A vegetable intelligent cultivation facility monitoring and regulating system

Abstract

The application belongs to the technical field of vegetable cultivation and management, and specifically discloses an intelligent vegetable cultivation facility monitoring and regulating system, which comprises a global state evaluation module, a regulating event identification module, a regulating event analysis module and a regulating execution terminal. The global state evaluation module is used for analyzing a combination state of multiple environmental parameters and outputting a comprehensive score. The regulating event identification module is used for identifying parameter out-of-limit events, trend out-of-limit events and comprehensive imbalance events based on the score or parameter trend. The regulating event analysis module is used for analyzing the cooperation, conflict or irrelevant influence among actions by means of a dynamic influence matrix, calculating a priority in combination with the out-of-limit degree of the event and generating an ordered instruction queue. The regulating execution terminal is used for executing the regulation in the order of the queue and supporting dynamic adjustment. The application realizes the identification of hidden imbalance of the facility environment, the early warning of deterioration trend, the avoidance of conflict of multiple regulating actions and the smooth and efficient execution of the regulating process, and greatly improves the precision and stability of the environmental control and saves energy consumption.

Description

Technical Field

[0001] This invention belongs to the field of vegetable cultivation and management technology, and more specifically, relates to an intelligent monitoring and control system for vegetable cultivation facilities. Background Technology

[0002] Environmental control in vegetable cultivation directly determines the quality of vegetables. Traditional environmental control mainly relies on human experience, using simple instruments to monitor basic environmental parameters such as temperature and humidity, and manually or through timed control to turn on and off equipment such as ventilation, irrigation, and shading.

[0003] Existing technologies, such as the intelligent integrated control system for agricultural vegetable greenhouses disclosed in Chinese invention patent application number 202411415148.1, are greenhouse control systems based on the STM32F103C8T6 core board, equipped with various sensors and ESP8266 Wi-Fi module, and achieve energy saving by using low-power components.

[0004] In vegetable cultivation, the current method mainly relies on fixed thresholds to trigger the regulation of the corresponding cultivation environment to maintain its stability. However, when all parameters are within the thresholds but in a non-optimal combination, they may inhibit vegetable growth. Moreover, when environmental parameters are at the threshold boundaries and show a rapid tendency to change in an unfavorable direction, it is currently impossible to capture this deteriorating trend in time, leading to untimely regulation.

[0005] Secondly, as can be seen from the existing technology, the current independent control method, although using low-power components to achieve energy saving, may have contradictory physical effects between environmental parameters when multiple environmental parameters need to be adjusted at the same time. Performing only independent adjustment may not only cause internal cancellation, but also lead to repeated fluctuations in the cultivation environment, thus preventing the energy saving effect from reaching the expected level and failing to quickly ensure that the cultivation environment reaches a stable state. Summary of the Invention

[0006] In view of this, in order to solve the above problems, a monitoring and control system for intelligent vegetable cultivation facilities is proposed.

[0007] The objective of this invention can be achieved through the following technical solution: This invention provides a monitoring and control system for intelligent vegetable cultivation facilities. The system includes: a global status assessment module, which performs an overall analysis of the combined status of all environmental parameters collected in real time and outputs a comprehensive score of the combined environmental status.

[0008] The regulation event identification module identifies at least one type of regulation event based on the environmental combination state score and / or the real-time collected values ​​and changing trends of each environmental parameter. The regulation event category consists of environmental parameter exceeding limits, environmental trend exceeding limits, and comprehensive environmental imbalance.

[0009] The regulation event analysis module identifies all pending regulation events, assesses the impact type between the regulation actions corresponding to each regulation event, creates regulation priorities for regulation events based on the impact type and the degree of exceeding limits of the regulation events, and generates a regulation instruction queue.

[0010] The control execution terminal, based on the control instruction queue, drives the corresponding environmental control terminal to execute the corresponding control in sequence.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) By conducting an overall analysis of the combination state of all environmental parameters, the present invention can identify the overall state imbalance caused by poor coordination between parameters when all individual environmental parameters are within the preset threshold range, thereby overcoming the current defect that it is impossible to identify and correct hidden unfavorable environmental combinations due to focusing only on the over-limit of a single parameter, and further maintaining the stability of the cultivation environment.

[0012] (2) This invention identifies environmental trend exceeding control events based on changing trends, and can trigger environmental trend exceeding control events in advance when the actual value of the parameter has not yet exceeded the threshold but its predicted trajectory indicates that it is about to exceed the threshold range. This solves the problem of response lag in traditional threshold control due to reliance on instantaneous value judgment, and thus realizes the transformation from post-remediation to pre-intervention.

