Logistics enterprise financial abnormal behavior big data analysis integrated early warning system and device

Abstract

The invention relates to a logistics enterprise financial abnormal behavior big data analysis integrated early warning system and device, and discloses a logistics enterprise financial abnormal behavior big data analysis integrated early warning system which is characterized in that through cooperation of data acquisition, storage, analysis, early warning and other modules, multi-channel data is acquired and preprocessed, abnormity is detected by using an algorithm, early warning is performed in multiple modes, and meanwhile, a data security module guarantees safety. The invention relates to a system and a device for realizing big data analysis and early warning of abnormal financial behaviors of logistics enterprises. The system is characterized by comprising a data acquisition module, a data storage module, a data analysis module, a financial early warning module and a data secrecy module, signal interaction is established between the data acquisition module and the data secrecy module, and signal interaction is established between the data storage module and the data secrecy module. Signal interaction is established between the financial early warning module and the data secrecy module, and the data acquisition module is responsible for comprehensively collecting relevant data of logistics enterprises and providing basic data support for subsequent analysis and early warning.

Description

Technical Field

[0001] The present invention discloses an integrated early warning system and device for big data analysis of abnormal financial behavior of logistics enterprises, which relates to an integrated early warning system and device for early warning of abnormal financial behavior of logistics enterprises through multi-module collaboration, belonging to the technical field of financial early warning, and particularly to a system and device for big data analysis and early warning of abnormal financial behavior of logistics enterprises through collaboration of modules such as data collection, storage, analysis and early warning, collecting and preprocessing multi-channel data, using algorithms to detect anomalies, issuing early warnings in multiple ways, and ensuring security with a data confidentiality module. Background Art

[0002] With the continuous development of the global economy, the logistics industry plays a vital role in modern business. The business scale of logistics companies continues to expand, and the links involved are becoming increasingly complex, including transportation, warehousing, distribution and other aspects. However, while logistics companies are developing rapidly, financial risks are also inevitable. Traditional financial early warning methods for logistics companies often rely on limited data sources, such as simple financial statement data, which is difficult to fully and accurately reflect the actual financial status of the company. At the same time, traditional methods are not deep enough in data analysis and cannot timely discover potential financial abnormalities. In addition, with the intensification of market competition and changes in the external environment, such as adjustments to policies and regulations, changes in the competitive situation in the same industry, the financial risks faced by logistics companies are more diversified and complex. In this context, there is an urgent need for an innovative, integrated early warning system and device based on big data analysis that can comprehensively collect multi-channel data, deeply explore data value, accurately detect financial abnormalities, and promptly issue early warnings to relevant personnel to ensure the stable operation and sustainable development of logistics companies.

[0003] Publication No. CN117009907B discloses a financial system early warning method and system based on trend outlier detection, the method includes the following steps: 1. The records in the financial data table are respectively composed of financial status vectors of each time node according to time; 2. A financial status trend model is constructed based on a Q-LSTM network, and the audited financial status vectors are used as a training data set to train a normal financial status trend model; 3. The financial status vectors in the time period that needs to be detected for outliers are input into the normal financial status trend model to perform outlier detection. If the model output prompts that it is an outlier, it is determined that the financial data table data in the time period is abnormal; the system includes a financial status vector construction module, a financial status trend model training module and a financial data table data anomaly detection module. The above-mentioned financial system early warning method and system rely on limited data sources, such as simple financial statement data, which is difficult to fully and accurately reflect the actual financial status of the enterprise. At the same time, the traditional method is not deep enough in data analysis and cannot timely discover potential financial abnormal behaviors. Summary of the invention

[0004] In order to improve the above situation, the logistics enterprise financial abnormal behavior big data analysis integrated early warning system and device of the present invention provides a system and device that collects multi-channel data and pre-processes it through the collaboration of data collection, storage, analysis and early warning modules, uses algorithms to detect anomalies, and issues early warnings in multiple ways. At the same time, the data confidentiality module ensures security, thereby realizing big data analysis and early warning of abnormal financial behaviors of logistics enterprises.

