Industrial chain analysis-based regional economic development index evaluation method and equipment

Abstract

The invention discloses a regional economic development index evaluation method and device based on industrial chain analysis, and the method comprises the steps: obtaining historical industrial chain data of a new energy industrial chain in a target region under a target environment parameter; performing climate environment influence analysis on the historical industry chain data to obtain a node layer influence factor set and a link layer influence factor set of the new energy industry chain; based on the node layer influence factor set and the link layer influence factor set, constructing an environment pressure scene of the new energy industry chain; testing real-time industry chain data of the new energy industry chain under the target environment parameters in the environment pressure scene to obtain test data; obtaining the economic influence weight of the new energy industry chain on the regional economic development index; and calculating a regional economic development index according to the test data and the economic influence weight to obtain a regional economic development index evaluation result. The evaluation accuracy of the regional economic development index can be improved.

Description

Technical Field

[0001] The present application relates to the field of regional economic development assessment, and specifically to a method and device for evaluating a regional economic development index based on industrial chain analysis. Background Art

[0002] With the deepening development of economic globalization, the new energy industry, as a key component of strategic emerging industries, has a significant impact on the sustainable development of regional economies due to its stability and resilience. Regional economic development index evaluation is an important means of measuring the level of regional economic development. Scientific and reasonable evaluation methods are of great significance in guiding regional economic policy formulation and industrial layout.

[0003] Existing assessments of the impact of new energy industries on regional economies primarily focus on direct economic contributions, such as investment, employment opportunities, and the economic output of the industry chain. However, the efficiency and output of new energy industries, particularly those that rely on natural resources (such as wind and solar energy), are significantly affected by climatic and environmental factors. For example, the efficiency of wind turbines decreases significantly in low wind conditions, while the power generation of solar panels decreases significantly on rainy days. These climatic and environmental factors affect the stable operation of the industry chain, making existing assessments of the new energy industry chain incomplete and, therefore, unable to accurately assess the regional economic development index. Summary of the Invention

[0004] This application provides a regional economic development index evaluation method based on industrial chain analysis, which is used to improve the accuracy of evaluating regional economic development index.

[0005] In a first aspect of the present application, a regional economic development index evaluation method based on industrial chain analysis is provided, which is applied to a server. The method includes: obtaining historical industrial chain data of a new energy industry chain in a target area under target environmental parameters; performing climate and environmental impact analysis on the historical industrial chain data to obtain a node layer influencing factor set and a link layer influencing factor set of the new energy industry chain; constructing an environmental pressure scenario for the new energy industry chain based on the node layer influencing factor set and the link layer influencing factor set; testing the real-time industrial chain data of the new energy industry chain under target environmental parameters in the environmental pressure scenario to obtain test data; obtaining the economic impact weight of the new energy industry chain on the regional economic development index; calculating the regional economic development index based on the test data and the economic impact weight to obtain a regional economic development index evaluation result.

[0006] Optionally, the historical industrial chain data includes the node operation data of each industrial chain node under the target environmental parameters, and the link interaction data between multiple industrial chain nodes. The climate and environmental impact analysis is performed on the historical industrial chain data to obtain the node layer influencing factor set and the link layer influencing factor set of the new energy industry chain. Specifically, it includes: identifying the climate response characteristics of the node operation data under the target environmental parameters; extracting the first influencing factor from the climate response characteristics to generate the node layer influencing factor set; identifying the link stability characteristics of the link interaction data under the target environmental parameters; extracting the second influencing factor from the link stability characteristics to generate the link layer influencing factor set.

[0007] Optionally, identifying the climate response characteristics of the node operation data under the target environmental parameters specifically includes: establishing a correspondence between the node operation data and the target environmental parameters, generating a node-environment mapping matrix; based on the node-environment mapping matrix, calculating the sensitivity index of the node operation data to the target environmental parameters; and determining the climate response characteristics of the industrial chain nodes according to the sensitivity index.

[0008] Optionally, an environmental pressure scenario for the new energy industry chain is constructed based on the node layer influencing factor set and the link layer influencing factor set, specifically including: performing a climate environment fluctuation analysis on the node layer influencing factor set and the link layer influencing factor set to obtain the fluctuation characteristics of the new energy industry chain under the target environmental parameters; and setting the environmental pressure scenario for the new energy industry chain according to the fluctuation characteristics.

[0009] Optionally, an environmental pressure scenario for the new energy industry chain is set according to the fluctuation characteristics, specifically including: constructing an environmental pressure conduction model based on the fluctuation characteristics, the environmental pressure conduction model is used to characterize the pressure conduction path of the new energy industry chain under target environmental parameters; identifying target sensitive units of the new energy industry chain according to the environmental pressure conduction model, the target sensitive unit is a target industry chain node in the new energy industry chain whose stability does not meet preset requirements; setting an environmental pressure intensity coefficient based on the target sensitive unit; and setting an environmental pressure scenario according to the environmental pressure intensity coefficient.

[0010] Optionally, the regional economic development index is calculated based on the test data and the economic impact weight to obtain the regional economic development index evaluation result, specifically including: calculating the node response index and link response index of the new energy industry chain under the target environmental parameters based on the test data; performing weighted calculation on the node response index and the link response index to obtain the industry chain environmental adaptability index; correcting the industry chain environmental adaptability index according to the pressure intensity coefficient to obtain the target industry chain environmental adaptability index; calculating the regional economic development index based on the target industry chain environmental adaptability index and the economic impact weight.

[0011] Optionally, after calculating the regional economic development index based on the test data and the economic impact weight to obtain the regional economic development index evaluation result, the method also includes: calculating the risk index of the target sensitive unit based on the test data; classifying the target sensitive unit into risk levels according to the risk index; and generating a regional economic development risk assessment report based on the regional economic development index evaluation result and the risk level.

[0012] In a second aspect of the present application, a regional economic development index evaluation system based on industrial chain analysis is provided, including: a first acquisition module, used to obtain historical industrial chain data of the new energy industry chain in the target region under target environmental parameters; an analysis module, used to perform climate and environmental impact analysis on the historical industrial chain data, and obtain a node layer influencing factor set and a link layer influencing factor set of the new energy industry chain; a construction module, used to construct an environmental pressure scenario of the new energy industry chain based on the node layer influencing factor set and the link layer influencing factor set; a testing module, used to test the real-time industrial chain data of the new energy industry chain under target environmental parameters in the environmental pressure scenario, and obtain test data; a second acquisition module, used to obtain the economic impact weight of the new energy industry chain on the regional economic development index; a calculation module, used to calculate the regional economic development index based on the test data and the economic impact weight, and obtain a regional economic development index evaluation result.

