A method for identifying low-sensitivity areas in centralized ventilation control of roadway incubators

Abstract

The present invention belongs to the technical field of data processing, and proposes a method for identifying low-sensitivity areas in centralized ventilation control of roadway incubators, specifically as follows: First, identify the active ventilation area and the passive ventilation area in the roadway incubator, obtain the temperature image of the passive ventilation area through an infrared thermal imager, extract the temperature array of the hatching positions through the temperature image, then regularly collect the temperature array and conduct a failure analysis of the passive ventilation temperature to obtain the failure analysis value, and finally identify the low-sensitivity areas in the roadway incubator through the failure analysis value, that is, the areas where the temperature regulation quality is defective. Since the failure analysis value is obtained through the time-series analysis of the temperature arrays of each hatching position, it can effectively quantify the degree of temperature control defects suffered by each hatching position in the scenario where the air volume coverage cannot be guaranteed during the hatching process in the roadway incubator, thus providing a mathematical basis for identifying the low-sensitivity areas in the passive ventilation area, effectively reducing the hatching failure risk of the roadway incubator, improving the flexibility and autonomous adaptability of the incubator in the scenario of popularizing automation, and further improving the quality and efficiency of breeding management.

Description

Technical Field

[0001] The present invention belongs to the technical fields of data processing and automatic control, and particularly relates to a method for identifying low-sensitivity areas in centralized ventilation control of a roadway incubator. Background Art

[0002] In the large-scale hatching industry, the production performance of the hatchery is closely related to the ultimate economic benefits of the breeding farm. The roadway incubator effectively combines the changing temperature requirements of eggs at different hatching stages, controls the temperature through the scientific layout of the ventilation system, and can also ensure the temperature balance inside the incubator and the control accuracy of oxygen and humidity. The roadway incubator has the characteristics of a large number of eggs to be hatched and the dynamic adjustment characteristics of zoning control ventilation. Therefore, the ideal control of 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 brought by the energy consumption of the ventilation system, the commonly used technical method in this field is to apply the centralized ventilation control method in the roadway incubator. This method centralizes the control of the ventilation system, arranges the eggs on different hatching racks according to different hatching stages, and does not ventilate each hatching rack in the entire incubator separately. Eggs with high temperature requirements are placed in the upwind position, and those with low temperature requirements are placed in the downwind position. Although it can achieve energy-saving effects, it will cause the lack of temperature control accuracy in the racks with passive ventilation. Especially when the system cannot quickly adjust the air flow direction, the local temperature fluctuation and the decline of gas circulation effect increase the risk of hatching failure. The area with insufficient temperature supply quality is the low-sensitivity area in the incubator. Therefore, there is an urgent need for a method for identifying low-sensitivity areas in centralized ventilation control of a roadway incubator to identify and warn the positions in the roadway incubator 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 method for identifying low-sensitivity areas in centralized ventilation control of a roadway incubator to solve one or more technical problems in the prior art and at least provide a beneficial choice or create conditions.

[0004] To achieve the above purpose, according to one aspect of the present invention, there is provided a method for identifying low-sensitivity areas in centralized ventilation control of a roadway incubator, the method comprising the following steps:

[0005] Identify the active ventilation area and the passive ventilation area in the roadway incubator;

[0006] Obtain the temperature image of the passive ventilation area through an infrared thermal imager, and extract the temperature array of the hatching position from the temperature image;

[0007] Regularly collect the temperature array and conduct passive ventilation temperature failure analysis to obtain the failure analysis value;

[0008] Identify the hypo-sensitive areas in the tunnel incubator through failure analysis values, i.e., the areas with defects in temperature regulation quality;

[0009] Furthermore, the method for identifying the active ventilation area and the passive ventilation area in the tunnel incubator is as follows: There are several hatching racks in the tunnel incubator, and each hatching rack is arranged along the ventilation direction of the air flow. Each hatching rack has a corresponding air outlet. Each hatching rack measures the passive ventilation temperature through a preset temperature sensor. When the passive ventilation temperature exceeds the preset temperature range of the hatching rack, the corresponding air outlet of the hatching rack is activated, and hot air or cold air for adjusting the air temperature is output. If the air outlet is activated, the hatching rack belongs to the active ventilation area; otherwise, it is the passive ventilation area.

[0010] The passive ventilation temperature refers to the air temperature measured by the hatching rack before the air outlet is activated, that is, the air temperature when the corresponding air outlet of the hatching rack is not activated.

