PID performance evaluation method

Abstract

The invention provides a PID performance evaluation method, which comprises the following steps: selecting a PID chip sample, and selecting a bearing device; preparing detection tools, wherein the detection tools comprise a chip operation system adjusting tool, a material strength detection tool, a material shape detection tool, a material insulativity detection tool, an internal signal transmission detection tool, a chip operation state recording tool, a carrier operation state monitoring tool and a compression-resistant environment manufacturing room; detecting the material performance of the chip and the use effect of the chip; 4, performing centralized processing on the detection result in the step 3, constructing an evaluation model based on multi-dimensional space vector transformation and an information entropy theory, and performing combined comparison and calculation on the data according to the evaluation model to obtain an evaluation result; according to the method, the evaluation model based on the multi-dimensional space vector transformation and the information entropy theory is constructed, and the machine learning algorithm optimization result and the comprehensive detection process are introduced, so that the problems in the prior art can be effectively solved, and more accurate and more comprehensive evaluation of the PID chip performance is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of chip performance evaluation, and particularly to a PID performance evaluation method. Background Art

[0002] The performance of PID control is mainly reflected in its ability to accurately and stably control the system to reach the preset target value. The performance of a PID controller is mainly adjusted and optimized through its three main parameters: proportional gain (KP), integral time (TI), and derivative time (TD). In practical applications, in order to obtain the best PID control performance, it is usually necessary to debug and optimize the parameters of the PID controller through experiments and simulations. At the same time, it is also necessary to consider the influence of factors such as hardware limitations and noise interference of the control system on the PID control performance.

[0003] There is a prior art method and device for evaluating the performance of a PID control loop with the publication number CN117192971A. The method includes the following steps: after obtaining the operation data of the control loop, first perform health detection on the valve data and instrument data in the operation data of the control loop. If there is an abnormality in the valve data or instrument data, an alarm message is sent for an engineer to solve. If neither the valve data nor the instrument data is abnormal, then evaluate the parameter performance in the operation data of the control loop. If the performance result of the control loop is excellent or good, the process ends and the performance evaluation of the PID control loop is completed; if the performance result of the control loop is medium or poor, an alarm message is sent for the engineer to handle, and then the operation data of the control loop is obtained again and detected again until the quantitative evaluation result of the parameter is excellent or good, thereby completing the performance evaluation of the PID control loop. This method standardizes the performance evaluation process of the PID control loop, making the performance evaluation process of the PID control loop easy and easy to operate.

[0004] The above method standardizes the performance evaluation process of the PID control loop and improves the convenience of the performance evaluation process of the PID control loop. It and the existing detection methods both adopt the method of detecting data through the operation effect of the loop. This method can quickly evaluate the performance effect of the PID chip under smooth operation. However, it cannot accurately detect and evaluate other performances of the PID chip, such as its serviceable time.

[0005] Therefore, there is an urgent need in the art for a PID performance evaluation method to solve the above technical problems. Summary of the Invention

[0006] The present invention provides a PID performance evaluation method, which has the effect of comprehensively detecting and evaluating the performance data of the PID chip, aiming to solve the technical problems proposed in the above background art.

[0007] The present invention provides a PID performance evaluation method, including: Step 1: Select PID chip samples and choose a carrier device; Step 2: Prepare detection tools; Step 3: Detect the material properties of the chip and the usage effect of the chip; Step 4: Centralize the processing of the detection results in Step 3, separately transmit the material, structure data, material data, and working data of the product to the computer, combine, compare, and calculate each data, and obtain an evaluation result.

[0008] Preferably, the detection tools include a chip operating system adjustment tool, a material strength detection tool, a material shape detection tool, a material insulation detection tool, an internal signal transmission detection tool, a chip operating state recording tool, a carrier operating state monitoring tool, and a compressive environment manufacturing chamber.

[0009] Preferably, in Step 1, for the selection of PID chip samples, according to the preset chip performance index range, select the chips that meet the requirements from multiple batches of chips as samples; the preset chip performance index range is obtained through statistical analysis of the performance parameters of mainstream PID chips on the market; In Step 1, for the selection of the carrier device, select a suitable carrier device from existing various carrier devices according to the size specifications, interface types, and heat dissipation and electromagnetic compatibility requirements during operation of the PID chip samples.

[0010] Preferably, in Step 2, the preparation of the detection tools includes: Use the material strength detection tool, the material shape detection tool, the material insulation detection tool, and the internal signal transmission detection tool to detect the strength, standardness of the shape, insulation effect, and signal transmission efficiency of the chip body respectively; Adjust the temperature system inside the controller where the PID chip is located to between 20% - 60%, the flow system to between 40% - 100%, the pressure system to between 30% - 70%, and the liquid level system to between 20% - 80% through the chip operating system adjustment tool; Install the product inside the carrier device, and install the chip operating state recording tool and the carrier operating state monitoring tool inside the carrier device, connect the chip operating state recording tool and the product with each other by signal, and connect the carrier operating state monitoring tool and the carrier device with each other by signal; Place the product in a conventional environment or adjust the operating environment corresponding to the product and the carrier device in the compressive environment manufacturing chamber to perform detection, and obtain different product operating data.

[0011] Preferably, the step of placing the product in a conventional environment or a controlled compressive environment manufacturing chamber to adjust the operating environment corresponding to the requirements of the product and the bearing device for detection to obtain different product operation data includes: Conventional environment detection: Under common environmental conditions, the specific working parameters of the power, working intensity, working time, function opening degree, temperature, flow rate, pressure, and liquid level received by the PID chip are adjusted respectively for detection to obtain detailed detection data; Compressive environment detection: The temperature of the internal high and low temperature detection environment of the compressive environment manufacturing chamber is adjusted to between -50 °C and 80 °C, the internal magnetic field strength of the electromagnetic interference environment is adjusted to between 0 - 500 NT, the humidity environment is adjusted to between 0 - 90% humidity, so that the internal voltage of the bearing device remains within different fluctuation ranges, and the dust content in the environment remains between 0 - 90%. Under different environments, the above-mentioned conventional environment detection steps are repeated.

