Method for detecting risk parameters and data detection system

Abstract

The embodiment of the invention provides a method for detecting risk parameters and a data detection system.The method comprises the steps that under the condition that a task request sent by a first detection subsystem is received, a target task matched with the task request is determined from multiple tasks; selecting target data from the multiple groups of object data according to the target task, and generating a detection demand parameter of the target task by using the target data; and sending the detection demand parameter to the first detection subsystem to instruct the first detection subsystem to detect a risk parameter of the target object in a preset time period based on the detection demand parameter. Through the risk detection method and device, the problem that the accuracy of risk detection is low in the prior art is solved, and the effect of improving the accuracy of risk detection is achieved.

Description

Technical Field

[0001] The embodiments of the present application relate to the computer field, and more specifically, to a method for detecting risk parameters and a data detection system. Background Art

[0002] In traditional data detection systems, risk management is a key link, which involves evaluating and monitoring various potential risk factors in order to take appropriate preventive measures. These risk parameters can be financial risks, health risks, cybersecurity risks, etc. In order to effectively manage and control these risks, a method is needed to detect and evaluate risk parameters.

[0003] In related technologies, the detection of risk parameters relies on a single detection subsystem, which may lead to limitations and inaccuracies in the detection results because different detection subsystems may have different data sets and detection capabilities. In addition, these systems may lack an effective coordination mechanism, resulting in duplication or omission of detection tasks. Summary of the invention

[0004] The embodiments of the present application provide a method for detecting risk parameters and a data detection system to at least solve the problem of low accuracy of risk detection in the related art.

[0005] According to an embodiment of the present application, a method for detecting risk parameters is provided, which is applied to a data detection system, wherein the data detection system includes a plurality of detection subsystems, and the method includes:

[0006] Upon receiving a task request sent by the first detection subsystem, a target task matching the task request is determined from multiple tasks, wherein the first detection subsystem is any one of the multiple detection subsystems, and the task request is used to request acquisition of data on risk parameters for detecting a target object; target data is selected from multiple groups of object data according to the target task, and detection requirement parameters of the target task are generated using the target data; the detection requirement parameters are sent to the first detection subsystem to instruct the first detection subsystem to detect the risk parameters of the target object within a preset time period based on the detection requirement parameters.

[0007] In an exemplary embodiment, upon receiving a task request sent by the first detection subsystem, a target task matching the task request is determined from the multiple tasks, including: parsing the system parameters of the first detection subsystem and the object category of the target object included in the task request, wherein the target object includes an object with a risk change trend; upon determining based on the system parameters that the first detection subsystem has parameter detection authority and is at a preset detection level in the data detection system, searching for a task matching the object category from multiple tasks to obtain the target task.

[0008] In an exemplary embodiment, before selecting target data from multiple groups of object data according to the target task and using the target data to generate the detection requirement parameters of the target task, the method further includes: generating acquisition configuration information, wherein the acquisition configuration information includes at least one of the following: data acquisition time, acquired data type, acquired data field, and address information of the data source; acquiring multiple groups of the object data from one or more data sources according to the acquisition configuration information; receiving a data management request, and responding to the data management request to manage the multiple groups of the object data, wherein the data management request includes at least one of the following: data query, data reset, data export, and data denoising; when it is determined that the multiple groups of the object data are stored in the database, performing data maintenance operations on the multiple groups of the object data according to a preset time period, so that the multiple groups of the object data are in a waiting-to-access state within the preset time period, wherein the data maintenance operations include at least one of the following: an operation of confirming the integrity of the object data, an operation of suspending the display of the object data, an operation of activating the object data, an operation of resetting the object data, an operation of downloading the object data to other devices, and an operation of uploading the object data to other devices.

[0009] In an exemplary embodiment, target data is selected from multiple groups of object data according to the target task, and detection requirement parameters of the target task are generated using the target data, including: when it is determined that the target task is a risk detection task, real-time supply and demand data and historical supply and demand data are selected from multiple groups of object data to obtain the target data; the real-time supply and demand data and the historical supply and demand data are converted into numerical data to obtain a real-time numerical set and a historical numerical set respectively; the data features of the data points in the real-time numerical set and the historical numerical set are converted into vector representations to obtain a real-time vector set and a historical vector set respectively; a feature vector of the real-time vector set is constructed to obtain a real-time feature vector; a feature vector of the historical vector set is constructed to obtain a historical feature vector; according to the time series between the real-time supply and demand data and the historical supply and demand data, the real-time feature vector and the historical feature vector are data stored to obtain the detection requirement parameters, wherein the detection requirement parameters are used to represent the changing trends of the real-time supply and demand data and the historical supply and demand data, and the correlation between the real-time supply and demand data and the historical supply and demand data.

[0010] In an exemplary embodiment, after the detection requirement parameters are sent to the first detection subsystem to instruct the first detection subsystem to detect the risk parameters of the target object within a preset time period based on the detection requirement parameters, the method further includes: receiving a data visualization chart sent by the first detection subsystem, wherein the data visualization chart is generated by the first analysis system based on the real-time feature vector and the historical feature vector included in the detection requirement parameters, the data visualization chart includes the risk parameters and the changing trend of the risk parameters within the preset time period, and the risk parameters include at least one of the following: the degree of fluctuation of the target object within the preset time period, the fluctuation index of the target object relative to the historical supply and demand data within the preset time period, the degree of loss of the target object within the preset time period, and the degree of correlation between the target object and other objects within the preset time period; generating risk warning information of the target object within the preset time period based on the data visualization chart; and sending the risk warning information to other detection subsystems.