[0011] The reason for arranging each hatching rack along the ventilation direction of the air flow is to ensure the uniformity and stability of the hatching environment. Such an arrangement can make the air circulation smoother, ensure that each hatching 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 heat and humidity loss, thus reducing energy consumption.

[0012] The preset temperature range of the hatching rack corresponds to the preset according to the hatching stage of the poultry eggs respectively.

[0013] Furthermore, the method for obtaining the temperature image of the passive ventilation area through an infrared thermal imager and extracting the temperature array of the hatching positions from the temperature image is as follows: Select each hatching rack in the passive ventilation area as the monitoring rack, and define each hatching poultry egg in the monitoring rack as a hatching position; collect a thermal image of the monitoring rack through an infrared thermal imager, and intercept the thermal image interception area of each hatching position through a preset image area or target detection algorithm. The average value in the thermal image interception area 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 number of the remaining pixels in the thermal image interception area is the low-temperature point ratio. The temperature array of the hatching position is composed of the constant temperature value and the low-temperature value ratio of the hatching position.

[0014] The preset image area refers to: Under the condition of the same camera angle, by presetting the area of each hatching position in advance, the area of each hatching position in the thermal image measured by the same hatching rack can be repeatedly located; 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.

[0015] Further, regularly collect temperature arrays and conduct passive ventilation temperature failure analysis. The method for obtaining the failure analysis value is as follows: Record each moment when the temperature array is obtained as the monitoring moment, and conduct passive ventilation temperature failure analysis on each temperature array within the egg-turning cycle. When the proportion of low-temperature values at a monitoring moment is greater than 0.5, mark this monitoring moment as a high-proportion point; otherwise, it is a low-proportion point. The moments when the extreme values appear among the low-temperature value proportions within the egg-turning cycle are the proportion inflection points. 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 rounded up to obtain the inflection point standard length InfLen.

[0016] Define any continuously occurring several high-proportion points as a high-proportion interval.

[0017] Among them, there are also constraints in the process of defining the high-proportion interval as above, that is, the monitoring moment before the first high-proportion point of the high-proportion interval and the monitoring moment after the corresponding high-proportion point at the end of the high-proportion interval do not belong to the high-proportion points. The setting of this constraint is to prevent the occurrence of intersecting high-proportion intervals, making each high-proportion interval independent of each other.

[0018] If the length of the high-proportion interval is less than 3, define each high-proportion point within the high-proportion interval as a compatible point and eliminate this high-proportion interval. Define any high-proportion interval as the current high-proportion interval, and traverse from the current high-proportion interval in the reverse time direction until the first-occurring high-proportion interval is searched. Combine all low-proportion points and compatible points during the traversal process with the current high-proportion interval to obtain the analysis interval.

[0019] Since the analysis interval is formed by merging the current high-proportion interval and several detection moments traversed in its reverse time direction, this analysis interval is actually a continuous time interval.

[0020] For the same analysis interval, record the average value of each constant temperature value in the high-proportion interval corresponding to the analysis interval as the first constant temperature level. Define the monitoring moment with a constant temperature value less than the first constant temperature level as a low-temperature point, otherwise as 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, then define it as a low-temperature inflection point.

[0021] The maximum value among the constant temperature values searched in the reverse time direction of the low-temperature inflection point is recorded as the reverse peak value. The difference between the constant temperature value of the low-temperature inflection point and the reverse peak value is the first integral quantity. The cumulative value of all first integral quantities is the interval failure quantity. The ratio of the length of the analysis interval to the number of low-temperature inflection points is the inflection point density. Calculate the weighted average value of the interval failure quantities corresponding to all analysis intervals with the inflection point density as the weight. The obtained value is the failure analysis value of the hatching position.

[0022] The length of the analysis interval refers to the number of monitoring moments in the analysis interval.

