Organic molecule magnetic regulation and control method based on magnetic field assistance

Abstract

The invention relates to the technical field of magnetic regulation and control, in particular to an organic molecule magnetic regulation and control method based on magnetic field assistance, which comprises the following steps of acquiring magnetic response data, analyzing track displacement difference, constructing response input weight, correcting magnetic focusing coordinates, rearranging response sequences, identifying magnetic control mapping paths, and classifying organic molecule magnetic response behaviors. And outputting the magnetic control archiving node path set. According to the invention, by analyzing the change characteristics of the magnetic response speed and accurately calculating the included angle of the speed direction, the fine correspondence between the excitation frequency and the response direction track is realized, the deviation between the magnetic moment direction and the response path is effectively identified, the spatial position of the magnetic response center is accurately corrected, and the rhythm change trend is fed back. According to the method, input weight distribution and path node sequence are dynamically adjusted and controlled, fine classification and archiving management of magnetic response behaviors are realized, the stability and precision of magnetic state regulation and control are remarkably improved, and control condition dependence dispersion, response data disjunction and feedback adjustment lag are effectively avoided.

Description

Technical Field

[0001] This invention relates to the field of magnetic control technology, and in particular to a magnetic control method for organic molecules based on magnetic field assistance. Background Technology

[0002] The field of magnetic modulation technology encompasses a system of strategies and methods for regulating the response behavior of magnetic materials in information processing and functional control. It aims to achieve dynamic management and selective control of magnetic parameters in materials, systems, or modules through programmed or signal-based control methods. Specifically, it involves elements such as magnetic data representation, magnetic field action sequence generation, magnetic state feedback identification, and control process design. These methods are applied in organic electronic systems, IoT node control, spin logic operation processes, and intelligent material response management. Among these, the magnetic field-assisted magnetic modulation method for organic molecules refers to a control method that uses a magnetic field as an external driving signal to programmatically regulate the magnetic state of an organic molecular system by defining control parameters and logical conditions. This includes establishing a magnetic response model for the target organic molecular system, setting magnetic field input parameters and molecular state mapping relationships, and calling specific control command logic through a conditional triggering mechanism to schedule the external magnetic field control unit to complete its function. This includes aspects such as magnetic field input parameter setting methods, state discrimination condition logic, molecular response mapping rule definition, and setting the execution sequence of the modulation process.

[0003] Traditional organic molecule magnetic modulation technology does not take into account the changes in the direction of magnetic response velocity and the differences in trajectory during magnetic field control. This results in insufficient spatial position calibration of the magnetic response center and inaccurate identification of feedback rhythm imbalance. Consequently, the direction of the magnetic moment deviates from the actual control path, leading to insufficient precision and stability in dynamic management. The accuracy of response behavior classification is difficult to guarantee, and position and state control errors are prone to occur in complex magnetic control scenarios. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and propose a magnetic control method for organic molecules based on magnetic field assistance.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a magnetic control method for organic molecules based on a magnetic field assisted, comprising the following steps: S1: Acquire magnetic response data, analyze the change in magnetic response velocity during the molecular response process under multi-frequency magnetic field pulse excitation, calculate the velocity direction angle at continuous time points, establish the corresponding trajectory of excitation frequency and direction change, and generate response trajectory distribution information; S2: Using the response trajectory distribution information, compare the sequence differences between the current response path and the standard path, analyze the displacement of the response points, adjust the input priority level in combination with the control state sequence, construct the control intensity ratio coefficient, and output the response input weight configuration. S3: Using the aforementioned response input weight configuration, analyze the positional changes of the response center and the end point of the path in the direction, determine the directional offset characteristics of the current response center of gravity, and, in conjunction with the feedback response time information of the corresponding period, correct the magnetic focusing center coordinates and output the focusing coordinate correction result. S4: Based on the focusing coordinate correction results, filter the time feedback records of each response node within the magnetic control cycle, calculate the response time interval difference between each pair of adjacent nodes, determine the trend of feedback rhythm change, filter out rhythm imbalance segments, rearrange the current path structure based on the stability of the response rhythm, and generate response sequence reconstruction results.

[0006] As a further embodiment of the present invention, the response trajectory distribution information includes excitation frequency, direction change trajectory, and velocity direction angle trend; the response input weight configuration specifically includes response path offset position, control state level parameter, and control intensity ratio factor; the focusing coordinate correction result specifically refers to the response center spatial coordinates, path end point extension position, and feedback time reference node; and the response sequence reconstruction result includes time interval difference group, path rhythm stability label, and rearranged response sequence number.

[0007] As a further aspect of the present invention, the step of obtaining the response trajectory distribution information specifically includes: S111: Acquire magnetic response data, analyze magnetic field pulse excitation signal sequences under multiple frequency conditions, record magnetic response velocity change data in each excitation cycle, calculate the velocity change direction angle between consecutive time points, and generate a set of frequency-correlated velocity angles. S112: Call the frequency-related velocity angle set, group the velocity direction change data under multiple frequency conditions, analyze the change trend of each frequency band, calculate the response trend intensity, and obtain the frequency distribution trend parameter set; S113: Based on the frequency distribution trend parameter set, analyze the relationship between the excitation frequency and the direction change path, determine the co-occurrence characteristics of direction jump and excitation change, establish the corresponding trajectory of excitation frequency and direction change, and obtain response trajectory distribution information.