[0005] The logistics enterprise financial abnormal behavior big data analysis integrated early warning system and device of the present invention are implemented as follows: the logistics enterprise financial abnormal behavior big data analysis integrated early warning system of the present invention comprises a data acquisition module, a data storage module, a data analysis module, a financial early warning module and a data confidentiality module. It is characterized in that the data acquisition module and the data confidentiality module establish signal interaction, the data storage module and the data confidentiality module establish signal interaction, and the financial early warning module and the data confidentiality module establish signal interaction. Data collection module: responsible for comprehensively collecting relevant data of logistics enterprises and providing basic data support for subsequent analysis and early warning. The data collection module includes data source module, collection method module and data preprocessing module. Preferably, the data source module collects data from three channels: first, the financial system, covering data such as accounting vouchers, account books, and financial statements; second, the business management system, including data from order management, transportation management, warehouse management, customer management, and other systems; third, external data, including obtaining financial and business data of companies in the same industry for comparative analysis, and understanding the impact of changes in relevant policies and regulations on the financial status of the company. Preferably, the acquisition method module can obtain data in the following four ways: first, using the identity storage module interface to establish a connection with the internal identity storage module of the enterprise and directly read the data; second, supporting file import, and can import files of various formats, such as Excel, CSV, XML, etc., and can regularly download data files from external data sources and import them into the system; third, for public external data sources, such as industry data platforms, news websites, etc., web crawler technology can be used to obtain data; fourth, by installing sensors in warehouses, transport vehicles and other places of logistics companies for data collection, the location, temperature, humidity and other information of the goods and the driving status of the vehicle can be obtained in real time, and the sensor data can be transmitted to the system with the help of Internet of Things technology. Preferably, the data preprocessing module includes a data cleaning module, a data conversion module and a data integration module. The data cleaning module can use methods such as data verification, data deduplication, and outlier detection to remove duplicate data, erroneous data, and outliers to ensure the accuracy and integrity of the data. The data conversion module can convert data in different formats into a unified format to facilitate subsequent analysis and processing, for example, unifying the date format into a standard format, unifying the currency unit, etc. The data integration module can integrate data from different data sources, establish a unified data view, and realize data integration and fusion. The data acquisition module and the data storage module establish signal interaction, Data storage module: to achieve safe and efficient storage of massive data for data analysis and comparison. The data storage module includes a storage technology module, a data partitioning module and a data indexing module. Preferably, the storage technology module uses a large-capacity, high-reliability storage technology, such as a distributed file system or a data warehouse, to store the massive data collected by the data collection module. These data include not only current real-time data but also historical data for trend analysis and comparison. Preferably, the data partitioning module partitions and stores the data according to attributes such as time, business type, and geographic location based on the characteristics of the data and query requirements to improve query efficiency. Preferably, the data indexing module creates indexes for key data fields according to specific query requirements to speed up data retrieval. The data storage module and the data analysis module establish signal interaction, Data analysis module: deeply explores data value and detects abnormal financial behavior. The data analysis module includes anomaly detection algorithm module, correlation analysis module and trend analysis module. Preferably, the anomaly detection algorithm module detects whether the data exceeds the normal range by calculating statistical indicators such as mean, standard deviation, median, etc. Preferably, the association analysis module analyzes the relationship between the business data and financial data of the logistics enterprise to find out potential abnormal behaviors. Preferably, the trend analysis module includes short-term trend analysis and long-term trend analysis. The short-term trend analysis analyzes short-term data by focusing on the changing trend of recent financial data and using methods such as moving average method and exponential smoothing method to timely discover abnormal fluctuations. The long-term trend analysis uses methods such as regression analysis and time series model to analyze long-term data, find trend lines and cyclical changes, and evaluate whether the financial status of the enterprise is stable. The data analysis module and the financial early warning module establish signal interaction, Financial warning module: Issue financial abnormality warnings to relevant personnel in a timely manner so that they can take countermeasures. The financial warning module includes warning rule settings, warning methods and warning feedback. Preferably, the early warning rules are set according to the business characteristics and risk preferences of the enterprise, and different levels of early warning rules and thresholds are set. The anomaly detection algorithm in the data analysis module, the correlation between business data and financial data, and the trend change of financial data can be selected to calculate and set the corresponding early warning rules. The early warning rules can also be customized according to the specific circumstances of the enterprise. Preferably, the early warning method can be used for early warning in the following three ways: first, system pop-up window, which pops up early warning information on the system interface to remind relevant personnel to handle it in time; second, SMS notification, which sends SMS early warning to enterprise managers to ensure timely knowledge; third, email notification, which sends a detailed early warning report to a designated mailbox for in-depth analysis; Preferably, the early warning report also includes data analysis results, abnormal situation description, recommended treatment measures, etc. Preferably, the warning method can also push warning information to users through a mobile phone APP, which is convenient for users to view anytime and anywhere. The APP can provide more interactive functions, such as viewing historical warning records, replying to warning processing status, etc. Preferably, appropriate measures are taken according to the severity and type of the warning. For minor abnormalities, data verification and adjustment can be carried out; for serious financial risks, in-depth investigation and analysis are required, and corresponding solutions are formulated. Preferably, the financial early warning module issues an early warning before receiving feedback to prevent omissions. Data confidentiality module: The data confidentiality module includes an identity authentication device, an identity storage module and an identity authentication system. The data confidentiality module is used to perform identity authentication before data collection and after early warning. The identity storage module stores fingerprint and facial information for identity verification, and also records the heart rate value of the operator during each fingerprint verification and the specific time of fingerprint verification and facial verification. Preferably, the identity storage module can store fingerprints and facial information of each failed verification. The identity verification device is composed of a support shell, a display screen, a support plate, a fixing plate, a connecting hole, a lifting motor, a threaded rod, a sliding groove, a moving shell, a camera, a sliding block, a guide column, a photoelectric sensor plate, a ramp hole and a fingerprint collection plate. The supporting shell is a square structure and a hollow structure, and the side of the supporting shell is an arc-shaped structure. The display screen is fixed on the top surface of the support shell and is placed near the middle. The top of the support plate is fixedly connected to the inner side of the top of the support shell, and the bottom of the support plate is fixedly connected to the inner side of the bottom of the support shell. The support plate is in a U-shaped structure, and the support plate consists of a horizontal plate and two vertical plates. Preferably, the support plate is placed obliquely, and the inclination angle with the bottom surface of the support shell is about 75 degrees. The fixing plate is fixedly connected to the inner side of the supporting plate, the fixing plate is placed close to the bottom end of the supporting plate, and the fixing plate is vertically arranged to the horizontal plate of the supporting plate. The top surface of the support shell is provided with a connection hole, the connection hole is a straight parallelepiped structure, a bottom surface of the connection hole intersects with the inner side surface of the support plate transverse plate and is on the same plane, and the length of the bottom surface of the connection hole is less than the width of the support plate transverse plate. One end of the support plate is fixedly connected to the top surface of the fixing plate, and the other end of the support plate is fixedly connected to the inner side surface of the top of the connecting hole. Preferably, the support plate is in a cylindrical structure. The lifting motor is fixed on the inner side of the horizontal plate of the support plate, and the lifting motor is placed at the bottom of the fixed plate. One end of the threaded rod passes through the fixed plate and is fixedly connected to the motor shaft of the lifting motor, and a support bearing is placed between the threaded rod and the fixed plate. The other end of the threaded rod is rotatably connected to the top of the support shell through the bearing. The support plate and the threaded rod are symmetrically arranged with respect to the support plate. The inner side transverse plate of the support plate is provided with a sliding groove, and the sliding groove is arranged parallel to the guide column. The moving shell is a rectangular parallelepiped structure, the moving shell is parallel to the support plate cross plate, one side of the moving shell is provided with a sliding rib, the moving shell is slidably connected with the support plate through the sliding groove and the sliding rib, and the moving shell has an opening structure corresponding to the surface thereof. The camera card is placed in the opening of the mobile shell. The baffle is fixed on the top of the moving shell. A sliding block is fixedly disposed on two opposite sides of the moving shell, respectively. The sliding block is placed close to the bottom of the moving shell, and is in a rectangular structure. One of the sliding blocks has a hole, and the sliding block is slidably connected to the guide column through the hole. The other sliding block has a threaded hole inside, and the sliding block is threadedly connected to the threaded rod through the threaded hole. The photoelectric sensor board is fixed on the top surface of the supporting shell. Preferably, the photoelectric sensor board is arranged in a long strip shape, and the display screen is placed between the camera and the photoelectric sensor board. The green LED light is fixed on the top surface of the support shell. Preferably, the green LED light is arranged in a long strip shape and is placed close to the photoelectric sensor board. The top surface of the support shell is provided with a slope hole, the slope hole is in a rectangular parallelepiped structure, and the height of the slope hole gradually increases from one end close to the display screen to the other end. The fingerprint collection plate is fixed on the bottom surface of the slope hole, and the inclination angle between the fingerprint collection plate and the bottom surface of the support shell is about 15 degrees. The identity verification system includes a signal converter, a data processor and a controller. The signal converter is placed in the supporting shell, and the data processor is placed in the supporting shell. The camera, fingerprint collection board and photoelectric sensing board are connected via data lines and signal converters respectively. The lifting motor and the green LED lamp are connected to the controller via a data line. The data processor and the signal converter perform information exchange, Furthermore, the bottom surface of the support shell is fixed with anti-skid particles, a plurality of the anti-skid particles are provided, and are respectively placed near the four corners of the bottom surface of the support shell, and the anti-skid particles are made of rubber. Furthermore, a light blocking plate is arranged between the green LED lamp and the photoelectric sensor plate, and the light blocking plate is in a long strip structure. Beneficial Effects

