Low-sensitivity area identification method for centralized ventilation control of roadway type incubator

Abstract

The invention belongs to the technical field of data processing, and provides a low-sensitivity area identification method for centralized ventilation control of a roadway type incubator, which specifically comprises the following steps of: firstly, identifying an active ventilation area and a passive ventilation area in the roadway type incubator, and acquiring a temperature image of the passive ventilation area through an infrared thermal imager; the method comprises the steps of extracting a temperature array of an incubation position through a temperature image, then regularly collecting the temperature array and carrying out passive ventilation temperature failure analysis to obtain a failure analysis value, and finally identifying a low-sensitivity area in a roadway type incubator through the failure analysis value, namely an area with temperature regulation quality defects. The failure analysis value is obtained by performing time sequence analysis on the temperature array of each incubation position, so that the temperature control defect degree suffered by each incubation position in the scene of passive ventilation which cannot guarantee complete air volume coverage in the incubation process of the roadway type incubator can be effectively quantified; therefore, identification mathematical support is provided for identifying the low-sensitivity area of the passive ventilation area, the hatching failure risk of the roadway type hatching machine is effectively reduced, the working flexibility and self-adaptability of the hatching machine in an automatic popularization scene are improved, and the breeding management quality and efficiency are further improved.

Description

Technical Field

[0001] The invention belongs to the technical field of data processing and automatic control, and in particular relates to a low-sensitivity area identification method for centralized ventilation control of a lane-type incubator. Background Art

[0002] In the large-scale hatching industry, the production performance of the hatchery is closely related to the ultimate economic benefits achieved by the breeder farm. The tunnel incubator effectively combines the temperature changes of poultry eggs at different hatching stages, controls the temperature through the scientific layout of the ventilation system, and can take into account the temperature balance in the incubator and the control accuracy of oxygen and humidity. The tunnel incubator has the characteristics of a large number of hatching eggs and the dynamic adjustment characteristics of zoned ventilation control. Therefore, the control of the ideal hatching temperature and humidity is inseparable from the energy consumption of the ventilation system. In the scenario of large-scale hatching, in order to effectively reduce the production cost caused by the energy consumption of the ventilation system, the commonly used technical method in this field is to apply a centralized ventilation control method in the tunnel incubator. This method centralizes the control of the ventilation system and arranges the eggs on different hatching shelves according to the different hatching stages, so that each hatching shelf in the entire incubator is not ventilated separately, and the eggs with high temperature requirements are arranged in the upwind position and the eggs with low temperature requirements are arranged in the downwind position. Although energy-saving effects can be achieved, it will cause the temperature control accuracy of the shelves in passive ventilation to be lost, especially when the system cannot quickly adjust the airflow direction. The local temperature fluctuation and the decrease in gas circulation effect increase the risk of hatching failure. This area with insufficient temperature supply quality is the low-sensitivity area in the incubator. Therefore, there is an urgent need for a low-sensitivity area identification method for centralized ventilation control of tunnel incubators to identify and warn the locations in the tunnel incubators where there is a risk of hatching failure caused by insufficient ventilation energy efficiency. Summary of the invention

[0003] The purpose of the present invention is to propose a low-sensitivity area identification method for centralized ventilation control of a lane-type incubator to solve one or more technical problems existing in the prior art and at least provide a beneficial option or create conditions.

[0004] In order to achieve the above object, according to one aspect of the present invention, a method for identifying low-sensitivity areas of centralized ventilation control in a lane-type incubator is provided, the method comprising the following steps: Identify active and passive ventilation zones in multi-stage incubators; The temperature image of the passive ventilation area is obtained by an infrared thermal imager, and the temperature array of the incubation position is extracted by the temperature image; Regularly collect temperature arrays and perform passive ventilation temperature failure analysis to obtain failure analysis values; Identify low-sensitivity areas in multi-stage incubators, i.e. areas with deficient temperature regulation quality, through failure analysis values; Furthermore, a method for identifying active ventilation zones and passive ventilation zones in a tunnel incubator is as follows: a plurality of incubation racks are arranged in the tunnel incubator, each incubation rack is arranged along the wind and ventilation direction, each incubation rack has a corresponding air outlet, and each incubation rack measures the passive ventilation temperature through a preset temperature sensor. When the passive ventilation temperature exceeds the preset temperature range of the incubation rack, the corresponding air outlet of the incubation rack is activated, and hot air or cold air for regulating the air temperature is output. If the air outlet is activated, the incubation rack belongs to the active wind zone, otherwise it is a passive wind zone.

[0005] The passive ventilation temperature refers to the air temperature measured on the incubation shelf before the air outlet is activated, that is, the air temperature when the air outlet corresponding to the incubation shelf is not activated.

