Identity recognition method and system for hydrological information forecasting system

Abstract

The invention relates to the technical field of identity authentication, in particular to an identity recognition method and system for a hydrological information forecasting system, and the method comprises the steps: obtaining hydrological data collected by a hydrological monitoring terminal, and generating an encrypted hydrological data packet; a worker logs in and uploads first identity authentication information to the cloud for verification; and obtaining a result returned by the identity authentication cloud, including accessing and verifying the encrypted hydrological data packet when authentication succeeds, and uploading the encrypted hydrological data packet to a data analysis end. And when the authentication fails, refusing to access the encrypted data packet. When the response is abnormal, logging in again by using the second identity authentication information; distribution information sent by the distribution channel is obtained, the distribution information is verified, a verification result is obtained, and the distribution information is obtained after the analysis and processing result of the data analysis end is processed by a spreading end worker according to the distribution characteristics. A user can be authenticated by adopting a local standby stricter identity authentication mode when the cloud is abnormal, so that the response efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of identity authentication, and in particular to an identity recognition method and system for a hydrological information forecasting system. Background Art

[0002] Flood reporting is an indispensable part of the flood prevention and disaster reduction system, which involves the collection, analysis and release of water information from various places during the flood season. This work is led by professional hydrological departments to ensure that relevant government departments, public institutions and ordinary people can obtain the latest water situation in a timely manner so as to make effective response measures.

[0003] During the flood reporting process, hydrological stations at all levels are distributed around important water bodies such as rivers and lakes. They observe hydrological elements such as water level, flow rate, flow, rainfall, temperature, etc. at fixed times every day, and quickly transmit the data to the competent hydrological department through specially established communication lines. After receiving the data transmitted by each station, the hydrological department will immediately process and analyze it. After a series of rigorous calculations and audits, an accurate and reliable water situation report is finally formed. Subsequently, the processed information will be sent to relevant government departments, emergency management departments, scientific research units and public media platforms to ensure that all stakeholders can receive the latest and accurate information.

[0004] There is an existing identity authentication management system that manages the identity information of multiple user departments in a unified manner in the cloud. The general process is that the user submits access credentials and then operates the data based on the permissions corresponding to the access credentials. This method improves processing efficiency, but when an abnormality occurs in the cloud, identity authentication cannot be performed in time, which will cause the information to be unable to be effectively transmitted. Summary of the invention

[0005] The purpose of the present invention is to provide an identity recognition method and system for a hydrological information forecasting system, aiming to use a local backup more stringent identity authentication method to authenticate users when an abnormality occurs in the cloud, thereby improving response efficiency and allowing corresponding data to be processed and transmitted in a timely manner.

[0006] To achieve the above-mentioned object, in a first aspect, the present invention provides an identity recognition method for a hydrological information forecasting system, comprising obtaining hydrological data collected by a hydrological monitoring terminal, and generating an encrypted hydrological data packet;

[0007] Obtain the first identity authentication information generated by the monitoring station staff when logging in and upload it to the identity authentication cloud;

[0008] When the authentication success information returned by the identity authentication cloud is obtained, the encrypted hydrological data packet is accessed and verified, and the verified hydrological data is uploaded to the data analysis end; when the authentication failure information returned by the identity authentication cloud is obtained, the access to the encrypted hydrological data packet is denied; when the response exception information returned by the identity authentication cloud is obtained, the second identity authentication information is used to log in and verify the hydrological data, and the verified hydrological data is uploaded to the data analysis end;

[0009] After the hydrological data is uploaded to the data analysis end, the distribution information sent by the distribution channel is obtained, and the distribution information is verified to obtain a verification result. The distribution information is the result of the analysis and processing by the data analysis end and is obtained after being processed by the communication end staff according to the distribution characteristics.

[0010] The specific steps of obtaining the hydrological data collected by the hydrological monitoring terminal and generating an encrypted hydrological data packet include:

[0011] Obtaining hydrological data collected by a hydrological monitoring terminal at predetermined time intervals;

[0012] Cleaning the hydrological data;

[0013] The cleaned hydrological data is encrypted by AES to obtain an encrypted hydrological data packet.

[0014] Wherein, the hydrological monitoring terminal includes a water level meter, a rain gauge and a flow meter.

[0015] The specific steps of obtaining the first identity authentication information generated by the monitoring station staff when logging in and uploading it to the identity authentication cloud include:

[0016] Obtaining identity authentication credentials entered by a staff member through a terminal device, wherein the identity authentication credentials include a user name and a password;

[0017] Encrypting the identity verification certificate for transmission to obtain an encrypted certificate;

[0018] Transmit the encrypted credentials to the identity cloud.

