Automatic investment supervision and early warning method, device and system

Abstract

The invention relates to an automatic investment supervision and early warning method, device and system. The method comprises the following steps: periodically acquiring investment business data according to a preset frequency; aiming at the investment business data acquired in the current period, respectively acquiring investment amounts corresponding to n risk time points in the current period, and respectively calculating an investment amount difference value between two adjacent risk time points in the n risk time points; calculating the investment floating and deficit rate of each investment service in the time periods corresponding to the n risk time points by using the investment service data; outputting early warning information based on the investment business data under the condition that the investment floating and deficit rate is smaller than or equal to a preset floating and deficit rate; and adjusting the time interval of the n risk time points corresponding to the current period based on the investment amount difference value to obtain n risk time points corresponding to the next period. According to the scheme, the flexibility of investment risk monitoring is improved.

Description

Technical Field

[0001] This disclosure relates to the field of financial risk supervision technology, and in particular to an automated investment supervision and early warning method, device and system. Background Technology

[0002] In the context of rapid globalization and financial innovation, investment has become a core means of capital appreciation and risk management. However, with the diversification of investment channels (such as stocks, bonds, trusts, and asset management plans) and the expansion of transaction volume, risks in the investment field are becoming more complex, concealed, and frequent. Traditional automated investment monitoring and early warning methods typically monitor investment behavior according to fixed rules, which is insufficient to address the complex, frequent, and concealed nature of risks in the investment field, resulting in poor monitoring effectiveness. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this disclosure provides an automated investment monitoring and early warning method, device and system.

[0004] According to a first aspect of the present disclosure, an automated investment monitoring and early warning method is provided, comprising:

[0005] Investment business data is acquired periodically at a preset frequency; the investment business data includes the investment amount.

[0006] For the investment data acquired in the current period, the investment amount corresponding to each of the n risk time points within the current period is obtained, and the difference in investment amount between two adjacent risk time points is calculated for each of the n risk time points; n is an integer greater than 2.

[0007] Calculate the floating loss rate of each investment business within the time period corresponding to each of the n risk time points using the investment business data;

[0008] If the unrealized loss rate of an investment is less than or equal to the preset unrealized loss rate, an early warning message will be output based on the investment business data corresponding to the unrealized loss rate of the investment that is less than or equal to the preset unrealized loss rate.

[0009] If the investment difference does not meet the preset conditions, the time interval of the n risk time points corresponding to the current period is adjusted based on the investment difference to obtain the n risk time points corresponding to the next period.

[0010] The next period is taken as the current period, and the process of returning to execute the investment business data obtained for the current period, and obtaining the investment amount corresponding to n risk time points within the current period, is carried out respectively.

[0011] In some embodiments of this disclosure, the step of adjusting the time interval of the n risk time points corresponding to the current period based on the investment difference when the investment difference does not meet the preset conditions, to obtain the n risk time points corresponding to the next period, includes:

[0012] For each investment difference, obtain multiple preset difference ranges, and the first risk coefficient corresponding to each difference range;

[0013] From the plurality of difference intervals, determine the target difference interval to which the investment amount difference belongs, and the template first risk coefficient corresponding to the target difference interval;

[0014] Based on the first risk coefficient, determine whether the investment amount difference meets the preset condition;

[0015] If the investment amount difference does not meet the preset conditions, the time interval of the n risk time points corresponding to the current period is adjusted based on the investment amount difference to obtain the n risk time points corresponding to the next period.

[0016] In some embodiments of this disclosure, determining whether the investment difference based on the first risk coefficient meets the preset condition includes:

[0017] Obtain multiple investment ranges and the corresponding second risk coefficient for each investment range;

[0018] From the plurality of investment amount ranges, determine the target investment amount range to which the investment amount associated with the investment amount difference belongs, and the second risk coefficient corresponding to the target investment amount range;

[0019] Add the first risk coefficient and the second risk coefficient to obtain the comprehensive risk coefficient;

[0020] If the overall risk coefficient is greater than or equal to the preset risk coefficient, it is determined that the difference in investment amount does not meet the preset condition;

[0021] If the overall risk coefficient is less than the preset risk coefficient, the difference in investment amount is determined to satisfy the preset condition.

