53results about How to "Avoid misdiagnosis" patented technology

The invention relates to the technical field of automobile data updating, in particular to a data updating method of automobile diagnosis equipment and the automobile diagnosis equipment. The method comprises the following steps: acquiring a historical running log of a vehicle; vehicle owner driving behavior multi-point sampling is carried out on the vehicle historical operation log, and different scene behavior characteristic evolution is carried out, so that different scene driving behavior models of a vehicle owner are obtained; extracting vehicle time sequence state parameters based on the vehicle historical operation log; performing multi-scene operation simulation on the vehicle time sequence state parameters according to different scene driving behavior models of the vehicle owner, and generating vehicle state operation simulation data of each scene; and carrying out future time period vehicle state rolling prediction and fault time window probability calculation on the vehicle state operation simulation data of each scene to obtain a vehicle abnormal fault part positioning point and a fault time window probability curve. According to the invention, the data updating efficiency and group accuracy of the automobile diagnosis equipment are realized.

The invention provides a clinical auxiliary decision-making method based on artificial intelligence and a related product, and the method comprises the steps: building a knowledge graph covering medical entities and relationships through learning authoritative medical data and medical records; a doctor inputs patient information, and data standardization and cleaning are carried out; based on the multi-dimensional information of the patient, recommending suspected diagnosis and differential diagnosis; recommending a preset examination scheme according to the suspected diagnosis and differential diagnosis results; when a doctor's advice is issued, the system checks conflicts between inspection and inspection items in real time and provides risk prompts; similar medical records are recommended based on patient clinical information, and diagnosis and treatment reference is provided for doctors. The clinical aid decision-making method based on artificial intelligence and related products have the beneficial effects that the disease list of suspected diagnosis and definite diagnosis is generated by automatically analyzing the illness state of the patient, and examination, inspection and treatment schemes are recommended in a targeted manner, so that the problems of missed diagnosis and misdiagnosis are effectively avoided.

The invention provides a satellite attitude control system fault diagnosis and fault-tolerant reconstruction method and system, and relates to the technical field of satellites, and the method comprises the steps: obtaining attitude data through a Kalman filtering state observer, building a fault diagnosis model, generating a residual sequence, and carrying out the adaptive dynamic threshold calculation to obtain a fault feature vector; and inputting the feature vector into a pre-trained deep belief network for fault diagnosis, and determining the fault type and degree. According to a diagnosis result, a mixed integer programming model considering attitude control precision and actuator power constraint is established, an improved genetic algorithm is adopted for solving, an actuator reconstruction scheme and controller parameters are obtained, and fault-tolerant control is achieved. According to the invention, faults can be diagnosed rapidly and accurately, fault-tolerant reconstruction is carried out according to fault types and degrees, the reliability and stability of a satellite attitude control system are effectively improved, and the satellite attitude control precision is ensured.

The invention discloses a wind power digital twinning robustness detection method based on time-frequency revolving door perception matching, and the method comprises the steps: obtaining a fan detection signal generated by digital twinning and a fan detection signal collected by a sensor, the fan detection signal being the temperature, radial vibration, axial vibration, current, voltage or power of a fan; the two kinds of fan detection signals are preprocessed through a rotating door algorithm SDA so that the characteristic frequency amplitude error of the preprocessed fan detection signals can be smaller than a preset threshold value; and respectively updating receptive fields of the corresponding convolutional neural networks based on the preprocessed fan detection signals so as to determine the number of convolutional layers of the convolutional neural networks, and outputting the difference between the number of convolutional layers of the convolutional neural networks of the two fan detection signals as a robustness detection result of digital twinning. The invention aims to improve the data processing capability, solve the problem of misdiagnosis possibly caused by spectrum deviation and aliasing and signal abrupt change when the signal stably changes, and improve the accuracy of robustness judgment.

The invention discloses an equipment operation and maintenance fault checking method and device, equipment and a medium. Through the pre-trained equipment fault diagnosis model, in combination with the real-time operation data, the historical fault case and the instruction template, the fault reason of the to-be-analyzed equipment can be quickly positioned, the maintenance suggestion is determined, the troubleshooting efficiency is improved, the time required by manual troubleshooting is reduced, and the overall operation and maintenance efficiency is improved. Through the reasoning layer in the equipment fault diagnosis model, the target troubleshooting thinking chain can be determined based on the input troubleshooting prompt, and the troubleshooting problem and the execution relation of each level are determined, so that the diagnosis process is organized and clear, and redundant and repeated troubleshooting work is reduced. The real-time operation data is checked in sequence according to the execution relation through the grading processing layer in the equipment fault diagnosis model, the fault problem can be quickly locked, and blindness and low efficiency of traditional manual checking are avoided. And the equipment fault removal knowledge base provides powerful knowledge support for fault diagnosis.

