78 results about "Rate analysis" patented technology

The invention discloses a software test case generation method and system based on a large language model, and relates to the technical field of software testing, and the system comprises a to-be-tested module detection subsystem which is used for constructing a to-be-tested module set; the test case generation subsystem is used for constructing a prompt template according to the to-be-tested module and generating a test case by utilizing a program generation model based on an adaptive focus context generation mechanism and the prompt template; the correction verification subsystem is used for performing multi-stage correction processing on the test case, eliminating errors and generating an initial test suite; and the test case supplement subsystem is used for carrying out coverage rate analysis and variation test on the initial test suite, constructing a model input text according to a result, and generating a supplement test case by utilizing a program generation model so as to update the initial test suite and obtain a final test suite. By combining the functional annotation of the to-be-tested module with the logical reasoning ability of the LLM, misjudgment caused by assertion errors can be reduced, and the accuracy of test feedback is ensured.

The invention discloses a quantitative evaluation system and method for context retention capability of multi-round dialogues of a large language model, and relates to the technical field of natural language processing evaluation. In order to solve the problems of standard missing and subjective dependence of LLM in multi-round dialogue ability evaluation, the scheme adopted by the invention comprises a dynamic scene generation module for cooperatively generating multi-round dialogue streams containing marked information points based on a rule template and LLM; the interference injection module is used for inserting controllable interference into the dialogue stream; the three-layer evaluation module takes the dialogue stream with interference as input, quantifies basic memory decline, topic logic correlation degree and anti-interference robustness of LLM through a memory precision layer, a correlation depth layer and an interference resistance layer, and calculates an information attenuation rate, an attenuation index and an anti-interference coefficient; the attenuation rate analysis module is used for correspondingly generating a curve based on a calculation result and intuitively presenting the performance change trend of the LLM; and the visual report module is used for integrating the outputs to generate an interactive evaluation report. The method is used for objective evaluation of the LLM context understanding capability.

The invention relates to the field of material performance detection, and discloses a corrugated paper moisture regain resistance detection method and system, and the method comprises the steps: firstly obtaining a corrugated paper sample storage environment, collecting environment data, carrying out dynamic moisture absorption analysis according to the environment data, and calculating a moisture permeation rate; analyzing the damp state of the structure in combination with the rate, querying a moisture regain index, and then carrying out layered humidity detection to obtain interlayer moisture change data; calculating anti-moisture regaining residual strength based on the data, generating an anti-moisture efficiency index by combining temperature and humidity, and dividing moisture regaining risk grades; and finally, configuring multi-order early warning indexes according to grades, positioning an edge moisture absorption area, calculating a diffusion gradient value, generating a humidity stable signal, sending the humidity stable signal to the terminal, and formulating a moisture regain detection strategy according to feedback. The corrugated paper moisture regaining detection accuracy can be guaranteed.

The invention relates to a human body health state assessment system and assessment method based on multi-point acquisition, and the system comprises an event sensing module which is used for collecting an image data signal, a physiological signal and an environment data signal of a human body, and outputting an asynchronous pulse event flow when the signal change is detected; the pulse coding module is used for generating a space-time pulse sequence through a preset adaptive threshold coding technology; the neural network processing module is used for performing event driving processing by utilizing leakage integral distribution neurons in a preset pulse neural network according to the space-time pulse sequence to obtain a current health characteristic pulse mode; and the health state decoding module is used for obtaining a health state score and a risk classification result of the current human body through a preset pulse distribution rate analysis algorithm and a preset time sequence decoding technology according to the current health characteristic pulse mode. Therefore, the problems of high power consumption, high delay, low data transmission efficiency, low early pathological feature recognition accuracy and the like of a human health state evaluation system are solved.

The invention provides a performance evaluation method for short packet coding communication, and belongs to the technical field of short packet communication. The method comprises the following steps that a system model of short packet coding communication is constructed, the system model comprises a ground base station, an unmanned aerial vehicle, a legal user side and a non-target user side which are in communication connection, and a reconfigurable intelligent surface is configured on the unmanned aerial vehicle; and according to all channel state information of the system model, carrying out average reachable secrecy rate analysis, average decoding error probability analysis and secrecy interruption probability analysis on the system model in sequence, and evaluating the performance of the system model according to all analysis results. According to the invention, the safety and reliability of performance evaluation of short packet coding communication can be improved.

