CN118091520B - Intelligent regulation and control method and system for 10us square wave surge testing equipment - Google Patents
Intelligent regulation and control method and system for 10us square wave surge testing equipment Download PDFInfo
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Abstract
The invention relates to the technical field of regulation and control of electrical test equipment, in particular to an intelligent regulation and control method and system of 10us square wave surge test equipment. Acquiring a preset test scheme of target test equipment, controlling the target test equipment to perform a pre-test based on the preset test scheme, and acquiring an actual waveform diagram of a square wave signal generated by the target test equipment in the pre-test process; evaluating the target test equipment according to the actual waveform diagram and a preset waveform diagram; if the square wave surge is the second evaluation result, performing association analysis on each element in the target test equipment to obtain suspicious elements which cause the square wave surge generated by the target test equipment to be inconsistent with the requirements; if the posterior probability of the suspicious element in the fault state is not greater than the preset probability, a regulation and control scheme is generated, the reliability and stability of the equipment can be effectively improved through the method, and the equipment can be ensured to stably and reliably generate the required square wave surge.
Description
Technical Field
The invention relates to the technical field of regulation and control of electrical test equipment, in particular to an intelligent regulation and control method and system of 10us square wave surge test equipment.
Background
The 10us square wave surge testing device is a device which is specially used for testing the surge resistance of electrical equipment or electronic equipment. In actual operation, electrical equipment may be subjected to transient overvoltages from the power supply lines, such as voltage surges caused by lightning strikes, switching operations, etc., which may damage the equipment or cause the equipment to function abnormally. These transient overvoltages can thus be simulated by means of a square wave surge testing device, which tests the endurance of the device in this case. The square wave surge testing device is used for testing the electrical equipment, so that the resistance of the equipment to transient overvoltage can be evaluated, and equipment manufacturers and users are helped to know the reliability and stability of the equipment. In order to ensure the accuracy and reliability of the test result, the square wave surge test device needs to stably generate a square wave surge signal meeting the standard requirement, and the aging or failure problem of the square wave surge test device itself may cause that the device cannot generate the square wave surge signal meeting the standard requirement, so that the distortion of the test result is caused, and the reliability of the test result is affected.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides an intelligent regulation and control method and system for 10us square wave surge testing equipment.
The technical scheme adopted by the invention for achieving the purpose is as follows:
The invention discloses an intelligent regulation and control method of 10us square wave surge testing equipment, which comprises the following steps:
Acquiring a preset test scheme of target test equipment, controlling the target test equipment to perform a pre-test based on the preset test scheme, and acquiring an actual waveform diagram of a square wave signal generated by the target test equipment in the pre-test process;
Formulating a preset waveform diagram of square wave signals generated by target test equipment in the test process, and performing evaluation processing on the target test equipment according to the actual waveform diagram and the preset waveform diagram to generate a first evaluation result or a second evaluation result;
If the first evaluation result is the first evaluation result, the target test equipment is indicated to be used for carrying out the substantive test on the product; if the square wave surge is the second evaluation result, performing association analysis on each element in the target test equipment to obtain suspicious elements which cause the square wave surge generated by the target test equipment to be inconsistent with the requirements;
Performing fault analysis on the suspicious element, and if the posterior probability of the suspicious element in a fault state is greater than a preset probability, generating a fault report; if the posterior probability of the suspicious element in the fault state is not greater than the preset probability, a regulation scheme is generated, and the regulation scheme is sent to a control terminal of the target test equipment so as to regulate and control the target test equipment.
Further, in a preferred embodiment of the present invention, a preset test scheme of the target test device is obtained, the target test device is controlled to perform a pre-test based on the preset test scheme, and an actual waveform diagram of a square wave signal generated by the target test device in the pre-test process is obtained, which specifically includes:
acquiring a preset test scheme of target test equipment, controlling the target test equipment to perform a pre-test based on the preset test scheme, and acquiring square wave signals generated by the target test equipment through an oscilloscope in the target pre-test process;
Filtering, denoising and downsampling the acquired square wave signals to obtain processed square wave signals, and performing digital conversion on the processed square wave signals through an analog-to-digital converter to obtain digital signals;
and according to the digital signals and in combination with data visualization software, performing visualization processing on square wave signals generated by the target test equipment to obtain an actual waveform diagram of the square wave signals generated by the target test equipment in the pre-test process.
Further, in a preferred embodiment of the present invention, the evaluation process is performed on the target test device according to the actual waveform diagram and the preset waveform diagram, so as to generate a first evaluation result or a second evaluation result, which specifically is:
Constructing a two-dimensional coordinate system, and mapping the actual waveform diagram and a preset waveform diagram into the two-dimensional coordinate system; wherein the abscissa of the two-dimensional coordinate system represents time and the ordinate of the two-dimensional coordinate system represents amplitude;
acquiring coordinate values corresponding to the actual waveform diagram and the preset waveform diagram on the same time node in a two-dimensional coordinate system, and calculating Euclidean distances between the actual waveform diagram and the preset waveform diagram on the same time node based on the coordinate values to obtain a plurality of Euclidean distances;
Summing a plurality of Euclidean distances, and then taking an average value to obtain an average Euclidean distance, and determining the coincidence ratio of the actual oscillogram and a preset oscillogram according to the average Euclidean distance; comparing the contact ratio with a preset threshold value;
If the overlap ratio is larger than a preset threshold value, indicating that square wave surge generated by target test equipment meets the requirement, generating a first evaluation result; and if the overlap ratio is not greater than the preset threshold value, indicating that the square wave surge generated by the target test equipment does not meet the requirement, and generating a second evaluation result.
