Dynamic safety management method for project cost data

Abstract

The invention relates to the technical field of data security processing, in particular to a dynamic security management method for project cost data. The method comprises the steps of selecting target cost data, and calculating a dynamic risk exposure degree according to an updating frequency; in combination with the relevance between the target data and other data, the relevance sensitivity degree is calculated through exposure degree weighting; constructing an interaction risk degree based on the data entry time duration and the topological structure layer number difference; fusing the correlation sensitivity degree and the interaction risk degree to generate an adjustment coefficient, and dynamically correcting the initial infection proportionality coefficient of the SIR model to obtain an optimized infection proportionality coefficient; and carrying out encryption level setting on the cost data. According to the method, the space-time interaction characteristic dynamic correction risk conduction model of the cost data is introduced, so that the problem of risk assessment deviation caused by the fact that a traditional method cannot adapt to the dynamic characteristics of the cost data is solved, and precision and dynamics of safety grading of the project cost data are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data security processing, in particular to an engineering cost data dynamic security management method. BACKGROUND

[0002] In the field of engineering cost management, the accuracy and security of data are directly related to the cost control and economic benefits of the project. With the advancement of digitalization in the construction industry, enterprises have accumulated vast amounts of historical engineering cost data, including multi-level data systems from basic labor and machine prices to sub-item engineering cost, single-item engineering cost, and project total cost. These data are not only the core assets of the enterprise, but also an important basis for subsequent bidding and cost estimation, so a perfect data security protection mechanism needs to be established.

[0003] The existing engineering cost data management system mainly adopts a static access control strategy based on roles (RBAC), that is, the permissions are pre-set according to the user's position, and fixed encryption is used for core fields. This mode is effective in static archiving, but has obvious defects in dynamic scenarios. First, the static strategy cannot perceive the real-time state of the data. For example, during the bidding price adjustment period, the risk exposure of high-frequency updated basic material prices increases dramatically, and the existing system cannot identify this "active risk" and dynamically improve protection. Second, the existing technology ignores the strong coupling and associated risks of cost data. Changes in basic data will affect the total cost, and isolated defense strategies cannot detect the risk of indirectly damaging core data by tampering with basic data.

[0004] In order to solve the risk or information transmission problem in complex networks, SIR model (Susceptible-Infected-Recovered, susceptible-infected-recovered model) as a classic propagation dynamics model is often used to analyze the spread process of viruses, rumors or risks among network nodes. In the field of data security, existing research has attempted to use the SIR model to simulate the propagation path of computer viruses in the network. However, the existing application of SIR model mainly focuses on virus defense at the network topology level, and directly applying the traditional SIR model cannot quantify the characteristics of hierarchical logical association and other characteristic information of cost data, resulting in errors in the calculation of risk infection proportion coefficient, and unable to accurately calculate the comprehensive sensitivity of a single data in a specific time window. SUMMARY

[0005] In order to solve the technical problem that the existing technology does not consider the characteristics of engineering cost data when using the SIR model to perform security grading on engineering cost data, resulting in the infection proportion coefficient obtained cannot effectively represent the data risk, the purpose of the present application is to provide an engineering cost data dynamic security management method, and the technical solution adopted is as follows: The application provides an engineering cost data dynamic security management method, which comprises the following steps: History engineering cost data of any level category in the total project engineering cost database is taken as target data, and the dynamic risk exposure degree of the target data is obtained according to the update frequency information of the target data. The target data and any other history engineering cost data are taken as a to-be-analyzed group, the correlation between the two data in the to-be-analyzed group is obtained, the correlation is weighted by using the dynamic risk exposure degree, and the correlation sensitivity degree of the to-be-analyzed group is obtained. The interactive risk degree is obtained according to the entry time length of the target data in the total project engineering cost database and the difference in the topological structure layer number between the two data in the to-be-analyzed group in the total project engineering cost database. The adjustment coefficient is obtained according to the correlation sensitivity degree and the interactive risk degree, the initial infection proportion coefficient of the to-be-analyzed group in the SIR infectious disease model is adjusted by using the adjustment coefficient, the optimized infection proportion coefficient is obtained, and the encryption level is set according to the optimized infection proportion coefficient between the target data and all other history engineering cost data.

