Agricultural planting greenhouse environment monitoring regulation and control system based on multi-source data analysis

Abstract

The invention belongs to the technical field of agricultural planting greenhouse supervision, and particularly relates to an agricultural planting greenhouse environment monitoring regulation and control system based on multi-source data analysis, which comprises a multi-source environment parameter sensing module, a crop growth suitability evaluation module, a dynamic regulation and control strategy generation module, an intelligent environment precise regulation and control module and an agricultural remote monitoring end. According to the invention, the crop growth suitability evaluation module accurately identifies the suitability of the environment and the current growth period of crops, and an environment regulation and control strategy is generated in a targeted manner when the suitability limited signal is generated, so that the operation of related regulation equipment is automatically controlled, and the environment is quickly recovered to a suitable state; the regulation and control effect is quantitatively evaluated to avoid accumulative influence on crops caused by environmental discomfort, the growth effect of the crops in the planting greenhouse is accurately judged when an effective qualified signal is generated, reasonable improvement measures are taken in time, and effective supervision on the agricultural planting greenhouse is achieved. And the refinement, automation and intelligence level of agricultural planting shed management is comprehensively improved.

Description

Technical Field

[0001] This invention relates to the field of agricultural greenhouse monitoring technology, specifically an agricultural greenhouse environmental monitoring and control system based on multi-source data analysis. Background Technology

[0002] With the development of modern agricultural technology, agricultural greenhouses, as an important carrier of controllable environment agriculture, directly affect crop growth quality and yield through the accurate monitoring and intelligent control of internal environmental parameters. Currently, various technologies related to environmental monitoring and control of agricultural greenhouses have emerged, aiming to achieve automated control of the agricultural greenhouse environment by collecting environmental data through sensors and linking them with adjustment equipment.

[0003] For example, Chinese invention patent with publication number CN117311422A discloses a method and system for regulating the internal environment of a greenhouse for crop cultivation. The technical solution of this invention is to collect greenhouse environmental data and combine environmental prediction models and genetic algorithms to solve the optimal regulation strategy, and then carry out environmental regulation accordingly. However, in practical applications, the aforementioned patented technologies still fail to adequately assess the environmental adaptability of agricultural greenhouses and automatically adjust them to accommodate the differences in the growth periods of different crop varieties. They also make it difficult to make progressive and accurate judgments and timely warnings regarding the effectiveness of environmental control, crop growth, and the degree of potential risks in the coordination of regulatory equipment. This hinders the effective supervision of agricultural greenhouses. Therefore, a solution is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide an agricultural greenhouse environment monitoring and control system based on multi-source data analysis, so as to solve the technical defects mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an agricultural greenhouse environment monitoring and control system based on multi-source data analysis, comprising a multi-source environmental parameter sensing module, a crop growth adaptability assessment module, a dynamic control strategy generation module, an intelligent environment precision control module, and an agricultural remote monitoring terminal; The multi-source environmental parameter sensing module comprehensively monitors and collects real-time data from the agricultural planting shed through a number of deployed sensors, and generates and outputs a planting shed dataset based on the collected monitoring data. The crop growth adaptability assessment module analyzes the adaptability between the environment and crop growth based on the planting shed dataset and crop growth characteristics, and sends the adaptability analysis results to the dynamic control strategy generation module. When the dynamic control strategy generation module receives an adaptability limitation signal, it generates a corresponding environmental control strategy and sends it to the intelligent environment precision control module and the agricultural remote monitoring terminal. The intelligent environment precision control module automatically controls the operation of relevant adjustment equipment in the planting shed based on the environmental control strategy to restore the environment in the planting shed.

