Multi-source sensor data fusion system for switch cabinet energy optimization

Abstract

The invention discloses a switch cabinet energy optimization-oriented multi-source sensor data fusion system and system, and relates to the technical field of switch cabinet optimization, and the system comprises a data acquisition layer which is used for obtaining real-time information data from different types of sensors; the data fusion layer is used for preprocessing and fusing the information data from the data uptake layer to generate fused data with advanced features; the decision engine layer is used for combining an artificial intelligence algorithm with the fusion data to generate an energy-saving suggestion and a high-energy-consumption alarm of the switch cabinet; the data display layer is used for displaying energy consumption analysis and providing an interactive instrument board; wherein the interactive instrument board is used for controlling the operation of the switch cabinet. Data from heterogeneous data sources are fused at a proper data fusion level and processed to generate operable, interpretable, personalized and persuasive suggestions at a proper time, and the user's response to the generated suggestions triggers the actuators to perform respective energy-saving operations.

Description

Technical Field

[0001] The present invention relates to the technical field of switch cabinet optimization, and in particular to a multi-source sensor data fusion system and system for switch cabinet energy optimization. Background Art

[0002] Switchgear plays a key role in distributing and controlling current in the power grid. They usually consume a certain amount of electrical energy to maintain operations, such as cooling systems and monitoring equipment. By optimizing the energy consumption of these systems, energy waste in the entire power grid system can be directly reduced. The energy efficiency framework based on behavioral change involves analyzing energy consumption footprints and related environmental background information to provide end users with tailored recommendations to optimize their energy use to achieve greater economic and ecological benefits.

[0003] The current research focuses on the use of energy-saving smart energy Internet technologies, which play an important role in designing a powerful energy-efficient ecosystem for remote collection and control, i.e. collecting data on indoor and outdoor environmental conditions, equipment energy consumption, end-user habits / activities and preferences. By tailoring different energy-saving measures to the end-user's behavior, about 20% of energy can be saved.

[0004] There are already some energy consumption prediction and optimization frameworks in the field of switchgear. (“Smart Home Energy Prediction and Optimization Based on Weather Metric Weight Coefficients”, Sensors 23, No. 7 (2023): 3640) proposed an optimization technique to solve the trade-off between energy conservation and user convenience in switchgear. In the study, not only temperature and humidity were considered, but also weather indicators such as air pressure, dew point and wind speed were introduced to more effectively optimize the indoor environment settings of the switchgear, thereby reducing energy costs. Although many advances have been made in the development of energy efficiency optimization frameworks in the field of switchgear, the main challenges that hinder the establishment of an effective framework for power equipment are mainly related to the following aspects: There is heterogeneity in data from different types of sensors. How to effectively fuse these data and extract useful information is a problem that needs to be solved. Traditional data processing methods often cannot cope with the complexity of multi-source data fusion. Summary of the invention

[0005] In view of the problems existing in the existing multi-source sensor data fusion and system for switch cabinet energy optimization, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is that it is impossible to effectively solve various types of data.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a multi-source sensor data fusion system for switch cabinet energy optimization, which includes:

[0009] The data ingestion layer is used to obtain real-time information data from different types of sensors;

[0010] The data fusion layer is used to preprocess and fuse the information data from the data ingestion layer to generate fused data with advanced features;

[0011] The decision engine layer combines artificial intelligence algorithms with fused data to generate energy-saving suggestions and high-energy consumption alarms for switchgear;

[0012] Data presentation layer, used to display energy consumption analysis and provide interactive dashboards;

[0013] Wherein, the interactive dashboard is used to control the operation of the switch cabinet.

[0014] As a preferred solution of the multi-source sensor data fusion system for switch cabinet energy optimization of the present invention, wherein: the data fusion layer includes low-level data fusion and mid-level data fusion;

[0015] The low-level data fusion is used to fuse the information data of the sensors to measure the same variable;

[0016] The intermediate data fusion is used to fuse information data from different sensors and extract high-level information from low-level fusion features.

