An audit data storage and management system based on data analysis

Abstract

The present invention belongs to the technical field of audit data management, and specifically relates to an audit data storage and management system based on data analysis, which includes an audit data capture module, an audit data storage module, an audit data analysis module, a result display module, and a storage risk assessment module; the present invention collects various types of data generated during the audit process through the audit data capture module, the audit data storage module classifies and stores the audit data, the audit data analysis module deeply mines and analyzes the audit data by using data mining and machine learning technologies, and the result display module visually displays the data analysis results, which can achieve comprehensive monitoring and intelligent analysis of audit data, improve the efficiency and accuracy of audit work, and through the storage risk assessment module, supervise all memories, back up the audit data in the corresponding memories in a timely manner and take reasonable treatment measures, significantly reducing the storage risk of audit data.

Description

Technical Field

[0001] The present invention relates to the technical field of audit data management, and specifically to an audit data storage and management system based on data analysis. Background Art

[0002] Audit data generally refers to all data information provided by the audited entity, on which auditors rely for audit analysis, investigation and evidence collection, and then form audit conclusions. These data include original transaction data, processed accounting statements and other data;

[0003] With the rapid development of information technology, audit data storage and management systems have been widely used in various industries. However, traditional audit data storage and management systems have problems such as poor data processing and analysis capabilities and insufficient data security, which are difficult to meet the needs of modern audit work, and cannot reasonably analyze and timely warn the risk status of all memories, which is not conducive to reducing the storage risk of audit data;

[0004] In view of the above technical defects, a solution is proposed now. Summary of the Invention

[0005] The purpose of the present invention is to provide an audit data storage and management system based on data analysis, which solves the problems of poor data processing and analysis capabilities and insufficient data security in the prior art, is difficult to meet the needs of modern audit work, and cannot reasonably analyze and timely warn the risk status of all memories, which is not conducive to reducing the storage risk of audit data and has a low degree of intelligence.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] An audit data storage and management system based on data analysis includes an audit data capture module, an audit data storage module, an audit data analysis module, a result display module and a storage risk assessment module; the audit data capture module collects various data generated during the audit process, including financial data, business data and personnel behavior data, converts the collected audit data into a unified format, and sends the converted audit data to the audit data storage module; the audit data storage module uses distributed storage technology to classify and store the received audit data according to preset rules;

[0008] The audit data analysis module uses data mining and machine learning data analysis technologies to deeply mine and analyze the audit data stored in the audit data storage module, identify potential risk points and abnormal behaviors, and send the data analysis results to the result display module, and the result display module visually displays the data analysis results in the form of charts and reports;

[0009] The audit data storage module stores audit data through several groups of memories. The storage risk assessment module supervises all memories, marks the corresponding memory as g, and g is a natural number greater than 1. Through memory-by-memory analysis, the memory g is marked as a risky object or a non-risky object, and the risky object is sent to the result display module and the smart terminals of the corresponding management personnel. The corresponding management personnel back up the audit data in the risky object and take corresponding treatment measures for the risky object.

[0010] Furthermore, the specific analysis process of the audit data analysis module is as follows:

[0011] Using data mining techniques, through preset algorithms and models, comprehensively and deeply analyze the audit data, including statistical descriptions of the audit data, and capture potential laws and patterns in the audit data through methods such as association rule mining, clustering analysis, and classification analysis. Among them, the corresponding laws and patterns reveal risk points, abnormal behaviors, or non-compliant operations in the audit process.

[0012] Through machine learning techniques, learn from historical audit data and perform pattern recognition to establish a prediction model to predict potential risks. When faced with a new audit case, use the trained model for risk assessment; and perform automated anomaly detection based on machine learning techniques. Through learning a large amount of audit data, establish an automated anomaly detection model to identify abnormal audit data that deviates from the normal pattern.

[0013] Furthermore, the specific analysis process of memory-by-memory analysis is as follows:

[0014] Collect the production date of memory g, mark the interval duration between the current date and the production date as the production duration value, and collect the inspection and maintenance times of memory g at each historical stage. Mark the interval duration between adjacent inspection and maintenance times as the inspection and maintenance interval value. Compare the inspection and maintenance interval value with the preset inspection and maintenance interval threshold, and mark the number of inspection and maintenance interval values that exceed the preset inspection and maintenance interval threshold as the inspection and maintenance over-interval value. Through numerical calculation of the production duration value of memory g and the inspection and maintenance over-interval value, obtain the memory property degradation value. Compare the memory property degradation value with the preset memory property degradation threshold. If the memory property degradation value exceeds the preset memory property degradation threshold, mark memory g as a risky object.

