50 results about "Dynamic demand" patented technology

The invention relates to the technical field of hydropower station optimization scheduling, in particular to a hydropower station multi-target scheduling decision-making method and system, and the method comprises the steps: obtaining the multi-source heterogeneous data of a target cascade hydropower station, and constructing and dynamically updating a scheduling knowledge graph fusing the cascade hydraulic coupling and collaborative operation association relationship; identifying a reference scheduling time period and a non-reference scheduling time period and establishing a differential output constraint; performing feature compression on the scheduling knowledge graph, extracting a key feature sub-graph influencing a scheduling decision, and predicting a state evolution path of related scheduling elements of the target cascade hydropower station in a future scheduling time domain; constructing and solving a multi-target dynamic decision model, and generating a candidate scheduling scheme set; and performing cross-scale conflict detection based on the candidate scheduling scheme set, performing hierarchical re-optimization on the candidate scheduling scheme set according to a detection result, and outputting and executing a scheduling decision result. The objective of the invention is to adapt to the dynamic demand of the power market for cascade hydropower station scheduling and realize rapid and accurate collaborative scheduling decision.

The invention discloses a flexible dynamic distribution method and system for a power module based on a charging pile, and relates to the field of power distribution, and the method comprises the steps: collecting dynamic demand parameters of a charging terminal in real time, and calculating a target power demand in combination with a battery safety constraint; obtaining the total number of available power modules and rated power, and generating an effective module set; calculating the minimum module demand number according to the total demand and the module average power; if the modules are sufficient, an efficiency priority strategy is adopted, and distribution is optimized through a greedy algorithm; if not, high-power terminals are preferentially distributed, and then residual power is distributed in a balanced mode; through real-time data, distribution is dynamically adjusted when a threshold value is exceeded; starting dormancy for the module with the load rate lower than 30%, migrating the terminal to other modules, and awakening when the load rises to 40%, so as to improve the efficiency and the resource utilization rate. The method has the advantages that through real-time monitoring and dynamic distribution of the power modules, the charging efficiency and safety are optimized, and the resource utilization rate and the system reliability of the charging pile are remarkably improved.

The invention relates to the technical field of virtual power plant resource configuration, and discloses a virtual power plant resource dynamic coordination configuration method and system, and the method comprises the following steps: collecting the operation data of various resources in a virtual power plant and the external power grid environment data in real time, and carrying out the dynamic coordination configuration of the resources based on the operation data; extracting operation characteristic parameters reflecting response speed differences of various resources; clustering analysis is carried out on the multiple resources to form resource clusters with different response speed levels, and the collaborative deployment demand intensity of the power grid in the current time period is evaluated; predicting a power grid load balance state in a future target time period, and determining the callable response capability of each resource cluster in the target time period; and carrying out multi-target optimization calculation meeting the stability constraint of the power grid, and generating a dynamic coordination configuration scheme of various resources in the target time period. According to the method, through the synergistic effect of multi-level technical characteristics, the matching problem between resource characteristic differences and dynamic requirements is fundamentally solved.

The invention relates to a tail end constant-pressure water supply strategy adjusting method and system based on AI, and belongs to the technical field of secondary water supply.The constant-pressure water supply strategy adjusting method comprises the steps that the flow of a header pipe and real-time health state data of each running pump are periodically collected; according to the header pipe flow, the dynamic demand lift is calculated, and all feasible pump set combination strategies are generated in combination with the dynamic demand lift and the real-time health state data; the score of each candidate strategy is calculated, the candidate strategy with the highest score is used as a pump set scheduling strategy, the pump with the most matched performance is selected from candidate pump sets corresponding to the pump set scheduling strategy to serve as a frequency conversion main guide pump, and the other pumps serve as frequency conversion auxiliary pumps; according to the set minimum service pressure and the dynamic demand lift, the target pressure of a pump outlet is calculated; and according to the target pressure and the actually-collected pump outlet pressure, the rotating speed of the variable-frequency main guide pump is adjusted. The method has the beneficial effects that the power of the pump set is matched with the actual requirement, so that the operation cost is reduced.