[0012] Preferably, in step three, the step of detecting the material properties of the chip includes: The product is separately placed in different environments for 1 - 1000 minutes, and the data of the strength, high-temperature resistance effect, insulation, anti-interference effect, and signal transmission efficiency of the product are detected, and the detected data are transmitted, stored, and analyzed; The product is installed inside the bearing device, and the operating power of the bearing device is adjusted between 1% - 100%. Then it is placed in different environments for 1 - 1000 minutes respectively. After that, the product is taken out, and the data of the strength, high-temperature resistance effect, insulation, anti-interference effect, and signal transmission efficiency of the product are detected, and the detected data are transmitted, stored, and analyzed; During the data detection process, use the formula Calculate the comprehensive index of material properties; Among them, is the comprehensive index of material properties; is the number of detection items; is the weight of each detection item, determined by the analytic hierarchy process according to the influence degree of each detection item on the chip performance; is the data processing function of the i-th detection item, constructed respectively according to the characteristics of the detection item.

[0013] Preferably, in step three, the step of detecting the usage effect of the chip includes: The bearing device equipped with the chip operation status recording tool and the carrier operation status monitoring tool has its operating power adjusted between 1% - 100%, and it is placed in different environments and continuously operated for 1 - 1000 minutes; The working status of the PID chip is detected and data is collected through a chip operation status recording tool. The operation status of the carrying device is detected and data is collected through a carrier operation status monitoring tool, and various data of the operation of the carrying device are transmitted, stored, and analyzed.

[0014] Preferably, in step four, an evaluation model is constructed based on multi-dimensional space vector transformation and information entropy theory, and each data is combined, compared, and calculated according to the evaluation model; Using the formula Calculate the information entropy of the chip working status ; Among them, is the number of feature dimensions of the chip working status, is the probability of the j-th feature dimension state occurring, which is used to measure the stability of the chip working status.

[0015] Preferably, in step four, the evaluation model is:

[0016] Among them, is the final evaluation result; is the number of samples; is the weight of the k-th sample, which is preset according to the representativeness and reliability of the sample; is the comprehensive material performance index of the k-th sample; is the information entropy of the chip working status of the k-th sample.

[0017] Preferably, in step four, after obtaining the evaluation result it further includes: Using a machine learning algorithm to optimize the evaluation result ; The machine learning algorithm is based on a neural network model and is trained through historical data to improve the accuracy of the evaluation data; It also includes: Visualizing the evaluation result and presenting the evaluation data intuitively in the form of charts and curves, which is convenient for users to quickly understand the performance status of the PID chip; The visualization display interface customizes the display parameters and display methods according to user needs.

[0018] Compared with the prior art, the beneficial effects of the present application are as follows: By using the PID chip in various environments, detailed test data can be provided for the PID operation performance evaluation, thereby obtaining clear and detailed evaluation data, and combined with the physical test data of each item in the PID chip, the evaluation data of the PID chip performance is more comprehensive. This application not only tests the PID chip under conventional conditions, but also uses a pressure-resistant environment manufacturing room to simulate extremely complex environments, such as controlling the high and low temperature test environment temperature at -50 degrees Celsius to 80 degrees Celsius, and adjusting the electromagnetic interference environment magnetic field strength to 0-500NT, etc.; compared with the existing technology that only tests under conventional conditions, it can more comprehensively understand the performance of the chip under different working conditions and ensure its reliability in actual complex environments.

[0019] This application constructs an evaluation model based on multi-dimensional space vector transformation and information entropy theory, and uses formulas to calculate comprehensive material performance indicators, chip working status information entropy and final evaluation results; compared with existing technologies that rely on empirical formulas and simple statistical analysis, it can more accurately quantify chip material performance, working status stability and its relationship with the final evaluation results, and tap the potential value of the data.

[0020] This application uses a machine learning algorithm based on a neural network model to optimize the evaluation results and sets weights according to sample representativeness and reliability; it overcomes the problems of insufficient sample consideration and unscientific weight determination in the prior art, effectively improves the accuracy of evaluation data, and provides a more reliable basis for chip performance judgment.

[0021] The evaluation results are displayed visually, and the display interface can customize display parameters and methods according to user needs; compared with the single presentation method of evaluation results in the existing technology, it is easier for users to quickly and intuitively understand the performance status of the PID chip, thereby improving the practicality and application value of the evaluation results.

[0022] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0023] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a flow chart of a PID performance evaluation method provided by an embodiment of the present invention; Figure 2It is the information diagram of the product material detection in the present invention; Figure 3 It is the information range diagram for detecting the operation effect of the PID chip of the present invention; Figure 4 It is the range diagram of the detection effect data of the present invention; Figure 5 It is the range diagram of the detection tool types of the present invention; Figure 6 It is the range diagram of the chip sample requirements of the present invention; Figure 7 It is the range diagram of the requirements for the carrying device of the present invention; Figure 8 It is the detection data table of a PID performance evaluation method provided by an embodiment of the present invention. Specific implementation mode

[0025] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustration and explanation of the present invention, and are not used to limit the present invention.

[0026] Embodiment 1: An embodiment of the present invention provides a PID performance evaluation method. Please refer to Figures 1 - 8 , including: Step 1, select PID chip samples and select a carrying device; Step 2, prepare detection tools. The detection tools include a chip operation system adjustment tool, a material strength detection tool, a material shape detection tool, a material insulation detection tool, an internal signal transmission detection tool, a chip operation status recording tool, a carrier operation status monitoring tool, and a compressive environment manufacturing chamber; Step 3, detect the chip material performance (roughly evaluate the chip performance; install the product inside the carrying device or directly place the product itself in an appropriate environment, and use various detection tools to detect the various performances of the product to obtain the material data of the product) and the chip usage effect (install some detection tools inside the carrying device, start the carrying device to drive the product to run, and perform real-time detection on the operation status of the chip and the operation status of the carrying device to obtain the working data of the PID chip); Step 4, centrally process the detection results in Step 3. Transmit the material, structure data, material data, and working data of the product to the inside of the computer respectively. Build an evaluation model based on multi-dimensional space vector transformation and information entropy theory. According to the evaluation model, combine, compare, and calculate each data to obtain an evaluation result.

