Environment zoning monitoring and grading control method for Chinese saprophytic boletus material storage yard

Abstract

The invention discloses an environment zoning monitoring and grading control method for a Chinese saprophytic boletus material storage yard, and relates to the technical field of edible fungus material storage yard management, a storage yard is used as an environment similar unit, a representative pile is selected to arrange multiple layers of monitoring points, and the water activity and the temperature and humidity field of each control unit are calculated according to a fungus material formula; calculating a risk index and a regional risk index of the control unit, establishing a risk budget and risk consumption relationship with bacterial material batches, performing partition ventilation, spraying, pile turning and pile-out sequence decision according to a risk level and a batch risk budget, and adjusting a risk weight and a threshold value according to a cultivation condition; the effects of yard environment perception, risk measurement and material utilization path regulation and control are achieved, the fungus material utilization rate and the finished product stability can be improved, and the method is suitable for large-scale production.

Description

Technical Field

[0001] This invention relates to the field of edible fungi substrate storage management technology, specifically to a method for environmental zoning monitoring and graded control of Boletus chinensis substrate storage yards. Background Technology

[0002] With the development of artificial cultivation and industrialized production of *Boletus saprophyticus*, more and more enterprises are choosing a production model of centralized bag making, large-scale inoculation, and batch mushroom production. The substrate storage area is the preliminary step in the procurement, mixing, bag making, and fermentation of raw materials for *Boletus saprophyticus*. It is mostly located in open or semi-open areas on one side of the factory area, where raw materials such as sawdust, cottonseed hulls, and corn cobs, along with pre-treated substrate mixed with water, are piled up. The piles are large in volume and size, and frequently rotated. They are significantly affected by seasonal climate, ventilation, ground drainage, and shading structures, making them a fundamental link influencing the contamination rate, yield, and quality of *Boletus saprophyticus* substrate.

[0003] In existing production methods, companies typically equip bag-making workshops, cultivation rooms, and fruiting rooms with temperature, humidity, and carbon dioxide monitoring devices to control the indoor environment. They divide the substrate storage areas based on experience and conduct manual inspections, with thermometers only placed in a few locations or simple records of outside temperature and rainfall. Some companies refer to composting processes by turning over, providing rain protection, or simple ventilation in certain sections of the pile, but there are no specific monitoring and evaluation methods for the characteristics of *Boletus chinensis* substrate. Due to the large space of the storage area and the difficulty in placing numerous sensors inside the pile, managers cannot promptly understand changes in temperature, moisture content, and gas levels within the core of the pile, relying solely on touch, visual inspection, and odor experience to determine if the pile is abnormal.

[0004] Under the aforementioned conditions, significant spatial and temporal differences can easily occur within the mushroom substrate storage area: Different locations within the storage area are affected by sunlight, wind direction, and ground humidity, resulting in a drier surface and the core remaining in a high-temperature, high-humidity state for extended periods. After rain or during hot seasons, some core sections are prone to self-heating, oxygen deficiency, or even localized decay. Different batches of mushroom substrate remain in the storage area for varying durations and are recombined during turning and replenishment processes, leading to significant differences in temperature, moisture content, gas composition, and microbial load during their storage phases. Failure to promptly identify high-risk piles and batches will result in higher-risk substrate entering the bag-making and inoculation process, increasing the probability of contaminated substrate, poor mycelial development, or even the complete failure of the entire storage area. Conversely, repeatedly turning the entire storage area and uniformly ventilating and spraying it to reduce risk increases energy consumption and labor costs. Therefore, in the context of industrialized production of Boletus edulis in China, the key technical problem that urgently needs to be solved in production is whether we can have a relatively objective understanding of the internal environmental state and quality risks of the mushroom material at different spatial locations and at different stages of storage in an open, heterogeneous, and time-varying storage environment, and provide a basis for subsequent storage management and usage sequence arrangement. Summary of the Invention

[0005] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides an environmental zoning monitoring and graded control method for *Boletus edulis* (Chinese saprophytic mushroom) substrate storage yards. Using the storage yard as an environmentally similar unit, multiple layers of monitoring points are set up on representative piles. Based on the substrate formula, the water activity, temperature, and humidity fields of each control unit are calculated. The risk index and regional risk index of each control unit are calculated, and a risk budget and risk consumption relationship are established with the substrate batches. Based on the risk level and batch risk budget, decisions are made regarding the order of zoning ventilation, spraying, turning, and removal from the pile. Furthermore, the risk weights and thresholds are adjusted according to the cultivation situation, achieving the effects of storage yard environmental perception, risk measurement, and substrate usage path control; thus solving the technical problems described in the background art.

[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: The method for environmental zoning monitoring and hierarchical control of Boletus saprophyticus substrate storage yard includes: spatial modeling of Boletus saprophyticus substrate storage yard; dividing control units according to the layout of the storage yard and wind direction; selecting representative piles within the control units and setting up multiple layers of environmental monitoring points; and calculating the moisture activity and three-dimensional environmental parameters of the substrate within each control unit based on the monitoring data of the representative piles and the substrate formula. The water activity of the fungal material in each control unit is periodically acquired and the risk index is calculated. The regional risk index is obtained by time accumulation and adjacent superposition. The risk consumption of each batch of fungal material is accumulated based on the regional risk index and the residence time of the batch of fungal material in the control unit and is matched with the preset risk budget. Based on the regional risk index and the risk consumption and risk budget of the mushroom substrate batches within the control unit, the priority of each control unit is determined, and environmental control and mushroom substrate removal arrangements are implemented accordingly. The weight parameters of the environmental risk index and the risk classification threshold are adjusted based on the mushroom substrate batch cultivation results.

[0007] Furthermore, based on the plan dimensions of the storage yard, the dominant natural wind direction, the direction of mechanical ventilation, the ground slope, the drainage layout, and the shading and enclosure structure, the mushroom material storage yard is divided into multiple areas with similar environmental conditions. Then, within each area, multiple control units are divided according to the stacking arrangement, stacking spacing, and the width of the turning machinery.

[0008] Furthermore, the representative pile is equipped with at least a bottom monitoring point near the ground, a core monitoring point in the middle of the pile, and a surface monitoring point near the pile surface in the height direction. An air monitoring point is also set above the representative pile. Temperature, relative humidity, and carbon dioxide concentration are collected at each monitoring point to characterize the environmental conditions of the fungal material and the surrounding air at different heights.

