45 results about "Environmental indicator" patented technology

The invention discloses a water quality monitoring method and system based on large model data analysis, and the method comprises the steps: collecting multi-dimensional environment index real-time data through a remote sensing device and an underwater mobile sensor, completing the noise filtering and format standardization through an edge calculation node, and inputting the data into a machine learning model; the method comprises the following steps: automatically extracting space-time distribution characteristics and intelligently identifying potential abnormal modes, immediately starting multi-parameter correlation analysis when an abnormal degree exceeds a threshold value, constructing a deviation correlation map to accurately invert pollution source space coordinates, further performing time sequence prediction modeling in combination with a historical data sequence, and deducing a future diffusion path track of pollutants. And finally, deeply overlapping and fusing the predicted trajectory and the ecological sensitive area map, quantitatively calculating the comprehensive risk score distribution of each intersection area by using a risk assessment algorithm, automatically generating a visual environment monitoring report, and updating a historical sequence to form closed-loop learning at the same time. According to the invention, the emergency response speed, the traceability accuracy and the risk prevention and control capability of the sudden pollution event of the water environment are greatly improved.

The invention discloses an intelligent fire prevention and control and operation and maintenance optimization method and system for an electric power supply network, and the method comprises the steps: collecting temperature, humidity and smoke concentration data in real time through a sensor network, generating a comprehensive environment index data flow, and obtaining a fire initial signal through abnormality judgment; calling a medium selection model, and outputting an optimal environment-friendly insulating medium and release parameters thereof by querying an environment-friendly medium and insulating characteristic parameter library and performing matching calculation based on the equipment type; utilizing a response speed optimization algorithm to generate an accurate release instruction sequence containing a release path, a dose and a time sequence; controlling the medium atomization device to synchronously release the medium according to the instruction; and collecting equipment protection and power supply continuity data, performing risk assessment to generate an efficiency index, and updating the prevention and control strategy library to optimize a subsequent decision. Through intelligent decision making and closed-loop optimization, power supply continuity and equipment safety are guaranteed to the maximum extent while rapid and accurate fire extinguishing is achieved, and transformation from post-disaster disposal to operation and maintenance level prevention and control is completed.

The invention discloses a method for constructing a health influence prediction model based on multi-source air data coupling, and relates to the technical field of air quality health monitoring, and the method comprises the steps: obtaining multi-source air environment data in a selected space as an environment index, and obtaining a multi-source physiological signal of a person in the selected space as a physiological index; on the basis of the feature vectors of the environment indexes and the physiological indexes, the coupling relation between the air environment features and the physiological features is constructed, modeling is conducted on the nonlinear coupling relation between the air features and the physiological features through a time sequence deep learning model, and the action rule of air pollution accumulative exposure on dynamic changes of the physiological indexes is captured. Through feature extraction and cross-modal coupling, the comprehensive influence rule of air pollution on the blood pressure, blood oxygen, body temperature, heart rate, heart rate variability and blood glucose physiological indexes of the human body can be revealed, and dynamic prediction of health risks can be achieved through a deep learning model.

The invention provides an enterprise environment influence assessment method, device and equipment and a computer storage medium, and relates to the technical field of environment assessment, and the method comprises the steps: collecting environment index original data with a timestamp, and constructing a multi-source environment data set in combination with enterprise resource consumption data and emission factor data; carrying out credibility evaluation and weighted fusion on the data set, inputting the fused data into a pre-training evaluation model, and outputting a comprehensive environmental impact evaluation value; the contribution degree of each input feature is analyzed and calculated through model attribution, key factors are identified based on metadata, and a customized improvement measure list of priority ranking is generated in combination with a measure knowledge base and a multi-objective optimization algorithm; the invention further provides a matched portable detection device, electronic equipment and a storage medium, dynamic quantification, accurate attribution and closed-loop optimization of enterprise environment influences are achieved, and the method is suitable for intelligent environment management of high-emission industrial enterprises.

