indirect risk factors, an audit checklist is established, comprehensively considering the components themselves and their associated impacts. Physical connection relationships are captured in real time. When a component audit fails, associated components are promptly marked and risk increments are calculated, dynamically updating the audit checklist. Continuous iterations are performed until a complete computer room component path is formed, enabling refined management of computer room component installation audits. This effectively reduces installation risks, improves installation quality and efficiency, ensures the stable operation of the computer room system, and provides scientific and reliable technical support for computer room construction.

[0022] By collecting environmental data, classifying external risks, assessing the completeness of emergency plans, and calculating external coefficients, we can comprehensively consider various influencing factors in computer room construction. By building a flexible construction schedule model based on external coefficients and adjusting component paths in real time, we can flexibly respond to uncertainties in the construction process and effectively reduce schedule deviations. When component review fails, the incremental indirect risk factors are corrected, further enhancing risk response capabilities. This solution improves the accuracy and rationality of construction schedule planning, reduces construction delays caused by external factors, ensures on-time delivery of the project, improves the overall efficiency and competitiveness of the project, and provides strong support for the smooth implementation of the computer room construction project.

[0023] By establishing a construction period monitoring mechanism to collect data in real time, we can accurately monitor the actual project progress. By determining the component path contribution value based on the construction period update, we can quantify the impact of each component on the path. We can further calculate the component's contribution to the overall risk propagation path and identify high-risk components, enabling early identification of potential risks. Finally, we can adjust the project construction period based on high-risk components, making construction period adjustments more targeted and scientific.

[0024] By dynamically dividing risk impact zones and combining contribution rate propagation rules with physical distance thresholds to accurately identify the impact range of high-risk components, a functional association network model is introduced to quantify the strength of dependencies between components. The risk diffusion boundary is controlled based on a maximum hop count and contribution rate attenuation mechanism, thereby achieving intelligent optimization of project schedules and risk mitigation. This solution integrates multiple factors, focusing on both the characteristics of the components themselves and taking into account their associations and propagation patterns. It effectively identifies and manages project risks, providing strong guarantees for smooth project progress and mitigating losses caused by risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Schematic diagram of the process of a computer room equipment management method based on cloud computing according to an embodiment of the present invention; Figure 2 This is a structural block diagram of a computer room equipment management system based on cloud computing according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0027] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the described features. In the description of this application, "plurality" means two or more, unless otherwise specifically specified.

[0028] In the description of this application, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art will recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0029] Example 1: Figure 1 This is a flow chart of a computer room equipment management method based on cloud computing according to a first embodiment of the present invention, comprising the following steps: S1, according to the computer room design drawings and actual needs, build a computer room component engineering BIM model based on actual size.

[0030] S2, obtaining the design values of the basic information of the pre-installed components in the computer room, and performing a recheck measurement of the building structure in the computer room to obtain actual measurement values.

[0031] The design values of basic information refer to the key parameters and indicators of components or equipment specified in the design drawings. This information is an important basis for the installation and commissioning of equipment in the computer room, including but not limited to size, location, specifications, installation angle, and connection method.

[0032] S3, based on the actual measured values and design values of the components, calculate the direct and indirect risk factors of each component and establish a component review list.

[0033] Among them, the component review list includes the design value, actual measurement value, direct risk factor (a quantitative indicator reflecting the degree of difference between the actual measurement value and the design value of the component), indirect risk factor (a quantitative indicator reflecting the potential impact of the status of the associated component on the target component) and achievability rate of each component.

[0034] Specifically, direct and indirect risk factors are calculated for each component, including: For each component in the list, calculate the difference between its actual measured value and its designed value.

[0035] The direct risk factor of each component is calculated based on the difference between the actual measured value and the designed value.

[0036] The direct risk factor calculation formula is:

[0037] is the normalized difference between the actual measured value and the designed value, , , is the dynamic weight coefficient, is the design value, is the actual value.