[0009] Furthermore, the height, width, shape, time of entry into the pile, and age of each pile were collected, as well as the proportion of sawdust, cottonseed hulls, and corn cobs in the fungal substrate formula and the initial moisture content. Based on the monitoring data of the representative pile and the fungal substrate formula, a correspondence between relative humidity, moisture content and fungal substrate water activity was established. Virtual monitoring points were generated in each control unit to estimate the distribution of fungal substrate water activity at different heights and locations.

[0010] Furthermore, the calculation of the control unit risk index simultaneously considers the degree of deviation of the fungal material water activity from the suitable range, the temperature and humidity differences between the core and the surface of the reactor and the air above the reactor, the degree of deviation of the average carbon dioxide concentration from the target range, the current reactor age, and the duration of the above parameters exceeding the suitable range, and assigns a higher weight to the degree of deviation of the fungal material water activity than to other parameters.

[0011] Furthermore, the regional risk index is obtained by accumulating the risk index of the control unit over time and superimposing it in space. The time accumulation is based on the superposition of the risk index of the same control unit according to the continuous sampling period. When superimposing in space, the risk propagation amount of the high-risk control unit to the adjacent control unit is calculated based on the relative position between the control units, the direction of the dominant airflow and the contact relationship of the stack body, and the risk index of the adjacent control unit is added accordingly.

[0012] Furthermore, the risk budget value set for each batch of fungi is pre-set based on the fungi formula, target product grade, and seasonal working conditions. The risk consumption of a batch of fungi is obtained by accumulating the regional risk index corresponding to the period during which the batch of fungi stays in each control unit. When the risk consumption of a certain batch of fungi reaches a preset ratio, the batch of fungi is marked as a priority batch, and in step three, it is restricted that the priority batch will no longer enter a new high-risk control unit.

[0013] Furthermore, based on the control unit risk index and the regional risk index, each control unit is divided into suitable, slightly deviated, high-risk, and severe-risk levels. The dominant risk type of the control unit is determined based on the relative magnitude of each parameter. The dominant risk types include high moisture activity, low moisture activity, core self-heating, oxygen deficiency, and neighborhood propagation. The risk level and the dominant risk type are used together as input conditions for selecting control actions.

[0014] Furthermore, control actions are configured according to the dominant risk type: control units with low water activity use fine mist spraying and reduce ventilation intensity; control units with high water activity use enhanced ventilation and reduced spraying, combined with shallow turning; control units with self-heating core and anoxic core generate turning tasks and activate exhaust ventilation; and batches of fungal materials in severe risk control units are limited to composting processes and other low-risk processes, rather than being used for the cultivation of Boletus edulis.

[0015] Furthermore, control resource budgets are set based on the power and operating time of blowers, spray pumps, and turning machinery, and equipment start-up and shutdown are allocated according to the control unit control priority. Mushroom material batches are classified according to the risk consumption of each batch and the risk level of the control unit where they are located when leaving the pile, and are corresponding to the bag making and sterilization process parameters. Mushroom material batch contamination rate and yield are collected and compared with the corresponding risk consumption and historical risk index to adjust the weights and thresholds used when calculating the risk index.

[0016] (III) Beneficial Effects This invention provides a method for environmental zoning monitoring and graded control of Boletus saprophyticus substrate storage yards, which has the following beneficial effects: Control units were divided according to the layout of the storage yard and the prevailing wind direction. In each control unit, a representative pile was selected to set up multiple layers of temperature, relative humidity and carbon dioxide monitoring points. The water activity of the fungal material and the three-dimensional environmental field were calculated by combining the pile height, width, shape and fungal material formula. This transformed the environmental measurement of the fungal material storage yard from zero point measurement to overall measurement, identified the environmental differences of different piles and different heights, and established a risk index at the control unit level. The risk index was based on the deviation of fungal material water activity, and was added to the temperature difference, humidity difference, carbon dioxide deviation and pile age risk index of the pile core and pile surface. The influence of adjacent control units was superimposed in time, and the regional risk index was superimposed in space. This transformed the risk characterization from single-point exceedance judgment to long-term exposure and spatial transmission risk characterization.

[0017] By calculating the risk consumption of each batch of mushroom substrate based on the risk index accumulated within the area where the substrate batch resides in each control unit, the environmental risks of the stockpile are divided into batches of mushroom substrate. This ensures that each batch of substrate has a precise risk history, and that batches of substrate with different risk levels are rationally sorted in the order of removal from the stockpile and the material usage path. Based on the risk level and main risk types of the control unit, the combined actions of ventilation, spraying, and turning are determined. At the same time, control resources are allocated within a certain range of fan power, spraying, and turning time based on the control priority of the control unit. A correspondence is established between the risk level of the substrate batch and the bag making and sterilization process parameters, realizing the risk measurement relationship between the zoned and graded control of the stockpile and the material usage decision of the substrate batch. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the process for environmental zoning monitoring and hierarchical control of the Boletus saprophyticus material storage yard of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 This invention provides a method for environmental zoning monitoring and graded control of *Boletus chinensis* fungal material storage yards, including: Step 1: In the industrialized production of Boletus edulis, the substrate storage yard serves as a buffer and transition point between the raw material entry and bag making and material preparation. The temperature, moisture content, and gas composition inside the storage yard directly affect the contamination risk of subsequent substrate sticks and the basis for mycelial growth.

[0021] However, in actual production sites, stockpiles are often in open or semi-open environments, with numerous, large, and complexly arranged stockpiles. Factors such as prevailing wind direction, localized obstructions, and ground seepage contribute to significant differences in the internal environment of stockpiles at different locations. Relying solely on surface thermometers at a few points and manual inspections makes it difficult to understand the environmental state of each stockpile at any given moment from the perspective of the entire stockpile. Furthermore, it is difficult to determine which height sections within a particular stockpile might experience self-heating, oxygen deficiency, or excessive dryness, thus failing to provide reliable input for subsequent risk assessment and differentiated management.

[0022] For the open, multi-unit, and high-altitude field of mushroom material storage yards, a spatial modeling and state acquisition method is established that can reflect both large-scale environmental changes and different height positions within the stacks. Each control unit corresponds to a set of important environmental parameters that can be tracked and used for calculation, especially the mushroom material water activity, which is the available moisture state of the mushroom material.

