A method and system for monitoring and optimizing operation energy consumption of a radio frequency automation test platform

Abstract

The present invention relates to the field of radio frequency test equipment monitoring technology, and in particular to a method and system for monitoring and optimizing the operating energy consumption of a radio frequency automation test platform. Compare and analyze each actual feature data after cleaning with the corresponding standard feature data, and analyze the status of each actual feature data in the target radio frequency automation test platform; obtain the actual feature data of the target radio frequency automation test platform whose status is abnormal, and trace the equipment to be monitored in the target radio frequency automation test platform according to the actual feature data of the abnormal status; perform fault analysis on the equipment to be monitored in the target radio frequency automation test platform, and generate an optimization plan according to the faulty equipment in the target radio frequency automation test platform and the fault type of the faulty equipment. The present invention can accurately analyze the equipment that needs to be optimized in the test platform, and formulate corresponding optimization plans, which can effectively improve work efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of radio frequency test equipment monitoring, and in particular to a method and system for monitoring and optimizing the operating energy consumption of a radio frequency automated test platform. Background Art

[0002] The RF automation test platform is a dedicated hardware and software test system, which is mainly used to automatically detect and verify the RF performance of wireless communication equipment (such as mobile phones, wireless routers, satellite communication equipment, etc.). It integrates RF signal generators, receivers, signal analyzers and advanced software control, and can perform a series of predefined test tasks, such as signal strength test, spectrum analysis, modem performance test, etc., without manual intervention, which greatly improves the accuracy, efficiency and consistency of the test. The energy consumption of the RF automation test platform is mainly reflected in the transmission and reception of RF signals, the operation of test equipment and the control of the test platform. Since the transmission and reception of RF signals consume a lot of electricity, and the operation of test equipment also requires the support of electricity, the energy consumption of the RF automation test platform is relatively large. The existing RF automation test platform has a certain degree of energy waste during operation, such as energy loss during the transmission and reception of RF signals, no-load operation of test equipment, and heating due to aging and failure of equipment; there is a lack of effective energy consumption optimization means, and it is impossible to optimize the platform according to the real-time monitored energy consumption data to achieve the purpose of reducing energy consumption. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a method and system for monitoring and optimizing the operating energy consumption of a radio frequency automation test platform.

[0004] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is:

[0005] The first aspect of the present invention discloses a method for monitoring and optimizing the operation energy consumption of a radio frequency automation test platform, comprising the following steps:

[0006] Acquire the real-time working environment of the target radio frequency automation test platform, and determine various standard characteristic data of the target radio frequency automation test platform when working under the current real-time working environment conditions according to the real-time working environment;

[0007] Extracting the standard energy consumption loss value of the target radio frequency automation test platform when working under the current real-time working environment conditions from the standard feature data; acquiring the power information of the power supply equipment of the target radio frequency automation test platform within a preset time period, and calculating the total energy consumption loss value of the target radio frequency automation test platform within the preset time period according to the power information;

[0008] If the total energy consumption loss value is greater than the standard energy consumption loss value, then continuously collecting actual feature data of the target radio frequency automation test platform at multiple preset time nodes, and performing data cleaning processing on each actual feature data to obtain each cleaned actual feature data;

[0009] Compare and analyze the cleaned actual feature data with the corresponding standard feature data to analyze the status of the actual feature data in the target RF automation test platform;

[0010] Acquire actual characteristic data of abnormal status in the target radio frequency automation test platform, and trace the device to be monitored in the target radio frequency automation test platform according to the actual characteristic data of abnormal status;

[0011] Perform fault analysis on the equipment to be monitored in the target RF automation test platform to obtain the faulty equipment and the fault type of the faulty equipment in the target RF automation test platform, and generate an optimization plan based on the faulty equipment and the fault type of the faulty equipment in the target RF automation test platform.

[0012] Further, in a preferred embodiment of the present invention, the real-time working environment of the target radio frequency automation test platform is obtained, and the standard characteristic data of the target radio frequency automation test platform when working under the current real-time working environment conditions are determined according to the real-time working environment, specifically:

[0013] Obtaining operation log information of the target radio frequency automation test platform, and obtaining various standard characteristic data of the target radio frequency automation test platform when it works normally under various preset working environment combination conditions according to the operation log information;

[0014] Constructing a knowledge base, and importing various standard feature data of the target radio frequency automation test platform when it works normally under various preset working environment combination conditions into the knowledge base;

[0015] When monitoring the target radio frequency automation test platform, the real-time working environment of the target radio frequency automation test platform is obtained; wherein the working environment includes temperature, humidity, electromagnetic intensity and dust concentration;

[0016] Calculating an average Euclidean distance value between the real-time working environment and various preset working environments based on a Euclidean distance algorithm, and calculating similarities between the real-time working environment and various preset working environments according to the corresponding average Euclidean distance values ​​to obtain multiple similarities;

[0017] Extracting the maximum similarity from the multiple similarities, obtaining a preset working environment corresponding to the maximum similarity, and matching various standard feature data of the target radio frequency automation test platform when working under the current real-time working environment conditions in the knowledge base according to the preset working environment corresponding to the maximum similarity;

[0018] The characteristic data include CPU usage, memory occupancy, hard disk read and write speed, operating voltage, operating current, operating resistance, internal temperature and energy consumption value.