[0027] As Figure 2 shown, it is the information diagram of the product material detection in the present invention, including: Idle state, undetected inspection information: strength (hardness and toughness), high-temperature resistance effect, insulating shoes; Working state: high-temperature resistance effect, insulating shoes, anti-interference effect, signal transmission efficiency; Figure 3 It is the information range diagram for detecting the operation effect of the PID chip of the present invention; Conventional inspection environment: power, working intensity, working time, function opening degree, temperature system, flow system, pressure system, liquid level system; Compression resistance inspection environment: high and low temperature environment, electromagnetic interference environment, humidity environment, voltage stability, dust environment; Figure 4 It is the range diagram of the detection effect data of the present invention; Detection effect data: running time, operation smoothness, operation error rate, induction speed, maximum running temperature, chip hardness, chip integrity, conduction effect.

[0028] In use, the user selects an appropriate number of PID chip samples of the same material, same batch, and same specification, and a carrier device of the same material, same batch, and same specification. The temperature system inside the controller where the PID chip is located is adjusted to 40% by the chip operating system adjustment device, the flow system is adjusted to 70%, the pressure system is adjusted to 50%, and the liquid level system is adjusted to 50%. The product is placed under common environmental conditions, and the specific working data of the power, working intensity, working time, function opening degree, temperature system, flow system, pressure system, and liquid level system received by the PID chip are adjusted respectively. Detection is carried out using a detection tool to obtain detailed detection data. The temperature of the internal high and low temperature detection environment in the compressive environment manufacturing room is adjusted to 0 degrees Celsius, the internal magnetic field strength of the electromagnetic interference environment is adjusted to be between 200 NT, and the humidity environment is adjusted to be between 80% humidity, so that the fluctuation range of the internal voltage of the device remains between 110V - 300V, and the dust content in the environment remains at 60%. Detection is carried out in different environments. The strength, standardness of shape, insulation effect, and signal transmission efficiency of the chip body are detected using a material strength detection tool, a material shape detection tool, a material insulation detection tool, and an internal signal transmission detection tool respectively to obtain different product operation data. The product is separately placed in different environments for 1000 minutes, and data on the strength, high temperature resistance effect, and insulation of the product are detected. The detection data are transmitted, stored, and analyzed. The product is installed inside the carrier device, and the operating power of the carrier device is adjusted to be between 100%. Then it is separately placed in different environments for 1000 minutes. After that, the product is taken out, and data on the strength, high temperature resistance effect, and insulation of the product are detected. The detection data are transmitted, stored, and analyzed. For the carrier device equipped with a chip operating status recording device and a carrier operating status monitoring device, the operating power of the carrier device is adjusted to be between 100%, and it is placed in different environments and continuously operated for 1000 minutes. The working status of the PID chip is detected and data are collected by the chip operating status recording device, and the operating status of the carrier device is detected and data are collected by the carrier operating status monitoring device. And all the data of the device operation are transmitted, stored, and analyzed. The material and structure data of the product are sorted out, combined with the material detection data, and the performance of the PID chip is roughly evaluated. The operation detection data of the PID chip are sent into the intelligent computer, and calculation and analysis are carried out on it through the existing evaluation calculation formula in combination with the above performance evaluation data to obtain more detailed and accurate performance evaluation data.

[0029] The principles and beneficial effects of the above embodiments are as follows: The evaluation model is constructed based on multi-dimensional space vector transformation and information entropy theory. First, chip samples are selected and suitable carrying devices are chosen, and a variety of detection tools are prepared. Through these tools, multi-environment and multi-parameter detections are carried out on the chip material performance and usage effects to obtain rich data. The various types of data are input into a computer, and the evaluation model is used to comprehensively process the data. The multi-dimensional space vector transformation is used to analyze the complex relationships between the data, and the information entropy theory measures the stability of the chip working state, and finally the evaluation result is obtained. Using a variety of detection tools to detect in different environments can comprehensively obtain chip performance data and avoid one-sided evaluation caused by a single detection environment. Constructing a model based on specific theories and calculating relevant indicators using formulas can accurately quantify the relationships between chip material performance, working state stability, etc. and the evaluation result, improving the evaluation accuracy. With the help of a computer and the model to process data, the limitations of manual experience evaluation are overcome, and the evaluation efficiency and scientificity are improved.

[0030] To further optimize the above embodiments, in step one, PID chip samples are selected, and according to the preset chip performance index range, chips that meet the requirements are screened out from multiple batches of chips as samples; the preset chip performance index range is obtained through statistical analysis of the performance parameters of mainstream PID chips on the market.

[0031] It should be noted that the user should select an appropriate number of PID chip samples of the same material, same batch, and same specification; with the help of professional electronic component database platforms, such as Arrow, Digi-Key, etc., these platforms provide rich chip specification parameter information; at the same time, consult industry research reports and official documents of chip manufacturers to collect data on key performance indicators such as response time, control accuracy, power consumption, and operating temperature range of PID chips of different brands and models; in addition, participate in industry exhibitions and technical seminars, communicate with chip manufacturers and users to obtain first-hand performance parameter feedback information; subsequently, use statistical analysis software, such as the data analysis libraries of SPSS, Python (Pandas, NumPy, Scikit-learn, etc.) to process the collected data; first, perform data cleaning on each performance indicator to remove outliers and duplicate data; then, calculate statistics such as the mean, median, and standard deviation of each performance indicator to determine the central tendency and dispersion degree of the data; by drawing visual charts such as histograms and box plots, visually observe the distribution of the data, and then determine a reasonable performance indicator range; for example, if the response time of most mainstream PID chips is concentrated in the range of 0.1 - 0.5 seconds, the preset response time indicator range can be set near this interval and an appropriate margin can be considered; finally, screen multiple batches of chips according to the determined preset chip performance indicator range; for chip batches purchased in bulk, a certain number of chips can be randomly selected for preliminary testing; use high-precision testing equipment, such as oscilloscopes, logic analyzers, power analyzers, etc., to actually test the performance indicators of the sampled chips; compare the test results with the preset range, and select the chips that meet the requirements as samples; at the same time, record the relevant data during the screening process, including chip batch information, test data, etc., for subsequent traceability and analysis.

