16 results about "Diagnosis Type" patented technology

The invention discloses a heat supply energy consumption diagnosis and analysis system fusing a big language model and cloud-mist edge collaboration, and the system comprises an edge processing layer which is used for monitoring the energy consumption related data and heat supply operation data of a heat supply system diagnosis object; the fog processing layer is used for extracting strong correlation data characteristics influencing energy consumption and establishing an energy consumption prediction model of a heat supply system diagnosis object; the analysis module is also used for establishing an energy consumption anomaly analysis knowledge graph according to the heat supply operation data and energy consumption anomaly analysis mechanism knowledge; the cloud processing layer is used for training a heat supply system energy consumption abnormity diagnosis large model according to the energy consumption predicted value of the heat supply system diagnosis object in combination with a preset energy consumption diagnosis standard value; and the server is also used for obtaining an energy consumption abnormality diagnosis type and an abnormality diagnosis object according to the heat supply system energy consumption abnormality diagnosis large model, performing question and answer intention recognition and constructing an energy consumption abnormality analysis question and answer agent based on the large language model in combination with the energy consumption abnormality analysis knowledge graph, and generating an energy consumption abnormality analysis and energy-saving recommendation scheme answer.

The invention provides a liver function data detection method and system for liver cancer high-risk groups, and relates to the field of health management. Context snapshots based on user IDs are created and updated in real time in a high-speed memory cache region, and a user diagnosis type, a medication state and a medication timestamp are combined to detect the liver function data of the liver cancer high-risk groups. According to the method and the device, the liver function data is subjected to differential judgment on the basis of the preset logic tree, so that the problems of misinformation and missing report caused by lack of context information in the prior art are effectively solved, the liver function data can be subjected to differential judgment accurately in real time according to the diagnosis type and the medication state of the user, misinformation and missing report are effectively avoided, and the user experience is improved. And the accuracy and the reliability of the alarm system are improved.

The invention discloses a grain diagnosis model training method, a grain diagnosis method, a grain diagnosis device and electronic equipment, and relates to the field of intelligent warehousing, the method comprises the following steps: collecting multiple groups of training data according to preset diagnosis types, each group of training data corresponding to one preset diagnosis type, the multiple groups of training data comprise grain storage information, warehouse environment information, warehouse equipment information and grain circulation information; extracting features of each group of training data, and obtaining a plurality of diagnosis models corresponding to each group of training data according to preset neural network training; and adopting a plurality of diagnosis models to build and obtain a grain diagnosis system. According to the method, data of various aspects of grain diagnosis are summarized, then the grain diagnosis model is trained, the grain diagnosis system is built, and the diagnosis system with an integrated evaluation function is built, so that the efficiency of grain storage management work is improved.

The invention provides a pneumonia CT image diagnosis model training method, diagnosis method and equipment, and the training method comprises the steps: inputting a 3D chest CT image into a multi-task deep learning model, enabling a shared encoder in the model to extract multi-scale feature data, and enabling a connection module and a decoder to obtain pneumonia focus region prediction result data according to the multi-scale feature data, the classification head obtains pneumonia diagnosis type prediction result data according to the multi-scale feature data; determining the joint loss of the model in the current iteration round and updating model parameters; and if the current multi-task deep learning model satisfies a training termination condition, outputting the current model as a pneumonia CT image diagnosis model. According to the method, the problems of low model feature utilization rate and low pneumonia diagnosis process efficiency caused by incapability of simultaneously completing focus segmentation and type classification due to task simplification of an existing pneumonia diagnosis model can be solved.

The invention discloses a fault intelligent diagnosis type thermal power plant electric power operation and maintenance method and system, and particularly relates to the technical field of thermal power plant electric power operation and maintenance and intelligent diagnosis, the system adopts an edge-cloud-site three-layer collaborative architecture, and comprises a sensing module, an edge processing module, a cloud core module, a site execution module and an optimization iteration module; the method is based on the system, combines technologies of federated learning, digital twinning and the like, and forms a closed-loop mechanism through full-dimension data acquisition and preprocessing, three-stage twinning model construction and updating, three-mode fusion fault diagnosis, differentiated operation and maintenance treatment and model self-optimization. The problems that traditional operation and maintenance cross-equipment fault tracing is difficult, and data safety and sharing are contradictory are solved, the diagnosis accuracy is larger than or equal to 98.5%, early warning level faults are pre-judged 3-6 hours in advance, the operation and maintenance cost is reduced by 30% or above, operation and maintenance are promoted to be transformed to active pre-judgment and intelligent optimization, stable operation of a unit is guaranteed, and practicability is remarkable.

