Communication equipment energy-saving control system and method and storage medium

Abstract

The invention relates to the technical field of communication equipment energy saving, and particularly discloses a communication equipment energy-saving control system and method and a storage medium, through a state data acquisition and storage module, operation parameters, environmental parameters and historical energy consumption records are acquired in real time, and a comprehensive state database is established through preprocessing and classified storage, so that a data foundation is laid for accurate analysis; the exception recognition and response evaluation module is used for recognizing exceptions, generating reports and monitoring response key parameters, exception response capability classification processing is achieved, and exception handling pertinence and timeliness are improved; through a strategy generation and efficiency evaluation module, an energy-saving strategy is generated in combination with abnormal conditions and operation data, process parameters are analyzed to judge whether the efficiency reaches the standard or not, and efficient adaptation of the strategy is guaranteed. The system realizes the dynamic monitoring and intelligent adjustment of the energy consumption of the communication equipment, reduces the invalid energy consumption, improves the energy utilization efficiency, reduces the operation and maintenance cost, enhances the operation stability of the equipment, and provides reliable technical support for the energy conservation and consumption reduction of the communication system.

Description

Technical Field

[0001] This invention relates to the field of energy-saving technology for communication equipment, and in particular to energy-saving control systems, methods and storage media for communication equipment. Background Technology

[0002] Existing energy-saving technologies for communication equipment have formed a certain system in terms of control systems: control systems mostly adopt modular collaborative devices (including decision, control, and communication modules) or sensor and controller combination systems to achieve energy consumption management by real-time monitoring and analysis of data and generation of control commands; energy-saving methods based on business traffic prediction (shutting down some equipment under low load but difficult to cope with sudden peaks), dynamic adjustment methods based on multi-data fusion (adjusting equipment status by combining historical and real-time data), and comprehensive strategy optimization methods (including power adjustment, hibernation, topology optimization, etc.).

[0003] For example, Chinese invention patent CN112654080B discloses an energy-saving system and method for wireless communication devices, belonging to the field of wireless communication technology. The energy-saving system includes a data acquisition system for dividing wireless access points into multiple wireless access groups, where all wireless access points in each group have identical wireless access characteristics. The data acquisition system then collects and outputs the wireless access data from each wireless access point in each group. A data decision system, connected to the data acquisition system, determines an energy-saving scheme for each wireless access group based on the wireless access data. A network management system, connected to the data decision system, issues corresponding control commands to the wireless access points in each wireless access group according to the energy-saving scheme, thereby controlling each wireless access point to enter its corresponding energy-saving state.

[0004] For example, Chinese invention patent CN118215108A discloses an energy-saving control method for a communication device, an electronic device, and a readable storage medium. The method includes: acquiring target parameters of the communication device under the condition of meeting energy-saving requirements, wherein the target parameters include at least one of the following: temperature in each time unit within a first time period, current humidity of the communication device, and current temperature of the communication device; acquiring the current power consumption of the communication device; and controlling the communication device to perform a first energy-saving stage in a third time period under the condition of meeting a second condition; the second condition includes one of the following: the current temperature of the communication device is greater than a preset absolute temperature threshold; the current power consumption of the communication device is greater than a preset power consumption threshold; the first energy-saving stage includes: turning off a first part of the communication device, wherein the ratio of the power decrease due to turning off the first part of the device to the total power of the communication device is equal to a first threshold.

[0005] The aforementioned technologies suffer from at least the following technical problems: When communication equipment experiences abnormalities such as high energy consumption, overheating, or overload, the abnormal situation identification and response mechanism is not sensitive. For example, under high energy consumption conditions, it is easy to cause disorder in the equipment's operating parameters. This disordered parameter state will interfere with the sensor's capture of effective features, causing the abnormal identification mechanism to fail to pinpoint the root cause in a timely manner. Data acquisition delays, missing data, or insufficient accuracy will cause strategy generation to be delayed or biased due to inaccurate input. The computing power of the strategy generation module will be squeezed out by non-core business operations and frequent synchronization with external systems, thereby reducing the efficiency of strategy generation and making it difficult to meet the real-time and accuracy requirements of energy-saving control. Summary of the Invention

[0006] To address the technical problems of untimely response to abnormal situations and low efficiency in generating energy-saving strategies in existing technologies, embodiments of the present invention provide an energy-saving control system, method, and storage medium for communication equipment. The technical solution is as follows: On the one hand, an energy-saving control system for communication equipment is provided. This system includes: a status data acquisition and storage module, used to collect comprehensive status data of the communication equipment in real time through data acquisition equipment, perform preprocessing, classify and store the preprocessed data, thereby establishing a comprehensive status database of the communication equipment. This comprehensive status data reflects the operating status of the communication equipment, the environmental conditions, and energy consumption-related information. On the other hand, an anomaly identification and response evaluation module is used to extract the operating data of the communication equipment from the comprehensive status database and compare it with preset reference data, thereby identifying abnormal situations of the communication equipment, generating anomaly reports, and monitoring and acquiring the communication equipment's response actions to abnormal situations. Key parameters are used to classify and process the abnormal response capabilities of communication equipment. These key parameters reflect the timeliness of the communication equipment's response to abnormal situations. A strategy generation and performance evaluation module is used to generate energy-saving control strategies based on the abnormal situations and operational data of the communication equipment. It analyzes and obtains the process parameters for generating the energy-saving control strategies to determine whether the efficiency of the energy-saving control strategies generated by the communication equipment meets the standards. Furthermore, by dynamically adjusting resource allocation and strategy execution rhythm, energy-saving control of the communication equipment is achieved. The operational data reflects the real-time operating status of the communication equipment under current conditions; the process parameters reflect the efficiency of the energy-saving control strategy generation process.

[0007] On the other hand, an energy-saving control method for communication equipment is provided. This method includes: Step 1: Real-time acquisition of comprehensive status data of the communication equipment using a data acquisition device, followed by preprocessing. The preprocessed data is then categorized and stored to establish a comprehensive status database for the communication equipment. This comprehensive status data reflects the operating status of the communication equipment, the environmental conditions, and energy consumption-related information. Step 2: Extracting operating data from the comprehensive status database and comparing it with preset reference data to identify abnormal situations of the communication equipment, generating an abnormal situation report, and monitoring and acquiring key parameters of the communication equipment's response to abnormal situations. This allows for the categorization of the communication equipment's abnormal situation response capability. The key parameters reflect the timeliness of the communication equipment's response to abnormal situations. Step 3: Generating an energy-saving control strategy based on the abnormal situations and operating data of the communication equipment, analyzing and acquiring process parameters for the generation of the energy-saving control strategy, thereby determining whether the efficiency of the energy-saving control strategy generation by the communication equipment meets the standards. Furthermore, by dynamically adjusting resource allocation and strategy execution rhythm, energy-saving control of the communication equipment is achieved. The operating data reflects the real-time operating status of the communication equipment under current operating conditions; the process parameters reflect the efficiency during the generation of the energy-saving control strategy.

[0008] On the other hand, a computer-readable storage medium is provided, which stores at least one instruction, wherein the at least one instruction is loaded and executed by a processor to implement the aforementioned energy-saving control method for communication devices. The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: (1) This invention provides an energy-saving control system, method, and storage medium for communication equipment. Through closed-loop management of the entire process, it achieves precise control and efficient operation and maintenance of communication equipment energy consumption. Based on high-quality data acquisition and combined with dynamic anomaly identification and hierarchical response mechanisms, the system can quickly identify abnormal scenarios such as excessive energy consumption and high temperature, and continuously optimize energy utilization efficiency through personalized energy-saving strategies. At the same time, through regular review and parameter iteration, the system adaptability is continuously improved, ultimately achieving the goal of reducing ineffective energy consumption and reducing operation and maintenance costs.

