System-based operation control method, device, computer equipment and storage medium

Abstract

The present application relates to the field of artificial intelligence technology, and provides an operation control method, device, computer device, and storage medium based on a system. The method includes: receiving a startup request triggered by a user for a preset system; wherein the startup request carries user information; authenticating the user based on the user information, and determining whether the authentication is passed; if the authentication is passed, starting the preset system, and controlling the preset system to execute a preset business operation; monitoring the user's behavior through a preset monitoring component, analyzing and processing the detected behavior characteristics of the user to obtain a behavior label corresponding to the behavior characteristics; determining a processing strategy corresponding to the behavior label, and controlling the preset system to execute corresponding operations based on the processing strategy. The present application can improve the intelligence of the operation control of the preset system and reduce the occurrence of misoperations of the preset system. The present application can also be applied to the blockchain field, and data such as the above-mentioned behavior characteristics can be stored on the blockchain.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and particularly to an operation control method, device, computer device, and storage medium based on a system. Background Art

[0002] With the continuous increase in the trading volume of the current securities market and the increasing richness of trading behaviors, various electronic trading behaviors have emerged with the development of calculator hardware, software upgrades, and network bandwidth expansion. Some traders can even complete the trading of stocks, bonds, and funds automatically in the securities market through a new electronic trading system. There is currently an automated market-making system that automatically obtains market quotes, calculates reasonable quotes, and executes transactions, and traders are responsible for monitoring the program operation and judging whether the quotes are reasonable. Generally, it is called a human-machine combined market-making system in the industry.

[0003] However, abnormal situations may occur in such human-machine combined market-making systems. That is, when traders monitor the human-machine combined market-making system, they may encounter some emergencies and need to leave their seats temporarily, or the mental state of the traders is not good, and they may forget to turn off the market-making system, resulting in the automated market-making system completing automated quotes without anyone. Once there is a major market change, unreasonable prices may be reported, or even an "erroneous order" event may occur. Therefore, some existing business systems are prone to misoperations during operation, leading to serious consequences, and there is a lack of intelligence in the operation control of business systems. Summary of the Invention

[0004] The main purpose of this application is to provide an operation control method, device, computer device, and storage medium based on a system, aiming to solve the technical problem that existing business systems are prone to misoperations during operation, leading to serious consequences, and there is a lack of intelligence in the operation control of business systems.

[0005] This application proposes an operation control method based on a system. The method includes the steps of:

[0006] Receiving a startup request for a preset system triggered by a user; wherein the startup request carries user information;

[0007] Authenticating the user based on the user information and determining whether the authentication is passed;

[0008] If the authentication is passed, starting the preset system and controlling the preset system to execute a preset business operation;

[0009] Monitoring the user's behavior through a preset monitoring component, analyzing and processing the detected behavior characteristics of the user to obtain a behavior label corresponding to the behavior characteristics;

[0010] Determine a processing strategy corresponding to the behavior tag, and control the preset system to perform corresponding operations based on the processing strategy.

[0011] Optionally, the step of monitoring the user's behavior through a preset monitoring component, analyzing and processing the detected behavior characteristics of the user, and obtaining a behavior tag corresponding to the behavior characteristics includes:

[0012] Obtain a facial image within a specified area based on the monitoring component;

[0013] Obtain facial behavior characteristics corresponding to the facial image;

[0014] Based on the facial behavior characteristics, detect whether the user is in a fatigued state;

[0015] If in a fatigued state, use being in a fatigued state as the behavior tag;

[0016] If not in a fatigued state, use not being in a fatigued state as the behavior tag.

[0017] Optionally, the number of the facial behavior characteristics includes multiple, and the step of detecting whether the user is in a fatigued state based on the facial behavior characteristics includes:

[0018] Count the occurrence times of each of the facial behavior characteristics within a preset time period respectively;

[0019] Obtain weights corresponding to each of the facial behavior characteristics respectively;

[0020] Perform arithmetic processing on each of the occurrence times based on each of the weights to obtain a corresponding fatigue score;

[0021] Judge whether the fatigue score is greater than a preset score threshold;

[0022] If greater than the score threshold, determine that the user is in a fatigued state.

[0023] Optionally, after the step of judging whether the fatigue score is greater than a preset score threshold, it includes:

[0024] If not greater than the score threshold, establish a 3D facial model based on the facial image;

[0025] Determine the fixation position of the user's line of sight based on the 3D facial model;

[0026] Judge whether the fixation position is within a preset range;

[0027] If not within the preset range, obtain the stay time of the user's line of sight at the fixation position;

[0028] Determine whether the residence time is greater than a preset time threshold;

[0029] If it is greater than the time threshold, determine that the user is in a fatigued state, and use being in a fatigued state as the behavior label;

[0030] If it is not greater than the preset time threshold, determine that the user is not in a fatigued state, and use not being in a fatigued state as the behavior label.

[0031] Optionally, the behavior label includes being in a fatigued state or not being in a fatigued state. The steps of determining a processing strategy corresponding to the behavior label and controlling the preset system to perform corresponding operations based on the processing strategy include:

[0032] If the behavior label is being in a fatigued state, control the preset system to stop executing the service operation; and,

[0033] Obtain a preset warning message and display it on the current interface;

[0034] If the behavior label is not being in a fatigued state, control the preset system to continue executing the service operation.

[0035] Optionally, the steps of authenticating the user based on the user information and determining whether the authentication is passed include:

[0036] Obtain a pre-stored standard voice corresponding to the user information, and display the text information corresponding to the standard voice on the current interface;

[0037] Collect the voice to be verified generated after the user reads the text information;

[0038] Extract the voiceprint feature vector to be verified from the voice to be verified, and obtain the standard voiceprint feature vector corresponding to the standard voice;

[0039] Call a preset distance calculation formula to calculate the voiceprint similarity between the voiceprint feature vector to be verified and the standard voiceprint feature vector; and,

[0040] Based on a preset probability calculation algorithm, calculate the conditional probability that the voice to be verified and the standard voice belong to the same sentence;

[0041] Obtain the voiceprint weight corresponding to the voiceprint similarity, and obtain the probability weight corresponding to the conditional probability;

[0042] Based on the voiceprint weight and the probability weight, perform arithmetic processing on the voiceprint similarity and the conditional probability to obtain the corresponding authentication score;

[0043] Determine whether the authentication score is greater than a preset authentication score threshold;

[0044] If it is greater than the authentication score threshold, determine that the authentication is passed;

[0045] If it is not greater than the authentication score threshold, determine that the authentication fails.