The invention relates to a method and a device for optimizing water quality evaluation based on interpretable machine learning. The method comprises the following steps: constructing a water quality data set; the contribution degree of the environmental indexes is determined through principal component analysis, and scores of the environmental indexes are obtained based on a penalty mechanism deviating from the optimal range so as to calculate water quality indexes; performing k-fold cross validation on the plurality of machine learning models, and selecting a prediction model according to the comprehensive evaluation value; evaluating the influence of each environmental index on the water quality index by adopting a plurality of importance evaluation methods to obtain comprehensive importance; and gradually inputting the environment indexes into the prediction model for training according to the sequence of gradually decreasing comprehensive importance, and determining an optimal prediction model according to the amplitude of increasing the performance of the model. According to the method, the monitoring purpose is achieved through a better environmental index combination, unnecessary environmental indexes are reduced, and the water quality evaluation efficiency is remarkably improved on the premise that high accuracy is guaranteed; the optimal prediction model can effectively reduce the monitoring cost and improve the operability.

The present application relates to a kind of ecological island building fill unit, fill unit middle is provided with through hole, fill unit includes shell surface layer, middle structural layer and internal filling layer;Shell surface layer is made of neutral material, to ensure that the sea bottom water quality and related environmental indicators where fill unit is after sinking to bottom are not affected;Structural layer uses high-performance corrosion-resistant material, for bearing the water head pressure after fill unit sinking to bottom, unit stacking transmission pressure and mutual collision force, the whole outside of structural layer is covered by shell surface layer, and internal full-closed cavity is formed;Filling layer uses the full-closed cavity surrounded by structural layer, and fills crushed construction waste.The present application has the advantages of simple structure, large specific weight, easy production and transportation, etc.

The invention discloses a lake water quality multi-parameter real-time monitoring method based on the Internet of Things. The method comprises the following steps: acquiring environmental indexes through a data acquisition device, and binding the environmental indexes with position depth information to generate three-dimensional data; cleaning data by adopting distributed calculation and marking anomalies, extracting normal data subsets, and reporting and converging the normal data subsets into regional feature parameters; dividing monitoring zones based on geographical and hydrological characteristics, and analyzing a parameter trend to construct a local dynamic sensing model; when it is detected that parameter linkage is abnormal, auxiliary parameter weights are analyzed through fusion learning to generate comprehensive monitoring indexes; and extracting full-depth data, performing dimension reduction to obtain global perception link data, and finally generating an optimized layout scheme through classification prediction analysis. According to the invention, multi-parameter and multi-depth accurate real-time monitoring and dynamic early warning of lake water quality are realized, the layout of monitoring equipment is optimized through data driving, and the monitoring efficiency and the management scientificity are obviously improved.

This invention discloses a computer-based method for evaluating the complexity of the working environment of unmanned ground vehicles (UGVs). First, it determines environmental complexity evaluation indicators and establishes an evaluation indicator system. Then, it acquires and normalizes multi-source environmental data using onboard sensors and a deep learning model. Next, it determines the weights of static environmental indicators using the Analytic Hierarchy Process (AHP) and calculates the static environmental complexity index using a nonlinear mapping function. Simultaneously, it establishes a potential energy function incorporating distance, speed, and predicted collision time, calculating the total environmental potential energy to output the dynamic environmental complexity index. For the state evolution complexity evaluation indicator, it constructs a state evolution complexity quantification model based on state change entropy, outputting the state evolution complexity index. Finally, it outputs the working environment complexity index by establishing a multi-dimensional coupled complexity model. This invention achieves real-time quantification and evaluation of working environment complexity, providing reliable data for autonomous decision-making, path planning, and safety control of UGVs.

The present application belongs to the technical field of environmental protection monitoring, and particularly relates to an environmental protection monitoring and inspection robot, which comprises a shell, a monitoring rotating module is installed at the upper end of the shell, and a high-definition camera and an infrared thermal imager are respectively installed at the left and right sides of the monitoring rotating module. The present application can realize the collection of omnibearing visible light and infrared images, and through visual intelligent algorithm analysis technology, the robot can autonomously identify various machines and equipment in a factory, automatically classify pollution source types, monitor various environmental indexes of an industrial enterprise production line in the process of robot intelligent inspection and monitoring, divide the early warning level based on good or extreme weather, and link with the equipment to assist in decision-making of whether the operation of each process of the industrial enterprise and the equipment continues, strengthen the supervision of the operation of the enterprise workshop, ensure the rationalization of production in the workshop, and improve the informatization and intelligentization level of the ecological environmental bureau in monitoring the environmental quality.

