Cloud platform-based instrument sharing management method and system

Abstract

The present application relates to the technical field of sharing management, in particular to an instrument sharing management method and system based on a cloud platform, comprising the following steps: obtaining a task generation structure sequence, screening equipment to generate and publish a list, constructing a channel list to generate a partition table, and binding resource output management records.In the present application, the behavior structure relationship is realized by automatically extracting the experimental task text content for fine analysis, the cooperation relationship between devices is accurately split according to the operation sequence and behavior difference, the device response capability and resource occupation characteristics are identified by multi-dimensional index quantization, the execution conflict relationship is jointly determined by difference analysis and resource intersection, the device operation sequencing and channel scheduling process are constructed to form a priority execution path according to the response level, the operation time period arrangement adopts the execution proportion and the start interval to jointly deduce the accurate division of the task block, and the automatic closed-loop linkage management of task analysis, device identification, conflict detection, priority sequencing, block division, resource allocation and sharing registration is realized.

Description

Technical Field

[0001] This invention relates to the field of shared management technology, and in particular to a cloud-based instrument shared management method and system. Background Technology

[0002] The field of shared resource management technology mainly involves methods for the coordinated use and unified allocation of reusable resources. This includes resource information registration, application approval, scheduling and allocation mechanisms, usage process monitoring, and post-return evaluation record management. It is commonly applied in various shared resource scenarios such as scientific research equipment, office facilities, and public transportation. Among these, instrument sharing management methods address the shared use needs of various experimental instruments and equipment in research or teaching institutions. These methods typically employ manual registration, offline reservations, paper-based approvals, and manual scheduling to manage instrument usage. The focus is on achieving unified management and allocation of different instruments, including user identification, access control verification, reservation scheduling, and usage record registration. Management records are generally maintained by establishing usage ledgers, assigning management personnel to verify applications, relying on management experience to determine the usage order, and manually filling out usage records.

[0003] In the current use and management of laboratory experimental equipment, manual registration is the main method. However, the lack of structured recognition of operational behavior under manual registration leads to errors in equipment allocation based on subjective judgment. Offline reservation processes cannot cover dynamic tasks, causing resource conflicts and time overlaps. Paper-based approval processes limit the efficiency of information exchange, resulting in delays in approval progress. Equipment usage records rely on manual filling, which can lead to omissions and ambiguities. Access control cannot accurately match actual operational needs. The use of ledger management makes it difficult to adjust the scheduling order in a timely manner when faced with changes in high-frequency tasks. Managers' reliance on experience to arrange the execution order may lead to decreased execution efficiency and resource waste when there are differences in the responsiveness of different equipment. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a cloud-based instrument sharing and management method.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a cloud platform-based instrument sharing and management method, comprising the following steps:

[0006] S1: Obtain the experimental task text content, perform character segmentation, match each group of operation words with preset operation category labels, determine the corresponding experimental behavior label for each group of operation words, and generate a task behavior structure sequence.

[0007] S2: Based on the experimental behavior labels in the task behavior structure sequence, identify the corresponding experimental devices and sort them according to the position index. Compare and calculate the sorting difference between the operations of each device, filter the concurrent collaboration units, and generate the experimental device concurrent structure table.

[0008] S3: Based on the experimental equipment concurrency structure table, collect the unit time response rate label and resource occupation code of the corresponding experimental equipment, identify experimental equipment pairs whose response rate label difference is greater than a set threshold, and at the same time determine that experimental equipment pairs with overlapping resource occupation codes are conflict groups, construct the experimental equipment execution sorting, and generate a list of concurrent control execution channels.

[0009] S4: Based on the experimental equipment sorting in the concurrent control execution channel list, read the execution time period of the corresponding task, calculate the start and end duration of each operation and leave a start interval between adjacent devices, mark the task execution block, construct the operation level scheduling partition layout, and generate the device scheduling time partition table.

[0010] S5: Based on the partition layout in the device scheduling time partition table, detect the availability status of the experimental equipment that can be called in the corresponding time period in the current laboratory, perform resource allocation for the equipment that meets the conditions and bind the scheduling partition and operation instruction information, and output the cloud platform smart laboratory instrument sharing management record.

[0011] As a further embodiment of the present invention, the task behavior structure sequence includes operation stage identifier, experimental behavior type, and behavior combination sequence; the experimental equipment concurrency structure table includes concurrent equipment group identifier, control continuity status, and equipment operation position index; the concurrent control execution channel list includes channel number, equipment priority level, and resource conflict flag; the equipment scheduling time partition table includes scheduling time period number, operation execution block division, and equipment start interval mark; and the cloud platform smart laboratory instrument sharing management record includes equipment resource allocation status information, scheduling partition correspondence, and instruction binding information.

[0012] As a further aspect of the present invention, the step of obtaining the task behavior structure sequence specifically includes:

[0013] S111: Obtain the experimental task text content, perform character segmentation on the experimental task text content, identify each segmented text unit, perform semantic clustering classification on the word groups in the text unit, perform annotation comparison on each operation word group, match operation category labels, and generate an operation semantic annotation result set;

[0014] S112: Based on the operation semantic annotation result set, all operation phrases are grouped into similar categories according to operation category labels. The merged operation sets are sequentially aggregated, and a linear sorting mapping is established according to the sequential arrangement structure in the operation sequence to generate a task operation combination sequence set.

[0015] S113: Based on the task operation combination sequence set, extract the corresponding operation category label for each set of operations in the sequence, match the experimental behavior type, aggregate all matching experimental behavior labels in sequence, establish a mapping between operations and behaviors, and obtain the task behavior structure sequence.

[0016] As a further aspect of the present invention, the step of obtaining the concurrent structure table of the experimental equipment specifically includes:

[0017] S211: Obtain the experimental behavior tags in the task behavior structure sequence, perform semantic retrieval on the set of operation phrases associated with each tag, extract the experimental equipment name in the operation phrases, perform the first indexing and locating operation in the experimental task text content, construct the location information table of equipment terms according to the original order of the task text, and generate the equipment location index mapping table.

[0018] S212: Based on the device location index mapping table, all experimental devices are rearranged in ascending order according to the first occurrence index value, the index difference between adjacent devices is calculated, an index difference sequence matrix between devices is constructed, and experimental device combination items with continuous control difference less than the continuous control difference threshold are filtered out to obtain a non-continuous control device combination set.

[0019] S213: Based on the set of non-continuous control devices, perform synchronization relationship detection on each set of devices, and perform comparative analysis based on the temporal difference characteristics between the experimental behaviors of the experimental devices to determine the concurrent units and obtain the experimental device concurrency structure table.