[0015] Further, if the memory degradation value does not exceed the preset memory degradation threshold, set the management period, collect the total duration of data transmission and storage of memory g during the management period and mark it as the total transmission and storage value, and collect the real-time transmission and storage speed during the data transmission and storage process. Compare the real-time transmission and storage speed with the preset real-time transmission and storage speed threshold. If the real-time transmission and storage speed does not exceed the preset real-time transmission and storage speed threshold, it is determined that memory g is in an inefficient transmission and storage state;

[0016] Obtain the duration of memory g being in an inefficient transmission and storage state during the management period and mark it as the inefficient transmission and storage occupancy value, and collect the number of operation interruptions that occur during the data transmission and storage process of memory g during the management period and mark it as the transmission interruption condition value. Sum up the duration of each operation interruption during the management period to obtain the transmission interruption value. Calculate the transmission inspection and evaluation value by numerically calculating the total transmission and storage value, the inefficient transmission and storage occupancy value, the transmission interruption condition value, and the transmission interruption value. Compare the transmission inspection and evaluation value with the preset transmission inspection and evaluation threshold. If the transmission inspection and evaluation value exceeds the preset transmission inspection and evaluation threshold, mark memory g as a risk list object.

[0017] Further, if the transmission inspection and evaluation value does not exceed the preset transmission inspection and evaluation threshold, collect the real-time temperature of memory g, mark the deviation value between the real-time temperature and the preset appropriate standard temperature as the storage temperature condition value, and collect the environmental humidity and environmental dust concentration of the environment where memory g is located and mark them as the storage environmental humidity value and the storage environmental ash value respectively. Mark the vibration data of the location where memory g is located as the storage vibration inspection value. Calculate the initial storage inspection value by numerically calculating the storage temperature condition value, the storage environmental humidity value, the storage environmental ash value, and the storage vibration inspection value. Compare the initial storage inspection value with the preset initial storage inspection threshold. If the initial storage inspection value exceeds the preset initial storage inspection threshold, it is determined that memory g is in a vulnerable state;

[0018] Obtain the total duration of memory g being in a vulnerable state per unit time and mark it as the total storage vulnerable time condition value, calculate the average value of all initial storage inspection values of memory g per unit time to obtain the average storage inspection value, and mark the single duration of each time memory g is in a vulnerable state as the vulnerable single duration value. Mark the number of vulnerable single duration values that exceed the preset vulnerable single duration threshold per unit time as the high vulnerable occupancy condition value. Calculate the memory evaluation and inspection value by numerically calculating the total storage vulnerable time condition value, the average storage inspection value, and the high vulnerable occupancy condition value. Compare the memory evaluation and inspection value with the preset memory evaluation and inspection threshold. If the memory evaluation and inspection value exceeds the preset memory evaluation and inspection threshold, mark memory g as a risk list object; if the memory evaluation and inspection value does not exceed the preset memory evaluation and inspection threshold, mark memory g as a non-risk object.

[0019] Further, the result display module is communicatively connected to the storage management evaluation module. The storage management evaluation module is used to set an evaluation period, collect the moment when the corresponding management personnel receive the risk form object and mark it as the risk form reception moment, and collect the moment when the corresponding management personnel take treatment measures and mark it as the risk form treatment moment. The interval duration between the risk form treatment moment and the risk form reception moment is marked as the risk treatment duration; obtain all the risk treatment durations within the evaluation period, calculate the average value of all the risk treatment durations to obtain the risk treatment detection value, compare the risk treatment duration with the preset risk treatment duration threshold. If the risk treatment duration exceeds the preset risk treatment duration threshold, mark the corresponding risk treatment duration as the abnormal treatment duration, and calculate the ratio of the number of abnormal treatment durations to the number of risk treatment durations within the evaluation period to obtain the abnormal treatment detection value;

[0020] By performing numerical calculation on the risk treatment detection value and the abnormal treatment detection value to obtain the storage management evaluation value, compare the storage management evaluation value with the preset storage management evaluation threshold. If the storage management evaluation value exceeds the preset storage management evaluation threshold, generate a storage management unqualified signal. If the storage management evaluation value does not exceed the preset storage management evaluation threshold, generate a storage management qualified signal, and send the storage management unqualified signal to the result display module. When the result display module receives the storage management unqualified signal, it issues a corresponding warning.

