Enterprise financial comprehensive management system based on cloud computing

Abstract

The invention discloses an enterprise finance integrated management system based on cloud computing, and relates to the technical field of enterprise finance management, and the system comprises a server, an authority management module and a storage management module. Through a machine learning classification algorithm and a natural language processing technology, automatically classifying the financial data and generating tags and keywords related to the financial data; performing association analysis on the word segments, the responsibility tags of the employees and the keywords to determine part of financial data corresponding to each employee; different sensitive values are set for the importance of the sensitive words to calculate the sensitivity of part of financial data allocated to each employee; the performance information of the storage nodes is dynamically monitored, and the sensitivity of part of financial data allocated by employees is combined to select the storage node with the best performance and the lowest risk to store the data in real time, so that efficient and reliable data storage is ensured; and meanwhile, by periodically updating node performance information, each storage operation is ensured to be based on the latest data, and a distribution strategy is dynamically adjusted.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise financial management, and in particular to an enterprise financial integrated management system based on cloud computing. Background Art

[0002] The financial data of an enterprise is particularly important because it not only reflects the financial status of the enterprise, but also involves the core secrets and business strategies of the enterprise. An important goal of financial management is to protect these financial data and prevent data leakage or improper use. Therefore, corporate financial management is particularly important for enterprises.

[0003] Many companies currently do not have strict enough control over access to financial data in terms of financial management, which allows unauthorized personnel to access, modify or delete sensitive financial information, increasing the risk of internal leaks. In addition, some companies store all financial data in a single server or database, increasing the risk of single point failure and data leakage. If this storage location is attacked or abused by internal personnel, it will lead to the leakage or loss of all financial data, causing immeasurable losses to the company. Summary of the invention

[0004] Based on this, it is necessary to provide a cloud computing-based enterprise financial integrated management system to address the problems mentioned in the above background technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A cloud computing-based enterprise financial integrated management system, including a server, a rights management module, and a storage management module;

[0006] The server stores the enterprise’s financial data, performance information of each storage node, and historical risk information;

[0007] The authority management module breaks down and distributes the company's financial data based on the scope of each employee's responsibilities, so that each employee can obtain the same part of the financial data as his or her scope of responsibilities;

[0008] The storage management module allocates partial financial data of each employee to the appropriate storage node for storage. The specific steps are as follows:

[0009] Step 1: Conduct sensitivity analysis on some of the financial data of each employee to obtain the sensitivity of some of the financial data of each employee.

[0010] Rank the employees in descending order according to the sensitivity of the corresponding financial data; select the employee with the highest sensitivity as the calibration employee;

[0011] Step 2: retrieve the performance information of each storage node at each collection time, where the performance information includes storage margin, read / write speed, and throughput, and record them as Cp, Vp, and Tp respectively; use the set formula CT = d1×Cp+d2×Vp+d3× Tp Calculate the performance value CT of the storage node at each collection time, where η

[0012] Where d1, d2, and d3 are the set proportional coefficients, and η is the risk accumulation coefficient;

[0013] Step 3: Sort the storage nodes in descending order according to their corresponding performance values, and select the storage node with the largest performance value as the calibration storage node, and send part of the financial data of the calibration employee to the calibration storage node for storage; after the storage operation is completed, re-acquire the performance information of the storage node and update it to step 2;

[0014] Step 4: Repeat steps 1 to 3 until all employees' partial financial data are stored separately.

[0015] In some embodiments, the specific process of performing sensitivity analysis on part of the financial data of each employee is as follows:

[0016] It is assumed that there are several sensitive words, each of which corresponds to a sensitivity value; the number of sensitive words in each word segment and the sensitivity value corresponding to each sensitive word are identified; the sensitivity value is compared and analyzed with the set sensitivity interval to divide the sensitive words corresponding to the sensitivity value into highly sensitive words, moderately sensitive words and low sensitive words, and the cumulative number of highly sensitive words, moderately sensitive words and low sensitive words in the word segment is counted respectively, and they are recorded as L1, L2 and L3 respectively; the sensitivity values ​​corresponding to the highly sensitive words, moderately sensitive words and low sensitive words are summed up to obtain the highly sensitive value, moderately sensitive value and low sensitive value, and they are recorded as L4, L5 and L6 respectively;

[0017] Substitute L1, L2, L3, L4, L5 and L6 into the given formula The word segment sensitivity value Lb is calculated, where are the set proportional coefficients, and b1>b2>b3>1; the word segment sensitivity values ​​of each word segment in the partial financial data corresponding to the employee are summed up to obtain the sensitivity of the partial financial data, thereby obtaining the sensitivity of the partial financial data corresponding to each employee.