Further, in a preferred embodiment of the present invention, if the second evaluation result is the second evaluation result, performing association analysis on each element in the target test device to obtain a suspicious element that triggers the square wave surge generated by the target test device to be inconsistent with the requirement, specifically:
If the actual waveform diagram is the second evaluation result, the Euclidean distance between the actual waveform diagram and the preset waveform diagram at the same time node is compared with the preset Euclidean distance one by one, and the time node corresponding to the Euclidean distance larger than the preset Euclidean distance is marked as an abnormal time node;
Acquiring an operation log of target test equipment, and extracting various actual operation parameters of the target test equipment at an abnormal time node from the operation log; acquiring various preset operation parameter threshold ranges of the target test equipment in the abnormal time node in a preset test scheme;
Respectively judging whether each actual operation parameter is within a corresponding preset operation parameter threshold range; if the operation parameters are located, marking the actual operation parameters as normal operation parameters; if not, marking the actual operation parameter as an abnormal operation parameter;
Acquiring functional characteristic information of each element in the target test equipment, and carrying out association analysis on the abnormal operation parameters and the functional characteristic information of each element to obtain association between the abnormal operation parameters and each element; marking the elements with the association degree larger than the preset association degree as suspicious elements;
Wherein the operating parameters include frequency, amplitude, duty cycle, rise time, and fall time.
Further, in a preferred embodiment of the present invention, fault analysis is performed on the suspicious element, and if the posterior probability that the suspicious element is in a fault state is greater than a preset probability, a fault report is generated; if the posterior probability of the suspicious element in the fault state is not greater than the preset probability, generating a regulation scheme, and sending the regulation scheme to a control terminal of target test equipment, wherein the regulation scheme specifically comprises the following steps:
Acquiring each actual electrical parameter of the suspicious element in the pre-test process according to the operation log, and calculating the difference between the actual electrical parameter of the suspicious element and the corresponding preset electrical parameter to obtain each electrical parameter difference of the suspicious element; wherein the electrical parameter includes current, voltage, and power;
Comparing each electrical parameter difference value of the suspicious element with a preset difference value; if the difference value of each electrical parameter of a certain suspicious element is not larger than the preset difference value, marking the suspicious element as a normal element, and marking the working state of the suspicious element as a normal state;
If at least one electrical parameter difference value of a suspicious element is larger than a preset difference value, marking the suspicious element as an abnormal element, marking the working state of the suspicious element as an abnormal state, and marking the actual electrical parameter of which the electrical parameter difference value is larger than a corresponding item of the preset difference value as an abnormal electrical parameter;
acquiring various actual electrical parameters of the abnormal element in a pre-test process and actual environmental parameters within a preset range of the actual electrical parameters according to the operation log;
Leading each actual electrical parameter of the abnormal element in the pre-test process and the actual environmental parameter within the preset range of the actual electrical parameter into a Bayesian network model for fault diagnosis to obtain the posterior probability of the abnormal element in a fault state;
If the posterior probability that the abnormal element is in the fault state is greater than the preset probability, marking the abnormal element as a fault element, acquiring the fault type and the fault position information of the fault element, generating a fault report according to the fault type and the fault position information of the fault element, and generating the fault report on a preset platform;
If the posterior probability of the abnormal element in the fault state is not greater than the preset probability, acquiring the abnormal electrical parameter condition of the abnormal element, generating a regulation and control scheme according to the abnormal electrical parameter condition, and sending the regulation and control scheme to a control terminal of target test equipment.
Further, in a preferred embodiment of the present invention, if the posterior probability that the abnormal element is in the fault state is not greater than the preset probability, the abnormal electrical parameter condition of the abnormal element is obtained, and a regulation scheme is generated according to the abnormal electrical parameter condition, and the regulation scheme is sent to the control terminal of the target test device, specifically:
setting regulation and control schemes corresponding to abnormal conditions of various electrical parameters of elements in target test equipment in advance, constructing a knowledge graph, and importing the regulation and control schemes corresponding to abnormal conditions of various electrical parameters of elements in the target test equipment which are set in advance into the knowledge graph;
If the posterior probability that the abnormal element is in the fault state is not greater than the preset probability, acquiring the abnormal electrical parameter condition of the abnormal element, and generating a search tag according to the abnormal electrical parameter condition of the abnormal element;
Searching the knowledge graph based on the search tag to obtain a corresponding regulation and control scheme; the regulation and control scheme is sent to a control terminal of target test equipment, so that the abnormal electrical parameters of the abnormal element are regulated and controlled based on the regulation and control scheme, and the abnormal electrical parameters of the abnormal element are regulated to a normal range;
and after the abnormal electrical parameters of the abnormal element are regulated to the normal range, controlling the target test equipment to perform the pre-test again based on the preset test scheme.
Further, in a preferred embodiment of the present invention, each actual electrical parameter of the abnormal element in the pre-test process and an actual environmental parameter within a preset range thereof are imported into a bayesian network model for fault diagnosis, so as to obtain a posterior probability that the abnormal element is in a fault state, which specifically is:
Establishing a Bayesian network, and defining nodes and edges of the Bayesian network, wherein the state and the observed variable of each element are expressed as a node, and the association between the nodes is expressed by directed edges;
Collecting historical electrical parameter data and historical environmental parameter data of each element in an operation log of target test equipment as observation data, and calibrating the working state of each element among various observation data conditions; the working state comprises a normal state and a fault state;
Based on the observation data, estimating maximum likelihood estimation values among all nodes in the Bayesian network by using a maximum likelihood estimation method, and determining the conditional probability distribution of all the nodes in the Bayesian network according to the maximum likelihood estimation values to obtain a Bayesian network model; wherein the conditional probability describes a dependency between the element state and the observed variable;
And importing each actual electrical parameter of the abnormal element in the pre-test process and the actual environmental parameter within the preset range of the actual electrical parameter into a Bayesian network model, and carrying out fault inference on the abnormal element by using the learned conditional probability distribution to obtain the posterior probability that the abnormal element is in a fault state.