The invention discloses a belt conveyor fault intelligent inspection and diagnosis method and system based on digital twinning, and the method comprises the steps: building a digital twinning model of a belt conveyor, and fusing a three-dimensional geometric model and a physical behavior model, constructing a fault simulation model for simulating the incidence relation between different fault types and physical parameters based on a digital twinborn model; collecting and preprocessing multi-source data, loading a fault detection model to recognize an image, and fusing physical parameter data; performing fault reasoning by the intelligent reasoning model; the digital twin model reproduces a fault scene based on the fault simulation model and loads solution simulation; if the result is valid, issuing an instruction; according to the diagnosis method, a digital twin model associated with equipment behaviors is built, comprehensive diagnosis is performed by combining image data and physical parameter data, fault and behavior association is realized, and faults are accurately positioned; the fault simulation model cooperates with the digital twin model to reproduce a fault scene, and loads reasoning result simulation and a virtual-real combined mechanism control instruction to guarantee operation and maintenance safety.

The invention discloses a child developmental hip joint dysplasia auxiliary diagnosis system based on deep learning. The system comprises a trained instance segmentation model and a feature point positioning model. The instance segmentation model is used for segmenting double-side closed pores and ilium from the pelvis image of the child; the feature point positioning model is used for accurately positioning eight hip joint feature points from the pelvis image of the child; the diagnosis method comprises the following steps: inputting a to-be-diagnosed pelvic image of a child into an instance segmentation model, segmenting double-side closed pores and ilium, and obtaining the maximum transverse diameter and area of the closed pores and ilium and the rotation index and area ratio of the closed pores and ilium so as to judge the symmetry of the pelvic image; if the pelvis image is judged to be symmetrical, the quality control standard is met; and after the quality control process is finished, the images or all the images meeting the quality control standard are selected to be sent to a feature point positioning model for feature point detection and IHDI classification. According to the invention, rapid and accurate image quality control and grading diagnosis of children's developmental dysplasia of hip joints can be realized.

The invention provides a CT data correction method, electronic equipment and a CT machine. The CT data correction method is used for pet CT machine scanning data processing and comprises the steps that actual scanning data at each measurement angle in a CT machine are collected in real time; acquiring endpoint channel data from the actual scanning data; judging whether the channel data is shielded or not; actual measurement current data and actual measurement channel data of the shielding channel are obtained; determining a function relationship between the actually measured current data and the actually measured channel data; obtaining correction current data of an unshielded channel; and substituting the correction current data into the function relation to obtain standard channel data of the shielding channel. The problem that the CT value of a pet CT image drifts due to the fact that current feedback of a pet CT machine is not timely is solved, and the accuracy of CT scanning is improved. In the image generation process, a CT image which is more practical can be formed, and misdiagnosis caused by CT value drift is prevented.

The invention discloses an oil tank shake recognition method, device and equipment and a storage medium, and relates to the technical field of automobile monitoring, the oil tank shake recognition method comprises the steps that a fuel oil liquid level initial voltage value and a power supply voltage are obtained, the power supply voltage is compensated, and a compensated fuel oil liquid level sensor output voltage is obtained; according to the fuel oil liquid level initial voltage value, an initial fuel oil liquid level percentage is obtained through the relation between the voltage and the liquid level, and low-pass filtering processing is conducted on the initial fuel oil liquid level percentage to obtain a target fuel oil liquid level; the absolute value of the difference value between the initial fuel oil liquid level percentage and the target fuel oil liquid level is calculated; when the absolute value of the difference value is larger than the limit value and lasts for a certain time, an oil tank shaking signal is sent out, and the diagnostic function of the evaporation leakage system is forbidden. By monitoring the original signal value and the filtering signal value of the liquid level of the oil tank, the shaking condition of the oil tank is recognized, diagnosis of the evaporation leakage system is stopped during shaking, and therefore the diagnosis accuracy and the safety of the fuel system are improved.

