New energy station full-time-space multi-dimensional data sensing system based on Internet of Things

Abstract

The invention provides a new energy station full-time-space multi-dimensional data sensing system based on the Internet of Things. The system comprises a data sensing layer, a communication transmission layer, a data processing analysis layer and a user service layer. The data sensing layer is used for collecting multi-dimensional data of a new energy station through a multi-dimensional sensor; the communication transmission layer is used for efficiently and safely transmitting the acquired multi-dimensional data to the subsequent processing layer; the data processing analysis layer is used for storing, sorting and analyzing the multi-dimensional data and generating optimization and decision support information; the user service layer is used for providing visual data monitoring and management functions for users; according to the invention, through full-time-space multi-dimensional data perception and intelligent analysis, accurate monitoring, personalized decision support and efficient management of operation of the new energy station are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy power generation systems, and in particular to a full-time and space multi-dimensional data perception system for new energy stations based on the Internet of Things. Background Art

[0002] With the rapid development of renewable energy power generation technology, wind power, photovoltaic power generation and energy storage technology have gradually become important components of modern power systems; however, due to the intermittent and uncertain nature of renewable energy power generation, how to efficiently manage station operations, conduct in-depth analysis of multi-dimensional data, and provide accurate decision support for different user roles has become a core issue of concern in the industry; existing new energy monitoring and management systems mostly focus on the collection and prediction of overall operating data, but are insufficient in personalized data analysis, matching of user needs and real-time decision support; at the same time, with the development of Internet of Things technology, the demand for real-time communication between devices within the station is increasing, which puts higher requirements on data security, transmission efficiency and edge computing capabilities.

[0003] According to the related disclosed technical solutions, the technology with the publication number CN114977490A proposes a new energy power generation monitoring platform, including: hardware in one zone, hardware in two zones, hardware in three zones and an external network system; hardware in one zone includes a control center and a wind, light and storage power station; the control center is used to collect the operation data of the wind, light and storage power station; hardware in two zones includes a prediction server, a power grid dispatching and trading center, a dispatching data network switch and a workstation; the power grid dispatching and trading center exchanges data with the prediction server through the dispatching data network switch; the workstation is used to perform centralized prediction and monitoring; centralized prediction includes predictions of meteorological resources and output correlations in different time dimensions and different regional ranges; hardware in three zones includes a meteorological server, and the external network system includes a numerical meteorological center; the meteorological server is used to obtain meteorological data from the numerical meteorological center. The platform provided by this application fills the gap in the prior art, realizes independent power prediction and overall regional prediction of wind, light and storage power stations, has better monitoring performance and a wider range of application; however, the solution focuses on the power prediction and overall regional monitoring of wind, light and storage power stations, lacks personalized support for user roles, and cannot provide customized data provision, analysis and optimization suggestions according to the responsibilities of different users. Summary of the invention

[0004] The purpose of the present invention is to propose a full-time and space multi-dimensional data perception system for new energy stations based on the Internet of Things in response to the current shortcomings.

[0005] The present invention adopts the following technical solution:

[0006] A full-time and space multidimensional data perception system for new energy stations based on the Internet of Things, the system comprising a data perception layer, a communication transmission layer, a data processing and analysis layer and a user service layer; the data perception layer is used to collect multidimensional data of new energy stations through multidimensional sensors; the communication transmission layer is used to efficiently and securely transmit the collected multidimensional data to a subsequent processing layer; the data processing and analysis layer is used to store, organize and analyze multidimensional data to generate optimization and decision support information; the user service layer is used to provide users with visual data monitoring and management functions.

[0007] The data perception layer includes a multidimensional sensor module and an edge computing module; the multidimensional sensor module includes a plurality of different types of data acquisition sensors for collecting real-time multidimensional data of new energy stations; the edge computing module is used to perform local preprocessing operations on the multidimensional data collected in real time, thereby reducing the amount of redundant data uploaded.

[0008] The communication transmission layer includes a wireless communication module and an encryption authentication module; the wireless communication module is used to transmit the multidimensional data obtained by the data perception layer to the subsequent processing layer through wireless communication; the encryption authentication module is used to provide encryption protection for the data during the data transmission process, and perform identity authentication on the data transmission end and the receiving end to ensure the security of the transmission process and the integrity of the data.