[0009] (2) Real-time collection of equipment operating parameters, environmental data and historical energy consumption records to build a comprehensive status database, laying a solid data foundation for energy-saving control. Preprocessing ensures the accuracy and integrity of the data, while classified storage allows data from different dimensions (such as the energy consumption curve of the equipment in a high-temperature environment and the power change during light load periods) to be accurately retrieved, so that the subsequent energy-saving strategy can be closely aligned with the actual energy consumption characteristics of the equipment, improving the accuracy of energy-saving control from the source and providing reliable data basis for tapping the potential of equipment to reduce energy consumption.

[0010] (3) By accurately identifying abnormal situations of communication equipment and classifying and processing response capabilities, a dynamic safeguarding mechanism for energy-saving targets is formed. The comparison of operating data with preset reference values ​​can promptly capture abnormal states that deviate from the normal energy consumption trajectory, preventing such states from continuously consuming redundant energy due to lack of detection. The graded handling of response capabilities can match the appropriate intervention intensity according to the degree of impact of the abnormality on energy consumption, ensuring that energy-saving adjustment resources are tilted towards high-priority scenarios and improving overall energy-saving efficiency. At the same time, the monitoring of key parameters of response actions can continuously optimize the execution logic of energy-saving strategies, reduce energy waste caused by response lag or improper adjustment, and provide a guarantee for the continuous effectiveness of energy-saving control from both the timeliness and accuracy of abnormal handling.

[0011] (4) Based on the anomaly type matching strategy template, personalized energy-saving solutions are generated by combining real-time business volume, load distribution and other parameters. This avoids energy waste caused by forced adjustments regardless of equipment status and business needs, and also eliminates the problem of affecting the normal operation of core businesses due to excessive energy saving. This makes energy-saving control more in line with actual needs and finds a precise balance between cost reduction and stability. By comparing the efficiency index with the threshold, the resource allocation for strategy generation is dynamically optimized to ensure that the strategy is implemented quickly. Closed-loop verification of the strategy execution effect, combined with regular summary analysis and strategy library iteration, continuously improves the adaptability and effectiveness of energy-saving strategies, and ultimately achieves a balance between energy consumption reduction and stable equipment operation. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of the energy-saving control system structure for communication equipment provided in an embodiment of the present invention; Figure 2 This is a flowchart of the energy-saving control method for communication equipment provided in an embodiment of the present invention; Figure 3 This is a flowchart of a method for constructing a comprehensive state database and identifying abnormal situations provided in an embodiment of the present invention; Figure 4 This is a flowchart of the abnormal situation response capability classification method for communication devices provided in an embodiment of the present invention; Figure 5 This is a flowchart of a method for generating manual intervention instructions provided in an embodiment of the present invention; Figure 6 This is a flowchart of the efficiency judgment method for generating energy-saving control strategies for communication devices provided in an embodiment of the present invention; Figure 7 This is a flowchart of a method for determining the effectiveness of an energy-saving strategy provided in an embodiment of the present invention; Figure 8 This is a screenshot of the homepage of the device status monitoring platform provided in this embodiment of the invention; Figure 9 This is a diagram of the anomaly management interface of the equipment status monitoring platform provided in this embodiment of the invention. Detailed Implementation

[0014] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0015] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0016] This invention provides an energy-saving control system for communication equipment. For example... Figure 1 The diagram shows the structure of an energy-saving control system for communication equipment. The system includes: a status data acquisition and storage module, an anomaly identification and response evaluation module, a strategy generation and performance evaluation module, and a comprehensive status database.

[0017] The status data acquisition and storage module is connected to the anomaly identification and response evaluation module, which in turn is connected to the strategy generation and performance evaluation module. The status data acquisition and storage module, the anomaly identification and response evaluation module, and the strategy generation and performance evaluation module are all connected to a comprehensive status database. The aforementioned comprehensive status database is used to store various parameters involved in the energy-saving control system of the communication equipment.

[0018] The status data acquisition and storage module is used to collect comprehensive status data of communication devices in real time through data acquisition equipment, perform preprocessing, and classify and store the preprocessed data to establish a comprehensive status database of communication devices. This comprehensive status data reflects the operating status of the communication devices, the environmental conditions, and energy consumption information. It should be explained that the aforementioned communication devices may be, for example, centralized core communication devices deployed in large data center server rooms, undertaking backbone / core network data exchange and processing; or distributed edge communication devices deployed near user edge nodes, undertaking localized data processing and low-latency communication. The comprehensive status data includes the operating parameters, environmental parameters, and historical energy consumption records of the communication devices. The operating parameters include, but are not limited to, power consumption, CPU load rate, and port traffic. Environmental parameters include, but are not limited to, server room temperature, humidity, and external light intensity. Historical energy consumption records include energy consumption curves under different time periods, load states, and environmental conditions. The preprocessing refers to removing outliers caused by sensor malfunctions and supplementing missing data using linear interpolation. The processed data is then classified and stored according to device number and acquisition timestamp.

[0019] The anomaly identification and response evaluation module is used to extract the operating data of communication equipment from a comprehensive status database and compare it with preset reference data to identify abnormal situations of the communication equipment, generate anomaly reports, monitor and acquire key parameters of the communication equipment's response actions to abnormal situations, and classify the communication equipment's anomaly response capabilities. These key parameters reflect the timeliness of the communication equipment's response actions to abnormal situations. It should be noted that the aforementioned operating data of the communication equipment includes, but is not limited to, equipment load rate, equipment signal strength, and power supply stability. Identifying abnormal situations of the communication equipment refers to retrieving real-time operating data (such as temperature) from the comprehensive status database and comparing it with preset reference data (such as the equipment's optimal operating parameter range) from multiple dimensions. When real-time data exceeds the reference range (such as a sudden increase in energy consumption of more than 10%), the system determines it as an abnormal situation, ensuring the objectivity and standardization of anomaly identification. Generating anomaly reports refers to automatically generating a report containing key information for the identified anomalies, including the time of the anomaly, the unique identifier of the equipment involved, specific abnormal parameters and values, and the duration of the anomaly, providing clear information support for subsequent responses.

[0020] In a specific example embodiment, the specific abnormal situation report is as follows: the specific time of the abnormality, the unique identifier of the device involved is 5G-Macro-BS-072 (a 5G macro base station deployed in a commercial center area of ​​a certain city), and the specific abnormal parameters and values ​​are: the transmission power reaches 49W (the baseline power at this time is 32W, exceeding it by 53.1%), and at the same time, the temperature of the device motherboard rises to 68℃ (the normal operating temperature range is 35-60℃), and the duration of the abnormality is 12 minutes.

[0021] In another specific example embodiment, the specific abnormal situation report is as follows: the specific time of the abnormality, the unique identifier of the device involved is DC-Server-108 (rack server in the 3rd row of racks in Area A of the data center), and the specific abnormal parameters and values ​​are: the CPU utilization rate is maintained at 94% (the threshold is 80%), the real-time power consumption reaches 320W (the average power consumption under normal load is 180W), the hard disk IOPS (input / output operations per second) is 1200 (the historical average under the same load is 800), and the duration of the abnormality is 23 minutes.

