Intelligent temperature control method and system for gastrodia elata factory pure fungus bag three-dimensional cultivation

Abstract

The invention discloses an intelligent temperature control method and system for gastrodia elata factory pure fungus bag three-dimensional cultivation, and belongs to the technical field of temperature control, and the method specifically comprises the steps: obtaining fungus bag related data of different heights and different areas in a cultivation frame, the fungus bag related data comprises sensing data, environmental data and fungus bag group metabolism heat release distribution data, based on the relevant data of the fungus bags, combined with growth cycle characteristics of gastrodia elata symbiotic fungi, generating a staged temperature control curve by adopting a logic mode of combining threshold judgment and condition triggering, and according to the staged temperature control curve, independently heating or cooling the fungus bags at different heights and areas, and performing zoned temperature control; according to the application, differential precise temperature control of fungus bags of different levels is realized, and an adjustable temperature gradient can be formed while the stability of a cultivation environment is ensured.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of temperature control, in particular to an intelligent temperature control method and system for stereoscopic cultivation of pure fungus bags of Gastrodia elata in a factory. BACKGROUND

[0002] Gastrodia elata is a kind of precious Chinese medicinal material. Its unique biological characteristics of no leaves, no roots and no chlorophyll determine its growth completely dependent on the nutrition supply of symbiotic fungi such as Armillaria. Traditional wild Gastrodia elata is mostly distributed in mountainous environments, and its yield is extremely low. The quality is significantly affected by natural conditions, and it has been difficult to meet market demand for a long time. Although artificial domestication and semi-wild cultivation methods have been used, there are still problems such as long cycle, low survival rate and strong environmental dependence.

[0003] In recent years, factory fungus bag cultivation has gradually become an important way to improve the yield and quality of Gastrodia elata. By preparing pure fungus bags and carrying out stereoscopic cultivation in an artificial environment, the yield and consistency can be improved to some extent. However, the demand for temperature and humidity of Gastrodia elata and symbiotic fungi is obviously different in different growth periods. The traditional constant temperature control method cannot meet the dynamic environmental requirements. The environment of upper and lower layers of fungus bags in stereoscopic cultivation structure is quite different, and there is often uneven temperature difference and unbalanced humidity distribution, resulting in disorder in mycelium expansion and fruit body induction stages. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides an intelligent temperature control method and system for stereoscopic cultivation of pure fungus bags of Gastrodia elata in a factory.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions: The intelligent temperature control method for stereoscopic cultivation of pure fungus bags of Gastrodia elata in a factory comprises: Obtaining fungus bag related data at different heights and different areas in the cultivation rack, wherein the fungus bag related data includes sensing data, environmental data and fungus bag group metabolic heat distribution data; Based on the fungus bag related data, combined with the growth period characteristics of Gastrodia elata symbiotic fungi, a threshold determination and condition triggering combined logic method is used to generate a phased temperature control curve; According to the phased temperature control curve, the fungus bags at different heights and areas are independently heated or cooled for zoned temperature control.

[0006] Specifically, based on the fungus bag related data, combined with the growth period characteristics of Gastrodia elata symbiotic fungi, a threshold determination and condition triggering combined logic method is used to generate a phased temperature control curve, comprising: The fungus bag related data is time-aligned and space-calibrated, a hierarchical data matrix is constructed according to cultivation levels and areas, and data is excluded and windowed and summarized according to preset abnormal threshold and consistency threshold; The windowed summary data is compared with a stored growth cycle characteristic table of the symbiotic fungus of Gastrodia, to determine a growth period of a current phase, when there is a phase conflict between adjacent time windows, a single phase determination is made according to a phase priority table, and the growth period includes a mycelium expansion period, a mycelium stable period, or a combination period of Armillaria and Gastrodia; On the basis of the phase determination result, a trigger condition set corresponding to the phase is formed, the trigger condition set includes a temperature rise trigger, a temperature drop trigger, and a day-night switch trigger, each trigger condition includes a trigger threshold, a minimum duration, and a hysteresis interval; According to the trigger condition set, a temperature control curve segment matched with the determined phase is selected from a preset curve segment library, and the initial staged temperature control curve is obtained by splicing in time sequence under the premise of meeting the continuity constraint; The initial staged temperature control curve is subjected to conflict checking to generate a staged temperature control curve, and switching rules and rollback rules are set for the staged temperature control curve, the switching rules include phase replacement triggers, sensor stability flag triggers, and abnormal event triggers, the rollback rules call a preset bottom curve, and a curve and a switching instruction set for zoned temperature control execution are output.

[0007] Specifically, on the basis of the phase determination result, a trigger condition set corresponding to the phase is formed, including: According to the phase determination result, a corresponding reference threshold library is called, and threshold intervals related to the phase are extracted from sensor data, environmental data, and group metabolic heat release distribution data of the fungus bag; The threshold intervals are combined with time duration, change rate, and spatial distribution characteristics to generate a plurality of candidate condition elements; The candidate condition elements are compared with each other, when there are mutually exclusive or contradictory threshold combinations, the preset priority rules are executed for elimination and sorting to obtain a candidate condition sequence; The candidate condition sequence is packaged according to the logical operation order to generate a trigger condition set corresponding to the phase.

[0008] Specifically, according to the trigger condition set, a temperature control curve segment matched with the determined phase is selected from a preset curve segment library, and the initial staged temperature control curve is obtained by splicing in time sequence under the premise of meeting the continuity constraint, including: According to the trigger condition set, a corresponding reference threshold library is called, and threshold intervals related to the phase are extracted from sensor data, environmental data, and group metabolic heat release distribution data of the fungus bag; The time dimension of the candidate segment set is mapped and matched with the phase start and end time interval to determine the available start point and end point of each segment; The matched segments are checked before splicing in chronological order to detect the time span and temperature change rate between adjacent segments. When the continuity constraint rule is exceeded, a compensation segment is inserted or the splicing order is adjusted. On the basis of the check, the candidate segments are spliced one by one in chronological order to obtain a complete initial staged temperature control curve.

[0009] Specifically, the initial staged temperature control curve is checked for conflicts to generate a staged temperature control curve, including: Conflict factors are extracted from the initial staged temperature control curve, including temperature interval overlap, lift rate contradiction, and adjacent section span anomaly. According to the phase type and trigger condition set, a preset multi-level judgment rule is called to map the conflict factors to the corresponding judgment level one by one. The conflict factors in the same time period are decomposed one by one, and the multi-level judgment rule is selected to retain or replace the curve segment to generate a conflict-free candidate curve sequence. The candidate curve sequence is subjected to consistency check to generate a staged temperature control curve, and switching rules and rollback rules are set for the staged temperature control curve.

[0010] Specifically, according to the staged temperature control curve, different height and area of the bacteria bag are independently heated or cooled for zoned temperature control, including: The time segments of the staged temperature control curve are mapped with the spatial coordinates of different heights and areas in the three-dimensional cultivation rack to generate a correspondence table of sections and curve segments. According to the correspondence table, the target temperature control parameters of each section are decomposed into a heating instruction set and a cooling instruction set, and the corresponding execution time sequence is marked. The instruction sets of each section in the same time period are interactively checked. When there is a risk of spatial overlap or temperature mutual influence, a preset scheduling logic is called to adjust the execution sequence. After the interactive check is completed, the corrected heating or cooling instructions are respectively sent to the control channels of the corresponding sections for independent adjustment of each section. During the execution of the instructions, the real-time environmental parameters of each section are sampled and compared with the target values of the staged temperature control curve as input basis for the next round of zoned temperature control mapping.

