Energy station monitoring and early warning method, system, medium and equipment

Abstract

The invention provides an energy station monitoring and early warning method and system, a medium and equipment. A plurality of sensors are adopted to collect monitoring data of a plurality of key points of an energy station; wherein the plurality of sensors comprise a plurality of modes; fusing a plurality of pieces of monitoring data, and calculating to obtain state parameters of the energy station; if the state parameter is greater than a state threshold value, triggering an alarm; wherein the state threshold value is dynamically adjusted according to historical monitoring data and current environment data of the energy station; according to the method, key points are monitored through a plurality of sensors, all monitoring data are fused to obtain global monitoring data of the energy station, state data of the energy station are calculated in combination with all the monitoring data, and an alarm is given when the state data exceed a threshold value, so that the global monitoring performance is improved; and the state threshold value is dynamically adjusted according to historical data, so that the reasonability of the state threshold value is ensured, and the monitoring accuracy is further improved.

Description

Technical Field

[0001] This application relates to the field of energy station monitoring technology, specifically to an energy station monitoring and early warning method, system, medium, and equipment. Background Technology

[0002] The energy station includes a circulating water system, compressed air system, chilled water, pure water, domestic water, and fire protection system. Each system may experience abnormalities or malfunctions during actual use. To ensure the normal operation of the energy station, it is necessary to monitor and regularly inspect each system (especially critical points). Currently, the main monitoring method involves setting up monitoring components (such as sensors) at each critical point to achieve individual monitoring and control. However, such monitoring data is clearly too isolated and lacks global analytical capabilities, and its accuracy needs improvement. To compensate for the limitations of monitoring accuracy, regular maintenance by maintenance personnel is also required. This maintenance is time-consuming, consumes significant manpower, and is prone to human error, making it difficult to guarantee consistency in maintenance. Therefore, a comprehensive and accurate monitoring method for the energy station is needed. Summary of the Invention

[0003] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a method, system, medium, and device for monitoring and early warning of energy stations.

[0004] According to one aspect of this application, an energy station monitoring and early warning method is provided, comprising: using multiple sensors to collect monitoring data of multiple key points of the energy station respectively; wherein the multiple sensors include multiple modes; fusing the multiple monitoring data to calculate the state parameters of the energy station; and triggering an alarm if the state parameters are greater than a state threshold; wherein the state threshold is dynamically adjusted based on the historical monitoring data of the energy station and the current environmental data.

[0005] In one embodiment, the plurality of sensors are respectively disposed at the plurality of key points, and a wireless transmission device is also disposed at each key point. The wireless transmission device is connected to the corresponding sensor and the monitoring platform, and is used to transmit the monitoring data collected by the sensors to the monitoring platform. The wireless transmission device includes an edge calculator. The process of using the plurality of sensors to collect monitoring data of the plurality of key points of the energy station includes: using the plurality of sensors to collect current data of the plurality of key points; using the edge calculator to clean and compress the current data to obtain the monitoring data, and storing the monitoring data.

[0006] In one embodiment, the category of the status parameter is the same as one category of the plurality of monitoring data; the process of fusing the plurality of monitoring data to calculate the status parameter of the energy station includes: based on the monitoring data of the plurality of monitoring data that is different from the category of the status parameter, correcting the monitoring data that is the same as the category of the status parameter to obtain the monitoring parameter.

[0007] In one embodiment, the category of the status parameter is different from the categories of the multiple monitoring data; the process of fusing the multiple monitoring data to calculate the status parameter of the energy station includes: weighting the multiple monitoring data to obtain the status parameter.

[0008] In one embodiment, the energy station monitoring and early warning method further includes: calculating the variance between historical monitoring data for each mode; if there is a mode with a variance greater than a set value, then increasing the weight of the corresponding monitoring data.

[0009] In one embodiment, the adjustment method of the state threshold includes: acquiring historical monitoring data and current environmental data of the energy station after the most recent maintenance; inputting the historical monitoring data and the current environmental data into a trained long short-term memory network to obtain the state threshold.