The invention discloses an ultra-low energy consumption building power utilization intelligent regulation and control method and system based on photovoltaic power generation, and the method comprises the steps: collecting photovoltaic voltage and current fluctuation and equipment power data in real time, and extracting supply instability features; classifying the dynamic demands by using a clustering algorithm according to the features, solving diffusion coefficients of each group of demands, and distinguishing high and low demands; extracting path candidates from high demands, and iteratively optimizing to obtain a complete electric energy distribution scheme; after the simulated energy utilization rate meets the ultra-low energy consumption requirement, the scheme is deployed as a timing scheduling execution plan; power deviation is continuously monitored during operation, if demand changes exist, diffusion coefficients are updated, demand groups are rearranged, an electric energy utilization report containing efficiency statistics is generated, and closed-loop control is achieved. Through dynamic optimization and real-time regulation and control, the electric energy distribution efficiency and the system stability are remarkably improved, and the requirements of ultra-low energy consumption buildings are met.

The invention belongs to the technical field of power distribution network power supply capacity potential tapping, and provides a power distribution network power supply capacity potential tapping method for multi-type load recombination, which comprises the following steps of: identifying a high-risk area in a power distribution network power supply area by performing power utilization time concentration analysis on the power distribution network power supply area; the method comprises the following steps: acquiring a flexible load and a rigid load of a high-risk area in a historical period, marking loads of different historical periods in the historical period, performing traversal analysis on a marking result, extracting all to-be-analyzed period groups in the historical period, and performing adjustability analysis on the flexible load of the historical periods in the to-be-analyzed period groups to obtain the flexible load of the high-risk area in the historical period. Outputting an adjustable high time period group in the to-be-analyzed time period group; according to the method, the multi-period collaborative optimization model is constructed, the optimal transfer scheme is solved in combination with the mixed integer linear programming and the branch and bound method, the residual capacity in the low-load period is fully utilized, and collaborative optimization of the dynamic demand response and the power supply capacity is achieved.

The invention relates to the technical field of kitchen appliance control, and discloses a kitchen appliance integrated control system based on dynamic power distribution, which comprises a power monitoring module, a load analysis module, a dynamic distribution module, a safety protection module, a user interaction module and a network integration module. The current, voltage and instantaneous power consumption value of each device are collected in real time through a power monitoring module, and whether a safe power interval is exceeded is judged; analyzing a total load state and a dynamic demand trend based on a load analysis module, and predicting a potential overload risk; the dynamic allocation module dynamically adjusts the power allocation proportion of key and non-key equipment in combination with a user priority rule; the safety protection module monitors in real time and automatically executes graded protection measures; and the user interaction module provides a graphical interface to support priority configuration and power upper limit setting so as to realize strategy closed-loop optimization.

The application relates to the technical field of order allocation, in particular to a dynamic distribution order allocation optimization method considering the supply-demand state of a transport capacity system.S1, a dynamic pickup and delivery problem model is constructed, the dynamic pickup and delivery problem system state and the action set are determined, and the dynamic pickup and delivery problem model is constructed;S2, a rolling time sequence mechanism is adopted to describe the dynamic nature of the urban dynamic pickup and delivery problem; and S3, a mixed allocation strategy is adopted to allocate the dynamic distribution order.The dynamic distribution order allocation optimization method considering the supply-demand state of the transport capacity system optimizes the design of the order allocation, efficiently allocates a scheme and a solution algorithm, realizes the matching of efficient dynamic time requirements and distribution resources, reduces the distribution cost and improves the distribution punctuality rate; meanwhile, the efficient order allocation strategy and the distribution optimization search operator based on the transport capacity supply-demand are designed, the response speed of the distribution system to the dynamic time requirements is improved, the distribution service quality of the distribution enterprise to the market and the customer requirements can be improved, and the distribution cost is optimized.