[0011] Specifically, according to the correspondence table, the target temperature control parameters of each section are decomposed into a heating instruction set and a cooling instruction set, and the corresponding execution time sequence is marked, including: The target temperature control parameters of each section in the correspondence table are read and decomposed into temperature interval values and time span values. According to the deviation direction of the temperature interval value relative to the real-time temperature of the current section, the target temperature control parameter is converted into a heating candidate instruction or a cooling candidate instruction; The candidate instruction is divided according to the time span value to form a sub-instruction unit with a clear starting point and an ending point; The sub-instruction unit is arranged in time sequence, and an execution priority identifier is embedded in each unit to generate a heating instruction set and a cooling instruction set with time sequence, and the corresponding execution time sequence is marked.

[0012] Specifically, the instruction set of each section in the same time period is interactively checked, and when there is a risk of spatial overlap or temperature mutual influence, a preset scheduling logic is called to adjust the execution sequence, including: In a preset time window, the heating and cooling instruction sets belonging to different sections are synchronously extracted to generate a parallel candidate task list; The spatial position and temperature gradient of the parallel candidate task list are compared to identify interference factors that exist spatial overlap or temperature reverse driving; The interference factors are input into a preset scheduling logic rule library one by one to perform rule mapping operation to obtain task identifiers for priority execution and task identifiers for delayed execution; According to the priority and delay identifiers, the parallel candidate task list is sequentially rearranged, and an updated scheduling sequence list is generated.

[0013] Specifically, after the interactive checking, the corrected heating or cooling instructions are respectively issued to the control channels of the corresponding sections for independent adjustment of each section, including: The heating and cooling instructions after interactive checking are classified according to section identifiers, and a one-to-one mapping table of instructions and control channels is established; The instruction set of each section is sequentially packaged according to the updated scheduling sequence list to form an execution instruction with a starting point and a duration; According to the mapping table, the corresponding control channel is called one by one, and the packaged execution instruction is injected into the execution queue of the channel; After the call is completed, the status signals returned by each control channel are confirmed one by one, and when the confirmation is correct, the execution instruction of the section is marked as having been issued for independent adjustment of each section.

[0014] The intelligent temperature control system for factory pure Ganoderma lucidum package three-dimensional cultivation is used to realize the intelligent temperature control method for factory pure Ganoderma lucidum package three-dimensional cultivation, comprising a data acquisition module, a temperature control curve generation module and a partition temperature control module. The data acquisition module is configured to acquire related data of the fungus bags at different heights and different areas in the cultivation frame, wherein the related data of the fungus bags comprises sensing data, environmental data and fungus bag colony metabolic heat release distribution data. The temperature control curve generation module is configured to generate a phased temperature control curve based on the related data of the fungus bags and growth cycle characteristics of the symbiotic fungus of Gastrodia elata in combination with a threshold determination and a condition triggering. The partition temperature control module is configured to independently heat or cool the fungus bags at different heights and areas according to the phased temperature control curve to perform partition temperature control.

[0015] Compared with the prior art, the present application has the following beneficial effects: The present application provides an intelligent temperature control method and system for factory cultivation of pure fungus bags of Gastrodia elata, which realizes differentiated and accurate temperature control of fungus bags at different levels by partition layout of a three-dimensional space, multi-point collection of environmental and metabolic signals, dynamic generation and conflict checking of a phased temperature control curve, and interactive scheduling and independent execution based on section instructions, can form an adjustable temperature gradient while ensuring the stability of the cultivation environment, solves the problems of extensive environmental control, large temperature difference between upper and lower layers and uneven growth of fungus bags in existing factory cultivation of Gastrodia elata, and provides a new intelligent cultivation scheme for factory cultivation of Gastrodia elata. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The present application provides an intelligent temperature control method and system for factory cultivation of pure fungus bags of Gastrodia elata, which realizes differentiated and accurate temperature control of fungus bags at different levels by partition layout of a three-dimensional space, multi-point collection of environmental and metabolic signals, dynamic generation and conflict checking of a phased temperature control curve, and interactive scheduling and independent execution based on section instructions, can form an adjustable temperature gradient while ensuring the stability of the cultivation environment, solves the problems of extensive environmental control, large temperature difference between upper and lower layers and uneven growth of fungus bags in existing factory cultivation of Gastrodia elata, and provides a new intelligent cultivation scheme for factory cultivation of Gastrodia elata. Figure 2 The present application provides an intelligent temperature control method and system for factory cultivation of pure fungus bags of Gastrodia elata, which realizes differentiated and accurate temperature control of fungus bags at different levels by partition layout of a three-dimensional space, multi-point collection of environmental and metabolic signals, dynamic generation and conflict checking of a phased temperature control curve, and interactive scheduling and independent execution based on section instructions, can form an adjustable temperature gradient while ensuring the stability of the cultivation environment, solves the problems of extensive environmental control, large temperature difference between upper and lower layers and uneven growth of fungus bags in existing factory cultivation of Gastrodia elata, and provides a new intelligent cultivation scheme for factory cultivation of Gastrodia elata. Figure 3 The present application provides an intelligent temperature control method and system for factory cultivation of pure fungus bags of Gastrodia elata, which realizes differentiated and accurate temperature control of fungus bags at different levels by partition layout of a three-dimensional space, multi-point collection of environmental and metabolic signals, dynamic generation and conflict checking of a phased temperature control curve, and interactive scheduling and independent execution based on section instructions, can form an adjustable temperature gradient while ensuring the stability of the cultivation environment, solves the problems of extensive environmental control, large temperature difference between upper and lower layers and uneven growth of fungus bags in existing factory cultivation of Gastrodia elata, and provides a new intelligent cultivation scheme for factory cultivation of Gastrodia elata. DETAILED DESCRIPTION

[0017] The present application will be described in detail below with reference to specific embodiments. The following examples will help those skilled in the art to further understand the present application, but do not limit the present application in any form. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application. These all belong to the protection scope of the present application.

[0018] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0019] It should be noted that the various features of the embodiments of the present application can be combined with each other, and are within the protection scope of the present application, if there is no conflict. In addition, although the functional modules are divided in the device schematic diagram, and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a manner different from the module division in the device or the order in the flowchart. In addition, the "first", "second", "third" and the like used in the present application do not limit the data and execution order, but only distinguish the same items or similar items with basically the same function and effect.

[0020] Unless otherwise defined, all technical and scientific terms used in the present application have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used in the present application are only for the purpose of describing the specific embodiments of the present application, and are not used to limit the present application. The term "and / or" used in the present application includes any and all combinations of one or more related listed items.

[0021] Embodiment 1 Please refer to Figures 1-2 An embodiment provided by the present application: an intelligent temperature control method for industrial pure fungus bag three-dimensional cultivation of Gastrodia elata, comprising the following specific steps: Step S1: obtaining fungus bag related data at different heights and different areas in the cultivation rack, wherein the fungus bag related data comprises sensing data, environmental data and fungus bag group metabolic heat release distribution data.

