29results about How to "Early detection" patented technology

The invention provides a multi-mode anomaly detection system based on digital twinning, and relates to the technical field of information technologies. Comprising seven main subsystems including a digital twin modeling system, a real-time data acquisition and sensor network, a multi-modal data fusion and processing system, a real-time data analysis and anomaly detection algorithm, a decision support and feedback system, a virtual simulation and visualization system and a safety and compliance management system. The multi-mode anomaly detection system based on digital twinning further comprises six additional subsystems, namely an automatic response and adjustment system, an extensible model updating and management system, an intelligent prediction and early warning system, a deep learning and mode recognition system, a real-time optimization and resource allocation system and an intelligent feedback and user interface optimization system. Through application of a real-time data analysis and anomaly detection algorithm and in combination with a decision support and feedback system, the system can quickly generate decision suggestions or responses, and a decision result is visually displayed through a virtual simulation and visualization system.

The invention belongs to the technical field of multi-sensor data fusion, and discloses a flight path fusion method for a low-altitude anti-unmanned aerial vehicle system. The anti-unmanned aerial vehicle system comprises a radar detection module, a photoelectric detection module and a fusion processing module, and the fusion processing module receives sensor track data of a target within a period of time and performs data calibration, data correlation, state estimation and other operations on the sensor track data of the target. Radar track data and photoelectric track data of a target can be obtained at the same time, and the method has the advantages of long detection distance, early discovery, accurate angle measurement, few false alarms and the like; and the fusion processing module receives the radar track data and the photoelectric track data, then carries out coordinate conversion and matching, and finally carries out fusion processing on the radar track data and the photoelectric track data of the target, so that target fusion track information with more comprehensive information is obtained, and subsequent target identification is facilitated.

The invention discloses a laboratory safety IOT (Internet of Things) intelligent early warning system, which comprises a laboratory environment early warning system, a laboratory equipment early warning system, a laboratory personnel behavior early warning system, a self-adaptive learning system and an integrated control system, wherein the laboratory environment early warning system, the laboratory equipment early warning system, the laboratory personnel behavior early warning system and the adaptive learning system are connected with the integrated control system through a wireless communication network; the adaptive learning system is connected with the laboratory environment early warning system, the laboratory equipment early warning system and the laboratory personnel behavior early warning system through a wireless communication network; the self-adaptive learning system is used for optimizing early warning models of the subsystems, and the integrated control system is used for efficient integration of the subsystems to ensure data sharing and cooperative work among the subsystems. According to the invention, the safety and management efficiency of the laboratory are effectively improved, and the potential risk is reduced.

The invention discloses application of HOXD1 in diagnosis, prognosis and treatment of liver cancer, and particularly relates to application of a reagent for detecting expression of a biomarker HOXD1 in preparation of a product for diagnosing a liver cancer object. The invention discloses application of a reagent for detecting expression of a biomarker HOXD1 in preparation of a product for prognosis of a liver cancer object. The invention discloses an application of HOXD1 as a target in screening medicines for treating liver cancer. The HOXD1 expression inhibitor is applied to preparation of a medicine for treating liver cancer. Differential expression of the HOXD1 in hepatocellular carcinoma tissues and normal hepatocellular carcinoma tissues is clear through research, high expression of the HOXD1 is closely related to the survival reduction period of clinical hepatocellular carcinoma patients, specific knock-down of the HOXD1 can obviously reduce the proliferation capacity of the hepatocellular carcinoma, and a new diagnosis / prognosis marker and a treatment target are provided for early discovery, early treatment and prognosis of the hepatocellular carcinoma. And a new treatment strategy and direction are provided.

The invention discloses a sliding surface feature monitoring method and system based on acoustic emission, and relates to the technical field of landslide monitoring. The method comprises the following steps: mounting an acoustic emission monitoring device; establishing a wave velocity field; dividing a sliding surface area; monitoring and analyzing acoustic emission signals; processing and analyzing data; positioning and modeling a sliding surface; and real-time monitoring and early warning are realized. According to the invention, real-time monitoring and early warning of landslide risks are realized by installing a high-sensitivity acoustic emission monitoring device, establishing a wave velocity field, dividing a sliding surface area, monitoring acoustic emission signals in real time, accurately positioning the position of the sliding surface and constructing a three-dimensional model through professional data processing and analysis. According to the method, the precision and the real-time performance of landslide monitoring can be effectively improved, and powerful technical support is provided for prevention and control of landslide disasters.