[0019] Among them, when obtaining the abnormal response information returned by the identity authentication cloud, logging in with the second identity authentication information and verifying the hydrological data, and uploading the verified hydrological data to the data analysis end, the specific steps include:

[0020] Obtaining response exception information and issuing a request to obtain second identity authentication information;

[0021] After the request is approved, the second identity authentication information collection module is started to collect the user's appearance information, wherein the appearance information includes clothing images and facial images;

[0022] Compare the appearance information with the local authentication database. After successful comparison, log in and verify the hydrological data, and upload the verified hydrological data to the data analysis terminal.

[0023] The specific steps of comparing the appearance information with the local authentication database, logging in and verifying the hydrological data after the comparison is successful, and uploading the verified hydrological data to the data analysis terminal include:

[0024] Collect user work videos at preset time intervals and generate judgment data sets;

[0025] Recognize clothing images and facial images based on the judgment data set, and generate clothing image data sets and facial image data sets respectively;

[0026] Train the YOLO model based on the facial image dataset to obtain a facial feature recognition model;

[0027] Classifying the clothing image dataset based on the user category identified by the facial feature recognition model to obtain a clothing dataset for each user;

[0028] Based on the clothing data sets of each user, the recognition model is trained respectively to obtain the clothing feature recognition model of each user;

[0029] The facial data in the appearance information is input into the facial feature recognition model to obtain user information through matching;

[0030] Inputting clothing data in the appearance information into the corresponding clothing feature recognition model for clothing matching;

[0031] If the clothing matches successfully, the user will log in and verify the hydrological data, and then upload the verified hydrological data to the data analysis terminal.

[0032] If the match is successful, the hydrological data is logged in and verified, and after uploading the verified hydrological data to the data analysis terminal, the steps further include:

[0033] Store the appearance information when logging in and continuously send login request information to the cloud;

[0034] After successfully obtaining the login request information, upload the appearance information for backup.

[0035] The distribution information is the result of the analysis and processing by the data analysis end, and is edited by the dissemination end staff according to the distribution channel. The specific steps include:

[0036] Obtain the hydrological detection results returned by the data analysis terminal and transmit them to the second local terminal;

[0037] Obtain the third identity authentication information generated by the communication end staff when logging in to the second local terminal;

[0038] When the third identity authentication information is passed, the communication end staff obtains the hydrological detection results and sends them out through the corresponding distribution channel; when the third identity authentication is abnormal, the access failure mark is returned, and the fourth identity authentication information is used to log in to the second local terminal and obtain the hydrological detection results, which are sent out through the corresponding distribution channel.

[0039] In a second aspect, the present invention also provides an identity recognition system for a hydrological information forecasting system, comprising a data collection terminal, a first local terminal, a data analysis terminal, a second local terminal and an identity authentication cloud;

[0040] The data collection terminal is used to collect hydrological data;

[0041] The first local terminal is used for the identity recognition method for the hydrological information forecasting system;

[0042] The data analysis end is used to analyze the verified hydrological data to obtain processing results;

[0043] The second local terminal is used to obtain the processing result and generate a distribution message, which is sent through a distribution channel;

[0044] The identity authentication cloud is used to authenticate the first identity authentication information.

[0045] The identity recognition method and system for the hydrological information forecasting system of the present invention, the hydrological monitoring terminal is responsible for collecting real-time hydrological data in water environments such as rivers and lakes. These data include information in multiple dimensions such as water level, flow rate, temperature, water quality, etc. In order to ensure the security and confidentiality during data transmission, the system will encrypt the original hydrological data and generate one or more encrypted hydrological data packets. When the staff of the monitoring station need to log in to the system, they must provide personal identity authentication information (such as user name and password), which will be uploaded to a special identity authentication cloud server for verification. This process is designed to confirm the legal identity of the operator and prevent unauthorized access.

[0046] If the authentication cloud returns a successful authentication message, the system allows the user to decrypt and access the previously generated encrypted hydrological data package. The verified data will then be uploaded to the data analysis end for further analysis. If the authentication fails, the system automatically rejects any attempt to access the encrypted hydrological data package, thereby protecting sensitive information from being leaked. In the event of an abnormal response, that is, when the authentication cloud fails to give a clear success or failure response, the system will trigger the backup authentication mechanism and require the user to provide a second authentication information (such as a one-time verification code or biometric recognition). Once this round of verification is passed, the hydrological data can also be checked as necessary and submitted to the data analysis end. Finally, after completing all internal processing processes, the system will also receive distribution information generated and optimized by professionals according to specific rules from the data analysis end. This information represents the content of the warning or report to be released to the outside world. The system will strictly verify these distribution information again to ensure that their source is reliable and conforms to the expected format before they are officially released for public reference.