[0032] To further optimize the above embodiment, in step one, select a carrier device, and select a suitable carrier device from various existing carrier devices according to the size specification, interface type, and heat dissipation and electromagnetic compatibility requirements during the operation of the PID chip sample.

[0033] It should be noted that the user should select an appropriate number of carrier devices of the same material; first, collect information on various existing carrier devices in the market, including size specifications, interface types, heat dissipation methods (such as natural heat dissipation, heat sink heat dissipation, fan heat dissipation, etc.), and electromagnetic compatibility indicators (such as shielding effectiveness, grounding methods, etc.); this information can be obtained by querying manufacturer product manuals, online electronic component platform materials, and participating in industry exhibitions, and after sorting, it is entered into the database for convenient subsequent retrieval and screening; Use measuring tools with appropriate accuracy, such as calipers and micrometers, to measure the length, width, and height of the PID chip sample, ensuring that the data is accurate to millimeters or even smaller units; use professional interface testing equipment to identify the interface type of the chip, and record the number of pins, arrangement, and electrical characteristics of the interface; use thermal imagers, power consumption testers, and other equipment to estimate the heat dissipation of the chip during operation, combined with the chip's operating power and ambient temperature requirements; use electromagnetic interference testing equipment to test the chip's own electromagnetic emission intensity and sensitivity to external electromagnetic interference, so as to determine the chip's electromagnetic compatibility requirements; Compare various parameters of chip samples with data in the carrier information database; prioritize carriers with dimensions that can accommodate chip samples and have suitable interface matching to ensure that the chip can be physically installed and electrically connected; for heat dissipation requirements, select carriers with corresponding heat dissipation capabilities according to the heat dissipation and heat dissipation method requirements of the chip, such as for chips with large heat dissipation, select carriers with high-efficiency heat sinks or fans; for electromagnetic compatibility requirements, check whether the electromagnetic shielding design and grounding measures of the carrier meet the chip requirements, and select carriers that can effectively shield external electromagnetic interference and prevent electromagnetic leakage of the chip itself; Conduct actual tests on the initially selected compatible carriers; install chip samples on the carriers to simulate the actual working environment of the chip and conduct performance tests; use temperature sensors to monitor the temperature changes of the chip during operation to verify whether the heat dissipation effect meets the requirements; use electromagnetic interference detectors to detect the electromagnetic environment of the chip in the carrier to evaluate whether the electromagnetic compatibility meets the standards; if the test results are not ideal, re-screen other carriers or improve and optimize the carriers, such as adding additional heat dissipation components or electromagnetic shielding layers, until all requirements of the chip are met.

[0034] In order to further optimize the above embodiment, in step 2, preparing the detection tool includes: Use material strength testing tools, material shape testing tools, material insulation testing tools and internal signal transmission testing tools to test the chip body's strength, shape standardization, insulation effect and signal transmission efficiency respectively; Use the chip running system adjustment tool to adjust the temperature system inside the controller where the PID chip is located to between 20%-60%, the flow system to between 40%-100%, the pressure system to between 30%-70%, and the liquid level system to between 20%-80%; Install the product inside the carrier, and install the chip operation status recording tool and the carrier operation status monitoring tool inside the carrier, connect the chip operation status recording tool and the product with each other, and connect the carrier operation status monitoring tool and the carrier with each other; Place the product in a conventional environment or a controlled compressive environment manufacturing chamber to adjust the operating environment corresponding to the requirements of the product and the carrying device for detection, and obtain different product operation data.

