Financial management service intelligent analysis method and system based on Internet of Things

Abstract

The invention relates to the field of data analysis, in particular to a financial management service intelligent analysis method and system based on the Internet of Things, and the method comprises the steps: setting a transaction frequency according to an output result of a financial comprehensive scoring system, and carrying out the adjustment through a cash flow coverage rate, and carrying out the risk division; calculating a transaction frequency correction coefficient according to the market fluctuation rate, the transaction cost rate and the market mobility factor, adjusting the transaction frequency according to a comparison result of the transaction frequency correction coefficient and a preset transaction frequency correction contrast coefficient, and compensating the transaction frequency according to the cash flow coverage rate; and performing dual verification according to the obtained final transaction frequency and cash flow data, and establishing an integrated financial early warning model for early warning.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and in particular to an intelligent analysis method and system for financial management business based on the Internet of Things. Background Art

[0002] With the accelerated development of the global digital economy and the rapid development of Internet of Things technology, the financial industry is facing the need to generate and process a large amount of data. The traditional financial business model faces core pain points such as data silos, lagging risk control, and low efficiency. Traditional financial management methods can no longer meet the needs of real-time analysis and decision-making, and the existing technology still has the following problems: the massive data recorded by Internet of Things devices has not been effectively integrated into financial decision-making, and there is no comprehensive scoring analysis combined with effective data to reasonably divide the risk situation, resulting in a lack of rationality in resource allocation; the transaction frequency is not accurately regulated according to the transaction frequency correction coefficient, and the changes in cash flow coverage cannot be monitored in real time, and the transaction frequency cannot be dynamically adjusted and effectively compensated, resulting in a certain imbalance between risks and returns; there is a lack of monitoring based on the adjusted transaction frequency and cash flow coverage gap, and there is no active management of financial risks, resulting in misjudgment or delayed response.

[0003] Therefore, there is a need for an intelligent analysis method and system for financial management business based on the Internet of Things. Summary of the invention

[0004] The present invention provides an intelligent analysis method and system for financial management business based on the Internet of Things, the purpose of which is to effectively integrate the massive data generated by Internet of Things devices, combine with other relevant data sources, and improve the accuracy and scientificity of financial decision-making through comprehensive scoring analysis; according to the comprehensive scoring results, reasonable risk classification is carried out for different transaction objects and situations to ensure the efficient use of financial resources; real-time monitoring of changes in cash flow coverage, and dynamic adjustment of transaction frequency using transaction frequency correction coefficients to ensure flexible response in different market environments; active identification and management of financial risks based on adjusted transaction frequencies and cash flow coverage gaps; balance risks and returns through reasonable transaction frequency regulation and risk management to improve the security and benefits of overall financial management.

[0005] The technical solution of the present invention is specifically as follows: A financial management business intelligent analysis method based on the Internet of Things includes the following steps: Step S1. Set the transaction frequency according to the output of the financial comprehensive scoring system and perform risk classification; Step S2. Calculate the transaction frequency correction coefficient according to market volatility, transaction cost rate, and market liquidity factors, adjust the transaction frequency according to the comparison result of the transaction frequency correction coefficient and the preset transaction frequency correction comparison coefficient, and compensate the transaction frequency according to the cash flow coverage ratio; Step S3: Double check the final transaction frequency and cash flow data to establish an integrated financial early warning model for early warning.

[0006] Further, the obtained financial user behavior data information is analyzed. Specifically, a user behavior analysis model is established to obtain a comprehensive user behavior risk score P; set , is the threshold of the data comprehensive scoring system, and ;if , it is a low risk state; if , it is a medium risk state; if , it is a high-risk state.

[0007] Further, step S1 specifically includes: When the data comprehensive scoring system determines that the risk is low, the transaction frequency is set to , ; When the data comprehensive scoring system determines that the risk is medium, the transaction frequency is set to , ; When the data comprehensive scoring system determines that the risk is high, the transaction frequency is set to , ; in, , , Respectively represent the first, second and third transaction frequency adjustment coefficients; Indicates the defined base transaction frequency.

[0008] Further, step S2 specifically includes: in, Indicates the transaction frequency correction factor; represents the transaction cost rate; Indicates market liquidity; represents the normalization coefficient; represents the market volatility, Indicates The price of the day, Indicates the number of sample days.

