Abnormity correction method and system for signal generator

Abstract

The invention discloses an anomaly correction method and system for a signal generator, and relates to the technical field related to signal processing, and the method comprises the steps: extracting a first transaction record in block chain transaction records; dynamically acquiring real-time detection signals, and calculating first signal similarity; when a preset similarity limit value is reached, an abnormal alarm prompt is sent out; analyzing and generating first abnormal alarm information, and adding the first abnormal alarm information to a candidate correction list; obtaining a target descending list of the candidate correction list; adding to the first abnormal alarm information to form first target abnormal alarm information; and performing abnormity correction processing on the target signal generator. According to the invention, the technical problems of poor signal abnormity correction effect and poor precision and reliability of the output result of the signal generator due to the lack of consideration of real-time and dynamic factors in the abnormity correction of the existing signal generator are solved, and the technical effect of improving the accuracy and stability of the output signal of the signal generator is achieved.

Description

Technical Field

[0001] The present invention relates to the field related to signal processing technology, and in particular to an abnormality correction method and system for a signal generator. Background Art

[0002] Signal generators are widely used in electronic testing, communication systems, scientific research experiments and other fields. As key equipment in the field of electronic testing, signal generators are responsible for generating various analog or digital signals to provide necessary test conditions for performance evaluation, fault diagnosis and scientific research experiments of electronic equipment, circuits and systems. However, due to equipment aging, environmental factors (such as temperature, humidity, electromagnetic interference, etc.), operating errors or slight differences in the manufacturing process, signal generators may have output abnormalities during long-term operation or use, which manifests as inaccurate amplitude of the output signal, frequency offset, waveform distortion, phase error, etc. These output deviations will directly affect the accuracy and reliability of the test results, and may even lead to misjudgment or damage to electronic equipment and systems. It is particularly important to perform abnormal correction on signal generators. Although existing correction technologies can improve the output accuracy of signal generators to a certain extent, due to the design limitations of the equipment itself, component aging or the influence of the external environment, the correction accuracy is often difficult to reach the theoretical optimal level.

[0003] Therefore, in the current event denoising convergence related technologies, there is a technical problem that the load data feature acquisition is inaccurate, resulting in an inaccurate denoising model, which makes the accuracy and reliability of the event denoising convergence results low. Summary of the invention

[0004] The present invention provides a method and system for correcting anomalies of a signal generator, and adopts technical means such as similarity analysis and smart contracts to solve the technical problem that the existing abnormal correction of signal generators lacks consideration of real-time and dynamic factors, resulting in poor signal abnormality correction effect and poor accuracy and reliability of the output result of the signal generator, so as to achieve the technical effect of improving the accuracy and stability of the output signal of the signal generator.

[0005] The present invention provides an abnormality correction method for a signal generator, the method comprising: extracting a first transaction record in a blockchain transaction record, the first transaction record comprising a first detection signal, a first abnormality information and a first correction data, and the first transaction record corresponds to a first time on a timestamp; dynamically acquiring a real-time detection signal of a target signal generator at a target time, and calculating a first signal similarity between the real-time detection signal and the first detection signal; when the first signal similarity reaches a predetermined similarity limit, issuing an abnormality alarm prompt; based on the abnormality alarm prompt, analyzing a first abnormality type and a first abnormality degree in the first abnormality information and generating first abnormality alarm information, and adding the first correction data to a candidate correction list; based on a first correction effect in the first correction data, descending a first correction strategy in the first correction data to obtain a target descending list of the candidate correction list; adding a target correction strategy in the target descending list to the first abnormality alarm information to form a first target abnormality alarm information; triggering a smart contract according to the first target abnormality alarm information to perform abnormal correction processing on the target signal generator.

[0006] In a possible implementation, the dynamic acquisition of the real-time detection signal of the target signal generator at the target time and the calculation of the first signal similarity between the real-time detection signal and the first detection signal specifically include: performing time domain feature acquisition on the first signal timing sequence obtained based on the first time and the first detection signal to obtain time domain feature information of the first signal; performing frequency domain feature acquisition on the first signal spectrum obtained by fast Fourier transform of the first signal timing sequence to obtain frequency domain feature information of the first signal; the first signal time domain feature information and the first signal frequency domain feature information constitute a first feature set of the first detection signal; acquiring the real-time feature set of the real-time detection signal; reading a similarity analysis plan, and obtaining the first signal similarity between the first feature set and the real-time feature set based on the similarity analysis plan.

[0007] In a possible implementation, a method for correcting anomalies of a signal generator performs the following processing: marking the real-time feature set according to the similarity analysis plan to obtain a real-time feature vector; marking the first feature set according to the similarity analysis plan to obtain a first feature vector; comparing the real-time feature vector with the first feature vector and calculating the first signal similarity.

