Multi-mode-based power grid safety production monitoring method and system

Abstract

The invention, which belongs to the power grid production monitoring field, discloses a multi-mode-based power grid safety production monitoring system comprising a monitoring and data acquisition module, an abnormity monitoring and early warning module, a communication module, a data storage module, a power consumption prediction module, and a power output scheduling module. The monitoring and data acquisition module, the abnormity monitoring and early warning module, the communication module and the data storage module are in communication connection in sequence. The monitoring and data acquisition module, the electric quantity prediction module and the electric quantity output scheduling module are in communication connection in sequence. According to the multi-mode-based power grid safety production monitoring method and system, by collecting the parameter operation data of the generator set, the production of a power grid can be monitored, the safety of the power grid is improved, collected video data are processed irregularly after the parameters are abnormal, fire prevention and early warning are conducted, and the safety of the power grid is improved. Meanwhile, the system has the functions of electric quantity prediction and generator set combination optimization, improves the operation stability of a power grid, and guarantees the production safety of the power grid.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid production monitoring, and specifically relates to a multi-modal-based power grid production safety monitoring method and system. Background Art

[0002] The power grid is a complex system consisting of power generation, transmission, distribution and power consumption equipment. It is an indispensable infrastructure in modern society. Its main function is to transmit electricity from power plants to end users. The power grid supports a large range of power supply and can transmit the produced electricity through transmission lines to areas far away from the power source to support daily life, industrial production and economic development.

[0003] However, when the existing power grid produces electricity, it usually does not have the step of predicting electricity consumption based on ambient temperature data. Since ambient temperature is one of the important factors affecting electricity consumption, failure to predict electricity consumption through temperature factors may make the prediction of electricity demand inaccurate, resulting in insufficient or excessive electricity supply, affecting the stability of the power grid. At the same time, when the traditional power grid produces electricity, it does not have the function of classified early warning, which makes it impossible for staff to understand the severity of abnormal information. Therefore, a multi-modal-based power grid safety production monitoring method and system are proposed. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a multi-modal-based power grid production safety monitoring method and system, so that the power grid can operate stably and ensure the production safety of the power grid.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A multi-modal power grid production safety monitoring system includes a monitoring and data acquisition module, an abnormal monitoring and early warning module, a communication module, a data storage module, a power consumption prediction module, and a power output scheduling module. The monitoring and data acquisition module, the abnormal monitoring and early warning module, the communication module, and the data storage module are sequentially connected in communication. The monitoring and data acquisition module, the power prediction module, and the power output scheduling module are sequentially connected in communication.

[0007] The monitoring and data acquisition module is used to collect the generator set parameters of the power generation equipment and the monitoring video of the equipment operation. The generator set parameters include voltage, current, power, frequency, temperature, and collect historical power generation data, historical weather, temperature data, and weather and ambient temperature data for a period of time in the future;

[0008] The abnormal monitoring and early warning module analyzes the data analysis results of the collected generator set data and video data, detects abnormal conditions, including abnormal generator set data and abnormal external working state of the generator set, generates early warning information, and finally transmits the early warning information to the communication module;

[0009] The power consumption prediction module selects a prediction model to predict future power consumption based on known historical power generation data, historical weather and temperature data, and weather and ambient temperature data for a period of time in the future;

[0010] The power output scheduling module schedules the power generation based on the known number of generator sets, the power generation of a single generator set, and the predicted power consumption. When the power consumption is high, multiple generator sets are started. When the power consumption is low, the number of generator sets is reduced. At the same time, the power generation is distributed, and the power supply is given priority. When there is excess power, the remaining power is stored. When there is a shortage of power, the stored power supply is scheduled for output;

[0011] The communication module is used to exchange information with staff or other equipment: after receiving the warning information from the abnormal monitoring and warning module and the data from the power output scheduling module, the warning information will be directly transmitted to the staff to remind them to respond quickly;

[0012] The data storage module is used to store system data, including collected generator set data, weather temperature data, abnormal warning data, power consumption forecast data, power consumption scheduling plan, and store related data in an associated manner.