[0009] Further, the first preset transaction frequency correction comparison coefficient is defined as , the second preset transaction frequency correction reference coefficient is , , the transaction frequency correction factor is now Respectively , Compare and adjust according to the comparison results. The specific process is as follows: if , then adjust the transaction frequency to , ; if , then adjust the transaction frequency to , ; if , then adjust the transaction frequency to , ; in, represents the frequency reduction factor; Indicates the frequency increase factor.

[0010] Further, the benchmark cash flow coverage ratio is defined as , the cash flow coverage ratio Cash flow coverage ratio Make a comparison and determine how to compensate the transaction frequency based on the comparison results. The specific process is as follows: if , then there is no need to compensate for the transaction frequency, or when When the price fluctuates around the base value, the trading frequency is only slightly adjusted; if , then compensation for transaction frequency is needed.

[0011] Further, based on the benchmark cash flow coverage ratio Cash flow coverage ratio Calculate the transaction frequency compensation factor , and make compensation. The specific process is as follows: if , , adjust the transaction frequency to , ; if , , adjust the transaction frequency to , ; in, represents the first compensation factor; Indicates the second compensation factor; sets the frequency lower limit , frequency cap , that is, the final transaction frequency .

[0012] Furthermore, an integrated financial early warning model is established, and the transaction frequency deviation rate is , the adjusted cash flow coverage gap is , the specific process is as follows: in, It represents the integrated financial early warning model indicator; , Represent the corresponding weight coefficients respectively; take graded measures according to the severity of the triggering conditions: when or , which is a yellow warning, requires manual review of the data to check whether it is a misjudgment due to short-term fluctuations; when or , which is a red alert, requiring forced reduction of positions, suspension of high-risk transactions, and activation of liquidity emergency plans; when , which is a black warning, requiring a complete suspension of trading or liquidation of some assets to replenish cash flow.

[0013] An intelligent analysis system for financial management business based on the Internet of Things, including the following contents: Financial related data information acquisition module, financial comprehensive scoring module, financial business intelligent adjustment module, double verification financial early warning module; The financial business intelligent adjustment module includes a transaction frequency correction unit, a compensation judgment unit, and a transaction frequency compensation unit; The financial data information acquisition module is used to collect financial data information and monitor market trends through IoT devices, sensors and smart terminals; The financial comprehensive scoring module is used to analyze financial related data after acquiring the data, generate comprehensive scores, and divide risk levels according to the comprehensive scores; The financial business intelligent adjustment module intelligently adjusts financial business strategies and transaction frequencies based on comprehensive scores; The double-check financial early warning module is responsible for monitoring and warning potential financial risks, and performs double-checking based on the final transaction frequency and cash flow data; The transaction frequency correction unit calculates the transaction frequency correction coefficient based on market volatility, transaction cost rate, and market liquidity factors, and adjusts the transaction frequency; The compensation judgment unit adjusts the transaction frequency according to the comparison result between the transaction frequency correction coefficient and the preset transaction frequency correction comparison coefficient, and compensates the transaction frequency according to the cash flow coverage ratio; The transaction frequency compensation unit compares the cash flow coverage ratio with the benchmark cash flow coverage ratio and determines how to compensate for the transaction frequency based on the comparison results.

[0014] Beneficial effects: 1. By combining the Internet of Things technology, the present invention can monitor market changes in real time, dynamically adjust trading strategies, and enhance flexibility. According to the analysis results of the comprehensive scoring system, it can more accurately evaluate the timing and frequency of transactions and improve the effectiveness of trading decisions. Risk classification can help identify high-risk and low-risk trading objects, optimize resource allocation, and reduce potential losses; risk classification helps to comply with relevant financial regulations, reduce compliance risks, and ensure business sustainability.

[0015] 2. The present invention monitors the changes in cash flow coverage ratio in real time and dynamically adjusts the transaction frequency, thereby optimizing the efficiency of capital use under the premise of controllable risks; the cash flow coverage ratio CFC dynamic compensation model achieves a balance between risk and return through nonlinear response characteristics, that is, the adjustment amplitude is proportional to the degree of CFC deviation, rather than a simple "on / off" switch. Moreover, by setting upper and lower limits of the frequency, it ensures that the strategy can still operate under extreme market conditions.

[0016] 3. When cash flow is sufficient, traditional models are often unable to proactively capture market opportunities, resulting in idle funds. The CFC dynamic compensation model allows institutions to moderately increase transaction frequency within a safe range through a dynamic frequency increase mechanism. Traditional risk control models usually use a static threshold trigger mechanism. For example, when the cash flow coverage ratio is lower than a certain fixed value, trading is directly suspended or extreme measures are taken. The drawback of this method is that the response is too rigid, which can easily cause frequent switching of strategies and even cause market shocks. In contrast, the CFC dynamic compensation model achieves smoother risk control through proportional adjustment and compensation coefficients.