[0006] 1. It covers multiple modules including data collection, data storage, data analysis, financial early warning and data confidentiality, forming a closed-loop data processing process, which can more effectively identify and prevent financial risks and improve the company's financial management level.

[0007] Second, the use of multiple data collection methods and advanced data analysis technologies can quickly and accurately uncover potential financial abnormalities. At the same time, combined with the real-time warning function of the financial early warning module, it can send early warning information to relevant personnel at the first time, helping companies to take timely response measures and reduce financial losses.

[0008] 3. Through multiple authentication mechanisms such as identity authentication devices, identity storage modules and identity authentication systems, it is ensured that only authorized personnel can access and use the data in the system. In addition, the identity storage module also records the information of each verification failure, which helps to track and analyze potential security threats, further enhancing the security and confidentiality of the system.

[0009] 4. The green LED light and photoelectric sensor board work together to detect heart rate and determine the operator's status, further enhancing safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a three-dimensional structural diagram of the identity authentication device of the present invention; Figure 2It is a structural schematic diagram of the identity authentication device of the present invention; Figure 3 A three-dimensional structural diagram of the second embodiment of the identity authentication device of the present invention Figure 4 It is a three-dimensional structural diagram of Example 3 of the identity authentication device of the present invention. Attached photos

[0011] The invention comprises: a supporting shell (1), a display screen (2), a camera (3), a moving shell (4), a connecting hole (5), a baffle (6), a ramp hole (7), a fingerprint collection plate (8), a green LED lamp (9), a photoelectric sensor plate (10), a supporting plate (11), a guide column (12), a threaded rod (13), a sliding block (14), a sliding groove (15), a fixing plate (16), a lifting motor (17), anti-skid particles (18), and a light blocking plate (19). DETAILED DESCRIPTION Example 1