[0023] Due to the phenomenon of solidification in the form of interval division in the time series when calculating the failure analysis value through the analysis interval, it will lead to the problem of underfitting risk within the analysis interval. However, the existing technologies cannot solve the underfitting problem caused by this solidification of the division form. Especially when it is at the edge of the hatching rack, the frequent change in the proportion of low-temperature values obtained leads to a more serious underfitting problem. Therefore, in order to better solve this problem and eliminate the distortion of the quantification result caused by underfitting, the present invention proposes a more preferable solution as follows:

[0024] Furthermore, the method of regularly collecting temperature arrays and performing passive ventilation temperature failure analysis to obtain the failure analysis value is as follows: Record each moment when the temperature array is obtained as the monitoring moment, and perform passive ventilation temperature failure analysis on each temperature array within the egg-turning cycle:

[0025] Where the egg-turning cycle refers to the time interval when the incubator regularly turns the eggs during the hatching process. Each egg-turning cycle of the incubator is denoted as Rv.TP, and the value of the egg-turning cycle ranges from 0.5 hours to 3 hours;

[0026] Write each low-temperature value proportion into a sequence, denoted as the low-proportion sequence. Obtain each extreme point within the low-proportion sequence, denoted as the first marked extreme value. The time period between the monitoring moments corresponding to any two first marked extreme values is used as an extreme value interval. The standard deviation of each low-temperature value proportion within the extreme value interval is denoted as the domain sudden change amount; Calculate the product of the minimum value among all the first marked extreme values within the extreme value interval and the range of all constant-temperature values, denoted as the failure accumulation amount LapsV of this extreme value interval;

[0027] The number of the first marked extreme values within the extreme value interval except for the two first marked extreme values at both ends that construct the extreme value interval is denoted as the interval marked capacity. The set composed of the interval marked capacities of all extreme value intervals is denoted as the marked capacity set, and all extreme value intervals with a marked capacity set quantity of 0 are excluded; The interval formed by the median and the lower quartile of the marked capacity set is denoted as the low-capacity value range. If the interval marked capacity value of an extreme value interval is within the low-capacity value range, it is defined as a low-capacity interval; The interval formed by the median and the upper quartile of the marked capacity set is denoted as the high-capacity value range. If the interval marked capacity value of an extreme value interval is within the high-capacity value range, it is defined as a high-capacity interval;

[0028] For a low-capacity interval, form a mapping relationship between this low-capacity interval and each high-capacity interval respectively. Within the same mapping relationship, set the first mapping condition as: There are at least two overlapping first marked extreme values between the low-capacity interval and the high-capacity interval; Where the overlapping first marked extreme values refer to the first marked extreme values that exist in both the low-capacity interval and the high-capacity interval;

[0029] The second mapping condition is that the domain sudden change amount in the low-capacity interval is greater than that in the high-capacity interval;

[0030] Among them, the mapping relationship refers to the unique relationship between two objects, namely a 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 with a repeated combination of a low-capacity interval and a high-capacity interval, that is, the mapping relationship is unique.

[0031] Screen and retain each mapping relationship that simultaneously satisfies the first mapping condition and the second mapping condition; calculate the failure analysis value FDV according to each mapping relationship: ;

[0032] Among them, lg() is the logarithmic function with the natural constant 10 as the base, i1 is the accumulation variable, N_i1 is the number of retained mapping relationships, SV.L i1 and LapsV.L i1 are the low-temperature weight value and the failure accumulation amount of the low-capacity interval in the i1-th mapping relationship, and LapsV.H i1 is the failure accumulation amount of the high-capacity interval in the i1-th mapping relationship;

[0033] The calculation method of the low-temperature weight value 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 average value. Calculate the difference between the minimum value of all constant temperature average values and the current constant temperature average value, which is the low-temperature weight value; the current constant temperature average value is the constant temperature average value of the low-capacity interval corresponding to the low-temperature weight value to be calculated.

[0034] Beneficial effect: Since the failure analysis value is obtained by time-series analysis of the temperature arrays of each hatching position, it can effectively quantify the degree of temperature control defects suffered by each hatching position in the scenario where passive ventilation cannot ensure complete air volume coverage during the hatching process in a tunnel incubator, thereby providing a mathematical support for identifying low-sensitivity areas in the passive ventilation area.

[0035] Furthermore, the method for identifying low-sensitivity areas, that is, areas with defective temperature regulation quality, in a tunnel incubator through the failure analysis value is as follows: At a monitoring moment, if the percentile value of the failure analysis value of a hatching position among all hatching positions exceeds 75%, the hatching position triggers a record at this monitoring moment. The Z-score value of the failure analysis value of the hatching position at this monitoring moment in the analysis value record table is the defect record score. Record the moments of each trigger record as the record moments of the hatching position, and obtain the average value of the record scores of each record moment as the defect level of the hatching position; the analysis value record table is the set of all failure analysis values obtained from the same hatching rack during the egg-turning cycle;

[0036] Among them, the egg turning cycle refers to the time interval when the incubator regularly turns the eggs during the hatching process. Each egg turning cycle of the incubator is denoted as Rv.TP, and the value of the egg turning cycle ranges from 0.5 hours to 3 hours;

[0037] If the proportion of the recorded time moments of the hatching points in the egg turning cycle exceeds 50%, 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 value range of the defect threshold is 1.5 - 2.5; When a hatching point meets both the high trigger condition and the high deviation condition simultaneously, it is defined that this hatching position belongs to the low-sensitivity area, and it is considered that there are defects in the temperature regulation quality, and each low-sensitivity area is sent to the administrator client.

