A blueberry substrate cultivation nutrient solution comprehensive monitoring system and method

Abstract

The application provides a blueberry substrate cultivation nutrient solution comprehensive monitoring system and method, wherein the system comprises: a monitoring item acquisition module, which is used to acquire monitoring items of the blueberry substrate cultivation nutrient solution, and the monitoring items at least include pH value, EC value and volume change; a cultivation condition information acquisition module, which is used to acquire cultivation condition information of the blueberry substrate cultivation; a monitoring task triggering module, which is used to trigger a monitoring task according to the monitoring items and the cultivation condition information; and a monitoring module, which is used to perform corresponding monitoring according to the monitoring task. The blueberry substrate cultivation nutrient solution comprehensive monitoring system and method provided by the application introduces monitoring items and cultivation condition information, adaptively triggers a monitoring task according to the cultivation condition information and the monitoring items, does not require staff to repeatedly go to the monitoring place, reduces labor cost, and greatly improves the nutrient solution monitoring efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of measurement, in particular to a blueberry substrate cultivation nutrient solution comprehensive monitoring system and method. BACKGROUND

[0002] Blueberry substrate cultivation refers to a technology of using substrate to replace soil for blueberry soilless cultivation, which is usually applied in areas with poor cultivation conditions. In blueberry substrate cultivation, nutrient solution is the key to provide nutrients required for the growth of blueberries, for example, blueberries require an acidic environment and a specific nutrient ratio to ensure their healthy growth. Therefore, the preparation and management of nutrient solution must be particularly careful, and the nutrient solution that meets the needs of blueberries must be precisely formulated, and the supply and adjustment of the nutrient solution must be strictly controlled during cultivation to ensure that blueberries can grow well under substrate cultivation conditions, thereby improving the yield and quality of blueberries.

[0003] However, the current monitoring of blueberry substrate nutrient solution is generally through manual regular manual monitoring, and the staff generally sets the monitoring period according to experience, and when the nutrient solution has abnormal indicators, it cannot be monitored in time, and the labor cost is also high.

[0004] Therefore, there is an urgent need for a blueberry substrate cultivation nutrient solution comprehensive monitoring system and method to at least solve the above problems. SUMMARY

[0005] One of the purposes of the present application is to provide a blueberry substrate cultivation nutrient solution comprehensive monitoring system and method, which introduces monitoring items and cultivation condition information, and triggers monitoring tasks according to the cultivation condition information and the monitoring items, without the need for staff to repeatedly monitor, reducing labor costs and greatly improving the monitoring efficiency of the nutrient solution.

[0006] The blueberry substrate cultivation nutrient solution comprehensive monitoring system provided by the embodiment of the present application comprises:

[0007] A monitoring item acquisition module is configured to acquire monitoring items of the blueberry substrate cultivation nutrient solution, and the monitoring items at least include pH value, EC value and volume change;

[0008] A cultivation condition information acquisition module is configured to acquire cultivation condition information of the blueberry substrate cultivation;

[0009] A monitoring task triggering module is configured to trigger a monitoring task according to the monitoring items and the cultivation condition information;

[0010] A monitoring module is configured to perform corresponding monitoring according to the monitoring task.

[0011] Preferably, the monitoring task triggering module triggers the monitoring task according to the monitoring items and the cultivation condition information, comprising:

[0012] determining whether a real-time monitoring value of the monitoring item can be directly obtained based on the automatic device;

[0013] if yes, triggering the real-time monitoring task;

[0014] if no, obtaining a historical monitoring time closest to the current time for the monitoring item;

[0015] obtaining a first monitoring value of the monitoring item at the historical monitoring time;

[0016] obtaining an influence factor based on the cultivation condition sub-information obtained after the historical monitoring time;

[0017] determining a second monitoring value based on the first monitoring value and the influence factor;

[0018] if the second monitoring value falls within a warning interval of the monitoring item, triggering the manual monitoring task.

[0019] Preferably, the monitoring task triggering module obtains the influence factor based on the cultivation condition sub-information obtained after the historical monitoring time, including:

[0020] obtaining a condition target type of the cultivation condition sub-information;

[0021] obtaining an affected experiment record of the monitoring item;

[0022] determining whether the condition target type is consistent with an influence target type in the affected experiment record;

[0023] if the condition target type is completely consistent with the influence target type, obtaining the influence factor based on an experimental result of the corresponding affected experiment record and a condition target value;

[0024] if the condition target type is not completely consistent with the influence target type, obtaining an affected simulation model of the monitoring item based on the affected experiment record;

[0025] configuring the affected simulation model based on the cultivation condition sub-information to obtain a simulation result of the affected simulation model;

[0026] performing affected experiment verification based on the simulation result;

[0027] obtaining the influence factor based on a result of the affected experiment verification.

