Fishery breeding daily management system and method based on data analysis

Abstract

The invention relates to the technical field of fishery breeding management, in particular to a fishery breeding daily management system and method based on data analysis, and the system comprises a management and control platform, a water quality supervision unit, a risk collection unit, an early warning execution unit, a management unit and a delay evaluation unit. According to the method, the working efficiency of the detection equipment is improved by collecting the state data of the detection equipment in the culture pond and performing fault risk assessment analysis, and when the detection equipment is normal, the water quality of the culture pond is supervised so that the water quality can be managed and controlled in time, and the water quality of the culture pond is improved; in addition, the water quality disqualification condition and the detection equipment abnormal condition are combined, and analysis is performed in a progressive mode, so that the loss and harm degree caused by the fact that the water quality of the culture pond cannot be treated in time due to data delay can be clearly known, management and remediation are accurately and reasonably performed, the unnecessary loss risk is reduced, and the economic benefit is improved. And the early warning effect on the culture pond is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fishery culture management, and in particular to a fishery culture daily management system and method based on data analysis. BACKGROUND

[0002] Water for aquaculture is the living environment of the farmed animals, and each kind of aquatic animals needs a water quality environment suitable for its survival, and the quality of the water quality environment is directly related to the growth and development of the aquatic animals, thereby related to the yield, quality and economic benefits of the aquaculture industry, and the 24-hour on-duty system usually equipped by the aquaculture enterprises in China adopts the manual monitoring method (i.e. the staff measures on site with a portable instrument, or sends the sample to the laboratory for analysis, which is the most commonly used monitoring means for the aquaculture businesses in China at present), and if high-frequency monitoring is to be realized, the manpower and the detection and monitoring cost will greatly increase. However, in the prior art, the detection of the water quality of the water body for water quality aquaculture cannot achieve the effect of timely early warning, and it is impossible to ensure whether the early warning equipment is normally operated, and the equipment for collecting data cannot be monitored and fed back, so that the water quality of the aquaculture pond cannot be timely treated due to the delay in data processing, thereby increasing the aquaculture risk, and in addition, the loss caused by the fact that the water quality of the aquaculture pond cannot be timely treated due to the delay in data processing cannot be reasonably and effectively remedied according to the degree of loss. In view of the above technical defects, a solution is proposed. SUMMARY

[0003] The present application aims to provide a fishery culture daily management system and method based on data analysis to solve the above technical defects, which combines the unqualified water quality and the abnormal condition of the detection equipment, and analyzes in a progressive manner to clearly understand the degree of loss and harm caused by the fact that the water quality of the aquaculture pond cannot be timely treated due to the delay in data processing, and then accurately and reasonably remedy the management, thereby reducing unnecessary loss risk, and in addition, the early warning performance of the equipment is supervised through data feedback and supervision to ensure that the equipment normally warns and the early warning is timely and effective, improve the early warning effect of the equipment, and thereby help to improve the timely maintenance of the alarm light by the staff, reduce the alarm failure risk probability, and improve the early warning effect of the aquaculture pond.

[0004] The purpose of the present application can be achieved by the following technical solution: a fishery culture daily management system based on data analysis, comprising a management and control platform, a water quality supervision unit, a risk collection unit, an early warning execution unit, a management unit and a delay evaluation unit. When the management and control platform generates the operation and management instruction, and sends the operation and management instruction to the water quality supervision unit and the collection risk unit, the collection risk unit immediately collects the state data of the internal detection equipment of the breeding pond after receiving the operation and management instruction, the state data includes the processing time length and the running current, and carries out fault risk evaluation analysis on the state data, sends the obtained normal signal to the water quality supervision unit, and sends the obtained abnormal signal to the early warning execution unit and the delay evaluation unit; The early warning execution unit immediately collects the reaction data of the alarm lamp after receiving the abnormal signal, the reaction data includes the brightness value characteristic curve and the line loss value characteristic curve of the alarm lamp, and carries out monitoring feedback analysis on the reaction data, and sends the obtained risk signal to the management unit through the delay evaluation unit; The water quality supervision unit immediately collects the water quality data in the breeding pond after receiving the operation and management instruction and the normal signal, the water quality data includes the dissolved oxygen rate value, the ammonia nitrogen content value and the bacteria content value, carries out collection processing and water quality evaluation analysis on the water quality data, obtains the unqualified signal, the qualified signal and the water quality evaluation coefficient S, sends the unqualified signal to the delay evaluation unit, and sends the qualified signal to the early warning execution unit; The delay evaluation unit immediately calls the delay risk value and the water quality evaluation coefficient S after receiving the abnormal signal and the unqualified signal, and then carries out comprehensive delay evaluation analysis on the delay risk value and the water quality evaluation coefficient S, and sends the obtained first-level remediation signal, second-level remediation signal and third-level remediation signal to the management unit.