[0047] In summary, this identification method effectively improves the security of the hydrological information forecasting system through multi-layer protection measures, while ensuring the authenticity and reliability of the transmitted information. When the cloud is abnormal, a more stringent local backup identity authentication method can be used to authenticate the user, thereby improving the response efficiency and allowing the corresponding data to be processed and transmitted in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 It is a flow chart of the identity recognition method for the hydrological information forecasting system of the present invention.

[0050] Figure 2 It is a flow chart of the present invention for obtaining hydrological data collected by a hydrological monitoring terminal and generating an encrypted hydrological data packet.

[0051] Figure 3 This is a flow chart of the present invention for obtaining the first identity authentication information generated by the monitoring station staff when logging in and uploading it to the identity authentication cloud.

[0052] Figure 4It is a flowchart of the present invention for obtaining the abnormal response information returned by the identity authentication cloud, logging in with the second identity authentication information and verifying the hydrological data, and uploading the verified hydrological data to the data analysis terminal.

[0053] Figure 5 It is a flowchart of the present invention that compares appearance information with a local authentication database, logs in and verifies hydrological data after successful comparison, and uploads the verified hydrological data to a data analysis terminal.

[0054] Figure 6 It is a flowchart of the distribution information obtained by editing the results of the analysis and processing by the data analysis end of the present invention according to the distribution channel by the dissemination end staff.

[0055] Figure 7 It is a structural diagram of the identity recognition system used in the hydrological information forecasting system of the present invention.

[0056] Data collection terminal 101, first local terminal 102, data analysis terminal 103, second local terminal 104, identity authentication cloud 105. DETAILED DESCRIPTION

[0057] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0058] First embodiment

[0059] See also Figure 1 to Figure 6 The present invention provides an identity recognition method for a hydrological information forecasting system, comprising:

[0060] S101 obtains hydrological data collected by a hydrological monitoring terminal and generates an encrypted hydrological data packet;

[0061] The specific steps include:

[0062] S201 obtains hydrological data collected by a hydrological monitoring terminal at a predetermined time interval;

[0063] The hydrological monitoring terminal includes a water level meter, a rain gauge and a flow meter.

[0064] Key hydrological information is collected through hydrological monitoring terminals distributed throughout the region. These terminals automatically operate at pre-set time intervals to ensure continuous monitoring and recording of hydrological changes.

[0065] Specifically, the hydrological monitoring terminal includes the following important equipment:

[0066] Water level gauge: used to measure the water level of water bodies such as rivers, lakes or reservoirs, which is crucial for flood prevention and early warning.

[0067] Rain gauge: Used to accurately record precipitation and help predict floods or droughts.

[0068] Flow meter: Measuring the speed of water flow is important for understanding the dynamic characteristics of the river and its transportation capacity.

[0069] These devices take a measurement at every predetermined point in time and send the resulting data back.

[0070] S202 cleans up the hydrological data;

[0071] Once the raw data from the hydrological monitoring terminal is received, the next step is to perform necessary cleaning on the data. This step aims to remove any outliers or erroneous records to improve the accuracy of subsequent analysis. The cleaning process includes but is not limited to:

[0072] Detect and remove abnormal readings due to sensor failure or other external factors.

[0073] Correct measurement deviations caused by changes in environmental conditions (such as temperature and humidity).

[0074] Handle missing data points and fill in the gaps through interpolation or other statistical methods.

[0075] S203 performs AES encryption on the cleaned hydrological data to obtain an encrypted hydrological data packet.

[0076] Finally, after ensuring that all hydrological data are clean and reliable, the system will apply the Advanced Encryption Standard (AES) algorithm to encrypt the data. AES is a widely recognized secure encryption method that can effectively protect data from unauthorized access. By encrypting the cleaned hydrological data with AES, a secure encrypted hydrological data packet can be generated. This encrypted data packet not only maintains the integrity and confidentiality of the original data, but also provides additional security for data transmission, ensuring that only authorized users can decrypt and use this important hydrological information.

[0077] In summary, S101 is a process that integrates multiple sub-steps, from data collection to the final formation of a secure encrypted data packet. Each step is indispensable and together constitutes the core function of a modern hydrological monitoring system.

[0078] S102 obtains the first identity authentication information generated by the monitoring station staff when logging in and uploads it to the identity authentication cloud;

[0079] The specific steps include:

[0080] S301 obtains identity authentication credentials input by a staff member through a terminal device, wherein the identity authentication credentials include a user name and a password;

[0081] At this stage, when the staff of the monitoring station need to access the system or perform specific operations, they must log in through a terminal device (such as a computer, tablet or other mobile device). During the login process, the staff needs to provide their authentication credentials, which include at least the following two:

[0082] Username: A unique string of characters used to identify a user, usually consisting of letters and numbers.

