An adaptive security remote drive control method and system

Abstract

The application discloses a kind of self-adapting security remote drive control method and system, acquire field temperature data and image data;According to the region where collection point is, carry out partition storage, region is provided with time index table, space index table and equipment index table;The temperature data collected is preprocessed, determine whether there is abnormal data;When abnormal data appears, determine single-point abnormal data, multi-point abnormal data or multi-region abnormal data;According to the type of abnormal data, match corresponding strategy analysis to confirm abnormal condition;According to the type of generated abnormal condition, match corresponding execution strategy;According to the fourth strategy, the abnormal condition area is monitored in real time, when judging that abnormal condition disappears, end corresponding execution strategy;By adopting temperature time axis matching, temperature data situation under different environments can be more accurately reflected, so that the determination of abnormal data is more accurate, reduces the influence of abnormality on security system, and guarantees the safe and stable operation of energy storage power station.

Description

Technical Field

[0001] The present invention relates to the field of energy storage power station security technology, and in particular to an adaptive security remote drive control method and system. Background Art

[0002] With the rapid development of energy storage technology, energy storage power stations are playing an increasingly important role in energy storage and management. They not only effectively regulate energy supply and demand and improve energy efficiency, but also enhance the stability and reliability of power systems, playing an indispensable role in the large-scale integration of renewable energy and the efficient utilization of distributed energy. However, the safety of energy storage power stations is essential for their stable operation and is therefore crucial. Energy storage power stations typically contain a large number of energy storage batteries, power conversion equipment, and complex electrical systems. These devices generate heat during operation. Failure to effectively monitor and manage temperature can easily lead to serious safety incidents such as thermal runaway and fire, resulting in significant economic losses and even casualties.

[0003] The existing method is to detect temperature through simple sensors and issue an alarm when the temperature is high. However, due to the unreasonable layout of existing sensors, some key areas (such as the center of the battery module, key nodes of the heat dissipation channel, etc.) cannot be effectively monitored. Once the temperature in these areas rises abnormally, it is difficult to detect it in time. At the same time, the simple sensor detection method lacks comprehensive consideration of environmental factors. The environment inside the energy storage power station is complex and changeable, and various factors may affect the measurement results of the temperature sensor. However, the existing detection methods usually do not effectively identify environmental interference factors or other interference factors, resulting in large errors in the measured temperature data and failing to truly reflect the actual temperature of the energy storage equipment. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention aims to provide an adaptive security remote drive control method and system.

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

[0006] An adaptive security remote drive control method includes acquiring on-site temperature data and image data;

[0007] The temperature data and image data are stored in partitions according to the areas where the collection points are located. Each area is equipped with a time index table, a space index table, and a device index table.

[0008] Pre-process the collected temperature data to determine whether there is any abnormal data;

[0009] When abnormal data appears, it is determined whether it is single-point abnormal data, multi-point abnormal data, or multi-region abnormal data;

[0010] When it is a single point of abnormal data, further analyze the abnormal data according to the first strategy to confirm the abnormal situation;

[0011] When there are multiple abnormal data points, further analyze the abnormal data according to the second strategy to confirm the abnormal situation;

[0012] When the data is abnormal in multiple regions, the abnormal data is further analyzed according to the third strategy to confirm the abnormal situation;

[0013] Match the corresponding execution strategy according to the generated exception type;

[0014] After the execution strategy is sent to the corresponding execution unit, the abnormal situation area is monitored in real time according to the fourth strategy. When it is determined that the abnormal situation disappears, the corresponding execution strategy is terminated.

[0015] In the present invention, preferably, the preprocessing specifically includes: each collection point corresponds to an independent temperature-time axis, determining the comparison interval on the temperature-time axis according to the time when the temperature data is acquired, judging whether the temperature data is within the comparison interval, and when it is within the range, marking it as normal data, and when it exceeds the range, marking the temperature data as abnormal data.

