Adaptive data acquisition method, device and equipment for dynamic environment monitoring unit and storage medium

Abstract

The invention discloses a power and environment monitoring unit adaptive data acquisition method, device and equipment and a storage medium, and relates to the technical field of data management, and the method comprises the steps: obtaining historical operation data of a monitoring object, and extracting a feature index corresponding to the historical operation data; constructing a weight calculation model based on the characteristic indexes, and determining a state weight value of the historical operation data according to the weight calculation model; and adjusting the original acquisition frequency according to the state weight value to obtain an adjusted acquisition frequency, and performing data acquisition on the current operation data of the monitored object through the adjusted acquisition frequency. As the acquisition frequency can be dynamically adjusted according to the state weight value of the operation data, resource waste possibly caused by fixed-period acquisition is avoided, the real-time performance and pertinence of data acquisition are improved, fluctuation and change of data can be captured more timely, and the response speed of a dynamic environment monitoring system to abnormal conditions is improved.

Description

Technical Field

[0001] The present application relates to the field of data management technology, and in particular to a method, device, equipment and storage medium for adaptive data acquisition of a dynamic environment monitoring unit. Background Art

[0002] With the rapid development of the information and communications industry, the Field Supervision Unit (FSU) system is playing an increasingly important role in ensuring the safe and stable operation of critical infrastructure such as communication base stations and power equipment rooms. The FSU system is primarily responsible for real-time collection and monitoring of environmental parameters (such as temperature, humidity, smoke, and water intrusion), power equipment (such as distribution cabinets, UPS, and battery packs), and security systems (such as access control and video surveillance).

[0003] However, in practical applications, existing FSU systems still face numerous challenges in terms of real-time data collection, reliability, and the timeliness of alarm reporting. Due to a lack of intelligent analysis and adaptive capabilities, FSU systems lack dynamic perception and control of data collection frequency and changing trends. This inability to flexibly adjust collection strategies and reporting mechanisms based on field conditions leads to wasted resources, data delays, and inefficient fault location, compromising the timeliness of FSU data collection. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, equipment and storage medium for adaptive data acquisition of a dynamic environment monitoring unit, aiming to solve the technical problem of low timeliness of data collected by the existing FSU system.

[0005] To achieve the above objectives, the present application proposes an adaptive data acquisition method for a dynamic environment monitoring unit, the method comprising:

[0006] Obtaining historical operating data of the monitored object and extracting characteristic indicators corresponding to the historical operating data;

[0007] Constructing a weight calculation model based on the characteristic indicators, and determining the state weight value of the historical operation data according to the weight calculation model;

[0008] The original acquisition frequency is adjusted according to the state weight value to obtain an adjusted acquisition frequency, and the current operation data of the monitored object is collected using the adjusted acquisition frequency.

[0009] In one embodiment, the step of obtaining historical operating data of the monitored object and extracting characteristic indicators corresponding to the historical operating data includes:

[0010] Acquiring operating data of a monitored object based on a preset adjustment period, wherein the operating data is collected by a plurality of collection channels;

[0011] Characteristic indicators of change amplitude, trend slope and historical fluctuation frequency are extracted from the operating data.

[0012] In one embodiment, the step of constructing a weight calculation model based on the characteristic index and determining the state weight value of the historical operation data according to the weight calculation model includes:

[0013] Determine the monitoring scene to which the monitored object belongs, and obtain a weight allocation rule corresponding to the monitoring scene, where the weight allocation rule is a preset optimal weight combination of the characteristic indicators in the monitoring scene;

[0014] A weight calculation model is constructed according to the weight allocation rule in combination with the numerical value of the characteristic index, and the state weight value corresponding to each of the acquisition channels is calculated based on the weight calculation model.

[0015] In one embodiment, the step of adjusting the original acquisition frequency according to the state weight value to obtain the adjusted acquisition frequency, and collecting the current operating data of the monitored object using the adjusted acquisition frequency includes:

[0016] Obtaining the original acquisition frequency and the acquisition frequency upper limit corresponding to each acquisition channel;

[0017] Substituting the state weight value, the original acquisition frequency, and the acquisition frequency upper limit into a first preset acquisition frequency adjustment formula to obtain an adjusted acquisition frequency corresponding to each acquisition channel;

[0018] Data is collected in each collection channel according to the corresponding adjusted collection frequency.

[0019] In one embodiment, the method further comprises:

[0020] Obtain the data response time and original collection interval of the monitored object, and set the upper limit value and lower limit value of the collection interval corresponding to the monitored object;

[0021] When the data response time is greater than a preset collection interval threshold, substituting the data response time, the original collection interval, the collection interval upper limit, and the collection interval lower limit into a second preset collection interval adjustment formula to obtain an adjusted collection interval;

[0022] Data collection is performed using the adjusted collection interval.