The invention discloses a method and a system for detecting the health state of a transformer in real time, relates to the technical field of transformer detection, is used for solving the problem of fault early warning lag, and aims at solving the problem of fault early warning lag by monitoring the output voltage of the primary side and the secondary side of the transformer, calculating the fluctuation ratio, analyzing the stable operation state and counting the abnormal duration. After entering a temperature rise analysis mode based on duration judgment, detecting oil temperature and winding temperature, calculating a difference value, calling a cooling system start-stop instruction to obtain a cooling response value, calculating a response lag rate, collecting data of gas dissolved in oil, calculating a gas anomaly ratio, and analyzing thermoelectric synergy anomaly characteristics in combination with the response lag rate; the method comprises the following steps: acquiring an insulation aging grade and an oil product replacement period from a historical maintenance database, evaluating an insulation degradation coefficient according to the insulation aging grade and the oil product replacement period, analyzing a health state in combination with thermoelectric collaborative abnormal characteristics, selecting an operation optimization or shutdown maintenance mechanism, improving fault early warning capability, optimizing a maintenance strategy, and prolonging the service life of equipment.

The invention relates to the technical field of rate calculation, and discloses an intelligent logistics rate analysis method based on dynamic region division. Comprising the following steps: acquiring regional data information and storing as an administrative province library; service province division information is acquired and stored as a service province library; establishing a nationwide multi-dimensional rate calculation model, constructing a hierarchical priority rule base by combining a business province base and an administrative province base with a multi-dimensional condition, and automatically matching an optimal rate according to a sequence; screening rates step by step according to a sequence by adopting a tree-shaped decision-making model; when a plurality of rate rules meet conditions, automatic selection is carried out according to priority weights; receiving a region type switching command, and automatically replacing historical data in batches by adopting a transaction lock mechanism; buried point data are collected and analyzed, the data are stored in a time sequence database, and monitoring indexes are displayed and implemented through an instrument panel; the method has the advantages that nationwide multi-dimensional dynamic matching is supported, and the method adapts to complex service scenes.

The invention discloses an engineering data dynamic matching method based on an iBIM platform. The method comprises the following steps that S1, existing engineering data are input and stored in a storage module; s2, an analysis module analyzes the engineering data stored in the storage module; s3, an auditing module calls the analyzed engineering data and audits the quality score based on the engineering data; s4, an engineering cost index data analysis model in the iBIM platform performs automatic matching analysis on the analyzed and audited engineering data based on an automatic matching rule to obtain a data analysis result; s5, matching and identifying abnormal data in the data analysis result by a matching rate analysis module, and automatically correcting an automatic matching rule according to the abnormal data to improve the automatic matching quality; and S6, according to a plurality of engineering data analysis results and the formed database, measuring and calculating the new project based on the measuring and calculating model, thereby effectively improving the accuracy and efficiency of engineering data dynamic matching, and reducing the labor cost.

The invention relates to a non-transparent vapor chamber internal working medium phase change rate analysis and calculation method. The method comprises the following steps: step 1, establishing a vapor chamber cold end surface temperature phase change rate inversion model; 2, vapor chamber parameters and cold end surface temperatures under corresponding working conditions are obtained; step 3, based on the cold end surface temperature, analyzing by using the vapor chamber cold end surface temperature phase change rate inversion model to obtain a phase change rate distribution condition; and 4, optimizing and improving the structure of the wick according to the obtained phase change rate distribution condition. According to the method, on the premise that geometric parameters and working condition parameters of the vapor chamber are obtained, the phase change rate of the working medium in the vapor chamber is calculated through the cold end surface temperature of the vapor chamber, then the phase change rate distribution field of the vapor chamber is obtained, finally, the internal structure of the vapor chamber is optimally designed according to the phase change rate distribution field and actual engineering requirements, and the design efficiency of the vapor chamber is improved. The problem that the phase change rate of the internal working medium cannot be accurately analyzed under the actual working condition of the vapor chamber is solved.

An object of the present disclosure is to accurately estimate a time discount rate of a user without depending on a measurement method using questionnaires.Therefore, the present disclosure provides a time discount rate estimation apparatus that estimates a time discount rate in a learning phase, the time discount rate estimation apparatus including: a model learning unit configured to calculate an error between a value obtained by standardizing values of a plurality of behavior features of a user and then multiplying each of the standardized values by a model parameter indicating each coefficient serving as a weight and a time discount rate serving as ground-truth data based on an answer by the user and to perform machine learning on the model parameter so as to reduce the error.