[0005] Preferably, the fault risk evaluation analysis process of the collection risk unit is as follows: SS1: Each area in the breeding pond in the breeding area is provided with a detection equipment, each collection data sensor in the detection equipment is marked as i, i is a natural number greater than zero, the time length from the beginning of breeding to the end of breeding in the breeding pond is collected, and is marked as a time threshold, the time length from the beginning of collection of each collection data sensor to the information sending time within the time threshold is obtained, and is marked as a processing time length, at the same time, the processing time length is compared and analyzed with a preset processing time length threshold, if the processing time length is greater than the preset processing time length threshold, the part of the processing time length exceeding the preset processing time length threshold is obtained, and is marked as a time lag value, marked as SJi, the number of sensors corresponding to the time lag value SJi exceeding the preset time lag value threshold is obtained, and is marked as a lag risk number, at the same time, the average value of the part of the time lag value SJi exceeding the preset time lag value threshold is obtained, and is marked as an average risk value, and the product of the lag risk number and the average risk value is marked as a delay risk value; SS2: divide the processing duration into o sub-time nodes, o is a natural number greater than zero, obtain the running current of each collection data sensor in each sub-time node, mark it as YDio, construct a set A of running current YDio, obtain the difference between two subsets connected in set A, and mark it as a floating value, obtain the ratio of the number of floating values exceeding the preset floating value to the total number of floating values, and mark it as a risk bias value FPi, obtain the maximum and minimum values of the risk bias value FPi in the processing duration, and mark the difference between the maximum and minimum values of the risk bias value FPi as the maximum span bias value; SS3: compare and analyze the delay risk value and the maximum span bias value with the preset delay risk value threshold and the preset maximum span bias value threshold recorded in the internal storage: If the delay risk value is less than or equal to the preset delay risk value threshold, and the maximum span bias value is less than or equal to the preset maximum span bias value threshold, a normal signal is generated; If the delay risk value is greater than the preset delay risk value threshold, or the maximum span bias value is greater than the preset maximum span bias value threshold, an abnormal signal is generated.

[0006] Preferably, the monitoring feedback analysis process of the early warning execution unit is as follows: First step: collect the time between the moment when the early warning execution unit receives the abnormal signal and the moment when the alarm lamp starts working, and mark it as the execution duration, and obtain the brightness value characteristic curve of the alarm lamp within the time threshold, obtain the ratio of the segment number corresponding to the brightness descending segment and the brightness rising segment from the brightness value characteristic curve, and mark it as the dark tendency value, and mark the product of the dark tendency value and the execution duration as the execution risk value; Second step: obtain the line loss value characteristic curve of the alarm lamp within the time threshold, and draw a preset threshold curve in the line loss value characteristic curve, obtain the area surrounded by the line segment of the line loss value characteristic curve above the preset threshold curve and between the preset threshold curve, and mark it as the abnormal loss value, and compare and analyze the execution risk value and the abnormal loss value with the preset execution risk value threshold and the preset abnormal loss value threshold recorded in the internal storage: If the execution risk value is less than or equal to the preset execution risk value threshold, and the abnormal loss value is less than or equal to the preset abnormal loss value threshold, no signal is generated; If the execution risk value is greater than the preset execution risk value threshold, or the abnormal loss value is greater than the preset abnormal loss value threshold, a risk signal is generated.

[0007] Preferably, the water quality monitoring unit collects and processes the water quality data as follows: S1: divide the culture pond into h sub-regional blocks, h is a natural number greater than zero, obtain the dissolved oxygen rate value of each sub-regional block within the time threshold, obtain the mean value of the dissolved oxygen rate value within the time threshold, and mark it as the average dissolved oxygen value, obtain the average dissolved oxygen value in each sub-time period in the culture pond, and establish a rectangular coordinate system with time as the X-axis and the average dissolved oxygen value as the Y-axis, and draw the average dissolved oxygen value curve by dotting, obtain the starting value, maximum peak value, minimum valley value and end value in the curve, and obtain the mean value of the average dissolved oxygen value corresponding to the remaining sub-time period after removing the starting value, maximum peak value, minimum valley value and end value, and mark it as the dissolved oxygen risk value; S12: obtain the ammonia nitrogen content value of each sub-regional block within the time threshold, compare and analyze the ammonia nitrogen content value with the preset ammonia nitrogen content value threshold, if the ammonia nitrogen content value is greater than the preset ammonia nitrogen content value threshold, obtain the total area of the sub-regional block corresponding to the ammonia nitrogen content value greater than the preset ammonia nitrogen content value threshold, and mark it as the risk area, and mark the ratio of the risk area to the total area of the culture pond as the ammonia nitrogen risk proportion value; S13: obtain the pathogen content value of the culture pond within the time threshold, and divide the time threshold into k sub-time periods, k is a natural number greater than zero, obtain the difference between the pathogen content values in the two connected sub-time periods, and mark it as the pathogen content change value, and compare and analyze the pathogen content change value with the preset pathogen content change value threshold, obtain the total number of sub-time periods corresponding to the pathogen content change value greater than the preset pathogen content change value, and mark it as the pathogen risk number, at the same time, obtain the total number of sub-time periods corresponding to the pathogen content change value less than or equal to the preset pathogen content change value, and mark it as the pathogen normal number, and then mark the ratio of the pathogen risk number to the pathogen normal number as the pathogen hazard value.

