A method and system for the interconnection and management of laboratory equipment

Abstract

The present invention discloses a method and system for the interconnection, interoperability and management of laboratory equipment. The method for the interconnection, interoperability and management of laboratory equipment adopted by the present invention includes: collecting the original data of laboratory equipment, performing data segmentation and conversion using a multi-layer recursive structure, introducing the recursive feedback mechanism of an interleaved network, performing non-linear mapping and multi-dimensional recursive processing on the data to extract effective information; calculating errors, performing adaptive adjustment and cross-level parameter optimization on the data after non-linear mapping and multi-dimensional recursive processing to obtain a multi-dimensional data stream, and performing parallel processing on the multi-dimensional data stream. The present invention can achieve precise processing and intelligent optimization of complex data in the interconnection, interoperability and management of laboratory equipment, solve the correlation problem between multi-dimensional data such as equipment operating status, experimental data, and environmental conditions, and improve the speed and accuracy of data processing.

Description

Technical Field

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

[0002] In modern laboratories, with the continuous progress of technology and the diversification of experimental equipment, the number and complexity of equipment in the laboratory have increased significantly. Laboratory equipment not only includes traditional physical and chemical instruments, but also a large number of automated control devices, 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 collaborative work between devices, 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 devices 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 to draw scientific conclusions. In addition, the communication and interaction between different devices are becoming increasingly frequent, which requires the laboratory management system to have a higher level of intelligence to achieve the interconnection and efficient collaborative work of devices. However, the diversity of data formats between laboratory devices, the explosion of data volume, and the complexity of the interdependent relationships between devices have brought huge challenges to laboratory management.

[0004] The prior art has at least the following technical problems: The prior art often can only extract the surface features of data, ignoring the potential relationships in the data, resulting in limitations in the accuracy and depth of the analysis results. In the process of multi-device data processing, there is a lack of sufficient stability and flexibility, making it difficult to adapt to the diverse and dynamically changing data requirements in the laboratory environment, prone to error accumulation, resulting in instability of 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, and environmental conditions, and improve the speed and accuracy of data processing.

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

[0007] In a first aspect, the present invention provides a method for the interconnection and management of laboratory equipment, which includes the steps:

[0008] S1. Collect the original data of laboratory equipment, perform data segmentation and transformation using a multi-layer recursive structure, introduce the recursive feedback mechanism of the interleaved network, and perform non-linear mapping and multi-dimensional recursive processing on the data to extract effective information;

[0009] S2. Calculate the error, perform adaptive adjustment and cross-layer parameter optimization on the data after non-linear mapping and multi-dimensional recursive processing, obtain a multi-dimensional data stream, and perform parallel processing on the multi-dimensional data stream to achieve precise processing and intelligent optimization of 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, and environmental conditions, and improve the speed and accuracy of data processing.

[0010] Preferably, the S1 specifically includes:

[0011] Adopt a multi-layer recursive structure, and each layer of recursion interacts with other layers through an interleaved network to form a multi-dimensional recursive network;

[0012] During the recursion process, segment and transform the original data of the collected laboratory equipment, and perform different-level allocation and processing according to the importance and characteristics of the data;

[0013] By performing weighted summation on the output results of different recursive layers and processing the data feedback between different layers in an exponentially decaying manner, the interleaved network can be dynamically adjusted to ensure the stability of data processing and the balance of multi-dimensional recursive paths.

[0014] Preferably, in the S1, the segmenting and transforming the original data of the collected laboratory equipment and performing different-level allocation and processing according to the importance and characteristics of the data specifically include:

[0015] Input the original data of the laboratory equipment into the first layer of recursion for preliminary processing, which is to segment the original data into multiple subsets. Each subset is a set of data points with similar characteristics. After segmentation, the subsets are directly regarded as initial data blocks and used as the input of the multi-layer recursive structure. The data blocks are processed in each layer of recursion and generate the input of the next layer; through the sine and cosine transformation of the data, non-linear mapping and enhancement processing of the data are realized.

[0016] Preferably, in the S1, the formula for each layer of recursive processing is as follows:

[0017]

[0018] Wherein, represents the th data block of the th layer of recursion, which is the output after processing the data block of the th layer of recursion; is a subset of the data set, represents the i th data point, and are the mean and standard deviation of the k th subset respectively; is the th data block in the j th layer of recursion and is the output of the th layer of recursion; is the angle adjustment coefficient of the recursive layer, used to adjust the data processing weight in the recursive process; is the th layer of recursive backflow interleaving adjustment amount.

[0019] Preferably, in S1, when dynamically adjusting the interleaving network, the th layer of recursive backflow interleaving adjustment amount is calculated by the following formula:

[0020]

[0021] where is used to feedback and correct the output of the th layer of recursion; is the m th data block weight coefficient, reflecting the importance between different data blocks; is the interleaving strength coefficient of the recursive layer, controlling the coupling degree between different levels; and are the number of hierarchical nodes in the recursive layer; and are respectively the th data block and the m th data block in the n th layer of recursion, is a constant to prevent division by zero.

[0022] Preferably, in S2, to ensure the accuracy and stability of the final output, error calculation and adaptive adjustment are performed after each layer of recursion;

[0023] When performing error calculation, by calculating the deviation between the output of each layer of recursion and the expected result, and quantifying and regularizing this deviation to ensure the convergence and robustness of data processing;

[0024] Based on the error calculation, the internal parameters of each layer of recursion are adaptively adjusted to optimize the data processing path and parameter configuration. The internal parameters of each layer of recursion include the angle adjustment coefficient and the backflow interleaving adjustment amount.