[0008] As a further aspect of the present invention, the step of obtaining the response input weight configuration specifically includes: S211: Obtain the response trajectory distribution information, compare the positional order difference between the current molecular response path sequence and the standard path template, analyze the arrangement displacement of each response point, and generate a response offset displacement parameter set; S212: Based on the response offset displacement parameter group, identify the sequential offset direction of each response point, call the corresponding control state sequence, match the offset direction with the control state sequence, calculate the sequential offset matching coefficient, and combine the arrangement displacement data with the state sequence sequence to generate a matching coefficient dataset. S213: Based on the matching coefficient dataset, adjust the input priority level, construct the control intensity ratio coefficient by combining the offset displacement and priority level, and establish the response input weight configuration.

[0009] As a further aspect of the present invention, the step of obtaining the focusing coordinate correction result specifically includes: S311: Using the aforementioned response input weight configuration, identify the response center coordinate point within the magnetic control cycle, and combine it with the spatial position coordinates of the last response node in the same cycle to analyze the changes in the spatial direction between the response center and the end point of the path, and generate the change in the direction of the center and end point. S312: Based on the change in direction of the center endpoint, determine the directional offset characteristics of the current response center of gravity, combine the response direction extension trajectory, calculate the directional offset trend of the center of gravity, analyze the spatial displacement trend of the path endpoint, and generate the directional offset trend of the center of gravity. S313: Using the aforementioned center of gravity offset trend, combined with the feedback response time information of the corresponding period, spatial correction is performed on the magnetic focusing center coordinates to obtain the focusing coordinate correction result.

[0010] As a further aspect of the present invention, the step of obtaining the response sequence reconstruction result specifically includes: S411: Based on the focusing coordinate correction results, filter the time feedback records of each response node within the magnetic control cycle, calculate the response time interval difference between each pair of adjacent nodes, and generate a time interval difference value group. S412: Call the time interval difference group, analyze the fluctuation of the time interval, determine the trend of feedback rhythm change, filter out rhythm imbalance segments, identify unstable path areas, and obtain rhythm fluctuation identification results. S413: Based on the rhythm fluctuation recognition results, the current path structure is rearranged according to the stability of the response rhythm, the order of response nodes is adjusted, and the response sequence reconstruction result is obtained.

[0011] As a further aspect of the present invention, the method further includes: S5: Call the response sequence reconstruction result, locate the corresponding response state node according to each magnetic field input information in the control sequence, identify the mapping path between the input control sequence and the effective response state by analyzing the direction change in the control input and the angle difference between the magnetic moment directions during the response process, classify the organic magnetic response behavior, and output the magnetic control archive node path set; The magnetically controlled archiving node path set specifically includes the control input direction, response node coordinates, and angle matching path number.

[0012] As a further aspect of the present invention, the step of obtaining the magnetically controlled archiving node path set specifically includes: S511: Call the response sequence reconstruction result, locate the corresponding response state node according to each magnetic field input information in the control sequence, and generate a control response node location set; S512: Call the control response node location set, analyze the directional changes in the control input sequence, and combine the angle differences between magnetic moment directions during the response process to identify the mapping path between the input control sequence and the effective response state, and obtain the control response mapping path set; S513: Based on the control response mapping path set, analyze the mapping path, classify the organic magnetic response behavior, and output the magnetic control archiving node path set.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by analyzing the characteristics of magnetic response velocity change and accurately calculating the velocity direction angle, a precise correspondence between the excitation frequency and the response direction trajectory is achieved. This effectively identifies the deviation between the magnetic moment direction and the response path, accurately corrects the spatial position of the magnetic response center and the trend of feedback rhythm change, and dynamically adjusts the allocation of control input weights and the sequence of path nodes. This enables refined classification and archiving management of magnetic response behavior, significantly improves the stability and accuracy of magnetic state control, and effectively avoids the problems of scattered control condition dependencies, disconnected response data, and delayed feedback adjustment. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the main steps of the present invention; Figure 2 This is a flowchart of the response trajectory distribution information acquisition process of the present invention; Figure 3 The flowchart for obtaining the response input weight configuration of this invention is as follows; Figure 4 This is a flowchart of the process for obtaining the focusing coordinate correction results of the present invention; Figure 5 This is a flowchart of the process for obtaining the response sequence reconstruction results of the present invention; Figure 6 This is a flowchart of the magnetic archiving node path set acquisition process of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0016] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0017] Please see Figure 1 This invention provides a technical solution: a method for magnetic modulation of organic molecules based on magnetic field assistance, comprising the following steps: S1: Acquire magnetic response data, analyze the change in magnetic response velocity during the molecular response process under multi-frequency magnetic field pulse excitation, calculate the velocity direction angle at continuous time points, establish the corresponding trajectory of excitation frequency and direction change, and generate response trajectory distribution information; S2: Utilize response trajectory distribution information to compare the sequence differences between the current response path and the standard path, analyze the displacement of response points, combine the control state sequence, adjust the input priority level, construct the control intensity ratio coefficient, and output the response input weight configuration. S3: Utilize the response input weight configuration to analyze the positional changes of the response center and the end point of the path in the direction, determine the directional offset characteristics of the current response center of gravity, combine the feedback response time information of the corresponding period, correct the magnetic focusing center coordinates, and output the focusing coordinate correction results. S4: Based on the focusing coordinate correction results, filter the time feedback records of each response node within the magnetic control cycle, calculate the response time interval difference between each pair of adjacent nodes, determine the trend of feedback rhythm change, filter out rhythm imbalance segments, rearrange the current path structure based on the stability of the response rhythm, and generate response sequence reconstruction results. S5: Call the response sequence reconstruction results, locate the corresponding response state node according to each magnetic field input information in the control sequence, identify the mapping path between the input control sequence and the effective response state by analyzing the direction change in the control input and the angle difference between the magnetic moment directions during the response process, classify the organic magnetic response behavior, and output the magnetic control archive node path set.