[0006] Furthermore, the specific analysis process of the crop growth adaptability assessment module is as follows: The system calls upon a pre-defined crop growth characteristic database, which stores environmental parameter information for different crops at various growth stages. Based on the crop variety and growth stage in the current planting shed, the system extracts the corresponding parameters to be monitored, the appropriate data range, and the pre-defined growth influence weight values. Extract real-time monitoring data of the parameters to be monitored from the planting shed dataset, calculate the difference between the real-time monitoring data and the median of the corresponding suitable data range and take the absolute value to obtain the monitoring deviation, and multiply the monitoring deviation by the corresponding preset growth influence weight value to obtain the growth influence coefficient. The growth influence coefficients of all parameters to be monitored are obtained and summed to obtain the fitness limitation index. The fitness limitation index is compared with the corresponding preset fitness limitation index threshold. If the fitness limitation index exceeds the corresponding preset fitness limitation index threshold, a fitness limitation signal is generated; if the fitness limitation index does not exceed the corresponding preset fitness limitation index threshold, a fitness qualified signal is generated.

[0007] Furthermore, the agricultural remote monitoring terminal communicates with the control effectiveness decision module. The control effectiveness decision module is used to set the detection period, analyze the effectiveness of environmental control in the agricultural planting shed during the detection period, generate an effectiveness anomaly signal or effectiveness qualified signal through analysis, and send the effectiveness anomaly signal or effectiveness qualified signal to the agricultural remote monitoring terminal. When the agricultural remote monitoring terminal receives the effectiveness anomaly signal, it issues a corresponding warning.

[0008] Furthermore, the specific analysis process of the control effectiveness decision module is as follows: A rectangular coordinate system is established with time as the X-axis and the fitness restriction index as the Y-axis. Based on all fitness restriction indices within the detection period, fitness restriction curves are plotted in the first quadrant of the rectangular coordinate system, with the starting point of the fitness restriction curve located on the Y-axis. A restriction boundary ray is drawn in the first quadrant of the rectangular coordinate system, parallel to the X-axis and with its endpoint located on the Y-axis. The Y-axis coordinate value corresponding to the restriction boundary ray is a preset fitness restriction index threshold. The area enclosed by the portion of the fill adaptability constraint curve above the constraint boundary ray and the constraint boundary ray is collected, and the area of ​​the corresponding fill region is marked as the fill coefficient. The fill coefficients of all fill regions are summed to obtain the fill feature value. The fill feature value is compared with the preset fill feature threshold. If the fill feature value exceeds the preset fill feature threshold, an validity anomaly signal is generated.

[0009] Furthermore, if the filling feature value does not exceed the preset filling feature threshold, the filling coefficient of the corresponding filling area is compared with the preset filling coefficient threshold. If the filling coefficient exceeds the preset filling coefficient threshold, the corresponding filling area is marked as an abnormal area. The number of abnormal regions is obtained and marked as filling anomaly values, and the filling coefficient with the largest value is marked as the filling area value. The effectiveness impact decision value is calculated by weighted summation of the filling feature value, filling anomaly value and filling area value. The effectiveness impact decision value is compared with the preset effectiveness impact decision threshold. If the effectiveness impact decision value exceeds the preset effectiveness impact decision threshold, an effectiveness anomaly signal is generated. If the effectiveness impact decision value does not exceed the preset effectiveness impact decision threshold, an effectiveness qualified signal is generated.

[0010] Furthermore, the control effectiveness decision module is connected to the crop growth analysis module. The control effectiveness decision module sends an effectiveness qualified signal to the crop growth analysis module. When the crop growth analysis module receives the effectiveness qualified signal, it analyzes the crop growth effect and generates a long-term early warning signal or a long-term normal signal through analysis. The long-term early warning signal or the long-term normal signal is then sent to the agricultural remote monitoring terminal. When the agricultural remote monitoring terminal receives the long-term early warning signal, it issues a corresponding early warning.

[0011] Furthermore, the specific analysis process of the crop growth analysis module is as follows: The crop density of several planting areas in the agricultural planting shed is obtained. The percentage decrease of the crop density compared with the corresponding standard density is marked as the crop density reduction value. The average growth height of the crops in the corresponding planting area is obtained. The crop height value is calculated by comparing the corresponding standard height with the average growth height. The average chlorophyll content of the crop leaves in the corresponding planting area is obtained. The crop greenness value is calculated by comparing the corresponding standard chlorophyll content with the average chlorophyll content. The growth characteristic value is calculated by weighted summation of crop density reduction value, crop height value, and crop greening value. The growth characteristic value is compared with a preset growth characteristic threshold. If the growth characteristic value exceeds the preset growth characteristic threshold, the corresponding planting area is marked as an alarm area. The percentage of alarm areas in the agricultural planting shed is obtained and marked as crop alarm area condition value. The crop alarm area condition value is compared with a preset crop alarm area condition threshold. If the crop alarm area condition value exceeds the preset crop alarm area condition threshold, a long-term early warning signal is generated.