[0025] Preferably, in S2, after the internal parameters of each layer of recursion are adaptively adjusted, the parameters across layers in the entire multi-layer recursive structure are globally optimized to ensure the best processing path. The parameters across layers in the multi-layer recursive structure include the interleaving strength coefficient of the recursive layer and the weight coefficient of the data block.

[0026] Preferably, the global optimization formula for the parameters across layers in the multi-layer recursive structure is:

[0027]

[0028] where, is the parameter update value after the -th layer of recursion at the -th iteration; is the weight matrix of the interleaving network, controlling the coupling strength between different recursive layers; is the node number in the -th layer, used to identify the data block or subset in the -th layer; is the node number in another layer that interacts with the -th layer of the current recursive layer; is the number of nodes in the -th layer; is the number of nodes in another layer that interacts with the -th layer of the current recursive layer; is the parameter update value after the -th layer of recursion at the -th iteration; p is the standard deviation, representing the fluctuation degree of the parameters of the -th layer of recursion; is a constant to prevent division by zero.

[0029] Preferably, in S2, the specific implementation process of parallel processing is as follows:

[0030]

[0031] where, is the parallel processing result of the -th layer of recursion, and respectively represent different data dimensions in the -th layer of recursion, and both I and J are the number of data blockades in the -th layer of recursion; is the angle adjustment parameter for parallel processing, used to control the interaction strength of data streams in different dimensions; is a constant to prevent division by zero; Indicates The mean of the recursive data.

[0032] By quantifying the interactions between different data dimensions, data consistency and stability in multi-dimensional parallel processing are ensured.

[0033] In a second aspect, the present invention provides a laboratory equipment interconnection and management system, comprising:

[0034] 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;

[0035] 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.

[0036] The present invention has the following beneficial effects:

[0037] 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;

[0038] 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;

[0039] 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;

[0040] 4. The parallel processing mechanism of multi-dimensional data flow after recursive processing in the present invention can process multiple data dimensions simultaneously, realize the interaction and coordination between data, which greatly improves the processing speed, and at the same time ensures the comprehensiveness of data processing, can make full use of the multi-dimensional information generated by laboratory equipment, thus providing more accurate basic data for the intelligent scheduling and collaborative work of equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. As shown by the drawings, the above-mentioned and other objects, features, and advantages of the present invention will be more clearly presented. The same reference numerals in all the drawings indicate the same parts, and the drawings are not deliberately drawn to scale in actual size, with the focus on showing the gist of the present invention.

[0042] Figure 1 It is a flowchart of a method for the interconnection and management of laboratory equipment according to the present invention;

[0043] Figure 2 It is a structural diagram of a system for the interconnection and management of laboratory equipment according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0045] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0046] The following will specifically describe the specific solution of a method for the interconnection and management of laboratory equipment provided by the present invention in conjunction with the drawings.

[0047] Referring to the attached Figure 1 , which shows a flowchart of a method for the interconnection and management of laboratory equipment provided by an embodiment of the present invention. The steps of the method are as follows:

[0048] S1. Collect the original data of laboratory equipment, perform data segmentation and transformation using a multi-layer recursive structure, introduce the recursive feedback mechanism of the interleaved network, and perform non-linear mapping and multi-dimensional recursive processing on the data to extract effective information.

[0049] Collect the original data of laboratory equipment, specifically including: real-time collection of equipment operation status data through an intelligent gateway, covering key parameters such as operation mode, fault status, operation duration, 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 with equipment operation status data for correlation analysis in subsequent processing; during the experiment, obtain core experimental data generated by the equipment through sensors, such as chemical reaction rate, current, and voltage; in addition, to achieve the collaborative work and interconnection of equipment, collect communication data between equipment, including instruction transmission and synchronization signals, for analyzing the interaction mode between equipment and optimizing the collaborative work efficiency.

[0050] In the management of laboratory equipment, the relationships between data are complex and changeable, and single-layer processing methods often cannot fully capture the deep-level correlations between data. Therefore, a multi-layer recursive structure is used to process the data repeatedly, gradually extracting the effective information in the data and enhancing the depth and breadth of data analysis. With a multi-layer recursive structure, each layer of recursion interacts with other layers through an interleaved network to form a multi-dimensional recursive network.

[0051] During the recursive process, the original data of the collected laboratory equipment is segmented and transformed, and different levels of allocation and processing are performed according to the importance and characteristics of the data. Specifically, the original data of the laboratory equipment is input into the first layer of the recursive structure for preliminary processing. The first step is to segment the original data into multiple subsets , and the subsets are based on specific segmentation rules, such as data importance, timestamp, data type, etc. The segmentation formula is as follows:

[0052]

[0053] where represents the i-th data point; and are the mean and standard deviation of the k-th subset respectively; is the threshold value used to determine whether a data point belongs to this subset, and is set through statistical analysis or empirical methods based on the distribution characteristics of the data. The data set is classified according to the mean and standard deviation to ensure the consistency of data characteristics in each subset.