The invention discloses an engineering machinery lease management method and system based on dynamic demand matching, and relates to the technical field of engineering machinery lease management, and the method comprises the following steps: recording a construction site of a leasee, and enabling the leasee to upload the use state and demand state of engineering machinery in real time through a lease APP; performing dynamic demand matching on the engineering machinery based on the use states and demand states uploaded at different construction sites; based on a dynamic demand matching result and a construction site, analyzing an optimal matching result under the condition of considering the transportation cost; the engineering machinery is transported based on the optimal matching result and the construction site; the method is used for solving the problems of low utilization rate and high transportation cost of the engineering machinery due to the fact that the transportation and the use of the engineering machinery cannot be dynamically balanced based on the requirements of different leasers in the existing engineering machinery leasing management technology.

The invention relates to the technical field of energy monitoring, in particular to a smart energy monitoring system based on the Internet of Things technology, which comprises an Internet of Things energy monitoring center, a state module, a performance analysis module, a dynamic demand module, a period monitoring module and a rear-end feedback module. According to the method, analysis is carried out from the perspective of multiple data sources of the energy equipment, auxiliary support is carried out in combination with historical multi-source data, whether the state and energy consumption performance of the energy equipment are normal or not is judged, the state and energy consumption performance conditions of the energy equipment can be visually known, targeted management is carried out on the energy equipment based on feedback information, and the energy equipment management efficiency is improved. And meanwhile, along with energy utilization supervision and period dynamic adjustment analysis, analysis feedback is performed from the aspects of energy utilization supervision and prediction short-term stable performance, so that the energy effective utilization rate of the energy equipment is improved, and meanwhile, dynamic adjustment of the monitoring period of the energy equipment is realized. And thus, the whole energy monitoring cost is reduced.

The invention discloses an energy storage power station output power control method and device based on the dynamic demand of an energy storage power station, a storage medium, electronic equipment and a computer program product. The control method comprises the following steps: acquiring energy storage meter power data, load meter power data and allowable output power data of an energy storage power station in real time; determining maximum demand data of the energy storage power station according to the energy storage meter power data and the load meter power data; determining the dynamic demand of the energy storage power station according to the preset fixed demand data, the preset initial power data, and the maximum pure load power data and the maximum demand data of the current period; and determining output power data of the energy storage power station according to the preset fixed demand data, the dynamic demand, the load table power data and the allowable output power data, so as to control the output power of the energy storage power station according to the output power data.

The application discloses a method and system for material demand prediction and distribution path planning for a factory building machine, and relates to the technical field of demand prediction and path planning.The method comprises the following steps: obtaining design parameters of a target factory building; obtaining coordinates of the factory building machine in real time and calculating construction progress; running an adaptive speed prediction model to output future advance speed prediction; obtaining a future construction progress curve based on the predicted speed integral, combining with dynamic calculation of replenishment demand nodes to form a dynamic demand node set by mapping the predicted demand time and the factory building machine position; taking the dynamic demand node set as input, establishing a multi-objective vehicle path planning model, and solving an optimal distribution scheme; and finally issuing the scheme and periodically rolling and re-planning.The application realizes dynamic and accurate cooperation of material distribution and factory building machine construction, effectively improving the construction efficiency and industrialization level of the factory building machine.

The invention relates to the technical field of rail transit vehicle dynamics and intelligent suspension control, in particular to a high-speed train rotating arm node reverse dynamics design method based on dynamic requirements, which comprises the following steps: S1, constructing a mapping relation between vehicle dynamics performance and node rigidity, the method comprises the steps of S1, establishing a mapping relation between node rigidity and a magneto-sensitive elastomer material and structure, S3, establishing a magneto-sensitive elastomer node database, S4, carrying out reverse design based on a dynamic demand matrix, and S5, carrying out semi-active rigidity regulation and hardware-in-the-loop verification based on a controllable magnetic field. According to the invention, material and structure design parameters of the damping element can be deduced reversely, a multi-dimensional parameter database can be established, computable performance reverse indexing can be realized, a simulation-test-database self-learning mechanism is formed by combining a mechanical model and a test, and a closed-loop verification platform is constructed between a whole vehicle simulation model and a rack system. And engineering landing of the design method is realized.