[0038] Furthermore, it also includes abnormal handling in combination with the low-sensitivity area. The specific method is: when the low-sensitivity area is identified, open the corresponding air outlet of the hatching rack to ensure uniform ventilation temperature of the hatching rack; or place the eggs in the low-sensitivity area in the active ventilation area.

[0039] Preferably, among them, for all variables not defined in the present invention, if there is no clear definition, they can all be manually set thresholds.

[0040] The present invention also provides a low-sensitivity area recognition system for centralized ventilation control of a roadway incubator. The low-sensitivity area recognition system for centralized ventilation control of a roadway incubator includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it realizes the steps in the low-sensitivity area recognition method for centralized ventilation control of a roadway incubator. The low-sensitivity area recognition system for centralized ventilation control of a roadway incubator can run on computing devices such as a desktop computer, a laptop computer, a palm computer, and a cloud data center. The operable system can include, but is not limited to, a processor, a memory, and a server cluster. The processor executes the computer program and runs in the following units of the system:

[0041] Ventilation area recognition unit, used to recognize the active ventilation area and the passive ventilation area in the roadway incubator;

[0042] Temperature data acquisition unit, used to obtain the temperature image of the passive ventilation area through an infrared thermal imager and extract the temperature array of the hatching position through the temperature image;

[0043] Hatching position failure analysis unit, used to regularly collect the temperature array and conduct passive ventilation temperature failure analysis to obtain the failure analysis value;

[0044] Low-sensitivity area recognition unit, used to recognize the low-sensitivity area in the roadway incubator through the failure analysis value, that is, the area where there are defects in the temperature regulation quality.

[0045] The beneficial effects of the present invention are as follows: The present invention provides a method for identifying low-sensitivity areas in centralized ventilation control of a roadway incubator. Since the failure analysis value is obtained by time-series analysis of the temperature arrays at each incubation position, it can effectively quantify the degree of temperature control defects suffered by each incubation position in the scenario where passive ventilation cannot ensure complete air volume coverage during the incubation process in a roadway incubator. Thus, it provides mathematical support for identifying low-sensitivity areas in the passive ventilation area, effectively reducing the incubation failure risk of the roadway incubator, enhancing the flexibility and self-adaptability of the incubator's operation in the scenario of popularizing automation, and further improving the quality and efficiency of breeding management. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] By describing the embodiments shown in the accompanying drawings in detail, the above and other features of the present invention will become more obvious. The same reference numerals in the drawings of the present invention represent the same or similar elements. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings:

[0047] Figure 1 Shown is a flowchart of a method for identifying low-sensitivity areas in centralized ventilation control of a roadway incubator;

[0048] Figure 2 Shown is a structural diagram of a system for identifying low-sensitivity areas in centralized ventilation control of a roadway incubator. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The following will clearly and completely describe the concept, specific structure, and technical effects generated by the present invention in combination with the embodiments and the drawings, so as to fully understand the purpose, solution, and effects of the present invention. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0050] Figure 1 Shown is a flowchart of a method for identifying low-sensitivity areas in centralized ventilation control of a roadway incubator. The following will describe Figure 1 a method for identifying low-sensitivity areas in centralized ventilation control of a roadway incubator according to an embodiment of the present invention. The method includes the following steps:

[0051] Embodiment 1

[0052] Identify the active ventilation area and the passive ventilation area in the roadway incubator;

[0053] Obtain the temperature image of the passive ventilation area through an infrared thermal imager, and extract the temperature array of the incubation position from the temperature image;

[0054] Regularly collect the temperature array and conduct passive ventilation temperature failure analysis to obtain the failure analysis value;

[0055] Identify the low-sensitivity area in the tunnel incubator through the failure analysis value, that is, the area with defects in temperature regulation quality;

[0056] Furthermore, the method for identifying the active ventilation area and the passive ventilation area in the tunnel incubator is as follows: There are several hatching shelves in the tunnel incubator, and each hatching shelf is arranged along the ventilation direction of the wind. Each hatching shelf has a corresponding air outlet. Each hatching shelf measures the passive ventilation temperature through a preset temperature sensor. When the passive ventilation temperature exceeds the preset temperature range of the hatching shelf, the corresponding air outlet of the hatching shelf is activated, and hot air or cold air for adjusting the air temperature is output. If the air outlet is activated, the hatching shelf belongs to the active ventilation area, otherwise it is the passive ventilation area.