[0008] In a possible implementation, a method for correcting an abnormality of a signal generator further performs the following processing: constructing a historical signal timing of the target signal generator based on the first signal timing; reading a predetermined health correlation indicator, and performing feature collection on the historical signal timing based on the predetermined health correlation indicator to obtain a historical signal health feature; introducing a health assessment function to analyze the historical signal health feature to obtain an initial health index of the target signal generator.

[0009] In a possible implementation, the predetermined health-related indicators include historical abnormality frequency, average abnormality degree, and average correction effect.

[0010] In a possible implementation, a health assessment function is introduced to analyze the health characteristics of the historical signal to obtain the initial health index of the target signal generator, and the following processing is also performed: the expression of the health assessment function is as follows:

[0011]

[0012] Wherein, f(x) refers to the initial health index of the target signal generator at the target time x; k i T i (x) refers to the feature T corresponding to the i-th health correlation index in the health feature of the historical signal i (x) and its corresponding weight coefficient k i The product of , n refers to the number of the predetermined health-related indicators, and n=3; μ refers to the feedback adjustment coefficient, and μ is at least related to signal delay, signal jitter, and signal acquisition loss.

[0013] In a possible implementation, a health assessment function is introduced to analyze the health characteristics of the historical signal to obtain the initial health index of the target signal generator, and the following processing is performed: the real-time detection signal is added to the historical signal timing to obtain the target signal timing; the real-time detection signal is compared with a predetermined signal benchmark to obtain a real-time comparison deviation, and a real-time deviation index is generated based on the real-time comparison deviation; the initial health index is weighted based on the real-time deviation index to obtain a real-time health index; when the real-time health index does not meet the health limit, a second target abnormal alarm information is generated; according to the second target abnormal alarm information, the smart contract is triggered to perform abnormal correction processing on the target signal generator.

[0014] The present invention also provides a signal generator abnormality correction system, comprising:

[0015] A transaction record extraction module, the transaction record extraction module is used to extract the first transaction record in the blockchain transaction record, the first transaction record includes a first detection signal, a first abnormal information and a first correction data, and the first transaction record corresponds to the first time on the timestamp; a real-time detection signal acquisition module, the real-time detection signal acquisition module is used to dynamically acquire the real-time detection signal of the target signal generator at the target time, and calculate the first signal similarity between the real-time detection signal and the first detection signal; an abnormal alarm prompt module, the abnormal alarm prompt module is used to issue an abnormal alarm prompt when the first signal similarity reaches a predetermined similarity limit; a first abnormal alarm information generation module, the first abnormal alarm information generation module analyzes the first abnormality based on the abnormal alarm prompt. The first abnormality type and the first abnormality degree in the information are used to generate the first abnormal alarm information, and the first correction data is added to the candidate correction list; a target descending list acquisition module, the target descending list acquisition module descends the first correction strategy in the first correction data based on the first correction effect in the first correction data, and obtains the target descending list of the candidate correction list; a first target abnormal alarm information acquisition module, the first target abnormal alarm information acquisition module is used to add the target correction strategy in the target descending list to the first abnormal alarm information to form the first target abnormal alarm information; an abnormal correction processing module, the abnormal correction processing module is used to trigger the smart contract according to the first target abnormal alarm information to perform abnormal correction processing on the target signal generator.

[0016] It is intended that through the abnormal correction method and system of a signal generator proposed by the present invention, the first transaction record in the blockchain transaction record is extracted; the real-time detection signal is dynamically acquired, and the similarity of the first signal is calculated; when the predetermined similarity limit is reached, an abnormal alarm prompt is issued; the first abnormal alarm information is analyzed and generated, and added to the candidate correction list; the target descending list of the candidate correction list is obtained; it is added to the first abnormal alarm information to form the first target abnormal alarm information; and the target signal generator is subjected to abnormal correction processing. The present invention solves the technical problem that the existing signal generator abnormal correction lacks consideration of real-time and dynamic factors, resulting in poor signal abnormal correction effect, and poor accuracy and reliability of the signal generator output result, and achieves the technical effect of improving the accuracy and stability of the signal generator output signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure are briefly introduced below. In the present invention, a flow chart is used to illustrate the operations performed by the system according to the embodiment of the present invention. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0018] Figure 1 A schematic flow chart of a method for correcting abnormality of a signal generator provided by an embodiment of the present invention;

[0019] Figure 2 A schematic diagram of the structure of an abnormality correction system for a signal generator provided in an embodiment of the present invention.