[0038] Analyze the physical connection relationship between each component and other components and determine the associated components.

[0039] Determining associated components involves understanding the layout and connection relationships of each component within the computer room, identifying the physical connection points between each component and other components, and recording each component's associated component information, including the associated component number, name, and connection type. Based on the identified physical connection relationships, an association matrix is established. The rows and columns in the matrix represent different components, and the element values indicate the connection relationships between components. If component i is physically connected to component j, the value of the corresponding position (i, j) in the matrix is 1 (or some other indicator indicating presence); otherwise, it is 0.

[0040] Based on historical data and real-time status information, a model of the impact of the status of associated components on the target components is established to predict the current construction association index and the maximum engineering association index.

[0041] Among them, establishing a model for the impact of the status of associated components on the target component involves collecting historical data from past similar computer room equipment installation projects, including the installation status of components, quality issues, and the impact of associated components. The data should cover a variety of situations, such as different time periods, different environmental conditions, and different construction teams, to ensure the generalization ability of the model. Analyze the factors that may affect the status of associated components on the target component in the historical data, such as the installation sequence, installation quality, and material properties of the associated components. Define quantitative indicators for these influencing factors and determine their relationship with the status of the target component. Based on the defined influencing factors and quantitative indicators, select a machine learning algorithm to establish a model for the impact of the status of associated components on the target component. Use historical data to train the model, and adjust the model parameters to optimize the prediction performance.

[0042] To predict the current component association index and the project's maximum association index, for each component in the current computer room equipment installation project, we use the previously established association matrix and physical connection relationships. By counting the number of nonzero elements in each row (or column) of the association matrix, we calculate the number of associated components (i.e., the current component association index). Within the entire computer room equipment installation project, we identify the component with the greatest number of associated components; this number represents the project's maximum association index. These current component association index and the project's maximum association index are then fed into the previously established impact model, allowing us to leverage the model's predictions to promptly update our understanding of the current component associations and the overall project's associations.

[0043] Calculate the indirect risk factor of each component based on the current construction correlation index and the maximum engineering correlation index.

[0044] The calculation formula for indirect risk factors is:

[0045] is the normalized difference between the actual measured value and the designed value, is the dynamic weight coefficient, , , is the number of associated indicators of the current component, It is the maximum number of associated indicators of the project.

[0046] and The initial value is determined by historical data statistics. .

[0047] S4, obtain the importance parameter of each component, obtain the correction parameter according to the importance parameter, and calculate the correction factor of the direct and indirect risk factors.

[0048] The calculation formula of importance parameter is:

[0049] at this time, and The value of is dynamically adjusted according to the stage, for example, the design stage can increase (focus on design deviation), reduce The construction phase can be increased (pay attention to associated risks), reduce .

[0050] S5. Calculate the feasibility of the component path based on the corrected direct and indirect risk factors, and judge the audit checklist results based on the feasibility.

[0051] Among them, the modified direct risk factor formula is:

[0052] α is the correction coefficient (default α=0.2), It is the importance parameter of all components in the current stage.

[0053] The modified indirect risk factor formula is:

[0054] β is the correction coefficient (default β=0.2), The maximum number of associated indicators of components in the project.

[0055] The formula for calculating the achievable rate is: .

[0056] Specifically, set the achievable rate threshold, if there is a component If the achievable rate threshold is reached or exceeded, the corresponding component Plan to the component path, modify the component status to successful status on the review list, and the review is successful.

[0057] S6, obtains the physical connection relationship between constructions in real time. If a component fails the review, the system automatically marks its associated components and calculates the increment of its indirect risk factor.