[0006] Further, the method for obtaining the dynamic risk exposure degree comprises the following steps: The update frequency information comprises the update frequency and the average update time interval. The average update time interval is negatively correlated, the negatively correlated mapping result is multiplied by the update frequency, the product is normalized to obtain the dynamic risk exposure degree.

[0007] Further, the method for obtaining the correlation comprises the following steps: The absolute value of the Spearman rank correlation coefficient of the two data in the to-be-analyzed group is obtained, the preset logical relationship identifier between the corresponding levels of the two data is obtained, the absolute value of the Spearman rank correlation coefficient is weighted and summed with the preset logical relationship identifier according to the preset weight, and the correlation is obtained.

[0008] Further, the method for obtaining the correlation sensitivity degree comprises the following steps: The dynamic risk exposure degree is multiplied by the correlation, the product is normalized to obtain the correlation sensitivity degree.

[0009] Further, the method for obtaining the interactive risk degree comprises the following steps: The layer number proportion of the topological structure layer number difference in the total topological structure layer number is obtained, the ratio between the latest entry time length of the target data and the average update time interval is taken as a time decay constraint coefficient, and the sum of the layer number proportion and the time decay constraint coefficient is taken as the interactive risk degree.

[0010] Further, the adjustment coefficient acquisition method comprises: The product of the correlation sensitivity and the interaction risk degree is normalized to obtain the adjustment coefficient.

[0011] Further, the optimization infection ratio coefficient acquisition method comprises: The adjustment ratio is obtained by adding the adjustment coefficient to the positive integer 1, and the optimization infection ratio coefficient is obtained by multiplying the adjustment ratio by the initial infection ratio.

[0012] Further, the encryption level setting according to the optimization infection ratio coefficient between the target data and all other historical engineering cost data comprises: The optimization infection ratio coefficients of all to-be-analyzed groups corresponding to the target data are counted, the average value of the optimization infection ratio coefficients is taken as an encryption reference value, and the encryption level of the target data is determined according to the size of the encryption reference value.

[0013] Further, the encryption level setting according to the size of the encryption reference value comprises: If the encryption reference value is less than a first threshold value, the encryption level is set to public reading; if the encryption reference value is greater than or equal to the first threshold value and less than a second threshold value, the encryption level is set to internal access; if the encryption reference value is greater than or equal to the second threshold value and less than a third threshold value, the encryption level is set to authorized access; and if the encryption reference value is greater than or equal to the third threshold value, the encryption level is set to strict approval authorization.

[0014] Further, the first threshold value is set to 0.1, the second threshold value is set to 0.4, and the third threshold value is set to 0.8.

[0015] The present application has the following beneficial effects: The application considers that the engineering cost data has the characteristics of high frequency update, and there is a hierarchical association between different data, so when analyzing the data, the dynamic risk exposure of the target data is determined by introducing the update frequency information, and the correlation sensitivity between the data is weighted and corrected, solving the problem that the traditional SIR model only relies on the static topological structure and ignores the dynamic update characteristics of the cost data. The data that is updated more frequently is given a higher propagation weight, so that the sensitive cost indicators that change frequently have stronger risk transmission ability in the model, accurately simulating the actual diffusion path of sensitivity in the cost system. Further considering the hierarchical structure and the entry time between the data, combining the entry time length and the difference in the number of topological structures, the interactive risk degree is constructed from the time dimension and the space dimension. The entry time length reflects the exposure window period of data operation, and the difference in the number of topological structures reflects the influence depth of data change on the project structure. The space-time interaction characteristics are used as the adjustment coefficient of the SIR model, the dynamic calibration of the initial infection proportion coefficient is realized, and it is ensured that the model not only reflects the static association of the data, but also can sensitively capture the potential risks brought by the current operation behavior, so as to implement more accurate encryption grading for cost data of different risk levels. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0017] Figure 1 A kind of engineering cost data dynamic security management method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following will combine the drawings and the preferred embodiments to specifically describe the specific implementation, structure, features and effects of the engineering cost data dynamic security management method according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0020] The specific scheme of the engineering cost data dynamic security management method provided by the present application will be specifically described below in combination with the drawings.

