Power management system and method for sharing experimental instruments based on two-dimensional code scanning

Abstract

The application discloses a two-dimensional code scanning-based experimental instrument shared power management system and method, relates to the technical field of data processing, and comprises the following steps: obtaining real-time experimental instrument state information sets, transmitting the real-time experimental instrument state information sets to a cloud platform for time sequence characteristic correction analysis, and obtaining corrected real-time experimental instrument state characteristic sets; disassembling user access information extracted from the cloud platform, combining the corrected real-time experimental instrument state characteristic sets to perform experimental instrument matching, obtaining user-experimental instrument matching results, and sending the user-experimental instrument matching results to an experimental instrument terminal set; a user scans a two-dimensional code; the experimental instrument terminal set verifies a two-dimensional code scanning result; if the verification is passed, a power management instruction is generated to perform power management on the experimental instrument. The application solves the technical problems that user-device matching is not accurate and state data has poor timeliness in the process of sharing and using experimental instruments in the prior art, and achieves the technical effects of improving user-experimental instrument matching accuracy and sharing and using process real-time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to an experimental instrument sharing power management system and method based on two-dimensional code scanning. BACKGROUND

[0002] Experimental instruments are widely used in scientific research institutions and testing agencies. In order to improve resource utilization efficiency, a sharing mechanism is gradually introduced. Users find available experimental equipment through a platform and make reservations. In order to determine whether the equipment is available, the platform relies on the state information uploaded by the equipment and allocates the equipment based on user demand. However, in actual application, due to the fact that the equipment state information is not updated in time, or the state characteristics are not fully analyzed and processed, the platform is difficult to accurately reflect the real availability of the equipment. At the same time, the user's access information such as use location and time demand is not fully included in the matching strategy, resulting in inaccurate allocation between users and equipment, affecting the sharing experience and resource allocation efficiency. SUMMARY

[0003] The present application provides an experimental instrument sharing power management system and method based on two-dimensional code scanning, which is used to solve the technical problems of inaccurate user and equipment matching and poor timeliness of state data in the process of experimental instrument sharing in the prior art.

[0004] In view of the above problems, the present application provides an experimental instrument sharing power management system and method based on two-dimensional code scanning.

[0005] In a first aspect of the present application, an experimental instrument sharing power management system based on two-dimensional code scanning is provided, which comprises:

[0006] An experimental instrument state acquisition module is used to acquire experimental instrument state information by using a set of experimental instrument terminals arranged on a set of experimental instruments, and a set of real-time experimental instrument state information is obtained; a time sequence characteristic correction analysis module is used to transmit the set of real-time experimental instrument state information to a cloud platform for time sequence characteristic correction analysis, and a set of corrected real-time experimental instrument state characteristics is obtained; an experimental instrument matching module is used to disassemble user access information extracted from the cloud platform, and combine the set of corrected real-time experimental instrument state characteristics to perform experimental instrument matching, and obtain a user-experimental instrument matching result; and a power management module is used to send the user-experimental instrument matching result to the set of experimental instrument terminals, and a user scans a two-dimensional code, and the set of experimental instrument terminals verifies a two-dimensional code scanning result, and if the verification is passed, a power management instruction is generated, and power management is performed on the experimental instrument.

[0007] In a second aspect of the present application, an experimental instrument sharing power management method based on two-dimensional code scanning is provided, which comprises:

[0008] The experimental instruments are monitored using a set of terminals deployed on a set of experimental instruments to obtain a set of real-time experimental instrument status information. This real-time experimental instrument status information is then transmitted to a cloud platform for time-series feature correction analysis to obtain a corrected set of real-time experimental instrument status features. User access information extracted from the cloud platform is analyzed and combined with the corrected set of real-time experimental instrument status features to perform experimental instrument matching, resulting in a user-experimental instrument matching result. This user-experimental instrument matching result is then sent to the set of experimental instrument terminals. The user scans a QR code, and the experimental instrument terminals verify the QR code scan result. If the verification is successful, a power management command is generated to manage the power of the experimental instrument.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] This application utilizes a set of experimental instrument terminals deployed on a set of experimental instruments to collect the status of the experimental instruments, obtaining a set of real-time experimental instrument status information. This real-time experimental instrument status information is then transmitted to a cloud platform for time-series feature correction analysis to obtain a corrected set of real-time experimental instrument status features. User access information extracted from the cloud platform is analyzed and combined with the corrected set of real-time experimental instrument status features to perform experimental instrument matching, obtaining a user-experimental instrument matching result. This user-experimental instrument matching result is sent to the set of experimental instrument terminals. The user scans a QR code, and the experimental instrument terminals verify the QR code scan result. If the verification is successful, a power management command is generated to manage the power of the experimental instrument. This invention solves the technical problems of inaccurate user-device matching and poor timeliness of status data in the prior art during the shared use of experimental instruments. By combining QR code scanning-based identity recognition with cloud platform time-series feature correction analysis, it achieves the technical effect of improving the accuracy of user-experimental instrument matching and the real-time performance of the shared use process. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A schematic diagram of the structure of a shared power management system for experimental instruments based on QR code scanning, provided in an embodiment of this application;

[0013] Figure 2 A schematic diagram of the shared power management method for experimental instruments based on QR code scanning, provided in an embodiment of this application.