[0057] Furthermore, the method for obtaining the temperature image of the passive ventilation area through an infrared thermal imager and extracting the temperature array of the hatching position from the temperature image is as follows: Select each hatching shelf in the passive ventilation area as the monitoring shelf, and define each hatching egg in the monitoring shelf as the hatching position; Collect thermal images of the monitoring shelf through an infrared thermal imager, intercept the thermal image interception area of each hatching position through the target detection algorithm, and record the average value in the thermal image interception area as the constant temperature value. The ratio of the number of pixels with temperature values lower than the constant temperature value to the number of the remaining pixels in the thermal image interception area is the low-temperature point ratio. The temperature array of the hatching position is composed of the constant temperature value and the low-temperature value ratio. The target detection algorithm is the YOLO algorithm; The infrared thermal imager is an infrared thermal imaging camera.

[0058] Furthermore, the method for regularly collecting the temperature array and conducting passive ventilation temperature failure analysis to obtain the failure analysis value is as follows: Record each moment when the temperature array is obtained as the monitoring moment, and obtain each temperature array within the egg turning cycle for passive ventilation temperature failure analysis:

[0059] Write each low-temperature value ratio into a sequence and record it as the low-ratio sequence. Obtain each extreme point in the low-ratio sequence and record it as the first marked extreme value. The time period between the monitoring moments corresponding to any two first marked extreme values is used as an extreme value interval. The standard deviation of each low-temperature value ratio in the extreme value interval is recorded as the domain mutation variable; Calculate the product of the minimum value of all the first marked extreme values and the range of all the constant temperature values within the extreme value interval, and record it as the failure accumulation quantity LapsV of the extreme value interval;

[0060] The number of the first marked extreme values other than the two ends of the extreme value interval that constructs the extreme value interval is denoted as the interval marking capacity. The set composed of the interval marking capacities of all extreme value intervals is denoted as the marking capacity set, and all extreme value intervals with a marking capacity set quantity of 0 are excluded; the interval formed by the median and the lower quartile of the marking capacity set is denoted as the low-capacity value range. If the interval marking capacity value of an extreme value interval is within the low-capacity value range, it is defined as a low-capacity interval; the interval formed by the median and the upper quartile of the marking capacity set is denoted as the high-capacity value range. If the interval marking capacity value of an extreme value interval is within the high-capacity value range, it is defined as a high-capacity interval;

[0061] For a low-capacity interval, a mapping relationship is formed between the low-capacity interval and each high-capacity interval respectively. Within the same mapping relationship, the first mapping condition is set as: there are at least two overlapping first marked extreme values between the low-capacity interval and the high-capacity interval;

[0062] 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;

[0063] Screen and retain each mapping relationship that simultaneously satisfies the first mapping condition and the second mapping condition; calculate the failure analysis value FDV according to each mapping relationship: ;

[0064] where lg() is the logarithmic function with the natural constant 10 as the base, i1 is the cumulative variable, N_i1 is the number of retained mapping relationships, SV.L i1 and LapsV.L i1 are the low-temperature weight value and the failure product quantity of the low-capacity interval in the i1-th mapping relationship, and LapsV.H i1 is the failure product quantity of the high-capacity interval in the i1-th mapping relationship;

[0065] Furthermore, the method for identifying the low-sensitivity area in the roadway incubator, that is, the area with defective temperature regulation quality, by the failure analysis value is: 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 a record at this monitoring moment. The Z-score value of the failure analysis value of the hatching position in the analysis value record table at this monitoring moment is the defect record score. Each moment when a trigger record occurs is denoted as the record moment of the hatching position, and the average value of the record scores at each record moment is obtained as the defect level of the hatching position; the analysis value record table is the set of all failure analysis values obtained for the same hatching layer rack within the turning cycle;

[0066] where the turning cycle refers to the time interval for the incubator to regularly turn the eggs during the hatching process. Each turning cycle of the incubator is denoted as Rv.TP, and the turning cycle takes a value of 1 hour;

[0067] If the proportion of the recording moments of the hatching points in the egg-turning cycle exceeds 50%, 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 set to 1.5. When a hatching point meets both the high-trigger condition and the high-deviation condition, it is defined that the hatching position belongs to the low-sensitivity area, and it is considered that there is a defect in the temperature regulation quality, and each low-sensitivity area is sent to the administrator client.