[0013] (3) In view of the potential conflict of equipment actions when multiple environmental parameters need to be adjusted at the same time, this invention constructs and applies a dynamic influence matrix that quantitatively describes the relationship between equipment actions and environmental parameters. This allows for the accurate assessment of the mutual interference effect between different control actions in advance, providing objective data for identifying action conflicts. This reduces the shortcomings of the previous extensive collaborative control method that relied on experience or fixed rules and could not predict the internal offsetting effect.

[0014] (4) By integrating the environmental urgency of the event and the intensity of the action conflict, the present invention dynamically calculates the control priority and generates a control instruction queue that can be executed sequentially. This ensures that repeated environmental oscillations and energy consumption caused by physical contradictions can be effectively avoided when performing multiple tasks, so that the cultivation environment can quickly return to and stabilize at the target state in a more stable and efficient manner. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1This is a schematic diagram of the system module connections of the present invention.

[0017] Figure 2 This is a schematic diagram of the overall implementation process of the present invention.

[0018] Figure 3 This is a schematic diagram of the dynamic influence matrix construction process 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 Figures 1 to 2 As shown, the present invention provides a monitoring and control system for intelligent vegetable cultivation facilities, which includes a global status assessment module, a control event identification module, a control event analysis module, and a control execution terminal.

[0021] In the above, the regulation event identification module is connected to the global state assessment module and the regulation event analysis module, respectively, and the regulation event analysis module is connected to the regulation execution terminal.

[0022] The global state assessment module performs an overall analysis of the combined state of all environmental parameters collected in real time and outputs a comprehensive score of the combined environmental state.

[0023] Specifically, the implementation process for the overall analysis of the combined state of all environmental parameters collected in real time is as follows:

[0024] First, based on the specific growth stage of the vegetables (e.g., seedling stage, flowering stage), a combination of standard environmental parameters corresponding to that stage is selected. This combination not only defines each environmental parameter (e.g., temperature, humidity, light intensity, etc.) The system defines an independent suitable range of values ​​for concentration, as well as the ideal correlation between environmental parameters and the expected proportion of each correlated parameter relative to a preset value.

[0025] Secondly, a two-dimensional deviation calculation is performed, which includes an individual dimension and a combined dimension.

[0026] At the individual level, for each environmental parameter, it is determined whether its real-time collected value exceeds its independent standard range. If it does not exceed the range, the parameter is considered to be in an ideal state, and its individual deviation is recorded as 0. If it exceeds the range, it is calculated according to the following formula: .

[0027] In the formula, This indicates the individual deviation of environmental parameters. This represents the collected values ​​of environmental parameters at the current moment. This indicates the closest boundary value of the environmental parameter at the current moment relative to the independent standard range. Indicates the width of the independent standard range. This indicates the cumulative duration during which environmental parameters exceed the range of independent standards. Indicates the preset reference duration. To represent an extremum function, use The function ensures that the calculation result does not exceed 1, so as to facilitate subsequent calculations with a unified numerical dimension.

[0028] The normalized out-of-limit amplitude term has a numerator representing the difference between the current collected value of the environmental parameter and the nearest boundary value of the independent standard range (out-of-limit quantity), and a denominator representing the difference between the upper and lower limits of the independent standard range, i.e., the independent standard range. The width of the independent standard range is introduced to normalize the out-of-limit quantity, making the deviations of different parameters and different dimensions comparable. The amplitude deviation term is used to reflect the current degree of deviation.

[0029] The normalized over-limit duration term represents the proportion of cumulative over-limit duration to a preset reference duration. This term reflects the sustained impact of the deviation, thereby distinguishing between short-term and long-term over-limits. The preset reference duration... It can be determined based on the average transition time of environmental parameters from normal to exceeding the limit in historical data, for example, set to 30 minutes.

[0030] In the combination dimension, for each pair of associated parameters (such as temperature and light) defined in the standard combination, the actual ratio between the real-time collected values ​​of the two parameters (such as the actual light-temperature ratio) is calculated, and the absolute value of the relative deviation between the actual ratio and the preset expected ratio is calculated as the combination deviation degree of the parameter pair. The larger the value, the more serious the imbalance of the synergistic relationship between the parameters.