[0046] Optionally, after the step of monitoring the user's behavior through a preset monitoring component, it includes:

[0047] If the user's behavior characteristics are not detected, control the preset system to suspend the execution of the business operation;

[0048] Determine whether the user's behavior characteristics are detected within a preset duration;

[0049] If the user's behavior characteristics are not detected within the preset duration, generate a stop processing instruction;

[0050] Based on the stop processing instruction, control the preset system to stop executing the business operation.

[0051] This application also provides an operation control device based on a system, including:

[0052] A receiving module, configured to receive a startup request for a preset system triggered by a user; wherein, the startup request carries user information;

[0053] An authentication module, configured to authenticate the user based on the user information and determine whether the authentication is passed;

[0054] A first control module, configured to, if the authentication is passed, start the preset system and control the preset system to execute a preset business operation;

[0055] An analysis module, configured to monitor the user's behavior through a preset monitoring component, analyze and process the detected user's behavior characteristics, and obtain a behavior label corresponding to the behavior characteristics;

[0056] A second control module, configured to determine a processing strategy corresponding to the behavior label and control the preset system to execute corresponding operations based on the processing strategy.

[0057] This application also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the above method are implemented.

[0058] The present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0059] The operation control method, device, computer device and storage medium based on the system provided in the present application have the following beneficial effects:

[0060] In the operation control method, device, computer device and storage medium based on the system provided in the present application, after receiving a startup request for a preset system triggered by a user and determining that the user has passed the identity verification, the preset system is started, and the preset system is controlled to execute a preset service operation. Then, the behavior of the user is monitored, and the detected behavior characteristics of the user are analyzed and processed to obtain a behavior label corresponding to the behavior characteristics. Finally, a processing strategy corresponding to the behavior label is determined, and the preset system is controlled to execute corresponding operations based on the processing strategy. Through the present application, during the process of the preset system executing the service operation, by analyzing the behavior characteristics of the user and adopting a matching processing strategy based on the obtained behavior label to control the preset system to execute corresponding operations, the occurrence of misoperations of the preset system can be avoided, and the intelligence of the operation control of the preset system can be effectively improved. Description of the Drawings

[0061] Figure 1 is a schematic flowchart of an operation control method based on the system according to an embodiment of the present application;

[0062] Figure 2 is a schematic structural diagram of an operation control device based on the system according to an embodiment of the present application;

[0063] Figure 3 is a schematic structural diagram of a computer device according to an embodiment of the present application.

[0064] The realization, functional features and advantages of the purpose of the present application will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0065] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0066] Those skilled in the art of the present technology can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as that generally understood by those of ordinary skill in the art to which the present invention belongs. It should also be understood that those terms defined in a general dictionary, such as those terms, should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless specifically defined as here.

[0067] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, Artificial Intelligence (AI) is a 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.

[0068] Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. Artificial intelligence software technologies mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0069] Referring to Figure 1 , an operation control method based on a system according to an embodiment of the present application includes:

[0070] S10: Receive a startup request for a preset system triggered by a user; wherein, the startup request carries user information;

[0071] S20: Authenticate the user based on the user information and determine whether the authentication is passed;

[0072] S30: If the authentication is passed, start the preset system and control the preset system to execute a preset business operation;

[0073] S40: Monitor the user's behavior through a preset monitoring component, analyze and process the detected behavior characteristics of the user to obtain a behavior label corresponding to the behavior characteristics;

[0074] S50: Determine a processing strategy corresponding to the behavior label and control the preset system to execute corresponding operations based on the processing strategy.

[0075] As described in the above steps S10 to S50, the execution subject of the method embodiment is an operation control device based on a system. In practical applications, the above-mentioned operation control device based on a system can be implemented by a virtual device, such as software code, or by an entity device written with or integrated with relevant execution code, and can interact with users through a keyboard, mouse, remote control, touchpad, voice control device, etc. The operation control device based on a system in this embodiment can avoid the occurrence of misoperations of the preset system and effectively improve the intelligence of the operation control of the preset system. Specifically, first, a startup request for the preset system triggered by the user is received. Among them, the startup request carries user information. In addition, the startup request is a request triggered by the user to start the preset system. The preset system can be a human-machine integrated market-making system, and the user can be a trader monitoring the human-machine integrated market-making system. In addition, the user information may include the user's name or user ID information. Then, the user is authenticated based on the user information, and it is judged whether the authentication is passed. Among them, for the specific implementation process of authenticating the user based on the user information, this application will further describe the details in subsequent specific embodiments and will not elaborate too much here.

[0076] If the authentication is passed, the preset system is started, and the preset system is controlled to execute preset business operations. Among them, the preset system can be a human-machine integrated market-making system, or an automated market-making system, and the preset business operation can be an automated market-making operation. Subsequently, the user's behavior is monitored through a preset monitoring component, and the detected behavior characteristics of the user are analyzed and processed to obtain a behavior label corresponding to the behavior characteristics. Among them, the behavior label may include being in a fatigue state or not being in a fatigue state. In addition, for the specific implementation process of monitoring the user's behavior through a preset monitoring component, analyzing and processing the detected behavior characteristics of the user, and obtaining a behavior label corresponding to the behavior characteristics, this application will further describe the details in subsequent specific embodiments and will not elaborate too much here. Finally, a processing strategy corresponding to the behavior label is determined, and the preset system is controlled to execute corresponding operations based on the processing strategy. Among them, the corresponding processing strategy can be intelligently determined according to the content of the behavior label. When the behavior label is not in a fatigue state, the preset system will be controlled to continue executing the business operation. When the behavior label is in a fatigue state, the preset system will be intelligently controlled to stop executing the business operation.