The invention provides an on-line management method and system for an engineering project, and the method comprises the steps: obtaining a continuous video stream of a monitoring region, and collecting environment data through an environment sensing network; performing timestamp alignment and space coordinate binding through a space-time synchronization unit to obtain a space-time synchronization data stream; inputting the time-space synchronization data stream into equipment for edge calculation to obtain an initial contour data set, and generating a dynamic deformation feature vector through continuous inter-frame displacement vector calculation and deformation rate analysis; inputting a pre-trained machine learning classifier, and analyzing to obtain a risk cause classification result; and performing grading response matching on the risk cause classification result, triggering a grading alarm mechanism to execute a corresponding instruction, starting environment linkage control equipment when the risk is caused by environmental factors, and generating a security event report containing a behavior mode label when the risk is caused by human factors, and pushing the security event report to the terminal. According to the invention, real-time monitoring of material deformation of construction safety risks, intelligent identification of risk causes and triggering of graded response are realized.

The application discloses a kind of unmanned vehicle video intelligent encoding method and related device, it is related to video encoding technical field, the method includes: to IMU data and height information are preprocessed;To the IMU data after pre-processing is weighted fusion and data mapping, obtain motion factor, segmented linear compensation analysis is carried out based on the height information after pre-processing, obtain height factor;Rate analysis is carried out based on motion factor and height factor, obtain target code rate;To the video to be encoded is canny edge detection and edge density analysis, obtain edge density information, determine IMU motion energy using motion energy model based on the IMU data after pre-processing;Determine key frame insertion information based on edge density information and IMU motion energy, and encoder is based on target code rate and key frame insertion information and is encoded to the video to be encoded processing.The application has significantly improved encoding efficiency and precision, while guaranteeing real-time, reduce bandwidth occupancy.

The invention discloses a radiation monitoring method and gate-type radiation monitoring equipment, and relates to the technical field of radiation monitoring. The method comprises the following steps: performing pulse analysis processing on a real-time gamma ray pulse signal to obtain a real-time net counting rate and a pulse PSD distribution histogram; determining a compensation coefficient according to the environmental data, and performing real-time compensation on the real-time net counting rate to obtain a compensated real-time net counting rate; performing compensation counting rate calculation according to the reference parameter, the compensated real-time net counting rate and the pulse PSD distribution histogram to obtain a compensation counting rate; and determining a target alarm type according to the compensation counting rate, the real-time net counting rate and a preset alarm condition corresponding to each alarm type, and performing alarm control according to an alarm mode corresponding to the target alarm type. Compared with the prior art, the method does not depend on single dose rate measurement or total count rate analysis, at the moment, it can be guaranteed that detection is more accurate, the method is not affected by environmental fluctuation any more, and then the problems of frequent false alarms and the high false alarm rate can be solved.

The invention discloses an electronic component detection system and method, and relates to the technical field of electronic component detection. Image features, functional parameter features and environmental response features are quantized, a unified feature data set is generated, component states are classified in combination with a support vector machine (SVM), normal, suspicious and potential fault components are effectively distinguished, and the detection accuracy is improved. Marking a detection priority according to a classification result in combination with a production demand; in addition, through classification error rate analysis and loss function optimization, the model prediction accuracy is improved; based on the detection priority, an improved multi-target scheduling algorithm MOEA / D is adopted, detection time and detection cost are brought into optimization targets, and a resource allocation strategy is dynamically adjusted; high-priority components are allocated to core detection resources, and low-priority components use shared resources or are arranged in off-peak periods, so that the resource utilization rate and the scheduling efficiency are effectively improved; and the weight vector is dynamically adjusted to further enhance the adaptability of the scheduling algorithm.

The invention discloses a software testing method and device, a medium and a product, and relates to the technical field of software test.The software testing method includes the steps that coverage rate data of unit testing, integration testing and system testing of target software are collected through a code coverage rate analysis tool, and the coverage rate data are converted into target text files; the coverage rate data comprises execution times and coverage rates of the codes; storing each target text file in a corresponding target directory, and reading and analyzing the target text file in each target directory to obtain corresponding analyzed data; the analyzed data comprises a total row number, a total branch number, a total function number, a total coverage row number, a total coverage branch number and a total coverage function number; and merging and storing the analyzed data into a preset nested dictionary, traversing the preset nested dictionary to determine the code coverage rate of the target software based on the traversed analyzed data, and determining the software defect of the target software by using the code coverage rate. The quality and stability of software can be improved through testing.