[0054] Each subset is a set of data points with similar characteristics. After segmentation, the subset is directly regarded as the initial data block , as the input of the multi-layer recursive structure, i.e., ; data block 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:

[0055]

[0056] where, represents the -th data block in the -th layer, which is the output after processing the data blocks in the -th layer; is the -th data block in the recursive structure of the j-th layer, which is the output of the -th layer; is the angle adjustment coefficient of the recursive layer, used to adjust the data processing weight in the recursive process; is the backflow interleaving adjustment amount for the -th layer of recursion. Through the sine and cosine transformation of the data, the non-linear mapping of the data and the enhancement of the processing effect are realized.

[0057] In the multi-layer recursive structure, although the simple layer-by-layer processing can extract the high-order features of the data, due to the lack of direct connection between the recursive layers, it may lead to insufficient information flow, thus affecting the overall processing effect. To solve this problem, a recursive backflow mechanism of the interleaving network is introduced, aiming to enhance the coherence and comprehensiveness of data processing through information interleaving and feedback between layers.

[0058] During the processing, there is also a complex interleaving network between the recursive layers. The interleaving network is not just a simple data transfer, but through the backflow interleaving mechanism, the processing results of different levels affect each other, forming a multi-dimensional data processing path. The backflow interleaving adjustment amount of the -th layer of recursion is calculated by the following formula:

[0059]

[0060] where, is the backflow interleaving adjustment amount of the -th layer of recursion, used to feedback and correct the output of the -th layer of recursion; is the weight coefficient of the m-th data block, reflecting the importance between different data blocks; is the interleaving intensity coefficient of the recursive layer, controlling the coupling degree between different levels; and are the number of hierarchical nodes in the recursive layer; and They are respectively the m-th data block and the n-th data block in the layer recursive structure; is a constant to prevent division by zero. By performing weighted summation on the output results of different recursive layers and processing the data backflow between different levels in an exponentially decaying manner, the interleaving network can be dynamically adjusted to ensure the stability of data processing and the balance of multi-dimensional recursive paths.

[0061] S2. Perform error calculation, adaptive adjustment, and cross-layer parameter optimization on the data after non-linear mapping and multi-dimensional recursive processing to obtain a multi-dimensional data stream, and perform parallel processing on the multi-dimensional data stream to achieve precise processing and intelligent optimization of 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, and environmental conditions, and improve the speed and accuracy of data processing.

[0062] As the depth of the recursive layer increases, the complexity of data processing gradually increases. To ensure the accuracy and stability of the final output, error calculation and adaptive adjustment are performed after each layer of recursion. The error calculation formula for each layer is:

[0063]

[0064] where is the error function of the layer, which 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, that is, the output result after the layer recursion; is the number of matching pairs of the target value and the actual value in the layer recursive structure; is the regularization coefficient, which is used to control the model complexity and prevent overfitting; is the layer recursive structure, the number of data blocks; represents the mean value of the layer recursive data. By calculating the deviation between the output of each layer of recursion and the expected result, and quantifying and regularizing this deviation, the convergence and robustness of data processing are ensured.

[0065] Based on the error calculation, the internal parameters of each layer of recursion are adaptively adjusted to optimize the data processing path and parameter configuration. The internal parameters of each layer of recursion include the angle adjustment coefficient and the backflow interleaving adjustment amount . The specific adjustment formulas are as follows:

[0066]

[0067] Among them, is the adaptive adjustment parameter for the v-th iteration, is the learning rate, which determines the step size of the adjustment, is the error function with respect to the parameter gradient. The recursive parameters are dynamically adjusted by the gradient descent method of the error to optimize the data processing path, enabling the algorithm adopted by the present invention to converge quickly in the multi-layer recursive structure and improving the accuracy of data processing.

[0068] After the internal parameters of each layer of recursion are adaptively adjusted, the cross-layer parameters in the entire multi-layer recursive structure are globally optimized to ensure the best processing path, which can not only accurately process data at the current level but also dynamically adapt to different data and processing requirements. The cross-layer parameters in the multi-layer recursive structure include the interleaving strength coefficient , weight coefficient . The update formula for the cross-layer parameters of the multi-layer recursive structure is:

[0069]

[0070] Among them, is the parameter update value after the -th layer of recursion at the -th iteration, is the weight matrix of the interleaving network, which controls the coupling strength between different recursive layers; is the node number in the -th layer, used to identify the data block or subset in the -th layer; is the node number in another layer that interacts with the -th layer of the current recursive layer; is the number of nodes in the -th layer; is the number of nodes in another layer that interacts with the -th layer of the current recursive layer; is the parameter update value after the -th layer of recursion at the -th iteration; is the standard deviation, representing the fluctuation degree of the recursive parameters of the p-th layer; is the strength control coefficient of the multi-layer recursive structure, which controls the data flow rate between different layers. By dynamically updating the parameters, it ensures adaptation to different data processing paths and requirements, enhancing the flexibility and adaptability of data processing.

[0071] In the present invention, the combination of the multi-layer recursive structure and the interleaved network realizes the refined processing and intelligent optimization of data through adaptive adjustment and error correction. In addition, a parallel processing mechanism for multi-dimensional data streams is introduced during the recursive process, and by independently and interactively processing data streams of different dimensions, the processing speed and accuracy are significantly improved. The specific implementation of the parallel processing mechanism is as follows:

[0072]

[0073] Wherein, is the parallel processing result of the th layer, and respectively represent different data dimensions in the th layer of recursion. Both I and J are the number of data blockades in the th layer of the recursive structure. is the angle adjustment parameter for parallel processing, which is used to control the interaction intensity of data streams of different dimensions. By quantifying the interaction of different data dimensions, the data consistency and stability in multi-dimensional parallel processing are ensured.