[0006] The reason why each incubation layer is arranged along the wind and ventilation direction is to ensure the uniformity and stability of the incubation environment. This arrangement can make the air circulation smoother, ensuring that each incubation layer can obtain uniform temperature and humidity, thereby improving the hatching rate and hatching quality. In addition, one of the more significant effects is to reduce the loss of heat and humidity and thus reduce energy consumption.

[0007] The temperature ranges preset on the incubation shelves are preset according to the incubation stages of the eggs.

[0008] Furthermore, a temperature image of the passive ventilation zone is obtained by an infrared thermal imager, and a method for extracting a temperature array of the hatching position by the temperature image is as follows: each hatching shelf in the passive ventilation zone is selected as a monitoring shelf, and each poultry egg being hatched in the monitoring shelf is defined as a hatching position; a thermal image of the monitoring shelf is collected by an infrared thermal imager, and a thermal image capture domain of each hatching position is captured by a preset image area or target detection algorithm, and the average value in the thermal image capture domain is recorded as a constant temperature value, and the ratio of the number of pixels with temperature values ​​lower than the constant temperature value to the remaining pixels in the thermal image capture domain is a low temperature point ratio, and the temperature array of the hatching position is composed of the ratio of the constant temperature value to the low temperature value.

[0009] The preset image area refers to: when the camera angle is the same, the area of ​​each hatching position in the thermal image measured each time of the same hatching rack can be repeatedly located by presetting the area of ​​each hatching position in advance; the target detection algorithm is any one of the Faster R-CNN or YOLO algorithms; the infrared thermal imager is an infrared thermal imaging camera.

[0010] Furthermore, the temperature array is collected regularly and the passive ventilation temperature failure analysis is performed. The method for obtaining the failure analysis value is as follows: each moment of obtaining the temperature array is recorded as the monitoring moment, and each temperature array in the egg-turning cycle is obtained to perform the passive ventilation temperature failure analysis. When the low temperature value ratio at a monitoring moment is greater than 0.5, the monitoring moment is marked as a high ratio point, otherwise it is a low ratio point; the moment when each extreme value in each low temperature value ratio in the egg-turning cycle appears is the ratio inflection point, the number of monitoring moments between any two consecutive ratio inflection points is the inflection point length, and the average value of all inflection point lengths is recorded as the inflection point standard length InfLen; Define any consecutive high-proportion points as high-proportion intervals; The above definition also has a constraint condition in the process of constructing the high-proportion interval, that is, the previous monitoring time of the first high-proportion point of the high-proportion interval and the next monitoring time of the high-proportion point corresponding to the end of the high-proportion interval are not high-proportion points. This constraint condition is set to prevent the occurrence of intersecting high-proportion intervals, so that each high-proportion interval is independent of each other; If the length of the high-proportion interval is less than 3, then define each high-proportion point in the high-proportion interval as a compatible point and remove the high-proportion interval; define any high-proportion interval as the current high-proportion interval, traverse in reverse time from the current high-proportion interval until the first high-proportion interval is found, and merge all low-proportion points and compatible points in the traversal process with the current high-proportion interval to obtain the analysis interval; Since the analysis interval is formed by merging the current high-proportion interval and several detection moments traversed in the reverse time direction, the analysis interval is actually a continuous time interval.

[0011] For the same analysis interval, the average value of each constant temperature value of the high-proportion interval corresponding to the analysis interval is recorded as the first constant temperature level. The monitoring moment when the constant temperature value is less than the first constant temperature level is defined as a low temperature point, otherwise it is a non-low temperature point. If the InfLen monitoring moments in the reverse time direction of a low temperature point are all non-low temperature points, it is defined as a low temperature inflection point; The maximum value among the constant temperature values ​​searched in the reverse time direction of the low-temperature inflection point is recorded as the inverse peak value, the difference between the constant temperature value of the low-temperature inflection point and the inverse peak value is the first product, and the cumulative value of all first products is the interval failure amount; the ratio of the length of the analysis interval to the number of low-temperature inflection points is the inflection point density; the weighted average of the corresponding interval failure amounts of all analysis intervals is calculated using the inflection point density as the weight, and the obtained value is the failure analysis value of the incubation position.