The application discloses a kind of RSF performance test methods based on Jmeter.The method includes developing Jmeter plug-in;Set the basic data of request message;Determine whether the format of request message meets the requirements, and if not, carry out request message format conversion;The content of request message is converted into the message type set;The converted request message is sent to the server registered by remote service framework RSF;Response message returned by the server is received, and the received response message is checked, if the response message meets the expectation, this request is set as success, otherwise, this request is set as failure;Using the performance statistical tool of Jmeter, the number of transactions per second and transaction response time analysis are carried out according to the received response message, and success rate analysis is carried out according to the checking result, and then the analyzed content is displayed.The application greatly reduces the test time, and reduces the learning cost of test personnel.

The invention relates to the technical field of warehouse management, in particular to an AGV scheduling and warehouse stock decision-making system and method based on data analysis. Comprising an inventory management module used for obtaining storage data, sales data and to-be-warehoused data, the storage data comprises real-time inventory data, and the storage quantity and the storage amount of each type of goods, the sales data comprises the sales amount of each type of goods, and the to-be-warehoused data comprises to-be-warehoused goods and the to-be-warehoused quantity; the area analysis module is used for analyzing the occupancy rate of each current inventory area according to the real-time inventory data, and the inventory area with the occupancy rate lower than a preset occupancy threshold value is marked as a low-occupancy area mark; and the turnover rate analysis module is used for calculating the stock turnover rate warehousing grade of the goods in the preset period according to the storage amount and the sales amount of the goods in the preset period, and is used for determining the warehousing grade of the goods to be warehoused according to the to-be-warehoused quantity and the storage amount turnover rate of the goods to be warehoused.

The application discloses an InSAR surface deformation multi-source vertical contribution analytical method based on deformation signal spectrum decomposition and belongs to the technical field of surface deformation monitoring. The steps are as follows: first, long-time SAR images are acquired, vertical deformation data and related rates and deformation variables are obtained through SBAS-InSAR technology processing; then, a plurality of auxiliary conditions are fused, regional spatial partitioning is realized through K-Means clustering; subsequently, CEEMDAN decomposition and mean threshold multi-scale reconstruction are performed on deformation signals of each partition, high-frequency items, low-frequency items and trend items are obtained; finally, ICA blind source separation and variance contribution rate analysis are performed, and shallow, medium and deep surface deformation information is inversed through directional reorganization; the K-Means clustering algorithm is adopted to perform spatial partitioning on the regional deformation field, the homogeneity and precision of subsequent signal processing are improved; and the CEEMDAN decomposition and multi-scale reconstruction method is introduced, adaptive and fine scale separation of deformation time series signals is realized; the blind source separation technology is applied to multi-scale component reorganization, and the physical attribution of the vertical position of the subsidence source is realized.

The invention discloses an exchange rate time sequence determination method and device based on a lightweight generative adversarial network, and relates to the financial science and technology field, and the method comprises the steps: obtaining source domain data and target domain data according to a preset window time length, employing a maximum mean value difference algorithm to quantify a distribution difference parameter, and obtaining an exchange rate time sequence; under the condition that the distribution difference parameter is greater than a preset detection threshold value, judging that a drift condition occurs, freezing a main body network weight of the basic exchange rate time sequence model, and adjusting a tail linear layer of the basic exchange rate time sequence model by utilizing a fine adjustment data set, the fine tuning data set is obtained by merging a pseudo drift sample generated by the lightweight generative adversarial network with target domain data; and outputting exchange rate time sequence parameters of a preset time step length by utilizing the adjusted basic exchange rate time sequence model. The technical problem that the accuracy of a model output result is reduced due to the fact that market data in a drifting condition cannot be adaptively adjusted in an exchange rate analysis mode in related technologies is solved.

The invention relates to the technical field of pressure sensor operation state monitoring, in particular to a pressure sensor operation state diagnosis method based on deviation rate analysis, which comprises the following steps: firstly, pressure sensor data sets in a plurality of time periods are extracted from historical data, and each data set comprises an actual measured value and a set value; and the running state of the sensor is reflected by calculating the deviation rate of each data point. Secondly, preprocessing the deviation rate data, and removing noise and abnormal values; thirdly, performing time sequence analysis on the deviation rate data by adopting an autoregression (AR) model, and capturing the variation trend of the deviation rate; and finally, dynamically judging whether the sensor needs to be replaced or not by combining a set threshold value based on an analysis result. The operation state of the sensor can be monitored in real time, early signs of performance degradation can be captured in time, and the replacement opportunity can be accurately judged in a data driving mode. According to the method, the production efficiency is guaranteed, and the maintenance cost and unnecessary resource waste of enterprises are effectively reduced.