[0020] As a further aspect of the present invention, the determination of concurrent units specifically means that if the related behaviors of two sets of experimental devices in the experimental task text content have a non-nested structure and no control dependency relationship, then the corresponding experimental device combination item is determined as a concurrent unit.

[0021] As a further aspect of the present invention, the step of obtaining the concurrent control execution channel list specifically includes:

[0022] S311: Collect the unit time response rate label and resource occupation code of each experimental device in the experimental device concurrency structure table, and perform structural parsing on the response rate label of each experimental device to construct the experimental device response rate index set. At the same time, perform standardized filling operation on the resource occupation code of each experimental device according to the binary encoding format to construct the device response and resource code set.

[0023] S312: Based on the device response and resource coding set, perform a difference calculation operation on the response rate label of each pair of concurrent experimental devices. If the difference is greater than the response difference threshold, the experimental device pair is marked as a candidate conflict pair. Perform a bitwise AND operation on the resource occupancy code bits of the candidate conflict pair. If any bit in the result is 1, it is considered that there is a resource intersection. Calculate and obtain the normalized response conflict score index, and summarize to establish a resource conflict judgment set.

[0024] S313: Based on the resource conflict determination set, all experimental equipment pairs that do not have resource intersections are sorted in descending order of response rate number, and a linear execution chain is constructed according to the sorting order. Each experimental equipment is assigned an independent channel number, and the numbers are numbered sequentially according to the sorting order to obtain the concurrent control execution channel list.

[0025] As a further aspect of the present invention, the step of obtaining the device scheduling time partition table specifically includes:

[0026] S411: Based on the sorting order of each experimental device in the concurrent control execution channel list, read the start time and end time corresponding to each experimental device, allocate the required execution time according to the proportion, count the start and end time periods of each experimental device in the total task time interval, and generate a device time period allocation matrix.

[0027] S412: Based on the device time period allocation matrix, extract the time period boundary positions of adjacent experimental device combinations, subtract the start position of each time period from the end position of the previous device, and if the difference is less than the fixed start interval benchmark value, shift the start time of the adjacent experimental device backward to the position that meets the minimum start interval condition. The updated time period record covers the original time period configuration, forming a non-overlapping separation structure, and obtain the scheduling start interval configuration set.

[0028] S413: Based on the start and end time period configuration in the scheduling start interval configuration set, mark the task execution area of ​​each device in chronological order, draw corresponding blocks in units of time intervals, bind each block with the corresponding experimental device number, perform block ownership mapping and scheduling map encoding, and generate a device scheduling time partition table.

[0029] As a further aspect of the present invention, the steps for obtaining the cloud platform smart laboratory instrument sharing management records are as follows:

[0030] S511: Based on the partition layout data frame in the device scheduling time partition table, extract the start time and end time corresponding to each experimental device task, construct the task time period index interval, perform logical judgment operation on the available status identifier of the experimental device and establish a list of callable devices, and generate a set of available status of partitioned devices.

[0031] S512: Based on the device information of each time interval in the available status set of the partitioned devices, perform a mapping and binding operation between the time interval and the experimental device number, retrieve the experimental device task identifier in the scheduling block, aggregate and match the control instruction set and the time interval identifier, bind them into a joint data structure, and establish a partitioned operation instruction binding structure frame.

[0032] S513: Based on the joint structure record in the binding structure frame of the partition operation instruction, perform synchronous write operation on all binding relationships, map each record to the cloud platform, and perform index encoding operation on each record to construct a data record sequence with a unique identifier structure, and obtain the cloud platform smart laboratory instrument sharing management record.

[0033] As a further aspect of the present invention, the logical judgment operation performed on the available status flag of the experimental equipment is specifically as follows: if the status flag of the experimental equipment is 1, it is determined to be in a callable state; otherwise, it is in a non-callable state.

[0034] A cloud-based instrument sharing management system includes:

[0035] The task semantic parsing module is used to perform S1: obtain the experimental task text content, perform character segmentation, match preset operation category labels for each group of operation words, determine the experimental behavior label corresponding to each group of operation words, and generate a task behavior structure sequence;

[0036] The device operation identification module is used to execute S2: based on the experimental behavior labels in the task behavior structure sequence, identify the corresponding experimental devices and sort them according to the position index, compare and calculate the sorting difference between each device operation, filter concurrent collaborative units, and generate an experimental device concurrent structure table.

[0037] The conflict factor rearrangement module is used to execute S3: based on the experimental equipment concurrency structure table, collect the unit time response rate label and resource occupation code of the corresponding experimental equipment, identify experimental equipment pairs whose response rate label difference is greater than a set threshold, and at the same time determine that experimental equipment pairs with intersection between resource occupation codes are conflict groups, construct the experimental equipment execution sorting, and generate a list of concurrency control execution channels.

[0038] The scheduling time configuration module is used to execute S4: according to the experimental equipment sorting in the concurrent control execution channel list, read the execution time period of the corresponding task, calculate the start and end duration of each operation and leave a start interval between adjacent devices, mark the task execution block, construct the operation level scheduling partition layout, and generate the device scheduling time partition table.

[0039] The platform scheduling landing module is used to execute S5: based on the partition layout in the device scheduling time partition table, detect the availability status of the experimental equipment that can be called in the corresponding time period in the current laboratory, perform resource allocation for the equipment that meets the conditions and bind the scheduling partition and operation instruction information, and output the cloud platform smart laboratory instrument sharing management record.

[0040] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0041] In this invention, the text content of experimental tasks is automatically extracted to achieve refined analysis of behavioral structure relationships. The collaborative relationships between devices are precisely divided according to the operation sequence and behavioral differences. The response capabilities and resource occupancy characteristics of devices are quantitatively identified through multi-dimensional indicators. Execution conflict relationships are jointly determined by difference analysis and resource intersection. The device operation sorting and channel scheduling process constructs priority execution paths based on response levels. The operation time period arrangement uses execution ratio and start interval to jointly deduce and accurately divide task blocks. The availability status of devices is combined with real-time detection of time periods to complete resource binding. The scheduling execution results are uniformly archived to generate shared records, realizing automatic closed-loop linkage management of task parsing, device identification, conflict detection, priority sorting, block division, resource allocation and sharing registration. Attached Figure Description

[0042] Figure 1 This is a flowchart of the main steps of the present invention;

[0043] Figure 2 This is a flowchart for obtaining the task behavior structure sequence of the present invention;

[0044] Figure 3 This is a flowchart of the concurrent structure table acquisition process for the experimental equipment of this invention.

[0045] Figure 4 This is a flowchart illustrating the process of obtaining the concurrent control execution channel list in this invention.