[0017] 4. The present invention realizes active management of financial risks through transaction frequency adjustment and cash flow coverage gap monitoring. When market volatility intensifies, it automatically reduces transaction frequency, reduces the number of executions of high-risk transactions, and avoids systemic collapse caused by liquidity exhaustion or sharp price fluctuations. This model only intervenes when risks exceed the standard, retains profit opportunities under normal market conditions, and realizes a progressive response from "manual review" to "forced liquidation" through yellow / red / black three-level warnings to avoid misjudgment or delayed response. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a method for intelligent analysis of financial management services based on the Internet of Things of the present invention; Figure 2 This is a module diagram of a financial management business intelligent analysis system based on the Internet of Things of the present invention; Figure 3 This is a schematic diagram of the intelligent adjustment module for financial services of the present invention. DETAILED DESCRIPTION

[0019] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should also be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0020] Refer to the attached Figure 1 This embodiment provides a financial management business intelligent analysis method based on the Internet of Things, comprising the following steps: S1. Set the transaction frequency based on the output of the comprehensive financial scoring system, and adjust and divide the risks through cash flow coverage ratio.

[0021] In the IoT environment, financial data information is collected through sensors and deployed devices, including but not limited to customer transaction records, account information, user behavior data, market data, economic indicators, etc. These data are usually stored in the company's database and can be obtained through API interfaces or crawled from relevant websites using crawler technology. For data that does not need to be updated in real time, it can be extracted from the database regularly and uploaded to the analysis platform. At the same time, raw data often contains noise and missing values, so data cleaning and processing are required to remove duplicate data and ensure that each data entry is unique; fill in missing values: fill in missing data according to business rules or use algorithms; data conversion: convert data into a format suitable for analysis, such as standardization and normalization.

[0022] The transaction frequency is set according to the output results of the comprehensive data scoring system, and is adjusted and risk warnings are issued through cash flow coverage.

[0023] Analyze the acquired financial user behavior data information. Specifically, establish a user behavior analysis model to obtain a comprehensive score of user behavior risk. The specific process is as follows: Assume the user set is , each user The behavior sequence is represented as in, Indicates the type of user behavior, such as login, transfer, etc. Indicates a timestamp; Represents the user behavior feature vector; The joint probability of the behavior sequence is expressed as: ,in, Expressing behavior The transition probability of Define the optimization goal: ,in, Indicates Clusters, represents the cluster center; User behavior prediction through LSTM: hidden state update The behavior probability is ;in, Indicates the hidden state, , Represents the weight matrix and bias term; Perform user behavior risk scoring: ,in, Represents risk feature vectors, such as transaction frequency, amount fluctuation, etc. ; The risk alert system is triggered.

[0024] At the same time, set , is the threshold of the data comprehensive scoring system, and ;if , it is a low risk state; if , it is a medium risk state; if , it is a high risk state; in the embodiment of the present invention, usually Take 0.8, Take 0.5.

[0025] If it is assessed as low risk, it means that better financial products and interest rates can be considered; if it is assessed as medium risk, it means that credit monitoring needs to be strengthened and higher collateral or guarantees may be required; if it is assessed as high risk, it means that credit limits need to be restricted or credit applications need to be rejected if necessary.

[0026] When the data comprehensive scoring system determines that the risk is low, the transaction frequency is set to , ; When the data comprehensive scoring system determines that the risk is medium, the transaction frequency is set to , ; When the data comprehensive scoring system determines that the risk is high, the transaction frequency is set to , ; in, , , Respectively represent the first, second and third transaction frequency adjustment coefficients; Indicates the defined base transaction frequency.

[0027] By combining the Internet of Things technology, the present invention can monitor market changes in real time, dynamically adjust trading strategies, and enhance flexibility. According to the analysis results of the comprehensive scoring system, it can more accurately evaluate the timing and frequency of transactions and improve the effectiveness of trading decisions. Risk classification can help identify high-risk and low-risk trading objects, optimize resource allocation, and reduce potential losses; risk classification helps to comply with relevant financial regulations, reduce compliance risks, and ensure business sustainability.

[0028] S2. Based on market volatility and transaction cost rate , Market Liquidity Calculate the transaction frequency correction coefficient based on factors such as , according to the transaction frequency correction factor The comparison result with the preset transaction frequency correction coefficient is used to adjust the transaction frequency and calculate the cash flow coverage ratio. Compensate for transaction frequency.

