Supply chain trade invoice-based early warning method, apparatus and device, and medium

Abstract

The invention relates to a transaction data security technology, and discloses an early warning method, device and equipment based on a supply chain trade invoice, and a medium, and the method comprises the steps: obtaining invoice data of a preset enterprise, carrying out the invoice authenticity verification of the invoice data, and obtaining an authenticity result; extracting transaction data from the invoice data according to the authenticity result, and performing time sequence difference calculation on the transaction data to obtain abnormal data; performing transaction analysis on the transaction data based on the abnormal data to obtain an abnormal transaction mode result; and generating an early warning signal according to the abnormal transaction mode result and the authenticity result, and performing invoice early warning on the enterprise according to the early warning signal. According to the invention, the accuracy and adaptability of invoice early warning can be improved.

Description

Technical Field

[0001] The present invention relates to the field of transaction data security technology, and in particular to an early warning method, device, equipment and medium based on supply chain trade invoices. Background Art

[0002] In modern corporate finance and enterprise management, invoice management and verification is a crucial task, which is directly related to the financial health and tax compliance of the enterprise. In order to improve the efficiency and accuracy of invoice management, enterprises can use advanced technical means to build an automated invoice statistics and monitoring system.

[0003] Most of the existing invoice early warning mechanisms rely on the enterprise resource planning (ERP) system, which verifies the consistency and integrity of invoice data through preset rules and thresholds, and then detects anomalies. This process can identify some basic anomalies, such as abnormal amounts, duplicate invoices, date errors, etc. However, since the preset rules and thresholds are based on static standards, it is difficult to adapt to the complex and changing business environment, and the recognition of gradually changing or hidden fraudulent behaviors (such as slowly growing invoice amounts, related-party transactions, etc.) is insufficient. In addition, the adjustment of the rules and thresholds of the ERP system requires manual intervention, which lags behind business changes, resulting in limited response speed and adaptability of the early warning system. Therefore, the current invoice early warning method still has the problems of low accuracy and insufficient adaptability of invoice early warning. Summary of the invention

[0004] The present invention provides an early warning method, device, equipment and medium based on supply chain trade invoices, the main purpose of which is to solve the problems of low accuracy and insufficient adaptability of invoice early warnings.

[0005] To achieve the above purpose, the present invention provides an early warning method based on supply chain trade invoices, comprising:

[0006] Obtain invoice data of a preset enterprise, perform invoice authenticity check on the invoice data, and obtain an authenticity result;

[0007] Extracting transaction data from the invoice data according to the authenticity result, performing time series difference calculation on the transaction data to obtain abnormal data;

[0008] Performing transaction analysis on the transaction data based on the abnormal data to obtain abnormal transaction pattern results;

[0009] An early warning signal is generated according to the abnormal transaction pattern result and the authenticity result, and an invoice early warning is issued to the enterprise according to the early warning signal.

[0010] Optionally, performing an invoice authenticity check on the invoice data to obtain an authenticity result includes:

[0011] Constructing an invoice request according to the invoice data;

[0012] Sending the invoice request to a preset invoice verification server, and obtaining a response result returned by the invoice verification server;

[0013] The response result is analyzed to obtain a truth or falsehood result.

[0014] Optionally, performing time series difference calculation on the transaction data to obtain abnormal data includes:

[0015] Arranging the transaction data in time order to obtain a time series;

[0016] Performing difference calculation on the time series to obtain a difference series;

[0017] Performing outlier identification on the differential sequence to obtain outlier data points;

[0018] The abnormal data points are confirmed to be abnormal to obtain abnormal data.

[0019] Optionally, performing abnormal confirmation on the abnormal data point to obtain abnormal data includes:

[0020] Extracting transaction information corresponding to the abnormal data point;

[0021] Reviewing the description information of the transaction description according to the transaction data to obtain a review result;

[0022] The abnormal data points are deleted according to the review results to obtain abnormal data.

[0023] Optionally, performing transaction analysis on the transaction data based on the abnormal data to obtain abnormal transaction pattern results includes:

[0024] Classifying the abnormal data to obtain abnormal categories;

[0025] Performing sequence pattern mining on the abnormal categories to obtain a time series pattern of abnormal transactions;

[0026] Abnormal trading pattern results are constructed based on the time series pattern.