[0021] Further, the storage management evaluation module is communicatively connected to the storage difficulty detection module. The storage management evaluation module sends the storage management unqualified signal to the storage difficulty detection module. When the storage difficulty detection module receives the storage management unqualified signal, it generates a storage difficult to manage signal or a storage easy to manage signal through storage difficulty analysis, and sends the storage difficult to manage signal to the result display module. When the result display module receives the storage difficult to manage signal, it issues a corresponding warning.

[0022] Further, the specific analysis process of the storage difficulty analysis is as follows:

[0023] Set several detection time periods within the evaluation period, collect the total number of times all memories are marked as risk form objects within the detection time periods and mark it as the risk form number situation value, calculate the average value of the risk form number situation values of all the detection time periods to obtain the risk form number analysis value, compare the risk form number analysis value with the preset risk form number analysis threshold. If the risk form number analysis value exceeds the preset risk form number analysis threshold, generate a storage difficult to manage signal;

[0024] If the risk table analysis value does not exceed the preset risk table analysis threshold, then the risk table condition value is numerically compared with the preset risk table condition threshold. If the risk table condition value exceeds the preset risk table condition threshold, the corresponding detection period is marked as a difficult management period; the number of difficult management periods within the evaluation period is obtained and marked as the difficult management condition value. By numerically calculating the difficult management condition value and the risk table analysis value, the storage difficulty value is obtained. The storage difficulty value is numerically compared with the preset storage difficulty threshold. If the storage difficulty value exceeds the preset storage difficulty threshold, a storage difficult management signal is generated; if the storage difficulty value does not exceed the preset storage difficulty threshold, a storage easy management signal is generated.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0026] 1. In the present invention, through the audit data capture module, various types of data generated during the audit process are collected. The audit data storage module uses distributed storage technology to classify and store the audit data. The audit data analysis module uses data mining and machine learning technologies to deeply mine and analyze the audit data. The result display module visually displays the data analysis results, enabling comprehensive monitoring and intelligent analysis of the audit data, improving the efficiency and accuracy of the audit work. Moreover, through the storage risk assessment module, all memories are supervised, and the risk table objects are determined through memory-by-memory analysis, enabling reasonable analysis and timely warning of the risk status of all memories, which is beneficial to ensuring the storage security of audit data.

[0027] 2. In the present invention, through the storage management evaluation module, the management performance of the memory during the evaluation period is reasonably analyzed and accurately feedback. When a storage management unqualified signal is generated, the work supervision of the corresponding management personnel is strengthened. And when a storage management unqualified signal is generated, the storage difficulty analysis is carried out through the storage difficulty detection module. When a storage difficult management signal is generated, additional management personnel are added and the supervision of the management personnel is strengthened to ensure the safe and stable operation of the memory, further reducing the storage risk of audit data, with a high degree of intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] For the convenience of those skilled in the art to understand, the present invention is further described below with reference to the accompanying drawings;

[0029] Figure 1 It is the system block diagram of Embodiment 1 in the present invention;

[0030] Figure 2 It is the system block diagram of Embodiment 2 and Embodiment 3 in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0031] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0032] Embodiment 1: As Figure 1 shown, an audit data storage and management system based on data analysis proposed by the present invention includes an audit data capture module, an audit data storage module, an audit data analysis module, a result display module, and a storage risk assessment module; the audit data capture module collects various types of data generated during the audit process, including financial data, business data, and personnel behavior data, etc., and converts the collected audit data into a unified format, and sends the converted audit data to the audit data storage module; the audit data storage module uses distributed storage technology to classify and store the received audit data according to preset rules, and adopts distributed storage technology to ensure the integrity and security of the data, effectively preventing data leakage and tampering;

[0033] The audit data analysis module uses data mining and machine learning data analysis technologies to deeply mine and analyze the audit data stored in the audit data storage module, identify potential risk points and abnormal behaviors, and send the data analysis results to the result display module. The result display module visualizes the data analysis results in the form of charts, reports, etc. By adopting advanced data analysis technologies, it can quickly identify risk points and abnormal behaviors in the audit data, improve the accuracy of the audit work, and by presenting complex analysis results in an intuitive form, it is convenient for auditors to quickly understand the audit situation and reduce the operation difficulty; the specific analysis process of the audit data analysis module is as follows:

[0034] Through preset algorithms and models, a comprehensive and in-depth analysis of the audit data is carried out. This analysis not only includes statistical descriptions and visualizations of the data, but more importantly, through methods such as association rule mining, clustering analysis, and classification analysis, potential laws and patterns in the data are discovered. These laws and patterns may reveal risk points, abnormal behaviors, or non-compliant operations in the audit process, providing important clues and bases for auditors;

[0035] Secondly, machine learning technology plays a crucial role in the data analysis module. Machine learning can establish a prediction model to predict potential risks through learning historical audit data and pattern recognition. Auditors can input the data of historical cases into the machine learning model. Through the analysis and learning of these data, the model can discover the rules and patterns hidden in the data. When faced with new audit cases, the trained machine learning model can be used for risk assessment and anomaly detection, thus improving the accuracy and efficiency of auditing;

[0036] Moreover, in the process of data analysis, machine learning can also help auditors with automated anomaly detection. Traditional anomaly detection is usually based on rules and experience, which has subjectivity and limitations. Machine learning can establish an automated anomaly detection model through learning a large amount of data, and can more accurately identify the abnormal data that deviates from the normal pattern. These abnormal data may represent potential fraud, illegal operations or other risk issues, providing important warnings and clues for auditors.

[0037] The audit data storage module stores audit data through several groups of memories. The storage risk assessment module supervises all memories, marks the corresponding memory as g, and g is a natural number greater than 1; through the per-memory analysis, the memory g is marked as a risk table object or a non-risk object, and the risk table object is sent to the result display module and the intelligent terminals of the corresponding management personnel. The corresponding management personnel back up the audit data in the risk table object and take corresponding treatment measures for the risk table object (such as replacing the corresponding memory or checking and maintaining the corresponding memory, etc.), significantly reducing the storage risk of audit data, with a high degree of intelligence and reducing the supervision difficulty for memories; the specific analysis process of the per-memory analysis is as follows:

[0038] Collect the production date of the memory g, mark the interval duration between the current date and the production date as the production duration value, and collect the inspection and maintenance times of the memory g in the historical stage. Mark the interval duration between two adjacent inspection and maintenance times as the inspection and maintenance interval value. Compare the inspection and maintenance interval value with the preset inspection and maintenance interval threshold, and mark the number of inspection and maintenance interval values that exceed the preset inspection and maintenance interval threshold as the inspection and maintenance over-interval value;

[0039] The production duration value XWg of the memory g and the inspection, maintenance, and ultra-separation value XQg are numerically calculated through the formula XFg = a1*XWg / (a2 + 0.637)+a2*XQg to obtain the memory performance degradation value XFg, where a1 and a2 are preset proportionality coefficients, and a2 > a1 > 0; moreover, the larger the numerical value of the memory performance degradation value XFg, the greater the storage risk of the memory g, and the more it tends to be scrapped; the memory performance degradation value XFg is numerically compared with the preset memory performance degradation threshold. If the memory performance degradation value XFg exceeds the preset memory performance degradation threshold, it indicates that the memory g tends to be scrapped and the storage risk is relatively high, then the memory g is marked as a risky object.

[0040] Moreover, if the memory performance degradation value XFg does not exceed the preset memory performance degradation threshold, it indicates that the performance status of the memory g during the management period is good, then a management period is set. Preferably, the management period is thirty-six hours; the total duration of data transmission and storage of the memory g during the management period is collected and marked as the total transmission and storage value, and the real-time transmission and storage speed during the data transmission and storage process is collected. The real-time transmission and storage speed is numerically compared with the preset real-time transmission and storage speed threshold. If the real-time transmission and storage speed does not exceed the preset real-time transmission and storage speed threshold, it indicates that the transmission and storage efficiency of the audit data at the corresponding moment is slow, then it is determined that the memory g is in a low transmission and storage efficiency state.

[0041] The duration of the memory g being in the low transmission and storage efficiency state during the management period is obtained and marked as the low transmission and storage occupancy value, and the number of operation interruptions that occur during the data transmission and storage process of the memory g during the management period is collected and marked as the transmission and storage interruption condition value, and the sum of the durations of each operation interruption during the management period is calculated to obtain the transmission and storage interruption value.