[0008] Preferably, the water quality monitoring unit performs water quality evaluation and analysis process on the water quality data as follows: According to the formula obtain the water quality quality evaluation coefficient of the culture pond, wherein a1, a2 and a3 are respectively the preset proportion factor coefficients of the dissolved oxygen risk value, the ammonia nitrogen risk proportion value and the pathogen hazard value, a4 is a preset correction coefficient, the value is 1.386, a1, a2 and a3 are all positive numbers greater than zero, S is the water quality quality evaluation coefficient of the culture pond, and the water quality quality evaluation coefficient S is compared and analyzed with the preset water quality quality evaluation coefficient threshold recorded and stored inside: If the water quality quality evaluation coefficient S is less than or equal to the preset water quality quality evaluation coefficient threshold, an unqualified signal is generated; if the water quality quality evaluation coefficient S is less than or equal to the preset water quality quality evaluation coefficient threshold, a qualified signal is generated.

[0009] Preferably, the comprehensive delay evaluation analysis process of the delay evaluation unit is as follows: The delay risk value and the water quality evaluation coefficient S within the time threshold are obtained, the delay risk value is marked as YW, the delay evaluation remediation rate P is obtained according to the formula, and the delay evaluation remediation rate P is compared and analyzed with the preset delay evaluation remediation rate interval recorded in the internal storage: If the delay evaluation remediation rate is greater than the maximum value in the preset delay evaluation remediation rate interval, a first-level remediation signal is generated; if the delay evaluation remediation rate is within the preset delay evaluation remediation rate interval, a second-level remediation signal is generated; and if the delay evaluation remediation rate is less than the minimum value in the preset delay evaluation remediation rate interval, a third-level remediation signal is generated.

[0010] The beneficial effects of the present application are as follows: The present application is by collecting the state data of the detection equipment inside the breeding pond and performing fault risk evaluation analysis to judge the running state of the detection equipment, so as to timely early warning maintenance, improve the working efficiency of the detection equipment, at the same time, it is helpful to improve the supervision of the breeding pond, and the delay risk value and the maximum span deviation value are evaluated from two dimensions, which improves the accuracy of the analysis result, and when the detection equipment is normal, the water quality of the breeding pond is monitored, so as to timely control the water quality, improve the water quality of the breeding pond, and further improve the survival rate and growth rate of the fish fry; The present application also combines the unqualified water quality and the abnormal condition of the detection equipment, and analyzes in a progressive manner, so as to clearly understand the loss and harm degree caused by the delay of the data leading to the water quality of the breeding pond cannot be timely treated, and further accurately and reasonably manage the remediation, reduce unnecessary loss risk, in addition, through the data feedback and supervision, the early warning performance of the equipment is supervised, so as to ensure the normal early warning of the equipment, and the timeliness and effectiveness of the early warning, improve the early warning effect of the equipment, and further help to improve the timely maintenance of the alarm lamp by the staff, so as to reduce the alarm failure risk probability, and improve the early warning effect of the breeding pond. BRIEF DESCRIPTION OF DRAWINGS

[0011] The present application will be further described below in combination with the drawings; Fig. 1 is a system flowchart of the present application; Fig. 2 is a method flowchart of the present application. DETAILED DESCRIPTION