[0028] Preferably, the monitoring task triggering module obtains the affected simulation model of the monitoring item based on the affected experiment record, including:

[0029] determining an influence target type group in each affected experiment record;

[0030] determining whether the affected experiment record meets a training screening condition based on the influence target type group;

[0031] If yes, the affected simulation model of the training monitoring item is trained according to the corresponding affected experimental record;

[0032] The training screening condition comprises:

[0033] The number m of the same type items of the influence target type group and the condition target type is greater than the number n of the different type items;

[0034] The influence target type corresponding to the different type items is associated with the condition target type corresponding to the at least n same type items;

[0035] The difference m-n between the number of the same type items and the number of the different type items is greater than or equal to a preset target threshold.

[0036] Preferably, the monitoring task triggering module triggers a manual monitoring task, comprising:

[0037] Obtaining a standard monitoring action flow of the manual monitoring task;

[0038] According to the standard monitoring action flow, the monitoring personnel are reminded in real time.

[0039] Preferably, the monitoring task triggering module reminds the monitoring personnel in real time according to the standard monitoring action flow, comprising:

[0040] According to the standard monitoring action target, the standard monitoring action flow is divided into a plurality of local analysis monitoring action flows;

[0041] When the monitoring personnel starts manual monitoring, the local analysis monitoring action flows are analyzed in sequence, and a first slice analysis value of the action flow slice being analyzed is obtained;

[0042] If the first slice analysis value does not reach a trigger value, relay analysis is performed;

[0043] If the first slice analysis value reaches the trigger value, a first feature value set is obtained by performing feature extraction on the first slice analysis value and a second slice analysis value of a previous action flow slice of the action flow slice being analyzed; wherein the previous action flow slice and the action flow slice being analyzed belong to the same local analysis monitoring action flow;

[0044] The first feature value set and a second feature value set in an action correction feature library corresponding to the local analysis monitoring action flow are subjected to feature matching;

[0045] If the feature matching is successful, subsequent action flow slices after the action flow slice being analyzed in the local analysis monitoring action flow are reorganized according to the second feature value set; wherein the subsequent action flow slices and the action flow slice being analyzed belong to the same local analysis monitoring action flow;

[0046] If the feature matching fails, the first slice analysis value is reminded.

[0047] Preferably, the monitoring task triggering module reorganizes the subsequent action flow slice after the action flow slice being analyzed in the local analysis monitoring action flow according to the second feature value set, comprising:

[0048] According to the second feature value set, the first correction action flow remaining uncorrected in the action correction situation is obtained;

[0049] The subsequent action flow slice is replaced by the first correction action flow, the action flow slice being analyzed and the subsequent action flow slice;

[0050] When the third slice analysis value of the action slice being analyzed reaches the triggering value, the feature extraction is performed on the action correction situation, the third slice analysis value and the current personnel correction action, and the third feature value set is obtained;

[0051] Abnormal correction analysis is performed according to the third feature value set;

[0052] The second correction action flow generated in the abnormal correction analysis process is obtained;

[0053] If the acquisition is successful, the second correction action flow is used to replace the first correction action flow remaining unexecuted;

[0054] If the acquisition fails, the manual monitoring task is triggered from the beginning.

[0055] The blueberry substrate cultivation nutrient solution comprehensive monitoring method provided by the embodiment of the application comprises:

[0056] Step 1: obtaining the monitoring items of the blueberry substrate cultivation nutrient solution, wherein the monitoring items at least include pH value, EC value and volume change;

[0057] Step 2: obtaining the cultivation condition information of the blueberry substrate cultivation;

[0058] Step 3: triggering the monitoring task according to the monitoring items and the cultivation condition information;

[0059] Step 4: performing corresponding monitoring according to the monitoring task.

[0060] Preferably, step 3: triggering the monitoring task according to the monitoring items and the cultivation condition information, comprising:

[0061] determining whether the real-time monitoring value of the monitoring item can be directly obtained based on the automatic device;

[0062] If yes, triggering the real-time monitoring task;

[0063] If no, obtaining the historical monitoring time of the monitoring item closest to the current time;

[0064] obtaining the first monitoring value of the monitoring item at the historical monitoring time;

[0065] According to the cultivation condition sub-information acquired after the historical monitoring moment, an influence factor is acquired;

[0066] According to the first monitoring value and the influence factor, a second monitoring value is determined;

[0067] If the second monitoring value falls into the early warning interval of the monitoring item, a manual monitoring task is triggered.