[0016] In the present invention, preferably, the temperature time axis is a set of historical temperature data with a unit length of year, and each collection point is matched with a separate temperature time axis, which is arranged in sequence at intervals of days or hours, corresponding to the historical regular temperature data at each time.

[0017] In the present invention, preferably, the specific steps of determining whether it is single-point abnormal data, multi-point abnormal data or multi-region abnormal data include:

[0018] When abnormal data occurs, count the number of collection points where the abnormal data occurs and the areas where the collection points are located.

[0019] When a single abnormal data collection point appears in the same area, it is determined to be single-point abnormal data;

[0020] When there are more than two abnormal data collection points in a single area, it is determined to be multi-point abnormal data;

[0021] When multiple abnormal data collection points appear in different areas at the same time, it is determined to be multi-area abnormal data.

[0022] In the present invention, preferably, the first strategy targets single-point abnormal data. When single-point abnormal data occurs, the temperature data of the past hour corresponding to the collection point is extracted according to the time index table, and the temperature change rate is calculated. When the temperature change rate exceeds the preset threshold, it is determined that there is a potential risk and marked as a single-point abnormal situation.

[0023] In the present invention, preferably, the second strategy targets multi-point abnormal data in a single area. When multi-point abnormal data occurs, the equipment corresponding to the collection point location is extracted according to the equipment index table, the operating status data of the corresponding equipment is obtained, and the operating status data is input into the simulation model to determine whether the equipment has a fault and the type of fault. When a fault exists, it is marked as a single-area equipment fault; when there is no equipment fault, an abnormal prompt message is generated.

[0024] In the present invention, preferably, the third strategy targets multi-region abnormalities. When a multi-region abnormality occurs, the temperature data of several other collection points at the same time are extracted according to the time index table, and the number of collection points whose temperature data exceeds the median value of the comparison interval is counted. When more than half of the collection points are higher than the median value, the current indoor and outdoor temperatures are synchronously obtained, and the indoor and outdoor temperature data of the same period on the historical time axis are extracted from the historical database. If the current temperature data is also higher than the historical temperature of the same period, and the difference exceeds a certain range, it is determined to be overheating caused by environmental factors and marked as an environmental abnormality.

[0025] According to the time index table, the temperature data of several other collection points at the same time are extracted. If no more than half of the collection points are higher than the median value, the corresponding device is further determined according to the device index table to determine whether the device is in the same task list. If so, it is marked as a device abnormality. If not in the same list, the operating status data of the corresponding device is obtained and the operating status data is input into the simulation model to determine whether the device has a fault and the type of fault. When the fault and fault type are output, it is determined to be a device abnormality. Otherwise, the corresponding collection points are summarized and an abnormal prompt message is generated.

[0026] In the present invention, preferably, different execution strategies are matched according to different types of abnormal situations generated. When it is a single-point abnormal situation, the execution strategy is to retrieve the image data of the corresponding acquisition point to perform gas-liquid leakage image recognition, and at the same time determine the specific location based on the spatial index table, generate a first-level maintenance instruction and send it to the maintenance personnel to perform fixed-point maintenance and confirm local equipment or sensor problems; when it is a single-area equipment failure or equipment abnormal situation, a maintenance instruction is generated according to the specific equipment failure type given, and the current maintenance personnel are matched while the maintenance instruction is generated, and the maintenance instruction is sent to the maintenance personnel; when it is an environmental abnormal situation, the air-conditioning system of the corresponding area is started or the ventilation is adjusted, or the cooling power and ventilation volume are dynamically adjusted.

[0027] In the present invention, preferably, the current maintenance personnel are matched while the maintenance instruction is generated, the maintenance instruction is issued to the maintenance personnel, the authority of the maintenance personnel in the corresponding area is simultaneously enabled, and the subsequent maintenance situation is followed up.