The embodiment of the application discloses a data diagnosis method and device, a diagnosis equipment and a storage medium. The method comprises the following steps: receiving a diagnosis request of a target diagnosis type of a target service sent by a diagnosis platform; the diagnosis request comprises a target domain name required to be called when executing the target service and network attribute information of a network used for executing the target service; intermediate data generated when the target service is executed based on the target domain name and the network attribute information is acquired; the intermediate data comprises address data required in the process of executing the target service; diagnosis reference data of the target diagnosis type of the target service is acquired; the intermediate data is diagnosed based on the diagnosis reference data, and a diagnosis result for the target service is obtained; the diagnosis result is used for indicating that a target address indicated by the target domain name can be accessed or cannot be accessed when the target service is executed based on the network attribute information. By using the application, the diagnosis efficiency and diagnosis accuracy for the target service can be improved.

The application provides a liver function data detection method and system for a high-risk population of liver cancer, relates to the field of health management, and effectively solves the false alarm and missed alarm problems caused by the lack of context information in the prior art by creating and updating a context snapshot based on a user ID in a high-speed memory cache area in real time, combining a user diagnosis type, a medication state and a medication timestamp, and differentiating liver function data based on a preset logic tree, so that the liver function data can be differentiated according to the diagnosis type and the medication state of the user in real time, false alarms and missed alarms can be effectively avoided, and the accuracy and reliability of the alarm system are improved.

The invention relates to the technical field of intelligent management, and particularly discloses a DRG-based profit and loss analysis method and system, and the method comprises the steps: S1, obtaining the case treatment information of a hospital, carrying out the grouping of the case treatment information through a disease diagnosis related group DRG, obtaining the DRG group information corresponding to each case, and enabling the case treatment information to comprise the treatment cost information; s2, determining profit and loss information of each case based on the treatment cost information of each case and a DRG preset payment standard of a case diagnosis type corresponding to the case; s3, based on the DRG group information and the profit and loss information corresponding to each case, obtaining a profit and loss analysis result of each case, and integrating the profit and loss analysis result with the profit and loss analysis result of the comprehensive profit and loss analysis type of the hospital to obtain a comprehensive profit and loss analysis result of the hospital; the method is used for efficiently carrying out profit and loss analysis on different cases in hospitals.

The application provides a fetal head MR image intelligent diagnosis method, comprising the following steps: extracting a preset image sequence from a patient DICOM image corresponding to an examination item; judging whether the image sequence is a T2WI image quality meeting a first preset condition, and if yes, defining the T2WI image as a first image; performing regional segmentation on the first image according to a first segmentation rule, and correcting the first image; defining the corrected first image as a second image; performing regional segmentation on the second image based on a second segmentation rule; analyzing the first image or the second image based on one or more preset diagnosis types and coordinate data of a region corresponding to the diagnosis type, automatically generating diagnosis data of each diagnosis type; and generating a diagnosis impression according to the diagnosis data by an image structured report. The application further discloses a fetal head MR image intelligent diagnosis system. The application can comprehensively, rapidly and intelligently provide image diagnosis data, and immediately know important dangerous signs.

This invention discloses a treatment pathway recommendation method based on IBS multidimensional data. This invention relates to the field of intelligent diagnosis and treatment technology, and includes the following steps: S1, constructing an IBS treatment plan library. The treatment plan library contains standardized treatment plans corresponding to different diagnostic types and severity levels of IBS. The standardized treatment plan includes at least follow-up recommendations, medication recommendations, probiotic recommendations, and nursing guidance, and each treatment plan is associated with a corresponding IBS multidimensional data feature threshold. This treatment pathway recommendation method based on IBS multidimensional data overcomes the limitations of existing technologies with single-dimensional diagnostic data. It comprehensively integrates four core dimensions: patient symptom data, physical sign data, laboratory test data, and medical history data. Symptom data is obtained through a combination of patient self-reporting and doctor consultation to ensure no omissions; physical sign data relies on professional medical equipment to ensure objectivity; and laboratory test data is obtained synchronously from the hospital's testing system to reduce errors.

The invention relates to the technical field of automobile electronic diagnosis, in particular to an automobile online diagnosis method and device, diagnosis equipment and a storage medium, and the method comprises the steps: receiving an encrypted script file which is downloaded from a server and is matched with a diagnosis type according to the diagnosis type selected by a user; decrypting the encrypted script file by using a private key to obtain an executable diagnosis script; and calling an interpreter to run the executable diagnosis script, and calling a dynamic link library function in the diagnosis function dependency library through an interface to complete diagnosis operation on the target vehicle. According to the application, the security of the program is improved, and the risk that the program is cracked is reduced.