[0021] Please see Figure 1 The diagram illustrates a flowchart of a dynamic security management method for engineering cost data according to an embodiment of the present invention. The method includes: Step S1: Take the historical project cost data of any level in the total project cost database as the target data, and obtain the dynamic risk exposure of the target data based on the update frequency information of the target data.

[0022] It should be noted that the overall project cost database contains various types of project cost data, such as basic project data, construction data, price information data, and labor and material data. In this embodiment of the invention, each type of historical project cost data is constructed into several sequences according to time order to obtain historical project cost data. Furthermore, it is divided into levels such as overall project cost, individual project cost, unit project, subsidiary company project, and sub-item project, completing the hierarchical classification. This results in historical project cost data sequences of multiple levels. The database can be stored using a topological structure based on the hierarchical relationship; this is a technique well-known to those skilled in the art and will not be elaborated upon here.

[0023] The present invention aims to determine the encryption level of engineering data in the database. Therefore, the present invention targets each piece of data in the database. Thus, the present invention uses historical engineering cost data of any level in the total project engineering cost database as the target data. The following description uses the target data as the object for example. The processing method of other data is the same as that of the target data.

[0024] For a given target data, this embodiment of the invention considers that traditional SIR models rely solely on static topology and ignore the dynamic updating characteristics of cost data. Since more frequently updated cost data indicates greater importance and sensitivity, it should have a higher weight in the SIR model, enabling frequently changing sensitive cost indicators to have a stronger risk transmission capability within the model. Therefore, this embodiment further obtains the dynamic risk exposure of the target data based on its update frequency at the current moment. A higher dynamic risk exposure indicates greater sensitivity, and thus a higher infection rate should be present in the SIR model.

[0025] Preferably, in this embodiment of the invention, the method for obtaining dynamic risk exposure includes: The update frequency information includes the update frequency and the average update time interval. It should be noted that the update frequency information should be statistically obtained within the time period in which the current moment occurs, used to describe the update frequency characteristics at the current moment. In this embodiment of the invention, data from the previous six months are statistically analyzed to obtain the update frequency and the average update time interval. During the statistical analysis, the update frequency is measured in times per month, and the average update time interval is the average time interval between two consecutive updates, which can be set to days.

[0026] After negatively mapping the average update time interval, the negative correlation mapping result is multiplied by the update frequency, and the product is normalized to obtain the dynamic risk exposure.

[0027] In this embodiment of the invention, a negative correlation mapping is performed on the average update time interval using a reciprocal form. To avoid the denominator being 0, the result of adding the average update time interval to a positive integer 1 is used as the denominator, and the positive integer 1 is used as the numerator, resulting in the negative correlation mapping result. The final normalization method used is set to range standardization, that is, the product of all data is calculated, and the normalization is achieved based on the maximum and minimum values. This is a technique well known to those skilled in the art and will not be elaborated here.

[0028] Step S2: Combine the target data with any other historical engineering cost data as a group to be analyzed; obtain the correlation between the two data within the group to be analyzed, and use dynamic risk exposure to weight the correlation to obtain the correlation sensitivity of the group to be analyzed.