The embodiment of the invention provides a control method of a control switch, the control switch, a system, equipment and a medium, and the method comprises the steps: receiving a plurality of environment parameters collected by a sensor, determining a target parameter of current adjustment from each environment parameter according to a preset environment index and priority of each environment parameter, determining a first device group for adjusting the target parameter, wherein the first device group comprises at least one smart home device; and according to a pre-stored mapping relationship between the environmental parameters and light colors, obtaining and indicating an indicator lamp of the control switch to display a target color corresponding to the target parameter, meanwhile, enabling the control switch to respond to the control operation, and controlling the first equipment group according to the control operation, therefore, the user can directly operate to realize adjustment of the target parameters without thinking and active switching, the steps of manual operation required by the user in a complex smart home environment are reduced, and the user experience and the control efficiency are remarkably improved.

The invention discloses a method and related equipment for predicting available cadmium and methane emission of soil under a dry-wet alternating condition, and belongs to the technical field of agricultural information, and the method comprises the following steps: obtaining soil physicochemical property data of a to-be-predicted target area under the dry-wet alternating condition; inputting the data into a soil environment index prediction model to obtain a soil environment index prediction result; wherein the soil environment index prediction model is constructed on the basis of an available cadmium prediction model and a methane emission prediction model; the effective-state cadmium prediction model and the methane emission prediction model are obtained by training a training data set. By inputting the soil physicochemical property data into the soil environment index prediction model, the available cadmium content and methane emission of the soil under alternation of wetting and drying can be predicted at the same time, the prediction efficiency and prediction precision can be effectively improved, the manpower and material resource cost is reduced, scientific guidance data is provided for targeted regulation and control of the soil physicochemical property, and the method is suitable for popularization and application. And the practicability of large-scale application is improved.

Environmental monitors for monitoring a predetermined environmental exposure, for example, an historical heat exposure such as a cumulative ambient heat exposure and / or a peak ambient heat exposure may include an environmental indicator material and a security material. The environmental monitor may be configured to attach to a host product for monitoring the predetermined environmental exposure of the host product, and additionally serve as an anti-counterfeiting indicator.

This application relates to a method, system, equipment, and medium for assessing the ESG resilience of heating facilities. The method includes: first, acquiring facility type data and dynamic environmental indicators; then, filtering and extracting environmental performance parameters and social demand indicators for heating scenarios to form a preliminary screening dataset; using a random forest algorithm to calculate the impact degree of dynamic environmental factors, obtaining a subset of high-risk environmental performance; extracting governance factor data and training a risk prediction model using a support vector machine algorithm; based on the equilibrium point output by the model, iteratively optimizing to obtain factor balance index data; simultaneously acquiring emission relationship index data and using a random forest algorithm for regression analysis to construct an emission relationship model; finally, extracting allocation fairness features, matching social demand to form an assessment framework, and integrating data to determine the adjustment results for the heating facility resilience assessment. This method can accurately reflect the actual resilience level of heating facilities and improve the scenario adaptability and generalization ability of the assessment method.

The present application relates to the technical field of edge computing, in particular to a groundwater pollution monitoring method and system based on edge computing, comprising the following steps: acquiring groundwater sequence data and calculating unit time fluctuation intensity parameters, comparing threshold interval to generate sampling frequency, collecting physicochemical indexes to generate deviation value set, extracting emergency weight parameters, combining edge node residual computing power cycle to generate task scheduling strategy, collecting environmental indexes to write into deviation cumulative matrix and accumulate row by row, comparing with the set risk threshold to generate pollution risk grade. In the present application, by adaptively matching the sampling frequency according to the fluctuation intensity state, the fixed interval blind area is effectively discarded and the mutation signal is captured, the scheduling strategy is constructed by combining the multi-dimensional deviation characteristics and the node computing power to generate the scheduling strategy, the dynamic allocation and computing power balance of the task are completed on the edge side, the link congestion caused by redundant baseline transparent transmission is avoided, the pollution trend is tracked based on the cumulative matrix, and the prepositioned research and judgment are realized.

The application provides a kind of water environment multi-index data space-time field establishment and early warning method, comprising the following steps: S1, detection data acquisition: obtain environmental detection data;S2, time segmentation: based on detection time information, environmental detection data is divided in multiple time slices;S3, uniform grid construction: based on the coordinate position of all detection points, construct uniform two-dimensional space grid;S4, spatial interpolation reconstruction: environmental index rasterization space-time field is generated on two-dimensional space grid using spatial interpolation method;S4, standardization processing: based on the average and standard deviation of interpolation result calculation standardization processing, to dimensionless rasterization space-time field;S6, multivariate two-dimensional function construction: based on the dimensionless rasterization space-time field of each environmental index, construct multivariate two-dimensional function data object;S7, multivariate function principal component analysis, obtain spatial characteristic function, principal component score and variance contribution;S8, baseline modeling and anomaly detection, output abnormal early warning information.