[0046] Figure 5 This is a flowchart of the process for obtaining the device scheduling time partition table in this invention;

[0047] Figure 6 This is a flowchart illustrating the process of obtaining records for the shared management of smart laboratory instruments on the cloud platform of this invention. Detailed Implementation

[0048] 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.

[0049] 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.

[0050] Please see Figure 1 The cloud-based instrument sharing management method includes the following steps:

[0051] S1: Obtain the experimental task text content, perform character segmentation on the experimental task text content, perform semantic classification filtering operation, retain the experimental operation phrase set, match the preset operation category label for each group of operation phrases, combine the task operation sequence according to the matching results, determine the experimental behavior label corresponding to each group of operations, and generate the task behavior structure sequence.

[0052] S2: Based on the experimental behavior labels in the task behavior structure sequence, identify the corresponding experimental equipment from the operation phrases, extract the first appearance position of the experimental equipment in the task text, sort according to the position index, compare and calculate the sorting difference between the operations of each equipment, filter the equipment group that does not meet the continuous control relationship as the concurrent coordination unit, and generate the experimental equipment concurrent structure table.

[0053] S3: Based on the experimental equipment concurrency structure table, collect the unit time response rate label and resource occupation code of the corresponding experimental equipment. Through the analysis of the difference in response rate labels between experimental equipment, identify the equipment pairs with a difference greater than the set threshold. At the same time, determine the binary intersection between resource occupation codes, mark the equipment pairs with intersection as conflict groups, and build the equipment execution sorting according to the response rate label from high to low to generate a list of concurrent control execution channels.

[0054] S4: Based on the order of each device in the concurrent control execution channel list, read the execution time period of the corresponding task, calculate the start and end time of each operation in a proportional manner, and leave a start interval between adjacent devices in a fixed increment manner. Mark the task execution blocks in sequence according to the time period, construct the operation-level scheduling partition layout, and generate the device scheduling time partition table.

[0055] S5: Based on the partition layout in the equipment scheduling time partition table, detect the availability status of the experimental equipment that can be called in the corresponding time period in the current laboratory, perform resource allocation for the equipment that meets the conditions and bind the scheduling partition and operation instruction information, and output the cloud platform smart laboratory instrument sharing management record.

[0056] The task behavior structure sequence includes operation phase identifiers, experimental behavior types, and behavior combination sequences. The experimental equipment concurrency structure table includes concurrent equipment group identifiers, control continuity status, and equipment operation location indexes. The concurrent control execution channel list includes channel numbers, equipment priority levels, and resource conflict flags. The equipment scheduling time partition table includes scheduling time period numbers, operation execution block divisions, and equipment start interval markers. The cloud platform smart laboratory instrument sharing management record includes equipment resource allocation status information, scheduling partition correspondence, and instruction binding information.

[0057] Please see Figure 2 Step S1 is as follows:

[0058] S111: Obtain the experimental task text content, perform character segmentation on the experimental task text content, identify each segmented text unit, perform semantic clustering classification on the word groups in the text unit, perform annotation comparison on each operation word group, match operation category labels, and generate an operation semantic annotation result set;

[0059] To obtain the experimental task text and perform character segmentation, the text must first be traversed character by character to identify sentence breaks (such as commas, semicolons, and periods). Based on this, the text is split into independent semantic sentence units. Then, for each sentence unit, an operation dictionary feature matrix is ​​built. This matrix is ​​constructed using the word segmentation index of an industry-standard operation dictionary, with defined field dimensions such as word length, part-of-speech tagging, and context window frequency. The resulting two-dimensional matrix is ​​structured as sentence unit × feature item. Part-of-speech tagging uses a professional natural language processing part-of-speech tagging system, such as "VV" for verbs and "NN" for nouns. For example, if a sentence element is "adjust the temperature to 60 degrees Celsius", then this sentence element is decomposed into {adjust / VV, temperature / NN, to / P, 60 / M, degrees Celsius / NN}. In the feature matrix, the verb tag dimension corresponding to this entry will be assigned a value of 1, and other dimensions will be filled according to the context feature values. After the matrix is ​​constructed, the above feature matrix is ​​then filtered using the operational semantics as the filtering criterion. The filtering criteria are items whose operational verbs appear more than twice and whose part of speech is "VV" or "VC". The frequency threshold for operational semantics is set to 2. This threshold is obtained through statistical analysis of sample texts from 10 historical experimental tasks, as shown in the table below:

[0060] Table 1. Frequency Statistics of Operational Verbs

[0061]

[0062] As shown in Table 1, the average frequency of operation phrases such as "stirring" and "transferring" exceeds 2, indicating that a frequency threshold of 2 is representative. Next, label matching is performed based on an operation dictionary. The operation dictionary is pre-defined as {stirring → mixing, heating → temperature control, cooling → temperature control, transfer → transport}, etc. The matching process is as follows: for each selected operation phrase, an equivalent match is performed against the operation dictionary. If a match is successful, the corresponding semantic label is recorded. If no match is found, a secondary operation is performed: fuzzy matching is conducted based on a thesaurus (e.g., "mixing" is a synonym of "stirring"). During the matching process, if the matching distance is lower than the set word distance threshold ε=1 (calculated using edit distance), the corresponding category label is assigned, ultimately generating an operation semantic annotation result set. This result indicates that the selected text contains operation instruction phrases that can be used for behavior recognition, and subsequent steps can use this to establish the experimental task structure.

[0063] S112: Based on the operation semantic annotation result set, all operation phrases are grouped into similar categories according to the operation category label. The merged operation sets are then sequentially aggregated, and a linear sorting mapping is established according to the sequential arrangement structure in the operation sequence to generate a task operation combination sequence set.

[0064] Based on the operation semantic annotation result set, firstly, each operation phrase and its semantic label information in the set are read to construct a structured data item {operation phrase, category, position index} triple. For example, if a task text is "Start stirring, heat to 70 degrees, maintain for 5 minutes and then add solution", the corresponding operation semantic annotation result set is {stirring-mixing category-index1, heating-temperature control category-index2, adding-addition category-index3}. Based on this set, the operation phrases need to be merged according to their semantic categories. The operation is as follows: traverse all triples, merge similar pairs according to the "category" field, and then reorder the operation order according to the "position index" field of the operation to reflect the order of operation execution in the original task. The specific data can be organized as follows:

[0065] Operation set:

[0066] Mixing type: Stirring (index 1);

[0067] Temperature control: Heating (Index 2);

[0068] Add class: Add (index 3);

[0069] After merging, a linear sorting structure is constructed for each type of operation set after merging. The sorting structure is represented in the form of key-value pairs {operation category: operation phrase sequence}. In the example above, the corresponding generated structure is {mixing category: [stirring], temperature control category: [heating], addition category: [adding]}. Each type of operation set is further combined in order of its position index value in the original task to establish a combination mapping. That is, the combination order is [stirring → heating → adding]. The mapping forms a unified structured queue, and finally generates a task operation combination sequence set. This sequence set provides basic data support for the construction of subsequent behavior structure sequences.

