Transaction early warning method, device, equipment, medium and program based on microseismic signal

Abstract

The invention discloses a microseismic signal-based transaction early warning method, device, equipment, medium and program. The method comprises the following steps: receiving an encrypted microseismic signal transmitted by terminal transaction equipment in a transaction action execution process of a client; carrying out decryption and feature extraction on the encrypted micro-seismic signal to obtain a current micro-seismic feature corresponding to the encrypted micro-seismic signal; inputting the current micro-seismic features into a pre-trained state recognition model for individual state prediction, and judging whether the customer is in an abnormal emotional state or not according to an individual state prediction result; and if it is determined that the customer is in the abnormal emotion state, abnormal emotion prompt information is generated to carry out early warning on the current transaction behavior of the customer. According to the embodiment of the invention, by receiving and analyzing the encrypted microseismic signal generated in the transaction action execution process of the customer, an irrational transaction decision made by the customer when the emotion is unstable is avoided, meanwhile, passive transaction behaviors caused by external factors are prevented, and fraudulent behaviors are effectively prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing, is applicable to the field of fintech, and particularly relates to a transaction warning method, device, equipment, medium and program based on microseismic signals. Background Art

[0002] At the present stage, when customers handle bank transactions offline, they usually use password verification, biometric verification or SMS verification to ensure the security of transactions. As a traditional identity verification method, the security of passwords is relatively low. Once the password is cracked or stolen, the customer's account funds will be at risk of being misappropriated; biometric verification cannot rule out the possibility of users being coerced into transactions; as a secondary verification method, the security of SMS verification completely depends on the security of the mobile terminal and the communication network. Once the mobile phone is stolen or the communication network is attacked, SMS verification will lose its protective effect.

[0003] When customers operate by themselves, the above identity verification methods cannot perceive whether the customer is in an extreme emotion (such as a coerced transaction), so they cannot effectively notify the staff to intervene in time to ensure the security of transactions and the rights and interests of customers. Summary of the Invention

[0004] Based on this, the present invention provides a transaction warning method, device, equipment, medium and program based on microseismic signals to solve the problem that traditional transaction verification methods cannot perceive whether there is a coerced transaction by users.

[0005] In a first aspect, an embodiment of the present invention provides a transaction warning method based on microseismic signals, and the method includes:

[0006] Receiving an encrypted microseismic signal transmitted by a terminal transaction device during a customer's transaction operation;

[0007] Decrypting the information of the encrypted microseismic signal and extracting features to obtain a current microseismic feature corresponding to the encrypted microseismic signal;

[0008] Inputting the current microseismic feature into a pre-trained state recognition model for individual state prediction, and judging whether the customer is in an abnormal emotion state according to the individual state prediction result;

[0009] If it is determined that the customer is in an abnormal emotion state, generating an abnormal emotion prompt message to warn the customer's current transaction behavior.

[0010] In a second aspect, an embodiment of the present invention further provides a transaction warning device based on microseismic signals, and the device includes:

[0011] A microseismic signal receiving module, configured to receive an encrypted microseismic signal transmitted by a terminal transaction device during a customer's transaction operation;

[0012] A microseismic feature extraction module, configured to decrypt the information of the encrypted microseismic signal and extract features, so as to obtain the current microseismic features corresponding to the encrypted microseismic signal;

[0013] An abnormal state judgment module, configured to input the current microseismic features into a pre-trained state recognition model for individual state prediction, and judge whether the customer is in an abnormal emotional state according to the individual state prediction result;

[0014] An abnormal state warning module, configured to generate an abnormal emotion prompt message to warn the customer's current trading behavior if it is determined that the customer is in an abnormal emotional state.

[0015] In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] 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 a trading warning method based on microseismic signals according to any embodiment of the present invention.

[0019] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores computer instructions, and the computer instructions are used to implement a trading warning method based on microseismic signals according to any embodiment of the present invention when executed by a processor.

[0020] In a fifth aspect, an embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program, and the computer program implements a trading warning method based on microseismic signals according to any embodiment of the embodiments of the present invention when executed by a processor.