1. Name of the product in this design: Graphical User Interface for Rural Environmental Monitoring in Electronic Equipment. 2. Intended use of this design: for use in an electronic device. 3. The key design features of this product are: the layout of the graphical user interface elements. 4. Images or photos that best illustrate the design key points: Interface state change diagrams. 5. Purpose of the graphical user interface: Enter the username and password in the main view to enter the interface change state diagram. The interface change state diagram is the main user interface for rural environmental monitoring and management, including the air quality index monitoring section to display the air quality index, the noise index monitoring section to display the specific noise value, the environmental status indicator light section for alarms and indicator light flashing when the environment is abnormal, the environmental parameter value section to display the real-time changes of specific environmental parameters by clicking, the water quality monitoring to display water quality statistics, and the real-time data display of the real-time changes and historical curves of different environmental indicators.

The application belongs to the technical field of pollutant detection and environmental protection, and discloses a detection method and application of cephalosporin in atmospheric environmental medium, which comprises the following steps: S1, determining key environmental indicators; S2, sampling atmospheric particulates; S3, extracting cephalosporin; S4, batch detection and data analysis. The application also discloses the application of the detection method in environmental and health risk assessment. The application proposes a specific and feasible key detection scheme with low cost through a whole-process strategy of'screening key environmental indicators, optimizing sampling network, sample pretreatment enrichment and purification, high-efficiency separation, high-sensitivity detection, method verification and risk assessment', develops and standardizes the detection method, and qualitatively and quantitatively detects different generations of structural characteristics, so as to improve the detection efficiency, reduce the detection cost, improve the overall monitoring capability of atmospheric environmental safety, and be applicable to activities such as atmospheric environmental health risk assessment.

The application discloses a kind of multi-sensor fire detection methods based on long short-term memory network and environmental information fusion.The method is when detecting fire, first using multi-sensor system continuously acquires temperature, smoke concentration, carbon monoxide concentration and carbon dioxide concentration data in environment, constructs the time series of monitoring value corresponding to environmental index;Second, based on long short-term memory network, the environmental change degree extraction module and the environmental level extraction module in the deep environmental information extraction model are constructed, with the help of long short-term memory network processing ability to long-distance dependence relationship of time series data, the change degree of environmental index and the level of environmental index are predicted;Then, using multilayer perceptron, the obtained various environmental information is fused, and comprehensive fire state information is obtained;Finally, the comprehensive fire state information value is compared with the best threshold value learned in system operation, and fire judgment is completed.

The application provides an open area radon emission rate prediction method based on VMD-LSTM-ELMAN, first, the radon emission rate historical data is decomposed into three components with different characteristic information by VMD, then the data training and prediction are carried out by selecting the LSTM model, the ELMAN neural network model and the normal distribution model, and the environmental factors affecting the radon emission are taken as the prediction index to input the model, so that the influence of the environmental index on the radon emission can be more truly reflected, the model prediction result can accurately reflect the emission law of the radon emission, and the prediction precision is high. The radon emission rate prediction model is established by jointly using VMD, LSTM and ELMAN, the lagging effect of the environmental index on the radon emission rate is specially considered, the radon emission rate value curve is decomposed and the change trend is predicted, and guidance is provided for radon prevention and control and environmental management.

This invention discloses a method for quantifying the spatial heterogeneity of microhabitats in mountainous rivers, comprising the following steps: Step 1: Setting up sampling sections and sampling points; Step 2: Measuring average water depth, average flow velocity, transparency, turbidity, sediment particle size distribution, and water environment indicators at each sampling point; Step 3: Determining key parameters related to hydrology, water environment indicators, and organisms; Step 4: Statistically analyzing the measurement results of key parameters indoors and calculating the microhabitat heterogeneity index of mountainous rivers; Step 5: Verifying the reliability of the calculated results of the spatial heterogeneity index of microhabitats in mountainous rivers. This invention's method for investigating microhabitats in mountainous rivers combines environmental factor screening with actual field measurements, enabling the quantification of the complexity of spatial habitat structure. Specifically, for the three-dimensional benthic habitat investigation method in mountainous rivers, it calculates the spatial heterogeneity of microhabitats by statistically analyzing river flow patterns and sediment particle size, thus evaluating the living space characteristics and quality of organisms in mountainous rivers.