[0013] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any of the methods described above.

[0014] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any one of the above methods is executed.

[0015] In summary, one or more technical solutions provided by this application have at least the following technical effects or advantages: 1. By obtaining the historical industry chain data of the new energy industry chain in the target area under the target environmental parameters, and conducting a climate and environmental impact analysis on the historical data, the node layer influencing factor set and the link layer influencing factor set of the new energy industry chain are extracted. On this basis, an environmental pressure scenario is constructed, so that the system can comprehensively simulate the impact of climate and environmental changes on all links of the new energy industry chain, thereby effectively making up for the shortcomings of existing technologies that only evaluate based on direct economic contribution while ignoring the impact of environmental factors on the stability of the industry chain operation; by testing real-time industry chain data in the constructed environmental pressure scenario, the operating performance of the new energy industry chain in the actual environment is obtained, so that the evaluation results of the regional economic development index can truly reflect the dynamic impact of environmental changes on the industry chain; further, by combining the economic impact weight of the new energy industry chain on the regional economic development index, the environmental impact factors and the economic impact factors are integrated into the model, realizing a multi-dimensional comprehensive evaluation of the regional economic development index and improving the accuracy of the regional economic development assessment.

[0016] 2. By introducing historical industrial chain data containing node operation data and link interaction data, the climate response characteristics of the node layer and link layer are extracted under the target environmental parameters. By constructing a node-environment mapping matrix and calculating the sensitivity index, the first influencing factor and the second influencing factor are extracted to generate the corresponding influencing factor set, which realizes the quantitative expression of the response characteristics of the industrial chain under climate change conditions, thereby more accurately identifying potential weak links and unstable factors in the industrial chain, providing a highly reliable input basis for the construction of subsequent environmental pressure scenarios, and thus enhancing the ability of the regional economic development index evaluation to reflect environmental adaptability, and improving the scientific nature and dynamic response ability of regional economic assessment in the context of new energy.

[0017] 3. By constructing an environmental pressure transmission model based on fluctuation characteristics, the pressure transmission path of the new energy industry chain under the target environmental parameters is clarified, and then the target sensitive units whose stability does not meet the preset requirements are identified, and the environmental pressure intensity coefficient is set accordingly, thereby achieving accurate modeling of environmental pressure scenarios and improving the ability to identify weak links in the industrial chain under the influence of environmental changes; further, the system uses test data under environmental pressure scenarios to calculate the node response index and link response index, obtain the environmental adaptability index of the industrial chain, and correct it in combination with the pressure intensity coefficient. Finally, the regional economic development index is calculated based on the corrected adaptability index and economic impact weight, so that the regional economic evaluation results not only reflect the environmental adaptability of the new energy industry chain, but also integrate its actual contribution to the regional economy, effectively improving the accuracy and guiding value of regional economic assessment in complex environmental driving scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1This is a system architecture diagram involved in a regional economic development index evaluation method or system based on industrial chain analysis in an embodiment of the present application; Figure 2 This is a flow chart of a method for evaluating a regional economic development index based on industrial chain analysis in an embodiment of the present application; Figure 3 This is a structural diagram of a regional economic development index evaluation system based on industrial chain analysis in an embodiment of the present application; Figure 4 It is a structural diagram of an electronic device in an embodiment of the present application.

[0019] Explanation of the accompanying drawings: 301, first acquisition module; 302, analysis module; 303, construction module; 304, testing module; 305, second acquisition module; 306, calculation module; 307, evaluation module; 401, processor; 402, communication bus; 403, user interface; 404, network interface; 405, memory. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0021] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0022] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0023] Figure 1 An exemplary system architecture 100 is shown in which an embodiment of a method or system for evaluating a regional economic development index based on industrial chain analysis of the present application can be applied.

[0024] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is a medium for providing communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0025] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as model training applications, video recognition applications, web browser applications, social platform software, etc.

[0026] Terminal devices 101, 102, 103 can be hardware or software. When terminal devices 101, 102, 103 are hardware, they can be various electronic devices with display screens, including but not limited to smart phones, tablet computers, e-book readers, MP3 (Moving Picture Experts Group Audio Layer III, Moving Picture Experts Compression Standard Audio Layer 3) players, MP4 (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Compression Standard Audio Layer 4) players, laptop computers and desktop computers, etc. When terminal devices 101, 102, 103 are software, they can be installed in the electronic devices listed above. It can be implemented as multiple software or software modules (for example, multiple software or software modules used to provide distributed services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0027] When terminals 101, 102, and 103 are hardware, they may also be equipped with a video capture device. The video capture device may be any device capable of capturing video, such as a camera, a sensor, and the like. Users can use the video capture device on terminals 101, 102, and 103 to capture video.

[0028] The server 105 may be a server that provides various services, such as a background server that processes data displayed on the terminal devices 101, 102, and 103. The background server may analyze and process the received data, and may feed back the processing results (such as recognition results) to the terminal device.

[0029] It should be noted that the server can be either hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When the server is software, it can be implemented as multiple software or software modules (e.g., multiple software or software modules used to provide distributed services), or as a single software or software module. No specific limitations are given here.

[0030] It should be understood that Figure 1 The number of terminal devices, networks, and servers in the above description is merely illustrative. Any number of terminal devices, networks, and servers may be used as needed. In particular, if target data does not need to be acquired remotely, the above system architecture may not include a network, but may instead include only terminal devices or servers.

[0031] Figure 2 It is a flow chart of a method for evaluating a regional economic development index based on industrial chain analysis in an embodiment of the present application.

[0032] See also Figure 2 In an embodiment of the present application, a method for evaluating a regional economic development index based on industrial chain analysis is applied to a server. The method includes: S201, obtaining historical industry chain data of the new energy industry chain in the target area under target environmental parameters; In step S201, historical industry chain data for the new energy industry chain in the target region under target environmental parameters is obtained to provide reliable historical data support for subsequent climate and environmental impact analysis and environmental pressure scenario construction. As a complex industrial system highly sensitive to the natural environment, the operating efficiency, economic benefits, and stability of the new energy industry chain are significantly affected by various external environmental factors.