[0023] In one embodiment, after the step of collecting the current operating data of the monitored object using the adjusted collection frequency, the method further includes:

[0024] Performing alarm status determination on the collected current operating data to identify critical alarm states;

[0025] triggering an event-driven mechanism associated with the critical alarm state;

[0026] According to the event-driven mechanism, alarm data is extracted from the current operation data and reported.

[0027] In one embodiment, the step of extracting and reporting alarm data from the current operating data according to the event-driven mechanism includes:

[0028] Determining, according to the event-driven mechanism, a correlation data collection strategy corresponding to the critical alarm state;

[0029] determining associated data in the current operation data based on the associated data collection strategy;

[0030] Dividing the associated data into alarm data of different alarm levels according to the preset alarm levels, and generating a current alarm reporting list according to the priority order corresponding to each of the alarm levels;

[0031] Based on the current alarm reporting list, alarm data of each alarm level is reported in sequence.

[0032] In addition, to achieve the above-mentioned purpose, the present application also proposes an adaptive data acquisition device for a dynamic environment monitoring unit, the device comprising:

[0033] A data acquisition module is used to obtain historical operating data of the monitored object and extract characteristic indicators corresponding to the historical operating data;

[0034] A state evaluation module is used to construct a weight calculation model based on the characteristic indicators and determine the state weight value of the historical operation data according to the weight calculation model;

[0035] The policy control module is used to adjust the original collection frequency according to the state weight value to obtain the adjusted collection frequency, and collect the current operation data of the monitored object through the adjusted collection frequency.

[0036] In addition, to achieve the above-mentioned purpose, the present application also proposes an adaptive data acquisition device for a dynamic environment monitoring unit, which includes: a memory, a processor, and an adaptive data acquisition program for a dynamic environment monitoring unit stored on the memory and runnable on the processor, and the adaptive data acquisition program for the dynamic environment monitoring unit is configured to implement the steps of the adaptive data acquisition method for the dynamic environment monitoring unit as described above.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, on which a dynamic environment monitoring unit adaptive data acquisition program is stored. When the dynamic environment monitoring unit adaptive data acquisition program is executed by a processor, the steps of the dynamic environment monitoring unit adaptive data acquisition method as described above are implemented.

[0038] The present application discloses an adaptive data collection method for a dynamic environment monitoring unit, comprising: obtaining historical operating data of a monitored object and extracting characteristic indicators corresponding to the historical operating data; constructing a weight calculation model based on the characteristic indicators, and determining the state weight value of the historical operating data according to the weight calculation model; adjusting the original collection frequency according to the state weight value to obtain an adjusted collection frequency, and collecting data from the current operating data of the monitored object using the adjusted collection frequency. Because the present application can dynamically adjust the collection frequency according to the state weight value of the operating data, it avoids the resource waste that may be caused by fixed-period collection, improves the real-time and targeted nature of data collection, can capture data fluctuations and changes more promptly, and improves the response speed of the dynamic environment monitoring system to abnormal situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0041] Figure 1 This is a flow chart of the first embodiment of the adaptive data acquisition method for a dynamic environment monitoring unit of the present application;

[0042] Figure 2 This is a flow chart of the second embodiment of the adaptive data acquisition method for a dynamic environment monitoring unit of the present application;

[0043] Figure 3 This is a flow chart of the third embodiment of the adaptive data acquisition method for a dynamic environment monitoring unit of the present application;

[0044] Figure 4 This is a schematic diagram of the module structure of the first embodiment of the adaptive data acquisition device for the dynamic environment monitoring unit of the present application;

[0045] Figure 5 This is a structural diagram of the adaptive data acquisition device for the dynamic environment monitoring unit of this application.

[0046] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0048] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0049] The present application provides a method for adaptive data acquisition of a dynamic environment monitoring unit, referring to Figure 1 , Figure 1 This is a flow chart of a first embodiment of the adaptive data acquisition method for a dynamic environment monitoring unit of the present application. In this embodiment, the method includes steps S10 to S30:

[0050] Step S10: Acquire historical operating data of the monitored object, and extract characteristic indicators corresponding to the historical operating data.

[0051] It should be noted that the execution entity of the method of this embodiment can be a computing service device with data processing, network communication, and program execution capabilities, such as a dynamic environment data management server or a monitoring host, or other electronic device capable of accessing the FSU system. This embodiment and the following embodiments will be specifically described using an adaptive data acquisition device for a dynamic environment monitoring unit (hereinafter referred to as the "FSU system") capable of accessing the FSU system as an example.

[0052] It should be understood that the monitoring objects may be environmental parameters, power equipment, etc. in different scenarios where the FSU system needs to collect and monitor real-time data. The number of monitoring objects may not be unique, and the operating data of different monitoring objects may be collected from different collection channels.

[0053] The FSU system can communicate with parameter sensors and device serial ports in various scenarios through multi-protocol access methods (such as modbus, RS485, RS232, Ethernet, etc.), thereby periodically obtaining various types of raw data of the monitored object during operation, namely the above-mentioned historical operation data.

