247 results about "System diagnosis" patented technology

The invention provides an integrated test scene prediction method and system based on multi-dimensional data, and the method comprises the steps: carrying out the multi-source data collection of an integrated test scene based on a dynamic sampling strategy, obtaining integrated test multi-dimensional data, carrying out the multi-dimensional data preprocessing of the integrated test multi-dimensional data, and obtaining a pre-trained multi-dimensional data analysis model; inputting the pre-processed integrated test multi-dimensional data into the multi-dimensional data analysis model to carry out sub-module data analysis, obtaining a pre-trained intelligent regulation and control model, importing a system diagnosis report and a key index deviation degree report into the intelligent regulation and control model as inputs, generating an intelligent regulation and control decision, and carrying out intelligent regulation and control. By means of the method, system faults, performance bottlenecks and potential problems occurring in the integrated test scene can be accurately predicted, intelligent regulation and control decisions are provided for solving the problems, and accurate system performance evaluation and optimization services can be provided for users.

The invention relates to a sonar fault intelligent diagnosis method and system based on a knowledge graph and a large language model. The sonar fault intelligent diagnosis method comprises the following steps: receiving an initial fault description text input by a user through a natural language interaction interface; carrying out integrity analysis on the initial fault description text, and judging whether the initial fault description text contains all preset key information dimensions or not; if the judgment result of the integrity analysis is no, starting an interactive guide mechanism until a complete fault description text is obtained; based on the complete fault description text, performing reasoning in a pre-trained large language model configured with a sonar fault knowledge graph; and based on the conclusion of the joint reasoning, generating and outputting a structured fault diagnosis report. According to the invention, by introducing a fault description integrity evaluation and interaction guide mechanism, the problem that the system diagnosis accuracy is reduced due to fuzzy or incomplete fault description input by a user in the prior art is solved, and the accuracy and stability of overall diagnosis are improved.

The invention discloses a multi-mode coordinated ship main and auxiliary power automatic switching system, which relates to the technical field of ship power systems and comprises a monitoring system, a diagnosis decision system, an execution system, a switching verification system, a maintenance management system and a data communication system. According to the invention, the airborne weather radar, the ship weather instrument and the weather fax receiver are used for monitoring the current weather condition of navigation and predicting the weather condition of a future navigation route, so that a prediction basis is provided for a power system operation mode selected by future ship navigation; meanwhile, the ship weather instrument monitors surrounding wind speed, wind direction, air temperature and humidity data in real time when the ship sails, the current data is stored in the data storage, a database is formed, and a follow-up maintenance management system can predict potential risks according to historical data conveniently.

The invention discloses a Petri network-based fault diagnosis method and system and a storage medium, and the method comprises the steps: S1, obtaining a time sequence signal of substation equipment, the time sequence signal comprising alarm information and accident and displacement information; s2, on the basis of the time sequence signal of the substation equipment, constructing a fault judgment rule base and setting a front part weight, a rule starting threshold value and a back part credibility; s3, establishing a fuzzy function based on the preceding component weight, the rule starting threshold and the consequent component credibility; s4, on the basis of the Petri network and the fuzzy function, a network topology model of the substation equipment is established, and the network topology model of the substation equipment comprises a fault intelligent judgment rule base; and S5, based on the network topology model of the substation equipment, obtaining a current time sequence signal of the substation equipment, and judging a fault condition of the substation equipment. By establishing the network topology model of the substation equipment, the fault diagnosis analysis speed is increased, the fault diagnosis efficiency is improved, and the diagnosis effect of the power system is optimized.

The invention discloses an intelligent vibration analysis and fault diagnosis method, and particularly relates to the technical field of rotating machinery state monitoring and fault diagnosis. Collecting a signal through a vibration sensor and carrying out standardization preprocessing; performing parallel analysis on the preprocessed signal by using the constructed multi-scale feature extraction and fusion network, extracting low-frequency trend and high-frequency detail features, and fusing the low-frequency trend and high-frequency detail features to generate a comprehensive feature vector; inputting the vector into a classifier based on an attention mechanism to carry out preliminary fault identification; meanwhile, performing similarity matching on the comprehensive feature vector of the current case and a fault knowledge base in which historical cases are stored; and finally, an enhanced diagnosis conclusion is generated by fusing an output result of the classifier and matched historical case experience, and a new case subjected to field verification is continuously updated to the knowledge base. According to the method, adaptive extraction of deep features of the vibration signals, interpretability enhancement of a diagnosis decision process and continuous evolution of system diagnosis knowledge are realized.