[0019] Furthermore, in a preferred embodiment of the present invention, data cleaning is performed on each actual feature data to obtain each cleaned actual feature data, specifically:

[0020] Each actual feature data is regarded as a cluster, and each actual feature data in each cluster is regarded as an independent data point;

[0021] The Euclidean distance between each data point in each cluster is calculated respectively, and the two data points with the closest Euclidean distance are merged to form a primary cluster group, thereby obtaining several primary cluster groups, and the mean of all actual feature data in each primary cluster group is calculated to obtain the cluster center position of each primary cluster group;

[0022] Each primary cluster group is regarded as an independent data point, and the Euclidean distance of the data point is calculated respectively. The two data points with the closest Euclidean distance are merged to form a secondary cluster group, thereby obtaining several secondary cluster groups, and calculating the mean of all actual feature data in each secondary cluster group to obtain the cluster center position of each secondary cluster group;

[0023] By analogy, the data points in each cluster are continuously iterated and clustered to obtain a number of n-fold clustering groups and the cluster center positions of the n-fold clustering groups;

[0024] Compare the cluster center position of the n-th cluster group with the cluster center position of the last cluster group until the cluster center position of the n-th cluster group no longer changes from the cluster center position of the last cluster group, then stop the iteration;

[0025] After the iteration is stopped, the actual feature data corresponding to the cluster group with the largest number of data points is extracted and retained from the n cluster groups, and the actual feature data corresponding to the remaining cluster groups are deleted to obtain the cleaned actual feature data; and so on, each actual feature data is cleaned to obtain each cleaned actual feature data.

[0026] Furthermore, in a preferred embodiment of the present invention, the actual characteristic data after cleaning are compared and analyzed with the corresponding standard characteristic data to analyze the status of the actual characteristic data in the target radio frequency automation test platform, specifically:

[0027] Acquire each standard feature data and each actual feature data after cleaning of the radio frequency automation test platform; construct a plurality of two-dimensional coordinate systems, and generate actual feature data curve graphs in corresponding two-dimensional coordinate systems based on each actual feature data after cleaning;

[0028] and generating a standard characteristic data curve graph in a corresponding two-dimensional coordinate system based on each standard characteristic data; and analyzing the degree of overlap between the actual characteristic data curve graph and the standard characteristic data curve graph in each two-dimensional coordinate system;

[0029] If the overlap between the actual characteristic data curve graph and the standard characteristic data curve graph in a certain two-dimensional coordinate system is greater than a preset overlap, the corresponding actual characteristic data is marked as actual characteristic data in a normal state;

[0030] If the overlap degree between the actual characteristic data curve graph and the standard characteristic data curve graph in a certain two-dimensional coordinate system is not greater than the preset overlap degree, the corresponding actual characteristic data is marked as actual characteristic data in an abnormal state.

[0031] Furthermore, in a preferred embodiment of the present invention, actual characteristic data of an abnormal state in a target radio frequency automation test platform is obtained, and the device to be monitored in the target radio frequency automation test platform is traced back according to the actual characteristic data of an abnormal state, specifically:

[0032] Acquire characteristic attribute information of each device in the target radio frequency automation test platform, perform correlation analysis on each characteristic data according to the characteristic attribute information of each device in the target radio frequency automation test platform, and obtain the correlation degree between each device and each characteristic data;

[0033] Defining devices with a correlation degree greater than a preset correlation degree as devices having correlation with corresponding feature data, and obtaining the correlation devices of each feature data; constructing a knowledge graph, and importing the correlation devices of each feature data into the knowledge graph;

[0034] Acquire actual feature data of abnormal status in the target radio frequency automation test platform, import the actual feature data of abnormal status into the knowledge graph for matching, and obtain a plurality of related devices through matching;

[0035] The matched associated devices are marked as devices to be monitored.

[0036] Further, in a preferred embodiment of the present invention, a fault analysis is performed on the equipment to be monitored in the target radio frequency automation test platform to obtain the faulty equipment and the fault type of the faulty equipment in the target radio frequency automation test platform, and an optimization solution is generated according to the faulty equipment and the fault type of the faulty equipment in the target radio frequency automation test platform, specifically:

[0037] Obtaining fault log information of the device to be monitored, and extracting fault feature data corresponding to various types of faults occurring in the device to be monitored based on the fault log information;

[0038] Acquire real-time operation data of the device to be monitored, and analyze the overlap between the real-time operation data and each fault characteristic data;

[0039] Comparing the overlap between the real-time operation data and each fault characteristic data with a preset overlap threshold value respectively;

[0040] If each overlap degree is not greater than the preset overlap degree threshold, it means that the device to be monitored is not a faulty device, indicating that no optimization processing is required;

[0041] If there is at least one overlap greater than the preset overlap threshold, it means that the device to be monitored is a faulty device and needs to be optimized, then the fault feature data corresponding to the overlap greater than the preset overlap threshold is obtained, and the fault type of the faulty device is determined according to the fault feature data corresponding to the overlap greater than the preset overlap threshold;

[0042] If the device to be monitored is a faulty device, an optimization plan is generated according to the fault type of the faulty device, and the optimization plan is transmitted to a preset terminal for display.

[0043] The second aspect of the present invention discloses an operation energy consumption monitoring and optimization system of a radio frequency automated test platform, the operation energy consumption monitoring and optimization system comprising a memory and a processor, the memory storing an operation energy consumption monitoring and optimization method program, when the operation energy consumption monitoring and optimization method program is executed by the processor, the following steps are implemented:

[0044] Acquire the real-time working environment of the target radio frequency automation test platform, and determine various standard characteristic data of the target radio frequency automation test platform when working under the current real-time working environment conditions according to the real-time working environment;

[0045] Extracting the standard energy consumption loss value of the target radio frequency automation test platform when working under the current real-time working environment conditions from the standard feature data; acquiring the power information of the power supply equipment of the target radio frequency automation test platform within a preset time period, and calculating the total energy consumption loss value of the target radio frequency automation test platform within the preset time period according to the power information;

[0046] If the total energy consumption loss value is greater than the standard energy consumption loss value, then continuously collecting actual feature data of the target radio frequency automation test platform at multiple preset time nodes, and performing data cleaning processing on each actual feature data to obtain each cleaned actual feature data;

[0047] Compare and analyze the cleaned actual feature data with the corresponding standard feature data to analyze the status of the actual feature data in the target RF automation test platform;

[0048] Acquire actual characteristic data of abnormal status in the target radio frequency automation test platform, and trace the device to be monitored in the target radio frequency automation test platform according to the actual characteristic data of abnormal status;

[0049] Perform fault analysis on the equipment to be monitored in the target RF automation test platform to obtain the faulty equipment and the fault type of the faulty equipment in the target RF automation test platform, and generate an optimization plan based on the faulty equipment and the fault type of the faulty equipment in the target RF automation test platform.

