32 results about "Cost index" patented technology

The invention provides a computing power scheduling method, device and equipment for large model heterogeneous reasoning and a medium, and belongs to the technical field of computing power scheduling, the method comprises the following steps: in response to a model deployment request, obtaining performance index data and cost index data of each candidate deployment unit in a heterogeneous computing resource pool; determining a dynamic weight of each candidate deployment unit, and calculating a comprehensive cost performance score of each candidate deployment unit based on the dynamic weight of each candidate deployment unit and the performance index data and the cost index data of each candidate deployment unit; and selecting the candidate deployment unit with the highest comprehensive cost performance score as a target deployment unit, and scheduling the target large model to heterogeneous computing resources corresponding to the target deployment unit for execution. According to the large model heterogeneous reasoning-oriented computing power scheduling method provided by the invention, the scheduling decision is made by comprehensively considering the dynamic weight, the performance and the cost of the candidate deployment unit, so that the real-time performance, the accuracy and the adaptability of computing power scheduling are improved.

The application discloses a kind of digital engineering cost index determination method, system, equipment and storage medium, wherein, method includes: receiving enterprise index subject and enterprise standard list uploaded by system administrator;Receive the configuration input of system administrator;According to the configuration input, the association of enterprise index subject and enterprise standard list is configured, and the calculation rule of each index in enterprise index subject;Obtain the data input by user for enterprise standard list;From the data of enterprise standard list associated with enterprise index subject, obtain the target data required for calculating the index in enterprise index subject;According to calculation rule, target data is processed, and the information for determining the index value in enterprise index subject is obtained.The application can upload enterprise index subject and enterprise standard list according to the needs of enterprise, and when calculating the index in enterprise index subject, the required data for calculation can be automatically obtained from the associated enterprise standard list.

The invention provides a data processing method and device, electronic equipment, a storage medium and a program product, relates to the technical field of information, and is used for improving the accuracy and reliability of data resource evaluation. The method comprises the steps that data resource index parameters of multiple dimensions of a basic cost index, a quality index, an application index and a resource relation index of a target entity are collected, the basic cost index is processed through a target resource model, and basic cost information of a first data resource of the target entity is obtained, on the basis of the basic cost information and the resource relation index, calculating resource dependence cost of the second data resource; calculating the value score of the first data resource based on the quality index and the application index through an analytic hierarchy process; determining the expected feedback rate corresponding to the value score interval in which the value score is located as the expected feedback rate of the first data resource; and based on the basic cost information, the resource dependence cost and the expected feedback rate of the first data resource, obtaining an evaluation result of the first data resource.

The present application relates to the technical field of engineering cost information processing, and discloses a cost index library construction and updating method and system based on version mapping. The method comprises: obtaining to-be-stored engineering data; identifying the pricing specification version, cost caliber version and list coding version corresponding to the engineering data to generate a version identification set; converting the list item, cost component item and measurement caliber based on a preset version mapping rule to obtain standardized engineering data; extracting engineering feature data and performing comparability verification with historical index samples in the target index library; and performing direct storage updating, corrected storage updating or restricted storage updating on the standardized engineering data according to the verification result. The present application can realize the unified conversion and controllable updating of different version engineering cost data, reduce the risk of index distortion and index pollution, and improve the consistency, accuracy and reliability of the updating result of the cost index library.

The present application relates to the technical field of base station power optimization scheduling, in particular to a base station scheduling method considering load power consumption and user satisfaction loss risk. The method comprises the following steps: S1, establishing a load power consumption cost index and a user satisfaction loss risk index under base station scheduling; S2, constructing a multi-objective optimization scheduling model based on the load power consumption cost index and the user satisfaction loss risk index; S3, inputting parameters into the optimization scheduling model; S5, selecting an optimal scheme from the multiple candidate scheduling schemes, and performing scheduling control on the base station based on the optimal scheme. The present application establishes the relationship between base station power scheduling and the load power consumption cost index and the user satisfaction loss risk index, realizes the dual goals of reducing power consumption cost and controlling satisfaction loss risk, carries out systematic scheduling from the overall operation efficiency of the base station, realizes the comprehensive benefits and sustainability of improving the operation of the base station, and has important significance and engineering practical value.