[0022] The strategy generation and performance evaluation module is used to generate energy-saving control strategies based on abnormal conditions and operational data of communication equipment. It analyzes and obtains process parameters for generating these strategies to determine whether the efficiency of the generated strategies meets the standards. Furthermore, it dynamically adjusts resource allocation and strategy execution rhythm to achieve energy-saving control of the communication equipment. The operational data reflects the real-time operating status of the communication equipment under current conditions; the process parameters reflect the efficiency of the energy-saving control strategy generation process. It should be noted that generating energy-saving control strategies refers to using identified abnormal conditions (such as excessively high temperatures) as a guide, combined with real-time operational data of the communication equipment (such as service load), matching basic templates from the strategy library, and adapting parameters. For example, for "abnormal temperature under high load," the system extracts data such as current concurrent service volume and CPU utilization, dynamically adjusts parameters such as power adjustment range and heat dissipation start threshold in the template, and generates a personalized strategy that includes adjustment targets, step-by-step execution steps, and expected energy-saving effects, ensuring a high degree of match between the strategy and the actual state of the equipment. It should be explained that the above preprocessing refers to removing outliers caused by sensor malfunctions, supplementing missing data using linear interpolation, and storing the processed data according to device number and acquisition timestamp.

[0023] like Figure 3 The flowchart of the method for constructing a comprehensive status database and identifying abnormal situations provided in this embodiment of the invention is shown. The process starts from the beginning, then collects data, preprocesses the data, classifies and stores it, establishes a comprehensive status database, then identifies abnormal situations of communication devices and generates abnormal situation reports, and then obtains the abnormal situation response lag index.

[0024] like Figure 8 As shown in the homepage diagram of the device status monitoring platform provided in this embodiment of the invention, the top displays the real-time total energy consumption (1248kWh, including comparison with the same period yesterday), today's energy saving (356kWh, including comparison with yesterday), and unprocessed anomalies (3) in the form of data cards; the middle area presents the energy consumption trend of the past 7 days through a line chart, and the energy consumption fluctuation can be observed by switching between daily and monthly dimensions; the right column lists the top 5 energy-saving devices and their corresponding energy saving data; the bottom displays today's strategy execution log in chronological order, showing the strategy name, associated device, execution operation and status, covering device energy efficiency optimization tasks such as reducing power supply and adjusting CPU utilization, intuitively presenting the overall picture of device energy consumption management and strategy execution.

[0025] Specifically, the abnormal situation response capability of communication equipment is classified and processed. The specific classification process is as follows: First, by analyzing the key parameters of the communication equipment's response actions to abnormal situations, an abnormal situation response lag index is obtained and compared with the lag type I threshold and lag type II threshold. It should be explained that the aforementioned lag type I threshold is a preset critical value in the comprehensive status database used to distinguish between acceptable and slightly unacceptable response lag. The aforementioned lag type II threshold is a preset critical value in the comprehensive status database used to distinguish between slightly acceptable and severely unacceptable response lag.

[0026] When the abnormal situation response lag index is lower than the lag category threshold, the abnormal situation response capability of the communication equipment is classified as having acceptable response lag. Simultaneously, the response data for this abnormal situation is recorded, entered into the communication equipment performance file, and marked as a high-response case. The recorded response data for this abnormal situation includes basic scenario information, response process data, and environmental correlation data. The performance file stores all high-response cases categorized by equipment number. Through long-term accumulation, a "response capability baseline" for the equipment can be formed, providing data support for assessing the equipment's health status. The cases marked as high-response can serve as optimization samples for the strategy library, enabling response strategy reuse and providing high-quality reference samples for energy-saving strategy optimization.

[0027] When the abnormal situation response lag index is between the lag type I threshold and the lag type II threshold, the abnormal situation response capability of the communication equipment is classified as slightly unqualified in response lag, and the response capability of the communication equipment to abnormal situations is optimized. When the abnormal situation response lag index is not lower than the lag type II threshold, the abnormal situation response capability of the communication equipment is classified as severely unqualified in response lag, and the response capability of the communication equipment to abnormal situations is adjusted.

[0028] Specifically, the optimization process for the communication equipment's response to abnormal situations involves: obtaining a type I deviation value based on the abnormal situation response lag index and a type I lag threshold; and optimizing the communication equipment's response to abnormal situations based on this type I deviation value. This type I deviation value refers to the result of subtracting the type I lag threshold from the abnormal situation response lag index. A sensor data sampling interval reduction coefficient is then matched based on the type I deviation value, thereby reducing the sensor data sampling interval of the communication equipment. Finally, an amplification coefficient for the enforcement of control measures is matched based on the type I deviation value, thereby increasing the enforcement strength of the communication equipment's control measures.

[0029] It should be explained that the above-mentioned sensor data sampling interval reduction coefficient, based on matching a type of deviation value, is determined as follows: A comprehensive state database is pre-set with sensor data sampling interval reduction coefficients corresponding to each type of deviation value interval. The obtained type of deviation value is input into the database, which then matches the corresponding type of deviation value interval. The sensor data sampling interval reduction coefficient corresponding to this interval is the required reduction coefficient. Multiplying the obtained sensor data sampling interval reduction coefficient by the original sensor data sampling interval yields the desired adjusted sensor data sampling interval. The above-mentioned sensor data sampling interval reduction coefficient is less than 1. This indicates the percentage that the sensor data sampling interval needs to be reduced. Shortening the sensor data sampling interval allows the system to capture changes in equipment operating parameters more frequently, detect early signs of anomalies in a timely manner, avoid delays in anomaly judgment caused by data update lags, and enable anomalies (such as the initial signs of abnormal energy consumption fluctuations) to be detected earlier. This avoids delays in anomaly identification caused by excessively long sampling intervals, and early detection of anomalies can provide more time for energy-saving control to intervene. Adjustment measures can be initiated in the early stages of energy consumption deviating from the normal trajectory, thereby minimizing redundant energy consumption during the period of anomaly, reducing energy losses caused by the expansion of anomalies, and improving the timeliness and predictability of energy-saving control.

[0030] The above-mentioned adjustment measure execution intensity amplification coefficient is determined based on a type of deviation value. The specific matching process is as follows: The database pre-sets the adjustment measure execution intensity amplification coefficients corresponding to each type of deviation value interval. The obtained type of deviation value is input into the database, and the database can match the corresponding type of deviation value interval. The adjustment measure execution intensity amplification coefficient corresponding to this interval is the required amplification coefficient. Multiplying the obtained adjustment measure execution intensity amplification coefficient by the original adjustment measure execution intensity yields the required adjustment measure execution intensity. An adjustment measure execution intensity amplification coefficient greater than 1 indicates that the adjustment measure execution intensity needs to be increased by a certain factor. Strengthening the adjustment measures during abnormal responses can more effectively curb the upward trend of energy consumption under abnormal conditions. When equipment exhibits a tendency to lag in response, increasing the adjustment intensity can quickly pull energy consumption back to a reasonable range, avoiding the continuous accumulation of abnormal energy consumption due to insufficient adjustment. It can quickly eliminate the interference of abnormalities on energy-saving targets while ensuring stable equipment operation, and reduce the additional energy consumption caused by repeated adjustments, thereby improving the accuracy and effectiveness of energy-saving control.