[0012] The length of the analysis interval refers to the number of monitoring moments in the analysis interval; Due to the solidification of the interval division form in the time series in the method of calculating the failure analysis value through the analysis interval, the risk of underfitting will appear in the analysis interval. However, the existing technology cannot solve the underfitting problem caused by the solidification of the division form, especially when it is at the edge of the incubation layer, the low temperature value ratio changes frequently, which makes the underfitting problem more serious. Therefore, in order to better solve this problem and eliminate the distortion of the quantitative results caused by underfitting, the present invention proposes a more preferred solution as follows: Furthermore, the temperature array is collected regularly and the passive ventilation temperature failure analysis is performed. The method for obtaining the failure analysis value is: record each time when the temperature array is obtained as the monitoring time, and obtain each temperature array in the egg turning cycle to perform the passive ventilation temperature failure analysis: The egg turning cycle refers to the time interval for the incubator to regularly turn over the eggs during the incubation process. Each egg turning cycle of the incubator is recorded as Rv.TP, and the value of the egg turning cycle is between 0.5 hours and 3 hours; Write each low temperature value ratio into a sequence as a low ratio sequence, obtain each extreme value point in the low ratio sequence as a first marked extreme value, and take the period between any two first marked extreme values ​​corresponding to the monitoring time as an extreme value interval. The standard deviation of each low temperature value ratio in the extreme value interval is recorded as a domain step variable; calculate the product of the minimum value of all first marked extreme values ​​in the extreme value interval and the extreme difference of all constant temperature values, and record it as the failure product LapsV of the extreme value interval; The number of the first marked extreme values ​​in the extreme value interval except the first marked extreme values ​​at the two ends of the extreme value interval is recorded as the interval marking capacity, the set of interval marking capacities of all extreme value intervals is recorded as the marking capacity set, and all extreme value intervals with 0 number of marking capacity sets are eliminated; the interval composed of the median and lower quartile of the marking capacity set is recorded as the low capacity value domain, if the interval marking capacity value of an extreme value interval is within the low capacity value domain, it is defined as a low capacity interval; the interval composed of the median and upper quartile of the marking capacity set is recorded as the high capacity value domain, if the interval marking capacity value of an extreme value interval is within the high capacity value domain, it is defined as a high capacity interval; For a low-capacity interval, a mapping relationship is formed between the low-capacity interval and each high-capacity interval, and within the same mapping relationship, a first mapping condition is set as follows: there are at least two overlapping first marking extreme values ​​between the low-capacity interval and the high-capacity interval; wherein the overlapping first marking extreme values ​​refer to first marking extreme values ​​existing in the low-capacity interval and in the high-capacity interval; The second mapping condition is: the domain step variable of the low capacity interval is greater than the domain step variable of the high capacity interval; The mapping relationship refers to the unique relationship between the two objects, the low-capacity interval and any high-capacity interval. In computer programming, the mapping relationship belongs to a class, which is composed of a low-capacity interval and a high-capacity interval. In each mapping relationship, there is no mapping relationship consisting of a repeated combination of low-capacity intervals and high-capacity intervals, that is, the mapping relationship is unique.

[0013] Screen and retain each mapping relationship that satisfies both the first mapping condition and the second mapping condition; calculate the failure analysis value FDV according to each mapping relationship: ; Where lg() is the logarithmic function with the natural constant 10 as the base, i1 is the accumulated variable, N_i1 is the number of retained mapping relationships, SV.L i1 and Laps V.L. i1 is the low temperature weight and failure product of the low capacity interval in the i1th mapping relationship, LapsV.H i1 is the failure product of the high capacity interval in the i1th mapping relationship; The calculation method of the low-temperature weight of the low-capacity interval is as follows: define the average value of all constant temperature values ​​in the low-capacity interval as the constant temperature mean, calculate the difference between the minimum value of all constant temperature means and the current constant temperature mean as the low-temperature weight; the current constant temperature mean is the constant temperature mean of the low-capacity interval corresponding to the low-temperature weight to be calculated.

[0014] Beneficial effect: Since the failure analysis value is obtained by time-series analysis of the temperature array of each incubation position, it can effectively quantify the degree of temperature control defects of each incubation position in the scenario of passive ventilation during the incubation process of the tunnel incubator, which cannot guarantee complete air volume coverage, thereby providing mathematical support for identifying low-sensitivity areas in the passive ventilation area.

[0015] Further, the method for identifying the low-sensitivity area in the tunnel incubator, that is, the area with defective temperature regulation quality, through the failure analysis value is: at a monitoring moment, if the percentile value of the failure analysis value of the hatching position in all the hatching positions exceeds 75%, the hatching position triggers the record at the monitoring moment, and the Z-score value of the failure analysis value of the hatching position at the monitoring moment in the analysis value record table is the defect record score, the moment of each trigger record is recorded as the record moment of the hatching position, and the average value of the record score at each record moment is obtained as the defect level of the hatching position; the analysis value record table is a collection of all failure analysis values ​​obtained on the same hatching shelf during the egg turning cycle; The egg turning cycle refers to the time interval for the incubator to regularly turn over the eggs during the incubation process. Each egg turning cycle of the incubator is recorded as Rv.TP, and the value of the egg turning cycle is between 0.5 hours and 3 hours; If the number of recorded moments of an incubation point accounts for more than 50% of the egg turning cycle, it is defined as meeting the high trigger condition. If the defect level is greater than or equal to the defect threshold, it is defined as meeting the high deviation condition. The defect threshold value range is 1.5-2.5. When an incubation point meets both the high trigger condition and the high deviation condition, it is defined as a low-sensitivity area, and its temperature regulation quality is considered to be defective. Each low-sensitivity area is sent to the administrator client.