[0021] The technical solution of the embodiment of the present invention receives and analyzes the encrypted microseismic signals generated by the customer during the execution of trading actions, and monitors the customer's emotional state in real time. If the customer is in an abnormal emotional state, an abnormal emotion prompt message will be generated immediately, thereby preventing the customer from making trading decisions that may damage their own interests when they are emotionally unstable, and realizing intelligent monitoring and management of the trading process. At the same time, this method can also effectively prevent fraud, effectively prevent passive trading behaviors caused by external factors, protect the customer's capital security and personal information from being violated, and promote the application and development of microseismic signals in the bank trading link.

[0022] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become readily understood from the following description. Brief Description of the Drawings

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings used in the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0024] Figure 1 is a flowchart of a trading warning method based on microseismic signals according to Embodiment 1 of the present invention;

[0025] Figure 2 is a flowchart of another trading warning method based on microseismic signals according to Embodiment 2 of the present invention;

[0026] Figure 3 is a schematic structural diagram of a trading warning device based on microseismic signals according to Embodiment 3 of the present invention;

[0027] Figure 4 is a schematic structural diagram of an electronic device of a trading warning method based on microseismic signals according to Embodiment 4 of the present invention. Detailed Embodiments

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention shall fall within the protection scope of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0030] Embodiment 1

[0031] Figure 1 The figure is a flowchart of a transaction early warning method based on microseismic signals provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of judging whether a transaction is abnormal according to the emotional state of offline transaction customers. This method can be executed by a transaction early warning device based on microseismic signals. The device can be implemented in the form of hardware and / or software, and the device can be configured in a cloud computing server that communicates with a terminal transaction device. As Figure 1 shown, the method includes:

[0032] S110. Receive the encrypted microseismic signal transmitted from the terminal transaction device during the customer's execution of the transaction action.

[0033] In the embodiment of the present invention, the terminal transaction device can be an ATM or a tablet computer for electronic signature at the manual counter. The microseismic signal can come from the slight vibration generated when the customer touches the screen, presses the button or performs other transaction-related actions on the terminal transaction device. The cloud computing server needs to receive the encrypted microseismic signal from the terminal transaction device. The purpose of encryption is to ensure the security and privacy of the signal and prevent it from being intercepted or tampered with by unauthorized personnel during the transmission process.

[0034] Optionally, the encrypted microseismic signal is obtained by encrypting the real-time microseismic signal that meets the validity verification condition, and the real-time microseismic signal is generated by the customer touching the terminal transaction device to execute the transaction action.

[0035] Among them, the validity verification condition includes at least one of the following: the real-time microseismic signal is a time-sequential continuous signal, the amplitude of the real-time microseismic signal is within a preset amplitude range, and the frequency-domain signal matching the real-time microseismic signal is within a preset human microseismic frequency range.

[0036] The above content indicates that the encrypted microseismic signal is obtained by the terminal transaction device encrypting the real-time microseismic signal. When a customer performs a transaction action (such as touching the screen, pressing a button, etc.) on the terminal transaction device, a real-time microseismic signal will be generated, which reflects the slight vibration of the customer's hand when performing the transaction action.

[0037] And the validity verification condition is used to determine whether the obtained microseismic signal is available. Specifically, the real-time microseismic signal needs to be temporally continuous, that is, the signal should be continuous in time without interruption or jump; the amplitude of the real-time microseismic signal needs to be within a preset amplitude range. The amplitude reflects the intensity of the signal. If the amplitude is too large or too small, it may mean that the signal is interfered or abnormal. Therefore, it is necessary to screen through the preset amplitude range; the frequency-domain signal matching the real-time microseismic signal needs to be within the preset human microseismic frequency range. The frequency-domain signal reflects the frequency distribution of the signal. By restricting the frequency range of the frequency-domain signal, it can be further ensured that the collected signal is a microseismic signal from the human body rather than an interference signal from other sources. By verifying the temporal continuity, amplitude range, and frequency-domain signal range of the real-time microseismic signal, invalid or interference signals can be excluded, and the accuracy of subsequent state recognition can be improved.

[0038] S120. Decrypt the information of the encrypted microseismic signal and extract features to obtain the current microseismic features corresponding to the encrypted microseismic signal.

[0039] After receiving the encrypted microseismic signal, the cloud computing server will first perform a decryption operation to restore the original microseismic signal. Then, the server will extract features from the decrypted microseismic signal. Feature extraction is a process of converting the original signal into a format that is easier to analyze and understand, involving extracting useful information (such as amplitude, frequency, phase, etc.) from the original microseismic signal, and these information can reflect the hand microseismic features of the customer when performing the transaction.