[0070] S113: Based on the task operation combination sequence set, extract the corresponding operation category label for each set of operations in the sequence, match the experimental behavior type, aggregate all matching experimental behavior labels in sequence, establish a mapping between operations and behaviors, and obtain the task behavior structure sequence.

[0071] Based on the task operation combination sequence set, a label extraction operation is performed on each group of operation terms in the sequence. Specifically, the "category" field contained in each operation set is read, and the corresponding matching is performed with the preset behavior template in the operation dictionary. The operation dictionary structure is set as a key-value mapping, such as {mixing category: stirring operation → experimental behavior A1, temperature control category: heating or cooling operation → experimental behavior A2, addition category: solution addition → experimental behavior A3}, etc. A one-to-one relationship is set in the matching operation, requiring that the operation category and the behavior template be completely consistent. If there are multiple operation terms corresponding to the same category in a certain operation set, only the behavior label corresponding to the first occurrence of the term needs to be extracted as the representative behavior of that category. An example of the operation behavior label mapping results is shown in Table 2.

[0072] Table 2 Tag Mapping Results

[0073]

[0074] Next, based on the original sorting order of the task operation combination sequence set, the corresponding behavior labels are sequentially combined into a structured output, namely the behavior label sequence [A1→A2→A3]. Further, a behavior structure mapping table for the experimental task is constructed. The mapping table adopts a key-value mapping structure, where the key is the operation phrase and the value is the behavior label. An example structure is {stirring: A1, heating: A2, adding: A3}. This structure facilitates the direct acquisition of the task behavior structure sequence based on the behavior information associated with the corresponding operation in subsequent processes. This result shows that all operable units in the original experimental task have been mapped to specific experimental behavior units, thus completing the semantic-to-behavior conversion process.

[0075] Please see Figure 3 Step S2 is as follows:

[0076] S211: Obtain the experimental behavior labels in the task behavior structure sequence, perform semantic retrieval on the set of operation phrases associated with each label, extract the experimental equipment names in the operation phrases, perform the first indexing and locating operation in the experimental task text content, construct the location information table of equipment terms according to the original order of the task text, and generate the equipment location index mapping table.

[0077] After obtaining the experimental behavior labels from the task behavior structure sequence, the sequence is first traversed item by item according to the behavior label order. The set of operation phrases associated with each behavior label is extracted. Then, lexical analysis is performed on each phrase in the operation phrases to identify terms with equipment noun attributes. Equipment noun identification is based on part-of-speech classification (NN) and matching with the established experimental equipment noun database. If a term matches an equipment name in the database, such as "centrifuge," "magnetic stirrer," or "heating jacket," the term and its corresponding character starting position in the original task text are recorded. The character position acquisition operation is based on the task text's character indexing system, counting each character. When the first complete occurrence of a device term is detected in a string segment, its starting character number is recorded as the index position. For example, for the task text "Place the reaction solution in a heating jacket for heating," if the term "heating jacket" appears at character number 8, then the index position of this device term is 8. The same process is performed on all identified device terms. Finally, all device terms and their corresponding first positions are combined into key-value pairs to construct structured tabular data, as shown below:

[0078] Table 3. Index of Experimental Equipment Terms

[0079]

[0080] As shown in Table 3, the first occurrence of the device name in the task text can be accurately located by using character indexing, providing basic data for subsequent operation sorting and control relationship analysis. This process ultimately yields the device location index mapping table.

[0081] S212: Based on the device location index mapping table, all experimental devices are rearranged in ascending order according to the first occurrence index value. The index difference between adjacent devices is calculated to construct the index difference sequence matrix between devices. Experimental device combination items with continuous control difference less than the continuous control difference threshold are filtered out to obtain the non-continuous control device combination set.

[0082] Based on the equipment location index mapping table, each equipment term is sorted in ascending order by its index field. The sorting process uses a bubble sort algorithm to arrange the index values ​​from smallest to largest, resulting in a sequential list such as {heating mantle, magnetic stirrer, constant temperature water bath, vacuum pump}. Then, a difference calculation operation is performed on the index position values ​​between adjacent equipment terms. The calculation formula is: current equipment position minus the previous equipment position, to construct an index difference sequence. For example, if the index of "heating mantle" is 8 and "magnetic stirrer" is 25, the difference is 17. The smaller the difference, the closer the operating positions of the two devices are, providing a quantitative basis for determining whether a continuous control relationship exists. This is done by setting the index. The difference threshold is 3. This threshold is set based on the experience of the average character spacing between device operations in the experimental task. In the actual experimental text, the device distribution of 10 sets of task documents was tested. The average character spacing was 12, the minimum was 2, and the maximum was 28. Therefore, setting the difference threshold of 3 means that there must be at least 3 characters between devices to be considered as a non-continuous relationship. For example, if the difference of a device combination is 2, its position in the task text is too close and does not meet the concurrency condition. If the difference is 6, it is considered that there is a possibility of parallel operation. Finally, the combination items with an index difference greater than or equal to 3 are selected from all device combinations, and the data rows that do not meet the condition are removed to obtain the set of non-continuous control device combinations.

[0083] S213: Based on the set of non-continuous control equipment combinations, perform synchronization relationship detection on each equipment combination item, and conduct comparative analysis based on the temporal difference characteristics between the experimental behaviors of the experimental equipment to determine the concurrent units and obtain the experimental equipment concurrent structure table.

[0084] Based on the set of discontinuous control devices, a synchronization relationship detection operation is performed on each device combination. During the detection process, the order of the experimental behaviors corresponding to the devices in the task behavior structure sequence must first be analyzed. Based on this order, it is determined whether there is a logical dependency or nested structure between the behaviors performed by the two devices. If there is a sequential dependency between the corresponding behaviors of the two devices (e.g., "reaction" must follow "stirring"), the combination is excluded; otherwise, it is retained as a concurrent candidate. Furthermore, a time-series difference analysis is performed on the retained combinations, setting a time window threshold of 10 seconds to determine the timing... The system checks whether two devices can start simultaneously within a given time interval. If there is no overlap in the operation time intervals, the combination is excluded. For example, if the start time of the "magnetic stirrer" operation is 30 seconds and the end time is 60 seconds, and the start time of the "constant temperature water bath" is 40 seconds and the end time is 90 seconds, then there is an overlap interval of 20 seconds, which meets the condition for concurrency. Finally, the device combinations that meet the conditions of no dependent behavior and overlapping time intervals are selected and recorded in a table. The fields include device pair, behavior pair, whether it is synchronized, overlap duration, etc., generating an experimental equipment concurrency structure table.