[0023] At the overall scale of the stockpile, areas with similar environments are divided according to the geometry of the stockpile and its environmental influencing factors. Within each area, several control units are formed to ensure that the stockpiles within each control unit have similar wind, light, and drainage conditions. Within each control unit, representative stockpiles are selected and monitoring points are set up along the height direction to obtain basic data such as temperature, relative humidity, and carbon dioxide concentration. Combined with the size of each stockpile, its age, and the formula of the fungal material, the temperature field, moisture field, and fungal material water activity field at different height positions of each stockpile within the control unit are obtained, ultimately forming an environmental field supported by actual measurement points and virtual monitoring points.

[0024] The prerequisite for actual yard design and renovation is to select areas with environmental differences on a planar scale. For example, the walls of nearby factory buildings are sheltered from the wind, and natural ventilation is less affected by natural ventilation, while areas near entrances, passages, or exhaust vents are easily affected by air convection; areas near the ground with drainage ditches or sump pits are prone to high humidity; some areas have fixed sunshades, while others are exposed to direct sunlight for extended periods; these visible environmental differences should be combined to divide the area.

[0025] Based on the original site plan and combined with the results of the on-site survey, several environmental impact factors were defined, such as the relative angle of the prevailing wind direction, the degree of unobstructedness, and the density of surface drainage paths, to delineate and divide the site into zones. Within each environmental zone, the long-term average levels of wind direction, sunlight, and surface humidity for the future stockpiles are relatively similar, thus allowing for the sharing of representative stockpiles' monitoring information in subsequent calculations. To facilitate subsequent stockpile marking and data management, each environmental zone was assigned a unique number. Each zone was further divided into multiple control units according to the stockpile direction. The same control unit covered several stockpile rows or several stockpile locations. The boundaries of each control unit were clearly marked and fixed on the ground, ensuring a one-to-one correspondence between on-site personnel and their system numbers.

[0026] In application, the complex external environment of the stockpile is explicitly decomposed into several environmental zones and control units. This allows subsequent calculations of the internal environment of the stockpile to be performed without starting from completely different boundary conditions, but rather using similar boundary assumptions within each zone. This provides a structured carrier for the spatial information of the stockpile at the data level, facilitating correlation with representative stockpile monitoring data and enabling horizontal comparisons between different control units, thus forming a clear spatial hierarchy.

[0027] After completing the zoning of the yard environment and the division of control units, one or two representative stacks are selected in each control unit as the basis for environmental monitoring and internal state estimation of that unit.

[0028] The selection of a representative stack needs to take into account the stack size, shape, and typicality of its location within the control unit. For example, if there are both tall and short stacks within a control unit, a stack with a height between the two can be selected as the representative stack to avoid calculation errors caused by extreme heights. If there are differences between the inlet and outlet sides of the control unit along the wind direction, a representative stack should be selected in the central region to reflect the average state of the unit.

[0029] When forming the piles, the height, width, and shape of each pile are recorded along with its number. Then, based on the number and geometric distribution of the piles, one or two piles are selected as representative piles. Temperature, humidity, and carbon dioxide sensors are installed at the bottom, core, surface, and upper air positions of these representative piles. These sensors can be installed at fixed proportions based on the pile height, such as one-quarter, one-half, and three-quarters of the pile height, allowing for comparison of monitoring results at different positions and heights. Other non-representative piles within the control unit are recorded based on their differences from the representative piles in terms of horizontal position, height, and shape. The internal state of these non-representative piles is then inferred from the representative pile data, corresponding one-to-one with the geometric information of all piles within the control unit. Subsequently, the monitoring results of the representative piles are extrapolated by position and height to obtain estimates of the temperature and humidity fields within the entire control unit. This method avoids the cost and construction difficulties of deploying numerous sensors inside each pile, providing a geometric basis for estimating the moisture activity of the fungal material.

[0030] After the representative stack sensors in the control unit are installed, temperature, humidity, and carbon dioxide levels can be collected from different locations in the four layers (bottom, middle, surface, top, and top) at time intervals. Since the internal environment of the stack generally changes slowly and is subject to localized disturbances, to ensure that subsequent calculations do not suffer from interlayer differences or misjudgments due to occasional small fluctuations, the collected time series needs to be organized and the interlayer relationships characterized. Within each sampling period, the sampling values ​​of the same height layer are smoothed multiple times in chronological order using a sliding window to obtain the layer temperature, layer humidity, and layer carbon dioxide concentration representing the steady-state or slowly changing state of that period. The temperature and humidity differences between different height layers at the same time (such as the difference between the core temperature and the surface temperature, and the difference between the core temperature and the air temperature above) are statistically analyzed to obtain the interlayer gradient representing the longitudinal heat transfer and ventilation status. By recording the changes in these interlayer gradients over different time periods, the evolution of the stack from new stacking, slow heating to self-heating peak, and then to mitigation can be recorded. This also allows for the identification of abnormal stacks with relatively late ages that maintain large temperature differences.

[0031] In practice, the monitoring data from multiple layers of the representative stack are processed and smoothed, with a focus on extracting the interlayer temperature and humidity gradients and carbon dioxide accumulation characteristics. This ensures that subsequent calculations of the substrate moisture state and ventilation conditions do not rely on readings at a single time point, but are based on interlayer relationships that exist stably over a certain period. This reduces interference from random factors, improves the representativeness of the calculation results to the actual state of the stack, and also provides a more reliable environmental input for the subsequent construction of a substrate moisture activity model.

[0032] After obtaining stable temperature and humidity data for each height layer of the stack, these environmental parameters need to be combined with the substrate formulation information to calculate the water activity of the substrate at the corresponding height layer. Since different raw materials have different water adsorption capacities, it is difficult to accurately characterize the water state in the substrate that can be utilized by Boletus sinensis simply by relying on moisture content. Therefore, it is necessary to establish a functional relationship between relative humidity, substrate moisture content, and substrate water activity, so that the environmental characterization of the stack extends from the air state to the internal state of the substrate.