The invention provides a chip testing device and optimization method based on multi-station parallelism, and relates to the technical field of chip testing, and the method comprises the steps: executing the chip testing of a first testing link according to a first priority sequence, and outputting a first testing result set; performing feature extraction according to the second associated features to obtain a second feature set, inputting the first test result set and the second feature set into a second fault prediction unit, and outputting a second prediction probability set; and setting a second priority sequence according to the second prediction probability set to execute the chip test of the second test link. According to the chip testing method and device, the technical problems that the chip testing priority setting is unreasonable, the testing resource utilization rate is low and the chip testing efficiency is influenced due to the fact that the chip fault risk cannot be accurately evaluated in an existing chip testing method can be solved, the scientificity and the accuracy of the chip testing priority setting can be improved, and the chip testing efficiency is improved. Potential faults can be found and processed earlier, and follow-up testing time is shortened.

The invention discloses a data acquisition system for intelligent supervision based on steel structure production, which comprises a data acquisition module, a network transmission module and a data analysis system, and relates to the technical field of data management. According to the data acquisition system for intelligent supervision based on steel structure production, the data acquisition system is arranged, a sensor is used for realizing data acquisition, deviation repair is carried out on the position of a steel structure through a data analysis system, and abnormal characteristics of the surface of the steel structure are determined based on data analysis and in combination with a three-dimensional parameter model; then, an execution instruction is generated according to the abnormal characteristics, the mode of processing while acquisition is completed is completed, product positioning consistency in the steel structure production process is kept constantly, the abnormality of the surface of the steel structure is found and analyzed in advance, the loss rate is effectively reduced, and meanwhile the intelligent supervision efficiency of steel structure production is improved; and comprehensive real-time monitoring and accurate data acquisition of the steel structure production process are realized.

The present invention relates to urine metabolite markers for gastric cancer detection and their applications. Based on the metabolomics method, the present invention detects and analyzes the metabolites of urine samples of gastric cancer patients, patients with non-cancerous gastric diseases and healthy controls, and screens 12 metabolite markers with high correlation with the development of gastric diseases. Multivariate multi-classification ROC curve analysis shows that the AUC value reaches 0.842, and the 12 differential metabolite markers have good diagnostic efficacy in distinguishing health, non-cancerous gastric diseases and gastric cancer. Further selecting 8 or 4 combinations from the 12 markers also has the same good diagnostic effect. And the above combination is further confirmed in the validation group. The metabolite combination provided by the present invention can accurately judge whether it is healthy, whether it has non-cancerous gastric diseases, and whether it has gastric cancer, which will help in the early prevention, treatment and prognosis of gastric cancer, etc., with good treatment effect and reduced treatment cost.

The monitoring device includes: an acquisition unit that acquires information on whether a learning model for detecting the state of a storage element is a first mode or a second mode; and a change unit that changes the operation of an equalizing circuit that equalizes the voltage of the storage element from a given state when the learning model is the first mode.

This invention relates to a multimodal orbital classification and recognition method, terminal, and medium based on edge AI. The method, applied to a mobile terminal, includes the following steps: acquiring a two-dimensional color image of a face to be recognized; performing region segmentation on the two-dimensional color image of the face to obtain a color image of the orbital region; acquiring three-dimensional face data corresponding to the two-dimensional color image of the face; obtaining a face depth image based on the three-dimensional face data; performing region segmentation on the face depth image to obtain a depth image of the orbital region; and using the color image of the orbital region and the depth image of the orbital region as input to a lightweight multimodal recognition model to obtain the classification result. Compared with existing technologies, this invention has advantages such as high reliability and small scale.

The invention belongs to the technical field of plant disease-resistant molecule identification, and particularly discloses an early identification method of rape clubroot resistance. The method comprises the following steps: planting aseptic seedlings of to-be-detected rape, known clubroot-resistant rape and clubroot-susceptible rape, inoculating plasmodiophora brassicae to soil at the root of the rape in the seedling stage, extracting DNA of root tissues of the to-be-detected rape and performing qPCR detection on specific genes of the plasmodiophora brassicae on the 5th day after inoculating the plasmodiophora brassicae, taking the known clubroot-resistant rape as a control group, and performing quantitative PCR detection on the specific genes of the plasmodiophora brassicae. Calculating the relative content of the gene, and identifying the anti-susceptibility of the rape to be detected to clubroot according to the relative content of the gene. The plasmodiophora brassicae DNA is taken as a detection target for the first time, pathogens can be found earlier through DNA level detection compared with RNA level detection, DNA serving as the detection target is more reliable than RNA and has high stability, and therefore the requirement for early-stage accurate identification is met.