The invention provides an electric energy loss abnormity identification method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring electrical parameters of the photovoltaic inverter system within a specified duration; wherein the photovoltaic inverter system comprises at least one inverter, the inverter comprises at least one maximum power point tracking module, and the electrical parameters comprise DC voltages of the at least one maximum power point tracking module at a plurality of acquisition moments; judging whether the photovoltaic inverter system is in a power generation limiting state or not; and if the photovoltaic inverter system is not in the power generation limiting state, identifying whether an electric energy loss abnormity occurs in the inverter according to the DC voltage. According to the method and the device, the electric energy loss abnormity can be identified on the basis of the least and most easily obtained parameters, the abnormity identification difficulty is reduced without depending on uncontrollable external data, misdiagnosis can be avoided, and the abnormity identification accuracy is improved.

The invention relates to the technical field of data processing and state monitoring, and discloses an intelligent ring main unit fault real-time diagnosis method and system, and the method comprises the steps: constructing a fault feature over-complete dictionary; collecting and preprocessing high-frequency operation data streams of the ring main unit in real time; carrying out online sparse decomposition and manifold mapping based on the dictionary, and converting a high-dimensional signal into a low-dimensional sparse coefficient and manifold features; and a lightweight classification model is used for rapid reasoning, and fault early warning and data uploading are triggered when abnormity is detected. The system comprises a data acquisition module, a preprocessing unit, an edge computing engine, an intelligent diagnosis module and a communication gateway. According to the method, depth dimension reduction and feature enhancement are realized through a manifold sparse coding technology, the bandwidth pressure and the calculation overhead are reduced on the premise of ensuring the fault feature integrity, and the real-time performance and the accuracy of diagnosis are improved.

The invention relates to the technical field of fault diagnosis, in particular to a zero-sample speed reducer composite fault diagnosis method based on fast spectral kurtosis, which comprises the following steps: arranging multiple types of sensors at the output and input ends of a fault speed reducer, and collecting data according to a preset sampling frequency; the collected real-time signals are processed into one-dimensional time domain signals, the one-dimensional time domain signals are converted into an actual spectral kurtosis graph after accuracy and integrity analysis and adjustment, a single fault feature set is constructed by combining real-time temperature values and pressure values, and single fault categories are obtained through classification by using a clustering algorithm; dividing samples under a single fault category to construct a diagnosis model, performing combined simulation on the single fault category to construct a composite fault sample set and labeling, performing preliminary diagnosis by the diagnosis model to obtain an initial result, finally determining a final diagnosis result according to the initial result and a composite fault inference rule, evaluating a classification recognition effect, and adjusting a preset sampling frequency according to the final diagnosis result. Therefore, the accuracy of a fault diagnosis result is improved.

The present invention discloses an intelligent motor fault diagnosis method based on digital twins, belonging to the field of motor fault diagnosis. In the model training stage, the method selects feature quantities through the random forest method to train an extreme learning machine model with an autoregressive cycle function as a digital twin motor. In the open circuit fault diagnosis stage, the method collects the physical entity motor feature quantity and inputs it into the digital twin virtual motor to obtain the digital twin current, and makes a difference between the actual winding current data and the digital twin winding current to obtain the current residual, and uses the difference value of the current residual as a threshold to perform open circuit fault diagnosis, and adopts the sliding average window method to locate the fault; in the turn-to-turn short circuit fault diagnosis stage, the current symmetrical mean absolute error is used as a judgment standard to diagnose the short circuit fault. The present invention does not require precise design parameters and motor fault data of the motor, but only requires data when the motor is in normal operation to quickly and real-time diagnose the motor open circuit fault and turn-to-turn short circuit fault, and has strong robustness.

The invention discloses a data uplink transmission method and system for intelligent hospital Internet of Things medical terminal equipment, and relates to the technical field of Internet of Things medical terminals, and the method comprises the following steps: obtaining various parameter information generated in the uploading process of an electrocardiograph monitor data packet, including data processing capability and network reliability; by acquiring data processing capacity and network reliability parameters in the uploading process of the electrocardiograph monitor, the system accurately analyzes buffer overflow and packet loss retransmission mechanism failure, and a transmission quality coefficient is generated in combination with machine learning. The intelligent mechanism detects the high-risk uploading process in real time, data packet error sequence or loss is reduced, the integrity of electrocardiogram (ECG) data is ensured, and misdiagnosis is effectively avoided. The high-risk uploading process is divided into an unstable type, a continuous type and a burst type, and corresponding early warning measures are taken respectively, so that timely processing of critical data is ensured, the safety and stability of data transmission are improved, and the safety of a patient is guaranteed.