[0040] S130. Input the current microseismic features into a pre-trained state recognition model for individual state prediction, and judge whether the customer is in an abnormal emotional state according to the individual state prediction result.

[0041] The state recognition model can be a deep learning model and has been trained to recognize the hand microseismic features of customers in different emotional states. The individual prediction result can be a probability distribution describing the emotional state, indicating the possibility of the customer being in various emotional states. According to this probability distribution and a preset threshold, it is judged whether the customer is in an abnormal emotional state.

[0042] Furthermore, after judging whether the customer is in an abnormal emotional state according to the individual state prediction result, it may further include:

[0043] If it is determined that the customer is in a normal emotional state, request the customer identity information of the customer from the terminal trading device;

[0044] Perform consistency matching on the current microseismic characteristics according to the customer identity information fed back by the terminal trading device;

[0045] When it is determined that the consistency matching fails, send a transaction termination instruction to the terminal trading device.

[0046] When the state recognition model determines that the customer is in a normal emotional state, the customer's current emotional state in the normal emotional state is suitable for conducting transactions. However, to ensure the security of the transaction, the customer's identity needs to be further verified. Therefore, by sending a request to the terminal trading device, the identity information of the customer currently performing the transaction operation is obtained. If the consistency matching fails, it is considered that the current transaction operation may be executed by unauthorized personnel. To ensure the security of the transaction, the system will immediately send a transaction termination instruction to the terminal trading device to prevent the further progress of the transaction. The above consistency matching process is used to verify the legality of the transaction, helps prevent identity theft or fraud, and enhances the security of the transaction.

[0047] Optionally, performing consistency matching on the current microseismic characteristics according to the customer identity information fed back by the terminal device may include:

[0048] Call the pre-stored microseismic characteristic template bound to the customer identity in the database according to the customer identity information, where the template is the microseismic signal characteristics collected and stored by the customer during historical transactions;

[0049] Use a similarity measurement algorithm to calculate the similarity between the current microseismic characteristics and the pre-stored microseismic characteristic template, and compare the calculated similarity with a matching threshold;

[0050] When the similarity is greater than or equal to the matching threshold, it is determined that the consistency matching is passed; when the similarity is less than the matching threshold, it is determined that the consistency matching fails.

[0051] The customer's identity information is usually associated with the characteristics of the microseismic signal. By searching in the database for the pre-stored microseismic characteristic template bound to this identity information, which is the microseismic signal characteristics collected and stored by the customer during historical transactions, and then using a similarity measurement algorithm to calculate the similarity between the current microseismic characteristics and the pre-stored template. This comparison aims to confirm that the person performing the current transaction is indeed the legitimate holder of this identity information. The similarity measurement algorithm can be a distance-based algorithm (such as Euclidean distance, Manhattan distance, etc.) or a similarity coefficient-based algorithm (such as cosine similarity, Pearson correlation coefficient, etc.), which is used to quantify the similarity between the current microseismic characteristics and the template. The present invention does not limit this. The matching threshold is a value set based on experience or statistical methods, which is used to determine whether the similarity between the current microseismic characteristics and the template is high enough to consider the transaction to be real.

[0052] If the calculated similarity is greater than or equal to the matching threshold, it indicates that the similarity between the current microseismic characteristics and the pre-stored microseismic characteristic template is high enough, so it is determined that the consistency match is passed; if the calculated similarity is less than the matching threshold, it is considered that the similarity between the current microseismic characteristics and the pre-stored microseismic characteristic template is not high enough, so it is determined that the consistency match is not passed. This situation means that there is a risk or fraud in the transaction because the current microseismic characteristics do not match the microseismic signal characteristics stored in history. At this time, further security measures are taken, such as terminating the transaction or requiring the customer to perform additional identity verification, etc.

[0053] S140. If it is determined that the customer is in an abnormal emotional state, an abnormal emotion prompt message is generated to warn the customer's current transaction behavior.

[0054] Once it is determined that the customer is in an abnormal emotional state, an abnormal emotion prompt message is generated. This message will be displayed on the screen of the terminal transaction device and sent to the administrator side of the terminal transaction device at the same time. The purpose is to remind the customer to pay attention to their emotional state and guide them to pause or reconsider the current transaction behavior, which helps to prevent the customer from making wrong transaction decisions in an impulsive or unstable state.