The invention discloses a MoE model intensified training method based on classification data, and relates to the technical field of MoE model intensified training, and the method comprises the following steps: collecting the correctly distributed number of various samples in each batch in the MoE model intensified training process, and carrying out the preprocessing to obtain sample correct rate data and expert matching rate data; performing sample accuracy analysis to obtain first weight feature data; performing expert matching rate analysis to obtain second weight feature data; based on the first weight feature data and the second weight feature data, dynamically adjusting the proportion of various samples in the batch of enhanced training; the method is used for solving the problem that when an existing MoE model intensive training technology carries out intensive training on a MoE model used for classifying data, the proportion of various samples in each training batch cannot be dynamically adjusted according to the number of correctly distributed samples in each training batch, the efficiency of intensive training of the MoE model is improved, and meanwhile the training efficiency of the MoE model is improved. And the classification precision of the MoE model is improved.

The invention relates to the technical field of drilling machine drilling speed analysis, in particular to a mechanical drilling speed prediction method and device based on seismic frequency division attributes, electronic equipment and a storage medium, and the method comprises the steps: obtaining the seismic frequency division attributes and the mechanical drilling speed of a completed drilling well along a well trajectory after well-seismic matching calibration; inputting the seismic frequency division attribute and the mechanical drilling speed of the completed drilling well along the well trajectory after well-seismic matching calibration to a mechanical drilling speed prediction model to obtain a three-dimensional space mechanical drilling speed prediction value; and extracting a mechanical drilling speed predicted value of the design well from the mechanical drilling speed predicted values in the three-dimensional space by using the design well trajectory of the design well. The model provided by the invention can fully learn the nonlinear relationship between the mechanical drilling speed of a completed well and the seismic frequency division attribute, and the prediction accuracy of the mechanical drilling speed in a three-dimensional space of the model is improved, so that the prediction accuracy of a mechanical drilling speed prediction value of a designed well is improved; and the predicted value of the mechanical drilling speed is used for well drilling parameter optimization and tool optimization, so that the well drilling efficiency is improved, and the operation cost is reduced.

The invention discloses an advertisement creation failure reason diagnosis method, system and device and a medium. The method specifically comprises the steps of collecting multi-source data in an advertisement creation process; constructing a knowledge graph of the advertisement creation process based on the multi-source data; node embedding and relation reasoning are carried out on the knowledge graph through a graph neural network, and associated features in the advertisement creation process are extracted; based on the association features, performing cross-modal alignment and fusion on the text description, the system log and the user operation video by using a Transform model, and generating a fine-grained reason diagnosis result of the failed task; and constructing a success rate prediction model according to historical data and real-time features based on the associated features and the fine-grained reason diagnosis result, and dynamically adjusting advertisement budget allocation based on a prediction result of the success rate prediction model. According to the method, automatic and intelligent diagnosis of the advertisement creation failure reason is realized, the efficiency and accuracy of advertisement creation success rate analysis and optimization are improved, and advertisement putting decision scientization is facilitated.

The invention relates to the technical field of corrosion layer inversion, in particular to a steel corrosion layer depth inversion method based on LIBS spectral correlation analysis, which comprises the following steps: S1, acquiring a spectral sequence from a covering corrosion layer to a metal matrix, performing layer-by-layer variation rate analysis on each wave band spectral line, and constructing a spectral depth variation path set changing along with the denudation process; s2, identifying positions with sudden change or stability turning of spectral line features, marking the corresponding denudation step number as a transition position between corrosion layers, and constructing a transition index set comprising the transition position, the mutation rate sudden enhancement degree and the spectral line group linkage degree dimension according to the transition position, the mutation rate sudden enhancement degree and the spectral line group linkage degree dimension; and S3, inputting the transition index set into a trained structure mapping model, and outputting the total corrosion layer depth, the thickness estimation value of each sub-layer and the confidence level of the target corrosion area in combination with the multi-type corrosion sample library. The method improves the detection resolution and process adaptability, and can be widely applied to industrial scenes sensitive to the corrosion layered structure.