[0074] This embodiment also provides a structural diagram of a laboratory equipment interconnection and management system, as shown in Figure 2 and includes:

[0075] Data processing module: Collect the original data of laboratory equipment, perform data segmentation and conversion using a multi-layer recursive structure, introduce the recursive backflow mechanism of the interleaved network, and perform non-linear mapping and multi-dimensional recursive processing on the data;

[0076] Precision processing and intelligent optimization module: Calculate errors, perform adaptive adjustment and cross-layer parameter optimization on the data after non-linear mapping and multi-dimensional recursive processing, obtain multi-dimensional data streams, and perform parallel processing on the multi-dimensional data streams to achieve precise processing and intelligent optimization of complex data in laboratory equipment interconnection and management.

[0077] 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 through software, hardware, and their combinations. The above-mentioned modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above-mentioned modules. For the specific limitations of a laboratory equipment interconnection and management system, refer to the limitations of a laboratory equipment interconnection and management method described above. The two have the same functions and effects and will not be elaborated here.

[0078] Example 1: Acquisition and multi-layer recursive processing of original data of laboratory equipment

[0079] This experiment focuses on the real-time monitoring and optimized management of the core equipment in the substation - transformers, switchgear, and protection equipment. The goal of the experiment is to deeply analyze the raw data collected from the experimental equipment through a multi-layer recursive algorithm, thereby optimizing the operating efficiency of grid equipment, warning of faults, and enhancing the collaborative working efficiency of equipment.

[0080] Specific experimental equipment includes:

[0081] Transformer (T1): Used for voltage conversion, and the key data includes current, voltage, temperature, and load.

[0082] Switchgear (S1, S2): Used for switching control of current, and the key data includes the switching frequency, current, voltage, temperature, etc.

[0083] Protection equipment (P1): Used for grid protection, and the key data includes the fault status, current, voltage, etc.

[0084] Environmental monitoring equipment: Temperature and humidity sensors, air quality monitoring sensors, providing environmental data of the laboratory.

[0085] Equipment communication data: Instruction transfer and synchronization signals between monitoring devices.

[0086] 2. Data Acquisition and Preliminary Preprocessing

[0087] 2.1 Raw Data Collected

[0088] At the beginning of the experiment, data was collected in real-time through the intelligent gateway and sensors of each device and stored in the data center. The following is the initially collected data:

[0089] Transformer T1:

[0090] Voltage: 240 kV

[0091] Current: 10 A

[0092] Temperature: 80 °C

[0093] Load: 90%

[0094] Switchgear S1:

[0095] Switching frequency: 5 times / hour

[0096] Switch status: Normal (protection not triggered)

[0097] Voltage: 238 kV

[0098] Current: 9.5 A

[0099] Temperature: 78 °C

[0100] Protection equipment P1:

[0101] Fault status: Not triggered

[0102] Overload protection status: Normal

[0103] Current: 8 A

[0104] Voltage: 230 kV

[0105] Environmental monitoring equipment:

[0106] Temperature: 22 °C

[0107] Humidity: 45%

[0108] Air quality (AQI): 60 (Good)

[0109] Equipment room communication data:

[0110] Instruction: From equipment T1 to S1, Instruction: Enable protection

[0111] Synchronization signal: Synchronization switch signals of S1 and S2, Timestamp: 2024-12-24 10:30:00

[0112] 2.2 Data preprocessing

[0113] First of all, all the collected data needs to be standardized. Taking the temperature data of transformer T1 as an example, assuming its mean value is 75 °C and the standard deviation is 5 °C, then the standardized temperature data is .

[0114] Similarly, standardize the voltage, current and temperature data of other equipment.

[0115] Next, use a denoising algorithm to filter out abnormal data caused by sensor errors or external interference. For example, in the current data of transformer T1, if a certain 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 12 A, which exceeds the normal fluctuation range, then use the interpolation method to correct it to 10 A.

[0116] 3. Data segmentation and preliminary recursive processing

[0117] After data standardization, the data is segmented into different time periods according to the timestamp. For example, it is divided by hour. The data of each time period corresponds to a subset, and the subset is sent as input into a multi-layer recursive algorithm.

[0118] 3.1 Data segmentation

[0119] Assume that the temperature data of transformer T1 is segmented, and the time period is 1 hour. The temperature data is as follows:

[0120] 240 kV: 75 °C, 80 °C, 79 °C, 81 °C, 80 °C, 78 °C, 76 °C

[0121] First, calculate the mean value of the data in this time period, which is 79 °C, and the standard deviation is 1.6 °C.

[0122] According to the data segmentation formula:

[0123]

[0124] Assume the selected threshold , then the calculation results are:

[0125]

[0126]

[0127]

[0128]

[0129] Therefore, the data subset in this time period only contains the data of 80 °C, 79 °C, and 81 °C.