[0010] In one embodiment, the energy station monitoring and early warning method further includes: fitting a state change curve of the energy station based on historical state parameters and current state parameters of the energy station; predicting a predicted state parameter and a corresponding predicted time of the energy station based on the state change curve; comparing the actual state parameter calculated from the predicted time with the predicted state parameter; and triggering an alarm if the actual state parameter is greater than the predicted state parameter.

[0011] According to another aspect of this application, an energy station monitoring and early warning system is provided, comprising: a monitoring data acquisition module, used to acquire monitoring data of multiple key points of the energy station using multiple sensors; wherein the multiple sensors include multiple modes; a status data calculation module, used to fuse the multiple monitoring data to calculate the status data of the energy station; and an alarm status triggering module, used to trigger an alarm if the status data is greater than a status threshold; wherein the status threshold is dynamically adjusted based on the historical monitoring data and current environmental data of the energy station.

[0012] According to another aspect of this application, a computer-readable storage medium is provided, the storage medium storing a computer program for performing any of the methods described above.

[0013] According to another aspect of this application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; the processor being configured to perform any of the methods described above.

[0014] This application provides a monitoring and early warning method, system, medium, and device for an energy station. It employs multiple sensors to collect monitoring data from multiple key points of the energy station; these sensors include various modes; the monitoring data is fused to calculate the energy station's status parameters; if the status parameters exceed a status threshold, an alarm is triggered; the status threshold is dynamically adjusted based on historical monitoring data and current environmental data of the energy station. In other words, multiple sensors monitor key points, and all monitoring data are fused to obtain global monitoring data for the energy station. The status data of the energy station is calculated by combining all monitoring data, and an alarm is triggered when the status data exceeds a threshold, thereby improving global monitoring effectiveness. Furthermore, the status threshold is dynamically adjusted based on historical data to ensure its rationality, thus further improving monitoring accuracy. Attached Figure Description

[0015] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1 This is a flowchart illustrating an exemplary embodiment of the energy station monitoring and early warning method provided in this application.

[0017] Figure 2 This is a schematic diagram of the structure of an energy station monitoring and early warning system provided in an exemplary embodiment of this application.

[0018] Figure 3 This is a structural diagram of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation

[0019] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0020] Figure 1 This is a flowchart illustrating an exemplary embodiment of the energy station monitoring and early warning method provided in this application. Figure 1 As shown, the energy station monitoring and early warning method includes the following steps: Step 110: Use multiple sensors to collect monitoring data from multiple key points of the energy station.

[0021] The multiple sensors encompass various modes. Specifically, this application can install pressure sensors, temperature sensors, and level sensors at various key points such as circulating water systems, chilled water systems, pure water systems, domestic water systems, and fire protection systems to collect multimodal monitoring data such as water pressure, temperature, and level. Pressure sensors and temperature sensors can also be installed at key points such as compressed air systems to collect monitoring data such as pressure and temperature.

[0022] Step 120: Integrate multiple monitoring data to calculate the status parameters of the energy station.

[0023] After collecting monitoring data from multiple modes, the multimodal monitoring data is fused to calculate state parameters (i.e. evaluation indicators) used to evaluate the state of the energy station, such as circulating water pressure and the liquid level of the domestic water tank.

[0024] Step 130: If the status parameter is greater than the status threshold, an alarm is triggered.

[0025] The status thresholds are dynamically adjusted based on the energy station's historical monitoring data and current environmental data. If the calculated status parameters exceed the current status thresholds, it indicates that one or more systems in the energy station are experiencing an anomaly or malfunction. In this case, an alarm signal (such as an audible and visual signal) is issued to prompt maintenance personnel to perform repairs.