[0020] Explanation of the accompanying drawings: 10 - transaction record extraction module, 20 - real-time detection signal acquisition module, 30 - abnormal alarm prompt module, 40 - first abnormal alarm information generation module, 50 - target descending list acquisition module, 60 - first target abnormal alarm information acquisition module, 70 - abnormal correction processing module. DETAILED DESCRIPTION

[0021] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below.

[0022] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present invention. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present invention.

[0023] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of the objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of the invention. The terms used herein are for the purpose of describing the embodiments of the present invention only.

[0024] The embodiment of the present invention provides a method for correcting abnormality of a signal generator, such as Figure 1 As shown, the method includes:

[0025] Step S100, extracting the first transaction record in the blockchain transaction record, the first transaction record includes a first detection signal, a first abnormal information and a first correction data, and the first transaction record corresponds to the first time on the timestamp. In blockchain technology, transaction records are added to the blockchain in chronological order, each block contains a certain amount of transaction data and a corresponding timestamp, and the timestamp identifies the specific time when the block is added to the blockchain. The first transaction record refers to a transaction record in a block, including a first detection signal, a first abnormal information and a first correction data. Specifically, the first detection signal refers to a signal or event detected by the system or device when or before the transaction occurs; the first abnormal information refers to a problem or unexpected situation found during the transaction processing or verification process, for example, the abnormal information may include the error type, cause, location or other data related to the abnormality; the first correction data refers to if the transaction is detected as abnormal and needs to be corrected, the correction data will contain information for repairing or adjusting the transaction, including new transaction parameters, corrected errors, replacement data or any other information for restoring the correctness of the transaction; the first time refers to the timestamp associated with the first transaction record, which identifies the specific time when the transaction record was added to the blockchain.

[0026] Step S200, dynamically acquire the real-time detection signal of the target signal generator at the target time, and calculate the first signal similarity between the real-time detection signal and the first detection signal. Dynamically acquiring the real-time detection signal of the target signal generator at the target time means dynamically capturing or recording the real-time detection signal from the target signal generator at the target time using an oscilloscope or spectrum analyzer, etc. Dynamic acquisition means that as the target signal generator runs, the test equipment continuously captures and records the signal, and then calculates the similarity between the real-time detection signal and the first detection signal to obtain the first signal similarity, for example, directly comparing the waveforms or amplitudes of the two signals, or calculating the similarity of the two signals using a cross-correlation function, and finally obtaining the similarity between the real-time detection signal and the first detection signal.

[0027] In a possible implementation, step S200 further includes step S210, collecting time domain features of the first signal sequence obtained based on the first time and the first detection signal to obtain time domain feature information of the first signal. For a given first detection signal (usually a signal that changes over time), extract and record the features or attributes of the signal in the time domain according to its corresponding first time (i.e., the timestamp or time axis of the signal) to obtain time domain feature information of the first signal, including signal statistics (mean, maximum, minimum, peak-to-peak value, etc.), waveform features (period, amplitude, phase, rise time, fall time, etc.), rate of change (slope, speed change, etc.), etc. It also includes step S220, collecting frequency domain features of the first signal spectrum obtained by fast Fourier transforming the first signal sequence to obtain frequency domain feature information of the first signal. The first signal spectrum obtained by fast Fourier transforming the first signal time series is subjected to frequency domain feature acquisition, that is, the signal is converted from the time domain to the frequency domain, and the signal features are extracted and analyzed in the frequency domain to obtain the first signal frequency domain feature information, including extracting various statistics of the spectrum (total energy and average power of the spectrum), peak frequency (the frequency point with the largest amplitude in the spectrum), bandwidth (the frequency range of signal energy or power distribution), harmonic components (integer multiple frequency components of the fundamental frequency in the signal), etc., wherein Fourier transform is a mathematical tool widely used in signal processing, image processing and other fields, which can convert a signal from the time domain (or space domain) to the frequency domain, or from the frequency domain back to the time domain (or space domain), which is called Fourier transform and its inverse transform. It also includes step S230, wherein the first signal time domain feature information and the first signal frequency domain feature information constitute the first feature set of the first detection signal. The first signal time domain feature information and the first signal frequency domain feature information are combined to form a more comprehensive and integrated feature set, namely the first feature set, which contains the key information of the signal in the time domain and frequency domain, and can more accurately describe the characteristics and properties of the signal. The method also includes step S240, obtaining a real-time feature set of the real-time detection signal. Extracting multiple signal features from the real-time detection signal to form a real-time feature set. The method also includes step S250, reading a similarity analysis plan, and obtaining the first signal similarity between the first feature set and the real-time feature set based on the similarity analysis plan. Read the similarity analysis plan, select a method for similarity analysis, such as cosine similarity, prepare the first feature set and the real-time feature set, ensure that they have the same or comparable dimensions and feature types, use the selected similarity analysis method or algorithm, perform similarity analysis on the first feature set and the real-time feature set, and based on the results of the similarity analysis, interpret and evaluate the similarity between the first feature set and the real-time feature set in terms of the first signal, including comparing the differences or similarities between the two feature sets in key features.