[0029] According to market volatility, transaction cost rate , Market Liquidity Calculate the transaction frequency correction coefficient based on factors such as , used in quantitative trading strategies or portfolio management to adjust for the effects of different trading frequencies.

[0030] in, Indicates the transaction frequency correction factor; It represents the transaction cost rate, that is, the ratio of the fixed cost of each transaction (commission, slippage, etc.) to the transaction amount. ; Indicates market liquidity, reflects market depth and transaction friction, When liquidity is high, large orders can be executed quickly with low slippage, while when liquidity is low, transactions are difficult and easily cause price fluctuations; Represents the normalization coefficient, which can be set through historical backtesting or strategy requirements; represents the market volatility, Indicates The price of the day, Represents the number of sample days, 252 is assumed to be 252 trading days a year; the natural logarithm of the ratio of adjacent prices is calculated for each time point.

[0031] Correction factor based on transaction frequency The comparison result with the preset transaction frequency correction coefficient is used to adjust the transaction frequency and calculate the cash flow coverage ratio. Compensate for transaction frequency.

[0032] Define the first preset transaction frequency correction comparison coefficient as , the second preset transaction frequency correction reference coefficient is , , the transaction frequency correction factor is now Respectively , Compare and adjust according to the comparison results. The specific process is as follows: if , then adjust the transaction frequency to , ; if , then adjust the transaction frequency to , ; if , then adjust the transaction frequency to , ; in, Indicates the frequency reduction factor to prevent excessive contraction; Indicates the frequency increase factor to prevent over-expansion.

[0033] Define the base cash flow coverage ratio as ,in It is a pre-set threshold used to measure whether the debt repayment capacity has reached the expected level. The cash flow coverage ratio is Make a comparison and determine how to compensate the transaction frequency based on the comparison results. The specific process is as follows: if , then there is no need to compensate for the transaction frequency, or when When the index fluctuates around the benchmark value, the trading frequency is only slightly adjusted to avoid performance fluctuations caused by frequent changes in strategies; if , then compensation for transaction frequency is needed.

[0034] Based on the benchmark cash flow coverage ratio Cash flow coverage ratio Calculate the transaction frequency compensation factor , the specific process is as follows: if , ; if , ; in, represents the first compensation factor; Represents the second compensation factor.

[0035] Therefore, when When , ; when When , ; At the same time, set the frequency lower limit , to avoid excessive frequency reduction and strategy failure; set a frequency cap , to prevent excessive risk taking; that is, the final trading frequency .

[0036] The present invention monitors the changes in cash flow coverage ratio in real time and dynamically adjusts the transaction frequency, thereby optimizing the efficiency of capital use under the premise of controllable risks; the cash flow coverage ratio CFC dynamic compensation model achieves a balance between risk and return through nonlinear response characteristics, that is, the adjustment amplitude is proportional to the degree of CFC deviation, rather than a simple "on / off" switch, and by setting upper and lower limits on the frequency, it ensures that the strategy can still operate under extreme market conditions.

[0037] When cash flow is sufficient, traditional models are often unable to proactively capture market opportunities, resulting in idle funds. The CFC dynamic compensation model allows institutions to moderately increase trading frequency within a safe range through a dynamic frequency increase mechanism. Traditional risk control models usually use a static threshold trigger mechanism. For example, when the cash flow coverage ratio is lower than a certain fixed value, trading is directly suspended or extreme measures are taken. The drawback of this method is that the response is too rigid, which can easily cause frequent switching of strategies and even cause market shocks. In contrast, the CFC dynamic compensation model achieves smoother risk control through proportional adjustment and compensation coefficients.

[0038] S3. Based on the final transaction frequency obtained Double check with cash flow data and establish an integrated financial early warning model to issue early warnings to avoid misjudgment of a single indicator.

[0039] According to the final transaction frequency Double check with cash flow data to provide early warning and avoid misjudgment of a single indicator. The specific process is as follows: Define the transaction frequency deviation rate as , the specific process is as follows: in, Represents the historical average frequency.

[0040] Define the adjusted cash flow coverage gap as , which measures the system's expected cash flow coverage after dynamically adjusting the transaction frequency Still unable to meet the benchmark requirement (i.e., the benchmark cash flow coverage ratio is ), the specific process is as follows: , in, It represents the improvement adjustment coefficient, which is the efficiency of cash flow improvement caused by transaction frequency adjustment; This is the current cash flow coverage ratio.