[0033] Introducing the water activity parameter of fungal material And express it as ambient relative humidity. Moisture content of fungal material and the mass fraction of fibrous raw materials Functions, for example:

[0034] Among them, the water activity of fungal material : Dimensionless parameter, with a value range of Used to characterize the moisture state of fungal material that can be utilized by mycelia; relative humidity : Represents the relative humidity of the air at the corresponding altitude of the stack, with a value range of . ; Moisture content The moisture content of the substrate at this height is [value to be filled in]. mass fraction The mass fraction of fibrous raw materials such as sawdust in the fungal substrate formulation, and its value. ;coefficient ,coefficient ,coefficient A positive coefficient determined based on experiments or experience, used to reflect the intensity of the influence of relative humidity, moisture content, and fibrous raw materials on water activity.

[0035] When the relative humidity of the environment and the moisture content of fungal materials Water activity increases synchronously The trend is accelerating, while the mass fraction of fibrous raw materials... When the value increases, the denominator also increases, thereby inhibiting water activity. The increase in [a certain value] is more consistent with the actual situation of moisture binding in woody raw materials. By calibrating different formulation samples in the early stages of actual production, a set of coefficients suitable for long-term use in this stockpile can be selected. , and This makes the calculated water activity It is basically consistent with the intuitive experience of operators, making it easy to use the same set of judgment criteria in subsequent management.

[0036] Within the control unit, for different height locations of non-representative piles, a weighted mapping method can be used to extrapolate the temperature and relative humidity of the corresponding height layer of the representative pile to that location, based on the differences between the non-representative pile and the representative pile in terms of horizontal distance, pile height, and pile shape. For example, a weighted average temperature estimation formula can be constructed to obtain the corresponding ambient relative humidity at that location. Furthermore, considering the given moisture content of the fungal material in the formula for this pile of fungal materials... and fibrous raw material mass fraction Substitute the values ​​into the above function to calculate the corresponding water activity of the fungal material. .

[0037] By repeating this calculation at different piles and heights, a three-dimensional environmental field of temperature, humidity, carbon dioxide concentration, and fungal material water activity can be formed within each control unit. This allows for viewing the longitudinal distribution on a single pile cross-section and comparing the differences between different piles at the control unit level, providing unified and detailed basic data for the subsequent calculation of the immediate risk index and cumulative damage risk index.

[0038] By introducing the water activity of the fungal material And establish relative humidity with the environment Moisture content of fungal material and the mass fraction of fibrous raw materials The functional relationship between them allows the results of the stockpile space modeling to no longer be limited to the level of air temperature and humidity, but to directly reflect the moisture state inside the fungal material that is significant for the growth of Boletus sinensis.

[0039] Step 2: Based on the environmental parameters of the control unit obtained in Step 1, first construct the instantaneous risk index and regional risk index at the control unit scale, and then construct the risk budget and risk consumption at the mushroom material batch scale, so that the environmental risk of the stockpile can be transmitted between the control unit and the mushroom material batch along time and space, and finally form a risk level system that can be called upon in Step 3.

[0040] In step one, each control unit already contains parameters such as the moisture content of the core substrate, the temperature difference between the core and the surface, the temperature difference between the core and the air above, the humidity difference between the core and the surface, the carbon dioxide content within the reactor, and the reactor age. These parameters reflect the environmental state of the substrate within the control unit, but they differ in dimensions and ranges, making it impossible to directly compare which unit experiences a risk at which moment.

[0041] On the other hand, the fungal substrate moves between multiple control units through operations such as mixing, stacking, turning, replenishing, and transshipment. The risk levels of each control unit vary at different times. If we only look at the environmental parameters at a certain moment and location, we cannot determine whether the risk borne by a batch of fungal substrate during the entire stockpile stage has approached the acceptable limit. Therefore, it is necessary to construct a unified risk index at the control unit level and introduce the concepts of risk budget and risk consumption at the batch level of fungal substrate, linking environmental risks to the historical exposure process of specific materials.

[0042] The control unit risk index provides an environmental basis for batch risk budgeting, while the batch risk budgeting in turn gives the control unit risk index a meaning related to specific fungal materials. The two support each other, forming a logical closed loop between environmental zoning monitoring and batch management of fungal materials.

[0043] At the control unit level, the first step is to map different physical quantities to the same dimensionless space, so that the contributions of moisture activity deviation, temperature gradient, humidity gradient, carbon dioxide deviation, and pile age to risk can be weighted and superimposed in the same expression. To this end, several deviation functions are introduced to convert the deviations of each physical quantity from its target value or reference range into non-negative deviations, which are then combined according to weights to form an instantaneous risk index.

[0044] During the configuration phase, a target water activity is set for the core microbial material. This value can be determined based on long-term production experience with *Boletus edulis* fungus. At the sampling time... Upon arrival, the system reads the moisture activity of the fungal material at the core location of the control unit from the three-dimensional environmental field of step one. The water activity deviation function was calculated using the exponential deviation function. :

[0045] Among them, the water activity deviation function Dimensionless quantity, used to represent the intensity of the deviation of the core substrate water activity from the target water activity, with a value range of [value missing]. ;Bacteria water activity : Control unit core location at time point The water activity of the fungal material was taken as a value. Target water activity The target water activity value set for the *Boletus chinensis* mycelium is as follows: ; Water activity sensitivity coefficient : Positive coefficient, used to control the rate of deviation amplification, value... ,when When the value is small, the exponential term is close to 1, and the water activity deviation function... Approaching zero; as the deviation increases, the water activity deviation function... It rose rapidly in an exponential manner.

[0046] A temperature deviation function can be defined to represent the temperature difference between the reactor core and the surface layer, and the temperature difference between the reactor core and the air above it. For example, the absolute temperature difference is set to zero when it is less than a pre-set allowable temperature difference, and increases linearly proportionally to the excess when it exceeds the allowable temperature difference, thus making the temperature gradient larger. The larger the value, the greater the humidity difference between the core and the surface. A humidity deviation function is defined. The value is small when the humidity difference is within the allowable range, and increases when the core or surface is significantly wet.

[0047] For carbon dioxide concentration, define the carbon dioxide deviation function. The inflection point is set at the upper limit of the reference concentration. The value is smaller when the concentration is below the upper limit of the reference concentration, and increases proportionally when the concentration exceeds the upper limit.