[0055] The technical solution of the embodiment of the present invention, by receiving and analyzing the encrypted microseismic signal generated by the customer during the execution of the transaction action, monitors the emotional state of the customer in real time. If the customer is in an abnormal emotional state, an abnormal emotion prompt message will be immediately generated, thus avoiding the customer from making transaction decisions that may damage their own interests when their emotions are unstable, and realizing the intelligent monitoring and management of the transaction process. At the same time, this method can also effectively prevent fraud, effectively prevent passive transaction behaviors caused by external factors, protect the customer's capital security and personal information from being violated, and promote the application and development of microseismic signals in the bank transaction link.

[0056] Embodiment Two

[0057] Figure 2 This is a flowchart of another trading warning method based on microseismic signals provided in the second embodiment of the present invention. This embodiment is refined based on the above embodiment. As Figure 2 shown, the method includes:

[0058] S210. Receive the encrypted microseismic signal transmitted from the terminal trading device during the customer's execution of the trading action.

[0059] S220. Decrypt the information of the encrypted microseismic signal and extract features to obtain the current microseismic feature corresponding to the encrypted microseismic signal.

[0060] S230. Input the current microseismic feature into the pre-trained state recognition model, and obtain the emotional state probability value output by the state recognition model for the current microseismic feature.

[0061] This probability value represents the possibility that the customer is currently in a certain emotional state (such as normal emotion or abnormal emotion). The range of the probability value is between 0 and 1. The closer it is to 1, the greater the possibility. This threshold interval is set according to the training results of the state recognition model and the actual application requirements, and is used to distinguish normal emotion and abnormal emotion.

[0062] Optionally, before inputting the current microseismic feature into the pre-trained state recognition model for individual state prediction, it may further include:

[0063] Use the microseismic signal collector embedded in the terminal trading device to collect the hand microseismic signal data of multiple customers when trading in different emotions, and obtain multiple historical training samples;

[0064] Add the matching emotion labels to each historical training sample to obtain a labeled data set;

[0065] Determine the parameter values for each parameter in the pre-selected deep learning model according to the data characteristics of the historical training samples and the task requirements, and construct an initial state recognition model;

[0066] Divide the labeled data set into a training set and a test set according to a preset ratio, and use the training set to train the initial state recognition model to obtain an alternative recognition model with the threshold interval of the normal emotional state determined;

[0067] Use the test set to evaluate the alternative recognition model, and continuously optimize the alternative recognition model according to the evaluation results until the alternative recognition model meets the preset recognition accuracy condition;

[0068] Determine the alternative recognition model that has completed iterative optimization as the state recognition model.

[0069] The microseismic signal collector can capture the tiny vibrations generated when the customer touches the device. These vibrations contain information about the customer's emotional state. By collecting a large number of hand microseismic signals under different emotional states, a rich historical training sample can be constructed for subsequent training and model optimization. The emotional labels are obtained by recording and analyzing the customer's state during collection. They can be categories such as "happy", "calm", "nervous", etc. After matching the emotional labels with the microseismic signal data, a labeled data set is constructed for subsequent model training.

[0070] The deep learning model is a complex mathematical framework, and its performance depends to a large extent on the choice of parameters. Therefore, when constructing the initial state recognition model, it is necessary to carefully analyze the data characteristics of the historical training samples, such as the amplitude and frequency of the signals, as well as the task requirements (such as recognition accuracy, processing speed, etc.), and determine the parameter values of the model accordingly. During the training process, the model continuously optimizes its parameters by learning the relationship between the microseismic signal characteristics in the training set and their corresponding emotional labels. Through multiple iterative trainings, the model can gradually learn how to extract useful information from the microseismic signals to predict the customer's emotional state. After the training is completed, the model will determine a threshold range for the normal emotional state for subsequent judgment and early warning.

[0071] The test set contains some data samples that have not participated in the training, and they are used to evaluate the generalization ability of the model. During the evaluation process, the model will make predictions on the samples in the test set and compare them with the true emotional labels. According to the comparison results, performance indicators such as the recognition accuracy of the model can be calculated. If the performance of the model does not meet the preset recognition accuracy conditions, the model needs to be optimized, such as adjusting parameters, increasing training samples, etc.

[0072] After multiple iterative optimizations, the performance of the alternative recognition model will be significantly improved. When it meets the preset recognition accuracy conditions, it can be determined as the final state recognition model.

