Laboratory equipment interconnection and management method and system

Abstract

The invention discloses a laboratory equipment interconnection and management method and system. The laboratory equipment interconnection and management method adopted by the invention comprises the following steps: collecting original data of laboratory equipment, performing data segmentation and conversion by adopting a multi-layer recursive structure, introducing a recursive backflow mechanism of an interleaving network, and performing nonlinear mapping and multi-dimensional recursive processing on the data so as to extract effective information; and performing error calculation, adaptive adjustment and cross-level parameter optimization on the data subjected to nonlinear mapping and multi-dimensional recursion processing to obtain a multi-dimensional data stream, and performing parallel processing on the multi-dimensional data stream. According to the invention, accurate processing and intelligent optimization of complex data in interconnection and management of laboratory equipment can be realized, the problem of relevance among multi-dimensional data such as equipment operation states, experimental data and environmental conditions is solved, and the speed and accuracy of data processing are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent laboratory management, and in particular to a laboratory equipment interconnection and management method and system. Background Art

[0002] In modern laboratories, with the continuous advancement of science and technology and the diversification of experimental equipment, the number and complexity of equipment in the laboratory have increased significantly. Laboratory equipment not only covers traditional physical and chemical instruments, but also includes a large number of automated control equipment, sensor networks and intelligent detection devices. As the complexity and scale of experimental data continue to increase, how to effectively manage these devices, ensure the coordination of equipment, improve experimental efficiency, and ensure the accuracy and security of data has become an important issue in laboratory management.

[0003] The operation of modern laboratories involves various types of equipment, and the data generated by these equipment has multi-dimensional characteristics, such as time, space, environmental conditions, etc. These data affect each other during the experiment, and need to be comprehensively analyzed after the experiment in order to draw scientific conclusions. In addition, the communication and interaction between different equipment are becoming more and more frequent, which requires the laboratory management system to have a higher level of intelligence to achieve the interconnection and efficient collaboration of equipment. However, the diversity of data formats between laboratory equipment, the surge in data volume, and the complexity of the interdependence between equipment have brought huge challenges to laboratory management.

[0004] There are at least the following technical problems with the existing technology: the existing technology can often only extract the surface features of the data and ignores the potential relationships in the data, resulting in limited accuracy and depth of the analysis results. In the process of multi-device data processing, it lacks sufficient stability and flexibility, and is difficult to adapt to the diverse and dynamically changing data requirements in the laboratory environment. It is prone to error accumulation, resulting in instability in the processing results, and increasing the complexity and time cost of data processing. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the above-mentioned prior art and provide a method and system for the interconnection and management of laboratory equipment to accurately process and intelligently optimize the complex data in the interconnection and management of laboratory equipment, solve the correlation problem between multi-dimensional data such as equipment operating status, experimental data, environmental conditions, etc., and improve the speed and accuracy of data processing.

[0006] To this end, the present invention adopts the following technical solution.

[0007] In a first aspect, the present invention provides a laboratory equipment interconnection and management method, which comprises the steps of: S1. Collect the original data of laboratory equipment, use a multi-layer recursive structure to segment and transform the data, introduce the recursive reflux mechanism of the interwoven network, perform nonlinear mapping and multi-dimensional recursive processing on the data to extract effective information; S2. Perform error calculation, adaptive adjustment and cross-level parameter optimization on the data after nonlinear mapping and multidimensional recursive processing to obtain a multidimensional data stream, and perform parallel processing on the multidimensional data stream to achieve accurate processing and intelligent optimization of complex data in the interconnection and management of laboratory equipment, solve the correlation problem between multidimensional data such as equipment operating status, experimental data, environmental conditions, etc., and improve the speed and accuracy of data processing.

[0008] Preferably, the S1 specifically includes: A multi-layer recursive structure is adopted, and each layer of recursion interacts with other layers through an interwoven network to form a multi-dimensional recursive network; In the recursive process, the raw data collected from laboratory equipment are segmented and transformed, and different levels of distribution and processing are performed according to the importance and characteristics of the data; By taking the weighted sum of the output results of different recursive layers and processing the data backflow between different levels in an exponential decay manner, the interleaved network can be dynamically adjusted to ensure the stability of data processing and the balance of multi-dimensional recursive paths.

[0009] Preferably, in S1, the raw data collected from the laboratory equipment is segmented and converted, and different levels of distribution and processing are performed according to the importance and characteristics of the data, specifically including: The raw data of laboratory equipment is input into the first layer of recursion for preliminary processing, which is to divide the raw data into multiple subsets. Each subset is a set of data points containing similar features. After the segmentation is completed, the subset is directly regarded as the initial data block. As the input of the multi-layer recursive structure, the data block is processed in each layer of recursion and generates the input of the next layer; through the sine and cosine transform of the data, the nonlinear mapping and enhancement processing of the data are realized.

[0010] Preferably, in S1, the formula for recursive processing at each layer is as follows:

[0011] in, Indicates The recursive layer The data block is composed of The output of the recursive data block after processing; is a subset of the dataset, Indicates i data points, and Respectively k The mean and standard deviation of the subsets; It is The first layer of recursion j The data block is Output of layer recursion; is the angle adjustment coefficient of the recursive layer, which is used to adjust the data processing weight in the recursive process; For the Layer recursive interleaving adjustment amount.

[0012] Preferably, in said S1, when the interlaced network is dynamically adjusted, Layer recursive interleaving adjustment Calculated by the following formula:

[0013] in, Used for Feedback and correction of layer-by-layer recursive output; For the m The weight coefficient of each data block reflects the importance of different data blocks; is the interleaving strength coefficient of the recursive layer, which controls the degree of coupling between different levels; and is the number of hierarchical nodes in the recursive layer; and They are The first layer of recursion m data blocks and n data blocks, is a constant to prevent division by zero.

[0014] Preferably, in S2, in order to ensure the accuracy and stability of the final output, error calculation and adaptive adjustment are performed after each layer of recursion; When calculating the error, the deviation between the output of each layer of recursion and the expected result is calculated, and the deviation is quantified and regularized to ensure the convergence and robustness of data processing; Based on the error calculation, the internal parameters of each recursive layer are adaptively adjusted to optimize the data processing path and parameter configuration, wherein the internal parameters of each recursive layer include an angle adjustment coefficient and a reflow interleaving adjustment amount.