The invention provides a defoaming agent for digestive endoscopy as well as a preparation method and a use method thereof, and belongs to the technical field of medicines. The defoaming agent comprises 1%-10% of organic silicone oil and 90%-99% of polyethylene glycol; the preparation method comprises the following steps: weighing the organic silicone oil and the polyethylene glycol according to a set proportion, and respectively heating to 75-85 DEG C to form a hot melt; carrying out isothermal mixing on the two components to obtain a mixed hot melt, and carrying out intermittent shearing and stirring in a wall breaking machine to obtain an emulsified melt; flatly spreading the emulsified molten liquid in a pre-cooled container, sealing a cover, and freezing to obtain a white waxy sheet block; and crushing and sieving the white waxy sheet blocks to obtain white organic silicone oil dry milk powder, namely the defoaming agent. The method comprises the following steps: adding water into a defoaming agent and uniformly mixing to prepare a silicone oil emulsion before digestive endoscopy; the composition is orally taken or sprayed on an examined part. The dry emulsion type defoaming agent provided by the invention is simple in formula, free of additives, free of any potential safety risk, safe and reliable, good in stability and excellent in defoaming effect.

The utility model relates to the technical field of livestock medical equipment, and disclose livestock veterinarian multifunctional animal holding frame, including base, the surface fixedly connected with support frame at the four corners of base upper end, and the outer surface hinged connection of base one side is connected with the treading plate, the upper end of support frame is provided with the holding frame, and one end of holding frame is provided with the open -and -shut structure. The utility model discloses by pulling down the pull rod, make first hinged link and second hinged link hinged rotation, and drive first positioning frame hinged rotation and reset, when the other end of first hinged link and hinged block hinged rotation, and drive the other end of hinged block and go up and hinged rotation, one end of hinged block drives second positioning frame hinged rotation and reset simultaneously, thereby can bind the head of animal, avoid the condition that animal struggles or escapes because of discomfort or panic, and the practicability of equipment is improved to some extent.

The invention relates to the technical field of scanning devices, in particular to a quick response X-ray scanning device based on a micro-electro-mechanical chip, which comprises a detection table, a photoelectric sensor is arranged in the detection table, a plurality of positioning blocks are arranged on the detection table, and a table top of the detection table is used for bearing an object to be imaged; the X-ray source is arranged above the detection table and can emit X-rays to the direction of the detection table, and the X-ray source is connected with a movable support capable of driving the X-ray source to move; the detector is connected with the detection table and can receive the X-rays penetrating through the object to be imaged and convert the X-rays into digital signals; the detection table fixes the position of an object to be imaged through the positioning block, the image acquisition module acquires an image of the detection table, and the control module adjusts the position of the X-ray source according to the position of the object to be imaged on the image so as to completely scan the object to be imaged. By accurately positioning the position of the object to be imaged and standardizing the image of the object to be imaged, the accuracy and reliability of the whole scanning process are improved, and the scanning efficiency and the imaging quality are improved.

The invention relates to the technical field of power system intelligent maintenance, and discloses a high-voltage circuit breaker fault intelligent diagnosis method and system based on deep learning, and the method comprises the following steps: 1) data collection: collecting multi-dimensional operation parameters of a high-voltage circuit breaker through a sensor system; 2) data preprocessing: carrying out noise filtering, abnormal point elimination, data normalization and other operations on the collected data; 3) deep learning model training: inputting historical fault data and normal operation data into a deep learning network for training, and optimizing model weight through back propagation; and 4) real-time diagnosis: inputting the data acquired in real time into the trained deep learning model, and analyzing the data by the model to obtain a fault diagnosis result. According to the method, technologies such as noise filtering (such as wavelet analysis) and abnormal point elimination (such as an isolated forest algorithm) are adopted, so that the data quality can be effectively improved, interference in model training is reduced, and the diagnosis accuracy is enhanced.

The present disclosure relates to an electric vehicle on-board charger, a relay control method of the electric vehicle on-board charger, and an electric vehicle two-way charging system including the electric vehicle on-board charger, the electric vehicle on-board charger including: a vehicle port configured to be connected to an external charger, the external charger includes a first relay located on an internal first power supply path; a power converter; a second relay located on a second power supply path between the vehicle port and the power converter; and a controller configured to open the first relay and the second relay in response to triggering a vehicle-to-grid (V2G) protection operation based on the power-related parameter.