[0009] Furthermore, the data processing and analysis layer includes a data storage module, an association classification module, an intelligent analysis module and a decision support module; the data storage module is used to format and store multidimensional data; the association classification module is used to classify and sort the stored multidimensional data according to the role requirements of different users; the intelligent analysis module is used to combine the multidimensional data and the association classification results to provide targeted in-depth analysis and prediction information for users of different roles; the decision support module is used to provide corresponding decision support information for users of different roles based on the results generated by the intelligent analysis module.

[0010] Furthermore, the association classification module completes the classification and sorting processing for each user role and multi-dimensional data in the following manner;

[0011] S11: Obtain data access records for each role user according to the user operation log recorded by the third-party system;

[0012] S12: Establish continuous time windows and obtain the access record matrix of the user role in each time window;

[0013] A={a ij};

[0014] Among them, A is the access record matrix of the user role in a certain time window, a ijis the number of visits to the jth data dimension by the i-th user role within the time window;

[0015] S13: For the access record matrix of the user role in the current time window, an access demand matrix is ​​generated in combination with the user role's attention to each dimension of data:

[0016]

[0017] Among them, S is the access demand matrix of the user role in the current time window, s ij is the access demand intensity of the i-th user role to the j-th data dimension in the current time window, is the access time decay weight of the i-th user role to the j-th data dimension in the current time window, is the attention weight of the i-th user role on the j-th data dimension in the current time window;

[0018] S14: For each user role in the access demand matrix, sort them from large to small according to the access demand intensity of each data dimension. The higher the ranking of the data dimension, the greater the correlation between the data dimension and the user role.

[0019] Furthermore, the intelligent analysis module includes a model analysis unit and an analysis result matching unit; the model analysis unit is used to combine multi-dimensional data to provide in-depth analysis and prediction of indicators, equipment status and abnormal events related to the operation of new energy stations; the analysis result matching unit is used to combine the associated classification results and the output of the model analysis unit to provide each user role with analysis and prediction results related to its needs.

[0020] Furthermore, the model analysis unit includes a shared input layer, a multi-model layer and a result output layer; the shared input layer is used to receive multi-dimensional data, and perform feature processing on the multi-dimensional data to provide a unified feature data input interface for the multi-model layer; the multi-model layer includes multiple different analysis models, each analysis model runs independently for different analysis targets and provides output results corresponding to the analysis targets; the result output layer is used to integrate the output results of the multi-model layer to provide a unified joint output interface.

[0021] Furthermore, each analysis model in the multi-model layer completes the analysis processing of the corresponding analysis target by extracting multiple data dimensions related to its analysis target from the shared input layer; the output result of each analysis model includes the analysis result of the analysis target and the contribution of various data dimensions related to the analysis model to the analysis result.

[0022] Furthermore, the specific workflow of the analysis result matching unit is as follows:

[0023] S21: Obtaining the association classification result of the association classification module, including the access requirement intensity of the user role to each data dimension;

[0024] S22: Obtain the output results of each analysis model in the multi-model layer, including the analysis results and the contribution of various data dimensions related to the analysis model to the analysis results;

[0025] S23: For each user role, obtain the relevant demand evaluation parameters for each analysis result:

[0026] S24: For each user role, sort the analysis results from large to small according to the relevant demand evaluation parameter corresponding to each user role. The analysis results with higher relevant demand evaluation parameters have higher correlation with user needs.

[0027] Furthermore, the decision support module generates optimization suggestions and decision support information related to the responsibilities of each user role by accessing a third-party database and combining the user role type and the analysis results of the intelligent analysis module.

[0028] Furthermore, the user service layer provides users with visual data monitoring functions including:

[0029] Data monitoring function: Display key operating indicators of new energy stations in multi-dimensional data through a visual interface.

[0030] Personalized service for user roles: For each user role type, a customized data management display interface is provided; the content of the data management display interface includes data dimension information sorted by access demand intensity within the scope of the user role authority and analysis results sorted by relevant demand evaluation parameters, as well as optimization establishment and decision support information corresponding to the analysis results.

[0031] Furthermore, the management functions provided by the user service layer for users include comprehensive evaluation of each user's system usage efficiency and work performance to support the management and optimization of user roles.