[0027] Optionally, performing sequence pattern mining on the abnormal category to obtain a time series pattern of abnormal transactions includes:

[0028] constructing a time series set according to the anomaly categories;

[0029] Extracting a frequent pattern sequence from the time series set;

[0030] Perform pattern combination on the frequent pattern sequence to obtain a time series pattern.

[0031] Optionally, generating a warning signal according to the abnormal transaction mode result and the authenticity result includes:

[0032] extracting abnormal invoice data from the invoice data according to the authenticity result;

[0033] generating an abnormal invoice description according to the abnormal invoice data;

[0034] Performing risk level assessment on the abnormal invoice description and the abnormal transaction pattern result to obtain risk level invoice data;

[0035] The risk level invoice data is packaged into messages to obtain an early warning signal.

[0036] In order to solve the above problems, the present invention also provides an early warning device based on supply chain trade invoices, characterized in that the device comprises:

[0037] An invoice authenticity verification module is used to obtain invoice data of a preset enterprise, perform invoice authenticity verification on the invoice data, and obtain an authenticity result;

[0038] A data screening module, used to extract transaction data from the invoice data according to the authenticity result, and perform time series difference calculation on the transaction data to obtain abnormal data;

[0039] A transaction analysis module, configured to perform transaction analysis on the transaction data based on the abnormal data to obtain abnormal transaction pattern results;

[0040] The transaction warning module is used to generate a warning signal according to the abnormal transaction mode result and the authenticity result, and to issue an invoice warning to the enterprise according to the warning signal.

[0041] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0042] at least one processor; and,

[0043] a memory communicatively connected to the at least one processor; wherein,

[0044] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned early warning method based on supply chain trade invoice.

[0045] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned early warning method based on supply chain trade invoices.

[0046] The embodiment of the present invention directly migrates the enterprise's planning to the blockchain network by integrating the interface of the enterprise resource planning system and the asset information database into the blockchain network, thereby avoiding the problem of inaccurate duplicate checking caused by untimely database updates. At the same time, the distributed storage and tamper-proof nature of the blockchain network are utilized to ensure the historical integrity of transaction records and improve data security. By performing keyword analysis on the target financial asset data and extracting key asset feature words, transaction records related to specific financial assets can be efficiently and accurately screened out from massive data, reducing the burden of manual screening and improving the accuracy and efficiency of duplicate checking. By using a pattern recognition algorithm to identify repeated patterns in transaction records and using the smart contract function of the blockchain network to perform automated transaction verification, repeated financing behaviors can be discovered and prevented in a timely manner, significantly reducing missed detections and misjudgments caused by human errors. Based on the preliminary duplicate checking results, a risk assessment model is used to calculate risk scores, and a duplicate checking report is generated by integrating various aspects of information, which can provide comprehensive and intuitive information, facilitate targeted measures, effectively control and reduce potential financial risks, and improve the overall efficiency of asset management and financing activities. Therefore, the early warning method, device and medium based on supply chain trade invoice proposed in the present invention can solve the problems of low accuracy and insufficient adaptability of invoice early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A schematic diagram of a process of an early warning method based on supply chain trade invoices provided by an embodiment of the present invention;

[0048] Figure 2 A functional module diagram of an early warning device based on supply chain trade invoices provided by an embodiment of the present invention;

[0049] Figure 3 A schematic diagram of the structure of an electronic device for implementing the early warning method based on supply chain trade invoices provided in an embodiment of the present invention.

[0050] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0051] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0052] The embodiment of the present application provides an early warning method based on supply chain trade invoices. The execution subject of the early warning method based on supply chain trade invoices includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the early warning method based on supply chain trade invoices can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0053] Reference Figure 1 FIG. 1 is a flow chart of an early warning method based on supply chain trade invoices provided by an embodiment of the present invention. In this embodiment, the early warning method based on supply chain trade invoices includes:

[0054] S1. Obtain invoice data of a preset enterprise, verify the authenticity of the invoice data, and obtain an authenticity result.

[0055] In an embodiment of the present invention, the invoice data refers to the detailed information of all invoices issued or received by the enterprise, including the invoice number, date, amount, buyer and seller information, etc. The authenticity verification refers to calling the invoice verification interface of the State Taxation Bureau and using the verification service to verify the unique number of the invoice to confirm that it has not been tampered with or reused and is issued by a legally registered company.