[0012] The technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. Example 1

[0013] Please see Figs. 1-2 As shown, this invention is a daily management system for aquaculture based on data analysis, including a control platform, a water quality monitoring unit, a risk collection unit, an early warning execution unit, a management unit, and a delay assessment unit. The control platform has a one-way communication connection with the water quality monitoring unit and the risk collection unit, a one-way communication connection with the early warning execution unit, a one-way communication connection with the delay assessment unit, a one-way communication connection with the delay assessment unit, and a one-way communication connection with the management unit. When the control platform generates an operation and management instruction, it sends the instruction to the water quality monitoring unit and the risk acquisition unit. Upon receiving the instruction, the risk acquisition unit immediately collects status data from the monitoring equipment inside the aquaculture pond, including processing time and operating current. The unit then performs a fault risk assessment analysis on the data to determine the operational status of the monitoring equipment, enabling timely early warning and maintenance, improving the equipment's efficiency, and enhancing the monitoring of the aquaculture pond. The specific fault risk assessment analysis process is as follows: Detection equipment is installed in each area of ​​the breeding pond within the breeding area. The sensors inside each detection device are labeled i, where i is a natural number greater than zero. The duration from the start to the end of the breeding period is collected and marked as a time threshold. The duration from the start of data collection to the information transmission time of each data sensor within the time threshold is obtained and marked as the processing duration. The processing duration is compared with a preset processing duration threshold. If the processing duration exceeds the preset threshold, the portion exceeding the threshold is obtained and marked as a time lag value, labeled SJi. The number of sensors whose time lag value SJi exceeds the preset time lag value threshold is obtained and marked as the lag risk number. The average value of the portion of time lag value SJi exceeding the preset time lag value threshold is obtained and marked as the average risk value. The product of the lag risk number and the average risk value is marked as the delay risk value. It should be noted that the larger the delay risk value, the worse the operating status of each sensor in the detection equipment, and the greater the error in the detection data results. The processing time is divided into o sub-time nodes, where o is a natural number greater than zero. The operating current of each data acquisition sensor within each sub-time node is obtained and labeled YDio. A set A of operating currents YDio is constructed. The difference between two consecutive subsets in set A is obtained and marked as a floating value. The ratio of the number of floating values ​​exceeding a preset floating value to the total number of floating values ​​is obtained and marked as a risk bias value FPi. The maximum and minimum values ​​of risk bias values ​​FPi within the processing time are obtained, and the difference between the maximum and minimum values ​​of risk bias values ​​FPi is marked as the maximum span bias value. It should be noted that the larger the maximum span bias value, the worse the operating status of the detection equipment, the greater the risk of sensor failure within the detection equipment, and the greater the risk of abnormal data acquisition. The delay risk value and the maximum span bias value are compared and analyzed with the preset delay risk value threshold and the preset maximum span bias value threshold stored internally. If the delay risk value is less than or equal to the preset delay risk value threshold, and the maximum span bias value is less than or equal to the preset maximum span bias value threshold, then a normal signal is generated and sent to the water quality monitoring unit. If the delay risk value exceeds the preset delay risk value threshold, or the maximum span deviation value exceeds the preset maximum span deviation value threshold, an abnormal signal is generated and sent to the early warning execution unit and the delay assessment unit. Upon receiving the abnormal signal, the early warning execution unit immediately controls the alarm light on the outer shell of the detection equipment to flash yellow, thereby reminding the staff to promptly inspect the sensors inside the detection equipment in order to improve the accuracy and effectiveness of data collection. Example 2