[0014] Figure labeling: Experimental instrument status acquisition module 11, timing feature correction and analysis module 12, experimental instrument matching module 13, power management module 14. Detailed Implementation

[0015] This application provides a shared power management system and method for experimental instruments based on QR code scanning. It addresses the technical problems of inaccurate matching between users and equipment and poor timeliness of status data in the process of sharing experimental instruments in the prior art. By combining QR code scanning-based identity recognition with cloud platform time-series feature correction analysis, it achieves the technical effect of improving the accuracy of matching between users and experimental instruments and the real-time performance of the sharing process.

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0017] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0018] Example 1, as Figure 1 As shown in the embodiment of this application, a shared power management system for experimental instruments based on QR code scanning is provided. The system includes:

[0019] The experimental instrument status acquisition module 11 is used to acquire the status of experimental instruments by utilizing the set of experimental instrument terminals respectively deployed on the experimental instrument set, and to obtain a set of real-time experimental instrument status information.

[0020] In this embodiment, the experimental instrument status acquisition module 11 is used to collect the current usage status of the experimental instruments in real time through a set of experimental instrument terminals deployed on each experimental instrument. Each experimental instrument terminal includes a power module, a barcode scanning module, a control module, a sensor module, and a communication module. The power module, consisting of an insulating shell, an input interface, an output interface, a locator, and a circuit board, is used to supply power to the terminal. The sensor module is responsible for collecting usage status information of the experimental instruments, such as parameters like battery level, current, voltage, and operating time. The control module includes a microprocessor and a memory. The microprocessor performs preliminary processing on the data collected by the sensors, and the memory stores the current device status and operating data. The communication module sends the collected status information to the cloud platform in a standard data format using communication methods such as 4G or Wi-Fi. Through the collaborative work of these modules, the experimental instrument status acquisition module 11 obtains a real-time set of experimental instrument status information, including whether the device is powered on, whether it is being used, and whether the battery is sufficient.

[0021] The time-series feature correction and analysis module 12 is used to transmit the real-time experimental instrument status information set to the cloud platform for time-series feature correction and analysis, and obtain the corrected real-time experimental instrument status feature set.

[0022] In this embodiment, the time-series feature correction and analysis module 12 is used to transmit the real-time experimental instrument status information set to the cloud platform for time-series feature correction and analysis. In this process, firstly, the historical experimental instrument status information sequence set stored in the cloud platform is called, and the historical experimental instrument status trend feature set is extracted from it; then, the current real-time experimental instrument status information set is traversed, and the real-time experimental instrument status feature set is extracted; finally, the historical trend features and real-time features are compared and fused, and the time-series feature correction is completed using a feature deviation correction method, generating a stable, continuous, and matching-compatible corrected real-time experimental instrument status feature set.

[0023] Furthermore, in the system provided in the application embodiment, the timing feature correction and analysis module 12 further includes:

[0024] The trend analysis module is used to retrieve the historical experimental instrument status information sequence set stored on the cloud platform, perform historical experimental instrument status trend analysis on the historical experimental instrument status information sequence set, and determine the historical experimental instrument status trend feature set; the status feature analysis module is used to traverse the real-time experimental instrument status information set to perform status feature analysis and obtain the real-time experimental instrument status feature set; the feature correction module is used to use the historical experimental instrument status trend feature set to perform time-series feature correction on the real-time experimental instrument status feature set and determine the corrected real-time experimental instrument status feature set.

[0025] In this embodiment, the trend analysis module first calls a set of historical experimental instrument status information sequences stored in the cloud platform. This set of sequences contains data such as equipment on / off status, power changes, voltage values, and usage frequency over multiple time periods. Next, the trend analysis module extracts trends from the historical experimental instrument status information sequences using a pre-built experimental instrument status analysis channel. This analysis channel is constructed based on the mapping relationship between sample experimental instrument status information and corresponding status features, and is used to identify typical characteristic patterns under different operating conditions. Then, this channel is used to parse the historical experimental instrument status information sequence set one by one, extracting a set of historical experimental instrument status feature sequences. Finally, combining the temporal evolution relationship within the sequences, feature trend analysis is performed, outputting a set of historical experimental instrument status trend features reflecting the long-term operating patterns of the equipment.

[0026] Next, the state feature analysis module performs state feature analysis on the real-time experimental instrument state information set. During the analysis, based on the experimental instrument state analysis channel constructed above, a feature mapping operation is performed on each piece of real-time data to extract feature information corresponding to the current usage state, forming a structurally unified set of real-time experimental instrument state features. For example, the set of real-time experimental instrument state features is shown in Table 1.