[0077] In this embodiment, after receiving a startup request for a preset system triggered by a user and determining that the user has passed the identity verification, the preset system will be started, and the preset system will be controlled to execute preset business operations. Then, the behavior of the user will be monitored, and the detected behavior characteristics of the user will be analyzed and processed to obtain a behavior label corresponding to the behavior characteristics. Finally, a processing strategy corresponding to the behavior label will be determined, and the preset system will be controlled to execute corresponding operations based on the processing strategy. Through this embodiment, during the process of the preset system executing business operations, by analyzing the behavior characteristics of the user and adopting a matching processing strategy based on the obtained behavior label to control the preset system to execute corresponding operations, the occurrence of misoperations of the preset system can be avoided, and the intelligence of the operation control of the preset system can be effectively improved.

[0078] Further, in an embodiment of the present application, the above step S40 includes:

[0079] S400: Obtain a facial image within a specified area based on the monitoring component;

[0080] S401: Obtain facial behavior characteristics corresponding to the facial image;

[0081] S402: Based on the facial behavior characteristics, detect whether the user is in a fatigued state;

[0082] S403: If in a fatigued state, use being in a fatigued state as the behavior label;

[0083] S404: If not in a fatigued state, use not being in a fatigued state as the behavior label.

[0084] As described in the above steps S400 to S404, the step of monitoring the user's behavior through a preset monitoring component, analyzing and processing the detected behavior characteristics of the user, and obtaining a behavior label corresponding to the behavior characteristics may specifically include: First, obtain a facial image within a specified area based on the monitoring component. Herein, the specified area refers to the area where the user's face is located, and the user can adjust the specified area by adjusting the angle and direction of the monitoring component. Additionally, the monitoring component may be a camera device built into the device. Then, obtain the facial behavior characteristics corresponding to the facial image. Among them, the facial image of the user can be obtained through the monitoring component, and then the occurrence times of various facial feature behaviors of the user can be respectively identified and counted by using face detection, facial feature point positioning, image recognition, and target tracking technologies in the existing machine vision technology. Additionally, the facial feature behaviors can select the feature changes of the eyes, mouth, and the whole head. The specific facial feature behaviors are: blinking, yawning, and lowering the head. Subsequently, based on the facial behavior characteristics, detect whether the user is in a fatigued state. Regarding the specific implementation process of detecting whether the user is in a fatigued state based on the facial behavior characteristics, this application will further describe the details in the subsequent specific embodiments and will not elaborate too much here. If in a fatigued state, then the state of being in a fatigued state is used as the behavior label. If not in a fatigued state, then the state of not being in a fatigued state is used as the behavior label. In this embodiment, by obtaining the facial image of the user and then determining the corresponding facial behavior characteristics from the facial image, it is possible to quickly and accurately detect whether the user is in a fatigued state based on the facial behavior characteristics, so as to generate a behavior label corresponding to the behavior characteristics according to the obtained detection result, which is beneficial to subsequently determining a processing strategy corresponding to the behavior label and controlling the preset system to execute corresponding operations based on the processing strategy, thereby effectively avoiding misoperations of the preset system and improving the intelligence of the operation control of the preset system.

[0085] Further, in an embodiment of the present application, the number of the facial behavior characteristics includes multiple, and the above step S402 includes:

[0086] S4020: respectively count the occurrence times of each of the facial behavior characteristics within a preset time period;

[0087] S4021: obtain the weights respectively corresponding to each of the facial behavior characteristics;

[0088] S4022: perform arithmetic processing on each of the occurrence times based on each of the weights to obtain the corresponding fatigue score;

[0089] S4023: determine whether the fatigue score is greater than a preset score threshold;

[0090] S4024: If it is greater than the score threshold, it is determined that the user is in a fatigued state.

[0091] As described in the above steps S4020 to S4024, the number of the facial behavior features includes a plurality. The step of detecting whether the user is in a fatigued state based on the facial behavior features may specifically include: First, count the occurrence times of each of the facial behavior features within a preset time period respectively. Among them, the value of the preset time period is not limited and can be set according to actual needs. For example, it can be within the first 30 minutes from the current time. Then, obtain the weights respectively corresponding to each of the facial behavior features. Among them, the values of the weights respectively corresponding to each of the facial behavior features are not limited and can be set according to actual needs. After that, perform arithmetic processing on each of the occurrence times based on each of the weights to obtain the corresponding fatigue score. Among them, the facial behavior features include blinking, yawning, and lowering the head behavior. Specifically, the fatigue score can be calculated based on the following calculation formula: S1 = z * W1 + h * W2 + d * W3. S1 is the fatigue score, z is the occurrence times of the user blinking within the preset time period, W1 is the weight corresponding to the blinking behavior, h is the occurrence times of the user yawning within the preset time period, W2 is the weight corresponding to the yawning behavior, d is the occurrence times of lowering the head, and W3 is the weight corresponding to the lowering the head behavior. Finally, determine whether the fatigue score is greater than a preset score threshold. Among them, the value of the score threshold is not specifically limited and can be set according to actual needs. If it is greater than the score threshold, it is determined that the user is in a fatigued state. Among them, if the calculated fatigue score is greater than the score threshold, it can be directly determined that the user is currently in a fatigued state. And if the calculated fatigue score is not greater than the score threshold, it may be further necessary to further determine whether the user is currently in a fatigued state based on the 3D facial model of the user subsequently. In this embodiment, by analyzing and processing the occurrence times and weights of the facial behavior features of the user obtained within the preset time period to calculate the corresponding fatigue score, and then the numerical comparison result between the fatigue score and the corresponding score threshold can be used to quickly and accurately detect whether the user is in a fatigued state, which is beneficial to generating the behavior label corresponding to the behavior feature according to the obtained detection result subsequently, so that the processing strategy corresponding to the behavior label can be determined, and the preset system can be controlled to execute the corresponding operation based on the processing strategy, thereby effectively avoiding misoperations of the preset system and improving the intelligence of the operation control of the preset system.