[0022] In some embodiments of this disclosure, when the investment difference does not meet a preset condition, adjusting the time interval between the n risk time points corresponding to the current period based on the investment difference to obtain the time interval between the n risk time points corresponding to the next period includes:

[0023] In the event that the investment amount difference does not meet the preset conditions, the time interval of the n risk time points corresponding to the current period is adjusted based on the comprehensive risk coefficient to obtain the n risk time points corresponding to the next period.

[0024] In some embodiments of this disclosure, adjusting the time interval between n risk time points corresponding to the current period based on the comprehensive risk coefficient to obtain the time interval between n risk time points corresponding to the next period includes:

[0025] Determine the first risk time point and the second risk time point corresponding to the comprehensive risk coefficient; the first risk time point is adjacent to the second risk time point, and the first risk time point is before the second risk time point.

[0026] Obtain the risk time point interval adjustment method that matches the comprehensive risk coefficient;

[0027] Based on the risk time points and the risk time point interval adjustment method, the time interval of the n risk time points corresponding to the current cycle is adjusted to obtain the n risk time points corresponding to the next cycle.

[0028] In some embodiments of this disclosure, the method for adjusting the risk time interval that matches the comprehensive risk coefficient includes:

[0029] Obtain a first mapping table including the comprehensive risk coefficient and the time interval scaling value; the time interval scaling value is the scaling value of the time interval between two adjacent risk time points;

[0030] Determine a scaling value that matches the comprehensive risk coefficient, and determine that the risk time point interval adjustment method is to scale the interval between n risk time points according to the scaling value;

[0031] The step of adjusting the time interval of the n risk time points corresponding to the current period based on the risk time point and the risk time point interval adjustment method to obtain the n risk time points corresponding to the next period includes:

[0032] The intervals between the n risk inspection points are scaled according to the scaling value to obtain the n risk time points corresponding to the next cycle.

[0033] According to a second aspect of the present disclosure, an automated investment monitoring and early warning device is provided, comprising:

[0034] The acquisition unit is used to periodically acquire investment business data at a preset frequency; the investment business data includes the investment amount.

[0035] The first calculation unit is used to obtain the investment amount corresponding to n risk time points in the current period based on the investment business data obtained in the current period, and to calculate the difference in investment amount between two adjacent risk time points in the n risk time points; n is an integer greater than 2;

[0036] The second calculation unit is used to calculate the floating loss rate of each investment business within the time period corresponding to each of the n risk time points using the investment business data.

[0037] The early warning unit is used to output early warning information based on the investment business data corresponding to the investment loss rate that is less than or equal to the preset loss rate when there is an investment loss rate less than or equal to the preset loss rate.

[0038] The adjustment unit is used to adjust the time interval of the n risk time points corresponding to the current period based on the investment difference when there is an investment difference that does not meet the preset conditions, so as to obtain the n risk time points corresponding to the next period.

[0039] The execution unit is used to take the next period as the current period, return to execute the investment business data obtained for the current period, and obtain the investment amount corresponding to n risk time points in the current period respectively.

[0040] According to a third aspect of the present disclosure, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in any one of the first aspects.

[0041] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of the first aspects.

[0042] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method as described in any one of the first aspects.

[0043] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects: periodically acquiring investment business data according to a preset frequency; the investment business data includes investment amount; for the investment business data acquired in the current period, acquiring the investment amount corresponding to n risk time points in the current period respectively, and calculating the investment amount difference between two adjacent risk time points in the n risk time points respectively; using the investment business data to calculate the investment floating loss rate of each investment business in the time period corresponding to each of the n risk time points; if there is an investment floating loss rate less than or equal to a preset floating loss rate, outputting early warning information based on the investment business data corresponding to the investment floating loss rate less than or equal to the preset floating loss rate; if there is an investment amount difference that does not meet the preset condition, adjusting the time interval of the n risk time points corresponding to the current period based on the investment amount difference to obtain the n risk time points corresponding to the next period; taking the next period as the current period, returning to execute the steps of acquiring the investment amount corresponding to the n risk time points in the current period for the investment business data acquired in the current period respectively. By using the difference in investment amount to dynamically adjust the interval between risk time points used for risk monitoring, the distribution of monitoring time can be dynamically adjusted according to the actual investment business situation. This improves the monitoring intensity during high-risk periods without wasting computing resources, enhances the flexibility and rationality of investment risk monitoring, and ultimately improves the accuracy of investment risk monitoring.