[0083] Password: A secret string of characters associated with a username that proves the user's identity.

[0084] The system will prompt the staff to enter the above information and receive this data through a secure interface to ensure that the input process cannot be spied on or intercepted. In addition, for enhanced security, it will require the use of complex password policies, such as including uppercase and lowercase letters, numbers and special characters, and changing passwords regularly.

[0085] S302 encrypts the identity verification certificate for transmission to obtain an encrypted certificate;

[0086] Once the authentication credentials provided by the staff are received, the next step is to encrypt these credentials to protect sensitive information from being intercepted or tampered with. The advanced Transport Layer Security (TLS) protocol or similar strong encryption algorithms are used here to achieve data encryption. Specifically, the authentication credentials are encrypted using recognized security encryption algorithms such as AES (Advanced Encryption Standard) and RSA. Ensure that the keys used in the encryption and decryption process are properly kept and only authorized parties can access them. Attach a message authentication code (MAC) or other form of verification mechanism to ensure that the data has not been modified during transmission.

[0087] S303 transmits the encrypted credentials to the identity authentication cloud.

[0088] The last step is to securely send the encrypted credentials to the identity authentication cloud located on a remote server. The key to this step is to ensure the security and reliability of the transmission channel: using the SSL / TLS protocol to establish an encrypted communication tunnel to prevent man-in-the-middle attacks. The encrypted credentials are uploaded to the cloud through the established secure connection. The cloud server is responsible for storing, managing and verifying these credentials. Each upload activity will be recorded for subsequent auditing and troubleshooting. At the same time, real-time monitoring can help detect any abnormal behavior or potential threats.

[0089] S103 performs access verification on the encrypted hydrological data packet when the authentication success information returned by the identity authentication cloud is obtained, and uploads the verified hydrological data to the data analysis end; when the authentication failure information returned by the identity authentication cloud is obtained, access to the encrypted hydrological data packet is denied; when the response exception information returned by the identity authentication cloud is obtained, log in with the second identity authentication information and verify the hydrological data, and upload the verified hydrological data to the data analysis end;

[0090] After completing the staff's identity authentication process, the system enters the S103 stage, which describes in detail the corresponding processing and operation of the encrypted hydrological data packet based on the different types of response information returned by the identity authentication cloud.

[0091] When the system receives the authentication success information returned by the identity authentication cloud, it means that the currently logged-in staff has been confirmed as a legitimate user. The system will perform access rights checks on the stored encrypted hydrological data packets to ensure that only users who have successfully authenticated can decrypt and access the data. Use the same key or algorithm (such as AES) used to encrypt the hydrological data packet to securely decrypt the data packet and verify the integrity and consistency of the data again to ensure that it has not been tampered with. Once the verification is passed, the system will package the decrypted hydrological data and transmit it to the data analysis end through a secure channel for further analysis and utilization.

[0092] If the identity authentication cloud returns an authentication failure message, it means that the user currently trying to log in has failed to pass the identity authentication. In this case, the system will immediately block any access requests to the encrypted hydrological data package to prevent unauthorized users from obtaining sensitive information. At the same time, the system will record the failed authentication attempt, including timestamps, IP addresses and other information for subsequent audits and security reviews.

[0093] Abnormal response information received from the identity authentication cloud indicates that there is a technical failure or network problem that prevents the authentication process from being completed normally. At this time, the staff is required to provide additional identity authentication information as a secondary verification method. This can be part of a multi-factor authentication, such as a one-time password (OTP), biometric features (fingerprint, facial recognition), or a hardware token. Once the secondary verification is passed, the system will decrypt and verify the encrypted hydrological data packet according to the steps in case one. Finally, the verified data is uploaded to the data analysis end to ensure that the data processing process is not affected.

[0094] In addition, throughout the process, the system should maintain real-time monitoring and log recording to promptly detect and resolve problems that arise, ensuring data security and system stability. This multi-level identity authentication and data processing mechanism not only improves system security, but also provides users with a more reliable working environment.

[0095] When obtaining the abnormal response information returned by the identity authentication cloud, the specific steps of logging in with the second identity authentication information and verifying the hydrological data, and uploading the verified hydrological data to the data analysis end include:

[0096] S401 obtains response exception information and issues a request to obtain second identity authentication information;

[0097] When the system receives an abnormal response from the identity authentication cloud, it indicates that the current identity authentication process has encountered a problem, which is caused by a network failure, a cloud server error, or other unknown reasons. After identifying the abnormal response, the corresponding processing mechanism is triggered immediately. A prompt message is sent to the staff member who is trying to log in, informing them that the initial authentication was unsuccessful and preparing for additional identity authentication. The system sends a request to the terminal device to obtain the second identity authentication information, requiring the user to provide additional verification materials.