[0018] In some embodiments, the risk accumulation coefficient is solved as follows:

[0019] Retrieve the risk information of each storage node, where the risk information includes historical hardware failure information and historical security events; historical hardware failure information includes the number of hardware failures and the duration of each failure; historical security events include the number of successful interceptions and the number of failed interceptions, which are recorded as S1 and S2;

[0020] Compare and analyze the fault duration with the set duration interval. When the fault duration is greater than the maximum value in the set duration interval, a severe fault is accumulated; when the fault duration is within the set duration interval, a moderate fault is accumulated; when the fault duration is less than the minimum value in the set duration interval, a low fault is accumulated; the cumulative number of severe faults, moderate faults and low faults are counted respectively, and recorded as Z1, Z2 and Z3 respectively; the fault duration corresponding to each fault is averaged and the average fault duration is calculated and recorded as Z4;

[0021] Substitute the number of successful interception S1, the number of failed interception S2, the cumulative number of severe faults Z1, the cumulative number of moderate faults Z2, the cumulative number of minor faults Z3 and the average fault length Z4 into the set formula The risk coefficient η is calculated, where f1, f2, f3, and f4 are respectively set proportional coefficients, and f1>f2>f3>0.

[0022] In some embodiments, the specific process of decomposing and allocating the financial data of the enterprise based on the scope of responsibility of each employee is as follows:

[0023] 401: According to the responsibilities of employees in different positions within the enterprise, set several tags related to each employee's responsibilities, and each tag corresponds to several keywords, thereby obtaining the tags of each employee and the keywords corresponding to each tag;

[0024] 402: Use classification algorithms in machine learning to preliminarily classify financial data, thereby breaking down the financial data into several word segments; use advanced natural language processing technologies such as word embedding or BERT to understand the semantics of each word segment to generate several labels related to its semantics and each label corresponds to several keywords; thereby, the word segment can be accurately located using several labels and the keywords corresponding to the labels;

[0025] 403: Perform correlation analysis between each word segment and each employee to obtain the correlation value between each word segment and the employee, set a correlation threshold corresponding to each employee, compare and analyze the correlation value of the word segment with the correlation threshold of the employee, and when the correlation value is greater than or equal to the set threshold, the word segment is assigned to the employee. In this way, each word segment can be assigned to each employee. Each employee can view the corresponding part of the financial data after successfully logging in through the corresponding identity authentication.

[0026] In some embodiments, the specific process of performing correlation analysis between each word segment and each employee is as follows:

[0027] 501: Compare the label of the segment with the label of the employee. If the labels of the segment are the same as the label of the employee, the employee is recorded as the full coverage segment of the segment, and the same label is recorded as the full coverage label; when the label of the segment partially overlaps with the label of the employee, the overlapping label is recorded as a semi-coverage label and the segment is recorded as the semi-coverage segment of the employee; extract the keywords under the full coverage label corresponding to the full coverage segment and the employee respectively, and compare the keywords of the two one by one to obtain the number of intersection keywords and the number of union keywords under each full coverage label;

[0028] 502: Similarly, extract keywords under the semi-covered segments and the corresponding semi-covered labels of employees respectively, and compare the keywords of the two one by one to obtain the number of intersection keywords and the number of union keywords under each semi-covered label, and record them as M1j and M2j respectively; wherein j = 1, 2, 3 ... J, J is a positive integer, J represents the total number of semi-covered labels in the semi-covered segments, i represents the serial number in any semi-covered label, and record them as N1i and N2i respectively; wherein i = 1, 2, 3 ... I, I is a positive integer, I represents the total number of full-covered labels in the segment, and i represents the serial number in any full-covered label;