[0044] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0046] Figure 1 This is a flowchart illustrating an automated investment monitoring and early warning method according to an exemplary embodiment.

[0047] Figure 2 This is a block diagram illustrating an automated investment monitoring and early warning device according to an exemplary embodiment.

[0048] Figure 3 This is a block diagram illustrating an apparatus for an automated investment monitoring and early warning method, according to an exemplary embodiment. Detailed Implementation

[0049] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0050] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. The singular forms “a” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0051] It should be understood that although the terms first, second, third, etc., may be used to describe various information in embodiments of this disclosure, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of embodiments of this disclosure, and similarly, second information may also be referred to as first information. Depending on the context, the words “if” and “suppose” as used herein may be interpreted as “when”, “when”, or “in response to a determination”.

[0052] Furthermore, various forms of processes shown in the embodiments of this disclosure can be used to reorder, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and no limitation is imposed herein.

[0053] In the context of rapid globalization and financial innovation, investment has become a core means of capital appreciation and risk management. However, with the diversification of investment channels (such as stocks, bonds, trusts, and asset management plans) and the expansion of transaction volume, risks in the investment field are becoming more complex, concealed, and frequent. Traditional automated investment monitoring and early warning methods typically monitor investment behavior according to fixed rules, which is insufficient to address the complex, frequent, and concealed nature of risks in the investment field, resulting in poor monitoring effectiveness.

[0054] To address the aforementioned issues, this disclosure provides an automated investment monitoring and early warning method, apparatus, and system. The method involves periodically acquiring investment business data at a preset frequency. This data includes investment amounts. For the investment business data acquired in the current period, the method obtains the investment amounts corresponding to n risk time points within the current period and calculates the investment amount difference between any two adjacent risk time points. The method uses the investment business data to calculate the investment loss rate for each investment business within the time period corresponding to each of the n risk time points. If the investment loss rate is less than or equal to a preset loss rate, an early warning message is output based on the investment business data corresponding to the investment loss rate less than or equal to the preset loss rate. If the investment amount difference does not meet a preset condition, the time interval between the n risk time points in the current period is adjusted based on the investment amount difference to obtain the n risk time points for the next period. The next period is then used as the current period, and the method returns to execute the steps of obtaining the investment amounts corresponding to the n risk time points within the current period based on the investment business data acquired in the current period. By using the difference in investment amount to dynamically adjust the interval between risk time points used for risk monitoring, the distribution of monitoring time can be dynamically adjusted according to the actual investment business situation. This improves the monitoring intensity during high-risk periods without wasting computing resources, enhances the flexibility and rationality of investment risk monitoring, and ultimately improves the accuracy of investment risk monitoring.

[0055] Figure 1 This is a flowchart illustrating an automated investment monitoring and early warning method according to an exemplary embodiment, such as... Figure 1 As shown, it should be noted that the investment automated monitoring and early warning method of this disclosure embodiment is applied in an investment automated monitoring and early warning device. For example... Figure 1 As shown, the method may include the following steps:

[0056] Step 101: Periodically acquire investment business data according to a preset frequency.

[0057] The investment data includes the investment amount, and may also include unrealized gains and losses on stocks, the ending balance of stock investments, unrealized gains and losses on bonds, and the ending balance of bond investments.

[0058] In one embodiment, the preset frequency can be preset according to actual needs, for example, 1 minute can be set as a cycle.

[0059] Step 102: For the investment business data obtained in the current period, obtain the investment amount corresponding to the n risk time points in the current period, and calculate the difference in investment amount between two adjacent risk time points in the n risk time points.

[0060] Where n is an integer greater than 2.

[0061] In one embodiment, n is a fixed value preset according to actual needs (e.g., actual computing power).