A method includes receiving an indication of an inspection of an industrial automation device being triggered, displaying, via a display of a mobile computing device, instructions for performing the inspection of the industrial automation device, receiving, via one or more sensors of the mobile computing device, one or more sensors communicatively coupled to the mobile computing device, or both, inspection data associated with the industrial automation device, processing the inspection data associated with the industrial automation device to generate results of the inspection of the industrial automation device, and displaying, via the display of the mobile computing device, the results of the inspection of the industrial automation device.

The invention relates to a thermal power plant fault early warning and diagnosis method based on a nebula system, and the method comprises the steps: collecting the multi-dimensional operation time sequence data of a thermal power plant, carrying out the feature extraction and lexical element processing of the multi-dimensional operation time sequence data through an encoder, and obtaining a unified equipment state lexical element sequence; based on the equipment state lexical element sequence, constructing a dynamic star map representing the operation state of the whole power plant; inputting the dynamic star map into a space-time fusion backbone network; the space-time fusion backbone network performs iterative processing on the dynamic star map and generates a health degree attenuation trajectory; when the slope of the health degree attenuation trajectory exceeds a preset threshold value, dynamic early warning and system diagnosis are triggered, and a natural language diagnosis report containing a causal reasoning chain is generated; new multi-dimensional operation time sequence data are collected in real time, and an incremental learning algorithm is used to update the encoder and the space-time fusion backbone network online; compared with the prior art, the system can continuously adapt to working condition changes and has high self-optimization capacity.

The invention belongs to the technical field of fault diagnosis, and particularly discloses a numerical control system fault diagnosis system and method fusing forward design and reverse case data, the numerical control system fault diagnosis system comprises a knowledge base and a large model, the knowledge base is used for determining cue words based on user questions, and the large model is used for outputting fault diagnosis results according to the cue words; the knowledge base is a fault diagnosis knowledge graph, a knowledge graph entity is extracted from forward design data and reverse case data of the numerical control system, the forward design data comprises a PLC file and a bottom layer alarm file of the numerical control system, and the reverse case data comprises historical fault diagnosis work order information of the numerical control system. According to the method, the interactivity and flexibility of the knowledge base can be improved, the multi-round interaction capability in the fault diagnosis process is optimized, and the method is of great significance in improving the system diagnosis efficiency and accuracy.

A dynamic testing system includes a test station and at least one controller. The test station includes a hydraulic actuator configured to drive an actuation of a test subject using a hydraulic fluid flow, and a control valve that includes a housing, a valve body contained within the housing having a position that controls a flow rate and direction of the hydraulic fluid flow, a valve body driver configured to adjust the position of the valve body based on a reference signal, and a valve position sensor configured to output a position signal that is indicative of the position of the valve body. The at least one controller is configured to obtain a current position of the valve body using the position signal, obtain a diagnostic reference, detect a diagnostic condition based on a difference between the current position and the diagnostic reference, and generate a diagnostic signal.

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.

A data acquirer acquires voltage data of each cell of a battery pack in which multiple cells are connected in series or voltage data of each parallel cell block of a battery pack in which parallel cell blocks, which each include multiple cells connected in parallel, are connected in series. A judgment unit judges, when a fluctuation in a voltage of each of two cells adjacent among the multiple cells or two parallel cell blocks adjacent among the multiple parallel cell blocks exceeds a threshold, that there is an abnormality in a voltage measurement system between a connection point of the two cells or the two parallel cell blocks and a voltage measurement unit.

The invention relates to the technical field of fluidized bed roamers, in particular to an intelligent anti-blocking system, method and device for a gas distribution plate of a fluidized bed roaster. Installing two groups of nozzle arrays on the side wall of the fluidized bed roaster, and adjusting the spraying direction of nozzles to be aligned with the small hole area of the distribution plate; high-precision temperature and pressure sensors are installed at the edge and corner blockage high-incidence area of the distribution plate, real-time and continuous monitoring of temperature-pressure data is achieved, and key supporting data are provided for system diagnosis and control. An integrated programmable logic controller system is introduced, when a sensor captures signals indicating that small holes are possibly blocked, such as abnormal temperature drop or obvious pressure rise, a PLC immediately judges the existence and position of blockage and triggers a preset blockage clearing program, a nozzle is driven to spray high-pressure pulse airflow, and blockages are efficiently cleared. The system has the characteristics of intelligence, automation, high efficiency and the like, can prevent the problem of blockage of the gas distribution plate of the fluidized bed roaster, and ensures high efficiency and stability of equipment operation.