[0029] When the SIR model is applied to the field of data security encryption, it can model the transmission process of risks or threats between data nodes, especially in data systems with logical dependencies or related structures, such as the multi-level engineering cost data targeted in this embodiment of the invention. Therefore, before using the SIR model to determine the security level of the target data, it is necessary to first analyze the correlation between the target data and other historical engineering cost data. For ease of analysis, this embodiment of the invention treats the target data and any historical engineering cost data as a group to be analyzed, that is, one target data corresponds to multiple groups to be analyzed, and then analyzes the correlation between the two data within the group to be analyzed. The greater the correlation, the stronger the correlation between the two data, and the greater the risk of data interaction. If one data is in a high-risk state such as tampering or leakage, it may further affect the other data. Therefore, combined with dynamic risk exposure, if the dynamic risk exposure is large, it indicates that the target data has frequent dynamic changes, which will generate strong security risks. At the same time, if the correlation is large, it will lead to the impact on other data. Therefore, the correlation is weighted using the dynamic risk exposure to obtain the correlation sensitivity of the group to be analyzed. The greater the correlation sensitivity, the stronger the correlation between the two data points in the analysis group, and the more likely it is to generate exposure risk.

[0030] Preferably, in this embodiment of the invention, considering the logical correlation between engineering data, such as the cost of a single project (e.g., the total cost of a residential building) and the cost of a unit project (e.g., the civil engineering cost of a residential building), where the cost of a single project equals the sum of the costs of multiple unit projects, these two data points have a strong correlation. However, the correlation itself may not be very apparent. Therefore, when analyzing the correlation, one should not only analyze the correlation at the data level but also consider the specific logical relationship. Specifically, the method for obtaining the correlation includes: Obtain the absolute value of the Spearman rank correlation coefficient between two data points in the group to be analyzed, and obtain the preset logical relationship identifier between the corresponding levels of the two data points; according to the preset weight, perform a weighted summation of the absolute value of the Spearman rank correlation coefficient and the preset logical relationship identifier to obtain the correlation.

[0031] It should be noted that Spearman's rank correlation coefficient is a nonparametric statistic used to measure the strength and direction of the monotonic relationship between two variables. It does not depend on the distribution of the data, but is based on the order (rank) of the variable values ​​rather than the original values. Therefore, it is robust to outliers and suitable for nonlinear but monotonic relationships (such as exponential or logarithmic relationships). Its value ranges from -1 to 1, where 1 represents a perfectly positive monotonic correlation, -1 represents a perfectly negative monotonic correlation, and 0 represents no monotonic association. Since the engineering cost data addressed in this embodiment of the invention does not only exhibit correlation through positive monotonic correlation, but negative correlation can also demonstrate correlation. For example, the precast component ratio data and on-site labor costs are clearly negatively correlated. Therefore, this embodiment of the invention needs to use the absolute value of the Spearman rank correlation coefficient to characterize the correlation at the data level.

[0032] In this embodiment of the invention, logical relationship identifiers are set to 1 and 0. 1 indicates a logical relationship between two cost data points, and 0 indicates no logical relationship between them. These logical relationship identifiers are pre-assigned by staff based on the project cost type and are preset values ​​that can be directly called in actual use. This is only a brief example of the types corresponding to these two identifiers; specific settings need to be configured according to the specific types included in the actual implementation. This embodiment of the invention will not elaborate further. (1) Logical relationship identifier is 1: For example, parent-child summary relationship, such as the single project cost and unit project cost in the above examples, which belong to parent-child summary relationship, so the data of the two are strongly correlated; For example, cost composition reference relationship, such as the comprehensive unit price of sub-project (such as C30 concrete beam) and material cost information price (such as C30 commercial concrete guidance price). The former is directly obtained by multiplying the latter by the consumption, and the two are strongly correlated.

[0033] (2) Logical relationship identifier is 0: For example, parallel or no relationship, indicating that the data are in different branches and have no calculation dependency. For example, earthwork and door and window engineering, although they belong to the same project, are two independent parallel tables in the cost calculation book. Modifying the door and window price usually will not directly trigger the recalculation logic of the layout earthwork data.

[0034] It should be noted that in this embodiment of the invention, the absolute value of the Spearman rank correlation coefficient is weighted at 0.6, and the logical relation identifier is weighted at 0.4. The two features are weighted and summed according to these weights, indicating that the final result focuses more on the correlation at the data level. In other implementations of this invention, the weights can be set independently, and the weight ratios can be adjusted according to the correlation tendency; these will not be elaborated upon here.