This invention discloses a demand response scheduling method and system that considers the fairness of nodal carbon potential location, belonging to the field of low-carbon optimization scheduling technology. The demand response scheduling method considering the fairness of nodal carbon potential location includes: establishing a comprehensive energy system equipment model and a distribution network carbon emission flow model; establishing a hybrid index model considering the fairness of nodal carbon potential location; establishing a load-side dynamic demand response model based on the hybrid index model to generate carbon price signals; constructing a two-stage low-carbon optimization scheduling model considering the uncertainty of renewable energy output, with system operating costs, nodal carbon potential location fairness index, and carbon emission costs as optimization objectives. The optimal scheduling scheme is obtained through iterative solutions by determining unit output plans in the pre-scheduling stage and adjusting load distribution in the subsequent scheduling stage. This invention reduces system operating costs, achieves refined allocation and fair control of carbon emission responsibility in the comprehensive energy system, and improves the overall low-carbon operation level and the degree of reasonable carbon responsibility sharing of the system.

The invention discloses a regulation and control method for a fixed rail transfer type port power conversion system, and relates to the technical field of port equipment energy management. The system comprises a distributed intelligent control system, a multi-sensor fusion monitoring and feedback mechanism, an intelligent algorithm scheduling module, a classified battery replacement strategy oriented to different equipment and operation modes, a dynamic demand response mechanism and a battery full life cycle management method. Through distributed node cooperative control, real-time data fusion and intelligent path planning, high-efficiency, stable and adaptive scheduling of a battery replacement process is realized. The method can significantly improve the port battery replacement efficiency, optimize the battery resource configuration, enhance the system reliability, and reduce the operation cost, and is suitable for rapid battery replacement and energy management scenes of various port electric devices.

The invention relates to a coal mine unloading station driving route generation method and system, and relates to the technical field of automatic driving. According to the scheme, the method can be executed or configured on unloading equipment, path coordinate points and corresponding first heading such as an area entrance point, an area exit point, a backing point and an unloading point are determined in a driving area of a coal mine unloading station, and an initial driving route is generated based on the path coordinate points, the first heading and motion parameters of the unloading equipment; in the process of executing coal mine unloading operation along the initial driving route, the initial driving route is adjusted in response to the setting request of the passing points in the driving area and the second course of the passing points, so that the target driving route is obtained to continue operation. The scheme adapts to actual requirements of unloading equipment, and an initial driving route is determined according to the coordinate point and the course; and in the operation process, the route is adjusted according to the setting based on the passing points and the course, so that the method adapts to the dynamic demand change of equipment, operation and working conditions, and has better flexibility and accuracy.

The invention relates to the technical field of information recommendation, in particular to a ticket information recommendation system and method, and the system comprises a data fusion construction module which collects and standardizes multi-source heterogeneous data of an industrial environment; constructing an industrial entity relationship network based on the multi-source heterogeneous data; the dynamic demand response module is associated with the data fusion construction module and is used for monitoring a trigger event of the multi-source heterogeneous data in real time and positioning associated industrial scene elements; in response to the trigger event, matching ticket objects associated with the industrial scene elements in the industrial entity relationship network; and applying the industrial scene operation parameters as constraint conditions, and generating a ticket business recommendation scheme and a decision-making basis which meet the constraint conditions. According to the method, through graph structure model construction, real-time event response, constraint condition verification, decision basis interpretation and a feedback learning mechanism, accurate recommendation of ticket business objects in an industrial scene is realized, and the blank of the prior art in the field of industrial internet intelligent recommendation is filled.