[0073] S240. Determine whether the emotional state probability value falls within the threshold range of the normal emotional state.

[0074] S250. If the probability value does not fall within the threshold range, determine whether the customer is in an abnormal emotional state.

[0075] The above determination is based on the output of the state recognition model and a preset threshold range, which is an important part of the early warning mechanism. The threshold range is set according to the training results of the state recognition model and the actual application requirements, and is used to distinguish normal emotions from abnormal emotions. If the probability value falls within the threshold range, it can be considered that the customer is in a normal emotional state; if not, corresponding measures are taken to intervene in the trading process. The embodiments of the present invention combine multiple technical links such as microseismic signal acquisition, encrypted transmission, feature extraction, deep learning model prediction, and emotional state evaluation to provide a real-time early warning mechanism based on emotional state, which helps banks detect and respond to potential trading risks in a timely manner.

[0076] S260. If it is determined that the customer is in an abnormal emotional state, an abnormal emotion prompt message is generated to give an early warning to the customer's current trading behavior.

[0077] Once it is determined that the customer is in an abnormal emotional state, corresponding measures are taken, such as generating an abnormal emotion prompt message to give an early warning to the customer's current trading behavior, to ensure the security of the transaction and the interests of the customer.

[0078] The technical solution of the embodiments of the present invention details the overall solution, mainly elaborates on constructing and optimizing the state recognition model for individual state prediction and how to determine whether the customer is in an abnormal emotional state. Specifically, by receiving and processing the microseismic signal data of the customer in real time, the emotional state of the customer is quickly judged, providing timely decision support for transaction risk prevention and control. In addition, a state recognition model with high accuracy and strong generalization ability is constructed by using deep learning technology, so as to more reliably judge whether the customer is in an abnormal emotional state. By generating early warning information in time, potential trading risks are avoided, providing a new idea and method for bank transaction risk prevention and control, and having significant technical innovation value.

[0079] Embodiment III

[0080] Figure 3 FIG. is a schematic structural diagram of a trading early warning device based on microseismic signals provided by Embodiment III of the present invention. As Figure 3 shown, the device includes:

[0081] A microseismic signal receiving module 310, configured to receive encrypted microseismic signals transmitted from a terminal trading device during a customer's execution of a trading action;

[0082] A microseismic feature extraction module 320, configured to decrypt the information of the encrypted microseismic signals and extract features to obtain current microseismic features corresponding to the encrypted microseismic signals;

[0083] An abnormal state judgment module 330 is configured to input the current microseismic feature into a pre-trained state recognition model for individual state prediction, and judge whether the customer is in an abnormal emotional state according to the individual state prediction result;

[0084] An abnormal state warning module 340 is configured to generate an abnormal emotion prompt message to warn the current trading behavior of the customer if it is determined that the customer is in an abnormal emotional state.

[0085] The technical solution of the embodiment of the present invention receives and analyzes the encrypted microseismic signal generated by the customer during the execution of the trading action, and monitors the emotional state of the customer in real time. If the customer is in an abnormal emotional state, an abnormal emotion prompt message will be generated immediately, so as to prevent the customer from making a trading decision that may damage their own interests when they are emotionally unstable, realizing intelligent monitoring and management of the trading process. At the same time, this method can also effectively prevent fraud, effectively prevent passive trading behaviors caused by external factors, protect the customer's capital security and personal information from being violated, and promote the application and development of microseismic signals in the bank trading link.

[0086] Optionally, based on the above embodiments, the encrypted microseismic signal is obtained by encrypting the real-time microseismic signal that meets the validity verification condition by the terminal device, and the real-time microseismic signal is generated by the customer touching the terminal device to execute the trading action;

[0087] Wherein, the validity verification condition includes at least one of the following: the real-time microseismic signal is a time-sequential continuous signal, the amplitude of the real-time microseismic signal is within a preset amplitude range, and the frequency domain signal matching the real-time microseismic signal is within a preset human microseismic frequency range.