The present invention discloses a sliding surface feature monitoring method and system based on acoustic emission, and relates to the technical field of landslide monitoring. The process of the present invention includes: installation of acoustic emission monitoring device; establishment of wave velocity field; division of sliding surface area; monitoring and analysis of acoustic emission signals; data processing and analysis; positioning and modeling of sliding surface; real-time monitoring and early warning. The present invention installs a highly sensitive acoustic emission monitoring device, establishes a wave velocity field, divides the sliding surface area, monitors the acoustic emission signal in real time, and accurately locates the sliding surface position through professional data processing and analysis, constructs a three-dimensional model, and realizes real-time monitoring and early warning of landslide risks. This method can effectively improve the accuracy and real-time performance of landslide monitoring, and provide strong technical support for the prevention and control of landslide disasters.

The present invention relates to a method, an application system, and a cloud platform for detecting abrupt changes in spatial data for national territorial spatial planning, wherein the steps in the method for detecting abrupt changes in spatial data for national territorial spatial planning include pre-processing data for detecting abrupt changes and detecting abrupt changes; the application system applies the method for detecting abrupt changes in spatial data for national territorial spatial planning, so as to carry out the abrupt change detection; and the cloud platform is deployed with the application system. In the implementation of technical solution of the present invention, after pre-processing by a higher-level user, all comparison computations are based on a comparison of attribute data, and the efficiency is higher by an order of magnitude than that of graphical computations, which has the advantages of low computational consumption and high efficiency. In addition, a spatial planning cloud support platform is used to carry out computations in a distributed manner, which not only reduces the workload of a higher-level spatial planning management department, but also shortens the process and time for a submission for review to the cloud client by a lower-level spatial planning and compilation department; secondly, it can ensure the accuracy of the spatial data for national territorial spatial planning and correct spatial data tampering caused by mis-operation at any time in a timely manner.

The invention discloses an optimal configuration method for an energy storage system in a power distribution area, and relates to the technical field of energy storage optimization, and the method comprises the steps: constructing an intelligent fault diagnosis strategy, carrying out the real-time monitoring of key parameters of the energy storage system, introducing a fault prediction algorithm, carrying out the system state analysis, recognizing a potential fault, and predicting a possible risk. The predicted risks are classified according to fault types and severity; an automatic fault handling and self-healing judgment mechanism is adopted, a handling strategy is automatically generated, operation parameters are adjusted or a standby system is switched to according to the diagnosed fault, and it is ensured that stable operation can still be maintained under the fault condition. According to the method, an intelligent fault diagnosis strategy is constructed, key parameters of the energy storage system are monitored in real time, and a fault prediction algorithm is introduced, so that potential faults can be effectively identified, and possible risks can be predicted. Compared with traditional manual monitoring, problems can be found earlier and more accurately, and further deterioration of faults is avoided.

The utility model discloses a device for detecting the lifting blockage of cigarette cartons, which comprises two brackets respectively arranged on the left side and the right side of a cigarette carton channel; the hinge is arranged above the cigarette carton channel, and an upper connecting piece of the hinge is connected with the two supports; the cigarette carton channel is covered with the movable baffle, and the upper end of the movable baffle is connected with the lower connecting piece of the hinge; and the detection system is arranged on the movable baffle and used for detecting whether the movable baffle is lifted or not, and if the movable baffle is lifted, the detection system triggers a signal through the controller to stop the packaging machine. According to the device for detecting the lifting blockage of the cigarette cartons, due to the application of the device, obliquely conveyed cigarette cartons, deformed cigarette cartons and transparent cigarette cartons which are not tightly wrapped can be found as soon as possible, so that the cigarette cartons trigger the movable plate, the machine is stopped as soon as possible, and unqualified cigarette cartons are prevented from entering the next working procedure. The quality of the cigarette carton can be quickly and conveniently detected, and meanwhile, the equipment stability is improved.