[0092] Further, in an embodiment of the present application, after the above step S4023, it includes:

[0093] S40230: If it is not greater than the score threshold, establish a 3D facial model based on the facial image;

[0094] S40231: Determine the fixation position of the user's line of sight based on the 3D facial model;

[0095] S40232: Determine whether the fixation position is within a preset range;

[0096] S40233: If it is not within the preset range, obtain the dwell time of the user's line of sight staying at the fixation position;

[0097] S40234: Determine whether the dwell time is greater than a preset time threshold;

[0098] S40235: If it is greater than the time threshold, determine that the user is in a fatigue state, and use being in a fatigue state as the behavior label;

[0099] S40236: If it is not greater than the preset time threshold, determine that the user is not in a fatigue state, and use not being in a fatigue state as the behavior label.

[0100] As described in the above steps S40230 to S40236, after the step of determining whether the fatigue score is greater than a preset score threshold, it includes: if it is not greater than the score threshold, first establish a 3D facial model based on the facial image. Wherein, the facial image is specifically a three-dimensional facial image. Since face recognition is easily affected by factors such as environment, pose, and expression, compared with two-dimensional facial images, three-dimensional facial images can provide more complete and richer recognition information. Therefore, three-dimensional face recognition has stronger robustness to factors such as illumination, pose, and expression. Thus, by using the obtained three-dimensional facial image to complete the detection of the user's fatigue state, the purpose of improving the detection accuracy can be achieved. In addition, the monitoring component can specifically be a depth camera, and the depth camera can detect the depth distance of the three-dimensional facial image. Then the device can use this facial image to establish a 3D facial model. Specifically, a three-dimensional facial image within a specified area can be obtained through a binocular vision face recognition algorithm. The binocular vision face recognition algorithm is based on a binocular stereo vision system with a simple structure. By adjusting the relative positions of the left and right cameras and the human face, the two-dimensional image of the human face is collected, so that the image collection can be completed economically and efficiently; then during the recognition process, the active shape model (ASM) technology is used to automatically locate the two-dimensional feature points of the two-dimensional image, and the three-dimensional coordinates of the feature points are obtained by combining the internal and external parameters of the camera, thus avoiding complex three-dimensional face reconstruction; finally, backpropagation (BP) neural network is used for recognition to obtain a three-dimensional facial image. Then determine the fixation position of the user's line of sight based on the 3D facial model. Among them, the pupil positions of the user's two eyes can be determined through the 3D facial model, and the line-of-sight directions of the two eyes can be determined according to the pupil positions. Then the intersection point of the two lines of sight is used as the fixation position of the user's line of sight. And determine whether the fixation position is within a preset range. Among them, after determining the fixation position of the user's line of sight through the 3D facial model, it is judged whether this fixation position is within the preset range. This preset range is the range where the line of sight is located when the user normally monitors the device corresponding to the device, usually the area where the device is located. In addition, according to the differences in the user's personal information, a corresponding preset range can also be set for him, further improving the accuracy of fatigue state detection. For example, for Liang who is relatively tall, the preset range can be appropriately shifted downward, and for Li who is relatively short, the preset range can be appropriately shifted upward. If it is not within the preset range, obtain the dwell time of the user's line of sight staying at the fixation position. If it is greater than the time threshold, it is determined that the user is in a fatigue state, and the fatigue state is used as the behavior label.Among them, if the user is in a fatigued state, there may be a situation where the line of sight stays at an abnormal position for a long time. Therefore, when it is detected that the fixation position is not within the preset range, it is determined whether the fixation time exceeds the preset time threshold. If it exceeds, it is considered that the user is in a fatigued state. In addition, the dwell time is the time when the user's line of sight stays at the fixation position. When the dwell time exceeds this time threshold, it proves that the user's line of sight has deviated from the normal monitoring position for a long time. At this time, it is determined that the user is in a fatigued state. The preset time threshold mentioned here is the maximum value allowed for the time when the line of sight deviates from the normal monitoring position in the normal monitoring state. This time threshold can be set by the manufacturer itself or can be a value determined by relevant technical personnel according to a specific calculation method. The determination method of the time threshold is not specifically limited. Optionally, when the dwell time does not exceed the time threshold, it proves that the user's line of sight has deviated from the normal monitoring position for a short time, and it cannot be determined that the user is in a fatigued state. At this time, a prompt message for line of sight deviation can also be sent so that the user can retract the line of sight in time to maintain the normal business monitoring state. Subsequently, it is determined whether the dwell time is greater than the preset time threshold. In addition, after it is determined that the user is in a fatigued state, a warning signal can also be sent so that the user can wake up in time to avoid misoperations of the preset system. If it is not greater than the preset time threshold, it is determined that the user is not in a fatigued state, and not being in a fatigued state is used as the behavior label. In this embodiment, a 3D facial model is established by using the facial image obtained in the specified area, and then the fixation position of the user's line of sight is determined by using the 3D facial model to judge whether the fixation position of the user's line of sight at this time is within the preset range determined according to the normal monitoring situation. If not, and the dwell time is too long, it is determined that the user is in a fatigued state. After obtaining the facial image based on the image acquisition technology in this embodiment, it is detected whether the user is in a fatigued state by using the established 3D facial model, which improves the accuracy of detecting the user's fatigued state and can effectively reduce the occurrence of fatigue misjudgment situations.

[0101] Further, in an embodiment of the present application, the behavior label includes being in a fatigued state or not being in a fatigued state. The above step S50 includes:

[0102] S500: If the behavior label is being in a fatigued state, control the preset system to stop executing the business operation; and,

[0103] S501: Obtain a preset warning message and display it on the current interface;

[0104] S502: If the behavior label is not being in a fatigued state, control the preset system to continue executing the business operation.

[0105] As described in the above steps S500 to S502, the behavior label includes being in a fatigued state or not being in a fatigued state. The steps of determining a processing strategy corresponding to the behavior label and controlling the preset system to perform corresponding operations based on the processing strategy may specifically include: If the behavior label is being in a fatigued state, control the preset system to stop executing the service operation. And obtain a preset warning message and display it on the current interface. Among them, the warning message can be pre-written and generated according to relevant service requirements and stored in the device. There is no limitation on the specific content of the warning message, and it can be set according to actual needs. If the behavior label is not being in a fatigued state, control the preset system to continue executing the service operation. In this embodiment, by intelligently determining the corresponding processing strategy according to the content of the behavior label. Only when the behavior label is not being in a fatigued state, will the preset system be controlled to continue executing the service operation. When the behavior label is being in a fatigued state, the preset system will be intelligently controlled to stop executing the service operation to avoid the situation that the preset system automatically makes incorrect operations without user monitoring, improving the intelligence of the operation control of the preset system.