[0042] Through the formula The total transmission and storage value FKg, the low transmission and storage occupancy value FRg, the transmission and storage interruption condition value FMg, and the transmission and storage interruption value FNg are numerically calculated to obtain the transmission and storage inspection and evaluation value FXg, where eq1, eq2, eq3, and eq4 are preset proportionality coefficients, and eq3 > eq4 > eq2 > eq1 > 0; moreover, the larger the numerical value of the transmission and storage inspection and evaluation value FXg, the worse the transmission and storage performance of the memory g during the management period; the transmission and storage inspection and evaluation value FXg is numerically compared with the preset transmission and storage inspection and evaluation threshold. If the transmission and storage inspection and evaluation value FXg exceeds the preset transmission and storage inspection and evaluation threshold, it indicates that the transmission and storage performance of the memory g during the management period is poor and the existing security risk is relatively high, then the memory g is marked as a risky object.

[0043] Furthermore, if the transfer and storage evaluation value FXg does not exceed the preset transfer and storage evaluation threshold, indicating that the transfer and storage performance of memory g during the management period is good, then the real-time temperature of memory g is collected, the deviation value between the real-time temperature and the preset appropriate standard temperature is marked as the storage temperature condition value, and the environmental humidity and environmental dust concentration of the environment where memory g is located are collected and marked as the storage environmental humidity value and the storage environmental ash value respectively, and the vibration data of the location where memory g is located is marked as the storage vibration inspection value;

[0044] The storage temperature condition value WFg, the storage environmental humidity value WKg, the storage environmental ash value WPg, and the storage vibration inspection value WZg are numerically calculated through the formula WXg = (kp1 * WFg + kp2 * WKg + kp3 * WPg + kp4 * WZg) / 4 to obtain the initial storage inspection value WXg, where kp1, kp2, kp3, and kp4 are preset proportionality coefficients, and the values of kp1, kp2, kp3, and kp4 are all positive numbers; moreover, the larger the value of the initial storage inspection value WXg, the greater the operating risk of memory g at the corresponding moment; the initial storage inspection value WXg is numerically compared with the preset initial storage inspection threshold. If the initial storage inspection value WXg exceeds the preset initial storage inspection threshold, indicating that the operating risk of memory g at the corresponding moment is relatively large, then it is determined that memory g is in a vulnerable state;

[0045] The total duration of memory g being in a vulnerable state within a unit time is obtained and marked as the total storage damage time condition value, and the average value of all initial storage inspection values of memory g within a unit time is calculated to obtain the average storage inspection value, and the single continuous duration of memory g being in a vulnerable state each time is marked as the vulnerable single continuous value, and the vulnerable single continuous value is numerically compared with the preset vulnerable single continuous threshold, and the number of vulnerable single continuous values exceeding the preset vulnerable single continuous threshold within a unit time is marked as the high proportion of vulnerable continuous state value;

[0046] Through the formula The total storage damage time condition value GMg, the average storage inspection value GKg, and the high proportion of vulnerable continuous state value GSg are numerically calculated to obtain the memory evaluation value GYg, where tq1, tq2, and tq3 are preset proportionality coefficients, and the values of tq1, tq2, and tq3 are all greater than zero; moreover, the larger the value of the memory evaluation value GYg, the greater the storage safety hazard of memory g; the memory evaluation value GYg is numerically compared with the preset memory evaluation threshold. If the memory evaluation value GYg exceeds the preset memory evaluation threshold, indicating that the storage safety hazard of memory g is relatively large, then memory g is marked as a risky object; if the memory evaluation value GYg does not exceed the preset memory evaluation threshold, indicating that the storage safety hazard of memory g is relatively small, then memory g is marked as a non-risky object.

[0047] Embodiment 2: As Figure 2As shown in the figure, the difference between this embodiment and Embodiment 1 is that the result display module is communicatively connected to the storage management evaluation module. The storage management evaluation module is used to set an evaluation period. Preferably, the evaluation period is fifteen days. The time when the corresponding management personnel receive the risk form object is collected and marked as the risk form reception time, and the time when the corresponding management personnel take treatment measures is collected and marked as the risk form treatment time. The time interval between the risk form treatment time and the risk form reception time is marked as the risk treatment duration. All risk treatment durations within the evaluation period are obtained, and the average value of all risk treatment durations is calculated to obtain the risk treatment detection value. The risk treatment duration is numerically compared with the preset risk treatment duration threshold. If the risk treatment duration exceeds the preset risk treatment duration threshold, the corresponding risk treatment duration is marked as an abnormal treatment duration. The ratio of the number of abnormal treatment durations to the number of risk treatment durations within the evaluation period is calculated to obtain the abnormal treatment detection value.