[0068] Furthermore, it also includes abnormal handling in combination with the low-sensitivity area. The specific method is: when the low-sensitivity area is identified, the eggs in the low-sensitivity area are placed in the active ventilation area.

[0069] Embodiment 2

[0070] Embodiment 2 adopts the same method for identifying low-sensitivity areas in centralized ventilation control of a roadway incubator as in Embodiment 1. The difference is that the temperature array is collected regularly and the passive ventilation temperature failure analysis is carried out. The method for obtaining the failure analysis value is replaced by: recording each moment when the temperature array is obtained as the monitoring moment, obtaining each temperature array within the egg-turning cycle for 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 appears among the low-temperature value ratios within the egg-turning cycle is the ratio inflection point, and the number of monitoring moments between any two consecutive ratio inflection points is the inflection point length. The average value of all inflection point lengths is rounded up to be recorded as the inflection point standard length InfLen;

[0071] Define any continuously appearing several high-ratio points as a high-ratio interval; if the length of the high-ratio interval is less than 3, define each high-ratio point within the high-ratio interval as a compatible point and remove the high-ratio interval; define any high-ratio interval as the current high-ratio interval, traverse from the current high-ratio interval in the reverse time direction until the first appeared high-ratio interval is searched, and merge all the low-ratio points and compatible points during the traversal process with the current high-ratio interval to obtain the analysis interval;

[0072] For the same analysis interval, record the average value of each constant temperature value of the high-ratio interval corresponding to the analysis interval as the first constant temperature level, and define the monitoring moment with a constant temperature value less than the first constant temperature level 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, then it is defined as a low-temperature inflection point;

[0073] The maximum value among the constant temperature values searched in the reverse time direction at the low-temperature inflection point is denoted as the reverse peak value. The difference between the constant temperature value at the low-temperature inflection point and the reverse peak value is the first integrated quantity, and the cumulative value of all the first integrated quantities is the interval failure quantity. The ratio of the length of the analysis interval to the number of low-temperature inflection points is the inflection point density. Using the inflection point density as the weight, calculate the weighted average of the interval failure quantities corresponding to all the analysis intervals, and the obtained value is the failure analysis value of the hatching position.

[0074] An insensitive area recognition system for centralized ventilation control of a roadway incubator provided by an embodiment of the present invention, as Figure 2 shown in the structure diagram of an insensitive area recognition system for centralized ventilation control of a roadway incubator of the present invention. The insensitive area recognition system for centralized ventilation control of a roadway incubator in this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the embodiment of the above-mentioned insensitive area recognition method for centralized ventilation control of a roadway incubator.

[0075] The system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it runs in the following units of the system:

[0076] A ventilation area recognition unit, configured to recognize an active ventilation area and a passive ventilation area in a roadway incubator;

[0077] A temperature data acquisition unit, configured to obtain a temperature image of the passive ventilation area through an infrared thermal imager and extract a temperature array of the hatching position from the temperature image;

[0078] A hatching position failure analysis unit, configured to regularly collect the temperature array and perform passive ventilation temperature failure analysis to obtain a failure analysis value;

[0079] An insensitive area recognition unit, configured to recognize an insensitive area in the roadway incubator through the failure analysis value, that is, an area with a defect in temperature regulation quality.

[0080] The low-sensitivity area recognition system for centralized ventilation control of a roadway incubator can operate on computing devices such as desktop computers, laptop computers, handheld computers, and cloud servers. The low-sensitivity area recognition system for centralized ventilation control of a roadway incubator, the operable system may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above examples are only examples of the low-sensitivity area recognition system for centralized ventilation control of a roadway incubator, and do not constitute a limitation on the low-sensitivity area recognition system for centralized ventilation control of a roadway incubator. It may include more or fewer components than the examples, or combine certain components, or different components. For example, the low-sensitivity area recognition system for centralized ventilation control of a roadway incubator may also include input / output devices, network access devices, buses, etc.

[0081] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the operating system of the low-sensitivity area recognition system for centralized ventilation control of a roadway incubator, and uses various interfaces and lines to connect all parts of the operable system of the low-sensitivity area recognition system for centralized ventilation control of a roadway incubator.