[0031] Finally, the maximum value is selected from all calculated individual deviations (representing the most severe single-parameter problem), and the maximum value is selected from all combined deviations (representing the most severe inter-parameter misalignment problem). The maximum individual deviation and the maximum combined deviation are linearly weighted and calculated to output a comprehensive environmental state score. The higher the score, the more severe the deviation of the current overall environmental state from the ideal growth state.

[0032] Considering that crop growth stagnation is often caused by the "weakest link" effect—that is, a single extremely unfavorable parameter (such as a brief period of high temperature) or a severely imbalanced parameter relationship (such as high humidity accompanied by low temperature) can cause damage, rather than a slight average deviation of all parameters—this invention uses maximum values ​​instead of average values ​​to more accurately detect abnormal environmental problems.

[0033] Meanwhile, in complex greenhouse environments, it's easy to encounter situations where individual environmental parameters meet standards while the overall coordination between these parameters is poor. For example, sufficient light but low temperature; both may be within independent standard ranges individually, but their combination inhibits photosynthetic efficiency. Traditional single-threshold methods are completely incapable of diagnosing such problems. This invention effectively solves this diagnostic blind spot by introducing expected proportional relationships between parameters based on agronomic knowledge and calculating the degree of deviation in combinations.

[0034] The following supplementary explanations are required in the overall analysis process described above:

[0035] The independent standard ranges of each environmental parameter, the correlations between parameters, and the expected proportions of each correlated parameter to a preset value are defined in the standard environmental parameter range combination, derived from the cultivation manuals or agricultural technical standards of the vegetable varieties corresponding to this invention. The expected proportions can be determined based on regression analysis of historical high-yield data of the target vegetable variety or by domain expert experience, and are stored in the system as configurable parameters. The standard environmental parameter range combination and associated expected proportions support manual configuration and updates to adapt to the needs of different crops or cultivation stages.

[0036] Individual deviation reflects the most severe single parameter problem, which often directly and quickly causes crop stress or even damage (such as instantaneous high temperature burns). Therefore, it is given a high weight. As a preferred example, the weights of the maximum individual deviation and the maximum combined deviation can be set to 0.7 and 0.3, respectively.

[0037] The regulation event identification module identifies at least one type of regulation event based on the environmental combination state score and / or the real-time collected values ​​and changing trends of each environmental parameter. The regulation event category consists of environmental parameter exceeding limits, environmental trend exceeding limits, and comprehensive environmental imbalance.

[0038] Specifically, the workflow for identifying at least one type of control event in the control event identification module follows a progressive judgment rule: first, determine if the control event exceeds the limit; then, determine the trend; and finally, determine the imbalance. That is, for environmental parameters that have not triggered the environmental parameter limit exceeding control event, the environmental trend limit exceeding control event is initiated. If neither the environmental trend limit exceeding control event nor the environmental parameter limit exceeding control event is triggered, the identification of the comprehensive environmental imbalance control event is initiated to ensure the efficiency and hierarchy of the judgment.

[0039] As a preferred embodiment of the present invention, the specific implementation process of the regulation event identification module to identify at least one type of regulation event is as follows: the process is to cyclically perform the identification of environmental parameter limit exceeding regulation event, environmental trend limit exceeding regulation event, and comprehensive environmental imbalance regulation event at a fixed period (e.g., every minute).

[0040] The process for identifying environmental parameter limit exceedance events is as follows: Based on the real-time collected values ​​of each environmental parameter, a time series sequence of each parameter is constructed. The time series sequence of each environmental parameter is then judged in real time: it is checked whether the latest collected value exceeds the preset static threshold range for that parameter (e.g., the temperature threshold is set to 15 to 30°C). If it does, it is further determined whether the exceedance duration has lasted for a first preset duration (e.g., 10 consecutive minutes). Only when both the value and duration meet the requirements is an environmental parameter limit exceedance event triggered, thus avoiding false triggering due to momentary interference or noise.

[0041] The process for identifying environmental trend exceeding control limits is as follows: For all environmental parameters that have not triggered such events, a sliding window linear fit is performed on their time-series data (e.g., data from the most recent 30 minutes) to obtain a fitted straight line. The slope of the line represents the rate of change (increase or decrease) of the parameter in the current time period, and the intercept is the baseline value. Based on this fitted straight line, the value of the environmental parameter at the end of a preset future time window (e.g., the next 15 minutes) is predicted and recorded as the predicted value. If the predicted value exceeds the static threshold range of the environmental parameter, an environmental trend exceeding control limits event is triggered.