[0027] Table 1: Data Table of Uniform State Characteristics of Experimental Instruments

[0028]

[0029] Finally, the feature correction module corrects the real-time experimental instrument state feature set using a sliding window differential analysis method. Specifically, the real-time experimental instrument state feature set is first divided into multiple fixed-length sliding windows in chronological order, for example, every 10 sampling points form a group. Within each window, the change in key characteristic indicators, such as power change value, voltage change rate, or number of state transitions, is calculated. Simultaneously, trend features at the corresponding time scale are extracted from the historical experimental instrument state trend feature set as a reference standard. Then, the real-time features of each window are compared one by one with their corresponding historical trend features. The degree of deviation is identified using absolute difference calculation. If the feature change within a window exceeds a set deviation threshold (e.g., power change deviation exceeds 5%, voltage rate difference exceeds 10mV / s), the window is determined to be an abnormal window. For windows marked as abnormal, linear interpolation or trend extension methods are used to correct and fill in the gaps based on the feature values ​​of two adjacent normal windows, thereby smoothing out non-trend jumps and sampling anomalies in the data. The final output is a corrected real-time experimental instrument state feature set with continuity, stability, and trend alignment.

[0030] Furthermore, in the system provided in the application embodiments, the trend analysis module further includes:

[0031] The analysis channel construction submodule is used to pre-build an experimental instrument status analysis channel, wherein the experimental instrument status trend analysis channel is generated by constructing a mapping relationship between sample experimental instrument status information and sample experimental instrument status features; the status analysis submodule is used to perform status analysis on the historical experimental instrument status information sequence set using the experimental instrument status analysis channel to obtain a historical experimental instrument status feature sequence set; the feature trend analysis submodule is used to perform intra-sequence feature trend analysis based on the historical experimental instrument status feature sequence set to determine the historical experimental instrument status trend feature set.

[0032] In this embodiment, the analysis channel construction submodule collects operational data of the sample experimental instruments under different usage states, and extracts the sample experimental instrument status information and corresponding sample experimental instrument status features required to construct the mapping relationship. The sample status information includes multiple dimensions, such as raw monitoring values ​​for electricity, voltage, current, power, and usage duration; the sample status features are extracted using statistical methods to obtain the mean, rate of change, and operating mode features for each time period. Subsequently, a function mapping relationship between the input status information and the output features is established using a method based on Support Vector Regression (SVR). SVR has good generalization ability and is suitable for constructing nonlinear mapping models in scenarios with a limited number of features and low data noise. The final generated mapping model is the experimental instrument status trend analysis channel.

[0033] Next, the state analysis submodule utilizes the previously constructed experimental instrument state analysis channel to process the historical experimental instrument state information sequence set one by one. Each historical data point serves as the input sample for the SVR channel. The channel infers and outputs the corresponding state feature value according to a preset mapping function, completing the transformation from raw signal to structured feature vector. This processing employs the state vector inference method, which outputs fixed-dimensional features based on the channel's regression model, such as "30-minute power change rate," "number of on / off cycles per unit time," and "voltage stability," converting each historical record into a feature vector in a unified format. All feature vectors are arranged in chronological order, forming a historical experimental instrument state feature sequence set.

[0034] Next, the feature trend analysis submodule performs intra-sequence feature trend analysis based on the historical experimental instrument state feature sequence set. In this process, a first historical experimental instrument state feature sequence is randomly selected from the feature sequence set, and its first and second feature values ​​are extracted. The feature difference coefficient between the two is calculated to measure the degree of change of the feature value over time. If the difference coefficient exceeds a preset feature difference threshold, time-series trend interaction analysis is triggered. Starting from the second feature, trend recursion is performed on the entire feature sequence to generate an interactive historical experimental instrument state trend feature set. This set is then averaged to obtain a preliminary trend representative value. Subsequently, all historical feature sequences are traversed, the above process is repeated, and intra-sequence feature trend identification is performed, ultimately forming a stable historical experimental instrument state trend feature set.

[0035] Furthermore, in the system provided in the application embodiments, the feature trend analysis submodule further includes:

[0036] An extraction unit is used to randomly extract a first historical experimental instrument state feature sequence from the set of historical experimental instrument state feature sequences, and extract the first historical experimental instrument state feature and the second historical experimental instrument state feature of the first historical experimental instrument state feature sequence; a difference coefficient calculation unit is used to calculate a first feature difference coefficient of the first historical experimental instrument state feature and the second historical experimental instrument state feature; a judgment unit is used to judge whether the first feature difference coefficient is greater than or equal to a preset feature difference coefficient threshold. If so, the first historical experimental instrument state feature and the second historical experimental instrument state feature are subjected to time-series trend interaction to obtain interactive historical experimental instrument state trend features, and the first historical experimental instrument state feature sequence is subjected to feature trend analysis sequentially with the second historical experimental instrument state feature as the starting point to obtain an interactive historical experimental instrument state trend feature set; a mean processing unit is used to perform mean processing on the interactive historical experimental instrument state trend feature set to obtain a first historical experimental instrument state trend feature; a trend recognition unit is used to traverse the set of historical experimental instrument state feature sequences to perform intra-sequence feature trend recognition to obtain the historical experimental instrument state trend feature set.

[0037] In this embodiment, the extraction unit first randomly extracts a first historical experimental instrument state feature sequence from the set of historical experimental instrument state feature sequences. This step uses a random walk sampling method to ensure that the extraction process is representative. After extraction, the first and second time-based feature vectors in the first historical experimental instrument state feature sequence are located and used as the first and second historical experimental instrument state features, respectively.