[0082] The memory can be used to store the computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory and invoking the data stored in the memory, the processor realizes various functions of the low-sensitivity area recognition system for centralized ventilation control of the roadway incubator. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0083] Although the description of the present invention has been quite detailed and several of the described embodiments have been described in particular, 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 has been described above with embodiments foreseeable by the inventor for the purpose of providing a useful description, and non-substantive changes to the present invention that are not currently foreseeable may still represent equivalent changes to the present invention.

Claims

1. A method for identifying a low-sensitivity area in centralized ventilation control of a roadway incubator, characterized in that, The method includes the following steps: Identify the active ventilation area and the passive ventilation area in the tunnel incubator; Obtain the temperature image of the passive ventilation area through an infrared thermal imager, and extract the temperature array of the hatching position through the temperature image; Regularly collect the temperature array and conduct passive ventilation temperature failure analysis to obtain the failure analysis value; Identify the low-sensitivity area in the tunnel incubator through the failure analysis value, that is, the area with defective temperature regulation quality; Among them, the method of regularly collecting the temperature array and conducting passive ventilation temperature failure analysis is: extract the ratio of the constant temperature value to the low temperature value through the temperature array, obtain the high ratio points by horizontally comparing the low temperature value ratios within the egg turning cycle, divide each high ratio interval from the high ratio points, divide the analysis interval through the high ratio interval, calculate the first constant temperature level using the constant temperature value in the analysis interval and identify the low temperature inflection point, calculate the interval failure amount using the low temperature inflection point, and obtain the failure analysis value through the interval failure amount.

2. The low-sensitivity area recognition method for centralized ventilation control of a roadway incubator according to claim 1, characterized in that, The method of identifying the active ventilation area and the passive ventilation area in the tunnel incubator is: there are several hatching racks in the tunnel incubator, each hatching rack is arranged along the ventilation direction of the wind, each hatching rack has a corresponding air outlet, each hatching rack measures the passive ventilation temperature through a preset temperature sensor, when the passive ventilation temperature exceeds the preset temperature range of the hatching rack, activate the corresponding air outlet of the hatching rack and output hot air or cold air to adjust the air temperature, if the air outlet is activated, the hatching rack belongs to the active ventilation area, otherwise it is the passive ventilation area.

3. The low-sensitivity area recognition method for centralized ventilation control of a roadway 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: select each hatching rack in the passive ventilation area as the monitoring rack, and define each hatching egg in the monitoring rack as the hatching position; collect the thermal image of the monitoring rack through the infrared thermal imager, intercept the thermal image interception area of each hatching position through the preset image area or target detection algorithm, record the average value in the thermal image interception area as the constant temperature value, and record 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 area as the low temperature point ratio, and form its temperature array from the constant temperature value and the low temperature value ratio of the hatching position.

4. A method for identifying a low-sensitivity area in centralized ventilation control of a roadway incubator according to claim 1, characterized in that, The method of regularly collecting the temperature array and conducting passive ventilation temperature failure analysis to obtain the failure analysis value is: record each moment when the temperature array is obtained as the monitoring moment, obtain each temperature array within the egg turning cycle for passive ventilation temperature failure analysis, when the low temperature value ratio at a monitoring moment is greater than 0.5, mark this monitoring moment as a high ratio point, otherwise it is a low ratio point; the moment when each extreme value appears among the low temperature value ratios within the egg turning cycle 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 rounded up to be recorded as the inflection point standard length InfLen; define any continuously occurring several high ratio points as a high ratio interval; If the length of the high-proportion interval is less than 3, define each high-proportion point within the high-proportion interval as a compatible point and eliminate the high-proportion interval; define any high-proportion interval as the current high-proportion interval, traverse in the reverse time direction from the current high-proportion interval until the first-occurring high-proportion interval is searched, and merge all low-proportion points and compatible points during the traversal with the current high-proportion interval to obtain the analysis interval; For the same analysis interval, record the average value of the constant temperature values of each high-proportion interval corresponding to the analysis interval as the first constant temperature level, define the monitoring moment with a constant temperature value less than the first constant temperature level as a low-temperature point, and if the InfLen monitoring moments in the reverse time direction of a low-temperature point are all non-low-temperature points, then define it as a low-temperature inflection point; Record the maximum value among the constant temperature values searched in the reverse time direction of the low-temperature inflection point as the reverse peak value, the difference between the constant temperature value of the low-temperature inflection point and the reverse peak value as the first integration quantity, and the cumulative value of all first integration quantities as the interval failure quantity; the ratio of the length of the analysis interval to the number of low-temperature inflection points is the inflection point density; calculate the weighted average value of the interval failure quantities corresponding to all analysis intervals with the inflection point density as the weight, and the obtained value is the failure analysis value of the hatching position.