[0098] S402: after the request is approved, the second identity authentication information collection module is started to collect the user's appearance information, wherein the appearance information includes clothing images and facial images;

[0099] Once the user's terminal device receives the above request, it will enter the second identity authentication stage. This stage mainly involves the collection of user appearance information, including but not limited to clothing images and facial images:

[0100] The second identity authentication information collection module on the terminal device is activated. The module is usually equipped with a high-definition camera and other necessary sensors. The collection module collects clothing images (for auxiliary verification) and facial images (as the main biometric feature) of the user in normal working state. This step is also combined with ambient light detection to ensure image quality, thereby improving the success rate of subsequent comparisons.

[0101] S403 compares the appearance information with the local authentication database, logs in and verifies the hydrological data after the comparison is successful, and uploads the verified hydrological data to the data analysis terminal.

[0102] After the collection is completed, the system will conduct a strict comparison and verification of the obtained appearance information. The specific steps include:

[0103] S501 collects user work videos at preset time intervals and generates a judgment data set;

[0104] The system sets a preset time interval (e.g. every hour or more frequently as needed) to automatically trigger the camera on the terminal device to record video. Each time, a short working video clip is recorded, which will serve as the basic material for subsequent analysis. Key frames are extracted from each video clip to form a judgment data set containing clothing images and facial images for subsequent recognition and comparison.

[0105] S502 recognizes the clothing image and the facial image based on the judgment data set, and generates a clothing image data set and a facial image data set respectively;

[0106] Use image processing algorithms to separate the clothing image and facial image of each frame from the judgment data set, and construct two independent data sets. Perform necessary preprocessing on the images in these two data sets, such as cropping, resizing, and contrast enhancement, to improve recognition accuracy.

[0107] S503 trains a YOLO model based on the facial image data set to obtain a facial feature recognition model;

[0108] In order to ensure that the model can accurately identify and extract facial feature points, a high-quality facial image dataset must be prepared. This dataset should contain rich and diverse facial samples, including expressions of all workers in the working environment and images under lighting conditions. In addition, each image in the dataset should have accurately labeled facial location information, that is, bounding box coordinates, and possible key points (such as the location of eyes, nose, and mouth).

[0109] Then, necessary preprocessing operations are performed on the original images, such as resizing and normalization, so that they meet the requirements of the model input. Data enhancement techniques (such as random cropping, flipping, color jittering, etc.) are used to increase the diversity of the data and further improve the robustness of the model. The configuration parameters of the YOLO model, such as network structure, learning rate, batch size, etc., are adjusted according to specific hardware resources and requirements.

[0110] The YOLO model is trained using the prepared facial image dataset. During the training process, the model will continuously optimize its weights to minimize the error between the output results and the true labels. During this period, a transfer learning strategy can also be introduced to use the weights of the pre-trained model as the initial value to accelerate convergence and improve the final performance.

[0111] S504 classifies the clothing image dataset based on the user category identified by the facial feature recognition model to obtain clothing datasets of each user;

[0112] Use the facial feature recognition model trained in the previous step to classify each sample in the clothing image dataset to determine which user the clothing image belongs to. Create a corresponding clothing dataset for each user to facilitate the next step of personalized model training.

[0113] S505: training recognition models based on clothing data sets of each user to obtain clothing feature recognition models of each user;

[0114] Create a unique clothing dataset for each user. This requires collecting a large number of images or video clips from the user's daily wear to ensure that the dataset contains the user's clothing changes in different scenes, seasons, and activities. In order to ensure the generalization and robustness of the model, the dataset should also cover various lighting conditions, shooting angles, and background environments as much as possible.

[0115] In addition to still images, dynamic video frames can also be included to capture the natural deformation of clothing as the body moves.

[0116] Label annotation: Each image or video frame needs to be accurately labeled with key information such as clothing type (such as tops, pants, shoes), color, pattern, brand logo, etc.

[0117] Train a clothing feature recognition model for each user. Considering the wide variety of clothing and different styles, model selection and architecture design are crucial. Convolutional neural network (CNN) can usually be used as the basic architecture because it is good at processing spatial hierarchical structures in images. Depending on specific needs, different pre-trained models (such as ResNet, VGG, EfficientNet, etc.) can be selected as the starting point, and then transfer learning can be performed to adapt to the clothing characteristics of specific users.

[0118] S506: input the facial data in the appearance information into the facial feature recognition model, and obtain the user information by matching;

[0119] When there is new appearance information that needs to be verified, the facial data is first input into the previously trained facial feature recognition model. The model outputs the most relevant user information. If the match is successful, it proceeds to the next step; otherwise, access is denied.