The invention relates to the technical field of computer and intelligent mine program operation and maintenance, in particular to an operation and maintenance system and method for mine program management and state monitoring. A database operation and maintenance module, a port monitoring module, a service management module, a system diagnosis module, a subscription data publishing and tracking module, a distributed service detection module and a data management module are fused, so that the capability of monitoring general configuration or states of machines and services is realized, and data formats are unified; and fault diagnosis and troubleshooting are carried out on modules with problems rapidly. According to the application, various operations of related applications of the intelligent mine platform in a whole business process period such as deployment, updating, troubleshooting and configuration information updating are fused, and the method is very suitable for deployment maintenance and troubleshooting operation and maintenance of deployment personnel and developers on the intelligent mine application in a mine scene.

The invention discloses a rolling bearing fault diagnosis method and equipment based on an optimal spectrum weight index, belongs to the technical field of bearing fault diagnosis, and is used for solving the problems that fault signals and normal signals are difficult to distinguish due to the fact that various signals collected by an existing rotating machinery fault diagnosis method are mixed and a large number of interference signals exist. And frequent occurrence of a misdiagnosis problem is caused. The method comprises the following steps: carrying out feature extraction on an acquired operation vibration signal of a rolling bearing of a rotating machine, and inputting the extracted signal features into an abnormal working condition discriminator for abnormal diagnosis; when detecting that the signal is abnormal, activating a system diagnosis module; performing symplectic geometric mode decomposition on the abnormal signals to obtain mode components forming the abnormal signals; calculating an optimal spectrum weight index of each modal component of the abnormal signal based on the health signal reference component; and according to the optimal spectrum weight index, screening fault characteristic components in the modal components, performing envelope spectrum fault diagnosis, and identifying a fault type.

The invention discloses an intelligent engine monitoring system, belongs to the technical field of engine monitoring, and solves the technical problem that a traditional engine monitoring system is inconvenient to expand monitoring parameters. The system comprises an electric control monitoring system of an engine, and further comprises an external mounting type sensor monitoring system, a diagnosis management platform and a mobile terminal, the external mounting type sensor monitoring system comprises an independent power supply module, a wireless receiver and a wireless intelligent sensor, the wireless receiver is installed beside an ECU of the engine and connected with the ECU through a bus, and the wireless intelligent sensor is connected with the external mounting type sensor monitoring system. The independent power supply module supplies power to the wireless receiver and the wireless intelligent sensor, detection data of the wireless intelligent sensor is transmitted to the ECU through the wireless receiver, the ECU uploads monitored parameters to the diagnosis management platform, and the diagnosis management platform sends monitored parameter information to the mobile terminal through wireless communication. Monitoring parameters can be conveniently expanded through the externally-mounted sensor monitoring system, wire harnesses and sensors of an original machine are fixed, and the circuit is simple and good in stability.

The invention discloses a digital twinning-based fault diagnosis system, diagnosis method and application, and the system comprises a hardware platform, a digital twinning fault diagnosis model and a cloud, and the hardware platform is used for collecting vibration, temperature and sound signals during the operation of equipment; the cloud is used for receiving data sent by the hardware platform and the controller and sending a fault state; the digital twinning fault diagnosis model comprises a physical data input layer, a digital twinning modeling layer, a virtual-real data fusion layer, a causal reasoning layer and an output layer, data collected by the hardware platform is input into the physical data input layer, modeling is carried out on key components of equipment through the digital twinning modeling layer, virtual fault data is generated, and the virtual fault data is sent to the hardware platform; and according to the virtual-real data fusion layer and the causal reasoning layer, distributing weights to each branch, and displaying a fault diagnosis result through an output layer. The invention can be applied to refrigeration equipment. According to the invention, fault prediction is carried out in advance, the fault type can be identified, the labor cost and the time cost are saved, and the identification accuracy is improved.