[0062] It should be noted that the above investment amounts refer to the investment amounts for a single transaction. The investment amounts corresponding to the above n risk time points refer to the investment amounts for all investment transactions within the time period corresponding to each risk checkpoint. For example, a certain period includes 5 risk time points, and the time period from the beginning of that period to the first risk time point corresponds to the first risk time point.

[0063] In one embodiment, the aforementioned investment difference can be the difference between the investment amounts of any two transactions belonging to two adjacent time periods.

[0064] Step 103: Calculate the floating loss rate of each investment business within the corresponding time period for each of the n risk time points using investment business data.

[0065] In one embodiment, the unrealized loss rate of an investment may include the unrealized loss rate of stock investments and the unrealized loss rate of bond investments, wherein the unrealized loss rate of stock investments = unrealized profit or loss of stocks / stock investment balance at the end of the period, and the unrealized loss rate of bond investments = unrealized profit or loss of bonds / bond investment balance at the end of the period.

[0066] Step 104: If there is an investment unrealized loss rate that is less than or equal to a preset unrealized loss rate, output early warning information based on the investment business data corresponding to the investment unrealized loss rate that is less than or equal to the preset unrealized loss rate.

[0067] In one embodiment, the early warning information may include investment business data and an early warning level, which can be determined based on the magnitude of the investment unrealized loss rate.

[0068] Step 105: If the investment amount difference does not meet the preset conditions, adjust the time interval of the n risk time points corresponding to the current period based on the investment amount difference to obtain the n risk time points corresponding to the next period.

[0069] In one embodiment, the detection interval can be shortened promptly when there is significant risk, and appropriately increased when the risk is low, so that the detection time points are distributed as evenly as possible. This allows for dynamic adjustment of the risk time point distribution based on the detected investment data, achieving dynamic investment risk monitoring.

[0070] In some embodiments of this application, step 105 may specifically include the following steps:

[0071] Step a1: For each investment difference, obtain multiple preset difference ranges and the first risk coefficient corresponding to each difference range.

[0072] Step a2: Determine the target difference interval to which the investment amount difference belongs from multiple difference intervals, and the template first risk coefficient corresponding to the target difference interval.

[0073] Step a3: Determine whether the difference in investment amount meets the preset conditions based on the first risk coefficient.

[0074] In one embodiment, the risk level corresponding to the investment amount difference, i.e. the first risk coefficient, can be determined based on the mapping relationship between the preset difference range and the first risk coefficient. The larger the value of the first risk coefficient, the higher the risk level.

[0075] In some embodiments of this application, step a3 may specifically include the following steps:

[0076] Step a31: Obtain multiple investment amount ranges and the second risk coefficient corresponding to each investment amount range;

[0077] Step a32: Determine the target investment range to which the investment amount associated with the investment amount difference belongs and the second risk coefficient corresponding to the target investment range from multiple investment ranges;

[0078] Step a33: Add the first risk coefficient and the second risk coefficient to obtain the comprehensive risk coefficient;

[0079] Step a34: If the comprehensive risk coefficient is greater than or equal to the preset risk coefficient, determine that the difference in investment amount does not meet the preset conditions.

[0080] Step a35: If the overall risk coefficient is less than the preset risk coefficient, determine that the difference in investment amount meets the preset conditions.

[0081] In one embodiment, in addition to the difference in investment amount, the size of the investment amount is taken as a second factor in the investment risk assessment. That is, by adding the first risk coefficient and the second risk coefficient, the difference in investment amount and the investment amount are combined, and the risk level of the relevant investment business is judged based on the comprehensive risk coefficient obtained by the combination, thereby improving the rationality and accuracy of risk assessment.

[0082] Step a4: If the investment amount difference does not meet the preset conditions, adjust the time interval of the n risk time points corresponding to the current period based on the investment amount difference to obtain the n risk time points corresponding to the next period.

[0083] In some embodiments of this application, step a4 may specifically include the following steps: when there is an investment difference that does not meet the preset conditions, adjust the time interval of the n risk time points corresponding to the current period based on the comprehensive risk coefficient to obtain the n risk time points corresponding to the next period.