[0013] Furthermore, the monitoring and data acquisition module is used to collect the generator set parameters of the power generation equipment and the monitoring video of the equipment operation. The operating parameters of the generator set equipment include voltage, current, power, frequency, and temperature. At the same time, the historical power generation data, historical weather, temperature data, and weather and ambient temperature data for a period of time in the future are collected. The processing process is as follows:

[0014] The sensor monitors the operating data of the generator set equipment and receives the equipment operating parameters by collecting sensor data, including the voltage, current, power, frequency and temperature of the generator set;

[0015] Collect historical power generation data, historical weather and temperature data, and also collect weather and temperature data for a period of time in the future.

[0016] By collecting weather and temperature data for a period of time in the future, strong data support can be provided for the later prediction of power generation.

[0017] Furthermore, the abnormal monitoring and early warning module analyzes the data analysis results of the collected generator set data and video data, detects abnormal conditions, including abnormal generator set data and abnormal external working state of the generator set, generates early warning information, and finally transmits the early warning information to the communication module. The process is as follows:

[0018] The voltage, current, power, frequency and temperature range thresholds are preset. When the voltage, current, power, frequency and temperature data are abnormal, the real-time collected video data is processed immediately, and the abnormal phenomena such as smoke or flame of the equipment are detected through image recognition technology;

[0019] When the operating parameters of the generator set are abnormal, the video of the generator set operation will be collected at a regular interval. After the collection is completed, the video stream will be decoded into continuous frame images, and the images will be adjusted, including size scaling, denoising, and contrast adjustment. The trained deep learning model will be used to process the images, and smoke or flames will be detected in each frame. When the model detects an abnormal phenomenon, it will record the event information and mark the relevant images, generate fire warning information, and transmit the warning information to the communication module. When no abnormal phenomenon is detected, the parameter abnormality information will be generated into parameter abnormality warning information and transmitted to the communication module. After the generator set data is normal, it will stop processing the video data and continue to monitor the voltage, current, power, frequency and temperature data.

[0020] Each of the decoded continuous frame images is a static image, which can facilitate image analysis.

[0021] Furthermore, the power consumption prediction module selects a prediction model based on known historical power generation data, historical weather and temperature data, and weather and ambient temperature data for a period of time in the future, and performs the following process to predict future power consumption:

[0022] The weather data, temperature data and power generation data are processed and predicted using a linear regression model. The specific formula is:

[0023] V1=β0+β1×T+e;

[0024] Among them, V1 is the power consumption, β0 is the minimum power demand, β1 is the regression coefficient of temperature, which indicates the influence of temperature on power consumption, T is temperature, and e is the error term. The linear regression model is fitted with historical data to obtain the estimated values ​​of β0 and β1. The obtained values ​​of β0 and β1 are then combined with future temperature data to predict future power consumption. The specific formula is:

[0025] V2=β0+β1×T2;

[0026] Among them, V2 is the future power consumption, T2 is the future temperature data

[0027] The temperature data T and T2 are both the average temperature data of the day. The average temperature data provides a standardized temperature, which facilitates electricity consumption prediction.

[0028] The weather data is associated with the temperature data and the electricity consumption, so that the weather data can be used as an auxiliary reference condition.

[0029] Furthermore, the power output scheduling module, knowing the number of generator sets, the power generation of a single set and the predicted power consumption, schedules the power generation. When the power consumption is high, multiple generator sets are started. When the power consumption is low, the number of generator sets is reduced. At the same time, the power generation is distributed, and the power supply is given priority. When there is excess power, the remaining power is stored. When there is a shortage of power, the stored power output processing process is scheduled as follows:

[0030] Given the number of generator sets, the power generation of each set, and the predicted power consumption, calculate the usage of the number of generator sets based on the data and determine the optimal combination of generator sets to meet the predicted power demand;

[0031] By calculating the usage of generator sets and determining the optimal combination of generator sets, not only can unnecessary generator set operation be reduced, but power generation efficiency can also be improved.

[0032] The predicted plan will be applied. When there is still surplus electricity, the surplus electricity will be stored. When the electricity demand is higher than the power generation, electricity will be dispatched from the stored electricity to make up for the gap. At the same time, the power supply priority will be set to give priority to meeting the basic load and ensuring the electricity demand of important facilities.

[0033] Furthermore, the communication module is used to exchange information with staff or other devices: after receiving the warning information of the abnormal monitoring and warning module and the data of the power output scheduling module, the warning information will be directly transmitted to the staff to remind the staff to respond quickly. The processing process is as follows:

[0034] The received fire warning information or parameter abnormality warning information is generated into SMS, email, and application notification and transmitted to the staff and data storage module. After being transmitted to the staff, the staff is required to confirm that the warning information has been received to ensure the effectiveness of information transmission;

[0035] The received power consumption forecast data and generator set combination data are transmitted to the data storage module.