This invention discloses a cost data optimization method and system based on a flexible task work order mechanism, belonging to the field of work order allocation technology. The system includes a dynamic sensing module, an efficiency analysis module, a cost optimization module, and a data storage module. The dynamic sensing module receives task work orders from clients and collects task records from all service objects within the park. The efficiency analysis module analyzes all work order records completed by each service object in each cycle based on the task records, calculates the cost index, and fits the efficiency index relationship by combining the interval duration of the work order records. The cost optimization module calculates the cost index of each task work order, sorts them, and performs the first round of allocation to service objects. Based on the efficiency index relationship, it predicts the efficiency index changes of each service object, determines the interval duration, and iteratively allocates task work orders. The data storage module generates a task list for each service object, records the process information of executing task work orders, and saves it to the task record.

The invention discloses an intelligent work order scheduling method and system based on multi-factor cost prediction, and belongs to the technical field of work order management. According to the method, a multi-factor dynamic cost model comprehensively considering in-transit time, skill matching degree, work order priority and service time limit SLA risk is constructed by acquiring work order and personnel states in real time, and a cost index is calculated for each potential scheduling scheme. According to the system, a variable neighborhood search VNS optimization algorithm is adopted, and global solution is carried out on the basis of a cost matrix so as to find an optimal task allocation scheme with the lowest total cost. According to the method, the weight can be dynamically adjusted and optimized according to operation states such as real-time traffic and personnel load, self-adaptive intelligent decision making is realized, and finally, an optimization scheme is automatically distributed to a personnel terminal, so that the work order scheduling efficiency, the resource utilization rate and the SLA fulfillment rate are comprehensively improved.

The invention relates to the technical field of digital twinning, in particular to a layout generation optimization method and system for a digital twinning body of a space facility, and the method comprises the steps: constructing a two-dimensional data matrix for each layout scheme of the space facility through employing the evaluation indexes of a preset number of channels in a discretization grid; constructing a social force model based on the two-dimensional data matrix, and determining a layout cost index; analyzing evaluation indexes about efficiency safety, energy consumption and a flexible channel in the two-dimensional data matrix to obtain a multi-dimensional evaluation vector, obtaining a dynamic weight vector by adopting a Bayesian network, obtaining a close degree score by utilizing a decision analysis method, obtaining scene adaptation robustness in combination with an element numerical value dispersion degree of the dynamic weight vector, and determining a fitness function, so as to obtain the scene adaptation robustness. And adopting a particle swarm optimization algorithm to obtain an optimal layout scheme. The invention aims to enhance the comprehensive performance of the space facility layout scheme.

The application relates to the technical field of engineering cost supervision, and discloses a process execution supervision system and method suitable for engineering cost consultation projects. The method acquires a project whole-cycle data set through a distributed data acquisition interface, and the data set at least contains contract files and progress payment vouchers; the data set is subjected to structured feature analysis to generate a multi-dimensional project feature matrix containing time nodes, cost indexes and resource allocation amounts; a pre-constructed engineering cost benchmark parameter library is called, real-time deviation degrees of the multi-dimensional project feature matrix and the benchmark parameters are calculated based on a dynamic time warping algorithm; when the real-time deviation degrees exceed a preset threshold range, an abnormal procedure identification engine is automatically triggered to extract procedure feature vectors including a construction period delay rate, a cost overrun rate and a material loss rate from deviated procedures; the procedure feature vectors are input into a pre-trained compliance verification model to output an abnormal diagnosis report containing a violation type, a risk level and a responsible subject.