[0084] In one embodiment, the degree of risk is determined by a comprehensive risk coefficient, thereby dynamically adjusting the time interval between risk points according to the magnitude of the risk, which improves the flexibility and accuracy of risk monitoring.

[0085] In some embodiments of this application, step a4 may specifically include the following steps:

[0086] Step a41: Determine the first and second risk time points corresponding to the comprehensive risk coefficient. The first and second risk time points are adjacent to each other and precede the second risk time point.

[0087] Step a42: Obtain the risk time interval adjustment method that matches the comprehensive risk coefficient.

[0088] In some embodiments of this application, step a42 may specifically include the following steps:

[0089] Obtain the first mapping table including the comprehensive risk coefficient and the time interval scaling value; the time interval scaling value is the scaling value of the time interval between two adjacent risk time points;

[0090] Determine the scaling value that matches the comprehensive risk coefficient, and determine the risk time point interval adjustment method as scaling the interval between n risk time points according to the scaling value;

[0091] The time intervals of the n risk time points corresponding to the current period are adjusted based on the risk time points and the risk time point interval adjustment method to obtain the n risk time points corresponding to the next period, including:

[0092] The intervals between n risk inspection points are scaled according to the scaling value to obtain the n risk time points corresponding to the next cycle.

[0093] In one embodiment, when the overall risk coefficient is high, the corresponding scaling value reduces the time interval significantly; conversely, when the overall risk coefficient is low, the corresponding scaling value amplifies the time interval significantly.

[0094] In one embodiment, according to the first mapping table, the interval between risk points is flexibly scaled based on the comprehensive risk coefficient, thereby achieving the effect of dynamically quantifying and adjusting the risk monitoring time interval based on the risk level of the current investment business, further improving the flexibility and accuracy of risk monitoring.

[0095] Step a43: Adjust the time interval of the n risk time points corresponding to the current cycle based on the risk time point and the risk time point interval adjustment method to obtain the n risk time points corresponding to the next cycle.

[0096] Step 106: Take the next period as the current period, return to execute the investment business data obtained for the current period, and obtain the investment amount corresponding to the n risk time points within the current period.

[0097] In one embodiment, the next cycle's automated investment monitoring and early warning is executed according to the risk time point after the scaling process described above.

[0098] According to the investment automated monitoring and early warning method proposed in this disclosure, investment business data is acquired periodically at a preset frequency. The investment business data includes investment amount. For the investment business data acquired in the current period, the investment amount corresponding to n risk time points in the current period is obtained, and the investment amount difference between two adjacent risk time points in the n risk time points is calculated. The investment loss rate of each investment business in the time period corresponding to each of the n risk time points is calculated using the investment business data. If the investment loss rate is less than or equal to a preset loss rate, an early warning information is output based on the investment business data corresponding to the investment loss rate that is less than or equal to the preset loss rate. If the investment amount difference does not meet the preset condition, the time interval of the n risk time points corresponding to the current period is adjusted based on the investment amount difference to obtain the n risk time points corresponding to the next period. The next period is taken as the current period, and the process returns to the step of acquiring the investment amount corresponding to the n risk time points in the current period for the investment business data acquired in the current period. By using the difference in investment amount to dynamically adjust the interval between risk time points used for risk monitoring, the distribution of monitoring time can be dynamically adjusted according to the actual investment business situation. This improves the monitoring intensity during high-risk periods without wasting computing resources, enhances the flexibility and rationality of investment risk monitoring, and ultimately improves the accuracy of investment risk monitoring.

[0099] Figure 2 This is a block diagram illustrating an automated investment monitoring and early warning device according to an exemplary embodiment. (Refer to...) Figure 2 The device includes an acquisition unit 201, a first calculation unit 202, a second calculation unit 203, an early warning unit 204, an adjustment unit 205, and an execution unit 206.

[0100] The acquisition unit 201 is used to periodically acquire investment business data according to a preset frequency; the investment business data includes the investment amount.

[0101] The first calculation unit 202 is used to obtain the investment amount corresponding to n risk time points in the current period based on the investment business data obtained in the current period, and to calculate the difference in investment amount between two adjacent risk time points in the n risk time points; n is an integer greater than 2;

[0102] The second calculation unit 203 is used to calculate the floating loss rate of each investment business within the time period corresponding to each of the n risk time points using investment business data.