[0031] Example 1: A communication device detects an anomaly of sudden high power consumption in an edge computing server: the instantaneous power jumps from a baseline of 300W to 480W and remains there for 5 minutes without decreasing. The communication device automatically executes the corresponding energy-saving strategy under this anomaly, namely reducing the power supply of non-core modules by 10%. At the same time, it triggers the anomaly identification and response mechanism. Analyzing the anomaly identification process, it is found that the communication device's anomaly response capability is slightly unqualified in terms of response lag. Then, based on the preset mapping rules in the comprehensive status database, the original execution intensity is increased, that is, the reduction in power supply of non-core modules is expanded to 25%.

[0032] Example 2: A communication device detects an anomaly where the bit error rate of a certain channel in an optical transmission device far exceeds the baseline. The communication device automatically executes the corresponding energy-saving strategy under this anomaly, namely, adjusting the power of the optical module of that channel by 5%. At the same time, it triggers the anomaly identification and response mechanism. Analyzing the anomaly identification process, it is found that the communication device's anomaly response capability is slightly unqualified in terms of response lag. Then, based on the preset mapping rules in the comprehensive status database, the original execution intensity is increased, that is, the power of the optical module of that channel is adjusted by 8%.

[0033] The optimized anomaly response lag index is reacquired and marked as the secondary response lag coefficient to determine whether to generate a manual intervention command. Further, the key parameters of the communication equipment's response to anomalies are analyzed. The specific analysis process is as follows: key parameters include the communication equipment's anomaly determination time, command transmission time, and equipment execution delay time. Based on the processed key parameters, the anomaly determination time factor, command transmission time factor, and equipment execution delay factor are obtained as core evaluation parameters. By pre-setting the effect coefficients of each factor in the comprehensive state database, their weight contribution to the anomaly response lag index is quantified. Finally, a weighted average fusion algorithm is used to synthesize the anomaly response lag index.

[0034] It should be explained that the aforementioned key parameters include, but are not limited to, parameters related to anomaly detection (such as feature matching time), parameters related to instruction transmission (such as protocol conversion delay), and parameters related to execution feedback (such as result return delay). The anomaly detection time factor represents the ratio of the communication device's anomaly detection time to its threshold value; the instruction transmission time factor represents the ratio of the communication device's instruction transmission time to its threshold value; and the device execution delay factor represents the ratio of the communication device's device execution delay to its threshold value. The anomaly response lag index refers to the degree of lag in the communication device's response to sudden anomalies, and the specific evaluation method is as follows: ; ; ; ; In the formula, ASRLI is the abnormal situation response lag index, AJDF is the abnormal judgment time factor of the communication device, AJDT is the abnormal judgment time of the communication device, DAJD is the preset defined abnormal judgment time in the comprehensive state database, CTTF is the instruction transmission time factor of the communication device, CTT is the instruction transmission time of the communication device, DCTT is the preset defined instruction transmission time in the comprehensive state database, DEDF is the device execution delay factor of the communication device, DEDT is the device execution delay time of the communication device, DDED is the preset defined device execution delay time in the comprehensive state database, qa is the effect coefficient corresponding to the preset abnormal judgment time factor in the comprehensive state database, qc is the effect coefficient corresponding to the preset instruction transmission time factor in the comprehensive state database, and qd is the effect coefficient corresponding to the preset device execution delay factor in the comprehensive state database.

[0035] It should be explained that the anomaly determination time of the aforementioned communication equipment refers to the total time from the time the communication equipment's sensor or monitoring module first collects abnormal feature data (such as temperature anomaly) to the time the system completes data analysis, compares with baseline standards, and finally determines it as an "abnormal situation." This is obtained by recording the difference between the time of the first collection of abnormal feature data and the time of the system's determination result output. The instruction transmission time of the aforementioned communication equipment refers to the time from when the adjustment instruction generated by the system is issued to when it is received and confirmed by the communication equipment after the anomaly is determined. This is obtained by recording the difference between the time the adjustment instruction is issued and the time the equipment receives and confirms it. The equipment execution delay time of the aforementioned communication equipment refers to the time from when the communication equipment receives and confirms the adjustment instruction to when it actually begins to execute the instruction and produces a measurable effect (such as temperature drop). This is obtained by recording the difference between the time the equipment receives and confirms the instruction and the time when the instruction is executed and produces the first effective effect.

[0036] The above-mentioned definition of anomaly determination time refers to the maximum value of anomaly determination time within the specified range; the above-mentioned definition of instruction transmission time refers to the maximum value of instruction transmission time within the specified range; the above-mentioned definition of device execution delay time refers to the maximum value of device execution delay time within the specified range.

[0037] The anomaly detection time is a prerequisite for response initiation, and its length directly determines the start time of command transmission. The command transmission time follows the detection result, and its fluctuations will compress or squeeze the time window for subsequent execution. The device execution latency is the final step in response implementation. If the cumulative time of the first two steps is too long, it may exacerbate the lag pressure in the execution phase. Extending any of these three steps will increase the total response cycle. Conversely, if one step is optimized and shortened, it can reserve a more reasonable time buffer for other steps, ultimately affecting the level of response lag index.

[0038] The effect coefficients corresponding to the above-mentioned anomaly determination duration factor indicate that when the anomaly determination duration factor changes by a unit amplitude, the anomaly response lag index will change accordingly. Similarly, the effect coefficients corresponding to the above-mentioned instruction transmission time factor indicate that when the instruction transmission time factor changes by a unit amplitude, the anomaly response lag index will change accordingly. The effect coefficients corresponding to the above-mentioned device execution delay factor indicate that when the device execution delay factor changes by a unit amplitude, the anomaly response lag index will change accordingly. The comprehensive state database stores the mapping relationships between the anomaly determination duration factor and its corresponding effect coefficient, the instruction transmission time factor and its corresponding effect coefficient, and the device execution delay factor and its corresponding effect coefficient. For example, when the anomaly determination duration factor, instruction transmission time factor, and device execution delay factor are input into the comprehensive state database, the comprehensive state database generates the corresponding effect coefficients for the anomaly determination duration factor, instruction transmission time factor, and device execution delay factor based on preset mapping rules, and the numerical range of each effect coefficient is strictly controlled between 0 and 1.

[0039] The larger the anomaly determination time factor, the more significant the contribution of the anomaly determination process to the lag, directly pushing up the anomaly response lag index; the larger the instruction transmission time factor, the more severe the lag in the instruction transmission process, and the stronger the positive pull on the anomaly response lag index; the larger the equipment execution delay factor, the greater the degree to which the actual execution delay deviates from the limit value, which will directly lead to an increase in the anomaly response lag index.

[0040] like Figure 4The flowchart of the abnormal situation response capability classification method for communication equipment provided in this embodiment of the invention shows that the obtained abnormal situation response lag index is compared with the preset lag type I threshold and lag type II threshold in the comprehensive status database. Based on the comparison result, the abnormal response capability of the equipment is classified and processed. If the abnormal situation response lag index is less than the lag type I threshold, the abnormal response capability of the communication equipment is determined to be qualified, and the response data of this abnormal situation is recorded in the communication equipment performance file and marked as a high-response case. If the abnormal situation response lag index is between the lag type I threshold and the lag type II threshold, the abnormal response capability of the communication equipment is determined to be slightly unqualified. Based on the type I deviation value, a sensor data sampling interval reduction coefficient is matched to shorten the sampling interval. The system improves data update frequency and strengthens the implementation of adjustment measures, and re-acquires the optimized abnormal situation response lag index, which is marked as the secondary response lag coefficient. If the abnormal situation response lag index is greater than or equal to the lag type II threshold, the abnormal response capability of the communication equipment is deemed seriously unqualified. Hardware and link diagnostic instructions are generated to locate the root cause of the abnormality. Based on the diagnostic results, the response strategy is redesigned. After testing and verification, the optimized response logic firmware is burned into the equipment control system to realize online updates of the abnormal handling strategy and equipment self-optimization. After the above abnormal response processing is completed, the system enters the strategy generation and performance evaluation module, which generates an energy-saving control strategy based on the abnormal situation report and the real-time operating data of the communication equipment, and evaluates the strategy generation efficiency index.