[0028] In a possible implementation, step S250 further includes step S251, marking the real-time feature set according to the similarity analysis plan to obtain a real-time feature vector. In the marking process stage, according to the requirements in the plan, the key features or attributes in the real-time feature set are marked to represent specific information, categories, states or importance, etc. After the marking process, the data in the real-time feature set is converted into the form of a feature vector to obtain a real-time feature vector. It also includes step S252, marking the first feature set according to the similarity analysis plan to obtain a first feature vector. Similarly, the first feature set is marked to obtain the corresponding first feature vector. It also includes step S253, comparing the real-time feature vector with the first feature vector and calculating the similarity of the first signal. The real-time feature vector captured and processed in real time is compared with the first feature vector, and the similarity between the two feature vectors is calculated by a similarity measurement algorithm, so as to obtain their similarity in describing a certain signal (i.e., the first signal).

[0029] Step S300, when the similarity of the first signal reaches a predetermined similarity limit, an abnormal alarm prompt is issued. In the process of dynamically monitoring and calculating the similarity between the real-time detection signal of the target signal generator at the target time and the pre-recorded first detection signal, if the calculated signal similarity metric value (such as correlation coefficient, cross-correlation function value, etc.) reaches a preset threshold value (i.e., a predetermined similarity limit), the system will automatically trigger an abnormal alarm prompt, which can be presented in various forms, such as sound alarms, visual prompts (such as flashing or color change on the screen), etc., wherein the predetermined similarity limit is a standard value set based on historical data or expert experience, etc., and is used to determine whether the difference between the real-time detection signal and the first detection signal exceeds an acceptable range.

[0030] Step S400, based on the abnormal alarm prompt, analyze the first abnormal type and the first abnormal degree in the first abnormal information and generate the first abnormal alarm information, and add the first correction data to the candidate correction list. When the system detects that the first signal similarity reaches the predetermined similarity limit and issues an abnormal alarm prompt, it starts to analyze the abnormal alarm prompt, that is, analyze the first abnormal information, which refers to the abnormal information associated with the real-time detection signal, and identify the corresponding abnormal type and abnormal degree. Specifically, the abnormal information may contain a variety of different types of abnormalities, such as signal amplitude abnormality, frequency offset, phase misalignment, noise interference, etc., and the abnormal type of the first abnormal information is one of them. At the same time, the severity or impact of the abnormality is evaluated, for example, by calculating the deviation value, the rate of change, etc. to determine the degree of abnormality, and finally generate the first abnormal alarm information. After determining the abnormal type and degree, retrieve and obtain the correction data corresponding to the abnormal information, that is, the first correction data, and finally add the first correction data to the candidate correction list, which includes correction options that can be used to repair or alleviate the current abnormality.

[0031] Step S500, based on the first correction effect in the first correction data, the first correction strategy in the first correction data is sorted in descending order to obtain the target descending list of the candidate correction list. For the first correction data, the effect of the corresponding correction strategy in practical application is evaluated, which is usually measured based on a series of indicators, such as the stability of the corrected signal, the response time of the correction, the performance indicators of the corrected system, etc. According to the evaluation results, a score is assigned to each correction strategy, which reflects the effect of the correction strategy, that is, to what extent it can solve or alleviate the current abnormal problem, and then all correction strategies are sorted in descending order according to their corresponding scores. The higher the score, the higher the correction strategy is ranked, indicating that the better its correction effect is. After sorting in descending order, a list arranged from high to low according to the correction effect is obtained, that is, the target descending list. This list is the sorting result of the candidate correction list, which reflects the priority of different correction strategies in solving the current abnormal problem.

[0032] Step S600, add the target correction strategy in the target descending list to the first abnormal alarm information to form the first target abnormal alarm information. The evaluated and sorted correction strategy (i.e., the target correction strategy in the target descending list) is merged or supplemented with the original first abnormal alarm information to form the final first target abnormal alarm information. Specifically, the first abnormal alarm information usually contains key information about the abnormality type, abnormality degree, occurrence time, etc., which is crucial for understanding and handling abnormalities; the target correction strategy in the target descending list is based on the current abnormal situation and system status, and is the optimal or suboptimal correction scheme obtained by evaluating the effects of different correction strategies. Adding the target correction strategy to the first abnormal alarm information can form a more complete and specific first target abnormal alarm information, which can locate and resolve abnormalities more quickly and accurately.