[0012] Furthermore, if the crop alarm zone value does not exceed the preset crop alarm zone threshold, the average growth characteristic values ​​of all planting areas in the agricultural planting shed are used to calculate the crop growth anomaly value, and the growth characteristic value with the largest value is marked as the crop growth anomaly value. The long-term decision value of crops is calculated by weighted summation of crop alarm zone condition value, crop growth anomaly value, and crop growth anomaly value. The long-term decision value of crops is compared with the preset long-term decision threshold. If the long-term decision value of crops exceeds the preset long-term decision threshold, a long-term warning signal is generated; if the long-term decision value of crops does not exceed the preset long-term decision threshold, a long-term normal signal is generated.

[0013] Furthermore, the crop growth analysis module communicates with the multi-device coordination and cooperation hidden danger output module. The crop growth analysis module sends a long-term normal signal to the multi-device coordination and cooperation hidden danger output module. When the multi-device coordination and cooperation hidden danger output module receives the long-term normal signal, it analyzes the coordination and cooperation hidden dangers of all regulating equipment in the agricultural planting shed. Through analysis, it generates a coordination and cooperation early warning signal or a coordination and cooperation safety signal, and sends the coordination and cooperation early warning signal or coordination and cooperation safety signal to the agricultural remote monitoring terminal. When the agricultural remote monitoring terminal receives the coordination and cooperation early warning signal, it issues a corresponding warning.

[0014] Furthermore, the specific analysis process of the multi-device coordinated hidden danger output module is as follows: The average response delay of the corresponding regulating equipment to the regulating command during the detection period is collected and marked as the delay characteristic value. The number of times the corresponding regulating equipment fails during the detection period is marked as the fault frequency characteristic value. The time of the last inspection and maintenance of the corresponding regulating equipment is obtained and marked as the target time. The time difference between the current time and the target time is calculated, and the ratio of the time difference result to the corresponding preset standard value is calculated to obtain the detection interval characteristic value. The adjustment hazard coefficient is calculated by weighted summation of the delay characteristic value, fault frequency characteristic value, and detection isolation characteristic value. The ratio of the adjustment hazard coefficient to the corresponding preset adjustment hazard coefficient threshold is used to calculate the hazard emergency value of the corresponding adjustment equipment. The average of the hazard emergency values ​​of all adjustment equipment in the agricultural planting shed is used to calculate the coordination and cooperation impact value. The coordination and cooperation impact value is compared with the preset coordination and cooperation impact threshold. If the coordination and cooperation impact value exceeds the preset coordination and cooperation impact threshold, a coordination and cooperation early warning signal is generated; if the coordination and cooperation impact value does not exceed the preset coordination and cooperation impact threshold, a coordination and cooperation safety signal is generated.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. In this invention, by collecting multi-dimensional real-time data from agricultural planting sheds and combining it with a crop characteristic database, the environmental adaptability is accurately determined. When an adaptability limitation signal is generated, a targeted control strategy is generated and output in real time. Based on the strategy, the operation of relevant adjustment equipment is automatically controlled to enable the planting shed to quickly restore a suitable environment. This helps to ensure the stable growth of crops in agricultural planting sheds and significantly reduces the difficulty of management.

[0016] 2. In this invention, the control effectiveness decision module quantifies and evaluates the control effect. When a qualified effectiveness signal is generated, the crop growth analysis module judges the crop growth effect in the planting shed. When an abnormal effectiveness signal is generated, the multi-device coordination and cooperation hidden danger output module analyzes the coordination and cooperation hidden dangers of all control devices in the agricultural planting shed, thereby realizing effective supervision of the agricultural planting shed and comprehensively improving the level of refinement, automation and intelligence of agricultural planting shed management. Attached Figure Description

[0017] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiment 2 of the present invention. Detailed Implementation

[0018] 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.