[0048] For pile age, define the pile age deviation function. The values ​​are lower in the early stages of stacking and increase as the maximum permissible stacking time approaches, thus reflecting the difference in the intensity of the same environmental deviation at different stacking ages. After obtaining these deviation functions, the system combines them into an instantaneous risk index according to set weights. :

[0049] Among them, the real-time risk index : is a dimensionless quantity used to characterize the control unit at a given time point. The overall environmental risk intensity, with a value range of [value range missing]. Weighting coefficient : is the water activity deviation function Corresponding weights, weight coefficients Temperature deviation function Corresponding weights, weight coefficients Humidity deviation function Corresponding weights, weight coefficients Carbon dioxide deviation function Corresponding weights, weight coefficients age deviation function The corresponding weights, each weight coefficient taking values ​​that are non-negative, and satisfying the following conditions: .

[0050] In practical configurations, the water activity deviation function Often assigned a large weighting coefficient To demonstrate the dominant role of water activity in the suitability of *Boletus sinensis* fungal materials, temperature deviation function was used. Deviation function with carbon dioxide The weighting coefficient can be set to a medium level, and the humidity deviation function... With age deviation function The weighting coefficients can be set based on specific experience. In practice, different environmental parameters are normalized to a deviation function and combined to form an immediate risk index. This provides a unified benchmark for controlling environmental risks, laying the foundation for addressing both time-accumulated and spatially propagated risks.

[0051] Real-time risk index This reflects the instantaneous deviation of the control unit at a certain moment. However, changes in the quality of the fungal material are often closely related to the cumulative process of long-term exposure to adverse environments. The same deviation, depending on its duration, will have different degrees of impact. Furthermore, airflow channels and pile contact relationships exist between control units in the stockpile. A high-risk state in one control unit may spread to adjacent control units along the ventilation direction or pile direction. Therefore, a regional risk index is introduced. This process smooths and accumulates the immediate risk index over time, and superimposes the influence of neighboring control units in space, thus forming a risk expression that has both temporal memory and spatial propagation meaning.

[0052] Furthermore, at each sampling time point Update the regional risk index according to the following formula. :

[0053] Among them, the regional risk index : For the control unit at a given time point The comprehensive risk index, with a value of Regional risk index at the previous time point : for a point in time The regional risk index is used to retain historical risk information; the attenuation coefficient... : A constant between 0 and 1, with values ​​ranging from 0 to 1. This is used to control the proportion of historical risk retained in the current regional risk, when the attenuation coefficient... When the coefficient is close to 1, historical risk has a large impact, and when the attenuation coefficient is close to 1... When the price approaches zero, the immediate impact of current risks accounts for a large proportion. Real-time risk index This control unit at time point Real-time risk index; neighborhood set This refers to the set of indexes of control units that have direct air passage or stack contact with this control unit in planar location. This set is determined during the yard layout design phase based on stack number and wind direction; neighborhood weighting coefficient. For adjacent control units The weighting of the influence on the risk index of this control unit area ranges from [value range missing]. Normalization can be performed on the neighborhood set of the same control unit to make the weight coefficients of all neighborhoods equal. The sum is 1; the immediate risk index of the adjacent control unit. For adjacent control units At the point of time The real-time risk index is used to express the transmission of risk from neighboring areas; Spatial propagation coefficient : is a non-negative constant, and its value is The proportion of the real-time risk index of adjacent control units in the risk index of this control unit area. When using, the regional risk index By incorporating historical accumulation in time and neighborhood propagation in space, risk assessment is transformed from instantaneous and localized judgments into comprehensive judgments based on historical and spatial memory, providing a more accurate baseline value for subsequent batch risk budget calculations of fungal materials.

[0054] After the risk index for the control unit area is constructed, this index needs to be accumulated over time along the spatial movement trajectory of batches of fungal materials in the storage yard, so that the environmental exposure process of each batch of fungal materials in the storage yard stage can be described by a risk consumption curve. At the same time, in order to prevent the same batch of fungal materials from staying in the storage yard for too long or being exposed to high-risk areas for a long time, a risk budget value needs to be set for each batch of fungal materials in advance, fixing the upper limit of the tolerable risk.

[0055] When the substrate is mixed and ready to be piled, a unique batch number is generated for each batch of substrate. Based on the mass ratio of sawdust, cottonseed hulls, and corn cobs in the substrate formula, the initial moisture content, and the intended grade of the *Boletus edulis* product to be produced, a batch risk budget value is set for that batch. .

[0056] Risk Budget Value This is a dimensionless quantity used to represent the upper limit of cumulative risk that this batch can withstand throughout the entire stockyard phase, and can be given by historical production experience or test results. During stockyard operation, the system is based on sampling time points. Record the regional risk index of each control unit Simultaneously, by scanning the relationship between the stack location number and the batch number, the time point of each batch of fungal material can be determined. The control unit where it is located, thus obtaining the time point of this batch. The corresponding regional risk index. To depict the cumulative process of risk over time, the risk consumption of each batch of fungal material is calculated. Defined from the time of heap entry Start time point The cumulative risk intensity value for each sampling interval during the period, where the risk intensity is expressed by the regional risk index at the corresponding time point, and the sampling interval length is determined by the time series configuration.

[0057] Through gradual accumulation, risk consumption The risk increases monotonically as time progresses, approaching the batch risk budget value for the fungal material. This indicates that the batch has approached its acceptable risk limit during the yard stage and should be released from the yard as soon as possible.

[0058] In one embodiment, three batches of mycelium material are produced consecutively within a week at the stockpile, with batch numbers B1, B2, and B3. After being placed in the stockpile, the three batches are located in different control units, and subsequently moved to a more inward location within the stockpile as the stockpile is turned and its position adjusted. At each sampling time point, the system updates the control units where the three batches of mycelium are located based on the scanning records and assigns the corresponding regional risk index. Accumulated into their respective risk consumption amounts Up. A few days later, B1's risk consumption was still far below its risk budget. B2's risk consumption was close to the risk budget value because it had been repeatedly moved into the high-risk control unit; B3's risk consumption was in between. After reviewing the batch risk consumption list at the morning meeting, the shift leader decided to prioritize B2's removal from the reactor that day, followed by B3, while temporarily keeping B1 in a lower-risk area to avoid it remaining in a high-risk environment.