[0033] Step S700, triggering the smart contract to perform abnormal correction processing on the target signal generator according to the first target abnormal alarm information. Among them, the smart contract is an automatically executed computer program written based on blockchain technology. When the system detects that the target signal generator is abnormal and generates the first target abnormal alarm information containing the target correction strategy, the execution of the smart contract is triggered. The smart contract will read the target correction strategy in the first target abnormal alarm information. According to the read target correction strategy, the smart contract will send corresponding control instructions to the target signal generator or perform corresponding operations to perform abnormal correction on it, for example, adjust the parameters of the signal generator, switch the working mode, restart the device, etc.

[0034] In a possible implementation, step S700 further includes step S710, constructing the historical signal timing of the target signal generator based on the first signal timing. Using the known first signal timing data, such as interpolation, fitting, model prediction, etc., simulate or reconstruct the signal timing generated by the target signal generator in a certain time period in the past. It also includes step S720, reading a predetermined health correlation index, and performing feature acquisition on the historical signal timing based on the predetermined health correlation index to obtain the historical signal health characteristics. In applications such as signal processing, equipment monitoring or fault diagnosis, key indicators related to the health status of the signal generator are first determined, and then these indicators are used to extract feature information that can reflect the health status of the signal generator from the historical signal timing. Specifically, the predetermined health correlation index is a series of parameters related to the health status that are predetermined based on the design, working principle, historical operation data, and maintenance experience of the signal generator. Based on the predetermined health correlation index, feature information that can reflect the health status of the signal generator is extracted from the historical signal timing, and a series of feature parameters or feature vectors that can reflect the historical health status of the signal generator are obtained, that is, historical signal health characteristics. The method further includes step S730, wherein a health assessment function is introduced to analyze the health characteristics of the historical signal to obtain an initial health index of the target signal generator. The extracted health characteristics of the historical signal are evaluated using the health assessment function to obtain a quantitative index, namely, the initial health index, which is used to describe the current health status of the target signal generator. Specifically, the health assessment function obtains a value reflecting the health status of the target signal generator through a series of calculations and comparisons based on a predetermined health correlation index and the extracted health characteristics of the historical signal, namely, the initial health index, which is usually a floating point number between 0 and 1, where 1 indicates complete health, 0 indicates complete failure, and intermediate values ​​indicate different degrees of sub-health or failure status.

[0035] In a possible implementation, the predetermined health-related index in step S720 includes historical abnormal frequency, average abnormal degree and average correction effect. Among the predetermined health-related indexes, the historical abnormal frequency, average abnormal degree and average correction effect are three important parameters closely related to the health status of the signal generator, which are used to quantitatively evaluate the operating status of the equipment. Specifically, the historical abnormal frequency refers to the number or frequency of abnormal conditions of the signal generator in the past period of time, reflecting the stability and reliability of the equipment. A higher historical abnormal frequency means a potential failure risk or insufficient maintenance of the equipment. By monitoring the changes in the historical abnormal frequency, the abnormal trend of the equipment can be discovered in time, and corresponding maintenance measures can be taken to avoid the occurrence of equipment failure; the average abnormal degree refers to the average degree of performance degradation or deviation of the signal generator from the normal state when an abnormal condition occurs, which measures the performance of the equipment in an abnormal state. A higher average abnormal degree means that the equipment will be greatly affected when a failure occurs, and may even cause equipment shutdown or production interruption; the average correction effect refers to the average value of the effect of restoring the equipment to the normal state through maintenance, adjustment or calibration after the equipment has an abnormal condition, reflecting the effectiveness of equipment maintenance and repair. A higher average correction effect indicates that the maintenance and repair of the equipment is relatively successful and can effectively restore the normal performance of the equipment. The three predetermined health-related indicators, namely historical abnormality frequency, average abnormality degree and average correction effect, provide a comprehensive quantitative basis for evaluating the health status of the signal generator, which helps to achieve equipment health management, fault diagnosis and predictive maintenance.

[0036] In a possible implementation, step S730 further includes: the expression of the health assessment function is as follows:

[0037]

[0038] Wherein, f(x) refers to the initial health index of the target signal generator at the target time x; k i T i (x) refers to the feature T corresponding to the i-th health correlation index in the health feature of the historical signal i (x) and its corresponding weight coefficient k i The product of , n refers to the number of the predetermined health-related indicators, and n=3; μ refers to the feedback adjustment coefficient, and μ is at least related to signal delay, signal jitter, and signal acquisition loss.