[0054] In a specific implementation, the FSU system can obtain the historical operation data of the monitored object based on a preset adjustment period. The historical operation data can reflect the data changes of the monitored object in the previous adjustment period. Then, the historical operation data can be analyzed in turn for the change amplitude (ΔA), trend slope (K), historical fluctuation frequency (f hist ) Feature index value extraction of three feature indicators.

[0055] Step S20: constructing a weight calculation model based on the characteristic index, and determining the state weight value of the historical operation data according to the weight calculation model.

[0056] It should be understood that since the number of monitored objects is not unique, that is, the historical operating data of each monitored object comes from different collection channels. Therefore, a corresponding weight calculation model can be built for each collection channel, so that a "state weight value" is dynamically calculated for the historical operating data of each collection channel, reflecting the current importance and volatility of the data.

[0057] To specifically illustrate the construction process of the weight calculation model, step S20 includes: steps S201 to S202:

[0058] Step S201: Determine the monitoring scene to which the monitoring object belongs, and obtain a weight allocation rule corresponding to the monitoring scene, where the weight allocation rule is a preset optimal weight combination of the feature indicators in the monitoring scene.

[0059] It should be noted that different monitoring objects can be divided according to the monitoring scene, and then the optimal weight combination corresponding to different monitoring can be pre-set as the weight allocation rule. For example, the weight allocation rule in this application can be shown in Table 1 below, which is a weight allocation rule table.

[0060] Table 1 Weight distribution rules

[0061]

[0062] Step S202: constructing a weight calculation model according to the weight allocation rule and the numerical value of the characteristic index, and calculating the state weight value corresponding to each of the acquisition channels based on the weight calculation model.

[0063] It should be understood that the weight calculation model can be expressed as follows:

[0064]

[0065] Among them, S is the state weight value corresponding to a single acquisition channel, ΔA is the change amplitude, K is the trend slope, f hist is the historical fluctuation frequency, ω ΔA is the change amplitude weight, ωΔK is the trend slope weight, is the historical fluctuation frequency weight.

[0066] In the specific implementation, the extracted three characteristic indicator values ​​are substituted into the constructed weight calculation model to calculate the state weight value S of each monitored object's operating data. This state weight value can comprehensively reflect the importance and volatility of the operating data during the previous adjustment cycle, providing a quantitative basis for subsequent collection frequency adjustments.

[0067] Step S30: adjusting the original acquisition frequency according to the state weight value to obtain an adjusted acquisition frequency, and collecting data on the current operation data of the monitored object using the adjusted acquisition frequency.

[0068] It should be understood that the original collection frequency can be dynamically adjusted based on the state weight value. If the state weight value of the data is high, it means that the data is fluctuating or is relatively important. In this case, the collection frequency should be appropriately increased to capture the details of the data changes more promptly. Conversely, if the state weight value is low, it means that the data is relatively stable. The collection frequency can be reduced to reduce unnecessary resource consumption.

[0069] Specifically, if the data changes (such as drastic fluctuations in voltage and current data), the collection frequency is increased; if the data is highly stable (such as the air conditioner on / off status remains unchanged for a long time), the collection frequency is reduced.

[0070] Furthermore, a corresponding frequency collection strategy can be configured for key equipment in the monitored object or high-priority data in the historical operation data. The frequency collection strategy can be updated based on the aforementioned adjustment period: after obtaining the state weight value calculated above, the original collection frequency in the frequency collection strategy can be adjusted according to the state weight value.

[0071] In practice, data collection is re-performed at the adjusted collection frequency. This adaptive collection method enables more accurate acquisition of operational data from monitored objects, enabling refined management of the dynamic environment monitoring system, improving the system's real-time performance and reliability, optimizing the utilization of communication link resources, and ensuring the safe and stable operation of critical infrastructure such as communication base stations and power equipment rooms.

[0072] This embodiment obtains the historical operating data of the monitored object and extracts characteristic indicators corresponding to the historical operating data; constructs a weight calculation model based on the characteristic indicators, and determines the state weight value of the historical operating data according to the weight calculation model; adjusts the original collection frequency according to the state weight value to obtain the adjusted collection frequency, and collects the current operating data of the monitored object using the adjusted collection frequency. Because this embodiment can dynamically adjust the collection frequency according to the state weight value of the operating data, it avoids the resource waste that may be caused by fixed-period collection, improves the real-time and targeted nature of data collection, can capture data fluctuations and changes more promptly, and improves the system's response speed to abnormal situations.

[0073] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 , Figure 2 This is a flow chart of the second embodiment of the adaptive data acquisition method for a dynamic environment monitoring unit of the present application.

[0074] In this embodiment, in order to specifically illustrate how to perform data collection according to the adjusted collection frequency, step S30 includes: steps S301 to S303:

[0075] Step S301: obtaining the original acquisition frequency and the acquisition frequency upper limit corresponding to each acquisition channel.

[0076] It should be understood that the original acquisition frequency may be the real-time acquisition frequency of the FSU system for the monitoring objects of each acquisition channel, and the acquisition frequency upper limit may be preset by the user based on personalized acquisition requirements or FSU system performance.