[0022] In the present embodiment, by arranging multiple types of sensing units at different heights and different areas of the cultivation rack, multi-dimensional collection of fungus bag related data is realized. Specifically, the temperature and humidity sensor and the carbon dioxide concentration probe are used to collect the basic environmental parameters of the cultivation space, and the surface and local temperature field distribution measured by the infrared thermal imaging or thermocouple array can obtain the heat release of the fungus bag group during the metabolic process. By comparing the temperature and humidity gradients collected at different spatial levels, the non-uniform distribution characteristics of the environment around the fungus bag can be obtained. By correlating the metabolic heat release and the gas concentration change, the relationship between the mycelium expansion rate and the group metabolic level is deduced. By mapping these data to the predefined spatial matrix, the environmental state of each section in the cultivation rack at a specific time can be obtained.

[0023] Step S2: based on the fungus bag related data, combining the growth cycle characteristics of the symbiotic fungus of Gastrodia elata, a threshold determination and condition triggering combined logic method is used to generate a phased temperature control curve.

[0024] As Figure 2 shown, the specific steps of step S2 are: Step S201: Time alignment and space calibration are performed on the fungus bag related data, a hierarchical data matrix is constructed according to the cultivation levels and regions, and abnormal data is removed and windowed according to preset abnormal threshold and consistency threshold.

[0025] In this embodiment, through multi-dimensional data regularization and screening, the fungus bag related data collected in different regions can have comparability and analysis value under a unified space-time reference. Specifically, first, time alignment is performed on the sensing data, the sampling results of each sensing node are interpolated or truncated according to a unified time step, and the time offset caused by sampling delay is eliminated. Then, space calibration is performed, the coordinates of the sensor collection points are corresponded to the cultivation shelf levels and region numbers, a space index is formed, and a hierarchical data matrix is constructed. It should be noted that an abnormal data removal mechanism is introduced in the hierarchical matrix, that is, isolated points deviating from the normal range are identified according to a preset abnormal threshold, and the mutual consistency degree of multi-point data collected in the same region is judged by a consistency threshold. When the consistency is insufficient, the data window is removed or corrected. Through the above operations, the original data is finally obtained after time and space regularization and quality verification, and a windowed and summarized hierarchical data matrix is obtained.

[0026] Step S202: The windowed and summarized data is compared with the stored growth cycle characteristic table of Armillaria mellea, the growth period of the current phase is determined, and when there is a phase conflict between adjacent time windows, single phase determination is performed according to a phase priority table. The growth period includes mycelium expansion period, mycelium stable period, or Armillaria mellea and Gastrodia combination period.

[0027] In this embodiment, the determination of the current growth period is realized through dynamic comparison between data and biological characteristic table. Specifically, the windowed and summarized data obtained in step S201 is input into the determination process and compared with the pre-stored growth cycle characteristic table of Armillaria mellea. The characteristic table defines the characteristic indexes of mycelium expansion period, mycelium stable period, and Armillaria mellea and Gastrodia combination period in temperature fluctuation range, humidity distribution mode, carbon dioxide release level, and metabolic heat curve form. When the matching degree of window data and a certain phase characteristic reaches a preset determination condition, it is determined that the current window is in the corresponding growth period. It should be noted that when adjacent time windows present different phase results, a phase conflict occurs. At this time, a phase priority table is introduced for arbitration. The priority table predefines the hierarchical relationship between different phases, for example, the rule that Armillaria mellea and Gastrodia combination period is prior to mycelium stable period, and mycelium stable period is prior to mycelium expansion period, to resolve the conflict, so as to determine a unique phase label among multiple windows. Through the above comparison and priority arbitration steps, a single growth period determination result meeting continuity and logic is finally obtained.

[0028] Step S203: on the basis of the phase determination result, a trigger condition set corresponding to the phase is formed, the trigger condition set including temperature rise trigger, temperature drop trigger and day-night switch trigger, each trigger condition including trigger threshold, minimum duration and hysteresis interval.

[0029] The specific steps of step S203 are: Step S2031: according to the phase determination result, the corresponding reference threshold library is called, and the threshold interval related to the phase is extracted from the sensing data, environmental data and population metabolic heat release distribution data of the fungus bag.

[0030] In the present embodiment, the call of the threshold library is driven by the phase determination result, realizing the directional extraction of environmental parameters and metabolic characteristics; specifically, when the growth period is determined to be one of the mycelium expansion period, the mycelium stable period or the Armillaria and Gastrodia combination period, the threshold item corresponding to the phase is called in the pre-established reference threshold library, and the threshold item is divided into subsets such as temperature, humidity, carbon dioxide concentration and metabolic heat distribution according to the parameter category; then, the sensing data, environmental data and metabolic heat distribution are matched with the called threshold item item by item, and the value set falling within the specified interval range is extracted; it should be noted that different growth periods have different sensitivities to parameter intervals, for example, the mycelium expansion period pays attention to the upper limit interval of temperature and humidity, and the Armillaria and Gastrodia combination period is more sensitive to the day-night temperature difference and metabolic heat fluctuation interval, so the extraction process will automatically limit the filtering dimension according to the phase characteristic table; in this way, the threshold interval set corresponding to the current phase is obtained.

[0031] Step S2032: combine the threshold interval with time duration, change rate and spatial distribution characteristics to generate a plurality of candidate condition elements.

[0032] In the present embodiment, the single threshold interval is expanded to a condition element set with execution significance through the combination of multi-dimensional elements; specifically, first, on the basis of the threshold interval obtained in step S2031, the time duration is introduced to limit the threshold interval within a specific time sequence range, ensuring that the trigger condition has a clear action period; then, the change rate is combined to describe the dynamic characteristics of the upper and lower limits of the threshold interval, so that the condition element can reflect the trend of parameter change, not just the static range; further, the spatial distribution characteristics are superimposed on the threshold interval to indicate the applicability of the threshold at different heights or regions, realizing the spatial positioning of the condition in the three-dimensional cultivation frame; it should be noted that after the combination of the above three types of elements and the threshold interval, a plurality of candidate condition elements are generated, each element simultaneously carrying time, rate and space attributes, and the original parameters are abstracted and converted into specific condition units that can be used for logical judgment and subsequent triggering.

[0033] Step S2033: pairwise comparison is performed on the candidate condition elements, and when a threshold combination of mutual exclusion or contradiction occurs, elimination and sorting are performed according to a preset priority rule to obtain a candidate condition sequence.

[0034] In this embodiment, the candidate condition elements generated initially are sorted into a consistent sequence through logical comparison and priority decision mechanism. Specifically, first, pairwise comparison is performed on the candidate condition elements to identify whether there is a mutual exclusion relationship in the threshold range or trigger direction, for example, both requiring temperature rise and temperature drop in the same time period, or there is an overlap conflict in the upper and lower limits of the humidity setting. Then, the identified conflicts are compared with the preset priority rule for decision, which is set according to the key control parameters of the growth phase, such as preferentially retaining the diurnal temperature difference related condition in the combination period of Armillaria and Gastrodia, and preferentially retaining the high humidity trigger condition in the mycelium expansion period. After priority decision, the retained candidate conditions are sorted according to the trigger sequence and dependency logic to finally form a linear candidate condition sequence.