[0050] The present invention solves the technical defects existing in the background technology, and the present invention has the following beneficial effects: obtaining the total energy consumption loss value within a preset time period; if the total energy consumption loss value is greater than the standard energy consumption loss value, then collecting the actual characteristic data and performing cleaning processing; comparing and analyzing each actual characteristic data after cleaning with the corresponding standard characteristic data, analyzing the status of each actual characteristic data in the target radio frequency automation test platform; obtaining the actual characteristic data of the target radio frequency automation test platform whose status is abnormal, tracing the equipment to be monitored in the target radio frequency automation test platform according to the actual characteristic data of the abnormal status; performing fault analysis on the equipment to be monitored in the target radio frequency automation test platform, and generating an optimization plan according to the faulty equipment in the target radio frequency automation test platform and the fault type of the faulty equipment. The present invention can accurately analyze the equipment that needs to be optimized in the test platform, and formulate corresponding optimization plans, which can effectively improve work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0052] Figure 1A first method flow chart of a method for monitoring and optimizing the operating energy consumption of a radio frequency automation test platform;

[0053] Figure 2 A second method flow chart of the method for monitoring and optimizing the operating energy consumption of a radio frequency automation test platform;

[0054] Figure 3 This is the system block diagram of the operation energy consumption monitoring and optimization system of the RF automation test platform. DETAILED DESCRIPTION

[0055] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0057] like Figure 1 As shown, the first aspect of the present invention discloses a method for monitoring and optimizing the operating energy consumption of a radio frequency automation test platform, comprising the following steps:

[0058] S102: Acquire the real-time working environment of the target radio frequency automation test platform, and determine various standard characteristic data of the target radio frequency automation test platform when working under the current real-time working environment conditions according to the real-time working environment;

[0059] S104: extracting from the standard feature data a standard energy consumption value of the target radio frequency automation test platform when it is working under the current real-time working environment conditions; obtaining power information of the power supply equipment of the target radio frequency automation test platform within a preset time period, and calculating the total energy consumption value of the target radio frequency automation test platform within the preset time period according to the power information;

[0060] S106: If the total energy consumption loss value is greater than the standard energy consumption loss value, then continuously collecting actual feature data of the target radio frequency automation test platform at multiple preset time nodes, and performing data cleaning processing on each actual feature data to obtain cleaned actual feature data;

[0061] S108: Compare and analyze the cleaned actual feature data with the corresponding standard feature data to analyze the status of the actual feature data in the target radio frequency automation test platform;

[0062] S110: Acquire actual characteristic data of abnormal status in the target radio frequency automation test platform, and trace the device to be monitored in the target radio frequency automation test platform according to the actual characteristic data of abnormal status;

[0063] S112: Perform fault analysis on the equipment to be monitored in the target radio frequency automation test platform to obtain the faulty equipment in the target radio frequency automation test platform and the fault type of the faulty equipment, and generate an optimization plan according to the faulty equipment in the target radio frequency automation test platform and the fault type of the faulty equipment.

[0064] Further, in a preferred embodiment of the present invention, the real-time working environment of the target radio frequency automation test platform is obtained, and the standard characteristic data of the target radio frequency automation test platform when working under the current real-time working environment conditions are determined according to the real-time working environment, specifically:

[0065] Obtaining operation log information of the target radio frequency automation test platform, and obtaining various standard characteristic data of the target radio frequency automation test platform when it works normally under various preset working environment combination conditions according to the operation log information;

[0066] Constructing a knowledge base, and importing various standard feature data of the target radio frequency automation test platform when it works normally under various preset working environment combination conditions into the knowledge base;

[0067] When monitoring the target radio frequency automation test platform, the real-time working environment of the target radio frequency automation test platform is obtained; wherein the working environment includes temperature, humidity, electromagnetic intensity and dust concentration;

[0068] Calculating an average Euclidean distance value between the real-time working environment and various preset working environments based on a Euclidean distance algorithm, and calculating similarities between the real-time working environment and various preset working environments according to the corresponding average Euclidean distance values ​​to obtain multiple similarities;

[0069] Among them, the Euclidean distance algorithm is a similarity calculation method based on geometric distance, which is used to measure the similarity between two sample points. The value calculated by the Euclidean distance algorithm can measure the similarity between the real-time working environment and various preset working environments. If the Euclidean distance between the real-time working environment and the preset working environment is small, it means that the similarity between the two is high; conversely, if the Euclidean distance between the real-time working environment and the preset working environment is large, it means that the similarity between the two is low;

[0070] Extracting the maximum similarity from the multiple similarities, obtaining a preset working environment corresponding to the maximum similarity, and matching various standard feature data of the target radio frequency automation test platform when working under the current real-time working environment conditions in the knowledge base according to the preset working environment corresponding to the maximum similarity;

[0071] The characteristic data include CPU usage, memory occupancy, hard disk read and write speed, operating voltage, operating current, operating resistance, internal temperature and energy consumption value.

[0072] It should be noted that the operation log information of the RF automation test platform refers to the information that records various operations, events, status, performance indicators and other data during the operation of the platform. This information includes the start, stop, operation status, test items, test results, test time, equipment utilization, system errors, etc. of the test equipment.