[0035] Preferably, in this embodiment of the invention, since both dynamic risk exposure and correlation are positively correlated with correlation sensitivity, the method for obtaining correlation sensitivity includes: The dynamic risk exposure is multiplied by the correlation, and the product is normalized to obtain the correlation sensitivity. The normalization here can still be performed using range standardization, a technique well-known to those skilled in the art, and will not be elaborated upon here.

[0036] Step S3: Based on the entry time of the target data in the total project cost database and the difference in the topological layer of the two data in the analysis group in the total project cost database, obtain the degree of interaction risk.

[0037] In the overall project cost database, the risk sensitivity of historical project cost data can be easily quantified. Anomalies can be monitored by observing access to and tampering of the data, leveraging user behavior characteristics and the correlation between the data and project cost data. However, the data in the database is not static after entry; new project cost data is constantly being entered and the database is dynamically updated. Therefore, when conducting risk analysis, it is necessary to further assess the spatial risk information of the group to be analyzed within the database by considering the data's topological structure (e.g., head office-branch-subsidiary-individual project) and the entry time.

[0038] It should be noted that for two data points in the analysis group, the greater the difference in the number of topological layers, the wider the impact of the data within the hierarchical structure. The deeper and broader the data updates involve, the greater the structural impact on the overall cost system, and thus the higher the security risk of that data. Additionally, a longer data entry time indicates potential stagnation or complexity during the entry process. An excessively long period of open or editable system exposure suggests a higher risk of data tampering or abnormal data manipulation. Therefore, the degree of interaction risk in the analysis group can be assessed by considering the difference in topological layers and database operation behavior. A higher level of interaction risk indicates a higher infection rate within the analysis group, and a higher security level for the target data.

[0039] Preferably, in this embodiment of the invention, the method for obtaining the level of interaction risk includes: The percentage of the difference in the number of topological layers in the total number of topological layers is obtained. The ratio between the most recent entry time of the target data and the average update time interval is used as the time decay constraint coefficient. The sum of the percentage of the number of layers and the time decay constraint coefficient is used as the interaction risk level. That is, the larger the percentage of the number of layers, the greater the structural influence depth of the current group to be analyzed, which means that the combination of two data in the group to be analyzed has a larger impact range in the hierarchical structure, and thus the greater the interaction risk level. The time decay constraint coefficient is a normalized or standardized process of the most recent entry time with the average update time interval as the denominator. The larger the coefficient, the larger the most recent entry time of the target data, which may even be significantly greater than the average update time interval, and thus the greater the corresponding interaction risk level.

[0040] It should be noted that in the early stages of database construction, it is mainly in a centralized initialization and data entry state, with a single data flow (primarily unidirectional writing). Complex network-like relationships and frequent external interactions have not yet formed, and there are no large-scale data updates. Therefore, the method proposed in this embodiment will not be executed. At this time, the data exposure surface is extremely small, and there is no risk transmission effect caused by multi-party collaboration, frequent changes, and cascading calculations (i.e., the diffusion scenario simulated by the SIR model has not yet formed). Therefore, the initial security risks are low and controllable, and there is no need to use the computationally expensive dynamic SIR model for real-time classification. Once the data accumulates to a statistically significant period (such as completing the first project settlement cycle or running for a full month), the dynamic classification mechanism of this solution will be automatically activated.

[0041] Step S4: Obtain adjustment coefficients based on the degree of correlation sensitivity and interaction risk. Use the adjustment coefficients to adjust the initial infection ratio coefficient for the group to be analyzed in the SIR infectious disease model to obtain the optimized infection ratio coefficient. Set the encryption level based on the optimized infection ratio coefficient between the target data and all other historical engineering cost data.

[0042] After the above processing steps, for the target data, the correlation sensitivity and interaction risk levels of the corresponding groups to be analyzed can be obtained. Then, by positively fusing these two features, an adjustment coefficient can be obtained. The larger the adjustment coefficient, the greater the initial infection proportion coefficient for the groups to be analyzed in the SIR infectious disease model should be, ultimately yielding the optimized infection proportion coefficient. By statistically analyzing the optimized infection proportion coefficients for all groups to be analyzed, the encryption level of the target data can be determined.