[0058] Specifically, the physical connection information between all components in the system is collected, including the connection method (such as welding, bolt connection, electrical connection, etc.), connection strength, connection position, etc. Graph theory is used to build a component physical connection relationship model. Each component is regarded as a node in the graph, and the physical connection between components is regarded as an edge in the graph. The physical connection relationship model is updated at any time in the BIM model. If the achievability rate does not reach the achievable rate threshold, the component status is changed to failed on the audit list, and the audit fails. Based on the component physical connection relationship model, the associated components directly connected to the audit-failed component are identified, marked in the system, and the indirect risk factor increment of the audit-failed component is calculated.

[0059] The calculation formula for the increment of indirect risk factors is:

[0060] The physical meaning of the correlation index number The larger the component, the lower the probability of failure of the component itself ( The higher the risk, the more likely the risk is to spread through the associated path. The diffusion coefficient (γ) is used to control the intensity of risk transmission and can be dynamically adjusted through historical data or machine learning. The default value is γ = 0.3 (empirical value, indicating a 30% risk transmission ratio). Used for normalization to ensure ΔI∈[0,γ].

[0061] S7, re-establish the component review list based on the increment of indirect risk factors until the complete computer room component path is achieved.

[0062] Specifically, the feasibility rate of the component is recalculated based on the increment of the indirect risk factor, and the iterative update of the audit list is completed until the feasibility rate of all components in the computer room exceeds the feasibility rate threshold, forming a complete computer room component path.

[0063] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: Based on the computer room design drawings and actual requirements, a BIM model is constructed to accurately capture component information. By calculating direct and indirect risk factors, an audit checklist is established, comprehensively considering the components themselves and their associated impacts. Physical connection relationships are captured in real time. When a component audit fails, associated components are promptly marked and risk increments are calculated, dynamically updating the audit checklist. Continuous iterations are performed until a complete computer room component path is formed, enabling refined management of computer room component installation audits. This effectively reduces installation risks, improves installation quality and efficiency, ensures the stable operation of the computer room system, and provides scientific and reliable technical support for computer room construction.

[0064] Example 2 In Example 1, when the risk prediction model for computer room components is calculated solely based on BIM relationships and static design deviations, while it can identify direct and indirect risks, the feasibility of component installation can be significantly affected by factors such as excessive temperature and humidity, natural disasters, and the completeness of emergency plans during actual construction. Because the impact of these external factors on risk factors varies significantly across different construction phases and geographical locations, using only a fixed parameter model would result in a lack of flexibility in adjusting construction schedules and making it difficult to accurately respond to emergencies.

[0065] Therefore, the embodiments of the present application are optimized based on the above embodiments.

[0066] In some embodiments, in step S5, calculating the feasibility of the component path further includes: S51, collect environmental data of the location where the computer room is built, normalize environmental interference factors, classify external risk impacts by level, and evaluate the completeness of the emergency plan.

[0067] For example, consider the impact of common local natural disasters, such as earthquakes, floods, and typhoons, on the computer room. Based on the likelihood of risk occurrence and the degree of impact, external risks are divided into three levels: high, medium, and low. Provide a detailed description of each identified external risk, including the risk name, risk source, possible stage of occurrence, scope of impact, etc. Based on the risk level classification results, the risks are included in the corresponding level list. Use expert evaluation methods to check whether the emergency plan covers all possible external risk events, including risk warning mechanisms, emergency response processes, rescue measures, etc. At the same time, check whether the emergency plan clearly defines the materials, equipment, funds and other resources required for the emergency, as well as the storage and deployment methods of resources. In addition, understand the emergency plan's rehearsal plan and actual rehearsal situation, and evaluate the rehearsal effect and improvements to the plan.

[0068] The plan completeness score is calculated based on the scores of each indicator. The formula is as follows:

[0069] in, Score the completeness of the emergency plan. is the weight of the i-th evaluation indicator, is the score of the i-th evaluation indicator, and n is the number of evaluation indicators. The completeness of the emergency plan is evaluated based on the completeness score. For example, a score of 80 or above is complete, 60-80 is basically complete, and below 60 is incomplete.

[0070] S52, calculating the external coefficient of the impact of environmental data on the component path of the computer room.