Disclosed is a thermal transfer ribbon for use with a media processing device. The thermal transfer ribbon includes a carrier layer and a transfer layer operatively coupled to the carrier layer. The transfer layer includes an environmental indicator material encapsulated in a plurality of microcapsules. The plurality of microcapsules prevent the environmental indicator material from producing an observable effect in response to exposure to a predetermined environmental condition. The environmental indicator material is released from the plurality of microcapsules in response to the plurality of microcapsules being ruptured. After the environmental indicator material is released and exposed to the predetermined environmental condition, the environmental indicator material produces the observable effect. The observable effect results from a change in the environmental indicator material from an original state to a second state in response to the predetermined environmental condition.

The application discloses a shale organic matter enrichment master control factor evaluation method and system, including: collecting shale sample geochemical data to construct an original data set; obtaining a standardized feature subset after preprocessing and constructing a multi-dimensional feature matrix, wherein the independent variable is a paleoenvironment index, and the dependent variable is total organic carbon content; grouping all samples according to geological sections, and training a random forest model for each section; using a SHAP model to interpret each section model, calculating the quantitative contribution percentage of each paleoenvironment index and determining the master control factor of the section; obtaining a contribution percentage set of each section, and comparing and analyzing the dynamic evolution law of the contribution percentage of each index with the stratigraphic stage. Through the method, the qualitative sorting to quantitative attribution is realized.

This invention relates to the field of edge computing technology, specifically to a groundwater pollution monitoring method and system based on edge computing. The method includes the following steps: acquiring groundwater sequence data and calculating unit-time fluctuation intensity parameters; comparing threshold intervals to generate sampling frequencies; collecting physicochemical indicators to generate deviation difference sets and extracting urgency weight parameters; combining the remaining computing power of edge nodes to generate task scheduling strategies; collecting environmental indicators and writing them into a deviation accumulation matrix and accumulating them row by row; and comparing with set risk thresholds to generate pollution risk levels. In this invention, by adaptively matching the sampling frequency based on the fluctuation intensity state, the fixed interval blind zone is effectively eliminated and abrupt signals are captured. Combining multi-dimensional deviation features and node computing power to construct an allocation index to generate a scheduling strategy, dynamic task allocation and computing power balancing are completed at the edge, avoiding link congestion caused by redundant baseline transmission. Based on the accumulation matrix, pollution trends are tracked, achieving proactive assessment.

The invention relates to the technical field of data processing, in particular to a high-carbon-nitrogen-ratio pollutant full-cycle treatment process evaluation method and system, and the method comprises the steps: building a multi-objective optimization model; monitoring an environmental load contribution rate through real-time life cycle list data, and dynamically determining an environmental index weight; coupling operation parameters influencing upstream and downstream unit load balance are determined as model input; solving the model by adopting a hotspot sensing particle swarm optimization algorithm, wherein the algorithm introduces a dynamic penalty term related to an environment index weight and a contribution rate into a fitness function; and generating an operation strategy decision report according to the optimal solution set. According to the method, the defects of static state and low efficiency of the existing evaluation model are overcome, the real-time balance of the environmental and economic targets and the rapid generation of the operation strategy are realized, and the perception capability and the real-time guidance value of the evaluation method to the environmental risk are obviously enhanced. The method is suitable for guiding operation scheduling and parameter optimization of a high-carbon-nitrogen-ratio pollutant treatment process.

The invention relates to the technical field of data processing, in particular to a coal mine building foundation settlement monitoring and early warning system based on an intelligent sensor, which comprises a processor and a memory, the method comprises the following steps: acquiring an initial threshold value of any monitoring index at any monitoring point of a target coal mine building at the current moment; obtaining a target environment index of which the environment data exceeds the limit, and performing adaptive adjustment on the initial threshold according to the influence of the data change of each target environment index on the monitoring data of any monitoring index and the data change condition of any monitoring index in a local time range up to the current moment, according to the method, the self-adaptive threshold value of any monitoring index at any monitoring point at the current moment is obtained, monitoring and early warning are carried out on the foundation settlement risk of the target coal mine building at the future moment according to the self-adaptive threshold value of each monitoring index at each monitoring point, and the early warning accuracy is improved.