[0068] Preferably, according to the cultivation condition sub-information acquired after the historical monitoring moment, the influence factor is acquired, including:

[0069] A condition target type of the cultivation condition sub-information is acquired;

[0070] An affected experiment record of the monitoring item is acquired;

[0071] It is judged whether the condition target type is consistent with an influence target type in the affected experiment record;

[0072] If the condition target type is completely consistent with the influence target type, according to the experimental result of the corresponding affected experiment record and the condition target value, the influence factor is acquired;

[0073] If the condition target type is not completely consistent with the influence target type, an affected simulation model of the monitoring item is acquired according to the affected experiment record;

[0074] The affected simulation model is configured according to the cultivation condition sub-information, and a simulation result of the affected simulation model is acquired;

[0075] According to the simulation result, affected experiment verification is performed;

[0076] According to the affected experiment verification result, the influence factor is acquired.

[0077] The beneficial effects of the present application are:

[0078] The present application introduces the monitoring item and the cultivation condition information, and adaptively triggers the monitoring task according to the cultivation condition information and the monitoring item, without the need for staff to repeatedly monitor, reduces the labor cost, and greatly improves the nutrient solution monitoring efficiency.

[0079] Other features and advantages of the present application will be described in the following description, and some will become apparent from the description, or will be understood from the practice of the present application. The purposes and other advantages of the present application can be achieved and obtained by the structures specifically pointed out in the present application document.

[0080] The technical solutions of the present application will be further described in detail below with the help of the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0081] The accompanying drawings are included to provide a further understanding of the application, and are incorporated in and constitute a part of this specification, illustrate embodiments of the application, and together with the description serve to explain the application, and do not limit the application. In the drawings:

[0082] Figure 1 It is a schematic view of a blueberry substrate cultivation nutrient solution comprehensive monitoring system in an embodiment of the application.

[0083] Figure 2 It is a schematic view of a blueberry substrate cultivation nutrient solution comprehensive monitoring method in an embodiment of the application. DETAILED DESCRIPTION

[0084] The preferred embodiments of the application are described below in conjunction with the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to explain and illustrate the application, and do not limit the application.

[0085] The embodiment of the application provides a blueberry substrate cultivation nutrient solution comprehensive monitoring system, as shown in the figure, comprising: Figure 1

[0086] The monitoring item acquisition module 1 is used for acquiring the monitoring item of the blueberry substrate cultivation nutrient solution, and the monitoring item at least includes: pH value, EC value and volume change;

[0087] The cultivation condition information acquisition module 2 is used for acquiring the cultivation condition information of the blueberry substrate cultivation;

[0088] The monitoring task triggering module 3 is used for triggering the monitoring task according to the monitoring item and the cultivation condition information;

[0089] The monitoring module 4 is used for corresponding monitoring according to the monitoring task.

[0090] The working principle and beneficial effects of the above technical solution are:

[0091] ​The monitoring item is a monitoring item type of a blueberry substrate cultivation nutrient solution, such as a pH value, an EC value, and a volume change, and when the monitoring item is acquired, the monitoring item can be acquired by analyzing a monitoring task set by a person or can be acquired by analyzing the monitoring task of the blueberry substrate cultivation nutrient solution from big data; the cultivation condition information is environmental information of the blueberry substrate cultivation, such as temperature, humidity, illumination, irrigation frequency, and the like, and the cultivation condition information can be automatically detected and acquired by a sensor of a corresponding information type; the monitoring task is monitoring of which monitoring item is triggered at which time; finally, corresponding monitoring is performed according to the monitoring task, such as when a liquid level sensor is pre-set in a blueberry planting pot, the liquid level sensor in the nutrient solution is automatically triggered to perform real-time nutrient solution quantity detection; when the liquid level sensor is not pre-set in the nutrient solution, when the real-time nutrient solution quantity estimated according to the temperature, humidity, illumination, irrigation frequency, and the most recently measured nutrient solution quantity is less than a pre-set nutrient solution quantity threshold, the cultivation personnel are notified to perform nutrient solution quantity detection.

[0092] The present application introduces the monitoring item and the cultivation condition information, and adaptively triggers the monitoring task according to the cultivation condition information and the monitoring item, without the need for staff to repeatedly monitor, reduces the labor cost, and greatly improves the nutrient solution monitoring efficiency.