The invention discloses an intelligent regulation and control system of 10us square wave surge testing equipment, which comprises a memory and a processor, wherein a 10us square wave surge testing equipment intelligent regulation and control method program is stored in the memory, and when the 10us square wave surge testing equipment intelligent regulation and control method program is executed by the processor, the following steps are realized:
Acquiring a preset test scheme of target test equipment, controlling the target test equipment to perform a pre-test based on the preset test scheme, and acquiring an actual waveform diagram of a square wave signal generated by the target test equipment in the pre-test process;
Formulating a preset waveform diagram of square wave signals generated by target test equipment in the test process, and performing evaluation processing on the target test equipment according to the actual waveform diagram and the preset waveform diagram to generate a first evaluation result or a second evaluation result;
If the first evaluation result is the first evaluation result, the target test equipment is indicated to be used for carrying out the substantive test on the product; if the square wave surge is the second evaluation result, performing association analysis on each element in the target test equipment to obtain suspicious elements which cause the square wave surge generated by the target test equipment to be inconsistent with the requirements;
Performing fault analysis on the suspicious element, and if the posterior probability of the suspicious element in a fault state is greater than a preset probability, generating a fault report; if the posterior probability of the suspicious element in the fault state is not greater than the preset probability, a regulation scheme is generated, and the regulation scheme is sent to a control terminal of the target test equipment so as to regulate and control the target test equipment.
The invention solves the technical defects existing in the background technology, and has the following beneficial effects: the method can intelligently analyze the aging or fault problems of the equipment, so that the opposite wave surge testing equipment can be intelligently regulated and controlled, the reliability and stability of the equipment are improved, the equipment is ensured to stably and reliably generate the required square wave surge, and the equipment testing precision is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is an overall flow chart of the intelligent regulation and control method of the wave surge testing equipment;
FIG. 2 is a partial flow chart of the intelligent regulation method of the wave surge testing device;
FIG. 3 is a system block diagram of the intelligent regulation and control system of the wave surge testing device.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the first aspect of the present invention discloses an intelligent regulation method for a 10us square wave surge testing device, comprising the following steps:
S102: acquiring a preset test scheme of target test equipment, controlling the target test equipment to perform a pre-test based on the preset test scheme, and acquiring an actual waveform diagram of a square wave signal generated by the target test equipment in the pre-test process;
S104: formulating a preset waveform diagram of square wave signals generated by target test equipment in the test process, and performing evaluation processing on the target test equipment according to the actual waveform diagram and the preset waveform diagram to generate a first evaluation result or a second evaluation result;
s106: if the first evaluation result is the first evaluation result, the target test equipment is indicated to be used for carrying out the substantive test on the product; if the square wave surge is the second evaluation result, performing association analysis on each element in the target test equipment to obtain suspicious elements which cause the square wave surge generated by the target test equipment to be inconsistent with the requirements;
s108: performing fault analysis on the suspicious element, and if the posterior probability of the suspicious element in a fault state is greater than a preset probability, generating a fault report; if the posterior probability of the suspicious element in the fault state is not greater than the preset probability, a regulation scheme is generated, and the regulation scheme is sent to a control terminal of the target test equipment so as to regulate and control the target test equipment.
The target test equipment is 10us square wave surge test equipment.
It should be noted that, the preset test scheme and the preset waveform chart are both prepared in advance by a technician according to specific test requirements. The pre-test is a series of readiness testing steps performed before the formal square wave surge test is performed, the pre-test aims at ensuring that the testing equipment and the tested equipment are in proper states and can work normally to perform subsequent square wave surge tests, potential problems or errors can be found before the formal test by performing the pre-test, and correction and adjustment can be performed in time, so that the subsequent square wave surge test can be performed smoothly and reliable testing results can be obtained.
Further, in a preferred embodiment of the present invention, a preset test scheme of the target test device is obtained, the target test device is controlled to perform a pre-test based on the preset test scheme, and an actual waveform diagram of a square wave signal generated by the target test device in the pre-test process is obtained, which specifically includes:
acquiring a preset test scheme of target test equipment, controlling the target test equipment to perform a pre-test based on the preset test scheme, and acquiring square wave signals generated by the target test equipment through an oscilloscope in the target pre-test process;
Filtering, denoising and downsampling the acquired square wave signals to obtain processed square wave signals, and performing digital conversion on the processed square wave signals through an analog-to-digital converter to obtain digital signals;
and according to the digital signals and in combination with data visualization software, performing visualization processing on square wave signals generated by the target test equipment to obtain an actual waveform diagram of the square wave signals generated by the target test equipment in the pre-test process.
Wherein the data visualization software includes Matplotlib, seaborn, etc. The digitized square wave signal is processed by a signal processing algorithm, which includes processing steps such as filtering, denoising, downsampling, etc., to extract the main features of the square wave and remove the interference. Reconstructing the square wave waveform from the digital signal can be achieved by interpolation, fitting, etc. techniques, so that the waveform has higher resolution and accuracy in visualization.
As shown in fig. 2, in a further preferred embodiment of the present invention, the evaluation process is performed on the target test device according to the actual waveform diagram and the preset waveform diagram, so as to generate a first evaluation result or a second evaluation result, which specifically are:
S202: constructing a two-dimensional coordinate system, and mapping the actual waveform diagram and a preset waveform diagram into the two-dimensional coordinate system; wherein the abscissa of the two-dimensional coordinate system represents time and the ordinate of the two-dimensional coordinate system represents amplitude;
S204: acquiring coordinate values corresponding to the actual waveform diagram and the preset waveform diagram on the same time node in a two-dimensional coordinate system, and calculating Euclidean distances between the actual waveform diagram and the preset waveform diagram on the same time node based on the coordinate values to obtain a plurality of Euclidean distances;
s206: summing a plurality of Euclidean distances, and then taking an average value to obtain an average Euclidean distance, and determining the coincidence ratio of the actual oscillogram and a preset oscillogram according to the average Euclidean distance; comparing the contact ratio with a preset threshold value;
S208: if the overlap ratio is larger than a preset threshold value, indicating that square wave surge generated by target test equipment meets the requirement, generating a first evaluation result; and if the overlap ratio is not greater than the preset threshold value, indicating that the square wave surge generated by the target test equipment does not meet the requirement, and generating a second evaluation result.