[0026] This application provides a monitoring and early warning method for an energy station. It employs multiple sensors to collect monitoring data from multiple key points within the energy station. These sensors include various modes of operation. The monitoring data is fused to calculate the energy station's status parameters. If the status parameters exceed a status threshold, an alarm is triggered. The status threshold is dynamically adjusted based on historical monitoring data and current environmental data. In other words, multiple sensors monitor key points, and all monitoring data is fused to obtain global monitoring data for the energy station. The status data of the energy station is calculated by combining all monitoring data, and an alarm is triggered when this status data exceeds a threshold. This improves global monitoring effectiveness, and the status threshold is dynamically adjusted based on historical data to ensure its rationality, thereby further improving monitoring accuracy.

[0027] In one embodiment, multiple sensors are respectively set at multiple key points, and wireless transmission devices are also set at the key points. The wireless transmission devices are connected to the corresponding sensors and the monitoring platform. The wireless transmission devices are used to transmit the monitoring data collected by the sensors to the monitoring platform. The wireless transmission devices include an edge calculator. The specific implementation of the above step 110 can be: using multiple sensors to collect current data of multiple key points respectively; using an edge calculator to clean and compress the current data to obtain monitoring data, and storing the monitoring data.

[0028] This application enables wireless transmission of sensor-collected monitoring data to a monitoring platform by installing wireless transmission devices at key points. This allows the platform to aggregate and analyze all monitoring data, achieving global monitoring. Furthermore, this application utilizes an edge calculator at the wireless transmission device to clean the monitoring data collected by the corresponding sensors, improving data accuracy and reducing transmission volume. Simultaneously, the monitoring data is compressed to further reduce data transmission volume, and the data is stored locally for a certain period (e.g., 30 days) to ensure data traceability.

[0029] In one embodiment, the category of the status parameter is the same as one category of multiple monitoring data; the specific implementation of step 120 above may be: based on the monitoring data of different categories from the status parameter among the multiple monitoring data, the monitoring data of the same category as the status parameter is corrected to obtain the monitoring parameter.

[0030] If the status parameter (which can be preset) is one of multiple monitoring data, such as the water pressure at a certain key point, a pressure sensor is also set at that key point to collect the water pressure. Under normal conditions, the water pressure value collected by the pressure sensor should be the most accurate. However, the pressure sensor also suffers some wear and tear during use, which may lead to inaccurate data collection. Therefore, this application uses data from other sensors to correct the water pressure and improve its accuracy. Specifically, this application can define the path where the key point is located as the target path, acquire monitoring data collected by sensors of the same mode on all branch paths of the target path, and comprehensively judge whether the monitoring data on the target path is normal based on the monitoring data on the branch paths. If normal, the data collected by the sensors on the target path is used as the monitoring data of the target path. If abnormal, the monitoring data of the target path is calculated based on the data collected by the sensors on all branches of the target path, and the monitoring data of the target path is verified by the data collected by sensors of other modes on the target path. For example, if the target path is a waterway and the monitoring data of the target path is water pressure, after calculating the water pressure of the target path according to the branch paths of the target path, the water pressure is verified based on the temperature and liquid level data of the target path. If the verification passes, the water pressure is determined to be the monitoring data of the target path; otherwise, an alarm signal is issued.

[0031] In one embodiment, the category of the status parameter is different from the category of multiple monitoring data; the specific implementation of step 120 above can be: weighting multiple monitoring data to obtain the status parameter.

[0032] If the category of the state parameter is different from the category of data collected by all sensors, meaning the state parameter cannot be directly obtained by the sensors, the state parameter for that path can be obtained by weighting the data collected by sensors of different modes along the same path. For example, for the same path, the state parameter can be calculated by weighting the water pressure, temperature, and liquid level data along that path. The weights of each data point differ across different paths (or systems); for example, in a water path, the weight of water pressure data is greater than the weight of liquid level data, and the weight of liquid level data is greater than the weight of temperature data. Preferably, this application can also use data from all branch paths of a certain path to verify the path, thereby improving the accuracy of the data.

[0033] In one embodiment, the above-mentioned energy station monitoring and early warning method may further include: calculating the variance between historical monitoring data of each mode; if there is a mode with a variance greater than a set value, then increasing the weight of the corresponding monitoring data.