[0011] In an exemplary embodiment, risk warning information of the target object within the preset time period is generated based on the data visualization chart, including: when the visualization chart includes a target broken line, determining the risk floating interval of the target object from the target broken line; determining the maximum value, minimum value and golden section value of the target object floating within multiple continuous time periods from the risk floating interval, wherein the golden section value is used to represent the value corresponding to the risk parameter indicated by the golden section point of the target interval with the minimum value as the lower limit and the maximum value as the upper limit; obtaining the values ​​corresponding to the risk parameter for multiple time periods after the target time period corresponding to the golden section value; when the values ​​corresponding to the risk parameter for multiple time periods after the target time period are greater than or equal to the golden section value, determining that the change trend of the risk parameter is increasing, and generating first risk warning information; when the values ​​corresponding to the risk parameter for multiple time periods after the target time period are less than the golden section value, determining that the change trend of the risk parameter is decreasing, and generating second risk warning information.

[0012] In an exemplary embodiment, the data detection system also includes a data audit service. After selecting target data from multiple groups of object data according to the target task and using the target data to generate the detection requirement parameters of the target task, the method also includes: sending the detection requirement parameters to the data audit service; parsing the parameter type and parameter value of the detection requirement parameters through the data audit service, and auditing whether the parameter type corresponds to the type allowed to be recognized by the first detection subsystem, and auditing whether the parameter value is in a valid state to obtain an audit result.

[0013] According to another embodiment of the present application, a data detection system is provided. The data detection system includes a plurality of detection subsystems. The data detection system implements the steps of the above method when executed.

[0014] According to another embodiment of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0015] According to another embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when run.

[0016] According to another embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0017] Through this application, by allocating tasks among multiple detection subsystems, it is ensured that each subsystem can use its specific data set and detection capabilities to detect risk parameters. This method not only improves the efficiency of risk detection, but also enhances the accuracy and comprehensiveness of the detection results. Improve the efficiency and accuracy of risk detection. Therefore, it can solve the problem of low accuracy of risk detection in related technologies, and achieve the effect of improving the accuracy of risk detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a hardware structure block diagram of a server device of a method for detecting risk parameters in an embodiment of the present application;

[0019] Figure 2 is a flow chart of a method for detecting risk parameters according to an embodiment of the present application;

[0020] Figure 3 is an architectural diagram of a data detection system according to an embodiment of the present application;

[0021] Figure 4 is a data flow diagram of a data detection system according to a specific embodiment of the present application;

[0022] Figure 5 is a relationship diagram between various services according to an embodiment of the present application;

[0023] Figure 6 is a schematic diagram of data query and quotation adjustment according to an embodiment of the present application;

[0024] Figure 7 is a schematic diagram of data repair according to an embodiment of the present application;

[0025] Figure 8 is a schematic diagram of a project group configuration according to an embodiment of the present application;

[0026] Fig. 9 is a schematic diagram of a data studio according to an embodiment of the present application;

[0027] Fig.10 is a schematic diagram constructed according to data of an embodiment of the present application;

[0028] Fig.11 is a schematic diagram of an audit according to an embodiment of the present application;

[0029] Fig.12 It is a schematic diagram of a trial calculation according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] The embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0032] The embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0034] The method embodiments provided in the embodiments of the present application can be executed in a server device or a similar computing device. Taking running on a server device as an example, Figure 1 1 is a hardware structure block diagram of a server device of a method for detecting risk parameters in an embodiment of the present application. Figure 1 As shown, the server device may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the server device may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above server device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0035] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for detecting risk parameters in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the server device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0036] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the server device. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0037] In this embodiment, a method for detecting risk parameters is provided, which is applied to a data detection system. The data detection system includes multiple detection subsystems. Figure 2 is a flow chart of a method for detecting risk parameters according to an embodiment of the present application. Figure 2 As shown, the process includes the following steps:

[0038] Step S202, upon receiving a task request sent by a first detection subsystem, determining a target task matching the task request from a plurality of tasks, wherein the first detection subsystem is any one of the plurality of detection subsystems, and the task request is used to request acquisition of data on risk parameters of a detection target object;

[0039] Step S204, selecting target data from multiple groups of object data according to the target task, and generating detection requirement parameters of the target task using the target data;

[0040] Step S206: sending the detection requirement parameters to the first detection subsystem to instruct the first detection subsystem to detect the risk parameters of the target object within a preset time period based on the detection requirement parameters.

[0041] Optionally, this embodiment can be applied to scenarios where real-time or regular detection and evaluation of risk parameters of target objects are required. For example, in banks or financial institutions, the system can be used to monitor the risk level of loans, investments or transactions. The first detection subsystem can be a monitoring system specific to a certain financial product, and the target task can be a risk assessment task for assessing the risk parameters of a specific financial product within a preset time period. In a network security scenario, the system can be used to monitor the risk of network attacks. The first detection subsystem can be an intrusion detection system, and the target task can be to identify specific types of network threats and perform real-time risk assessment based on detection requirement parameters. In an industrial environment, the system can be used to monitor the risk of equipment failure or operational errors. The first detection subsystem can be a sensor network for collecting equipment status data, and the target task can be a predictive maintenance task for assessing the risk of equipment failure in a future time period. In traffic management, the system can be used to monitor the risk of traffic accidents. The first detection subsystem can be a traffic monitoring camera and sensor, and the target task can be to assess the risk of accidents based on real-time traffic flow data and take corresponding traffic control measures.

[0042] Optionally, in step S202, it is assumed that there is a security monitoring system, which includes multiple detection subsystems, such as video monitoring, audio monitoring and sensor monitoring. The following is a specific embodiment:

[0043] Step 1: Receiving a task request. The first detection subsystem (eg, a video surveillance system) receives a task request for acquiring data of risk parameters of a detection target object (eg, an intruder).

[0044] Step 2: Analyze the task request, analyze the task request and extract key information, such as the type of the target object to be detected (intruder), risk parameters (e.g., behavior, speed, direction, etc.).