[0043] Preferably, in this embodiment of the invention, the method for obtaining the adjustment coefficient includes: The adjustment coefficient is obtained by normalizing the product of the association sensitivity and the interaction risk. That is, the two features are positively fused through a product to obtain the final result. Furthermore, the normalization setting makes the magnitude relationship of the adjustment coefficients more significant, facilitating subsequent calculations. The normalization operation has been described in the above embodiments and will not be repeated here.

[0044] Furthermore, since the adjustment coefficient is a normalized data point with a value range between 0 and 1, adding the adjustment coefficient to the positive integer 1 yields the adjustment ratio. Multiplying this adjustment ratio by the initial infection ratio gives the optimized infection ratio coefficient. That is, the larger the adjustment coefficient, the larger the adjustment ratio, ultimately adjusting the initial infection ratio through multiplication. Conversely, a smaller adjustment coefficient indicates a weaker correlation between the two data points in the analysis group and a lower exposure risk for the target data, eliminating the need for targeted adjustments. The initial infection ratio in traditional algorithms can be used to characterize the relationship between the two data points.

[0045] Preferably, in this embodiment of the invention, the encryption level is set based on an optimized infection ratio coefficient between the target data and all other historical project cost data, including: The optimized infection rate coefficients of all groups to be analyzed corresponding to the target data are statistically analyzed. The average value of the optimized infection rate coefficients is used as the encryption reference value, and the encryption level of the target data is determined by the magnitude of the encryption reference value.

[0046] It should be noted that in other implementations of the present invention, a weighted summation method can be used to perform weighted fusion of all optimized infection ratio coefficients, or graph neural networks, PageRank algorithms, or path-based cumulative risk models can be used for integration. The specific choice can be made according to the actual implementation scenario. These are all technical means well known to those skilled in the art, and will not be elaborated or limited here.

[0047] Preferably, in this embodiment of the invention, determining the encryption level of the target data by the magnitude of the encryption reference value includes: If the encryption reference value is less than the first threshold, the encryption level is set to public read; if the encryption reference value is greater than or equal to the first threshold and less than the second threshold, the encryption level is set to internal access; if the encryption reference value is greater than or equal to the second threshold and less than the third threshold, the encryption level is set to authorized access; if the encryption reference value is greater than or equal to the third threshold, the encryption level is set to strict approval authorization.

[0048] It should be noted that, since the coefficient for optimizing the infection ratio in the embodiments of the present invention is based on the adjustment of the initial infection ratio, the value range of the initial infection ratio is between 0 and 1, and the value range of the adjustment ratio is between 1 and 2, the value range of the final optimized infection ratio and the encryption reference value is between 0 and 2. Therefore, for the setting of the threshold, the embodiments of the present invention set the first threshold to 0.3, the second threshold to 0.8, and the third threshold to 1.3. In other specific implementations of the embodiments of the present invention, the range of each security level can be divided according to the needs of the implementer based on other thresholds, which will not be elaborated or limited here.

[0049] In summary, this invention selects target cost data and calculates dynamic risk exposure based on its update frequency; it combines the correlation between target data and other data to calculate the correlation sensitivity using exposure weighting; it constructs the interaction risk level based on the data entry time and the difference in the number of topological layers; it integrates the correlation sensitivity and interaction risk level to generate adjustment coefficients, dynamically corrects the initial infection ratio coefficient of the SIR model, and obtains the optimized infection ratio coefficient; and it sets the encryption level of the cost data accordingly. This invention solves the risk assessment bias problem caused by the inability of traditional methods to adapt to the dynamic characteristics of cost data by introducing the spatiotemporal interaction characteristics of cost data to dynamically correct the risk transmission model, thus achieving precise and dynamic security classification of engineering cost data.