[0073] It should be noted that the standard characteristic data of the RF automation test platform are different when working under different working environment conditions, mainly because the working environment conditions have a great impact on the performance and test results of the test platform. Under different environmental conditions, the equipment performance, signal transmission, test accuracy and other aspects of the test platform will change, thus affecting the accuracy and reliability of the test results. For example, environmental factors such as temperature, humidity, and electromagnetic interference will affect the energy consumption of the power supply equipment of the test platform, resulting in different standard energy consumption values ​​of the test platform when working under different working conditions.

[0074] In summary, this method can further determine the standard characteristic data of the test platform when it is working under the current real-time working environment conditions according to the real-time working environment of the test platform, which can ensure that the monitoring results are more accurate and reliable and avoid monitoring errors caused by environmental factors.

[0075] Furthermore, in a preferred embodiment of the present invention, data cleaning is performed on each actual feature data to obtain each cleaned actual feature data, specifically:

[0076] Each actual feature data is regarded as a cluster, and each actual feature data in each cluster is regarded as an independent data point;

[0077] The Euclidean distance between each data point in each cluster is calculated respectively, and the two data points with the closest Euclidean distance are merged to form a primary cluster group, thereby obtaining several primary cluster groups, and the mean of all actual feature data in each primary cluster group is calculated to obtain the cluster center position of each primary cluster group;

[0078] Each primary cluster group is regarded as an independent data point, and the Euclidean distance of the data point is calculated respectively. The two data points with the closest Euclidean distance are merged to form a secondary cluster group, thereby obtaining several secondary cluster groups, and calculating the mean of all actual feature data in each secondary cluster group to obtain the cluster center position of each secondary cluster group;

[0079] By analogy, the data points in each cluster are continuously iterated and clustered to obtain a number of n-fold clustering groups and the cluster center positions of the n-fold clustering groups;

[0080] Compare the cluster center position of the n-th cluster group with the cluster center position of the last cluster group until the cluster center position of the n-th cluster group no longer changes from the cluster center position of the last cluster group, then stop the iteration;

[0081] After the iteration is stopped, the actual feature data corresponding to the cluster group with the largest number of data points is extracted and retained from the n cluster groups, and the actual feature data corresponding to the remaining cluster groups are deleted to obtain the cleaned actual feature data; and so on, each actual feature data is cleaned to obtain each cleaned actual feature data.

[0082] It should be noted that the power information of the power supply equipment of the target RF automation test platform within the preset time period is obtained, and the total energy consumption loss value of the target RF automation test platform within the preset time period is calculated based on the power information; if the total energy consumption loss value of the test platform within the preset time period is greater than the standard energy consumption loss value, it means that the energy consumption loss of the test platform is abnormal. At this time, the actual characteristic data of the target RF automation test platform is continuously collected at multiple preset time nodes. However, in the process of characteristic data collection, noise data may be generated due to various reasons, which will affect the accuracy of data analysis. Therefore, it is necessary to clean the collected characteristic data to improve the quality of the data and ensure the accuracy and reliability of the data. In the present invention, feature data is clustered based on the principle of hierarchical clustering. When the position of the cluster center of the last clustering no longer changes from the position of the cluster center of the previous clustering, it means that the data has been clustered. At this time, several cluster groups are obtained, including effective feature data cluster groups and noise data cluster groups. After the noise data cluster groups are clustered, the corresponding feature data in the noise data cluster groups are deleted. Therefore, the method can realize the function of quickly cleaning the actual feature data, and can ensure the quality and reliability of the data.

[0083] Furthermore, in a preferred embodiment of the present invention, the actual characteristic data after cleaning are compared and analyzed with the corresponding standard characteristic data to analyze the status of the actual characteristic data in the target radio frequency automation test platform, specifically:

[0084] Acquire each standard feature data and each actual feature data after cleaning of the radio frequency automation test platform; construct a plurality of two-dimensional coordinate systems, and generate actual feature data curve graphs in corresponding two-dimensional coordinate systems based on each actual feature data after cleaning;

[0085] and generating a standard characteristic data curve graph in a corresponding two-dimensional coordinate system based on each standard characteristic data; and analyzing the degree of overlap between the actual characteristic data curve graph and the standard characteristic data curve graph in each two-dimensional coordinate system;

[0086] If the overlap between the actual characteristic data curve graph and the standard characteristic data curve graph in a certain two-dimensional coordinate system is greater than a preset overlap, the corresponding actual characteristic data is marked as actual characteristic data of a normal state; wherein the preset overlap is set to 98%;

[0087] If the overlap degree between the actual characteristic data curve graph and the standard characteristic data curve graph in a certain two-dimensional coordinate system is not greater than the preset overlap degree, the corresponding actual characteristic data is marked as actual characteristic data in an abnormal state.

[0088] It should be noted that the horizontal axis of the two-dimensional coordinate system is the time point, and the vertical axis is the characteristic data value. By judging the overlap between the actual characteristic data curve graph and the standard characteristic data curve graph in the two-dimensional coordinate system, the degree of overlap between the actual characteristic data curve graph and the standard characteristic data curve graph is obtained. If the degree of overlap between a certain actual characteristic data curve graph and the standard characteristic data curve graph is greater than the preset overlap degree, it means that the actual characteristic data and the standard characteristic data of the test platform are highly overlapped during the working process, and the corresponding actual characteristic data is marked as the actual characteristic data of the normal state. If the degree of overlap between a certain actual characteristic data curve graph and the standard characteristic data curve graph is not greater than the preset overlap degree, it means that the overlap between the actual characteristic data and the standard characteristic data of the test platform during the working process is low, and the corresponding actual characteristic data is marked as the actual characteristic data of the abnormal state.