[0014] Upon receiving operational instructions and normal signals, the water quality monitoring unit immediately collects water quality data from the aquaculture ponds. This data includes dissolved oxygen rate, ammonia nitrogen content, and pathogen content. The unit then performs a water quality assessment and analysis to determine if the water quality in the ponds is up to standard, enabling timely water quality control and ultimately improving the survival rate and growth rate of the fish. The specific water quality assessment and analysis process is as follows: The aquaculture pond is divided into h sub-regions, where h is a natural number greater than zero. The dissolved oxygen rate value of each sub-region within a time threshold is obtained, and the average dissolved oxygen rate value within the time threshold is obtained and marked as the average dissolved oxygen value. The average dissolved oxygen value in the aquaculture pond within each sub-time period is obtained, and a rectangular coordinate system is established with time as the X-axis and average dissolved oxygen value as the Y-axis. The average dissolved oxygen value curve is plotted by plotting points, and the starting value, maximum peak value, minimum trough value, and end value of the curve are obtained. After removing the starting value, maximum peak value, minimum trough value, and end value, the average dissolved oxygen value corresponding to the remaining sub-time periods is obtained and marked as the dissolved oxygen risk value. It should be noted that the larger or smaller the dissolved oxygen risk value, the greater the risk of fish farming. The ammonia nitrogen content values ​​of each sub-region block within the time threshold are obtained. The ammonia nitrogen content values ​​are compared with the preset ammonia nitrogen content threshold. If the ammonia nitrogen content value is greater than the preset ammonia nitrogen content threshold, the total area of ​​the sub-region block corresponding to the ammonia nitrogen content value greater than the preset ammonia nitrogen content threshold is obtained and marked as the risk area. The ratio of the risk area to the total area of ​​the aquaculture pond is marked as the ammonia nitrogen risk ratio value. It should be noted that the larger the ammonia nitrogen risk ratio value, the greater the risk to the survival of the aquaculture. The pathogen content values ​​of the aquaculture pond within a time threshold are obtained, and the time threshold is divided into k sub-time periods, where k is a natural number greater than zero. The difference between the pathogen content values ​​in two consecutive sub-time periods is obtained and marked as the pathogen content change value. The pathogen content change value is compared and analyzed with the preset pathogen content change value threshold. The total number of sub-time periods corresponding to pathogen content change values ​​greater than the preset pathogen content change value is obtained and marked as the pathogen risk number. At the same time, the total number of sub-time periods corresponding to pathogen content change values ​​less than or equal to the preset pathogen content change value is obtained and marked as the pathogen normal number. Then, the ratio of the pathogen risk number to the pathogen normal number is marked as the pathogen hazard value. The dissolved oxygen risk value, ammonia nitrogen risk ratio value, and pathogen hazard value are respectively labeled as RY, DF, and XW. According to the formula The water quality assessment coefficients of the aquaculture pond are obtained. Here, a1, a2, and a3 are preset proportional factor coefficients for dissolved oxygen risk value, ammonia nitrogen risk ratio value, and pathogen hazard value, respectively. These proportional factor coefficients are used to correct deviations in the calculation of various parameters, thus making the calculation more accurate and the parameter data more reliable. a4 is a preset correction coefficient with a value of 1.386. a1, a2, and a3 are all positive numbers greater than zero. S is the water quality assessment coefficient of the aquaculture pond. The water quality assessment coefficient S is then compared and analyzed with the preset water quality assessment coefficient thresholds stored internally. If the water quality assessment coefficient S is less than or equal to the preset water quality assessment coefficient threshold, an unqualified signal is generated and sent to the delay assessment unit. If the water quality assessment coefficient S is less than or equal to the preset water quality assessment coefficient threshold, a qualified signal is generated and sent to the early warning execution unit. After receiving the qualified signal, the early warning execution unit immediately displays the aquaculture pond in green to intuitively understand the water quality of each aquaculture pond. Upon receiving abnormal or non-compliant signals, the delay assessment unit immediately retrieves the delay risk value and water quality assessment coefficient S. It then conducts a comprehensive delay assessment analysis based on these two values ​​to clearly understand the losses and harm caused by the inability to promptly address water quality issues in aquaculture ponds due to data delays. This allows for precise and appropriate management and remedial measures to reduce unnecessary losses and risks. The specific comprehensive delay assessment analysis process is as follows: Obtain the delay risk value and water quality assessment coefficient S within the time threshold, and label the delay risk value as YW; According to the formula The delay assessment recovery rate is obtained, where b1 and b2 are the preset weighting coefficients of the delay risk value and water quality assessment coefficient, respectively, b3 is the preset fault tolerance compensation coefficient, and b1, b2, and b3 are all positive numbers greater than zero. P is the delay assessment recovery rate, and the delay assessment recovery rate P is compared and analyzed with the preset delay assessment recovery rate range entered and stored internally. If the delay assessment recovery rate is greater than the maximum value in the preset delay assessment recovery rate range, a first-level recovery signal is generated; If the delay assessment recovery rate is within the preset delay assessment recovery rate range, a secondary recovery signal is generated; If the delay assessment recovery rate is less than the minimum value in the preset delay assessment recovery rate range, a three-level recovery signal is generated. The recovery management level corresponding to the first-level recovery signal, the second-level recovery signal, and the third-level recovery signal decreases in that order. The first-level recovery signal, the second-level recovery signal, and the third-level recovery signal are sent to the management unit. Upon receiving the first-level recovery signal, the second-level recovery signal, and the third-level recovery signal, the management unit immediately formulates the preset management plan corresponding to the first-level recovery signal, the second-level recovery signal, and the third-level recovery signal, so as to avoid losses and harms caused by the inability to treat the water quality of the aquaculture pond in a timely manner due to data delays. In this way, precise and reasonable management and recovery can be carried out to reduce unnecessary loss risks. Upon receiving an abnormal signal, the early warning execution unit immediately collects the alarm light's response data, including the alarm light's brightness characteristic curve and line loss characteristic curve. The unit then performs monitoring and feedback analysis on the response data to determine whether the alarm light is functioning correctly, thus ensuring its operational efficiency. The specific monitoring and feedback analysis process is as follows: The time from when the early warning execution unit receives the abnormal signal to when the alarm light starts working is collected and marked as the execution duration. At the same time, the brightness value characteristic curve of the alarm light within the time threshold is obtained. The ratio of the number of segments corresponding to the brightness decrease segment to the number of segments corresponding to the brightness increase segment is obtained from the brightness value characteristic curve and marked as the dark trend value. It should be noted that the larger the value of the dark trend value, the lower the efficiency of the alarm light and the worse the early warning efficiency. The product of the dark trend value and the execution duration is marked as the execution risk value. The characteristic curve of the line loss value of the alarm light within the time threshold is obtained, and a preset threshold curve is plotted on the characteristic curve of the line loss value. The area enclosed by the line segment of the line loss value characteristic curve above the preset threshold curve and the preset threshold curve is obtained and marked as an abnormal loss value. It should be noted that the larger the value of the abnormal loss value, the greater the stability risk when the alarm light is working. The execution risk value and the abnormal loss value are compared and analyzed with the preset execution risk value threshold and the preset abnormal loss value threshold recorded and stored in its internal database. If the execution risk value is less than or equal to the preset execution risk value threshold, and the abnormal loss value is less than or equal to the preset abnormal loss value threshold, then no signal will be generated; If the execution risk value exceeds the preset execution risk value threshold, or the abnormal loss value exceeds the preset abnormal loss value threshold, a risk signal is generated and sent to the management unit via the delay assessment unit. Upon receiving the risk signal, the management unit immediately issues a warning in the form of the text "Warning Abnormality". This helps staff to maintain the alarm lights in a timely manner, thereby reducing the probability of alarm failure and improving the early warning effect on the aquaculture pond. Example 3