[0045] Step 3: Retrieve the task library, and retrieve the task matching the task request in the system's task library. The task library contains multiple tasks, each of which has a corresponding task description, risk parameters, and detection methods.

[0046] Step 4: Determine the matching target task, and filter out the tasks that match the request from the task library based on the key information of the task request. In this example, two tasks that match the task request can be found: Task A (detecting the behavior of the intruder) and Task B (detecting the speed of the intruder).

[0047] Step 5: Evaluate the task priority and evaluate the matching tasks to determine which task better meets the current detection needs. In this example, the priorities of Task A and Task B can be evaluated, and it is found that Task A (detecting the behavior of intruders) better meets the current detection needs.

[0048] Step 6: Select the target task. According to the priority evaluation result, select the target task. In this example, select Task A as the target task.

[0049] Step 7: Execute the target task and obtain the risk parameter data of the target object. In this example, execute task A and obtain the behavior data of the intruder.

[0050] Through the above steps, a target task matching the task request can be determined from multiple tasks, and the task can be executed to obtain risk parameter data for detecting the target object.

[0051] Alternatively, for example, in a financial scenario, the data detection system is used to monitor risk parameters of the financial market, such as credit risk, market risk, etc. The specific process may include the following:

[0052] The first detection subsystem (e.g., risk management subsystem) sends a task request to obtain risk parameter data for a specific customer or asset portfolio. After receiving the task request, the data detection system determines a target task that matches the request from multiple preset tasks. For example, if the request is to obtain credit risk parameters, the system will select tasks related to credit risk. The system selects relevant customer data and market data from the database according to the target task, such as the customer's credit score, historical repayment record, market interest rate changes, etc. Using the selected target data, the system generates detection requirement parameters, which may include input variables required by the risk assessment model, such as default probability, expected loss, etc. The system sends these detection requirement parameters back to the first detection subsystem, instructing it to perform risk parameter detection based on these parameters within a preset time period (e.g., one month or one quarter). The first detection subsystem performs risk assessment based on the provided parameters and outputs risk reports, such as credit risk level, market risk exposure, etc.

[0053] For another example, in the scenario of network security, the data detection system is used to monitor the security risks of the network system, such as intrusion detection, data leakage risks, etc. Specifically, it includes the following contents:

[0054] The first detection subsystem (e.g., a network security monitoring system) sends a task request to obtain security risk parameter data for a specific network asset. After receiving the task request, the data detection system determines a target task that matches the request from multiple preset tasks. For example, if the request is to obtain data leakage risk parameters, the system will select tasks related to data protection. The system selects relevant data from log files, network traffic data, and user behavior data according to the target task, such as abnormal login attempts, sensitive data access records, etc. Using the selected target data, the system generates detection requirement parameters, which may include features required for security event classification and abnormal behavior identification. The system sends these detection requirement parameters back to the first detection subsystem, instructing it to perform risk parameter detection based on these parameters within a preset time period (e.g., the past 24 hours). The first detection subsystem performs a security risk assessment based on the provided parameters and outputs a security report, such as potential intrusion attempts, data leakage risk level, etc.

[0055] Through the above steps, by allocating tasks among multiple detection subsystems, it is ensured that each subsystem is able to detect risk parameters using its specific data set and detection capabilities. This method not only improves the efficiency of risk detection, but also enhances the accuracy and comprehensiveness of the detection results. It improves the efficiency and accuracy of risk detection. Therefore, it can solve the problem of low accuracy of risk detection in related technologies and achieve the effect of improving the accuracy of risk detection.

[0056] In an exemplary embodiment, upon receiving a task request sent by a first detection subsystem, a target task matching the task request is determined from the multiple tasks, including: parsing system parameters of the first detection subsystem and an object category of the target object included in the task request, wherein the target object includes an object with a risk change trend; upon determining based on the system parameters that the first detection subsystem has parameter detection authority and is at a preset detection level in the data detection system, searching for a task matching the object category from multiple tasks to obtain the target task.

[0057] Optionally, the detection subsystem in this embodiment refers to a system for detecting specific target objects (e.g., pedestrians, vehicles, objects, etc.), including sensors, algorithms, and data processing units. System parameters may include, but are not limited to: sensor type, for example, whether a camera, radar, lidar, or other type of sensor is used. The target object category refers to the type of object that the system is designed to detect. For example: pedestrian detection, vehicle detection, object detection, etc. In order to provide accurate information, it may be necessary to collect and analyze data from the detection subsystem, including detection logs, performance reports, and user feedback.

[0058] For example, in a financial scenario, a financial company needs to monitor its customer accounts for abnormal transaction behavior to prevent fraudulent activities. Therefore, a data detection system is deployed, which includes multiple detection subsystems, one of which is a subsystem dedicated to monitoring account activities.

[0059] The first detection subsystem sends a task request to monitor the transaction behavior of a specific customer account (target object). The system parses the system parameters in the task request, including the customer account ID and the account category (such as personal account or corporate account). The system checks the permissions of the first detection subsystem to confirm that it has access to the customer account data and is at the preset monitoring level. The system searches for tasks that match the account category. For example, if the account is a personal account, the system may search for tasks related to abnormal transaction monitoring of personal accounts. Once the target task is found, the system will execute the task, monitor the transaction behavior of the specified account, and analyze whether there are any anomalies based on the preset rules.

[0060] For another example, in the scenario of network security, an enterprise needs to monitor its network traffic to detect and prevent potential network attacks, such as DDoS attacks, malware propagation, etc. A network security detection system is deployed, which includes multiple detection subsystems, one of which is a network traffic monitoring subsystem. The network traffic monitoring subsystem sends a task request to analyze network traffic from a specific IP address (target object). The system parses the system parameters in the task request, including the IP address and traffic type (such as inbound traffic or outbound traffic). The system checks the permissions of the network traffic monitoring subsystem and confirms that it has access to network traffic data and is at the preset monitoring level. The system looks for tasks that match the traffic type. For example, if the request is for inbound traffic, the system may look for tasks related to detecting DDoS attacks. Once the target task is found, the system will execute the task, analyze the network traffic from the specific IP address, and detect whether there is a threat based on the preset rules.