The invention discloses an intelligent material allocation method and device suitable for a new energy station. The method comprises the steps that basic parameters needed by operation and maintenance of the new energy station are acquired; according to a material demand parameter, a cost parameter, a time period parameter and a total inventory capacity in the basic parameters, a supply chain comprehensive cost index is determined, and a target function with the supply chain comprehensive cost index minimization as a target is constructed; according to a material demand parameter, a cost parameter, a time period parameter, a boundary constraint parameter and a dynamic safety inventory coefficient in the basic parameters, constructing constraint conditions taking total supply and demand balance, a storage upper limit, a material demand and a resource investment upper limit as constraints; and based on the objective function and the constraint condition, constructing a multi-category material collaborative allocation model, solving the model, determining the preparation amount of each category of materials in each time period, and forming a new energy station multi-category material allocation scheme. According to the invention, accurate allocation of multiple types of materials in the new energy station can be realized, and reliable support is provided for fine operation and maintenance of the new energy station.

1. Name of the product in this design: Computer with a graphical user interface for managing the entire process of engineering cost indicators. 2. Purpose of this design: This product is used for displaying the interface and running programs. 3. The key design features of this product are the graphical user interface content on the screen. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Other views of the product are standard design, so other views are omitted, including the top view of Design 1 and Design 2; other views of the product are standard design, so other views are omitted, including the bottom view of Design 1 and Design 2; other views of the product are standard design, so other views are omitted, including the rear view of Design 1 and Design 2; other views of the product are standard design, so other views are omitted, including the left view of Design 1 and Design 2; other views of the product are standard design, so other views are omitted, including the right view of Design 1 and Design 2. 6. Design 2 is designated as the basic design. 7. Purpose of the graphical user interface: For the management of engineering cost indicators throughout the entire life cycle of construction projects. 8. The area of ​​the graphical user interface in the product: the entire area of ​​the computer screen. 9. Human-computer interaction method of graphical user interface: Users can perform different functions by clicking different areas, icons or buttons displayed on the interface with the mouse. 10. Description of the changing states of the graphical user interface: The specific description of the changing states of the interface is as follows: The interface shown in the main view of Design 1 is the main interface for the management of the whole process engineering cost indicators. Design 1 Change State Figure 1 shows the interface displayed after the user clicks "Project" in the main view menu. The interface shown in Design 1 Change State Diagram 2 is the interface displayed after the user clicks on "Structure" in the operation column of Design 1 Change State Diagram 1. This interface is for project structure display and cost index rules and project characteristic information management in the budget stage. The interface shown in Design 1 Change State Diagram 3 is the interface displayed after the user clicks on the "Cost Information" tab in Design 1 Change State Diagram 2. This interface is the management interface for cost index rules and project characteristic information for all stages of the project structure except for the preliminary estimate. The interface shown in the main view of Design 2 is the main interface for managing the cost indicators of the entire project process. The interface shown in Figure 1, which represents the change in state of Design 2, is the interface displayed after the user clicks "Project" in the main view menu. This interface contains a list of projects covering all stages of the entire project process. The interface shown in Design 2 Change State Diagram 2 is the interface displayed after the user clicks "View" in the operation column of Design 2 Change State Diagram 1 and then clicks the "Project Features" tab. This interface displays the project structure nodes and their project feature information involved in the project stage of the project. The interface shown in Design 2 Change State Diagram 3 is the interface displayed after the user clicks the "Project Cost List" tab in Design 2 Change State Diagram 2. This interface displays the details of the project cost data in the project cost document and the index attribution information of each cost data component. The interface shown in Design 2 Change State Diagram 4 is the interface displayed after the user clicks the "Engineering Indicators" tab in Design 2 Change State Diagram 3. This interface displays the engineering cost indicator results, which include quantity and price indicator information. The circular gray block in the upper right corner of the main view of Design 1 and Design 2, as well as the various change state diagrams in the design, belongs to the content screen.

The invention discloses a low-carbon building scheme comparison and selection method and device, a storage medium and a computer program product, and relates to the technical field of low-carbon resource management, and the method comprises the steps: defining a building component in each candidate low-carbon building scheme as a first global adjustable parameter, associating the first global adjustable parameter with a first full life cycle carbon emission factor and a first cost factor; capturing a parameter adjustment instruction sent by a user, and calculating a carbon cost index based on the parameter adjustment instruction, the first full life cycle carbon emission factor and the first cost factor; obtaining an optimization target and a constraint condition set by a user, and setting a multi-target optimization algorithm according to the optimization target and the constraint condition; and inputting the carbon cost index into the multi-objective optimization algorithm for iterative calculation to obtain a Pareto optimal solution set as a low-carbon building scheme comparison and selection result. And full-life-cycle multi-objective collaborative optimization comparison and selection of the candidate building schemes are realized.