[0041] Specifically, the process for determining whether to generate a manual intervention command is as follows: the secondary response lag coefficient is compared with the lag threshold of type I; if the secondary response lag coefficient is lower than the lag threshold of type I, it is determined that no manual intervention command will be generated, and the sensor data sampling interval and the intensity of the adjustment measures will be restored; if the secondary response lag coefficient is not lower than the lag threshold of type I, it is determined that a manual intervention command will be generated.

[0042] It should be explained that the aforementioned manual intervention command contains detailed information about the abnormal scenario. The purpose is to compensate for the shortcomings of the automatic response mechanism through the professional judgment and manual operation of the operation and maintenance personnel. For example, if the system has shortened the sensor sampling interval and increased the adjustment intensity, but the equipment response lag still has not improved, manual intervention may include remotely logging into the equipment to check the hardware status, correcting the response logic parameters, or temporarily switching to backup equipment to share the load, thereby quickly curbing the aggravation of abnormal lag and avoiding the risk of equipment damage or business interruption.

[0043] Adjusting the communication equipment's response capability to abnormal situations involves the following process: First, generating hardware and link diagnostic commands to pinpoint the cause of the abnormality. Then, replanning the response strategy based on the diagnostic results. After testing and confirming the effectiveness of the replanned response strategy, importing it into the system enables online updates of the abnormality handling strategy.

[0044] It's important to explain that generating hardware and link diagnostic commands is fundamental to accurately locating problems. These commands trigger the device's built-in detection modules and link monitoring tools to perform a comprehensive scan of the operational status of key hardware (such as sensors) and data transmission links (such as network bandwidth), outputting a diagnostic report that includes fault points and link bottlenecks. This avoids ineffective optimizations caused by blind adjustments. The core of redesigning the response strategy based on the diagnostic results is to address problems specifically. For example, if the diagnosis reveals that "sensor data delays cause a lag in anomaly detection," a multi-sensor cross-validation mechanism is added to the new strategy. If link congestion causes slow command transmission, the command data packet structure is optimized to reduce transmission volume. The redesigned response strategy needs to be validated through simulation testing to ensure that it can shorten response lag time under different load and environmental conditions.

[0045] like Figure 5 As shown in the flowchart of the method for generating a manual intervention command provided in this embodiment of the invention, if the secondary response lag coefficient is less than the lag threshold, that is, the original sampling interval of the sensor and the original execution strength of the adjustment measures are restored, and no manual intervention command is generated; otherwise, a manual intervention command is generated and pushed to the maintenance team for manual intervention.

[0046] Specifically, the process parameters for generating energy-saving control strategies are analyzed and obtained. The specific analysis process includes: the strategy algorithm iteration cycle, the strategy parameter verification time, and the multi-objective conflict resolution time. Based on the processed key parameters, the strategy algorithm iteration cycle factor, the strategy parameter verification time factor, and the multi-objective conflict resolution time factor are obtained as core evaluation parameters. At the same time, the final value of the response lag in abnormal situations is obtained as a basic performance indicator. By pre-setting the effect coefficients of each parameter in the comprehensive state database, their weight contribution value to the energy-saving strategy generation efficiency index is quantified. Finally, a weighted average fusion algorithm is used to synthesize the energy-saving strategy generation efficiency index.

[0047] It should be explained that the above process parameters include, but are not limited to, resource occupancy rate, external data interaction latency, and parameter iteration count; the above strategy algorithm iteration cycle factor represents the ratio of the communication device's strategy algorithm iteration cycle to its threshold value; the above strategy parameter verification time factor represents the ratio of the communication device's strategy parameter verification time to its threshold value; the above multi-objective conflict resolution time factor represents the ratio of the communication device's multi-objective conflict resolution time to its threshold value; and the above abnormal situation response lag final value represents the final abnormal situation response lag index of the communication device, which is classified as having acceptable response lag. The energy-saving strategy generation efficiency index refers to the efficiency of the communication device in generating energy-saving control strategies based on identified abnormal situations and operational data. The specific evaluation method is as follows: ; ; ; ; In the formula, ESSGEI is the energy-saving strategy generation efficiency index, FYRLAS is the lag final value of the abnormal situation response, SAICF is the strategy algorithm iteration cycle factor of the communication equipment, SAIC is the strategy algorithm iteration cycle of the communication equipment, DSAIC is the default definition strategy algorithm iteration cycle in the comprehensive state database, SPVTF is the strategy parameter verification time factor of the communication equipment, SPVT is the strategy parameter verification time of the communication equipment, DSPVT is the default definition strategy parameter verification time in the comprehensive state database, MOCRDF is the multi-objective conflict resolution time factor of the communication equipment, MOCRD is the multi-objective conflict resolution time of the communication equipment, DMOCRD is the default definition target conflict resolution time in the comprehensive state database, ga is the effect coefficient corresponding to the default strategy algorithm iteration cycle factor in the comprehensive state database, gp is the effect coefficient corresponding to the default strategy parameter verification time factor in the comprehensive state database, gm is the effect coefficient corresponding to the default multi-objective conflict resolution time factor in the comprehensive state database, and gf is the effect coefficient corresponding to the default abnormal situation response lag final value in the comprehensive state database.

[0048] It should be explained that the above-mentioned strategy algorithm iteration cycle refers to the complete time interval between the current version and the next optimized version of the communication device's strategy algorithm, which can be directly extracted from the device management log; the above-mentioned strategy parameter verification time represents the ratio of the actual time spent on the strategy parameter verification phase to the preset standard time spent, which can also be directly extracted from the device management log; the above-mentioned multi-objective conflict resolution time refers to the total time spent by the system to coordinate priorities and balance target weights through algorithms to finally output a conflict-free strategy when multiple optimization objectives conflict during strategy generation, which is obtained by recording the difference in timestamps from the time the system identifies the target conflict to the time it outputs the coordinated strategy.

[0049] The aforementioned definition of the strategy algorithm iteration cycle represents the maximum value of the strategy algorithm iteration cycle within a specified range; the aforementioned definition of the strategy parameter verification time represents the maximum value of the strategy parameter verification time within a specified range; the aforementioned definition of the multi-objective conflict resolution time represents the maximum value of the multi-objective conflict resolution time within a specified range. The strategy parameter verification time directly constrains the strategy algorithm iteration cycle. Verification time exceeding the standard will prolong the testing phase of a single iteration, forcing a longer iteration cycle. Meanwhile, the multi-objective conflict resolution time, as a crucial step in strategy generation, affects the input quality and efficiency of parameter verification, thus indirectly impacting the verification time. Conversely, a reasonable iteration cycle allows sufficient time for parameter verification and conflict resolution, avoiding insufficient verification or rushed conflict handling due to an excessively short cycle.