[0089] like Figure 2 As shown, further, in a preferred embodiment of the present invention, actual characteristic data of an abnormal state in a target radio frequency automation test platform is obtained, and the device to be monitored in the target radio frequency automation test platform is traced back according to the actual characteristic data of an abnormal state, specifically:

[0090] S202: Acquire characteristic attribute information of each device in the target radio frequency automation test platform, perform correlation analysis on each characteristic data according to the characteristic attribute information of each device in the target radio frequency automation test platform, and obtain the correlation between each device and each characteristic data;

[0091] S204: defining devices with a correlation greater than a preset correlation as devices having correlation with the corresponding feature data, and obtaining the correlation devices of each feature data; constructing a knowledge graph, and importing the correlation devices of each feature data into the knowledge graph;

[0092] S206: Acquire actual feature data of abnormal status in the target radio frequency automation test platform, import the actual feature data of abnormal status into the knowledge graph for matching, and obtain a plurality of related devices through matching;

[0093] S208: Marking the matched related devices as devices to be monitored.

[0094] It should be noted that in the RF automation test platform, the devices that are correlated with the corresponding characteristic data refer to certain devices in the test platform that can have a substantial impact on the characteristic data. There is a certain causal relationship between these devices and the characteristic data. By analyzing these devices, we can understand the changing rules and influencing factors of the characteristic data, so as to formulate targeted energy consumption optimization of the test platform. For example, in the RF automation test platform, signal generators, signal receivers, test instruments and other equipment are correlated with the characteristic data. The output signal quality of the signal generator, the receiving performance of the signal receiver, the measurement accuracy of the test instrument, etc. will affect the test results, that is, they are correlated with the characteristic data. For another example, the circuit voltage in the test platform is associated with the voltage stabilizer and the power supply equipment, that is, the voltage stabilizer and the power supply equipment are associated devices of the circuit voltage.

[0095] If the analysis shows that the status of a certain item of actual feature data in the test platform is abnormal, the actual feature data is input into the knowledge graph constructed in advance for pairing, so as to pair the device that is relevant to the actual feature data, that is, the associated device. At this time, the matched several associated devices are marked as the devices to be monitored. The purpose of this step is to further screen out the related devices in the test platform that may affect the status of the actual feature data by combining the actual feature data with abnormal status, and then conduct fault analysis on these related devices in a targeted manner, which can effectively improve the analysis speed and improve the response efficiency of the system.

[0096] Further, in a preferred embodiment of the present invention, a fault analysis is performed on the equipment to be monitored in the target radio frequency automation test platform to obtain the faulty equipment and the fault type of the faulty equipment in the target radio frequency automation test platform, and an optimization solution is generated according to the faulty equipment and the fault type of the faulty equipment in the target radio frequency automation test platform, specifically:

[0097] Obtaining fault log information of the device to be monitored, and extracting fault feature data corresponding to various types of faults occurring in the device to be monitored based on the fault log information;

[0098] Among them, fault log information refers to the information that records various faults, errors, anomalies, etc. that occur during the operation of the equipment. This information includes the time, location, cause, phenomenon, treatment measures, treatment results, etc. of the fault, which can help understand the operating status of the equipment, timely discover and handle equipment faults, and improve the stability and reliability of the equipment;

[0099] Acquire real-time operation data of the device to be monitored, and analyze the overlap between the real-time operation data and each fault characteristic data;

[0100] The principle of analyzing the overlap between the real-time operation data and each fault characteristic data is the same as the principle of analyzing the overlap between the actual characteristic data and the standard characteristic data described above, and will not be elaborated here.

[0101] The operating data include but are not limited to amplitude, frequency, phase, voltage, current, temperature, sensitivity, signal-to-noise ratio and bit error rate;

[0102] Comparing the overlap between the real-time operation data and each fault characteristic data with a preset overlap threshold value respectively; wherein the preset overlap threshold value is set to 95%;

[0103] If each overlap degree is not greater than the preset overlap degree threshold, it means that the device to be monitored is not a faulty device, indicating that no optimization processing is required;

[0104] If there is at least one overlap greater than the preset overlap threshold, it means that the device to be monitored is a faulty device and needs to be optimized, then the fault feature data corresponding to the overlap greater than the preset overlap threshold is obtained, and the fault type of the faulty device is determined according to the fault feature data corresponding to the overlap greater than the preset overlap threshold;

[0105] If the device to be monitored is a faulty device, an optimization plan is generated according to the fault type of the faulty device, and the optimization plan is transmitted to a preset terminal for display.

[0106] It should be noted that if each overlap is not greater than the preset overlap threshold, it means that the device to be monitored is a non-faulty device, indicating that no optimization is required; if there is at least one overlap greater than the preset overlap threshold, it means that the device to be monitored is a faulty device, then an optimization plan is generated based on the faulty device in the target RF automation test platform and the fault type of the faulty device, and the optimization plan is transmitted to the preset terminal for display, so that relevant technicians can optimize the equipment in the test platform in a targeted manner, such as repairing or replacing the faulty equipment. This method can accurately analyze the equipment that needs to be optimized in the test platform, and formulate corresponding optimization plans, which can effectively improve work efficiency.