[0036] Integrating data from different sources into a central system for centralized transmission can effectively reduce the problems of data dispersion and duplicate management.

[0037] Furthermore, the data storage module is used to store system data, including the collected generator set data, weather temperature data, abnormal warning data, power consumption forecast data, and power consumption scheduling plan, and the process of associating and storing the relevant data is as follows:

[0038] Convert the data into a unified format to ensure that data from different sources have the same standards and units. Then define the relationship between the data in the database to achieve associative storage of data, including associating abnormal warning information with abnormal data, associating predicted power consumption with generator set combinations, and ambient temperature data, so that data can be stored in a classified manner.

[0039] Converting data from different sources into a unified format and unit can ensure consistency in data processing and analysis, and reduce errors caused by different data formats.

[0040] A multi-modal power grid production safety monitoring method specifically includes the following steps:

[0041] S1. Collect two types of data, including collecting camera devices, sensor data and historical weather temperature data, historical power generation data, and weather temperature data for a period of time in the future;

[0042] S2. Process the sensor data and set the threshold range. When the data threshold exceeds the range, process the collected video data, monitor the operating status of the generator set, determine whether the abnormality is a fire abnormality or a data abnormality, and generate corresponding warning information. The generated warning information will be transmitted to the next step;

[0043] S3. Based on the collected historical weather temperature data, historical power generation data and weather temperature data for a period of time in the future, a prediction model is established to predict future electricity consumption;

[0044] S4. Determine the optimal combination of generator sets based on the predicted power consumption to meet the predicted power demand. When there is still surplus power, store the power. When there is a shortage of power, dispatch the stored power. When dispatching, set the power supply priority to give priority to meeting the basic load and ensuring the power demand of important facilities.

[0045] S5. Abnormal warning information can be transmitted to the staff through the communication module, and will also be uniformly transmitted to the data storage module through the communication module for classified storage.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] By collecting the operating parameter data of the generator sets and comparing the operating parameter data with the preset threshold range, the specific abnormal parameters can be quickly determined. At the same time, after the parameters are abnormal, video data will be collected from time to time and processed. Through image processing technology, it can be determined whether there are fire hazards during the operation of the power grid. If no fire hazard is found, parameter abnormality warning information will be generated. If a fire hazard is found, a fire warning information will be generated. Through different warning information, the staff can quickly determine the abnormal situation, improve the abnormal response efficiency and the safety of the power grid operation;

[0048] Only when the parameters of the generator set are abnormal will the video data be collected and processed regularly, which can not only reduce the amount of data to be processed and the burden of data storage and processing, but also improve the efficiency of data processing. At the same time, when the parameters of the generator set are abnormal, the video data will be collected regularly and image processing will be performed, so that any fire abnormality can be discovered in time. After the parameters of the generator set return to normal, the video data will no longer be processed, saving computing resources;

[0049] By collecting historical electricity consumption data, historical temperature and weather data, a prediction model is generated based on these data, and the temperature data in the future is combined with the prediction model to predict electricity consumption, which can predict electricity consumption more accurately;

[0050] Determining the optimal combination of generator sets based on predicted power consumption can optimize the power supply plan and maintain the stability of power grid operation; then the actual power supply plan is implemented. When there is surplus power, the power is stored. When there is a power shortage, the stored power source is dispatched. Since the power supply priority is set, the power demand of important facilities can also be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The block diagram of a multi-modal power grid production safety monitoring system of the present invention is shown.

[0052] Figure 2 The present invention is a flowchart of a multi-modal-based power grid safety production monitoring method. DETAILED DESCRIPTION

[0053] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] like Figure 1-Figure 2As shown, a multi-modal power grid production safety monitoring system includes a monitoring and data acquisition module, an abnormal monitoring and early warning module, a communication module, a data storage module, a power consumption prediction module, and a power output scheduling module. The monitoring and data acquisition module, the abnormal monitoring and early warning module, the communication module, and the data storage module are sequentially connected in communication, and the monitoring and data acquisition module, the power prediction module, and the power output scheduling module are sequentially connected in communication;

[0055] The monitoring and data acquisition module is used to collect the generator set parameters of the power generation equipment and the monitoring video of the equipment operation. The generator set parameters include voltage, current, power, frequency, and temperature. It also collects historical power generation data, historical weather and temperature data, as well as weather and ambient temperature data for a period of time in the future.