[0050] A larger strategy algorithm iteration cycle factor indicates a greater lag in strategy algorithm updates compared to environmental changes, making it difficult for energy-saving strategies to quickly adapt to new scenarios and leading to a decrease in the energy-saving strategy generation efficiency index. A larger strategy parameter verification time factor means a longer parameter verification process, slowing down the strategy deployment pace and reducing the overall energy-saving strategy generation efficiency index. A larger multi-objective conflict resolution time factor indicates increased decision-making blocking time during strategy generation, directly lowering the energy-saving strategy generation efficiency index. A larger abnormal situation response lag value indicates a more significant lag in equipment handling abnormalities, and this lag in abnormality handling interferes with the execution of energy-saving strategies, leading to a decrease in the energy-saving strategy generation efficiency index.

[0051] The effect coefficients corresponding to the above-mentioned strategy algorithm iteration cycle factor indicate that when the strategy algorithm iteration cycle factor changes by a unit magnitude, the energy-saving strategy generation efficiency index will change accordingly. The effect coefficients corresponding to the above-mentioned strategy parameter verification time factor indicate that when the strategy parameter verification time factor changes by a unit magnitude, the energy-saving strategy generation efficiency index will change accordingly. The effect coefficients corresponding to the above-mentioned multi-objective conflict resolution duration factor indicate that when the multi-objective conflict resolution duration factor changes by a unit magnitude, the energy-saving strategy generation efficiency index will change accordingly. The effect coefficients corresponding to the above-mentioned abnormal situation response lag final value indicate that when the abnormal situation response lag final value changes by a unit magnitude, the energy-saving strategy generation efficiency index will change accordingly. The comprehensive state database stores the mapping relationships between policy algorithm iteration cycle factors and their corresponding effect coefficients, policy parameter verification time factors and their corresponding effect coefficients, multi-objective conflict resolution duration factors and their corresponding effect coefficients, and lag final values ​​of abnormal situation responses and their corresponding effect coefficients. For example, when the policy algorithm iteration cycle factor, policy parameter verification time factor, multi-objective conflict resolution duration factor, and lag final value of abnormal situation responses are input into the comprehensive state database, the comprehensive state database generates the corresponding effect coefficients for the policy algorithm iteration cycle factor, policy parameter verification time factor, multi-objective conflict resolution duration factor, and lag final value of abnormal situation responses based on preset mapping rules, and the numerical range of each effect coefficient is strictly controlled between 0 and 1.

[0052] like Figure 6 The flowchart of the efficiency judgment method for generating energy-saving control strategies in communication equipment provided in this embodiment of the invention shows that the energy-saving strategy generation efficiency index is compared with the preset energy-saving strategy generation efficiency threshold in the comprehensive status database to determine whether the strategy generation efficiency meets the standard. If the energy-saving strategy generation efficiency index is greater than or equal to the energy-saving strategy generation efficiency threshold, the strategy generation efficiency is determined to meet the standard, and an execution progress report of the energy-saving strategy is generated according to a preset time. Then, the step of judging the effectiveness of the energy-saving strategy is entered. If the energy-saving strategy generation efficiency index is less than the energy-saving strategy generation efficiency threshold, the strategy generation efficiency is determined to be substandard. Based on the difference between the energy-saving strategy generation efficiency index and the efficiency threshold, i.e., the energy-saving strategy generation efficiency deviation value, the synchronization frequency is matched. The coefficient is reduced to decrease the synchronization frequency between the strategy generation module and external systems, reduce data interaction, and lower the processing priority of non-core business (such as daily inspections). The adjusted energy-saving strategy generation efficiency index is re-acquired and marked as the final value of energy-saving strategy generation efficiency. If the final value of energy-saving strategy generation efficiency is greater than or equal to the energy-saving strategy generation efficiency threshold, the energy-saving strategy validity is judged. If the final value of energy-saving strategy generation efficiency is less than the energy-saving strategy generation efficiency threshold, an early warning is issued for the energy-saving strategy generation process. During the execution of all processes, the abnormal scenario handling process and the effect of energy-saving strategies are summarized and analyzed regularly. Effective strategies are updated to the strategy library and various thresholds are adjusted to continuously improve the accuracy and efficiency of the system's energy-saving control.

[0053] Furthermore, to determine whether the efficiency of the energy-saving control strategy generated by the communication equipment meets the standard, the specific judgment process is as follows: compare the energy-saving strategy generation efficiency index with the energy-saving strategy generation efficiency threshold; the aforementioned energy-saving strategy generation efficiency threshold refers to the minimum value of the energy-saving strategy generation efficiency index preset in the comprehensive status database within the specified range.

[0054] When the energy-saving strategy generation efficiency index is not lower than the energy-saving strategy generation efficiency threshold, it is determined that the efficiency of the communication equipment in generating the energy-saving control strategy meets the standard. At the same time, an execution progress report is generated according to the preset time, and it is determined whether the energy-saving strategy is effective after execution. The specific judgment process is as follows: obtain the actual energy consumption reduction value after the energy-saving strategy is executed, and compare it with the preset target energy consumption reduction threshold in the comprehensive status database; if the actual energy consumption reduction value is not lower than the target energy consumption reduction threshold, it is determined that the energy-saving strategy is effective after execution; if the actual energy consumption reduction value is lower than the target energy consumption reduction threshold, it is determined that the energy-saving strategy is not effective after execution, and a new energy-saving strategy is regenerated.

[0055] It should be explained that the aforementioned target energy consumption reduction threshold refers to the planned target energy consumption reduction value after implementing the energy-saving strategy. Specifically, the process of regenerating a new energy-saving strategy involves first reviewing the parameter settings, real-time equipment status, and environmental interference factors of the original strategy to pinpoint the key failure point (e.g., "power adjustment range not matching the energy consumption characteristics of the aging equipment"). Then, a more suitable basic template from the strategy library is called, and the updated operating data is used to perform dynamic parameter calculations, generating a strategy scheme that includes the adjusted adjustment target, step-by-step execution logic, and new expected effects. Finally, after rapid simulation testing to verify feasibility, the scheme is pushed to the equipment for execution. Simultaneously, the cause of failure is associated with the new strategy parameters and archived to provide a reference for similar scenarios in the future, ensuring that the energy-saving strategy always dynamically adapts to the actual equipment status and environmental changes, avoiding the continuous accumulation of ineffective energy consumption.

[0056] It should be explained that the core of generating execution progress reports at preset times is to set fixed time nodes based on the strategy cycle (such as daily for long-term optimization) and periodically output phased reports containing multi-dimensional information. The report content covers completed adjustment steps, changes in real-time equipment operating parameters, deviation analysis from the preset plan, and environmental interference factors. This report not only provides continuous data support for the system to subsequently determine the effectiveness of the strategy, but also allows maintenance personnel to promptly detect execution anomalies and intervene. At the same time, all reports will be archived in the database, providing practical evidence for strategy iteration and optimization.

[0057] When the energy-saving strategy generation efficiency index is lower than the energy-saving strategy generation efficiency threshold, it is determined that the efficiency of the communication equipment in generating energy-saving control strategies is substandard. Based on the energy-saving strategy generation efficiency index and the energy-saving strategy generation efficiency threshold, the energy-saving strategy generation efficiency deviation value is obtained. Based on the energy-saving strategy generation efficiency deviation value, a synchronization frequency reduction coefficient is matched to reduce the synchronization frequency between the strategy generation module and the external system, and at the same time, the processing priority of non-core services is reduced, thereby reducing the resource consumption of non-core services. The energy-saving strategy generation efficiency index is obtained again and marked as the final value of energy-saving strategy generation efficiency, so as to determine whether to issue an early warning for the energy-saving strategy generation process.