[0015] Data-driven aquaculture management methods include the following steps: Step 1: Collect status data of the detection equipment inside the aquaculture pond and conduct a fault risk assessment analysis to determine whether the detection equipment is operating normally. If it is normal, proceed to Step 2; if it is abnormal, proceed to Step 3 and Step 4. Step 2: Collect water quality data inside the breeding pond and conduct water quality assessment and analysis to determine whether the water quality in the breeding pond is up to standard, so as to issue timely water quality warnings. If the water quality is up to standard, the breeding pond will be marked in green. If the water quality is not up to standard, proceed to Step 3. Step 3: Retrieve the delay risk value and water quality assessment coefficient S, and then conduct a comprehensive delay assessment analysis on the delay risk value and water quality assessment coefficient S in order to understand the degree of harm caused by the failure to treat the substandard water quality in the aquaculture pond due to abnormal testing equipment. Step 4: Collect the alarm light's response data and perform monitoring and feedback analysis to determine whether the alarm light is providing normal warnings, thus ensuring the alarm light's working efficiency. At the same time, provide warnings through text feedback, which helps staff to maintain the alarm light in a timely manner.

[0016] In summary, this invention collects status data from the monitoring equipment inside the aquaculture pond and performs fault risk assessment analysis to determine the operational status of the equipment. This allows for timely early warning and maintenance, improving the efficiency of the monitoring equipment and enhancing the supervision of the aquaculture pond. The assessment is conducted from two dimensions: delay risk value and maximum span bias value, improving the accuracy of the analysis results. When the monitoring equipment is functioning normally, water quality monitoring in the aquaculture pond allows for timely water quality control, improving water quality and thus contributing to increased fish survival and growth rates. Furthermore, by combining analysis of substandard water quality and abnormal monitoring equipment conditions in a progressive manner, the invention clearly understands the losses and harms caused by delayed water quality management due to data delays, enabling precise and reasonable management and remedial measures to reduce unnecessary losses. In addition, data feedback and monitoring monitor the early warning performance of the equipment to ensure normal and timely early warning, improving the early warning effect. This, in turn, helps staff maintain the alarm lights promptly, reducing the probability of alarm failure and enhancing the early warning effect for the aquaculture pond.

[0017] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; it is acceptable as long as it does not affect the ratio between the parameter and the quantized value. The size of the coefficient is to provide a specific value obtained by quantizing each parameter to facilitate subsequent comparison. The size of the coefficient depends on the amount of sample data and the corresponding operating coefficient initially set by those skilled in the art for each set of sample data; it is acceptable as long as it does not affect the ratio between the parameter and the quantized value.

[0018] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A daily management system for aquaculture based on data analysis, characterized in that, It includes a control platform, a water quality monitoring unit, a risk data collection unit, an early warning execution unit, a management unit, and a delay assessment unit; When the control platform generates an operation and management instruction, it sends the instruction to the water quality monitoring unit and the risk collection unit. Upon receiving the instruction, the risk collection unit immediately collects the status data of the monitoring equipment inside the aquaculture pond, including processing time and operating current. It then performs a fault risk assessment and analysis on the status data, sends the obtained normal signals to the water quality monitoring unit, and sends the obtained abnormal signals to the early warning execution unit and the delay assessment unit. Upon receiving an abnormal signal, the early warning execution unit immediately collects the response data of the alarm light, including the characteristic curve of the brightness value of the alarm light and the characteristic curve of the line loss value. The unit then monitors and analyzes the response data and sends the obtained risk signal to the management unit via the delay assessment unit. Upon receiving the operation and management instructions and normal signals, the water quality monitoring unit immediately collects water quality data inside the aquaculture pond. The water quality data includes dissolved oxygen rate, ammonia nitrogen content, and pathogen content. The water quality data is collected, processed, and analyzed to obtain unqualified signals, qualified signals, and water quality assessment coefficient S. The unqualified signal is sent to the delay assessment unit, and the qualified signal is sent to the early warning execution unit. Upon receiving abnormal or unqualified signals, the delay assessment unit immediately retrieves the delay risk value and water quality assessment coefficient S, and then performs a comprehensive delay assessment analysis on the delay risk value and water quality assessment coefficient S, sending the resulting first-level remedial signal, second-level remedial signal, and third-level remedial signal to the management unit.