[0017] As a preferred solution of the multi-source sensor data fusion system for switch cabinet energy optimization of the present invention, the operation steps of the low-level data fusion include:

[0018] Determine whether the number of sensors of the same type is greater than the value N. If it is greater than the value N, clean the acquired information data;

[0019] Remove the maximum and minimum values ​​of the information data, and calculate the average value of the remaining information data;

[0020] and calculating the difference between the maximum value and the average value of the information data and the difference between the minimum value and the average value of the information data, and comparing the sizes of the two differences;

[0021] When the difference between the maximum value and the average value is greater than the difference between the minimum value and the average value, the maximum value of the information data is removed and the average value of the remaining information data is calculated;

[0022] When the difference between the maximum value and the average value is less than the difference between the minimum value and the average value, the minimum value of the information data is removed and the average value of the remaining information data is calculated;

[0023] When the difference between the maximum value and the average value is equal to the difference between the minimum value and the average value, the average value of the information data is directly calculated;

[0024] Wherein, the value N≥4;

[0025] The data cleaning includes removing noise, filling missing values ​​and standardizing data formats.

[0026] As a preferred solution of the multi-source sensor data fusion system for switch cabinet energy optimization of the present invention, the operation steps of the intermediate data fusion include:

[0027] Obtaining information data after low-level data fusion, and determining the type of the information data;

[0028] According to the matching information of the preset type, the matching information associated with the type of the information data is obtained, and the type of the matched information data is obtained;

[0029] Determine whether the matched information data type and the acquired information data type are from the same batch. If the two sets of information data are from the same batch, merge the two sets of information data. If they are not from the same batch, it indicates that the information data is missing.

[0030] Among them, the judgment conditions for whether the two sets of information data are from the same batch are:

[0031] Get the calculation time of two sets of information data, and determine whether the time interval is greater than the set value. When the time interval is greater than the set value, it means that the two sets of information data are not from the same batch. When the time interval is not greater than the set value, it means that the two sets of information data are from the same batch.

[0032] As a preferred solution of the multi-source sensor data fusion system for switch cabinet energy optimization described in the present invention, when the information data is fused, based on the historical working status of the switch cabinet, the historical information data closest to the two sets of information data is queried, and the timestamp of the historical information data is obtained, and the operating status or peripheral environment of the switch cabinet at that time is obtained according to the timestamp.

[0033] As a preferred solution of the multi-source sensor data fusion system for switch cabinet energy optimization of the present invention, the operation steps of the decision engine layer include:

[0034] Obtain fused data after fusing two sets of intermediate data, perform high-level fusion on the two sets of fused data, and generate high energy consumption alerts and energy-saving suggestions for each energy optimization use case;

[0035] When generating high energy consumption alerts and energy-saving suggestions for each energy optimization use case, based on the historical working status of the switch cabinet, when one set of fused data is within the set range and other fused data is not within the range, the weight of the fused data is adaptively adjusted;

[0036] The high energy consumption alert and energy saving suggestion are formulated as a time series classification task.

[0037] As a preferred solution of the multi-source sensor data fusion system for switch cabinet energy optimization of the present invention, wherein: 7. When displaying energy consumption analysis, the data display layer will judge the received information, and the specific judgment method is:

[0038] Generate a set table according to the content to be displayed, extract the corresponding information from the database, and fill the information into the set table in sequence;

[0039] Extract the same table from the repository, collect the information of the same table, compare the information of the two sets of tables, and determine whether the difference of the information is less than the error value;

[0040] When it is less than the error value, the corresponding switch cabinet energy usage status information in the integrated storage library is used to give the same energy-saving suggestions and high-energy consumption alarms. Then, the energy-saving suggestions and high-energy consumption alarms are compared with the energy-saving suggestions and high-energy consumption alarms output by the decision engine layer. If the results are the same, the energy-saving suggestions and high-energy consumption alarms are presented. If the results are different, a reminder is issued to the staff, who then confirm the energy-saving suggestions and high-energy consumption alarms for the second time.

[0041] If it is not less than the error value, no judgment is made and the energy-saving suggestions and high energy consumption alarms output by the decision engine layer are directly presented.

[0042] As a preferred solution of the multi-source sensor data fusion system for switch cabinet energy optimization described in the present invention, it also includes a data fusion method, which specifically includes the following steps:

[0043] Through different types of sensors equipped in the switch cabinet, the environment of different granularities can be sensed and monitored, the data information of the switch cabinet can be obtained, and it can be transmitted to the corresponding data pipeline;

[0044] Fusion of switchgear data information, application of different AI algorithms to input data, and acquisition of patterns and insights on energy consumption, generation of energy-saving recommendations and high-energy consumption alerts;

[0045] Present energy analytics of energy consumption and energy bills at the desired granularity using energy saving recommendations and high energy consumption alerts stored in the database.