[0038] Next, the difference coefficient calculation unit calculates the first feature difference coefficient of the first historical experimental instrument state feature and the second historical experimental instrument state feature. Specifically, the cosine similarity calculation method is adopted. By calculating the cosine value of the angle between the two feature vectors, the similarity between the two is quantified, and the difference coefficient is obtained by calculating 1-similarity, thereby quantifying the state change amplitude and outputting the first feature difference coefficient for trend determination.

[0039] The judgment unit then compares the calculated first feature difference coefficient based on a threshold determination method. The first feature difference coefficient is compared with a preset feature difference coefficient threshold (e.g., 0.3). If the first feature difference coefficient is greater than or equal to the threshold, the current sequence is considered to contain an identifiable trend structure. At this point, a time-series trend interaction operation is performed. Starting with the second feature vector, the differences between each subsequent feature vector in the sequence and the feature vector at the previous time point are calculated sequentially. The Euclidean distance method is used to calculate the difference between each pair of adjacent feature vectors, and each difference vector is recorded in chronological order, ultimately forming an interactive historical experimental instrument state trend feature set.

[0040] Subsequently, the mean processing unit uses the dimension-by-dimensional arithmetic mean method to aggregate the difference vectors in the set of interactive historical experimental instrument state trend features. Specifically, it calculates the arithmetic mean of each dimension of the difference feature in the set of interactive historical experimental instrument state trend features, forming a new single vector that represents the average difference behavior in that trend change. The final output is the first historical experimental instrument state trend feature of the current analysis sequence.

[0041] Finally, the trend identification unit uses a sliding window traversal method to perform trend analysis on the entire historical experimental instrument state feature sequence set. A fixed-length sliding window (e.g., 10 feature vectors) is set, and the operations of extraction, difference calculation, trend judgment, and mean processing are repeatedly performed in the sequence set. The trend feature vectors obtained from each analysis are summarized, and finally a complete historical experimental instrument state trend feature set is constructed.

[0042] Furthermore, the system provided in the application embodiments also includes:

[0043] If the first feature difference coefficient is less than the preset feature difference coefficient threshold, then the first historical experimental instrument state feature is taken as the starting point for feature trend analysis, and the first historical experimental instrument state feature sequence is sequentially subjected to feature trend analysis to obtain the interactive historical experimental instrument state trend feature set.

[0044] In this embodiment, when the first feature difference coefficient is less than a preset feature difference coefficient threshold, it is determined that the initial stage of the current sequence has a small change amplitude and belongs to a stationary state sequence. At this time, a complete trend analysis mechanism is activated, taking the first historical experimental instrument state feature as the starting point of the feature trend analysis, and performing continuous trend calculation on the entire first historical experimental instrument state feature sequence. First, a sequential traversal method is used, starting from the first item of the sequence, to read each pair of adjacent historical feature vectors in turn. The difference calculation of each pair of vectors uses the Euclidean distance method, performing a dimension-by-dimensional difference between the feature vector at the current time point and the feature vector at the next time point, calculating the change value in each dimension and performing square root of the sum of squares to quantify the amplitude of the state change.

[0045] After the difference calculation is completed, each set of difference results is recorded in vector form and appended sequentially to the interactive historical experimental instrument state trend feature set in chronological order. Each item in this set corresponds to the state change of the equipment within a continuous time interval, which can completely describe the characteristic evolution trend of the equipment at each time period during operation.

[0046] The experimental instrument matching module 13 is used to decompose the user access information extracted from the cloud platform, combine it with the modified real-time experimental instrument status feature set to perform experimental instrument matching, and obtain the user-experimental instrument matching result.

[0047] In this embodiment, the experimental instrument matching module 13 is used to decompose the user access information extracted from the cloud platform, including key fields such as appointment time and user location, and match it with the modified real-time experimental instrument status feature set. The experimental instrument matching module 13 analyzes the appointment time and user location in the user access information, combines them with the modified real-time experimental instrument status feature set, and filters equipment resources from two dimensions: time satisfaction and location proximity, to determine a candidate set of experimental instruments. Then, based on the status features of each candidate instrument, a matching score is calculated, the device with the highest score is selected as the matching experimental instrument, and it is bound to the user access information to generate a user-experimental instrument matching result.

[0048] Furthermore, in the system provided in the application embodiment, the experimental instrument matching module 13 also includes:

[0049] The information acquisition module is used to acquire the experimental instrument reservation time and user location based on the user access information; the filtering module is used to filter the modified real-time experimental instrument status feature set according to the experimental instrument reservation time and user location from two dimensions: time satisfaction and location proximity, and obtain a candidate experimental instrument set based on the filtering results; the candidate scoring determination module is used to determine the candidate scores of the candidate experimental instrument set based on the modified real-time experimental instrument status feature set, and select the candidate experimental instrument corresponding to the maximum candidate score as the matching experimental instrument; the association module is used to associate the user access information with the matching experimental instrument to obtain the user-experimental instrument matching result.