2. The daily management system for fishery aquaculture based on data analysis according to claim 1, characterized in that, The fault risk assessment and analysis process of the data acquisition risk unit is as follows: SS1: Detection equipment is installed in each area of ​​the breeding pond within the breeding area. The sensors inside the detection equipment that collect data are labeled i, where i is a natural number greater than zero. The duration between the start and end of the breeding in the pond is collected and marked as a time threshold. The duration between the start of data collection and the information transmission time of each data sensor within the time threshold is obtained and marked as the processing duration. At the same time, the processing duration is compared with the preset processing duration threshold. If the processing duration is greater than the preset processing duration threshold, the portion of the processing duration exceeding the preset processing duration threshold is obtained and marked as a time lag value, labeled SJi. The number of sensors corresponding to the time lag value SJi exceeding the preset time lag value threshold is obtained and marked as the lag risk number. At the same time, the average value of the portion of the time lag value SJi exceeding the preset time lag value threshold is obtained and marked as the average risk value. The product of the lag risk number and the average risk value is marked as the delay risk value. SS2: Divide the processing time into o sub-time nodes, where o is a natural number greater than zero. Obtain the operating current of each data acquisition sensor within each sub-time node, labeled as YDio. Construct a set A of operating currents YDio. Obtain the difference between two consecutive subsets in set A and mark it as a floating value. Obtain the ratio of the number of floating values ​​exceeding the preset floating value to the total number of floating values ​​and mark it as a risk bias value FPi. Obtain the maximum and minimum values ​​of risk bias value FPi within the processing time and mark the difference between the maximum and minimum values ​​of risk bias value FPi as the maximum span bias value. SS3: Compare and analyze the delay risk value and maximum span bias value with the preset delay risk value threshold and preset maximum span bias value threshold that are entered and stored internally. If the delay risk value is less than or equal to the preset delay risk value threshold, and the maximum span bias value is less than or equal to the preset maximum span bias value threshold, then a normal signal is generated. If the delay risk value is greater than the preset delay risk value threshold, or the maximum span bias value is greater than the preset maximum span bias value threshold, an abnormal signal will be generated.

3. The daily management system for fishery aquaculture based on data analysis according to claim 1, characterized in that, The monitoring feedback analysis process of the early warning execution unit is as follows: Step 1: Collect the time between the moment the early warning execution unit receives the abnormal signal and the moment the alarm light starts working, and mark it as the execution duration. At the same time, obtain the brightness value characteristic curve of the alarm light within the time threshold, obtain the ratio of the number of segments corresponding to the brightness decrease segment to the number of segments corresponding to the brightness increase segment from the brightness value characteristic curve, and mark it as the dark trend value. Mark the product of the dark trend value and the execution duration as the execution risk value. Step 2: Obtain the characteristic curve of the line loss value of the alarm light within the time threshold, and plot the preset threshold curve on the characteristic curve of the line loss value. Obtain the area enclosed by the line segment of the line loss value characteristic curve above the preset threshold curve and the preset threshold curve, and mark it as an abnormal loss value. Then, compare and analyze the execution risk value and the abnormal loss value with the preset execution risk value threshold and the preset abnormal loss value threshold that are recorded and stored internally. If the execution risk value is less than or equal to the preset execution risk value threshold, and the abnormal loss value is less than or equal to the preset abnormal loss value threshold, then no signal will be generated; If the execution risk value is greater than the preset execution risk value threshold, or the abnormal loss value is greater than the preset abnormal loss value threshold, a risk signal will be generated.