It should be noted that, a two-dimensional coordinate system is constructed by software such as CAD, solidWorks, and then the actual waveform chart and the preset waveform chart are mapped into the two-dimensional coordinate system by using methods such as equidistant projection and orthographic projection. Then obtaining coordinate values corresponding to the actual waveform diagram and the preset waveform diagram on the same time node, namely coordinate values of the actual waveform diagram and the preset waveform diagram on each time node, and dividing the actual waveform diagram and the preset waveform diagram into a plurality of discrete point pairs in such a way that the abscissa of each discrete point pair is the same; then, according to the Euclidean distance between the calculated actual waveform diagram and the preset waveform diagram at the same time node, namely the Euclidean distance between each discrete point pair; and calculating to obtain an average Euclidean distance, thereby obtaining the coincidence degree of the actual oscillogram and the preset oscillogram, wherein the smaller the average Euclidean distance is, the higher the coincidence degree is. If the overlap ratio is greater than a preset threshold value, the actual waveform diagram is similar to the preset waveform diagram in height, and the square wave surge generated by the target test equipment meets the requirement; otherwise, if the overlap ratio is not greater than the preset threshold value, the actual waveform diagram is lower in similarity with the preset waveform diagram, and the square wave surge generated by the target test equipment is not in accordance with the requirement. The method can rapidly and reliably judge whether the square wave surge generated by the target test equipment meets the requirement.
Further, in a preferred embodiment of the present invention, if the second evaluation result is the second evaluation result, performing association analysis on each element in the target test device to obtain a suspicious element that triggers the square wave surge generated by the target test device to be inconsistent with the requirement, specifically:
If the actual waveform diagram is the second evaluation result, the Euclidean distance between the actual waveform diagram and the preset waveform diagram at the same time node is compared with the preset Euclidean distance one by one, and the time node corresponding to the Euclidean distance larger than the preset Euclidean distance is marked as an abnormal time node;
Acquiring an operation log of target test equipment, and extracting various actual operation parameters of the target test equipment at an abnormal time node from the operation log; acquiring various preset operation parameter threshold ranges of the target test equipment in the abnormal time node in a preset test scheme;
Respectively judging whether each actual operation parameter is within a corresponding preset operation parameter threshold range; if the operation parameters are located, marking the actual operation parameters as normal operation parameters; if not, marking the actual operation parameter as an abnormal operation parameter;
Acquiring functional characteristic information of each element in the target test equipment, and carrying out association analysis on the abnormal operation parameters and the functional characteristic information of each element to obtain association between the abnormal operation parameters and each element; marking the elements with the association degree larger than the preset association degree as suspicious elements;
Wherein the operating parameters include frequency, amplitude, duty cycle, rise time, and fall time.
It should be noted that the operation log of the square wave surge test device is an electronic record file for recording information such as the operation state, operation record, test condition and the like of the device. The functional characteristic information of the element refers to specific properties and behavior characteristics of the element during actual operation, such as a waveform generator for generating a square wave surge test signal. And the relevance analysis can be carried out on the abnormal operation parameters and the functional characteristic information of each element by calculating the pearson correlation coefficient, mutual information and the like. And acquiring various preset operation parameter threshold ranges of the target test equipment in the abnormal time node in a preset test scheme, wherein the preset operation parameter threshold ranges are normal parameter ranges. If the association degree between the abnormal operation parameter and a certain element is larger than the preset association degree, the element is marked as a suspicious element.
Further, in a preferred embodiment of the present invention, fault analysis is performed on the suspicious element, and if the posterior probability that the suspicious element is in a fault state is greater than a preset probability, a fault report is generated; if the posterior probability of the suspicious element in the fault state is not greater than the preset probability, generating a regulation scheme, and sending the regulation scheme to a control terminal of target test equipment, wherein the regulation scheme specifically comprises the following steps:
Acquiring each actual electrical parameter of the suspicious element in the pre-test process according to the operation log, and calculating the difference between the actual electrical parameter of the suspicious element and the corresponding preset electrical parameter to obtain each electrical parameter difference of the suspicious element; wherein the electrical parameter includes current, voltage, and power;
Comparing each electrical parameter difference value of the suspicious element with a preset difference value; if the difference value of each electrical parameter of a certain suspicious element is not larger than the preset difference value, marking the suspicious element as a normal element, and marking the working state of the suspicious element as a normal state;
If at least one electrical parameter difference value of a suspicious element is larger than a preset difference value, marking the suspicious element as an abnormal element, marking the working state of the suspicious element as an abnormal state, and marking the actual electrical parameter of which the electrical parameter difference value is larger than a corresponding item of the preset difference value as an abnormal electrical parameter;
acquiring various actual electrical parameters of the abnormal element in a pre-test process and actual environmental parameters within a preset range of the actual electrical parameters according to the operation log;
Leading each actual electrical parameter of the abnormal element in the pre-test process and the actual environmental parameter within the preset range of the actual electrical parameter into a Bayesian network model for fault diagnosis to obtain the posterior probability of the abnormal element in a fault state;
If the posterior probability that the abnormal element is in the fault state is greater than the preset probability, marking the abnormal element as a fault element, acquiring the fault type and the fault position information of the fault element, generating a fault report according to the fault type and the fault position information of the fault element, and generating the fault report on a preset platform;
If the posterior probability of the abnormal element in the fault state is not greater than the preset probability, acquiring the abnormal electrical parameter condition of the abnormal element, generating a regulation and control scheme according to the abnormal electrical parameter condition, and sending the regulation and control scheme to a control terminal of target test equipment.