[0088] Optionally, based on the above embodiments, it may further include a state recognition model training unit, which is configured to collect the hand microseismic signal data of multiple customers during trading in different emotions by using the microseismic signal collector embedded in the terminal trading device to obtain a plurality of historical training samples before inputting the current microseismic feature into the pre-trained state recognition model for individual state prediction;

[0089] Add a matching emotion label to each historical training sample to obtain a labeled data set;

[0090] Determine the parameter values for each parameter in the pre-selected deep learning model according to the data characteristics and task requirements of the historical training samples, and construct an initial state recognition model;

[0091] Divide the labeled data set into a training set and a test set according to a preset ratio, and use the training set to train the initial state recognition model to obtain an alternative recognition model that determines the threshold interval of the normal emotional state;

[0092] Evaluate using the alternative recognition model described in the test set, and continuously optimize the alternative recognition model according to the evaluation results until the alternative recognition model meets the preset recognition accuracy condition;

[0093] Determine the alternative recognition model that has completed iterative optimization as the status recognition model.

[0094] Optionally, based on the above embodiments, the abnormal state judgment module 330 may include:

[0095] An emotional state prediction unit, configured to input the current microseismic feature into a pre-trained status recognition model, and obtain the emotional state probability value output by the status recognition model for the current microseismic feature;

[0096] An emotional state comparison unit, configured to determine whether the emotional state probability value falls within the threshold range of the normal emotional state;

[0097] An abnormal emotion determination unit, configured to determine whether the customer is in an abnormal emotional state if the probability value does not fall within the threshold range.

[0098] Optionally, based on the above embodiments, it may further include: a customer identity matching unit, configured to, after determining whether the customer is in an abnormal emotional state according to the individual state prediction result, if it is determined that the customer is in a normal emotional state, request the customer identity information of the customer from the terminal trading device;

[0099] Perform consistency matching on the current microseismic feature according to the customer identity information fed back by the terminal device;

[0100] When it is determined that the consistency matching fails, send a termination transaction instruction to the terminal device.

[0101] Optionally, based on the above embodiments, the customer identity matching unit may further be configured to, according to the customer identity information, call a pre-stored microseismic feature template bound to the customer identity in the database, where the template is the microseismic signal feature collected and stored during the historical transactions of the customer;

[0102] Calculate the similarity between the current microseismic feature and the pre-stored microseismic feature template using a similarity measurement algorithm, and compare the calculated similarity with a matching threshold;

[0103] When the similarity is greater than or equal to the matching threshold, it is determined that the consistency matching is passed; when the similarity is less than the matching threshold, it is determined that the consistency matching fails.

[0104] The trading warning device based on microseismic signals provided by the embodiments of the present invention can execute the trading warning method based on microseismic signals provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0105] It should be noted that the information collected is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure, and application of relevant data, etc., all comply with the relevant laws, regulations, and standards of relevant countries and regions, take necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0106] In addition, a corresponding operation entrance is provided for users to choose to agree or refuse the automated decision-making result; if the user chooses to refuse, the expert decision-making process will be entered.

[0107] Embodiment Four

[0108] Figure 4 The structural schematic diagram of the electronic device 10 that can be used to implement the embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0109] As Figure 4 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0110] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0111] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a transaction warning method based on microseismic signals.

[0112] That is: receiving an encrypted microseismic signal transmitted from a terminal transaction device during a customer's transaction action;

[0113] Decrypting the information and extracting the features of the encrypted microseismic signal to obtain the current microseismic features corresponding to the encrypted microseismic signal;

[0114] Inputting the current microseismic features into a pre-trained state recognition model for individual state prediction, and judging whether the customer is in an abnormal emotional state according to the individual state prediction result;

[0115] If it is determined that the customer is in an abnormal emotional state, an abnormal emotion prompt message is generated to warn the customer's current transaction behavior.

[0116] In some embodiments, a transaction method based on microseismic signals can be implemented as a computer program, which is tangibly included in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of a transaction method based on microseismic signals described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute a transaction method based on microseismic signals in any other suitable manner (e.g., by means of firmware).

[0117] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0118] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing device, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0119] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0120] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0121] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0122] A computing system can include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0123] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0124] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A trading warning method based on microseismic signals, which is executed by a cloud computing server, characterized in that Including: Receiving an encrypted micro-vibration signal transmitted from a terminal transaction device during a customer's execution of a transaction action; Decrypting the information of the encrypted micro-vibration signal and extracting features to obtain a current micro-vibration feature corresponding to the encrypted micro-vibration signal; Inputting the current micro-vibration feature into a pre-trained state recognition model for individual state prediction, and judging whether the customer is in an abnormal emotional state according to the individual state prediction result; If it is determined that the customer is in an abnormal emotional state, generating an abnormal emotional prompt message to give an early warning to the customer's current transaction behavior.