[0028] An adaptive security remote drive control system includes a master control platform, an acquisition unit and an execution unit. The acquisition unit includes a temperature and humidity sensor and a video collector. The execution unit includes an air cooling system. The execution unit is also connected to a personnel management system. The master control platform is provided with a data storage unit, a real-time data matching unit, a data processing unit, an execution strategy generation unit and a feedback unit. The data storage unit includes acquisition data storage groups corresponding to each area, each storage group is provided with a time index table, a space index table and a device index table. The real-time data matching unit includes several temperature time axes, each temperature time axis corresponds to a collection point. The collected temperature data is compared with the temperature time axis data to determine whether there is abnormal data. When abnormal data exists, the data processing unit processes it to determine the abnormal situation. The execution strategy generation unit generates an execution strategy based on the abnormal situation and transmits it to the execution unit for execution. While executing the execution strategy, the feedback unit obtains real-time data of the corresponding acquisition point and feeds back at a high frequency, and dynamically adjusts or stops the execution strategy based on the feedback.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The method of the present invention uses precise multi-source data and optimizes the data storage indexing method, which can comprehensively and accurately obtain on-site data and improve the monitoring accuracy and timeliness of abnormal data; by adopting temperature-time axis matching, it can more accurately reflect the temperature data conditions in different environments, thereby making the judgment of abnormal data more accurate and providing a reliable basis for subsequent processing; different execution strategies are matched according to different abnormal situations, and combined with a feedback mechanism to monitor the execution situation, the execution strategy is adaptively adjusted based on feedback, reducing the impact of abnormalities on the security system and ensuring the safe and stable operation of the energy storage power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 The figure is a flow chart of an adaptive security remote drive control method according to the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0034] See Figure 1 A preferred embodiment of the present invention provides an adaptive security remote drive control method, which is mainly used in energy storage power stations to perform daily inspection and maintenance on the equipment of energy storage power stations. By adopting accurate multi-source data and optimizing the data storage index method, it can comprehensively and accurately obtain on-site data, and improve the accuracy and timeliness of monitoring abnormal data; by adopting temperature-time axis matching, it can more accurately reflect the temperature data conditions in different environments, thereby making the judgment of abnormal data more accurate and providing a reliable basis for subsequent processing; different execution strategies are matched according to different abnormal situations, and combined with a feedback mechanism, the execution status is monitored, and the execution strategy is adaptively adjusted based on feedback, reducing the impact of abnormalities on the security system and ensuring the safe and stable operation of the energy storage power station.

[0035] The specific steps include:

[0036] S1. Obtain on-site temperature data and image data:

[0037] Various sensors, including temperature and humidity sensors, video collectors, gas sensors, and sound sensors, are installed at multiple collection points in the energy storage power station to achieve all-round monitoring of the energy storage power station equipment. There are many cases of data anomalies or false alarms caused by temperature, so only the temperature data collected by the temperature sensor is improved in combination with the image data processing method.

[0038] S2. The temperature data and image data are partitioned and stored according to the areas where the collection points corresponding to the temperature data and image data are located. Each area is provided with a time index table, a space index table, and a device index table.

[0039] Specifically, due to the large overall scale of the energy storage power station, it is partitioned for management, and the collected temperature data and image data are stored in partitions. A time index table is established when the data is stored. The time index table contains the temperature data collected by each collection point according to the time point. At the same time, a spatial index table is also provided. The spatial index table associates each collection point with its specific location, and the specific location is represented by the corresponding code; a device index table is also provided. The device index table associates the corresponding device collected by each collection point, and the specific device is also represented by the corresponding device code. By establishing the index table, the subsequent extraction and analysis of the collected data is facilitated, and the data processing efficiency is accelerated.

[0040] S3. Pre-process the collected temperature data to determine whether there is any abnormal data.

[0041] Specifically, the preprocessing includes: each collection point corresponds to an independent temperature-time axis, the comparison interval on the temperature-time axis is determined according to the time when the temperature data is acquired, and whether the temperature data is within the comparison interval is judged. When it is within the range, it is marked as normal data; when it exceeds the range, the temperature data is marked as abnormal data.