[0077] It should be noted that the original collection frequency is the reciprocal of the default interval at which the FSU system collects data for a channel, ignoring factors such as the data state weight. For example, for a temperature and humidity monitoring channel, the original collection frequency might be every 5 minutes. This frequency is set based on general monitoring requirements and the system's default configuration, and is used for periodic data collection of monitored objects under normal circumstances.

[0078] The upper limit of the acquisition frequency specifies the maximum acquisition frequency that can be achieved for a channel. Regardless of how the data's state weight changes, the acquisition frequency will not exceed this upper limit. For example, the acquisition frequency upper limit for a power equipment current monitoring channel might be set to 10 acquisitions per second. When the data's state weight is very high, the acquisition frequency can approach or even reach this upper limit to ensure that rapid data changes can be captured in a timely manner.

[0079] Step S302: Substitute the state weight value, the original acquisition frequency, and the acquisition frequency upper limit into a first preset acquisition frequency adjustment formula to obtain an adjusted acquisition frequency corresponding to each acquisition channel.

[0080] It should be noted that in order to comprehensively consider the current state importance of the data (reflected by the state weight value), the original acquisition frequency basis and system resource limitations (reflected by the acquisition frequency upper limit), and reasonably determine the new acquisition frequency, a first preset acquisition frequency adjustment formula can be provided to adjust the acquisition frequency. The first preset acquisition frequency adjustment formula can be expressed as follows:

[0081] f=min(f max , f base +a·S)

[0082] Among them, f is the adjusted acquisition frequency, f base is the original acquisition frequency, f max is the upper limit of the acquisition frequency, a is the adjustment coefficient, and s is the aforementioned state weight value.

[0083] Step S303: collecting the current operating data of the monitored object in each collection channel according to the corresponding adjusted collection frequency.

[0084] In a specific implementation, the adjusted collection frequency corresponding to each collection channel is calculated based on the first preset collection frequency adjustment formula. During data collection, the FSU system sequentially reads data from each collection channel according to the adjusted collection frequency. For channels with increased collection frequencies, the FSU system initiates data collection requests at shorter intervals to more quickly obtain the current operating data of the monitored object, thereby more promptly reflecting data trends and anomalies.

[0085] Through the above steps, the original acquisition frequency is dynamically adjusted according to the state weight value of the monitored object's operating data, and data is collected according to the adjusted acquisition frequency, thereby more accurately meeting the data collection needs of different monitored objects in different operating states and improving the real-time performance, reliability and resource utilization efficiency of the dynamic environment monitoring system.

[0086] In addition, in addition to adjusting the acquisition frequency corresponding to each acquisition channel based on the state weight value, the acquisition frequency can also be adjusted based on the data response sequence of each monitored object. Therefore, in this embodiment, the method further includes: Steps: S001-S002:

[0087] Step S001: Acquire the data response time and original collection interval of the monitored object, and set the upper limit value and lower limit value of the collection interval corresponding to the monitored object.

[0088] It should be understood that data response time refers to the interval between the FSU sending a data collection request to a monitored object and actually receiving the operational data returned by the monitored object. It reflects the speed at which the monitored object responds to data collection requests and is affected by various factors, including the monitored object's processing capabilities, the quality of the communication link, and the status of the data transmission network.

[0089] The original collection interval is the interval initially set by the FSU system to collect operational data for the monitored object. This interval is used to periodically collect data. The original collection interval is preconfigured based on general monitoring requirements and device characteristics, and determines the frequency of data collection under normal circumstances. For example, the original collection interval for an ambient temperature and humidity sensor might be set to collect data every 800ms.

[0090] The upper and lower limits of the collection interval are used to restrict the range of collection interval values, ensuring that the collection interval is within a reasonable time range. The upper limit is the maximum possible collection interval. When long data response times or other factors require an increase in the collection interval, the collection interval will not exceed this upper limit. The lower limit is the minimum possible collection interval. This ensures that when data changes frequently or high-frequency collection is required, the collection interval will not fall below this lower limit, allowing timely data changes to be captured.

[0091] By setting the upper and lower limits of the collection interval, you can ensure that the collection frequency (the inverse of the collection interval) is within a reasonable range, which can meet the monitoring needs without causing excessive consumption of system resources or unnecessary burden on the equipment.

[0092] Step S002: When the data response time is greater than the preset collection interval threshold, the data response time, the original collection interval, the collection interval upper limit and the collection interval lower limit are substituted into a second preset collection interval adjustment formula to obtain an adjusted collection interval.

[0093] It should be noted that the preset time interval threshold can be used to determine whether the data response time is too long. When the data response time exceeds this threshold, it is considered that the current collection interval may not meet the need for timely data acquisition, or the current collection plan does not match the actual response capability of the monitored object, and the collection interval needs to be adjusted.