[0046] In a second aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, it implements any step of the above-mentioned multi-source sensor data fusion system for switch cabinet energy optimization.

[0047] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned multi-source sensor data fusion system for switch cabinet energy optimization is implemented.

[0048] The beneficial effects of the present invention are: data from heterogeneous data sources (environmental sensors, cameras, plug data, etc.) are fused at an appropriate data fusion level and processed to generate actionable, explainable, personalized and persuasive recommendations when appropriate, and user responses to the generated recommendations trigger actuators to perform respective energy-saving operations and provide more personalized future recommendations;

[0049] By initially fusing data from sensors of the same type, we can reduce data redundancy and increase data accuracy. By combining data from different types of sensors, we can extract higher-level information and finally integrate low-level and mid-level fused data to generate comprehensive energy consumption analysis results. After data fusion, we use advanced artificial intelligence algorithms (such as machine learning and deep learning) to analyze the fused data, identify energy consumption patterns, detect anomalies, and predict future energy consumption trends. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0051] Figure 1 Scenario diagram of a multi-source sensor data fusion system for switchgear energy optimization.

[0052] Figure 2 Schematic diagram of the multi-source sensor data fusion system for switchgear energy optimization.

[0053] Figure 3 Schematic diagram of the process of multi-source sensor data fusion system for switchgear energy optimization.

[0054] Figure 4 Schematic diagram of the method of multi-source sensor data fusion system for switchgear energy optimization. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0058] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0059] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0060] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0061] Example 1

[0062] Reference Figure 1 to Figure 3, which is the first embodiment of the present invention, and provides a multi-source sensor data fusion system for switch cabinet energy optimization, including:

[0063] Data ingestion layer, which is used to obtain real-time information data from different types of sensors.

[0064] The data fusion layer is used to preprocess and fuse the information data from the data ingestion layer to generate fused data with advanced features.

[0065] The data fusion layer includes low-level data fusion and mid-level data fusion;

[0066] The low-level data fusion is used to fuse the information data of the sensors to measure the same variable;

[0067] The intermediate data fusion is used to fuse information data from different sensors and extract high-level information from low-level fusion features;

[0068] First, the data of sensors of the same type are initially fused through low-level fusion to reduce data redundancy and increase data accuracy. Then, the data of sensors of different types are combined through intermediate fusion to extract higher-level information.

[0069] The sensor reads multiple temperatures (T 1 ,T 2 ,…,T n )、Light(L 1 ,L 2 ,…,L n ),humidity Sensors are processed through the data fusion layer to obtain fused data, such as fused temperature, fused light, fused humidity and fused CO 2 .

[0070] The operation steps of the low-level data fusion include:

[0071] Determine whether the number of sensors of the same type is greater than the value N. If it is greater than the value N, clean the acquired information data;

[0072] Remove the maximum and minimum values ​​of the information data, and calculate the average value of the remaining information data;

[0073] and calculating the difference between the maximum value and the average value of the information data and the difference between the minimum value and the average value of the information data, and comparing the sizes of the two differences;

[0074] When the difference between the maximum value and the average value is greater than the difference between the minimum value and the average value, the maximum value of the information data is removed and the average value of the remaining information data is calculated;

[0075] When the difference between the maximum value and the average value is less than the difference between the minimum value and the average value, the minimum value of the information data is removed and the average value of the remaining information data is calculated;

[0076] When the difference between the maximum value and the average value is equal to the difference between the minimum value and the average value, the average value of the information data is directly calculated;

[0077] Wherein, the value N≥4;

[0078] The data cleaning includes removing noise, filling missing values ​​and standardizing data formats;

[0079] For example, check whether the number of sensors of the same type is greater than N. In this example, the number of sensors is 5, which is greater than N (N=4), so data cleaning is required to obtain the maximum and minimum values ​​of all information data, where the maximum value is 23.5 and the minimum value is 20.5. After removing the maximum and minimum values, calculate the average value of the remaining information data. The data after removal is: [21.5, 22, 22.5]. The calculated average value = (21.5 + 22.0 + 22.5) / 3 = 22. Calculate the difference between the maximum value and the average value of the information data, and the difference between the minimum value and the average value of the information data, that is, the difference between the maximum value and the average value: 23.5-22.0 = 1.5, the difference between the minimum value and the average value: 22.0-20.5 = 1.5. In this example, the difference between the maximum value and the average value is equal to the difference between the minimum value and the average value (1.5 = 1.5). Therefore, the average value of the information data is directly calculated.