[0035] It should be noted that: 1. Detecting the performance of the chip body includes: Material strength detection: Use a universal material testing machine to fix the chip on a fixture, and select an appropriate loading speed and loading method according to the material characteristics of the chip, such as tensile, compressive or bending tests, to measure the strength data of the chip under stress; for tiny chips, high-precision micro sensors can be equipped to accurately measure tiny deformations and stress conditions; Material shape detection: Observe the chip using an optical microscope or an electron microscope, take surface images of the chip, and measure the dimensional accuracy, flatness of the chip, as well as the shape and position accuracy of the pins through image analysis software, and compare with the standard shape to evaluate the standardness of the shape; Material insulation detection: Use an insulation resistance tester, contact the electrodes of the tester with different pins or components of the chip, apply a certain test voltage, and measure the insulation resistance value between different parts of the chip to determine whether the insulation effect of the chip meets the requirements; Internal signal transmission detection: Use a high-speed oscilloscope and a signal generator. The signal generator inputs an electrical signal with a specific frequency and amplitude to the chip, and the oscilloscope monitors the signal transmission conditions at different nodes inside the chip, measures parameters such as signal transmission delay, attenuation, and signal integrity, and evaluates the signal transmission efficiency; 2. Adjusting the chip operating environment includes: Temperature system adjustment: Through the temperature controller in the chip operating system adjustment tool, control the heating or cooling device to raise or lower the temperature of the internal environment of the controller where the chip is located; devices such as a thermoelectric cooler (TEC) or heating wires can be used, combined with the temperature data real-time feedback by the temperature sensor, to accurately adjust the temperature between 20% - 60% (assuming the full-scale temperature is 0 - 100 °C); Flow, pressure, and liquid level system adjustment: For the flow system, install a flow sensor and a flow regulating valve in the fluid channel of the chip, and control the fluid flow by adjusting the valve opening according to the preset flow range; for the pressure system, use a pressure sensor and a pressure regulating pump to adjust the output pressure of the pump to maintain the system pressure between 30% - 70%; for the liquid level system, use a liquid level sensor and a liquid level controller to realize the adjustment of the liquid level between 20% - 80% by controlling the injection or discharge of the liquid; 3. Installing and connecting monitoring tools includes: Carefully install the chip at the designated position on the carrying device to ensure good electrical connection between the chip and the carrying device; use welding or plug-in connection methods to ensure reliable connection between the pins and the circuit board of the carrying device; Connect a chip operation status recording tool (such as a data acquisition card, microcontroller, etc.) to the chip through a suitable communication interface (such as SPI, I2C, etc.) to ensure that the working status data of the chip, such as voltage, current, temperature, etc., can be collected in real time; Install a carrier operation status monitoring tool (such as an acceleration sensor, gyroscope, etc.) on the carrier device and connect it to the data processing device by wired or wireless means to monitor the operation status data of the carrier device, such as vibration and displacement; Furthermore, the steps of placing the product in a conventional environment or a controlled compressive environment manufacturing chamber to adjust the operating environment corresponding to the requirements of the product and the carrier device for detection to obtain different product operation data include: Conventional environment detection: Under common environmental conditions, adjust the specific working parameters of the power, working intensity, working time, function opening degree, temperature, flow rate, pressure, and liquid level received by the PID chip for detection respectively to obtain detailed detection data; Compressive environment detection: Adjust the temperature of the internal high and low temperature detection environment in the compressive environment manufacturing chamber to between -50 °C and 80 °C, adjust the internal magnetic field strength of the electromagnetic interference environment to between 0 - 500 NT, adjust the humidity environment to between 0 - 90% humidity, keep the internal voltage range of the carrier device within different fluctuation amplitudes, and keep the dust content in the environment between 0 - 90%. Under different environments, repeat the above conventional environment detection steps; It should be noted that for conventional environment detection: In a conventional environment of normal temperature, normal pressure, and no strong electromagnetic interference in the laboratory, adjust the power, working intensity, working time, function opening degree, etc. of the chip according to the set parameters, and use various detection tools to synchronously collect the operation data of the chip and the carrier device. For example, record the change of the chip's electrical signal through an oscilloscope, and use a data acquisition card to record various physical quantity data collected by the sensor; Compressive environment detection: Place the chip and the carrier device in the compressive environment manufacturing chamber, and adjust parameters such as the temperature, humidity, electromagnetic interference intensity, voltage fluctuation range, and dust content in the chamber through the environmental control system to make it meet the set range; Under each different combination of environmental parameters, repeat the steps of conventional environment detection and record the corresponding operation data; At the same time, ensure that the detection equipment in the compressive environment manufacturing chamber maintains stable communication with the external data processing equipment for timely data transmission and analysis.

[0036] To further optimize the above embodiments, in step three, the steps of detecting the material properties of the chip include: Place the product separately in different environments for 1 - 1000 minutes, and conduct data detection on the strength, high-temperature resistance effect, insulation, anti-interference effect, and signal transmission efficiency of the product, and transmit, store, and analyze the detection data; Install the product inside the carrying device and adjust the operating power of the carrying device between 1% and 100%. Then place it in different environments for 1 to 1000 minutes. After that, take out the product and conduct data detection on the strength, high-temperature resistance effect, insulation, anti-interference effect, and signal transmission efficiency of the product. Transmit, store, and analyze the detected data; During the data detection process, use the formula to calculate the comprehensive index of material properties; where is the comprehensive index of material properties; is the number of detection items; is the weight of each detection item, which is determined by the analytic hierarchy process according to the influence degree of each detection item on the chip performance; is the data processing function of the i-th detection item, which is constructed separately according to the characteristics of the detection item.

[0037] It should be noted that for the detection of the product placed alone, prepare multiple environmental simulation chambers, which can simulate different environments such as high temperature, low temperature, humidity, and strong electromagnetic interference. For example, the temperature range of the high-temperature environmental simulation chamber is set to 80°C - 150°C, the temperature range of the low-temperature environmental simulation chamber is set to -50°C - 0°C, the humidity range of the humidity environmental simulation chamber is set to 60% - 95%, and the electromagnetic interference environmental simulation chamber can generate electromagnetic fields with different frequencies and intensities. Place the product in these simulation chambers respectively, and set different placement durations for each environment, ranging from 1 minute to 1000 minutes, and accurately control the time through a timer. During the placement period, use professional detection equipment to detect the performance of the product in real time. For strength detection, use a high-precision pressure sensor to measure the pressure-bearing capacity of the product at different time points. For the high-temperature resistance effect detection, use an infrared thermal imager to monitor the surface temperature change of the product and the presence of abnormal heat generation points. For insulation detection, use an insulation resistance tester to measure the insulation resistance value between different parts of the product at regular intervals. For anti-interference effect detection, observe whether the working state of the product is affected by emitting interference signals with specific frequencies and intensities. For signal transmission efficiency detection, use a signal generator and an oscilloscope to detect parameters such as signal transmission delay and attenuation of the product. The detected data is sent to a data storage device, such as a server or a large-capacity hard disk, in real time through wired or wireless transmission methods; For post-installation detection on the carrying device, install the product in the carrying device and connect devices such as circuits and sensors. Use a power adjustment device, such as a programmable power supply, to adjust the operating power of the carrying device between 1% and 100%. Set a fixed power value each time for adjustment, such as 5%, 10%, etc., and record the adjusted power value. Similar to the detection of the product placed alone, put the carrying device with the product installed into different environmental simulation boxes, and set the placement time to 1 - 1000 minutes. During the placement process, use the same detection equipment to detect the strength, high-temperature resistance effect, insulation, anti-interference effect, and signal transmission efficiency of the product. Also, transmit and store the detection data in real time. After each detection is completed, take out the product from the carrying device, check for physical damage or performance changes, and record the relevant situation. For calculating the comprehensive index of material properties, use the analytic hierarchy process to construct a hierarchical structure model that includes each detection item (strength, high-temperature resistance effect, insulation, anti-interference effect, signal transmission efficiency). Based on experience, compare and score the relative importance of each detection item pairwise to construct a judgment matrix. By calculating the eigenvector and the maximum eigenvalue of the judgment matrix, obtain the weights of each detection item. For example, if experts believe that the strength has a slightly more important impact on the chip performance than the anti-interference effect, the score may be 3 (1 - 9 scale method), and so on to construct a complete judgment matrix. Construct a data processing function according to the characteristics of each detection item. For strength detection, assume the detection data is the pressure value P, and the data processing function can be calculated based on the deviation between the pressure value and the standard strength value, such as is the strength standard value required for the chip to work properly. For signal transmission efficiency detection, if the detection data is the signal transmission delay t and attenuation a, the data processing function can be a function that comprehensively considers the delay and attenuation, such as (only for example, actually need to be constructed according to specific situations). Process the data of each detection item according to these functions, and then combine the determined weights to calculate the comprehensive index M of the material properties using the formula