[0019] Example 1: As Figure 1 As shown, the agricultural greenhouse environment monitoring and control system based on multi-source data analysis proposed in this invention includes a multi-source environmental parameter sensing module, a crop growth adaptability assessment module, a dynamic control strategy generation module, an intelligent environment precision control module, a control effectiveness decision-making module, a crop growth analysis module, and an agricultural remote monitoring terminal. The multi-source environmental parameter sensing module comprehensively monitors and collects real-time data within the agricultural planting greenhouse through a variety of sensors (including temperature sensors, soil detection sensors, light sensors, carbon dioxide sensors, etc.). Based on the collected monitoring data, a planting greenhouse dataset is generated and output to the crop growth suitability assessment module, providing accurate and comprehensive raw data support for all subsequent analysis and assessment stages, thus ensuring the scientific nature of environmental monitoring and control from the source.

[0020] The crop growth adaptability assessment module, based on the planting greenhouse dataset and crop growth characteristics, analyzes the adaptability between the environment and crop growth, and sends the adaptability analysis results to the dynamic regulation strategy generation module. This allows for accurate assessment of the degree of adaptability between the environment and the current growth stage of the crop, avoiding the subjectivity of traditional experience-based judgments and promptly identifying the risk of environmental incompatibility, thus providing a clear basis for the generation of dynamic regulation strategies. The specific analysis process of the crop growth adaptability assessment module is as follows: The system calls upon a pre-defined crop growth characteristic database, which stores environmental parameter information (including temperature, light intensity, soil moisture, etc.) for different crops at various growth stages (such as seedling stage, flowering stage, fruiting stage, etc.). Based on the crop variety and growth stage in the current planting shed, the system extracts the corresponding parameters to be monitored, as well as the appropriate data range and pre-defined growth influence weight values. Real-time monitoring data of the parameters to be monitored are extracted from the planting shed dataset. The difference between the real-time monitoring data and the median of the corresponding suitable data range is calculated and the absolute value is taken. Based on this, the monitoring deviation of the corresponding parameter to be monitored is obtained. The monitoring deviation is multiplied by the corresponding preset growth influence weight value to obtain the growth influence coefficient. It should be noted that the more important the corresponding parameter is to the growth of the planted crop, the larger the value of the preset growth influence weight value that matches it. The growth influence coefficients of all monitored parameters are obtained and summed to obtain the adaptability limitation index. The adaptability limitation index is compared with the corresponding preset adaptability limitation index threshold. If the adaptability limitation index exceeds the corresponding preset adaptability limitation index threshold, it indicates that the current environmental conditions are not suitable for the growth of the planted crop, and an adaptability limitation signal is generated. If the adaptability limitation index does not exceed the corresponding preset adaptability limitation index threshold, it indicates that the current environmental conditions are relatively suitable for the growth of the planted crop, and an adaptability qualified signal is generated.

[0021] When the dynamic control strategy generation module receives a signal indicating limited adaptability, it generates a corresponding environmental control strategy (containing several control commands, such as temperature control commands and light control commands) and sends it to the intelligent environmental precision control module and the agricultural remote monitoring terminal. This not only enables the precise issuance of control commands but also allows managers to grasp the control direction in real time, balancing the efficiency of automated control with the controllability of manual supervision.

[0022] The intelligent environment precision control module automatically controls the operation of relevant adjustment equipment (such as heating equipment, irrigation equipment, and lighting equipment) in the planting greenhouse based on environmental control strategies, so as to restore the environment inside the planting greenhouse. It also sends the control information to the agricultural remote monitoring terminal to realize the automatic adaptive adjustment of the environment inside the agricultural planting greenhouse. This helps to ensure that the agricultural planting greenhouse is always in a suitable state for crop growth, improves the timeliness and accuracy of environmental control, and significantly reduces the difficulty of planting supervision. In addition, managers can send manual adjustment commands to the intelligent environment precision control module through the agricultural remote monitoring terminal to realize manual intervention control and management.