[0094] The preset time interval threshold can be set based on a comprehensive analysis of factors such as system performance requirements, historical data response time, and communication network conditions. For example, for a computer room with good communication network conditions, the preset collection interval threshold might be set to 1 second. If the data response time of a monitored object exceeds 1 second, the collection interval adjustment mechanism is triggered.

[0095] It should be understood that in order to comprehensively consider the data response time, the original collection interval, and the upper and lower limits of the collection interval, and dynamically determine a more reasonable collection interval, a second preset collection interval adjustment formula can be provided to adjust the collection interval. The second preset collection interval adjustment formula can be expressed as follows:

[0096] T gap =min(T max ,max(T min , T resp β))

[0097] Among them, T gap is the adjusted collection interval (the inverse of the collection frequency); T max The upper limit of the collection interval can be set to 800ms. min is the lower limit of the acquisition interval, which can be set to 100ms; β is the response rate adjustment coefficient.

[0098] In a specific implementation, when the data response time is detected to be greater than the preset collection interval threshold, the corresponding data response time, the original collection interval, the collection interval upper limit, and the collection interval lower limit are substituted into the above formula to calculate the adjusted collection interval. This allows the collection interval to be appropriately extended in the event of a long data response time, avoiding the waste of system resources or communication congestion caused by frequent collection requests. This also ensures that the collection interval does not exceed the set upper limit, ensuring the minimum frequency requirement for data collection.

[0099] Step S003: collecting the current operating data of the monitored object at the adjusted collection interval.

[0100] It should be understood that after determining the adjusted collection interval, the FSU system can collect data from the monitored object according to this new interval: the FSU system will set a scheduled task or trigger mechanism based on the adjusted collection interval. When the collection time point is reached, it will automatically send a data collection request to the monitored object and wait for the return data to obtain the current operation data of the monitored object.

[0101] For example, for a monitoring object whose collection interval is adjusted to 30 seconds according to the above calculation, the FSU system will send a data collection instruction to it every 30 seconds to obtain the latest operating data.

[0102] Furthermore, the collected data is promptly transmitted to the FSU system for subsequent processing and analysis. The FSU also monitors and provides feedback on the status of the collection process to ensure the normal progress of the collection process and the accuracy of the data. If data anomalies or collection failures are detected during the collection process, the FSU system will use appropriate mechanisms to handle them, such as re-collection and alarm prompts, to ensure the reliable operation of the monitoring system and the integrity of the data.

[0103] This embodiment dynamically adjusts the original acquisition frequency according to the state weight value of the monitored object's operating data, and collects data according to the adjusted acquisition frequency, thereby more accurately meeting the data acquisition requirements of different monitored objects in different operating states; in addition, the acquisition interval can also be dynamically adjusted according to the data response time of the monitored object, so that the data acquisition process can better adapt to the different monitored object characteristics and communication environments, optimize the utilization of system resources, and further improve the efficiency and reliability of data acquisition.

[0104] Based on the first and second embodiments of the present application, in the third embodiment of the present application, the same or similar contents as those in the first and second embodiments can be referred to above and will not be described in detail. Figure 3 , Figure 3 This is a flow chart of the third embodiment of the adaptive data acquisition method for a dynamic environment monitoring unit of the present application.

[0105] In this embodiment, in order to realize real-time alarm for real-time collected data, after step S30, the following steps are further included: steps S401 to S403:

[0106] Step S401: performing alarm status determination on the collected current operation data to identify a critical alarm status.

[0107] It should be understood that the FSU system can pre-set corresponding alarm determination rules for various types of operating data based on the normal operating range of the monitored object and possible abnormal conditions. These alarm determination rules can include data threshold ranges, change rate limits, specific pattern recognition, etc. For example, for the temperature data of the computer room, an alarm can be set to trigger when the temperature exceeds 30°C or falls below 15°C; for the UPS battery voltage, an alarm can be set to trigger when the voltage falls below a certain safety lower limit.

[0108] In specific implementation, the FSU system can conduct real-time comparative analysis of the collected current operating data with pre-set alarm judgment rules: comparing each data point one by one to see whether the value exceeds the set normal range, or whether there is a situation that meets a specific abnormal pattern.

[0109] For example, when the FSU system detects that the temperature of a certain area in the computer room rises sharply in a short period of time and exceeds a preset threshold range, it determines that the temperature data of the area is in an alarm state and identifies this critical alarm state.

[0110] Step S402: triggering an event-driven mechanism associated with the critical alarm state.

[0111] It should be understood that an association relationship between a critical alarm state and a corresponding event-driven mechanism may be pre-established in the FSU system to define which related event-driven operations should be triggered when a specific critical alarm state occurs.

[0112] For example, when a critical alarm state of low UPS battery voltage is identified, the associated event-driven mechanism may include immediately starting a deep inspection of the battery pack, triggering the startup preparation operation of the backup power supply, etc.

[0113] In practice, event triggers are pre-set for critical states (such as power failures and smoke alarms) to achieve event-driven monitoring. Once the event-driven mechanism is triggered, the FSU system can immediately adopt a high-frequency data collection and upload alarm data. Simultaneously, a correlated data enhancement collection strategy is initiated, enabling chain-linked monitoring.