This invention discloses a method for assessing enterprise ESG environmental indicators based on IoT data, belonging to the field of intelligent industrial environmental management technology. It addresses the technical problems of ambiguous correlations between production factors and environmental indicators, lack of scientific basis for anomaly judgment, and low efficiency in source tracing and handling in existing technologies. The method establishes a ledger for the coding of equipment, processes, and ESG environmental indicators throughout the entire production process; collects IoT data to filter core correlation groups; builds a three-level correlation mapping table and establishes a dynamic update and traceability mechanism; establishes a threshold ledger for different operating conditions; constructs an abnormal feature library of enterprise ESG environmental indicators; and uses threshold comparison and cosine similarity methods to determine true anomalies; generates a source tracing list based on the three-level correlation mapping table; filters abnormal elements hierarchically; and integrates information to form an anomaly root cause location report. This invention achieves standardized and quantitative binding of production factors and ESG indicators, improves the rationality and accuracy of anomaly judgment, and effectively enhances the efficiency and scientific basis of enterprise ESG environmental indicator assessment and anomaly handling.

A data logging device includes an activation circuit, an environmental sensor, a processing module, and an environmental indicator. The activation circuit is operably coupled to place the data logging device in a ready mode based on an activation signal. The environmental sensor is operable to sense an environmental condition associated with the data logging device in response to a log command to produce sensed data. The processing module is operably coupled to generate the log command; receive the sensed data; determine whether the sensed data triggers an environmental violation; and, when the sensed data triggers the environmental violation, generate an environmental violation signal. The environmental indicator is operably coupled to provide a visual indication of the environmental violation based the environmental violation signal.

The application discloses a water quality anomaly dynamic correlation gain identification method based on machine learning, relates to the technical field of water environment monitoring, and comprises data acquisition, model construction, training and application steps. Based on a multi-order gain coupling activation mechanism, the following core steps are used to complete water quality anomaly identification and tracing: a three-dimensional coupling activation mechanism library of environment-index-gain is constructed, a dynamic coupling correlation network is generated based on the mechanism library, and a special calculation model is designed to quantize coupling gain activation. In the application, the multi-order gain coupling activation mechanism, the three-dimensional coupling activation mechanism library, the dynamic coupling correlation network, the 128-bit coupling fingerprint coding and the CGED-NN neural network are used to solve the problems of static modeling, neglect of environmental regulation and multi-order coupling in the prior art, which lead to water quality anomaly misjudgment, tracing failure and inability to cope with complex pollution scenarios. The application realizes accurate identification of water quality anomaly, efficient tracing, early prediction and dynamic adaptation of monitoring points, and reduces monitoring cost.

This invention relates to a method and system for monitoring the diffusion of toxic waste gas based on machine learning, comprising the following steps: S1, collecting environmental indicator data in real time using gas sensors and meteorological sensors deployed in the monitoring area, wherein the environmental indicator data includes at least pollutant concentration, wind speed, wind direction, ambient temperature, and humidity; S2, constructing a pollution source feature vector P=(p1,p2) based on the environmental indicator data collected in step S1, wherein p1 represents the pollutant emission intensity index calculated based on real-time monitoring data, and p2 represents the type identifier obtained based on the chemical composition analysis of pollutants; S3, calculating the risk score of each monitoring point using an improved entropy weight-TOPSIS comprehensive evaluation algorithm; S4, generating toxic waste gas diffusion early warning information based on the calculated risk score and combined with a dual threshold judgment mechanism, thereby better preventing environmental pollution and safety accidents caused by toxic waste gas leakage.

We provide technology that supports the design of products that can evaluate environmental performance throughout their entire lifecycle. [Solution] The design support system includes a storage unit that stores product component configuration information, alternative parts information, usage scenario information that defines product usage scenarios, regulatory information that specifies regulations concerning the product, improvement item information that includes improvement items for the product's environmental indicators, and constraint information that the product must satisfy; an improvement item identification unit that identifies target values ​​for environmental indicators that must be satisfied throughout the product's lifecycle according to at least one of the usage scenarios, regulations, and improvement items; an alternative parts identification unit that identifies one or more alternative parts that can be replaced while satisfying the constraints; a calculation unit that identifies predicted values ​​for the environmental indicators of the substituted product; and an output unit that outputs alternative parts whose predicted values ​​for environmental indicators exceed the target values.