Operation evaluation method and device of integrated energy system, and management and control system

Abstract

The invention discloses an operation evaluation method and device of an integrated energy system and a management and control system. The method comprises the following steps: obtaining operation data of each subsystem in the integrated energy system; based on a pre-constructed comprehensive energy index library, screening out corresponding evaluation features from the operation data of each subsystem; a principal component analysis method is adopted to analyze the evaluation features, and core features are given; according to the similarity between each evaluation feature and the core feature, determining a target evaluation feature and giving an evaluation weight corresponding to the target evaluation feature; and based on the target evaluation features of each subsystem in the integrated energy system, fusing the evaluation weights corresponding to the target evaluation features, and giving operation evaluation data of the integrated energy system. The operation evaluation data is obtained by collecting the operation data of the integrated energy system, performing feature extraction on the data by applying the principal component analysis method, selecting the target evaluation features with high correlation, and setting the evaluation weight in combination with the information entropy, so that the accuracy of the evaluation of the integrated energy system is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy system operation efficiency evaluation, and specifically relates to an integrated energy system operation evaluation method, device and control system. Background Art

[0002] In today's energy field, integrated energy systems involve the production, conversion, storage and consumption of multiple energy sources, and their complexity is increasing.

[0003] Traditional assessment methods have numerous limitations when processing data from integrated energy systems. These include the difficulty in extracting key features from massive amounts of data, the subjectivity of indicator selection, and the lack of scientific rationality in setting indicator weights. These issues result in incomplete, inaccurate, and inefficient assessments of integrated energy systems, failing to meet the demands of energy system optimization and sustainable development.

[0004] Patent application CN116227646A discloses a method and system for evaluating the operating status of an integrated energy system. The method comprises the following steps: establishing an operating status evaluation system for the integrated energy system and determining the weights of each indicator in the operating status evaluation system; constructing a multi-objective optimization scheduling model for the integrated energy system and, based on the weights of each indicator in the operating status evaluation system, establishing the coupling relationship between different forms of energy for each indicator in the operating status evaluation system; modeling the state probabilities of energy conversion equipment under multiple time periods and, based on the multi-objective optimization scheduling model for the integrated energy system and the coupling relationship between different forms of energy for each indicator in the operating status evaluation system, determining the importance of different energy conversion equipment in the integrated energy system; and determining whether the operating status of the integrated energy system is at risk based on the importance of different energy conversion equipment in the integrated energy system. The method can be widely used in the field of operation scheduling and control. The indicators in the evaluation system in this patent are manually set and are highly subjective, making the evaluation of the integrated energy system incomplete and inaccurate.

[0005] How to determine the evaluation indicators under the premise of meeting the complexity and diversity of the integrated energy system in order to achieve a scientific, comprehensive and accurate evaluation of the integrated energy system is a problem that needs to be solved at present. Summary of the Invention

[0006] In response to the defects in the above-mentioned prior art, the present invention provides an operation evaluation method, device and control system for an integrated energy system. The method includes: obtaining the operation data of each subsystem in the integrated energy system; based on a pre-built integrated energy index library, screening out corresponding evaluation features from the operation data of each subsystem; using principal component analysis to analyze the evaluation features and give core features; based on the similarity between each evaluation feature and the core feature, determining the target evaluation feature and giving the evaluation weight corresponding to the target evaluation feature; based on the target evaluation features of each subsystem in the integrated energy system, integrating the evaluation weights corresponding to the target evaluation features, and giving the operation evaluation data of the integrated energy system. By collecting the operation data of the integrated energy system, using principal component analysis to extract features from the data and select target evaluation features with high correlation, and setting evaluation weights in combination with information entropy, the operation evaluation data is finally obtained, thereby improving the accuracy of the integrated energy system evaluation.

[0007] In a first aspect, the present invention provides an operation evaluation method for an integrated energy system, comprising the following steps: Obtain operating data of each subsystem in the integrated energy system; Based on the pre-built comprehensive energy indicator library, the corresponding evaluation features are screened from the operating data of each subsystem; The principal component analysis method is used to analyze the evaluation characteristics and give the core characteristics; According to the similarity between each evaluation feature and the core feature, the target evaluation feature is determined and the corresponding evaluation weight of the target evaluation feature is given; Based on the target evaluation characteristics of each subsystem in the integrated energy system and the evaluation weights corresponding to the target evaluation characteristics, the operation evaluation data of the integrated energy system is given.

[0008] Furthermore, the operating data of each subsystem in the integrated energy system is obtained, including: Obtaining initial operational data of the integrated energy system; Replace or delete outliers in the initial operation data to complete data cleaning of the initial operation data; The initial operation data after data cleaning is standardized to obtain operation data.

[0009] Furthermore, outliers in the initial operating data are replaced or deleted to complete data cleaning of the initial operating data, specifically including: Obtain historical operation data corresponding to the initial operation data; Based on the historical operation data, the standard deviation and mean of the historical operation data are obtained, and the normal fluctuation range corresponding to the initial operation data is given; According to the normal fluctuation range, outliers in the initial operation data are identified and replaced or deleted to complete the data cleaning of the initial operation data.

[0010] Furthermore, the comprehensive energy index database includes supply indicators, consumption indicators, conversion indicators, equipment indicators and environmental indicators; The pre-construction of the comprehensive energy indicator library is determined by the following steps: Obtain historical operating data of the integrated energy system, where the operating data is multi-parameter data, and the parameter dimension of each parameter is one of the monitoring data of each subsystem, equipment operation logs, energy conversion records, and environmental monitoring data; Normalize historical operation data and build an operation database with a multi-layer index structure. The multi-layer index includes timestamps, subsystems, and devices within subsystems. By mining patterns for each parameter dimension in the operation database, key parameters in each parameter dimension are identified and determined as initial indicators, forming an initial indicator set for each parameter dimension; Screen the initial indicator sets corresponding to the monitoring data of each subsystem and provide initial indicators for supply and consumption; Conduct correlation analysis on the initial indicators of supply and consumption. Based on the proportion of each combination of initial indicators of supply and consumption in the monitoring data of each subsystem, obtain the indicator correlation network of supply and consumption, and give the supply indicators and consumption indicators based on the correlation conditions. Based on the supply and consumption indicators, the initial indicator set of each parameter dimension is updated to form the indicator set of each parameter dimension; The sliding time window is used to analyze the sensitivity of each indicator in the indicator set. Combined with the dynamic weight allocation mechanism, each indicator is weighted and marked to construct a comprehensive energy indicator library.