[0114] Step S403: extracting and reporting alarm data from the current operation data according to the event-driven mechanism.

[0115] It should be understood that the FSU system can extract data directly related to critical alarm conditions from current operational data based on the definition of the event-driven mechanism. This data may include the value of the specific data point that triggered the alarm, the timestamp of the alarm occurrence, and the associated device identifier. For example, after identifying the critical alarm state of a smoke alarm on a device in the computer room, the system can extract the smoke concentration data at that moment, the device location information, and related ambient temperature and humidity data to help analyze the cause and severity of the alarm.

[0116] It should also be noted that the FSU system can also encapsulate the extracted alarm data according to a preset format and protocol and send it to the northbound platform or monitoring center via the communication module. Alarm data can be reported in real time, ensuring that the monitoring center receives alarm information immediately and can take timely response measures. For example, HTTP, TCP, MQTT, and other protocols can be used to transmit alarm data to the monitoring center's server, allowing users or other maintenance personnel to view and process alarm prompts on the monitoring interface.

[0117] Furthermore, different critical alarm states can be classified into alarm levels: level 1, level 2, level 3, and level 4, with level 1 being the highest level and level 4 being a common alarm. Furthermore, a priority reporting strategy can be set based on the priority classification. High-priority alarms will immediately interrupt the current reporting queue for rapid upload, while medium and low-priority alarms will use a buffer queue mechanism for unified scheduling and upload.

[0118] Therefore, step S403 specifically includes steps S4031 to S4034:

[0119] Step S4031: Determine, according to the event-driven mechanism, a correlation data collection strategy corresponding to the critical alarm state.

[0120] It should be understood that in the event-driven mechanism, correlation data collection strategies for different critical alarm states can also be predefined. The correlation data collection strategy specifies what correlation data needs to be collected in addition to the directly related alarm data when a specific critical alarm state occurs, and how to collect this data.

[0121] For example, when a power equipment fault alarm occurs, the associated data collection strategy may include simultaneously collecting the equipment's operating data such as current, voltage, and temperature, as well as data of associated upper-level power distribution equipment and lower-level load equipment.

[0122] In the specific implementation, after triggering the event-driven mechanism, the FSU system can determine the associated data collection strategy corresponding to the current critical alarm status based on the preset association relationship, and then identify the associated data type, collection frequency, collection duration and other parameters that need to be collected.

[0123] For example, for a computer room air conditioner failure alarm, the determined associated data collection strategy may be to collect the air conditioner's compressor pressure, current, outlet temperature and other data every 5 minutes over the next 30 minutes, as well as the ambient temperature and humidity data in the computer room, in order to comprehensively analyze the cause of the failure and the scope of impact.

[0124] Step S4032: determining associated data in the current running data based on the associated data collection strategy.

[0125] It should be understood that, according to the determined associated data collection strategy, associated data that meets the requirements is screened out from the current running data.

[0126] Specifically, data related to critical alarm states can be found from a large amount of collected data based on conditions such as data type, device identification, and time range.

[0127] Furthermore, the selected related data can be integrated and organized into logically related data sets for subsequent analysis and processing. For example, related data can be arranged in chronological order, or different types of related data can be categorized and organized. This allows maintenance personnel at the monitoring center or data analysis systems to quickly and accurately obtain the required information for fault diagnosis and decision-making.

[0128] Step S4033: Divide the associated data into alarm data of different alarm levels according to the preset alarm levels, and generate a current alarm reporting list according to the priority order corresponding to each of the alarm levels.

[0129] It should be understood that alarms can be divided into different levels according to factors such as the urgency, severity and scope of impact of the alarm, such as level 1 alarm (the highest level, indicating extremely serious emergencies, such as fire alarms, serious equipment failures, etc.), level 2 alarm (indicating more serious abnormal conditions, such as serious deviations of key equipment parameters from the normal range, etc.), level 3 alarm (indicating general abnormalities, such as minor abnormalities in equipment operating status, etc.), and level 4 alarm (ordinary alarm, such as minor failures of non-critical equipment, etc.).

[0130] In practice, the FSU system can classify the extracted correlated data into alarms of different levels based on pre-set alarm classification criteria. For example, a failure alarm for a critical power device that could potentially disrupt power to the entire computer room would be classified as a Level 1 alarm. A minor failure in a non-critical component would, however, impact performance but not immediately endanger system operations, resulting in a Level 3 alarm.

[0131] Next, the alarm data of different alarm levels can be sorted according to the priority order corresponding to each alarm level (from high to low, such as level 1 alarm has the highest priority, and so on), and the current alarm reporting list is generated. This list clarifies the reporting order of alarm data, ensuring that high-priority alarms are processed and responded to first.

[0132] Step S4034: reporting the alarm data of each alarm level in sequence based on the current alarm reporting list.