[0016] Furthermore, it also includes abnormal handling in combination with low-sensitivity areas. The specific method is: when a low-sensitivity area is identified, the corresponding air outlet of the incubation shelf is opened to ensure uniform ventilation temperature of the incubation shelf; or, the poultry eggs in the low-sensitivity area are placed in the active ventilation area.

[0017] Preferably, all undefined variables in the present invention, if not clearly defined, can be manually set thresholds.

[0018] The present invention also provides a low-sensitivity area identification system for centralized ventilation control of a lane-type incubator, the low-sensitivity area identification system for centralized ventilation control of a lane-type incubator comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, the processor implementing the steps in the low-sensitivity area identification method for centralized ventilation control of a lane-type incubator when executing the computer program, the low-sensitivity area identification system for centralized ventilation control of a lane-type incubator can be run in computing devices such as desktop computers, laptop computers, PDAs, and cloud data centers, the executable system may include, but is not limited to, a processor, a memory, and a server cluster, the processor executing the computer program runs in the following system units: A ventilation zone identification unit is used to identify active ventilation zones and passive ventilation zones in a multi-lane incubator; A temperature data acquisition unit is used to obtain a temperature image of the passive ventilation area through an infrared thermal imager, and to extract a temperature array of the incubation position through the temperature image; The incubation position failure analysis unit is used to regularly collect the temperature array and perform passive ventilation temperature failure analysis to obtain failure analysis values; The low-sensitivity area identification unit is used to identify low-sensitivity areas in the multi-stage incubator, i.e. areas where the temperature regulation quality is defective, by means of failure analysis values.

[0019] The beneficial effects of the present invention are as follows: the present invention provides a method for identifying low-sensitivity areas of centralized ventilation control of tunnel-type incubators. Since the failure analysis value is obtained by time-series analysis of the temperature array of each incubation position, it can effectively quantify the degree of temperature control defects of each incubation position in the scenario of passive ventilation in the tunnel-type incubator process where complete air volume coverage cannot be guaranteed, thereby providing mathematical support for identifying low-sensitivity areas in passive ventilation areas, effectively reducing the risk of hatching failure of tunnel-type incubators, improving the flexibility and autonomous adaptability of incubators in scenarios of popularization of automation, and further improving the quality and efficiency of breeding management. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other features of the present invention will become more obvious by describing in detail the embodiments shown in the accompanying drawings. The same reference numerals in the accompanying drawings of the present invention represent the same or similar elements. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work. In the accompanying drawings: Figure 1 Shown is a flow chart of a low-sensitivity area identification method for centralized ventilation control of a tunnel-type incubator; Figure 2 Shown is a structural diagram of a low-sensitivity area identification system for centralized ventilation control of a tunnel-type incubator. DETAILED DESCRIPTION

[0021] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention, so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0022] Figure 1 The figure shows a flow chart of a low-sensitivity area identification method for centralized ventilation control of a lane-type incubator. Figure 1 To illustrate a method for identifying low-sensitivity areas of centralized ventilation control of a lane-type incubator according to an embodiment of the present invention, the method comprises the following steps:

[0023] Example 1 Identify active and passive ventilation zones in multi-stage incubators; The temperature image of the passive ventilation area is obtained by an infrared thermal imager, and the temperature array of the incubation position is extracted by the temperature image; Regularly collect temperature arrays and perform passive ventilation temperature failure analysis to obtain failure analysis values; Identify low-sensitivity areas in multi-stage incubators, i.e. areas with deficient temperature regulation quality, through failure analysis values; Furthermore, a method for identifying active ventilation zones and passive ventilation zones in a tunnel incubator is as follows: a plurality of incubation racks are arranged in the tunnel incubator, each incubation rack is arranged along the wind and ventilation direction, each incubation rack has a corresponding air outlet, and each incubation rack measures the passive ventilation temperature through a preset temperature sensor. When the passive ventilation temperature exceeds the preset temperature range of the incubation rack, the corresponding air outlet of the incubation rack is activated, and hot air or cold air for regulating the air temperature is output. If the air outlet is activated, the incubation rack belongs to the active wind zone, otherwise it is a passive wind zone.