It should be noted that, according to the operation log, each actual electrical parameter of the suspicious element in the pre-test process is obtained, and a difference value between the actual electrical parameter of the suspicious element and a corresponding preset electrical parameter is calculated, for example, a difference value between the actual voltage parameter of the suspicious element and a preset voltage parameter (the preset voltage parameter can be obtained from a product specification of the corresponding element) is calculated, so as to obtain each electrical parameter difference value of the suspicious element. If the difference value of each electrical parameter of a suspicious element is not larger than the preset difference value, the parameters of the suspicious element are normal, and if the square wave surge generated by the target test equipment is not required to be caused by the element, the suspicious element is marked as a normal element, and the working state of the suspicious element is marked as a normal state.
If at least one electrical parameter difference value of a suspicious element is larger than a preset difference value, the situation that square wave surge generated by target test equipment is not in accordance with the requirement is caused by the element is indicated to be very likely, the suspicious element is marked as an abnormal element, at the moment, each actual electrical parameter of the abnormal element in the pre-test process and the actual environment parameter in the preset range of the abnormal element are predicted according to a Bayesian network model, the posterior probability that the abnormal element is in a fault state is obtained, if the posterior probability that the abnormal element is in the fault state is larger than the preset probability, the situation that the element has a fault is indicated to be very high, the abnormal element is marked as a fault element, the fault type and the fault position information of the fault element are obtained, a fault report is generated according to the fault type and the fault position information of the fault element, and the fault report is generated to a preset platform, and accordingly a technician can carry out targeted overhaul on the equipment.
Further, in a preferred embodiment of the present invention, if the posterior probability that the abnormal element is in the fault state is not greater than the preset probability, the abnormal electrical parameter condition of the abnormal element is obtained, and a regulation scheme is generated according to the abnormal electrical parameter condition, and the regulation scheme is sent to the control terminal of the target test device, specifically:
setting regulation and control schemes corresponding to abnormal conditions of various electrical parameters of elements in target test equipment in advance, constructing a knowledge graph, and importing the regulation and control schemes corresponding to abnormal conditions of various electrical parameters of elements in the target test equipment which are set in advance into the knowledge graph;
the technical scheme is characterized in that a regulation scheme corresponding to abnormal conditions of various electrical parameters of each element in target test equipment is formulated in advance by a technician; if the internal circuit of the waveform generator is aged to a certain extent, and the circuit resistance is larger, the voltage of the circuit can be increased by adjusting the booster so as to overcome the aging problem to a certain extent;
If the posterior probability that the abnormal element is in the fault state is not greater than the preset probability, acquiring the abnormal electrical parameter condition of the abnormal element, and generating a search tag according to the abnormal electrical parameter condition of the abnormal element;
Searching the knowledge graph based on the search tag to obtain a corresponding regulation and control scheme; the regulation and control scheme is sent to a control terminal of target test equipment, so that the abnormal electrical parameters of the abnormal element are regulated and controlled based on the regulation and control scheme, and the abnormal electrical parameters of the abnormal element are regulated to a normal range;
and after the abnormal electrical parameters of the abnormal element are regulated to the normal range, controlling the target test equipment to perform the pre-test again based on the preset test scheme.
It should be noted that, if the posterior probability of the abnormal element in the fault state is not greater than the preset probability, it is indicated that the element does not fail, but a certain degree of aging problem may occur, at this time, the abnormal electrical parameter condition of the abnormal element is obtained, and a corresponding regulation and control scheme is obtained by searching in a knowledge graph according to the abnormal electrical parameter condition, and the regulation and control scheme is sent to the control terminal of the target test device, and then the abnormal electrical parameter of the abnormal element is regulated and controlled according to the regulation and control scheme, so as to adjust the abnormal electrical parameter of the abnormal element to the normal range, and after the abnormal electrical parameter of the abnormal element is adjusted to the normal range, the target test device is controlled to perform the pre-test based on the preset test scheme, and whether the square wave surge generated by the target test device meets the requirement is again judged.
Further, in a preferred embodiment of the present invention, each actual electrical parameter of the abnormal element in the pre-test process and an actual environmental parameter within a preset range thereof are imported into a bayesian network model for fault diagnosis, so as to obtain a posterior probability that the abnormal element is in a fault state, which specifically is:
Establishing a Bayesian network, and defining nodes and edges of the Bayesian network, wherein the state and the observed variable of each element are expressed as a node, and the association between the nodes is expressed by directed edges;
Collecting historical electrical parameter data and historical environmental parameter data of each element in an operation log of target test equipment as observation data, and calibrating the working state of each element among various observation data conditions; the working state comprises a normal state and a fault state;
Based on the observation data, estimating maximum likelihood estimation values among all nodes in the Bayesian network by using a maximum likelihood estimation method, and determining the conditional probability distribution of all the nodes in the Bayesian network according to the maximum likelihood estimation values to obtain a Bayesian network model; wherein the conditional probability describes a dependency between the element state and the observed variable;
And importing each actual electrical parameter of the abnormal element in the pre-test process and the actual environmental parameter within the preset range of the actual electrical parameter into a Bayesian network model, and carrying out fault inference on the abnormal element by using the learned conditional probability distribution to obtain the posterior probability that the abnormal element is in a fault state.
It should be noted that, by establishing a bayesian network, the network includes states (normal or fault) of respective elements and observation variables (such as electrical parameters, environmental parameters, etc.) related to the states and the observation variables, in the bayesian network, each state and the observation variables of the elements are represented as a node, and association between the nodes is represented by directed edges, for example, the node of the states of the elements may be connected to the node of the observation variables of the electrical parameters. If the posterior probability of failure of a certain element is high, it is determined that the element may have failure. And corresponding maintenance or replacement measures are adopted according to the diagnosis result. Performing component fault diagnosis by using a Bayesian network requires establishing a proper network model, collecting sufficient observation data and performing parameter learning, then performing fault diagnosis and decision making by using an inference algorithm, and finally continuously optimizing and updating the network model to improve the diagnosis effect.
In summary, the aging or fault problems of the equipment can be intelligently analyzed through the method, so that the opposite wave surge testing equipment can be intelligently regulated and controlled, the reliability and stability of the equipment are improved, the equipment is ensured to stably and reliably generate the required square wave surge, and the equipment testing precision is improved.