[0011] Furthermore, a sliding time window is used to analyze the sensitivity of each indicator in the indicator set. Combined with the dynamic weight allocation mechanism, each indicator is weighted and marked to construct a comprehensive energy indicator library. The specific contents include: Based on the preset sliding step, the time window is moved to analyze the indicator sensitivity of each indicator in each time window to obtain the indicator sensitivity data of each indicator; Substitute the indicator-sensitive data into the pre-built weight adjustment function to obtain the assigned weights of each indicator; Based on the allocated weights of each indicator, each indicator is weighted and marked to construct a comprehensive energy indicator library.

[0012] Furthermore, principal component analysis was used to analyze the evaluation features and provide core features, including: Analyze the linear correlation between each evaluation feature and construct the covariance matrix of the evaluation features; The covariance matrix is decomposed to give multiple eigenvalues; According to the variance explanation ratio of each eigenvalue and the preset explanation threshold, multiple eigenvalues are screened to obtain the core features.

[0013] Furthermore, the linear correlation between each evaluation feature is analyzed, and the covariance matrix of the evaluation features is constructed, including: According to the elements in each evaluation feature and the feature mean of the corresponding evaluation feature, the deviation feature of each evaluation feature is given; Based on the deviation characteristics of any two evaluation features, the mean of the product of the two deviation characteristics is given to obtain the degree of linear correlation between the evaluation features; The linear correlation degree between each evaluation feature is integrated to construct the covariance matrix of the evaluation features.

[0014] Furthermore, multiple eigenvalues are specifically expressed as: ; Where C is the covariance matrix of the evaluation features, is a diagonal matrix, and each element on the diagonal of the diagonal matrix is the eigenvalue. is the identity matrix, is the transposed matrix of V, V is the eigenvector matrix, and the elements in the eigenvector matrix are the eigenvectors corresponding to the eigenvalues.

[0015] Furthermore, based on the variance explanation ratio of each eigenvalue and the preset explanation threshold, multiple eigenvalues are screened to obtain core features, including: According to the variance explanation ratio of each eigenvalue, the cumulative variance explanation ratio corresponding to each eigenvalue is given; Compare the cumulative variance explanation ratio corresponding to each eigenvalue with the preset explanation threshold to obtain the comparison result; According to the comparison results, the eigenvalues are screened and the eigenvectors corresponding to the screened eigenvalues are given; The standard data matrix is projected into a vector matrix composed of eigenvectors corresponding to the filtered eigenvalues to obtain the core features.

[0016] Furthermore, based on the similarity between each evaluation feature and the core feature, the target evaluation feature is determined and the evaluation weight corresponding to the target evaluation feature is given, specifically including: Based on the similarity between the evaluation features and the core features, combined with the preset similarity range, the evaluation features are screened to give the target evaluation features; Combined with the information content of the target evaluation feature, the evaluation weight corresponding to the target evaluation feature is given.

[0017] Furthermore, based on the information content of the target evaluation features, the evaluation weights corresponding to the target evaluation features are given, including: According to the value of each element in the target evaluation feature, the information content of the target evaluation feature is given; Combined with the information content of the target evaluation features, the degree of variation of the target evaluation features is analyzed; Based on the degree of variation of all target evaluation features and combined with the degree of variation of the target evaluation features, the evaluation weight corresponding to the target evaluation features is given.

[0018] Furthermore, the evaluation weight corresponding to the target evaluation feature is specifically expressed as: ; in, is the evaluation weight corresponding to the j-th target evaluation feature, is the information content of the j-th target feature weight, and m is the number of target evaluation features.

[0019] Furthermore, based on the target evaluation characteristics of each subsystem in the integrated energy system and the evaluation weights corresponding to the target evaluation characteristics, the operation evaluation data of the integrated energy system is given, including: Based on the target evaluation characteristics of each subsystem in the integrated energy system, quantifying and / or standardizing the characteristic values in the target evaluation characteristics to obtain standard characteristic values; The evaluation weights corresponding to the fusion target evaluation features are combined with the standard eigenvalues for weighted summation to give the operation evaluation data.

[0020] In a second aspect, the present invention further provides an operation evaluation device for an integrated energy system, which adopts any of the above-mentioned operation evaluation methods for an integrated energy system, comprising: Data acquisition module, used to obtain the operating data of each subsystem in the integrated energy system; The feature screening module is used to screen out corresponding evaluation features from the operating data of each subsystem based on the pre-built comprehensive energy indicator library; The core determination module is used to analyze the evaluation features using principal component analysis and give the core features; The weight determination module is used to determine the target evaluation feature and give the evaluation weight corresponding to the target evaluation feature based on the similarity between each evaluation feature and the core feature; The operation evaluation module is based on the target evaluation characteristics of each subsystem in the integrated energy system, integrates the evaluation weights corresponding to the target evaluation characteristics, and provides the operation evaluation data of the integrated energy system.

[0021] In a third aspect, the present invention also provides a management and control system for an integrated energy system, comprising a memory, a processor, and a computer program stored in the memory. When the computer program is run by the processor, it executes instructions according to the above method.

[0022] The present invention provides an operation evaluation method, device, and control system for an integrated energy system, which have at least the following beneficial effects: (1) By collecting the operation data of the integrated energy system, the principal component analysis method is used to extract the features of the data. The core features obtained are analyzed for correlation with the pre-established integrated energy index library, and the target evaluation features with high correlation with the core features are selected. The weights of the target evaluation features are set based on the information entropy of the target evaluation features. Finally, the operation evaluation data of the integrated energy system are obtained, which improves the accuracy of the operation evaluation of the integrated energy system.

[0023] (2) By cleaning and standardizing the collected initial operating data of the integrated energy system, the noise interference in the data was effectively removed, the comparability and analyzability of the data were enhanced, and a solid foundation was laid for subsequent accurate evaluation.

[0024] (3) By combining principal component analysis with correlation analysis, target evaluation features with high correlation are screened out, avoiding the subjectivity and one-sidedness of traditional methods. This method can accurately reflect the core characteristics and operational nature of the integrated energy system and ensure the scientific nature and pertinence of the evaluation index system.

[0025] (4) The evaluation weight is determined by the information entropy of the target evaluation characteristics, so that the weight distribution is completely based on the information characteristics of the data itself, avoiding the interference of human factors, ensuring the objectivity and scientific nature of the weight setting, and making the evaluation results more convincing and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flow chart of an operation evaluation method for an integrated energy system provided in an embodiment of the present invention; Figure 2 A flowchart for determining core features provided by an embodiment of the present invention; Figure 3 A flowchart of constructing a covariance matrix of evaluation features provided by an embodiment of the present invention; Figure 4 A flowchart of screening core features from multiple feature values provided by an embodiment of the present invention; Figure 5 A flowchart for determining target evaluation features and evaluation weights provided by an embodiment of the present invention; Figure 6 A flowchart for determining evaluation weights provided in an embodiment of the present invention; Figure 7 This is a structural block diagram of the operation evaluation device of the integrated energy system provided by an embodiment of the present invention.