[0024] Furthermore, the temperature image of the passive ventilation area is obtained by an infrared thermal imager, and the method of extracting the temperature array of the hatching position by the temperature image is as follows: each hatching shelf in the passive ventilation area is selected as a monitoring shelf, and each egg being hatched in the monitoring shelf is defined as a hatching position; the thermal image of the monitoring shelf is collected by the infrared thermal imager, and the thermal image interception domain of each hatching position is intercepted by the target detection algorithm, and the average value in the thermal image interception domain is recorded as the constant temperature value, and the ratio of the number of pixels with temperature values ​​lower than the constant temperature value to the remaining pixels in the thermal image interception domain is the low temperature point ratio, and the constant temperature value and low temperature value ratio of the hatching position constitute its temperature array. The target detection algorithm is the YOLO algorithm; and the infrared thermal imager is an infrared thermal imaging camera.

[0025] Furthermore, the temperature array is collected regularly and the passive ventilation temperature failure analysis is performed. The method for obtaining the failure analysis value is: record each time when the temperature array is obtained as the monitoring time, and obtain each temperature array in the egg turning cycle to perform the passive ventilation temperature failure analysis: Write each low temperature value ratio into a sequence as a low ratio sequence, obtain each extreme value point in the low ratio sequence as a first marked extreme value, and take the period between any two first marked extreme values ​​corresponding to the monitoring time as an extreme value interval. The standard deviation of each low temperature value ratio in the extreme value interval is recorded as a domain step variable; calculate the product of the minimum value of all first marked extreme values ​​in the extreme value interval and the extreme difference of all constant temperature values, and record it as the failure product LapsV of the extreme value interval; The number of the first marked extreme values ​​in the extreme value interval except the first marked extreme values ​​at the two ends of the extreme value interval is recorded as the interval marking capacity, the set of interval marking capacities of all extreme value intervals is recorded as the marking capacity set, and all extreme value intervals with 0 number of marking capacity sets are eliminated; the interval composed of the median and lower quartile of the marking capacity set is recorded as the low capacity value domain, if the interval marking capacity value of an extreme value interval is within the low capacity value domain, it is defined as a low capacity interval; the interval composed of the median and upper quartile of the marking capacity set is recorded as the high capacity value domain, if the interval marking capacity value of an extreme value interval is within the high capacity value domain, it is defined as a high capacity interval; For a low-capacity interval, a mapping relationship is formed between the low-capacity interval and each high-capacity interval, and within the same mapping relationship, a first mapping condition is set as: there are at least two overlapping first marking extreme values ​​between the low-capacity interval and the high-capacity interval; The second mapping condition is: the domain step variable of the low capacity interval is greater than the domain step variable of the high capacity interval; Screen and retain each mapping relationship that satisfies both the first mapping condition and the second mapping condition; calculate the failure analysis value FDV according to each mapping relationship: ; Where lg() is the logarithmic function with the natural constant 10 as the base, i1 is the accumulated variable, N_i1 is the number of retained mapping relationships, SV.L i1 and Laps V.L. i1 is the low temperature weight and failure product of the low capacity interval in the i1th mapping relationship, LapsV.H i1 is the failure product of the high capacity interval in the i1th mapping relationship; Further, the method for identifying the low-sensitivity area in the tunnel incubator, that is, the area with defective temperature regulation quality, through the failure analysis value is: at a monitoring moment, if the percentile value of the failure analysis value of the hatching position in all the hatching positions exceeds 75%, the hatching position triggers the record at the monitoring moment, and the Z-score value of the failure analysis value of the hatching position at the monitoring moment in the analysis value record table is the defect record score, the moment of each trigger record is recorded as the record moment of the hatching position, and the average value of the record score at each record moment is obtained as the defect level of the hatching position; the analysis value record table is a collection of all failure analysis values ​​obtained on the same hatching shelf during the egg turning cycle; The egg turning cycle refers to the time interval during which the incubator turns over the eggs regularly during the incubation process. Each egg turning cycle of the incubator is recorded as Rv.TP, and the egg turning cycle is 1 hour. If the number of recorded moments of an incubation point accounts for more than 50% of the egg-turning cycle, it is defined as meeting the high trigger condition. If the defect level is greater than or equal to the defect threshold, it is defined as meeting the high deviation condition, and the defect threshold is 1.5. When an incubation point meets both the high trigger condition and the high deviation condition, it is defined as a low-sensitivity area, and its temperature regulation quality is considered to be defective. Each low-sensitivity area is sent to the administrator client.