[0012] The logistics enterprise financial abnormal behavior big data analysis integrated early warning system and device of the present invention are implemented as follows: the logistics enterprise financial abnormal behavior big data analysis integrated early warning system of the present invention comprises a data acquisition module, a data storage module, a data analysis module, a financial early warning module and a data confidentiality module. It is characterized in that the data acquisition module and the data confidentiality module establish signal interaction, the data storage module and the data confidentiality module establish signal interaction, the financial early warning module and the data confidentiality module establish signal interaction, the data acquisition module and the data storage module establish signal interaction, the data storage module and the data analysis module establish signal interaction, and the data analysis module and the financial early warning module establish signal interaction. Data collection module: responsible for comprehensively collecting relevant data of logistics enterprises and providing basic data support for subsequent analysis and early warning. The data collection module includes data source module, collection method module and data preprocessing module. Preferably, the data source module collects data from three channels: first, the financial system, covering data such as accounting vouchers, account books, and financial statements; second, the business management system, including data from order management, transportation management, warehouse management, customer management, and other systems; third, external data, including obtaining financial and business data of companies in the same industry for comparative analysis, and understanding the impact of changes in relevant policies and regulations on the financial status of the company. Preferably, the acquisition method module can obtain data in the following four ways: first, using the identity storage module interface to establish a connection with the internal identity storage module of the enterprise and directly read the data; second, supporting file import, and can import files of various formats, such as Excel, CSV, XML, etc., and can regularly download data files from external data sources and import them into the system; third, for public external data sources, such as industry data platforms, news websites, etc., web crawler technology can be used to obtain data; fourth, by installing sensors in warehouses, transport vehicles and other places of logistics companies for data collection, the location, temperature, humidity and other information of the goods and the driving status of the vehicle can be obtained in real time, and the sensor data can be transmitted to the system with the help of Internet of Things technology. Preferably, the data preprocessing module includes a data cleaning module, a data conversion module and a data integration module. The data cleaning module can use methods such as data verification, data deduplication, and outlier detection to remove duplicate data, erroneous data, and outliers to ensure the accuracy and integrity of the data. The data conversion module can convert data in different formats into a unified format to facilitate subsequent analysis and processing, for example, unifying the date format into a standard format, unifying the currency unit, etc. The data integration module can integrate data from different data sources, establish a unified data view, and realize data integration and fusion. Data storage module: to achieve safe and efficient storage of massive data for data analysis and comparison. The data storage module includes a storage technology module, a data partitioning module and a data indexing module. Preferably, the storage technology module uses a large-capacity, high-reliability storage technology, such as a distributed file system or a data warehouse, to store the collected massive data, which includes not only current real-time data but also historical data for trend analysis and comparison. Preferably, the data partitioning module partitions and stores the data according to attributes such as time, business type, and geographic location based on the characteristics of the data and query requirements to improve query efficiency. Preferably, the data index module can create indexes for key data fields according to specific query requirements to speed up data retrieval. Data analysis module: deeply explores data value and detects abnormal financial behavior. The data analysis module includes anomaly detection algorithm module, correlation analysis module and trend analysis module. Preferably, the anomaly detection algorithm module detects whether the data exceeds the normal range by calculating statistical indicators such as mean, standard deviation, median, etc. Preferably, the association analysis module analyzes the relationship between the business data and financial data of the logistics enterprise to find out potential abnormal behaviors. Preferably, the trend analysis module includes short-term trend analysis and long-term trend analysis. The short-term trend analysis analyzes short-term data by focusing on the changing trend of recent financial data and using methods such as moving average method and exponential smoothing method to timely discover abnormal fluctuations. The long-term trend analysis uses methods such as regression analysis and time series model to analyze long-term data, find trend lines and cyclical changes, and evaluate whether the financial status of the enterprise is stable. Financial warning module: Issue financial abnormality warnings to relevant personnel in a timely manner so that they can take countermeasures. The financial warning module includes warning rule settings, warning methods and warning feedback. Preferably, the early warning rules are set according to the business characteristics and risk preferences of the enterprise, and different levels of early warning rules and thresholds are set. The corresponding early warning rules can be set by calculating any one of the anomaly detection algorithm, the correlation between business data and financial data, and the trend change of financial data. The early warning rules can also be customized according to the specific circumstances of the enterprise. Preferably, the early warning method can be used for early warning in the following three ways: first, system pop-up window, which pops up early warning information on the system interface to remind relevant personnel to handle it in time; second, SMS notification, which sends SMS early warning to enterprise managers to ensure timely knowledge; third, email notification, which sends a detailed early warning report to a designated mailbox for in-depth analysis; Preferably, the early warning report may include data analysis results, abnormal situation description, suggested treatment measures, etc. Preferably, the warning method can also push warning information to users through a mobile phone APP, which is convenient for users to check anytime and anywhere. The APP can provide more interactive functions, such as viewing historical warning records, replying to warning processing status, etc. Preferably, appropriate measures are taken according to the severity and type of the warning. For minor abnormalities, data verification and adjustment can be carried out; for serious financial risks, in-depth investigation and analysis are required, and corresponding solutions are formulated. Preferably, the financial early warning module issues an early warning before receiving feedback to prevent omissions. Data confidentiality module: The data confidentiality module includes an identity authentication device, an identity storage module and an identity authentication system. The data confidentiality module is used to perform identity authentication before data collection and after early warning. The identity storage module stores fingerprint and facial information for identity verification, and also records the heart rate value of the operator during each fingerprint verification and the specific time of fingerprint verification and facial verification. Preferably, the identity storage module can store fingerprints and facial information of each failed verification. The identity verification device comprises a supporting shell (1), a display screen (2), a supporting plate (11), a fixing plate (16), a connecting hole (5), a lifting motor (17), a threaded rod (13), a sliding groove (15), a moving shell (4), a camera (3), a sliding block (14), a guide column (12), a photoelectric sensor plate (10), a slope hole (7) and a fingerprint collection plate (8). The support shell (1) is a square structure