[0027] Among them, 201 is a data acquisition module; 202 is a feature screening module; 203 is a core determination module; 204 is a weight determination module; and 205 is an operation evaluation module. DETAILED DESCRIPTION

[0028] To better understand the above technical solution, the following will be described in detail with reference to the accompanying drawings and specific implementation methods. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0029] The terms used in the embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The singular forms "a," "an," "the," and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, and unless the context clearly indicates otherwise, "a plurality" generally includes at least two.

[0030] It should also be noted that the terms "include," "comprises," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a product or device comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such product or device. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the product or device comprising the element.

[0031] Currently, integrated energy system evaluation methods lack data cleaning and standardization processes. They directly aggregate data from disparate sources, at varying levels, and in different units, resulting in noisy and poor comparability. Indicator selection relies heavily on empirical judgment and a focus on conventional indicators, resulting in an incomplete evaluation system that fails to accurately reflect the complex nature of integrated energy systems. Indicator weighting is often subjective, heavily influenced by personal experience, resulting in a lack of objectivity and consistency in weight allocation, making it ineffective in supporting energy system decision-making.

[0032] The present invention provides an operation evaluation method for an integrated energy system, comprising the following steps: obtaining operation data of each subsystem in the integrated energy system; screening corresponding evaluation features from the operation data of each subsystem based on a pre-built integrated energy index library; analyzing the evaluation features using principal component analysis to determine core features; determining evaluation weights corresponding to the evaluation features based on the similarity between each evaluation feature and the core features; and integrating the evaluation weights corresponding to the evaluation features based on the evaluation features of each subsystem in the integrated energy system to determine operation evaluation data for the integrated energy system. By establishing a comprehensive data collection and preprocessing process, using a principal component analysis (PCA) algorithm to extract key features, and combining correlation analysis to screen out indicators closely related to system performance, and then using an entropy weight method to set objective weights based on the data's inherent information characteristics, a comprehensive, accurate, and objective evaluation of the integrated energy system is ultimately achieved, providing a solid and reliable scientific basis for system management and decision-making, and promoting the optimized development and efficient operation of the integrated energy system.

[0033] like Figure 1 As shown, an embodiment of the present invention provides an operation evaluation method for an integrated energy system, and the specific steps are as follows: S101: Obtaining operating data of each subsystem in the integrated energy system.

[0034] It is understood that an integrated energy system primarily consists of an energy supply network, energy coupling equipment, energy storage devices, and users. Electricity, gas, heat, and cold energy are transmitted to users via the energy supply network. Exchange equipment includes combined heat and power (CHP), electric chillers, gas boilers, gas turbines, and absorption chillers. Energy storage includes electricity, gas, heat, and cold storage. In other words, an integrated energy system comprises multiple subsystems, including the power subsystem, thermal subsystem, natural gas subsystem, and cooling subsystem. Raw operating data from all subsystems and equipment within the integrated energy system is collected, including but not limited to energy production data (such as the output of various energy sources and operating parameters of production equipment), energy consumption data (energy consumption and consumption patterns by user or region), energy conversion data (such as conversion efficiency and losses of conversion equipment), storage data (storage capacity of energy storage equipment, charging and discharging parameters), and environmental data (such as ambient temperature and humidity). This raw operating data is collected from various sensors, monitoring systems, metering devices, and information management systems to ensure comprehensiveness and accuracy.

[0035] The initial operation data is cleaned, including: Obtain historical operation data corresponding to the initial operation data; Based on the historical operation data, the standard deviation and mean of the historical operation data are obtained, and the normal fluctuation range corresponding to the initial operation data is given; Based on the normal fluctuation range, outliers in the initial running data are identified and replaced or deleted.

[0036] In a specific example, statistical analysis and anomaly detection methods were used to remove outliers and erroneous data from initial operational data. Taking energy data as an example, by analyzing the distribution and trends of historical data and combining the data's standard deviation and mean, the normal fluctuation range of the data was determined. Data outside this range was then identified and processed. Data outside this range was marked as outliers and replaced or deleted based on adjacent data.

[0037] After completing the data cleaning of the initial operation data, perform data standardization and scale the initial operation data to an appropriate range to obtain the operation data, which is specifically expressed as: ; ; in, For After standardization, the running data is the jth eigenvalue of the i-th group of initial running data, is the mean of the initial running data of group i, is the standard deviation of the i-th group of initial running data, and n is the data size of the i-th group of initial running data.

[0038] Because the initial operational data of integrated energy systems comes from a wide range of sources and is of diverse types, this invention effectively addresses the issues of data integration and scaling by cleaning and standardizing the collected initial operational data, making the data analyzable and laying the foundation for subsequent evaluation. By applying statistical analysis and anomaly detection methods to clean the data, noise interference in the data is effectively removed. Data standardization, which unifies data of different magnitudes and units to the same scale, enhances data comparability and analyzability, facilitates subsequent model processing and analysis, and lays a solid foundation for subsequent accurate evaluation.

[0039] S102: Based on the pre-built comprehensive energy indicator library, corresponding evaluation features are screened out from the operating data of each subsystem.

[0040] The comprehensive energy index library includes supply indicators, consumption indicators, conversion indicators, equipment indicators and environmental indicators. The pre-construction of the comprehensive energy index library is determined by the following steps: Obtain historical operating data of the integrated energy system, where the operating data is multi-parameter data, and the parameter dimension of each parameter is one of the monitoring data of each subsystem, equipment operation logs, energy conversion records, and environmental monitoring data; Normalize historical operation data and build an operation database with a multi-layer index structure. The multi-layer index includes timestamps, subsystems, and devices within subsystems. By mining patterns for each parameter dimension in the operation database, key parameters in each parameter dimension are identified and determined as initial indicators, forming an initial indicator set for each parameter dimension; Screen the initial indicator sets corresponding to the monitoring data of each subsystem and provide initial indicators for supply and consumption; Conduct correlation analysis on the initial indicators of supply and consumption. Based on the proportion of each combination of initial indicators of supply and consumption in the monitoring data of each subsystem, obtain the indicator correlation network of supply and consumption, and give the supply indicators and consumption indicators based on the correlation conditions. Based on the supply and consumption indicators, the initial indicator set of each parameter dimension is updated to form the indicator set of each parameter dimension; The sliding time window is used to analyze the sensitivity of each indicator in the indicator set. Combined with the dynamic weight allocation mechanism, each indicator is weighted and marked to construct a comprehensive energy indicator library.