[0023] The crop growth adaptability assessment module sends the adaptability limitation index to the control effectiveness decision module. The control effectiveness decision module is used to set the detection period, preferably five days. The module analyzes the effectiveness of environmental control in agricultural greenhouses during the detection period and generates an effectiveness anomaly signal or effectiveness qualified signal through analysis. Furthermore, it sends either an abnormal effectiveness signal or a qualified effectiveness signal to the agricultural remote monitoring terminal. Upon receiving an abnormal effectiveness signal, the agricultural remote monitoring terminal issues a corresponding early warning, enabling timely detection of control failures or ineffective control measures and prompting managers to take appropriate corrective actions to avoid the cumulative impact of prolonged environmental unsuitability on crop growth. The specific analysis process is as follows: A rectangular coordinate system is established with time as the X-axis and the fitness restriction index as the Y-axis. Based on all fitness restriction indices within the detection period, fitness restriction curves are plotted in the first quadrant of the rectangular coordinate system, with the starting point of the fitness restriction curve located on the Y-axis. A restriction boundary ray is drawn in the first quadrant of the rectangular coordinate system, parallel to the X-axis and with its endpoint located on the Y-axis. The Y-axis coordinate value corresponding to the restriction boundary ray is a preset fitness restriction index threshold. The area enclosed by the portion of the fit constraint curve above the constraint boundary ray and the constraint boundary ray (if the end point of the fit constraint curve is above the constraint boundary ray, then draw a line segment perpendicular to the constraint boundary ray downward from the end point to close the end area), the area of ​​the corresponding filled area is collected and marked as the filling coefficient, and the filling coefficients of all filled areas are summed to obtain the filling feature value. The filling feature value is compared with the preset filling feature threshold. If the filling feature value exceeds the preset filling feature threshold, it indicates that the environmental control performance in the agricultural planting shed is poor during the detection period, and an effectiveness anomaly signal is generated.

[0024] Furthermore, if the filling feature value does not exceed the preset filling feature threshold, the filling coefficient of the corresponding filling area is compared with the preset filling coefficient threshold. If the filling coefficient exceeds the preset filling coefficient threshold, the corresponding filling area is marked as an abnormal area. The number of abnormal areas is obtained and marked as filling anomaly values, and the filling coefficient with the largest value is marked as the filling area value. The effectiveness impact decision value is obtained by weighted summation of the filling feature value, filling anomaly value, and filling area value. Specifically, each of the filling feature value, filling anomaly value, and filling area value is assigned a corresponding preset weight coefficient, and then each is multiplied by its respective preset weight coefficient. The sum of these three products is then labeled as the effectiveness impact decision value. It should be noted that a larger effectiveness impact decision value indicates a worse overall performance of environmental control within the agricultural planting shed during the detection period. The effectiveness impact decision value is compared with the preset effectiveness impact decision threshold. If the effectiveness impact decision value exceeds the preset effectiveness impact decision threshold, it indicates that the overall performance of environmental control in the agricultural planting shed during the testing period is poor, and an effectiveness anomaly signal is generated. If the effectiveness impact decision value does not exceed the preset effectiveness impact decision threshold, it indicates that the overall performance of environmental control in the agricultural planting shed during the testing period is good, and an effectiveness qualified signal is generated.