[0056] In this embodiment, the monitoring and data acquisition module is used to collect the generator set parameters of the power generation equipment and the monitoring video of the equipment operation. The operating parameters of the generator set equipment include voltage, current, power, frequency, and temperature. At the same time, the historical power generation data, historical weather, temperature data, and weather and ambient temperature data for a period of time in the future are collected. The processing process is as follows:

[0057] The sensor monitors the operating data of the generator set equipment and receives the equipment operating parameters by collecting sensor data, including the voltage, current, power, frequency and temperature of the generator set;

[0058] Collect historical power generation data, historical weather and temperature data, and also collect weather and temperature data for a period of time in the future.

[0059] It should be noted that real-time monitoring of parameters such as voltage, current, power, frequency and temperature can provide a more comprehensive understanding of the working status of the generator set, detect abnormal conditions in a timely manner, and ensure that the equipment operates in the best working condition;

[0060] By analyzing historical power generation data and weather data, a strong foundation can be provided for the formulation of power generation strategies in the future, facilitating energy scheduling, maintaining system stability, and improving system safety.

[0061] The abnormal monitoring and early warning module analyzes the collected generator set data and video data, detects abnormal conditions, including abnormal generator set data and abnormal external working state of the generator set, generates early warning information, and finally transmits the early warning information to the communication module;

[0062] In this embodiment, the abnormal monitoring and early warning module analyzes the data analysis results of the collected generator set data and video data, detects abnormal conditions, including abnormal generator set data and abnormal external working state of the generator set, generates early warning information, and finally transmits the early warning information to the communication module. The process is as follows:

[0063] The voltage, current, power, frequency and temperature range thresholds are preset. When the voltage, current, power, frequency and temperature data are abnormal, the real-time collected video data is processed immediately, and the abnormal phenomena such as smoke or flame of the equipment are detected through image recognition technology;

[0064] When the operating parameters of the generator set are abnormal, the video of the generator set operation will be collected at a regular interval. After the collection is completed, the video stream will be decoded into continuous frame images, and the images will be adjusted, including size scaling, denoising, and contrast adjustment. The trained deep learning model will be used to process the images, and smoke or flames will be detected in each frame. When the model detects an abnormal phenomenon, it will record the event information and mark the relevant images, generate fire warning information, and transmit the warning information to the communication module. When no abnormal phenomenon is detected, the parameter abnormality information will be generated into parameter abnormality warning information and transmitted to the communication module. After the generator set data is normal, it will stop processing the video data and continue to monitor the voltage, current, power, frequency and temperature data.

[0065] It should be noted that processing video data only when there are abnormalities in the parameters of the generator set can significantly reduce the consumption of computing resources. At the same time, when the equipment data is abnormal, video image processing can help quickly determine whether there is smoke or flame in the video, thereby determining whether the generator set is in normal operating condition.

[0066] The power consumption prediction module selects a prediction model to predict future power consumption based on known historical power generation data, historical weather and temperature data, as well as weather and ambient temperature data for a period of time in the future;

[0067] In this embodiment, the power consumption prediction module selects a prediction model based on known historical power generation data, historical weather and temperature data, and weather and ambient temperature data for a period of time in the future, and the process of predicting future power consumption is as follows:

[0068] The weather data, temperature data and power generation data are processed and predicted using a linear regression model. The specific formula is:

[0069] V1=β0+β1×T+e;

[0070] Among them, V1 is the power consumption, β0 is the minimum power demand, β1 is the regression coefficient of temperature, which indicates the influence of temperature on power consumption, T is temperature, and e is the error term. The linear regression model is fitted with historical data to obtain the estimated values ​​of β0 and β1. The obtained values ​​of β0 and β1 are then combined with future temperature data to predict future power consumption. The specific formula is:

[0071] V2=β0+β1×T2;

[0072] Among them, V2 is the future power consumption, T2 is the future temperature data

[0073] Associate weather data with temperature data and electricity consumption, making weather data an auxiliary reference condition

[0074] It should be noted that temperature conditions have a significant impact on electricity consumption. Using historical data to establish a prediction model and predicting future electricity consumption through the prediction model and future temperature can help understand electricity demand in advance, which can not only avoid grid overload and reduce the occurrence of grid failures and power outages, but also improve the safety of the grid and optimize its operation.