[0015] Preferably, in S2, after the internal parameters of each recursive layer are adaptively adjusted, the cross-layer parameters in the entire multi-layer recursive structure are optimized as a whole to ensure the best processing path, and the cross-layer parameters in the multi-layer recursive structure include the interleaving strength coefficient of the recursive layer and the weight coefficient of the data block.

[0016] Preferably, the overall optimization formula for the cross-level parameters of the multi-layer recursive structure is:

[0017] in, It is The layer recursion is in The parameter update value after iterations; is the weight matrix of the interleaved network, controlling the coupling strength between different recursive layers; It is The node number in the layer, used to identify the A block or subset of data in a layer; Is the same as the current recursive layer The node number in another layer that the layer interacts with; It is The number of nodes in the layer; Is the same as the current recursive layer The number of nodes in another layer that a layer interacts with; It is The layer recursion is in The parameter update value after iterations; is the standard deviation, indicating the p The degree of fluctuation of the layer recursion parameters; It is the strength control coefficient of the multi-layer recursive structure, controlling the data flow rate between different levels; is a constant to prevent division by zero.

[0018] Preferably, in S2, the specific implementation process of parallel processing is as follows:

[0019] in, For the The parallel processing results of layer recursion, and Respectively represent Different data dimensions in layer recursion, I and J are The number of data containments in the layer recursion; It is the angle adjustment parameter for parallel processing, which is used to control the interaction intensity of data streams of different dimensions; is a constant to prevent division by zero; Indicates The mean of the recursive data.

[0020] By quantifying the interactions between different data dimensions, data consistency and stability are ensured in multi-dimensional parallel processing.

[0021] In a second aspect, the present invention provides a laboratory equipment interconnection and management system, which includes: Data processing module: collects raw data from laboratory equipment, uses a multi-layer recursive structure to segment and convert data, introduces a recursive reflux mechanism of an interwoven network, and performs nonlinear mapping and multi-dimensional recursive processing on the data; Precision processing and intelligent optimization module: perform error calculation, adaptive adjustment and cross-level parameter optimization on the data after nonlinear mapping and multidimensional recursive processing to obtain multidimensional data streams, and perform parallel processing on the multidimensional data streams to achieve precise processing and intelligent optimization of complex data in the interconnection and management of laboratory equipment.

[0022] The present invention has the following beneficial effects: 1. The present invention repeatedly processes the collected raw data of laboratory equipment through a multi-layer recursive structure, gradually extracts effective information from the data, and enhances the depth and breadth of data analysis. Each layer of recursive processing can further mine hidden features in the data, overcome the limitations of traditional single-layer data processing, and can more accurately capture the complex relationship between data, thereby improving the intelligent level of laboratory equipment management; 2. By introducing the recursive reflux mechanism of the interwoven network, the processing results at different levels influence each other, forming a multi-dimensional data processing path, making the data processing process more coherent and comprehensive; the dynamic adjustment of the interwoven network ensures the stability of data processing, and optimizes the data processing path through the adaptive adjustment mechanism, which can flexibly respond to the diverse data in the laboratory environment and maintain efficient and accurate data processing capabilities in complex and changeable experimental environments; 3. The present invention gradually reduces the error in data processing and ensures the convergence of the system by performing error calculation and adaptive adjustment after each layer of recursion; the error calculation can not only quantify the deviation of data processing, but also control the complexity of the model through regularization to prevent the occurrence of overfitting; the adaptive adjustment mechanism further optimizes the recursive parameters, so that the system can converge quickly in multi-level recursion, improving the accuracy and efficiency of data processing; 4. The present invention implements a parallel processing mechanism for multi-dimensional data streams after recursive processing, which can process multiple data dimensions at the same time and realize interaction and coordination between data. This improvement greatly improves the processing speed while ensuring the comprehensiveness of data processing. It can make full use of the multi-dimensional information generated by laboratory equipment, thereby providing more accurate basic data for the intelligent scheduling and collaborative work of equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. The above and other purposes, features and advantages of the present invention will be more clear as shown in the drawings. The same reference numerals in all the drawings indicate the same parts, and the drawings are not deliberately scaled according to the actual size. The focus is on illustrating the main purpose of the present invention.

[0024] Figure 1 A flow chart of a laboratory equipment interconnection and management method of the present invention; Figure 2 This is a structural diagram of a laboratory equipment interconnection and management system of the present invention. DETAILED DESCRIPTION

[0025] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0027] The specific scheme of the laboratory equipment interconnection and management method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0028] See attached Figure 1 , which shows a flow chart of a laboratory equipment interconnection and management method provided by an embodiment of the present invention, the steps of the method are as follows: S1. Collect the original data of laboratory equipment, use a multi-layer recursive structure to segment and convert the data, introduce the recursive reflux mechanism of the interwoven network, perform nonlinear mapping and multi-dimensional recursive processing on the data to extract effective information.

[0029] Collecting raw data from laboratory equipment, specifically including: real-time collection of equipment operating status data through intelligent gateways, covering key parameters such as operating mode, fault status, operation time, temperature and pressure, and preliminary filtering; real-time collection of environmental conditions in the laboratory, such as temperature, humidity and air quality, through environmental sensors, and combining them with equipment operating status data for correlation analysis in subsequent processing; during the experiment, core experimental data generated by the equipment, such as chemical reaction rate, current and voltage, are obtained through sensors; in addition, in order to achieve collaborative work and interconnection of equipment, communication data between devices is collected, including command transmission and synchronization signals, etc., which are used to analyze the interaction mode between devices and optimize collaborative work efficiency.

[0030] In laboratory equipment management, the relationship between data is complex and changeable, and a single-layer processing method often cannot fully capture the deep relationship between data. Therefore, a multi-layer recursive structure is used to repeatedly process the data, gradually extract the effective information in the data, and enhance the depth and breadth of data analysis. With a multi-layer recursive structure, each layer of recursion interacts with other layers through an interwoven network to form a multi-dimensional recursive network.