[0033] Target environmental parameters refer to a set of environmental variables that can reflect the climate and ecological environment characteristics of the target area and have a significant impact on the operation of the new energy industry chain. They include but are not limited to: temperature, humidity, wind speed, wind direction, precipitation, solar radiation intensity, air quality index, PM2.5 / PM10 concentration, surface temperature, soil moisture, etc. The selection of target environmental parameters is customized based on the energy type, geographical area characteristics, and industry chain structure. For example, for the wind power industry chain, wind speed and wind direction are key environmental parameters; for the photovoltaic industry chain, more attention is paid to solar radiation intensity and atmospheric transmittance; and in the hydrogen energy storage and transportation link, temperature and humidity may have a direct impact on material performance and safety.

[0034] During the specific implementation process, the geographical scope of the target area and the coverage of the new energy industry chain will be determined first. The new energy industry chain can include nodes and links in the upstream raw material supply, midstream production and manufacturing, and downstream energy storage and sales corresponding to industries such as wind power, photovoltaic power generation, biomass energy, and hydrogen energy. On this basis, by accessing multi-source data platforms such as local statistical bureau databases, industry supervision platforms, enterprise ERP (Enterprise Resource Planning) systems, and industrial park data centers, historical operating data of the new energy industry chain in recent years (such as the past 5 to 10 years) will be obtained. Historical industry chain data includes but is not limited to: production capacity, output value, energy consumption, carbon emissions, operating status of key equipment, raw material sources and delivery cycles, product inventory and outbound frequency, upstream and downstream collaborative relationships, logistics routes and costs, etc. of each node enterprise.

[0035] At the same time, by connecting to data from the National Meteorological Administration, the Ministry of Ecology and Environment, remote sensing satellite systems, and local meteorological monitoring stations, we extracted target environmental parameter data for the target area within the corresponding historical time period. To ensure effective data matching, we used a timestamp alignment mechanism and spatial gridding methods to synchronously map environmental data with industrial chain data.

[0036] S202. Analyze the impact of climate and environment on historical industrial chain data to obtain a set of node-layer impact factors and a set of link-layer impact factors for the new energy industry chain; Step S202 may include the following steps: identifying the climate response characteristics of the node operation data under the target environmental parameters; extracting the first influencing factor from the climate response characteristics to generate a node layer influencing factor set; identifying the link stability characteristics of the link interaction data under the target environmental parameters; extracting the second influencing factor from the link stability characteristics to generate a link layer influencing factor set.

[0037] Specifically, a climate and environmental impact analysis was conducted on historical industry chain data to obtain a set of node-level and link-level influencing factors for the new energy industry chain. This analysis aims to reveal the mechanism by which target environmental parameters affect different structural levels of the new energy industry chain, thereby constructing environmental pressure scenarios with realistic constraints. Because the operational characteristics of the new energy industry chain are highly sensitive to climate factors, and its structure typically includes multiple functional nodes (such as production, energy storage, and distribution) and links connecting these nodes (such as logistics, information transmission, and energy circulation), it is necessary to identify the specific influencing factors of environmental changes at both the node and link levels.

[0038] Among them, identifying the climate response characteristics of node operation data under target environmental parameters specifically includes the following steps: establishing a corresponding relationship between node operation data and target environmental parameters to generate a node-environment mapping matrix; based on the node-environment mapping matrix, calculating the sensitivity index of node operation data to the target environmental parameters; and determining the climate response characteristics of the industrial chain nodes according to the sensitivity index.

[0039] In one embodiment, to effectively identify the climate response characteristics of each node in the new energy industry chain under target environmental parameters, it is first necessary to establish a correspondence between node operating data and target environmental parameters to generate a node-environment mapping matrix. The node-environment mapping matrix uses the time series of node operating data as the primary dimension and environmental parameters (such as temperature, humidity, wind speed, solar radiation intensity, etc.) as the characteristic dimension. The two are associated through timestamp alignment and spatial position matching to ensure that the operating status of each node under specific time and environmental conditions is accurately mapped.

[0040] After generating the node-environment mapping matrix, sensitivity analysis can be used to calculate the responsiveness of each node's operating indicator to various environmental parameters, generating a sensitivity index. The sensitivity index can be quantified using correlation coefficients, regression coefficients, information gain, or feature importance scores based on machine learning models, reflecting the impact of changes in environmental variables on the node's operating status. To improve analysis accuracy, it is preferable to use a sliding window mechanism for local modeling of time series data and to introduce normalization to mitigate interference caused by different dimensions.

[0041] Finally, based on the numerical distribution of the sensitivity index, the response discrimination threshold is set, the highly sensitive variables are screened, and the characteristic variables that can significantly reflect the fluctuations of the node's operating performance with climate change are extracted, thereby determining the climate response characteristics of the node.

[0042] After constructing the node-environment mapping matrix, calculating the sensitivity index based on this mapping matrix, and determining the climate response characteristics of the industry chain nodes, the system extracts the primary impact factor from each node's climate response characteristics to generate a set of node-level impact factors in order to further quantify and structure the specific impact paths and response variables of climate perturbations on the functional nodes of the new energy industry chain. This set of node-level impact factors serves as a key input for subsequent environmental stress modeling and system response simulation. It characterizes the node's response elasticity, fluctuation amplitude, and reaction speed to different dimensions of environmental perturbations and forms the basis for constructing environmental stress transmission models and identifying target sensitive units.

[0043] Specifically, after identifying the climate response characteristics of each node, the system further performs statistical analysis and variable importance evaluation on its characteristic variables, extracting the dominant response variables that have a significant impact on the node's operating status, including but not limited to key climate factors that affect the node's operating efficiency, output capacity, and stability. To ensure the scientific nature of factor screening, the system uses principal component analysis (PCA), information gain ranking, mutual information scoring, or tree-based feature importance assessment methods to reduce the dimension and screen all response characteristics, eliminating redundant variables and noise variables, and retaining only the core climate factors that best represent the node response pattern as the first influencing factors. The extracted first influencing factors retain the corresponding response direction, response intensity, and fluctuation characteristics, and are structured and organized according to the node dimension to form a node-level influencing factor set. This influencing factor set not only describes the direct impact of environmental disturbances on individual nodes, but also has a unified data format that is quantifiable, comparable, and can be used for modeling, providing a data foundation for subsequent systemic pressure modeling and simulation.