[0039] In a possible implementation, step S700 further includes step S740, adding the real-time detection signal to the historical signal time sequence to obtain the target signal time sequence. The signal captured and detected in real time (i.e., the real-time detection signal) is added to the end of the existing historical signal time sequence (i.e., the historical signal time sequence), thereby constructing a continuous time sequence containing past and current signal data, i.e., the target signal time sequence. It also includes step S750, comparing the real-time detection signal with the predetermined signal benchmark to obtain a real-time comparison deviation, and generating a real-time deviation index based on the real-time comparison deviation. By comparing the real-time detection signal with the predetermined signal benchmark, the difference between the two, i.e., the real-time comparison deviation, can be calculated to measure the degree of difference between the real-time detection signal and the benchmark. Based on the real-time comparison deviation, a real-time deviation index can be further calculated or generated. The real-time deviation index is a quantitative indicator used to more intuitively reflect the degree of difference between the real-time detection signal and the benchmark. It also includes step S760, weighting the initial health index based on the real-time deviation index to obtain a real-time health index. According to the size of the real-time deviation index, the initial health index is weighted and adjusted to obtain the real-time health index, so as to more accurately reflect the health status of the monitored object or system at the current moment. Specifically, when the real-time deviation index is large, it means that the deviation between the current state of the monitored object or system and the health benchmark is large, and a larger weight is given to adjust the initial health index. It also includes step S770, when the real-time health index does not meet the health limit, a second target abnormal alarm information is formed. When the real-time health index does not meet the health limit, that is, the real-time health index does not meet the preset health limit range, it is judged that the signal generator is currently in an abnormal state, and the abnormal alarm mechanism is triggered to form abnormal alarm information, that is, the second target abnormal alarm information. It also includes step S780, according to the second target abnormal alarm information, triggering the smart contract to perform abnormal correction processing on the target signal generator. When the second target abnormal alarm information is triggered, it means that the target signal generator may have an abnormality or failure. This alarm information will trigger the smart contract previously deployed on the blockchain. The smart contract will perform corresponding operations according to the preset conditions and rules in the contract, and perform abnormal correction processing on the target signal generator. For example, adjust the parameters of the signal generator, perform reset operations, call backup systems or equipment, etc., to achieve automated management and control of the target signal generator, timely discover and solve potential problems, and ensure the stability and performance safety of the system.

[0040] In the above, refer to Figure 1 A method for correcting abnormality of a signal generator according to an embodiment of the present invention is described in detail. Figure 2 A signal generator abnormality correction system according to an embodiment of the present invention is described.

[0041] According to an embodiment of the present invention, a signal generator abnormality correction system is used to solve the technical problem that the existing signal generator abnormality correction lacks consideration of real-time and dynamic factors, resulting in poor signal abnormality correction effect, and poor accuracy and reliability of the signal generator output result, thereby achieving the technical effect of improving the accuracy and stability of the signal generator output signal. A signal generator abnormality correction system includes: a transaction record extraction module 10, a real-time detection signal acquisition module 20, an abnormality alarm prompt module 30, a first abnormality alarm information generation module 40, a target descending list acquisition module 50, a first target abnormality alarm information acquisition module 60, and an abnormality correction processing module 70.

[0042] A transaction record extraction module 10, wherein the transaction record extraction module 10 is used to extract a first transaction record in a blockchain transaction record, wherein the first transaction record includes a first detection signal, first abnormal information, and first correction data, and the first transaction record corresponds to a first time on a timestamp;

[0043] A real-time detection signal acquisition module 20, the real-time detection signal acquisition module 20 is used to dynamically acquire a real-time detection signal of a target signal generator at a target time, and calculate a first signal similarity between the real-time detection signal and the first detection signal;

[0044] An abnormal alarm prompt module 30, wherein the abnormal alarm prompt module 30 is used to issue an abnormal alarm prompt when the similarity of the first signal reaches a predetermined similarity limit;

[0045] A first abnormality warning information generating module 40, wherein the first abnormality warning information generating module 40 analyzes a first abnormality type and a first abnormality degree in the first abnormality information based on the abnormality warning prompt and generates first abnormality warning information, and adds the first correction data to a candidate correction list;

[0046] a target descending list obtaining module 50, wherein the target descending list obtaining module 50 performs descending order on the first correction strategy in the first correction data based on the first correction effect in the first correction data to obtain a target descending list of the candidate correction list;

[0047] A first target abnormality alarm information acquisition module 60, the first target abnormality alarm information acquisition module 60 is used to add the target correction strategy in the target descending list to the first abnormality alarm information to form the first target abnormality alarm information;

[0048] The abnormality correction processing module 70 is used to trigger the smart contract to perform abnormality correction processing on the target signal generator according to the first target abnormality alarm information.

[0049] The specific configuration of the real-time detection signal acquisition module 20 will be described in detail below. The real-time detection signal acquisition module 20 is specifically used for: performing time domain feature acquisition on the first signal time series obtained based on the first time and the first detection signal to obtain time domain feature information of the first signal; performing frequency domain feature acquisition on the first signal spectrum obtained by fast Fourier transform of the first signal time series to obtain frequency domain feature information of the first signal; the first signal time domain feature information and the first signal frequency domain feature information constitute the first feature set of the first detection signal; obtaining the real-time feature set of the real-time detection signal; reading the similarity analysis plan, and obtaining the first signal similarity between the first feature set and the real-time feature set based on the similarity analysis plan.