[0106] Further, in an embodiment of the present application, the above step S20 includes:

[0107] S200: Obtain the standard voice corresponding to the user information stored in advance, and display the text information corresponding to the standard voice on the current interface;

[0108] S201: Collect the voice to be verified generated after the user reads the text information;

[0109] S202: Extract the voiceprint feature vector to be verified from the voice to be verified, and obtain the standard voiceprint feature vector corresponding to the standard voice;

[0110] S203: Call a preset distance calculation formula to calculate the voiceprint similarity between the voiceprint feature vector to be verified and the standard voiceprint feature vector; and,

[0111] S204: Based on a preset probability calculation algorithm, calculate the conditional probability that the voice to be verified and the standard voice belong to the same sentence;

[0112] S205: Obtain the voiceprint weight corresponding to the voiceprint similarity, and obtain the probability weight corresponding to the conditional probability;

[0113] S206: Based on the voiceprint weight and the probability weight, perform arithmetic processing on the voiceprint similarity and the conditional probability to obtain the corresponding identity verification score;

[0114] S207: Determine whether the authentication score is greater than a preset authentication score threshold;

[0115] S208: If it is greater than the authentication score threshold, determine that the authentication is passed;

[0116] S209: If it is not greater than the authentication score threshold, determine that the authentication fails.

[0117] As described in the above steps S200 to S209, the steps of authenticating the user based on the user information and determining whether the authentication is passed may specifically include: First, obtain the standard voice corresponding to the user information pre-stored and display the text information corresponding to the standard voice on the current interface. Wherein, the standard voice is the voice pre-input by a legitimate user for authentication. Then collect the voice to be verified generated after the user reads the text information aloud. After obtaining the voice to be verified, extract the voiceprint feature vector to be verified from the voice to be verified, and obtain the standard voiceprint feature vector corresponding to the standard voice. Then call a preset distance calculation formula to calculate the voiceprint similarity between the voiceprint feature vector to be verified and the standard voiceprint feature vector. Wherein, the distance calculation formula may be: Let a be the voiceprint feature vector to be verified, and b be the standard voiceprint feature vector. And based on a preset probability calculation algorithm, calculate the conditional probability that the voice to be verified and the standard voice belong to the same sentence. Among them, the probability calculation algorithm is specifically the Naive Bayes algorithm. The Naive Bayes method is a classification method based on Bayes' theorem and the assumption of feature conditional independence. It uses probability statistics knowledge to classify the sample data set. Specifically, it is a method based on Bayes' theorem and assuming that the feature conditions are independent of each other. First, through the given training set, with the premise assumption that the feature words are independent of each other, learn the joint probability distribution from input to output. Then, based on the learned model, input X to find the output Y that maximizes the posterior probability. In this embodiment, the keywords included in the standard voice can be extracted, which can be represented by the set X = {A1, A2, ···, Am}. Then, use the Naive Bayes algorithm to determine whether the standard voice reserved by the user and the voice to be verified input online are the same sentence, which is represented by C = (Y1, Y2). If the obtained result C = 1, it can be represented as the same sentence. If C = 2, it can be represented as not the same sentence. Calculate the corresponding conditional probabilities P(Y1|X) and P(Y2|X). The specific calculation method is not limited. Thus, the conditional probability N that the standard voice reserved by the user and the voice to be verified input online are the same sentence is obtained. In addition, based on the Naive Bayes algorithm, text similarity training can be performed on the pre-collected samples until the constraint conditions are met to obtain the corresponding classification model. Then, subsequently, the conditional probability that the voice to be verified and the standard voice belong to the same sentence can be calculated by using this classification model. The constraint conditions can be determined by using the optimization direction of the stochastic gradient descent algorithm. Subsequently, obtain the voiceprint weight corresponding to the voiceprint similarity, and obtain the probability weight corresponding to the conditional probability. Among them, the values of the voiceprint weight and the probability weight are not specifically limited and can be set according to actual needs. For example, they can be generated by analyzing historical data, or the parameters can be determined by using genetic algorithms, etc. After obtaining the voiceprint weight and the probability weight, based on the voiceprint weight and the probability weight, perform arithmetic processing on the voiceprint similarity and the conditional probability to obtain the corresponding identity verification score. Among them, record the voiceprint similarity as R, the voiceprint weight coefficient corresponding to the voiceprint similarity R as W4, and the probability weight coefficient corresponding to the conditional probability T as W5. Then, the identity verification score can be calculated by the formula S2 = R * W4 + T * W5. Finally, determine whether the identity verification score is greater than the preset verification score threshold. Among them, the value of the verification score threshold is not specifically limited and can be set according to historical experience values. If it is greater than the verification score threshold, it is determined that the identity verification is passed. If it is not greater than the verification score threshold, it is determined that the identity verification is not passed.In this embodiment, by performing voiceprint analysis and text analysis on the voice to be verified based on the standard voice, and generating the corresponding identity verification score according to the obtained voiceprint analysis result and text analysis result, the user can be accurately authenticated by comparing the identity verification score with the preset verification score threshold, so that the identity verification result of the user can be quickly generated, effectively improving the accuracy and reliability of the identity verification. In addition, only when the user passes the identity verification, the subsequent startup request of the user for the preset system will be responded to, effectively ensuring the processing standardization and security of the startup request.

[0118] Further, in an embodiment of the present application, after the above step S40 of monitoring the behavior of the user through the preset monitoring component, it includes:

[0119] S410: If the behavior characteristics of the user are not detected, control the preset system to suspend the execution of the service operation;

[0120] S411: Determine whether the behavior characteristics of the user are detected within a preset time period;

[0121] S412: If the behavior characteristics of the user are not detected within the preset time period, generate a stop processing instruction;

[0122] S413: Based on the stop processing instruction, control the preset system to stop executing the service operation.