[0041] It is understood that by normalizing historical operating data and scaling it to a specific range, the dimensional differences between historical operating data are reduced, allowing historical operating data to be compared and processed on the same scale. Based on the results of the normalization process, an operating database with a multi-layer index structure is constructed. The multi-layer index includes timestamps, subsystems, and devices within the subsystem. Each piece of historical operating data includes at least three layers of indexes, which can be used to search for the corresponding historical operating data in the operating database.

[0042] In the embodiments provided herein, pattern mining for each parameter dimension in the operational database specifically employs a prototype clustering algorithm to cluster the parameters in the operational database. By combining a preset minimum intra-cluster variance threshold and a maximum number of iterations, cluster centers are obtained. Key parameters for each cluster center are extracted and determined as initial indicators, forming an initial indicator set for each parameter dimension. In this example, the prototype clustering algorithm is implemented using the K-prototypes clustering algorithm.

[0043] In a specific example, the various parameter dimensions in the operational database are input into a prototype clustering algorithm, which then divides each parameter dimension into different clusters based on parameter similarity. Parameter similarity can be expressed as the distance between parameters; samples with similar distances are grouped into the same cluster. For example, in power system data, similarity between samples is calculated based on Euclidean distance. Samples with similar voltage, current patterns, and equipment operating states are grouped together. Clustering is completed when the intra-cluster variance of each cluster falls below the minimum intra-cluster variance threshold or the maximum number of iterations is reached. The parameters within each cluster in the clustering results are analyzed to identify representative parameters within each cluster. Parameter importance can be measured by calculating the intra-cluster variance or information gain metrics for each parameter. For example, if the variance of voltage fluctuation in a cluster is large while the variances of other parameters are relatively small, voltage fluctuation may be a key parameter in that cluster. Parameter differences between clusters can also be observed. If the values of a parameter vary significantly between clusters, it may be an important parameter for distinguishing different operating states. For example, the device operation status code has different distributions among different clusters, which may indicate that it is critical for distinguishing the normal operation and abnormal operation status of the device and can be used as a key parameter.

[0044] Based on the correspondence between the monitoring data of each subsystem and the parameters in the initial indicator set, the parameters in the initial indicator set are classified and screened to produce supply-related initial indicators and consumption-related initial indicators. It is understood that whether an initial indicator in the initial indicator set is a supply-related initial indicator or a consumption-related initial indicator is determined by the corresponding monitoring data itself. For example, if the monitoring data is energy intensity, the corresponding initial indicator is a consumption-related initial indicator. For another example, if the monitoring data is comprehensive energy consumption, the corresponding initial indicator is a supply-related initial indicator.

[0045] Perform correlation analysis on the initial indicators of supply and consumption. According to the proportion of each combination of initial indicators of supply and consumption in the monitoring data of each subsystem, obtain the indicator correlation network of supply and consumption, and give the supply indicators and consumption indicators based on the correlation conditions, including: According to the preset minimum support threshold and confidence threshold, association rule mining is used to analyze the relationship between each supply category initial indicator and each consumption category initial indicator to obtain the indicator association network.

[0046] It should be understood that association rule mining is a method of finding relationships between variables in a large database. The Apriori algorithm is a classic algorithm in association rule mining, which generates association rules by finding frequent item sets. In the scenario of energy supply and consumption indicator analysis, this method can help understand the relationship between different energy indicators and build a multi-dimensional indicator system. In the implementation method provided by the present invention, the minimum support threshold and the confidence threshold must first be set. Among them, the minimum support represents the frequency of the item set in all transactions. If the support of an item set is higher than the minimum support threshold, then it is considered to be a frequent item set. The confidence represents the probability of the consequent occurring when the antecedent occurs. If the confidence of a rule is higher than the confidence threshold, then it is considered to be a strong association rule.

[0047] Analyze the relationship between initial supply indicators and initial consumption indicators. Combined with the relationship between the proportion of each combination of initial supply indicators and initial consumption indicators in the monitoring data of each subsystem and the minimum support threshold, an indicator association network is constructed. For example, consider multiple transaction data, each of which includes a transaction number and consumption data for at least one of coal, oil, natural gas, and electricity. Use the Apriori algorithm to identify frequent itemsets. When the proportion of a combination of an initial supply indicator and its corresponding initial consumption indicator in the monitoring data of a subsystem reaches the minimum support threshold, the combination is considered a frequent itemset. For example, the frequent itemset {coal, electricity}: In all transaction data, the frequency of both coal and electricity occurring together exceeds the minimum support threshold. Then, association rules are generated, such as the rule: If coal is supplied, then electricity is consumed (coal → electricity). If the confidence of the association rule exceeds the confidence threshold, the association rule is considered a strong association rule. Parameter groups that meet the strong association criteria are included in the indicator association network. For example, if the association rule between coal and electricity is a strong association, the connection between these two parameters is marked in the indicator association network. Thus, multiple strong association rules constitute the indicator association network.

[0048] According to the supply indicators and consumption indicators, the initial indicator set of each parameter dimension is updated to form an indicator set of each parameter dimension. Specifically, based on the supply indicators and consumption indicators in the indicator association network, the initial indicators in the initial indicator set are screened. If the initial indicator is included in the indicator association network, it is determined as an indicator in the indicator set.

[0049] Furthermore, a sliding time window is used to analyze the sensitivity of each indicator in the indicator set. Combined with the dynamic weight allocation mechanism, each indicator is weighted and marked to construct a comprehensive energy indicator library. The specific contents include: Based on the preset sliding step, the time window is moved to analyze the indicator sensitivity of each indicator in each time window to obtain the indicator sensitivity data of each indicator; Substitute the indicator-sensitive data into the pre-built weight adjustment function to obtain the assigned weights of each indicator; Based on the allocated weights of each indicator, each indicator is weighted and marked to construct a comprehensive energy indicator library.

[0050] It can be understood that a sliding time window is a method for analyzing time series data, analyzing local data features by sliding a fixed-size time window. In the embodiments provided herein, the time window size (e.g., a month or a quarter) and the time step size (e.g., a sliding step) for each sliding window are first determined. Within each time window, the sensitivity of each indicator is analyzed, that is, the impact of indicator changes on system energy efficiency.

[0051] For example, let's analyze the monthly data of a power plant with a one-month time window and a one-week sliding step. We need to calculate the relationship between coal consumption and power generation within each month.

[0052] Indicator sensitivity analysis refers to the evaluation of the impact of indicator changes on system performance (such as energy efficiency).

[0053] Indicator sensitive data, specifically expressed as: ; in, For indicators in the indicator association network Sensitive data of the system energy efficiency Y indicator, For indicators in the indicator association network and the covariance of system energy efficiency Y, For indicators in the indicator association network The standard deviation of is the standard deviation of the system energy efficiency Y.