[0050] The specific configuration of the real-time detection signal acquisition module 20 will be described in detail below. The reading of the similarity analysis plan and obtaining the first signal similarity between the first feature set and the real-time feature set based on the similarity analysis plan may further include: marking the real-time feature set according to the similarity analysis plan to obtain a real-time feature vector; marking the first feature set according to the similarity analysis plan to obtain a first feature vector; comparing the real-time feature vector with the first feature vector and calculating the first signal similarity.

[0051] The specific configuration of the abnormality correction processing module 70 will be described in detail below. The abnormality correction processing module 70 may further include: constructing the historical signal timing of the target signal generator based on the first signal timing; reading a predetermined health correlation index, and performing feature collection on the historical signal timing based on the predetermined health correlation index to obtain a historical signal health feature; introducing a health assessment function to analyze the historical signal health feature to obtain an initial health index of the target signal generator.

[0052] The specific configuration of the abnormality correction processing module 70 will be described in detail below. The abnormality correction processing module 70 further includes: the predetermined health-related indicators include historical abnormality frequency, average abnormality degree and average correction effect.

[0053] The specific configuration of the abnormality correction processing module 70 will be described in detail below. The abnormality correction processing module 70 further includes: the expression of the health assessment function is as follows:

[0054]

[0055] Wherein, f(x) refers to the initial health index of the target signal generator at the target time x; k i T i (x) refers to the feature T corresponding to the i-th health correlation index in the health feature of the historical signali (x) and its corresponding weight coefficient k i The product of , n refers to the number of the predetermined health-related indicators, and n=3; μ refers to the feedback adjustment coefficient, and μ is at least related to signal delay, signal jitter, and signal acquisition loss.

[0056] The specific configuration of the abnormality correction processing module 70 will be described in detail below. The abnormality correction processing module 70 may further include: adding the real-time detection signal to the historical signal timing to obtain the target signal timing; comparing the real-time detection signal with the predetermined signal reference to obtain the real-time comparison deviation, and generating a real-time deviation index based on the real-time comparison deviation; weighting the initial health index based on the real-time deviation index to obtain a real-time health index; when the real-time health index does not meet the health limit, forming a second target abnormality alarm information; triggering the smart contract to perform abnormality correction processing on the target signal generator according to the second target abnormality alarm information.

[0057] A signal generator abnormality correction system provided by an embodiment of the present invention can execute a signal generator abnormality correction method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects of the execution method.

[0058] Although the present invention makes various references to certain modules in the system according to the embodiments of the present invention, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0059] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for correcting abnormality of a signal generator, characterized in that: include: Extracting a first transaction record from the blockchain transaction record, where the first transaction record includes a first detection signal, first abnormal information, and first correction data, and the first transaction record corresponds to a first time on a timestamp; Dynamically acquiring a real-time detection signal of a target signal generator at a target time, and calculating a first signal similarity between the real-time detection signal and the first detection signal; When the similarity of the first signal reaches a predetermined similarity limit, an abnormal alarm is issued; Based on the abnormality warning prompt, analyzing the first abnormality type and the first abnormality degree in the first abnormality information and generating first abnormality warning information, and adding the first correction data to a candidate correction list; Based on the first correction effect in the first correction data, the first correction strategy in the first correction data is sorted in descending order to obtain a target descending list of the candidate correction list; Adding the target correction strategy in the target descending list to the first abnormal warning information to form the first target abnormal warning information; According to the first target abnormal alarm information, the smart contract is triggered to perform abnormal correction processing on the target signal generator.

2. The method for correcting abnormality of a signal generator according to claim 1, characterized in that: The dynamically acquiring a real-time detection signal of a target signal generator at a target time, and calculating a first signal similarity between the real-time detection signal and the first detection signal, specifically includes: Perform time domain feature collection on a first signal time sequence obtained based on the first time and the first detection signal to obtain first signal time domain feature information; Performing frequency domain feature acquisition on a first signal spectrum obtained by performing fast Fourier transform on the first signal time series to obtain frequency domain feature information of the first signal; The first signal time domain feature information and the first signal frequency domain feature information constitute a first feature set of the first detection signal; Acquire a real-time feature set of the real-time detection signal; A similarity analysis plan is read, and based on the similarity analysis plan, the first signal similarity between the first feature set and the real-time feature set is obtained.