[0123] As described in the above steps S410 to S413, after the step of monitoring the user's behavior through a preset monitoring component, the following steps may further be included: If the user's behavior characteristics are not detected, control the preset system to suspend the execution of the service operation. Among them, there may also be a situation where the user's behavior characteristics are not detected. For example, when the user is currently not in front of the device corresponding to the device, the preset system will first suspend the execution of the service operation. Then, it is determined whether the user's behavior characteristics are detected within a preset duration. If the user's behavior characteristics are not detected within the preset duration, a stop processing instruction is generated. Subsequently, based on the stop processing instruction, control the preset system to stop the execution of the service operation. Among them, the value of the preset duration is not specifically limited and can be set according to actual needs. For example, it can be set to 1 minute. If the user's behavior characteristics are still not detected within this preset duration, it will be determined that the user has left for other things and forgotten to turn off the preset system, and the preset system will be stopped from executing the service operation. In this embodiment, when the user's behavior characteristics are not detected, the preset system will first suspend the execution of the service operation, and will further detect whether the user's behavior characteristics are detected within the preset duration. If the user's behavior characteristics are still not detected within this preset duration, the preset system will be intelligently stopped from executing the service operation, thereby avoiding the occurrence of misoperations of the preset system and effectively improving the processing intelligence of controlling the preset system to execute service operations.

[0124] The operation control method based on the system in the embodiments of the present application can also be applied to the blockchain field, such as storing the above behavior characteristics and other data on the blockchain. By using the blockchain to store and manage the above behavior characteristics, the security and immutability of the above behavior characteristics can be effectively ensured.

[0125] The above blockchain is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a string of data blocks generated by using cryptographic methods. Each data block contains information on a batch of network transactions, which is used to verify the validity (anti-counterfeiting) of the information and generate the next block. The blockchain can include a blockchain underlying platform, a platform product service layer, and an application service layer, etc.

[0126] The underlying blockchain platform may include processing modules such as user management, basic services, smart contracts, and operation monitoring. Among them, the user management module is responsible for the identity information management of all blockchain participants, including maintaining the generation of public and private keys (account management), key management, and the maintenance of the correspondence between the real identity of the user and the blockchain address (permission management). And under authorized circumstances, it supervises and audits the transaction situations of certain real identities, and provides the rule configuration for risk control (risk control and audit); the basic service module is deployed on all blockchain node devices to verify the validity of business requests, and after reaching a consensus on valid requests, records them in storage. For a new business request, the basic service first performs interface adaptation parsing and authentication processing (interface adaptation), then encrypts the business information through a consensus algorithm (consensus management), transmits it to the shared ledger in a complete and consistent manner after encryption (network communication), and performs record storage; the smart contract module is responsible for the registration and issuance of contracts, contract triggering, and contract execution. Developers can define contract logic through a certain programming language, publish it to the blockchain (contract registration), trigger the execution by calling keys or other events according to the logic of the contract terms, complete the contract logic, and also provide functions for contract upgrade and cancellation; the operation monitoring module is mainly responsible for the deployment, configuration modification, contract setting, cloud adaptation during the product release process, and the visual output of the real-time state during product operation, such as: alarming, monitoring network conditions, monitoring the health status of node devices, etc.

[0127] Referring to Figure 2 , in an embodiment of the present application, an operation control device based on the system is further provided, including:

[0128] A receiving module 1, configured to receive a startup request for a preset system triggered by a user; wherein, the startup request carries user information;

[0129] A verification module 2, configured to authenticate the user based on the user information and determine whether the authentication is passed;

[0130] A first control module 3, configured to, if the authentication is passed, start the preset system and control the preset system to execute a preset business operation;

[0131] An analysis module 4, configured to perform behavior monitoring on the user through a preset monitoring component, analyze and process the detected behavior characteristics of the user, and obtain a behavior label corresponding to the behavior characteristics;

[0132] A second control module 5, configured to determine a processing strategy corresponding to the behavior label and control the preset system to execute corresponding operations based on the processing strategy.

[0133] In this embodiment, the operations respectively performed by the above-mentioned modules or units correspond one by one to the steps of the operation control method based on the system in the foregoing embodiment, and will not be elaborated here.

[0134] Further, in an embodiment of the present application, the above-mentioned analysis module 4 includes:

[0135] A first acquisition unit, configured to acquire a facial image within a specified area based on the monitoring component;

[0136] A second acquisition unit, configured to acquire facial behavior characteristics corresponding to the facial image;

[0137] A detection unit, configured to detect whether the user is in a fatigued state based on the facial behavior characteristics;

[0138] A first determination unit, configured to, if in a fatigued state, use being in a fatigued state as the behavior label;

[0139] A second determination unit, configured to, if not in a fatigued state, use not being in a fatigued state as the behavior label.

[0140] In this embodiment, the operations respectively performed by the above-mentioned modules or units correspond one by one to the steps of the operation control method based on the system in the foregoing embodiment, and will not be elaborated here.

[0141] Further, in an embodiment of the present application, the number of the facial behavior characteristics includes a plurality. The above-mentioned detection unit includes:

[0142] A statistics subunit, configured to respectively count the occurrence times of each of the facial behavior characteristics within a preset time period;

[0143] A first acquisition subunit, configured to acquire weights respectively corresponding to each of the facial behavior characteristics;

[0144] A calculation subunit, configured to perform arithmetic processing on each of the occurrence times based on each of the weights to obtain corresponding fatigue scores;

[0145] A first judgment subunit, configured to judge whether the fatigue score is greater than a preset score threshold;

[0146] A first determination subunit, configured to, if greater than the score threshold, determine that the user is in a fatigued state.

[0147] In this embodiment, the operations respectively performed by the above-mentioned modules or units correspond one by one to the steps of the operation control method based on the system in the foregoing embodiment, and will not be elaborated here.

[0148] Further, in an embodiment of the present application, the above-mentioned detection unit includes:

[0149] A sub-unit is established to build a 3D facial model based on the facial image if it is not greater than the score threshold.

[0150] A determination sub-unit is used to determine the fixation position of the user's line of sight based on the 3D facial model.

[0151] A second judgment sub-unit is used to judge whether the fixation position is within a preset range.