The invention relates to the technical field of financial risk prediction, and discloses a financial transaction risk dynamic prediction system based on artificial intelligence. A financial data acquisition unit of the system obtains real-time market information flow and associated entity information flow of global multi-market transaction objects, a financial space-time cube is formed through space-time alignment processing, and the data flow and the information flow comprise multiple types of information such as an object asset price fluctuation sequence and a market depth change sequence; the risk factor separation unit executes deep tensor decomposition on the financial space-time cube, and extracts a core factor tensor and an abnormal mode indication tensor; a risk strategy generation unit generates a multi-market collaborative real-time risk prevention and control strategy containing subject asset position adjustment and market open control instructions based on the core factor tensor and the mechanism risk preference map; the dynamic strategy adjustment unit identifies an emergency risk event according to the abnormal mode indication tensor, revises the strategy in real time to generate a new prevention and control strategy, and can effectively deal with the dynamic risk of the financial market.

The utility model discloses a self-diagnosis detection device for the working state of an intermediate relay, relates to the technical field of steel production, and solves the technical problem that a conventional intermediate relay has no detection function when a contact breaks down. Comprising a controller and an intermediate relay, and further comprises an external power supply, the signal output end of the controller is connected with the coil access end of the intermediate relay, the second terminal of the intermediate relay is connected with the first signal input end of the controller, and the third terminal of the intermediate relay is connected with the positive electrode of the external power supply. And a fifth terminal of the intermediate relay is connected with a second signal input end of the controller, a sixth terminal of the intermediate relay is connected with a positive electrode of an external power supply, and a seventh terminal of the intermediate relay is connected with a load. According to the utility model, the contact diagnosis of the intermediate relay is realized, and the self-sensing capability of the system to the state of part of key output control signals is improved.

The invention relates to an early disease detection device based on high-depth cell-free nucleic acid sequencing, and the device can comprise a library preparation part which is used for preparing a library set comprising a whole genome sequencing library, a whole epigenome sequencing library, a transcriptome sequencing library and an epitranscriptome sequencing library based on cell-free nucleic acid extracted from blood; the sequencing part is used for sequencing the library set to generate high-depth cell-free nucleic acid sequencing data; the signal calculation part is used for mapping the high-depth cell-free nucleic acid sequencing data to a genome reference sequence so as to determine the position of the high-depth cell-free nucleic acid sequencing data on a genome, and calculating at least one disease-specific signal value based on the mapped high-depth cell-free nucleic acid sequencing data; a prediction signal value generation section for processing the at least one disease-specific signal value to generate a disease prediction signal value; and a prediction model generation unit for generating a disease prediction model using the marker information relating to the blood and the disease prediction signal value to detect the occurrence or absence of a disease in the input blood and the type of the disease.

A method, process, and computer program for determining an order in which a food product is successively cultured with a set of bacterial cultures in a process for producing a fermented food product, each bacterial culture of the set of bacterial cultures including at least one bacterial strain, wherein the set of bacterial cultures comprises a first subset of one or more bacterial cultures comprising one or more unknown bacterial strain, and a second subset of one or more bacterial cultures comprising one or more known bacterial strains, wherein each of the bacterial strains of the second subset is known; wherein the process is carried out with an initial order of the set of bacterial cultures; wherein during culturing with each bacterial culture of the first subset a process sample is collected, wherein a culture sample of a bacterial culture of the second subset is exposed to the collected process samples in order to determine bacteriophage sensitivities of the one or more bacterial cultures of the second subset to bacteriophages present in the collected process sample, wherein an adapted order of the set of bacterial cultures is determined based on the determined bacteriophage sensitivities such as to reduce common bacteriophage sensitivities in successive bacterial cultures.

The present invention discloses an intelligent optometry and glasses fitting system, including an optometry room, an optometry table, a chair and an optometry instrument, wherein the optometry table, the chair and the optometry instrument are installed in the optometry room as a set; a human feature acquisition device and a controller are provided in the optometry room, wherein the human feature acquisition device is directly opposite to the entrance of the optometry room, and the human feature acquisition device is connected to the controller by signal; a lifting swing arm is provided between the optometry table and the optometry instrument, wherein the chair is an electrically controlled lifting chair, and wherein the controller adjusts the height of the chair and the extension and retraction amount of the swing arm according to the human body height parameter detected by the human feature acquisition device. Beneficial effects: The present invention adjusts the seat height and the extension and retraction amount of the swing arm by the measured human body height and leg length, and can guide the patient to select the frame and the staff to process and assemble the lens by the measured cheek width and pupil distance, and can establish a patient file to automatically store the data.