[0085] Please see Figure 4Step S3 is as follows:

[0086] S311: Collect the unit time response rate label and resource occupation code of each experimental device in the experimental equipment concurrency structure table, perform structural parsing on the response rate label of each experimental device, construct the experimental equipment response rate index set, and perform standardized filling operation on the resource occupation code of each experimental device according to the binary encoding format to construct the device response and resource code set.

[0087] After collecting the device number, unit time response rate label, and resource occupancy code from the experimental equipment concurrency structure table, the device numbers are first standardized, and a unified device naming rule is adopted, such as D1, D2, D3, etc. Then, the response of each device in the experimental task is monitored and recorded to obtain its response frequency per unit time, with the unit of response frequency being times / second. A continuous triggering method is used; after each device responds three times, its average trigger interval is taken as the basis for back-calculating the frequency. Assuming that the three response intervals of D1 are 0.6 seconds, 0.4 seconds, and 0.5 seconds, its frequency is... The response rate is calculated as 40 times / second, then scaled up to the system standard unit time. Similarly, records are executed for D2 to D4 to obtain the following response rate labels. Simultaneously, device resource occupancy is retrieved from the system control configuration table, mapping resource allocation information to 8-bit binary codes, each indicating whether a device occupies one of the eight logical resource positions (R1 to R8). If a bit is 1, it indicates occupancy. For example, D1's code is 11010010, indicating that resources R1, R2, R4, and R7 are occupied. All device data is shown in Table 4.

[0088] Table 4 Device Response Rate and Resource Code Points

[0089]

[0090] As shown in Table 4, the response rate of device D1 is 40 times / second, and it occupies resource code bit 11010010. The resource occupancy characteristics of other devices are also encoded through a unified data structure to form a device response and resource code set.

[0091] S312: Based on the device response and resource coding set, perform a difference calculation operation on the response rate labels of each pair of concurrent experimental devices. If the difference is greater than the response difference threshold, the experimental device pair is marked as a candidate conflict pair. Perform a bitwise AND operation on the resource occupancy code bits of the candidate conflict pair. If any bit in the result is 1, it is considered that there is a resource intersection, using the formula:

[0092] ;

[0093] The normalized response conflict score index is obtained through calculation, and a resource conflict determination set is established by summarizing the scores; among which... and They represent the first The unit response rate label of the equipment, This represents a normalized reference value for the response rate. and Indicates the first For the equipment in the The resource occupancy code bit binary value. This represents the total number of resource code points. A normalized response conflict scoring index;

[0094] Based on device response and resource coding sets, the response rate difference is calculated for any pair of concurrent devices. Let the rate difference between device pairs D1 and D2 be... The response difference judgment threshold is set to 8, which is based on the maximum acceptable response time difference of the device per unit time in the task scheduling cycle of the system control module. If the rate difference between devices exceeds this threshold, the system will not be able to guarantee the stability of the synchronous control of the operation flow. This value is related to the distribution density of device response performance levels. In the standard device level classification, the response rate is generally distributed in the range of 20 to 45 times / second, with an average interval of about 6 to 9 times / second. Therefore, 8 times / second is used as the critical breakpoint for the coordination capability of device response rhythm, and it is dynamically fine-tuned in the scheduling strategy update according to the change of the maximum number of supported devices. Since 12 is greater than 8, D1 and D2 are marked as candidate conflict pairs. Then, their resource occupation code bits are extracted and bitwise AND logical operation is performed. D1 is 11010010, D2 is 10011000, and the result after bitwise AND is 10010000, that is, resources R1 and R4 intersect. This information is substituted into the calculation formula:

[0095] set up This represents the upper limit of the standard reference rate designation defined by the system. The values ​​are as follows:

[0096] , The rate deviation is ;

[0097] The binary intersection of resource usage is 10010000, where the first and fourth bits are 1, and the remaining bits are 0;

[0098] The sum of the intersection terms is: ;

[0099] Therefore:

[0100] ;

[0101] The conflict judgment benchmark value is set to 1.5. This value comes from the resource conflict tolerance limit set in the system's concurrent scheduling strategy, used to distinguish between reconcilable device groups and absolutely mutually exclusive device groups. This value is directly related to the sparsity of resource code bits occupied by each device in the system. When the average intersection bit length exceeds 2 bits and the corresponding rate deviation ratio is greater than 0.25, a conflict will be triggered. Considering the influence intensity of the above two interference factors and the task execution density, 1.5 is taken as the screening standard for joint conflict degree, and dynamic threshold control is performed when the resource capacity of multi-thread scheduling changes. If the conflict judgment benchmark value is set to 1.5, then D1 and D2 are conflicting device pairs. The calculation for device pairs D2-D4 is as follows: the rate difference is 7 (28-21), which is less than 8, so they are not included in the candidate set. The difference for D1-D3 is calculated to be 7, so they are also excluded. Thus, only D1-D2 enter the resource conflict judgment set, as shown in Table 5.

[0102] Table 5 Conflict Calculation and Judgment Results

[0103]

[0104] As shown in Table 5, only D1-D2 satisfy the condition of simultaneously having rate deviation and resource intersection.

[0105] The formula's calculation logic is based on a joint measurement of the risk of the overlap between the deviation in response rate and resource usage between devices during the scheduling process. The first term in the formula... This represents the relative difference in the response rate of two devices per unit time, used to measure the degree of synchronization in their response rhythms. Using absolute values ​​eliminates the interference of the difference direction on the result, retaining only the intensity of the difference. The denominator contains... This is used to normalize the difference to a dimensionless interval, making it easier to participate in addition operations with resource conflict terms; the second term Used to measure the shared resource bits of two devices, multiplication operation This represents the binary bitwise intersection, where the product is 1 only if both devices have the same resource bit set to 1, otherwise it is 0. This reflects the true situation of overlapping resource usage. The square root processing of each bit's result preserves the unit influence intensity of the resource intersection but reduces its weight, avoiding the amplification of interference from multiple weak conflicts on the overall result. Finally, the response deviation term is added to the resource intersection term to unify the device synchronization differences and resource contention intensity into the conflict risk measurement system. This structure can comprehensively measure the degree of interference of devices with resource logic and the degree of asynchronous time response in scheduling concurrency scenarios, thus providing a quantifiable basis for subsequent channel scheduling and mutual exclusion control.