[0044] After the node layer processing is completed, the system further conducts climate and environmental impact analysis on the data interaction relationship between the connected nodes in the new energy industry chain to identify the stability characteristics of the link layer structure under the disturbance of the target environmental parameters. As the functional circulation channel between nodes in the industrial chain, the stability of the link layer is directly related to the overall collaborative efficiency and continuity of the system. To identify the stability characteristics of the link layer, the system first collects historical link interaction data, including but not limited to logistics path timeliness, energy flow transmission on-off rate, link load change rate, communication delay and other indicators, and aligns them with the target environmental parameters in time to construct a link-environment mapping relationship, and then analyzes the state change law of each link under the influence of different climate variables.

[0045] Based on the aforementioned link-environment mapping, the system uses techniques such as volatility analysis, anomaly detection, and steady-state transition models to identify link stability characteristics. Specifically, these include the change in the link's stability range before and after a disturbance, the number of sudden changes, the distribution of recovery times, and the probability of link interruption. This process aims to extract key characteristic variables that reflect the link structure's ability to respond to external disturbances, characterizing the risk paths and critical characteristics of a link's transition from stable operation to an unstable state.

[0046] After obtaining the link stability characteristics, the system further screens and categorizes these characteristic variables, extracting those with a significant impact on link transmission capacity, coordination, or scheduling efficiency as secondary influencing factors. To ensure the representativeness and computational efficiency of these factors, the system also uses a feature selection algorithm to reduce the dimensionality of the link response variables, retaining only those variables that fluctuate significantly under environmental disturbances and significantly impact system coordination, ultimately forming a set of link-layer influencing factors.

[0047] S203. Construct an environmental pressure scenario for the new energy industry chain based on the node layer influencing factor set and the link layer influencing factor set; perform a climate environment fluctuation analysis on the node layer influencing factor set and the link layer influencing factor set to obtain the fluctuation characteristics of the new energy industry chain under the target environmental parameters; and set the environmental pressure scenario for the new energy industry chain based on the fluctuation characteristics.

[0048] In one embodiment, to accurately analyze the dynamic operating characteristics of the new energy industry chain under target environmental parameter perturbations, a multidimensional trajectory of its changes over historical time is constructed based on the extracted sets of node-layer and link-layer influencing factors. Specifically, each influencing factor is arranged by timestamp to form a multidimensional time series matrix. Local statistical features are extracted using a sliding time window, including metrics such as mean, standard deviation, skewness, kurtosis, and coefficient of variation, to characterize the underlying fluctuation trends under historical operating conditions.

[0049] Subsequently, to simulate the driving impact of changes in environmental parameters on the operation of the industrial chain, a climate fluctuation model was introduced to model the perturbations of the target environmental parameters. This model can be constructed based on national meteorological observation data, remote sensing monitoring data, and climate prediction model data (such as CMIP6, Coupled Model Intercomparison Project Phase 6). It uses the following three strategies to generate environmental perturbation sequences: First, a periodic perturbation model is constructed by extracting periodic fluctuations in parameters such as temperature and humidity using Fourier transforms. Second, a Monte Carlo simulation method is used to generate high-confidence extreme perturbation sequences using historical extreme weather sample sets. Third, a trend drift model is established to simulate long-term climate change by combining interannual trends with changes in the urban heat island effect.

[0050] After obtaining the environmental disturbance sequence, it is input as an exogenous variable, and coupled modeling techniques are used to simulate the disturbance sequence and the influencing factor sequence. Vector autoregression models, system dynamics models, or time series prediction models based on LSTM (Long Short-Term Memory) networks can be used to simulate the response of various influencing factors under different disturbance conditions. The simulation results are used to extract the performance changes of each node or link under the influence of disturbances, and further calculate their sensitivity distribution (such as the quantile of the change amplitude), response resilience (such as the disturbance recovery time), and stability interval (such as the probability interval of performance remaining within the normal fluctuation range).

[0051] Ultimately, this analysis revealed the overall fluctuation characteristics of the new energy supply chain under the context of target environmental parameter fluctuations. Specifically, these include the functional volatility at the node level, the changing trends in coupling strength at the link level, and the dynamic exposure of key bottleneck nodes and vulnerable links under various disturbance scenarios. These fluctuation characteristics not only serve as quantitative boundary conditions for constructing environmental stress scenarios but also provide data support for subsequent node-level real-time response testing and full-chain economic impact assessments.

[0052] In order to achieve a multi-dimensional and multi-scenario assessment of the operational response capability of the new energy industry chain under different climatic environmental disturbances, it is necessary to set environmental pressure scenarios based on the fluctuation characteristic data obtained above. Setting environmental pressure scenarios for the new energy industry chain according to the fluctuation characteristics may include the following steps: constructing an environmental pressure conduction model based on the fluctuation characteristics, the environmental pressure conduction model is used to characterize the pressure conduction path of the new energy industry chain under target environmental parameters; identifying target sensitive units of the new energy industry chain according to the environmental pressure conduction model, the target sensitive unit is the target industry chain node in the new energy industry chain whose stability does not meet the preset requirements; setting the environmental pressure intensity coefficient based on the target sensitive unit; setting the environmental pressure scenario according to the environmental pressure intensity coefficient.

[0053] In one embodiment, in order to achieve structured characterization of the operating status and response capability assessment of the new energy industry chain under the target environmental parameter fluctuation conditions, the system constructs an environmental pressure conduction model based on the obtained fluctuation characteristics to characterize the pressure conduction path of the new energy industry chain under the target environmental parameters, and accordingly identifies the target industry chain nodes whose stability does not meet the preset requirements as target sensitive units, and then sets the environmental pressure intensity coefficient and constructs the environmental pressure scenario to achieve response simulation and risk prediction of the industry chain under complex environmental disturbances.

[0054] Specifically, the system first relies on the existing industrial chain topology and its historical operational monitoring data, and combines the time series fluctuation characteristics of the node-layer influencing factor set and the link-layer influencing factor set to construct an environmental pressure transmission model. The pressure transmission model uses environmental disturbance factors as driving variables and maps their transmission paths to the operating status of each industrial chain node through multi-level coupling relationships, reflecting the directionality and intensity distribution of the impact of different disturbance sources on each structural unit within the system. In the pressure transmission model, environmental disturbances propagate to downstream nodes through link-layer coupling paths, causing node operating status deviations or functional degradation.