[0038] To further optimize the above embodiments, in step three, the steps for detecting the usage effect of the chip include: For the carrying device installed with the chip operating status recording tool and the carrier operating status monitoring tool, adjust the operating power of the carrying device between 1% and 100%, and place it in different environments for continuous operation for 1 - 1000 minutes. Detect and collect the working status data of the PID chip through the chip operating status recording tool, detect and collect the operating status data of the carrying device through the carrier operating status monitoring tool, and transmit and store and analyze the various data of the operation of the carrying device. Use the formula to calculate the information entropy of the chip operating state ; wherein, is the number of characteristic dimensions of the chip operating state, is the probability of the occurrence of the state of the j-th characteristic dimension, which is used to measure the stability of the chip operating state.

[0039] It should be noted that, first, adjust the operation of the carrier device and set the environment. Use a power regulation device, such as a programmable power supply or a power controller, to connect to the power supply line of the carrier device; through the operation interface or supporting software of the device, adjust the operating power of the carrier device in steps between 1% and 100%; for example, increase in steps of 5%, starting from 1% and gradually adjusting to 100%, and operate stably for a period of time after each adjustment to ensure that the chip and the carrier device reach a stable operating state; prepare a variety of simulation environment devices, such as a high and low temperature test chamber to simulate the temperature environment, and the temperature range can be set between -50°C and 80°C; a humidity test chamber to simulate the humidity environment, and the humidity range is 0 - 90%; an electromagnetic interference generator to simulate the electromagnetic interference environment, and the magnetic field intensity can be adjusted to 0 - 500 NT; place the carrier device with adjusted power into different simulation environment devices in sequence, and set the operating time between 1 and 1000 minutes. For example, for the high temperature environment, the operating time can be set to 100 minutes, and for the low temperature environment, it can be set to 300 minutes, etc., and accurately control the operating duration through a timer; Secondly, detect, collect, transmit and store the data; the chip operating state recording tool can adopt a high-precision data acquisition card and a supporting sensor. The sensor is connected to the key nodes of the PID chip, such as the power supply pin, the signal output pin, etc.; the data acquisition card collects parameters such as the operating voltage, operating current, signal output frequency of the chip at a set sampling frequency, such as 1000 times per second, so as to reflect the operating state of the chip; the collected data is transmitted to the data storage server in real time by wired (such as USB, Ethernet) or wireless (such as Wi-Fi, Bluetooth) methods; the carrier operating state monitoring tool is installed on the carrier device, such as an acceleration sensor for monitoring the vibration of the carrier device, and a displacement sensor for monitoring whether there is abnormal displacement of the carrier device; these sensors convert the monitored analog signals into digital signals, and after preliminary processing by the microcontroller, they are then transmitted to the data storage server; at the same time, monitor other operating parameters of the carrier device, such as the rotation speed of the cooling fan (if any), the internal temperature (using a temperature sensor), etc., and transmit and store them together; Finally, calculate the information entropy of the chip's working state; determine the characteristic dimensions used to measure its working state based on the chip's functions and application scenarios; for example, for a PID chip used for motor speed control, the four parameters of working voltage, working current, output control signal frequency, and control error can be used as characteristic dimensions, that is, m=4; perform statistical analysis on the data collected for each characteristic dimension; taking the working voltage as an example, divide the collected working voltage data according to a certain voltage interval, such as dividing the working voltage range of 0-5V into 10 intervals, each with a width of 0.5V; count the number of times the data appears in each interval, and then divide it by the total number of data to obtain the probability of the working voltage state corresponding to the interval. ; Other feature dimensions are analogous; the determined m and the calculated Substitute into the formula to calculate; For example, suppose the calculated probabilities of the four feature dimensions are =0.1, =0.2, =0.3, =0.4, then the information entropy is: E=-(0.1*log20.3+032*log20.2+0.3*log20.4+0.4*log20.4), the value of information entropy is calculated by a calculator or programming to measure the stability of the chip working state; a lower information entropy value means that the chip working state is more stable and has smaller fluctuations; a higher information entropy value means that the working state changes greatly and the stability is poor.

[0040] In order to further optimize the above embodiment, in step 4, the evaluation model constructed based on multidimensional space vector transformation and information entropy theory is:

[0041] in, For the final evaluation results; is the sample size; is the weight of the kth sample, which is pre-set according to the representativeness and reliability of the sample; is the comprehensive index of material performance of the kth sample; is the information entropy of the chip working status of the kth sample.