5. The low-sensitivity area identification method for centralized ventilation control of a roadway incubator according to claim 1, wherein Regularly collect the temperature array and perform passive ventilation temperature failure analysis. The method to obtain the failure analysis value is as follows: Record each moment when the temperature array is obtained as the monitoring moment, and perform passive ventilation temperature failure analysis for each temperature array within the turning cycle: Write each low-temperature value ratio into the sequence and record it as the low-proportion sequence, obtain each extreme value point within the low-proportion sequence and record it as the first marked extreme value, take the time period between the monitoring moments corresponding to any two first marked extreme values as an extreme value interval, and record the standard deviation of each low-temperature value ratio within the extreme value interval as the domain sudden change variable; Calculate the product of the minimum value among all first marked extreme values and the range of all constant temperature values within the extreme value interval, and record it as the failure integration quantity LapsV of the extreme value interval; The number of first marked extreme values other than the two first marked extreme values at both ends that construct the extreme value interval within the extreme value interval is recorded as the interval marked capacity, and the set composed of the interval marked capacities of all extreme value intervals is recorded as the marked capacity set, and eliminate all extreme value intervals with a marked capacity set quantity of 0; The interval formed by the median and the lower quartile of the marked capacity set is recorded as the low-capacity value range, and if the interval marked capacity value of an extreme value interval is within the low-capacity value range, then define it as a low-capacity interval; The interval formed by the median and the upper quartile of the marked capacity set is recorded as the high-capacity value range, and if the interval marked capacity value of an extreme value interval is within the high-capacity value range, then define it as a high-capacity interval; For a low-capacity interval, form a mapping relationship between the low-capacity interval and each high-capacity interval respectively. Within the same mapping relationship, set the first mapping condition as: There are at least two overlapping first marked extreme values between the low-capacity interval and the high-capacity interval; The second mapping condition is: The domain sudden change variable of the low-capacity interval is greater than the domain sudden change variable of the high-capacity interval; Screen and retain each mapping relationship that simultaneously satisfies the first mapping condition and the second mapping condition; Calculate the failure analysis value according to each mapping relationship.

6. The low-sensitivity area recognition method for centralized ventilation control of a roadway incubator according to claim 1, characterized in that, The method for identifying low-sensitivity areas in a roadway incubator, i.e., areas with defects in temperature regulation quality, through failure analysis values is as follows: At a monitoring moment, if the percentile value of the failure analysis value of an incubation position exceeds 75% among all incubation positions, the incubation position triggers a record at this monitoring moment. The Z-score value of the failure analysis value of the incubation position in the analysis value record table at this monitoring moment is the defect record score. Record the moments when each trigger occurs as the record moments of the incubation position, and obtain the average value of the record scores at each record moment as the defect level of the incubation position. The analysis value record table is a set of all failure analysis values obtained for the same incubation tier during the egg-turning cycle. If the proportion of the number of record moments of an incubation point in the egg-turning cycle exceeds 50%, 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 value range of the defect threshold is 1.5 - 2.

5. When an incubation point meets both the high-trigger condition and the high-deviation condition, it is defined that this incubation position belongs to the low-sensitivity area, and it is considered that there are defects in its temperature regulation quality. Send each low-sensitivity area to the administrator client.

7. A method for identifying a low-sensitivity area in centralized ventilation control of a roadway incubator according to claim 6, characterized in that, It also includes abnormal handling in combination with the low-sensitivity area. The specific method is as follows: When the low-sensitivity area is identified, open the corresponding air outlet of this incubation tier to ensure uniform ventilation temperature of the incubation tier; or place the eggs in the low-sensitivity area in the active ventilation area.

8. A low-sensitivity area recognition system for centralized ventilation control of a roadway incubator, characterized in that, The low-sensitivity area identification system for centralized ventilation control of a roadway incubator includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the method for identifying low-sensitivity areas in centralized ventilation control of a roadway incubator as described in any one of claims 1 - 7. The low-sensitivity area identification system for centralized ventilation control of a roadway incubator runs on computing devices such as desktop computers, laptop computers, handheld computers, and cloud data centers.

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