[0031] In the recursive process, the raw data collected from the laboratory equipment is segmented and transformed, and different levels of distribution and processing are performed according to the importance and characteristics of the data. Specifically, the raw data of the laboratory equipment is input into the first-level recursive structure for preliminary processing. The first step is to segment the raw data into multiple subsets. , the subset is based on specific segmentation rules, such as data importance, timestamp, data type, etc. The segmentation formula is as follows:

[0032] in, represents the i-th data point; and are the mean and standard deviation of the kth subset respectively; is the threshold used to determine whether a data point belongs to the subset. It is set based on the distribution characteristics of the data through statistical analysis or empirical methods. The data set is classified according to the mean and standard deviation to ensure the consistency of the data characteristics of each subset.

[0033] Each subset is a set of data points with similar features. After segmentation, the subset The data block is directly regarded as the initial , as the input of the multi-layer recursive structure, that is ; Data block It will be processed in each layer of recursion and generate the input for the next layer. The processing formula for each layer of recursion is as follows:

[0034] in, Indicates Layer The data block is composed of The processed output of the layer’s data blocks; It is The jth data block in the layer recursive structure is The output of the layer; is the angle adjustment coefficient of the recursive layer, which is used to adjust the data processing weight in the recursive process; For the The recursive interleaving adjustment of the layers. Through the sine and cosine transformation of the data, the nonlinear mapping and enhanced processing effect of the data are realized.

[0035] In a multi-layer recursive structure, although simple layer-by-layer processing can extract high-order features of the data, the lack of direct connection between the recursive layers may lead to insufficient information flow, thus affecting the overall processing effect. To solve this problem, the recursive reflux mechanism of the interwoven network is introduced, aiming to enhance the coherence and comprehensiveness of data processing through information interweaving and feedback between layers.

[0036] During the processing, there is a complex interweaving network between the recursive layers. The interweaving network is not just a simple data transmission, but through the mechanism of reflux interweaving, the processing results of different levels affect each other, forming a multi-dimensional data processing path. Layer recursive interleaving adjustment Calculated by the following formula:

[0037] in, It is The recursive interleaving adjustment amount for the first Feedback and correction of layer-by-layer recursive output; is the weight coefficient of the mth data block, reflecting the importance of different data blocks; is the interleaving strength coefficient of the recursive layer, which controls the degree of coupling between different levels; and is the number of hierarchical nodes in the recursive layer; and They are The mth data block and the nth data block in the layer recursive structure; It is a constant to prevent division by zero. By weighted summing of the output results of different recursive layers and processing the data backflow between different layers in an exponential decay manner, the interleaved network can be dynamically adjusted to ensure the stability of data processing and the balance of multi-dimensional recursive paths.

[0038] S2. Perform error calculation, adaptive adjustment and cross-level parameter optimization on the data after nonlinear mapping and multidimensional recursive processing to obtain a multidimensional data stream, and perform parallel processing on the multidimensional data stream to achieve accurate processing and intelligent optimization of complex data in the interconnection and management of laboratory equipment, solve the correlation problem between multidimensional data such as equipment operating status, experimental data, environmental conditions, etc., and improve the speed and accuracy of data processing.

[0039] As the recursive layer goes deeper, the complexity of data processing gradually increases. In order to ensure the accuracy and stability of the final output, error calculation and adaptive adjustment will be performed after each recursive layer. The error calculation formula for each layer is:

[0040] in, For the The error function of the layer is used to measure the deviation between the output of the current layer and the target value; is the target value, the expected output data; is the actual value, i.e. Output result after layer recursion; It is in The number of matching pairs between target values ​​and actual values ​​in the layer recursive structure; is the regularization coefficient, which is used to control the complexity of the model and prevent overfitting; It is The number of data blocks in the layer recursive structure; Indicates The mean of the layer recursive data. The deviation between the output of each layer of recursion and the expected result is calculated, and the deviation is quantified and regularized to ensure the convergence and robustness of data processing.

[0041] Based on the error calculation, the internal parameters of each recursive layer are adaptively adjusted to optimize the data processing path and parameter configuration. The internal parameters of each recursive layer include the angle adjustment coefficient and reflux interleaving adjustment The specific adjustment formula is as follows:

[0042] in, is the adaptive adjustment parameter for the vth iteration, is the learning rate, which determines the step size of the adjustment. is the error function Parameters The recursive parameters are dynamically adjusted through the error gradient descent method to optimize the data processing path, so that the algorithm adopted by the present invention can converge quickly in a multi-layer recursive structure and improve the accuracy of data processing.

[0043] After the internal parameters of each recursive layer are adaptively adjusted, the cross-level parameters in the entire multi-layer recursive structure are optimized as a whole to ensure the best processing path. Not only can the data be accurately processed at the current level, but it can also dynamically adapt to different data and processing requirements. The cross-level parameters in the multi-layer recursive structure include the interleaving strength coefficient , weight coefficient The update formula for cross-level parameters of a multi-layer recursive structure is:

[0044] in, It is The layer recursion is in The parameter update value after iterations is is the weight matrix of the interleaved network, which controls the coupling strength between different recurrent layers; It is The node number in the layer, used to identify the A block or subset of data in a layer; Is the same as the current recursive layer The node number in another layer that the layer interacts with; It is The number of nodes in the layer; Is the same as the current recursive layer The number of nodes in another layer that a layer interacts with; It is The layer recursion is in The parameter update value after iterations; is the standard deviation, which indicates the degree of fluctuation of the recursive parameter of the pth layer; It is the strength control coefficient of the multi-layer recursive structure, and controls the data flow rate between different levels. By dynamically updating the parameters, it ensures that it can adapt to different data processing paths and requirements, and enhances the flexibility and adaptability of data processing.

[0045] In the present invention, the combination of multi-layer recursive structure and interleaved network realizes refined processing and intelligent optimization of data through adaptive adjustment and error correction. In addition, the present invention introduces a parallel processing mechanism of multi-dimensional data streams in the recursive process, which greatly improves the processing speed and accuracy by independently and interactively processing data streams of different dimensions. The specific implementation of the parallel processing mechanism is as follows:

[0046] in, For the The parallel processing results of the layers, and Respectively represent Different data dimensions in layer recursion, I and J are The number of data confinements in the layer recursive structure, It is a parameter for adjusting the angle of parallel processing, which is used to control the interaction intensity of data streams of different dimensions. By quantifying the interaction of different data dimensions, it ensures data consistency and stability in multi-dimensional parallel processing.