[0048] The risk treatment detection value RW and the abnormal treatment detection value RY are numerically calculated through the formula RK = ey1 * RW + ey2 * RY to obtain the storage management evaluation value RK, where ey1 and ey2 are preset proportionality coefficients, and the values of ey1 and ey2 are both positive numbers. Moreover, the larger the value of the storage management evaluation value RK, the worse the management performance of the memory during the evaluation period.

[0049] The storage management evaluation value RK is numerically compared with the preset storage management evaluation threshold. If the storage management evaluation value RK exceeds the preset storage management evaluation threshold, indicating that the management performance of the memory during the evaluation period is poor, a storage management unqualified signal is generated. If the storage management evaluation value does not exceed the preset storage management evaluation threshold, indicating that the management performance of the memory during the evaluation period is good, a storage management qualified signal is generated, and the storage management unqualified signal is sent to the result display module. When the result display module receives the storage management unqualified signal, it issues a corresponding warning to strengthen the subsequent supervision of the corresponding management personnel in a timely manner, ensure the safe and stable operation of each memory, and further reduce the storage risk of audit data.

[0050] Embodiment Three: As Figure 2 shown in the figure, the difference between this embodiment and Embodiment 1 and Embodiment 2 is that the storage management evaluation module is communicatively connected to the storage management difficulty detection module. The storage management evaluation module sends the storage management unqualified signal to the storage management difficulty detection module. When the storage management difficulty detection module receives the storage management unqualified signal, it generates a storage difficult to manage signal or a storage easy to manage signal through storage management difficulty analysis, and sends the storage difficult to manage signal to the result display module. When the result display module receives the storage difficult to manage signal, it issues a corresponding warning to increase the management personnel for the memory in the subsequent process and strengthen the subsequent supervision of the management personnel, further ensure the safe and stable operation of the memory, and improve the storage security of audit data. The specific analysis process of the storage management difficulty analysis is as follows:

[0051] During the evaluation period, several detection time periods are set, the total number of times all memories are marked as risk list objects within the corresponding detection time periods is collected and marked as the risk list data condition value, the risk list data condition values of all detection time periods are averaged to obtain the risk list data analysis value, the risk list data analysis value is numerically compared with a preset risk list data analysis threshold. If the risk list data analysis value exceeds the preset risk list data analysis threshold, it indicates that it is difficult to manage all memories, and a storage management difficult signal is generated;

[0052] If the risk list data analysis value does not exceed the preset risk list data analysis threshold, then the risk list data condition value is numerically compared with a preset risk list data condition threshold. If the risk list data condition value exceeds the preset risk list data condition threshold, the corresponding detection time period is marked as a difficult management time period, the number of difficult management time periods within the evaluation period is obtained and marked as the difficult management time condition value;

[0053] The difficult management time condition value TP and the risk list data analysis value TL are numerically calculated through the formula TX = hp1 * TP + hp2 * TL to obtain the storage management difficulty value TX, where hp1 and hp2 are preset weight coefficients, and the values of hp1 and hp2 are both greater than zero; moreover, the larger the value of the storage management difficulty value TX, the greater the management difficulty for all memories; the storage management difficulty value TX is numerically compared with a preset storage management difficulty threshold. If the storage management difficulty value TX exceeds the preset storage management difficulty threshold, it indicates that it is difficult to manage all memories, and a storage management difficult signal is generated; if the storage management difficulty value TX does not exceed the preset storage management difficulty threshold, it indicates that it is relatively easy to manage all memories, and a storage management easy signal is generated.

[0054] The working principle of the present invention: When in use, various data generated during the audit process are collected through the audit data capture module, the audit data storage module uses distributed storage technology to classify and store the audit data according to preset rules, the audit data analysis module uses data mining and machine learning data analysis technologies to deeply mine and analyze the audit data stored in the audit data storage module, identify potential risk points and abnormal behaviors, and the result display module visually displays the data analysis results in the form of charts and reports, which can achieve comprehensive monitoring and intelligent analysis of audit data, improve the efficiency and accuracy of audit work, provide strong support for modern audit work, and through the storage risk assessment module, all memories are supervised, and the corresponding memories are marked as risk list objects or non-risk objects through memory-by-memory analysis. The corresponding management personnel timely back up the audit data in the risk list objects and take corresponding treatment measures for the risk list objects, which is beneficial to significantly reduce the storage risk of audit data.