[0050] In this embodiment, the information acquisition module first uses a field parsing method to perform structured processing on the user access information extracted from the cloud platform, extracting two key fields: experimental instrument reservation time and user location. The reservation time is parsed using a standard timestamp format to generate a specific start and end time period, which is used for comparison with the available time period of the equipment; the user location is extracted by extracting its latitude and longitude coordinates and converting them into a standard two-dimensional spatial vector.

[0051] Next, the filtering module filters the set of real-time experimental instrument status features from two dimensions: time availability and location proximity. In the time dimension, a time interval overlap judgment method is used to determine whether there is an overlap between the user's scheduled time and the current idle time period of each experimental instrument; overlap is considered as time availability. In the spatial dimension, the Euclidean distance calculation method is used to calculate the straight-line distance between the user's location and the current location of the experimental instrument, and a fixed threshold (e.g., 5 kilometers) is set; if the distance is less than this threshold, it is considered as location proximity. Devices that simultaneously meet the conditions of time overlap and location proximity constitute the candidate experimental instrument set.

[0052] The candidate scoring module then uses feature counting to score each device in the candidate experimental instrument set. In this process, it first determines whether the device's current voltage change is stable. It retrieves the device's recent voltage measurement sequence, such as sampling data every 30 seconds for the last 5 minutes. The standard deviation is used to statistically process this voltage sequence. If the standard deviation is less than a preset threshold (e.g., 0.05V), the voltage fluctuation is considered small and stable, earning 1 point. If the standard deviation is greater than or equal to the threshold, the voltage fluctuation is considered large and the operating state is unstable, earning no points. Next, it assesses whether the device's available time is sufficient to meet the user's reservation requirements. It reads the current idle time period recorded in the device's corrected real-time status feature set, extracts its end time, and uses a time difference calculation method to subtract the end time from the current time to obtain the remaining idle time. This is then compared to the user's reservation time requirement (i.e., the difference between the start and end time periods of the user's reservation). If the device's remaining idle time is greater than or equal to the user's requirement, 1 point is awarded; if it is less than the required time, the time is insufficient, and no points are awarded. Finally, it verifies whether the device type meets the user's functional requirements. The system extracts type fields (such as "optical analyzer" or "temperature chamber") from the device metadata and compares them with the user-submitted required device type using a one-to-one string matching method. If the types match perfectly, the device is considered to meet the functional requirements and receives 1 point; if the types do not match or the type field is missing, no points are awarded. Finally, the scores from the three dimensions are summed to generate a candidate score for the device, with a maximum score of 3 points. The device with the highest score among all candidate devices is selected as the matching experimental instrument; if multiple devices have the same score, the one with the longest remaining idle time is selected first.

[0053] Finally, the association module uses an identifier binding method to bind the matched experimental instrument with the user access information, generating a structured user-experimental instrument matching result. This result includes the user ID, device ID, matching time period, matching location, and the current status of the device.

[0054] The power management module 14 is used to send the user-experimental instrument matching result to the experimental instrument terminal set. The user scans the QR code, and the experimental instrument terminal set verifies the QR code scanning result. If the verification is successful, a power management command is generated to manage the power of the experimental instrument.

[0055] In this embodiment, after receiving the user-experimental instrument matching result, the power management module 14 first transmits the result to the corresponding experimental instrument terminal and stores it in the memory of the control module. After the user arrives at the site, he scans the device QR code through the scanning module. The control module reads the scanning result and verifies it with the matching result in the memory. After the verification is successful, the microprocessor generates and processes the power management instruction and finally sends a control signal to the power module to realize the power-on and power-off management of the experimental instrument.

[0056] Furthermore, in the system provided in the application embodiment, the power management module 14 further includes:

[0057] The transmission and storage module is used to transmit the user-experimental instrument matching results to the corresponding experimental instrument terminal and store them in the memory of the control module. The verification control module is used by the user to scan the QR code using the scanning module, and to verify the QR code scanning results by combining them with the user-experimental instrument matching results stored in the memory of the control module. If the verification is successful, a power management command is generated, and the corresponding power management command is processed by the microprocessor of the control module and a control signal is sent to the power module for power management.

[0058] In this embodiment, the transmission and storage module first transmits the user-experimental instrument matching result generated by the cloud platform to the corresponding experimental instrument terminal through a data communication channel. The matching result includes key fields such as user identifier, experimental instrument identifier, and authorized usage time period. After receiving the result, the control module in the terminal calls its local memory to store it completely as local reference data for subsequent QR code verification.

[0059] Upon arrival at the equipment site, the user scans the QR code bound to the equipment using the scanning module on their terminal. This QR code contains the equipment identifier and user identification information. After scanning, the process enters the verification control module. This module first retrieves the user-experimental instrument matching result stored in the control module's memory and compares it with the QR code scan result from the scanning module. The comparison process is executed by the microprocessor within the control module, using a field comparison method to verify whether the user ID, equipment ID, and authorization time are consistent. If all three pieces of information match, the verification control module generates a power management command.

[0060] The microprocessor in the control module further processes the power management command, including logic parsing, signal encoding, and control format encapsulation. After processing, the control signal is sent to the power module inside the experimental instrument, which controls the power on / off operation via its output interface, thus controlling the power supply to the experimental instrument. If a mismatch is found, the power management command is rejected, and a verification failure message is output to the terminal interface.