[0106] The Normalized Response Conflict Score is a dimensionless numerical measure of the risk of conflict between concurrent devices during task scheduling. It comprehensively considers two key factors: the relative difference in response rate per unit time between devices and the actual overlap in resource usage. By normalizing the rate difference and superimposing it with the intersection strength of resource code points, a unified numerical scoring system is formed. This score reflects whether there are problems of time rhythm misalignment and resource contention overlap during task execution. The higher the score, the stronger the interference of the device in the scheduling process, and the more likely it is to cause resource blocking or scheduling conflicts. Conversely, the lower the score, the more suitable it is for parallel configuration. The introduction of this index aims to provide a clear and calculable quantitative basis for conflict in scheduling strategies such as device channel allocation and concurrent control chain construction.

[0107] S313: Based on the resource conflict determination set, all experimental equipment pairs that do not have resource intersections are sorted in descending order of response rate number, and a linear execution chain is constructed according to the sorting order. Each experimental equipment is assigned an independent channel number, which is numbered sequentially according to the sorting order, and a list of concurrent control execution channels is obtained.

[0108] After obtaining the resource conflict determination set, conflicting device pairs are removed from the device response and resource code set, retaining only non-overlapping combinations such as D2-D4, D3-D4, etc. The response rate labels of each device are extracted and sorted in descending order, such as D3 being 33, D2 being 28, and D4 being 21, forming a linear sorting structure from high to low rate. Then, execution channel numbers are assigned to the devices in sequence, generating the channel allocation as follows: T1→D3, T2→D2, T3→D4. The control structure mapping chain is constructed in sequence, and finally, a list of concurrent control execution channels is generated.

[0109] Please see Figure 5 Step S4 is as follows:

[0110] S411: Based on the sorting order of each experimental device in the concurrent control execution channel list, read the start time and end time corresponding to each experimental device, allocate the required execution time proportionally, calculate the start and end time intervals of each experimental device within the total task time interval, and generate a device time interval allocation matrix.

[0111] Based on the device order in the concurrent control execution channel list, the channel number of each device is extracted. Then, using the device scheduling index set and the corresponding device number for that channel number, the "estimated start time" and "estimated end time" fields for that device in the task configuration parameter set are obtained. The original duration interval of the device's task is calculated. Next, the response rate labels of all devices are obtained and summed to construct a total rate benchmark. Finally, the response rate value of a single device is divided by the total rate benchmark to obtain its execution time percentage coefficient. For example, if D1's response rate is 40 times / second, D2's is 30 times / second, and D3's is 30 times / second, then D1's time percentage is... If the total task execution time is 300 seconds, then D1 is allocated 120 seconds. Time is allocated proportionally to each device in this manner to obtain its task time period. Then, a time period start offset matrix is ​​constructed based on the device's sorting order. D1, as the first task, starts at 0 seconds; D2 starts at the end time of D1 (120 seconds); and D3 starts at the end time of D2 (210 seconds). Three sets of time period data are constructed and saved as structured records, summarized into the following matrix:

[0112] Table 6 Equipment Time Period Allocation Matrix

[0113]

[0114] As shown in Table 6, D1-D3 are allocated to continuous and seamless time periods on the time axis according to the response rate ratio, and the device time period allocation matrix is ​​obtained.

[0115] S412: Based on the device time period allocation matrix, extract the time period boundary positions of adjacent experimental device combinations, subtract the start position of each time period from the end position of the previous device, and if the difference is less than the fixed start interval benchmark value, shift the start time of the adjacent experimental device backward to the position that meets the minimum start interval condition. The updated time period record covers the original time period configuration, forming a non-overlapping separation structure, and obtain the scheduling start interval configuration set.

[0116] Based on the start and end time fields in the device time period allocation matrix, the difference is extracted for the task boundaries of every two adjacent devices. Specifically, the start time (120 seconds) of device D2 and the end time (120 seconds) of device D1 are subtracted to obtain a time difference of 0 seconds. If this difference is less than the minimum start interval baseline value set in the device scheduling, the time period is shifted. The start interval baseline value is set to 5 seconds, which is based on the average processing time of the controller power-on, interface handshake, and resource loading during actual device startup. After evaluation by multiple scheduling records, its value range is generally between 3 and 6 seconds. Therefore, the fixed baseline value is set to 5 seconds and used as a static lower limit control item. The start time of D2 is shifted from 120 seconds to 125 seconds, and the end time is shifted from 210 seconds to 215 seconds. At the same time, the start time of D3 is updated from 210 seconds to 215 seconds, and the end time is adjusted to 305 seconds, forming the updated time period. The scheduling interval structure of tasks between devices is reconstructed to ensure non-overlap and meet the start buffer time condition, thus obtaining the scheduling start interval configuration set.

[0117] S413: Based on the start and end time period configuration in the scheduling start interval configuration set, mark the task execution area of ​​each device in time order, draw the corresponding block in time interval unit, bind each block with the corresponding experimental device number, perform block ownership mapping and scheduling map coding, and generate device scheduling time partition table;

[0118] Based on the start and end time fields of each device after the scheduling start interval configuration is updated, the system scheduling time axis is divided into blocks in sequence. First, a global time period list is created, from 0 seconds to the maximum end time of 305 seconds. The task scheduling timeline is constructed with seconds as the smallest time granularity. Then, each device task is assigned a device number identifier in its corresponding time interval. For example, D1 is set in the interval from 0 to 120 seconds, D2 is set in the interval from 125 to 215 seconds, and D3 is set in the interval from 215 to 305 seconds. The task scheduling structure table is generated by mapping the device number to the time period. This structure table is then compiled into the scheduling record mapping set. Each record contains the device number, block start and end time, channel number, and task number index. Finally, a structured data frame is output to generate the device scheduling time partition table.

[0119] Please see Figure 6 The S5 steps are as follows:

[0120] S511: Based on the partition layout data frame in the device scheduling time partition table, extract the start time and end time corresponding to each experimental device task, construct the task time period index interval, perform logical judgment operations on the available status identifier of the experimental device and establish a list of callable devices, and generate a set of available status of partitioned devices.

[0121] Based on the partition layout in the device scheduling time partition table, and based on the device number corresponding to each task block and its corresponding start and end time fields, a partition time index vector is constructed. This allows for the acquisition of the status identifier fields of all devices in the current laboratory status monitoring data frame at each moment. The partition time vector is then matched against the time axis of the device status data to obtain the set of all status markers for each device within the corresponding task interval. If all status markers in this set are "1", the device meets the availability condition within that time interval; otherwise, it is marked as unavailable. Subsequently, each device corresponding to all task partitions is evaluated, and the device index that meets the availability condition is selected. Its channel number, task identifier, and task time period information are recorded to form a table structure record. For example, if device E01's available status set is {1, 1, 1, 1, 1, 1} within the task time period from 10:00 to 10:30, then E01 is determined to be a callable device. If device E02's status set is {1, 1, 0, 1, 1} within the task period, then it is determined to be in an uncallable state. The following table is constructed:

[0122] Table 7. Set of Available Device Status for Each Zone

[0123]

[0124] As shown in Table 7, devices E01 and E03 are recorded as callable, while device E02 is marked as unavailable due to a status interruption, thus obtaining the available status set of partitioned devices.