[0120] S507: inputting the clothing data in the appearance information into the corresponding clothing feature recognition model for clothing matching;

[0121] The clothing data in the same appearance information is input into the corresponding clothing feature recognition model for a second verification. Only when both face and clothing verifications are successful, the user is considered to be a legitimate user.

[0122] S508 If the clothing is successfully matched, the user logs in and verifies the hydrological data, and uploads the verified hydrological data to the data analysis terminal.

[0123] Once the clothing is matched successfully, the user is allowed to log into the system and is granted access rights. The previously encrypted hydrological data package is decrypted and verified to ensure its integrity and consistency. Finally, the verified hydrological data is securely transmitted to the data analysis end for further analysis.

[0124] S509 stores the appearance information during login and continuously sends login request information to the cloud;

[0125] Record all appearance information collected during the login process, including facial and clothing images, and save them in a local database. Continue to send login request information to the identity authentication cloud to ensure that the connection can be maintained even when the network is unstable.

[0126] After successfully acquiring the login request information, S510 uploads the appearance information for backup.

[0127] When receiving a successful response from the cloud, all relevant appearance information is immediately uploaded to the cloud for backup. At the same time, the log of this login activity is recorded both in the cloud and locally to facilitate subsequent auditing and troubleshooting.

[0128] Through the above detailed steps, the system not only implements strict multi-factor identity authentication, but also ensures the safe processing and uploading of hydrological data. This highly automated and multi-level identity authentication mechanism greatly improves the reliability and security of the system, while also providing users with a more convenient and secure working environment.

[0129] After the hydrological data is uploaded to the data analysis terminal, S104 obtains the distribution information sent by the distribution channel, verifies the distribution information, and obtains the verification result. The distribution information is the result of the analysis and processing by the data analysis terminal and is obtained after being processed by the communication terminal staff according to the distribution characteristics.

[0130] The distribution information is the result of the analysis and processing by the data analysis end. The specific steps to obtain it through editing by the communication end staff according to the distribution channel include:

[0131] S601 obtains the hydrological detection result returned by the data analysis terminal and transmits it to the second local terminal;

[0132] The system first receives the analyzed and processed hydrological test results from the data analysis end. These results contain key conclusions and recommendations drawn from an in-depth analysis of the collected hydrological data. These hydrological test results are securely transmitted to the second local terminal, the device used by the dissemination end staff, using an encrypted communication protocol (such as TLS / SSL). This step ensures that the data will not be intercepted or tampered with during transmission.

[0133] S602 obtains the third identity authentication information generated by the communication end staff logging into the second local terminal;

[0134] When the hydrological test results arrive at the second local terminal, the system will prompt the dissemination end staff to log in to access these results. The staff needs to provide third-party identity authentication information, including but not limited to user name, password, and multi-factor authentication methods (such as one-time password, biometrics, etc.). This is to confirm the legitimacy of the staff's identity and prevent unauthorized personnel from accessing sensitive information.

[0135] S603 When the third identity authentication information is passed, the dissemination end staff obtains the hydrological detection results and sends them out through the corresponding distribution channel; when the third identity authentication is abnormal, the access failure mark is returned, and the fourth identity authentication information is used to log in to the second local terminal and obtain the hydrological detection results, which are sent out through the corresponding distribution channel.

[0136] Once the third-party identity authentication information is verified, the communication staff can legally access and obtain the hydrological test results. The staff will edit and format the hydrological test results appropriately according to the pre-set distribution characteristics (such as the target audience type, urgency, etc.) to make them suitable for specific distribution channels. Finally, the staff will send the processed distribution information through the corresponding distribution channels (such as email, social media, dedicated applications, etc.) to ensure that the information can be communicated to the target audience in a timely and effective manner.

[0137] If the third identity authentication information fails to pass the verification, the system will return an access failure mark, deny the access request, and record the event for subsequent audit. In order not to affect business continuity, the system allows staff to use additional fourth identity authentication information for secondary verification. This can be a higher level of authentication, such as hardware tokens, administrator approval, etc. If the fourth identity authentication information is successfully verified, the staff is allowed to re-login to the second local terminal and obtain the hydrological test results according to the normal process. After that, the staff continues to follow the steps in the above situation one to process the results and send them out through the corresponding distribution channels.

[0138] Throughout the S104 process, the system always focuses on security and reliability. Specific measures include: all login attempts, authentication results and distribution activities are recorded in detail for future review and troubleshooting. The system continuously monitors the entire process to promptly detect and respond to any abnormal behavior or potential security threats. Through multiple protection mechanisms such as multi-factor authentication, encrypted communication, and permission management, it ensures that only authorized users can access and process sensitive hydrological detection results.