[0026] Furthermore, it also includes abnormal handling in conjunction with low-sensitivity areas. The specific method is: when a low-sensitivity area is identified, the poultry eggs in the low-sensitivity area are placed in an active ventilation area.

[0027] Example 2 Example 2 adopts the same low-sensitivity area identification method for centralized ventilation control of tunnel incubators as Example 1, with the difference that the temperature array is regularly collected and passive ventilation temperature failure analysis is performed, and the method for obtaining the failure analysis value is replaced by: recording each moment of obtaining the temperature array as the monitoring moment, obtaining each temperature array in the egg turning cycle to perform passive ventilation temperature failure analysis, when the low temperature value ratio at a monitoring moment is greater than 0.5, the monitoring moment is marked as a high proportion point, otherwise it is a low proportion point; the moment when each extreme value in each low temperature value ratio in the egg turning cycle appears is the proportion inflection point, the number of monitoring moments between any two consecutive proportion inflection points is the inflection point length, and the average value of all inflection point lengths is recorded as the inflection point standard length InfLen; Define any number of high-proportion points that appear continuously as a high-proportion interval; if the length of a high-proportion interval is less than 3, define each high-proportion point in the high-proportion interval as a compatible point and exclude the high-proportion interval; define any high-proportion interval as the current high-proportion interval, traverse in reverse time from the current high-proportion interval until the first high-proportion interval is found, and merge all low-proportion points and compatible points in the traversal process with the current high-proportion interval to obtain the analysis interval; For the same analysis interval, the average value of each constant temperature value of the high-proportion interval corresponding to the analysis interval is recorded as the first constant temperature level, and the monitoring moment when the constant temperature value is less than the first constant temperature level is defined as a low temperature point. If the InfLen monitoring moments in the reverse time direction of a low temperature point are all non-low temperature points, it is defined as a low temperature inflection point; The maximum value among the constant temperature values ​​searched in the reverse time direction of the low-temperature inflection point is recorded as the inverse peak value, the difference between the constant temperature value of the low-temperature inflection point and the inverse peak value is the first product, and the cumulative value of all first products is the interval failure amount; the ratio of the length of the analysis interval to the number of low-temperature inflection points is the inflection point density; the weighted average of the corresponding interval failure amounts of all analysis intervals is calculated using the inflection point density as the weight, and the obtained value is the failure analysis value of the incubation position.

[0028] The embodiment of the present invention provides a low-sensitivity area recognition system for centralized ventilation control of a lane-type incubator, such as Figure 2 Shown is a structural diagram of a low-sensitivity area identification system for centralized ventilation control of a tunnel-type incubator of the present invention. A low-sensitivity area identification system for centralized ventilation control of a tunnel-type incubator of this embodiment comprises: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned embodiment of a method for identifying low-sensitivity areas for centralized ventilation control of a tunnel-type incubator are implemented.

[0029] The system comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to run in the following units of the system: A ventilation zone identification unit is used to identify active ventilation zones and passive ventilation zones in a multi-lane incubator; A temperature data acquisition unit is used to obtain a temperature image of the passive ventilation area through an infrared thermal imager, and to extract a temperature array of the incubation position through the temperature image; The incubation position failure analysis unit is used to regularly collect the temperature array and perform passive ventilation temperature failure analysis to obtain failure analysis values; The low-sensitivity area identification unit is used to identify low-sensitivity areas in the multi-stage incubator, i.e. areas where the temperature regulation quality is defective, by means of failure analysis values.

[0030] The low-sensitivity area identification system for centralized ventilation control of a tunnel-type incubator can be run in computing devices such as desktop computers, laptops, PDAs, and cloud servers. The low-sensitivity area identification system for centralized ventilation control of a tunnel-type incubator, the system that can run may include, but is not limited to, a processor, a memory. Those skilled in the art will understand that the example described is merely an example of a low-sensitivity area identification system for centralized ventilation control of a tunnel-type incubator, and does not constitute a limitation on a low-sensitivity area identification system for centralized ventilation control of a tunnel-type incubator, and may include more or fewer components than the example, or a combination of certain components, or different components, for example, the low-sensitivity area identification system for centralized ventilation control of a tunnel-type incubator may also include input and output devices, network access devices, buses, etc.

[0031] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the low-sensitivity area identification system for centralized ventilation control of a lane-type incubator, and uses various interfaces and lines to connect the various parts of the entire low-sensitivity area identification system for centralized ventilation control of a lane-type incubator.

[0032] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the low-sensitivity area identification system for centralized ventilation control of the lane-type incubator by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0033] Although the description of the present invention has been quite detailed and has been described in particular with respect to several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, so as to effectively cover the intended scope of the present invention. In addition, the present invention is described above with the embodiments foreseeable by the inventors, and its purpose is to provide a useful description, and those non-substantial changes to the present invention that are not currently foreseen may still represent equivalent changes of the present invention.