[0025] Furthermore, the control effectiveness decision module sends an effectiveness pass signal to the crop growth analysis module. Upon receiving the pass signal, the crop growth analysis module analyzes the crop growth effect and generates a long-term early warning signal or a long-term normal signal. This long-term early warning signal or long-term normal signal is then sent to the agricultural remote monitoring terminal. Upon receiving the long-term early warning signal, the agricultural remote monitoring terminal issues a corresponding warning to remind managers to promptly investigate the cause and take reasonable improvement measures. This also helps to quickly strengthen crop planting management in the greenhouse, ensuring stable crop growth within the greenhouse. The specific analysis process is as follows: The crop density of several planting areas in the agricultural planting shed is obtained. The percentage decrease of the crop density compared with the corresponding standard density is marked as the crop density reduction value. The average growth height of the crops in the corresponding planting area is obtained. The crop height value is calculated by comparing the corresponding standard height with the average growth height. The average chlorophyll content of the crop leaves in the corresponding planting area is obtained. The crop greenness value is calculated by comparing the corresponding standard chlorophyll content with the average chlorophyll content. The growth characteristic value is calculated by weighted summation of crop density reduction, crop height, and crop green condition values. Specifically, each of these values ​​is assigned a pre-defined weight coefficient, and then multiplied by its respective weight coefficient. The sum of these three products is then labeled as the growth characteristic value. It should be noted that a larger growth characteristic value indicates a worse overall crop growth trend in the corresponding planting area. The growth characteristic value is compared with the preset growth characteristic threshold. If the growth characteristic value exceeds the preset growth characteristic threshold, it indicates that the overall crop growth trend in the corresponding planting area is poor. The corresponding planting area is then marked as an alarm area. The percentage of alarm areas within the agricultural planting shed is obtained and marked as crop alarm area condition value. The crop alarm area condition value is compared with the preset crop alarm area condition threshold. If the crop alarm area condition value exceeds the preset crop alarm area condition threshold, it indicates that the crop growth management in the agricultural planting shed is poor and the crop growth effect is poor, and a long-term early warning signal is generated.

[0026] Furthermore, if the crop alarm zone value does not exceed the preset crop alarm zone threshold, the average growth characteristic values ​​of all planting areas in the agricultural planting shed will be used to calculate the crop growth anomaly value, and the growth characteristic value with the largest value will be marked as the crop growth anomaly value. The long-term crop decision value is calculated by weighting and summing the crop warning area value, crop growth anomaly value, and crop growth anomaly value. Specifically, each of these three values ​​is assigned a corresponding preset weight coefficient, and then multiplied by its respective preset weight coefficient. The sum of these three products is then marked as the long-term crop decision value. It should be noted that a higher long-term crop decision value indicates a worse overall crop growth effect within the agricultural greenhouse. The long-term decision value of crops is compared with the preset long-term decision threshold of crops. If the long-term decision value of crops exceeds the preset long-term decision threshold of crops, it indicates that the crop growth management in the agricultural planting greenhouse is not good and the overall crop growth effect is poor, so a long-term warning signal is generated; if the long-term decision value of crops does not exceed the preset long-term decision threshold of crops, it indicates that the overall crop growth effect in the agricultural planting greenhouse is good, so a long-term normal signal is generated.

[0027] Example 2: Figure 2 As shown, the difference between this embodiment and Embodiment 1 is that the crop growth analysis module is communicatively connected to the multi-device coordination and cooperation hidden danger output module. The crop growth analysis module sends a long-term normal signal to the multi-device coordination and cooperation hidden danger output module. When the multi-device coordination and cooperation hidden danger output module receives the long-term normal signal, it analyzes the coordination and cooperation hidden dangers of all regulating devices in the agricultural planting shed and generates a coordination and cooperation early warning signal or a coordination and cooperation safety signal through analysis. Furthermore, the coordination and cooperation early warning signal or coordination and cooperation safety signal is sent to the agricultural remote monitoring terminal. When the agricultural remote monitoring terminal receives the coordination and cooperation early warning signal, it issues a corresponding warning, which can accurately assess the degree of potential coordination and cooperation risks of the equipment, avoid subsequent control failures due to equipment factors, ensure the long-term stable operation of the control system, further reduce the difficulty of planting supervision, and help crops grow in a high-quality manner. The specific analysis process of the multi-equipment coordination and cooperation risk output module is as follows: The average response delay of the corresponding regulating equipment to the regulating command during the detection period is collected and marked as the delay characteristic value. The number of times the corresponding regulating equipment fails during the detection period is marked as the fault frequency characteristic value. The time of the last inspection and maintenance of the corresponding regulating equipment is obtained and marked as the target time. The time difference between the current time and the target time is calculated, and the ratio of the time difference result to the corresponding preset standard value is calculated to obtain the detection interval characteristic value. The adjustment hazard coefficient is obtained by weighted summation of the delay characteristic value, fault frequency characteristic value, and detection isolation characteristic value. Specifically, each of the delay characteristic value, fault frequency characteristic value, and detection isolation characteristic value is assigned a corresponding preset weight coefficient, and then each of these values ​​is multiplied by its respective preset weight coefficient. The sum of these three products is then labeled as the adjustment hazard coefficient. It should be noted that a higher adjustment hazard coefficient indicates a higher level of operational hazard for the corresponding adjustment equipment. The ratio of the adjustment hazard coefficient to the corresponding preset adjustment hazard coefficient threshold is used to calculate the hazard emergency value of the corresponding adjustment equipment. The average value of the hazard emergency values ​​of all adjustment equipment in the agricultural planting shed is used to calculate the coordination and cooperation impact value. The coordination and cooperation impact value is then compared with the preset coordination and cooperation impact threshold. If the impact value of coordination exceeds the preset threshold, it indicates a high risk of coordination problems for all regulating equipment in the agricultural planting shed, and a coordination warning signal is generated. If the impact value of coordination does not exceed the preset threshold, it indicates a low risk of coordination problems for all regulating equipment in the agricultural planting shed, and a coordination safety signal is generated.