[0041] In the embodiment of the present invention, if , Indicates the preset transaction frequency deviation rate threshold. When the actual transaction frequency deviates from the historical normal level by more than When the market activity is abnormal, , Indicates the preset cash flow coverage gap threshold. Even if the transaction frequency has been reduced, the cash flow coverage ratio cannot be restored to a safe level ( ), and the gap ratio exceeds When the pressure reaches 0, the pressure warning is triggered.

[0042] Establish an integrated financial early warning model. The specific process is as follows: in, It represents the integrated financial early warning model indicator; , They represent the corresponding weight coefficients respectively.

[0043] if , Represents the comprehensive warning threshold. When the weighted combined risk of frequency deviation and cash flow gap exceeds , the authentication system is in alarm state.

[0044] In summary, based on the severity of the triggering conditions, take phased measures: when or , which is a yellow warning, requires manual review of the data to check whether it is a misjudgment due to short-term fluctuations; when or , which is a red alert, requiring forced reduction of positions, suspension of high-risk transactions, and activation of liquidity emergency plans; when , which is a black warning, requiring a complete suspension of trading or liquidation of some assets to replenish cash flow.

[0045] The present invention realizes active management of financial risks through transaction frequency adjustment and cash flow coverage gap monitoring. When market volatility intensifies, it automatically reduces transaction frequency, reduces the number of executions of high-risk transactions, and avoids systemic collapse caused by liquidity exhaustion or sharp price fluctuations. This model only intervenes when risks exceed the standard, retains profit opportunities under normal market conditions, and realizes a progressive response from "manual review" to "forced liquidation" through yellow / red / black three-level warnings to avoid misjudgment or delayed response.

[0046] Refer to the attached Figure 2 This embodiment provides a financial management business intelligent analysis system based on the Internet of Things, including the following contents: Financial related data information acquisition module, financial comprehensive scoring module, financial business intelligent adjustment module, double verification financial early warning module; Refer to the attached Figure 3 ,The financial business intelligent adjustment module includes a transaction frequency correction unit, a compensation judgment unit, and a transaction frequency compensation unit; The financial data information acquisition module is used to collect real-time financial data information through IoT devices, sensors and smart terminals to monitor market trends and ensure the timeliness and accuracy of data; The financial comprehensive scoring classification module is used to analyze the data after acquiring the data, generate a comprehensive score, and classify the risk level according to the comprehensive score; The financial business intelligent adjustment module intelligently adjusts financial business strategies and transaction frequencies based on comprehensive scores; The double-check financial early warning module is responsible for monitoring and warning potential financial risks. It performs double checks based on the final transaction frequency and cash flow data to ensure the accuracy and timeliness of the early warning. The transaction frequency correction unit calculates the transaction frequency correction coefficient based on factors such as market volatility, transaction cost rate, and market liquidity, and adjusts the transaction frequency; The compensation judgment unit adjusts the transaction frequency according to the comparison result between the transaction frequency correction coefficient and the preset transaction frequency correction comparison coefficient, and compensates the transaction frequency according to the cash flow coverage ratio; The transaction frequency compensation unit compares the cash flow coverage ratio with the benchmark cash flow coverage ratio and determines how to compensate for the transaction frequency based on the comparison results.

[0047] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0048] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0049] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0050] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0051] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A financial management business intelligent analysis method based on the Internet of Things, characterized in that: The following steps are involved: Step S1. Set the transaction frequency according to the output of the financial comprehensive scoring system and perform risk classification; Step S2. Calculate the transaction frequency correction coefficient according to market volatility, transaction cost rate, and market liquidity factors, adjust the transaction frequency according to the comparison result of the transaction frequency correction coefficient and the preset transaction frequency correction comparison coefficient, and compensate the transaction frequency according to the cash flow coverage ratio; Step S3: Double check the final transaction frequency and cash flow data to establish an integrated financial early warning model for early warning.

2. According to the method of intelligent analysis of financial management business based on the Internet of Things in claim 1, it is characterized in that: In step S1, the obtained financial user behavior data information is analyzed. Specifically, a user behavior analysis model is established to obtain a comprehensive user behavior risk score P; , is the threshold of the data comprehensive scoring system, and ;if , it is a low risk state; if , it is a medium risk state; if , it is a high-risk state.