Claims

1. A method for identifying low-sensitivity areas of centralized ventilation control in lane-type incubators, characterized in that: The method comprises the following steps: Identify active and passive ventilation zones in multi-stage incubators; The temperature image of the passive ventilation area is obtained by an infrared thermal imager, and the temperature array of the incubation position is extracted by the temperature image; Regularly collect temperature arrays and perform passive ventilation temperature failure analysis to obtain failure analysis values; Identify low-sensitivity areas in multi-stage incubators, i.e. areas with deficient temperature regulation quality, through failure analysis values; Among them, the method of regularly collecting temperature arrays and conducting passive ventilation temperature failure analysis is: extracting the ratio of constant temperature values ​​to low temperature values ​​through the temperature array, obtaining high-proportion points based on the horizontal comparison of the low-temperature value ratio within the egg-turning cycle, dividing the high-proportion points into various high-proportion intervals, dividing the analysis intervals through the high-proportion intervals, using the constant temperature values ​​in the analysis intervals to calculate the first constant temperature level and identify the low-temperature inflection point, using the low-temperature inflection point to calculate the interval failure amount, and obtaining the failure analysis value through the interval failure amount.

2. A method for identifying low-sensitivity areas of centralized ventilation control in tunnel incubators according to claim 1, characterized in that: The method for identifying active ventilation areas and passive ventilation areas in a tunnel incubator is as follows: there are several incubation racks in the tunnel incubator, each of which is arranged along the wind and ventilation direction, each of which has a corresponding air outlet, and each of which measures the passive ventilation temperature through a preset temperature sensor. When the passive ventilation temperature exceeds the preset temperature range of the incubation rack, the corresponding air outlet of the incubation rack is activated, and hot air or cold air for adjusting the air temperature is output. If the air outlet is activated, the incubation rack belongs to the active wind zone, otherwise it is a passive wind zone.

3. A method for identifying low-sensitivity areas for centralized ventilation control of a lane-type incubator according to claim 1, characterized in that: The method of obtaining the temperature image of the passive ventilation area through an infrared thermal imager and extracting the temperature array of the hatching position through the temperature image is as follows: each hatching shelf in the passive ventilation area is selected as a monitoring shelf, and each poultry egg being hatched in the monitoring shelf is defined as a hatching position; the thermal image of the monitoring shelf is collected by the infrared thermal imager, and the thermal image interception domain of each hatching position is intercepted through a preset image area or target detection algorithm, the average value in the thermal image interception domain is recorded as the constant temperature value, the ratio of the number of pixels with temperature values ​​lower than the constant temperature value to the remaining pixels in the thermal image interception domain is the low temperature point ratio, and the temperature array of the hatching position is composed of the ratio of the constant temperature value to the low temperature value.

4. A method for identifying low-sensitivity areas for centralized ventilation control of a lane-type incubator according to claim 1, characterized in that: The temperature array is collected regularly and the passive ventilation temperature failure analysis is performed. The method for obtaining the failure analysis value is as follows: each moment of obtaining the temperature array is recorded as the monitoring moment, and each temperature array in the egg-turning cycle is obtained to perform the passive ventilation temperature failure analysis. When the low temperature value ratio at a monitoring moment is greater than 0.5, the monitoring moment is marked as a high proportion point, otherwise it is a low proportion point; the moment when each extreme value of each low temperature value ratio in the egg-turning cycle appears is the proportion inflection point, and the number of monitoring moments between any two consecutive proportion inflection points is the inflection point length. The average value of all inflection point lengths is recorded as the inflection point standard length InfLen; any number of consecutive high proportion points are defined as high proportion intervals; If the length of the high-proportion interval is less than 3, then define each high-proportion point in the high-proportion interval as a compatible point and remove the high-proportion interval; define any high-proportion interval as the current high-proportion interval, traverse in reverse time from the current high-proportion interval until the first high-proportion interval is found, and merge all low-proportion points and compatible points in the traversal process with the current high-proportion interval to obtain the analysis interval; For the same analysis interval, the average value of each constant temperature value of the high-proportion interval corresponding to the analysis interval is recorded as the first constant temperature level, and the monitoring moment when the constant temperature value is less than the first constant temperature level is defined as a low temperature point. If the InfLen monitoring moments in the reverse time direction of a low temperature point are all non-low temperature points, it is defined as a low temperature inflection point; The maximum value among the constant temperature values ​​searched in the reverse time direction of the low-temperature inflection point is recorded as the inverse peak value, the difference between the constant temperature value of the low-temperature inflection point and the inverse peak value is the first product, and the cumulative value of all first products is the interval failure amount; the ratio of the length of the analysis interval to the number of low-temperature inflection points is the inflection point density; the weighted average of the corresponding interval failure amounts of all analysis intervals is calculated using the inflection point density as the weight, and the obtained value is the failure analysis value of the incubation position.