[0042] The process for identifying comprehensive environmental imbalance control events is as follows: If neither environmental parameter over-limit control event nor environmental trend over-limit control event is triggered, the comprehensive environmental state score output in real time by the global state assessment module is obtained and compared with the preset comprehensive state score threshold for the current vegetable growth stage. As an example, when the normalized range of the comprehensive score is 0 to 1, the threshold can be set to 0.7 by default. If the real-time comprehensive score is lower than this threshold, it indicates that although none of the individual parameters have exceeded their limits and there is no short-term deterioration trend, the overall environment is in an uncoordinated sub-healthy state, and at this time, an comprehensive environmental imbalance control event is triggered.

[0043] In agricultural facility environments, problems of different natures have varying speeds and severity of impact on crops. Existing parameter exceedances (such as excessively high temperatures) typically cause direct damage to crops and therefore require priority identification and response. Trend exceedances indicate an impending problem; although not yet causing damage, it will soon occur without intervention and requires secondary priority identification. Overall imbalances indicate a general environmental disharmony, potentially affecting crop growth efficiency but not causing immediate harm, and can be considered last. Based on this, this invention establishes a progressive rule: first judging exceedances, then trends, and finally imbalances. This ensures the response order matches cultivation needs. Furthermore, by logically linking three independent yet complementary identification methods (threshold comparison, trend prediction, and comprehensive scoring), conflicts in regulatory objectives caused by the simultaneous triggering of multiple events can be avoided.

[0044] Meanwhile, by introducing trend prediction, early identification and warning of environmental parameters that are at the threshold boundary and rapidly changing in an unfavorable direction are achieved, thereby overcoming the shortcomings of traditional threshold control in terms of response lag and inability to capture deteriorating trends in a timely manner.

[0045] Secondly, by establishing a diagnostic step based on the comprehensive score of the combined environmental state, it is possible to accurately identify the hidden imbalance problem where all individual parameters have not exceeded their limits but the overall coordinated state has deviated from the optimal range, thus filling the blind spot in the cognition and control of traditional independent parameter control methods in such imbalance scenarios.

[0046] It is important to note that for environmental parameters that frequently exceed limits, a hysteresis range or a minimum control interval (e.g., setting a hysteresis range of 5 to 10 minutes or a control interval of 10 minutes) can be introduced to avoid frequent triggering.

[0047] The regulation event analysis module identifies all current regulation events to be processed, evaluates the impact type between the regulation actions corresponding to each regulation event, creates the regulation priority of the regulation events by combining the impact type and the degree of exceeding the limit of the regulation events, and generates a regulation instruction queue.

[0048] Specifically, the evaluation process for assessing the types of impact between regulatory actions corresponding to various regulatory events includes:

[0049] S1. When the control event identification module outputs multiple control events to be processed (e.g., a high temperature exceeding the limit event and a low humidity trend event), the control event analysis module first aggregates these events into a set of events to be processed.

[0050] S2. For each event in the set, record the environmental parameters that it controls to restore the environmental target as associated environmental parameters (e.g., for a high-temperature event, the associated parameter is temperature). Based on the preset correspondence between equipment and parameters, determine at least one environmental control terminal and its control action that needs to be activated to restore the environmental target. For example, if the environmental target is to reduce the temperature, the environmental control terminal that needs to be activated to restore the environmental target is either a wet curtain terminal or a fan terminal. That is, activate the wet curtain terminal to perform the opening action, and may link the fan terminal to perform the speed-up action.

[0051] S3. To quantify the mutual influence between different control actions, this step presets a dynamic influence matrix and obtains the expected change in environmental parameters associated with other events to be processed based on the matrix.

[0052] Please refer to Figure 3 As shown, the construction process of the dynamic influence matrix is ​​as follows: S31. During the normal operation of the facility in history, continuously and synchronously record every historical action of each environmental control terminal (such as sunshade curtains, supplemental lighting, ventilation fans, irrigation valves) (e.g., at a certain moment, the sunshade curtain is closed by 50%), and the environmental parameters (temperature, humidity, light intensity, etc.) immediately following this action. Historical change data (such as concentration) are integrated with historical action records and historical change data to form a training dataset.

[0053] S32. For each environmental control terminal, analyze the average change in various environmental parameters (such as an average temperature decrease of 0.5℃ and an average humidity increase of 3%) when it performs a standardized unit action, such as a 10% change in the opening of the shading curtain or an increase in the fan power by one level. Use the average change as the expected change.