[0103] Early warning unit 204 is used to output early warning information based on investment business data corresponding to the investment loss rate that is less than or equal to the preset loss rate when there is an investment loss rate less than or equal to the preset loss rate.

[0104] The adjustment unit 205 is used to adjust the time interval of the n risk time points corresponding to the current period based on the investment difference when there is an investment difference that does not meet the preset conditions, so as to obtain the n risk time points corresponding to the next period.

[0105] The execution unit 206 is used to take the next period as the current period, return the execution steps for the investment business data obtained for the current period, and obtain the investment amount corresponding to n risk time points in the current period respectively.

[0106] In some embodiments of this application, the adjustment unit 205 may specifically be used for:

[0107] For each investment difference, obtain multiple preset difference ranges, and the first risk coefficient corresponding to each difference range;

[0108] Determine the target difference interval to which the investment amount difference belongs from multiple difference intervals, and the template first risk coefficient corresponding to the target difference interval;

[0109] Determine whether the difference in investment amount meets the preset conditions based on the first risk coefficient;

[0110] If the investment amount difference does not meet the preset conditions, the time interval of the n risk time points corresponding to the current period is adjusted based on the investment amount difference to obtain the n risk time points corresponding to the next period.

[0111] In some embodiments of this application, the adjustment unit 205 may specifically be used for:

[0112] Obtain multiple investment ranges and the corresponding second risk coefficient for each investment range;

[0113] From multiple investment ranges, determine the target investment range to which the investment amount associated with the investment amount difference belongs, and the second risk coefficient corresponding to the target investment range;

[0114] Add the first risk coefficient and the second risk coefficient to obtain the comprehensive risk coefficient;

[0115] If the overall risk coefficient is greater than or equal to the preset risk coefficient, determining the difference in investment amount does not meet the preset conditions;

[0116] If the overall risk coefficient is less than the preset risk coefficient, the difference in investment amount is determined to meet the preset conditions.

[0117] In some embodiments of this application, the adjustment unit 205 may specifically be used for:

[0118] If the investment amount difference does not meet the preset conditions, the time interval of the n risk time points corresponding to the current period is adjusted based on the comprehensive risk coefficient to obtain the n risk time points corresponding to the next period.

[0119] In some embodiments of this application, the adjustment unit 205 may specifically be used for:

[0120] Determine the first and second risk time points corresponding to the comprehensive risk coefficient; the first risk time point is adjacent to the second risk time point and precedes the second risk time point.

[0121] Obtain the risk time interval adjustment method that matches the comprehensive risk coefficient;

[0122] The time interval between n risk time points in the current cycle is adjusted based on the risk time point and the risk time point interval adjustment method to obtain the n risk time points in the next cycle.

[0123] In some embodiments of this application, the adjustment unit 205 may specifically be used for:

[0124] Obtain the first mapping table including the comprehensive risk coefficient and the time interval scaling value; the time interval scaling value is the scaling value of the time interval between two adjacent risk time points;

[0125] Determine the scaling value that matches the comprehensive risk coefficient, and determine the risk time point interval adjustment method as scaling the interval between n risk time points according to the scaling value;

[0126] The intervals between n risk inspection points are scaled according to the scaling value to obtain the n risk time points corresponding to the next cycle.

[0127] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0128] According to the investment automated monitoring and early warning device proposed in this embodiment, investment business data is acquired periodically at a preset frequency. The investment business data includes investment amount. For the investment business data acquired in the current period, the investment amount corresponding to n risk time points in the current period is obtained respectively, and the investment amount difference between two adjacent risk time points in the n risk time points is calculated respectively. The investment loss rate of each investment business in the time period corresponding to each of the n risk time points is calculated using the investment business data. If there is an investment loss rate less than or equal to a preset loss rate, an early warning information is output based on the investment business data corresponding to the investment loss rate less than or equal to the preset loss rate. If there is an investment amount difference that does not meet the preset condition, the time interval of the n risk time points corresponding to the current period is adjusted based on the investment amount difference to obtain the n risk time points corresponding to the next period. The next period is taken as the current period, and the process returns to execute the steps of acquiring the investment amount corresponding to the n risk time points in the current period for the investment business data acquired in the current period. By using the difference in investment amount to dynamically adjust the interval between risk time points used for risk monitoring, the distribution of monitoring time can be dynamically adjusted according to the actual investment business situation. This improves the monitoring intensity during high-risk periods without wasting computing resources, enhances the flexibility and rationality of investment risk monitoring, and ultimately improves the accuracy of investment risk monitoring.