[0058] The aforementioned acquisition of the energy-saving strategy generation efficiency deviation value refers to subtracting the energy-saving strategy generation efficiency index from the energy-saving strategy generation efficiency threshold. The matching process for the synchronization frequency reduction coefficient based on the energy-saving strategy generation efficiency deviation value is as follows: the comprehensive state database stores the synchronization frequency reduction coefficients corresponding to each energy-saving strategy generation efficiency deviation value interval. The obtained energy-saving strategy generation efficiency deviation value is input into the database, and the database can match the corresponding synchronization frequency reduction coefficient. The obtained synchronization frequency reduction coefficient is multiplied by the original synchronization frequency, and the result is the synchronization frequency that needs to be adjusted. A synchronization frequency reduction coefficient less than 1 indicates that the synchronization frequency between the strategy generation module and the external system needs to be reduced by a certain percentage. Reducing the synchronization frequency between the strategy generation module and the external system reduces unnecessary data interaction energy consumption and resource occupation, while freeing the strategy generation module from frequent external responses, allowing more computing power to be concentrated on the core logic operations of the energy-saving strategy, accelerating strategy generation speed, avoiding delays in energy-saving timing due to insufficient efficiency, ensuring that energy-saving measures can intervene in equipment operation in a timely manner, and curbing the continuous growth of ineffective energy consumption.

[0059] The aforementioned reduction in the processing priority of non-core services involves the system assigning priority levels to different services and directly lowering the priority label of non-core services from the original setting (e.g., "P3") to "P4". This allows the scheduler to prioritize core services when allocating CPU time slices, indirectly alleviating the problem of insufficient policy generation efficiency without significantly affecting the core functions and overall stability of the equipment.

[0060] like Figure 7 The flowchart of the method for determining the effectiveness of an energy-saving strategy provided in this embodiment of the invention shows that the actual energy consumption reduction value after the energy-saving strategy is executed is obtained and compared with the preset target energy consumption reduction threshold. If the actual energy consumption reduction value is greater than or equal to the target energy consumption reduction threshold, the current energy-saving strategy is determined to be effective and the strategy continues to be executed. Otherwise, the reason for the strategy being ineffective is analyzed and the strategy is regenerated based on the abnormal situation and operating data.

[0061] Specifically, the process for determining whether to issue an early warning during the energy-saving strategy generation process involves comparing the final efficiency value of the generated energy-saving strategy with the energy-saving strategy generation efficiency threshold. If the final efficiency value is not lower than the energy-saving strategy generation efficiency threshold, no early warning is issued, and the effectiveness of the energy-saving strategy after execution is assessed. If the final efficiency value is lower than the energy-saving strategy generation efficiency threshold, an early warning is issued. The determination of the effectiveness of the executed energy-saving strategy is consistent with the process described earlier. The early warning for the energy-saving strategy generation process refers to pushing the warning information to the operation and maintenance platform. Regularly summarize and analyze the handling process of all abnormal scenarios and the effects of energy-saving strategies, update effective strategies to the strategy library, and adjust various thresholds in the comprehensive status database (such as adjusting the ambient temperature threshold according to seasonal changes) to continuously improve the accuracy and efficiency of the system's energy-saving control.

[0062] like Figure 9 As shown in the diagram of the anomaly management interface of the device status monitoring platform provided in this embodiment of the invention, the top has an anomaly filtering area that supports filtering anomalies by anomaly type, processing status, and time range; the middle anomaly list displays the anomaly number, device name, occurrence time, specific anomaly description (such as optical power below the threshold), processing status (unprocessed, processing, ignored), and operation; the right-hand automatic response log records the automatic response actions triggered by the anomaly according to the time (such as communication interruption detected, reconnection has been attempted), clearly presenting the anomaly identification, processing flow, and automated response trajectory, helping maintenance personnel track the progress of device fault handling.

[0063] In a specific example embodiment, the present invention provides an energy-saving control system for communication equipment, which, through a closed-loop management model covering the entire process, achieves refined control over the energy consumption of communication equipment and a leap in operational efficiency. Based on high-quality data acquisition, the system integrates dynamic anomaly identification and hierarchical response mechanisms, enabling it not only to quickly handle abnormal situations such as excessive energy consumption and overheating, but also to continuously improve energy utilization efficiency through personalized energy-saving strategies.

[0064] This invention provides an energy-saving control method for communication equipment, which can be implemented by an energy-saving control system for the communication equipment, such as... Figure 2The flowchart of the energy-saving control method for communication equipment shown below includes the following steps: Step 1: Real-time acquisition of comprehensive status data of the communication equipment using a data acquisition device, followed by preprocessing. The preprocessed data is then categorized and stored to establish a comprehensive status database for the communication equipment. This comprehensive status data reflects the operating status of the communication equipment, the environmental conditions, and energy consumption-related information. Step 2: Extracting the operating data of the communication equipment from the comprehensive status database and comparing it with preset reference data to identify abnormal situations. An abnormal situation report is generated, and key parameters of the communication equipment's response to abnormal situations are monitored and acquired. This allows for the categorization of the communication equipment's response capability to abnormal situations. These key parameters reflect the timeliness of the communication equipment's response to abnormal situations. Step 3: Generating an energy-saving control strategy based on the abnormal situations and operating data of the communication equipment. Analyzing and acquiring the process parameters for generating the energy-saving control strategy helps determine whether the efficiency of the energy-saving control strategy generation meets the standards. Furthermore, by dynamically adjusting resource allocation and strategy execution rhythm, energy-saving control of the communication equipment is achieved. The operating data reflects the real-time operating status of the communication equipment under the current operating conditions, and the process parameters reflect the efficiency during the generation of the energy-saving control strategy.

[0065] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An energy-saving control system for communication equipment, characterized in that, The method includes: The status data acquisition and storage module is used to collect comprehensive status data of the communication equipment in real time through the data acquisition device, perform preprocessing, classify and store the preprocessed data, thereby establishing a comprehensive status database of the communication equipment. The comprehensive status data is used to reflect the operating status of the communication equipment, the environmental status, and energy consumption related information. The anomaly identification and response evaluation module is used to extract the operating data of the communication equipment from the comprehensive status database and compare it with the preset reference data, thereby identifying the abnormal situation of the communication equipment, generating an anomaly report, monitoring and obtaining the key parameters of the communication equipment's response to the abnormal situation, and thus classifying the communication equipment's abnormal situation response capability. The key parameters are used to reflect the timeliness of the communication equipment's response to the abnormal situation. The strategy generation and performance evaluation module is used to generate energy-saving control strategies based on the abnormal conditions and operating data of the communication equipment, analyze and obtain the process parameters of the energy-saving control strategy generation, thereby determining whether the efficiency of the communication equipment in generating the energy-saving control strategy meets the standards, and then realize energy-saving control of the communication equipment by dynamically adjusting resource allocation and strategy execution rhythm. The operating data is used to reflect the real-time operating status of the communication equipment under the current working conditions; the process parameters are used to reflect the efficiency of the energy-saving control strategy generation process.

2. The energy-saving control system for communication equipment according to claim 1, characterized in that, The classification process for the abnormal response capability of communication equipment is as follows: First, by analyzing the key parameters of the communication equipment's response to abnormal situations, the abnormal situation response lag index is obtained and compared with the lag type I threshold and lag type II threshold. When the abnormal situation response lag index is lower than the lag class 1 threshold, the abnormal situation response capability of the communication equipment is classified as qualified response lag. At the same time, the response data of this abnormal situation is recorded, entered into the communication equipment performance file, and marked as a high response case. When the abnormal situation response lag index is between the lag type I threshold and the lag type II threshold, the abnormal situation response capability of the communication equipment is classified as slightly unqualified in terms of response lag, and the response capability of the communication equipment to abnormal situations is optimized. When the abnormal situation response lag index is not lower than the lag type II threshold, the abnormal situation response capability of the communication equipment is classified as severely unqualified in response lag, and the response capability of the communication equipment to abnormal situations is adjusted accordingly.