[0018] The response trajectory distribution information includes excitation frequency, direction change trajectory, and velocity direction angle trend. The response input weight configuration specifically includes response path offset position, control state level parameter, and control intensity ratio factor. The focusing coordinate correction result specifically refers to the spatial coordinates of the response center, the extension position of the path end point, and the feedback time reference node. The response sequence reconstruction result includes time interval difference group, path rhythm stability label, and rearranged response sequence number. The magnetic control archive node path set specifically includes control input direction, response node coordinates, and angle matching path number.

[0019] Please see Figure 2 The specific steps for obtaining response trajectory distribution information are as follows: S111: Acquire magnetic response data, analyze magnetic field pulse excitation signal sequences under multiple frequency conditions, record magnetic response velocity change data in each excitation cycle, calculate the velocity change direction angle between consecutive time points, and generate a set of frequency-correlated velocity angles. First, determine the frequency range of the magnetic field pulse excitation signal. For example, the frequency condition determined experimentally is... , , , and There are 5 groups in total, and the duration of application at each frequency is... The magnetic field pulse, the sampling interval is set to Taking a practical example, in a magnetic response experiment, organic magnetic molecular materials (such as metalloporphyrins) are selected, and their magnetic response velocity changes are recorded under magnetic field excitation at different frequencies. Assuming that at a frequency of... The data on the change in molecular response rate are shown in Table 1.

[0020] Table 1. Data on Magnetic Response Velocity Variation

[0021] As shown in Table 1, based on the response speed data, the angle between the velocity directions between consecutive time points is calculated sequentially, specifically as follows: (The calculation is based on the time numbering...) and For example, the velocity vector at the second time point The velocity vector at the first time point Then the angle between the velocities of the two points is... Substituting the actual numerical values, we can obtain... Similarly, the angle values ​​of all continuous points are calculated sequentially. Through the above process, the set of velocity angles under each frequency condition is obtained, that is, the set of frequency-related velocity angles is obtained.

[0022] S112: Call the frequency-correlated velocity angle set, group the velocity direction change data under multiple frequency conditions, analyze the changing trend of each frequency band, and use the following formula: ; Calculate the response trend intensity to obtain the frequency distribution trend parameter set; in, The strength of the response trend under frequency modulation. For the first The normalized value of the velocity direction angle at each response time point is obtained by comparing the original angle with the theoretical maximum angle. The arithmetic mean of the normalized angles of the velocity directions over all response sampling periods is obtained by averaging the normalized angle values ​​for each period. For the first The normalized value of the velocity increment within each response cycle is obtained by comparing the velocity increment of that cycle with the maximum velocity increment within that cycle. For the first The normalized value of the excitation frequency density for each cycle is obtained by comparing the excitation frequency of the current cycle with the maximum excitation frequency of all cycles. The minimum unit of the system for normalized sampling frequency is obtained by normalizing using the minimum sampling frequency set by the system. In response to the total number of sampling periods, Number the response sampling period; By extracting the first The velocity direction angle at each response time point First, normalize it, that is, normalize the actual included angle. Divide by the theoretical maximum angle For example, the first included angle value Normalization process yields Then, calculate the arithmetic mean of all the normalized angle values. For example, assume the arithmetic mean of 100 normalized angle values. Simultaneously, the response speed increment within each cycle... Normalization is performed, specifically taking the velocity increment of the second cycle as an example. Let's assume the velocity increment for this cycle is... The maximum speed increment is Then the normalized value of the velocity increment ; for periodic excitation frequency Normalization is performed using periodic frequency. For example, assuming the highest frequency of the experiment Then the normalized value of frequency density System minimum sampling frequency Set as sampling frequency Normalized value Then, substitute the normalized parameters above into the formula: ; The specific calculations are as follows: Pick Sometimes, , , Substitute into the formula to calculate: ; For all The calculation is performed sequentially for each sampling point, assuming the final summation result is... ,and Then substitute it into the calculation: ; The response trend intensity is a comprehensive dimensionless value characterizing the coordinated characteristics of directional fluctuations and velocity changes in the response process of an organic molecular system under magnetic field-assisted excitation. It reflects the strength of non-uniform response segments or abrupt change segments exhibited under different excitation frequencies and magnetic response behaviors. A larger response trend intensity value indicates that the magnetic response behavior exhibits more obvious directional fluctuations and velocity jumps within the observed period; a smaller value indicates a more stable response with limited variation. This parameter is used in various application scenarios such as magnetic control strategy adjustment, focusing response anomaly screening, or automatic optimization of magnetic response devices, enabling real-time feedback and strategy support for the dynamic behavior of the magnetic field control system. The results show that the calculated response trend intensity of the frequency distribution trend parameter set is... Its range is generally within The values ​​were verified and determined through multiple sets of experimental data. The formula, which uses both the velocity angle and velocity increment in the calculation, obtains a precise description of the magnetic response trend and can accurately characterize the response features of organic molecules under different frequency conditions.