[0152] A second acquisition sub-unit is used to acquire the dwell time of the user's line of sight staying at the fixation position if it is not within the preset range.

[0153] A third judgment sub-unit is used to judge whether the dwell time is greater than a preset time threshold.

[0154] A second determination sub-unit is used to determine that the user is in a fatigued state if it is greater than the time threshold, and use being in a fatigued state as the behavior label.

[0155] A third determination sub-unit is used to determine that the user is not in a fatigued state if it is not greater than the preset time threshold, and use not being in a fatigued state as the behavior label.

[0156] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the operation control method based on the system in the foregoing embodiment, and will not be elaborated herein.

[0157] Further, in an embodiment of the present application, the behavior label includes being in a fatigued state or not being in a fatigued state. The above second control module 5 includes:

[0158] A first control unit is used to control the preset system to stop executing the service operation if the behavior label is being in a fatigued state; and,

[0159] A display unit is used to acquire a preset warning message and display it on the current interface.

[0160] A second control unit is used to control the preset system to continue executing the service operation if the behavior label is not being in a fatigued state.

[0161] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the operation control method based on the system in the foregoing embodiment, and will not be elaborated herein.

[0162] Further, in an embodiment of the present application, the above verification module 2 includes:

[0163] A third acquisition unit is used to acquire a pre-stored standard voice corresponding to the user information and display the text information corresponding to the standard voice on the current interface.

[0164] A collection unit, configured to collect the voice to be verified generated after the user reads the text information aloud;

[0165] An extraction unit, configured to extract a voiceprint feature vector to be verified from the voice to be verified, and obtain a standard voiceprint feature vector corresponding to the standard voice;

[0166] A first calculation unit, configured to call a preset distance calculation formula to calculate the voiceprint similarity between the voiceprint feature vector to be verified and the standard voiceprint feature vector; and,

[0167] A second calculation unit, configured to calculate the conditional probability that the voice to be verified and the standard voice belong to the same sentence based on a preset probability calculation algorithm;

[0168] A fourth acquisition unit, configured to acquire a voiceprint weight corresponding to the voiceprint similarity, and acquire a probability weight corresponding to the conditional probability;

[0169] A third calculation unit, configured to perform arithmetic processing on the voiceprint similarity and the conditional probability based on the voiceprint weight and the probability weight to obtain a corresponding identity verification score;

[0170] A judgment unit, configured to judge whether the identity verification score is greater than a preset verification score threshold;

[0171] A first determination unit, configured to determine that the identity verification is passed if it is greater than the verification score threshold;

[0172] A second determination unit, configured to determine that the identity verification fails if it is not greater than the verification score threshold.

[0173] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the operation control method based on the system in the foregoing embodiment, and will not be elaborated herein.

[0174] Further, in an embodiment of the present application, the above operation control device based on the system includes:

[0175] A third control module, configured to control the preset system to suspend the execution of the service operation if the behavior characteristics of the user are not detected;

[0176] A judgment module, configured to judge whether the behavior characteristics of the user are detected within a preset time period;

[0177] A generation module, configured to generate a stop processing instruction if the behavior characteristics of the user are not detected within the preset time period;

[0178] The fourth control module is used to control the preset system to stop executing the service operation based on the stop processing instruction.

[0179] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the operation control method based on the system in the foregoing embodiment, and will not be elaborated herein.

[0180] Refer to Figure 3 , in the embodiment of the present application, a computer device is further provided. The computer device may be a server, and its internal structure may be as Figure 3 shown. The computer device includes a processor, a memory, a network interface, a display screen, an input device, and a database connected through a system bus. Among them, the processor designed for the computer device is used to provide computing and control capabilities. The memory of the computer device includes a storage medium and an internal memory. The storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the storage medium. The database of the computer device is used to store user information, behavior characteristics, behavior tags, and processing strategies. The network interface of the computer device is used to communicate with an external terminal through a network connection. The display screen of the computer device is an essential graphic and text output device in the computer, which is used to convert digital signals into optical signals so that text and graphics are displayed on the screen of the display screen. The input device of the computer device is the main device for information exchange between the computer and users or other devices, and is used to transmit data, instructions, and certain flag information into the computer. When the computer program is executed by the processor, it realizes an operation control method based on the system.

[0181] The above processor executes the steps of the above operation control method based on the system:

[0182] Receive a startup request for a preset system triggered by a user; wherein, the startup request carries user information;

[0183] Authenticate the user based on the user information and determine whether the authentication is passed;

[0184] If the authentication is passed, start the preset system and control the preset system to execute a preset service operation;

[0185] Monitor the user's behavior through a preset monitoring component, analyze and process the detected behavior characteristics of the user, and obtain a behavior tag corresponding to the behavior characteristics;

[0186] Determine a processing strategy corresponding to the behavior tag, and control the preset system to execute corresponding operations based on the processing strategy.

[0187] Those skilled in the art can understand,Figure 3 The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the devices and computer equipment to which the solution of this application is applied.

[0188] An embodiment of this application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements an operation control method based on a system, specifically:

[0189] Receive a startup request for a preset system triggered by a user; wherein, the startup request carries user information;

[0190] Authenticate the user based on the user information and determine whether the authentication is passed;

[0191] If the authentication is passed, start the preset system and control the preset system to execute a preset business operation;

[0192] Monitor the user's behavior through a preset monitoring component, analyze and process the detected behavior characteristics of the user to obtain a behavior label corresponding to the behavior characteristics;

[0193] Determine a processing strategy corresponding to the behavior label and control the preset system to execute corresponding operations based on the processing strategy.

[0194] In summary, in the operation control method, device, computer equipment and storage medium based on a system provided in the embodiments of this application, after receiving a startup request for a preset system triggered by a user and determining that the user has passed the authentication, the preset system will be started, and the preset system will be controlled to execute a preset business operation, and then the user's behavior will be monitored, and the detected behavior characteristics of the user will be analyzed and processed to obtain a behavior label corresponding to the behavior characteristics. Finally, a processing strategy corresponding to the behavior label will be determined, and the preset system will be controlled to execute corresponding operations based on the processing strategy. Through the embodiments of this application, during the process of the preset system executing a business operation, by analyzing the behavior characteristics of the user and adopting a matching processing strategy based on the obtained behavior label to control the preset system to execute corresponding operations, the occurrence of misoperations of the preset system can be avoided, and the intelligence of the operation control of the preset system can be effectively improved.