[0071] The calculation formula of the external coefficient is:

[0072] is the environmental interference (weighted average of temperature, humidity, vibration, and space limitations); is the external risk level; Score the plan's completeness; and is the weight coefficient (the default in this application is =0.6, =0.4, which can be optimized based on historical data in actual use).

[0073] S53, builds a flexible construction period model based on external coefficients and adjusts component paths in real time.

[0074] Specifically, according to the project's work breakdown structure and historical duration estimation methods, determine the original planned duration and set the elasticity coefficient. (The default value is 0.2, which means that for every 0.1 increase in E, the construction period will be extended by 2%). Establish a flexible construction period adjustment formula:

[0075] New is the new construction period, and Original is the original construction period.

[0076] Among them, the real-time adjustment of component path is based on the change of construction period, and the external coefficient is used to correct the achievable rate. Yes, for the corrected Re-evaluate the results of the audit list and plan the adjustment component path. Modify the calculation formula of the achievable rate as follows:

[0077] S54, if a component fails the review, correct the increment of its indirect risk factor.

[0078] The incremental calculation formula for the modified indirect risk factor is:

[0079] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: By collecting environmental data, classifying external risks, assessing the completeness of emergency plans, and calculating external coefficients, we can comprehensively consider various influencing factors in computer room construction. By building a flexible construction schedule model based on external coefficients and adjusting component paths in real time, we can flexibly respond to uncertainties in the construction process and effectively reduce schedule deviations. When component review fails, the incremental indirect risk factors are corrected, further enhancing risk response capabilities. This solution improves the accuracy and rationality of construction schedule planning, reduces construction delays caused by external factors, ensures on-time delivery of the project, improves the overall efficiency and competitiveness of the project, and provides strong support for the smooth implementation of the computer room construction project.

[0080] Example 3 In Example 2, while the overall adjustment of the construction period through the dynamic external coefficient was able to respond to environmental changes and external risks, it failed to accurately identify the differentiated impact of specific components on construction delays. Because the contribution of different components to the risk transmission path varies significantly, using only a unified adjustment coefficient will lead to an imbalance in resource allocation. Furthermore, the environmental coefficient correction in Example 2 relies primarily on macro data and lacks quantitative analysis of component-level risk transmission paths, making it difficult to cope with complex delay scenarios in multi-disciplinary cross-construction.

[0081] Therefore, the embodiments of the present application are optimized based on the above embodiments.

[0082] In some embodiments, in step S53, constructing the flexible construction period model further includes: S531, establish a construction period monitoring mechanism to collect actual project progress data in real time.

[0083] Among them, the progress data includes information such as the start time, completion time, and actual construction duration of each component, and the collected progress data is entered into the database.

[0084] S532: If the construction period needs to be updated, the component path contribution value of each component is determined according to the position of the construction period update time.

[0085] Specifically, the actual progress data and the original planned duration data of each component are extracted from the database. The construction when calculating the new duration is used as the defining point, and all successfully audited components that depend on this path are traced back. At this time, the component path (the component path that is temporarily interrupted due to the duration update before the complete computer room component path is realized) is the risk propagation path, and the contribution value of each component to the component path is calculated.

[0086] The calculation formula of path contribution value is:

[0087] and It is a dynamic adjustment parameter, the default =0.5, =1.5. In actual application, it can be dynamically adjusted according to environmental interference factors, external risk impacts and the completeness of emergency plans.

[0088] S533, quantify the contribution rate of each component to the overall risk propagation path and locate high-risk components.

[0089] Specifically, the path contribution value of each component on the risk propagation path is calculated ,According to the path contribution value, calculate the contribution rate of the component to the risk propagation path, sort the contribution rate in descending order, and define a high-risk threshold. Components exceeding this threshold are high-risk components. The high-risk threshold is defined as 30% in this application.