The application discloses a BIM model and construction progress, cost data heterogeneous fusion modeling method and system, relates to the building information model technical field, and the heterogeneous fusion modeling method comprises the following steps: analyzing a BIM source model, a construction progress plan and a cost list, extracting geometric, time sequence and cost characteristic vectors, and mapping to a unified characteristic space to generate characteristic points; a coupling degree tensor field containing time, space and cost coupling factors is constructed based on the characteristic points; under the constraint of the coupling degree tensor field, the total energy functional containing geometric deformation energy, progress deviation energy and cost potential energy is minimized, and an initial fusion model is generated; the coupling degree tensor field is updated in real time by accessing Internet of Things data and a cost index, and self-reconstruction is triggered when the energy gradient exceeds a threshold value, so that associated attributes are automatically adjusted. The application realizes deep intrinsic fusion of geometry, progress and cost, has dynamic self-adapting and intelligent early warning capabilities, and can be applied to whole life cycle management of building engineering.

The invention provides a memory optimization method for distributed deep learning, and relates to the technical field of deep learning. The method comprises the following steps: constructing a double-cost index containing memory exchange strategy extra time overhead and re-calculation strategy extra time overhead and an optimized value index based on a re-calculation value index according to a time sequence relationship between a data object generation node and a calling node; determining an upstream or downstream device label of each computing device according to the computing duration of forward propagation and reverse propagation of the device; performing priority calculation on the input gradient of the downstream label equipment, and determining an overlapping calculation time window for triggering back propagation of the upstream label equipment; and differential memory optimization processing aiming at different equipment tags is implemented by utilizing an overlapping calculation time window and a two-dimensional cost index, a corresponding memory management scheme is determined, and collaborative improvement of the memory utilization rate and the calculation efficiency is realized.

The invention provides a project cost index analysis method and system, and relates to the technical field of project cost, and the method comprises the following steps: S1, obtaining structured data and unstructured data of a project, respectively analyzing the two types of data through a hierarchical processing mode, and extracting feature information which can be used for cost analysis; s2, adopting an improved DBSCAN clustering algorithm to carry out clustering fusion on the feature information obtained through analysis in the step S1, and generating a fusion data set in a unified format; according to the method, structured data and unstructured data are converted into cost analysis data in a unified format through a hierarchical processing mode and a special analysis technology, the clustering accuracy of 98% is still kept under million-level data volume in combination with an improved DBSCAN clustering algorithm, and the problems of data splitting and low utilization rate of unstructured data in a traditional method are solved.

The invention discloses an automobile thermal manager life optimization method based on deep learning, and the method comprises the following steps: collecting data, recording a timestamp, and forming a multi-source time sequence; executing preprocessing and resampling, and constructing a running state vector sequence; constructing an input fragment sequence and an operation state prediction target sequence of the predictive coding network; setting an operation state prediction path and a life state prediction path, and generating an operation state prediction sequence and a life state prediction sequence; calculating an operation prediction error sequence, and calculating a life prediction error sequence; executing error-driven updating and updating parameters to obtain a predictive coding network subjected to life constraint optimization; calculating a life cost index and a thermal management performance index to jointly construct a control target; and updating a heat management control strategy, and generating and applying a control instruction sequence. The predictive coding network and deep learning are adopted, the service life constraint is introduced into the automobile thermal management control process, and service life evolution perception and active optimization are achieved.