[0195] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided in this application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0196] It should be noted that in this document, the terms "include", "comprise", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that includes a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "including one..." does not exclude the presence of additional identical elements in the process, apparatus, article, or method that includes the element.

[0197] The above are only the preferred embodiments of this application, and do not limit the patent scope of this application. Any equivalent structure or equivalent process transformation made using the content of the specification and drawings of this application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of this application.

Claims

1. A system-based operation control method, characterized in that, Including: Receiving a startup request for a preset system triggered by a user; wherein, the startup request carries user information; Authenticating the user based on the user information and determining whether the authentication is passed; If the authentication is passed, starting the preset system and controlling the preset system to execute a preset business operation; Monitoring the user's behavior through a preset monitoring component, analyzing and processing the detected behavior characteristics of the user, and obtaining a behavior label corresponding to the behavior characteristics; Determining a processing strategy corresponding to the behavior label and controlling the preset system to execute corresponding operations based on the processing strategy; The step of monitoring the user's behavior through a preset monitoring component, analyzing and processing the detected behavior characteristics of the user, and obtaining a behavior label corresponding to the behavior characteristics includes: Obtaining a facial image within a specified area based on the monitoring component; Obtaining facial behavior characteristics corresponding to the facial image; Based on the facial behavior characteristics, detecting whether the user is in a fatigued state; If in a fatigued state, taking being in a fatigued state as the behavior label; If not in a fatigued state, taking not being in a fatigued state as the behavior label; The number of the facial behavior characteristics includes multiple, and the step of detecting whether the user is in a fatigued state based on the facial behavior characteristics includes: Respectively counting the occurrence times of each of the facial behavior characteristics within a preset time period; Obtaining weights respectively corresponding to each of the facial behavior characteristics; Performing arithmetic processing on each of the occurrence times based on each of the weights to obtain a corresponding fatigue score; Judging whether the fatigue score is greater than a preset score threshold; If greater than the score threshold, determining that the user is in a fatigued state; The facial behavior characteristics include blinking, yawning, and head-down behavior; The fatigue score is calculated through the calculation formula: S1 = z*W1 + h*W2 + d*W3; Wherein, S1 is the fatigue score, z is the occurrence times of the user's blinking within a preset time period, W1 is the weight corresponding to the blinking behavior, h is the occurrence times of the user's yawning within the preset time, W2 is the weight corresponding to the yawning behavior, d is the occurrence times of head-down, and W3 is the weight corresponding to the head-down behavior.

2. The system-based operation control method according to claim 1, characterized in that, After the step of judging whether the fatigue score is greater than a preset score threshold, it includes: If not greater than the score threshold, establishing a 3D facial model based on the facial image; Determining the fixation position of the user's line of sight based on the 3D facial model; Judging whether the fixation position is within a preset range; If not within the preset range, obtaining the stay time of the user's line of sight at the fixation position; Judging whether the stay time is greater than a preset time threshold; If greater than the time threshold, determining that the user is in a fatigued state and taking being in a fatigued state as the behavior label; If not greater than the preset time threshold, determining that the user is not in a fatigued state and taking not being in a fatigued state as the behavior label.

3. The system-based operation control method according to claim 1, characterized in that, The behavior tags include being in a fatigued state or not being in a fatigued state. The steps of determining a processing strategy corresponding to the behavior tag and controlling the preset system to perform corresponding operations based on the processing strategy include: If the behavior tag is being in a fatigued state, control the preset system to stop executing the service operation; and, Obtain a preset warning message and display it on the current interface; If the behavior tag is not being in a fatigued state, control the preset system to continue executing the service operation.

4. The system-based operation control method according to claim 1, characterized in that, The steps of authenticating the user based on the user information and determining whether the authentication is successful include: Obtain a pre-stored standard voice corresponding to the user information and display the text information corresponding to the standard voice on the current interface; Collect the voice to be verified generated after the user reads the text information aloud; Extract a voiceprint feature vector to be verified from the voice to be verified, and obtain a standard voiceprint feature vector corresponding to the standard voice; Call a preset distance calculation formula to calculate the voiceprint similarity between the voiceprint feature vector to be verified and the standard voiceprint feature vector; and, Based on a preset probability calculation algorithm, calculate the conditional probability that the voice to be verified and the standard voice belong to the same sentence; Obtain a voiceprint weight corresponding to the voiceprint similarity and a probability weight corresponding to the conditional probability; Based on the voiceprint weight and the probability weight, perform arithmetic processing on the voiceprint similarity and the conditional probability to obtain a corresponding authentication score; Determine whether the authentication score is greater than a preset verification score threshold; If it is greater than the verification score threshold, determine that the authentication is successful; If it is not greater than the verification score threshold, determine that the authentication fails.

5. The operation control method based on the system according to claim 1, wherein, After the step of monitoring the user's behavior through a preset monitoring component, it includes: If the user's behavior characteristics are not detected, control the preset system to pause executing the service operation; Determine whether the user's behavior characteristics are detected within a preset time period; If the user's behavior characteristics are not detected within the preset time period, generate a stop processing instruction; Based on the stop processing instruction, control the preset system to stop executing the service operation.

6. An operation control device based on the system, wherein, Used to implement the operation control method based on the system according to any one of claims 1 to 5, including: A receiving module, used to receive a start request triggered by the user for the preset system; wherein, the start request carries user information; A verification module, used to authenticate the user based on the user information and determine whether the authentication is successful; A first control module, used to start the preset system and control the preset system to execute a preset service operation if the authentication is successful; An analysis module, used to monitor the user's behavior through a preset monitoring component, analyze and process the detected user's behavior characteristics, and obtain a behavior tag corresponding to the behavior characteristics; A second control module, used to determine a processing strategy corresponding to the behavior tag and control the preset system to perform corresponding operations based on the processing strategy.

7. A computer device, including a memory and a processor, wherein a computer program is stored in the memory, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium, on which a computer program is stored, wherein, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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