[0125] S512: Based on the device information of each time interval in the available status set of partitioned devices, perform mapping and binding operations between time intervals and experimental device numbers, retrieve experimental device task identifiers in the scheduling block, aggregate and match control instruction sets and time interval identifiers, bind them into a joint data structure, and establish a partitioned operation instruction binding structure frame.

[0126] Based on the device task records that are determined to be "yes" in the available status set of partitioned devices, the device number, start time, and end time fields are extracted from each task partition. Using this triplet as the index key, the task number field in the task configuration set is retrieved. The task number and device number are combined as a double key to retrieve the associated operation instruction set field in the operation instruction configuration table, and all control instruction parameter items under this field are extracted. Subsequently, an index mapping binding operation is performed on the device number, task time period, and control instruction field to form a device task control unit structure record. Each data in the record structure includes four items: device number, task number, operation instruction content, and task start and end time. Taking the execution of task T100 by device D01 in partition P01 as an example, its operation instruction is "parameter configuration - temperature 70, speed 1200", and the time period is from 10:00 to 10:30. Then, the complete structure record of this task is generated and added to the data structure sequence to obtain the partition operation instruction binding structure frame.

[0127] S513: Based on the joint structure record in the binding structure frame of the partition operation instruction, perform synchronous write operation on all binding relationships, map each record to the cloud platform, and perform index encoding operation on each record to construct a data record sequence with a unique identifier structure, and obtain the smart laboratory instrument sharing management record of the cloud platform.

[0128] Based on all structure record data in the partition operation instruction binding structure frame, each record is sorted according to the task time field. The sorted data structure is written into the cloud platform shared task master table structure one by one. A unique scheduling number is generated for each written record, and the record upload timestamp and resource binding status field are attached. Then, an index building operation is performed on the record, and the scheduling number is used as the primary key to uniquely identify the index mark. After the index is generated, the status of the scheduling record is updated to "registered". At the same time, the sharing status field is updated in the resource management record table with the device number as the key value and its value is set to "binding". The record entry process is completed, and the cloud platform smart laboratory instrument sharing management record is generated.

[0129] A cloud-based instrument sharing management system includes:

[0130] The task semantic parsing module is used to perform S1: obtain the experimental task text content, perform character segmentation, match preset operation category labels for each group of operation words, determine the experimental behavior label corresponding to each group of operation words, and generate a task behavior structure sequence;

[0131] The device operation identification module is used to execute S2: based on the experimental behavior labels in the task behavior structure sequence, identify the corresponding experimental devices and sort them according to the position index, compare and calculate the sorting difference between each device operation, filter concurrent collaborative units, and generate an experimental device concurrent structure table.

[0132] The conflict factor rearrangement module is used to execute S3: based on the experimental equipment concurrency structure table, it collects the unit time response rate label and resource occupation code of the corresponding experimental equipment, identifies experimental equipment pairs whose response rate label difference is greater than a set threshold, and determines that experimental equipment pairs with overlapping resource occupation codes are conflict groups, and constructs the experimental equipment execution sorting to generate a list of concurrency control execution channels.

[0133] The scheduling time configuration module is used to execute S4: according to the experimental equipment sorting in the concurrent control execution channel list, read the execution time period of the corresponding task, calculate the start and end duration of each operation and reserve the start interval between adjacent devices, mark the task execution block, construct the operation-level scheduling partition layout, and generate the device scheduling time partition table.

[0134] The platform scheduling landing module is used to execute S5: based on the partition layout in the equipment scheduling time partition table, it detects the availability status of the experimental equipment that can be called in the corresponding time period in the current laboratory, performs resource allocation for the equipment that meets the conditions, binds the scheduling partition and operation instruction information, and outputs the cloud platform smart laboratory instrument sharing management record.

[0135] 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 cloud-based instrument sharing and management method, characterized in that, Includes the following steps: S1: Obtain the experimental task text content, perform character segmentation, match each group of operation words with preset operation category tags, determine the corresponding experimental behavior tags for each group of operation words, and generate a task behavior structure sequence. S2: Based on the experimental behavior labels in the task behavior structure sequence, identify the corresponding experimental devices and sort them according to the position index. Compare and calculate the sorting difference between the operations of each device, filter the concurrent collaboration units, and generate the experimental device concurrent structure table. S3: Based on the experimental equipment concurrency structure table, collect the unit time response rate label and resource occupation code of the corresponding experimental equipment, identify experimental equipment pairs whose response rate label difference is greater than a set threshold, and at the same time determine that experimental equipment pairs with overlapping resource occupation codes are conflict groups, construct the experimental equipment execution sorting, and generate a list of concurrent control execution channels. S4: Based on the experimental equipment sorting in the concurrent control execution channel list, read the execution time period of the corresponding task, calculate the start and end duration of each operation and leave a start interval between adjacent devices, mark the task execution block, construct the operation level scheduling partition layout, and generate the device scheduling time partition table. S5: Based on the partition layout in the equipment scheduling time partition table, detect the availability status of the experimental equipment that can be called in the corresponding time period in the current laboratory, perform resource allocation for the equipment that meets the conditions and bind the scheduling partition and operation instruction information, and output the cloud platform smart laboratory instrument sharing management record. The specific steps for obtaining the concurrent structure table of the experimental equipment are as follows: S211: Obtain the experimental behavior tags in the task behavior structure sequence, perform semantic retrieval on the set of operation phrases associated with each tag, extract the experimental equipment name in the operation phrases, perform the first indexing and locating operation in the experimental task text content, construct the location information table of equipment terms according to the original order of the task text, and generate the equipment location index mapping table. S212: Based on the device location index mapping table, all experimental devices are rearranged in ascending order according to the first occurrence index value, the index difference between adjacent devices is calculated, an index difference sequence matrix between devices is constructed, and experimental device combination items with continuous control difference less than the continuous control difference threshold are filtered out to obtain a non-continuous control device combination set. S213: Based on the set of non-continuous control devices, perform synchronization relationship detection on each set of devices, and perform comparative analysis based on the temporal difference characteristics between the experimental behaviors of the experimental devices to determine the concurrent units and obtain the experimental device concurrency structure table. The specific steps for obtaining the concurrent control execution channel list are as follows: S311: Collect the unit time response rate label and resource occupation code of each experimental device in the experimental device concurrency structure table, and perform structural parsing on the response rate label of each experimental device to construct the experimental device response rate index set. At the same time, perform standardized filling operation on the resource occupation code of each experimental device according to the binary encoding format to construct the device response and resource code set. S312: Based on the device response and resource coding set, perform a difference calculation operation on the response rate label of each pair of concurrent experimental devices. If the difference is greater than the response difference threshold, the experimental device pair is marked as a candidate conflict pair. Perform a bitwise AND operation on the resource occupancy code bits of the candidate conflict pair. If any bit in the result is 1, it is considered that there is a resource intersection. Calculate and obtain the normalized response conflict score index, and summarize to establish a resource conflict judgment set. S313: Based on the resource conflict determination set, all experimental equipment pairs that do not have resource intersections are sorted in descending order of response rate number, and a linear execution chain is constructed according to the sorting order. Each experimental equipment is assigned an independent channel number, and the numbers are numbered sequentially according to the sorting order to obtain the concurrent control execution channel list.