[0059] In use, the risk index of the control unit area is mapped to a specific batch of fungal material along the time and space dimensions, forming a risk budget value for the fungal material batch. Risk consumption The combination of these factors allows for a clear understanding and management of the risk exposure process for each batch of fungal material during the stockpile stage.

[0060] Based on the established risk index of the control unit area and the risk consumption of fungal materials in batches, the numerical results need to be converted into risk level indicators that can be executed on-site. This will allow the zoning and hierarchical control and material scheduling in step three to be based directly on the level as the decision-making basis, without requiring on-site personnel to reinterpret complex numerical values.

[0061] Risk index for control unit area Several risk levels are defined, and control units whose regional risk index falls into the lowest level are marked as suitable risk level, control units whose index falls into the next lower level are marked as slightly deviated risk level, control units whose index falls into the higher level are marked as high risk level, and the highest level corresponds to severe risk level.

[0062] At the same time, utilize real-time risk index The relationship between the magnitudes of the various deviation functions determines the dominant risk type of the control unit: when the water activity deviation function... Significantly higher than the temperature deviation function and carbon dioxide deviation function When the dominant risk type is determined to be water activity deviation; when the temperature deviation function... Deviation function with carbon dioxide Simultaneously, the water activity deviation function is at a high value. At a moderate level, the dominant risk type is identified as self-heating and poor ventilation; when the humidity deviation function... When the situation is prominent, the dominant risk type is identified as uneven humidity distribution.

[0063] For batches of fungal materials, after obtaining the risk consumption amount Then, the risk consumption amount and the risk budget value are compared. The risk level of a mushroom batch is determined by combining the ratio of risk factors, the risk level of the control unit in which the batch has been located recently, and the dominant risk type. For example, when the risk consumption... Far below the risk budget value If a batch spends most of its time in a suitable risk level control unit, it can be classified as a Category 1 batch risk level and given priority for the main cultivation of *Boletus saprophyticus*. If the risk consumption is close to the risk budget value and the batch has passed through a high-risk level control unit but for a limited time, it can be classified as a Category 2 batch risk level. In subsequent use, it can be compensated for by increasing the sterilization temperature or extending the heat preservation time. If the risk consumption has exceeded the risk budget value and the batch has recently been in a severe risk level control unit, it can be classified as a Category 3 batch risk level. This type of batch will no longer enter the *Boletus saprophyticus* cultivation chain in subsequent processes, but will be used as compost material or for low-sensitivity applications.

[0064] When in use, control unit area risk index Risk consumption of fungal material batches The risk levels of the control unit and the batch risk levels of the fungal material are converted into risk levels of the control unit, so that the calculation results of step two can be seamlessly transferred to step three in the form of levels, realizing a direct correspondence between environmental zoning monitoring results and hierarchical control actions.

[0065] Step 3: Starting with the risk level of the control unit and the risk level of the substrate batch, determine the specific control actions and resource allocation of different control units in the spatial dimension, arrange the order of substrate removal and material usage in the material dimension, and continuously revise the risk assessment and control parameters using the cultivation results in the time dimension, so that the indicator system established in the first two steps can truly penetrate into daily operations and formula decisions, forming a unified technical chain.

[0066] In fact, in step two, the risk index of the control unit area has already formed the risk level of the control unit through the accumulation of time and the spread of neighboring areas. The batch of fungal material has also formed the batch risk level through risk budget and risk consumption. However, for managers, the actual stockpile and production site do not face abstract indices but rather the turning machine, fan, spray pipeline and bag making plan.

[0067] If risk information cannot be closely linked to these operational objects, it will remain in a state of being visible but unusable. This will lead to high-risk areas not receiving targeted treatment, low-risk areas being over-intervened, and even batches of high-risk fungal materials entering sensitive process stages, negating the value of previous monitoring and assessment investments. Therefore, in step three, the risk index must be systematically transformed into control actions, resource allocation, and material selection decisions, and this transformation process must be verified and corrected using cultivation results in long-term operation.

[0068] After completing the regional environmental risk assessment, each control unit has a clear risk level identifier and dominant risk type label at any given time, such as high risk, predominantly high moisture activity, slight deviation, predominantly low moisture activity, high risk, and predominantly self-heating and carbon dioxide accumulation. This information needs to be specifically interpreted on-site at the stockpile site to determine whether to activate a particular exhaust fan, whether to implement localized spraying, whether to schedule turning of the stockpile, and the combination of turning depths. The goal is to transform this textual risk description into a control action matrix with a fixed structure, so that each change in risk status can generate definite and repeatable operational instructions.

[0069] Define a control action sequence for each combination of risk level and dominant risk type. Taking a slight deviation risk level with low water activity as an example, the control action sequence can be set to first perform a short-term local fine mist spray, and then reduce the airflow of the ventilation equipment above the control unit, so that the newly added moisture has a chance to be absorbed by the fungal material instead of being carried away immediately.

[0070] For slightly risky levels where high moisture activity is the primary concern, the control sequence can be set to first increase local air supply or exhaust volume, then shorten the spraying cycle to alleviate excessive moisture in the reactor core. For high-risk levels where self-heating and carbon dioxide accumulation are the primary concerns, the control sequence can be set to first turn the reactor core over, bringing the high-temperature, anoxic substrate to the outer layer, followed by enhanced ventilation, and then determining whether to turn it over again based on feedback after the initial turn. For control units at severe risk levels, the control sequence can be expanded to include partial removal of the reactor core, separate storage, and temporary isolation measures to prevent the risk from spreading to the surrounding area through the reactor core contact surface.

[0071] When in use, each risk combination corresponds to a preset sequence of control actions, so that the environmental classification of the control unit is no longer just displayed, but is naturally linked to specific ventilation, spraying and turning operations, realizing a structured mapping of risk information to work arrangements, and improving the consistency and executability of yard management.

[0072] Even with a defined control action matrix, a critical issue remains in the stockpile: given the limited number of blowers, spraying capacity, turning machinery operating time, and on-duty manpower, it's impossible to simultaneously execute all actions on all control units requiring processing. This necessitates establishing a control priority calculation method in step three that integrates the risk index of the control unit area, the risk status of the batch of mycelium contained in the control unit, and the planned batch usage time. This ensures that, under limited control resources, the processing needs of the control units with the highest risk and closest relevance to the recent production plan are prioritized.