The invention belongs to the technical field of PH detection, particularly relates to a sauce PH detection technology, and particularly discloses an online PH value detector based on artificial intelligence, sauce distribution uniformity monitoring is synchronously achieved in the sauce pH detection process, vibration control is implemented based on sauce distribution uniformity feedback, it is ensured that detection is in a full mixing state, and the detection accuracy is improved. Meanwhile, through linkage analysis of pH sudden change recognition and the uniformity state, process disturbance and probe faults can be effectively distinguished under the continuous monitoring condition, false alarm and invalid maintenance are avoided, the intelligent level of system diagnosis is improved, and stable and reliable online pH detection is achieved; and meanwhile, liquid level offset monitoring is introduced when vibration control is implemented based on sauce uniformity feedback, and vibration adjustment is performed in combination with a dynamic coupling relationship between a uniformity index and liquid level offset, so that stirring is ensured to operate within a structural safety range, excessive vibration is avoided, and the system stability and operation safety are improved.

1. Name of the Design Product: Graphical User Interface for Controlling Numerical Control Machine Tools of Electronic Devices. 2. Use of the Design Product: Display information and run programs. 3. Design Key Points of the Design Product: Lie in the graphical user interface in the electronic device. 4. Picture or Photograph Best Showing the Design Key Points: Front View. 5. Use of the Graphical User Interface: Display the numerical control machine tool operation interface. 6. Human-Machine Interaction Method of the Graphical User Interface: The front view is the login interface. After entering the user name and password and clicking "Login", it enters the variant state diagram 1; clicking "Load File F1" enters the variant state diagram 2; clicking "Program View F2" enters the variant state diagram 3; clicking "Start Option" enters the variant state diagram 4; clicking "Tool Selection" enters the variant state diagram 5; clicking "Pattern Selection" enters the variant state diagram 6; clicking "Free Box Selection" enters the variant state diagram 7; clicking "Program Zero Point" enters the variant state diagram 8; clicking "Upper PIN" enters the variant state diagram 9; clicking "Settings" enters the variant state diagram 10; clicking "Forming Analysis" enters the variant state diagram 11; clicking "Errors and Warnings" enters the variant state diagram 12; clicking "System Diagnosis F3" enters the variant state diagram 13; clicking "Input" enters the variant state diagram 14; clicking "Output" enters the variant state diagram 15; clicking "Tool Management F11" enters the variant state diagram 16; clicking "Parameters" enters the variant state diagram 17; clicking "Dynamic Parameters" enters the variant state diagram 18; clicking "Tolerance" enters the variant state diagram 19; clicking "Tool Measurement" enters the variant state diagram 20; clicking "Broken Tool" enters the variant state diagram 21; clicking "Diameter Table" enters the variant state diagram 22; clicking "Index Table" enters the variant state diagram 23; clicking "Tool Length Calibration" enters the variant state diagram 24; clicking "Tool Diameter Calibration" enters the variant state diagram 25; clicking "Job Interface F5" enters the variant state diagram 26; clicking "Custom Function" enters the variant state diagram 27; clicking "Incremental Key" enters the variant state diagram 28; clicking "Z Plane" enters the variant state diagram 29; clicking "Processing Status" enters the variant state diagram 30; clicking "Mark" enters the variant state diagram 31; clicking "User Mark" enters the variant state diagram 32; clicking "Shift" enters the variant state diagram 33.

The disclosed system relates to the diagnosis and correction of autonomic nervous system imbalance through the recognition and improvement of parasympathetic tone of a user using paced breathing and vibratory neuromodulation. The system includes a wearable IBI detector, software for analyzing PSD data and determining recommended therapy, a vibratory stimulator, and optional smart phone guidance. The IBI detector reads the pulsatile waveform to produce IBI data that is used to compute PSD data. The PSD data is analyzed for ANS imbalance indicators and therapy is recommended based on continuously updated information stored in a database. Results from application of recommended therapy are stored and shared and can be used to build a personalized training regime to strengthen parasympathetic tone.

The invention discloses an AI-based power distribution equipment partial discharge online diagnosis and positioning method, which relates to the technical field of equipment detection, and comprises the following steps: acquiring high-frequency signals in power distribution equipment in real time, sequentially calculating a topological inconsistency index, an echoing characteristic response abnormal index and a burst pulse ring chaos index, and calculating a false index based on the three results, so as to realize the partial discharge online diagnosis and positioning of the power distribution equipment. The method is used for judging whether the signal is a real signal generated by partial discharge of equipment. And if the false index is lower than a threshold value, determining that the discharge signal is a real discharge signal, and performing type identification and accurate positioning through a preset AI model. According to the method, non-discharge interference signals can be effectively identified, the fault judgment accuracy is improved, the false alarm rate is reduced, the trust of operation and maintenance personnel on a system diagnosis result is enhanced, the manual inspection pressure is relieved, and the maintenance cost is reduced.