[0130] 3.2 First-layer recursive processing

[0131] For the segmented data subset , it is processed through the first-layer recursion. The recursive formula is as follows:

[0132]

[0133] Calculate the subset mean: 79.33

[0134] Calculate the standard deviation: 1.0

[0135] According to the above recursive formula, perform the calculation:

[0136]

[0137]

[0138]

[0139] Substitute these values into the recursive formula to obtain:

[0140]

[0141] Assume and , as well as the reflux adjustment amount , substitute and calculate to obtain:

[0142]

[0143] Therefore, the output after the first - layer recursion is .

[0144] 4. Second - layer recursive processing

[0145] Take the output of the first - layer recursion as the input and continue the second - layer recursion. Assume that in the second - layer recursion, the temperature and current data are jointly processed, and the data weights of different layers are adjusted through the recursive formula.

[0146] After the second - layer recursive processing, the output recursive result is:[[]]

[0147]

[0148]

[0148] In the above experimental process, through the multi - layer recursive processing of experimental data, several key conclusions are drawn based on the different changes of the front - and back - data, mainly reflected in the optimization of the equipment operation state, the adjustment of load scheduling, and the improvement of the collaborative work among devices. The following is the specific conclusion analysis:[[]]

[0149]

[0149] 1. Correlation between transformer temperature and load

[0150] In the experiment, the temperature data of transformer T1 has experienced the process from raw data acquisition to multi - layer recursive processing. In the initial data, the temperature of the transformer is 80 °C, and as the load increases (90%), the temperature also gradually rises. After standardization, denoising, and segmentation, the data enters the recursive processing layer.[[]]

[0151]

[0151] In the first - layer recursion, after the weighted average based on features and non - linear mapping processing, the standardized value of the transformer temperature becomes 0.92. Through the correlation analysis with the load data of the device (such as current, power, etc.), it can be seen that there is a certain positive correlation between temperature and load. This is enhanced by the reflux interleaving mechanism to the sensitivity of temperature changes.[[]]

[0152]

[0152] When transformer T1 is under a relatively high load, the temperature rising trend is obvious, but it still does not exceed the set safety threshold (such as 85 °C). This conclusion indicates that the temperature monitoring of the device is still within a reasonable range during high - load operation, and potential risks can be warned in time through real - time data processing and recursive analysis.[[]]

[0153]

[0153] 2. Load coordination and optimization among devices

[0154] In the experiment, the collaborative work of multiple devices is involved, especially the current, temperature, and switching states of transformer T1, switching devices S1, and S2. The experimental data shows that the state changes among these devices are interrelated. For example, the load and temperature of the transformer will affect the frequent start and stop of the switching devices, and the start and stop frequency of the switching devices will in turn affect the working load of the transformer.

[0155] Device collaborative optimization: During the second-layer recursive processing, through the recursive backflow mechanism of the intertwined network, the interaction between devices is optimized. Through the feedback of the backflow intertwining adjustment amount the scheduling strategy between devices is optimized. For example, when the frequent start and stop of switching device S1 causes current fluctuations, by adjusting the backflow mechanism, the start and stop frequency of the switching device is optimized, thereby reducing the impact of equipment load fluctuations on the transformer temperature.

[0156] Through recursive processing and backflow intertwining adjustment, the load and operating states between devices are effectively coordinated. The switching frequency of the switching device is reduced, and the load regulation of the transformer becomes more stable, reducing the risk of overload. This process helps to optimize the overall load distribution of the power grid and improve the operating efficiency and safety of the equipment.

[0157] 3. Verification of Fault Warning and Protection Mechanism

[0158] During the experiment, the state of protection device P1 is monitored. The experimental data shows that when the device is operating normally, the current and voltage of the protection device remain within the safe range and no fault state is triggered. However, with the increase in temperature and load, the system's early warning ability for potential faults is verified.

[0159] Fault state monitoring: By analyzing the real-time state of the device through a recursive algorithm, especially the data such as the current and voltage of the protection device, it is found that even when the temperature and load increase, the system can detect potential device anomalies in advance through sensor data and adjust the load distribution and operating strategy in a timely manner.

[0160] Through the processing of the protection device data by the recursive algorithm, the experiment concludes that when the system faces abnormal situations such as equipment overload and temperature rise, it can provide timely warnings and make adjustments, reducing the risk of equipment damage and system failures.

[0161] 4. Influence of Environmental Data on Device Operating State

[0162] Data such as the temperature, humidity, and air quality of the environmental monitoring device are also processed in the experiment. Through a multi-layer recursive algorithm, combining environmental conditions (such as temperature and humidity) with the operating data of the device (such as current, voltage, temperature, etc.), the potential impact of the environment on the device performance is analyzed.

[0163] Relationship between the environment and the equipment: Experiments show that at a relatively high ambient temperature (22°C) and relative humidity (45%), the temperature of transformer T1 rises slightly but remains within an acceptable range. Although the impact of environmental factors on the equipment temperature is relatively small, the indirect impact of environmental factors on the power grid load still needs to be considered during equipment scheduling.

[0164] Although the impact of environmental factors (such as temperature and humidity) on the equipment is small, it still needs to be considered in the equipment management system to improve the comprehensive assessment ability of the equipment operation status. By combining environmental data, optimizing the equipment load scheduling strategy and real-time adjustment, the reliability of the power grid operation can be further improved.

[0165] 5. Improvement of data accuracy by multi-dimensional parallel processing

[0166] In the experiment, a multi-dimensional parallel processing mechanism was adopted for the multi-dimensional data streams (such as voltage, current, temperature, etc.) between devices. During the multi-dimensional parallel processing, the recursive algorithm independently and interactively processes the data streams of each dimension, significantly improving the accuracy and speed of data processing.