[0054] In a specific example, the covariance of coal consumption X1 and power generation Y is 100, and the standard deviations are 10 and 20 respectively, then S1=100 / (10×20)=0.5.

[0055] The dynamic weight allocation mechanism adjusts the weight of each parameter according to the indicator sensitivity of each indicator in the indicator association network and the fluctuation of system energy efficiency.

[0056] The weight adjustment function is specifically expressed as: ; in, For indicators in the indicator association network The allocation weight of , α and β are parameters for adjusting the allocation weight.

[0057] In a specific example, α=2 and β=0.5, then the allocation weight of coal consumption W1=2×0.5+0.5=1.5.

[0058] Indicators in the indicator association network that are significantly correlated with system energy efficiency fluctuations are weighted and labeled. Indicators with higher weights are considered to be significantly correlated with system energy efficiency fluctuations. In a specific example, if coal consumption and natural gas consumption have weights of 1.5 and 1.2, respectively, coal consumption is labeled as the more important indicator.

[0059] In a specific example, it was found that in the past quarter, an increase in natural gas supply was significantly correlated with a decrease in electricity consumption. The weight assigned to the natural gas supply indicator can be increased to reflect its impact on system energy efficiency. Ultimately, a comprehensive energy indicator library is constructed, including five dimensions: supply indicators, consumption indicators, conversion indicators, equipment indicators, and environmental indicators. The indicators in each dimension are weighted according to their correlation with system energy efficiency fluctuations. In a specific example, the comprehensive energy indicator library includes: Supply indicators: coal consumption (allocation weight 1.5), natural gas consumption (allocation weight 1.2); consumption indicators: power generation (allocation weight 1.0); conversion indicators: thermal efficiency (allocation weight 0.8); equipment indicators: equipment utilization rate (allocation weight 0.9); environmental indicators: temperature (allocation weight 0.7).

[0060] In other implementations, the comprehensive energy index library may be integrated with mandatory monitoring parameters in industry specifications to further screen and eliminate redundant indicators in the comprehensive energy index library.

[0061] A comprehensive energy index library helps better understand and manage the energy efficiency of energy systems. By building a comprehensive energy index library, association rule mining techniques can be used to extract valuable information from complex energy data. This allows the identification and labeling of indicators significantly associated with system energy efficiency fluctuations, and the construction of a comprehensive index library that reflects the key performance indicators of energy systems and provides support for energy management and decision-making.

[0062] In a specific example, the comprehensive energy index library includes energy supply indicators, energy consumption indicators, energy conversion indicators, equipment operation indicators, and environmental indicators. Among them, energy supply indicators include energy production, energy supply stability (comprehensively considering key factors such as the frequency of energy supply interruptions (times / month), the average duration of interruptions, and the proportion of the affected range), energy self-sufficiency rate (the percentage of local energy production to total energy consumption), etc.; energy consumption indicators include energy consumption (the amount of different energy types consumed at different time granularities, in different regions (administrative regions, functional areas, etc.) and for different user types (residential, commercial, industrial, etc.)), energy consumption structure (the proportion of various energy types in total energy consumption), energy consumption elasticity coefficient (the ratio of energy consumption growth rate to economic growth rate), etc.; energy conversion indicators include energy conversion efficiency (cogeneration units, transformers, gas turbines, etc.). For different energy conversion equipment, the ratio of effective energy after conversion to input energy before conversion), energy conversion loss rate (the ratio of energy loss in the energy conversion process to total input energy), etc.; equipment operation indicators include equipment failure rate (the ratio of the number of equipment failures to the total equipment operation time), equipment mean time between failures (the average operating time of the equipment between two adjacent failures), equipment maintenance costs (regular maintenance costs (materials, labor, etc.), fault repair costs and equipment renewal and transformation shared costs, etc.); environmental indicators include carbon emission intensity (carbon dioxide emissions generated per unit of energy consumption), pollutant emission indicators (monitoring the emissions and emission concentrations of pollutants such as sulfur dioxide, nitrogen oxides, and particulate matter), etc.

[0063] By building a comprehensive energy indicator library, we initially screened out assessment features relevant to the operation of the integrated energy system from a large amount of operational data. This initial screening reduced the computational effort required for subsequent core feature analysis, improving the efficiency of the assessment process while ensuring the accuracy of the integrated energy system assessment.

[0064] S103: Use principal component analysis to analyze the evaluation features and provide core features.

[0065] Specifically, refer to Figure 2 ,include: Analyze the linear correlation between each evaluation feature and construct the covariance matrix of the evaluation features; The covariance matrix is decomposed to give multiple eigenvalues; According to the variance explanation ratio of each eigenvalue and the preset explanation threshold, multiple eigenvalues are screened to obtain the core features.

[0066] Further, refer to Figure 3 , construct the covariance matrix of the evaluation features, including: According to the elements in each evaluation feature and the feature mean of the corresponding evaluation feature, the deviation feature of each evaluation feature is given; Based on the deviation characteristics of any two evaluation features, the mean of the product of the two deviation characteristics is given to obtain the degree of linear correlation between the evaluation features; The linear correlation degree between each evaluation feature is integrated to construct the covariance matrix of the evaluation features.

[0067] The degree of linear correlation between each evaluation feature is specifically expressed as: ; in, is the linear correlation degree between the u-th evaluation feature and the v-th evaluation feature, m is the number of samples, is the kth eigenvalue in the uth evaluation feature, is the feature mean of the u-th evaluation feature, is the kth eigenvalue in the vth evaluation feature, is the feature mean of the vth evaluation feature.

[0068] Deviation feature refers to the matrix composed of the difference between each eigenvalue and the feature mean in the evaluation feature.

[0069] The covariance matrix is specifically expressed as: ; Where C is the covariance matrix, is the matrix composed of standardized evaluation features, m is the number of samples, for Transpose the matrix.

[0070] Furthermore, multiple eigenvalues are specifically expressed as: ; Where C is the covariance matrix of the evaluation features, is a diagonal matrix, and each element on the diagonal of the diagonal matrix is the eigenvalue. is the identity matrix, is the transposed matrix of V, V is the eigenvector matrix, and the elements in the eigenvector matrix are the eigenvectors corresponding to the eigenvalues.

[0071] In a specific embodiment, the covariance matrix C is subjected to eigendecomposition to obtain the eigenvalues and the corresponding eigenvector The eigendecomposition is specifically expressed as ,in, is a diagonal matrix whose diagonal elements are eigenvalues , and arranged in descending order. V is the eigenvector The matrix composed of .