[0055] After the pressure conduction model is constructed, the system conducts a stability assessment on the state response of each node in the industrial chain during the disturbance conduction process. To achieve standardization and objectivity in stability judgment, the system pre-sets the judgment threshold of the stability assessment as a preset requirement. The preset requirements are formulated based on the statistical analysis of historical operating data, combined with the operating tolerance capabilities, industrial policy requirements and equipment technical specifications of various typical nodes in the new energy industry chain. Specifically, they include: the maximum allowable output deviation rate of the node per unit time, the maximum allowable fluctuation range of the operating parameters, the maximum tolerance threshold of the response recovery time, and the acceptable upper limit of the fault frequency, among other dimensional indicators. These indicators are obtained by clustering analysis and parameter fitting of the node behavior patterns in normal operating conditions in historical samples, and are solidified as the stability judgment benchmark for system operation after evaluation by industry experts and adjustment of policy constraints.

[0056] The system compares and analyzes each node's response data under simulated disturbance conditions against preset requirements. If a node exhibits a state deviation exceeding any of the above-prescribed stability thresholds over multiple consecutive disturbance cycles, its stability is determined to be non-compliant with the preset requirements. Such nodes are identified as target sensitive units, indicating operational vulnerabilities or response instability in the current environmental disturbance transmission path. They may become trigger points for systemic risks and therefore should be prioritized for environmental stress scenario construction.

[0057] After identifying a target sensitive unit, the system further calculates its environmental pressure intensity coefficient based on its topological position, structural role, and response characteristics in the environmental pressure transmission model. This coefficient quantifies the actual impact of a disturbance on that node. It comprehensively considers factors such as the disturbance input amplitude, the node's response amplitude, and recoverability, forming a quantitative expression of the structural unit's risk tolerance under disturbance-driven pressure.

[0058] Ultimately, the system sets environmental pressure scenarios based on the environmental pressure intensity coefficient, forming a set of scenario parameters encompassing the disturbance variable type, disturbance intensity, disturbance action range, and action time period. These environmental pressure scenarios serve as boundary input conditions for subsequent system simulation and response testing, simulating the operational state of the new energy industry chain under diverse environmental disturbances. This provides structured support for subsequent node-level response testing, risk assessment of target sensitive units, and quantification of regional economic impacts.

[0059] S204. Testing real-time industry chain data of the new energy industry chain under target environmental parameters in an environmental pressure scenario to obtain test data; In one embodiment, to achieve high-precision simulation and assessment of the operating status of the new energy industry chain under specific environmental stress scenarios, it is necessary to test the dynamic response of the new energy industry chain to target environmental parameter perturbations within the constructed environmental stress scenarios and obtain test data. The purpose of this testing is to systematically assess the performance fluctuations, instability trends, and system coordination capabilities of each node and link in the new energy industry chain under environmental perturbations of varying types and intensities, thereby providing a quantifiable basis for subsequent operational adaptability optimization, risk classification, and control strategy formulation.

[0060] During the specific implementation process, first, under the set environmental pressure scenario, a test model corresponding to the new energy industry chain structure is constructed. The test model can adopt the real-time mapping model of the industry chain built on the digital twin platform. The core of the real-time mapping model of the industry chain includes: Node state mapping module: Based on the node-level influencing factor set, it establishes the physical attributes (such as production capacity, energy consumption, inventory), logical attributes (such as scheduling strategy, upstream and downstream dependencies) of each node and its response function to environmental disturbances; Link dynamic transmission module: Based on the link layer influencing factor set, it simulates the transmission delay, interruption probability, loss rate, etc. of material flow, information flow, and energy flow under different environmental fluctuations; Perturbation injection module: Injects target environmental parameter disturbance sequences (such as sudden temperature rise, sudden drop in wind speed, unstable light, etc.) into the model in the form of dynamic variables to drive the testing process.

[0061] The real-time industry chain mapping model is initialized by calling historical measured data or real-time monitoring data, and dynamically collects real-time industry chain operation data during the test process, including but not limited to: Node output change rate (such as capacity deviation value per unit time); Changes in node energy efficiency (e.g., fluctuations in the ratio of energy consumption to output); Link transmission delay (e.g., the difference between the actual delay and the expected delay of raw material transmission); Failure probability of key nodes (e.g., frequency of triggering loss of function under disturbance); The overall synergy index of the system (such as calculated by the link flow rate matrix).

[0062] During testing, data is sampled at a fixed time step (e.g., every 10 minutes) to form a multidimensional time series test dataset. To enhance test accuracy and data credibility, the test platform can introduce a dynamic error correction mechanism, using an extended Kalman filter to correct the deviation between the model's predicted output and the observed value in real time, further improving the simulation results' fit.

[0063] After the test is completed, the collected test data will be structured and stored and categorized. The output format can be multi-field JSON or multi-dimensional CSV format, and each set of data will be labeled with environmental scenario labels and response level labels.

[0064] S205. Obtain the economic impact weight of the new energy industry chain on the regional economic development index; In one embodiment, to accurately evaluate the regional economic development index based on the dynamic operation of the new energy industry chain under environmental disturbances, it is necessary to obtain the economic impact weight of the new energy industry chain on the regional economic development index. This economic impact weight is used to quantify the contribution of each node and link in the industry chain to various regional economic indicators. It constitutes the weighting factor in the final calculation of the regional economic development index and is the core data foundation for establishing the mapping relationship between industrial disturbances and economic impacts.

[0065] To this end, we first construct a structural model of the regional economic development index based on the macroeconomic indicator system of the target region. The model can include the following economic target dimensions: regional GDP, industrial added value, industrial tax contribution, unit energy consumption output ratio, employment absorption capacity, green investment ratio, etc., which are unified into a set of regional economic indicators I = {I1, I2, ..., I n}. Among them, I i represents the i-th regional economic indicator, such as regional GDP, added value of new energy industries, unit energy consumption-to-output ratio, proportion of green investment, number of clean energy employees, regional fiscal revenue, etc. The selection of indicators should be based on the evaluation system published by national or local statistical departments to ensure consistency with the caliber of regional economic evaluation.