[0061] This embodiment can achieve effective monitoring and risk management of the target object by parsing the parameters in the task request, determining the authority of the detection subsystem, and matching the corresponding tasks.

[0062] In an exemplary embodiment, before selecting target data from multiple groups of object data according to the target task and using the target data to generate the detection requirement parameters of the target task, the method further includes: generating acquisition configuration information, wherein the acquisition configuration information includes at least one of the following: data acquisition time, acquired data type, acquired data field, and address information of the data source; acquiring multiple groups of the object data from one or more data sources according to the acquisition configuration information; receiving a data management request, and responding to the data management request to manage the multiple groups of the object data, wherein the data management request includes at least one of the following: data query, data reset, data export, and data denoising; when it is determined that the multiple groups of the object data are stored in the database, performing data maintenance operations on the multiple groups of the object data according to a preset time period, so that the multiple groups of the object data are in a waiting-to-access state within the preset time period, wherein the data maintenance operation includes at least one of the following: an operation of confirming the integrity of the object data, an operation of suspending the display of the object data, an operation of activating the object data, an operation of resetting the object data, an operation of downloading the object data to other devices, and an operation of uploading the object data to other devices.

[0063] Optionally, the collection configuration information in this embodiment can be flexibly set based on different application scenarios and can be adjusted based on data sources in different scenarios.

[0064] For example, suppose the goal is to detect and analyze traffic flow in a city in order to optimize traffic management and reduce congestion. Here are the detailed implementation steps of the method:

[0065] Step 1, generate collection configuration information: Data collection time: Set the collection time to once a minute to monitor traffic flow in real time. Data types collected: The data to be collected include vehicle speed, flow, accident information, etc. Data fields collected: Specific fields may include timestamp, location, vehicle type, speed value, etc. Address information of data source: Data sources may include traffic cameras, traffic sensors, real-time traffic updates on social media, etc. Their address information needs to be clearly recorded.

[0066] Step 2, collecting data according to the collection configuration information: According to the above configuration, traffic data is collected in real time from multiple data sources (such as traffic cameras, sensors, etc.).

[0067] Step 3, receive data management requests and respond: The system receives data management requests from users or automatic systems, such as data query (querying traffic data for a specific time period), data reset (clearing expired data), data export (exporting data as a report) and data denoising (filtering out abnormal data points).

[0068] Step 4, data storage and maintenance: All collected data are stored in the database. The system performs data maintenance operations at a preset time period (e.g., every day) to ensure data integrity and availability. Data maintenance operations may include checking data integrity, stopping display of outdated data, activating new data, resetting erroneous data, downloading data to a backup server, or uploading data to cloud storage.

[0069] Step 5, select target data to generate detection requirement parameters: After data maintenance and storage, the system will select the data most relevant to traffic flow detection, such as vehicle flow and speed data during peak hours. Using these target data, the system generates detection requirement parameters, which may include flow thresholds, speed anomalies, etc., for subsequent traffic flow detection and analysis.

[0070] For another example, in a financial scenario, collection configuration information and data management may involve multiple types of data such as customer transaction data, risk assessment, market trends, etc. The following is a specific embodiment that describes how to generate collection configuration information, collect data, manage data, and perform data maintenance operations:

[0071] Step 1, generate collection configuration information: Data collection time: set to the trading time of each trading day, for example, from 9:30 am to 4:00 pm. Data types collected: including stock trading data, customer account information, market data, etc. Collected data fields: For stock trading data, it may include stock code, trading time, trading price, trading volume, etc.; for customer account information, it may include customer ID, account balance, transaction records, etc. Address information of data source: Data sources may include real-time data interfaces of exchanges, customer account databases of banks, financial market analysis tools, etc.

[0072] Step 2, collecting multiple sets of object data: According to the above collection configuration information, the system will collect multiple sets of object data from one or more data sources in real time.

[0073] Step 3, receive data management requests and respond: Data query: Provide an interface for users to query stock transaction records or customer account information within a specific time period. Data reset: Perform data reset operations when data errors are found or the system needs to be reinitialized. Data export: Allow users to export query results as CSV or Excel files for further analysis. Data denoising: Clean data and remove outliers or irrelevant information to improve data quality.

[0074] Step 4, data maintenance operation: When it is determined that multiple sets of object data are stored in the database, the system will perform data maintenance operations according to the preset time period (for example, every quarter). Confirm data integrity: Check whether the data is missing or damaged, and repair it. Suspend displaying data: For expired or no longer needed data, it can be temporarily hidden and no longer displayed to the public. Activate data: For hidden data that needs to be reused, it can be reactivated and displayed to the public. Reset data: For data that needs to be restarted, you can perform a reset operation to restore it to the initial state.

[0075] Through the above embodiments, the data processing process can be more efficient and secure, while ensuring the availability and reliability of data.

[0076] In an exemplary embodiment, target data is selected from multiple groups of object data according to the target task, and detection requirement parameters of the target task are generated using the target data, including: when it is determined that the target task is a risk detection task, real-time supply and demand data and historical supply and demand data are selected from multiple groups of object data to obtain the target data; the real-time supply and demand data and the historical supply and demand data are converted into numerical data respectively to obtain a real-time numerical set and a historical numerical set; the data features of data points in the real-time numerical set and the historical numerical set are converted into vector representations respectively to obtain a real-time vector set and a historical vector set; a feature vector of the real-time vector set is constructed to obtain a real-time feature vector; a feature vector of the historical vector set is constructed to obtain a historical feature vector; according to the time series between the real-time supply and demand data and the historical supply and demand data, the real-time feature vector and the historical feature vector are data stored to obtain the detection requirement parameters, wherein the detection requirement parameters are used to represent the changing trends of the real-time supply and demand data and the historical supply and demand data, and the correlation between the real-time supply and demand data and the historical supply and demand data.