[0167] Data consistency and stability: Experiments show that after parallel processing, the data streams of each dimension (the correlation between voltage and current) are more consistent, and the system response speed is accelerated. 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 a timely manner.

[0168] 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, the parallel processing of multi-dimensional data helps to identify potential risks in real time and make a quick response, thus improving the stability and security of the power grid.

[0169] Comprehensive conclusion

[0170] Through the multi-layer recursive processing of experimental data, especially through the in-depth analysis of device data, environmental data, and communication data between devices, the following main conclusions are obtained:

[0171] 1. Correlation between load and temperature: Under high load conditions, the temperature of the transformer shows an upward trend but does not exceed the safety threshold. The system can monitor and adjust the load in real time through the recursive algorithm to avoid overheating of the equipment.

[0172] 2. Equipment collaborative optimization: The load scheduling between devices is optimized through the reflux interleaving mechanism, reducing the start-stop frequency of switching devices and improving the smoothness of the transformer load.

[0173] 3. Fault warning ability: The system can identify potential equipment failure risks in advance and optimize the response of the load and protection equipment through the recursive algorithm.

[0174] 4. Influence of environmental factors: Environmental conditions have a certain impact on the operation of the equipment. However, by comprehensively considering environmental data, the system can optimize the operation state of the equipment and reduce the impact of environmental changes on the power grid load.

[0175] 5. Parallel processing improves accuracy: Multidimensional parallel processing significantly improves the speed and accuracy of data processing, enabling the system to identify and respond to changes in real time in complex power grid data.

[0176] 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.

[0177] Example 2: Error calculation and adaptive adjustment based on the results of recursive processing

[0178] Based on the results of the first and second layer recursive processing completed in Example 1, further deepen the recursive level for error calculation and adaptive adjustment. By optimizing the parameters of each layer of recursion, ensure the accuracy and stability of the final output. To this end, use the error function to evaluate the performance of each layer of recursion, and optimize the parameters through the gradient descent method. Finally, through the parameter optimization of multi-layer interleaved recursion, ensure that the entire model converges and adapts to complex data structures in multi-layer recursion.

[0179] Equipment and data

[0180] 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 within 24 hours.

[0181] 2. Switching devices S1 and S2: Data such as switching frequency, current, voltage, temperature, etc. Each device collects data once per hour.

[0182] 3. Protection device P1: Data such as fault status, current, voltage, etc. Monitor and collect data in real time.

[0183] 4. Environmental monitoring equipment: Data such as temperature, humidity, air quality, etc. Collect data every 15 minutes.

[0184] 2. Data preprocessing and pre-operation

[0185] 2.1 Results of the second layer of recursion

[0186] In Example 1, the results of the second layer of recursion are as follows:

[0187] Temperature of transformer T1:

[0188] Current of switching device S1:

[0189] Temperature of the environmental monitoring device:

[0190] Target value Is the actual expected value of these data. Set:

[0191] Target value of transformer T1 is 1.0

[0192] Target value of switchgear S1 is 1.0

[0193] Target value of the environmental monitoring device is 1.0

[0194] 3. Error calculation and adaptive adjustment

[0195] The error calculation formula is as follows:

[0196]

[0197] In the second - layer recursion, the difference between the target value and the actual value is:

[0198] For transformer T1:

[0199]

[0200] Assume the regularization coefficient , then:

[0201]

[0202] For switchgear S1:

[0203]

[0204] For the environmental monitoring device:

[0205]

[0206] 3.3 Total error calculation

[0207] The total error of the second layer is calculated by adding each error term :

[0208]

[0209] 4. Adaptive adjustment and optimization

[0210] 4.1 Internal parameter adjustment

[0211] Based on the error calculation, the next step is to perform adaptive adjustment. According to the formula of gradient descent method, the internal parameters of each layer of recursion need to be updated. The update formula is as follows:

[0212]

[0213] Set the learning rate , and assume the initial parameter values are:

[0214] Calculate the gradient of the error function with respect to :

[0215]

[0216] Update the angle adjustment coefficient according to the gradient:

[0217]

[0218] 4.2 Cross-layer parameter optimization

[0219] The optimization of cross-layer parameters is based on the interweaving of multiple layers in the recursive model. To ensure that cross-layer parameters can accurately transmit information, the following cross-layer parameter update formula is used:

[0220]

[0221] Assume the initial values of cross-layer parameters are:

[0222]

[0223] Calculate the cross-layer coupling weights of each layer's parameters , and the interweaving relationship between each parameter. Assume and When, the interweaving weight matrix has the following form:

[0224]

[0225] The interweaving coefficient and standard deviation are:

[0226]

[0227] Use the interweaving parameter update formula for calculation:

[0228]

[0229]

[0230] In the above experimental process, through the error calculation of the multi-layer recursive structure, the optimization of internal parameters and cross-layer parameters, the output accuracy of the model was gradually improved. Based on the differences in the front and back data, especially the error calculation and parameter optimization results after each layer of recursion, the following specific conclusions can be drawn:

[0231] 1. Changes brought about by error calculation and adjustment

[0232] After data preprocessing and preliminary recursive processing, preliminary error values were obtained. Taking transformer T1 as an example, the preliminary error was 0.0368, the error of switchgear S1 was 0.02075, and the error of the environmental monitoring device was 0.005. The overall error was 0.06255, indicating that without further optimization and adjustment, there was a large deviation between the system output and the target value, especially in the processing results of the transformer and switchgear.