[0035] Step S2034: the candidate condition sequence is packaged according to the logical operation order to generate a trigger condition set corresponding to the phase.

[0036] In this embodiment, the candidate condition sequence obtained in the previous link is converted into a trigger condition set that can directly drive the temperature control curve call through the arrangement of logical operation order. Specifically, first, the logical structure of the candidate condition sequence is analyzed to identify the sequence and parallel trigger relationship between conditions. Then, according to the preset logical operation order, the condition elements are divided into mandatory conditions that must be met and optional conditions that are triggered under the premise of being met, and hierarchical combination is established through logical symbolization. It should be noted that in this process, the index information of the time window and the spatial partition is also bound to the condition elements, so that the condition set not only has trigger logic, but also has the attributes of action range and time sequence. Finally, through packaging operation, the above multi-level condition combination is integrated into a complete trigger condition set, which directly corresponds to a specific growth phase.

[0037] Step S204: according to the trigger condition set, a temperature control curve segment matching the determined phase is selected from the preset curve segment library, and the selected curve segments are spliced in time sequence under the premise of meeting the continuity constraint to obtain an initial staged temperature control curve.

[0038] The specific steps of step S204 are as follows: Step S2041: the preset curve segment library is searched item by item according to the trigger condition set, and the segments that do not match the current phase label are excluded to form a candidate segment set.

[0039] In the embodiment, the preset curve segment library is directionally screened by the trigger condition set, to ensure that the selected segments are consistent with the requirements of the current growth phase; specifically, first, the phase label, threshold range and timing attribute in the trigger condition set are compared with the index information in the curve segment library item by item, taking the trigger condition set as the search key; when the phase identification of the curve segment does not match the current determined growth stage, the segment is directly excluded, to avoid irrelevant or conflicting control modes; then, the parameter requirements in the trigger condition set are continued to be compared in the remaining segments, to preliminarily screen out the segments that meet the current phase characteristics, and the segments are collected into a candidate segment set; it should be noted that the process actually directionally compresses the segment library originally containing multiple phases and multiple modes, so that the output candidate set only retains the content directly matching the current phase.

[0040] Step S2042: mapping and matching the time dimension in the candidate segment set with the phase start and end time interval, to determine the available start point and end point of each segment.

[0041] In the embodiment, the candidate segment set is constrained within an executable range through the corresponding relationship between the time dimension and the phase time interval, to ensure the rationality of subsequent splicing; specifically, first, the time length, start point reference and internal parameter change curve contained in each segment in the candidate segment set are read and mapped and compared with the start and end time interval of the current phase; when the segment start point is earlier than the phase start point or the segment end point is later than the phase end point, the exceeding part is cut or adjusted, to limit the actual available range of the segment; it should be noted that the continuity of the internal parameter change of the segment is preferentially ensured in the mapping process, to avoid mutation caused by cutting, and at the same time, in combination with the margin of the phase time span, the segment obviously exceeding the interval limit is screened out; through the above mapping and modification, the corresponding available start point and end point of each segment are determined, to realize accurate butt joint of the candidate segment set and the time boundary of the current growth phase.

[0042] Step S2043: pre-checking the matched segments in time sequence, to detect the time span and temperature change rate between adjacent segments, and when the continuity constraint rule is exceeded, inserting a compensation segment or adjusting the splicing order.

[0043] In the embodiment, the continuity check ensures that the overall logic of the spliced curve segments and the change rule of the environment remain coherent. Specifically, first, the segments mapped by time are sequentially sorted, and it is checked whether there is a discontinuity in the time span between adjacent segments and whether the temperature change rate exceeds the preset smooth interval. If it is found that the gap time exceeds the allowed range, a preset transition compensation segment is called in the interval to fill the missing period. If the temperature change rate difference between adjacent segments is too large, the splicing order of the segments or a buffer segment is inserted to reduce the mutation amplitude. It should be noted that the process not only focuses on the rationality of the parameters within a single segment, but also emphasizes the connection relationship between the segments to ensure the overall continuity of the spliced curve. Through the above checking and correction, the final segment sequence is formed, which can meet the phase requirements and keep smooth transition in time sequence and parameter change.

[0044] Step S2044: Based on the check, the candidate segments are spliced one by one in time sequence to obtain a complete initial staged temperature control curve.

[0045] In the embodiment, the candidate segments after the check are linearly spliced in time sequence to form an initial staged temperature control curve with complete phase coverage. Specifically, first, the segments that pass the continuity check are arranged one by one in the time order from the phase starting point to the ending point, and the start and end times of each segment are seamlessly connected with the previous and next segments. In the splicing process, the parameter change curve within the segment is not further divided, and only the boundary alignment is performed at the connection point to ensure the continuous coverage of the overall curve in the time dimension. It should be noted that if there is a small parameter jump at the connection point of adjacent segments, a smoothing processing rule is called to modify the joint for transition, thereby avoiding abrupt control instructions. Finally, all segments are spliced to form a complete initial staged temperature control curve, which covers the entire time interval of the target growth period and has a directly callable parameter time sequence.

[0046] Step S205: Conflict check is performed on the initial staged temperature control curve to generate a staged temperature control curve, and switching rules and rollback rules are set for the staged temperature control curve. The switching rules include phase replacement trigger, sensor stability flag trigger, and abnormal event trigger, and the rollback rules call a preset bottom curve to output a curve and a switching instruction set for zoned temperature control execution.

[0047] The specific steps of step S205 are as follows: Step S2051: Extract conflict factors from the initial staged temperature control curve, including temperature interval overlap, lift rate contradiction, and adjacent section span anomaly.

[0048] In the embodiment, potential logical contradictions or execution conflicts are extracted in the form of factors through structural analysis of the initial staged temperature control curve. Specifically, first, the temperature intervals of each section in the curve are compared one by one. When there is an interval overlap in the same time period, it is marked as a temperature interval overlap factor. Second, the temperature change rate difference between sections is detected. If the temperature change direction of a section is opposite to that of the previous section or the rate exceeds the preset smoothing limit, the case is extracted as a temperature change rate contradiction factor. Finally, the continuity of the time span of adjacent sections is checked. When there is a clear time fault or the span is too long between sections, it is recorded as a span anomaly factor. It should be noted that through this factorization processing method, potential inconsistencies in the complex curve are converted into explicit conflict labels.

[0049] Step S2052: According to the phase type and the trigger condition set, a preset multi-level decision rule is called to map the conflict factors to the corresponding decision levels one by one.

[0050] In the embodiment, the extracted conflict factors are hierarchically mapped through the constraint relationship between phase characteristics and trigger condition sets, thereby establishing a decision path for priority resolution. Specifically, first, the phase type of the current growth period is read, and the trigger condition set corresponding to the phase is called. The key control parameters in the phase are compared with the conflict factors one by one. When the conflict factor involves a core control parameter of the phase, it is directly mapped to the highest level of the decision rule. For example, the diurnal temperature difference factor of the combination period of Armillaria and Gastrodia is classified into the primary decision layer. If the conflict factor only involves general environmental parameters, it is mapped to the secondary level, such as secondary contradictions of humidity or gas concentration. For conflicts involving section span or rate transition, it is mapped to the continuity maintenance level. It should be noted that the multi-level decision rule has established level division and priority order when preset. After factor mapping, different processing strategies are executed according to the level in the subsequent resolution process, thereby ensuring that conflict resolution is phase-oriented and condition-matched, and finally forming a hierarchical conflict decision framework.