[0066] In order to obtain the influence weight of the new energy industry chain on the above economic indicators, the regional input-output analysis method is adopted. First, based on the national economic input-output table of the target region, the direct consumption coefficient matrix A between industries is constructed, where: A = [a ij ], where a ij represents the proportion of intermediate inputs used in the i-th industry in the total output of the j-th industry unit in the region; all a ij ∈[0,1], the matrix dimension is m×m, where m is the number of industries in the region. On this basis, calculate the complete demand coefficient matrix between industries (i.e., the Leontief inverse matrix): L = (EA) -1 , where E represents the m×m identity matrix; L = [l ij ], where l ij It means that when the final demand increases by one unit, the total output (including direct and indirect) of the i-th industry needs to increase in order to meet the demand; each column of the total demand coefficient matrix L represents the total pull effect of a certain industry on other industries. The new energy industry chain contains several industrial sectors corresponding to the regional input-output table. The set S = {s1, s2, ..., sn}, among which, j Represents the jth key node industry in the new energy industry chain (such as photovoltaic manufacturing, wind power equipment, energy storage, grid dispatching, etc.). Extract the row vector set corresponding to S in the complete demand coefficient matrix L to form the new energy industry chain's contribution matrix L to the overall regional economy. S Next, define the mapping matrix C between regional economic indicators and industry output = [c pi ], where c pi It represents the unit output contribution coefficient of the ith industry to the pth economic indicator. This coefficient can be obtained by fitting historical statistical data or set by policy guidelines. The dimension of C is n×m, where n is the number of economic indicators and m is the number of industries.

[0067] The driving effect of the new energy industry chain on the economic system S Multiplying it with the indicator mapping matrix C, we get the contribution vector V of the new energy industry chain to the regional economic indicators: in, is the total driving effect vector of each industrial node in the new energy industry chain (i.e. L S The column vector and V = {v1,v2,…,v n}, where v p Represents the relative contribution value of the new energy industry chain to the p-th regional economic indicator. Finally, the contribution vector V is normalized to obtain the economic impact weight vector W of the new energy industry chain on the regional economic development index: p=1,2,…,n, that is, W={w1,w2,…,w n}, Among them, w p represents the impact weight of the new energy industry chain on the pth regional economic indicator; n is the total number of regional economic indicators; the economic impact weight W will be used in the weighted calculation of the subsequent regional economic development index.

[0068] S206. Calculate the regional economic development index based on the test data and the economic impact weight to obtain an evaluation result of the regional economic development index.

[0069] Based on the test data, the node response index and link response index of the new energy industry chain under the target environmental parameters are calculated; the node response index and link response index are weighted to obtain the industry chain environmental adaptability index; the industry chain environmental adaptability index is corrected according to the pressure intensity coefficient to obtain the target industry chain environmental adaptability index; the regional economic development index is calculated based on the target industry chain environmental adaptability index and the economic impact weight.

[0070] In one embodiment, to achieve dynamic assessment and precise quantification of the regional economic development index, a regional economic development index calculation model adaptable to different environmental disturbance scenarios is constructed based on environmental stress test data and economic impact weights. The core of this model is to integrate the operational adaptability of the new energy industry chain under target environmental parameters with its contribution to various regional economic indicators, thereby outputting a regional economic development index evaluation result that is structurally responsive and economically sensitive.

[0071] During the specific implementation process, we first quantitatively analyze the response characteristics of the new energy industry chain under the target environmental parameter scenario based on the aforementioned test data. According to the multi-dimensional time series data output by the test model, the operating status parameters of each node and each link in the industry chain are extracted, such as node output deviation rate, energy efficiency fluctuation rate, link transmission interruption frequency, response delay, etc. Based on the above indicators, the node response index and link response index are constructed to respectively characterize the stability and coordination of the local structure of the industry chain under the disturbance of the target environmental parameters. Let the response index of the i-th node be: in, represents the response index of the i-th node; represents the theoretical output level of the i-th node under the benchmark environment; Represents the actual test output of the i-th node under the target environment parameters; the closer the response index is to 1, the more stable the node performance.

[0072] The calculation of the link response index is similar. Let the response index of the jth link be: in, represents the response index of the jth link; is the average transmission delay of the link under the benchmark environment; D j is the test delay of the link under the target environmental parameter conditions; ∈ is a constant (such as 0.001) to prevent the denominator from being zero; a larger value indicates a more stable link and a smaller transmission delay.

[0073] The environmental adaptability index of the new energy industry chain as a whole is obtained by weighted calculation of all node response indices and link response indices, which is expressed as: Among them, R chain represents the environmental adaptability index of the industrial chain; N is the total number of nodes, M is the total number of links; α, β∈[0,1] and α+β=1, α, β are the weights used to adjust the overall adaptability of nodes and links. The environmental adaptability index reflects the comprehensive operating performance of the entire industrial chain under the current environmental pressure.

[0074] Considering that the impact of the intensity of different environmental disturbances on the adaptability of the industrial chain has nonlinear characteristics, the pressure intensity coefficient is introduced to correct the environmental adaptability index of the industrial chain. Let the environmental pressure intensity coefficient be S p∈[0,1], which is determined by the environmental disturbance amplitude, duration and change rate. The final target industry chain environmental adaptability index is: R adj =R chain ·(1-S p ), where R adj It represents the environmental adaptability index of the industrial chain after correction of pressure intensity; when the environmental pressure intensity is greater (i.e. S p The closer it is to 1), the further the adaptability index of the industrial chain will be weakened, reflecting its actual resilience in extreme environments.

[0075] Finally, based on the target industry chain environmental adaptability index R adj The economic impact weight vector W = {w1,w2,…,w n}, perform weighted calculation on the regional economic indicator set to obtain the regional economic development index EDI: Among them, EDI is the final output regional economic development index; I p represents the standardized value of the p-th regional economic indicator, which can be normalized based on the historical average level or policy baseline; w p is the economic impact weight of the new energy industry chain on this indicator; R adj It plays a dynamic adjustment role, so that the index can truly reflect the actual contribution of the new energy industry chain to the regional economy under conditions of environmental disturbance.

[0076] Optional, in Figure 2 After step S206 of the illustrated embodiment, the following steps may be performed: Based on the test data, the risk index of the target sensitive unit is calculated; the risk level of the target sensitive unit is divided according to the risk index; based on the regional economic development index evaluation results and risk level, a regional economic development risk assessment report is generated.