[0090] The contribution rate calculation formula is:

[0091] is the sum of the contribution values of all components on the current risk propagation path, is the contribution value of a component on the risk propagation path, It is the contribution rate of a certain component on the risk transmission path.

[0092] S534, revise the project duration based on high-risk components.

[0093] The calculation formula for the revised project duration is as follows:

[0094] λ is the elasticity coefficient (default is 0.2), and every 0.1 unit change in E corresponds to a 2% construction period adjustment; k is the number of high-risk components; New is the new construction period.

[0095] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: By establishing a construction period monitoring mechanism to collect data in real time, we can accurately monitor the actual project progress. By determining the component path contribution value based on the construction period update, we can quantify the impact of each component on the path. We can further calculate the component's contribution to the overall risk propagation path and identify high-risk components, enabling early identification of potential risks. Finally, we can adjust the project construction period based on high-risk components, making construction period adjustments more targeted and scientific.

[0096] Example 4 While Example 3's component path contribution value and contribution rate thresholds can identify high-risk components and correct their duration, this approach only assesses the direct impact of a single component and fails to consider the multi-level propagation of risk within the component-dependent network. When projects are large and component dependencies are complex, delays in high-risk components can spread to remote, non-directly dependent components through functional or physical connections. The local correction strategy in Example 3 struggles to fully prevent this risk propagation, leading to cumulative deviations in duration predictions.

[0097] Therefore, the embodiments of the present application are optimized based on the above embodiments.

[0098] In some embodiments, step S533, quantifying the contribution of each component to the overall risk propagation path, further includes: 3A, generate the influence area for the core components and calculate the physical distance between the core components in the influence area.

[0099] Among them, the core component is defined as the component whose contribution rate is greater than the contribution value threshold.

[0100] For example, assume that for every x% increase in contribution rate, the impact zone expands by y associated components. For example, set x=10 and y=2, that is, when the contribution rate of a component increases by 10%, its impact zone expands by 2 associated components (components that are directly dependent or functionally bound). Based on the contribution rate of the component, the preliminary impact zone is calculated to determine the set of components that may be affected under the contribution rate propagation rule. Next, a physical distance threshold is set. For components within the preliminary impact zone, if the physical distance from the core component exceeds the physical distance threshold, they are removed from the impact zone (because they are less physically affected by the core component). If the components are closely functionally related, they will be retained even if the physical distance exceeds the limit.

[0101] 3B, analyze the functional associations between core components and construct a core component association network model.

[0102] Among them, functional associations include whether there are upstream and downstream dependencies, whether they jointly complete a specific function, etc. For components with close functional associations, even if they are physically far away, they may be affected by the core components and should be included in the scope of the impact zone. The core components are used as nodes, and the association relationships between components are used as edges to construct a component association network model. In the model, each node represents a component, and each edge represents the association relationship between components. The weight of the edge can be set according to the closeness of the association. For example, for associations with close physical connections and strong functional dependencies, a higher weight is set; for weaker associations, a lower weight is set.

[0103] 3C, sets the maximum number of hops for contribution rate propagation, starting from the component with the highest risk, and propagating all components within the hop range along the component-associated network to form a risk diffusion zone.

[0104] If the project is small, with a small number of components and simple relationships, the risk propagation path is relatively short, so the maximum number of hops n can be set to a smaller value, such as n=1 or n=2. For large, complex projects, with a large number of components and complex relationships, risks may propagate across multiple components. Therefore, a larger n value, such as n=3 or n=4, or even larger, is required to ensure that the risk impact zone covers all potentially affected components. If the risk propagates rapidly and may affect multiple components in a short period of time, a larger n value is required to promptly include potentially affected components in the risk impact zone.

[0105] For example, if n=3, starting from the component with the highest risk, all related components within 3 hops are included in the risk impact area.