[0107] The method for monitoring and optimizing the operation energy consumption of a radio frequency automation test platform further comprises the following steps:

[0108] If the total energy consumption loss value is greater than the standard energy consumption loss value, then obtaining various standard characteristic data of the target radio frequency automation test platform when working under the current real-time working environment conditions, and drawing a standard temperature distribution diagram of the target radio frequency automation test platform when working under the current real-time working environment conditions according to the standard characteristic data;

[0109] During the operation of the target radio frequency automation test platform, actual temperature values ​​of multiple preset position nodes are obtained, and an actual temperature distribution diagram of the target radio frequency automation test platform during the operation is constructed according to the actual temperature values ​​of the multiple preset position nodes;

[0110] The cosine similarity between the actual temperature distribution map and the standard temperature distribution map is calculated based on the cosine similarity algorithm. If the cosine similarity is not greater than the preset cosine similarity, the Kriging interpolation algorithm is introduced to convert the actual temperature distribution map and the standard temperature distribution map based on the Kriging interpolation algorithm to obtain an actual isotherm map and a standard isotherm map;

[0111] Coloring the actual isotherm map to obtain an actual isotherm map represented by a first color; and coloring the standard isotherm map to obtain a standard isotherm map represented by a second color; wherein the first color and the second color are different in color;

[0112] Constructing a fusion space, and importing the actual isotherm map and the standard isotherm map into the fusion space, and performing registration processing on the actual isotherm map and the standard isotherm map in the fusion space by using an iterative closest point algorithm to obtain a registered isotherm map;

[0113] The registered isotherm map is analyzed, and a curve area with a separate first color or a second color is marked in the registered isotherm map, which is defined as a temperature singularity area; the device corresponding to the temperature singularity area is marked as a temperature fault device, an optimization plan is generated based on the temperature fault device, and the optimization plan is transmitted to a preset terminal for display.

[0114] The cosine similarity value is between 0 and 1. The closer the value is to 1, the higher the similarity is; the closer the value is to 0, the lower the similarity is. In the present invention, the cosine similarity is set to 0.7. The first color may be black, and the second color may be red.

[0115] It should be noted that during the operation of the RF automation test platform, devices with abnormal temperatures will have a significant impact on energy consumption. This is because the performance and efficiency of the internal components (such as processors, power modules, signal amplifiers, etc.) of many electronic devices, including RF devices, will be affected by the temperature of the working environment. When the temperature of the device is too high, in order to ensure normal operation, the load of the cooling system needs to be increased, which consumes more electricity. At the same time, high temperature may cause components to overheat, affect their service life, and even trigger protection mechanisms, so that the equipment needs to be started and shut down more frequently, further increasing energy consumption. Therefore, ensuring that the equipment is within a suitable temperature range is an important part of optimizing the energy efficiency of the RF automation test platform. This method can quickly and effectively analyze and locate the temperature-singular devices of the test platform during operation, so as to formulate corresponding optimization plans, reduce unnecessary energy consumption, and ensure that each device can maintain the best operating state, thereby improving work efficiency and stability.

[0116] The method for monitoring and optimizing the operation energy consumption of a radio frequency automation test platform further comprises the following steps:

[0117] Obtaining operation log information of the target radio frequency automation test platform, and obtaining historical energy consumption data of the target radio frequency automation test platform under different working stages and different working environment conditions according to the operation log information;

[0118] Build a prediction model based on a deep learning network, and divide the historical energy consumption data of the target RF automation test platform under different working stages and working environment conditions into a training set and a test set;

[0119] The training set is imported into the prediction model for back propagation training, and the training error of the model is obtained. When the training error converges to a preset value, the model parameters are saved; the prediction model is tested according to the test set. If the prediction model meets the preset requirements, the saved model parameters are extracted to obtain a trained prediction model;

[0120] Obtaining the optimized time period and real-time working environment of the target radio frequency automation test platform, importing the optimized time period and real-time working environment of the target radio frequency automation test platform into the trained prediction model for prediction, and obtaining the predicted energy consumption loss data of the target radio frequency automation test platform at each time node in the optimized time period;

[0121] Calculate the estimated power consumption of the target RF automation test platform during the optimization period according to the predicted energy consumption loss data of the target RF automation test platform at each time node during the optimization period;

[0122] Obtaining a preset power consumption of the target radio frequency automation test platform in the optimization time period, comparing the estimated power consumption of the target radio frequency automation test platform in the optimization time period with the preset power consumption, and obtaining a power consumption deviation value;

[0123] If the power consumption deviation value is greater than a preset deviation threshold, an electric energy charging scheme is generated according to the power consumption deviation value, and the power supply equipment of the target radio frequency automation test platform is charged based on the electric energy charging scheme.

[0124] It should be noted that in the RF automation test platform, since the equipment is in continuous operation and needs to be tested frequently, the power consumption is high, while the battery capacity is limited. Manually formulating a charging plan may lead to untimely or unbalanced charging due to factors such as heavy workload, human negligence or changing workload, affecting the performance and service life of the equipment. The present invention can monitor the energy consumption of the equipment in real time and automatically formulate an electric energy charging plan, automatically adjust the charging time, frequency and intensity according to the preset strategy, ensure that the equipment has sufficient power when needed, and avoid damage caused by excessive charging and discharging, ensure the continuity and accuracy of the test process, improve overall work efficiency, reduce operating costs, and achieve refined and intelligent management.

[0125] like Figure 3 As shown, the second aspect of the present invention discloses an operation energy consumption monitoring and optimization system of a radio frequency automated test platform, the operation energy consumption monitoring and optimization system comprising a memory 20 and a processor 100, the memory 20 stores an operation energy consumption monitoring and optimization method program, when the operation energy consumption monitoring and optimization method program is executed by the processor 100, the following steps are implemented:

[0126] Acquire the real-time working environment of the target radio frequency automation test platform, and determine various standard characteristic data of the target radio frequency automation test platform when working under the current real-time working environment conditions according to the real-time working environment;

[0127] Extracting the standard energy consumption loss value of the target radio frequency automation test platform when working under the current real-time working environment conditions from the standard feature data; acquiring the power information of the power supply equipment of the target radio frequency automation test platform within a preset time period, and calculating the total energy consumption loss value of the target radio frequency automation test platform within the preset time period according to the power information;

[0128] If the total energy consumption loss value is greater than the standard energy consumption loss value, then continuously collecting actual feature data of the target radio frequency automation test platform at multiple preset time nodes, and performing data cleaning processing on each actual feature data to obtain each cleaned actual feature data;

[0129] Compare and analyze the cleaned actual feature data with the corresponding standard feature data to analyze the status of the actual feature data in the target RF automation test platform;

[0130] Acquire actual characteristic data of abnormal status in the target radio frequency automation test platform, and trace the device to be monitored in the target radio frequency automation test platform according to the actual characteristic data of abnormal status;

[0131] Perform fault analysis on the equipment to be monitored in the target RF automation test platform to obtain the faulty equipment and the fault type of the faulty equipment in the target RF automation test platform, and generate an optimization plan based on the faulty equipment and the fault type of the faulty equipment in the target RF automation test platform.