[0042] It should be noted that in this evaluation model, the comprehensive material performance index of each sample and the information entropy of the chip working state are regarded as vector elements in a multi-dimensional space. The core idea of the multi-dimensional space vector transformation is to integrate multiple different-dimensional data related to chip performance (here, the data related to material performance and working state stability) into a unified space framework for analysis. Through this transformation, data with different natures and different dimensions can be mathematically related to explore the potential complex relationships between the data, so as to more comprehensively and deeply reflect the comprehensive performance of the chip. For example, in practical applications, the comprehensive material performance index and the information entropy of the chip working state describe the chip characteristics from different angles, and the vector transformation can combine them organically to avoid the limitations of single-index evaluation. The information entropy is used to measure the stability of the chip working state. The lower the information entropy value, the more stable the chip working state, and its contribution in the evaluation model is relatively greater. On the contrary, the higher the information entropy value, the greater the fluctuation and the worse the stability of the chip working state. Introducing the information entropy into the formula is to fully consider the important factor of working state stability when evaluating the chip performance, so that the evaluation result is more in line with the actual situation. Because even if the chip performs well in some performance indicators, if the working state is unstable, it cannot be considered an excellent chip. The number of samples is l, and each sample has a corresponding weight, which is preset according to the representativeness and reliability of the sample. The representativeness of the sample depends on its typicality in the overall sample. For example, among the samples selected from multiple different production batches and different usage environments, those samples that can widely reflect the performance of the chip in various common scenarios have stronger representativeness. The reliability is related to the accuracy of the sample detection process, the credibility of the data, etc. The setting of the weight makes the evaluation model assign greater influence to more representative and reliable samples when calculating the final result, ensuring the scientificity and accuracy of the evaluation result. The formula comprehensively considers the comprehensive material performance index and the working state information entropy of each sample by weighted summing all samples and dividing by the total sum of sample weights, and obtains the final evaluation result P, comprehensively and accurately evaluating the performance of the PID chip.

[0043] To further optimize the above embodiment, in step four, after obtaining the evaluation result it further includes: using a machine learning algorithm to optimize the evaluation result The machine learning algorithm is based on a neural network model and is obtained through training with historical data, and is used to improve the accuracy of the evaluation data.

[0044] It should be noted that the process is as follows: First, a large amount of historical data related to PID chips is collected. This data covers material, structure, material quality, working data of different chip models under various detection conditions, as well as the corresponding manual evaluation results or actual application performance feedback; the data is cleaned to remove outliers and incorrect data to ensure data quality; then the data is divided into a training set, a validation set, and a test set, for example, divided according to a ratio of 7:2:1. The training set is used to train the neural network model, the validation set is used to adjust the model parameters, and the test set is used to evaluate the generalization ability of the model; Secondly, a suitable neural network architecture is selected, such as a multi-layer perceptron (MLP); the number of input layer nodes is determined according to the number of features of the input data. For example, if the input data contains 10 different features (such as various material property parameters, working state parameters, etc.), the number of input layer nodes is set to 10; the hidden layer can be set to 2 - 3 layers, and the number of nodes in each layer is determined based on experience and experiments, generally selectable between 32 - 128, such as set to 64, 32; the number of output layer nodes is 1, that is, the optimized evaluation result is output; the ReLU function is selected as the activation function for the hidden layer to increase the non-linear expression ability of the model, and a linear activation function can be selected for the output layer; Furthermore, the training set data is input into the neural network model, and appropriate training parameters are set, such as the learning rate (generally between 0.001 - 0.1, can be set to 0.001), the number of iterations (such as 500 times); during the training process, the model predicts the evaluation result according to the input data and compares it with the actual evaluation result in the training set. The prediction error is calculated through the backpropagation algorithm, and the weights and biases of each layer in the neural network are adjusted to make the prediction result gradually approach the actual result; every certain number of training times (such as 50 times), the validation set data is used to verify the model, observe the loss function value on the validation set. If the loss function value no longer decreases or even increases, the training is stopped to prevent the model from overfitting; Finally, the preliminary evaluation result obtained through the evaluation model is input into the trained neural network model, and the model outputs the optimized evaluation result; in actual applications, new chip evaluation data is continuously collected, and the neural network model is retrained regularly to enable it to adapt to new data distributions and changes, continuously improving the accuracy of the evaluation data.

[0045] To further optimize the above embodiments, it further includes: For the evaluation result Visual display is performed, and the evaluation data is intuitively presented in the form of charts and curves, facilitating users to quickly understand the performance status of the PID chip; the visual display interface customizes the display parameters and display methods according to user needs.

[0046] It should be noted that Matplotlib and Seaborn libraries in Python can be selected, or professional data visualization software such as Tableau and PowerBI can be used. These tools have powerful drawing functions and interactivity, and can meet diverse visualization needs. For example, Matplotlib is highly flexible and suitable for drawing various basic charts. Tableau is easy to operate and can quickly generate interactive dashboards. Extract the data to be displayed from the evaluation result dataset, such as the comprehensive material performance index, the information entropy of the chip working state, the final evaluation score, etc. Organize and calculate the data according to the display requirements. For example, calculate the average performance index of chips in different batches, or count the distribution of chip performance by specific classification. If you want to show trend changes, ensure that the data has an identifier for the time or order dimension. Set a drop-down menu or checkbox in the visualization interface for users to select the parameters to be displayed. For example, users can choose to view only the evaluation data of chips in specific batches and specific manufacturers, or only focus on certain key performance indicators. Provide multiple chart type switching buttons, allowing users to select appropriate charts to display data according to their needs. You can also set style options such as chart colors, axis labels, and data markers to meet personalized visual requirements. Utilize the interactive features of the visualization tool to add data tooltips that display detailed evaluation data when the user hovers the mouse over chart elements. Set zooming and panning functions to facilitate users to view chart details. Create link or drill-down functions that allow users to click on chart elements to view more detailed underlying data or relevant analysis reports. Furthermore, in some embodiments, the detection data can be summarized into a detection data table as shown in the appendix Figure 2 and displayed together.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A PID performance evaluation method, characterized in that, Including: Step 1: Select PID chip samples and choose a carrier device; Step 2: Prepare detection tools; Step 3: Detect the material properties and usage effects of the chip; Step 4: Centralize the processing of the detection results in Step 3. Transmit the material, structure data, material data, and working data of the product to the computer respectively, and perform combined comparison and calculation on each data to obtain an evaluation result.