[0061] Furthermore, the system provided in the application embodiments also includes:

[0062] The power module has a rectangular or cylindrical casing made of insulating material, an input interface that is a plug-in interface, and an output interface that is a DC or AC output interface.

[0063] In this embodiment, the power module's casing is a rectangular or cylindrical shell made of insulating material, preferably ABS engineering plastic or polycarbonate (PC), to effectively isolate the internal live components and improve electrical safety. This structural design gives the power module excellent protection against electric shock and thermal stability, adapting to the embedded installation requirements of various experimental instrument terminals. The power module is equipped with an input interface using a plug-in interface structure for modular and quick connection with the control module, simplifying wiring and improving maintenance convenience. The power module also has an output interface, which can be configured as a DC output interface or an AC output interface depending on the power supply type of the connected experimental instrument, to support the power needs of experimental equipment with different power characteristics, thereby ensuring stable output, safe connection, and strong compatibility.

[0064] Furthermore, the system provided in the application embodiments also includes:

[0065] The sensor modules of the experimental instrument terminal set are used to continuously monitor the power of the experimental instrument set. When the monitoring results do not meet the preset power threshold, a low power alarm is sent to the cloud platform.

[0066] In this embodiment, when continuously monitoring the battery level of the experimental instrument set using the sensor modules of the experimental instrument terminal set, the built-in power acquisition sensor periodically reads the current voltage value of the experimental instrument and calculates the remaining battery percentage by proportionally converting this voltage value to the device's calibrated full-charge voltage. The sensor module processes each monitoring result through the microprocessor in the control module and compares it with a preset battery threshold, for example, 30%. If the current battery percentage is lower than this threshold, it is determined to be in a low-battery state. At this time, the control module generates a low-battery alarm, including the device number, battery level, and alarm time information, and sends the alarm data to the cloud platform through the terminal's built-in communication module. Through this process, real-time battery monitoring of the experimental instrument set is achieved, and timely reporting of insufficient battery power enables remote early warning and management functions.

[0067] In summary, the embodiments of this application have at least the following technical effects:

[0068] This application utilizes a set of experimental instrument terminals deployed on a set of experimental instruments to collect the status of the experimental instruments, obtaining a set of real-time experimental instrument status information. This real-time experimental instrument status information is then transmitted to a cloud platform for time-series feature correction analysis to obtain a corrected set of real-time experimental instrument status features. User access information extracted from the cloud platform is analyzed and combined with the corrected set of real-time experimental instrument status features to perform experimental instrument matching, obtaining a user-experimental instrument matching result. This user-experimental instrument matching result is sent to the set of experimental instrument terminals. The user scans a QR code, and the experimental instrument terminals verify the QR code scan result. If the verification is successful, a power management command is generated to manage the power of the experimental instrument. This invention solves the technical problems of inaccurate user-device matching and poor timeliness of status data in the prior art during the shared use of experimental instruments. By combining QR code scanning-based identity recognition with cloud platform time-series feature correction analysis, it achieves the technical effect of improving the accuracy of user-experimental instrument matching and the real-time performance of the shared use process.

[0069] Example 2 is based on the same inventive concept as the QR code scanning-based shared power management system for experimental instruments in the previous examples, such as... Figure 2 As shown in the embodiment of this application, a method for managing shared power supplies for experimental instruments based on QR code scanning is provided. The method includes:

[0070] The experimental instruments are monitored using a set of terminals deployed on a set of experimental instruments to obtain a set of real-time experimental instrument status information. This real-time experimental instrument status information is then transmitted to a cloud platform for time-series feature correction analysis to obtain a corrected set of real-time experimental instrument status features. User access information extracted from the cloud platform is analyzed and combined with the corrected set of real-time experimental instrument status features to perform experimental instrument matching, resulting in a user-experimental instrument matching result. This user-experimental instrument matching result is then sent to the set of experimental instrument terminals. The user scans a QR code, and the experimental instrument terminals verify the QR code scan result. If the verification is successful, a power management command is generated to manage the power of the experimental instrument.

[0071] Furthermore, the method also includes:

[0072] The system retrieves a set of historical experimental instrument status information sequences stored on the cloud platform, performs historical experimental instrument status trend analysis on the set of historical experimental instrument status information sequences, and determines a set of historical experimental instrument status trend features; it then traverses the set of real-time experimental instrument status information and performs status feature analysis to obtain a set of real-time experimental instrument status features; finally, it uses the set of historical experimental instrument status trend features to perform time-series feature correction on the set of real-time experimental instrument status features, and determines a corrected set of real-time experimental instrument status features.

[0073] Furthermore, the method also includes:

[0074] A pre-constructed experimental instrument status analysis channel is used, wherein the experimental instrument status trend analysis channel is generated by constructing a mapping relationship between sample experimental instrument status information and sample experimental instrument status features; the experimental instrument status analysis channel is used to perform status analysis on the historical experimental instrument status information sequence set to obtain a historical experimental instrument status feature sequence set; based on the historical experimental instrument status feature sequence set, intra-sequence feature trend analysis is performed to determine the historical experimental instrument status trend feature set.