and a hollow structure, and the side of the support shell (1) is an arc-shaped structure. The display screen (2) is fixedly placed on the top surface of the support shell (1) and is placed near the middle. The top end of the support plate (11) is fixedly connected to the inner side surface of the top of the support shell (1), and the bottom end of the support plate (11) is fixedly connected to the inner side surface of the bottom of the support shell (1). The support plate (11) has a U-shaped structure and is composed of a horizontal plate and two vertical plates. Preferably, the support plate (11) is placed at an angle, and the angle of inclination with the bottom surface of the support shell (1) is about 75 degrees. The fixing plate (16) is fixedly connected to the inner side surface of the supporting plate (11), the fixing plate (16) is placed close to the bottom end of the supporting plate (11), and the fixing plate (16) and the horizontal plate of the supporting plate (11) are arranged vertically. The top surface of the support shell (1) is provided with a connection hole (5), the connection hole (5) is a straight parallelepiped structure, a bottom surface of the connection hole (5) intersects with the inner side surface of the horizontal plate of the support plate (11) and is on the same plane, and the length of the bottom surface of the connection hole (5) is less than the width of the horizontal plate of the support plate (11). One end of the support plate (11) is fixedly connected to the top surface of the fixing plate (16), and the other end of the support plate (11) is fixedly connected to the inner side surface of the top of the connecting hole (5). Preferably, the support plate (11) is a cylindrical structure. The lifting motor (17) is fixedly placed on the inner side of the horizontal plate of the support plate (11), and the lifting motor (17) is placed at the bottom of the fixed plate (16). One end of the threaded rod (13) passes through the fixed plate (16) and is fixedly connected to the motor shaft of the lifting motor (17), and a support bearing is arranged between the threaded rod (13) and the fixed plate (16). The other end of the threaded rod (13) is rotatably connected to the top of the support shell (1) through the bearing. The support plate (11) and the threaded rod (13) are symmetrically arranged with respect to the support plate (11). The inner side transverse plate of the support plate (11) is provided with a sliding groove (15), and the sliding groove (15) is arranged parallel to the guide column (12). The movable shell (4) is in a rectangular parallelepiped structure. The movable shell (4) is parallel to the horizontal plate of the support plate (11). A sliding rib is disposed on one surface of the movable shell (4). The movable shell (4) is slidably connected to the support plate (11) by cooperating with the sliding rib through the sliding groove (15). The movable shell (4) has an opening structure corresponding to the surface thereof. The camera (3) is inserted into the opening of the mobile housing (4). The baffle (6) is fixedly placed on the top of the moving shell (4). A sliding block (14) is fixedly mounted on two other opposite sides of the movable shell (4). The sliding block (14) is placed close to the bottom of the movable shell (4). The sliding block (14) is in a rectangular parallelepiped structure. One of the sliding blocks (14) has a hole, and the sliding block (14) is slidably connected to the guide column (12) through the hole. The other sliding block (14) has a threaded hole inside, and the sliding block (14) is threadedly connected to the threaded rod (13) through the threaded hole. The photoelectric sensor plate (10) is fixedly placed on the top surface of the supporting shell (1). Preferably, the photoelectric sensor board (10) is arranged in a long strip shape, and the display screen (2) is placed between the camera (3) and the photoelectric sensor board (10). A green LED light is fixedly placed on the top surface of the support shell (1). Preferably, the green LED light is arranged in a long strip shape and is placed close to the photoelectric sensor plate (10). The top surface of the support shell (1) is provided with a slope hole (7), the slope hole (7) is in a rectangular parallelepiped structure, and the height of the slope hole (7) gradually increases from one end close to the display screen (2) to the other end. The fingerprint collection plate (8) is fixedly placed on the bottom surface of the slope hole (7), and the fingerprint collection plate (8) and the bottom surface of the support shell (1) are inclined at an angle of approximately 15 degrees. The identity verification system includes a signal converter, a data processor and a controller. The signal converter is placed in the supporting shell (1), and the data processor is placed in the supporting shell (1). The camera (3), the fingerprint collection board (8) and the photoelectric sensing board (10) are connected via data lines and signal converters respectively. The lifting motor (17) and the green LED lamp (9) are connected to the controller via a data line. The data processor and the signal converter perform information exchange, When in use, identity authentication is required in front of the data acquisition module, and the password finger is placed on the fingerprint acquisition board (8), so that the fingerprint image can be collected and the detailed features of the fingerprint can be accurately obtained. The signal converter converts the analog signal collected by the fingerprint sensor into a digital signal. The data processor pre-processes the collected fingerprint image, including filtering, enhancement, binarization and refinement operations to eliminate noise and highlight the fingerprint features, and matches these feature points with the templates stored in the database. If there is a matching fingerprint in the database, the input of information is allowed. At the same time, when collecting fingerprint information, the green LED illuminates the wrist, and the photoelectric sensor captures the intensity change of the reflected light and converts it into The signal is converted into a digital signal by the signal converter. The data processor performs filtering and denoising operations. The heart rate value is obtained by calculating the period or frequency of the digital signal to detect whether the staff is nervous or has other abnormal conditions. The value is stored in the data storage module. After the financial information is input through the data acquisition module, the data preprocessing module removes duplicate data, erroneous data and abnormal values ​​to ensure the accuracy and completeness of the data. At the same time, data in different formats are converted into a unified format to facilitate subsequent analysis and processing. The data storage module stores the preprocessed data and deeply mines the data value through the data analysis module to detect financial abnormalities. When financial abnormalities are detected, A system pop-up window or SMS notification is often selected according to the severity and type of the financial anomaly. When the financial anomaly is minor, a system pop-up window is used to issue an early warning. When the situation is serious, a system pop-up window is issued and an SMS warning is sent to the enterprise management personnel at the same time. Regardless of whether the financial anomaly is serious or not, a detailed early warning report is sent to a designated mailbox via email. If there is no confirmation of receipt of the early warning, a regular early warning notification is required. When confirming receipt of the early warning, face recognition verification is required. The lifting motor (17) is started by the controller, and the threaded rod (13) is driven to rotate by the lifting motor (17), that is, the movable shell (4) and the upper camera (13) can be driven by the sliding block (14). 3) Move diagonally upward. When it moves to the top, the camera (3) captures the user's facial image. The signal converter converts the analog signal captured by the camera (3) into a digital signal. The data processor pre-processes the image captured by the camera (3), including operations such as denoising, contrast enhancement, and brightness adjustment. The data processor extracts facial feature information from the pre-processed image and compares it with the facial features pre-stored in the database. If a match is successful, the verification is successful. Otherwise, the system will store the feature information collected this time and remind you after the next successful verification. After the fingerprint match and facial match are successful, the display screen can display the person's information. Example 2