[0023] S113: Based on the frequency distribution trend parameter set, analyze the relationship between excitation frequency and direction change path, determine the co-occurrence characteristics of direction jump and excitation change, establish the corresponding trajectory of excitation frequency and direction change, and obtain response trajectory distribution information; The response trend strength obtained from paragraph 2 For example, first determine the analysis range of the excitation frequency and direction change path, that is, select... exist The intervals between these points serve as clear co-occurrence characteristic regions. Specifically, when analyzing response paths, the aforementioned frequencies are used. The corresponding data first identifies the locations of directional jumps during the response process. Specifically, a directional jump is defined as the change in the normalized value of the velocity-direction angle between two consecutive time points exceeding a threshold, which is set as the normalized value of the angle. For example, between time points 15 and 16, the normalized values ​​of the velocity direction angles are respectively and The difference is Exceeding the threshold Therefore, this point was determined to be the direction change point; secondly, based on the periodic characteristics of the excitation frequency, the corresponding magnetic field pulse excitation signal was selected. and The changes between them are recorded, and the corresponding excitation frequency jump data are recorded. By comparing the jump position of the excitation frequency with the jump position of the velocity direction, the co-occurrence of the two is determined and recorded as the corresponding trajectory of the excitation frequency and direction changes. The response trajectory distribution information is obtained from the actual data.

[0024] Please see Figure 3 The specific steps for obtaining the response input weight configuration are as follows: S211: Obtain response trajectory distribution information, compare the positional order differences between the current molecular response path sequence and the standard path template, analyze the arrangement displacement of each response point, and generate a response offset displacement parameter set; First, a set of actual magnetic response path sequences and standard path templates are selected. The positions of each response point within the sequence are compared one by one. Assuming the measured molecule has a total of 10 response points under the influence of a magnetic field, the standard path template numbers are sequentially increasing integers from 1 to 10, and the actual measured response point sequence is: [1,3,2,4,5,7,6,8,9,10]. This sequence represents the control state number of the response molecule under the actual influence of an external magnetic field. By calling the corresponding position number of the standard template one by one, taking position 2 of the actual response sequence (response point state number 3) as an example, the corresponding position number of the standard template is 2. The numerical difference is calculated by subtracting the values, thus obtaining the displacement difference of that response point. That is, the position is shifted one unit in the positive direction, and the same process is performed on the remaining response points one by one to obtain the displacement value data of each response point relative to the standard template, as shown in Table 2.

[0025] Table 2 Data on Displacement Difference at Response Points

[0026] As shown in Table 2, the position of each response point is calculated by the numerical difference between the actual response state number and the standard state number, and the offset displacement of each response point is obtained to form a response offset displacement parameter group.

[0027] S212: Based on the response offset displacement parameter group, identify the sequential offset direction of each response point, call the corresponding control state sequence, and match the offset direction with the control state sequence using the formula: ; Calculate the sequence offset matching coefficients, and combine the displacement data with the state sequence order to generate a matching coefficient dataset; in, For sequential offset matching coefficients, For the first The current control state number of each response point is obtained through the response point control command number. For the first Displacement of each response point The sign function value, if If positive, take 1; if negative, take -1; if zero, take 0. For the first Each standard state sequence reference state number is determined by the standard path template number. For the first The displacement of each response point relative to the standard path. This represents the total number of response points. The index number of the response point; First, call the displacement difference parameter of the response point ( The direction is determined one by one, and the sign of the displacement difference is determined by the displacement difference of the second response point. For example, because it is a positive value, the sign function... The calculation result is 1, the displacement difference of the 3rd response point. Then the symbolic function The value is -1, and so on. The control state sequence is called to perform a sequence matching process, and the actual response state sequence number is ( ) sequentially with the standard state number ( Numerical calculations are performed using the results of the offset displacement sign function, taking the second response point as an example, the response state number... Standard state number The sign function value is 1. Substituting this into the formula, the calculation is as follows: ; First, calculate the absolute value of the numerator, taking the second response point as an example: ; Calculate separately for all response points, such as response point number 3: ; The sum of the results from all 10 response points is: .

[0028] Next, calculate the square root operation of the second term in the numerator, taking the second response point as an example: ; The sum of all response points is calculated cumulatively. For example, the sum of all point displacements calculated by taking the square root of the total displacement is 6.656.

[0029] Next, calculate the denominator, taking response point number 2 as an example: ; Calculate each response point individually, and the total sum of the cumulative denominators is 4.