[0075] Furthermore, the method also includes:

[0076] A first historical experimental instrument state feature sequence is randomly extracted from the set of historical experimental instrument state feature sequences. The first and second historical experimental instrument state features of the first historical experimental instrument state feature sequence are then extracted. A first feature difference coefficient is calculated between the first and second historical experimental instrument state features. It is determined whether the first feature difference coefficient is greater than or equal to a preset feature difference coefficient threshold. If so, a time-series trend interaction is performed on the first and second historical experimental instrument state features to obtain interactive historical experimental instrument state trend features. Using the second historical experimental instrument state feature as the starting point for feature trend analysis, feature trend analysis is sequentially performed on the first historical experimental instrument state feature sequence to obtain a set of interactive historical experimental instrument state trend features. The set of interactive historical experimental instrument state trend features is then averaged to obtain a first historical experimental instrument state trend feature. Finally, the set of historical experimental instrument state feature sequences is traversed to identify intra-sequence feature trends, thereby obtaining the set of historical experimental instrument state trend features.

[0077] Furthermore, the method also includes:

[0078] If the first feature difference coefficient is less than the preset feature difference coefficient threshold, then the first historical experimental instrument state feature is taken as the starting point for feature trend analysis, and the first historical experimental instrument state feature sequence is sequentially subjected to feature trend analysis to obtain the interactive historical experimental instrument state trend feature set.

[0079] Furthermore, the method also includes:

[0080] Based on the user access information, the reservation time and user location of the experimental instrument are obtained; the modified real-time experimental instrument status feature set is filtered according to the reservation time and user location from two dimensions: time satisfaction and location proximity, and a candidate experimental instrument set is obtained based on the filtering results; the candidate scores of the candidate experimental instrument set are determined based on the modified real-time experimental instrument status feature set, and the candidate experimental instrument corresponding to the maximum candidate score is selected as the matching experimental instrument; the user access information is associated with the matching experimental instrument to obtain the user-experimental instrument matching result.

[0081] Furthermore, the method also includes:

[0082] The sensor modules of the experimental instrument terminal set are used to continuously monitor the power of the experimental instrument set. When the monitoring results do not meet the preset power threshold, a low power alarm is sent to the cloud platform.

[0083] Furthermore, the method also includes:

[0084] The user-experimental instrument matching result is transmitted to the corresponding experimental instrument terminal and stored in the memory of the control module. The user scans the QR code using the scanning module and verifies the QR code scanning result by combining it with the user-experimental instrument matching result stored in the memory of the control module. If the verification is successful, a power management command is generated, and the corresponding power management command is processed by the microprocessor of the control module and a control signal is sent to the power module for power management.

[0085] Furthermore, the method also includes:

[0086] The power module has a rectangular or cylindrical casing made of insulating material, an input interface that is a plug-in interface, and an output interface that is a DC or AC output interface.

[0087] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0088] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0089] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A shared power management system for experimental instruments based on QR code scanning, characterized in that, The system includes: The experimental instrument status acquisition module is used to acquire the status of experimental instruments by utilizing the set of experimental instrument terminals deployed on the experimental instrument set, and to obtain a set of real-time experimental instrument status information. The time-series feature correction and analysis module is used to transmit the set of real-time experimental instrument status information to the cloud platform for time-series feature correction and analysis, and to obtain the corrected set of real-time experimental instrument status features. The experimental instrument matching module is used to decompose the user access information extracted from the cloud platform, combine it with the modified real-time experimental instrument status feature set to perform experimental instrument matching, and obtain the user-experimental instrument matching result. The power management module is used to send the user-experimental instrument matching result to the experimental instrument terminal set. The user scans the QR code, and the experimental instrument terminal set verifies the QR code scanning result. If the verification is successful, a power management command is generated to manage the power of the experimental instrument. The time series feature correction analysis module includes: The trend analysis module is used to retrieve the set of historical experimental instrument status information sequences stored on the cloud platform, perform historical experimental instrument status trend analysis on the set of historical experimental instrument status information sequences, and determine the set of historical experimental instrument status trend features. The state feature analysis module is used to traverse the set of state information of the real-time experimental instrument to perform state feature analysis and obtain the set of state features of the real-time experimental instrument. The feature correction module is used to perform time-series feature correction on the real-time experimental instrument state feature set using the historical experimental instrument state trend feature set, and to determine the corrected real-time experimental instrument state feature set. The trend analysis module includes: The analysis channel construction submodule is used to pre-build the experimental instrument status analysis channel, wherein the experimental instrument status trend analysis channel is generated by constructing a mapping relationship between the sample experimental instrument status information and the sample experimental instrument status characteristics; The state analysis submodule is used to perform state analysis on the set of historical experimental instrument state information sequences using the experimental instrument state analysis channel to obtain a set of historical experimental instrument state feature sequences. The feature trend analysis submodule is used to perform intra-sequence feature trend analysis based on the set of historical experimental instrument state feature sequences to determine the set of historical experimental instrument state trend features. The feature trend analysis submodule includes: The extraction unit is used to randomly extract a first historical experimental instrument state feature sequence from the set of historical experimental instrument state feature sequences, and to extract the first historical experimental instrument state feature and the second historical experimental instrument state feature of the first historical experimental instrument state feature sequence. The difference coefficient calculation unit is used to calculate the first feature difference coefficient of the first historical experimental instrument state feature and the second historical experimental instrument state feature. The judgment unit is used to determine whether the first feature difference coefficient is greater than or equal to the preset feature difference coefficient threshold. If so, the first historical experimental instrument state feature and the second historical experimental instrument state feature are subjected to time-series trend interaction to obtain interactive historical experimental instrument state trend features. The second historical experimental instrument state feature is used as the starting point for feature trend analysis. The first historical experimental instrument state feature sequence is subjected to feature trend analysis in sequence to obtain an interactive historical experimental instrument state trend feature set. The time-series trend interaction operation is performed. Starting from the second feature vector, the difference between each subsequent feature vector in the sequence and the feature vector at the previous time point is calculated. The Euclidean distance method is used to calculate the difference between each pair of adjacent feature vectors, and each difference vector is recorded in chronological order. Finally, the interactive historical experimental instrument status trend feature set is formed. The mean processing unit is used to perform mean processing on the set of interactive historical experimental instrument state trend features to obtain the first historical experimental instrument state trend features. The arithmetic mean of the difference feature in each dimension of the interactive historical experimental instrument state trend feature set is calculated to form a new single vector, representing the average difference behavior in the trend change; the final output result is the first historical experimental instrument state trend feature of the current analysis sequence. The trend recognition unit is used to traverse the set of historical experimental instrument state feature sequences to perform intra-sequence feature trend recognition and obtain the set of historical experimental instrument state trend features. The trend identification unit uses a sliding window traversal method to perform trend analysis on the entire set of historical experimental instrument state feature sequences. A fixed-length sliding window is set up, and the operations of extraction, difference calculation, trend judgment and mean processing are repeatedly performed in the sequence set. The trend feature vectors obtained from each analysis are summarized, and finally a complete set of historical experimental instrument state trend features is constructed.