The invention discloses a cerebrospinal fluid sampling and detecting device, and belongs to the technical field of neurology. According to the cerebrospinal fluid sampling and detecting device, a detecting and distributing head, a puncture needle, a control cabinet and a detecting box are arranged on a device base, a sealing sleeve is arranged in the detecting and distributing head, a preliminary checking disc is installed in the sealing sleeve, and a three-way valve is installed between the detecting box and the detecting and distributing head. The cerebrospinal fluid sampling and detecting device solves the problems that an existing cerebrospinal fluid sampling and detecting device cannot carry out split charging to achieve automatic adjusting operation, and the detection effect is affected, according to detection data, the electric actuator is adjusted through the plc module, the three-way valve and the adjusting valve are controlled, and therefore according to the detection result, the cerebrospinal fluid can be firstly split charged, and then split charging is carried out. All optical and electrochemical signals are captured by an optical fiber sensing array plate and a detection box which are arranged in the detection distribution head, and are transmitted to a control cabinet through Bluetooth, so that different detection processing work is carried out in time, treatment schemes are effectively switched, and the detection effect is improved.

The present application provides a method, device, storage medium and electronic device for encoding a respiratory path, the method may include: obtaining a first tracheal diameter of the airway position measured by intra-airway radial ultrasound technology and / or intra-airway optical coherence tomography technology; obtaining a second tracheal diameter of the airway position obtained by computer tomography technology; determining the tracheal path by comparing the first tracheal diameter and the second tracheal diameter, wherein the tracheal path is used to provide a doctor with a reference for reaching the lesion. Some embodiments of the present application can improve the accuracy of tracheal path encoding and provide valuable reference suggestions for doctors.

The present invention provides a battery pack and an overheat diagnosis method which perform battery overheat diagnosis by measuring each of an internal temperature and an external temperature of the battery pack from different temperature sensors respectively provided in battery cells, a BMS, and a battery pack case of the battery pack for a predetermined period of time, and comparing the same with a predetermined reference value, in particular, it is possible to determine whether the diagnosed overheating is caused by external factors or internal factors of the battery pack.

The invention relates to a CEACAM6 targeting nuclide ligand, a PET probe and a preparation method and application of the CEACAM6 targeting nuclide ligand, the CEACAM6 targeting nuclide ligand is obtained by modifying a targeting molecule through a bifunctional chelating agent, the targeting molecule is a nuclide aptamer, an antibody or polypeptide capable of targeting CEACAM6, and the targeting molecule is preferably Glu-Trp-Gln-Arg-Leu-Lys-Trp-Phe-Asp-His or Gly-Arg-Gln-Tyr-Asp-Tyr-Grp-Gln-Gly. CEACAM6 can be used as an appropriate target spot for PET-based noninvasive diagnosis of malignant pulmonary nodules in the early stage of LUAD. According to the present invention, the good PET probe is obtained by radiolabeling the nuclide ligand targeting CEACAM6; wherein the PET probes 68Ga-NODA-P2 and 68Ga-NODA-P3 capable of noninvasively and specifically targeting CEACAM6 can be used as potential PET tracers for accurately diagnosing the early lung malignant nodules of LUAD, misdiagnosis and excessive treatment caused by the malignant risk that the nodules cannot be accurately evaluated by virtue of LDCT screening are avoided, and particularly, the 68Ga-NODA-P3 probe shows the high-specificity imaging capability in a CEACAM6 positive A549 xenotransplantation tumor model, so that the CEACAM6 positive A549 xenotransplantation tumor model has the advantages that the CEACAM6 positive A549 xenotransplantation tumor model has the high-specificity imaging capability, and the CEACAM6 positive A549 xenotransplantation tumor model has the high-specificity imaging capability; according to the present invention, the CEACAM6 positive tumor and the CEACAM6 negative tumor can be significantly distinguished, the imaging target is high, the metabolism clearance is rapid, the rapid imaging diagnosis is easily achieved, and the accurate identification of the LUAD early stage malignant pulmonary nodule is easily achieved.

The invention discloses a lithium battery micro internal short circuit fault diagnosis method based on dynamic mode decomposition and a radial basis function neural network. Comprising the following steps: S1, preprocessing original voltage time sequence data collected in an operation process of a lithium battery, and constructing an input matrix suitable for dynamic mode decomposition; s2, decomposing the input matrix based on a dynamic mode decomposition method, extracting a dominant dynamic mode, screening and recombining a key mode based on Pearson correlation analysis, and generating a low-dimensional and high-sensitivity fault feature vector; s3, taking the feature vector as an input of a radial basis function neural network, taking an internal short circuit equivalent resistance value as a label, and constructing a nonlinear mapping relation between the feature space and an internal short circuit state; and S4, comparing an internal resistance prediction value output by the radial basis function neural network with a set threshold value or a reference value, calculating a prediction error and evaluating the diagnosis precision. According to the invention, misdiagnosis caused by noise and aging effects is avoided.