The present application relates to the technical field of low-voltage droop power adjustment at the end of rural power grid, and discloses a self-adaptive droop power control system for treating low-voltage at the end of rural power grid. The self-adaptive droop power control system for treating low-voltage at the end of rural power grid collects voltage data and current data at the end of rural power grid, pre-processes the two kinds of data, calculates an adjustment factor according to the two deviation values, calculates the actual power in a collection cycle according to the actual voltage measurement value and the actual current measurement value after the adjustment factor is generated, combines the actual power in the collection cycle and the adjustment factor to calculate a final adjustment power value, and adjusts the droop power of low-voltage at the end of rural power grid according to the final adjustment power value, so as to realize real-time dynamic adjustment of the voltage at the end of rural power grid, avoid static voltage adjustment mode, correspond to load changes in real time, meet dynamic demand, be suitable for use in various regions, and improve power quality stability.

The present application relates to the technical field of power system planning, and particularly relates to a regional low-carbon power grid multi-scenario dynamic expansion deduction method and system, comprising: modeling steel-electricity coupling demand according to steel electric arc furnace data and regional steel production capacity distribution characteristics to obtain regional power demand fluctuation distribution; identifying power grid stability critical point according to the regional power demand fluctuation distribution; obtaining expansion demand ranking table according to power grid equipment capacity data corresponding to the power grid stability critical point; generating phased power grid capacity expansion path graph according to the expansion demand ranking table, simulating peak load distribution, and obtaining carbon emission intensity index; and generating a low-carbon dynamic expansion planning scheme of steel-electricity coupling according to the carbon emission intensity index and regional energy structure characteristic data. The present application realizes the collaborative optimization of decarburization of the steel industry and expansion of the power system by establishing a dynamic demand model of steel-electricity coupling and based on the identification of the power grid stability critical point and the low-carbon oriented expansion planning.

The invention belongs to the technical field of charging infrastructure planning, and discloses a charging station dynamic adaptive planning method based on model deduction, which comprises the following steps: dividing a target city into a plurality of planning areas, collecting multi-source heterogeneous data of each planning area, and constructing a data block containing time-space coupling information; establishing a simulation environment of the multi-agent game based on the data blocks; a reinforcement learning model comprising a spatial-temporal feature extraction module and a strategy evaluation module is constructed, an output game equilibrium result is used for training, and the long-term value of the station building action of each planning area is evaluated; deploying the trained reinforcement learning model in a virtual environment which can be dynamically updated, performing multi-step strategy deduction in combination with an optimization algorithm, and selecting an optimal construction scheme of the charging station; the problems that in the prior art, charging infrastructure layout and dynamic demand matching is insufficient, a multi-body game decision mechanism is lacked, and long-term adaptability optimization capacity is lacked are solved.

The application discloses a method and system for vehicle path optimization in a single-vehicle channel under dynamic demand, acquires a path point request and adds the path point request to a request queue; a vehicle path optimization model is constructed, and according to a current position of the vehicle and the dynamic request queue, an optimal order of the request queue is dynamically calculated within a certain time limit after the request is sent, a next request is dynamically selected to minimize total travel time, and the next path point is determined; a priority queue is divided from the request queue according to a time when the request is sent, for the priority queue, a current target, a next target and a second next target are determined in sequence; when the vehicle reaches the next target, the next target is updated to the current target, and the second next target is updated to the next target, and the process is sequentially and circularly promoted to form a processing order until the requests in the priority queue are all responded.