The invention discloses an electric drive system NVH dynamic test system and method based on XIL. The system comprises a driver-in-the-loop module, a real object-in-the-loop module and an NVH data acquisition system. The driver in-the-loop module is used for collecting a driver real-time operation signal and providing a virtual driving environment; the real object in-the-loop module receives a signal through a real-time simulation machine, and a co-simulation model is used for resolving vehicle dynamic operation parameters; and the rack system drives the dynamometer to apply a dynamic load to the tested electric driving system by adopting a composite control framework based on rotating speed closed-loop control and combined with dynamic compensation according to the parameters. By constructing a human-vehicle-road closed-loop coupling mechanism, the problem that transient load change in a complex driving scene cannot be reproduced in a traditional rack test is solved, working conditions such as urgent acceleration and braking can be simulated in a high-fidelity manner, vibration and noise response of an electric driving system in real dynamic operation can be accurately captured, and the reliability of the electric driving system is improved. And the authenticity and the effectiveness of the NVH test are obviously improved.

The present invention discloses a fire prediction method for fusing multi-source time series data suitable for edge computing, including collecting multi-view RGB images, multi-view infrared images and meteorological data, and preprocessing the data; constructing a Smokey Net prediction model with CNN as the backbone network, inputting the preprocessed data into the prediction model, and performing multi-source data fusion; selecting three attention mechanisms: sparse attention mechanism, strong attention mechanism and multi-head attention mechanism; adding an external register module and a long short-term memory network; and training the Smokey Net prediction model. The present invention solves the problem of insufficient sample data when predicting wild fires, and improves the accuracy of long-term monitoring. Thereby helping to reduce casualties and property losses caused by fires, and improving the efficiency and timeliness of responding to fires.

This invention discloses an online monitoring method for external leakage in aircraft hydraulic systems based on a mechanistic model, belonging to the field of hydraulic system technology. It aims to solve the technical bottlenecks of existing aircraft hydraulic system external leakage monitoring, such as poor real-time performance, high false alarm rate, and inability to detect the leakage amount and rate. The method includes the following steps: First, a mechanistic model is constructed based on the law of conservation of mass. Combined with temperature and pressure data monitored by airborne sensors, the oil volume inside each hydraulic component outside the tank is calculated according to volumetric deformation characteristics, deriving the theoretical value of the oil volume inside the self-pressurized tank. Second, the mechanistic model is corrected by comparing the theoretical value with the actual monitored value using flight parameter data under leak-free conditions, eliminating inherent errors. Finally, real-time flight parameter data is input to recalculate the theoretical value, and compared with the real-time monitoring value from the level sensor to determine if the system is leaking. If leakage occurs, the leakage amount and rate are calculated, and an early warning result is output. This invention relies on existing airborne sensors, requiring no additional equipment or sensors. It can effectively distinguish between normal oil fluctuations and actual leakage, improving monitoring accuracy and real-time performance. It provides data support for the handling and maintenance of aircraft hydraulic system leakage faults and has certain engineering application value.

The application relates to the technical field of intelligent crop pesticide application, and relates to a precision pesticide application method and system of an agricultural robot based on adaptive control, which comprises the following steps: crop pest state score identification is performed on a crop information set to obtain a pest state matrix and acquire a pest matrix change sequence; a risk area set in the pest matrix change sequence is identified according to a pest threshold value, and the total pest intensity of the risk area set is acquired; when the total pest intensity is greater than a pesticide application threshold value, the spraying suitability of an environment information set is identified; when the spraying suitability is greater than an implementation condition threshold value, drug type identification is performed on the crop information set to obtain first pesticide configuration information and second pesticide configuration information; a pest development trend identification operation is performed on the pest matrix change sequence to obtain a pest trend range; and the first pesticide is proportioned according to the first pesticide configuration information and the risk area set, and the second pesticide is proportioned according to the second pesticide configuration information and the pest trend range. The application can improve the accuracy of crop pesticide application.