[0056] In the embodiment of the present invention, by verifying the authenticity of the invoice data, the authenticity of the invoice can be ensured, thereby avoiding financial losses and legal risks to the enterprise caused by fake invoices.

[0057] In the embodiment of the present invention, the invoice data is subjected to an authenticity check to obtain an authenticity result, including:

[0058] Constructing an invoice request according to the invoice data;

[0059] Sending the invoice request to a preset invoice verification server, and obtaining a response result returned by the invoice verification server;

[0060] The response result is analyzed to obtain a truth or falsehood result.

[0061] In an embodiment of the present invention, by extracting key fields from invoice data, such as invoice code, invoice number, invoice date, purchaser's taxpayer identification number, seller's taxpayer identification number, amount excluding tax, tax amount, price-tax total and other field data, and encapsulating the key fields into a request structure, that is, a string in JSON format, an invoice request is obtained.

[0062] In an embodiment of the present invention, the invoice verification server refers to the invoice verification server of the State Taxation Bureau. By obtaining the address provided by the invoice verification server of the State Taxation Bureau, the invoice request is sent to the invoice verification server according to the address. The invoice verification server will process the request and return a response result, wherein the response result is usually a string in JSON or XML format.

[0063] In the embodiment of the present invention, the response character string is converted into a readable data object, and information about the authenticity of the invoice is searched from the decoded data object to obtain the authenticity result.

[0064] In the embodiments of the present invention, financial transparency is improved, the internal control capability of the enterprise is enhanced, and the trust between the enterprise and its suppliers and customers is improved.

[0065] S2. Extracting transaction data from the invoice data according to the authenticity result, performing time series difference calculation on the transaction data, and obtaining abnormal data.

[0066] In the embodiment of the present invention, time series analysis is used to identify data in the invoice data that does not conform to normal transaction patterns, thereby discovering potential abnormalities or fraudulent behaviors, providing a more accurate basis for subsequent early warning and intervention.

[0067] In an embodiment of the present invention, the transaction data is obtained by extracting specific transaction information from the invoice data whose authenticity result is true, such as transaction date, transaction amount, transaction parties, tax amount, transaction information description, etc., and performing invalid value processing on the transaction data, such as deleting any null value or negative transaction amount, etc., to obtain the transaction data.

[0068] In the embodiment of the present invention, performing time series difference calculation on the transaction data to obtain abnormal data includes:

[0069] Arranging the transaction data in time order to obtain a time series;

[0070] Performing difference calculation on the time series to obtain a difference series;

[0071] Performing outlier identification on the differential sequence to obtain outlier data points;

[0072] The abnormal data points are confirmed to be abnormal to obtain abnormal data.

[0073] In the embodiment of the present invention, the transaction date in the transaction data is extracted, and the transaction data is sorted in chronological order using the transaction date, so as to construct a time series arranged in chronological order of the transaction dates.

[0074] In the embodiment of the present invention, the difference between the transaction amount at each time point and the transaction amount at the previous time point is calculated to obtain a new sequence, namely a difference sequence. The difference sequence reflects the increase or decrease of the transaction amount over time.

[0075] In the embodiment of the present invention, the sequence values ​​in the differential sequence exceeding the preset threshold are marked as abnormal data points, and the abnormal data points indicate that the change of the transaction amount exceeds the normal range, which may be a signal of potential financial risk or fraudulent behavior.

[0076] In the embodiment of the present invention, the step of performing abnormal confirmation on the abnormal data point to obtain abnormal data includes:

[0077] Extracting transaction information corresponding to the abnormal data point;

[0078] Reviewing the description information of the transaction description according to the transaction data to obtain a review result;

[0079] The abnormal data points are deleted according to the review results to obtain abnormal data.

[0080] In detail, by extracting the transaction information corresponding to the abnormal data point from the transaction data, all relevant information of the abnormal data point transaction is included, such as the transaction date, transaction amount, names of both parties to the transaction, tax amount, etc.

[0081] In detail, by comparing the transaction information with the preset industry standards and the transaction data, it is evaluated whether the current transaction deviates from the normal pattern, thereby obtaining a difference score, and comparing the transaction information with the industry average transaction to obtain the review result; among which, the industry standards include the average transaction amount, transaction frequency, transaction volume within a specific time period, etc.