[0093] In one embodiment, the monitoring task triggering module triggers the monitoring task according to the monitoring item and the cultivation condition information, including:

[0094] determining whether the real-time monitoring value of the monitoring item can be directly acquired based on the automatic device;

[0095] if yes, triggering the real-time monitoring task;

[0096] if no, acquiring a historical monitoring time closest to the current time of the monitoring item;

[0097] acquiring a first monitoring value of the monitoring item at the historical monitoring time;

[0098] acquiring an influence factor according to the cultivation condition sub-information acquired after the historical monitoring time;

[0099] determining a second monitoring value according to the first monitoring value and the influence factor;

[0100] if the second monitoring value falls within an early warning interval of the monitoring item, triggering the manual monitoring task.

[0101] The working principle and beneficial effects of the above technical solution are:

[0102] The automatic device is a device that automatically performs monitoring value detection of the monitoring item, such as a pH detector pre-set in the blueberry substrate, and if the automatic device is configured, manual intervention in pH measurement is not required, and real-time monitoring can be performed;

[0103] If the monitoring item corresponding to the monitoring value cannot be directly and automatically obtained, the determination of manual detection time is very important. Frequent mobilization of manual detection can avoid missing the best intervention opportunity, but will consume a lot of manpower, and the mobilization cycle will be lengthened, which will lead to the problem of untimely monitoring. Therefore, the nearest historical monitoring time of the monitoring item to the current time is obtained, which is, for example, 12 hours ago, the first monitoring value is, for example, 4.7. The influence factor includes a plurality of influence values. The influence value is the influence coefficient of the information type of the cultivation condition sub-information on the monitoring item, which is determined by considering the influence relationship of the cultivation condition sub-information of different information types on the first monitoring value. The first monitoring value and each influence value in the influence factor are multiplied in turn to obtain the second monitoring value. The second monitoring value is the real-time prediction value of the monitoring value of the monitoring item that cannot be directly obtained. The warning interval of the monitoring item is set by human, for example, the suitable pH value of blueberry is 4.5-5.5, so the warning interval of the monitoring item is [4-4.5]∪[5.5-6] when the second monitoring value falls into the warning interval of the monitoring item. It is indicated that the corresponding actual monitoring value is likely to exceed the suitable range, and the manual monitoring task is triggered to dispatch human to detect.

[0104] The present application introduces the influence factor extraction template of the monitoring item, obtains the local cultivation condition information (cultivation condition sub-information) intercepted after the historical monitoring time, and uses the influence factor to correct the first monitoring value to obtain the second monitoring value. The second monitoring value and the warning interval are compared to determine whether the monitoring task is triggered. The triggering process of the monitoring task is more reasonable.

[0105] In one embodiment, the monitoring task triggering module obtains the influence factor extraction template of the monitoring item, including:

[0106] Obtain the condition target type of the cultivation condition sub-information;

[0107] Obtain the affected experiment record of the monitoring item;

[0108] Determine whether the condition target type and the influence target type in the affected experiment record are consistent;

[0109] If they are completely consistent, the influence factor is obtained according to the experimental results and the condition target value of the corresponding affected experiment record;

[0110] If they are not completely consistent, the affected simulation model of the monitoring item is obtained according to the affected experiment record;

[0111] The affected experiment simulation model is configured according to the cultivation condition sub-information, and the simulation result of the affected experiment simulation model is obtained;

[0112] According to the simulation result, the affected experiment verification is carried out;

[0113] According to the affected experiment verification result, an influence factor is obtained.

[0114] The working principle and beneficial effects of the above technical solution are:

[0115] The condition target type is a type of cultivation condition information in the cultivation condition sub-information, such as temperature, humidity; the affected experiment record is an experiment process record in which the monitoring item is affected by various factors, such as an experiment record in which the pH value is affected by temperature change, humidity change, and light change; the influence target type is the type of the above factors; if the condition target type and the influence target type are completely consistent, it means that the influence relationship of the cultivation condition sub-information on the monitoring item has been summarized based on the corresponding affected experiment record (for example, when the humidity is too low, the pH value rises, when the humidity is too high, the pH value drops, when the temperature is too high, the pH value rises, and when the temperature is too low, the pH value drops); based on the experiment result and the condition target type, the influence factor is obtained; if the condition target type and the influence target type in the affected experiment record are not completely consistent, the affected simulation model is trained according to the affected experiment record; the affected simulation model is an AI model that automatically analyzes the experiment process in which the monitoring item is affected by various factors, which is obtained by learning the analysis logic of the experiment analysis process in which the monitoring item is affected by human in the affected experiment record; the simulation result is a simulated experiment process in which the monitoring item is affected by various factors in the cultivation condition information, which is output by the affected experiment simulation model; the affected experiment verification result is the result of the adaptability of the monitoring item in the experiment scheme based on the simulation result, that is, when there is no related experiment in history to study the influence of the condition target type on the monitoring item, the affected experiment simulation model is configured according to the affected simulation model and the cultivation condition sub-information, the experiment scheme is adaptively formulated, and the influence factor is obtained, thereby improving the suitability of the influence factor acquisition.