[0080] If the difference between the maximum value and the average value is greater than the difference between the minimum value and the average value, the maximum value: 23.5 is deleted, and then the average values ​​of the remaining four sets of data are calculated and the calculated average value is output.

[0081] The operation steps of the intermediate data fusion include:

[0082] Obtaining information data after low-level data fusion, and determining the type of the information data;

[0083] According to the matching information of the preset type, the matching information associated with the type of the information data is obtained, and the type of the matched information data is obtained;

[0084] Determine whether the matched information data type and the acquired information data type are from the same batch. If the two sets of information data are from the same batch, merge the two sets of information data. If they are not from the same batch, it indicates that the information data is missing.

[0085] Among them, the judgment conditions for whether the two sets of information data are from the same batch are:

[0086] Obtain the calculation time of two sets of information data, and determine whether the time interval is greater than the set value. When the time interval is greater than the set value, it means that the two sets of information data are not from the same batch. When the time interval is not greater than the set value, it means that the two sets of information data are from the same batch.

[0087] For example,

[0088] Temperature data after low-level data fusion: (timestamp, 22:10);

[0089] Humidity data after low-level data fusion: (timestamp, 15:20);

[0090] The temperature data and humidity data are stored as lists respectively. In this example, the temperature data and humidity data are preset matching types. The judgment condition is: the time interval is not greater than the set value (such as 10 minutes). When the timestamps match, the temperature and humidity data are merged into the same record. The timestamps of the temperature data and humidity data in this example do not meet the judgment conditions, which means that the two sets of information data are not from the same batch and will not be merged.

[0091] When the information data is merged, based on the historical working status of the switch cabinet, the historical information data closest to the two sets of information data is queried, and the timestamp of the historical information data is obtained, and the operating status or peripheral environment of the switch cabinet at that time is obtained according to the timestamp.

[0092] The decision engine layer is used to combine artificial intelligence algorithms with fused data to generate energy-saving suggestions and high-energy consumption alarms for switch cabinets.

[0093] The operation steps of the decision engine layer include:

[0094] Obtain fused data after fusing two sets of intermediate data, perform high-level fusion on the two sets of fused data, and generate high energy consumption alerts and energy-saving suggestions for each energy optimization use case;

[0095] When generating high energy consumption alerts and energy-saving suggestions for each energy optimization use case, based on the historical working status of the switch cabinet, when one set of fused data is within the set range and other fused data is not within the range, the weight of the fused data is adaptively adjusted;

[0096] The high energy consumption alert and energy saving suggestion are formulated as a time series classification task.

[0097] Data presentation layer, used to display energy consumption analysis and provide interactive dashboards.

[0098] When displaying energy consumption analysis, the data display layer will judge the received information. The specific judgment method is:

[0099] Generate a set table according to the content to be displayed, extract the corresponding information from the database, and fill the information into the set table in sequence;

[0100] Extract the same table from the repository, collect the information of the same table, compare the information of the two sets of tables, and determine whether the difference of the information is less than the error value;

[0101] When it is less than the error value, the corresponding switch cabinet energy usage status information in the integrated storage library is used to give the same energy-saving suggestions and high-energy consumption alarms. Then, the energy-saving suggestions and high-energy consumption alarms are compared with the energy-saving suggestions and high-energy consumption alarms output by the decision engine layer. If the results are the same, the energy-saving suggestions and high-energy consumption alarms are presented. If the results are different, a reminder is issued to the staff, who then confirm the energy-saving suggestions and high-energy consumption alarms for the second time.

[0102] If the value is not less than the error value, no judgment is made and the energy-saving suggestions and high energy consumption alarms output by the decision engine layer are directly presented;

[0103] For example, current power consumption data: "time": 2024-07-24 10:00:00, energy: 150.0;

[0104] Historical power consumption data: "time": 2024-07-23 10:00:00, energy: 145.0;

[0105] Set an error threshold, such as 5.0, and check whether all differences are less than this error threshold. When the difference is less than the error value, comprehensively analyze the corresponding switch cabinet energy usage status information in the repository, generate energy-saving suggestions and high-energy consumption alarms, such as "turn off unnecessary equipment" and "the current energy consumption is too high, please check the equipment", obtain the energy-saving suggestions and high-energy consumption alarms output by the decision engine layer, compare the results of the comprehensive analysis with the output of the decision engine layer, and if the results are consistent, directly present the energy-saving suggestions and high-energy consumption alarms, that is, display the energy-saving suggestions and high-energy consumption alarms: "turn off unnecessary equipment" and "the current energy consumption is too high, please check the equipment".