[0054] It should be added that the positive and negative signs of the expected change are defined as follows: if the control action leads to an increase in the value of a certain environmental parameter, the expected change is positive; if it leads to a decrease in the value, it is negative; if there is no significant effect, it is recorded as zero.

[0055] It is also necessary to define the expected adjustment direction of the event when classifying the impact type. For a specific control event (such as excessively high temperature), the expected adjustment direction of its associated environmental parameter (temperature) is the direction required to restore the parameter value to its independent standard range. Specifically, if the current value of the parameter is higher than the standard range, the expected adjustment direction is negative (needs to decrease); if the current value is lower than the standard range, the expected adjustment direction is positive (needs to increase); if there is a comprehensive imbalance, the expected adjustment direction is to bring it towards the preset expected proportion or optimal value.

[0056] S33. Based on the expected changes in various environmental parameters, arrange them into the dynamic influence matrix with the environmental control terminal as the row and the environmental parameters as the column. This matrix can be directly queried when assessing the mutual influence between different control actions. That is, for any control action in the set of events to be processed, the expected changes in the associated environmental parameters of interest to other events can be quickly read from the matrix. For example, query the expected change in the environmental parameter of humidity caused by the action of turning on the evaporative cooling pad.

[0057] When dealing with multiple environmental issues simultaneously, the interrelationships between different execution terminal actions cannot be accurately predicted. For example, activating a evaporative cooling pad may drastically increase humidity, potentially interfering with ongoing dehumidification operations. Therefore, this step introduces a dynamic influence matrix design to accurately characterize the average influence intensity and direction of each control terminal unit action on various environmental parameters, thus providing a data foundation for the subsequent generation of conflict-free and highly efficient control command sequences.

[0058] Furthermore, by constructing and applying a dynamic influence matrix that quantitatively describes the relationship between equipment actions and environmental parameters, it is possible to accurately assess the mutual interference effects between different control actions in advance, providing objective data for identifying action conflicts. This reduces the shortcomings of the previous extensive collaborative control method that relied on experience or fixed rules and could not predict internal offsetting effects.

[0059] It should be noted that the dynamic impact matrix supports periodic or triggered updates. When the facility structure, equipment performance, or crop variety changes, the values ​​of environmental parameters are re-collected and the matrix is ​​updated to ensure the accuracy of the impact relationships.

[0060] S4. Based on the quantitative expected change obtained from the above query, the influence relationship between any two regulatory actions (belonging to event A and event B) is automatically classified into any one of the following: synergistic influence, conflicting influence, or unrelated influence. This yields the influence type between the regulatory actions corresponding to each regulatory event. The classification steps are as follows:

[0061] S41. Based on the environmental parameters associated with events A and B and the enabled control actions, query the expected change in the associated environmental parameters of event B caused by action A, denoted as... Simultaneously query the expected changes in environmental parameters associated with event A when event B is executed, denoted as . .

[0062] S42, if absolute value and If both are less than the preset change threshold (for example, 2% of the independent standard range width of the corresponding event-related environmental parameters can be taken, representing that the impact is negligible), then the impact type between the corresponding control actions of event A and event B is classified as unrelated impact.

[0063] S43. If the no-association condition is not met, then further judgment is made. and Symbols:

[0064] like and ,at the same time The symbol is the same as the expected adjustment direction of event B on its associated environmental parameters. If the symbol and event A have the same expected adjustment direction for their associated environmental parameters, then the influence type between the corresponding control actions of event A and event B is classified as synergistic influence. That is, performing action A helps improve the goal of event B, and performing action B also helps improve the goal of event A. For example, in a scenario where cooling and humidification are required at the same time, the influence type of the action of turning on the wet curtain and turning off the ventilation is synergistic influence.

[0065] like or (That is, at least one of them is negative), or and If the symbols in the diagram represent events whose expected adjustment directions to their associated environmental parameters differ, then the influence type between the corresponding control actions of events A and B is classified as conflicting influence. This indicates that the execution of at least one action will negatively impact the goal of the other event. For example, turning on a wet curtain can lower the temperature but will significantly increase the humidity. If there is also an event that requires dehumidification, then the two actions conflict.