[0047] This embodiment also provides a structure diagram of a laboratory equipment interconnection and management system, such as Figure 2 As shown, it includes: Data processing module: collects raw data from laboratory equipment, uses a multi-layer recursive structure to segment and convert data, introduces a recursive reflux mechanism of an interwoven network, and performs nonlinear mapping and multi-dimensional recursive processing on the data; Precision processing and intelligent optimization module: perform error calculation, adaptive adjustment and cross-level parameter optimization on the data after nonlinear mapping and multidimensional recursive processing to obtain multidimensional data streams, and perform parallel processing on the multidimensional data streams to achieve precise processing and intelligent optimization of complex data in the interconnection and management of laboratory equipment.

[0048] It should be noted that each module in the above-mentioned laboratory equipment interconnection and management system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. For the specific definition of a laboratory equipment interconnection and management system, please refer to the definition of a laboratory equipment interconnection and management method mentioned above. The two have the same functions and effects, which will not be repeated here.

[0049] Example 1: Raw data collection and multi-layer recursive processing of laboratory equipment This experiment focuses on the real-time monitoring and optimization management of the core equipment of the substation - transformers, switchgear and protection equipment. The goal of the experiment is to conduct in-depth analysis of the raw data collected by the experimental equipment through a multi-layer recursive algorithm, so as to optimize the operating efficiency of the power grid equipment, warn of faults and improve the efficiency of equipment collaboration.

[0050] Specific experimental equipment includes: Transformer (T1): used for voltage conversion, key data include current, voltage, temperature and load.

[0051] Switching devices (S1, S2): used for current switching control. Key data include switching frequency, current, voltage, temperature, etc.

[0052] Protection equipment (P1): used for power grid protection, key data includes fault status, current, voltage, etc.

[0053] Environmental monitoring equipment: temperature and humidity sensors, air quality monitoring sensors, providing laboratory environmental data.

[0054] Device communication data: monitors the transmission of instructions and synchronization signals between devices.

[0055] 2. Data Collection and Preliminary Preprocessing 2.1 Collected raw data At the beginning of the experiment, data was collected in real time through the smart gateway and sensors of each device and stored in the data center. The following is the preliminary data collected: Transformer T1: Voltage: 240kV Current: 10A Temperature: 80℃ Load: 90% Switching device S1: Switching frequency: 5 times / hour Switch status: Normal (protection not triggered) Voltage: 238kV Current: 9.5A Temperature: 78℃ Protective device P1: Fault status: Not triggered Overload protection status: Normal Current: 8A Voltage: 230 kV Environmental monitoring equipment: Temperature: 22℃ Humidity: 45% Air quality (AQI): 60 (good) Communication data between devices: Command: Device T1 to S1, Command: Enable protection Synchronous signal: S1 and S2 synchronous switch signal, timestamp: 2024-12-24 10:30:00 2.2 Data Preprocessing First, all collected data needs to be standardized. Taking the temperature data of transformer T1 as an example, assuming that its mean is 75°C and the standard deviation is 5°C, then the standardized temperature data is .

[0056] Similarly, the voltage, current and temperature data of other devices are standardized.

[0057] Next, a denoising algorithm is used to filter out abnormal data caused by sensor errors or external interference. For example, in the current data of transformer T1, if a data point is greater than a certain threshold, it will be considered an abnormal value and corrected. Assuming that the current data collected at the 10th second is 12A, which exceeds the normal fluctuation range, it will be corrected to 10A using interpolation.

[0058] 3. Data segmentation and preliminary recursive processing After the data is normalized, it is divided into different time periods according to the timestamp, for example, by hour. Each time period corresponds to a subset of data, which is fed into the multi-layer recursive algorithm as input.

[0059] 3.1 Data Segmentation Assume that the temperature data of transformer T1 is segmented into 1 hour time periods, and the temperature data is as follows: 240kV: 75℃, 80℃, 79℃, 81℃, 80℃, 78℃, 76℃ First, calculate the mean of the data for this time period, which is 79°C and the standard deviation is 1.6°C.

[0060] According to the data segmentation formula:

[0061] Assume that the threshold is selected , then we calculate:

[0062]

[0063]

[0064]

[0065] Therefore, the data subset for this time period only contains data at 80°C, 79°C, and 81°C.

[0066] 3.2 First-level recursive processing For the split data subset , processed through the first level of recursion. The recursive formula is as follows:

[0067] Calculate the subset mean: 79.33 Calculated standard deviation: 1.0 According to the above recursive formula, calculation is performed:

[0068]

[0069]

[0070] Substituting these values ​​into the recursive formula, we obtain:

[0071] Assumptions and , and the reflux adjustment , substitute into the calculation and get:

[0072] Therefore, the output after the first layer of recursion is .

[0073] 4. Second level recursive processing The output of the first recursive layer As input, the second layer recursion is continued. Assume that in the second layer recursion, the temperature and current data are processed together, and the data weights of different layers are adjusted through the recursive formula.

[0074] After the second layer of recursive processing, the output recursive result is:

[0075] In the above experiment, through multi-layer recursive processing of experimental data, based on the different changes of the data before and after, several key conclusions were drawn, which are mainly reflected in the optimization of equipment operation status, the adjustment of load scheduling, and the improvement of collaborative work between equipment. The following is a specific conclusion analysis: 1. Correlation between transformer temperature and load In the experiment, the temperature data of transformer T1 went through a process from raw data collection to multi-layer recursive processing. In the initial data, the temperature of the transformer was 80°C, and as the load increased (90%), the temperature gradually increased. After standardization, denoising, and segmentation, the data entered the recursive processing layer.

[0076] At the first recursive level, after feature-based weighted averaging and nonlinear mapping, the normalized value of the transformer temperature becomes 0.92. Through correlation analysis with the load data of the equipment (such as current, power, etc.), it can be seen that there is a certain positive correlation between temperature and load. This enhances the sensitivity to temperature changes through the backflow interleaving mechanism.