[0055] The above formulas are all dimensionless and take their numerical calculations. The formula is a formula obtained by collecting a large amount of data for software simulation to approximate the real situation as closely as possible. The preset parameters in the formula are set by those skilled in the art according to the actual situation. The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation manners only. Obviously, many modifications and variations can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principle and practical application of the present invention, so that those skilled in the relevant technical field can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An audit data storage and management system based on data analysis, characterized in that It includes an audit data capture module, an audit data storage module, an audit data analysis module, a result display module, and a storage risk assessment module; the audit data capture module collects various types of data generated during the audit process, converts the collected audit data into a unified format, and sends the converted audit data to the audit data storage module; the audit data storage module uses distributed storage technology to classify and store the received audit data according to preset rules; The audit data analysis module uses data mining and machine learning data analysis technologies to deeply mine and analyze the audit data stored in the audit data storage module, identify potential risk points and abnormal behaviors, and send the data analysis results to the result display module, and the result display module visualizes the data analysis results in the form of charts and reports; The audit data storage module stores the audit data through several groups of memories, and the storage risk assessment module supervises all memories, marks the corresponding memory as g, and g is a natural number greater than 1; Through memory-by-memory analysis, the memory g is marked as a risk table object or a non-risk object, and the risk table object is sent to the result display module and the intelligent terminals of the corresponding management personnel, and the corresponding management personnel back up the audit data in the risk table object and take corresponding treatment measures for the risk table object; The result display module is communicatively connected to the storage management evaluation module. The storage management evaluation module is used to set the evaluation period, collect the moment when the corresponding management personnel receive the risk table object and mark it as the risk table reception moment, and collect the moment when the corresponding management personnel take treatment measures and mark it as the risk table treatment moment, and mark the interval duration between the risk table treatment moment and the risk table reception moment as the risk treatment duration; Obtain all the risk treatment durations within the evaluation period, calculate the mean value of all the risk treatment durations to obtain the risk treatment detection value, compare the risk treatment duration with the preset risk treatment duration threshold value. If the risk treatment duration exceeds the preset risk treatment duration threshold value, mark the corresponding risk treatment duration as the abnormal treatment duration, and calculate the ratio of the number of abnormal treatment durations to the number of risk treatment durations within the evaluation period to obtain the abnormal treatment detection value; Calculate the storage management evaluation value by numerically calculating the risk treatment detection value and the abnormal treatment detection value. If the storage management evaluation value exceeds the preset storage management evaluation threshold value, generate a storage management unqualified signal. If the storage management evaluation value does not exceed the preset storage management evaluation threshold value, generate a storage management qualified signal, and send the storage management unqualified signal to the result display module. When the result display module receives the storage management unqualified signal, it issues a corresponding warning; The storage management evaluation module is communicatively connected to the storage difficulty detection module. The storage management evaluation module sends the storage management unqualified signal to the storage difficulty detection module. When the storage difficulty detection module receives the storage management unqualified signal, it generates a storage difficult management signal or a storage easy management signal through storage difficulty analysis, and sends the storage difficult management signal to the result display module. When the result display module receives the storage difficult management signal, it issues a corresponding warning; The specific analysis process of the storage difficulty analysis is as follows: Set several detection time periods within the evaluation period, collect the total number of times all memories are marked as risk list objects during the detection time period and mark it as the risk list data value, calculate the average value of the risk list data values of all detection time periods to obtain the risk list analysis value. If the risk list analysis value exceeds the preset risk list analysis threshold, generate a storage management difficult signal; If the risk list analysis value does not exceed the preset risk list analysis threshold, compare the risk list data value with the preset risk list data threshold. If the risk list data value exceeds the preset risk list data threshold, mark the corresponding detection time period as a difficult management time period; obtain the number of difficult management time periods within the evaluation period and mark it as the difficult management data value, calculate the storage management difficulty value by numerically calculating the difficult management data value and the risk list analysis value. If the storage management difficulty value exceeds the preset storage management difficulty threshold, generate a storage management difficult signal; if the storage management difficulty value does not exceed the preset storage management difficulty threshold, generate a storage management easy signal.