[0077] Optionally, this embodiment involves selecting specific data from multiple sets of data, and converting and processing these data to generate detection requirement parameters. These parameters are used to represent the change trend of data and the correlation between data. This process is applicable to risk detection tasks, such as financial scenarios and network security scenarios.

[0078] In financial scenarios, real-time supply and demand data may include real-time stock trading volume, price, etc., while historical supply and demand data may include stock trading records over a period of time in the past. The system selects relevant real-time and historical data from these data. Convert these data into numerical data, for example, convert trading volume and price into quantifiable values ​​for further analysis. Convert data points in real-time and historical data into vector representations, which can contain features such as price changes and trading volume changes. Construct feature vectors for real-time vector sets and feature vectors for historical vector sets, which can represent the changing trend of the data. Store the real-time feature vectors and historical feature vectors in time series to analyze the correlation and changing trend between them. The generated detection demand parameters can be used to predict market risks, such as identifying potential market manipulation or bubbles by analyzing abnormal changes in prices and trading volumes.

[0079] In network security scenarios, real-time supply and demand data may include real-time network traffic data, while historical supply and demand data may include past network traffic records. Convert network traffic data into numerical data, for example, convert traffic size, number of packets, etc. into quantifiable values. Convert data points in real-time and historical network traffic data into vector representations, which can contain features such as traffic anomalies and changes in packet size. Construct feature vectors for real-time vector sets and feature vectors for historical vector sets, which can represent the changing trend of network traffic. Store real-time feature vectors and historical feature vectors in time series to analyze the correlation and changing trend between them. The generated detection demand parameters can be used to detect network attacks, such as identifying DDoS attacks or other network threats by analyzing traffic anomalies.

[0080] This embodiment extracts useful information from a large amount of data and converts it into parameters that can be used for risk detection. These parameters can help predict and identify potential risks, so that corresponding preventive or countermeasure measures can be taken.

[0081] In an exemplary embodiment, after the detection requirement parameters are sent to the first detection subsystem to instruct the first detection subsystem to detect the risk parameters of the target object within a preset time period based on the detection requirement parameters, the method further includes: receiving a data visualization chart sent by the first detection subsystem, wherein the data visualization chart is generated by the first analysis system based on the real-time feature vector and the historical feature vector included in the detection requirement parameters, the data visualization chart includes the risk parameters and the changing trend of the risk parameters within the preset time period, the risk parameters include at least one of the following: the degree of fluctuation of the target object within the preset time period, the fluctuation index of the target object within the preset time period relative to the historical supply and demand data, the degree of loss of the target object within the preset time period, and the degree of association between the target object and other objects within the preset time period; generating risk warning information of the target object within the preset time period based on the data visualization chart; and sending the risk warning information to other detection subsystems.

[0082] Optionally, this embodiment generates a data visualization chart by receiving data from the first detection subsystem and based on specific analysis requirement parameters (including real-time and historical feature vectors). The system further generates risk warning information of the target object within a preset time period and sends it to other detection subsystems.

[0083] This system can be used for financial risk management, market analysis, supply chain management or other scenarios that require real-time monitoring and risk assessment.

[0084] For example, the first detection subsystem collects and sends data, which may include real-time data and historical data. The first analysis system receives this data and analyzes it based on detection requirement parameters (such as real-time feature vectors and historical feature vectors). These parameters may involve various attributes and behavior patterns of the target object. Based on the analysis results, the system generates data visualization charts. These charts include but are not limited to: the degree of fluctuation of the target object within a preset time period. The volatility index of the target object relative to historical supply and demand data. The degree of loss of the target object within a preset time period. The degree of association between the target object and other objects. The data visualization chart also shows the changing trend of risk parameters within a preset time period, which helps to understand the dynamic changes of risks over time. The system generates risk warning information based on the data visualization chart, which may include predictions or warnings of upcoming risk events. Finally, the system sends these risk warning information to other detection subsystems for further analysis, monitoring or preventive measures.

[0085] This embodiment enhances the decision support capability of the entire system through the sharing of visualization and risk warning information.

[0086] Optionally, risk warning information of the target object within the preset time period is generated based on the data visualization chart, including: when the visualization chart includes a target broken line, determining the risk floating interval of the target object from the target broken line; determining the maximum value, minimum value and golden section value of the target object floating within multiple consecutive time periods from the risk floating interval, wherein the golden section value is used to represent the value corresponding to the risk parameter indicated by the golden section point of the target interval with the minimum value as the lower limit and the maximum value as the upper limit; obtaining the values ​​corresponding to the risk parameter for multiple time periods after the target time period corresponding to the golden section value; when the values ​​corresponding to the risk parameter for multiple time periods after the target time period are greater than or equal to the golden section value, determining that the change trend of the risk parameter is increasing, and generating first risk warning information; when the values ​​corresponding to the risk parameter for multiple time periods after the target time period are less than the golden section value, determining that the change trend of the risk parameter is decreasing, and generating second risk warning information.

[0087] In this embodiment, the target polyline can show the changes of the relevant risk parameters of the target object within the preset time period. The risk floating interval of the target object reflects the change range of the risk parameters of the target object within the preset time period.