[0066] In actual operation, the specific combination of events that needs to be processed simultaneously each time is random and changes in real time. For example, in this case, it might be high temperature and low temperature. The next event combination might be high humidity and low light. If the corresponding control actions are executed in parallel, their physical effects may contradict each other, leading to canceling out the effects of the commands, repeated fluctuations in environmental parameters, additional energy consumption, and prolonged stabilization time. Therefore, it is necessary to pre-determine the type of interaction (coordination, conflict, or no correlation) between any two control actions before generating the execution queue.

[0067] Based on the above considerations, this invention obtains the expected changes in environmental parameters associated with other events by each regulatory action in the current set of events to be processed by querying a preset dynamic influence matrix, and quantitatively determines the type of influence between actions accordingly. This allows for proactive identification and avoidance of physical conflicts between devices, preventing energy consumption, repetitive execution, and environmental parameter oscillations, thereby ensuring that the cultivation environment quickly approaches and stabilizes at the environmental target.

[0068] Furthermore, the specific implementation process for creating the regulatory priority of regulatory events by combining the type of impact and the degree of exceeding the limits of the regulatory event is as follows:

[0069] For each event (denoted as event X) in the set of events to be processed, based on the type of influence between its control action and the control actions of other events, the total number of conflicting influences between its control action and the control action of event X is counted among all other events. For example, if there are currently 5 events to be processed, and the control action of event X conflicts with the control actions of 3 of these events, then the number of conflicts is 3. Then, this number of conflicts is divided by the total number of other events (i.e., the total number of events minus 1) to obtain the conflict influence strength. The conflict influence strength value ranges from 0 to 1; the higher the value, the higher the risk of triggering internal conflicts when executing the control action of that event.

[0070] For the same event X, obtain the real-time collected values ​​of its associated environmental parameters (i.e., the recovery environment target for the event) at the current moment, along with the corresponding target value (usually the optimal value within its independent standard range, such as the median value of the independent standard range). Calculate the absolute deviation of the current collected value relative to the target value, and normalize this deviation value (e.g., divide by the width of the independent standard range) to obtain a comparable value within the range of 0 to 1, which serves as the environmental urgency of the event. The larger this value, the further the parameter deviates from the ideal state, and the higher the urgency for correction.

[0071] The intensity of the conflict's impact and the urgency of the environment for each event are weighted and summed according to preset weights. As an example, both weights can be set to 0.5, indicating equal emphasis on conflict risk and environmental urgency. The resulting weighted sum is the priority indicator for managing that event. This indicator comprehensively reflects both the necessity of addressing the event (based on environmental deviation) and the risk of addressing it (based on the possibility of triggering conflict).

[0072] Based on the calculated control priority indicators of all pending events, they are sorted in descending order of value. The sorting result directly determines the execution order of control instructions, forming the final control instruction queue. The control instruction corresponding to the highest priority event (with the largest comprehensive indicator) will be at the front of the queue and executed first.

[0073] Traditional prioritization is often based solely on the severity (urgency) of a problem, but ignores the fact that in a facility environment, solving one problem may trigger conflicting actions. Furthermore, whether an event should be prioritized depends not only on how urgent it is, but also on whether handling it will increase the energy consumption of the process and the total processing time.

[0074] Therefore, this invention introduces the intensity of conflict impact as a second dimension in the calculation of control priority, prioritizing events with high conflict risk. These events can be resolved before other dependent actions are executed or when the environmental context is relatively simple. Furthermore, it reduces the oscillations caused by repeated equipment actions and back-and-forth adjustments of environmental parameters due to improper execution order. This allows the cultivation environment to smoothly transition to the environmental target with fewer steps, lower energy consumption, and shorter time. This fundamentally improves the efficiency and reliability of control execution, ultimately achieving the control goals of rapid stability and energy efficiency.

[0075] The control execution terminal drives the corresponding environmental control terminal to execute corresponding controls sequentially based on the control instruction queue.

[0076] Specifically, the control execution terminal receives a queue of control instructions generated by the control event analysis module. Each instruction explicitly specifies the environmental control terminal to be driven (e.g., a circulating fan), the specific action to be performed (e.g., setting to high speed), and optional execution parameters (such as the duration of the action). The execution process of the control execution terminal sequentially processes the instructions in the queue, as follows:

[0077] The control execution terminal reads the first instruction in the queue, parses it into a specific low-level control signal (such as a relay on / off instruction), and sends the signal to the corresponding environmental control terminal drive circuit, thereby initiating the specified action of the terminal.