2. The PID performance evaluation method according to claim 1, characterized in that The detection tools include a chip operation system adjustment tool, a material strength detection tool, a material shape detection tool, a material insulation detection tool, an internal signal transmission detection tool, a chip operation status recording tool, a carrier operation status monitoring tool, and a compressive environment manufacturing chamber.

3. A PID performance evaluation method according to claim 1, characterized in that, In Step 1, when selecting the PID chip samples, according to the preset chip performance index range, select the chips that meet the requirements from multiple batches of chips as samples; The preset chip performance index range is obtained through statistical analysis of the performance parameters of mainstream PID chips on the market; In Step 1, when choosing the carrier device, select a suitable carrier device from existing multiple carrier devices according to the size specifications, interface types, and heat dissipation and electromagnetic compatibility requirements during operation of the PID chip samples.

4. A PID performance evaluation method according to claim 2, characterized in that In Step 2, the preparation of the detection tools includes: Use the material strength detection tool, the material shape detection tool, the material insulation detection tool, and the internal signal transmission detection tool to detect the strength, standardness of the shape, insulation effect, and signal transmission efficiency of the chip body respectively; Adjust the temperature system inside the controller where the PID chip is located to between 20% - 60%, the flow system to between 40% - 100%, the pressure system to between 30% - 70%, and the liquid level system to between 20% - 80% through the chip operation system adjustment tool; Install the product inside the carrier device, and install the chip operation status recording tool and the carrier operation status monitoring tool inside the carrier device. Connect the chip operation status recording tool and the product with each other by signals, and connect the carrier operation status monitoring tool and the carrier device with each other by signals; Place the product in a normal environment or adjust the operating environment corresponding to the product and the carrier device in the controlled compressive environment manufacturing chamber for detection to obtain different product operation data.

5. A PID performance evaluation method according to claim 4, characterized in that The step of placing the product in a normal environment or adjusting the operating environment corresponding to the product and the carrier device in the controlled compressive environment manufacturing chamber for detection to obtain different product operation data includes: Normal environment detection: Under common environmental conditions, adjust the specific working parameters of the power, working intensity, working time, function opening degree, temperature, flow, pressure, and liquid level received by the PID chip respectively for detection to obtain detailed detection data; Compressive environment detection: Adjust the temperature of the internal high and low temperature detection environment in the compressive environment manufacturing chamber to between -50 °C and 80 °C, adjust the internal magnetic field strength of the electromagnetic interference environment to between 0 - 500 NT, adjust the humidity environment to between 0 - 90% humidity, keep the internal voltage range of the loading device within different fluctuation amplitudes, and keep the dust content in the environment between 0 - 90%. Repeat the above conventional environment detection steps under different environments.

6. The PID performance evaluation method according to claim 5, wherein In step three, the steps of detecting the material properties of the chip include: Place the product separately in different environments for 1 - 1000 minutes, and conduct data detection on the strength, high-temperature resistance effect, insulation, anti-interference effect, and signal transmission efficiency of the product, and transmit, store, and analyze the detection data; Install the product inside the loading device, and adjust the operating power of the loading device to between 1% and 100%. Then place it in different environments for 1 - 1000 minutes. After that, take out the product, conduct data detection on the strength, high-temperature resistance effect, insulation, anti-interference effect, and signal transmission efficiency of the product, and transmit, store, and analyze the detection data; During the data detection process, the comprehensive index of material properties is calculated using the formula ​ Among them, is the comprehensive index of material properties; is the number of detection items; is the weight of each detection item, which is determined by the analytic hierarchy process according to the influence degree of each detection item on the chip performance; is the data processing function of the i-th detection item, which is constructed respectively according to the characteristics of the detection item.

7. A PID performance evaluation method according to claim 6, characterized in that, In step three, the steps of detecting the usage effect of the chip include: For the loading device equipped with a chip operation status recording tool and a carrier operation status monitoring tool, adjust the operating power of the loading device to between 1% and 100%, and place it in different environments to continuously operate for 1 - 1000 minutes; Detect and collect data on the working status of the PID chip through the chip operation status recording tool, detect and collect data on the operating status of the loading device through the carrier operation status monitoring tool, and transmit, store, and analyze various data of the loading device operation.

8. A PID performance evaluation method according to claim 1, characterized in that In step four, construct an evaluation model based on multi-dimensional space vector transformation and information entropy theory, and conduct combined comparison and calculation on each data according to the evaluation model; Using the formula to calculate the information entropy of the chip's working state ; Among them, is the number of characteristic dimensions of the chip working state, is the probability of the occurrence of the state of the j-th characteristic dimension, which is used to measure the stability of the chip working state.

9. A PID performance evaluation method according to claim 8, characterized in that In step four, the evaluation model is: Among them, is the final evaluation result; is the number of samples; is the weight of the k-th sample, which is preset according to the representativeness and reliability of the sample; is the comprehensive material performance index of the k-th sample; is the information entropy of the chip working state of the k-th sample.

10. A PID performance evaluation method according to claim 9, characterized in that, In step four, the evaluation result is obtained It further includes: Use a machine learning algorithm to optimize the evaluation results for optimization; the machine learning algorithm is based on a neural network model and is obtained by training with historical data for improving the accuracy of evaluation data; It also includes: For the evaluation results Visualize the evaluation results, and intuitively present the evaluation data in the form of charts and curves, so as to facilitate users to quickly understand the performance of the PID chip; the visualization display interface customizes the display parameters and display methods according to user needs.

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