2. The method according to claim 1, characterized in that, The encrypted micro-vibration signal is obtained by encrypting a real-time micro-vibration signal that meets the validity verification conditions, and the real-time micro-vibration signal is generated by the customer touching the terminal transaction device to execute a transaction action; Wherein, the validity verification conditions include at least one of the following: the real-time micro-vibration signal is a time-sequential continuous signal, the amplitude of the real-time micro-vibration signal is within a preset amplitude range, and the frequency-domain signal matching the real-time micro-vibration signal is within a preset human micro-vibration frequency range.

3. The method according to claim 1, characterized in that, Before inputting the current micro-vibration feature into a pre-trained state recognition model for individual state prediction, it further includes: Using a micro-vibration signal collector embedded in the terminal transaction device to collect hand micro-vibration signal data of multiple customers during transactions in different emotions, obtaining a plurality of historical training samples; Adding a matching emotion label to each historical training sample to obtain a labeled data set; Determining parameter values for each parameter in a pre-selected deep learning model according to the data characteristics of the historical training samples and task requirements, and constructing an initial state recognition model; Dividing the labeled data set into a training set and a test set according to a preset ratio, and using the training set to train the initial state recognition model to obtain an alternative recognition model with a threshold interval for the normal emotional state determined; Evaluating the alternative recognition model using the test set, and continuously optimizing the alternative recognition model according to the evaluation result until the alternative recognition model meets the preset recognition accuracy condition; Determining the alternative recognition model that has completed iterative optimization as the state recognition model.

4. The method according to claim 1, wherein Inputting the current micro-vibration feature into a pre-trained state recognition model for individual state prediction, and judging whether the customer is in an abnormal emotional state according to the individual state prediction result, including: Inputting the current micro-vibration feature into a pre-trained state recognition model to obtain an emotional state probability value output by the state recognition model for the current micro-vibration feature; Judging whether the emotional state probability value falls within the threshold interval of the normal emotional state; If the probability value does not fall within the threshold interval, judging whether the customer is in an abnormal emotional state.

5. The method according to any one of claims 1-4, characterized in that, After judging whether the customer is in an abnormal emotional state according to the individual state prediction result, it further includes: If it is determined that the customer is in a normal emotional state, requesting the customer identity information of the customer from the terminal transaction device; Performing consistency matching on the current micro-vibration feature according to the customer identity information fed back by the terminal transaction device; When it is determined that the consistency matching fails, a transaction termination instruction is sent to the terminal transaction device.

6. The method according to claim 5, wherein Based on the customer identity information fed back by the terminal transaction device, the consistency matching of the current microseismic feature is performed, including: Calling a pre-stored microseismic feature template bound to the customer identity in the database according to the customer identity information, where the template is the microseismic signal feature collected and stored by the customer during the historical transaction process; Calculating the similarity between the current microseismic feature and the pre-stored microseismic feature template by using a similarity measurement algorithm, and comparing the calculated similarity with a matching threshold; When the similarity is greater than or equal to the matching threshold, it is determined that the consistency matching is passed; when the similarity is less than the matching threshold, it is determined that the consistency matching fails.

7. A trading warning device based on microseismic signals, characterized in that Including: A microseismic signal receiving module, configured to receive an encrypted microseismic signal transmitted from the terminal transaction device during the customer's execution of a transaction action; A microseismic feature extraction module, configured to decrypt the information of the encrypted microseismic signal and extract features to obtain a current microseismic feature corresponding to the encrypted microseismic signal; An abnormal state judgment module, configured to input the current microseismic feature into a pre-trained state recognition model for individual state prediction, and judge whether the customer is in an abnormal emotional state according to the individual state prediction result; An abnormal state warning module, configured to generate an abnormal emotion prompt message to warn the customer's current transaction behavior if it is determined that the customer is in an abnormal emotional state.

8. An electronic device, characterized in that, The electronic device includes: 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 a transaction warning method based on microseismic signals according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the processor to implement a transaction warning method based on microseismic signals according to any one of claims 1-6 when executed.

10. A computer program product, characterized in that, The computer program product includes a computer program, and the computer program implements a transaction warning method based on microseismic signals according to any one of claims 1-6 when executed by the processor.

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