[0032] The beneficial effects achieved by the present invention are:

[0033] The present invention collects multi-dimensional real-time data of new energy stations through the data perception layer, covering key information such as equipment status, energy output and environmental parameters, and realizes full-time and all-round data monitoring; through the data processing and analysis layer, the association classification module and the intelligent analysis module are used to conduct in-depth analysis of the multi-dimensional data, generate optimization suggestions and prediction information for user roles, and improve the quality of decision-making; through the user service layer, a customized data management interface and optimization suggestions are provided according to the user role type, ensuring that each user can quickly obtain key information related to his or her responsibilities, reducing the interference of irrelevant data, optimizing the user experience, and the streamlined data dimension information is easier for users to analyze and make decisions, meeting the personalized needs of users and improving the work efficiency of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily drawn to scale, but the emphasis is placed on illustrating the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0035] Figure 1 It is a schematic diagram of the overall module of the present invention.

[0036] Figure 2 Schematic diagram of the internal framework of the model analysis unit of the present invention.

[0037] Figure 3 Schematic diagram of the workflow of the association classification module of the present invention.

[0038] Figure 4 This is a schematic diagram of the workflow of the analysis result matching unit of the present invention. DETAILED DESCRIPTION

[0039] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with its embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention; for those skilled in the art, other systems, methods and / or features of the present embodiment will become apparent after reviewing the following detailed description; it is intended that all such additional systems, methods, features and advantages are included in this specification; included within the scope of the present invention and protected by the appended claims; additional features of the disclosed embodiments are described in the following detailed description, and these features will be apparent from the following detailed description.

[0040] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, it is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or component referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limitations on this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0041] The i, j, and k in the embodiments of the present invention are merely index symbols and have no specific physical or practical meanings.

[0042] Embodiment 1:

[0043] like Figure 1 As shown, this embodiment provides a full-time and space multi-dimensional data perception system for new energy stations based on the Internet of Things, the system includes a data perception layer, a communication transmission layer, a data processing and analysis layer and a user service layer; the data perception layer is used to collect multi-dimensional data of new energy stations through multi-dimensional sensors; the communication transmission layer is used to efficiently and securely transmit the collected multi-dimensional data to the subsequent processing layer; the data processing and analysis layer is used to store, organize and analyze the multi-dimensional data to generate optimization and decision support information; the user service layer is used to provide users with visual data monitoring and management functions;

[0044] The data perception layer includes a multidimensional sensor module and an edge computing module; the multidimensional sensor module includes a plurality of different types of data acquisition sensors for collecting real-time multidimensional data of new energy stations; the edge computing module is used to perform local preprocessing operations on the multidimensional data collected in real time, thereby reducing the amount of redundant data uploaded;

[0045] The communication transmission layer includes a wireless communication module and an encryption authentication module; the wireless communication module is used to transmit the multidimensional data acquired by the data perception layer to the subsequent processing layer through wireless communication; the encryption authentication module is used to provide encryption protection for the data during the data transmission process, and perform identity authentication on the data transmission end and the receiving end to ensure the security of the transmission process and the integrity of the data;

[0046] The data processing and analysis layer includes a data storage module, an association classification module, an intelligent analysis module and a decision support module; the data storage module is used to format and store multidimensional data; the association classification module is used to classify and sort the stored multidimensional data according to the role requirements of different users; the intelligent analysis module is used to combine the multidimensional data and the association classification results to provide targeted in-depth analysis and prediction information for users of different roles; the decision support module is used to provide corresponding decision support information for users of different roles based on the results generated by the intelligent analysis module;

[0047] Further, such as Figure 3 As shown, the association classification module completes the classification and sorting processing of each user role and multi-dimensional data in the following manner;

[0048] S11: Obtain data access records for each role user according to the user operation log recorded by the third-party system;

[0049] S12: Establish continuous time windows and obtain the access record matrix of the user role in each time window;

[0050] A={a ij};

[0051] Among them, A is the access record matrix of the user role in a certain time window, a ij is the number of visits to the jth data dimension by the i-th user role within the time window;

[0052] S13: For the access record matrix of the user role in the current time window, an access demand matrix is ​​generated in combination with the user role's attention to each dimension of data:

[0053]

[0054] Among them, S is the access demand matrix of the user role in the current time window, s ij is the access demand intensity of the i-th user role to the j-th data dimension in the current time window, is the access time decay weight of the i-th user role to the j-th data dimension in the current time window, is the attention weight of the i-th user role on the j-th data dimension in the current time window; satisfy:

[0055]

[0056] Among them, t ijis the time interval between the time point when the i-th user role last accessed the j-th data dimension in the current time window and the current time, and λ is a preset time decay factor;