[0106] It's important to note that during the propagation process, the contribution rate can be considered to decay. For example, the contribution rate decays by a certain percentage with each hop to more reasonably determine the scope of the risk spread. The decay rate per hop (e.g., 20%) is calculated. The final contribution rate = original contribution rate × (1 - decay rate) ^ number of hops. Termination conditions are: contribution rate < 5% or the maximum number of hops is reached.

[0107] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: By dynamically dividing risk impact zones and combining contribution rate propagation rules with physical distance thresholds to accurately identify the impact range of high-risk components, a functional association network model is introduced to quantify the strength of dependencies between components. The risk diffusion boundary is controlled based on a maximum hop count and contribution rate attenuation mechanism, thereby achieving intelligent optimization of project schedules and risk mitigation. This solution integrates multiple factors, focusing on both the characteristics of the components themselves and taking into account their associations and propagation patterns. It effectively identifies and manages project risks, providing strong guarantees for smooth project progress and mitigating losses caused by risks.

[0108] Furthermore, an embodiment of the present invention also provides a computer room equipment management system based on cloud computing.

[0109] Figure 2 It is a structural diagram of a computer room equipment management system based on cloud computing according to an embodiment of the present invention.

[0110] like Figure 2 As shown, a computer room equipment management system based on cloud computing includes: a modeling module, a calculation module, a management module, an adjustment module and an analysis module.

[0111] The modeling module is used to build a BIM model of the project based on the actual size according to the computer room design drawings and actual needs, and update the physical connection relationship of the components in real time; The calculation module is used to calculate the direct risk factor and indirect risk factor of the component and generate the correction factor in combination with the importance parameter; The management module is used to calculate the component realizability rate based on the revised risk factors, dynamically update the audit status, mark the associated components when the component audit fails, and calculate the indirect risk factor increment; The adjustment module is used to calculate the external coefficient according to environmental interference factors, external risk level and completeness of emergency plan, dynamically modify the construction period model, and adjust the achievable rate threshold of the component path.

[0112] It should be noted that other specific implementation contents of the computer room equipment management system based on cloud computing according to the embodiment of the present invention may refer to the above-mentioned computer room equipment management method based on cloud computing.

[0113] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0114] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0118] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0119] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A computer room equipment management method based on cloud computing, characterized in that: The method comprises: S1, build a computer room component model based on actual size; S2, obtain component design values and actual measurement values; S3, calculate the direct and indirect risk factors of each component based on S2 and establish a component review list; S4, obtain the importance parameters of each component and modify the direct and indirect risk factors; S5, based on the revised direct and indirect risk factors, calculates the feasibility of the component path and determines the audit checklist results; collects environmental data of the computer room, calculates the external coefficient of its impact on the computer room component path, and constructs a flexible construction period model to adjust the component path in real time; S6, if a component fails the review, the system automatically marks its associated components and calculates the increment of its indirect risk factor; S7, re-establish the component review list based on the increment of indirect risk factors until the complete computer room component path is achieved.

2. The computer room equipment management based on cloud computing according to claim 1 is characterized in that: The S4, Modification of Direct and Indirect Risk Factors, includes: The modified direct risk factor formula is: α is the correction coefficient, is the importance parameter of all components in the current stage; the direct risk factor calculation formula is: , , , is the dynamic weight coefficient, is the design value, is the actual value; The modified indirect risk factor formula is: β is the correction coefficient, is the maximum number of associated indicators of components in the project; the calculation formula for the indirect risk factor is: , is the dynamic weight coefficient, , , is the number of associated indicators of the current component, is the maximum number of related indicators of the project, .

3. The computer room equipment management based on cloud computing according to claim 1 is characterized in that: The increment of the indirect risk factor in S6 includes: if the component has an achievable rate that does not reach the achievable rate threshold, modifying the status of the component in the audit list to failure or audit failure, finding the associated components directly connected to the audit failed component, marking the identified associated components in the system, and calculating the increment of the indirect risk factor of the audit failed component; wherein the calculation formula for the increment of the indirect risk factor is: The physical meaning of the correlation index number The larger the value, the lower the probability of component failure itself; the diffusion coefficient γ controls the intensity of risk propagation. Used for normalization.