The invention relates to a polypeptide composition and application thereof, and relates to the technical field of brucellosis detection. The polypeptide composition comprises at least one of an OPM25 polypeptide library and an OPM31 polypeptide library; the OPM25 polypeptide library comprises at least one polypeptide, and the amino acid sequence of any polypeptide is the same as the amino acid sequence of a section of polypeptide in the OPM25 protein; the OPM31 polypeptide library comprises at least one polypeptide, and the amino acid sequence of any polypeptide is the same as the amino acid sequence of a section of polypeptide in the OPM31 protein. The application is the application of the polypeptide composition in preparation of a product for detecting brucellosis.

The present invention discloses a puncture device for treating pig ovarian cysts, including a B-ultrasound probe fixing device and a puncture needle, and a negative pressure system, etc. The B-ultrasound probe fixing device, the B-ultrasound probe matching sleeve is fixed to the position of the lumen near the convergence section, and the B-ultrasound probe connecting line connected to the B-ultrasound probe is led out of the lumen and connected to the input end of the B-ultrasound probe display. The front end of the puncture needle outer sleeve of the puncture needle guide assembly is an expansion head composed of a plurality of arc-shaped pieces, the rear port of the sleeve protrudes outward with a base, and the middle part of the sleeve has a clearance groove. The pressure rod of the pressing component is laterally pressed into the clearance groove by the elastic component and pressed against the side wall of the puncture needle body to fix it. After pressing the elastic component, the pressure rod can be separated from the puncture needle body. On the basis of visualization, the present invention uses puncture, extraction, local administration and other methods to treat pig follicular cysts in the early and middle stages of the disease, which can effectively prevent the disease from worsening, achieve early diagnosis and treatment and good prognosis, thereby increasing the service life of sows.

The utility model discloses a road construction safety early warning vehicle relates to road construction safety early warning technical field, including main part, the main part still includes early warning mechanism, and the bottom of main part is provided with a plurality of moving wheel, the utility model drives push plate to move down with electric telescopic link, and push plate moves down in the process of driving push rod to move down, and push rod moves down in the process of driving connecting strip synchronous down, and further make articulated guardrail expand outward to preset position, like this can accurate cover the dangerous range of construction area, let rear vehicle clear identification safety boundary, and electric telescopic link can reverse contraction, drive articulated guardrail automatic retraction, and the early warning vehicle is convenient for fastly removed to new construction position, and adapts many place operation demand, and articulated guardrail expands, and dangerous alarm flashing light passes through the light of high brightness scintillation, can remind rear vehicle in front of the construction area in several hundred meters, let the driver have enough time to slow down, change lane, and all -round improve the safety protection of construction area.

The invention discloses a cloud collaborative real-time traffic data visualization analysis method and system, and the method comprises the steps: carrying out the traffic data classification of obtained historical traffic data through a traffic classification model, determining the correlation degree between each type of traffic data and each type of security situation through a gray correlation degree method, and determining the sensitivity of each type of traffic data; classifying the preprocessed real-time traffic data by using a traffic classification model, determining the stability of the traffic data by using a weighting method based on the information entropy of the classified traffic data, and visually displaying the stability of the traffic data; and determining a security situation prediction result based on the information entropy of the classified traffic data and the security situation prediction model, evaluating the final security situation prediction result based on the security situation evaluation index system, and visually displaying the security situation evaluation result. According to the method, the accuracy, the real-time performance and the comprehensiveness of network security situation awareness and evaluation are remarkably improved.

The present invention proposes a method for constructing deep speech features and screening mental health, including obtaining speech data and preprocessing the speech data; performing fast Fourier transform on the preprocessed speech data to obtain a power spectrum, applying a Mel filter group to the power spectrum and taking the logarithm to obtain a Mel spectrogram; for each frame of the logarithmic Mel spectrogram, calculating its first-order and second-order differences; constructing a three-channel image based on the obtained logarithmic Mel spectrogram and its first-order and second-order differences; processing the three-channel image to extract deep temporal features and deep spatial features; splicing the deep temporal features with the deep spatial features and using a fully connected layer for dimensionality reduction to obtain a fused speech feature; for the fused speech feature, using a prediction network containing a multi-task learning strategy to screen mental health and predict the severity. The method proposed by the present invention can be applied to psychological and mental health monitoring to achieve early detection, early intervention, and early treatment.