[0057] for satisfy:

[0058]

[0059] Among them, Frep ij is the access attention coefficient of the i-th user role to the j-th data dimension in the current time window. β and ρ are nonlinear adjustment parameters used to control the sensitivity of attention weight to data changes, which are set by pre-experiments. ij satisfy:

[0060]

[0061] Among them, F prev is the access frequency of the i-th user role to the j-th data dimension in the previous time window of the current time window, F vurr is the access frequency of the i-th user role to the j-th data dimension in the current time window;

[0062] S14: For each user role in the access demand matrix, sort them from large to small according to the access demand intensity of each data dimension corresponding to the user role. The higher the ranking of the data dimension, the greater the relevance of the data dimension to the user role.

[0063] Furthermore, some functional implementation codes of the association classification module are as follows:

[0064]

[0065]

[0066]

[0067] New energy stations contain a large amount of dimensional data information, but not all data dimensions are highly relevant to user roles. Directly presenting all data will make it difficult for users to quickly find the required information. For each user role, highly relevant data dimensions are obtained through the above method and sorted and presented to the user. In the sorting process, the focus of different user roles may change over time, which can reduce the interference of irrelevant data and optimize the user experience. The streamlined data dimension information is easier for users to analyze and make decisions, meet the personalized needs of users, and improve the work efficiency of users.

[0068] Embodiment 2:

[0069] This embodiment should be understood to include at least all the features of any of the above embodiments, and further improve upon them;

[0070] This embodiment provides a full-time and space multi-dimensional data perception system for new energy stations based on the Internet of Things, the system includes a data perception layer, a communication transmission layer, a data processing and analysis layer, and a user service layer; the data perception layer is used to collect multi-dimensional data of new energy stations through multi-dimensional sensors; the communication transmission layer is used to efficiently and securely transmit the collected multi-dimensional data to the subsequent processing layer; the data processing and analysis layer is used to store, organize and analyze the multi-dimensional data to generate optimization and decision support information; the user service layer is used to provide users with visual data monitoring and management functions;

[0071] The data processing and analysis layer includes a data storage module, an association classification module, an intelligent analysis module and a decision support module; the data storage module is used to format and store multidimensional data; the association classification module is used to classify and sort the stored multidimensional data according to the role requirements of different users; the intelligent analysis module is used to combine the multidimensional data and the association classification results to provide targeted in-depth analysis and prediction information for users of different roles; the decision support module is used to provide corresponding decision support information for users of different roles based on the results generated by the intelligent analysis module;

[0072] Furthermore, the intelligent analysis module includes a model analysis unit and an analysis result matching unit; the model analysis unit is used to provide in-depth analysis and prediction of indicators, equipment status and abnormal events related to the operation of new energy stations in combination with multi-dimensional data; the analysis result matching unit is used to combine the associated classification results and the output of the model analysis unit to provide each user role with analysis and prediction results related to its needs;

[0073] Further, such as Figure 2 As shown, the model analysis unit includes a shared input layer, a multi-model layer and a result output layer; the shared input layer is used to receive multi-dimensional data, perform feature processing on the multi-dimensional data, and provide a unified feature data input interface for the multi-model layer; the multi-model layer includes a plurality of different analysis models, each of which independently runs for different analysis targets and provides output results corresponding to the analysis targets; the result output layer is used to integrate the output results of the multi-model layer and provide a unified joint output interface;

[0074] Furthermore, each analysis model in the multi-model layer completes the analysis processing of the corresponding analysis target by extracting multiple data dimensions related to its analysis target from the shared input layer; the output result of each analysis model includes the analysis result of the analysis target and the contribution of various data dimensions related to the analysis model to the analysis result;

[0075] Furthermore, the analysis model categories in the multi-model layer include but are not limited to statistical analysis models, deep learning models, and time series analysis models;

[0076] Further, such as Figure 4 As shown, the specific workflow of the analysis result matching unit is as follows:

[0077] S21: Obtaining the association classification result of the association classification module, including the access requirement intensity of the user role to each data dimension;

[0078] S22: Obtain the output results of each analysis model in the multi-model layer, including the analysis results and the contribution of various data dimensions related to the analysis model to the analysis results;

[0079] S23: For each user role, obtain the relevant demand evaluation parameters for each analysis result:

[0080]