[0106] Wherein, the interactive dashboard is used to control the operation of the switch cabinet.

[0107] In summary, data from heterogeneous data sources (environmental sensors, cameras, plug data, etc.) are fused at the appropriate data fusion level and processed to generate actionable, explainable, personalized and persuasive recommendations when appropriate. User responses to the generated recommendations trigger actuators to perform their respective energy-saving actions and provide more personalized future recommendations.

[0108] By initially fusing data from sensors of the same type, we can reduce data redundancy and increase data accuracy. By combining data from different types of sensors, we can extract higher-level information and finally integrate low-level and mid-level fused data to generate comprehensive energy consumption analysis results. After data fusion, we use advanced artificial intelligence algorithms (such as machine learning and deep learning) to analyze the fused data, identify energy consumption patterns, detect anomalies, and predict future energy consumption trends.

[0109] Example 2

[0110] Reference Figure 4 Based on the first embodiment, this embodiment further provides a multi-source sensor data fusion system for switch cabinet energy optimization, including a data fusion method, which specifically includes the following steps:

[0111] Through different types of sensors equipped in the switch cabinet, the environment of different granularities can be sensed and monitored, the data information of the switch cabinet can be obtained, and it can be transmitted to the corresponding data pipeline;

[0112] Fusion of switchgear data information, application of different AI algorithms to input data, and acquisition of patterns and insights on energy consumption, generation of energy-saving recommendations and high-energy consumption alerts;

[0113] Present energy analytics of energy consumption and energy bills at the desired granularity using energy saving recommendations and high energy consumption alerts stored in the database.

[0114] This embodiment also provides a computer device, which is suitable for a multi-source sensor data fusion system for switch cabinet energy optimization, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the multi-source sensor data fusion system for switch cabinet energy optimization as proposed in the above embodiment.

[0115] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0116] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a multi-source sensor data fusion system for switch cabinet energy optimization as proposed in the above embodiment is implemented.

[0117] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0118] Example 3

[0119] On the basis of the previous two embodiments, this embodiment provides a multi-source sensor data fusion system for switch cabinet energy optimization. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0120] Data generation: Generate simulated sensor data, including environmental sensors (temperature, humidity, etc.), electrical quantity sensors (voltage, current, etc.), and camera data. Each sensor data contains multiple measurement values.

[0121] Experimental data table

[0122]

[0123]

[0124] Through data cleaning in low-level data fusion, the accuracy of the data can be effectively improved, and through the setting of intermediate data fusion, the accuracy of energy consumption analysis can be increased. In addition, comprehensive analysis and artificial intelligence algorithms ensure the improvement of anomaly detection rate, and based on historical data, it can make a prediction of future energy consumption.

[0125] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the present invention.

Claims

1. A multi-source sensor data fusion system for switch cabinet energy optimization, characterized by: include, The data ingestion layer is used to obtain real-time information data from different types of sensors; The data fusion layer is used to preprocess and fuse the information data from the data ingestion layer to generate fused data with advanced features; The decision engine layer combines artificial intelligence algorithms with fused data to generate energy-saving suggestions and high-energy consumption alarms for switchgear; Data presentation layer, used to display energy consumption analysis and provide interactive dashboards; Wherein, the interactive dashboard is used to control the operation of the switch cabinet.

2. The multi-source sensor data fusion system for switch cabinet energy optimization according to claim 1, characterized in that: The data fusion layer includes low-level data fusion and mid-level data fusion; The low-level data fusion is used to fuse the information data of the sensors to measure the same variable; The intermediate data fusion is used to fuse information data from different sensors and extract high-level information from low-level fusion features.