[0051] Step S2053: The conflict factors in the same time period are decomposed one by one, and the curve segments to be retained or replaced are selected according to the multi-level decision rule to generate a conflict-free candidate curve sequence.

[0052] In the embodiment, for multiple conflict factors occurring in the same time period, a decomposition and screening mechanism is combined with multi-level decision rules to gradually form an executable conflict-free curve sequence. Specifically, first, the conflict factors in the same time window are decomposed item by item to determine their corresponding parameter categories, triggering directions, and action segments. Then, each conflict factor is matched with the multi-level decision rules. When the factor involves a priority level parameter, the corresponding curve segment is retained and the contradictory segment is removed. When the factor belongs to a secondary level, the missing segment is supplemented with a replacement segment or a smooth transition segment to avoid curve breakage. Further, for the case where multiple conflict factors act on the same segment, an iterative method is used to sequentially decompose and rule, until all factors are processed. It should be noted that after the above decomposition, decision, and replacement operations, the original initial curve containing conflicts is reconstructed into a candidate curve sequence without contradictory segments. The sequence retains the phase core control parameters and ensures the overall curve's executability in logic and timing.

[0053] Step S2054: performing consistency checking on the candidate curve sequence, generating a staged temperature control curve, and setting switching rules and rollback rules for the staged temperature control curve.

[0054] In the embodiment, the candidate curve sequence is converted into a staged temperature control curve with stable execution logic through consistency checking and rule setting. Specifically, first, the candidate curve sequence is subjected to global consistency checking to detect the compliance of each segment in terms of time continuity, temperature gradient smoothness, and phase boundary connection. If discontinuity or rate mutation is found locally, a transition correction mechanism is called to perform smooth adjustment to ensure the overall curve continuity. Then, based on the checking results, switching rules are set for the staged temperature control curve according to the transition requirements between different phases, i.e., when the next phase triggering condition is detected, the curve is switched to the corresponding new curve segment according to the preset logic. At the same time, rollback rules are set to allow the curve to rollback to the stable segment of the previous phase in case of parameter abnormality or triggering condition failure, thus maintaining the safety and recoverability of control. It should be noted that through consistency checking and binding of switching and rollback rules, the final staged temperature control curve not only satisfies the logical continuity but also has controllable properties for dynamic adjustment.

[0055] Figure 2The illustrated phased temperature control curve shows the temperature regulation mode and logical relationship of the industrial pure fungus bag of Gastrodia at different growth stages. Specifically, the curve is divided into three main growth periods according to the time axis: mycelium expansion period, mycelium stable period, and Armillaria and Gastrodia combination period. In the mycelium expansion period, the temperature control curve is first at the baseline temperature level. When the monitoring of the mycelium metabolic activity is improved, the temperature is gradually increased to the appropriate interval of the expansion period by triggering the temperature rising condition, promoting the rapid expansion of the mycelium. After entering the mycelium stable period, the temperature curve remains in a relatively constant platform state to ensure that the mycelium population is in a stable metabolic state, while avoiding excessive fluctuations that interfere with population growth. When the growth process enters the Armillaria and Gastrodia combination period, the temperature is lowered according to the trigger condition, and the curve gradually decreases from the platform section to the appropriate low-temperature interval for induction, thereby realizing the transformation from vegetative growth to reproductive growth. It should be noted that the triggering of temperature rise and fall is not a fixed point set by humans, but is determined by threshold judgment and condition trigger logic, combined with minimum duration and hysteresis interval to avoid frequent switching, so that the curve remains stable at the stage transition. Finally, the phased temperature control curve forms a dynamic temperature control scheme for different growth phases of Gastrodia, with time dimension as the sequence and temperature change as the adjustment object.

[0056] Step S3: According to the phased temperature control curve, independently heat or cool the fungus bag at different heights and regions to perform zoned temperature control.

[0057] The specific steps of step S3 are: Step S301: Map the time segments of the phased temperature control curve with the spatial coordinates of different heights and regions in the three-dimensional cultivation rack to generate a correspondence table of the sections and curve segments.

[0058] In this embodiment, the correspondence between the temperature control curve and the internal sections of the three-dimensional cultivation rack is established through bidirectional mapping of time segments and spatial coordinates. Specifically, first, the phased temperature control curve is divided into multiple time segments according to the phase requirements, and each segment corresponds to a specific temperature and humidity control mode. Then, read the spatial coordinate information of different heights and regions in the three-dimensional cultivation rack, and combine the cultivation level number and section division standard to discretize the spatial coordinates into identifiable regional units. Next, match the control parameters of each time segment with the regional units one by one, and generate a correspondence table according to the phase characteristics and target distribution law, so that the curve segments not only have a sequence in the time dimension, but also have a directional nature in the spatial dimension. It should be noted that this mapping process binds the time sequence curve with the region index of the three-dimensional structure, and the final correspondence table provides a clear spatial positioning basis for subsequent instruction disassembly and zoned regulation.

[0059] Step S302: According to the correspondence table, the target temperature control parameter of each section is decomposed into a heating instruction set and a cooling instruction set, and the corresponding execution time sequence is marked.

[0060] The specific steps of step S302 are: Step S3021: Read the target temperature control parameter of each section in the correspondence table, and decompose it into temperature interval value and time span value.

[0061] In this embodiment, the control target in the correspondence table is parameterized and decomposed to form the basis input for subsequent instruction generation. Specifically, first, read the corresponding entries of each section and curve segment in the correspondence table, and extract the target temperature control parameter defined therein. Then, decompose the parameter, split the temperature control target into a temperature interval composed of upper and lower limit values, and convert the duration of the control target into a time span value, thereby forming a two-element description of temperature interval-time span. It should be noted that this decomposition method converts the control requirements of each section from the original overall target into operational numerical boundaries and timing constraints, ensuring the uniformity of parameter expression between different sections and providing clear data units for subsequent candidate instruction generation and scheduling.

[0062] Step S3022: According to the deviation direction of the temperature interval value relative to the real-time temperature of the current section, convert the target temperature control parameter into a heating candidate instruction or a cooling candidate instruction.

[0063] In this embodiment, the target temperature control parameter is converted into a candidate control instruction with direction, thereby providing a clear operation path for subsequent partition adjustment. Specifically, first, compare the temperature interval value obtained in step S3021 with the real-time temperature of the current section, and determine whether the real-time temperature is lower than the lower limit of the interval or higher than the upper limit of the interval. When the real-time temperature is lower than the lower limit, convert the parameter into a heating candidate instruction to drive the subsequent warming action. When the real-time temperature is higher than the upper limit, convert it into a cooling candidate instruction to trigger the cooling measure. If the real-time temperature is within the interval range, generate a maintenance instruction to maintain the state. It should be noted that in this process, not only the numerical value is mapped to the instruction, but also a one-to-one correspondence between the temperature deviation direction and the control measure is established, so that each target temperature control parameter can be directly converted into a candidate instruction unit with execution significance.