[0081] Among them, P ik is the relevant demand evaluation parameter between the i-th user role and the k-th analysis result, m is the total number of data dimensions related to the k-th analysis model, c jk is the contribution of the j-th data dimension to the k-th analysis result; δ is the preset smoothing parameter, which is set according to the pre-experimental setting;

[0082] S24: for each user role, sort the analysis results from large to small according to the relevant demand evaluation parameter corresponding to each user role, and the analysis results with higher relevant demand evaluation parameters have higher relevance to the user demand;

[0083] Furthermore, the decision support module generates optimization suggestions and decision support information related to the responsibilities of each user role by accessing a third-party database and combining the user role type and the analysis results of the intelligent analysis module;

[0084] Furthermore, the user service layer provides users with visual data monitoring functions including:

[0085] Data monitoring function: Display key operating indicators of new energy stations in multi-dimensional data through a visual interface;

[0086] Personalized service for user roles: For each user role type, a customized data management display interface is provided; the content of the data management display interface includes data dimension information sorted by access demand intensity within the scope of the user role authority and analysis results sorted by relevant demand evaluation parameters, as well as optimization establishment and decision support information corresponding to the analysis results;

[0087] Furthermore, the management functions provided by the user service layer include comprehensive evaluation of each user's system usage efficiency and work performance to support the management and optimization of user roles;

[0088] Furthermore, the user service layer specifically completes the evaluation of the user in the following manner:

[0089] S31: Setting a continuous evaluation cycle;

[0090] S32: Obtaining evaluation indicators of the user in the current evaluation period, wherein the evaluation indicators include the frequency of the user using the system in the evaluation period, the number of tasks assigned to the user, and the number of tasks actually processed and completed by the user in the evaluation period;

[0091] S33: Calculate the user's performance evaluation parameters:

[0092]

[0093] Among them, Q is the performance evaluation parameter of the user in the current evaluation cycle, T c is the number of tasks actually completed by the user in the current evaluation period, T a is the number of tasks assigned to the user in the current evaluation period, H is the frequency of the user using the system in the current evaluation period, and H z It is a preset reference value of system usage frequency, used to measure the user's system usage activity;

[0094] Through the management function of the user service layer and combined with the user's performance evaluation parameters, the system can evaluate the user's usage efficiency and work performance in real time, and provide data support and decision-making basis for the management and optimization of user roles; for example, for users with low performance evaluation scores, provide targeted usage training, optimization suggestions and operation guidance for the user's use of the system, help users improve system usage efficiency and task processing capabilities, and thus help users better complete the operation and management tasks of new energy stations.

[0095] The contents disclosed above are only preferred feasible embodiments of the present invention, and do not limit the protection scope of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention specification and drawings are included in the protection scope of the present invention. In addition, the elements therein can be updated as technology develops.

Claims

1. A new energy station full-time and space multi-dimensional data perception system based on the Internet of Things, characterized by: The system includes a data perception layer, a communication transmission layer, a data processing and analysis layer, and a user service layer; the data perception layer is used to collect multidimensional data of new energy stations through multidimensional sensors; the communication transmission layer is used to efficiently and safely transmit the collected multidimensional data to the subsequent processing layer; the data processing and analysis layer is used to store, organize and analyze the multidimensional data to generate optimization and decision support information; the user service layer is used to provide users with visual data monitoring and management functions; The data perception layer includes a multidimensional sensor module and an edge computing module; the multidimensional sensor module includes a plurality of different types of data acquisition sensors for collecting real-time multidimensional data of new energy stations; The edge computing module is used to perform local preprocessing operations on multi-dimensional data collected in real time, thereby reducing the amount of redundant data uploaded; The communication transmission layer includes a wireless communication module and an encryption authentication module; the wireless communication module is used to transmit the multidimensional data obtained by the data perception layer to the subsequent processing layer through wireless communication; the encryption authentication module is used to provide encryption protection for the data during the data transmission process, and perform identity authentication on the data transmission end and the receiving end to ensure the security of the transmission process and the integrity of the data.

2. According to the Internet of Things-based new energy station full-time and space multi-dimensional data perception system according to claim 1, it is characterized in that: The data processing and analysis layer includes a data storage module, an association classification module, an intelligent analysis module and a decision support module; the data storage module is used to format and store multidimensional data; the association classification module is used to classify and sort the stored multidimensional data according to the role requirements of different users; The intelligent analysis module is used to combine multidimensional data and associated classification results to provide targeted in-depth analysis and prediction information for users of different roles; the decision support module is used to provide corresponding decision support information for users of different roles based on the results generated by the intelligent analysis module.