3. The multi-source sensor data fusion system for switch cabinet energy optimization according to claim 2, characterized in that: The operation steps of the low-level data fusion include: Determine whether the number of sensors of the same type is greater than the value N. If it is greater than the value N, clean the acquired information data; Remove the maximum and minimum values ​​of the information data, and calculate the average value of the remaining information data; and calculating the difference between the maximum value and the average value of the information data and the difference between the minimum value and the average value of the information data, and comparing the sizes of the two differences; When the difference between the maximum value and the average value is greater than the difference between the minimum value and the average value, the maximum value of the information data is removed and the average value of the remaining information data is calculated; When the difference between the maximum value and the average value is less than the difference between the minimum value and the average value, the minimum value of the information data is removed and the average value of the remaining information data is calculated; When the difference between the maximum value and the average value is equal to the difference between the minimum value and the average value, the average value of the information data is directly calculated; Wherein, the value N≥4; The data cleaning includes removing noise, filling missing values ​​and standardizing data formats.

4. The multi-source sensor data fusion system for switch cabinet energy optimization according to claim 3, characterized in that: The operation steps of the intermediate data fusion include: Obtaining information data after low-level data fusion, and determining the type of the information data; According to the matching information of the preset type, the matching information associated with the type of the information data is obtained, and the type of the matched information data is obtained; Determine whether the matched information data type and the acquired information data type are from the same batch. If the two sets of information data are from the same batch, merge the two sets of information data. If they are not from the same batch, it indicates that the information data is missing. Among them, the judgment conditions for whether the two sets of information data are from the same batch are: Get the calculation time of two sets of information data, and determine whether the time interval is greater than the set value. When the time interval is greater than the set value, it means that the two sets of information data are not from the same batch. When the time interval is not greater than the set value, it means that the two sets of information data are from the same batch.

5. The multi-source sensor data fusion system for switch cabinet energy optimization according to claim 4, characterized in that: When the information data is merged, based on the historical working status of the switch cabinet, the historical information data closest to the two sets of information data is queried, and the timestamp of the historical information data is obtained, and the operating status or peripheral environment of the switch cabinet at that time is obtained according to the timestamp.

6. The multi-source sensor data fusion system for switch cabinet energy optimization according to claim 5, characterized in that: The operation steps of the decision engine layer include: Obtain fused data after fusing two sets of intermediate data, perform high-level fusion on the two sets of fused data, and generate high energy consumption alerts and energy-saving suggestions for each energy optimization use case; When generating high energy consumption alerts and energy-saving suggestions for each energy optimization use case, based on the historical working status of the switch cabinet, when one set of fused data is within the set range and other fused data is not within the range, the weight of the fused data is adaptively adjusted; The high energy consumption alert and energy saving suggestion are formulated as a time series classification task.

7. The multi-source sensor data fusion system for switch cabinet energy optimization according to claim 6, characterized in that: When displaying energy consumption analysis, the data display layer will judge the received information. The specific judgment method is: Generate a set table according to the content to be displayed, extract the corresponding information from the database, and fill the information into the set table in sequence; Extract the same table from the repository, collect the information of the same table, compare the information of the two sets of tables, and determine whether the difference of the information is less than the error value; When it is less than the error value, the corresponding switch cabinet energy usage status information in the integrated storage library is used to give the same energy-saving suggestions and high-energy consumption alarms. Then, the energy-saving suggestions and high-energy consumption alarms are compared with the energy-saving suggestions and high-energy consumption alarms output by the decision engine layer. If the results are the same, the energy-saving suggestions and high-energy consumption alarms are presented. If the results are different, a reminder is issued to the staff, who then confirm the energy-saving suggestions and high-energy consumption alarms for the second time. If it is not less than the error value, no judgment is made and the energy-saving suggestions and high energy consumption alarms output by the decision engine layer are directly presented.

8. The multi-source sensor data fusion system for switch cabinet energy optimization according to any one of claims 1 to 7, characterized in that: It also includes data fusion methods, specifically The following steps are included: Through different types of sensors equipped in the switch cabinet, the environment of different granularities can be sensed and monitored, the data information of the switch cabinet can be obtained, and it can be transmitted to the corresponding data pipeline; Fusion of switchgear data information, application of different AI algorithms to input data, and acquisition of patterns and insights on energy consumption, generation of energy-saving recommendations and high-energy consumption alerts; Present energy analytics of energy consumption and energy bills at the desired granularity using energy saving recommendations and high energy consumption alerts stored in the database.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the multi-source sensor data fusion system for switch cabinet energy optimization according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-source sensor data fusion system for switch cabinet energy optimization according to any one of claims 1 to 8 are implemented.

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