The application provides a network effect comprehensive evaluation method, system, device and medium, relates to the technical field of network effect comprehensive evaluation, and comprises the following steps: through complex network attacks carried out by n attackers, attack cost index system and attack effect index system on both attack and defense sides are established; the attack cost index and index data are collected and standardized, and standardized index data on both sides is obtained; subjective weight and objective weight of the index are subjected to variable weight processing, and based on a game method, a comprehensive variable weight weight vector is obtained; the standardized index data on both sides and the comprehensive variable weight weight vector are combined, based on a combined embedded comprehensive evaluation model, corresponding evaluation results are obtained, and the evaluation results on both attack and defense sides are comprehensively obtained, so that the comprehensive evaluation result of the whole network attack action is obtained. The network effect comprehensive evaluation method can be used for comprehensive evaluation of both attack and defense sides in a complex network attack environment, and can comprehensively evaluate the security situation of the whole network.

The invention discloses a power distribution network project cost index construction method and system based on modularization. The method comprises the following steps: collecting data such as a design drawing, a technical specification, an equipment material list, a field image and historical cost, and constructing a unified data set based on a space-time reference; establishing a modular typical cost library, extracting key features from the data by using a deep learning model fused with an attention mechanism, and enhancing expression through a multi-scale feature pyramid network to obtain an engineering feature vector and a preliminary cost index; then generating an engineering characteristic fingerprint according to the static landmark, converting a two-dimensional drawing into three-dimensional data by utilizing three-dimensional space mapping, and checking a preliminary cost index; and finally, performing similarity matching with a typical cost library to automatically generate a budget file, and correcting cost indexes in combination with historical data when necessary to ensure accuracy. By implementing the method provided by the invention, the construction and application level of the power distribution network project cost index can be comprehensively improved.

The invention provides a project cost data management system, and relates to the technical field of project cost, the project cost data management system comprises a dynamic association engine, a cost index multi-dimensional model module and a distributed block chain storage module, the dynamic association engine is used for establishing an association relationship among a project quantity list, a quota library, a real-time market price and design change data; a cost result is automatically generated and a cost deviation is updated in real time based on the association relationship; the cost index multi-dimensional model module is used for constructing a cost index multi-dimensional model based on a data warehouse and an online analysis processing technology; according to the method, a four-dimensional binding model of the bill of quantity-quota library-real-time market price-design change is constructed through the dynamic association engine, automatic data association is realized by presetting association rules, and manual matching and input are not needed; the cost is updated in real time and the deviation is calculated when any data changes, compared with traditional manual operation, the efficiency is higher, the cost deviation rate is smaller, and the cost risk caused by manual errors is reduced.

The invention relates to an engineering machinery intelligent scheduling method and system based on vehicle-mounted OBD data, and belongs to the technical field of engineering machinery and intelligent construction. The method comprises the following steps: recording the working condition of an engine in real time from an OBD-II interface through a vehicle-mounted internet of things box deployed in a vehicle, capturing the operation dynamic state of the engineering machinery, and identifying the characteristic curves of the engine under different working conditions; engineering machinery information is identified through an engine working condition detection method, engine working conditions, driver information, order information and order data are fused, and an efficiency and cost index graph is generated; the working condition state of an engine is recognized through a dynamic data flow analysis technology, engineering mechanical equipment with the highest working efficiency in the order and operator configuration are output, and an intelligent scheduling strategy is generated; parameters of engineering machinery types and engine working conditions are analyzed according to the optimal engineering machinery equipment driver distribution diagram, and the optimal drivers and the engineering machinery with the highest efficiency are distributed.

The invention relates to the technical field of power system planning, and discloses a distributed wind power project intelligent economy evaluation method, system and device based on small sample adaptive optimization and multi-scene constraint, and a medium, and the method comprises the steps: obtaining historical data of different distributed wind power projects; calculating a historical construction cost index of each project and an investment income index reflecting different distributed wind power projects; based on the improved maximum information coefficient of the self-adaptive small sample, analyzing the correlation between the multi-dimensional variable and the two types of indexes in the historical data, and carrying out feature screening; constructing a special model for a wind power project for training; the prediction of the two types of data is realized; and the economic evaluation of the distributed wind power project is carried out. Aiming at the characteristic of rare data, a small sample adaptive improved maximum information coefficient algorithm is designed, and the accuracy and the stability of variable correlation analysis under the small sample condition are improved; and a geographical environment constraint item is added in the optimization objective function, so that the reasonability and reliability of a prediction result are improved.