[0129] Figure 3 This is a block diagram illustrating an apparatus for an automated investment monitoring and early warning method according to an exemplary embodiment. For example, apparatus 300 may be an electronic device, such as a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0130] Reference Figure 3 The device 300 may include one or more of the following components: a processing component 302, a memory 304, a power component 306, a multimedia component 308, an audio component 310, an input / output (I / O) interface 312, a sensor component 314, and a communication component 316.

[0131] Processing component 302 typically controls the overall operation of device 300, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 302 may include one or more processors 320 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 302 may include one or more modules to facilitate interaction between processing component 302 and other components. For example, processing component 302 may include a multimedia module to facilitate interaction between multimedia component 308 and processing component 302.

[0132] Memory 304 is configured to store various types of data to support the operation of device 300. Examples of this data include instructions for any application or method operating on device 300, contact data, phonebook data, messages, pictures, videos, etc. Memory 304 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0133] The power supply component 306 provides power to the various components of the device 300. The power supply component 306 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to the device 300.

[0134] Multimedia component 308 includes a screen that provides an output interface between the device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 308 includes a front-facing camera and / or a rear-facing camera. When the device 300 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0135] Audio component 310 is configured to output and / or input audio signals. For example, audio component 310 includes a microphone (MIC) configured to receive external audio signals when device 300 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 304 or transmitted via communication component 316. In some embodiments, audio component 310 also includes a speaker for outputting audio signals.

[0136] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0137] Sensor assembly 314 includes one or more sensors for providing status assessments of various aspects of device 300. For example, sensor assembly 314 may detect the on / off state of device 300, the relative positioning of components such as the display and keypad of device 300, changes in the position of device 300 or a component of device 300, the presence or absence of user contact with device 300, the orientation or acceleration / deceleration of device 300, and temperature changes of device 300. Sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 314 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 314 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0138] Communication component 316 is configured to facilitate wired or wireless communication between device 300 and other devices. Device 300 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 316 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 316 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0139] In an exemplary embodiment, the apparatus 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0140] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 304 including instructions, which can be executed by a processor 320 of the device 300 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0141] In an exemplary embodiment, a computer program product is also provided, including a computer program that implements the above-described method when executed by the processor 320 of the device 300.

[0142] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0143] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. An automated investment monitoring and early warning method, characterized in that, include: Investment business data is acquired periodically at a preset frequency; The investment data includes the investment amount; For the investment data acquired in the current period, the investment amount corresponding to each of the n risk time points within the current period is obtained, and the difference in investment amount between two adjacent risk time points is calculated for each of the n risk time points; n is an integer greater than 2. Calculate the floating loss rate of each investment business within the time period corresponding to each of the n risk time points using the investment business data; If the unrealized loss rate of an investment is less than or equal to the preset unrealized loss rate, an early warning message will be output based on the investment business data corresponding to the unrealized loss rate of the investment that is less than or equal to the preset unrealized loss rate. If the investment difference does not meet the preset conditions, the time interval of the n risk time points corresponding to the current period is adjusted based on the investment difference to obtain the n risk time points corresponding to the next period. The next period is taken as the current period, and the process of returning to execute the investment business data obtained for the current period, and obtaining the investment amount corresponding to n risk time points within the current period, is carried out respectively.