[0078] During the execution of environmental control terminal actions, changes in environmental parameters are monitored in real time. If, during this process, the control event identification module identifies a new emergency control event, it reassesses the event based on the latest environmental data and determines that it has been transformed into a conflict type or is inconsistent with the expected impact type. The module then feeds back the changes in the newly monitored environmental parameters, the newly identified event, and the reassessed impact relationship to the control event analysis module, requesting it to regenerate control instructions based on the latest information.

[0079] The control priority of the new control instruction is compared with the control priority of the event corresponding to the currently executing control instruction as follows:

[0080] If the new control instruction has a higher control priority, the control execution terminal interrupts the execution of the current instruction and feeds back the new control instruction along with all unexecuted instructions in the control instruction queue to the control event analysis module. The control event analysis module takes all currently pending events (including new events and unresolved events in the original queue) as a set, reassesses the impact types between events, calculates the control priority, and generates a new control instruction queue. Subsequently, the control execution terminal continues execution based on the newly generated queue.

[0081] If the priority of the new control instruction is equal to or lower than the priority of the currently executed instruction, the control execution terminal will not interrupt the current execution, but will only add the new control instruction to the current control instruction queue. The control event analysis module or the control execution terminal will then reorder the instructions in the updated queue according to their control priorities to form an updated execution sequence.

[0082] For instructions that execute normally and without interruption, after their corresponding actions are completed (usually determined by terminal feedback signals or preset execution duration), a preset stable waiting period (e.g., 30 seconds to 2 minutes) is inserted. During this period, relevant environmental parameters are continuously monitored until their changes tend to stabilize. When it is determined that the environmental state has reached a new relatively stable state, the current instruction is considered to have completed execution.

[0083] After completing the execution of the current instruction and stabilizing, the control and execution terminal removes the instruction from the queue, then automatically reads and begins executing the next instruction. This continues until all instructions in the queue have been executed sequentially.

[0084] It should be added that "tending to stability" means that during the waiting period, the standard deviation of the real-time collected values ​​of the environmental parameters associated with the control command is lower than the set fluctuation threshold. The specific value of the fluctuation threshold can be determined based on agronomic experience. For example, for tomatoes in the fruiting stage, the temperature fluctuation threshold can be set to 0.3℃ and the humidity fluctuation threshold can be set to 2% relative humidity.

[0085] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A monitoring and control system for intelligent vegetable cultivation facilities, characterized in that, The system includes: The global state assessment module performs a comprehensive analysis of the combined state of all environmental parameters collected in real time, and outputs a comprehensive score of the combined environmental state. The comprehensive analysis includes: Obtain a combination of standard environmental parameter ranges corresponding to the current vegetable growth stage, wherein the combination includes the independent standard range of each environmental parameter and the correlation between the parameters; For each environmental parameter, determine whether it exceeds the corresponding independent standard range. If it does, calculate the individual deviation of that environmental parameter; otherwise, assign the individual deviation a value of 0. The formula for calculating the individual deviation is as follows: ； In the formula, This indicates the individual deviation of environmental parameters. This represents the collected values ​​of environmental parameters at the current moment. This indicates the closest boundary value of the environmental parameter at the current moment relative to the independent standard range. Indicates the width of the independent standard range. This indicates that the preset time affects the weight. This indicates the cumulative duration during which environmental parameters exceed the range of independent standards. Indicates the preset reference duration. Represents an extremum function; Based on the correlation between parameters defined by the standard environmental parameter range combination, environmental parameter pairs with correlation are denoted as correlated parameter pairs; Calculate the actual ratio between the current collected values ​​of the two environmental parameters of the associated parameter pair, calculate the relative deviation between the actual ratio and the preset expected ratio of the parameter pair, and take the absolute value of the relative deviation as the combined deviation of the associated parameter pair. The maximum value is selected from the individual deviations of all environmental parameters to obtain the maximum individual deviation, and the maximum combined deviation is extracted from the combined deviations of all related parameter pairs. The maximum individual deviation and the maximum combined deviation are linearly weighted and the output is the comprehensive score of the environmental combination state. The regulation event identification module identifies at least one type of regulation event based on the environmental combination state score and / or the real-time collected values ​​and changing trends of each environmental parameter. The regulation event category consists of environmental parameter exceeding limits, environmental trend exceeding limits, and comprehensive environmental imbalance. The regulation event analysis module identifies all pending regulation events, assesses the impact type between the regulation actions corresponding to each regulation event, creates the regulation priority of the regulation events based on the impact type and the degree of exceeding the limit of the regulation events, and generates a regulation instruction queue. The control execution terminal, based on the control instruction queue, drives the corresponding environmental control terminal to execute the corresponding control in sequence.