[0072] Further, refer to Figure 4 , according to the variance explanation ratio of each eigenvalue and the preset explanation threshold, multiple eigenvalues are screened to obtain the core features, including: According to the variance explanation ratio of each eigenvalue, the cumulative variance explanation ratio corresponding to each eigenvalue is given; Compare the cumulative variance explanation ratio corresponding to each eigenvalue with the preset explanation threshold to obtain the comparison result; According to the comparison results, the eigenvalues are screened and the eigenvectors corresponding to the screened eigenvalues are given; The standard data matrix is projected into a vector matrix composed of eigenvectors corresponding to the filtered eigenvalues to obtain the core features.

[0073] In a specific implementation, the eigenvalues are arranged in descending order, and the core features are selected based on the cumulative variance explained ratio.

[0074] Calculate the cumulative proportion of variance explained for each eigenvalue: ; in, is the cumulative variance explained by the k-th eigenvalue, is the proportion of variance explained by the i-th eigenvalue, is the i-th eigenvalue, and m is the number of eigenvalues.

[0075] In a specific example, the preset interpretation threshold is T, and the selected The minimum value k is used as the number of principal components. After selecting k principal components, the standardized data matrix Projection to the matrix consisting of the first k eigenvectors On the above, we get the core feature Y, which is specifically expressed as: ; in, is composed of the first k eigenvectors Matrix, the core feature Y is The matrix of the core feature Y represents the score of the i-th sample on the j-th principal component.

[0076] S104: Determine the target evaluation feature based on the similarity between each evaluation feature and the core feature and give the evaluation weight corresponding to the target evaluation feature.

[0077] Specifically, refer to Figure 5 ,include: Based on the similarity between the evaluation features and the core features, combined with the preset similarity range, the evaluation features are screened to give the target evaluation features; Combined with the information content of the target evaluation feature, the evaluation weight corresponding to the target evaluation feature is given.

[0078] In the embodiment provided by the present invention, the similarity between each evaluation feature and the core feature is represented by the Pearson correlation coefficient. In other embodiments, other methods can be used to determine the similarity between each evaluation feature and the core feature, which is not limited to this.

[0079] The similarity between each evaluation feature and the core feature is specifically expressed as: ; in, To evaluate the similarity between feature p and core feature q, is the eigenvalue of the evaluation feature p in the I-th sample, is the feature mean of the evaluation feature p, is the eigenvalue of the core feature q of the I-th sample, is the feature mean of the core feature q, and n is the number of samples.

[0080] Pearson correlation coefficient The value range is between [-1, 1]. The closer the absolute value is to 1, the stronger the correlation between the evaluation feature p and the core feature q. A positive sign indicates a positive correlation, and a negative sign indicates a negative correlation.

[0081] In the embodiment provided by the present invention, by setting a similarity range, it is determined whether the similarity is within the preset similarity range. If so, the evaluation feature is used as the target evaluation feature. Otherwise, if the similarity is not within the preset similarity range, the evaluation feature is eliminated and not used as the target evaluation feature for evaluating the integrated energy system. In a specific example, the similarity range is 0.5-0.7, and the absolute value of the similarity is screened out. Evaluation characteristics within a preset similarity range. The target evaluation characteristics obtained after screening are closely related to the core characteristics, which can deeply reflect the key characteristics and operational essence of the integrated energy system, effectively avoid interference from irrelevant or weakly related indicators, significantly improve the pertinence and effectiveness of the evaluation, and ensure that the evaluation focuses on core elements and accurately understands system performance.

[0082] It is understood that traditional evaluation methods are highly subjective in their indicator selection, generally selecting common, unified indicators, making it difficult to accurately screen out the core characteristics that truly reflect the operational essence of integrated energy systems. This invention utilizes data mining, statistical analysis, and indicator correlation analysis techniques, combining principal component analysis with correlation analysis to screen out key indicators with high correlation (i.e., target evaluation characteristics). This avoids the subjectivity and one-sidedness of traditional methods, accurately reflects the core characteristics and operational essence of integrated energy systems, and ensures the scientific and targeted nature of the evaluation indicator system.

[0083] Further, refer to Figure 6 , giving the evaluation weights corresponding to the target evaluation features, including: According to the value of each element in the target evaluation feature, the information content of the target evaluation feature is given; Combined with the information content of the target evaluation features, the degree of variation of the target evaluation features is analyzed; Based on the degree of variation of all target evaluation features and combined with the degree of variation of the target evaluation features, the evaluation weight corresponding to the target evaluation features is given.

[0084] Furthermore, the evaluation weight corresponding to the target evaluation feature is specifically expressed as: ; in, is the evaluation weight corresponding to the j-th target evaluation feature, is the information content of the jth target feature weight, m is the number of target evaluation features, Evaluate the degree of variation of features for the target.

[0085] In the embodiment provided by the present invention, the entropy weight method is used to set the evaluation weights. The information entropy of each target evaluation feature is calculated to reflect the information amount of each target evaluation feature. The weights are allocated entirely based on the intrinsic information characteristics of the data to ensure the objectivity and scientificity of the weight setting.

[0086] In a specific embodiment, for the target evaluation feature j, the information entropy is calculated, which is specifically expressed as: ; in, is the information entropy corresponding to the target evaluation feature j, n is the number of eigenvalues of the target evaluation feature j, The target evaluation feature j is taken as The probability of Evaluate the eigenvalue of feature j for the target in the i-th sample, and ln is the natural logarithm function.

[0087] Information entropy reflects the amount of information contained in the target evaluation feature. The larger the information entropy of the target evaluation feature, the smaller the degree of variation of the target evaluation feature, the less information it provides, and the smaller its role in the comprehensive evaluation. Conversely, the smaller the information entropy of the target evaluation feature, the greater the degree of variation of the target evaluation feature, the more information it provides, and the greater the weight should be.

[0088] The evaluation weights are determined by the information entropy of the target evaluation characteristics, so that the weight distribution is completely based on the information characteristics of the data itself, avoiding the interference of human factors, ensuring the objectivity and scientific nature of the weight setting, and making the evaluation results more convincing and reliable.

[0089] S105: Based on the target evaluation characteristics of each subsystem in the integrated energy system, the evaluation weights corresponding to the target evaluation characteristics are integrated to provide the operation evaluation data of the integrated energy system.

[0090] Specifically, based on the target evaluation characteristics of each subsystem in the integrated energy system, characteristic values in the target evaluation characteristics are quantified and / or standardized to obtain standard characteristic values; The evaluation weights corresponding to the target evaluation features are integrated with the standard feature values to give the operation evaluation data.

[0091] Run the evaluation data, specifically expressed as: ; Among them, S is the operation evaluation data of the integrated energy system, is the evaluation weight of target evaluation feature i, is the standard feature value of target evaluation feature i, and k is the number of target evaluation features.