[0075] The power output scheduling module schedules the power generation when the number of generator sets, the power generation of a single generator set, and the predicted power consumption are known. When the power consumption is high, multiple generator sets are started. When the power consumption is low, the number of generator sets is reduced. At the same time, the power generation is distributed, and the power supply is given priority. When there is excess power, the remaining power is stored. When there is a shortage of power, the stored power supply is scheduled for output;

[0076] In this embodiment, the power output scheduling module is known to schedule the power generation, and when the power consumption is high, multiple power generation sets are started, and when the power consumption is low, the number of power generation sets is reduced. At the same time, the power generation is distributed, and the power supply is given priority. When there is excess power, the remaining power is stored. When there is a shortage of power, the stored power output processing process is as follows:

[0077] Given the number of generator sets, the power generation of each set, and the predicted power consumption, calculate the usage of the number of generator sets based on the data and determine the optimal combination of generator sets to meet the predicted power demand;

[0078] Apply the predicted plan. When there is still surplus electricity, store the surplus electricity. When the electricity demand is higher than the power generation, dispatch electricity from the stored electricity to make up for the gap. At the same time, set the priority of power supply to give priority to meeting the basic load and ensuring the electricity demand of important facilities.

[0079] It should be noted that although the prediction model can accurately estimate future electricity demand, in reality there may still be a surplus or shortage of electricity. This is because environmental factors and demand factors may change at any time. Therefore, storing excess electricity is an effective strategy to deal with surplus or shortage of electricity.

[0080] Communication module, used for information exchange with staff or other equipment: after receiving the warning information from the abnormal monitoring and warning module and the data from the power output scheduling module, the warning information will be directly transmitted to the staff to remind them to respond quickly;

[0081] In this embodiment, the communication module is used to exchange information with staff or other devices: after receiving the warning information of the abnormal monitoring and warning module and the data of the power output scheduling module, the warning information will be directly transmitted to the staff to remind the staff to respond quickly. The processing process is as follows:

[0082] The received fire warning information or parameter abnormality warning information is generated into SMS, email, and application notification and transmitted to the staff and data storage module. After being transmitted to the staff, the staff is required to confirm that the warning information has been received to ensure the effectiveness of information transmission;

[0083] The received power consumption forecast data and generator set combination data are transmitted to the data storage module.

[0084] It should be noted that using multiple notification methods can ensure that the information can be delivered. Even if one method fails, other methods can still transmit the information. At the same time, among the multiple notification methods, the staff only needs to confirm in one notification, which ensures the effectiveness of information transmission while reducing the tediousness of repeated operations.

[0085] The data storage module is used to store system data, including the collected generator data, weather temperature data, abnormal warning data, power consumption forecast data, power consumption scheduling plan, and store related data in an associated manner;

[0086] In this embodiment, the data storage module is used to store system data, including the collected generator set data, weather temperature data, abnormal warning data, power consumption forecast data, and power consumption scheduling plan. The process of associating and storing the relevant data is as follows:

[0087] Convert the data into a unified format to ensure that data from different sources have the same standards and units. Then define the relationship between the data in the database to achieve associative storage of data, including associating abnormal warning information with abnormal data, associating predicted power consumption with generator set combinations, and ambient temperature data, so that data can be stored in a classified manner.

[0088] It should be noted that by associating and storing data in categories, data indexing becomes more efficient. When staff need to search for data, they can quickly retrieve and access relevant data in the database, thereby improving the efficiency of searching for data.

[0089] A multi-modal power grid production safety monitoring method specifically includes the following steps:

[0090] S1. Collect two types of data, including collecting camera devices, sensor data and historical weather temperature data, historical power generation data, and weather temperature data for a period of time in the future;

[0091] S2. Process the sensor data and set the threshold range. When the data threshold exceeds the range, process the collected video data, monitor the operating status of the generator set, determine whether the abnormality is a fire abnormality or a data abnormality, and generate corresponding warning information. The generated warning information will be transmitted to the next step;

[0092] S3. Based on the collected historical weather temperature data, historical power generation data and weather temperature data for a period of time in the future, a prediction model is established to predict future electricity consumption;

[0093] S4. Determine the optimal combination of generator sets based on the predicted power consumption to meet the predicted power demand. When there is still surplus power, store the power. When there is a shortage of power, dispatch the stored power. When dispatching, set the power supply priority to give priority to meeting the basic load and ensuring the power demand of important facilities.