[0233] In the second - layer recursion, by calculating the error between the output of each layer and the target value and making adaptive adjustments according to the error gradient. By adjusting the angle adjustment coefficient , the changes in the error were observed:

[0234] The error of transformer T1 decreased from 0.0368 to 0.03 (the error decreased by about 18%).

[0235] The error of switchgear S1 decreased from 0.02075 to 0.015 (the error decreased by about 27%).

[0236] The error of the environmental monitoring device decreased from 0.005 to 0.0045 (the error decreased by about 10%).

[0237] This indicates that through error calculation and parameter adjustment, the output accuracy of the model has been improved, especially in the processing of transformers and switchgear, and the optimization effect is more obvious.

[0238] Conclusion

[0239] In each layer of recursion, through error calculation and adaptive parameter optimization, the system can gradually reduce the error from the target value, thereby improving the processing accuracy and stability. This process shows that each layer of recursion not only optimizes the output of the current layer but also optimizes the global performance of the entire model by adjusting cross - layer parameters.

[0240] 2. Influence of adaptive adjustment on parameters

[0241] During the parameter adjustment process, the internal parameters of each layer of recursion were adaptively optimized. By calculating the gradient and adjusting the parameters, new adjustment values were obtained:

[0242] The angle adjustment coefficient was adjusted from the initial value of 0.5 to 0.4985 (a slight change, indicating that the influence of this parameter on the current - layer output is small).

[0243] 3. Global effects brought about by cross - layer parameter optimization

[0244] The optimization of cross - level parameters is an important step to ensure a reasonable coupling relationship between layers in the entire recursive structure. In the experiment, the interweaving strength coefficient and weight coefficient between layers were optimized through the cross - level parameter update formula, and significant improvements were particularly demonstrated in the following aspects:

[0245] Optimization of the interweaving weight: In the initial experiment, the interweaving weight had a certain impact on information transfer between different layers, but no significant advantages were shown. However, after cross - level parameter optimization, the model can better handle the dependence relationship between different data. Especially when processing the data of transformer T1 and switchgear S1, the output accuracy was further improved.

[0246] Improvement of cross - level information transfer: After optimizing the cross - level parameters, the influence of each layer of the recursive model on other layers is more precise, enhancing the global stability of the model. Especially in the interweaving relationship between switchgear S1 and the environmental monitoring device, cross - level optimization greatly reduces the unnecessary redundancy and conflicts between the two, making the overall error value better controlled.

[0247] The cross - level parameter optimization significantly improves the global coordination and adaptability of the multi - layer recursive structure, enabling the model to effectively handle the dependence relationship between different data sources, and thus better adapt to complex data processing tasks. This process shows that cross - level adjustment not only optimizes the output of a single layer but also improves the performance of the multi - layer interweaving recursive system under different conditions.

[0248] 4. Optimization effect of the overall model

[0249] Through the layer - by - layer optimization of the multi - layer recursive structure, not only the error of each layer's output is reduced, but also the robustness and accuracy of the entire 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 significantly reduced. Especially in reducing the errors of transformer T1 and switchgear S1, the effect is particularly significant.

[0250] Transformer T1: The initial error of 0.0368 is reduced to 0.03, and the error is reduced by about 18%.

[0251] Switchgear S1: The initial error of 0.02075 is reduced to 0.015, and the error is reduced by about 27%.

[0252] Environmental monitoring device: The initial error of 0.005 is reduced to 0.0045, and the error is reduced by about 10%.

[0253] These changes reflect that through recursive error calculation and adaptive parameter adjustment, the system is gradually optimized at multiple levels and finally converges to a more ideal output result. Most importantly, the reduction of error not only enhances the accuracy of a single layer but also improves the robustness of the entire system, enabling it to better handle complex environments and data changes.

[0254] Example 3: Application of Multidimensional Parallel Processing and Interleaved Network in Power Grid Equipment Data Analysis

[0255] After multi-layer recursive processing, a parallel processing mechanism is used to independently process multi-dimensional data of each layer, and then the data of different dimensions are interactively processed through an interleaved network to finally obtain the parallel processing results of each layer. The introduction of this mechanism is to improve processing efficiency, ensure data consistency, and optimize data flow.

[0256] The formula for parallel processing is as follows:

[0257]

[0258] In the experiment, parallel processing is carried out for data dimensions such as the temperature and current of the transformer, the status of switchgear S1, and the status

[0259] of protection device P1.

[0260] Transformer T1 temperature (Dimension 1)

[0261] Transformer T1 current (Dimension 2)

[0262] Switchgear S1 current (Dimension 3)

[0263] Protection device P1 fault status (Dimension 4)

[0264] Weighted calculation is carried out by collecting data of each dimension. Set , , and the value of each dimension is as follows (assuming these data are obtained from the recursive process):

[0265] Transformer T1 temperature

[0266] Transformer T1 current

[0267] Switchgear S1 current

[0268] Protection device P1 fault status

[0269] These data are interactively processed through a parallel processing formula.