[0088] In this embodiment, the golden section value is a golden section point based on the minimum value and the maximum value, which is used to indicate a key value of the risk parameter. The calculation formula of the golden section value is: \[\text{golden section value}=\frac{\sqrt{5}-1}{2}\times(\text{maximum value}-\text{minimum value})+\text{minimum value}\].

[0089] In this embodiment, determining the change trend of the risk parameter and generating risk warning information includes: if the risk parameter values ​​of multiple time periods after the target period are greater than or equal to the golden section value, determining that the change trend of the risk parameter is increasing, and generating the first risk warning information. If the risk parameter values ​​of multiple time periods after the target period are less than the golden section value, determining that the change trend of the risk parameter is decreasing, and generating the second risk warning information.

[0090] Through the above steps, key information can be effectively extracted from the data visualization chart, and risk warning information of the target object within the preset time period can be generated based on this information. This method helps users better understand and predict the risk changes of the target object, so as to make more reasonable decisions.

[0091] In an exemplary embodiment, the data detection system also includes a data audit service. After selecting target data from multiple groups of object data according to the target task and using the target data to generate detection requirement parameters for the target task, the method also includes: sending the detection requirement parameters to the data audit service; parsing the parameter type and parameter value of the detection requirement parameters through the data audit service, and auditing whether the parameter type corresponds to the type allowed to be recognized by the first detection subsystem, and auditing whether the parameter value is in a valid state to obtain an audit result.

[0092] Optionally, in each scenario, the sending and review process of detection requirement parameters is crucial to ensure the accuracy and security of the data.

[0093] For example, in a financial scenario, a banking system collects credit score data from customers and sends this data to a data audit service as a detection requirement parameter. The data audit service parses the type (e.g., numeric) and value (actual credit score) of these parameters. The audit service checks whether the credit score parameters are of a type that the banking system allows to be recognized (e.g., numeric parameters) to ensure that they meet the bank's credit score system requirements. The audit service also checks whether the credit score value is in a valid state, for example, whether the score is in the range of 0 to 1000, and whether there are any abnormal values ​​or incorrect inputs. If both the parameter type and value meet the requirements, the audit result is passed, and the bank can continue to use this data for credit approval. If not, the audit result is failed, and the data needs to be collected or corrected again.

[0094] In a network security scenario, the security system collects network traffic data and sends it to the data audit service as a detection requirement parameter. The data audit service parses the type (for example, traffic size, packet type, etc.) and value (actual traffic value) of these parameters. The audit service checks whether the network traffic parameters are of the type that the security system allows to be identified to ensure that they meet the requirements of network security monitoring. The audit service also checks whether the network traffic values ​​are in a valid state, for example, whether the traffic is abnormally high or low, whether there are unusual packet types, etc. If both the parameter type and value meet the requirements, the audit result is passed, and the security system can continue to use this data for further anomaly detection and response. If not, the audit result is failed, and the data needs to be collected or corrected again.

[0095] In both scenarios, the purpose of the data audit service is to ensure that the input data is both in the expected format and within an acceptable range, thereby avoiding incorrect decisions and potential security risks.

[0096] This embodiment ensures that only parameters that meet specific standards are accepted and processed by the first detection subsystem, thereby ensuring the accuracy and reliability of the detection.

[0097] In this embodiment, a data detection system is provided. Figure 3 is an architecture diagram of a data detection system according to an embodiment of the present application, such as Figure 3 As shown, the data detection system includes: a plurality of detection subsystems, and the data detection system implements the steps of any one of the above methods when executed.

[0098] Optionally, the data detection system also includes a data acquisition service and a database, wherein the data acquisition service is used to acquire multiple sets of data required for multiple tasks, and the database is used to store the multiple sets of data, wherein one set of the data corresponds to one or more data sources.

[0099] The present application is described below in conjunction with specific embodiments:

[0100] This embodiment takes market risk management in a financial scenario as an example to illustrate that market data faces the following technical problems:

[0101] Large amounts of data can easily lead to duplication of work. Under the independent management model of project teams, because market data itself has certain commonalities, the independent management model can easily lead to duplication of work on the same data by different project teams. Because market data needs to be accessed and constructed daily, there will be a huge amount of data, which requires energy to maintain and manage. Under existing technologies, due to decentralized management, different project teams need to complete a complete set of maintenance and management processes, which will increase the workload and cause inaccurate analysis.

[0102] This specific embodiment provides a more efficient, accurate and intensive market risk market data detection system to achieve the purpose of reducing redundant work, lowering management thresholds, improving the accuracy of market data, enhancing the flexibility of market data management, and increasing industry competitiveness.

[0103] like Figure 4 As shown, it is a data flow diagram of the data detection system in this specific embodiment. The data detection system is mainly used to coordinate access to market data (i.e., multiple groups of object data) and transmit the constructed curves and surfaces (i.e., detection demand parameters) to the downstream system (i.e., the first detection system) for use.

[0104] The relationship between the services in the data detection system is as follows: Figure 5 As shown, the data detection system only has two services, one management service and one operation service. The management service has all permissions, and the operation service needs to comply with the permission requirements of the project team when operating the project team. The following is a detailed description of each functional service.

[0105] In order to realize the centralized management of market data in the risk middle office and adapt to the flexible configuration requirements of various projects (i.e. tasks), the data detection system must be built into a multi-layer configurable, flexible control platform that can be put online immediately. An important link is the access and management of internal and external data. The data mart has achieved data concentration. The data detection system serves as a unified centralized data supply center for the risk downstream project team, integrating the access of internal and external market data, and then the data detection system stores it uniformly. Users use market data to build curves and surfaces on demand, and then transmit the results to downstream systems. At the same time, in order to meet the needs of different project groups for market data, the data mart can also make personalized requirements for data acquisition time, data type, data field, etc.