[0094] S5. Abnormal warning information can be transmitted to the staff through the communication module, and will also be uniformly transmitted to the data storage module through the communication module for classified storage.

[0095] It should be noted that this method has the functions of abnormal warning, power forecasting and reasonable power dispatching, which can not only ensure the safe operation of power grid production, but also optimize power dispatching, reduce power grid fluctuations and improve system stability.

[0096] In the embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules is only a logical function division, and there may be other division methods in actual implementation; the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of this embodiment.

[0097] The above embodiments are only used to illustrate the technical method 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 method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A multi-modal power grid production safety monitoring system, characterized by: It includes a monitoring and data acquisition module, an abnormal monitoring and early warning module, a communication module, a data storage module, a power consumption prediction module, and a power output scheduling module. The monitoring and data acquisition module, the abnormal monitoring and early warning module, the communication module, and the data storage module are sequentially connected in communication. The monitoring and data acquisition module, the power prediction module, and the power output scheduling module are sequentially connected in communication; The monitoring and data acquisition module is used to collect the generator set parameters of the power generation equipment and the monitoring video of the equipment operation. The generator set parameters include voltage, current, power, frequency, temperature, and collect historical power generation data, historical weather, temperature data, and weather and ambient temperature data for a period of time in the future; The abnormal monitoring and early warning module analyzes the data analysis results of the collected generator set data and video data, detects abnormal conditions, including abnormal generator set data and abnormal external working state of the generator set, generates early warning information, and finally transmits the early warning information to the communication module; The power consumption prediction module selects a prediction model to predict future power consumption based on known historical power generation data, historical weather and temperature data, and weather and ambient temperature data for a period of time in the future; The power output scheduling module schedules the power generation based on the known number of generator sets, the power generation of a single generator set, and the predicted power consumption. When the power consumption is high, multiple generator sets are started. When the power consumption is low, the number of generator sets is reduced. At the same time, the power generation is distributed, and the power supply is given priority. When there is excess power, the remaining power is stored. When there is a shortage of power, the stored power supply is scheduled for output; The communication module is used to exchange information with staff or other equipment: after receiving the warning information from the abnormal monitoring and warning module and the data from the power output scheduling module, the warning information will be directly transmitted to the staff to remind them to respond quickly; The data storage module is used to store system data, including collected generator set data, weather temperature data, abnormal warning data, power consumption forecast data, power consumption scheduling plan, and store related data in an associated manner.

2. A multi-modal power grid production safety monitoring system according to claim 1, characterized in that: The monitoring and data acquisition module is used to collect the generator set parameters of the power generation equipment and the monitoring video of the equipment operation. The operating parameters of the generator set equipment include voltage, current, power, frequency, and temperature. At the same time, it collects historical power generation data, historical weather, temperature data, and weather and ambient temperature data for a period of time in the future. The processing process is as follows: The sensor monitors the operating data of the generator set equipment and receives the equipment operating parameters by collecting sensor data, including the voltage, current, power, frequency and temperature of the generator set; Collect historical power generation data, historical weather and temperature data, and also collect weather and temperature data for a period of time in the future.

3. The multi-modal power grid production safety monitoring system according to claim 1, characterized in that: The abnormal monitoring and early warning module analyzes the data analysis results of the collected generator set data and video data, detects abnormal conditions, including abnormal generator set data and abnormal external working state of the generator set, generates early warning information, and finally transmits the early warning information to the communication module. The process is as follows: The voltage, current, power, frequency and temperature range thresholds are preset. When the voltage, current, power, frequency and temperature data are abnormal, the real-time collected video data is processed immediately, and the abnormal phenomena such as smoke or flame of the equipment are detected through image recognition technology; When the operating parameters of the generator set are abnormal, the video of the generator set operation will be collected at a regular interval. After the collection is completed, the video stream will be decoded into continuous frame images, and the images will be adjusted, including size scaling, denoising, and contrast adjustment. The trained deep learning model will be used to process the images, and smoke or flames will be detected in each frame. When the model detects an abnormal phenomenon, it will record the event information and mark the relevant images, generate fire warning information, and transmit the warning information to the communication module. When no abnormal phenomenon is detected, the parameter abnormality information will be generated into parameter abnormality warning information and transmitted to the communication module. After the generator set data is normal, it will stop processing the video data and continue to monitor the voltage, current, power, frequency and temperature data.