[0270] For each pair and , calculate their mutual relationship. For example, for the first dimension and the second dimension , the relationship is:

[0271]

[0272] Similarly, perform the same calculation for other dimensions to finally obtain the entire parallel processing result. The final calculation result is:

[0273] In the experiment of Example 3, the temperature and current data of transformer T1 were respectively processed in parallel, and an interleaved feedback mechanism between different devices was incorporated.

[0274] During the first two layers of recursive processing, the temperature data of transformer T1 was 0.85 and the current data was 0.95.

[0275] Preliminary analysis shows that when the transformer operates under high load, the temperature will rise slightly but does not exceed the safe range.

[0276] During the parallel processing, by introducing parallel data stream interaction, it was found that the correlation between the transformer temperature and current was further amplified. The final output obtained through parallel calculation , compared with the temperature and current data, obtained a more accurate temperature increase trend.

[0277] 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. Through the parallel processing result, the ability to capture and warn of this trend was optimized.

[0278] This indicates that during the peak load period of the power grid, the temperature of the transformer may approach the upper limit of the 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 dispatching optimization and equipment warning.

[0279] For switch devices S1 and S2, through parallel processing and the adjustment of interleaved feedback, their possible impacts on the power grid during frequent start-stop processes were further analyzed.

[0280] The state of switch device S1 after the first layer of recursive processing was 1.05, and the result of the second layer of recursion was 0.98.

[0281] Preliminary analysis shows that the start-stop frequency of switchgears S1 and S2 is relatively high, but no abnormal conditions are observed.

[0282] With the support of parallel processing and interleaved feedback mechanisms, the state changes of switchgears S1 and S2 are further refined. Through parallel processing, the final output indicates that although there is a certain start-stop frequency for the switchgears under different load and environmental conditions, the limit state of the equipment is not reached.

[0283] The interleaved network mechanism further ensures the coordinated working state among devices, especially at the start and stop moments of the switchgears, where the collaborative working state among devices remains stable.

[0284] Conclusion:

[0285] Switchgears S1 and S2 operate stably under high load conditions. Although the start-stop frequency is relatively high, it does not cause equipment overload or failure, indicating that the protection mechanism of the power grid system effectively safeguards the equipment safety.

[0286] Through parallel processing, it is concluded that the collaborative working efficiency among power grid devices is relatively high, and the interconnection and interoperability among devices are crucial for optimizing the operating state of power grid devices and improving the accuracy of load dispatching.

[0287] Protection device P1 is mainly used to ensure the safety of the power grid system. When a device fails, it will activate the protection mechanism. Parallel processing analysis is performed on the data of protection device P1 to evaluate its performance under the current load.

[0288] The output of protection device P1 at the first-layer recursion is 1.10, and the output at the second-layer recursion is 1.03.

[0289] Preliminary analysis shows that during the experiment, the protection device did not trigger the protection mechanism, and the safe operating state of the power grid was not threatened.

[0290] After parallel processing, the state of protection device P1 is further stabilized. The final output shows that the fault state of the protection device remains within the safe range under power grid load fluctuations, and no faults or abnormal signals occur in the device.

[0291] The introduction of the interleaved network further optimizes the feedback mechanism of the protection device in multi-dimensional data stream interaction, making the device response more sensitive and stable.

[0292] Conclusion:

[0293] The collaborative working efficiency of power grid equipment has been improved under the optimization of parallel processing and interleaved networks, indicating that the power grid system can achieve more precise scheduling and collaborative operation among different devices, avoiding load overloading and equipment failures.

[0294] This result provides more reliable data support for the intelligent scheduling and equipment maintenance of the power grid. Especially in the case of large load fluctuations, it ensures that power grid equipment can work efficiently in coordination, improving the overall operation efficiency and stability.

[0295] Summary

[0296] Through the parallel processing mechanism and the feedback adjustment of the interleaved network, the operating status and collaborative efficiency of power grid equipment have been significantly optimized. The specific conclusions are as follows:

[0297] 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.

[0298] 2. Switching devices S1 and S2 operate stably under high load conditions and do not reach the equipment overload state, indicating that the protection mechanism of power grid equipment is effective.

[0299] 3. Protection device P1 is in a stable state and does not trigger the protection mechanism, further verifying the safety of the power grid.

[0300] 4. The collaborative working efficiency among devices has been improved. Through the interleaved network 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.

[0301] The above embodiments are only used to illustrate the technical solutions of the present invention, not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all 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, performing 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 performing parallel processing on the multidimensional data stream; 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 a weighted sum 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. 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 the 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 with 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 transformation of the data, the nonlinear mapping and enhancement processing of the data are realized; 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.

2. The laboratory equipment interconnection and management method according to claim 1, 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.

3. The laboratory equipment interconnection and management method according to claim 1, characterized in that: In S2, 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.

4. The laboratory equipment interconnection and management method according to claim 3, 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.

5. The laboratory equipment interconnection and management method according to claim 4, 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 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 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.

6. The laboratory equipment interconnection and management method according to claim 1, 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 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 recursive data.

7. A laboratory equipment interconnection and management system, used to implement the laboratory equipment interconnection and management method according to any one of claims 1 to 6, 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; Precise processing and intelligent optimization module: performs error calculation, adaptive adjustment and cross-level parameter optimization on the data after nonlinear mapping and multi-dimensional recursive processing to obtain multi-dimensional data streams, and performs parallel processing on the multi-dimensional data streams.

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