[0106] The parameter format of market data can include the following: Date and time: the timestamp of market data, recording the date and time of data. Price: the price of market transactions, including opening price, closing price, highest price, lowest price, etc. Other parameters: depending on the specific situation, other market data parameters may also be included, such as product attributes, daily calculation conventions, etc.

[0107] On the function interface, such as Figure 6 He Ru Figure 7 As shown, a data query and quotation adjustment menu is designed in the data detection system, which can not only easily check the data, but also repair the abnormal quotation data in the market through manual channels.

[0108] Project management mainly enables the project team to independently build and manage market data, as follows:

[0109] (1) Meet the needs of different curves and surfaces proposed by multiple project teams, iterate from the management and function, and avoid repeated development problems. Within the platform, data from different project teams can be managed and stored independently, and the background will strictly isolate the data to avoid data confusion.

[0110] (2) Information sharing among different project teams. Functions such as query and export that do not involve new additions or modifications will be open to all project teams, and information sharing can be achieved among project teams.

[0111] (3) The overall approval process refers to the current approval process at each level, but with the addition of auxiliary functions to facilitate management.

[0112] Project management is a comprehensive management module. When actually managing, it will be frequently interconnected with other services, that is, other services are basically within the framework of project management. Project team management includes project team configuration (corresponding to the project team configuration service mentioned above), market data configuration (corresponding to the market data configuration service mentioned above), approval and review management (corresponding to the first review service mentioned above), etc.

[0113] Among them, Figure 8 As shown, the project group configuration can be used for secondary allocation of permissions, and can also roughly view all project groups, curve and surface groups and curve and surface information, and provide a jump function to query and revise specific market data. During configuration, it is also equipped with a copy function, which can refer to the configuration of this group or other project groups, copy it, and then adjust it according to the needs of this project group. The configuration group can be distinguished as production configuration or test configuration. The market data configured in the production group will be updated regularly every day, and the data results and specific parameter information will be transmitted to the downstream system / downstream project group through the batch line to ensure the needs of the downstream system for market data.

[0114] like Fig. 9 As shown, the data studio meets the daily management needs of querying and downloading market data, and the authority is open to all relevant personnel.

[0115] Data construction (corresponding to the demand construction service mentioned above): used for the synthesis of quotation points, the construction of curves and surfaces. In order to meet the needs of various data, the data detection system is configured with dozens of market data parameter formats and construction models, and the data types that can be built are as follows:

[0116] (1) Quotation points: direct exchange rate and cross exchange rate.

[0117] (2) Curves: bond curve, credit spread curve, foreign exchange forward curve, commodity forward curve, money market interest rate curve, swap curve, implied yield curve, reference interest rate curve, etc.

[0118] (3) Surfaces: foreign exchange volatility surface, upper and lower limit volatility surfaces, swap option volatility surfaces, etc.

[0119] When constructing data, Fig.10 As shown, a three-layer format is adopted, that is, first set the product category attributes, then set the quotation attributes, and finally set the quotation points, curves and surfaces. The product category attributes are to combine the key term points with the same convention. For example, all points except the ON point on the USD swap curve are IRS. The daily calculation conventions and the linked targets of these IRS quotation points are consistent. In order to avoid repeated operations, a parameter of a product type attribute is established, and then all quotation points that meet the parameter are added to it; the quotation point is the standard point on the curve or surface, and the corresponding main parameter is the term; the curve / surface mainly configures the interpolation method and construction method of the curve / surface, and the object is the entire curve / surface.

[0120] like Fig.11 As shown in the figure, after each level is configured, if it needs to be used as production data, it needs to be reviewed and approved by two people to reduce operational risks and improve configuration accuracy.

[0121] Curve and Surface Laboratory: A derivative module built for curves and surfaces, which can be used for market data analysis or market data calculation. The laboratory mainly provides users with an independent space for data debugging. Users can use various methods and quotations in the system to build curves and surfaces and compare or conduct in-depth analysis to obtain the best results for production. The laboratory has advanced curve and surface modeling software and tools, which can perform complex analysis and calculations on market data, thereby helping companies make more informed decisions. Through the Curve and Surface Laboratory, companies can better understand market data and make more accurate predictions and analyses. At the same time, the Curve and Surface Laboratory can help companies quickly and efficiently model curves and surfaces, thereby saving time and costs and improving the timeliness and accuracy of modeling. In short, the Curve and Surface Laboratory provides companies with important technical support and tools to help them better respond to market changes, adjust the existing market data system in a timely manner, and achieve fast and smooth iteration of market data.

[0122] like Fig.12 As shown, during trial calculation, multiple trial calculation tasks are supported, and the quotation and parameters can be modified before trial calculation. After the data is generated, the data analysis can be more intuitive through the chart display function. The trial calculation data does not affect the production data, but it can be exported.

[0123] According to the above content, this specific embodiment integrates a coordinated market risk market data management system, integrates market data for combined analysis, and forms a unified risk market data output terminal; flexible project team management mode, fully shares knowledge, and supports project teams to configure or revise data through the system interface. A large amount of data can be managed in parallel on multiple lines, and duplication of work can be reduced. The data platform continuously outputs the latest market price of the curve surface every day, and delivers it to the downstream system for valuation and measurement. At the same time, the data platform takes into account a certain convergence of market data, and reduces the possible duplication of work of different project teams through a unified management method, construction logic, etc. Market risk market data and its functions are integrated in the same data platform, which is convenient for different project teams to share knowledge, and the business personnel of the project team can access the configuration information, update status, etc. of other project groups. Flexible management, the data platform sets many configuration functions as a mode that business personnel can revise, approve, and take effect directly through the interface, which improves management efficiency and makes data management more efficient and convenient. The data platform reduces the technical threshold requirements for business personnel. Through fool-proof operations (such as copy functions, etc.), the technical threshold is further reduced, so that business personnel without relevant experience can also quickly get started and perform data management.