[0030] The final result is the sequence offset matching coefficient: ; The sequence offset matching coefficient is a dimensionless index of overall deviation of the response sequence, used to measure the degree of overall offset in sequence and position between the current response path and the standard template during molecular magnetic modulation. A larger value indicates a more significant difference between the response point arrangement, offset direction, and control state sequence and the standard template, and a more prominent overall path anomaly. This coefficient can be used to dynamically adjust input priority and excitation intensity during magnetic modulation, and to determine and warn of drift risks, response anomalies, or reconstructed modulation paths, achieving automatic feedback and adaptive adjustment. The results show an offset matching coefficient of 4.164, which, experimentally verified, is consistent with the expected value. The value typically ranges from 0 to 10, with lower values ​​indicating higher matching accuracy. The current value is in the medium-high range (4.0 to 6.0), indicating that further adjustment is needed. The formula uses state number, displacement sign, and displacement amount together in the calculation to achieve quantitative analysis of the response path matching accuracy.

[0031] S213: Based on the matching coefficient dataset, adjust the input priority level, construct the control intensity ratio coefficient by combining the offset displacement and priority level, and establish the response input weight configuration; The matching coefficient calculated in paragraph 2 Based on this, the current matching coefficient value is determined to be between 4.0 and 6.0, indicating that the response path deviates from the standard path to a moderate to high degree. Therefore, the input priority level needs adjustment. Based on this range, the priority adjustment factor is set to 1.2. By calling the response offset displacement data one by one (for example, the displacement difference of response point 2 is 1, and the displacement difference of response point 3 is -1), the absolute value of the displacement is multiplied by the priority adjustment factor value to calculate the control intensity ratio coefficient for response point 2. The control intensity ratio coefficient for response point 3 is the same. Calculate the control intensity ratio coefficients corresponding to all response points in sequence, and establish response input weight configuration data, as shown in Table 3.

[0032] Table 3. Control Intensity Proportioning Coefficient Data Table

[0033] As shown in Table 3, the complete response input weight configuration dataset is obtained.

[0034] Please see Figure 4 The specific steps for obtaining the focusing coordinate correction results are as follows: S311: By configuring the response input weights, identify the coordinates of the response center within the magnetic control cycle. Combined with the spatial coordinates of the last response node in the same cycle, analyze the changes in the spatial direction between the response center and the end point of the path, and generate the change in the direction of the center and the end point. First, the sequence of response nodes within the current magnetic control cycle is retrieved. For example, if a total of 8 response nodes are measured in this cycle, the three-dimensional coordinate position data of each response node is acquired using actual measuring equipment. The first node is used as... The second node is And so on, until the coordinates of the last node are... Call the control strength ratio coefficient of each node, for example, the first node is... The second node is The coordinates of each node are multiplied by the control strength coefficient. The weighted sum of all nodes is then divided by the sum of the control strength coefficients to obtain the coordinates of the response center. For example, in the actual calculation process: the product of the first node's position and its weight is... The product of the second node position and the weight is Calculate the product of all nodes sequentially, then sum these results vector-wise, and finally divide by the sum of all control intensity ratio coefficients. For example, if the sum of the coefficients is... The location of the response center is then calculated as follows: Using this value as the response center coordinate position, the coordinates of the last response node are further called. Then, by subtracting the coordinates of the center and the last node one dimension at a time, the change in spatial direction is obtained. This ultimately generates the change in direction of the center and end points.

[0035] S312: Based on the change in direction of the center endpoint, determine the directional shift characteristics of the current response centroid, and combine this with the response direction extension trajectory, using the formula: ; Calculate the trend of center of gravity offset, analyze the spatial displacement trend at the end of the path, and generate the trend of center of gravity offset. in, This represents the trend of the center of gravity shifting in the direction of gravity. For the first The normalized spatial extension trajectory direction vector of the response node is obtained by using the first response node. The spatial trajectory direction vector of each response node is obtained by dividing the maximum magnitude of the spatial trajectory vectors of all nodes within that period. For the first The normalized spatial extension trajectory direction vector of each response node The index of the response node indicates the sequential number of the node in the current trajectory sequence. The total number of response nodes is obtained by counting the number of all valid response nodes within this period. Change in direction from center to end point Based on this, the first step is to retrieve the total number of nodes within the response period, for example, the total number of response nodes in this period. Taking the calculation of the spatial trajectory direction vector of adjacent nodes as an example, the first... Node positions and the Node positions Calculate the spatial trajectory vector For example, the trajectory vector from node 1 to node 2 is Calculate the trajectory vectors between all nodes sequentially, assuming the maximum magnitude of each vector is obtained. Then, the trajectory vector of each node is normalized one by one. For example, the trajectory vector of the first node... After normalization, we get The normalized spatial trajectory vectors of all nodes are calculated in the same manner, as shown in Table 4: Table 4. Node Normalized Trajectory Vector Table

[0036] As shown in Table 4, the vector difference is calculated one by one from adjacent normalized trajectory vectors, and then the magnitude of the difference vector is calculated. For example, the difference vector between node 1 and node 2 is... The module length is Then, sum the magnitudes of the difference vectors between all nodes, for example, the sum is... Then calculate the dot product of the trajectory vectors between nodes one by one. For example, the dot product of the vectors of node 1 and node 2 is... Assume the sum of the dot products of all nodes is Then substitute it into the formula to calculate: ; Among them, the center-of-gravity offset trend is a comprehensive index that quantifies the degree of spatial directional change of the response path throughout the entire magnetic control cycle. It reflects the overall degree of path reversal, jumps, or drift. The larger the value, the more discontinuous the spatial direction of the response sequence and the more abrupt changes; the smaller the value, the more continuous and stable the spatial trend of the response path. This parameter serves as a decision input for magnetic control adjustments such as magnetic focusing correction, spatial anomaly detection, and response reconstruction judgment, enabling automated calibration of spatial positioning and early warning of abnormal responses. The results show that the center-of-gravity offset trend is... This value typically ranges from 0 to 2, where 0 indicates no directional shift, and the closer the value is to 2, the stronger the shift trend. The current shift trend is moderately weak (0.5 to 1.0). The formula accurately assesses the spatial directional shift trend of the center of gravity by normalizing the magnitude of the spatial trajectory vector difference and performing a dot product operation.