[0050] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0051] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. An engineering cost data dynamic security management method, characterized in that, The method comprises: The history engineering cost data of any one level category in the total project engineering cost database is taken as target data, and the dynamic risk exposure degree of the target data is obtained according to the update frequency information of the target data; The target data and any one other history engineering cost data are taken as a to-be-analyzed group, the correlation between the two data in the to-be-analyzed group is obtained, the correlation sensitivity degree of the to-be-analyzed group is obtained by weighting the correlation using the dynamic risk exposure degree; The interactive risk degree is obtained according to the recording time length of the target data in the total project engineering cost database and the difference in the topological structure layer number between the two data in the to-be-analyzed group in the total project engineering cost database; The adjustment coefficient is obtained according to the correlation sensitivity degree and the interactive risk degree, the initial infection proportion coefficient for the to-be-analyzed group in the SIR infectious disease model is adjusted using the adjustment coefficient to obtain an optimized infection proportion coefficient, and the encryption level of the target data and all other history engineering cost data is set according to the optimized infection proportion coefficient.

2. The method for dynamic security management of construction cost data according to claim 1, characterized in that, The method for obtaining the dynamic risk exposure degree comprises: The update frequency information comprises the update frequency and the average update time interval; After the average update time interval is negatively correlated and mapped, the negatively correlated and mapped result is multiplied by the update frequency, and the product is normalized to obtain the dynamic risk exposure degree.

3. The method for dynamic security management of construction cost data according to claim 1, wherein, The method for obtaining the correlation comprises: The absolute value of the Spearman rank correlation coefficient of the two data in the to-be-analyzed group is obtained, and the preset logical relationship identifier between the corresponding levels of the two data is obtained; the absolute value of the Spearman rank correlation coefficient and the preset logical relationship identifier are weighted and summed according to the preset weight to obtain the correlation.

4. The method for dynamic security management of construction cost data according to claim 1, wherein, The method for obtaining the correlation sensitivity degree comprises: The dynamic risk exposure degree and the correlation are multiplied, and the product is normalized to obtain the correlation sensitivity degree.

5. The method for dynamic security management of construction cost data according to claim 2, wherein, The method for obtaining the interactive risk degree comprises: The layer number proportion of the topological structure layer number difference in the total topological structure layer number is obtained, the time decay constraint coefficient is obtained by taking the ratio between the latest recording time length of the target data and the average update time interval, and the sum of the layer number proportion and the time decay constraint coefficient is taken as the interactive risk degree.

6. The method for dynamic security management of construction cost data according to claim 1, wherein, The method for obtaining the adjustment coefficient comprises: The product of the correlation sensitivity degree and the interactive risk degree is normalized to obtain the adjustment coefficient.

7. The method for dynamic security management of construction cost data according to claim 1, wherein, The method for obtaining the optimized infection proportion coefficient comprises: After the adjustment coefficient is added to the positive integer 1, an adjustment proportion is obtained, the adjustment proportion is multiplied by the initial infection proportion to obtain the optimized infection proportion coefficient.

8. The method for dynamic security management of construction cost data according to claim 1, wherein, The encryption level setting of the target data and all other history engineering cost data according to the optimized infection proportion coefficient comprises: The optimized infection proportion coefficients of all to-be-analyzed groups corresponding to the target data are counted, the average value of the optimized infection proportion coefficients is taken as an encryption reference value, and the encryption level of the target data is determined according to the size of the encryption reference value.

9. The method for dynamic security management of construction cost data according to claim 8, wherein, The determination of the encryption level of the target data according to the size of the encryption reference value comprises: If the encrypted reference value is less than a first threshold, the encryption level is set to public read; if the encrypted reference value is greater than or equal to the first threshold and less than a second threshold, the encryption level is set to internal access; if the encrypted reference value is greater than or equal to the second threshold and less than a third threshold, the encryption level is set to authorized access; and if the encrypted reference value is greater than or equal to the third threshold, the encryption level is set to strict approval authorization.

10. The method of claim 9, wherein, The first threshold is set to 0.1, the second threshold is set to 0.4, and the third threshold is set to 0.8.

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