[0077] Under high load, the temperature of transformer T1 has a clear trend of rising, but it still does not exceed the set safety threshold (such as 85°C). This conclusion shows that the temperature monitoring of the equipment is still within a reasonable range when running at high load, and can timely warn of potential risks through real-time data processing and recursive analysis.

[0078] 2. Load coordination and optimization between devices In the experiment, multiple devices are involved in the coordinated work, especially the current, temperature and switch status of transformer T1, switch devices S1 and S2. Experimental data show that the state changes between these devices are interrelated. For example, the load and temperature of the transformer will affect the frequent start and stop of the switch device, and the start and stop frequency of the switch device will affect the working load of the transformer.

[0079] Device collaborative optimization: In the second-level recursive processing, the interaction between devices is optimized through the recursive reflux mechanism of the interwoven network. The feedback from the system optimizes the scheduling strategy between devices. For example, when the frequent start and stop of the switch device S1 causes current fluctuations, the start and stop frequency of the switch device is optimized by adjusting the reflux mechanism, thereby reducing the impact of equipment load fluctuations on the transformer temperature.

[0080] Through recursive processing and backflow interleaving adjustment, the load and operating status of the equipment are effectively coordinated. The switching frequency of the switchgear is reduced, the load regulation of the transformer becomes smoother, and the risk of overload is reduced. This process helps to optimize the overall load distribution of the power grid and improve the operating efficiency and safety of the equipment.

[0081] 3. Verification of fault warning and protection mechanisms During the experiment, the status of the protection device P1 was monitored. The experimental data showed that when the equipment was operating normally, the current and voltage of the protection device remained within the safe range and no fault status was triggered. However, as the temperature increased and the load increased, the system's early warning ability for potential faults was verified.

[0082] Fault status monitoring: Through recursive algorithms, the real-time status of the equipment is analyzed, especially the current, voltage and other data of the protection equipment. It is found that even if the temperature and load increase, the system can detect potential equipment abnormalities in advance through sensor data and adjust the load distribution and operation strategy in time.

[0083] Through the recursive algorithm to process the protection device data, the experiment concluded that when faced with abnormal situations such as equipment overload and temperature rise, the system can provide timely warnings and make adjustments, reducing the risk of equipment damage and system failure.

[0084] 4. Impact of environmental data on equipment operating status The temperature, humidity and air quality data of the environmental monitoring equipment were also processed in the experiment. Through a multi-layer recursive algorithm, the potential impact of the environment on the performance of the equipment was analyzed by combining the environmental conditions (such as temperature and humidity) with the operating data of the equipment (such as current, voltage, temperature, etc.).

[0085] Relationship between environment and equipment: Experiments show that under high ambient temperature (22°C) and relative humidity (45%), the temperature of transformer T1 rises slightly, but remains within an acceptable range. The impact of environmental factors on equipment temperature is relatively small, but the indirect impact of environmental factors on grid load still needs to be considered during equipment scheduling.

[0086] Although environmental factors (such as temperature and humidity) have little impact on equipment, they still need to be considered in the equipment management system to improve the comprehensive assessment of equipment operating status. By combining environmental data, optimizing equipment load scheduling strategies and real-time adjustments, the reliability of power grid operation can be further improved.

[0087] 5. Improvement of data accuracy by multi-dimensional parallel processing In the experiment, a multi-dimensional parallel processing mechanism was used for multi-dimensional data flows between devices (such as voltage, current, temperature, etc.). In the multi-dimensional parallel processing process, the recursive algorithm processes the data flow of each dimension independently and interactively, which significantly improves the accuracy and speed of data processing.

[0088] Data consistency and stability: Experiments show that after parallel processing, the data flow of each dimension (the relationship between voltage and current) is more consistent and the system response speed is faster. For example, through the parallel calculation between current and temperature, the trend of load overload can be quickly identified and the load distribution plan can be adjusted in time.

[0089] Multi-dimensional parallel processing significantly improves the efficiency of data processing and can maintain high accuracy and stability in complex data streams. Especially in load scheduling and equipment protection, multi-dimensional data parallel processing helps to identify potential risks in real time and respond quickly, thereby improving the stability and security of the power grid.

[0090] Summary Through multi-layer recursive processing of experimental data, especially through in-depth analysis of device data, environmental data, and inter-device communication data, the following main conclusions were drawn: 1. Correlation between load and temperature: Under high load conditions, the temperature of the transformer tends to rise, but does not exceed the safety threshold. The system can monitor and adjust the load in real time through a recursive algorithm to avoid overheating of the equipment.

[0091] 2. Equipment collaborative optimization: The load scheduling between equipment is optimized through the reflux interweaving mechanism, the start and stop frequency of switching equipment is reduced, and the stability of transformer load is improved.

[0092] 3. Fault warning capability: The system can identify potential equipment failure risks in advance and optimize the response of loads and protection equipment through recursive algorithms.

[0093] 4. Impact of environmental factors: Environmental conditions have a certain impact on equipment operation, but by comprehensively considering environmental data, the system can optimize the operating status of the equipment and reduce the impact of environmental changes on the grid load.

[0094] 5. Parallel processing improves accuracy: Multi-dimensional parallel processing significantly improves the speed and accuracy of data processing, enabling the system to identify and respond to changes in complex power grid data in real time.

[0095] Overall, through the multi-layer recursive algorithm, the intelligent management of power grid equipment has been optimized, providing strong support for the efficient operation and intelligent monitoring of the power grid.

[0096] Embodiment 2: Error calculation and adaptive adjustment based on recursive processing results Based on the results of the first and second recursive processing completed in Example 1, further deepen the recursive level to perform error calculation and adaptive adjustment. By optimizing the parameters of each layer of recursion, the accuracy and stability of the final output are guaranteed. To this end, the performance of each layer of recursion is evaluated using an error function, and the parameters are optimized by the gradient descent method. Finally, the parameters of multi-layer interleaved recursion are optimized to ensure that the entire model converges in multi-layer recursion and adapts to complex data structures.