[0028] The working principle of this invention is as follows: During use, the multi-source environmental parameter sensing module collects and outputs a planting greenhouse dataset. The crop growth adaptability assessment module accurately identifies the adaptability between the environment and the current growth stage of the crop. When an adaptability limitation signal is generated, the dynamic control strategy generation module generates a targeted environmental control strategy. The intelligent environment precision control module automatically controls the operation of relevant adjustment equipment according to the strategy, quickly restoring the environment to a suitable state, significantly reducing the cost of manual intervention. Furthermore, the control effectiveness decision module quantitatively evaluates the control effect, promptly identifies potential control risks and provides early warnings, avoiding the cumulative impact of environmental incompatibility on crops. When an effectiveness qualified signal is generated, the crop growth analysis module judges the crop growth effect in the planting greenhouse, promptly identifies growth abnormalities, and makes reasonable improvement measures. This achieves effective supervision of agricultural planting greenhouses and significantly reduces management difficulty, comprehensively improving the level of refinement, automation, and intelligence in agricultural planting greenhouse management.

[0029] In this invention, the threshold, preset value, or preset range settings are for result comparison and analysis to determine whether the result is good or bad. The magnitude of these values ​​is determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions. Similarly, the preset weight coefficients and influence factors are assigned specific values ​​based on the magnitude of each parameter's influence on the result, ultimately reflecting the impact on the result. These settings are also determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions.