[0042] The temperature time axis is a historical temperature data set with a length of 1 unit year. Each collection point is matched with a separate temperature time axis. Each time point in the temperature time axis is arranged in intervals of days or hours. Each time point corresponds to a historical temperature data set. The historical temperature data set includes the temperature values ​​collected at the same collection point in the previous few years. The historical temperature interval is formed based on the maximum and minimum values ​​of the historical temperature values. That is, each time point on the temperature time axis corresponds to a historical temperature interval, where the historical temperature values ​​do not include abnormal data. According to the time of the collection data, the same time point or the closest time point on the temperature time axis is matched to obtain the historical temperature interval corresponding to the time point. The historical temperature interval is the comparison interval. It is determined whether the temperature data is within the comparison interval. If it is within the interval, it is normal data. If it is out of range, it is marked as abnormal data. By adopting the temperature time axis comparison method, it can better adapt to environmental changes. At the same time, the latest normal data is included in the corresponding historical temperature data set every week or month.

[0043] S4. When abnormal data appears, determine whether it is single-point abnormal data, multiple-point abnormal data, or multi-region abnormal data;

[0044] When it is a single point of abnormal data, further analyze the abnormal data according to the first strategy to confirm the abnormal situation;

[0045] When there are multiple abnormal data points, further analyze the abnormal data according to the second strategy to confirm the abnormal situation;

[0046] When the data is abnormal in multiple regions, the abnormal data is further analyzed according to the third strategy to confirm the abnormal situation;

[0047] Match the corresponding execution strategy according to the generated exception type;

[0048] After the execution strategy is sent to the corresponding execution unit, the abnormal situation area is monitored in real time according to the fourth strategy. When it is determined that the abnormal situation disappears, the corresponding execution strategy is terminated.

[0049] Specifically, the steps for determining whether it is single-point abnormal data, multi-point abnormal data, or multi-region abnormal data include:

[0050] When abnormal data occurs, count the number of collection points where the abnormal data occurs and the areas where the collection points are located.

[0051] When a single abnormal data collection point appears in the same area, it is determined to be single-point abnormal data; if the collection point has frequently experienced abnormalities in the past period of time (such as more than 3 times in a week), the attention level of its abnormal situation is increased, and it is marked as "high-frequency single-point abnormality" and prioritized for analysis.

[0052] When there are more than two abnormal data collection points in a single area, it is determined to be multi-point abnormal data;

[0053] When multiple abnormal data collection points appear in different areas at the same time, it is determined to be multi-area abnormal data.

[0054] In this embodiment, the first strategy targets single-point abnormal data. When single-point abnormal data occurs, the temperature data of the past hour corresponding to the collection point is extracted according to the time index table, and the temperature change rate is calculated using methods such as linear regression. When the temperature change rate exceeds a preset threshold (such as a rise of 5°C per minute), it is determined that there is a potential risk and is marked as a single-point abnormal situation. The single-point abnormal situation may be a sensor problem or a problem in a part of the corresponding location equipment area.

[0055] In this embodiment, the second strategy targets multi-point abnormal data in a single area. When multi-point abnormal data occurs, the device corresponding to the collection point is located based on the device index table. The corresponding device's operating status data is then acquired. This operating status data is then input into a simulation model to model the device's multi-parameter data. The system then determines whether the device is faulty and what type of fault it is. If a fault exists, it is marked as a single-area device fault; if not, an abnormality prompt is generated. The simulation model utilizes a machine learning algorithm, obtained through preliminary training, and can quickly determine whether a device is faulty and what type of fault it is.