The invention provides an enterprise cost management method and system based on big data, and relates to the technical field of enterprise cost management. The method comprises the following steps: continuously obtaining department work task data, carrying out conversion rate analysis based on output value profit, and forming department production efficiency data; historical human resource data of a department is collected, and human efficiency analysis is carried out to form existing human efficiency data of the department; performing manpower demand analysis according to the department production efficiency data and the department existing manpower efficiency data to form manpower demand analysis data; and obtaining department dynamic manpower demand information, and establishing department manpower demand data in combination with the manpower demand analysis data. According to the method, the demand condition of an enterprise department for human resources can be determined more accurately, the human structure can be optimized while sufficient human resources are ensured, the enterprise can perform cost management based on the human resources more reasonably, and the effects of reducing cost and creating income are achieved.

This invention discloses a load rate analysis and evaluation method based on multi-source data fusion and deep learning. The main steps include: first, data preprocessing by collecting the actual and rated load rates of the power system; second, fusing the collected data from multiple data sources to obtain a dataset containing more information; training the dataset using a DBN model to establish a power system load rate prediction model; validating the model using a validation set; predicting the load rate of the power system using the trained model on new data; analyzing the actual load rate of the power system based on the prediction results and external factor data; and updating the model in a timely manner based on the analysis results to improve prediction accuracy and adaptability. Through load rate analysis and evaluation, the safety and stability of the power system can be improved, while ensuring the safe operation of the power system.

The invention belongs to the technical field of software testing, and provides a defect retrieval method and system for software development testing, which comprises the following steps: aiming at the problem of integrated logic test missing caused by overuse of a Mock object in a recognition unit test, and combining code coverage rate analysis and a Mock object optimization strategy to obtain a defect retrieval result; software defects caused by excessive Mock are accurately positioned and retrieved, the limited number of Mock objects in the unit test cases is determined according to the coverage rate of integrated logic codes during replacement of the Mock objects, unqualified unit test cases are identified in the unit test cases according to the limited number of the Mock objects, and necessity analysis is performed on the Mock objects in the unqualified unit test cases, so that the unit test cases are identified. By quantitatively evaluating the necessity of the Mock object, determining the reasonable use threshold and optimizing the test case, the integrity of the integrated logic test is effectively improved, the production environment defects caused by test distortion are reduced, and the software quality and stability are improved.

The invention provides a reference axis precision control method from a contraction section to a diffusion section, and belongs to the technical field of wind tunnel construction. A stainless steel plate control system is established as a bottom plate profile control reference, and a discontinuous point matrix and a contribution rate calculation model are constructed to realize key control point identification; a step point matrix and a virtual-real data comparison system are established to carry out precision control on a template profile, a multi-point joint observation network system is arranged to realize omnibearing monitoring, an intelligent contribution rate analysis system is utilized to carry out template installation correction, and a point stability matrix and a dynamic monitoring optimization equation set are established to realize real-time monitoring of a pouring process. A virtual-real data fusion analysis system is adopted for hole body axis fitting and optimization, and the technical problem that the terminal precision is poor due to the fact that the distance from a contraction section to a first diffusion section is long and measurement errors can be accumulated and amplified in the axis transmission process is solved.

The application discloses a low-voltage transformer area line loss rate analysis method, device, equipment and storage medium, the method comprises the following steps: obtaining the ideal line loss rate of the transformer in the ideal operation state of three-phase balance; using the preset phase equivalent resistance method to calculate the theoretical line loss rate according to the preset transformer area line loss related data; based on the ideal line loss rate, the theoretical line loss rate and the preset statistical line loss rate, the statistical deviation index and the technical deviation index are calculated respectively; according to the statistical deviation index, the technical deviation index and the deviation threshold, the management line loss measure and the technical line loss measure analysis are carried out, and the analysis result is obtained. The application can solve the technical problems that the existing technology cannot quantitatively analyze the line loss anomaly of the low-voltage transformer area, and the line loss analysis effect and the treatment scheme are poor.

The invention discloses an adaptive control system for industrial equipment, relates to the technical field of industrial equipment control, and comprises a data acquisition module, a data processing module, a rate analysis module, a matching module, and a control module. The invention solves the technical problem that when the extension length of a milling cutter is long, if the cutting rate of the milling cutter is high, the deflection and vibration of the milling cutter when cutting the outer contour of a workpiece are very likely to be large, and thus the workpiece processing inaccuracy is very likely to be caused. The actual cutting rate of the milling cutter is adjusted accordingly according to the cutting rate corresponding to the extension length range of the milling cutter, so that the deflection value of the milling cutter when cutting at different extension lengths is minimized and the vibration frequency is reasonable as much as possible, so that the inaccuracy of workpiece processing caused by the large deflection and vibration frequency of the milling cutter when cutting is reduced, and the processing accuracy of the milling cutter on the workpiece is improved.