[0139] Second embodiment

[0140] See also Figure 7The present invention also provides an identity recognition system for a hydrological information forecasting system, comprising a data collection terminal 101, a first local terminal 102, a data analysis terminal 103, a second local terminal 104 and an identity authentication cloud 105; the data collection terminal 101 is used to collect hydrological data; the first local terminal 102 is used to execute an identity recognition method for a hydrological information forecasting system; the data analysis terminal 103 is used to analyze the verified hydrological data to obtain a processing result; the second local terminal 104 is used to obtain the processing result and generate a distribution message, which is sent out through a distribution channel; the identity authentication cloud 105 is used to authenticate the first identity authentication information.

[0141] This invention provides a comprehensive and efficient identity recognition system designed for hydrological information forecasting systems to ensure data security, accuracy and timeliness. The system consists of multiple key components, each of which plays an indispensable role in ensuring overall functionality and security. The following is a detailed description of each component and its function:

[0142] The data collection terminal 101 collects raw hydrological data from various hydrological monitoring points. These monitoring points include but are not limited to rivers, lakes, reservoirs, etc., which are equipped with various sensors (such as water level gauges, rain gauges, and current meters) to monitor hydrological changes in real time. The operation is to automatically collect hydrological data at predetermined time intervals and transmit the data to subsequent processing links through a secure channel.

[0143] The first local terminal 102 mainly undertakes the following responsibilities: perform initial identity authentication for staff members who attempt to access the system, obtain and process the first identity authentication information (such as user name and password), and interact with the identity authentication cloud 105 to complete the authentication. Once the identity authentication is successful, the first local terminal 102 will clean and AES encrypt the collected hydrological data, generate an encrypted hydrological data packet, and securely transmit it to the data analysis terminal 103. When encountering an authentication failure or an abnormal response, the backup authentication process is started, such as the request and verification of the second identity authentication information.

[0144] The data analysis terminal 103 is a key node for in-depth analysis of verified hydrological data. Its core tasks include: receiving encrypted hydrological data packets from the first local terminal 102, decrypting them using the same key or algorithm, and verifying the integrity and consistency of the data. Using advanced analysis tools and techniques, the decrypted hydrological data is processed to extract valuable information and trend predictions, and generate detailed processing results. The processing results are packaged and sent to the second local terminal 104 through a secure communication protocol for further distribution.

[0145] The staff at the second local terminal 104 dissemination end needs to log in to the system through an additional identity authentication step (i.e., the third identity authentication information) to ensure that only authorized personnel can access the processing results. According to the preset distribution characteristics, the staff can appropriately edit and format the processing results to make them suitable for specific distribution channels. Finally, the staff sends the processed information through the corresponding distribution channel to ensure that the information can be communicated to the target audience in a timely and effective manner. If the third identity authentication fails, the fourth identity authentication information is allowed to be used for secondary verification to ensure that business continuity is not affected.

[0146] As the core guarantee of security in the entire system, the Identity Authentication Cloud 105 strictly verifies all received first identity authentication information to confirm the legitimacy of the user's identity. Manage and distribute keys used to encrypt and decrypt hydrological data to ensure the security of data transmission. Record all authentication activities in detail, including timestamps, IP addresses, authentication results and other information, to facilitate subsequent audits and troubleshooting. Continuously monitor any abnormal behavior or potential threats during the authentication process, and take timely measures to ensure the safe operation of the system.

[0147] Through the collaborative work of the above components, the identity recognition system provided by the present invention not only realizes the effective management of each link in the hydrological information forecast system, but also greatly improves the security and reliability of the system.

[0148] Strict authentication mechanisms are set up at every step from data collection to distribution to ensure that only legitimate users can access sensitive information.

[0149] Advanced encryption technology and data verification methods are used to ensure that hydrological data is not tampered with or leaked during the entire processing process.

[0150] Through customized distribution processes, we ensure that processing results can be delivered to relevant parties quickly and accurately, providing strong support for decision-making.

[0151] Detailed logging and real-time monitoring measures ensure that any abnormal behavior can be discovered and handled in a timely manner, enhancing the transparency and credibility of the system.

[0152] In summary, the identity recognition system provided by the present invention provides a safe and efficient solution for the hydrological information forecasting system, significantly improving the hydrological monitoring and early warning capabilities.

[0153] What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. An identity recognition method for a hydrological information forecasting system, characterized in that: include: Obtain hydrological data collected by the hydrological monitoring terminal and generate encrypted hydrological data packets; Obtain the first identity authentication information generated by the monitoring station staff when logging in and upload it to the identity authentication cloud; When the authentication success information returned by the identity authentication cloud is obtained, the encrypted hydrological data packet is accessed and verified, and the verified hydrological data is uploaded to the data analysis end; when the authentication failure information returned by the identity authentication cloud is obtained, the access to the encrypted hydrological data packet is denied; when the response exception information returned by the identity authentication cloud is obtained, the second identity authentication information is used to log in and verify the hydrological data, and the verified hydrological data is uploaded to the data analysis end; After the hydrological data is uploaded to the data analysis end, the distribution information sent by the distribution channel is obtained, and the distribution information is verified to obtain a verification result. The distribution information is the result of the analysis and processing by the data analysis end and is obtained after being processed by the communication end staff according to the distribution characteristics.