Device for influencing at least one operating parameter of an existing control and / or regulating device (11) of a technical system, in particular a refrigeration, air conditioning or HVAC system, characterized by (i) Means (2, 3) for determining an external control variable (in particular electricity price / cost time series, CO2 intensity, grid signal or flexibility signal) and for determining a price / cost index derived therefrom and / or a control variable (setpoint offset and / or demand limit); (ii) a communication interface (4) for writing at least one operating parameter, in particular a setpoint, operating mode and / or a power / demand limit, to an existing control / regulation device (11) via a fieldbus or data network-based connection, including but not limited to BACnet, Modbus, KNX, OPC UA, MQTT, HTTP / REST, WebSocket, Ethernet / IP, M-Bus or CAN; (iii) a failsafe mechanism (9) which automatically resets to a predefined safe operating state in the absence of valid control variables and / or in the event of communication / software errors, in particular by reverting set values, holding a last valid value for a maximum hold time and / or setting conservative default parameters.

The present application relates to a kind of engineering machinery intelligent scheduling method and system based on vehicle OBD data, belong to engineering machinery and intelligent construction technical field.Wherein, the method includes: through the vehicle-mounted Internet of Things box deployed in vehicle, from OBD-II interface real-time recording engine operating condition capture engineering machinery operation dynamic, identify the characteristic curve of engine under different operating conditions;Through engine operating condition detection method, identify engineering machinery information, engine operating condition, driver information, order information and order data are fused, and efficiency and cost index chart are generated;Through dynamic data flow analysis technology, identify engine operating condition state, output the highest work efficiency engineering machinery equipment and operator configuration in this order, and generate intelligent scheduling strategy;According to the best engineering machinery equipment driver allocation chart, analyze the parameters of engineering machinery type and engine operating condition, and distribute the best driver and the highest efficiency engineering machinery.

The invention relates to the technical field of rail transit technology, and discloses an underground shield interval derivation type line selection duct piece splicing method which specifically comprises the following steps: S1, establishing an area digital model, S2, formulating a comprehensive cost index, S3, carrying out derivation type line selection design, and S4, carrying out interval duct piece parameterization design. According to the derivation type line selection segment assembling method for the underground shield section, a digital visual three-dimensional model of a planned construction area of the underground shield section of rail transit is established, a comprehensive cost evaluation objective function formed by factors such as environmental risks, economic cost and operation and maintenance investment in the design process of the underground shield section is constructed, and a derivation type design principle is adopted; a plurality of design schemes are generated, a visual basis is provided for scheme elaboration and decision making by using the computing power of the cloud, the engineering comprehensive cost is greatly saved, the time of rail transit underground section BIM design is greatly shortened, and the design working efficiency is improved.

This invention discloses a method and system for dynamic conversion of engineering cost indicators based on a materials and equipment price database, belonging to the field of engineering cost management technology. By constructing a standardized basic database of materials and equipment and updating it in real time based on multi-source data, this invention first solves the problems of incomplete data coverage and delayed updates, providing a comprehensive and timely data foundation for accurate conversion. On this basis, by calculating a dual-dimensional price index based on time and brand differences, the indicator items are deconstructed and labeled, and adjustments are made for each material according to time and brand differences. These technical features work synergistically to achieve high-precision, high-efficiency, and automated dynamic conversion from historical cost indicators to current market prices, significantly improving the scientific nature of cost management and the reliability of decision support.

The invention discloses a construction project cost index analysis system based on big data, and belongs to the technical field of project cost management. The system comprises a data acquisition module, a data processing module, a data analysis module, an index application module and a data security module, and a multi-source data access unit and a data supplementation unit cooperatively acquire project basic data, cost file data and supplementary feature data, so that comprehensive coverage, accurate acquisition and efficient updating of cost data are realized; through structural processing, intelligent automatic matching and a multi-dimensional data correction mechanism, accurate binding of list subitems and indexes is efficiently completed, meanwhile, manual intervention of special scenes is supported, automation efficiency and flexible adaptability are considered, the problems of inaccurate data association and uneven quality are solved, and the data association efficiency is improved. Based on index generation, feature analysis and a diversified application unit, an intelligent application system covering the whole process of the cost is constructed, and the data security guarantee of the cost index is realized in combination with a triple protection design.