[0133] It should be noted that the FSU system may report the alarm data of each alarm level to the monitoring center in sequence according to the priority order determined in the current alarm reporting list.

[0134] The highest priority alarm data (such as level 1 alarms) is reported first. During the reporting process, special methods (such as highlighting and sound alarms) may be used to alert maintenance personnel to handle the situation. After completing the reporting of level 1 alarms, level 2 and level 3 alarm data are reported in sequence to ensure that all alarm information can be transmitted to the monitoring center in a timely and accurate manner.

[0135] Furthermore, alarm data can be reported in a variety of ways, such as sending it to the northbound platform's database via a communication module or displaying it in real time on the monitoring center's interface. The FSU system also monitors the reporting process to ensure successful delivery of alarm data, resending it or implementing other backup reporting methods when necessary.

[0136] In addition, the FSU system may also be provided with a data storage module for storing the operating data and alarm data collected at the current moment, so as to serve as the historical operating data of the next adjustment cycle and the data basis for subsequent collection frequency adjustment.

[0137] This embodiment, after collecting the current operating data of the monitored object, determines the alarm status and identifies key alarms on the data, triggers the event-driven mechanism, extracts and reports the alarm data according to the associated data collection strategy, and reports it in order of alarm level priority, ensuring that the monitoring center can obtain important alarm information in a timely manner, thereby improving the monitoring efficiency and emergency response capabilities of key infrastructure such as computer rooms.

[0138] It should be noted that the above examples are only used to understand this application and do not constitute a limitation on the adaptive data acquisition method of the dynamic environment monitoring unit of this application. More simple transformations based on this technical concept are all within the scope of protection of this application.

[0139] In addition, the present application also provides an adaptive data acquisition device for a dynamic environment monitoring unit, referring to Figure 4 , Figure 4 This is a schematic diagram of the module structure of the first embodiment of the adaptive data acquisition device for the dynamic environment monitoring unit of this application; Figure 4 As shown, the device includes:

[0140] The data acquisition module 401 is used to obtain historical operating data of the monitored object and extract characteristic indicators corresponding to the historical operating data;

[0141] A state evaluation module 402 is configured to construct a weight calculation model based on the characteristic indicators and determine a state weight value of the historical operation data according to the weight calculation model;

[0142] The policy control module 403 is configured to adjust the original acquisition frequency according to the state weight value to obtain an adjusted acquisition frequency, and collect the current operating data of the monitored object using the adjusted acquisition frequency.

[0143] This embodiment can dynamically adjust the collection frequency according to the state weight value of the operating data, avoiding the waste of resources that may be caused by fixed-period collection, improving the real-time and targeted nature of data collection, and can capture data fluctuations and changes more promptly, thereby improving the response speed of the dynamic environment monitoring system to abnormal situations.

[0144] Other embodiments or specific implementation methods of the adaptive data acquisition device for the dynamic environment monitoring unit described in this application can refer to the above-mentioned method embodiments and will not be repeated here.

[0145] The present application also provides an adaptive data acquisition device for a dynamic environment monitoring unit, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the adaptive data acquisition method for the dynamic environment monitoring unit in the above-mentioned embodiment one.

[0146] Reference below Figure 5 , Figure 5 This is a schematic diagram of the structure of the adaptive data acquisition device for the dynamic environment monitoring unit of the present application. The adaptive data acquisition device for the dynamic environment monitoring unit in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), etc., as well as fixed terminals such as digital TVs and desktop computers. Figure 5 The adaptive data acquisition device for the dynamic environment monitoring unit shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0147] like Figure 5As shown, the adaptive data acquisition device for the dynamic environment monitoring unit may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. Various programs and data required for the operation of the adaptive data acquisition device for the dynamic environment monitoring unit are also stored in RAM 1004. The processing device 1001, ROM 1002, and RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the adaptive data acquisition device of the dynamic environment monitoring unit to communicate wirelessly or wired with other devices to exchange data. Although the figure shows the adaptive data acquisition device of the dynamic environment monitoring unit with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have instead.

[0148] The adaptive data acquisition device for a dynamic environment monitoring unit provided in this application utilizes the adaptive data acquisition method for a dynamic environment monitoring unit described in the aforementioned embodiment, thereby resolving the technical issues surrounding adaptive data acquisition for dynamic environment monitoring units. Compared to the prior art, the adaptive data acquisition device for a dynamic environment monitoring unit provided in this application achieves the same beneficial effects as the adaptive data acquisition method for a dynamic environment monitoring unit described in the aforementioned embodiment. Other technical features of the adaptive data acquisition device for a dynamic environment monitoring unit are the same as those disclosed in the aforementioned embodiment and are not further elaborated upon here.

[0149] The present application also provides a computer-readable storage medium having computer-readable program instructions (ie, computer program) stored thereon, and the computer-readable program instructions are used to execute the adaptive data acquisition method for the dynamic environment monitoring unit in the above-mentioned embodiment.