Furthermore, the method comprises the following steps:
acquiring actual electromagnetic intensity corresponding to each preset node in a preset range of target test equipment in the process of pre-testing the target test equipment; constructing an actual electromagnetic distribution diagram according to the actual electromagnetic intensity corresponding to each preset node in the preset range of the target test equipment;
Acquiring a use instruction of target test equipment, acquiring preset electromagnetic intensity generated by each element in the target test equipment in the working process according to the use instruction, and constructing a preset electromagnetic distribution diagram according to the preset electromagnetic intensity generated by each element in the working process;
Calculating the similarity between the actual electromagnetic distribution diagram and a preset electromagnetic distribution diagram through a cosine similarity algorithm, and comparing the similarity with the preset similarity;
If the similarity is larger than the preset similarity, the working state of each element in the target test equipment is normal;
If the similarity is not greater than the preset similarity, performing feature extraction processing on the actual electromagnetic distribution diagram and the preset electromagnetic distribution diagram through an ORB algorithm to obtain an actual electromagnetic distribution diagram and a preset electromagnetic distribution diagram;
And performing pairing treatment on the actual electromagnetic curve graph and the preset electromagnetic curve graph, acquiring a region in which the curves of the actual electromagnetic curve graph and the preset electromagnetic curve graph are not coincident after the pairing treatment is completed, and marking the corresponding element in the region in which the curves are not coincident as a suspicious element.
It should be noted that, when an element in the target test device is abnormal, electromagnetic signals different from the normal operation state may be generated, the abnormal electromagnetic signals may be detected in the electromagnetic field around the device, and by analyzing the signals, it may be inferred whether the element has failed or is in the abnormal operation state. The suspicious element in the target test equipment can be positioned quickly by the method.
Furthermore, the method comprises the following steps:
If the fault element exists in the target test equipment, acquiring the fault type of the fault element, and estimating the maintenance time required for maintaining the target test equipment according to the fault type of the fault element;
Acquiring capacity information of the target test equipment, and calculating the yield shortage of the target test equipment in the overhaul time according to the overhaul time and the capacity information required for overhauling the target test equipment;
acquiring inventory information of a production workshop, and acquiring the standby yield of a target product in the production workshop according to the inventory information; judging whether the standby yield is greater than the absent yield;
If the number of the test equipment is larger than the number of the test equipment, acquiring nameplate information of the other test equipment in the idle state in the production workshop, and matching the nameplate information of the target test equipment with the nameplate information of the other test equipment in the idle state in the production workshop;
and acquiring the test equipment with the highest matching degree in the idle state, and marking the test equipment with the highest matching degree in the idle state as recommended test equipment.
It should be noted that, when maintaining the failed target test, if the required maintenance time is too long, the product delivery plan may be affected, so that the other test devices need to be arranged to carry out the supplementary production at this time.
As shown in fig. 3, the second aspect of the present invention discloses an intelligent regulation and control system for a 10us square wave surge test device, where the intelligent regulation and control system for a 10us square wave surge test device includes a memory 15 and a processor 20, where a 10us square wave surge test device intelligent regulation and control method program is stored in the memory 15, and when the 10us square wave surge test device intelligent regulation and control method program is executed by the processor 20, the following steps are implemented:
Acquiring a preset test scheme of target test equipment, controlling the target test equipment to perform a pre-test based on the preset test scheme, and acquiring an actual waveform diagram of a square wave signal generated by the target test equipment in the pre-test process;
Formulating a preset waveform diagram of square wave signals generated by target test equipment in the test process, and performing evaluation processing on the target test equipment according to the actual waveform diagram and the preset waveform diagram to generate a first evaluation result or a second evaluation result;
If the first evaluation result is the first evaluation result, the target test equipment is indicated to be used for carrying out the substantive test on the product; if the square wave surge is the second evaluation result, performing association analysis on each element in the target test equipment to obtain suspicious elements which cause the square wave surge generated by the target test equipment to be inconsistent with the requirements;
Performing fault analysis on the suspicious element, and if the posterior probability of the suspicious element in a fault state is greater than a preset probability, generating a fault report; if the posterior probability of the suspicious element in the fault state is not greater than the preset probability, a regulation scheme is generated, and the regulation scheme is sent to a control terminal of the target test equipment so as to regulate and control the target test equipment.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or optical disk, or the like, which can store program codes.