[0056] In this embodiment, the third strategy targets multi-region abnormalities. When a multi-region abnormality occurs, temperature data from several other collection points at the same time are extracted based on the time index table. The number of collection points whose temperature data exceeds the median value of the comparison interval is counted. When more than half of the collection points are higher than the median value, the current indoor and outdoor temperatures are synchronously obtained, and the indoor and outdoor temperature data for the same period on the historical time axis are extracted from the historical database. If the current temperature data is also higher than the historical temperature for the same period, and the difference exceeds a certain range (e.g., 5°C), it is determined to be overheating caused by environmental factors and marked as an environmental abnormality.

[0057] According to the time index table, the temperature data of several other collection points at the same time are extracted. If no more than half of the collection points are higher than the median value, the corresponding device is further determined according to the device index table to determine whether the devices are in the same task list. If they are in the same list, it is marked as a device abnormality. If they are not in the same list, the operating status data of the corresponding device is obtained and the operating status data is input into the simulation model (using machine learning algorithms (such as decision trees, support vector machines, etc.) to model the multi-parameter data of the device to determine whether the device has a fault and the type of fault). It is determined whether the device has a fault and the type of fault. When the fault and fault type are output, it is determined to be a device abnormality. Otherwise, the corresponding collection points are summarized and an abnormal prompt message is generated.

[0058] In this embodiment, the second strategy and the third strategy also extract the temperature data of the past hour corresponding to each collection point based on the time index table, use linear regression and other methods to calculate the temperature change rate, and judge whether the change rates are similar. When the change rates are the same, it can be determined that the abnormal situation is caused by the same factor. Then, the operating status data of some corresponding equipment is obtained to determine the specific equipment failure and failure type; when the change rates are different or partially the same, the original strategy is used for analysis and judgment.

[0059] S5. In this embodiment, different execution strategies are matched according to the different abnormal situation types generated. When it is a single-point abnormal situation, the execution strategy is to retrieve the image data of the corresponding acquisition point to perform gas-liquid leakage image recognition, and at the same time determine the specific location based on the spatial index table, generate a first-level maintenance instruction and send it to the maintenance personnel to perform fixed-point maintenance and confirm local equipment or sensor problems. When it is a single-area equipment failure or equipment abnormality, a maintenance instruction is generated based on the specific equipment failure type given. The maintenance instruction includes the specific equipment number and failure type. When the maintenance instruction is generated, the current maintenance personnel is matched. The current maintenance personnel is based on the current maintenance personnel list provided by the personnel management system and the personnel's corresponding maintenance area. The equipment number of the maintenance instruction is matched to the maintenance area, thereby matching the current maintenance personnel in the area, sending the maintenance instruction to the maintenance personnel, and simultaneously opening the authority of the maintenance personnel in the corresponding area to follow up on subsequent maintenance situations.

[0060] When an abnormal environment occurs, the air conditioning system in the corresponding area is activated or the ventilation is adjusted, or the cooling power and ventilation volume are dynamically adjusted. When an abnormal prompt message appears, a warning message must be generated in a timely manner and sent to all maintenance personnel for timely manual inspection and troubleshooting. At the same time, the image data of the corresponding area is activated and sent to the visualization unit for display. The visualization unit processes the image data of the corresponding area to determine whether there is any movement of personnel. If it is determined that there is no movement of personnel within the set time, that is, no personnel have arrived at the area for maintenance, a warning message will be generated for the area again; if it is determined that there is movement of personnel, the warning message for the corresponding area will be suspended, waiting for the personnel to feedback maintenance information.

[0061] In the event of an abnormal equipment failure, in addition to notifying maintenance personnel, the device can also be remotely controlled to enter a safe mode, or reduce the device's operating power, suspend operations, etc. to prevent the failure from further expanding.