[0037] S313: Using the trend of the center of gravity offset, combined with the feedback response time information of the corresponding period, the coordinates of the magnetic focusing center are spatially corrected to obtain the focusing coordinate correction result. The calculated trend quantity Based on this, the feedback response time data of the response nodes in this cycle is further invoked. For example, the response times of nodes 1 to 8 are as follows: The response time difference of each response node is calculated one by one, and the maximum difference in response time is determined. For example, the maximum difference within this period is set to a threshold. Assuming the maximum time difference within this period is If the time difference is less than the set threshold, it means the time difference meets the set conditions, and the response center coordinates are called. coordinates of the last node The spatial direction change is adjusted by multiplying the values, for example, by using the trend in the direction of the center of gravity. Multiply by direction change in each dimension The correction amount is obtained as Finally, the correction amount is vector-added with the original response center coordinates to obtain the center coordinates after focus correction. This generates the focus coordinate correction results.

[0038] Please see Figure 5 The specific steps for obtaining the response sequence reconstruction results are as follows: S411: Based on the focusing coordinate correction results, filter the time feedback records of each response node within the magnetic control cycle, calculate the response time interval difference between each pair of adjacent nodes, and generate a time interval difference value group. First, the response node time feedback records within the magnetic control cycle are retrieved. Assuming a total of 9 response nodes are recorded in this cycle, with feedback response times of [0.013, 0.025, 0.036, 0.049, 0.061, 0.073, 0.086, 0.098, 0.110] seconds, the response time values ​​of adjacent nodes are subtracted one by one. For example, the difference in response time interval between the second and first nodes is calculated as follows: The difference in response time between the third node and the second node is calculated as follows: The response time interval differences between all adjacent nodes are calculated sequentially and are shown in Table 5.

[0039] Table 5: Response Node Time Interval Difference Table

[0040] As shown in Table 5, the complete time interval difference set is obtained through the above calculation process.

[0041] S412: Call the time interval difference group, analyze the fluctuation of the time interval, determine the trend of the feedback rhythm change, filter out rhythm imbalance segments, identify unstable areas of the path, and obtain the rhythm fluctuation identification result. First, retrieve all response time interval difference data from Table 5, and calculate the fluctuation between adjacent differences one by one. For example, the first fluctuation is calculated by subtracting the first interval difference from the second interval difference. The fluctuation calculated from the difference between the third and second intervals is: Similarly, all fluctuation values ​​are calculated, resulting in fluctuation data of [0.001, 0.002, 0.001, 0.000, 0.001, 0.001, 0.000]s. Then, the set fluctuation threshold is applied. Each fluctuation is compared and calculated individually. When a fluctuation exceeds a threshold, a rhythmic imbalance segment is identified at that location. For example, the second fluctuation... Above the threshold If a rhythm imbalance segment is determined between nodes 2 and 4, the corresponding node position in this interval is marked as an unstable area of ​​the path. If the fluctuation amount of other regions is below the threshold, then other regions are identified as stable regions. Finally, the rhythm fluctuation identification result is that the path between nodes 2 and 4 is an unstable area.

[0042] S413: Based on the rhythm fluctuation identification results, the current path structure is rearranged according to the stability of the response rhythm, the order of response nodes is adjusted, and the response sequence reconstruction result is obtained; First, the previously identified unstable path regions, namely nodes 2 to 4, are accessed, and the original time feedback records for each node are retrieved, specifically for node 2: Node 3: Node 4: Then, the response times of the nodes in this area are reordered based on numerical differences. The reordering is based on the absolute value of the response time difference between nodes; for example, the interval between nodes 2 and 3 is... The interval between nodes 3 and 4 is The interval between nodes 3 and 4 is determined to be greater than the interval between nodes 2 and 3. Therefore, node 4 is moved to the position of node 3, node 3 is moved to the position of node 4, and the position of node 2 remains unchanged. This completes the adjustment of the node order. The rearranged response sequence is [node 2: 0.025s, node 4: 0.049s, node 3: 0.036s]. Then, all nodes are called again to reconstruct the complete response sequence. The rearranged segment is put back into the original path position. Finally, the reconstructed complete response sequence is obtained as follows: [node 1: 0.013s, node 2: 0.025s, node 4: 0.049s, node 3: 0.036s, node 5: 0.061s, node 6: 0.073s, node 7: 0.086s, node 8: 0.098s, node 9: 0.110s].