[0073] Therefore, a control unit can be introduced to control priority. , where index Identify the specific control unit. At a given sampling time. Control unit control priority It can be calculated using the following formula:

[0074] Among them, the control unit controls the priority. : Dimensionless quantity; a larger value indicates that the control unit needs priority access to ventilation, spraying, and turning resources at the current moment; weighting coefficient Weighting coefficients Weighting coefficients : A non-negative real number used to balance the impact of regional risk index, batch risk consumption level, and batch plan urgency. The sum of these three can be set as ; Regional Risk Index Control Unit At the point of time The regional risk index, corresponding to the risk measure introduced in step two after considering historical and neighborhood influences, is a non-negative real number; set : The control unit at the current moment The internal collection of fungal material batches is obtained by mapping stack locations to batch numbers; risk consumption. : Batch of fungal material At the point of time The amount of risk consumption corresponds to the risk curve value accumulated over time in step two, and is a non-negative real number. Risk Budget Value :batch The batch risk budget value for the fungal material corresponds to the upper limit of the tolerable cumulative risk for that batch, and is a non-negative real number; the urgency indicator to be used in the plan. : Batch of fungal material The urgency index of planned usage time can be obtained by mapping the difference between the scheduled usage date and the current date to a positive number; the smaller the value, the closer to the scheduled usage time. (Operator) In the control unit Internally, take the maximum value among the ratios of risk consumption to risk budget value for all batches; operator In the control unit Take the minimum value among all batch plans using urgency indicators.

[0075] Priority is controlled by the aforementioned control unit. The calculation of the regional risk index Risk consumption With risk budget value And plans to use urgency indicators They are all incorporated into a unified numerical framework. Before scheduling daytime operations at the yard, the control priorities of all control units can be determined. Sort them from highest to lowest to form a control task list.

[0076] The turner operation window and blower speed adjustment window are assigned to the control units in the order listed. When the available operating time and equipment capacity reach their maximum, the remaining control units are automatically postponed to the next shift. For some control units, even if the regional risk index... While not the highest, some of the batches included in this figure have risk consumption levels that are close to the risk budget. Or plans to use urgency indicators It is already in the lowest range, and its control priority is... This will also improve the coordination between environmental risks and batch risks at the resource allocation level.

[0077] When in use, control unit area risk index and batch risk budget value of fungal materials Risk consumption And plans to use urgency indicators Integrated into a control unit to control priority This allows for differentiated and tiered control that considers not only the intensity of environmental risks but also the progress of batch usage and historical exposure.

[0078] After the control unit's zoning and resource scheduling are completed, it is still necessary to establish a set of material usage paths and process parameter linkage rules at the batch level that match the batch risk level, so that batches of fungi with different risk levels enter the cultivation path appropriate to their risk status. Otherwise, even if the stockpile control is reasonable, if high-risk batches and low-risk batches are treated equally in the bag-making, sterilization, and inoculation stages, there will be a situation where high-risk batches are not adequately protected and low-risk batches are over-treated.

[0079] Establish a one-to-one correspondence table between the risk level of each batch of fungal material and the cultivation process. For example, classify the risk consumption amount... Far below the risk budget value Batches that have remained within the appropriate risk level control unit for an extended period are classified as batch risk level 1. These batches are designated as the main mycelial material for *Boletus chinensis* and are given priority in the bag-making and inoculation processes under standard formulations and routine sterilization conditions. Risk consumption is... Approaching risk budget value Batches that have passed through a higher-risk control unit but had a limited dwell time are classified as batch risk level two. These batches can be mixed with batches one in a certain proportion during the bag-making process, while the sterilization temperature is increased or the holding time is extended during sterilization; this reduces the risk consumption. It has exceeded the risk budget value. Furthermore, batches that have recently remained in the severe risk level control unit have been classified as batch risk level three. These batches are marked in the system as prohibited from being used for the cultivation of Boletus chinensis saprophyticus and are only permitted to be sent for composting or other uses where the strain is less sensitive.

[0080] In one embodiment, the bag-making workshop retrieves a list of available mycelium batches from the system before the start of the morning shift. Each batch in the list is marked with a batch number, batch risk level, and recommended use. The workshop manager determines the number of batches of *Boletus edulis* spawn needed for the day based on the production plan, prioritizing batch number one (highest risk level) as raw material. If batch number one is insufficient, a portion of batch number two is added proportionally, and the sterilization section is simultaneously notified to use upgraded sterilization parameters. Batch number three is scheduled to be transported to the composting area by forklift in the afternoon. Throughout the process, production personnel do not need to determine which batch of mycelium has a higher risk; they only need to follow the batch risk levels and recommended uses provided by the system, reducing quality fluctuations caused by human error.

[0081] When using, the batch risk budget value for fungal materials Risk consumption This is directly mapped to the selection of material usage paths and cultivation process parameters, so that the risk assessment of the storage yard is no longer limited to the front end, but runs through the entire process of bag making, sterilization and cultivation, forming a continuous technology chain through multi-level linkage.

[0082] Even if the weighting coefficients and risk level classification thresholds are reasonably set in steps one and two, deviations may still occur between the original parameters and the actual risks due to seasonal changes, adjustments to the fungal substrate formula, and equipment aging.

[0083] If the parameters are not adjusted for a long period of time, the regional risk index will... and batch risk budget value of fungal materials The representativeness of the data will decrease, and the reliability of the method will also decrease. Therefore, at the cultivation results level, a self-learning update method is applied. By measuring results indicators such as contamination rate, yield, and grade structure, key weights and thresholds are adjusted to ensure that the risk assessment and control logic is consistent with the actual production situation.

[0084] A certain weighting coefficient, such as the water activity deviation weighting coefficient. Defined as a correction formula based on cultivation results; assuming that several batches of cultivation results have been obtained within an evaluation period, an error index regarding the contribution of water activity deviation can be calculated. It is used to determine whether the current water activity deviation underestimates or overestimates the risk of the batch.

[0085] Weighting coefficient The update method can be represented as:

[0086] Among them, the water activity deviation weighting coefficient : No. The water activity deviation weighting coefficient before the last update is a non-negative real number; water activity deviation weighting coefficient : No. The updated water activity deviation weighting coefficient; learning step size : Positive constant, value This is used to control the magnitude of each parameter update; Error index The contribution error of water activity deviation obtained from the statistical analysis of several batches of cultivation results can be obtained by accumulating the error signals corresponding to the actual good performance of the predicted high-risk batches and the actual poor performance of the predicted low-risk batches.