2. The intelligent vegetable cultivation facility monitoring and control system as described in claim 1, characterized in that: The relationships between parameters defined in the standard environmental parameter range combination include the expected proportion of each associated parameter to the preset parameters.

3. The intelligent vegetable cultivation facility monitoring and control system as described in claim 1, characterized in that: The specific method for identifying at least one type of regulatory event is as follows: Based on the real-time collected values ​​of each environmental parameter, a time series sequence of each environmental parameter is constructed. If the time series data sequence contains environmental parameter values ​​that continuously exceed their preset static threshold range for a duration of a first preset duration, then an environmental parameter over-limit control event is triggered. For environmental parameters that have not triggered an event, a sliding window linear fitting is performed on their time series data sequence to obtain the slope and intercept of the fitting change, and the predicted value of the environmental parameter in the preset future time window is calculated accordingly. If the predicted value exceeds the preset static threshold range, an environmental trend over-limit control event is triggered. If neither the environmental parameter over-limit regulation event nor the environmental trend over-limit regulation event is triggered, then it is determined whether the comprehensive score of the environmental combination state is lower than the preset comprehensive state score threshold of the current vegetable growth stage. If it is lower, then the comprehensive environmental imbalance regulation event is triggered.

4. The intelligent vegetable cultivation facility monitoring and control system as described in claim 1, characterized in that: The process for assessing the type of influence between the regulatory actions corresponding to each regulatory event is as follows: Aggregate all currently pending regulatory events into a current pending event set; For each event in the set of events to be processed, the environmental parameters that it regulates for the purpose of restoring the environmental target are recorded as associated environmental parameters, and at least one environmental control terminal and its control action that need to be activated for the purpose of restoring the environmental target are determined. Based on the preset dynamic influence matrix, the expected change in environmental parameters associated with other events to be processed by each control action is obtained. Based on the expected change, the impact type between any two regulatory actions is classified into any one of the following: synergistic impact, conflicting impact, or unrelated impact, thereby obtaining the impact type between the regulatory actions corresponding to each regulatory event.

5. The intelligent vegetable cultivation facility monitoring and control system as described in claim 4, characterized in that: The dynamic influence matrix is ​​constructed in the following way: During the current normal growth period of vegetable cultivation, historical action records of each environmental control terminal and historical change data of each environmental parameter under the corresponding historical action records are collected simultaneously, and the collected historical action records and historical change data are integrated into a training dataset. For each environmental control terminal, based on the training dataset, the average change of each environmental parameter is analyzed when the control terminal performs a unit action, and the average change is used as the expected change. Based on the expected changes in various environmental parameters, the dynamic influence matrix is ​​constructed by arranging environmental control terminals as rows and environmental parameters as columns.

6. The intelligent vegetable cultivation facility monitoring and control system as described in claim 4, characterized in that: The steps for classifying the influence types between any two regulatory actions are as follows: Select any two events from the set of events to be processed, and label them as event A and event B respectively; Based on the environmental parameters associated with events A and B and the enabled control actions, query the expected changes in the environmental parameters associated with event B when event A is executed, and simultaneously query the expected changes in the environmental parameters associated with event A when event B is executed. If the absolute values ​​of the two expected changes are less than the preset change threshold, the influence between the corresponding control actions of events A and B will be classified as unrelated influence. Otherwise, when both expected changes are positive, the influence between the corresponding regulatory actions of event A and event B is classified as synergistic influence. When both expected changes are negative, or one of the expected changes is negative, the impact between the corresponding regulatory actions of events A and B is classified as conflict impact.

7. The intelligent vegetable cultivation facility monitoring and control system as described in claim 6, characterized in that: The control priorities for creating control events include: For each event in the set of events to be processed, based on the type of influence between its control action and the control actions of other events, the total number of control actions corresponding to other events that are judged to be conflicting influences with the control action corresponding to this event is counted, and the intensity of the conflicting influence of this event is obtained by comparing it with the total number of other events. For each event, the environmental urgency of the event is calculated based on the degree of deviation of the collected values ​​of its associated environmental parameters at the current moment from their target values. The regulatory priority index is calculated by weighting and summing the impact intensity of each event on the degree of environmental urgency.

8. The intelligent vegetable cultivation facility monitoring and control system as described in claim 7, characterized in that: The control instruction queue is obtained by sorting all pending events in descending order according to the control priority index.

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