[0062] In this embodiment, the fourth strategy is to establish a real-time feedback mechanism. The execution unit will feed back the execution status of the execution strategy, such as whether the air conditioner starts normally, whether the device enters safe mode, etc., as well as real-time on-site data, to the master control platform at a high frequency (e.g., once per second). Based on this feedback information, the master control platform uses an adaptive control algorithm to dynamically adjust the current execution strategy and adopts the RBF-VSG adaptive control algorithm to optimize the operating status of the energy storage system. When it is found that the temperature drops slowly after the air conditioner is started, the master control platform automatically increases the cooling power of the air conditioner. When the fourth strategy determines that the abnormal situation has disappeared, in addition to terminating the corresponding execution strategy, it also records and analyzes the abnormal situation in detail, updates the abnormal situation knowledge base, and provides a reference for subsequent abnormality handling.

[0063] Another preferred embodiment of the present invention provides an adaptive security remote drive control system, including a master control platform, an acquisition unit and an execution unit, the acquisition unit including a temperature and humidity sensor and a video collector, the execution unit including an air cooling system, and the execution unit is also connected to a personnel management system, the master control platform is provided with a data storage unit, a real-time data matching unit, a data processing unit, an execution strategy generation unit and a feedback unit, the data storage unit includes acquisition data storage groups corresponding to each area, each storage group is provided with a time index table, a space index table and a device index table, the real-time data matching unit includes several temperature time axes, each temperature time axis corresponds to a collection point, the collected temperature data is compared with the temperature time axis data to determine whether there is abnormal data, when there is abnormal data, the abnormal situation is determined by the data processing unit, the execution strategy generation unit generates an execution strategy based on the abnormal situation, and transmits it to the execution unit for execution, the feedback unit obtains the real-time data of the corresponding acquisition point while executing the execution strategy, and dynamically adjusts the execution strategy or stops the execution strategy based on the feedback.

[0064] In some other preferred embodiments of the present invention, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the method described in the above embodiment.

[0065] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0066] The above description is a detailed description of the preferred embodiments of the present invention, but the embodiments are not intended to limit the scope of the patent application of the present invention. Any equivalent changes or modifications completed under the technical spirit suggested by the present invention should fall within the patent scope covered by the present invention.

Claims

1. An adaptive security remote drive control method, characterized in that: Obtain on-site temperature data and image data; The temperature data and image data are stored in partitions according to the areas where the collection points are located. Each area is equipped with a time index table, a space index table, and a device index table. Pre-process the collected temperature data to determine whether there is any abnormal data; When abnormal data appears, it is determined whether it is single-point abnormal data, multi-point abnormal data, or multi-region abnormal data; When it is single-point abnormal data, the temperature data of the past hour corresponding to the collection point is extracted according to the time index table, and the temperature change rate is calculated. When the temperature change rate exceeds the preset threshold, it is determined that there is a potential risk and marked as a single-point abnormal situation; When there are multiple points of abnormal data, the corresponding equipment of the collection point is extracted according to the equipment index table, and the operating status data of the corresponding equipment is obtained. The operating status data is input into the simulation model to determine whether there is a fault in the equipment and the type of fault. If there is a fault, it is marked as a single-area equipment fault; if there is no equipment fault, an abnormal prompt message is generated; When there is abnormal data in multiple areas, the temperature data of several other collection points at the same time are extracted according to the time index table. The number of collection points whose temperature data exceeds the median value of the comparison interval is counted. When more than half of the collection points are higher than the median value, the current indoor and outdoor temperatures are obtained synchronously. The indoor and outdoor temperature data of the same period on the historical time axis are extracted from the historical database. If the current temperature data is also higher than the historical temperature of the same period, and the difference exceeds a certain range, it is determined to be overheating caused by environmental factors and marked as an environmental abnormality. Extract temperature data from several other collection points at the same time based on the time index table. If no more than half of the collection points are higher than the median value, further determine the corresponding device based on the device index table and determine whether the devices are in the same task list. If so, mark the device as abnormal. If they are not in the same list, the operating status data of the corresponding equipment is obtained and input into the simulation model to determine whether the equipment has a fault and the type of fault. If the fault and fault type are output, it is determined that the equipment is abnormal. Otherwise, the corresponding collection points are summarized and an abnormal prompt message is generated. Match the corresponding execution strategy according to the generated exception type; After the execution strategy is sent to the corresponding execution unit, a real-time feedback mechanism is established for the abnormal situation area. The execution unit will feed back the execution status of the execution strategy and the real-time data on site to the master control platform. The master control platform will dynamically adjust the current execution strategy based on the feedback information using the adaptive control algorithm. When it is determined that the abnormal situation has disappeared, the corresponding execution strategy will be terminated.