[0064] Step S3023: Cut the candidate instruction according to the time span value to form a sub-instruction unit with a clear starting point and ending point.

[0065] In the embodiment, the candidate instruction is further refined into a directly executable sub-level control unit, ensuring its accuracy in the time sequence dimension; specifically, first, the time span value obtained in step S3021 is called as a segmentation reference, and the candidate instruction is divided into several continuous time sections; then, the starting point and the ending point are clearly marked in each section, so that each sub-instruction unit has complete execution boundaries; it should be noted that during the segmentation process, consistency with the temperature interval value is maintained, that is, all sub-instruction units inherit the heating or cooling properties of the original candidate instruction, and are only disassembled in the time dimension, thereby ensuring the continuity and schedulability of the instruction after refinement; in this way, the candidate instruction is converted from a whole long-period operation into a group of sub-instruction units with clear boundaries.

[0066] Step S3024: arrange the sub-instruction units in chronological order, and embed execution priority identifiers in each unit, to generate heating instruction sets and cooling instruction sets with time sequences, and mark the corresponding execution time order.

[0067] In the embodiment, the dispersed sub-instruction units are organized into an instruction set with schedulability through time sequence arrangement and priority embedding; specifically, first, the sub-instruction units obtained in the previous step are sorted according to their starting time, to ensure that the instructions are sequentially connected in the time dimension; then, an execution priority identifier is embedded in each sub-instruction unit, which is set according to the importance of the control target and the phase characteristics, for example, to ensure stable control of the core temperature interval first, and then to allocate secondary adjustment tasks; next, the arranged sub-instruction units with priority are classified into heating instruction sets and cooling instruction sets, and the specific execution time order is marked in each unit to form a clear scheduling path; it should be noted that through the above process, the originally independent sub-instruction units are reconstructed into an instruction set with time continuity and execution priority, so that the partitioned temperature control can be implemented in a predetermined order in the subsequent scheduling stage.

[0068] Step S303: interactively check each section instruction set in the same time period, and when there is a risk of spatial overlap or temperature mutual influence, call the preset scheduling logic to adjust the execution order.

[0069] The specific steps of step S303 are: Step S3031: in a preset time window, the heating and cooling instruction sets belonging to different sections are extracted synchronously to generate a parallel candidate task list.

[0070] In the embodiment, the temperature control instructions distributed in different sections are parallelized and extracted through unified constraints of time windows, so as to be interactively checked in the same time frame. Specifically, a predefined time window corresponding to a continuous control period in the staged temperature control curve is first set, and then in the time window, the generated heating instruction set and the cooling instruction set in each section are retrieved respectively, and the instruction units overlapping with the time window are extracted. Through synchronous extraction, the operation requirements of different sections in the same time period are concentrated and summarized, and are arranged into a parallel candidate task list. It should be noted that the candidate task list not only retains the instruction properties of each section independently, but also realizes horizontal alignment under the unified time reference.

[0071] Step S3032: The parallel candidate task list is compared in space position and temperature gradient, and interference factors existing in space overlap or temperature reverse driving are identified.

[0072] In the embodiment, the parallel candidate task list is conflict screened through cross comparison of space and temperature gradient, and potential interference factors are identified. Specifically, the space coordinate section corresponding to each instruction in the candidate task list is first read, and space mapping is performed in the three-dimensional cultivation shelf model to detect whether different tasks act on mutually adjacent or overlapping areas. Secondly, gradient analysis is performed on the temperature regulation direction and amplitude of each instruction, and when it is found that there is opposite driving of heating and cooling in adjacent sections or the rate difference exceeds the preset threshold, the case is marked as temperature reverse driving conflict. It should be noted that the physical interference risk can be identified through space position comparison, and the logical contradiction is revealed through temperature gradient comparison, and the combination of the two realizes comprehensive identification of interference factors between candidate tasks, and finally forms a conflict label set for subsequent scheduling logic call.

[0073] Step S3033: The interference factors are input into the preset scheduling logic rule library one by one, rule mapping operation is performed, and the task identification of priority execution and the task identification of delayed execution are obtained.

[0074] In the embodiment, the identified interference factors are mapped and judged one by one by the scheduling logic rule base to form a task identification set with an execution sequence; specifically, first, the interference factors are input to the preset scheduling logic rule base, which establishes a judgment framework according to priority levels, for example, to ensure the temperature control instruction of the phase critical section first, and then to consider energy consumption optimization or balanced regulation of adjacent sections; then, for each interference factor, the corresponding rule item is called for matching and analysis, when the factor involves core parameters, the corresponding task is assigned a priority execution identifier, and when the factor only involves edge parameters or local influence, the corresponding task is assigned a delayed execution identifier; it should be noted that the rule mapping operation not only handles the judgment of a single factor, but also comprehensively judges according to the hierarchical relationship when multiple interferences exist, and finally outputs a set of clear priority execution tasks and delayed execution tasks.

[0075] Step S3034: According to the priority and delay identifiers, the parallel candidate task list is sequentially rearranged, and an updated scheduling sequence list is generated.

[0076] In the embodiment, the priority and delay identifiers obtained in the previous step are used as the basis for sorting to reorganize the parallel candidate task list to form a scheduling sequence list with clear timing; specifically, first, read the priority or delay identifier corresponding to each task, arrange the tasks with priority identifiers in the front section of the time sequence, and arrange the tasks with delay identifiers in the rear section; then, in the sorting process, combine the original start time and duration of the task to correct the time overlap or gap that may be caused by the sequence adjustment, so that the tasks remain coherent in the new timing structure; further, in the case where multiple priority tasks or delay tasks appear at the same time, introduce a secondary rule, such as reordering according to the phase criticality or spatial distribution range, to resolve potential conflicts between similar tasks; it should be noted that through the rearrangement process, the tasks that originally have interference relationship are converted into a scheduling sequence list that is optimized in timing.

[0077] Step S304: After completing the interactive review, the corrected heating or cooling instructions are respectively sent to the control channels of the corresponding sections for independent adjustment of each section.

[0078] The specific steps of step S304 are: Step S3041: Classify the heating and cooling instructions that have been interactively reviewed according to the section identifier, and establish a one-to-one mapping table of the instructions and control channels.

[0079] In the embodiment, the binding of the classification identified by the section and the channel resource makes the temperature control instruction after the interactive verification have a direct execution path for issuing. Specifically, first, the scheduling order list output in the interactive verification stage is read, and the heating instructions and the cooling instructions therein are classified and arranged according to the section identifier to form an instruction set indexed by a spatial position. Then, a preset channel configuration table in the cultivation rack control system is called to establish a one-to-one mapping relationship between the physical channel number of each section and the corresponding instruction set, so that the instruction and the hardware channel can be directly associated. It should be noted that in the process of establishing the mapping table, the channel occupation of adjacent sections is checked to avoid resource conflicts, and when multiple instructions compete for the same channel are detected, the mapping is automatically adjusted according to the task priority. Through the above steps, a one-to-one mapping table of instructions and control channels is finally generated.