[0132] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0133] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0134] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0135] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0136] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0137] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for monitoring and optimizing the operating energy consumption of a radio frequency automated test platform, characterized in that: The following steps are involved: Acquire the real-time working environment of the target radio frequency automation test platform, and determine various standard characteristic data of the target radio frequency automation test platform when working under the current real-time working environment conditions according to the real-time working environment; Extracting the standard energy consumption value of the target radio frequency automation test platform when working under the current real-time working environment conditions from the standard characteristic data; Acquire power information of a power supply device of a target radio frequency automation test platform within a preset time period, and calculate a total energy consumption value of the target radio frequency automation test platform within the preset time period according to the power information; If the total energy consumption loss value is greater than the standard energy consumption loss value, then continuously collecting actual feature data of the target radio frequency automation test platform at multiple preset time nodes, and performing data cleaning processing on each actual feature data to obtain each cleaned actual feature data; Compare and analyze the cleaned actual feature data with the corresponding standard feature data to analyze the status of the actual feature data in the target RF automation test platform; Acquire actual characteristic data of abnormal status in the target radio frequency automation test platform, and trace the device to be monitored in the target radio frequency automation test platform according to the actual characteristic data of abnormal status; Perform fault analysis on the equipment to be monitored in the target radio frequency automation test platform, obtain the faulty equipment in the target radio frequency automation test platform and the fault type of the faulty equipment, and generate an optimization plan according to the faulty equipment in the target radio frequency automation test platform and the fault type of the faulty equipment; Among them, the real-time working environment of the target radio frequency automation test platform is obtained, and the standard characteristic data of the target radio frequency automation test platform when working under the current real-time working environment conditions are determined according to the real-time working environment, specifically: Obtaining operation log information of the target radio frequency automation test platform, and obtaining various standard characteristic data of the target radio frequency automation test platform when it works normally under various preset working environment combination conditions according to the operation log information; Constructing a knowledge base, and importing various standard feature data of the target radio frequency automation test platform when it works normally under various preset working environment combination conditions into the knowledge base; When monitoring the target radio frequency automation test platform, the real-time working environment of the target radio frequency automation test platform is obtained; wherein the working environment includes temperature, humidity, electromagnetic intensity and dust concentration; Calculating an average Euclidean distance value between the real-time working environment and various preset working environments based on a Euclidean distance algorithm, and calculating similarities between the real-time working environment and various preset working environments according to the corresponding average Euclidean distance values ​​to obtain multiple similarities; Extracting the maximum similarity from the multiple similarities, obtaining a preset working environment corresponding to the maximum similarity, and matching various standard feature data of the target radio frequency automation test platform when working under the current real-time working environment conditions in the knowledge base according to the preset working environment corresponding to the maximum similarity; The characteristic data include CPU usage, memory occupancy, hard disk read and write speed, operating voltage, operating current, operating resistance, internal temperature and energy consumption value.

2. The method for monitoring and optimizing the operating energy consumption of a radio frequency automated test platform according to claim 1, characterized in that: Perform data cleaning on each actual feature data to obtain each cleaned actual feature data, specifically: Each actual feature data is regarded as a cluster, and each actual feature data in each cluster is regarded as an independent data point; The Euclidean distance between each data point in each cluster is calculated respectively, and the two data points with the closest Euclidean distance are merged to form a primary cluster group, thereby obtaining several primary cluster groups, and the mean of all actual feature data in each primary cluster group is calculated to obtain the cluster center position of each primary cluster group; Each primary cluster group is regarded as an independent data point, and the Euclidean distance of the data point is calculated respectively. The two data points with the closest Euclidean distance are merged to form a secondary cluster group, thereby obtaining several secondary cluster groups, and calculating the mean of all actual feature data in each secondary cluster group to obtain the cluster center position of each secondary cluster group; By analogy, the data points in each cluster are continuously iterated and clustered to obtain a number of n-fold clustering groups and the cluster center positions of the n-fold clustering groups; Compare the cluster center position of the n-th cluster group with the cluster center position of the last cluster group until the cluster center position of the n-th cluster group no longer changes from the cluster center position of the last cluster group, then stop the iteration; After the iteration is stopped, the actual feature data corresponding to the cluster group with the largest number of data points is extracted and retained from the n cluster groups, and the actual feature data corresponding to the remaining cluster groups are deleted to obtain the cleaned actual feature data; In this way, each actual feature data is cleaned to obtain cleaned actual feature data.

3. The method for monitoring and optimizing the operating energy consumption of a radio frequency automated test platform according to claim 1, characterized in that: Compare and analyze the cleaned actual feature data with the corresponding standard feature data to analyze the status of each actual feature data in the target RF automation test platform, specifically: Acquire each standard feature data and each actual feature data after cleaning of the radio frequency automation test platform; construct a plurality of two-dimensional coordinate systems, and generate actual feature data curve graphs in corresponding two-dimensional coordinate systems based on each actual feature data after cleaning; and generating a standard feature data curve graph in a corresponding two-dimensional coordinate system based on each standard feature data; Analyze the degree of overlap between the actual characteristic data curve graph and the standard characteristic data curve graph in each two-dimensional coordinate system; If the overlap between the actual characteristic data curve graph and the standard characteristic data curve graph in a certain two-dimensional coordinate system is greater than a preset overlap, the corresponding actual characteristic data is marked as actual characteristic data in a normal state; If the overlap degree between the actual characteristic data curve graph and the standard characteristic data curve graph in a certain two-dimensional coordinate system is not greater than the preset overlap degree, the corresponding actual characteristic data is marked as actual characteristic data in an abnormal state.