3. According to the Internet of Things-based new energy station full-time and space multi-dimensional data perception system according to claim 2, it is characterized in that: The association classification module completes the classification and sorting processing of each user role and multi-dimensional data in the following manner; S11: Obtain data access records for each role user according to the user operation log recorded by the third-party system; S12: Establish continuous time windows and obtain the access record matrix of the user role in each time window; A={a ij }; Among them, A is the access record matrix of the user role in a certain time window, a ij is the number of visits to the jth data dimension by the i-th user role within the time window; S13: For the access record matrix of the user role in the current time window, an access demand matrix is ​​generated in combination with the user role's attention to each dimension of data: Among them, S is the access demand matrix of the user role in the current time window, s ij is the access demand intensity of the i-th user role to the j-th data dimension in the current time window, is the access time decay weight of the i-th user role to the j-th data dimension in the current time window, is the attention weight of the i-th user role on the j-th data dimension in the current time window; S14: For each user role in the access demand matrix, sort them from large to small according to the access demand intensity of each data dimension. The higher the ranking of the data dimension, the greater the correlation between the data dimension and the user role.

4. According to the Internet of Things-based new energy station full-time and space multi-dimensional data perception system according to claim 3, it is characterized in that: The intelligent analysis module includes a model analysis unit and an analysis result matching unit; the model analysis unit is used to combine multi-dimensional data to provide in-depth analysis and prediction of indicators, equipment status and abnormal events related to the operation of new energy stations; the analysis result matching unit is used to combine the associated classification results and the output of the model analysis unit to provide each user role with analysis and prediction results related to its needs.

5. According to the Internet of Things-based new energy station full-time and space multi-dimensional data perception system according to claim 4, it is characterized in that: The model analysis unit includes a shared input layer, a multi-model layer and a result output layer; the shared input layer is used to receive multi-dimensional data, perform feature processing on the multi-dimensional data, and provide a unified feature data input interface for the multi-model layer; The multi-model layer includes multiple different analysis models, each of which runs independently for different analysis objectives and provides output results corresponding to the analysis objectives; the result output layer is used to integrate the output results of the multi-model layer and provide a unified joint output interface.

6. According to the Internet of Things-based new energy station full-time and space multi-dimensional data perception system according to claim 5, it is characterized in that: Each analysis model in the multi-model layer completes the analysis processing of the corresponding analysis target by extracting multiple data dimensions related to its analysis target from the shared input layer; the output result of each analysis model includes the analysis result of the analysis target and the contribution of various data dimensions related to the analysis model to the analysis result.

7. According to claim 6, a new energy station full-time and space multi-dimensional data perception system based on the Internet of Things is characterized in that: The specific workflow of the analysis result matching unit is as follows: S21: Obtaining the association classification result of the association classification module, including the access requirement intensity of the user role to each data dimension; S22: Obtain the output results of each analysis model in the multi-model layer, including the analysis results and the contribution of various data dimensions related to the analysis model to the analysis results; S23: For each user role, obtain the relevant demand evaluation parameters for each analysis result: S24: For each user role, sort the analysis results from large to small according to the relevant demand evaluation parameter corresponding to each user role. The analysis results with higher relevant demand evaluation parameters have higher correlation with user needs.

8. The full-time and space multi-dimensional data perception system of new energy stations based on the Internet of Things according to claim 7 is characterized in that: The decision support module generates optimization suggestions and decision support information related to the responsibilities of each user role by accessing a third-party database and combining the user role type with the analysis results of the intelligent analysis module.

9. The full-time and space multi-dimensional data perception system of new energy stations based on the Internet of Things according to claim 8 is characterized in that: The user service layer provides users with visual data monitoring functions including: Data monitoring function: Display key operating indicators of new energy stations in multi-dimensional data through a visual interface; Personalized service for user roles: For each user role type, a customized data management display interface is provided; the content of the data management display interface includes data dimension information sorted by access demand intensity within the scope of the user role authority and analysis results sorted by relevant demand evaluation parameters, as well as optimization establishment and decision support information corresponding to the analysis results.

10. The IoT-based new energy station full-time and space multi-dimensional data perception system according to claim 9 is characterized in that: The management functions provided by the user service layer include comprehensive evaluation of each user's system usage efficiency and work performance to support the management and optimization of user roles.

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