2. The automated investment monitoring and early warning method according to claim 1, characterized in that, When the investment difference does not meet the preset conditions, the time interval of the n risk time points corresponding to the current period is adjusted based on the investment difference to obtain the n risk time points corresponding to the next period, including: For each investment difference, obtain multiple preset difference ranges, and the first risk coefficient corresponding to each difference range; From the plurality of difference intervals, determine the target difference interval to which the investment amount difference belongs, and the template first risk coefficient corresponding to the target difference interval; Based on the first risk coefficient, determine whether the investment amount difference meets the preset condition; If the investment amount difference does not meet the preset conditions, the time interval of the n risk time points corresponding to the current period is adjusted based on the investment amount difference to obtain the n risk time points corresponding to the next period.

3. The automated investment monitoring and early warning method according to claim 2, characterized in that, The step of determining whether the investment difference based on the first risk coefficient meets the preset condition includes: Obtain multiple investment ranges and the corresponding second risk coefficient for each investment range; From the plurality of investment amount ranges, determine the target investment amount range to which the investment amount associated with the investment amount difference belongs, and the second risk coefficient corresponding to the target investment amount range; Add the first risk coefficient and the second risk coefficient to obtain the comprehensive risk coefficient; If the overall risk coefficient is greater than or equal to the preset risk coefficient, it is determined that the difference in investment amount does not meet the preset condition; If the overall risk coefficient is less than the preset risk coefficient, the difference in investment amount is determined to satisfy the preset condition.

4. The automated investment monitoring and early warning method according to claim 3, characterized in that, When the investment difference does not meet the preset conditions, adjusting the time interval between the n risk time points corresponding to the current period based on the investment difference to obtain the time interval between the n risk time points corresponding to the next period includes: In the event that the investment amount difference does not meet the preset conditions, the time interval of the n risk time points corresponding to the current period is adjusted based on the comprehensive risk coefficient to obtain the n risk time points corresponding to the next period.

5. The automated investment monitoring and early warning method according to claim 4, characterized in that, The adjustment of the time interval between the n risk time points corresponding to the current period based on the comprehensive risk coefficient to obtain the time interval between the n risk time points corresponding to the next period includes: Determine the first risk time point and the second risk time point corresponding to the comprehensive risk coefficient; the first risk time point is adjacent to the second risk time point, and the first risk time point is before the second risk time point. Obtain the risk time point interval adjustment method that matches the comprehensive risk coefficient; Based on the risk time points and the risk time point interval adjustment method, the time interval of the n risk time points corresponding to the current cycle is adjusted to obtain the n risk time points corresponding to the next cycle.

6. The automated investment monitoring and early warning method according to claim 5, characterized in that, The method for adjusting the risk time interval that matches the comprehensive risk coefficient includes: Obtain a first mapping table including the comprehensive risk coefficient and the time interval scaling value; the time interval scaling value is the scaling value of the time interval between two adjacent risk time points; Determine a scaling value that matches the comprehensive risk coefficient, and determine that the risk time point interval adjustment method is to scale the interval between n risk time points according to the scaling value; The step of adjusting the time interval of the n risk time points corresponding to the current period based on the risk time point and the risk time point interval adjustment method to obtain the n risk time points corresponding to the next period includes: The intervals between the n risk inspection points are scaled according to the scaling value to obtain the n risk time points corresponding to the next cycle.

7. An automated investment monitoring and early warning device, characterized in that, include: The acquisition unit is used to periodically acquire investment business data at a preset frequency; the investment business data includes the investment amount. The first calculation unit is used to obtain the investment amount corresponding to n risk time points in the current period based on the investment business data obtained in the current period, and to calculate the difference in investment amount between two adjacent risk time points in the n risk time points respectively. n is an integer greater than 2; The second calculation unit is used to calculate the floating loss rate of each investment business within the time period corresponding to each of the n risk time points using the investment business data. The early warning unit is used to output early warning information based on the investment business data corresponding to the investment loss rate that is less than or equal to the preset loss rate when there is an investment loss rate less than or equal to the preset loss rate. The adjustment unit is used to adjust the time interval of the n risk time points corresponding to the current period based on the investment difference when there is an investment difference that does not meet the preset conditions, so as to obtain the n risk time points corresponding to the next period. The execution unit is used to take the next period as the current period, return to execute the investment business data obtained for the current period, and obtain the investment amount corresponding to n risk time points in the current period respectively.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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