[0077] In one embodiment, in order to realize the structural risk identification and operational stability assessment of the regional economic system under multi-dimensional environmental disturbance conditions, the system, on the basis of completing the construction of the environmental pressure scenario of the new energy industry chain, further calculates the risk index and classifies the risk level of the target industry chain nodes whose stability does not meet the preset requirements, i.e., the target sensitive units, based on the test data, and generates a regional economic development risk assessment report in combination with the regional economic development index evaluation results, thereby realizing a multi-level, full-link, and structured risk expression of the regional economic operation status.

[0078] During implementation, the system first acquires the response data of each target sensitive unit under disturbance conditions. This response data includes multi-dimensional operational indicators such as the rate of change of node output, degree of performance fluctuation, state recovery time, and coupling path interruption. Based on these indicators, the system constructs a risk index calculation model to characterize the level of operational uncertainty of each target sensitive unit under specific environmental disturbances. The risk index is an important quantitative indicator for measuring the adaptability of structural unit systems. A larger value indicates that the node will exhibit stronger production capacity fluctuations, greater state fluctuations, or more significant functional degradation under stress scenarios, which may in turn have a greater impact on the overall regional economic operation.

[0079] The risk index calculation model constructs a weighted aggregation function after normalizing multidimensional response parameters. Weights are assigned based on the relative importance of different indicators in overall system stability, achieving a unified quantitative expression of risk characteristics. The system ensures that this calculation process is consistent with the aforementioned stability requirements, ensuring logical consistency and response sensitivity of upstream and downstream risk identification data.

[0080] After calculating the risk index for all target sensitive units, the system maps each node's risk index to discrete risk level intervals based on a pre-defined risk grading model. Risk grading criteria can be determined based on the distribution of historical risk event data, the node's influence within the industrial chain structure, and the statistical quantile of the risk index, forming a relatively stable grading system. Each level corresponds to a different risk management strategy priority, and the resulting grading will serve as input for subsequent regional economic risk analysis.

[0081] Based on the risk levels of target sensitive units, the system further integrates these risk ratings with the regional economic development index. The regional economic development index reflects the overall economic structure, development vitality, and industrial support of a region, while the risk level reveals the operational vulnerability of key nodes in the new energy industry chain under specific scenarios. Through structural mapping relationships, the system structurally couples the operational risks of the industry chain with macroeconomic development indicators, identifying key risk paths or bottlenecks that may adversely affect the regional economic development index.

[0082] Ultimately, the system generates a regional economic development risk assessment report based on the results of this integrated analysis. This report includes an analysis of the regional economic development index and its influencing factors, the risk index and risk level distribution of target sensitive units, the identification of key risk nodes and links, an analysis of potential risk transmission pathways, and risk response recommendations for different scenarios. The report is output in a structured format, supporting graphical presentation and strategy output, to assist industry management agencies or investment entities in implementing zoning control, optimizing industrial structure, or making risk prevention and control decisions.

[0083] See also Figure 3 , is a schematic diagram of the structure of a regional economic development index evaluation system based on industrial chain analysis provided by an embodiment of the present application. A regional economic development index evaluation system 300 based on industrial chain analysis specifically includes: The first acquisition module 301 is used to obtain historical industry chain data of the new energy industry chain in the target area under target environmental parameters; the analysis module 302 is used to analyze the climate and environmental impact of the historical industry chain data to obtain a set of node-layer impact factors and a set of link-layer impact factors of the new energy industry chain; A construction module 303 is used to construct an environmental pressure scenario of the new energy industry chain based on the node layer impact factor set and the link layer impact factor set; The testing module 304 is used to test the real-time industry chain data of the new energy industry chain under target environmental parameters in an environmental stress scenario to obtain test data; The second acquisition module 305 is used to obtain the economic impact weight of the new energy industry chain on the regional economic development index; The calculation module 306 is used to calculate the regional economic development index according to the test data and the economic impact weight to obtain the regional economic development index evaluation result.

[0084] Optionally, the analysis module 302 is specifically configured to: Identify the climate response characteristics of node operation data under target environmental parameters; extract the first influencing factor from the climate response characteristics to generate a node layer influencing factor set; identify the link stability characteristics of link interaction data under target environmental parameters; extract the second influencing factor from the link stability characteristics to generate a link layer influencing factor set.

[0085] Optionally, the analysis module 302 is further configured to: Establish the correspondence between node operation data and target environmental parameters to generate a node-environment mapping matrix; based on the node-environment mapping matrix, calculate the sensitivity index of node operation data to target environmental parameters; determine the climate response characteristics of the industrial chain nodes based on the sensitivity index.

[0086] Optionally, the construction module 303 is specifically configured to: The climate environment fluctuation analysis is conducted on the node layer influencing factor set and the link layer influencing factor set to obtain the fluctuation characteristics of the new energy industry chain under the target environmental parameters; the environmental pressure scenario of the new energy industry chain is set according to the fluctuation characteristics.

[0087] Optionally, the construction module 303 is further specifically configured to: An environmental pressure conduction model is constructed based on the fluctuation characteristics. The environmental pressure conduction model is used to characterize the pressure conduction path of the new energy industry chain under the target environmental parameters. According to the environmental pressure conduction model, the target sensitive units of the new energy industry chain are identified. The target sensitive units are target industry chain nodes in the new energy industry chain whose stability does not meet the preset requirements. The environmental pressure intensity coefficient is set based on the target sensitive units. The environmental pressure scenario is set according to the environmental pressure intensity coefficient.

[0088] Optionally, the calculation module 306 is further configured to: Based on the test data, the node response index and link response index of the new energy industry chain under the target environmental parameters are calculated; the node response index and link response index are weighted to obtain the industry chain environmental adaptability index; the industry chain environmental adaptability index is corrected according to the pressure intensity coefficient to obtain the target industry chain environmental adaptability index; the regional economic development index is calculated based on the target industry chain environmental adaptability index and the economic impact weight.

[0089] Optionally, the system further includes an evaluation module 307, specifically configured to: Based on the test data, the risk index of the target sensitive unit is calculated; the risk level of the target sensitive unit is divided according to the risk index; based on the regional economic development index evaluation results and risk level, a regional economic development risk assessment report is generated.