[0013] The difference between this embodiment and embodiment 1 is that: the bottom surface of the support shell (1) is fixed with anti-skid particles (18), a plurality of the anti-skid particles (18) are provided, and are respectively placed near the four corners of the bottom surface of the support shell (1), and the anti-skid particles (18) are made of rubber. When in use, the anti-skid particles (18) can prevent the identity verification device from sliding when collecting identity information, thereby avoiding errors that affect work efficiency; Example 3

[0014] The difference between this embodiment and embodiment 1 is that a light blocking plate (19) is disposed between the green LED lamp (9) and the photoelectric sensor plate (10), and the light blocking plate (19) is in the form of a long strip. When in use, the light blocking plate (19) can prevent the light source of the green LED lamp (9) from irradiating the photoelectric sensor plate (10), thereby avoiding the occurrence of errors. The design of the data collection module comprehensively covers various data sources of logistics enterprises, including financial systems, business management systems and external data, ensuring the comprehensiveness and diversity of data. At the same time, it adopts multiple collection methods to improve the efficiency and accuracy of data collection; The design of the data storage module adopts large-capacity and high-reliability storage technology, such as distributed file system or data warehouse, to achieve efficient storage of massive data. At the same time, through data partitioning and indexing technology, the query efficiency and retrieval speed of data are improved; The design of the data analysis module includes functions such as anomaly detection algorithm, correlation analysis and trend analysis, which can deeply mine the data value and detect financial abnormal behavior. These functions cooperate with each other to analyze data from multiple angles, thereby improving the accuracy and timeliness of detecting financial abnormal behavior; The design of establishing signal interaction between the data acquisition module and the data confidentiality module ensures that during the data acquisition process, all transmitted data is encrypted and securely verified, effectively preventing data leakage and tampering, and ensuring data integrity and security; The design of establishing signal interaction between the data storage module and the data confidentiality module can encrypt the stored data to prevent unauthorized access and data theft, thereby enhancing the security of data storage; The design of signal interaction between the financial warning module and the data confidentiality module can ensure the safe transmission of the warning information through the intervention of the data confidentiality module during the transmission of the financial warning information, prevent sensitive information from being intercepted or abused, and protect the financial security of the enterprise; The data confidentiality module is used to authenticate identity before data collection and after early warning. The identity storage module also records the information of each failed authentication. Through the multiple identity authentication mechanism, it ensures that only authorized personnel can access and use the data in the system, which enhances the security and confidentiality of the data and helps to track and analyze potential security threats. It is possible to achieve the goal of big data analysis and early warning of abnormal financial behavior of logistics companies through the collaboration of modules such as data collection, storage, analysis and early warning, collect and pre-process data from multiple channels, use algorithms to detect anomalies, and issue early warnings in various ways. At the same time, the data confidentiality module ensures security, thereby achieving the goal of big data analysis and early warning of abnormal financial behavior of logistics companies.

[0015] It should be noted that, unless otherwise clearly specified and limited, the terms "placed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection method such as a folding connection, a rivet connection, a pin connection, a bonding connection and a welding connection, or a detachable connection method such as a threaded connection, a snap connection and a hinge connection, or an integral connection, or an electrical connection, or a direct connection, or an indirect connection through an intermediate medium, or the internal connection of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0016] It should be further pointed out that, when describing the above specific embodiment, for the sake of simplicity and clarity, only the differences between the above specific embodiment and other embodiments are described, but those skilled in the art should know that the above specific embodiment itself is also an independent technical solution.