[0116] In one embodiment, the monitoring task triggering module obtains the affected simulation model of the monitoring item according to the affected experiment record, including:

[0117] Determine the influence target type group in each affected experiment record;

[0118] According to the influence target type group, determine whether the affected experiment record meets the training screening condition;

[0119] If it is met, the affected simulation model of the monitoring item is trained according to the corresponding affected experiment record;

[0120] The training screening condition includes:

[0121] The number m of the same type items of the influence target type group and the condition target type is greater than the number n of different type items;

[0122] The influence target type corresponding to the different type item is associated with the condition target type corresponding to at least n same type items;

[0123] The difference m-n between the number of same type items and the number of different type items is greater than or equal to a preset target threshold.

[0124] The working principle and beneficial effects of the above technical solution are:

[0125] The influence target type group is a combination of influence target types analyzed in each affected experiment record, for example, the monitoring item is nutrient solution pH, and the influence target type group is [temperature, humidity]; the training screening condition is used to screen the affected experiment records with training value, and specifically includes:

[0126] Condition 1: The number m of same type items of the influence target type group and the condition target type is greater than the number n of different type items, which restricts that the number of consistent analysis types is greater than the number of inconsistent analysis types, and improves the analysis scene similarity;

[0127] Condition 2: The influence target type corresponding to the different type item is associated with the condition target type corresponding to at least n same type items, which restricts that the more the number of different type items, the more the condition target types corresponding to the same type items that the influence target type corresponding to the different type item needs to be indirectly associated with, and improves the analysis correlation;

[0128] Condition 3: The difference m-n between the number of same type items and the number of different type items is greater than or equal to a preset target threshold, the target threshold is half of the total number of condition target types, which is obtained by rounding down, which restricts that the number of same type items is much more than the number of different type items, and further improves the analysis suitability.

[0129] The present application introduces the training screening condition to screen the affected experiment records, and improves the suitability of the subsequent affected simulation model simulation experiment.

[0130] In one embodiment, the monitoring task triggering module triggers a manual monitoring task, including:

[0131] Obtaining a standard monitoring action flow of the manual monitoring task;

[0132] According to the standard monitoring action target, the standard monitoring action flow is divided into a plurality of local analysis monitoring action flows;

[0133] When the monitoring personnel starts manual monitoring, the local analysis monitoring action flow is analyzed in sequence, and a first slice analysis value of the action flow slice being analyzed is obtained;

[0134] If the first slice analysis value does not reach the trigger value, relay analysis is performed;

[0135] If the first slice analysis value reaches the trigger value, feature extraction is performed on the first slice analysis value and a second slice analysis value of a previous action flow slice of the action flow slice being analyzed, to obtain a first feature value set; the previous action flow slice and the action flow slice being analyzed belong to the same local analysis monitoring action flow;

[0136] Feature matching is performed on the first feature value set and a second feature value set in an action correction feature library corresponding to the local analysis monitoring action flow;

[0137] If the feature matching is successful, subsequent action flow slices after the action flow slice being analyzed in the local analysis monitoring action flow are reorganized according to the second feature value set; the subsequent action flow slices and the action flow slice being analyzed belong to the same local analysis monitoring action flow;

[0138] If the feature matching fails, a reminder is given according to the first slice analysis value.

[0139] The working principle and beneficial effects of the above technical solution are as follows:

[0140] The standard monitoring action flow is: manual monitoring task specification execution, specification action sequence containing before and after action sequence; local analysis monitoring action flow is divided according to action target, such as: action targets are respectively: calibrating electrodes with standard solution of pH 4.0 and 7.0, inserting calibrated electrodes into nutrient solution, and cleaning and storing in KCl solution after use; the local analysis monitoring action flow is the local standard monitoring action flow corresponding to the standard monitoring action target; the action flow slice contains the action process mark and the specific action of the action process, when the action process and the artificial manual monitoring process are consistent, the analysis of the action flow slice (the action flow slice being analyzed) of the corresponding action process is triggered, the first slice analysis value is: the difference degree of the current manual monitoring action and the action corresponding to the action flow slice being analyzed; if the difference degree does not reach the trigger value, relay analysis (analysis of the action flow slice of the next action process) is performed; if the first slice analysis value reaches the trigger value, the first feature value set is obtained by feature extraction on the first slice analysis value and the second slice analysis value of the previous action flow slice of the action flow slice being analyzed, the first feature value includes: the feature representation of the action relationship between the manual monitoring action corresponding to the first slice analysis value and the manual monitoring action corresponding to the second slice analysis value, such as: the first slice analysis value corresponding manual monitoring action is to adjust the second slice analysis value corresponding manual monitoring action in what way; the action correction feature library corresponding to the local analysis monitoring action flow includes the feature representation of the related adjustment relationship between the actions in the specification adjustment situation of the local analysis monitoring action flow, that is, the second feature value set; the first feature value set and the second feature value set are matched, if the matching is successful, it means that the correction situation of the action target corresponding to the local analysis monitoring action flow is met, the subsequent action flow slice used for comparison of subsequent manual monitoring actions is adjusted, and the rationality and reliability of the monitoring basis are improved; if the feature matching fails, it means that neither the correction situation of the previous action nor the current specification action is met, and the monitoring personnel are reminded in time to improve the monitoring specification.

[0141] In one embodiment, the monitoring task triggering module reorganizes the subsequent action flow slice after the action flow slice being analyzed in the local analysis monitoring action flow according to the second feature value set, including:

[0142] According to the second feature value set, a first correction action flow which has not been corrected in the action correction situation is obtained;

[0143] The first correction action flow, the action flow slice being analyzed, and the subsequent action flow slice are used to replace the subsequent action flow slice;

[0144] When the third slice analysis value of the correction action slice being analyzed reaches the trigger value, the third feature value set is obtained by feature extraction on the action correction situation, the third slice analysis value, and the current personnel correction action;

[0145] performing an abnormal correction analysis according to the third feature value set;

[0146] obtaining a second correction action flow generated in the abnormal correction analysis process;

[0147] if the obtaining is successful, replacing the remaining first correction action flow with the second correction action flow;

[0148] if the obtaining fails, triggering the manual monitoring task from the beginning.

[0149] The working principle and beneficial effects of the above technical solution are as follows:

[0150] The correction action flow described by the second feature value set and the first correction action flow jointly constitute a complete correction action flow of the action correction situation, and the subsequent action flow slice is replaced with the first correction action flow, the action flow slice being analyzed, and the subsequent action flow slice. After the replacement, the system will re-analyze the action slice, and when the third slice analysis value of the correction action slice reaches the trigger value, it indicates that the correction action is not standardized or adjustment occurs. The feature extraction is performed on the action correction situation, the third slice analysis value and the current personnel correction action, and the third feature value set is obtained. The third feature value includes: the difference item of the current personnel correction action and the third slice analysis value corresponding to the standard correction action, the association relationship between the difference item and the action correction situation, etc. The abnormal correction analysis is: analyzing why the correction behavior is abnormal, for example: if the difference item and the action correction situation are related, it indicates that the correction action can be further adjusted. The second correction action flow generated in the abnormal correction analysis process is obtained, and the remaining first correction action flow is replaced with the second correction action flow. If the difference item and the action correction situation are not related, other situations that conflict with the current correction strategy may occur, and the correction cost is larger, so the manual monitoring task is triggered again.

[0151] The present application adaptively adjusts the standard monitoring task according to the sudden situation in the manual monitoring process, timely reminds the artificial to perform the standard monitoring, and improves the standard detection efficiency of the artificial intervention monitoring.

[0152] The embodiment of the present application provides a comprehensive monitoring method for blueberry substrate cultivation nutrient solution, as shown in the figure, comprising: Figure 2

[0153] Step 1: obtaining the monitoring items of the blueberry substrate cultivation nutrient solution, the monitoring items at least including: pH value, EC value and volume change;

[0154] Step 2: obtaining the cultivation condition information of the blueberry substrate cultivation;

[0155] Step 3: triggering the monitoring task according to the monitoring items and the cultivation condition information;

[0156] ​Step 4: Perform corresponding monitoring according to the monitoring task.