[0080] Step S3042: The instruction set of each section is sequentially packaged according to the updated scheduling order list to form an execution instruction with a starting point and a duration.

[0081] In the embodiment, the section instruction set is converted into an operation unit with a clear execution boundary through the sequential processing, which guarantees the controllability and traceability of the subsequent issuing process. Specifically, first, the heating or cooling instructions classified and arranged in each section are read one by one according to the updated scheduling order list, and arranged in time sequence. Then, the starting point and the duration of each instruction are set according to the spatial position of the section and the control target corresponding to the curve segment, so that it has complete execution definition in the time sequence dimension. Further, if overlapping or gap is detected between the starting points during the packaging process, a transition adjustment mechanism is called to correct, to ensure that the instruction set as a whole maintains continuity and reasonableness. It should be noted that after the above packaging processing, the section instruction originally only having directionality and target parameters is converted into an execution instruction with a clear time attribute.

[0082] Step S3043: According to the mapping table, the corresponding control channel is called to inject the packaged execution instruction into the execution queue of the channel.

[0083] In the embodiment, the encapsulated execution instructions are accurately allocated to the execution queue of the physical control channel through the mapping relationship, and the independent adjustment of the section level is realized. Specifically, first, the section identifier corresponding to the encapsulated instruction is read in pieces according to the one-to-one mapping table established in step S3041, and the control channel number corresponding to the section is found in the mapping table; then the execution interface of the channel is called, the instruction with the set starting point and duration is injected into the execution queue of the channel, and it waits for scheduling in time sequence. Further, in the instruction injection process, the load state of the queue also needs to be checked, when there are unfinished instruction units in the same channel, then according to the priority identifier, the insertion or delay is carried out, so as to avoid the instruction conflict or covering; it should be noted that through the way of calling and queue injection in pieces, the temperature control tasks of different sections can be accurately mapped to the respective execution channels, so as to form a distributed control pattern.

[0084] Step S3044: After the call is completed, the state signals returned by each control channel are confirmed item by item, and when it is confirmed that there is no error, the execution instruction of the section is marked as having been issued, and the independent adjustment of each section is carried out.

[0085] In the embodiment, through the verification of the control channel feedback signal, the closed-loop confirmation of the instruction issuing process is realized, so as to ensure that the independent adjustment of each section can be smoothly executed according to the preset logic; specifically, first, after the execution instruction is injected, the state signals returned by each control channel are read, including the receiving confirmation signal, the execution queue load state and the ready state of the hardware channel; then these state signals are compared with the expected execution conditions item by item, when it is confirmed that the channel has been correctly received and there is no conflict occupation, the execution instruction of the corresponding section is marked as having been issued; on this basis, the control channel of each section will enter the independent adjustment mode and execute the heating or cooling operation according to the predetermined time sequence; it should be noted that through the item-by-item confirmation and state comparison, the temperature control failure caused by channel failure or instruction loss is avoided, so as to establish a full-link checking mechanism from instruction generation to execution at the logic level.

[0086] Step S305: In the process of executing the instruction, the real-time environmental parameters of each section are collected and compared with the target value of the staged temperature control curve, which is used as the input basis for the next round of zoning temperature control mapping.

[0087] In the embodiment, through comparison of real-time parameter recovery and target curve, closed-loop correction of temperature control effect is realized, and dynamic input is provided for the next round of mapping; specifically, first, in the process of executing instructions, sensor nodes arranged in each section are called to collect environmental parameters such as temperature, humidity and carbon dioxide concentration in real time, and metabolic heat distribution of the fungus bag group is obtained by combining infrared thermal imaging or heat flow sensors; then the collected parameters are compared with the target values at corresponding time points in the phased temperature control curve, the deviation data is extracted and recorded in the section operation log; when the comparison result shows that the deviation exceeds the set tolerance range, the deviation marker is triggered to prompt the threshold correction or condition weight adjustment in the subsequent mapping link; it should be noted that this comparison and recording process not only forms immediate feedback on the current execution effect, but also provides an input basis verified by actual practice for the next round of zoned temperature control mapping, so that the temperature control process has the characteristics of iterative optimization cycle by cycle, thereby ensuring that the phased temperature control curve can dynamically fit the growth needs of the Gastrodia fungus bag.

[0088] Embodiment 2 For another embodiment of the present application, please refer to Figure 3 The present application provides another embodiment of an intelligent temperature control system for three-dimensional cultivation of pure fungus bags in Gastrodia factory, comprising a data acquisition module, a temperature control curve generation module and a zoned temperature control module. The data acquisition module is used to acquire fungus bag related data at different heights and different regions in the cultivation rack, wherein the fungus bag related data includes sensing data, environmental data and metabolic heat distribution data of the fungus bag group. The temperature control curve generation module is used to generate a phased temperature control curve based on the fungus bag related data and the growth cycle characteristics of Gastrodia symbiotic fungi by using a logic method combining threshold determination and condition triggering. The zoned temperature control module is used to independently heat or cool the fungus bags at different heights and regions according to the phased temperature control curve to perform zoned temperature control.

[0089] In addition, the part of the above technical solution in the embodiments of the present application that is consistent with the implementation principle of the corresponding technical solution in the prior art is not described in detail to avoid excessive repetition.

[0090] The specific embodiments described above further illustrate the purposes, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. An intelligent temperature control method for the stereoscopic cultivation of pure fungus packets in a Gastrodia elata factory, characterized in that, The method comprises the following steps: Obtain relevant data of the fungus bags at different heights and different areas in the cultivation frame, wherein the relevant data of the fungus bags include sensing data, environmental data, and heat release distribution data of the fungus bag group; Based on the relevant data of the fungus bags and the growth cycle characteristics of the symbiotic fungus of Gastrodia, a logic method combining threshold determination and conditional triggering is used to generate a phased temperature control curve; According to the phased temperature control curve, the fungus bags at different heights and areas are independently heated or cooled for zoned temperature control.

2. The intelligent temperature control method for the three-dimensional cultivation of the Gastrodia elata pure fungus package in a factory, according to claim 1, characterized in that, Based on the relevant data of the fungus bags and the growth cycle characteristics of the symbiotic fungus of Gastrodia, a logic method combining threshold determination and conditional triggering is used to generate a phased temperature control curve, which comprises: Align the relevant data of the fungus bags in time and space, construct a hierarchical data matrix according to the cultivation levels and areas, and exclude abnormal data and windowed aggregate data according to the preset abnormal threshold and consistency threshold; Compare the windowed aggregate data with the stored growth cycle characteristics table of the symbiotic fungus of Gastrodia to determine the current phase of the growth period. When there is a phase conflict in adjacent time windows, a single phase is determined according to the phase priority table. The growth period includes the mycelium expansion period, the mycelium stable period, or the Armillaria and Gastrodia combination period; Based on the phase determination result, a set of trigger conditions corresponding to the phase is formed, which includes temperature rising trigger, temperature falling trigger, and day-night switching trigger. Each trigger condition contains a trigger threshold, a minimum duration, and a hysteresis interval; According to the trigger condition set, select the temperature control curve segment matched with the determined phase from the preset curve segment library, and splice them in time sequence under the premise of meeting the continuity constraint to obtain an initial phased temperature control curve; The initial phased temperature control curve is checked for conflicts to generate a phased temperature control curve, and switching rules and rollback rules are set for the phased temperature control curve. The switching rules include phase replacement trigger, sensor stability trigger, and abnormal event trigger. The rollback rules call the preset bottom curve, and output the curve and switching instruction set for zoned temperature control execution.