[0034] This application determines the degree of change in historical monitoring data by calculating the variance among historical monitoring data under the same mode. When the variance exceeds a set value (indicating significant fluctuations among historical monitoring data), the weight of that monitoring data is increased to enhance its influence on threshold adjustments. For example, if the calculated water pressure monitoring data shows significant fluctuations, it indicates that water pressure, as a criterion for judging the operating status of the energy station, varies considerably. Therefore, the weight of water pressure on the status threshold is increased to enhance its weight in judging the monitoring status. Optionally, this application can calculate the variance of all historical monitoring data under the same mode, for example, calculating the variance separately for the monitoring data of each pressure sensor and then summing them to obtain the total variance.

[0035] Optionally, this application can set a threshold for each key point. If the data collected by the sensor at a single key point exceeds the corresponding threshold, an alarm signal will be issued and the location of the key point will be marked to facilitate maintenance personnel to carry out maintenance.

[0036] In one embodiment, the adjustment of the state threshold can be achieved by: acquiring historical monitoring data and current environmental data of the energy station after the most recent maintenance; inputting the historical monitoring data and current environmental data into the trained long short-term memory network to obtain the state threshold.

[0037] Since the monitoring data after the maintenance or replacement of parts at the energy station will change significantly compared to the historical data, this application can perform a phased clearing of the historical monitoring data after maintenance (i.e., the monitoring data before maintenance is not included in the current historical data for judgment, with the maintenance as the dividing line). Furthermore, this application trains a long short-term memory network to obtain a model that can automatically determine the state threshold based on historical monitoring data and environmental data. Thus, when historical monitoring data (such as pressure, temperature, liquid level, flow rate, etc.) and current environmental data (such as humidity, equipment operating time, etc.) are input, the current optimal state threshold can be generated.

[0038] In one embodiment, the above-mentioned energy station monitoring and early warning method may further include: fitting a state change curve of the energy station based on historical state parameters and current state parameters of the energy station; predicting the predicted state parameters and corresponding prediction time of the energy station based on the state change curve; comparing the actual state parameters calculated from the prediction time with the predicted state parameters; and triggering an alarm if the actual state parameters are greater than the predicted state parameters.

[0039] This application can determine the changing trend of the energy station's status parameters based on historical and current status parameters, and predict the energy station's status parameters and corresponding time for the next cycle (e.g., within the next week) based on this trend. Furthermore, it measures the energy station's status parameters in the next cycle. If the measured actual status parameters are greater than the predicted status parameters, it indicates that the energy station's operating state is worse than the predicted state, and an alarm can be triggered. Preferably, this application can set a threshold; an alarm is only triggered if the difference between the measured actual status parameters and the predicted status parameters exceeds this threshold.

[0040] Optionally, this application can also cluster historical state parameters according to the operating conditions of the energy station. The operating conditions include the operating time period and operating load of the energy station. Multiple clusters are obtained through clustering, and historical monitoring data in the same cluster are fitted and predicted to optimize the situation where the difference between historical monitoring data under different operating conditions is too large, resulting in low prediction accuracy.

[0041] Optionally, this application can also form a state parameter curve based on the state parameters within a fixed time period. For example, 24 data points of state parameter values ​​are collected hourly within a day. The state parameter curve for the day is obtained by fitting the 24 data points. By comparing the differences of the state parameter curves for each day, it can be determined whether the current state parameters are abnormal. Specifically, if the number of data points (out of 24 data points) where the difference between the current status parameter curve and the historical status parameter curve (status parameter curves before the current day) is greater than the first preset value (the absolute value of the difference between the data points of the current status parameter curve and the corresponding data points of the historical status parameter curve) is greater than the first preset value, it indicates that the current status parameters have changed significantly compared to the historical status parameters at certain times. In this case, an alarm is issued to remind staff to check other data at the corresponding time to determine the cause of the anomaly (equipment malfunction or abnormal use at a specific time). Otherwise, if the number of data points (out of 24 data points) where the difference between the current status parameter curve and the historical status parameter curve (the difference between the data points of the current status parameter curve and the corresponding data points of the historical status parameter curve) is greater than the second preset value (less than the first preset value) is greater than the second preset threshold (greater than the first preset threshold), it indicates that the current status parameters have generally improved compared to the historical status parameters. In this case, it is very likely that the equipment is malfunctioning, and an alarm signal is issued.