This invention proposes a method and system for fault diagnosis of permanent magnet synchronous motor (PMSM) sensors. The method includes: acquiring historical measurement data of the PMSM and constructing a dataset for training a classification model; training the classification model offline using the dataset; inputting the PMSM data collected in the current time window into the trained classification model and determining the reliability of the model's output; if reliable, stopping the diagnosis process in the current time window and outputting the diagnosis type and fault location; if unreliable, proceeding to the next window for diagnosis. Based on this method, a fault diagnosis system for PMSM sensors is also proposed. This invention features high scalability, strong self-learning capability, independence from the motor's physical model, and strong robustness to system noise and external disturbances, appropriately improving the diagnosis speed while ensuring accuracy.

The invention provides an acupuncture and moxibustion large model construction method, system and device based on retrieval enhancement generation, and relates to the technical field of acupuncture and moxibustion large models. Constructing an acupuncture knowledge base, an acupuncture scheme base and question and answer pair data; training the large language model base to obtain an acupuncture large model; receiving a user input request; converting the question and answer request into a query statement, and performing vector retrieval in an acupuncture knowledge base to obtain knowledge fragments; inputting the knowledge fragments into a large acupuncture model to generate an answer result; performing syndrome diagnosis on the symptom text of the diagnosis request through an acupuncture large model to obtain a syndrome result; a diagnosis vector obtained after the symptom text and the syndrome result are spliced is retrieved in an acupuncture scheme library, treatment scheme items most similar to the symptom text and the syndrome result are obtained and serve as candidate results, and the optimal treatment scheme item serves as a diagnosis and treatment basis. According to the scheme, the accuracy and credibility of the acupuncture large model are improved.

The invention relates to the technical field of medical care, in particular to a method for generating a targeted medical care scheme in combination with the traditional Chinese medicine constitution of old people. The method comprises the following steps: obtaining diagnosis results obtained by different diagnosis types of users with different physiques; according to the quantity distribution of different physiques with the same expression characteristics, determining the association degree of the diagnosis type and the physique; according to the type distribution of the medical care schemes corresponding to different samples, determining an adaptive proportion coefficient; determining the specific gravity evaluation of the medical care scheme by combining the characteristic extensibility and the adaptive specific gravity coefficient; determining the direct recommendation degree of each medical care scheme; determining a recommendation deviation in combination with the medical care schemes actually used by different samples and the recommended medical care schemes; and determining an inhibition coefficient according to the proportion evaluation and the recommendation deviation, and correcting the direct recommendation degree of the to-be-analyzed sample to the medical and nursing scheme according to the inhibition coefficient to obtain a corrected recommendation degree. According to the method, more accurate medical care scheme recommendation is realized, so that the obtained recommendation degree result is more accurate.

The invention discloses a vehicle Ethernet diagnosis method and system, and the method and system are applied to a master control system-on-chip, the master control system-on-chip comprises a main processor and a remote processor, and the main processor communicates with the remote processor through a remote processor message passing protocol. The method comprises the following steps: the main processor sends a diagnosis request to the remote processor through the remote processor message passing protocol, and the remote processor determines a communication component to be diagnosed and a corresponding diagnosis operation according to the diagnosis type information in response to the diagnosis request, the remote processor executes the diagnosis operation on the communication component to be diagnosed; the remote processor generates a diagnosis result according to the diagnosis operation; and the remote processor sends the diagnosis result to the main processor through the remote processor message passing protocol, so that a fault in a vehicle network can be accurately positioned.

The invention provides a transverse evaluation method and system for diagnosis and treatment capabilities of multiple hospitals, belongs to the technical field of medical big data analysis and hospital performance evaluation, and solves the problem of score distortion caused by data heterogeneity, weight immobilization and the like in the prior art. The method comprises the following steps: preprocessing detection data of a plurality of hospitals on various diagnosis types to obtain a standard diagnosis rate of each hospital on each diagnosis type; calculating the relative proportion of each hospital in the corresponding diagnosis type according to the standard diagnosis rates of all hospitals in the same diagnosis type; according to the relative proportion of all hospitals in the same diagnosis type, determining the comprehensive weight of the corresponding diagnosis type by using an entropy weight method; according to the detection data and the standard diagnosis rate of each hospital on each diagnosis type and the comprehensive weight of each diagnosis type, calculating a comprehensive diagnosis and treatment ability score of the corresponding hospital; and according to the comprehensive diagnosis and treatment capability scores of all hospitals, carrying out transverse evaluation on the diagnosis and treatment capabilities of the hospitals.