2. The identity recognition method for the hydrological information forecasting system according to claim 1, characterized in that: The specific steps of obtaining the hydrological data collected by the hydrological monitoring terminal and generating an encrypted hydrological data packet include: Obtaining hydrological data collected by a hydrological monitoring terminal at predetermined time intervals; Cleaning the hydrological data; The cleaned hydrological data is encrypted by AES to obtain an encrypted hydrological data packet.

3. The identity recognition method for the hydrological information forecasting system according to claim 2, characterized in that: The hydrological monitoring terminal includes a water level meter, a rain gauge and a flow meter.

4. The identity recognition method for the hydrological information forecasting system according to claim 3, characterized in that: The specific steps of obtaining the first identity authentication information generated by the monitoring station staff when logging in and uploading it to the identity authentication cloud include: Obtaining identity authentication credentials entered by a staff member through a terminal device, wherein the identity authentication credentials include a user name and a password; Encrypting the identity verification certificate for transmission to obtain an encrypted certificate; Transmit the encrypted credentials to the identity cloud.

5. The identity recognition method for the hydrological information forecast system according to claim 4, characterized in that: When obtaining the abnormal response information returned by the identity authentication cloud, the specific steps of logging in with the second identity authentication information and verifying the hydrological data, and uploading the verified hydrological data to the data analysis end include: Obtaining response exception information and issuing a request to obtain second identity authentication information; After the request is approved, the second identity authentication information collection module is started to collect the user's appearance information, wherein the appearance information includes clothing images and facial images; Compare the appearance information with the local authentication database. After successful comparison, log in and verify the hydrological data, and upload the verified hydrological data to the data analysis terminal.

6. The identity recognition method for the hydrological information forecasting system according to claim 5, characterized in that: The specific steps of comparing the appearance information with the local authentication database, logging in and verifying the hydrological data after the comparison is successful, and uploading the verified hydrological data to the data analysis terminal include: Collect user work videos at preset time intervals and generate judgment data sets; Recognize clothing images and facial images based on the judgment data set, and generate clothing image data sets and facial image data sets respectively; Train the YOLO model based on the facial image dataset to obtain a facial feature recognition model; Classifying the clothing image dataset based on the user category identified by the facial feature recognition model to obtain a clothing dataset for each user; Based on the clothing data sets of each user, the recognition model is trained respectively to obtain the clothing feature recognition model of each user; The facial data in the appearance information is input into the facial feature recognition model to obtain user information through matching; Inputting clothing data in the appearance information into the corresponding clothing feature recognition model for clothing matching; If the clothing matches successfully, the user will log in and verify the hydrological data, and then upload the verified hydrological data to the data analysis terminal.

7. The identity recognition method for the hydrological information forecasting system according to claim 6, characterized in that: If the match is successful, the hydrological data is logged in and verified, and after the verified hydrological data is uploaded to the data analysis terminal, the steps further include: Store the appearance information when logging in and continuously send login request information to the cloud; After successfully obtaining the login request information, upload the appearance information for backup.

8. The identity recognition method for the hydrological information forecasting system according to claim 7, characterized in that: The specific steps for the data analysis end to analyze and process the results and get the distribution information through the distribution end staff according to the distribution channel include: Obtain the hydrological detection results returned by the data analysis terminal and transmit them to the second local terminal; Obtain the third identity authentication information generated by the communication end staff when logging in to the second local terminal; When the third identity authentication information is passed, the communication end staff obtains the hydrological detection results and sends them out through the corresponding distribution channel; when the third identity authentication is abnormal, the access failure mark is returned, and the fourth identity authentication information is used to log in to the second local terminal and obtain the hydrological detection results, which are sent out through the corresponding distribution channel.

9. An identity recognition system for a hydrological information forecast system, characterized in that: It includes a data collection terminal, a first local terminal, a data analysis terminal, a second local terminal and an identity authentication cloud; The data collection terminal is used to collect hydrological data; The first local terminal is used to execute the identity recognition method for the hydrological information forecast system according to claim 1; The data analysis end is used to analyze the verified hydrological data to obtain processing results; The second local terminal is used to obtain the processing result and generate a distribution message, which is sent through a distribution channel; The identity authentication cloud is used to authenticate the first identity authentication information.

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