[0157] In one embodiment, step 3: Trigger the monitoring task according to the monitoring item and the cultivation condition information, comprising:

[0158] Determine whether the real-time monitoring value of the monitoring item can be directly obtained based on the automatic device;

[0159] If yes, trigger the real-time monitoring task;

[0160] If no, obtain the historical monitoring time closest to the current time for the monitoring item;

[0161] Obtain the first monitoring value of the monitoring item at the historical monitoring time;

[0162] Obtain the influence factor according to the cultivation condition sub-information obtained after the historical monitoring time;

[0163] Determine the second monitoring value according to the first monitoring value and the influence factor;

[0164] If the second monitoring value falls within the warning interval of the monitoring item, trigger the manual monitoring task.

[0165] In one embodiment, obtaining the influence factor according to the cultivation condition sub-information obtained after the historical monitoring time, comprises:

[0166] Obtain the condition target type of the cultivation condition sub-information;

[0167] Obtain the affected experiment record of the monitoring item;

[0168] Determine whether the condition target type and the influence target type in the affected experiment record are consistent;

[0169] If they are completely consistent, obtain the influence factor according to the experimental result and the condition target value of the corresponding affected experiment record;

[0170] If they are not completely consistent, obtain the affected simulation model of the monitoring item according to the affected experiment record;

[0171] Configure the affected simulation model according to the cultivation condition sub-information, and obtain the simulation result of the affected simulation model;

[0172] According to the simulation result, perform affected experiment verification;

[0173] According to the affected experiment verification result, obtain the influence factor.

[0174] Obviously, many modifications and variations of the present application are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A comprehensive monitoring system for nutrient solutions in blueberry substrate cultivation, characterized in that, include: The monitoring item acquisition module is used to acquire the monitoring items of the blueberry substrate cultivation nutrient solution. The monitoring items include at least: pH value, EC value and volume change. The cultivation condition information acquisition module is used to acquire cultivation condition information for blueberry substrate cultivation. The monitoring task triggering module is used to trigger monitoring tasks based on monitoring items and cultivation conditions. The monitoring module is used to perform corresponding monitoring according to the monitoring task; The monitoring task triggering module triggers monitoring tasks based on monitoring items and cultivation condition information, including: Determine whether the real-time monitoring values ​​of the monitored items can be obtained directly based on automated devices; If so, trigger a real-time monitoring task; If not, retrieve the most recent historical monitoring time for the monitored item. Obtain the first monitoring value of the monitoring item at the historical monitoring time; Influencing factors are obtained based on cultivation condition sub-information acquired after historical monitoring time. The second monitoring value is determined based on the first monitoring value and the influencing factor; If the second monitoring value falls within the warning range of the monitoring item, then the standard monitoring action flow for the manual monitoring task is obtained; Based on the standard monitoring action objectives, the standard monitoring action flow is divided into multiple local analysis monitoring action flows; When the monitoring personnel start manual monitoring, they analyze the local analysis monitoring action flow in sequence and obtain the first slice analysis value of the action flow slice being analyzed. If the analysis value of the first slice does not reach the trigger value, then continue the analysis; If the first slice analysis value reaches the trigger value, feature extraction is performed on the first slice analysis value and the second slice analysis value of the preceding action flow slice of the action flow slice being analyzed to obtain the first feature value set; wherein, the preceding action flow slice and the action flow slice being analyzed belong to the same local analysis monitoring action flow; Perform feature matching between the first feature set and the second feature set in the action correction feature library corresponding to the local analysis and monitoring action flow; If feature matching is successful, the subsequent action flow slices following the currently analyzed action flow slice in the local analysis monitoring action flow are reorganized according to the second feature value set; wherein, the subsequent action flow slice and the currently analyzed action flow slice belong to the same local analysis monitoring action flow. If feature matching fails, a warning will be issued based on the analysis value of the first slice.

2. The comprehensive monitoring system for blueberry substrate cultivation nutrient solution as described in claim 1, characterized in that, The monitoring task triggering module obtains influencing factors based on cultivation condition sub-information acquired after historical monitoring times, including: Conditional target type for obtaining cultivation condition sub-information; Obtain the affected experimental records for the monitored items; Determine whether there are any conditions whose target type is consistent with the impact target type in the affected experimental records; If they are completely identical, obtain the impact factor based on the experimental results and target values ​​of the corresponding affected experimental records; If they are not completely consistent, obtain the affected simulation model of the monitoring item based on the affected experimental records; Configure the simulation model of the affected experiment based on the cultivation condition sub-information, and obtain the simulation results of the simulation model of the affected experiment; Based on the simulation results, the affected experiments were verified. Based on the experimental verification results of the affected experiments, the impact factor is obtained.