[0092] In one specific embodiment, the selected target evaluation features are precisely quantified based on their respective characteristics and industry standards. For example, the target evaluation feature for energy supply stability can be derived by calculating factors such as the number of energy supply interruptions within a certain time period, their duration, and the proportion of affected users. The target evaluation feature for energy conversion efficiency can be determined based on the ratio of the actual converted effective energy to the input energy of different energy conversion devices (such as cogeneration units and gas turbines).

[0093] After completing the quantification of the target evaluation features, the min-max normalization method is used to standardize the quantified target evaluation features to obtain the standard feature values.

[0094] The standard eigenvalue is specifically expressed as: ; in, is the standard eigenvalue, x is the original eigenvalue or the quantized original eigenvalue, The minimum value of the target evaluation feature, The maximum value of the target evaluation feature, After standardization, The value range is [0,1].

[0095] The assessment weight associated with each target assessment feature reflects its relative importance in the evaluation of the integrated energy system. For example, in an energy system focused on renewable energy development, the renewable energy output ratio might be given a higher weight. By combining the information from each target assessment feature into a single assessment score, this weighted summation provides a clear picture of the overall performance of the integrated energy system.

[0096] Based on the operational evaluation data, the performance of the integrated energy system is categorized into different grades. For example, grades such as excellent (operational evaluation data between 0.8-1), good (operational evaluation data between 0.6-0.8), average (operational evaluation data between 0.4-0.6), poor (operational evaluation data between 0.2-0.4), and bad (operational evaluation data between 0-0.2) can be set. This grading helps to intuitively understand the relative position and performance level of the integrated energy system among similar systems. The grading can be adjusted based on industry averages, performance data of leading benchmark systems, and relevant policies, standards, and regulations, and is not limited to this.

[0097] Based on the performance levels corresponding to operational evaluation data, specific decision-making recommendations are provided for the optimization and development of integrated energy systems. Regarding energy supply, if supply stability is insufficient, consideration can be given to increasing backup energy supply equipment, optimizing energy scheduling strategies, or strengthening interconnection with surrounding energy systems to improve supply reliability. Regarding energy consumption, if the energy consumption structure is irrational and overly reliant on a single highly polluting or high-cost energy source, policies can be formulated to encourage the transition to clean energy, such as promoting energy-saving equipment and implementing energy subsidy policies. Regarding equipment operation, if the equipment failure rate is high, it is recommended to increase investment in equipment maintenance, establish a full lifecycle management system for equipment, and plan equipment upgrades in advance. Regarding environmental indicators, if carbon emissions exceed standards, measures such as promoting the construction of renewable energy projects and implementing carbon capture and storage technology pilots can be implemented to reduce the system's environmental impact and achieve the sustainable development of the integrated energy system.

[0098] The present invention provides an operation evaluation method for an integrated energy system, which specifically includes: obtaining operation data of each subsystem in the integrated energy system; based on a pre-built integrated energy index library, screening corresponding evaluation features from the operation data of each subsystem; using principal component analysis to analyze the evaluation features and provide core features; based on the similarity between each evaluation feature and the core feature, determining a target evaluation feature and providing an evaluation weight corresponding to the target evaluation feature; based on the target evaluation features of each subsystem in the integrated energy system, integrating the evaluation weights corresponding to the target evaluation features to provide operation evaluation data of the integrated energy system. By collecting the operation data of the integrated energy system, using the principal component analysis algorithm to extract features from the data, performing correlation analysis on the extracted features with the pre-built integrated energy index library, selecting target evaluation features with high correlation, and then setting the weights of the target evaluation features based on the information entropy of the features, the operation evaluation data of the integrated energy system is finally obtained, providing a scientific basis for the management and decision-making of the integrated energy system.

[0099] Reference Figure 7 , an embodiment of the present invention provides an operation evaluation device for an integrated energy system, comprising: The data acquisition module 201 is used to obtain the operating data of each subsystem in the integrated energy system; The feature screening module 202 is used to screen corresponding evaluation features from the operating data of each subsystem based on the pre-built comprehensive energy index library; The core determination module 203 is used to analyze the evaluation features using principal component analysis to obtain core features; The weight determination module 204 is used to determine the target evaluation feature and give the evaluation weight corresponding to the target evaluation feature based on the similarity between each evaluation feature and the core feature; The operation evaluation module 205 provides operation evaluation data of the integrated energy system based on the target evaluation characteristics of each subsystem in the integrated energy system and the evaluation weights corresponding to the target evaluation characteristics.

[0100] In addition, the present invention also provides a management and control system for an integrated energy system, comprising a memory, a processor, and a computer program stored in the memory. When the computer program is run by the processor, it executes instructions according to the above method.

[0101] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0102] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0103] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0104] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0105] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of code, which contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can be implemented using a dedicated hardware-based system that performs the specified functions or operations, or can be implemented using a combination of dedicated hardware and computer instructions.

[0106] The units described in the embodiments of the present disclosure may be implemented in software or hardware, wherein the name of a unit does not necessarily limit the unit itself.

[0107] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, the present invention is intended to include such changes and modifications as fall within the scope of the claims and their equivalents.

Claims

1. A method for evaluating the operation of an integrated energy system, characterized in that: include: Obtain operating data of each subsystem in the integrated energy system; Based on the pre-built comprehensive energy indicator library, the corresponding evaluation features are screened from the operating data of each subsystem; The principal component analysis method is used to analyze the evaluation characteristics and give the core characteristics; According to the similarity between each evaluation feature and the core feature, the target evaluation feature is determined and the corresponding evaluation weight of the target evaluation feature is given; Based on the target evaluation characteristics of each subsystem in the integrated energy system and the evaluation weights corresponding to the target evaluation characteristics, the operation evaluation data of the integrated energy system is given.

2. The operation evaluation method of the integrated energy system according to claim 1, characterized in that: Obtain operating data of each subsystem in the integrated energy system, including: Obtaining initial operational data of the integrated energy system; Replace or delete outliers in the initial operation data to complete data cleaning of the initial operation data; The initial operation data after data cleaning is standardized to obtain operation data.

3. The operation evaluation method of the integrated energy system according to claim 2, characterized in that: Replace or delete outliers in the initial running data to complete data cleaning of the initial running data, including: Obtain historical operation data corresponding to the initial operation data; Based on the historical operation data, the standard deviation and mean of the historical operation data are obtained, and the normal fluctuation range corresponding to the initial operation data is given; According to the normal fluctuation range, outliers in the initial operation data are identified and replaced or deleted to complete the data cleaning of the initial operation data.