3. According to claim 2, a financial management business intelligent analysis method based on the Internet of Things is characterized in that: The step S1 specifically includes: When the data comprehensive scoring system determines that the risk is low, the transaction frequency is set to , ; When the data comprehensive scoring system determines that the risk is medium, the transaction frequency is set to , ; When the data comprehensive scoring system determines that the risk is high, the transaction frequency is set to , ; in, , , Respectively represent the first, second and third transaction frequency adjustment coefficients; Indicates the defined base transaction frequency.

4. According to the method of intelligent analysis of financial management business based on the Internet of Things in claim 1, it is characterized in that: The step S2 specifically includes: in, Indicates the transaction frequency correction factor; represents the transaction cost rate; Indicates market liquidity; represents the normalization coefficient; represents the market volatility, Indicates The price of the day, Indicates the number of sample days.

5. According to claim 4, a financial management business intelligent analysis method based on the Internet of Things is characterized in that: The first preset transaction frequency correction comparison coefficient is defined in step S2 as: , the second preset transaction frequency correction reference coefficient is , , the transaction frequency correction factor is now Respectively , Compare and adjust according to the comparison results. The specific process is as follows: if , then adjust the transaction frequency to , ; if , then adjust the transaction frequency to , ; if , then adjust the transaction frequency to , ; in, represents the frequency reduction factor; Indicates the frequency increase factor.

6. The method for intelligent analysis of financial management business based on the Internet of Things according to claim 1, characterized in that: The benchmark cash flow coverage ratio is defined in step S2 as , the cash flow coverage ratio Cash flow coverage ratio Make a comparison and determine how to compensate for the frequency of financial transactions based on the comparison results. The specific process is as follows: if , there is no need to compensate for the frequency of financial transactions, or when When the price fluctuates around the base value, the trading frequency is only slightly adjusted; if , then compensation for the frequency of financial transactions is needed.

7. The method for intelligent analysis of financial management business based on the Internet of Things according to claim 6, characterized in that: In step S2, according to the benchmark cash flow coverage ratio Cash flow coverage ratio Calculate the transaction frequency compensation factor , and make compensation. The specific process is as follows: if , , adjust the transaction frequency to , ; if , , adjust the transaction frequency to , ; in, represents the first compensation factor; Indicates the second compensation factor; sets the frequency lower limit , frequency cap , that is, the final transaction frequency .

8. The method for intelligent analysis of financial management business based on the Internet of Things according to claim 1, characterized in that: The step S3 establishes an integrated financial early warning model, and the transaction frequency deviation rate , the adjusted cash flow coverage gap is , the specific process is as follows: in, It represents the integrated financial early warning model indicator; , Represent the corresponding weight coefficients respectively; take graded measures according to the severity of the triggering conditions: when or , which is a yellow warning, requires manual review of the data to check whether it is a misjudgment due to short-term fluctuations; when or , which is a red alert, requiring forced reduction of positions, suspension of high-risk transactions, and activation of liquidity emergency plans; when , which is a black warning, requiring a complete suspension of trading or liquidation of some assets to replenish cash flow.

9. A financial management business intelligent analysis system based on the Internet of Things, applied to the financial management business intelligent analysis method based on the Internet of Things according to claim 1, characterized in that: Includes the following: Financial related data information acquisition module, financial comprehensive scoring module, financial business intelligent adjustment module, double verification financial early warning module; The financial business intelligent adjustment module includes a transaction frequency correction unit, a compensation judgment unit, and a transaction frequency compensation unit; The financial-related data information acquisition module is used to collect financial-related data information through IoT devices, sensors and smart terminals to monitor market trends; The financial comprehensive scoring classification module is used to analyze the financial related data after acquiring the data, generate a comprehensive score, and classify the risk level according to the comprehensive score; The financial business intelligent adjustment module intelligently adjusts the financial business strategy based on the comprehensive score and adjusts the transaction frequency; The double-check financial early warning module is responsible for monitoring and warning potential financial risks, and performs double-checking based on the final transaction frequency and cash flow data obtained; The transaction frequency correction unit calculates the transaction frequency correction coefficient according to market volatility, transaction cost rate, and market liquidity factors, and adjusts the transaction frequency; The compensation judgment unit adjusts the transaction frequency according to the comparison result between the transaction frequency correction coefficient and the preset transaction frequency correction comparison coefficient, and compensates the transaction frequency according to the cash flow coverage ratio; The transaction frequency compensation unit compares the cash flow coverage ratio with the benchmark cash flow coverage ratio, and determines how to compensate the transaction frequency according to the comparison result.

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