[0087] If the batch cultivation results with high water activity deviation are generally good, then the error index A negative value indicates a negative weighting coefficient for water activity deviation. The error index will be reduced in the next update; conversely, if batches with low water activity deviation still show a high contamination rate, the error index will be reduced. A positive value indicates the weighting coefficient for water activity deviation. Increased in the next update.

[0088] Regional risk index is a parameter series that is gradually adjusted based on feedback from cultivation results. and batch risk budget value of fungal materials Over time, it gradually adapts to the actual performance of this stockpile, this formula, and this strain, enabling the entire environmental zoning monitoring and hierarchical control method to have self-evolutionary characteristics and avoid structural deviations after long-term use.

[0089] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0090] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0091] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0092] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0093] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for environmental zoning monitoring and graded control of *Boletus chinensis* fungal substrate storage yards, characterized by: include, Spatial modeling was performed on the Boletus saprophyticus substrate storage area. Control units were divided according to the storage area layout and wind direction. Multiple environmental monitoring points were set up in representative piles within the control units. Based on the monitoring data of the representative piles and the substrate formula, the water activity and three-dimensional environmental parameters of the substrate in each control unit were calculated. The water activity of the fungal material in each control unit is periodically acquired and the risk index is calculated. The regional risk index is obtained by time accumulation and adjacent superposition. The risk consumption of each batch of fungal material is accumulated based on the regional risk index and the residence time of the batch of fungal material in the control unit and is matched with the preset risk budget. Based on the regional risk index and the risk consumption and risk budget of the mushroom substrate batches within the control unit, the priority of each control unit is determined, and environmental control and mushroom substrate removal arrangements are implemented accordingly. The weight parameters of the environmental risk index and the risk classification threshold are adjusted based on the mushroom substrate batch cultivation results.

2. The environmental zoning monitoring and hierarchical control method according to claim 1, characterized in that: First, based on the plan dimensions of the storage yard, the dominant natural wind direction, the direction of mechanical ventilation, the ground slope, the drainage layout, and the shading and enclosure structure, the mushroom material storage yard is divided into multiple areas with similar environmental conditions. Then, within each area, multiple control units are divided according to the stacking arrangement, stacking spacing, and the width of the turning machinery.

3. The environmental zoning monitoring and hierarchical control method according to claim 2, characterized in that: The representative pile has at least a bottom monitoring point near the ground, a core monitoring point in the middle of the pile, and a surface monitoring point near the pile surface in the height direction. An air monitoring point is set above the representative pile. Temperature, relative humidity, and carbon dioxide concentration are collected at each monitoring point to characterize the environmental conditions of the fungal material and the surrounding air at different heights.

4. The environmental zoning monitoring and hierarchical control method according to claim 3, characterized in that: The height, width, shape, time of entry into the pile, and age of each pile were collected, as well as the proportion of sawdust, cottonseed hulls, and corn cobs in the fungal substrate formula and the initial moisture content. Based on the monitoring data of the representative pile and the fungal substrate formula, the correspondence between relative humidity, moisture content and fungal substrate water activity was established. Virtual monitoring points were generated in each control unit to estimate the distribution of fungal substrate water activity at different heights and locations.

5. The environmental zoning monitoring and hierarchical control method according to claim 4, characterized in that: The calculation of the control unit risk index takes into account the degree of deviation of the substrate water activity from the suitable range, the temperature and humidity difference between the core and the surface and the air above the stack, the degree of deviation of the average carbon dioxide concentration from the target range, the current stack age, and the duration of the above parameters exceeding the suitable range, and assigns a higher weight to the degree of deviation of substrate water activity than other parameters.

6. The environmental zoning monitoring and hierarchical control method according to claim 5, characterized in that: The regional risk index is obtained by accumulating the risk index of the control unit over time and superimposing it in space. The time accumulation is based on the superposition of the risk index of the same control unit according to the continuous sampling period. When superimposing in space, the risk propagation of the high-risk control unit to the adjacent control unit is calculated based on the relative position between the control units, the direction of the dominant airflow and the contact relationship of the stack body, and the risk index of the adjacent control unit is added accordingly.

7. The environmental zoning monitoring and hierarchical control method according to claim 6, characterized in that: The risk budget value set for each batch of fungal material is based on the fungal material formula, target product grade and seasonal working conditions. The risk consumption of a batch of fungal material is obtained by accumulating the regional risk index corresponding to the period during which the batch of fungal material stays in each control unit. When the risk consumption of a certain batch of fungal material reaches the preset ratio, the batch of fungal material is marked as a priority batch, and in step three, it is restricted that the priority batch will no longer enter a new high-risk control unit.

8. The environmental zoning monitoring and hierarchical control method according to claim 7, characterized in that: Based on the control unit risk index and the regional risk index, each control unit is divided into suitable, slightly deviated, high-risk, and severe-risk levels. The dominant risk type of the control unit is determined based on the relative magnitude of each parameter. The dominant risk types include high moisture activity, low moisture activity, core self-heating, oxygen deficiency, and neighborhood propagation. The risk level and the dominant risk type are used together as input conditions for selecting control actions.

9. The environmental zoning monitoring and hierarchical control method according to claim 8, characterized in that: Control actions are configured according to the dominant risk type: control units with low water activity use fine mist spraying and reduce ventilation intensity; control units with high water activity use enhanced ventilation and reduced spraying, combined with shallow turning; control units with self-heating core and anoxic core generate turning tasks and activate exhaust ventilation; and batches of fungal materials in severe risk control units are limited to use for composting processes and other low-risk processes, rather than for the cultivation of Boletus chinensis.

10. The environmental zoning monitoring and hierarchical control method according to claim 9, characterized in that: The control resource budget is set according to the power and operating time of the blower, spray pump and turning machine, and the equipment start and stop are allocated according to the control priority of the control unit. The batch of fungal material is classified according to the risk consumption of the batch and the risk level of the control unit where it is located when it leaves the pile, and is corresponding to the bag making and sterilization process parameters. The batch contamination rate and yield of fungal material are collected and compared with the corresponding risk consumption and risk index in history to adjust the weight and threshold used when calculating the risk index.

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