[0043] Please see Figure 6 The specific steps for obtaining the magnetic archiving node path set are as follows: S511: Call the response sequence reconstruction result, locate the corresponding response state node according to each magnetic field input information in the control sequence, and generate a control response node location set; First, the magnetic field input control sequence is invoked. For example, a total of 7 magnetic field input information items are invoked. Each input information item contains specific parameters such as the magnetic field application direction, magnetic field strength, and application duration. Taking the first magnetic field input information as an example, the magnetic field application direction is set to a three-dimensional spatial vector direction (0.4, 0.7, 0.6), the magnetic field strength is 1.2 mT, and the application duration is 0.02 s. Then, the spatial position and response time parameters of the nodes in the reconstructed response sequence are invoked sequentially. Node positioning is performed by comparing each item. For example, the application duration of the first input magnetic field (0.02 s) is compared with the node times in the response sequence. The response time of node 1 is 0.013 s, and that of node 2 is 0.025 s. The absolute differences are calculated as |0.02-0.013|=0.007 s and |0.02-0.025|=0.005 s, respectively. Based on the principle of minimizing absolute differences, node 2 is determined to be the response state node corresponding to the first magnetic field input, thus completing the positioning of the response node for the first magnetic field input information. Similarly, for the second magnetic field input information, the direction (0.5, 0.6, 0.5), intensity 1.0 mT, and duration 0.035 s are calculated by calling the time series of each response node, and finally positioning node 3. The above steps are repeated until the positioning of the response nodes for all 7 input information items is completed, forming a complete set of control response node positioning.

[0044] S512: Call the control response node location set, analyze the directional changes in the control input sequence, combine the angle differences between magnetic moment directions during the response process, identify the mapping path between the input control sequence and the effective response state, and obtain the control response mapping path set; First, the spatial direction vector of each magnetic field control input sequence is called. For example, the first direction vector is (0.4, 0.7, 0.6), and the second direction vector is (0.5, 0.6, 0.5). The angle change between adjacent direction vectors of the control input sequence is calculated sequentially. For example, the vector angle calculation process calls the direction vector dot product formula. Taking the first and second inputs as an example, the dot product of the two vectors is calculated as: 0.4×0.5+0.7×0.6+0.6×0.5=0.83. The vector magnitudes are calculated separately. The magnitude of the first vector is (0.4²+0.7²+0.6²)^0.5=1.0, and the magnitude of the second vector is (0.5²+0.6²+0.5²)^0.5=0.927. Then, the angle calculation formula cosθ=0.83 / (1.0×0.927)=0.895 is called to calculate the angle. With angle θ set to 26.56°, the angles between adjacent directions of all input sequences are repeatedly calculated. Simultaneously, the magnetic moment direction vectors of the corresponding response nodes are called. For example, the magnetic moment direction vectors of node 2 are (0.42, 0.69, 0.58) and node 3 are (0.51, 0.59, 0.51). The changes in the magnetic moment direction angles of the response nodes are calculated one by one, resulting in a magnetic moment direction angle of 25.50°. Then, the difference between the input direction and the response magnetic moment angle is calculated: |26.56° - 25.50°| = 1.06°. This method is used to analyze the angle differences between all magnetic field inputs and the corresponding response node magnetic moment directions one by one. A threshold of 2° is then set for the angle difference value. When the difference is lower than this threshold, it is considered a valid response state, and the input and response node are marked as a valid mapping relationship. Finally, the control response mapping path set is obtained.

[0045] S513: Based on the control response mapping path set, analyze the mapping path, classify the organic magnetic response behavior, and output the magnetic control archiving node path set; First, the marked valid control response mapping paths are invoked. For example, the first input is validly mapped to node 2, and the second input is validly mapped to node 3. The specific parameter information of each response node within the mapping path is then invoked, including the node's spatial location, magnetic moment direction, and response intensity. For example, node 2 has a spatial location of (1.3, 2.4, 1.2) mm, a magnetic moment direction of (0.42, 0.69, 0.58), and a response intensity of 0.95. Subsequently, the response nodes are categorized according to their parameter value ranges. For instance, the spatial location coordinates are categorized based on the x, y, and z directions, with ranges set as follows: x direction (1.0-1.5) mm, y direction (2.0-2.5) mm, and z direction (1.0-1.5) mm respectively. mm, the magnetic moment direction angle is divided into the same category from 0-30°, the response intensity is defined as the strong response range from 0.8-1.0, and node 2 is classified into the "spatial range A-strong response category" archiving category. In this way, all node parameters in the mapping path are called one by one to perform the same classification process, and finally the complete magnetic control archiving node path set is obtained.

[0046] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for controlling the magnetic properties of organic molecules based on a magnetic field-assisted process, characterized in that, Includes the following steps: S1: Acquire magnetic response data, analyze the change in magnetic response velocity during the molecular response process under multi-frequency magnetic field pulse excitation, calculate the velocity direction angle at continuous time points, establish the corresponding trajectory of excitation frequency and direction change, and generate response trajectory distribution information; S2: Using the response trajectory distribution information, compare the sequence differences between the current response path and the standard path, analyze the displacement of the response points, adjust the input priority level in combination with the control state sequence, construct the control intensity ratio coefficient, and output the response input weight configuration. S3: Using the aforementioned response input weight configuration, analyze the positional changes of the response center and the end point of the path in the direction, determine the directional offset characteristics of the current response center of gravity, and, in conjunction with the feedback response time information of the corresponding period, correct the magnetic focusing center coordinates and output the focusing coordinate correction result. S4: Based on the focusing coordinate correction results, filter the time feedback records of each response node within the magnetic control cycle, calculate the response time interval difference between each pair of adjacent nodes, determine the trend of feedback rhythm change, filter out rhythm imbalance segments, rearrange the current path structure based on the stability of the response rhythm, and generate response sequence reconstruction results.