[0042] Figure 2 This is a schematic diagram of the structure of an energy station monitoring and early warning system provided in an exemplary embodiment of this application. Figure 2As shown, the energy station monitoring and early warning system 20 includes: a monitoring data acquisition module 21, used to collect monitoring data of multiple key points of the energy station using multiple sensors; wherein, the multiple sensors include multiple modes; a status data calculation module 22, used to fuse multiple monitoring data to calculate the status data of the energy station; and an alarm status triggering module 23, used to trigger an alarm if the status data is greater than the status threshold; wherein, the status threshold is dynamically adjusted according to the historical monitoring data of the energy station and the current environmental data.

[0043] This application provides an energy station monitoring and early warning system. A monitoring data acquisition module 21 uses multiple sensors to collect monitoring data from multiple key points of the energy station. These sensors include various modes. A status data calculation module 22 fuses the multiple monitoring data to calculate the energy station's status parameters. If the status parameters exceed a status threshold, an alarm triggering module 23 triggers an alarm. The status threshold is dynamically adjusted based on the energy station's historical monitoring data and current environmental data. In other words, multiple sensors monitor key points, and all monitoring data is fused to obtain global monitoring data for the energy station. The system calculates the energy station's status data based on all monitoring data, and an alarm is triggered when the status data exceeds a threshold, thereby improving global monitoring effectiveness. Furthermore, the status threshold is dynamically adjusted based on historical data to ensure its rationality, further improving monitoring accuracy.

[0044] In one embodiment, multiple sensors are respectively set at multiple key points, and wireless transmission devices are also set at the key points. The wireless transmission devices are connected to the corresponding sensors and the monitoring platform. The wireless transmission devices are used to transmit the monitoring data collected by the sensors to the monitoring platform. The wireless transmission devices include an edge calculator. The monitoring data acquisition module 21 can be further configured to: use multiple sensors to collect current data of multiple key points respectively; use the edge calculator to clean and compress the current data to obtain monitoring data, and store the monitoring data.

[0045] In one embodiment, the category of the status parameter is the same as one category of multiple monitoring data; the aforementioned status data calculation module 22 can be further configured to: based on the monitoring data of different categories from the status parameter among the multiple monitoring data, correct the monitoring data of the same category as the status parameter to obtain the monitoring parameter.

[0046] In one embodiment, the category of the status parameter is different from the category of multiple monitoring data; the aforementioned status data calculation module 22 can be further configured to: weight multiple monitoring data to obtain the status parameter.

[0047] In one embodiment, the energy station monitoring and early warning system 20 described above can be further configured to: calculate the variance between historical monitoring data of each mode; if there is a mode with a variance greater than a set value, then increase the weight of the corresponding monitoring data.

[0048] In one embodiment, the adjustment of the state threshold can be achieved by: acquiring historical monitoring data and current environmental data of the energy station after the most recent maintenance; inputting the historical monitoring data and current environmental data into the trained long short-term memory network to obtain the state threshold.

[0049] In one embodiment, the energy station monitoring and early warning system 20 described above can be further configured to: fit a state change curve of the energy station based on the historical state parameters and current state parameters of the energy station; predict the predicted state parameters and corresponding prediction time of the energy station based on the state change curve; compare the actual state parameters calculated by the prediction time with the predicted state parameters; and trigger an alarm if the actual state parameters are greater than the predicted state parameters.

[0050] Below, for reference Figure 3 This application describes an electronic device according to embodiments thereof. The electronic device may be either or both of a first device and a second device, or a standalone device independent of them, which may communicate with the first device and the second device to receive acquired input signals from them.