[0097] Equipment and Data 1. Transformer T1: current, voltage, temperature and load data. The collection period is 1 hour, and the data volume is approximately 24 sets of data in 24 hours.

[0098] 2. Switching devices S1 and S2: switching frequency, current, voltage, temperature and other data. Data is collected once an hour for each device.

[0099] 3. Protection device P1: fault status, current, voltage and other data. Real-time monitoring and data collection.

[0100] 4. Environmental monitoring equipment: temperature, humidity, air quality and other data. Data is collected every 15 minutes.

[0101] 2. Data preprocessing and pre-operation 2.1 Second-level recursive results In Example 1, the result of the second level of recursion is as follows: Temperature of transformer T1:

[0102] Current of switching device S1:

[0103] Temperature of environmental monitoring equipment:

[0104] Target value is the actual expected value of these data. Set: Target value of transformer T1 1.0 Target value of switching device S1 1.0 Target value for environmental monitoring equipment 1.0 3. Error calculation and adaptive adjustment The error calculation formula is as follows:

[0105] In the second level of recursion, the difference between the target value and the actual value is: For transformer T1:

[0106] Assuming the regularization coefficient ,but:

[0107] For switching device S1:

[0108] For environmental monitoring equipment:

[0109] 3.3 Total error calculation The total error for the second layer is calculated by adding up each error term :

[0110] 4. Adaptive adjustment and optimization 4.1 Internal parameter adjustment Based on the error calculation, adaptive adjustment is then performed. According to the formula of the gradient descent method, the internal parameters of each layer of recursion need to be updated. The update formula is as follows:

[0111] Setting the learning rate , and assuming that the initial parameter values ​​are:

[0112] Calculate the error function for The gradient is:

[0113] According to the gradient, update the angle adjustment coefficient:

[0114] 4.2 Cross-level parameter optimization The optimization of cross-level parameters is based on the interweaving of multiple levels in the recursive model. In order to ensure that the cross-level parameters can accurately convey information, the following cross-level parameter update formula is used:

[0115] Assume that the initial values ​​of the cross-level parameters are:

[0116] Calculate the cross-layer coupling weights of each layer parameter , and the intertwined relationships between each parameter. Assume and When the interleaving weight matrix Has the following form:

[0117] The interleaving coefficient and standard deviation are:

[0118] Calculated using the interleaved parameter update formula:

[0119]

[0120] In the above experiment, the output accuracy of the model was gradually improved by optimizing the error calculation, internal parameters and cross-level parameters of the multi-layer recursive structure. Based on the difference between the previous and next data, especially the error calculation and parameter optimization results after each layer of recursion, the following specific conclusions can be drawn: 1. Changes caused by error calculation and adjustment After data preprocessing and preliminary recursive processing, preliminary error values ​​were obtained. Taking transformer T1 as an example, the preliminary error is 0.0368, the error of switchgear S1 is 0.02075, and the error of environmental monitoring equipment is 0.005. The overall error is 0.06255, which shows that without further optimization and adjustment, there is a large deviation between the output of the system and the target value, especially in the processing results of the transformer and switchgear.

[0121] In the second recursive layer, the error between the output of each layer and the target value is calculated, and adaptive adjustment is made according to the error gradient. , and see the change in error: The error of transformer T1 is reduced from 0.0368 to 0.03 (an error reduction of about 18%).

[0122] The error of switching device S1 was reduced from 0.02075 to 0.015 (approximately 27% error reduction).

[0123] The error of environmental monitoring equipment dropped from 0.005 to 0.0045 (a reduction of approximately 10%).

[0124] This shows that the output accuracy of the model has been improved through error calculation and parameter adjustment, especially in the processing of transformers and switchgear, where the optimization effect is more obvious.

[0125] in conclusion In each recursive layer, through error calculation and adaptive parameter optimization, the system can gradually reduce the error with the target value, thereby improving the accuracy and stability of processing. This process shows that each recursive layer not only optimizes the output of the current layer, but also optimizes the global performance of the entire model by adjusting the parameters across layers.

[0126] 2. Impact of adaptive adjustment on parameters During the parameter adjustment process, the recursive internal parameters of each layer are adaptively optimized. By calculating the gradient and adjusting the parameters, the new adjustment values ​​are obtained: Angle adjustment factor Adjusted from the initial value of 0.5 to 0.4985 (a small change, indicating that this parameter has little effect on the output of the current layer).

[0127] 3. Global effects brought by cross-level parameter optimization The optimization of cross-level parameters is an important step to ensure the reasonable coupling relationship between layers in the entire recursive structure. In the experiment, the cross-level parameter update formula was used to optimize the interleaving strength coefficient and weight coefficient between layers, especially in the following aspects: Optimization of interleaving weights: In the initial experiments, the interleaving weights had a certain impact on the information transfer between different layers, but did not show a significant advantage. However, after cross-layer parameter optimization, the model was able to better handle the dependencies between different data, especially when processing the data of transformer T1 and switchgear S1, the output accuracy was further improved.

[0128] Improvement of cross-level information transmission: After optimizing cross-level parameters, the influence of each layer of the recursive model on other layers is more accurate, which enhances the global stability of the model. Especially in the intertwined relationship between the switch device S1 and the environmental monitoring device, cross-level optimization greatly reduces the unnecessary redundancy and conflict between the two, so that the overall error value is better controlled.

[0129] Cross-level parameter optimization significantly improves the global coordination and adaptability of the multi-layer recursive structure, enabling the model to effectively handle the dependencies between different data sources, thus better adapting to complex data processing tasks. This process shows that cross-level adjustments not only optimize the output of a single layer, but also improve the performance of the multi-layer interleaved recursive system under different conditions.

[0130] 4. Optimization effect of the overall model By optimizing the multi-layer recursive structure layer by layer, not only the error of each layer output is reduced, but also the robustness and accuracy of the whole system are improved. The experimental results show that after error calculation, internal parameter adjustment and cross-level parameter optimization, the deviation between the system output and the target value is greatly reduced, especially in the error reduction of transformer T1 and switch device S1.

[0131] Transformer T1: The initial error of 0.0368 was reduced to 0.03, and the error was reduced by about 18%.