2. The method for controlling the magnetic properties of organic molecules based on a magnetic field assisted according to claim 1, characterized in that, The response trajectory distribution information includes excitation frequency, direction change trajectory, and velocity direction angle trend. The response input weight configuration specifically includes response path offset position, control state level parameter, and control intensity ratio factor. The focusing coordinate correction result specifically refers to the response center spatial coordinates, path end point extension position, and feedback time reference node. The response sequence reconstruction result includes time interval difference group, path rhythm stability label, and rearranged response sequence number.

3. The method for controlling the magnetic properties of organic molecules based on magnetic field assistance according to claim 1, characterized in that, The specific steps for obtaining the response trajectory distribution information are as follows: S111: Acquire magnetic response data, analyze magnetic field pulse excitation signal sequences under multiple frequency conditions, record magnetic response velocity change data in each excitation cycle, calculate the velocity change direction angle between consecutive time points, and generate a set of frequency-correlated velocity angles. S112: Call the frequency-related velocity angle set, group the velocity direction change data under multiple frequency conditions, analyze the change trend of each frequency band, calculate the response trend intensity, and obtain the frequency distribution trend parameter set; S113: Based on the frequency distribution trend parameter set, analyze the relationship between the excitation frequency and the direction change path, determine the co-occurrence characteristics of direction jump and excitation change, establish the corresponding trajectory of excitation frequency and direction change, and obtain response trajectory distribution information.

4. The method for controlling the magnetic properties of organic molecules based on a magnetic field assisted according to claim 3, characterized in that, The specific steps for obtaining the response input weight configuration are as follows: S211: Obtain the response trajectory distribution information, compare the positional order difference between the current molecular response path sequence and the standard path template, analyze the arrangement displacement of each response point, and generate a response offset displacement parameter set; S212: Based on the response offset displacement parameter group, identify the sequential offset direction of each response point, call the corresponding control state sequence, match the offset direction with the control state sequence, calculate the sequential offset matching coefficient, and combine the arrangement displacement data with the state sequence sequence to generate a matching coefficient dataset. S213: Based on the matching coefficient dataset, adjust the input priority level, construct the control intensity ratio coefficient by combining the offset displacement and priority level, and establish the response input weight configuration.

5. The method for controlling the magnetic properties of organic molecules based on a magnetic field assisted according to claim 4, characterized in that, The specific steps for obtaining the focusing coordinate correction result are as follows: S311: Using the aforementioned response input weight configuration, identify the response center coordinate point within the magnetic control cycle, and combine it with the spatial position coordinates of the last response node in the same cycle to analyze the changes in the spatial direction between the response center and the end point of the path, and generate the change in the direction of the center and end point. S312: Based on the change in direction of the center endpoint, determine the directional offset characteristics of the current response center of gravity, combine the response direction extension trajectory, calculate the directional offset trend of the center of gravity, analyze the spatial displacement trend of the path endpoint, and generate the directional offset trend of the center of gravity. S313: Using the aforementioned center of gravity offset trend, combined with the feedback response time information of the corresponding period, spatial correction is performed on the magnetic focusing center coordinates to obtain the focusing coordinate correction result.

6. The method for controlling the magnetic properties of organic molecules based on a magnetic field assisted according to claim 5, characterized in that, The specific steps for obtaining the response sequence reconstruction result are as follows: S411: Based on the focusing coordinate correction results, filter the time feedback records of each response node within the magnetic control cycle, calculate the response time interval difference between each pair of adjacent nodes, and generate a time interval difference value group. S412: Call the time interval difference group, analyze the fluctuation of the time interval, determine the trend of feedback rhythm change, filter out rhythm imbalance segments, identify unstable path areas, and obtain rhythm fluctuation identification results. S413: Based on the rhythm fluctuation recognition results, the current path structure is rearranged according to the stability of the response rhythm, the order of response nodes is adjusted, and the response sequence reconstruction result is obtained.

7. The method for controlling the magnetic properties of organic molecules based on a magnetic field assisted according to claim 1, characterized in that, The method further includes: S5: Call the response sequence reconstruction result, locate the corresponding response state node according to each magnetic field input information in the control sequence, identify the mapping path between the input control sequence and the effective response state by analyzing the direction change in the control input and the angle difference between the magnetic moment directions during the response process, classify the organic magnetic response behavior, and output the magnetic control archive node path set; The magnetically controlled archiving node path set specifically includes the control input direction, response node coordinates, and angle matching path number.

8. The method for controlling the magnetic properties of organic molecules based on a magnetic field assisted according to claim 7, characterized in that, The specific steps for obtaining the magnetically controlled archiving node path set are as follows: S511: Call the response sequence reconstruction result, locate the corresponding response state node according to each magnetic field input information in the control sequence, and generate a control response node location set; S512: Call the control response node location set, analyze the directional changes in the control input sequence, and combine the angle differences between magnetic moment directions during the response process to identify the mapping path between the input control sequence and the effective response state, and obtain the control response mapping path set; S513: Based on the control response mapping path set, analyze the mapping path, classify the organic magnetic response behavior, and output the magnetic control archiving node path set.

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