4. The method for monitoring and optimizing the operating energy consumption of a radio frequency automated test platform according to claim 1, characterized in that: Acquire actual characteristic data of abnormal status in the target radio frequency automation test platform, and trace the device to be monitored in the target radio frequency automation test platform according to the actual characteristic data of abnormal status, specifically: Acquire characteristic attribute information of each device in the target radio frequency automation test platform, perform correlation analysis on each characteristic data according to the characteristic attribute information of each device in the target radio frequency automation test platform, and obtain the correlation degree between each device and each characteristic data; Defining devices with a correlation degree greater than a preset correlation degree as devices having correlation with corresponding feature data, and obtaining the correlation devices of each feature data; constructing a knowledge graph, and importing the correlation devices of each feature data into the knowledge graph; Acquire actual feature data of abnormal status in the target radio frequency automation test platform, import the actual feature data of abnormal status into the knowledge graph for matching, and obtain a plurality of related devices through matching; The matched related devices are marked as devices to be monitored.

5. The method for monitoring and optimizing the operating energy consumption of a radio frequency automated test platform according to claim 1, characterized in that: Perform fault analysis on the equipment to be monitored in the target RF automation test platform to obtain the faulty equipment and fault types of the faulty equipment in the target RF automation test platform, and generate an optimization plan based on the faulty equipment and fault types of the faulty equipment in the target RF automation test platform, specifically: Obtaining fault log information of the device to be monitored, and extracting fault feature data corresponding to various types of faults occurring in the device to be monitored based on the fault log information; Acquire real-time operation data of the device to be monitored, and analyze the overlap between the real-time operation data and each fault characteristic data; Comparing the overlap between the real-time operation data and each fault characteristic data with a preset overlap threshold value respectively; If each overlap degree is not greater than the preset overlap degree threshold, it means that the device to be monitored is not a faulty device, indicating that no optimization processing is required; If there is at least one overlap greater than the preset overlap threshold, it means that the device to be monitored is a faulty device and needs to be optimized, then the fault characteristic data corresponding to the overlap greater than the preset overlap threshold is obtained, and the fault type of the faulty device is determined according to the fault characteristic data corresponding to the overlap greater than the preset overlap threshold; If the device to be monitored is a faulty device, an optimization plan is generated according to the fault type of the faulty device, and the optimization plan is transmitted to a preset terminal for display.

6. A system for monitoring and optimizing the operating energy consumption of a radio frequency automated test platform, characterized in that: The operation energy consumption monitoring and optimization system includes a memory and a processor. The memory stores an operation energy consumption monitoring and optimization method program. When the operation energy consumption monitoring and optimization method program is executed by the processor, the following steps are implemented: Acquire the real-time working environment of the target radio frequency automation test platform, and determine various standard characteristic data of the target radio frequency automation test platform when working under the current real-time working environment conditions according to the real-time working environment; Extracting the standard energy consumption value of the target radio frequency automation test platform when working under the current real-time working environment conditions from the standard characteristic data; Acquire power information of a power supply device of a target radio frequency automation test platform within a preset time period, and calculate a total energy consumption value of the target radio frequency automation test platform within the preset time period according to the power information; If the total energy consumption loss value is greater than the standard energy consumption loss value, then continuously collecting actual feature data of the target radio frequency automation test platform at multiple preset time nodes, and performing data cleaning processing on each actual feature data to obtain each cleaned actual feature data; Compare and analyze the cleaned actual feature data with the corresponding standard feature data to analyze the status of the actual feature data in the target RF automation test platform; Acquire actual characteristic data of abnormal status in the target radio frequency automation test platform, and trace the device to be monitored in the target radio frequency automation test platform according to the actual characteristic data of abnormal status; Perform fault analysis on the equipment to be monitored in the target radio frequency automation test platform, obtain the faulty equipment in the target radio frequency automation test platform and the fault type of the faulty equipment, and generate an optimization plan according to the faulty equipment in the target radio frequency automation test platform and the fault type of the faulty equipment; Among them, the real-time working environment of the target radio frequency automation test platform is obtained, and the standard characteristic data of the target radio frequency automation test platform when working under the current real-time working environment conditions are determined according to the real-time working environment, specifically: Obtaining operation log information of the target radio frequency automation test platform, and obtaining various standard characteristic data of the target radio frequency automation test platform when it works normally under various preset working environment combination conditions according to the operation log information; Constructing a knowledge base, and importing various standard feature data of the target radio frequency automation test platform when it works normally under various preset working environment combination conditions into the knowledge base; When monitoring the target radio frequency automation test platform, the real-time working environment of the target radio frequency automation test platform is obtained; wherein the working environment includes temperature, humidity, electromagnetic intensity and dust concentration; Calculating an average Euclidean distance value between the real-time working environment and various preset working environments based on a Euclidean distance algorithm, and calculating similarities between the real-time working environment and various preset working environments according to the corresponding average Euclidean distance values ​​to obtain multiple similarities; Extracting the maximum similarity from the multiple similarities, obtaining a preset working environment corresponding to the maximum similarity, and matching various standard feature data of the target radio frequency automation test platform when working under the current real-time working environment conditions in the knowledge base according to the preset working environment corresponding to the maximum similarity; The characteristic data include CPU usage, memory occupancy, hard disk read and write speed, operating voltage, operating current, operating resistance, internal temperature and energy consumption value.

Images