[0029] 503: Using TF-IDF information retrieval technology, identify the importance scores of the intersection keywords of all the full coverage labels in the full coverage segment, and record them as Fiq; wherein q = 1, 2, 3 ... Q, Q is a positive integer, Q represents the number of intersection keywords under a certain full coverage label, and q represents the serial number of any intersection keyword under a certain full coverage label; compare and analyze the importance scores corresponding to each intersection keyword with the set score range to divide the keywords corresponding to the important scores into high-level keywords, medium-level keywords and low-level keywords, respectively count the number of high-level keywords, medium-level keywords and low-level keywords, and perform formulaic calculation and analysis to obtain the key coefficients of all the full coverage labels in the full coverage segment, record them as βi;

[0030] 504: Similarly, the TF-IDF information retrieval technology is used to identify the importance scores of the intersection keywords of each half-covering label in the half-covering segment, and it is recorded as Fjk; k==1,2,3...K, K represents the number of intersection keywords under a certain half-covering label, and k represents the sequence number of any intersection keyword under a certain half-covering label; the importance scores corresponding to each intersection keyword of each half-covering label are compared and analyzed to obtain the key coefficient of each half-covering label in the half-covering segment, which is recorded as βj;

[0031] 505: Substitute the importance score (Fiq or Fjk) of each keyword, the number of intersection keywords (N1i or M1j), the number of union keywords (N2i or M2j), and the key coefficient (βi or βj) into the set formula group

[0032] The correlation value GF between each word segment and the employee is calculated, where g1, g2, g3, and g4 are respectively the set proportional coefficients; αi and αj are respectively the constant coefficients of the full coverage word segment and the half coverage word segment, and αi>αj>1.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. Through machine learning classification algorithms and natural language processing technology, financial data is automatically classified and related tags and keywords are generated; word segments are associated with employee job tags and keywords to determine the corresponding financial data of each employee, ensuring that employees can only access data that is highly relevant to their work content, greatly reducing the risk of abuse of authority and data leakage; the principle of least privilege is realized, and the automation and efficiency of authority management are realized;

[0035] 2. By setting different sensitivity values ​​for the importance of sensitive words and using a formula to calculate the sensitivity of part of the financial data assigned to each employee; by grading the data according to sensitivity, ensuring that highly sensitive data is properly stored on the nodes with the best performance, rather than treating all data equally, the security and effectiveness of data storage are improved; by dynamically monitoring the performance parameters of storage nodes (such as storage margin, read and write speed, and throughput), and combining the calculation of risk factors, the storage nodes with the best performance and lowest risk can be selected in real time to store data, ensuring efficient and reliable data storage; at the same time, by regularly updating node performance information, ensuring that each storage operation is based on the latest data, and dynamically adjusting the distribution strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the conventional technology, the drawings required for use in the embodiments or the conventional technology descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0039] like Figure 1 As shown, a cloud computing-based enterprise financial integrated management system includes: a server, an authorization management module and a storage management module;

[0040] The present invention takes large enterprises as specific application scenarios. For large enterprises, due to the large number of departments and employees, especially the large amount of data involved in the financial department, a single storage node is usually difficult to carry such a large amount of data, and there is a risk of failure. It is assumed that there are several financial employees in the large enterprise, and multiple storage nodes are set to reduce the memory limitations and failure risks of a single storage node.

[0041] The server stores the enterprise’s financial data, performance information of each storage node, and historical risk information;

[0042] The authority management module authorizes the company's financial data in different ways based on the nature of each employee's work, ensuring that each employee can only access the financial data related to his or her duties, implementing the principle of least privilege and avoiding the risk of data leakage caused by excessive allocation of permissions; specifically:

[0043] According to the responsibilities of employees in different positions within the company, set several tags related to each employee's responsibilities, and each tag corresponds to several keywords; for example, the financial director may be responsible for "budget management" and "financial planning", while the accountant may be related to "daily transactions" and "bookkeeping processing"; each tag can be further refined into multiple keywords, which can cover more specific business scopes. For example, the "budget management" tag may correspond to keywords such as "annual budget", "department budget", "expenditure control", etc.