Claims

1. A big data analysis integrated early warning system for abnormal financial behavior of logistics enterprises, including a data acquisition module, a data storage module, a data analysis module, a financial early warning module and a data confidentiality module, characterized in that: The data acquisition module and the data confidentiality module establish signal interaction, the data storage module and the data confidentiality module establish signal interaction, the financial early warning module and the data confidentiality module establish signal interaction, the data acquisition module: responsible for comprehensively collecting relevant data of logistics enterprises, providing basic data support for subsequent analysis and early warning, the data acquisition module includes a data source module, a collection method module and a data preprocessing module, the data acquisition module and the data storage module establish signal interaction, the data storage module: realizes safe and efficient storage of massive data for data analysis and comparison, the data storage module includes a storage technology module, a data partitioning module and a data indexing module, the data storage module and the data analysis module establish signal interaction, the data analysis module: deeply explores the value of data, detects financial abnormalities, the data The data analysis module includes an anomaly detection algorithm module, a correlation analysis module and a trend analysis module. The data analysis module and the financial early warning module establish signal interaction. The financial early warning module: promptly issues financial anomaly warnings to relevant personnel so that countermeasures can be taken. The financial early warning module includes early warning rule settings, early warning methods and early warning feedback. The data confidentiality module: the data confidentiality module includes an identity authentication device, an identity storage module and an identity authentication system. The data confidentiality module is used to perform identity authentication before data collection and after the early warning. The identity storage module stores fingerprints and facial information for identity verification, and also records the heart rate value of the operator during each fingerprint verification and the specific time of fingerprint verification and facial verification. The identity storage module can store the fingerprint and facial information of each failed verification.

2. According to claim 1, a logistics enterprise financial abnormal behavior big data analysis integrated early warning system is characterized by The data preprocessing module includes a data cleaning module, a data conversion module and a data integration module. The data cleaning module can use methods such as data verification, data deduplication, and outlier detection to remove duplicate data, erroneous data, and outliers to ensure the accuracy and integrity of the data. The data conversion module can convert data in different formats into a unified format to facilitate subsequent analysis and processing, for example, unifying the date format into a standard format, unifying the currency unit, etc. The data integration module can integrate data from different data sources, establish a unified data view, and realize data integration and fusion. The acquisition method module can obtain data in the following four ways: first, using the identity storage module interface to establish a connection with the enterprise's internal identity storage module and directly read data; second, supporting file import, and can import files in various formats, such as Excel, CSV, XML, etc., and can regularly download data files from external data sources and import them into the system; Third, for public external data sources, such as industry data platforms, news websites, etc., web crawler technology can be used to obtain data; Fourth, by installing sensors in warehouses, transport vehicles and other places of logistics companies for data collection, real-time information such as the location, temperature, humidity and other information of the goods and data such as the driving status of the vehicle can be obtained, and the sensor data can be transmitted to the system with the help of Internet of Things technology. The data source module collects data from three channels: first, the financial system, covering accounting vouchers, account books, financial statements and other data; second, the business management system, including data from order management, transportation management, warehousing management, customer management and other systems; third, external data, including obtaining financial and business data of companies in the same industry for comparative analysis, and understanding the impact of changes in relevant policies and regulations on the financial status of the company.

3. According to claim 1, a big data analysis and integrated early warning system for abnormal financial behavior of logistics enterprises is characterized by The storage technology module adopts large-capacity and high-reliability storage technology, such as distributed file system or data warehouse, to store the massive data collected by the data acquisition module. These data include not only current real-time data, but also historical data for trend analysis and comparison. The data partitioning module partitions and stores the data according to attributes such as time, business type, and geographic location based on the characteristics of the data and query requirements to improve query efficiency. The data indexing module establishes indexes for key data fields based on specific query requirements to speed up data retrieval.

4. According to claim 1, a big data analysis integrated early warning system for abnormal financial behavior of logistics enterprises is characterized by The anomaly detection algorithm module detects whether the data exceeds the normal range by calculating statistical indicators such as mean, standard deviation, and median. The association analysis module finds out potential abnormal behaviors by analyzing the relationship between the business data and financial data of the logistics company. The trend analysis module includes short-term trend analysis and long-term trend analysis. The short-term trend analysis focuses on the changing trend of recent financial data and uses methods such as moving average and exponential smoothing to analyze short-term data and promptly discover abnormal fluctuations. The long-term trend analysis uses methods such as regression analysis and time series models to analyze long-term data, find trend lines and cyclical changes, and evaluate whether the company's financial status is stable.

5. According to claim 1, a big data analysis and integrated early warning system for abnormal financial behavior of logistics enterprises is characterized by The warning rule setting sets warning rules and thresholds of different levels according to the business characteristics and risk preferences of the enterprise. The anomaly detection algorithm in the data analysis module, the correlation between business data and financial data, and the trend change of financial data can be selected to calculate and set the corresponding warning rules. The warning rules can also be customized according to the specific situation of the enterprise. The warning method can be used for warning in the following three ways: first, the system pop-up window pops up the warning information on the system interface to remind relevant personnel to deal with it in time; Second, SMS notifications, which send SMS warnings to enterprise managers to ensure timely notification; Third, email notification, sending detailed early warning reports to designated mailboxes for in-depth analysis; the early warning report also includes data analysis results, abnormal situation descriptions, recommended treatment measures, etc. The early warning method can also push early warning information to users through mobile phone APP, which is convenient for users to view anytime and anywhere. The APP can provide more interactive functions, such as viewing historical early warning records, replying to early warning processing status, etc., and taking corresponding treatment measures according to the severity and type of the warning. For minor abnormal situations, data verification and adjustment can be carried out; for serious financial risks, in-depth investigation and analysis are required, and corresponding solutions are formulated. The financial early warning module will issue early warnings regularly before receiving feedback to prevent omissions.