4. The daily management system for fishery aquaculture based on data analysis according to claim 1, characterized in that, The water quality monitoring unit collects and processes water quality data as follows: S1: Divide the aquaculture pond into h sub-regions, where h is a natural number greater than zero. Obtain the dissolved oxygen rate value of each sub-region within the time threshold. Obtain the mean of the dissolved oxygen rate values ​​within the time threshold and mark it as the average dissolved oxygen value. Obtain the average dissolved oxygen value in the aquaculture pond within each sub-time period. Establish a rectangular coordinate system with time as the X-axis and average dissolved oxygen value as the Y-axis. Plot the average dissolved oxygen value curve by plotting points. Obtain the starting value, maximum peak value, minimum trough value, and end value in the curve. After removing the starting value, maximum peak value, minimum trough value, and end value, obtain the mean of the average dissolved oxygen value corresponding to the remaining sub-time periods and mark it as the dissolved oxygen risk value. S12: Obtain the ammonia nitrogen content value of each sub-region block within the time threshold, compare the ammonia nitrogen content value with the preset ammonia nitrogen content value threshold, if the ammonia nitrogen content value is greater than the preset ammonia nitrogen content value threshold, obtain the total area of ​​the sub-region block corresponding to the ammonia nitrogen content value greater than the preset ammonia nitrogen content value threshold, and mark it as the risk area, and mark the ratio of the risk area to the total area of ​​the aquaculture pond as the ammonia nitrogen risk ratio value; S13: Obtain the pathogen content value of the aquaculture pond within the time threshold, and divide the time threshold into k sub-time periods, where k is a natural number greater than zero. Obtain the difference between the pathogen content values ​​in two consecutive sub-time periods and mark it as the pathogen content change value. Compare and analyze the pathogen content change value with the preset pathogen content change value threshold. Obtain the total number of sub-time periods corresponding to pathogen content change values ​​greater than the preset pathogen content change value and mark it as the pathogen risk number. At the same time, obtain the total number of sub-time periods corresponding to pathogen content change values ​​less than or equal to the preset pathogen content change value and mark it as the pathogen normal number. Then, mark the ratio of the pathogen risk number to the pathogen normal number as the pathogen hazard value.

5. The daily management system and method for fishery aquaculture based on data analysis according to claim 1, characterized in that, The process by which the water quality monitoring unit conducts water quality assessment and analysis on the water quality data is as follows: According to the formula The water quality assessment coefficients of the aquaculture pond are obtained, where a1, a2, and a3 are preset proportional factor coefficients for dissolved oxygen risk value, ammonia nitrogen risk ratio value, and pathogen hazard value, respectively; a4 is a preset correction coefficient with a value of 1.386; and a1, a2, and a3 are all positive numbers greater than zero. S is the water quality assessment coefficient of the aquaculture pond. The water quality assessment coefficient S is then compared and analyzed with the preset water quality assessment coefficient thresholds entered and stored internally. If the water quality assessment coefficient S is less than or equal to the preset water quality assessment coefficient threshold, an unqualified signal is generated; if the water quality assessment coefficient S is less than or equal to the preset water quality assessment coefficient threshold, a qualified signal is generated.

6. The daily management system for fishery aquaculture based on data analysis according to claim 1, characterized in that, The comprehensive delay assessment and analysis process of the delay assessment unit is as follows: The delay risk value and water quality assessment coefficient S within the time threshold are obtained, and the delay risk value is labeled as YW. The delay assessment remediation rate P is obtained according to the formula, and the delay assessment remediation rate P is compared and analyzed with the preset delay assessment remediation rate interval internally entered and stored. If the delay assessment recovery rate is greater than the maximum value in the preset delay assessment recovery rate range, a first-level recovery signal is generated; if the delay assessment recovery rate is within the preset delay assessment recovery rate range, a second-level recovery signal is generated; if the delay assessment recovery rate is less than the minimum value in the preset delay assessment recovery rate range, a third-level recovery signal is generated.

7. A data-driven method for daily management of aquaculture, employing the data-driven aquaculture daily management system as described in claims 1-6, characterized in that... Includes the following steps: Step 1: Collect status data of the detection equipment inside the aquaculture pond and conduct a fault risk assessment analysis to determine whether the detection equipment is operating normally. If it is normal, proceed to Step 2; if it is abnormal, proceed to Step 3 and Step 4. Step 2: Collect water quality data inside the breeding pond and conduct water quality assessment and analysis to determine whether the water quality in the breeding pond is up to standard, so as to issue timely water quality warnings. If the water quality is up to standard, the breeding pond will be marked in green. If the water quality is not up to standard, proceed to Step 3. Step 3: Retrieve the delay risk value and water quality assessment coefficient S, and then conduct a comprehensive delay assessment analysis on the delay risk value and water quality assessment coefficient S in order to understand the degree of harm caused by the failure to treat the substandard water quality in the aquaculture pond due to abnormal testing equipment. Step 4: Collect the alarm light's response data and perform monitoring and feedback analysis to determine whether the alarm light is providing normal warnings, thus ensuring the alarm light's working efficiency. At the same time, provide warnings through text feedback, which helps staff to maintain the alarm light in a timely manner.

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