2. The adaptive security remote drive control method according to claim 1, characterized in that: The preprocessing specifically includes: each collection point corresponds to an independent temperature-time axis, the comparison interval on the temperature-time axis is determined based on the time when the temperature data is acquired, and whether the temperature data is within the comparison interval is judged. When it is within the range, it is marked as normal data; when it exceeds the range, the temperature data is marked as abnormal data.

3. The adaptive security remote drive control method according to claim 2, characterized in that: The temperature time axis is a set of historical temperature data with a length of unit year. Each collection point is matched with a separate temperature time axis, which is arranged in sequence at intervals of days or hours, corresponding to the historical regular temperature data at each time.

4. The adaptive security remote drive control method according to claim 1, characterized in that: The specific steps for determining whether it is single-point abnormal data, multi-point abnormal data, or multi-region abnormal data include: When abnormal data occurs, count the number of collection points where the abnormal data occurs and the areas where the collection points are located. When a single abnormal data collection point appears in the same area, it is determined to be single-point abnormal data; When there are more than two abnormal data collection points in a single area, it is determined to be multi-point abnormal data; When multiple abnormal data collection points appear in different areas at the same time, it is determined to be multi-area abnormal data.

5. The adaptive security remote drive control method according to claim 1, characterized in that: Different execution strategies are matched according to the different types of abnormal situations generated. When it is a single-point abnormal situation, the execution strategy is to retrieve the image data of the corresponding collection point to perform gas-liquid leakage image recognition, and at the same time determine the specific location based on the spatial index table, generate a first-level maintenance instruction and send it to the maintenance personnel to perform fixed-point maintenance and confirm local equipment or sensor problems; when it is a single-area equipment failure or equipment abnormality, a maintenance instruction is generated according to the specific equipment failure type given, and the current maintenance personnel are matched while the maintenance instruction is generated, and the maintenance instruction is sent to the maintenance personnel; when it is an environmental abnormality, the air-conditioning system of the corresponding area is started or the ventilation is adjusted, or the cooling power and ventilation volume are dynamically adjusted.

6. The adaptive security remote drive control method according to claim 5, characterized in that: When generating maintenance instructions, the current maintenance personnel are matched and issued to the maintenance personnel. The maintenance instructions are also enabled to the maintenance personnel in the corresponding area, and the maintenance personnel are followed up on the subsequent maintenance situation.

7. An adaptive security remote drive control system, used to implement the adaptive security remote drive control method according to any one of claims 1 to 6, characterized in that: It includes a master control platform, an acquisition unit and an execution unit. The acquisition unit includes a temperature and humidity sensor and a video collector. The execution unit includes an air cooling system. The execution unit is also connected to a personnel management system. The master control platform is provided with a data storage unit, a real-time data matching unit, a data processing unit, an execution strategy generation unit and a feedback unit. The data storage unit includes acquisition data storage groups corresponding to each area. Each storage group is provided with a time index table, a space index table and a device index table. The real-time data matching unit includes several temperature time axes, each temperature time axis corresponds to a collection point. The collected temperature data is compared with the temperature time axis data to determine whether there is abnormal data. When there is abnormal data, the abnormal situation is determined by the data processing unit. The execution strategy generation unit generates an execution strategy based on the abnormal situation and transmits it to the execution unit for execution. While executing the execution strategy, the feedback unit obtains the real-time data of the corresponding acquisition point and feeds back at a high frequency, and dynamically adjusts the execution strategy or stops the execution strategy based on the feedback.

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