[0044] The financial manager is associated with the "budget management" tag, while the accountant is associated with the "daily transactions" tag; thus, each employee's tag and the keywords corresponding to each tag can be obtained;

[0045] Use classification algorithms in machine learning (such as decision trees, random forests, SVM, etc.) to preliminarily classify financial data; for example, financial data can be classified into different segments based on transaction type, account category, amount size and other features (before classification, financial data needs to be feature extracted and preprocessed, such as numerical normalization, category encoding, etc., to improve the performance and accuracy of the classification algorithm); after classification, financial data is decomposed into several "segments", each of which represents a specific category, such as "income", "expenditure", "assets and liabilities", etc.; using advanced natural language processing (NLP) technologies such as word embedding or BERT, a deeper semantic understanding of the content of each segment can be achieved. These technologies can identify the key meanings and themes in a segment by learning about the data context and word relationships. By analyzing the content of each segment, they can automatically generate tags related to its semantics and mark the main financial category or theme of the segment. Based on semantic analysis, they can extract the keywords that best represent the content of the segment. Technologies such as BERT can understand the precise meaning of words based on the context, so the extracted keywords are usually closely related to the semantics of the segment. Each segment is assigned multiple tags, each tag corresponds to several keywords, and these tags and keywords can be further used for permission control and refined management. Therefore, each segment can get several corresponding tags and keywords corresponding to each tag.

[0046] Compare the label of the segment with the label of the employee. If the labels of the segment are the same as the label of the employee, that is, the label of the segment is the same as the label of the employee or the label of the employee can cover the label of the segment; then record the employee as the full coverage segment of the segment, and record the same label as the full coverage label; when the label of the segment partially overlaps with the label of the employee, and record the overlapping label as the semi-coverage label, then record the segment as the semi-coverage segment of the employee; extract the keywords under the full coverage label corresponding to the full coverage segment and the employee respectively, and compare the keywords of the two one by one to obtain the number and sum of the intersection keywords under each full coverage label. The number of keywords in the set is N1i and N2i, respectively; where i = 1, 2, 3 ... I, I is a positive integer, I represents the total number of full coverage labels in the segment, and i represents the sequence number in any full coverage label; it should be noted that, for example, there is a full coverage label A, and the keywords corresponding to the full coverage label A in the segment are: A1, A5, A8, A9; the keywords corresponding to the full coverage label A in the employee are A2, A3, A5, A8, A9; then the intersection keywords of the full coverage label A are: A5, A8, A9; the union keywords are: A1, A2, A3, A5, A8, A9;

[0047] Extract keywords under the semi-covered words and employees' corresponding semi-covered labels respectively, and compare the keywords of the two one by one to obtain the number of intersection keywords and the number of union keywords under each semi-covered label, and record them as M1j and M2j respectively; where j = 1, 2, 3...J, J is a positive integer, J represents the total number of semi-covered labels in the semi-covered words, and i represents the serial number in any semi-covered label;

[0048] The TF-IDF (term frequency-inverse document frequency) information retrieval technology is used to identify the importance score of the intersection keywords of each full coverage label in the full coverage segment, and it is recorded as Fiq; where q = 1, 2, 3...Q, Q is a positive integer, Q represents the number of intersection keywords under a certain full coverage label, and q represents the serial number of any intersection keyword under a certain full coverage label; the importance score corresponding to each intersection keyword is compared and analyzed with the set score interval. When the importance score of the intersection keyword is greater than the maximum value in the set score interval, the intersection keyword is recorded as a high-level keyword; when the importance score of the intersection keyword is in the set score interval, the intersection keyword is recorded as a medium-level keyword; when the importance score of the intersection keyword is less than the minimum value in the set score interval, the intersection keyword is recorded as a low-level keyword; the number of high-level keywords, medium-level keywords and low-level keywords are counted respectively, and they are recorded as D1, D2, and D3 respectively; using the set formula The key coefficients are calculated, where they are the set proportional coefficients, and a1>a2>a3>0; thus, the key coefficients of each full coverage label are recorded as βi;

[0049] Similarly, the TF-IDF (term frequency-inverse document frequency) information retrieval technology is used to identify the importance score of the intersection keywords of each half-covering label in the half-covering segment, and it is recorded as Fjk; k==1,2,3...K, K represents the number of intersection keywords under a certain half-covering label, and k represents the sequence number of any intersection keyword under a certain half-covering label;

[0050] Similarly, the importance scores corresponding to the intersection keywords of each half-covering label are compared and analyzed to obtain the key coefficient of each half-covering label, which is recorded as βj;