[0132] Switching device S1: The initial error of 0.02075 was reduced to 0.015, and the error was reduced by about 27%.

[0133] Environmental monitoring equipment: The initial error of 0.005 was reduced to 0.0045, a reduction of about 10%.

[0134] These changes reflect that the system is gradually optimized at multiple levels through recursive error calculation and adaptive parameter adjustment, and finally converges to a more ideal output result. Most importantly, the reduction in error not only enhances the accuracy of a single layer, but also improves the robustness of the entire system, enabling it to better cope with complex environments and data changes.

[0135] Example 3: Application of multi-dimensional parallel processing and interwoven networks in power grid equipment data analysis After multiple layers of recursive processing, the parallel processing mechanism is used to independently process the multi-dimensional data of each layer, and then the data of different dimensions are interactively processed through the interleaving network, and finally the parallel processing results of each layer are obtained. The mechanism is introduced to improve processing efficiency, ensure data consistency, and optimize data flow.

[0136] The formula for parallel processing is as follows:

[0137] In the experiment, the temperature of the transformer and current , the state of the switching device S1 and the status of the protection device P1 Equal data dimensions for parallel processing.

[0138] Select the following dimensions for parallel processing: Transformer T1 temperature (1st dimension) Transformer T1 current (2nd dimension) Switching device S1 current (3rd dimension) Protection device P1 fault status (4th dimension) By collecting data from each dimension, weighted calculation is performed. , , and the values ​​of each dimension are as follows (assuming that these data are obtained from the recursive process): Transformer T1 temperature

[0139] Transformer T1 current

[0140] Switching device S1 current

[0141] Protection device P1 fault status

[0142] These data are processed interactively through parallel processing formulas.

[0143] For each pair and , and calculate the relationship between them. For example, the first dimension and the second dimension The relationship between:

[0144] Similarly, the same calculation is performed on other dimensions to finally obtain the entire parallel processing result. The calculation results are:

[0145] In the experiment of Example 3, the temperature and current data of the transformer T1 are processed in parallel, respectively, and an interleaved feedback mechanism between different devices is combined.

[0146] During the first two layers of recursive processing, the temperature data of transformer T1 is 0.85 and the current data is 0.95.

[0147] Preliminary analysis shows that when the transformer operates under high load, the temperature will increase slightly, but it does not exceed the safe range.

[0148] In the parallel processing process, by introducing parallel data stream interaction, it is found that the correlation between transformer temperature and current is further amplified. The final output obtained by parallel calculation is , and temperature and current Compared with the data from the previous study, a more accurate temperature increase trend was obtained.

[0149] There is a strong positive correlation between the temperature and current of transformer T1, that is, the greater the transformer load, the higher the temperature. By processing the results in parallel, the ability to capture and warn of this trend is optimized.

[0150] This shows that during peak load periods in the power grid, the temperature of the transformer may approach the upper limit of safe operation of the equipment. Although it did not exceed the safety threshold in the current experiment, this trend provides an important basis for load scheduling optimization and equipment early warning.

[0151] For switchgear S1 and S2, their possible impact on the power grid during frequent start-stop is further analyzed through the adjustment of parallel processing and interleaved feedback.

[0152] The state of the switch device S1 after the first level of recursive processing is 1.05, and the result of the second level of recursion is 0.98.

[0153] Preliminary analysis showed that the switchgear S1 and S2 had a high start and stop frequency, but did not show abnormal conditions.

[0154] With the support of parallel processing and interleaved feedback mechanism, the state changes of switch devices S1 and S2 are further refined. Through parallel processing, the final output result It shows that although the switchgear has a certain start-stop frequency under different load and environmental conditions, the limit state of the equipment is not reached.

[0155] The interwoven network mechanism further ensures the coordinated working status among various devices, especially at the start and stop moments of the switchgear, the coordinated working status among the devices remains stable.

[0156] in conclusion: The switchgear S1 and S2 operated stably under high load conditions. Although the start-stop frequency was high, it did not cause equipment overload or failure, indicating that the protection mechanism of the power grid system effectively ensured the safety of the equipment.

[0157] Through parallel processing, it is concluded that the collaborative working efficiency between power grid equipment is high, and the interconnection between equipment is crucial to optimizing the operating status of power grid equipment and improving the accuracy of load scheduling.

[0158] The protection device P1 is mainly used to ensure the safety of the power grid system. When the device fails, it will activate the protection mechanism. The data of the protection device P1 is processed and analyzed in parallel to evaluate its performance under the current load.

[0159] The output of the protection device P1 at the first recursive layer is 1.10, and the output of the second recursive layer is 1.03.

[0160] Preliminary analysis shows that during the experiment, the protection device did not trigger the protection mechanism and the safe operation status of the power grid was not threatened.

[0161] After parallel processing, the state of protection device P1 is further stabilized. The final output It shows that the fault status of the protection equipment remains within the safe range under the condition of grid load fluctuation, and the equipment has no fault or abnormal signal.

[0162] The introduction of interwoven networks further optimizes the feedback mechanism of protection devices in multi-dimensional data flow interaction, making the device's response more sensitive and stable.

[0163] in conclusion: The collaborative working efficiency of power grid equipment has been improved through the optimization of parallel processing and interwoven networks, indicating that the power grid system can achieve more accurate scheduling and coordinated operation between different devices, avoiding load overload and equipment failure.

[0164] This result provides more reliable data support for the intelligent dispatching and equipment maintenance of the power grid, especially in the case of large load fluctuations, ensuring that power grid equipment can work together efficiently and improve overall operating efficiency and stability.

[0165] Summarize Through the parallel processing mechanism and feedback adjustment of the interwoven network, the operating status and coordination efficiency of the power grid equipment have been significantly optimized. The specific conclusions are as follows: 1. There is a strong positive correlation between the load and temperature of transformer T1, and parallel processing optimizes the capture of this trend, providing a basis for load scheduling and equipment early warning.

[0166] 2. The operating status of switchgear S1 and S2 is stable under high load conditions and the equipment is not overloaded, indicating that the protection mechanism of the power grid equipment is effective.

[0167] 3. The status of protection device P1 is stable and the protection mechanism is not triggered, which further verifies the safety of the power grid.