The invention provides a bidirectional physical examination percussion hammer, and belongs to the field of medical equipment. Comprising a first handle, a second handle and a hinge part, the first handle and the second handle are arranged in a crossed mode, and the top ends are rotationally connected through the hinge part; the upper end of the hinge part is fixedly connected with a knocking assembly; and the knocking assembly comprises a main shell fixedly connected to the upper end of the hinge part, the outer surface of the left side of the main shell is fixedly connected with a micro air pump, and one side of the interior of the main shell is fixedly connected with a partition plate. According to the percussion device, the knocking assembly is arranged, when percussion work is conducted, medical staff can control the knocking height and the percussion strength in a standardized mode, percussion specifications are more uniform, the problem that some doctors with insufficient medical experience misdiagnose due to non-standard operation can be solved to a great extent, meanwhile, due to automatic control over the percussion strength, the percussion efficiency is improved, and the percussion efficiency is improved. And the problem that standardized percussion cannot be achieved due to the fact that different medical staff judge different percussion forces can also be avoided.

The invention discloses an elevator fault diagnosis system based on AI intelligent data analysis, relates to the technical field of elevator fault analysis, and solves the technical problems that effective data mining and analysis means are lacked and powerful support cannot be provided for fault diagnosis. Advanced data mining and knowledge extraction technologies are applied, and various elevator fault phenomena, causes, related parts and solutions are integrated. Therefore, fault diagnosis has comprehensive and structured knowledge support, common faults or rare faults can be quickly matched according to the atlas, the diagnosis efficiency is improved, comprehensive analysis is carried out in combination with historical fault records and current fault phenomena, a representation analysis result and a concrete analysis result are generated, and the fault diagnosis accuracy is improved. Not only can the reason of the back of the visual external presentation be quickly grasped, but also the source of the deep fault hidden danger hidden in the internal structure of the elevator and the deep position of the electrical circuit can be deeply excavated.

The invention provides a rapid rib fracture diagnosis device based on an artificial intelligence technology. The rapid rib fracture diagnosis device comprises a spiral CT examination equipment main body, an intelligent host and a display device, the intelligent host comprises a fracture image sample acquisition module, a three-dimensional convolution analysis and learning module and a fracture image analysis and diagnosis module; the three-dimensional convolution analysis and learning module is used for carrying out feature analysis and learning memory on the obtained diagnosed fracture image samples to form a fracture feature image library; the fracture image analysis and diagnosis module is used for acquiring a multi-layer spiral CT examination image of the clinical rib fracture image output by the spiral CT examination equipment main body and carrying out artificial intelligence clinical diagnosis. According to the method, the fracture feature database is formed through artificial intelligence on the basis of the condition that the image features obtained through multi-layer spiral CT examination are finer and the number of the images is large, a doctor is helped to carry out clinical rib fracture diagnosis on the basis, the diagnosis speed and accuracy can be remarkably improved, and the diagnosis efficiency is improved. And the labor intensity of workers and misdiagnosis or missed diagnosis caused by human factors are reduced.

The invention discloses a multi-modal intelligent osteoarthritis detection method based on loop optimization, a storage medium and electronic equipment. The multi-modal intelligent osteoarthritis detection method comprises the following steps of multi-modal data acquisition and preprocessing, modal-by-modal alternate training, test set division and uncertainty-based multi-modal fusion diagnosis; and finally, evaluating and optimizing the detection model. According to the method, a weighted aggregation mechanism based on uncertainty is introduced. In clinical scenarios, some modalities may have noise or missing. The model can automatically calculate the entropy value (uncertainty) of each modal prediction result, allocate a higher weight for a high-quality modal and allocate a low weight for a low-quality modal, so that misdiagnosis caused by poor quality of a single data source is avoided, and the diagnosis stability in a complex clinical environment is remarkably improved.

A BMS having a master BMS receiving power from an electric vehicle and at least one slave BMS, in which each of the master BMS and the slave BMS has a first ground terminal and a second ground terminal, the first ground terminal of the master BMS and the slave BMS being connected to a ground terminal of the electric vehicle, the second ground terminal of the master BMS being connected to the first ground terminal of the master BMS, and the second ground terminal of the slave BMS being connected to the second ground terminal of the slave BMS. And a second ground terminal of the slave BMS is connected to a second ground terminal of the master BMS.