3. The abnormality correction method of a signal generator according to claim 2, characterized in that: The reading of the similarity analysis plan and obtaining the first signal similarity between the first feature set and the real-time feature set based on the similarity analysis plan specifically includes: Marking the real-time feature set according to the similarity analysis plan to obtain a real-time feature vector; Marking the first feature set according to the similarity analysis plan to obtain a first feature vector; The real-time feature vector is compared with the first feature vector and the first signal similarity is calculated.

4. The method for correcting abnormality of a signal generator according to claim 2, characterized in that: Triggering the smart contract to perform abnormal correction processing on the target signal generator according to the first target abnormal alarm information specifically includes: Constructing a historical signal timing of the target signal generator based on the first signal timing; Reading a predetermined health correlation indicator, and collecting characteristics of the historical signal time series based on the predetermined health correlation indicator to obtain a historical signal health characteristic; A health evaluation function is introduced to analyze the health characteristics of the historical signal to obtain an initial health index of the target signal generator.

5. The method for correcting abnormality of a signal generator according to claim 4, characterized in that: The predetermined health-related indicators include historical abnormality frequency, average abnormality degree and average correction effect.

6. The method for correcting abnormality of a signal generator according to claim 4, characterized in that: The expression of the health assessment function is as follows: Wherein, f(x) refers to the initial health index of the target signal generator at the target time x; k i T i (x) refers to the feature T corresponding to the i-th health correlation index in the health feature of the historical signal i (x) and its corresponding weight coefficient k i The product of; n refers to the number of the predetermined health-related indicators, and n=3; μ refers to the feedback adjustment coefficient, and μ is at least related to signal delay, signal jitter, and signal acquisition loss.

7. The method for correcting abnormality of a signal generator according to claim 4, characterized in that: Also includes: Adding the real-time detection signal to the historical signal time sequence to obtain a target signal time sequence; Comparing the real-time detection signal with a predetermined signal reference to obtain a real-time comparison deviation, and generating a real-time deviation index based on the real-time comparison deviation; Weighting the initial health index based on the real-time deviation index to obtain a real-time health index; When the real-time health index does not meet the health limit, a second target abnormal alarm information is generated; The smart contract is triggered according to the second target abnormal alarm information to perform abnormal correction processing on the target signal generator.

8. A signal generator abnormality correction system, characterized in that: include: A transaction record extraction module, wherein the transaction record extraction module is used to extract a first transaction record from the blockchain transaction record, wherein the first transaction record includes a first detection signal, first abnormal information, and first correction data, and the first transaction record corresponds to a first time on a timestamp; A real-time detection signal acquisition module, the real-time detection signal acquisition module is used to dynamically acquire a real-time detection signal of a target signal generator at a target time, and calculate a first signal similarity between the real-time detection signal and the first detection signal; An abnormal alarm prompt module, wherein the abnormal alarm prompt module is used to issue an abnormal alarm prompt when the similarity of the first signal reaches a predetermined similarity limit; A first abnormality warning information generating module, which analyzes a first abnormality type and a first abnormality degree in the first abnormality information based on the abnormality warning prompt and generates first abnormality warning information, and adds the first correction data to a candidate correction list; a target descending list obtaining module, wherein the target descending list obtaining module performs descending order on the first correction strategy in the first correction data based on the first correction effect in the first correction data to obtain a target descending list of the candidate correction list; A first target abnormality alarm information acquisition module, wherein the first target abnormality alarm information acquisition module is used to add the target correction strategy in the target descending list to the first abnormality alarm information to form the first target abnormality alarm information; An abnormality correction processing module is used to trigger a smart contract to perform abnormality correction processing on the target signal generator according to the first target abnormality alarm information.

9. The abnormality correction system for a signal generator according to claim 8, characterized in that: The real-time detection signal acquisition module is specifically used to: perform time domain feature acquisition on the first signal time series obtained based on the first time and the first detection signal to obtain time domain feature information of the first signal; perform frequency domain feature acquisition on the first signal spectrum obtained by fast Fourier transform of the first signal time series to obtain frequency domain feature information of the first signal; the first signal time domain feature information and the first signal frequency domain feature information constitute a first feature set of the first detection signal; obtain a real-time feature set of the real-time detection signal; read a similarity analysis plan, and obtain the first signal similarity between the first feature set and the real-time feature set based on the similarity analysis plan.

10. The abnormality correction system of a signal generator according to claim 8, characterized in that: The abnormal correction processing module specifically includes: constructing the historical signal timing of the target signal generator based on the first signal timing; reading a predetermined health correlation indicator, and collecting characteristics of the historical signal timing based on the predetermined health correlation indicator to obtain historical signal health characteristics; introducing a health assessment function to analyze the historical signal health characteristics to obtain an initial health index of the target signal generator.

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