The invention discloses an intelligent control system and method of an argon recovery device, and relates to the professional technical field of argon recovery, and the intelligent control method of the argon recovery device comprises the following specific steps: step 1, collecting data generated in a historical argon recovery process of the argon recovery device, and predicting a dynamic demand based on a pre-trained dynamic demand prediction model; the method comprises the following steps of: 1, predicting energy consumption of the argon recovery device in different time periods, 2, traversing and collecting corresponding processing capacities and current load values of the argon recovery device in different key process units to carry out data analysis, and carrying out grade classification and structure marking according to a data analysis result, and 3, constructing a multi-objective optimization function. According to the method, full-link closed-loop management of data acquisition, load analysis and decision optimization is realized through modular design, and the comprehensive energy efficiency and the operation stability of the argon recovery device are remarkably improved.

The invention discloses a charging control method, a confluence charging device, equipment and a storage medium, and belongs to the technical field of power electronics. The confluence charging device is connected with electric equipment and a plurality of charging piles; determining the distribution power of each charging pile by splitting the required power of the electric equipment, and obtaining the maximum power which can be provided by each charging pile at present; and determining the target power of each charging pile based on the distribution power and the maximum power of each charging pile, and controlling each charging pile to charge the electric equipment based on the target power of each charging pile. According to the method, the demand of the electric equipment is disassembled and analyzed, and the target power of each charging pile is determined based on the distribution power and the maximum power of each charging pile, so that the dynamic scheduling of the plurality of charging piles is realized, the problem that the confluence charging device cannot meet the dynamic demand of the electric equipment is effectively solved, the charging resources are fully utilized, and the charging efficiency is improved. The charging efficiency is improved, and the safety and reliability of the confluence charging device are ensured.

The invention discloses a thermal power generation coordination control method, which belongs to the technical field of thermal power generation, and comprises the following steps: S1, obtaining the coal inventory of a coal yard; s2, generating capacity is set, and predicted coal consumption is calculated according to the generating capacity; s3, determining the purchased coal quantity according to the coal inventory and the predicted coal consumption; s4, dividing the real-time coal consumption by the actual load to obtain a power-coal ratio; s5, when the load instruction changes, dividing the heat consumption value corresponding to the load instruction by the heat consumption value at the moment of change to obtain a heat consumption coefficient; s6, multiplying the power-coal ratio, the heat consumption coefficient and the load instruction to obtain an accurate coal quantity required in a load instruction change process; s7, calculating according to the real-time accurate coal quantity and the time to obtain a dynamic demand coal quantity; s8, adjusting the purchased coal quantity according to the dynamic demand coal quantity; s9, determining the power generation cost; and S10, determining the purchase cost and the electricity price based on the power generation cost. The inventory is optimized, the power generation benefit can be accurately determined, and the power generation benefit can be increased.

The application provides a kind of energy storage device participates transformer demand control method and system.Step one: start control system and complete each unit self-checking, synchronous power grid metering device to obtain demand metering period, settlement period parameter;Load preset threshold and hybrid prediction model parameter, call historical load and energy storage operation parameter sample library to complete model preheating, energy storage device participates transformer demand control method and system.The energy storage device participates transformer demand control method and system provided by the application has a hybrid prediction model that combines LSTM and an improved slip algorithm, along with a dynamic demand calculation window and a metering calibration factor, which strictly controls the demand prediction error within 5%.The millisecond-level energy storage response and dynamic power adjustment can reduce the monthly maximum demand by 25%-40%, effectively reducing the basic electricity charge, and is compatible with the power grid metering system, without billing dispute risk.

The embodiment of the invention provides a new energy inertia enhancement control method and system for improving the self-healing capability of a distribution network, and the method comprises the steps: determining an energy storage configuration target of a new energy power station; determining a representative power value of the new energy power station according to the historical output power data of the new energy power station; configuring parameters of the virtual synchronous controller; according to the energy storage configuration target, based on the representative power value and the virtual synchronous controller parameters, energy storage power capacities required by the energy storage system to meet inertial support and primary frequency modulation are calculated respectively; calculating the energy storage capacity of the energy storage system according to the energy storage power capacity; and configuring an energy storage system according to the energy storage capacity, connecting the energy storage system to the new energy power station, responding to power grid disturbance through the virtual synchronous controller, and supporting self-healing operation of the power distribution network. The technical scheme provided by the invention can adapt to dynamic requirements in the self-healing process of the distribution network, and ensures stable operation and quick recovery of the distribution network under complex disturbance.