[0051] Using the set formula group

[0052] Calculate and obtain the association value GF between each word segment and the employee, where g1, g2, g3, and g4 are respectively the set proportional coefficients; αi and αj are respectively the constant coefficients of the full coverage word segment and the half coverage word segment, and αi>αj>1; set a corresponding association threshold for each employee, compare and analyze the association value of the word segment with the association threshold of the employee, and when the association value is greater than or equal to the set threshold, the word segment is assigned to the employee, so that each word segment can be assigned to each employee, and thus each employee can obtain the corresponding part of the financial data according to his or her professional responsibilities; each employee can view the corresponding part of the financial data after successfully logging in through the corresponding identity authentication;

[0053] Through machine learning classification algorithms and natural language processing technology, financial data is automatically classified and related labels and keywords are generated; word segments are associated with employees' job labels and keywords to determine the partial financial data corresponding to each employee, ensuring that employees can only access data that is highly relevant to their work content, greatly reducing the risk of authority abuse and data leakage; and realizing the principle of least privilege and the automation and efficiency of authority management.

[0054] The storage management module performs distributed storage based on the portion of financial data allocated to each employee to reduce the risk caused by single node storage failure and achieve safe and efficient storage of financial data; specifically:

[0055] Step 1: Obtain part of the financial data allocated to each employee (part of the financial data consists of several segments), set several sensitive words (it should be noted that sensitive words are set by technical personnel in this field according to the needs of the enterprise, such as account balance, accounts payable, shareholder list and each shareholder's income details, customer list and payment details corresponding to each customer, etc.); each sensitive word corresponds to a sensitivity value, the higher the sensitivity value, the more important the sensitive word is to the enterprise; identify the number of sensitive words in each segment and the sensitivity value corresponding to each sensitive word; compare and analyze the sensitive value with the set sensitivity range, and when When the sensitivity value is greater than the maximum value in the set sensitivity interval, a highly sensitive word is accumulated; when the sensitivity value is in the set sensitivity interval, a moderately sensitive word is accumulated; when the sensitivity value is less than the minimum value in the set sensitivity interval, a lowly sensitive word is accumulated; the cumulative number of highly sensitive words, moderately sensitive words and lowly sensitive words in the word segment is counted respectively, and they are recorded as L1, L2 and L3 respectively; the sensitivity values ​​corresponding to highly sensitive words, moderately sensitive words and lowly sensitive words are summed up to obtain highly sensitive values, moderately sensitive values ​​and lowly sensitive values, and they are recorded as L4, L5 and L6 respectively; using the set formula Calculate to obtain the word segment sensitivity value Lb, where they are the set proportional coefficients, and b1>b2>b3>1; sum the word segment sensitivity values ​​of each word segment in the partial financial data corresponding to the employee to obtain the sensitivity of the partial financial data, thereby obtaining the sensitivity of the partial financial data corresponding to each employee; sort the employees in order from large to small according to the sensitivity of the partial financial data corresponding to each employee; select the employee with the greatest sensitivity as the calibration employee;

[0056] Step 2: retrieve the performance information of each storage node at each collection time, where the performance information includes storage margin, read / write speed (I / O speed, a higher I / O speed means that the node can process data requests more quickly, thereby reducing the delay time of data access) and throughput (a higher throughput means that the node can handle a larger amount of data traffic, which is suitable for high-load application scenarios), and record them as Cp, Vp and Tp respectively; use the set formula CT = d1×Cp+d2×Vp+ d3×Tp Calculate the storage node

[0057] η

[0058] The performance value CT of the point at each collection time, where d1, d2, and d3 are the set proportional coefficients, and η is the risk accumulation coefficient;

[0059] The solution process of the risk accumulation coefficient is:

[0060] Retrieve the risk information of each storage node, where the risk information includes historical hardware failure information and historical security events; historical hardware failure information includes the number of hardware failures (hardware usually refers to hard disks, memory, CPU, etc., and nodes with frequent hardware failures are usually less stable and have higher risks) and the duration of each failure; historical security events include the number of successful interceptions (successful interception usually means that an alarm of the intrusion monitoring system has been triggered and successfully intercepted) and the number of failed interceptions, which are recorded as S1 and S2 respectively;