6. According to claim 1, a big data analysis and integrated early warning system for abnormal financial behavior of logistics enterprises is characterized by The identity verification device is composed of a support shell, a display screen, a support plate, a fixed plate, a connection hole, a lifting motor, a threaded rod, a sliding groove, a moving shell, a camera, a sliding block, a guide column, a photoelectric sensor plate, a ramp hole and a fingerprint collection plate. The display screen is fixedly placed on the top surface of the support shell and is placed near the middle. The top end of the support plate is fixedly connected to the inner side surface of the top of the support shell, and the bottom end of the support plate is fixedly connected to the inner side surface of the bottom of the support shell. The support plate is U-shaped, and the support plate is composed of a horizontal plate and two vertical plates. The fixed plate is fixedly connected to the inner side surface of the support plate, and the fixed plate is placed near the bottom end of the support plate. The fixing plate is vertically arranged with respect to the supporting plate transverse plate, the top surface of the supporting shell is provided with a connecting hole, the connecting hole is a straight parallelepiped structure, a bottom surface of the connecting hole intersects with the inner side surface of the supporting plate transverse plate and is on the same plane, the length of the bottom surface of the connecting hole is smaller than the width of the supporting plate transverse plate, one end of the supporting plate is fixedly connected to the top surface of the fixing plate, the other end of the supporting plate is fixedly connected to the inner side surface of the top of the connecting hole, the lifting motor is fixedly placed on the inner side surface of the supporting plate transverse plate, the lifting motor is placed at the bottom of the fixing plate, one end of the threaded rod passes through the fixing plate and is fixedly connected to the motor shaft of the lifting motor, and a supporting rod is arranged between the fixing plate and the fixing plate. The support bearing, the other end of the threaded rod is rotatably connected to the top of the support shell through the bearing, the support plate and the threaded rod are symmetrically arranged about the support plate, the inner side transverse plate of the support plate is provided with a sliding groove, the sliding groove and the guide column are arranged in parallel, one side of the movable shell is provided with a sliding rib, the movable shell is slidably connected to the support plate through the sliding groove and the sliding rib, the movable shell has an opening structure corresponding to the surface, the camera is placed in the opening of the movable shell, the baffle is fixed on the top of the movable shell, and a sliding block is fixed on the other two opposite sides of the movable shell, and the sliding block is close to the bottom of the movable shell. The sliding block is placed in the top surface of the support shell, the sliding block is in a rectangular structure, one of the sliding blocks is provided with a hole, and the sliding block is slidably connected to the guide column through the hole, and the other sliding block is provided with a threaded hole inside, and the sliding block is threadedly connected to the threaded rod through the threaded hole, the photoelectric sensor plate is fixedly placed on the top surface of the support shell, the green LED light is fixedly placed on the top surface of the support shell, the top surface of the support shell is provided with a slope hole, the slope hole is in a rectangular structure, and the height of the slope hole gradually increases from one end close to the display screen to the other end, the fingerprint collection board is fixedly placed on the bottom surface of the slope hole, and the inclination angle between the fingerprint collection board and the bottom surface of the support shell is about 15 degrees.

7. A logistics enterprise financial abnormal behavior big data analysis integrated early warning system according to claim 6, characterized in that The bottom surface of the support shell is fixed with anti-skid particles, a plurality of the anti-skid particles are provided, and are respectively placed near the four corners of the bottom surface of the support shell, and the anti-skid particles are made of rubber.

8. According to claim 6, a big data analysis and integrated early warning system for abnormal financial behavior of logistics enterprises is characterized by A light blocking plate is arranged between the green LED lamp and the photoelectric sensor plate, and the light blocking plate is in a long strip structure.

9. The big data analysis and integrated early warning system for abnormal financial behavior of logistics enterprises according to claim 6 is characterized in that The supporting shell has a square structure and a hollow structure, the side of the supporting shell has an arc-shaped structure, the supporting plate is placed at an angle and has an inclination angle of about 75 degrees to the bottom surface of the supporting shell, the supporting plate has a cylindrical structure, the moving shell has a rectangular structure, the moving shell is parallel to the horizontal plate of the supporting plate, the green LED light is arranged in a long strip shape and is placed close to the photoelectric sensing plate, the photoelectric sensing plate is arranged in a long strip shape, and the display screen is placed between the camera and the photoelectric sensing plate.

10. The big data analysis and integrated early warning system for abnormal financial behavior of logistics enterprises according to claim 1 is characterized in that The identity authentication system includes a signal converter, a data processor and a controller. The signal converter is placed in the supporting shell, the data processor is placed in the supporting shell, the camera, the fingerprint collection board and the photoelectric sensing board are respectively connected to the signal converter through a data line, the lifting motor and the green LED lamp are connected to the controller through a data line, and the data processor and the signal converter exchange information.

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