2. The audit data storage management system based on data analysis according to claim 1, wherein, The specific analysis process of the audit data analysis module is as follows: Using data mining techniques, through preset algorithms and models, conduct a comprehensive and in-depth analysis of audit data, including statistical descriptions of audit data, and capture potential rules and patterns in audit data through methods such as association rule mining, clustering analysis, and classification analysis; Through machine learning techniques, learn historical audit data and perform pattern recognition to establish a prediction model to predict potential risks. When facing new audit cases, use the trained model for risk assessment; and perform automated anomaly detection based on machine learning techniques. Through learning a large amount of audit data, establish an automated anomaly detection model to identify abnormal audit data that deviates from the normal pattern.

3. An audit data storage management system based on data analysis according to claim 1, characterized in that The specific analysis process of memory-by-memory evaluation is as follows: Collect the production date of memory g, mark the time interval between the current date and the production date as the production time value, and collect the time of each inspection and maintenance of memory g in the historical stage. Mark the time interval between adjacent two inspection and maintenance times as the inspection and maintenance interval value, and mark the number of inspection and maintenance interval values that exceed the preset inspection and maintenance interval threshold as the inspection and maintenance over interval value; calculate the memory property degradation value by numerically calculating the production time value of memory g and the inspection and maintenance over interval value. If the memory property degradation value exceeds the preset memory property degradation threshold, mark memory g as a risk list object.

4. An audit data storage management system based on data analysis according to claim 3, wherein If the memory property degradation value does not exceed the preset memory property degradation threshold, set a management period, collect the total time of data transmission and storage of memory g during the management period and mark it as the total transmission and storage time value, and collect the real-time transmission and storage speed during the data transmission and storage process. Compare the real-time transmission and storage speed with the preset real-time transmission and storage speed threshold. If the real-time transmission and storage speed does not exceed the preset real-time transmission and storage speed threshold, determine that memory g is in a low-efficiency state of data transmission and storage; Obtain the duration during which the memory g is in an inefficient transfer and storage state during the management period and mark it as the inefficient transfer and storage occupancy value, and collect the number of operation interruptions that occur during the data transfer and storage process of the memory g during the management period and mark it as the transfer interruption condition value, and sum up the duration of each operation interruption during the management period to obtain the transfer interruption time value. Calculate the transfer inspection and evaluation value through numerical calculation of the total transfer time value, the inefficient transfer and storage occupancy value, the transfer interruption condition value, and the transfer interruption time value. If the transfer inspection and evaluation value exceeds the preset transfer inspection and evaluation threshold, mark the memory g as a risky object.

5. An audit data storage management system based on data analysis according to claim 4, characterized in that, If the transfer inspection and evaluation value does not exceed the preset transfer inspection and evaluation threshold, collect the real-time temperature of the memory g, mark the deviation value between the real-time temperature and the preset appropriate standard temperature as the storage temperature condition value, and collect the environmental humidity and environmental dust concentration of the environment where the memory g is located and mark them as the storage environmental humidity value and the storage environmental ash value respectively, and mark the vibration data of the location where the memory g is located as the storage vibration inspection value. Calculate the initial storage inspection value through numerical calculation of the storage temperature condition value, the storage environmental humidity value, the storage environmental ash value, and the storage vibration inspection value. If the initial storage inspection value exceeds the preset initial storage inspection threshold, it is determined that the memory g is in a vulnerable state; Obtain the total duration during which the memory g is in a vulnerable state per unit time and mark it as the total storage vulnerability time condition value, calculate the average value of all the initial storage inspection values of the memory g per unit time to obtain the average storage inspection value, and mark the single duration of each time the memory g is in a vulnerable state as the vulnerable single duration value. Mark the number of vulnerable single duration values that exceed the preset vulnerable single duration threshold per unit time as the high occupancy condition value of vulnerable single duration. Calculate the memory evaluation and inspection value through numerical calculation of the total storage vulnerability time condition value, the average storage inspection value, and the high occupancy condition value of vulnerable single duration. If the memory evaluation and inspection value exceeds the preset memory evaluation and inspection threshold, mark the memory g as a risky object; If the memory evaluation and inspection value does not exceed the preset memory evaluation and inspection threshold, mark the memory g as a non-risky object.

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