2. The experimental instrument shared power management system based on QR code scanning as described in claim 1, characterized in that, If the first feature difference coefficient is less than the preset feature difference coefficient threshold, then the first historical experimental instrument state feature is taken as the starting point for feature trend analysis, and the first historical experimental instrument state feature sequence is sequentially subjected to feature trend analysis to obtain the interactive historical experimental instrument state trend feature set.

3. The experimental instrument shared power management system based on QR code scanning as described in claim 1, characterized in that, The experimental instrument matching module includes: The information acquisition module is used to acquire the experimental instrument reservation time and user location based on the user access information. The filtering module is used to filter the set of corrected real-time experimental instrument status features based on the reservation time of the experimental instrument and the user's location from two dimensions: time satisfaction and location proximity, and to obtain a set of candidate experimental instruments based on the filtering results. The candidate score determination module is used to determine the candidate scores of the candidate experimental instrument set based on the modified real-time experimental instrument state feature set, and select the candidate experimental instrument corresponding to the maximum candidate score as the matching experimental instrument. The association module is used to associate user access information with matching experimental instruments to obtain the user-experimental instrument matching results.

4. The experimental instrument shared power management system based on QR code scanning as described in claim 1, characterized in that, The sensor modules of the experimental instrument terminal set are used to continuously monitor the power of the experimental instrument set. When the monitoring results do not meet the preset power threshold, a low power alarm is sent to the cloud platform.

5. The experimental instrument shared power management system based on QR code scanning as described in claim 1, characterized in that, The power management module includes: The transmission and storage module is used to transmit the user-experimental instrument matching results to the corresponding experimental instrument terminal and store them in the memory of the control module. The verification control module is used by the user to scan the QR code using the scanning module. The QR code scanning result is verified by combining the user-experimental instrument matching result stored in the memory of the control module. If the verification is successful, a power management command is generated, and the corresponding power management command is processed by the microprocessor of the control module and a control signal is sent to the power module for power management.

6. The experimental instrument shared power management system based on QR code scanning as described in claim 5, characterized in that, The power module has a rectangular or cylindrical casing made of insulating material, an input interface that is a plug-in interface, and an output interface that is a DC or AC output interface.

7. A method for managing shared power supplies for experimental instruments based on QR code scanning, characterized in that, The method is executed by the experimental instrument shared power management system based on QR code scanning as described in any one of claims 1 to 6, including: The experimental instrument status is collected by using a set of experimental instrument terminals deployed on the experimental instrument set to obtain a set of real-time experimental instrument status information. The real-time experimental instrument status information set is transmitted to the cloud platform for time-series feature correction analysis to obtain the corrected real-time experimental instrument status feature set. The user access information extracted from the cloud platform is disassembled and combined with the modified real-time experimental instrument status feature set to perform experimental instrument matching, thereby obtaining the user-experimental instrument matching result. The user-experimental instrument matching result is sent to the experimental instrument terminal set. The user scans the QR code, and the experimental instrument terminal set verifies the QR code scanning result. If the verification is successful, a power management command is generated to perform power management on the experimental instrument.

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