[0030] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An agricultural planting shed environment monitoring and regulation system based on multi-source data analysis, characterized in that, The system comprises a multi-source environmental parameter sensing module, a crop growth adaptability evaluation module, a dynamic control strategy generation module, an intelligent environment precision control module, and an agricultural remote monitoring terminal. The multi-source environmental parameter sensing module comprehensively monitors and collects real-time data in the agricultural planting shed through several sensors, and forms a planting shed dataset based on the collected monitoring data and outputs the dataset. The crop growth adaptability evaluation module analyzes the adaptability of the environment and crop growth based on the planting shed dataset and crop growth characteristics. When the dynamic control strategy generation module receives a limited adaptability signal, it generates a corresponding environmental control strategy and sends it to the intelligent environment precision control module and the agricultural remote monitoring terminal. The intelligent environment precision control module automatically controls the operation of the related adjusting equipment in the planting shed based on the environmental control strategy. 2.The multi-source data analysis based agricultural planting shed environment monitoring and regulation system according to claim 1, characterized in that, The specific analysis process of the crop growth adaptability evaluation module is as follows: Obtain the growth influence coefficient of all monitored parameters and sum them up to obtain the adaptability limitation index. If the adaptability limitation index exceeds the corresponding preset adaptability limitation index threshold, a limited adaptability signal is generated; otherwise, a qualified adaptability signal is generated. 3.The multi-source data analysis based agricultural planting shed environment monitoring and regulation system according to claim 1, characterized in that, The agricultural remote monitoring terminal is communicatively connected to the control effectiveness decision module. The control effectiveness decision module analyzes the environmental control effectiveness in the agricultural planting shed during the detection period and sends the effectiveness abnormal signal or the effectiveness qualified signal to the agricultural remote monitoring terminal. 4.The multi-source data analysis based agricultural planting shed environment monitoring and regulation system according to claim 3, characterized in that, The specific analysis process of the control effectiveness decision module is as follows: Fill the area surrounded by the part of the adaptability limitation curve above the limit boundary ray and the limit boundary ray. Collect the area of the corresponding filled region and mark it as the filling coefficient. Sum all the filling coefficients of the filled regions to obtain the filling feature value. If the filling feature value exceeds the preset filling feature threshold, an effectiveness abnormal signal is generated. 5.The multi-source data analysis based agricultural planting shed environment monitoring and regulation system according to claim 4, characterized in that, If the filling feature value does not exceed the preset filling feature threshold, the filling feature value, the filling abnormal value, and the filling surface amplitude value are weighted and summed to obtain the effectiveness influence decision value. If the effectiveness influence decision value exceeds the preset effectiveness influence decision threshold, an effectiveness abnormal signal is generated; otherwise, an effectiveness qualified signal is generated. 6.The multi-source data analysis based agricultural planting shed environment monitoring and regulation system according to claim 5, characterized in that, The control effectiveness decision module is communicatively connected to the crop growth analysis module. When the crop growth analysis module receives the effectiveness qualified signal, it analyzes the crop growth effect and sends the long-term warning signal or the long-term normal signal to the agricultural remote monitoring terminal. 7.The multi-source data analysis based agricultural planting shed environment monitoring and regulation system according to claim 6, characterized in that, The specific analysis process of the crop growth analysis module is as follows: Calculate the growth feature value by weighting and summing the crop density reduction value, the crop height value, and the crop green value. If the growth feature value exceeds the preset growth feature threshold, the corresponding planting area is marked as a warning area. Obtain the number of warning areas in the agricultural planting shed and mark it as the crop warning area value. If the crop warning area value exceeds the preset crop warning area value threshold, a long-term warning signal is generated. 8.The multi-source data analysis based agricultural planting shed environment monitoring and regulation system according to claim 7, characterized in that, If the crop warning area condition value does not exceed the preset crop warning area condition threshold value, a long-term decision value of the crop is calculated by weighted summation of the crop warning area condition value, the crop growth abnormality value and the crop growth abnormality amplitude value, if the long-term decision value of the crop exceeds the preset long-term decision threshold value of the crop, a long-term early warning signal is generated; otherwise, a long-term normal signal is generated. 9.The multi-source data analysis based agricultural planting shed environment monitoring and regulation system according to claim 6, characterized in that, The crop growth analysis module is in communication connection with a multi-device coordination hidden danger output module, when the long-term normal signal is received by the multi-device coordination hidden danger output module, coordination hidden dangers of all adjusting devices in the agricultural planting shed are analyzed, a coordination early warning signal or a coordination safety signal is generated through the analysis, and the coordination early warning signal or the coordination safety signal is sent to the agricultural remote monitoring end. 10.The multi-source data analysis based agricultural planting shed environment monitoring and regulation system according to claim 9, characterized in that, The specific analysis process of the multi-device coordination hidden danger output module is as follows: The adjusting hidden danger coefficient is calculated by weighted summation of the should-delay characteristic value, the obstacle frequency characteristic value and the detection interval characteristic value, the hidden danger emergency value of the corresponding adjusting device is calculated by ratio calculation of the adjusting hidden danger coefficient and the corresponding preset adjusting hidden danger coefficient threshold value, the hidden danger emergency values of all adjusting devices in the agricultural planting shed are mean calculated to obtain a coordination influence value, if the coordination influence value exceeds the preset coordination influence threshold value, a coordination early warning signal is generated; otherwise, a coordination safety signal is generated.

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