[0082] Specifically, if an enterprise encounters a transaction amount that is significantly higher than the industry average, or the transaction occurs at an abnormal business time, these situations may indicate abnormal activity; in addition, when the transaction amount between an enterprise and a specific supplier increases sharply for no reason, the system will compare and analyze the detailed information of the transaction with the enterprise's past transaction history, and generate a transaction data score based on the abnormal increase in the transaction amount to assess the degree of abnormality of the transaction and its potential risks.

[0083] In detail, the abnormal data points are processed according to the review results. If a data point is confirmed to be abnormal, the data point is added to the data set of abnormal data to obtain abnormal data.

[0084] In the embodiment of the present invention, the transaction data of the supply chain trade invoice is deeply analyzed through the time series difference algorithm, abnormal data is effectively identified, and potential financial risks and fraudulent behaviors are discovered.

[0085] S3. Perform transaction analysis on the transaction data based on the abnormal data to obtain abnormal transaction pattern results.

[0086] In the embodiment of the present invention, by performing transaction analysis on the transaction data, potential financial risks and fraudulent behaviors can be discovered early, thereby improving the accuracy of invoice warnings.

[0087] In an embodiment of the present invention, the transaction analysis refers to the in-depth analysis process of abnormal transaction patterns in supply chain trade invoice data to understand the reasons behind abnormal transactions, such as abnormal transaction results caused by operational errors, fraudulent behavior or other abnormal business activities.

[0088] In the embodiment of the present invention, the performing transaction analysis on the transaction data based on the abnormal data to obtain abnormal transaction pattern results includes:

[0089] Classifying the abnormal data to obtain abnormal categories;

[0090] Performing sequence pattern mining on the abnormal categories to obtain a time series pattern of abnormal transactions;

[0091] Abnormal trading pattern results are constructed based on the time series pattern.

[0092] In the embodiment of the present invention, the abnormal data classification refers to using statistical methods to identify different types of abnormal transaction behaviors, such as fraudulent transactions, abnormally large transactions, atypical transactions, etc.

[0093] In the embodiment of the present invention, the performing of sequence pattern mining on the abnormal category to obtain the time series pattern of abnormal transactions includes:

[0094] constructing a time series set according to the anomaly categories;

[0095] Extracting a frequent pattern sequence from the time series set;

[0096] Perform pattern combination on the frequent pattern sequence to obtain a time series pattern.

[0097] In detail, for each type of anomaly, a set of time series is constructed, where each time series represents an instance of a specific anomaly class. Each series consists of consecutive abnormal transaction records, including key information such as transaction date and amount.

[0098] In detail, the occurrence frequency of transactions in the time series set is calculated, and the transactions are arranged in descending order according to the occurrence frequency to construct an initial frequent tree, that is, the transactions are added to the frequent tree in the order of occurrence frequency; the time series set is traversed again, and according to the constructed frequent tree, a transaction in the frequent tree is selected as the target frequent item. For all transaction records containing the target frequent item in the time series set, the remaining parts after removing the target frequent item constitute a new set, which is the conditional pattern base for the target frequent item; the frequent tree of the conditional pattern base is constructed again, and the frequent items in the frequent tree of the conditional pattern base, that is, the frequent pattern sequence, are found.

[0099] Further, the frequent pattern sequence is refined to identify key features in the pattern, that is, to identify the changing trend of the transaction amount in the frequent pattern sequence, such as whether there is a pattern of gradual increase or decrease in the amount, which may indicate fraud or operational error, and to analyze whether there are abnormal fluctuations in the transaction frequency in the frequent pattern sequence, such as an abnormal increase or decrease in the number of transactions in a short period of time, which may point to potential fraud or system errors, etc.; then, similarity calculation is performed on the frequent pattern sequence based on the identified key features, and frequent pattern sequences with similarity results greater than a preset similarity threshold are merged into one category, and association identification is performed on the merged frequent pattern sequences, that is, to determine whether the frequent pattern sequence is always accompanied by other frequent pattern sequences. If so, the frequent pattern sequence is merged with the other frequent pattern sequences to obtain a time series pattern.

[0100] In an embodiment of the present invention, a time series pattern is described, including information such as the frequency of occurrence of the pattern, the transaction types involved, and the amount range, to obtain a description result, and the time series pattern is formatted according to the pattern description result to obtain an abnormal transaction pattern result.