5. The method for identifying low-sensitivity areas of centralized ventilation control in tunnel incubators according to claim 1, characterized in that: The temperature array is collected regularly and the passive ventilation temperature failure analysis is performed. The method for obtaining the failure analysis value is as follows: each time when the temperature array is obtained is recorded as the monitoring time, and each temperature array in the egg-turning cycle is obtained to perform the passive ventilation temperature failure analysis: each low temperature value ratio is written into a sequence and recorded as a low ratio sequence, each extreme value point in the low ratio sequence is obtained and recorded as the first marked extreme value, and the period between any two first marked extreme values ​​corresponding to the monitoring time is regarded as an extreme value interval, and the standard deviation of each low temperature value ratio in the extreme value interval is recorded as the domain step variable; the product of the minimum value of all first marked extreme values ​​in the extreme value interval and the extreme difference of all constant temperature values ​​is calculated, and recorded as the failure product LapsV of the extreme value interval; The number of the first marked extreme values ​​in the extreme value interval except the first marked extreme values ​​at the two ends of the extreme value interval is recorded as the interval marking capacity, the set of interval marking capacities of all extreme value intervals is recorded as the marking capacity set, and all extreme value intervals with 0 number of marking capacity sets are eliminated; the interval composed of the median and lower quartile of the marking capacity set is recorded as the low capacity value domain, if the interval marking capacity value of an extreme value interval is within the low capacity value domain, it is defined as a low capacity interval; the interval composed of the median and upper quartile of the marking capacity set is recorded as the high capacity value domain, if the interval marking capacity value of an extreme value interval is within the high capacity value domain, it is defined as a high capacity interval; For a low-capacity interval, a mapping relationship is formed between the low-capacity interval and each high-capacity interval, and within the same mapping relationship, a first mapping condition is set as: there are at least two overlapping first marking extreme values ​​between the low-capacity interval and the high-capacity interval; The second mapping condition is: the domain step variable of the low capacity interval is greater than the domain step variable of the high capacity interval; Screen and retain each mapping relationship that satisfies both the first mapping condition and the second mapping condition; and calculate a failure analysis value according to each mapping relationship.

6. A method for identifying low-sensitivity areas for centralized ventilation control of a lane-type incubator according to claim 1, characterized in that: The method for identifying low-sensitivity areas in tunnel incubators, i.e., areas with defective temperature regulation quality, through failure analysis values ​​is as follows: at a monitoring moment, if the percentile value of the failure analysis value of the hatching position among all hatching positions exceeds 75%, the hatching position triggers recording at the monitoring moment, and the Z-score value of the failure analysis value of the hatching position at the monitoring moment in the analysis value recording table is the defect recording score, and the moment of each triggering record is recorded as the recording moment of the hatching position, and the average value of the recording score at each recording moment is obtained as the defect level of the hatching position; the analysis value recording table is a collection of all failure analysis values ​​obtained on the same hatching shelf during the egg turning cycle; If the number of recorded moments of an incubation point accounts for more than 50% of the egg turning cycle, it is defined as meeting the high trigger condition. If the defect level is greater than or equal to the defect threshold, it is defined as meeting the high deviation condition. The defect threshold value range is 1.5-2.

5. When an incubation point meets both the high trigger condition and the high deviation condition, it is defined as a low-sensitivity area, and its temperature regulation quality is considered to be defective. Each low-sensitivity area is sent to the administrator client.

7. A method for identifying low-sensitivity areas for centralized ventilation control of tunnel incubators according to claim 6, characterized in that: It also includes abnormal handling in conjunction with low-sensitivity areas. The specific method is: when a low-sensitivity area is identified, the corresponding air outlet of the incubation shelf is opened to ensure uniform ventilation temperature of the incubation shelf; or, the poultry eggs in the low-sensitivity area are placed in the active ventilation area.

8. A low-sensitivity area identification system for centralized ventilation control of lane-type incubators, characterized in that: The low-sensitivity area identification system for centralized ventilation control of an alley-type incubator comprises: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the low-sensitivity area identification method for centralized ventilation control of an alley-type incubator described in any one of claims 1 to 7 are implemented. The low-sensitivity area identification system for centralized ventilation control of an alley-type incubator runs on computing devices such as desktop computers, laptop computers, PDAs, and cloud data centers.

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