An intelligent catalyst discovery and electrolysis optimization system for the production of green hydrogen, consisting of: • a schematic water electrolysis unit configured to simulate the electrolytic splitting of water into hydrogen and oxygen by modeling the hydrogen evolution reaction (HER) at a cathode and the oxygen evolution reaction (OER) at an anode and enabling analysis of catalyst effectiveness in reducing energy input; • a catalyst structure analysis unit configured to visualise and simulate atomic and molecular structures of candidate catalyst materials, including base metals, transition metal-based nanostructures and two-dimensional materials, and to evaluate charge distribution, surface morphology and active site behaviour; • a computational modeling and simulation unit using density functional theory (DFT) for quantum simulations to evaluate the thermodynamic stability, electronic structure, reaction energetics and adsorption energy of potential catalysts; • a machine learning optimization module trained on simulated and experimental data, using supervised and unsupervised learning models to determine optimal catalyst descriptors and predict catalytic activity, stability, and durability; • an experimental validation and test interface comprising electrochemical test environments including three-electrode cells and electrolyzers for performance benchmarking under realistic electrolysis conditions and capable of logging overvoltage, efficiency and degradation behavior; • an energy integration unit configured to interface with renewable energy systems, including photovoltaic systems, to dynamically power the electrolysis process and manage energy consumption through a power management subsystem; • a data processing and control interface to manage cross-system communication between simulation, experimental, and machine learning modules, enabling real-time parameter tuning and visualization of system diagnostics; and • a secure storage and logging unit adapted for storing simulation data, machine learning training sets and experimental results, with features for offline retrieval and integration with cloud platforms for collaborative research.

The invention discloses a grid-connected inverter IGBT (Insulated Gate Bipolar Translator) fault diagnosis method and a grid-connected inverter IGBT fault diagnosis system based on a combined observer, and aims to improve the fault diagnosis robustness and accuracy of a switching device by an inverter system. Inverter inductive current, filter capacitor voltage, power grid inductive current and total disturbance including IGBT fault effect are considered; then, designing and operating a superhelix total disturbance observer, and estimating and separating the total disturbance of the system in real time; disturbance compensation is carried out through an estimation result of the interval sliding mode observer, and interval estimation of a system state and system output is generated; and finally, realizing IGBT fault diagnosis by combining two criteria of disturbance estimation deviation and state interval consistency. According to the method, the problems that a traditional method is poor in disturbance resistance and prone to false alarm and missing alarm are effectively solved, the stability, accuracy and reliability of a diagnosis system are enhanced, and the method is suitable for photovoltaic grid-connected inverter IGBT fault detection and diagnosis under the complex operation condition.

A dynamic testing system includes a test station and at least one controller. The test station includes a hydraulic actuator configured to drive an actuation of a test subject using a hydraulic fluid flow, and a control valve that includes a housing, a valve body contained within the housing having a position that controls a flow rate and direction of the hydraulic fluid flow, a valve body driver configured to adjust the position of the valve body based on a reference signal, and a valve position sensor configured to output a position signal that is indicative of the position of the valve body. The at least one controller is configured to obtain a current position of the valve body using the position signal, obtain a diagnostic reference, detect a diagnostic condition based on a difference between the current position and the diagnostic reference, and generate a diagnostic signal.

A method for operating and diagnosing faults in a laboratory instrument comprising a plurality of subsystems can include conducting an analysis sequence and a set of diagnostic steps. Such a method can be conducted using diagnostic reagents that contain paramagnetic particles but lack an antibody component. Such a method can also include evaluating a set of instrument subsystems in an order that is opposite to the order in which the subsystems are used during an analysis.