3. The intelligent temperature control method for the three-dimensional cultivation of the Gastrodia elata pure fungus package in a factory, according to claim 2, characterized in that, Based on the phase determination result, a set of trigger conditions corresponding to the phase is formed, which comprises: According to the phase determination result, a corresponding reference threshold library is called to extract the threshold interval related to the phase from the sensing data, environmental data, and heat release distribution data of the fungus bag group; Combine the threshold interval with the time duration, the change rate, and the spatial distribution characteristics to generate multiple candidate condition elements; Compare the candidate condition elements with each other. When there are mutually exclusive or contradictory threshold combinations, perform elimination and sorting according to the preset priority rules to obtain a candidate condition sequence; The candidate condition sequence is packaged according to the logical operation order to generate a set of trigger conditions corresponding to the phase.

4. The intelligent temperature control method for the three-dimensional cultivation of the Gastrodia elata pure fungus package in a factory, according to claim 3, characterized in that, According to the trigger condition set, select the temperature control curve segment matched with the determined phase from the preset curve segment library, and splice them in time sequence under the premise of meeting the continuity constraint to obtain an initial phased temperature control curve, which comprises: According to the trigger condition set, search the preset curve segment library item by item, eliminate the segments that do not match the current phase label, and form a candidate segment set; Mapping and matching the time dimension in the candidate fragment set with the phase start and end time interval to determine the available starting point and ending point of each fragment; Carrying out pre-splicing checking on the matched fragments in chronological order to detect the time span and temperature change rate between adjacent fragments, and inserting a compensation fragment or adjusting the splicing order when the continuity constraint rule is exceeded; On the basis of the checking, splicing the candidate fragments in chronological order to obtain a complete initial staged temperature control curve.

5. The intelligent temperature control method for the three-dimensional cultivation of the Gastrodia elata pure fungus package in a factory, as claimed in claim 4, wherein, Carrying out conflict checking on the initial staged temperature control curve to generate a staged temperature control curve, including: Extracting conflict factors from the initial staged temperature control curve, the conflict factors including temperature interval overlap, lift rate contradiction, and adjacent section span anomaly; According to the phase type and trigger condition set, calling preset multi-level judgment rules to map the conflict factors to the corresponding judgment levels one by one; Decomposing the conflict factors in the same time period one by one, selecting the retained or replaced curve segment according to the multi-level judgment rules, and generating a conflict-free candidate curve sequence; Performing consistency checking on the candidate curve sequence to generate a staged temperature control curve, and setting switching rules and rollback rules for the staged temperature control curve.

6. The intelligent temperature control method for the solid cultivation of the Gastrodia elata pure fungus package in a factory as claimed in claim 5, characterized in that, According to the staged temperature control curve, independently heating or cooling the fungus bags of different heights and areas for zoned temperature control, including: Mapping the time segments of the staged temperature control curve with the spatial coordinates of different heights and areas in the three-dimensional cultivation rack to generate a correspondence table of sections and curve segments; According to the correspondence table, decomposing the target temperature control parameters of each section into a heating instruction set and a cooling instruction set, and marking the corresponding execution time sequence; Interactively checking the instruction sets of each section in the same time period, and when there is a risk of spatial overlap or temperature mutual influence, calling preset scheduling logic to adjust the execution sequence; After completing the interactive checking, issuing the corrected heating or cooling instructions to the control channels of the corresponding sections to independently adjust each section; In the process of executing the instructions, the real-time environmental parameters of each section are collected and compared with the target values of the staged temperature control curve as the input basis for the next round of zoned temperature control mapping.

7. The intelligent temperature control method for the three-dimensional cultivation of the Gastrodia elata pure fungus package in a factory, as claimed in claim 6, wherein, According to the correspondence table, decomposing the target temperature control parameters of each section into a heating instruction set and a cooling instruction set, and marking the corresponding execution time sequence, including: Reading the target temperature control parameters of each section in the correspondence table and decomposing them into temperature interval values and time span values; According to the deviation direction of the temperature interval values relative to the real-time temperature of the current section, converting the target temperature control parameters into heating candidate instructions or cooling candidate instructions; Dividing the candidate instructions according to the time span values to form sub-instruction units with clear starting points and ending points; Arranging the sub-instruction units in chronological order and embedding execution priority identifiers in each unit to generate heating instruction sets and cooling instruction sets with time sequences and mark the corresponding execution time sequence.

8. The intelligent temperature control method for the three-dimensional cultivation of the Gastrodia elata pure bacterial packet in a factory, according to claim 7, characterized in that, The interaction check of each section instruction set in the same time period is performed, when there is a risk of spatial overlap or temperature mutual influence, a preset scheduling logic is called to adjust the execution sequence, including: In a preset time window, the heating and cooling instruction sets belonging to different sections are extracted synchronously to generate a parallel candidate task list; The spatial position and temperature gradient of the parallel candidate task list are compared to identify interference factors that exist spatial overlap or temperature reverse driving; The interference factors are input into the preset scheduling logic rule base one by one to perform rule mapping operation to obtain the task identification of priority execution and the task identification of delayed execution; According to the priority and delay identification, the parallel candidate task list is sequentially rearranged, and an updated scheduling sequence list is generated.

9. The intelligent temperature control method for the three-dimensional cultivation of the Gastrodia elata pure bacterial packet in a factory, according to claim 8, characterized in that, After the interaction check is completed, the corrected heating or cooling instructions are respectively sent to the control channel of the corresponding section for independent adjustment of each section, including: The heating and cooling instructions after the interaction check are classified according to the section identification, and a one-to-one mapping table of the instructions and the control channel is established; The instruction set of each section is sequentially packaged according to the updated scheduling sequence list to form an execution instruction with a starting point and a duration; According to the mapping table, the corresponding control channel is called one by one to inject the packaged execution instruction into the execution queue of the channel; After the call is completed, the status signals returned by each control channel are confirmed one by one, and when no error is found, the execution instruction of the section is marked as having been sent for independent adjustment of each section.

10. The intelligent temperature control system for the factory-based three-dimensional cultivation of pure Gastrodia elata fungus packets, used to implement the intelligent temperature control method for the factory-based three-dimensional cultivation of pure Gastrodia elata fungus packets according to any one of claims 1-9, characterized in that, Including: Data acquisition module, temperature control curve generation module and partition temperature control module; The data acquisition module is used for acquiring the related data of the mushroom bag in different heights and different areas in the cultivation frame, and the related data of the mushroom bag includes sensing data, environmental data and mushroom bag group metabolic heat distribution data; The temperature control curve generation module is used for generating a phased temperature control curve based on the related data of the mushroom bag, combining the growth cycle characteristics of the symbiotic fungus of Gastrodia elata, and using a logic method combining threshold judgment and condition triggering; The partition temperature control module is used for independently heating or cooling the mushroom bags in different heights and areas according to the phased temperature control curve to perform partition temperature control.

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