3. The comprehensive monitoring system for blueberry substrate cultivation nutrient solution as described in claim 2, characterized in that, The monitoring task triggering module obtains the affected simulation model of the monitored item based on the affected experimental records, including: Identify the target type group in each affected experimental record; Based on the target type group, determine whether the affected experimental records meet the training screening criteria; If the conditions are met, train the affected simulation model for the monitoring item based on the corresponding affected experimental records; The training selection criteria include: The number of identical type items (m) affecting the target type group and the condition target type is greater than the number of dissimilar type items (n); The target type corresponding to a dissimilar type item is associated with the condition target type corresponding to at least n identical type items; The difference mn between the number of items of the same type and the number of items of different types is greater than or equal to the preset target threshold.

4. The comprehensive monitoring system for blueberry substrate cultivation nutrient solution as described in claim 1, characterized in that, The monitoring task triggering module reorganizes the subsequent action flow slices in the monitored action flow based on the second feature set, following the action flow slice being analyzed. These include: Based on the second feature set, obtain the first correction action flow that is still uncorrected in the action correction case; Replace the subsequent action flow slice with the first corrected action flow, the action flow slice being analyzed, and the subsequent action flow slice; When the third slice analysis value of the corrected action slice being analyzed reaches the trigger value, feature extraction is performed on the action correction situation, the third slice analysis value, and the current personnel's corrected action to obtain the third feature value set. Anomaly correction analysis is performed based on the third eigenvalue set; Obtain the second correction action flow generated during the anomaly correction analysis process; If successful, replace the remaining unexecuted first correction action flow with the second correction action flow; If the acquisition fails, trigger the manual monitoring task from the beginning.

5. A method for comprehensive monitoring of nutrient solutions in blueberry substrate cultivation, characterized in that, include: Step 1: Obtain the monitoring items for the blueberry substrate cultivation nutrient solution. The monitoring items should include at least: pH value, EC value, and volume change. Step 2: Obtain information on cultivation conditions for blueberry substrate cultivation; Step 3: Trigger the monitoring task based on the monitoring items and cultivation conditions; Step 4: Conduct corresponding monitoring according to the monitoring task; Step 3: Based on the monitoring items and cultivation conditions, trigger the monitoring task, including: Determine whether the real-time monitoring values ​​of the monitored items can be obtained directly based on automated devices; If so, trigger a real-time monitoring task; If not, retrieve the most recent historical monitoring time for the monitored item. Obtain the first monitoring value of the monitoring item at the historical monitoring time; Influencing factors are obtained based on cultivation condition sub-information acquired after historical monitoring time. The second monitoring value is determined based on the first monitoring value and the influencing factor; If the second monitoring value falls within the warning range of the monitoring item, then the standard monitoring action flow for the manual monitoring task is obtained; Based on the standard monitoring action objectives, the standard monitoring action flow is divided into multiple local analysis monitoring action flows; When the monitoring personnel start manual monitoring, they analyze the local analysis monitoring action flow in sequence and obtain the first slice analysis value of the action flow slice being analyzed. If the analysis value of the first slice does not reach the trigger value, then continue the analysis; If the first slice analysis value reaches the trigger value, feature extraction is performed on the first slice analysis value and the second slice analysis value of the preceding action flow slice of the action flow slice being analyzed to obtain the first feature value set; wherein, the preceding action flow slice and the action flow slice being analyzed belong to the same local analysis monitoring action flow; Perform feature matching between the first feature set and the second feature set in the action correction feature library corresponding to the local analysis and monitoring action flow; If feature matching is successful, the subsequent action flow slices following the currently analyzed action flow slice in the local analysis monitoring action flow are reorganized according to the second feature value set; wherein, the subsequent action flow slice and the currently analyzed action flow slice belong to the same local analysis monitoring action flow. If feature matching fails, a warning will be issued based on the analysis value of the first slice.

6. The method for comprehensive monitoring of nutrient solution in blueberry substrate cultivation as described in claim 5, characterized in that, Based on the cultivation condition sub-information obtained after the historical monitoring time, influencing factors are obtained, including: Conditional target type for obtaining cultivation condition sub-information; Obtain the affected experimental records for the monitored items; Determine whether there are any conditions whose target type is consistent with the impact target type in the affected experimental records; If they are completely identical, obtain the impact factor based on the experimental results and target values ​​of the corresponding affected experimental records; If they are not completely consistent, obtain the affected simulation model of the monitoring item based on the affected experimental records; Configure the simulation model of the affected experiment based on the cultivation condition sub-information, and obtain the simulation results of the simulation model of the affected experiment; Based on the simulation results, the affected experiments were verified. Based on the experimental verification results of the affected experiments, the impact factor is obtained.

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