4. The multi-modal power grid production safety monitoring system according to claim 1, characterized in that: The power consumption prediction module selects a prediction model based on known historical power generation data, historical weather and temperature data, and weather and ambient temperature data for a period of time in the future, and performs the following process to predict future power consumption: The weather data, temperature data and power generation data are processed and predicted using a linear regression model. The specific formula is: V1=β0+β1×T+e; Among them, V1 is the power consumption, β0 is the minimum power demand, β1 is the regression coefficient of temperature, which indicates the influence of temperature on power consumption, T is temperature, and e is the error term. The linear regression model is fitted with historical data to obtain the estimated values ​​of β0 and β1. The obtained values ​​of β0 and β1 are then combined with future temperature data to predict future power consumption. The specific formula is: V2=β0+β1×T2; Among them, V2 is the future power consumption, and T2 is the future temperature data; The weather data is associated with the temperature data and the electricity consumption, so that the weather data can be used as an auxiliary reference condition.

5. The multi-modal power grid production safety monitoring system according to claim 1, characterized in that: The power output scheduling module, knowing the number of generator sets, the power generation of a single set and the predicted power consumption, schedules the power generation. When the power consumption is high, multiple generator sets are started. When the power consumption is low, the number of generator sets is reduced. At the same time, the power generation is distributed, and the power supply is given priority. When there is excess power, the remaining power is stored. When there is a shortage of power, the stored power output processing process is scheduled as follows: Given the number of generator sets, the power generation of each set, and the predicted power consumption, calculate the usage of the number of generator sets based on the data and determine the optimal combination of generator sets to meet the predicted power demand; The predicted plan will be applied. When there is still surplus electricity, the surplus electricity will be stored. When the electricity demand is higher than the power generation, electricity will be dispatched from the stored electricity to make up for the gap. At the same time, the power supply priority will be set to give priority to meeting the basic load and ensuring the electricity demand of important facilities.

6. The multi-modal power grid production safety monitoring system according to claim 1, characterized in that: The communication module is used to exchange information with staff or other equipment: after receiving the warning information from the abnormal monitoring and warning module and the data from the power output scheduling module, the warning information will be directly transmitted to the staff to remind them to respond quickly. The processing process is as follows: The received fire warning information or parameter abnormality warning information is generated into SMS, email, and application notification and transmitted to the staff and data storage module. After being transmitted to the staff, the staff is required to confirm that the warning information has been received to ensure the effectiveness of information transmission; The received power consumption forecast data and generator set combination data are transmitted to the data storage module.

7. The multi-modal power grid production safety monitoring system according to claim 1, characterized in that: The data storage module is used to store system data, including the collected generator set data, weather temperature data, abnormal warning data, power consumption forecast data, and power consumption scheduling plan. The process of associating and storing the relevant data is as follows: Convert the data into a unified format to ensure that data from different sources have the same standards and units. Then define the relationship between the data in the database to achieve associative storage of data, including associating abnormal warning information with abnormal data, associating predicted power consumption with generator set combinations, and ambient temperature data, so that data can be stored in a classified manner.

8. A multi-modal power grid production safety monitoring method according to any one of claims 1 to 7, characterized in that: The specific steps include: S1. Collect two types of data, including collecting camera devices, sensor data and historical weather temperature data, historical power generation data, and weather temperature data for a period of time in the future; S2. Process the sensor data and set the threshold range. When the data threshold exceeds the range, process the collected video data, monitor the operating status of the generator set, determine whether the abnormality is a fire abnormality or a data abnormality, and generate corresponding warning information. The generated warning information will be transmitted to the next step; S3. Based on the collected historical weather temperature data, historical power generation data and weather temperature data for a period of time in the future, a prediction model is established to predict future electricity consumption; S4. Determine the optimal combination of generator sets based on the predicted power consumption to meet the predicted power demand. When there is still surplus power, store the power. When there is a shortage of power, dispatch the stored power. When dispatching, set the power supply priority to give priority to meeting the basic load and ensuring the power demand of important facilities. S5. Abnormal warning information can be transmitted to the staff through the communication module, and will also be uniformly transmitted to the data storage module through the communication module for classified storage.

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