The invention discloses a multi-source domain rolling bearing fault diagnosis method and system based on a diffusion model. The diagnosis method comprises the steps that rolling bearing vibration signals under different rotating speed working conditions are collected; in the forward process of the diffusion model, converting a source domain label into a one-dimensional vector, and adding noise to destroy original distribution to obtain yT approximate to Gaussian distribution; replacing yT with a Gaussian distribution variable # imgabs0 # in a reverse process, learning noise distribution characteristics added in forward distribution through a noise prediction network, denoising # imgabs1 # step by step to obtain # imgabs2 # in the same distribution as y0, inputting source domain data into the noise prediction network to extract data characteristics, inputting target domain data into the noise prediction network, and outputting the target domain data into the noise prediction network; global noise priori and local noise priori corresponding to the target domain data are obtained; and inputting source domain data and target domain data into a noise prediction network, mapping source domain data features to a target domain by using a multi-source domain adaptive algorithm, learning noise distribution features related to the target domain data, and gradually denoising # imgabs3 # to obtain a fault type of the target domain data.

A method for heating, ventilation, and air conditioning (HVAC) system diagnostics includes sending a first instruction to a thermostat to shut down an HVAC system. A user is instructed to minimize background noise. A second instruction is sent to the thermostat to turn on the HVAC system. A third instruction is sent to the thermostat to set a temperature setpoint below or above a value of a room temperature. Outdoor unit sound data is captured for a second time period. The baseline sound data is subtracted from the outdoor unit sound data to determine normalized outdoor unit sound data. Expected sound signatures of the outdoor unit are identified. The normalized outdoor unit sound data is compared to the expected sound signatures. In response to determining that an expected sound signature for a compressor is missing from the normalized outdoor unit sound data, it is determined that the compressor has failed.

The invention discloses a Fast DDS-based QNX system soft diagnosis method and system and electronic equipment, and relates to the field of system diagnos.The method comprises the steps that S1, a Fast DDS-based global data space is constructed and serves as a real-time communication bus between a QNX system and an Android system, and decentralized interaction of dual-system diagnosis data is achieved; s2, deploying a soft diagnosis service in the QNX system; the soft diagnosis service comprises four core function modules: a black screen detection and self-healing module, a resource dynamic monitoring module, a virtual machine monitoring module and a burying point analysis module; s3, a diagnosis agent service is deployed in the Android system; the diagnosis agency service receives the alarm and data issued by the QNX system through the Fast DDS client; the method comprises the following steps: reading a log packaging file and a tcpdump file stored in a QNX system through a shared directory; and summarizing the data and uploading the summarized data to a cloud platform to realize visual display of the diagnostic data.

The server includes: identification means (314) configured to identify that a location of a defect is in a first range including a plurality of replaceable components in an image forming apparatus, and in a case where it is possible to identify that the location of the defect is in a second range included in the first range, further identify the second range; and reporting means (315) configured to, in a case where the identification means identifies the second range, report to a display apparatus such that the display apparatus displays information indicating the second range, and in a case where the identification means does not identify the second range, report to the display apparatus such that the display apparatus displays information indicating the first range.

Methods, computer systems, and computer storage media are provided for utilizing system diagnostics focused in areas of disruption to improve inventory and workforce management in a revenue cycle management system. Inventory input is received from a revenue cycle management system. The inventory input includes inventory in the revenue cycle management system, types of claims in the inventory, pair agreements of the claims, and a length of time the claims have been in the inventory. Workforce input is also received from the revenue cycle management system. The workforce input includes experience, type of education or technical training, specialty, and outcomes corresponding to a workforce of the client. A portion of the inventory is automatically assigned to an associate of the workforce. The portion of the inventory tailored to the associate based on the inventory input and the workforce input.

The invention relates to a dangerous chemical enterprise safety system diagnosis grading evaluation system and a dangerous chemical enterprise safety system diagnosis grading evaluation method, and aims to comprehensively and accurately evaluate the safety management level of an enterprise through a multi-dimensional and personalized evaluation method and provide scientific and objective evaluation results and improvement suggestions. The system comprises a plurality of evaluation modules for responsibility allocation rationality, planning ability, system execution ability, supervision and inspection ability, continuous improvement ability and the like, and customized evaluation can be carried out according to enterprise scales and types. Meanwhile, advanced technologies such as big data and artificial intelligence are introduced into the system, rapid and accurate evaluation data processing and analysis are achieved, improvement suggestions are automatically generated, and accurate safety management guidance is provided for enterprises. Through application of the system, dangerous chemical enterprises can find and solve problems in safety management in time, safety production efficiency and quality are improved, safety and stability of the production process are ensured, and a powerful guarantee is provided for sustainable development of the industry.