[0150] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0151] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned adaptive data acquisition method for a dynamic environment monitoring unit. This computer-readable storage medium can address the technical issues surrounding adaptive data acquisition for dynamic environment monitoring units. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the adaptive data acquisition method for a dynamic environment monitoring unit provided in the aforementioned embodiments, and are not further elaborated here.

[0152] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional elements in the process, method, article, or system comprising the element.

[0153] The serial numbers of the above-mentioned embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments. Moreover, they are only some embodiments of the present application and do not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the description and drawings of the present application under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for adaptive data acquisition of a dynamic environment monitoring unit, characterized in that: The method comprises: Obtaining historical operating data of the monitored object and extracting characteristic indicators corresponding to the historical operating data; Constructing a weight calculation model based on the characteristic indicators, and determining the state weight value of the historical operation data according to the weight calculation model; The original acquisition frequency is adjusted according to the state weight value to obtain an adjusted acquisition frequency, and the current operation data of the monitored object is collected using the adjusted acquisition frequency.

2. The method according to claim 1, wherein The step of obtaining historical operation data of the monitored object and extracting characteristic indicators corresponding to the historical operation data includes: Acquire historical operating data of the monitored object based on a preset adjustment period, wherein the historical operating data is collected by a plurality of collection channels; The characteristic indicators of the change amplitude, trend slope and historical fluctuation frequency are extracted from the historical operation data.

3. The method according to claim 2, wherein The step of constructing a weight calculation model based on the characteristic indicators and determining the state weight value of the historical operation data according to the weight calculation model includes: Determine the monitoring scene to which the monitored object belongs, and obtain a weight allocation rule corresponding to the monitoring scene, where the weight allocation rule is a preset optimal weight combination of the characteristic indicators in the monitoring scene; A weight calculation model is constructed according to the weight allocation rule in combination with the numerical value of the characteristic index, and the state weight value corresponding to each of the acquisition channels is calculated based on the weight calculation model.

4. The method according to claim 3, wherein The step of adjusting the original acquisition frequency according to the state weight value to obtain the adjusted acquisition frequency, and collecting the current operating data of the monitored object using the adjusted acquisition frequency includes: Obtaining the original acquisition frequency and the acquisition frequency upper limit corresponding to each acquisition channel; Substituting the state weight value, the original acquisition frequency, and the acquisition frequency upper limit into a first preset acquisition frequency adjustment formula to obtain an adjusted acquisition frequency corresponding to each acquisition channel; In each of the acquisition channels, data is collected on the current operating data of the monitored object according to the corresponding adjusted acquisition frequency.

5. The method according to claim 1, wherein: The method further comprises: Obtain the data response time and original collection interval of the monitored object, and set the upper limit value and lower limit value of the collection interval corresponding to the monitored object; When the data response time is greater than a preset collection interval threshold, substituting the data response time, the original collection interval, the collection interval upper limit, and the collection interval lower limit into a second preset collection interval adjustment formula to obtain an adjusted collection interval; The current operation data of the monitored object is collected at the adjusted collection interval.

6. The method according to claim 1, wherein After the step of collecting the current operating data of the monitored object using the adjusted collection frequency, the method further includes: Performing alarm status determination on the collected current operating data to identify critical alarm states; triggering an event-driven mechanism associated with the critical alarm state; According to the event-driven mechanism, alarm data is extracted from the current operation data and reported.

7. The method according to claim 6, wherein The step of extracting and reporting alarm data from the current operation data according to the event-driven mechanism includes: Determining, according to the event-driven mechanism, a correlation data collection strategy corresponding to the critical alarm state; determining associated data in the current operation data based on the associated data collection strategy; Dividing the associated data into alarm data of different alarm levels according to the preset alarm levels, and generating a current alarm reporting list according to the priority order corresponding to each of the alarm levels; Based on the current alarm reporting list, alarm data of each alarm level is reported in sequence.

8. An adaptive data acquisition device for a dynamic environment monitoring unit, characterized in that: The device comprises: A data acquisition module is used to obtain historical operating data of the monitored object and extract characteristic indicators corresponding to the historical operating data; A state evaluation module is used to construct a weight calculation model based on the characteristic indicators and determine the state weight value of the historical operation data according to the weight calculation model; The policy control module is used to adjust the original collection frequency according to the state weight value to obtain the adjusted collection frequency, and collect the current operation data of the monitored object through the adjusted collection frequency.

9. An adaptive data acquisition device for a dynamic environment monitoring unit, characterized in that: The device includes a memory, a processor, and a dynamic environment monitoring unit adaptive data acquisition program stored in the memory and runnable on the processor. When the dynamic environment monitoring unit adaptive data acquisition program is executed by the processor, the steps of the dynamic environment monitoring unit adaptive data acquisition method as described in any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that: The storage medium stores a dynamic environment monitoring unit adaptive data acquisition program, which, when executed by a processor, implements the steps of the dynamic environment monitoring unit adaptive data acquisition method according to any one of claims 1 to 7.

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