[0051] Figure 3 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0052] like Figure 3 As shown, the electronic device 10 includes one or more processors 11 and memory 12.

[0053] The processor 11 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0054] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may execute the program instructions to implement the methods of the various embodiments of this application described above and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.

[0055] In one example, the electronic device 10 may also include an input device 13 and an output device 14, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0056] When the electronic device is a standalone device, the input device 13 can be a communication network connector for receiving the collected input signals from the first device and the second device.

[0057] In addition, the input device 13 may also include, for example, a keyboard, a mouse, etc.

[0058] The output device 14 can output various information to the outside, including determined distance information, direction information, etc. The output device 14 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0059] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device 10 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device 10 may include any other suitable components depending on the specific application.

[0060] In addition to the methods and apparatus described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0061] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0062] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0063] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0064] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0065] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0066] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0067] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0068] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for monitoring and early warning of energy stations, characterized in that, include: Multiple sensors are used to collect monitoring data from multiple key points of the energy station; wherein, the multiple sensors include multiple modes; By integrating multiple monitoring data, the status parameters of the energy station are calculated. If the status parameter is greater than the status threshold, an alarm is triggered; wherein the status threshold is dynamically adjusted based on the historical monitoring data and current environmental data of the energy station.

2. The energy station monitoring and early warning method according to claim 1, characterized in that, The plurality of sensors are respectively set at the plurality of key points, and a wireless transmission device is also set at the key points. The wireless transmission device is connected to the corresponding sensor and the monitoring platform. The wireless transmission device is used to transmit the monitoring data collected by the sensor to the monitoring platform. The wireless transmission device includes an edge calculator. The method of using multiple sensors to collect monitoring data from multiple key points of the energy station includes: The current data of the multiple key points are collected using the multiple sensors respectively; The monitoring data is obtained by cleaning and compressing the current data using the edge calculator and then stored.

3. The energy station monitoring and early warning method according to claim 1, characterized in that, The category of the status parameter is the same as one category of the multiple monitoring data; The state parameters of the energy station calculated by integrating multiple monitoring data include: Based on monitoring data that are of a different category from the status parameter among multiple monitoring data, the monitoring data that are of the same category as the status parameter are corrected to obtain the monitoring parameter.

4. The energy station monitoring and early warning method according to claim 1, characterized in that, The category of the status parameter is different from the category of multiple monitoring data; The state parameters of the energy station calculated by integrating multiple monitoring data include: The status parameters are obtained by weighting multiple monitoring data.

5. The energy station monitoring and early warning method according to claim 4, characterized in that, The energy station monitoring and early warning method also includes: Calculate the variance between historical monitoring data for each modality; If a mode has a variance greater than the set value, the weight of the corresponding monitoring data will be increased.

6. The energy station monitoring and early warning method according to claim 1, characterized in that, The adjustment methods for the state threshold include: Acquire historical monitoring data and current environmental data of the energy station since its most recent maintenance. The historical monitoring data and the current environmental data are input into the trained Long Short-Term Memory network to obtain the state threshold.

7. The energy station monitoring and early warning method according to claim 1, characterized in that, The energy station monitoring and early warning method also includes: Based on the historical and current state parameters of the energy station, the state change curve of the energy station is obtained by fitting. Based on the state change curve, predict the predicted state parameters and corresponding prediction time of the energy station; Compare the actual state parameters calculated from the predicted time with the predicted state parameters; If the actual state parameter is greater than the predicted state parameter, an alarm is triggered.

8. An energy station monitoring and early warning system, characterized in that, include: The monitoring data acquisition module is used to collect monitoring data from multiple key points of the energy station using multiple sensors; wherein, the multiple sensors include multiple modes; The status data calculation module is used to integrate multiple monitoring data to calculate the status data of the energy station; An alarm status triggering module is used to trigger an alarm if the status data is greater than a status threshold; wherein the status threshold is dynamically adjusted based on the historical monitoring data and current environmental data of the energy station.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method described in any one of claims 1-7.

10. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is used to execute the method described in any one of claims 1-7.

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