2. The instrument sharing management method based on a cloud platform according to claim 1, characterized in that, The task behavior structure sequence includes operation stage identifiers, experimental behavior types, and behavior combination sequences. The experimental equipment concurrency structure table includes concurrent equipment group identifiers, control continuity status, and equipment operation location indexes. The concurrent control execution channel list includes channel numbers, equipment priority levels, and resource conflict flags. The equipment scheduling time partition table includes scheduling time period numbers, operation execution block divisions, and equipment start interval markers. The cloud platform smart laboratory instrument sharing management record includes equipment resource allocation status information, scheduling partition correspondence, and instruction binding information.

3. The instrument sharing management method based on a cloud platform according to claim 1, characterized in that, The steps for obtaining the task behavior structure sequence are as follows: S111: Obtain the experimental task text content, perform character segmentation on the experimental task text content, identify each segmented text unit, perform semantic clustering classification on the word groups in the text unit, perform annotation comparison on each operation word group, match operation category labels, and generate an operation semantic annotation result set; S112: Based on the operation semantic annotation result set, all operation phrases are grouped into similar categories according to operation category labels. The merged operation sets are sequentially aggregated, and a linear sorting mapping is established according to the sequential arrangement structure in the operation sequence to generate a task operation combination sequence set. S113: Based on the task operation combination sequence set, extract the corresponding operation category label for each set of operations in the sequence, match the experimental behavior type, aggregate all matching experimental behavior labels in sequence, establish a mapping between operations and behaviors, and obtain the task behavior structure sequence.

4. The instrument sharing management method based on a cloud platform according to claim 1, characterized in that, Specifically, if the related behaviors of two sets of experimental devices in the experimental task text content have a non-nested structure and no control dependency relationship, then the corresponding experimental device combination item is determined to be a concurrent unit.

5. The instrument sharing management method based on a cloud platform according to claim 1, characterized in that, The specific steps for obtaining the device scheduling time partition table are as follows: S411: Based on the sorting order of each experimental device in the concurrent control execution channel list, read the start time and end time corresponding to each experimental device, allocate the required execution time according to the proportion, count the start and end time periods of each experimental device in the total task time interval, and generate a device time period allocation matrix. S412: Based on the device time period allocation matrix, extract the time period boundary positions of adjacent experimental device combinations, subtract the start position of each time period from the end position of the previous device, and if the difference is less than the fixed start interval benchmark value, shift the start time of the adjacent experimental device backward to the position that meets the minimum start interval condition. The updated time period record covers the original time period configuration, forming a non-overlapping separation structure, and obtain the scheduling start interval configuration set. S413: Based on the start and end time period configuration in the scheduling start interval configuration set, mark the task execution area of ​​each device in chronological order, draw corresponding blocks in units of time intervals, bind each block with the corresponding experimental device number, perform block ownership mapping and scheduling map encoding, and generate a device scheduling time partition table.

6. The instrument sharing management method based on a cloud platform according to claim 1, characterized in that, The specific steps for obtaining the cloud platform smart laboratory instrument sharing management records are as follows: S511: Based on the partition layout data frame in the device scheduling time partition table, extract the start time and end time corresponding to each experimental device task, construct the task time period index interval, perform logical judgment operation on the available status identifier of the experimental device and establish a list of callable devices, and generate a set of available status of partitioned devices. S512: Based on the device information of each time interval in the available status set of the partitioned devices, perform a mapping and binding operation between the time interval and the experimental device number, retrieve the experimental device task identifier in the scheduling block, aggregate and match the control instruction set and the time interval identifier, bind them into a joint data structure, and establish a partitioned operation instruction binding structure frame. S513: Based on the joint structure record in the binding structure frame of the partition operation instruction, perform synchronous write operation on all binding relationships, map each record to the cloud platform, and perform index encoding operation on each record to construct a data record sequence with a unique identifier structure, and obtain the cloud platform smart laboratory instrument sharing management record.

7. The instrument sharing management method based on a cloud platform according to claim 6, characterized in that, The logical judgment operation performed on the available status flag of the experimental equipment is as follows: if the status flag of the experimental equipment is 1, it is determined to be in a callable state; otherwise, it is in a non-callable state.

8. A cloud-based instrument sharing management system, characterized in that, The system is used to implement the cloud-based instrument sharing management method according to any one of claims 1-7, including: The task semantic parsing module is used to perform S1: obtain the experimental task text content, perform character segmentation, match preset operation category labels for each group of operation words, determine the experimental behavior label corresponding to each group of operation words, and generate a task behavior structure sequence; The device operation identification module is used to execute S2: based on the experimental behavior labels in the task behavior structure sequence, identify the corresponding experimental devices and sort them according to the position index, compare and calculate the sorting difference between each device operation, filter concurrent collaborative units, and generate an experimental device concurrent structure table. The conflict factor rearrangement module is used to execute S3: based on the experimental equipment concurrency structure table, collect the unit time response rate label and resource occupation code of the corresponding experimental equipment, identify experimental equipment pairs whose response rate label difference is greater than a set threshold, and at the same time determine that experimental equipment pairs with intersection between resource occupation codes are conflict groups, construct the experimental equipment execution sorting, and generate a list of concurrency control execution channels. The scheduling time configuration module is used to execute S4: according to the experimental equipment sorting in the concurrent control execution channel list, read the execution time period of the corresponding task, calculate the start and end duration of each operation and leave a start interval between adjacent devices, mark the task execution block, construct the operation level scheduling partition layout, and generate the device scheduling time partition table. The platform scheduling landing module is used to execute S5: based on the partition layout in the device scheduling time partition table, detect the availability status of the experimental equipment that can be called in the corresponding time period in the current laboratory, perform resource allocation for the equipment that meets the conditions and bind the scheduling partition and operation instruction information, and output the cloud platform smart laboratory instrument sharing management record.

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