The embodiment of the invention provides a reactive power optimization scheduling method, device and equipment for a converter equipment cluster. The method comprises the following steps: acquiring output prediction data and real-time operation data of a target converter equipment cluster; based on the day-ahead reactive power optimization scheduling model, processing the output prediction data of the target converter equipment cluster to obtain a day-ahead scheduling plan of the target converter equipment cluster in the next day; based on a rolling update mechanism, according to the real-time operation data, correcting the output prediction data to obtain corrected output prediction data of the target converter equipment cluster; and performing calculation processing on the intra-day scheduling plan of the target converter equipment cluster and the corrected output prediction data by adopting an improved pollen algorithm to obtain a reactive power optimization scheduling strategy of the target converter equipment cluster. According to the method, the adaptability of the power distribution network to the distributed energy output randomness and the load dynamic demand can be remarkably improved.

The present application relates to the technical field of hydropower station optimal scheduling, in particular to a hydropower station multi-objective scheduling decision method and system, comprising: acquiring multi-source heterogeneous data of a target cascade hydropower station, constructing and dynamically updating a scheduling knowledge graph that fuses cascade hydraulic coupling and collaborative operation correlation; identifying a reference scheduling period and a non-reference scheduling period and establishing differentiated output constraints; performing feature compression on the scheduling knowledge graph, extracting key feature subgraphs that affect scheduling decisions, and predicting the state evolution path of relevant scheduling elements of the target cascade hydropower station in the future scheduling time domain; constructing and solving a multi-objective dynamic decision model to generate a candidate scheduling scheme set; performing cross-scale conflict detection based on the candidate scheduling scheme set, hierarchically re-optimizing the candidate scheduling scheme set according to the detection results, and outputting and executing the scheduling decision results. The purpose is to adapt to the dynamic demand of the power market for cascade hydropower station scheduling, and to realize fast and accurate collaborative scheduling decision.

The application provides a power distribution network bus single-phase grounding fault positioning method and system, comprising: simultaneously collecting multiple zero sequence current signals through a preset double-source zero sequence acquisition unit in a bus switch cabinet; taking a time point of fault occurrence as a reference time point, determining a zero sequence current average value according to zero sequence current signals of a preset power frequency cycle before the reference time point and taking the zero sequence current average value as a reference value, and calculating an external zero sequence current increment and a self-produced zero sequence current increment based on the reference value; compensating and correcting the obtained external zero sequence current increment through a preset correction model to obtain a corrected zero sequence current increment; determining a corresponding fault confidence based on the corrected zero sequence current increment, judging a fault position according to the fault confidence, and outputting final fault positioning information. The application realizes rapid and accurate positioning of 10kV bus single-phase grounding faults, improves power distribution network operation and maintenance efficiency and power supply reliability, and adapts to dynamic demands of different load scenarios.

This application relates to the field of project access control technology, and in particular to a method, system, device, and storage medium for generating a dynamic requirements management panel. The method includes: constructing a system-level access control index; performing project-level access control filtering on project data to obtain a set of accessible projects, and obtaining the workflow tree configured for each accessible project; parsing the configuration information of each workflow node in the workflow tree configured for the target project, determining the access control constraints corresponding to each workflow node, and aggregating and standardizing the configuration information into workflow rule objects; performing multi-dimensional access control convergence based on the system-level access control index, project-level access control filtering results, and access control constraints corresponding to each workflow node, determining the visible data boundaries of the logged-in user under the target project, and dynamically generating the requirements management panel corresponding to the logged-in user based on the visible data boundaries and workflow rule objects. This application can quickly adapt to dynamically changing business needs and reduce security risks caused by mismatches in information display scope.