4. The computer room equipment management based on cloud computing according to claim 1, characterized in that: The calculation of the external coefficient of its influence on the component path of the computer room includes: if there is a component that reaches or exceeds the achievable rate threshold, the corresponding component is planned into the component path, the external coefficient is calculated for the component path, and the achievable rate is corrected; wherein the calculation formula of the external coefficient is: For environmental interference, is the external risk level, and is the weight coefficient, Score the plan's completeness; , is the weight of the i-th evaluation indicator, is the score of the i-th evaluation indicator, and n is the number of evaluation indicators.

5. The computer room equipment management based on cloud computing according to claim 4 is characterized in that: The correcting the achievable rate includes: correcting the achievable rate using an external coefficient Yes, for the corrected Re-evaluate the audit checklist results; revise the achievable rate calculation formula as follows: ， in, .

6. The computer room equipment management based on cloud computing according to claim 1, characterized in that: Said S5, constructing a flexible schedule model, includes: collecting actual project progress data in real time; if the schedule needs to be updated, determining the component path contribution value of each component based on the location of the schedule update time; quantifying the contribution rate of each component to the overall risk propagation path, and locating high-risk components.

7. The computer room equipment management based on cloud computing according to claim 1 is characterized in that: S5, constructing a flexible duration model, includes: collecting actual project progress data in real time; if the duration needs to be updated, determining the component path contribution value of each component based on the location of the duration update time; quantifying the contribution rate of each component to the overall risk propagation path and locating high-risk components; and revising the project duration based on the high-risk components. The revised project duration calculation formula is as follows: λ is the elasticity coefficient, k is the number of high-risk components, is the contribution rate of a component on the risk transmission path, New is the new construction period, , Original refers to the original construction period.

8. The computer room equipment management based on cloud computing according to claim 7 is characterized in that: The contribution rate includes: based on the actual and planned duration data, taking the new duration calculation point as the benchmark, reversely tracing the successfully audited dependent components to form a risk propagation path, and calculating the path contribution value of each component on the risk propagation path , . and is a dynamic adjustment parameter; then the contribution rate is obtained, the contribution rates are sorted in descending order, and a high-risk threshold is defined. Components exceeding this threshold are considered high-risk components; the contribution rate calculation formula is: , is the sum of the contribution values of all components on the current risk propagation path, It is the contribution value of a component on the risk propagation path.

9. The computer room equipment management based on cloud computing according to claim 7, characterized in that: The method of quantifying the contribution rate of each component to the overall risk propagation path includes: identifying core components whose contribution rates exceed a threshold, generating their impact areas, and calculating physical distances; wherein, core components are defined as components whose contribution rates are greater than a contribution value threshold; analyzing the functional associations between core components and constructing an association network model; wherein, functional associations include whether there are upstream and downstream dependencies and whether a specific function is completed together; setting a maximum number of hops for contribution rate propagation, starting from the highest risk component, propagating along the network to determine the scope of the risk diffusion area, and quantifying the multi-level risk impact.

10. A computer room equipment management system based on cloud computing, applied to a computer room equipment management method based on cloud computing according to any one of claims 1 to 9, characterized in that: The system comprises: The modeling module is used to build a BIM model of the project based on the actual size according to the computer room design drawings and actual needs, and update the physical connection relationship of the components in real time; The calculation module is used to calculate the direct risk factor and indirect risk factor of the component and generate the correction factor in combination with the importance parameter; The management module is used to calculate the component realizability rate based on the revised risk factors, dynamically update the audit status, mark the associated components when the component audit fails, and calculate the indirect risk factor increment; The adjustment module is used to calculate the external coefficient according to environmental interference factors, external risk level and completeness of emergency plan, dynamically modify the construction period model, and adjust the achievable rate threshold of the component path.

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