[0090] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0091] This embodiment also discloses an electronic device, referring to Figure 4 The electronic device may include: at least one processor 401 , at least one communication bus 402 , a user interface 403 , a network interface 404 , and at least one memory 405 .

[0092] The communication bus 402 is used to implement the connection and communication between these components.

[0093] The user interface 403 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 403 may also include a standard wired interface and a wireless interface.

[0094] The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0095] The processor 401 may include one or more processing cores. The processor 401 utilizes various interfaces and lines to connect various parts of the entire server, and executes various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 405, as well as calling data stored in the memory 405. Optionally, the processor 401 may be implemented in the form of at least one hardware component selected from digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 401 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the processor 401 and may be implemented separately on a single chip.

[0096] Among them, the memory 405 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 405 includes a non-transitory computer-readable storage medium. The memory 405 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 405 may also be optionally at least one storage device located away from the aforementioned processor 401. As Figure 4 As shown, the memory 405 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a regional economic development index evaluation method based on industrial chain analysis.

[0097] exist Figure 4In the electronic device shown, the user interface 403 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 401 can be used to call an application stored in the memory 405 for a regional economic development index evaluation method based on industrial chain analysis. When executed by one or more processors 01, the electronic device executes one or more methods as in the above embodiments.

[0098] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0099] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0100] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0101] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0102] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0103] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory 405 and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory 405 includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disk.

[0104] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variations, uses or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A regional economic development index evaluation method based on industrial chain analysis, characterized in that: Applied to a server, the method includes: Obtain historical industry chain data of the new energy industry chain in the target area under target environmental parameters; Performing climate and environmental impact analysis on the historical industry chain data to obtain a node-layer impact factor set and a link-layer impact factor set of the new energy industry chain; Constructing an environmental pressure scenario for the new energy industry chain based on the node layer impact factor set and the link layer impact factor set; Testing real-time industry chain data of the new energy industry chain under the target environmental parameters in the environmental pressure scenario to obtain test data; Obtaining the economic impact weight of the new energy industry chain on the regional economic development index; The regional economic development index is calculated based on the test data and the economic impact weight to obtain a regional economic development index evaluation result.

2. The method according to claim 1, characterized in that The historical industry chain data includes node operation data of each industry chain node under the target environmental parameters, and link interaction data between multiple industry chain nodes. The climate environment impact analysis of the historical industry chain data is performed to obtain the node layer impact factor set and link layer impact factor set of the new energy industry chain, which specifically include: Identifying climate response characteristics of the node operation data under the target environmental parameters; Extracting a first influencing factor from the climate response characteristics to generate a node-level influencing factor set; Identifying link stability characteristics of the link interaction data under the target environment parameters; A second impact factor is extracted from the link stability feature to generate a link layer impact factor set.

3. The method according to claim 2, characterized in that The identifying the climate response characteristics of the node operation data under the target environmental parameters specifically includes: Establishing a correspondence between the node operation data and the target environment parameters to generate a node-environment mapping matrix; Calculating a sensitivity index of the node operation data to the target environment parameter based on the node-environment mapping matrix; The climate response characteristics of the industrial chain node are determined according to the sensitivity index.

4. The method according to claim 1, wherein The constructing of the environmental pressure scenario of the new energy industry chain based on the node layer impact factor set and the link layer impact factor set specifically includes: Performing climate environment fluctuation analysis on the node layer influencing factor set and the link layer influencing factor set to obtain fluctuation characteristics of the new energy industry chain under the target environmental parameters; The environmental pressure scenario of the new energy industry chain is set according to the fluctuation characteristics.

5. The method according to claim 4, characterized in that The step of setting the environmental pressure scenario of the new energy industry chain according to the fluctuation characteristics specifically includes: Building an environmental pressure conduction model based on the fluctuation characteristics, wherein the environmental pressure conduction model is used to characterize the pressure conduction path of the new energy industry chain under the target environmental parameters; identifying target sensitive units of the new energy industry chain according to the environmental pressure conduction model, wherein the target sensitive units are target industry chain nodes in the new energy industry chain whose stability does not meet preset requirements; Setting an environmental pressure intensity coefficient based on the target sensitive unit; The environmental pressure scenario is set according to the environmental pressure intensity coefficient.

6. The method according to claim 5, characterized in that The calculating of the regional economic development index based on the test data and the economic impact weight to obtain the regional economic development index evaluation result specifically includes: Based on the test data, calculating the node response index and link response index of the new energy industry chain under the target environmental parameters; Performing weighted calculation on the node response index and the link response index to obtain an industrial chain environment adaptability index; Correcting the environmental adaptability index of the industrial chain according to the pressure intensity coefficient to obtain the environmental adaptability index of the target industrial chain; The regional economic development index is calculated based on the target industrial chain environmental adaptability index and the economic impact weight.

7. The method according to claim 5, characterized in that After calculating the regional economic development index based on the test data and the economic impact weight to obtain a regional economic development index evaluation result, the method further includes: Calculating a risk index of the target sensitive unit based on the test data; Classifying the target sensitive units into risk levels according to the risk index; Based on the regional economic development index evaluation results and the risk level, a regional economic development risk assessment report is generated.

8. A regional economic development index evaluation system based on industrial chain analysis, characterized by: include: The first acquisition module is used to obtain historical industry chain data of the new energy industry chain in the target area under target environmental parameters; An analysis module, configured to perform climate and environmental impact analysis on the historical industry chain data to obtain a node-layer impact factor set and a link-layer impact factor set of the new energy industry chain; A construction module, configured to construct an environmental pressure scenario of the new energy industry chain based on the node layer impact factor set and the link layer impact factor set; A testing module, configured to test the real-time industry chain data of the new energy industry chain under the target environmental parameters in the environmental pressure scenario to obtain test data; A second acquisition module is used to obtain the economic impact weight of the new energy industry chain on the regional economic development index; The calculation module is used to calculate the regional economic development index based on the test data and the economic impact weight to obtain a regional economic development index evaluation result.

9. An electronic device, characterized in that: include: one or more processors and memory; The memory is coupled to the one or more processors, and is configured to store computer program codes, where the computer program codes include computer instructions. The one or more processors call the computer instructions to enable the electronic device to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on an electronic device, the electronic device is caused to execute the method according to any one of claims 1 to 7.

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