[0168] 4. The collaborative working efficiency between devices has been improved. Through interwoven networks and parallel processing, the power grid can effectively avoid equipment failures and overloads under high load conditions, ensuring the stable operation of the power grid.

[0169] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A laboratory equipment interconnection and management method, characterized in that: Includes steps: S1. Collect the original data of laboratory equipment, use a multi-layer recursive structure to segment and transform the data, introduce the recursive reflux mechanism of the interwoven network, and perform nonlinear mapping and multi-dimensional recursive processing on the data; S2. Perform error calculation, adaptive adjustment and cross-level parameter optimization on the data after nonlinear mapping and multidimensional recursive processing to obtain a multidimensional data stream, and process the multidimensional data stream in parallel to achieve accurate processing and intelligent optimization of complex data in the interconnection and management of laboratory equipment.

2. The laboratory equipment interconnection and management method according to claim 1, characterized in that: The S1 specifically includes: A multi-layer recursive structure is adopted, and each layer of recursion interacts with other layers through an interwoven network to form a multi-dimensional recursive network; In the recursive process, the raw data collected from laboratory equipment are segmented and transformed, and different levels of distribution and processing are performed according to the importance and characteristics of the data; By taking the weighted sum of the output results of different recursive layers and processing the data backflow between different levels in an exponential decay manner, the interleaved network can be dynamically adjusted to ensure the stability of data processing and the balance of multi-dimensional recursive paths.

3. The laboratory equipment interconnection and management method according to claim 2, characterized in that: In S1, the raw data collected from the laboratory equipment is segmented and converted, and different levels of distribution and processing are performed according to the importance and characteristics of the data, specifically including: The raw data of laboratory equipment is input into the first layer of recursion for preliminary processing, which is to divide the raw data into multiple subsets. Each subset is a set of data points containing similar features. After the segmentation is completed, the subset is directly regarded as the initial data block. As the input of the multi-layer recursive structure, the data block is processed in each layer of recursion and generates the input of the next layer; through the sine and cosine transform of the data, the nonlinear mapping and enhancement processing of the data are realized.

4. The laboratory equipment interconnection and management method according to claim 3, characterized in that: In S1, the formula for recursive processing at each layer is as follows: ， in, Indicates The recursive layer The data block is composed of The output of the recursive data block after processing; is a subset of the dataset, Indicates i data points, and Respectively k The mean and standard deviation of the subsets; It is The first layer of recursion j The data block is Output of layer recursion; is the angle adjustment coefficient of the recursive layer, which is used to adjust the data processing weight in the recursive process; For the Layer recursive interleaving adjustment amount.

5. The laboratory equipment interconnection and management method according to claim 2, 3 or 4, characterized in that: In S1, when the interlaced network is dynamically adjusted, Layer recursive interleaving adjustment Calculated by the following formula: ， in, Used for Feedback and correction of layer-by-layer recursive output; For the m The weight coefficient of each data block reflects the importance of different data blocks; is the interleaving strength coefficient of the recursive layer, which controls the degree of coupling between different levels; and is the number of hierarchical nodes in the recursive layer; and They are The first layer of recursion m data blocks and n data blocks, is a constant to prevent division by zero.

6. The laboratory equipment interconnection and management method according to claim 1, 2, 3 or 4, characterized in that: In S2, in order to ensure the accuracy and stability of the final output, error calculation and adaptive adjustment are performed after each layer of recursion; When calculating the error, the deviation between the output of each layer of recursion and the expected result is calculated, and the deviation is quantified and regularized to ensure the convergence and robustness of data processing; Based on the error calculation, the internal parameters of each recursive layer are adaptively adjusted to optimize the data processing path and parameter configuration, wherein the internal parameters of each recursive layer include an angle adjustment coefficient and a reflow interleaving adjustment amount.

7. The laboratory equipment interconnection and management method according to claim 6, characterized in that: In S2, after the internal parameters of each recursive layer are adaptively adjusted, the cross-layer parameters in the entire multi-layer recursive structure are optimized as a whole to ensure the best processing path. The cross-layer parameters in the multi-layer recursive structure include the interleaving strength coefficient of the recursive layer and the weight coefficient of the data block.

8. The laboratory equipment interconnection and management method according to claim 7, characterized in that: The overall optimization formula for cross-level parameters of multi-layer recursive structure is: ， in, It is The layer recursion is in The parameter update value after iterations; is the weight matrix of the interleaved network, controlling the coupling strength between different recursive layers; It is The node number in the layer, used to identify the A block or subset of data in a layer; Is the same as the current recursive layer The node number in another layer that the layer interacts with; It is The number of nodes in the layer; Is the same as the current recursive layer The number of nodes in another layer that a layer interacts with; It is The layer recursion is in The parameter update value after iterations; is the standard deviation, indicating the p The degree of fluctuation of the layer recursion parameters; It is the strength control coefficient of the multi-layer recursive structure, controlling the data flow rate between different levels; is a constant to prevent division by zero.

9. The laboratory equipment interconnection and management method according to claim 1, 2, 3 or 4, characterized in that: In S2, the specific implementation process of parallel processing is as follows: ， in, For the The parallel processing results of layer recursion, and Respectively represent Different data dimensions in layer recursion, I and J are both The number of data dimensions in the layer recursion; It is the angle adjustment parameter for parallel processing, which is used to control the interaction intensity of data streams of different dimensions; is a constant to prevent division by zero; Indicates The mean of the layer recursive data; By quantifying the interactions between different data dimensions, data consistency and stability are ensured in multi-dimensional parallel processing.

10. A laboratory equipment interconnection and management system, characterized in that: include: Data processing module: collects raw data from laboratory equipment, uses a multi-layer recursive structure to segment and convert data, introduces a recursive reflux mechanism of an interwoven network, and performs nonlinear mapping and multi-dimensional recursive processing on the data; Precision processing and intelligent optimization module: perform error calculation, adaptive adjustment and cross-level parameter optimization on the data after nonlinear mapping and multidimensional recursive processing to obtain multidimensional data streams, and perform parallel processing on the multidimensional data streams to achieve precise processing and intelligent optimization of complex data in the interconnection and management of laboratory equipment.

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