[0124] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0125] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0126] The embodiments of the present application also provide a computer program, which includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps of any one of the above method embodiments.

[0127] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0128] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0129] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0130] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0131] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0132] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0133] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for detecting risk parameters, characterized in that: Applied to a data detection system, the data detection system includes a plurality of detection subsystems, and the method includes: In the case of receiving a task request sent by a first detection subsystem, determining a target task matching the task request from a plurality of tasks, wherein the first detection subsystem is any one of the plurality of detection subsystems, and the task request is used to request acquisition of data on risk parameters of a detection target object; Selecting target data from multiple groups of object data according to the target task, and generating detection requirement parameters of the target task using the target data; The detection requirement parameters are sent to the first detection subsystem to instruct the first detection subsystem to detect the risk parameters of the target object within a preset time period based on the detection requirement parameters.

2. The method according to claim 1, characterized in that In the case of receiving a task request sent by the first detection subsystem, determining a target task matching the task request from the plurality of tasks includes: Parsing the system parameters of the first detection subsystem and the object category of the target object included in the task request, wherein the target object includes an object with a risk change trend; When it is determined based on the system parameters that the first detection subsystem has parameter detection authority and is at a preset detection level in the data detection system, a task matching the object category is searched from multiple tasks to obtain the target task.

3. The method according to claim 1, characterized in that Before selecting target data from multiple groups of object data according to the target task and generating detection requirement parameters of the target task using the target data, the method further includes: Generate collection configuration information, wherein the collection configuration information includes at least one of the following: data collection time, collected data type, collected data field, and address information of the data source; Collecting multiple groups of the object data from one or more data sources according to the collection configuration information; Receiving a data management request, and responding to the data management request to manage the plurality of sets of the object data, wherein the data management request comprises at least one of the following: data query, data reset, data export, data denoising; When it is determined that the multiple groups of object data are stored in the database, data maintenance operations are performed on the multiple groups of object data according to a preset time period, so that the multiple groups of object data are in a waiting-to-be-accessed state within the preset time period, wherein the data maintenance operations include at least one of the following: an operation of confirming the integrity of the object data, an operation of terminating the display of the object data, an operation of activating the object data, an operation of resetting the object data, an operation of downloading the object data to other devices, and an operation of uploading the object data to other devices.

4. The method according to claim 1, characterized in that: Selecting target data from multiple groups of object data according to the target task, and using the target data to generate detection requirement parameters of the target task, including: In the case where it is determined that the target task is a risk detection task, real-time supply and demand data and historical supply and demand data are selected from the plurality of groups of object data to obtain the target data; Respectively converting the real-time supply and demand data and the historical supply and demand data into numerical data to obtain a real-time numerical value set and a historical numerical value set; Respectively converting data features of data points in the real-time value set and the historical value set into vector representations to obtain a real-time vector set and a historical vector set; Constructing a feature vector of the real-time vector set to obtain a real-time feature vector; Constructing a feature vector of the historical vector set to obtain a historical feature vector; According to the time series between the real-time supply and demand data and the historical supply and demand data, the real-time feature vector and the historical feature vector are data stored to obtain the detection demand parameter, wherein the detection demand parameter is used to represent the change trend of the real-time supply and demand data and the historical supply and demand data, and the correlation relationship between the real-time supply and demand data and the historical supply and demand data.

5. The method according to claim 4, characterized in that After sending the detection requirement parameter to the first detection subsystem to instruct the first detection subsystem to detect the risk parameter of the target object within a preset time period based on the detection requirement parameter, the method further includes: Receive a data visualization chart sent by the first detection subsystem, wherein the data visualization chart is generated by the first analysis system based on the real-time feature vector and the historical feature vector included in the detection demand parameter, and the data visualization chart includes the risk parameter and the change trend of the risk parameter within the preset time period, and the risk parameter includes at least one of the following: the fluctuation degree of the target object within the preset time period, the fluctuation index of the target object relative to the historical supply and demand data within the preset time period, the loss degree of the target object within the preset time period, and the correlation degree between the target object and other objects within the preset time period; Generating risk warning information of the target object within the preset time period based on the data visualization chart; The risk warning information is sent to other detection subsystems.

6. The method according to claim 5, characterized in that Generating risk warning information of the target object within the preset time period based on the data visualization chart includes: In the case where the visualization chart includes a target broken line, determining the risk floating interval of the target object from the target broken line; Determine the maximum value, minimum value and golden section value of the target object floating in multiple continuous time periods from the risk floating interval, wherein the golden section value is used to represent the value corresponding to the risk parameter indicated by the golden section point of the target interval with the minimum value as the lower limit and the maximum value as the upper limit; Obtaining values ​​corresponding to the risk parameter in multiple time periods after the target time period corresponding to the golden section value; When the values ​​corresponding to the risk parameter in multiple time periods after the target time period are greater than or equal to the golden section value, determining that the change trend of the risk parameter is increasing, and generating first risk warning information; When the values ​​corresponding to the risk parameter in multiple time periods after the target time period are less than the golden section value, it is determined that the change trend of the risk parameter is decreasing, and second risk warning information is generated.

7. The method according to claim 1, characterized in that The data detection system further includes a data review service. After selecting target data from multiple sets of object data according to the target task and using the target data to generate detection requirement parameters of the target task, the method further includes: Sending the detection requirement parameters to the data review service; The data audit service parses the parameter type and parameter value of the detection requirement parameter, and audits whether the parameter type corresponds to the type allowed to be recognized by the first detection subsystem, and audits whether the parameter value is in a valid state to obtain an audit result.

8. A data detection system, comprising a plurality of detection subsystems, wherein the data detection system implements the steps of the method according to any one of claims 1 to 7 when executed.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method according to any one of claims 1 to 7 when executed by a processor.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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