[0101] In the embodiment of the present invention, by extracting abnormal transaction patterns, it is helpful for enterprises to deeply understand the reasons behind abnormal transactions, and the accuracy of invoice warnings can be improved and the adaptability of warning mechanisms can be enhanced, thereby improving warning efficiency and reducing financial risks.

[0102] S4. Generate a warning signal according to the abnormal transaction pattern result and the authenticity result, and issue an invoice warning to the enterprise according to the warning signal.

[0103] In the embodiment of the present invention, by generating a warning signal based on the abnormal transaction pattern results and the authenticity results, and using the warning signal to issue an invoice warning to the enterprise, the enterprise can quickly respond to potential financial risks and track and process related orders in a timely manner.

[0104] In the embodiment of the present invention, generating a warning signal according to the abnormal transaction mode result and the authenticity result includes:

[0105] extracting abnormal invoice data from the invoice data according to the authenticity result;

[0106] generating an abnormal invoice description according to the abnormal invoice data;

[0107] Performing risk level assessment on the abnormal invoice description and the abnormal transaction pattern result to obtain risk level invoice data;

[0108] The risk level invoice data is packaged into messages to obtain an early warning signal.

[0109] In an embodiment of the present invention, the invoice data is compared with the results of the authenticity check, invoices marked as suspected forgeries or having other problems are screened out, and the data of the suspected problem invoices are separated from the normal invoice data to form an abnormal invoice data set.

[0110] In an embodiment of the present invention, key features of abnormal invoice data, such as invoice number, invoice date, amount, etc., are extracted, and a detailed description is written based on the key features and the authenticity results to explain the specific problems of each abnormal invoice, such as "invoice number does not exist", "amount exceeds the normal range", etc.

[0111] In the embodiment of the present invention, ratings are performed based on the severity of the abnormal invoices and the suspiciousness of the transaction patterns, such as low, medium, and high risk, and a risk level is assigned to each abnormal invoice to form risk level invoice data.

[0112] In an embodiment of the present invention, key information in the risk level invoice data (such as invoice ID, risk level, and exception description) is integrated into a message, and different levels of early warning signals are customized according to different risk levels, such as issuing warnings through emails, text messages, or system pop-ups.

[0113] In the embodiment of the present invention, by generating early warning signals, the enterprise can not only promptly discover abnormal situations in invoices and transactions, but also respond quickly according to the risk level, effectively preventing financial loopholes and potential fraud.

[0114] like Figure 2 , which is a functional module diagram of an early warning device based on supply chain trade invoices provided by an embodiment of the present invention.

[0115] The early warning device 100 based on supply chain trade invoices of the present invention can be installed in an electronic device. According to the functions implemented, the early warning device 100 based on supply chain trade invoices can include an invoice authenticity verification module 101, a data screening module 102, a transaction analysis module 103 and a transaction early warning module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0116] In this embodiment, the functions of each module / unit are as follows:

[0117] The invoice authenticity verification module 101 is used to obtain invoice data of a preset enterprise, perform invoice authenticity verification on the invoice data, and obtain an authenticity result;

[0118] The data screening module 102 is used to extract transaction data from the invoice data according to the authenticity result, and perform time series difference calculation on the transaction data to obtain abnormal data;

[0119] The transaction analysis module 103 is used to perform transaction analysis on the transaction data based on the abnormal data to obtain abnormal transaction pattern results;

[0120] The transaction warning module 104 is used to generate a warning signal according to the abnormal transaction mode result and the authenticity result, and issue an invoice warning to the enterprise according to the warning signal.

[0121] In detail, each module described in the early warning device 100 based on supply chain trade invoices in the embodiment of the present invention is used in the same manner as described above. Figure 1 The above-mentioned early warning method based on supply chain trade invoice has the same technical means and can produce the same technical effect, so it will not be repeated here.

[0122] like Figure 3 FIG. 1 is a schematic diagram of the structure of an electronic device for implementing an early warning method based on supply chain trade invoices provided by an embodiment of the present invention.

[0123] The electronic device may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as an early warning program based on supply chain trade invoices.

[0124] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (for example, executing an early warning program based on a supply chain trade invoice, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device and process data.

[0125] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 11 may also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of the early warning program based on the supply chain trade invoice, but also can be used to temporarily store data that has been output or is to be output.