4. The operation evaluation method of the integrated energy system according to claim 1, characterized in that: The comprehensive energy index database includes supply indicators, consumption indicators, conversion indicators, equipment indicators and environmental indicators; The pre-construction of the comprehensive energy indicator library is determined by the following steps: Obtain historical operating data of the integrated energy system, where the operating data is multi-parameter data, and the parameter dimension of each parameter is one of the monitoring data of each subsystem, equipment operation logs, energy conversion records, and environmental monitoring data; Normalize historical operation data and build an operation database with a multi-layer index structure. The multi-layer index includes timestamps, subsystems, and devices within subsystems. By mining patterns for each parameter dimension in the operation database, key parameters in each parameter dimension are identified and determined as initial indicators, forming an initial indicator set for each parameter dimension; Screen the initial indicator sets corresponding to the monitoring data of each subsystem and provide initial indicators for supply and consumption; Conduct correlation analysis on the initial indicators of supply and consumption. Based on the proportion of each combination of initial indicators of supply and consumption in the monitoring data of each subsystem, obtain the indicator correlation network of supply and consumption, and give the supply indicators and consumption indicators based on the correlation conditions. Based on the supply and consumption indicators, the initial indicator set of each parameter dimension is updated to form the indicator set of each parameter dimension; The sliding time window is used to analyze the sensitivity of each indicator in the indicator set. Combined with the dynamic weight allocation mechanism, each indicator is weighted and marked to construct a comprehensive energy indicator library.

5. The operation evaluation method of the integrated energy system according to claim 4, characterized in that: The sliding time window is used to analyze the sensitivity of each indicator in the indicator set. Combined with the dynamic weight allocation mechanism, each indicator is weighted and marked to construct a comprehensive energy indicator library. The specific contents include: Based on the preset sliding step, the time window is moved to analyze the indicator sensitivity of each indicator in each time window to obtain the indicator sensitivity data of each indicator; Substitute the indicator-sensitive data into the pre-built weight adjustment function to obtain the assigned weights of each indicator; Based on the allocated weights of each indicator, each indicator is weighted and marked to construct a comprehensive energy indicator library.

6. The operation evaluation method of the integrated energy system according to claim 1, characterized in that: The principal component analysis method is used to analyze the evaluation characteristics and give the core characteristics, including: Analyze the linear correlation between each evaluation feature and construct the covariance matrix of the evaluation features; The covariance matrix is decomposed to give multiple eigenvalues; According to the variance explanation ratio of each eigenvalue and the preset explanation threshold, multiple eigenvalues are screened to obtain the core features.

7. The operation evaluation method of the integrated energy system according to claim 6, characterized in that: Analyze the degree of linear correlation between each evaluation feature and construct the covariance matrix of the evaluation features, including: According to the elements in each evaluation feature and the feature mean of the corresponding evaluation feature, the deviation feature of each evaluation feature is given; Based on the deviation characteristics of any two evaluation features, the mean of the product of the two deviation characteristics is given to obtain the degree of linear correlation between the evaluation features; The linear correlation degree between each evaluation feature is integrated to construct the covariance matrix of the evaluation features.

8. The operation evaluation method of the integrated energy system according to claim 6, characterized in that: Multiple eigenvalues, specifically expressed as: ； Where C is the covariance matrix of the evaluation features, is a diagonal matrix, and each element on the diagonal of the diagonal matrix is the eigenvalue. is the identity matrix, is the transposed matrix of V, V is the eigenvector matrix, and the elements in the eigenvector matrix are the eigenvectors corresponding to the eigenvalues.

9. The operation evaluation method of the integrated energy system according to claim 6, characterized in that: Based on the variance explanation ratio of each eigenvalue and the preset explanation threshold, multiple eigenvalues are screened to obtain core features, including: According to the variance explanation ratio of each eigenvalue, the cumulative variance explanation ratio corresponding to each eigenvalue is given; Compare the cumulative variance explanation ratio corresponding to each eigenvalue with the preset explanation threshold to obtain the comparison result; According to the comparison results, the eigenvalues are screened and the eigenvectors corresponding to the screened eigenvalues are given; The standard data matrix is projected into a vector matrix composed of eigenvectors corresponding to the filtered eigenvalues to obtain the core features.

10. The operation evaluation method of the integrated energy system according to claim 1, characterized in that: Based on the similarity between each evaluation feature and the core feature, the target evaluation feature is determined and the corresponding evaluation weight of the target evaluation feature is given, including: Based on the similarity between the evaluation features and the core features, combined with the preset similarity range, the evaluation features are screened to give the target evaluation features; Combined with the information content of the target evaluation feature, the evaluation weight corresponding to the target evaluation feature is given.

11. The operation evaluation method of the integrated energy system according to claim 10, characterized in that: Combined with the information content of the target evaluation features, the evaluation weights corresponding to the target evaluation features are given, including: According to the value of each element in the target evaluation feature, the information content of the target evaluation feature is given; Combined with the information content of the target evaluation features, the degree of variation of the target evaluation features is analyzed; Based on the degree of variation of all target evaluation features and combined with the degree of variation of the target evaluation features, the evaluation weight corresponding to the target evaluation features is given.

12. The operation evaluation method of the integrated energy system according to claim 11, characterized in that: The evaluation weight corresponding to the target evaluation feature is specifically expressed as: ; in, is the evaluation weight corresponding to the j-th target evaluation feature, is the information content of the j-th target feature weight, and m is the number of target evaluation features.

13. The operation evaluation method of the integrated energy system according to claim 1, characterized in that: Based on the target evaluation characteristics of each subsystem in the integrated energy system and the evaluation weights corresponding to the target evaluation characteristics, the operation evaluation data of the integrated energy system is given, including: Based on the target evaluation characteristics of each subsystem in the integrated energy system, quantifying and / or standardizing the characteristic values in the target evaluation characteristics to obtain standard characteristic values; The evaluation weights corresponding to the fusion target evaluation features are combined with the standard eigenvalues for weighted summation to give the operation evaluation data.

14. An operation evaluation device for an integrated energy system, characterized in that: The operation evaluation method of the integrated energy system according to any one of claims 1 to 13 comprises: Data acquisition module, used to obtain the operating data of each subsystem in the integrated energy system; The feature screening module is used to screen out corresponding evaluation features from the operating data of each subsystem based on the pre-built comprehensive energy indicator library; The core determination module is used to analyze the evaluation features using principal component analysis and give the core features; The weight determination module is used to determine the target evaluation feature and give the evaluation weight corresponding to the target evaluation feature based on the similarity between each evaluation feature and the core feature; The operation evaluation module is based on the target evaluation characteristics of each subsystem in the integrated energy system, integrates the evaluation weights corresponding to the target evaluation characteristics, and provides the operation evaluation data of the integrated energy system.

15. A management and control system for an integrated energy system, comprising a memory, a processor, and a computer program stored in the memory, characterized in that: When the computer program is executed by a processor, the computer program executes the instructions of the method according to any one of claims 1 to 13.

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