3. The energy-saving control system for communication equipment according to claim 2, characterized in that, The optimization process for improving the communication device's response to abnormal situations is as follows: Based on the abnormal situation response lag index and lag threshold, a type of deviation value is obtained, and the response capability of communication equipment to abnormal situations is optimized based on the type of deviation value. Based on a type of deviation value, a coefficient for reducing the sensor data sampling interval is determined, thereby reducing the sensor data sampling interval of the communication equipment. Based on a type of deviation value, a coefficient for increasing the intensity of the adjustment measures is determined, thereby increasing the intensity of the adjustment measures of the communication equipment. The optimized anomaly response lag index is reacquired and marked as the secondary response lag coefficient to determine whether to generate a manual intervention instruction. The adjustment process for the communication device's response to abnormal situations is as follows: First, hardware and link diagnostic commands are generated to locate the cause of the anomaly. Based on the diagnostic results, the response strategy is replanned. After testing confirms that the replanned response strategy is effective, the replanned response strategy is imported into the system, thereby realizing the online update of the anomaly handling strategy.

4. The energy-saving control system for communication equipment according to claim 2, characterized in that, The key parameters of the analysis and communication device's response to abnormal situations are analyzed, and the specific analysis process is as follows: The key parameters include the anomaly determination time, instruction transmission time, and device execution delay time of the communication device. Based on the processed key parameters, the anomaly determination time factor, instruction transmission time factor, and device execution delay factor are obtained. By pre-setting the effect coefficient of each factor in the comprehensive state database, their weight contribution value to the anomaly response lag index is quantified. Finally, a weighted average fusion algorithm is used to synthesize the anomaly response lag index, which refers to the degree of lag in the response of the communication device to sudden anomalies.

5. The energy-saving control system for communication equipment according to claim 3, characterized in that, The specific process for determining whether to generate a manual intervention instruction is as follows: The lag coefficient of the second response is compared with the lag threshold of type I; When the secondary response hysteresis coefficient is lower than the hysteresis threshold, it is determined that no manual intervention instruction will be generated, and the sensor data sampling interval and the intensity of the adjustment measures will be restored. When the lag coefficient of the secondary response is not lower than the lag threshold of type I, it is determined that a manual intervention instruction will be generated.

6. The energy-saving control system for communication equipment according to claim 1, characterized in that, The process of analyzing and obtaining the parameters for generating the energy-saving control strategy is as follows: The process parameters include the strategy algorithm iteration cycle, the strategy parameter verification time, and the multi-objective conflict resolution time. Based on the processed key parameters, the strategy algorithm iteration cycle factor, the strategy parameter verification time factor, and the multi-objective conflict resolution time factor are obtained. At the same time, the response lag final value of abnormal situations is obtained. By pre-setting the effect coefficient of each parameter in the comprehensive state database, their weight contribution value to the energy-saving strategy generation efficiency index is quantified. Finally, a weighted average fusion algorithm is used to synthesize the energy-saving strategy generation efficiency index, which refers to the efficiency of the communication equipment in generating energy-saving control strategies based on the identified abnormal situations and operating data.

7. The energy-saving control system for communication equipment according to claim 1, characterized in that, The specific process for determining whether the efficiency of the energy-saving control strategy generated by the communication equipment meets the standard is as follows: The efficiency index of energy-saving strategy generation is compared with the efficiency threshold of energy-saving strategy generation; When the energy-saving strategy generation efficiency index is not lower than the energy-saving strategy generation efficiency threshold, it is determined that the efficiency of the communication equipment in generating the energy-saving control strategy meets the standard. At the same time, an execution progress report is generated according to the preset time, and it is determined whether the energy-saving strategy is effective after execution. The specific judgment process is as follows: obtain the actual energy consumption reduction value after the energy-saving strategy is executed, and compare it with the preset target energy consumption reduction threshold in the comprehensive status database. If the actual energy consumption reduction is not lower than the target energy consumption reduction threshold, then the energy-saving strategy is considered effective. If the actual energy consumption reduction is lower than the target energy consumption reduction threshold, the energy-saving strategy is deemed ineffective and a new energy-saving strategy is generated. When the energy-saving strategy generation efficiency index is lower than the energy-saving strategy generation efficiency threshold, it is determined that the efficiency of the communication equipment in generating energy-saving control strategies is substandard. Based on the energy-saving strategy generation efficiency index and the energy-saving strategy generation efficiency threshold, the energy-saving strategy generation efficiency deviation value is obtained. Based on the energy-saving strategy generation efficiency deviation value, a synchronization frequency reduction coefficient is matched to reduce the synchronization frequency between the strategy generation module and the external system, and at the same time reduce the processing priority of non-core services, thereby reducing the resource consumption of non-core services. The energy-saving strategy generation efficiency index is reacquired and marked as the final value of the energy-saving strategy generation efficiency, thereby determining whether to issue an early warning for the energy-saving strategy generation process.

8. The energy-saving control system for communication equipment according to claim 7, characterized in that, The specific determination process for whether to issue an early warning during the energy-saving strategy generation process is as follows: The final value of the energy-saving strategy's generated efficiency is compared with the threshold value of the energy-saving strategy's generated efficiency; When the final value of the energy-saving strategy generation efficiency is not lower than the energy-saving strategy generation efficiency threshold, it is determined that no warning will be issued for the energy-saving strategy generation process, and at the same time, it is determined whether the energy-saving strategy is effective after execution. When the final value of the energy-saving strategy generation efficiency is lower than the energy-saving strategy generation efficiency threshold, an early warning will be issued for the energy-saving strategy generation process.

9. A method for energy-saving control of communication equipment, applied to the energy-saving control system of communication equipment as described in any one of claims 1 to 8, characterized in that: include: Step 1: Collect comprehensive status data of communication equipment in real time through data acquisition equipment, and preprocess the data. Classify and store the preprocessed data to establish a comprehensive status database of communication equipment. The comprehensive status data is used to reflect the operating status of communication equipment, the environmental status, and energy consumption related information. Step 2: Extract the operating data of the communication equipment from the comprehensive status database and compare it with the preset reference data to identify abnormal situations of the communication equipment, generate an abnormal situation report, monitor and obtain the key parameters of the communication equipment's response to abnormal situations, and classify the abnormal situation response capability of the communication equipment. The key parameters are used to reflect the timeliness of the communication equipment's response to abnormal situations. Step 3: Generate energy-saving control strategies based on the abnormal conditions and operating data of the communication equipment. Analyze and obtain the process parameters for generating the energy-saving control strategies to determine whether the efficiency of the communication equipment in generating the energy-saving control strategies meets the standards. Then, by dynamically adjusting resource allocation and strategy execution rhythm, energy-saving control of the communication equipment is achieved. The operating data is used to reflect the real-time operating status of the communication equipment under the current working conditions; the process parameters are used to reflect the efficiency of the energy-saving control strategy generation process.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code, which can be called by a processor to execute the energy-saving control system for communication equipment as described in any one of claims 1 to 8.

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