Or the above-described integrated units of the invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (6)
1. An intelligent regulation and control method for 10us square wave surge testing equipment is characterized by comprising the following steps:
Acquiring a preset test scheme of target test equipment, controlling the target test equipment to perform a pre-test based on the preset test scheme, and acquiring an actual waveform diagram of a square wave signal generated by the target test equipment in the pre-test process;
Formulating a preset waveform diagram of square wave signals generated by target test equipment in the test process, and performing evaluation processing on the target test equipment according to the actual waveform diagram and the preset waveform diagram to generate a first evaluation result or a second evaluation result;
If the first evaluation result is the first evaluation result, the target test equipment is indicated to be used for carrying out the substantive test on the product; if the square wave surge is the second evaluation result, performing association analysis on each element in the target test equipment to obtain suspicious elements which cause the square wave surge generated by the target test equipment to be inconsistent with the requirements;
Performing fault analysis on the suspicious element, and if the posterior probability of the suspicious element in a fault state is greater than a preset probability, generating a fault report; if the posterior probability of the suspicious element in the fault state is not greater than the preset probability, generating a regulation and control scheme, and sending the regulation and control scheme to a control terminal of target test equipment so as to regulate and control the target test equipment;
The evaluation processing is performed on the target test equipment according to the actual waveform diagram and the preset waveform diagram, and a first evaluation result or a second evaluation result is generated, specifically:
Constructing a two-dimensional coordinate system, and mapping the actual waveform diagram and a preset waveform diagram into the two-dimensional coordinate system; wherein the abscissa of the two-dimensional coordinate system represents time and the ordinate of the two-dimensional coordinate system represents amplitude;
acquiring coordinate values corresponding to the actual waveform diagram and the preset waveform diagram on the same time node in a two-dimensional coordinate system, and calculating Euclidean distances between the actual waveform diagram and the preset waveform diagram on the same time node based on the coordinate values to obtain a plurality of Euclidean distances;
Summing a plurality of Euclidean distances, and then taking an average value to obtain an average Euclidean distance, and determining the coincidence ratio of the actual oscillogram and a preset oscillogram according to the average Euclidean distance; comparing the contact ratio with a preset threshold value;
if the overlap ratio is larger than a preset threshold value, indicating that square wave surge generated by target test equipment meets the requirement, generating a first evaluation result; if the overlap ratio is not greater than a preset threshold value, indicating that square wave surge generated by the target test equipment does not meet the requirement, generating a second evaluation result;
If the second evaluation result is the second evaluation result, performing association analysis on each element in the target test equipment to obtain suspicious elements which cause square wave surge generated by the target test equipment to be inconsistent, wherein the suspicious elements specifically are as follows:
If the actual waveform diagram is the second evaluation result, the Euclidean distance between the actual waveform diagram and the preset waveform diagram at the same time node is compared with the preset Euclidean distance one by one, and the time node corresponding to the Euclidean distance larger than the preset Euclidean distance is marked as an abnormal time node;
Acquiring an operation log of target test equipment, and extracting various actual operation parameters of the target test equipment at an abnormal time node from the operation log; acquiring various preset operation parameter threshold ranges of the target test equipment in the abnormal time node in a preset test scheme;
Respectively judging whether each actual operation parameter is within a corresponding preset operation parameter threshold range; if the operation parameters are located, marking the actual operation parameters as normal operation parameters; if not, marking the actual operation parameter as an abnormal operation parameter;
Acquiring functional characteristic information of each element in the target test equipment, and carrying out association analysis on the abnormal operation parameters and the functional characteristic information of each element to obtain association between the abnormal operation parameters and each element; marking the elements with the association degree larger than the preset association degree as suspicious elements;
Wherein the operating parameters include frequency, amplitude, duty cycle, rise time, and fall time.
2. The intelligent regulation and control method of 10us square wave surge testing equipment according to claim 1, wherein a preset testing scheme of the target testing equipment is obtained, the target testing equipment is controlled to perform a pre-test based on the preset testing scheme, and an actual waveform diagram of a square wave signal generated by the target testing equipment in the pre-test process is obtained, specifically:
acquiring a preset test scheme of target test equipment, controlling the target test equipment to perform a pre-test based on the preset test scheme, and acquiring square wave signals generated by the target test equipment through an oscilloscope in the target pre-test process;
Filtering, denoising and downsampling the acquired square wave signals to obtain processed square wave signals, and performing digital conversion on the processed square wave signals through an analog-to-digital converter to obtain digital signals;
and according to the digital signals and in combination with data visualization software, performing visualization processing on square wave signals generated by the target test equipment to obtain an actual waveform diagram of the square wave signals generated by the target test equipment in the pre-test process.
3. The intelligent regulation and control method of the 10us square wave surge test equipment according to claim 1, wherein fault analysis is carried out on the suspicious element, and if the posterior probability of the suspicious element in a fault state is greater than a preset probability, a fault report is generated; if the posterior probability of the suspicious element in the fault state is not greater than the preset probability, generating a regulation scheme, and sending the regulation scheme to a control terminal of target test equipment, wherein the regulation scheme specifically comprises the following steps:
Acquiring each actual electrical parameter of the suspicious element in the pre-test process according to the operation log, and calculating the difference between the actual electrical parameter of the suspicious element and the corresponding preset electrical parameter to obtain each electrical parameter difference of the suspicious element; wherein the electrical parameter includes current, voltage, and power;
Comparing each electrical parameter difference value of the suspicious element with a preset difference value; if the difference value of each electrical parameter of a certain suspicious element is not larger than the preset difference value, marking the suspicious element as a normal element, and marking the working state of the suspicious element as a normal state;
If at least one electrical parameter difference value of a suspicious element is larger than a preset difference value, marking the suspicious element as an abnormal element, marking the working state of the suspicious element as an abnormal state, and marking the actual electrical parameter of which the electrical parameter difference value is larger than a corresponding item of the preset difference value as an abnormal electrical parameter;
acquiring various actual electrical parameters of the abnormal element in a pre-test process and actual environmental parameters within a preset range of the actual electrical parameters according to the operation log;
Leading each actual electrical parameter of the abnormal element in the pre-test process and the actual environmental parameter within the preset range of the actual electrical parameter into a Bayesian network model for fault diagnosis to obtain the posterior probability of the abnormal element in a fault state;
If the posterior probability that the abnormal element is in the fault state is greater than the preset probability, marking the abnormal element as a fault element, acquiring the fault type and the fault position information of the fault element, generating a fault report according to the fault type and the fault position information of the fault element, and generating the fault report on a preset platform;
If the posterior probability of the abnormal element in the fault state is not greater than the preset probability, acquiring the abnormal electrical parameter condition of the abnormal element, generating a regulation and control scheme according to the abnormal electrical parameter condition, and sending the regulation and control scheme to a control terminal of target test equipment.