[0061] Compare and analyze the fault duration with the set duration interval. When the fault duration is greater than the maximum value in the set duration interval, a severe fault is accumulated; when the fault duration is within the set duration interval, a moderate fault is accumulated; when the fault duration is less than the minimum value in the set duration interval, a low fault is accumulated; count the cumulative number of severe faults, moderate faults and low faults respectively, and record them as Z1, Z2 and Z3 respectively; calculate the average fault duration corresponding to each fault to obtain the average fault duration, and record them as Z4 respectively; use the set formula The risk coefficient η is calculated, where f1, f2, f3, and f4 are respectively the set proportional coefficients, and f1>f2>f3>0;

[0062] Step 3: Sort the storage nodes in descending order according to their corresponding performance values, and select the storage node with the largest performance value as the calibration storage node, and send part of the financial data of the calibration employee to the calibration storage node for storage; after the storage operation is completed, re-acquire the performance information of the storage node and update it to step 2;

[0063] Step 4: Repeat steps 1 to 3 until all employees' partial financial data are stored separately;

[0064] By setting different sensitivity values ​​for the importance of sensitive words and using a formula to calculate the sensitivity of part of the financial data assigned to each employee; data is graded according to sensitivity to ensure that highly sensitive data is properly stored on the nodes with the best performance, rather than treating all data equally, thereby improving the security and effectiveness of data storage; by dynamically monitoring the performance parameters of storage nodes (such as storage margin, read and write speed, and throughput), and combining it with the calculation of risk factors, it is possible to select the storage nodes with the best performance and the lowest risk in real time to store data, ensuring efficient and reliable data storage; at the same time, by regularly updating node performance information, it is ensured that each storage operation is based on the latest data and the distribution strategy is dynamically adjusted.

[0065] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0066] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A cloud computing-based enterprise financial integrated management system, including a rights management module and a storage management module; characterized in that: The authority management module breaks down and distributes the company's financial data based on the responsibilities of each employee, so that each employee gets the same part of the financial data as his or her responsibilities; The storage management module allocates some of the financial data of each employee to store them in the corresponding storage nodes. The specific steps are as follows: Step 1: Conduct sensitivity analysis on some of the financial data of each employee to obtain the sensitivity of some of the financial data of each employee; sort the employees in descending order according to the sensitivity of the corresponding part of the financial data of each employee; select the employee with the highest sensitivity as the calibration employee; Step 2: retrieve the performance information of each storage node at each collection time, where the performance information includes storage margin, read / write speed, and throughput, and record them as Cp, Vp, and Tp respectively; use the set formula CT = d1×Cp+d2×Vp+d3×Tp Calculate the performance value CT of the storage node at each collection time, where η Where d1, d2, and d3 are the set proportional coefficients, and η is the risk accumulation coefficient; Step 3: Sort the storage nodes in descending order according to their corresponding performance values, select the storage node with the largest performance value as the calibration storage node, and send part of the financial data of the calibration employee to the calibration storage node for storage; After the storage operation is completed, the performance information of the storage node is retrieved and updated to step 2.

2. The cloud computing-based enterprise financial integrated management system according to claim 1, characterized in that: The specific process of conducting sensitivity analysis on some of the financial data of each employee is as follows: It is assumed that there are several sensitive words, each of which corresponds to a sensitivity value; the number of sensitive words in each word segment and the sensitivity value corresponding to each sensitive word are identified; the sensitivity value is compared and analyzed with the set sensitivity interval to divide the sensitive words corresponding to the sensitivity value into highly sensitive words, moderately sensitive words and low sensitive words, and the cumulative number of highly sensitive words, moderately sensitive words and low sensitive words in the word segment is counted respectively, and the sensitivity values ​​corresponding to the highly sensitive words, moderately sensitive words and low sensitive words are summed up to obtain the highly sensitive value, moderately sensitive value and low sensitive value respectively; The cumulative number of highly sensitive words, the cumulative number of moderately sensitive words, the cumulative number of lowly sensitive words, the highly sensitive value, the moderately sensitive value and the lowly sensitive value are calculated and analyzed in a formula to obtain the segment sensitivity value; The sensitivity of the partial financial data is obtained by summing up the segment sensitivity values ​​of each segment in the partial financial data corresponding to the employee, thereby obtaining the sensitivity of the partial financial data corresponding to each employee.