[0126] The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0127] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0128] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0129] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0130] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0131] The early warning program based on supply chain trade invoices stored in the memory 11 in the electronic device is a combination of multiple instructions. When running in the processor 10, the early warning method based on supply chain trade invoices can be implemented.

[0132] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0133] Furthermore, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0134] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor of an electronic device, it can implement the above-mentioned early warning method based on supply chain trade invoices.

[0135] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, media and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0136] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0137] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0138] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0139] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is not limited only according to the above description, and it is intended that all changes within the meaning and scope of equivalent elements within the scope of protection are included in the present invention.

[0140] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0141] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. An early warning method based on supply chain trade invoices, characterized in that: The method comprises: Obtain invoice data of a preset enterprise, perform invoice authenticity check on the invoice data, and obtain an authenticity result; Extracting transaction data from the invoice data according to the authenticity result, performing time series difference calculation on the transaction data to obtain abnormal data; Performing transaction analysis on the transaction data based on the abnormal data to obtain abnormal transaction pattern results; An early warning signal is generated according to the abnormal transaction pattern result and the authenticity result, and an invoice early warning is issued to the enterprise according to the early warning signal.

2. The early warning method based on supply chain trade invoices as claimed in claim 1, characterized in that: The invoice data is subjected to an authenticity check to obtain an authenticity result, including: Constructing an invoice request according to the invoice data; Sending the invoice request to a preset invoice verification server, and obtaining a response result returned by the invoice verification server; The response result is analyzed to obtain a truth or falsehood result.

3. The early warning method based on supply chain trade invoice according to claim 1, characterized in that: The performing time series difference calculation on the transaction data to obtain abnormal data includes: Arranging the transaction data in time order to obtain a time series; Performing difference calculation on the time series to obtain a difference series; Performing outlier identification on the differential sequence to obtain outlier data points; The abnormal data points are confirmed to be abnormal to obtain abnormal data.

4. The early warning method based on supply chain trade invoices as claimed in claim 3 is characterized in that: The step of performing abnormal confirmation on the abnormal data point to obtain abnormal data includes: Extracting transaction information corresponding to the abnormal data point; Reviewing the description information of the transaction description according to the transaction data to obtain a review result; The abnormal data points are deleted according to the review results to obtain abnormal data.

5. The early warning method based on supply chain trade invoice according to claim 1, characterized in that: The performing transaction analysis on the transaction data based on the abnormal data to obtain abnormal transaction pattern results includes: Classifying the abnormal data to obtain abnormal categories; Performing sequence pattern mining on the abnormal categories to obtain a time series pattern of abnormal transactions; Abnormal trading pattern results are constructed based on the time series pattern.

6. The early warning method based on supply chain trade invoices as claimed in claim 5, characterized in that: The performing sequence pattern mining on the abnormal category to obtain the time series pattern of abnormal transactions includes: constructing a time series set according to the anomaly categories; Extracting a frequent pattern sequence from the time series set; Perform pattern combination on the frequent pattern sequence to obtain a time series pattern.

7. The early warning method based on supply chain trade invoices as claimed in claim 1, characterized in that: The generating of the warning signal according to the abnormal transaction mode result and the authenticity result includes: extracting abnormal invoice data from the invoice data according to the authenticity result; generating an abnormal invoice description according to the abnormal invoice data; Performing risk level assessment on the abnormal invoice description and the abnormal transaction pattern result to obtain risk level invoice data; The risk level invoice data is packaged into messages to obtain an early warning signal.

8. An early warning device based on supply chain trade invoices, characterized in that: The device comprises: An invoice authenticity verification module is used to obtain invoice data of a preset enterprise, perform invoice authenticity verification on the invoice data, and obtain an authenticity result; A data screening module, used to extract transaction data from the invoice data according to the authenticity result, and perform time series difference calculation on the transaction data to obtain abnormal data; A transaction analysis module, configured to perform transaction analysis on the transaction data based on the abnormal data to obtain abnormal transaction pattern results; The transaction warning module is used to generate a warning signal according to the abnormal transaction mode result and the authenticity result, and to issue an invoice warning to the enterprise according to the warning signal.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the early warning method based on supply chain trade invoice as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the early warning method based on supply chain trade invoice is implemented as described in any one of claims 1 to 7.

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