4. The intelligent regulation and control method of a 10us square wave surge test device according to claim 3, wherein if the posterior probability of the abnormal element in a fault state is not greater than a preset probability, acquiring the abnormal electrical parameter condition of the abnormal element, generating a regulation and control scheme according to the abnormal electrical parameter condition, and transmitting the regulation and control scheme to a control terminal of a target test device, specifically:
setting regulation and control schemes corresponding to abnormal conditions of various electrical parameters of elements in target test equipment in advance, constructing a knowledge graph, and importing the regulation and control schemes corresponding to abnormal conditions of various electrical parameters of elements in the target test equipment which are set in advance into the knowledge graph;
If the posterior probability that the abnormal element is in the fault state is not greater than the preset probability, acquiring the abnormal electrical parameter condition of the abnormal element, and generating a search tag according to the abnormal electrical parameter condition of the abnormal element;
Searching the knowledge graph based on the search tag to obtain a corresponding regulation and control scheme; the regulation and control scheme is sent to a control terminal of target test equipment, so that the abnormal electrical parameters of the abnormal element are regulated and controlled based on the regulation and control scheme, and the abnormal electrical parameters of the abnormal element are regulated to a normal range;
and after the abnormal electrical parameters of the abnormal element are regulated to the normal range, controlling the target test equipment to perform the pre-test again based on the preset test scheme.
5. The intelligent regulation and control method of the 10us square wave surge test equipment according to claim 3, wherein each actual electrical parameter of the abnormal element in the pre-test process and the actual environmental parameter within the preset range are led into a bayesian network model for fault diagnosis, and the posterior probability that the abnormal element is in a fault state is obtained specifically as follows:
Establishing a Bayesian network, and defining nodes and edges of the Bayesian network, wherein the state and the observed variable of each element are expressed as a node, and the association between the nodes is expressed by directed edges;
Collecting historical electrical parameter data and historical environmental parameter data of each element in an operation log of target test equipment as observation data, and calibrating the working state of each element among various observation data conditions; the working state comprises a normal state and a fault state;
Based on the observation data, estimating maximum likelihood estimation values among all nodes in the Bayesian network by using a maximum likelihood estimation method, and determining the conditional probability distribution of all the nodes in the Bayesian network according to the maximum likelihood estimation values to obtain a Bayesian network model; wherein the conditional probability describes a dependency between the element state and the observed variable;
And importing each actual electrical parameter of the abnormal element in the pre-test process and the actual environmental parameter within the preset range of the actual electrical parameter into a Bayesian network model, and carrying out fault inference on the abnormal element by using the learned conditional probability distribution to obtain the posterior probability that the abnormal element is in a fault state.
6. The intelligent regulation and control system of the 10us square wave surge testing equipment is characterized by comprising a memory and a processor, wherein a 10us square wave surge testing equipment intelligent regulation and control method program is stored in the memory, and when the 10us square wave surge testing equipment intelligent regulation and control method program is executed by the processor, the following steps are realized:
Acquiring a preset test scheme of target test equipment, controlling the target test equipment to perform a pre-test based on the preset test scheme, and acquiring an actual waveform diagram of a square wave signal generated by the target test equipment in the pre-test process;
Formulating a preset waveform diagram of square wave signals generated by target test equipment in the test process, and performing evaluation processing on the target test equipment according to the actual waveform diagram and the preset waveform diagram to generate a first evaluation result or a second evaluation result;
If the first evaluation result is the first evaluation result, the target test equipment is indicated to be used for carrying out the substantive test on the product; if the square wave surge is the second evaluation result, performing association analysis on each element in the target test equipment to obtain suspicious elements which cause the square wave surge generated by the target test equipment to be inconsistent with the requirements;
Performing fault analysis on the suspicious element, and if the posterior probability of the suspicious element in a fault state is greater than a preset probability, generating a fault report; if the posterior probability of the suspicious element in the fault state is not greater than the preset probability, generating a regulation and control scheme, and sending the regulation and control scheme to a control terminal of target test equipment so as to regulate and control the target test equipment;
The evaluation processing is performed on the target test equipment according to the actual waveform diagram and the preset waveform diagram, and a first evaluation result or a second evaluation result is generated, specifically:
Constructing a two-dimensional coordinate system, and mapping the actual waveform diagram and a preset waveform diagram into the two-dimensional coordinate system; wherein the abscissa of the two-dimensional coordinate system represents time and the ordinate of the two-dimensional coordinate system represents amplitude;
acquiring coordinate values corresponding to the actual waveform diagram and the preset waveform diagram on the same time node in a two-dimensional coordinate system, and calculating Euclidean distances between the actual waveform diagram and the preset waveform diagram on the same time node based on the coordinate values to obtain a plurality of Euclidean distances;
Summing a plurality of Euclidean distances, and then taking an average value to obtain an average Euclidean distance, and determining the coincidence ratio of the actual oscillogram and a preset oscillogram according to the average Euclidean distance; comparing the contact ratio with a preset threshold value;
if the overlap ratio is larger than a preset threshold value, indicating that square wave surge generated by target test equipment meets the requirement, generating a first evaluation result; if the overlap ratio is not greater than a preset threshold value, indicating that square wave surge generated by the target test equipment does not meet the requirement, generating a second evaluation result;
If the second evaluation result is the second evaluation result, performing association analysis on each element in the target test equipment to obtain suspicious elements which cause square wave surge generated by the target test equipment to be inconsistent, wherein the suspicious elements specifically are as follows:
If the actual waveform diagram is the second evaluation result, the Euclidean distance between the actual waveform diagram and the preset waveform diagram at the same time node is compared with the preset Euclidean distance one by one, and the time node corresponding to the Euclidean distance larger than the preset Euclidean distance is marked as an abnormal time node;
Acquiring an operation log of target test equipment, and extracting various actual operation parameters of the target test equipment at an abnormal time node from the operation log; acquiring various preset operation parameter threshold ranges of the target test equipment in the abnormal time node in a preset test scheme;
Respectively judging whether each actual operation parameter is within a corresponding preset operation parameter threshold range; if the operation parameters are located, marking the actual operation parameters as normal operation parameters; if not, marking the actual operation parameter as an abnormal operation parameter;
Acquiring functional characteristic information of each element in the target test equipment, and carrying out association analysis on the abnormal operation parameters and the functional characteristic information of each element to obtain association between the abnormal operation parameters and each element; marking the elements with the association degree larger than the preset association degree as suspicious elements;
Wherein the operating parameters include frequency, amplitude, duty cycle, rise time, and fall time.
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