The invention provides a new energy power system scheduling method and system considering source-storage collaboration, and the method is applied to a system comprising an operation cost evaluation module, an adjustment capability calculation module, a model construction module, a scheduling solving module and a strategy execution module, and specifically comprises the steps: quantifying the comprehensive operation cost based on an operation cost index evaluation system; respectively calculating the adjusting capability of the thermal power generating unit and the energy storage unit, and obtaining the overall adjusting capability in combination with the real-time operation state of the power system; setting a flexibility margin constraint, combining a unit operation constraint and a power balance constraint, and constructing a scheduling optimization model by taking the lowest comprehensive operation cost as an objective function; solving is carried out through an improved raccoon optimization algorithm fused with Gaussian chaotic mapping, and a unit scheduling strategy is generated. According to the method, the system flexibility is taken as a constraint condition, and the scheduling optimization model is solved through the improved raccoon optimization algorithm fused with Gaussian chaotic mapping, so that the system suitability is guaranteed, and the scheduling accuracy and efficiency are improved at the same time.

The application discloses a water network system balanced coordination optimization networking method and system, adopts an improved meta-analysis method to screen water network system balanced state indexes and networking cost indexes, constructs a water network system balanced state initial index system, constructs a networking scheme set and inputs a pre-constructed system dynamics model to obtain water network system balanced state index values corresponding to each networking scheme; adopts a CNN-BP neural network coupling dimension reduction method to reduce dimensions of the water network system balanced state initial index system, obtains a water network system balanced state reduced dimension index system, and calculates water network system balanced state reduced dimension index values; inputs the water network system balanced state reduced dimension index values of each networking scheme into a pre-constructed water network system balanced state decision model to screen out an optimal networking scheme; and inputs the optimal networking scheme into a pre-constructed multi-objective optimization model to obtain a water network system balanced coordination optimization networking scheme. The application realizes efficient, safe and sustainable operation of the water network system.

The invention belongs to the technical field of RCM systems, and discloses an RCM system division and analysis method and a related device. The RCM system division and analysis method comprises the steps that a three-dimensional analysis model is constructed according to preprocessed equipment data, the three-dimensional analysis model is trained, and the trained three-dimensional analysis model is obtained; inputting a maintenance cost index, a non-stop risk index and an environmental protection energy consumption index corresponding to each device of the unit system into the trained three-dimensional analysis model, and dividing the unit system into a plurality of subsystems through a clustering algorithm, extracting an overhaul cost index, a non-stop risk index and an environment-friendly energy consumption index of each subsystem through the trained three-dimensional analysis model, and analyzing the overhaul cost index, the non-stop risk index and the environment-friendly energy consumption index of each subsystem through an integrated learning algorithm; according to the invention, the performance of each subsystem in the aspects of maintenance cost, non-stop risk and environment-friendly energy consumption can be comprehensively and accurately analyzed.

The application discloses a kind of frame assembly structure optimization method, device and electronic equipment, belong to automobile technical field, method includes: S1: the node is established to the front axle of the frame to be optimized, rear axle longitudinal beam airfoil, the node is applied forced displacement to realize angle torsion, the torsional stiffness value between the shaft of the frame to be optimized is calculated;S2: the torsional stiffness value between the shaft of the frame to be optimized and the frame of benchmark pole vehicle type is compared, and the structural difference point of the frame to be optimized is identified;S3: the influence of each structural difference point on the torsional stiffness value between the shaft of frame is analyzed, and optimization scheme is determined according to the rationality of whole vehicle arrangement and cost.The application sets the torsional stiffness target value between the shaft of frame assembly, studies the influence of key parts of frame assembly on the torsional stiffness between the shaft, considers the demand of whole vehicle arrangement and cost index, uses parameterized modeling method to strengthen structure to meet the requirement of torsional stiffness target value between the shaft, solves the problem of poor followability.