3. The cloud computing-based enterprise financial integrated management system according to claim 1, characterized in that: The solution process of the risk accumulation coefficient is: Retrieve risk information of each storage node, where risk information includes historical hardware failure information and historical security events; historical hardware failure information includes the number of hardware failures and the duration of each failure; historical security events include the number of successful interceptions and the number of failed interceptions; Compare and analyze the fault duration with the set duration interval. When the fault duration is greater than the maximum value in the set duration interval, a severe fault is accumulated; when the fault duration is within the set duration interval, a moderate fault is accumulated; when the fault duration is less than the minimum value in the set duration interval, a low fault is accumulated; the accumulated times of severe faults, moderate faults and low faults are counted respectively; The fault duration corresponding to each fault is averaged to obtain the average fault duration; The number of successful interceptions, the number of failed interceptions, the cumulative number of severe faults, the cumulative number of moderate faults, the cumulative number of mild faults and the average fault length are normalized and their values ​​are taken, and the risk coefficient is obtained by numerical analysis.

4. The cloud computing-based enterprise financial integrated management system according to claim 1, characterized in that: The specific process of breaking down and allocating the company's financial data based on the responsibilities of each employee is as follows: 401: According to the responsibilities of employees in different positions within the enterprise, set several tags related to each employee's responsibilities, and each tag corresponds to several keywords, thereby obtaining the tags of each employee and the keywords corresponding to each tag; 402: Use classification algorithms in machine learning to preliminarily classify financial data, thereby breaking down the financial data into several word segments; use advanced natural language processing technologies such as word embedding or BERT to understand the semantics of each word segment to generate several labels related to its semantics and each label corresponds to several keywords; thereby, the word segment can be accurately located using several labels and the keywords corresponding to the labels; 403: Perform correlation analysis between each word segment and each employee to obtain the correlation value between each word segment and the employee, set a correlation threshold corresponding to each employee, compare and analyze the correlation value of the word segment with the correlation threshold of the employee, and when the correlation value is greater than or equal to the set threshold, the word segment is assigned to the employee. In this way, each word segment can be assigned to each employee. Each employee can view the corresponding part of the financial data after successfully logging in through the corresponding identity authentication.

5. The cloud computing-based enterprise financial integrated management system according to claim 4, characterized in that: The specific process of analyzing the correlation between each word segment and each employee is as follows: 501: Compare the label of the segment with the label of the employee. If the labels of the segment are the same as the label of the employee, the employee is recorded as a fully covered segment of the segment, and the same label is recorded as a fully covered label. When the label of the segment partially overlaps with the label of the employee, the overlapping label is recorded as a semi-covered label, and the segment is recorded as a semi-covered segment of the employee. Extract the keywords under the fully covered word segments and the fully covered labels of employees respectively, and compare the keywords of the two one by one to obtain the number of intersection keywords and the number of union keywords under each fully covered label; 502: Similarly, extract the keywords under the semi-covered word segments and the employee's corresponding semi-covered labels respectively, and compare the keywords of the two one by one to obtain the number of intersection keywords and the number of union keywords under each semi-covered label; 503: using TF-IDF information retrieval technology to identify the importance scores of the intersection keywords of all the fully covered labels in the fully covered segment; comparing and analyzing the importance scores corresponding to the intersection keywords with the set score range to classify the keywords corresponding to the importance scores into high-level keywords, medium-level keywords and low-level keywords, respectively counting the number of high-level keywords, medium-level keywords and low-level keywords, and performing formulaic calculation and analysis to obtain the key coefficient of all the fully covered labels in the fully covered segment; 504: Similarly, using TF-IDF information retrieval technology to identify the importance scores of the intersection keywords of each half-covering tag in the half-covering segment; comparing and analyzing the importance scores corresponding to each intersection keyword of each half-covering tag to obtain the key coefficient of each half-covering tag in the half-covering segment; 505: The importance score of each keyword, the number of intersection keywords, the number of union keywords, and the key coefficient are normalized and their values ​​are obtained, and the correlation value between each word segment and the employee is obtained by numerical analysis.

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