A workshop temperature and humidity data real-time monitoring, collecting and processing method

Abstract

The present application relates to the technical field of workshop environment data monitoring and collecting, in particular to a kind of workshop temperature and humidity data real-time monitoring and collecting processing method.The method comprises: collecting the temperature and humidity of target workshop when processing current batch of live sheep, obtaining temperature fluctuation threshold;Obtain initial transmission interval and the deviation index of each temperature of target workshop, and obtain the deviation index threshold of target workshop;The temperature of target workshop based on initial transmission interval transmission is recorded as normal transmission temperature;The temperature of target workshop is transmitted, and the temperature of the fluctuation coefficient or the deviation index greater than temperature fluctuation threshold or deviation index threshold is recorded as additional transmission temperature.Judge whether the next data transmission needs to update temperature fluctuation threshold and initial transmission interval;If update, then obtain updated temperature fluctuation threshold and transmission interval, and the humidity of target workshop is transmitted according to the transmission mode of temperature in the same way.The present application can effectively collect the temperature and humidity data of workshop.

Description

Technical Field

[0001] This invention relates to the field of workshop environmental data monitoring and acquisition technology, specifically to a method for real-time monitoring, acquisition, and processing of workshop temperature and humidity data. Background Technology

[0002] In modern industrial production, the workshop environment has a crucial impact on product quality, production efficiency, and equipment operational stability, with temperature and humidity being two of the most critical environmental parameters. Different production workshops, such as electronics manufacturing workshops, pharmaceutical workshops, and food processing workshops, have strict and specific requirements for temperature and humidity. In live sheep slaughter and fresh meat quick-freezing workshops, monitoring and collecting temperature and humidity data is crucial for ensuring fresh meat quality, production safety, and compliance with hygiene standards.

[0003] Because live sheep slaughtering and fresh meat quick-freezing workshops consist of multiple workshops with different functions, and temperature and humidity monitoring is usually conducted by multiple sensors, a large amount of monitoring data is generated. If all data is transmitted, a large amount of redundant data will be generated, and there is a possibility of data loss. Therefore, edge data filtering algorithms are generally used to preprocess the raw data on local devices (such as edge gateways and sensor nodes) to reduce invalid data transmission and cloud computing pressure, while ensuring that critical data is not lost. However, the dead zone filtering threshold for edge data is generally a fixed value, which may ignore brief but important anomalies (such as the temperature in the quick-freezing room rising to -10℃ and then recovering). Furthermore, a fixed threshold (such as a dead zone of 0.5℃) may not be able to adapt to changes in the workshop environment (such as greater humidity fluctuations in summer), which will affect the accuracy of the transmitted data results and thus reduce the accuracy of temperature and humidity monitoring in each workshop. Summary of the Invention

[0004] To address the aforementioned technical problems, the present invention aims to provide a method for real-time monitoring, acquisition, and processing of workshop temperature and humidity data. The specific technical solution adopted is as follows: One embodiment of the present invention provides a method for real-time monitoring, acquisition, and processing of workshop temperature and humidity data, the method comprising: The temperature and humidity of the target workshop when processing the current batch of live sheep are collected; the temperature of the target workshop is divided into windows and the local fluctuation coefficient of each window is obtained; the fluctuation coefficient of each temperature is calculated separately for each window centered on each temperature; cluster analysis is performed on the local fluctuation coefficients of each window corresponding to different batches of live sheep processed in the target workshop in history to obtain the temperature fluctuation threshold of the target workshop; The initial transmission interval is obtained based on the temperature at which different batches of live sheep were processed in the target workshop in the past; the deviation index of a temperature in the target workshop is obtained based on the deviation of a temperature from the historical temperature; and the deviation index threshold is obtained by performing cluster analysis on the deviation indices of each temperature in the target workshop in the past. The temperature of the target workshop transmitted based on the initial transmission interval is recorded as the normal transmission temperature; the temperature of the target workshop whose fluctuation coefficient or deviation index is greater than the temperature fluctuation threshold or deviation index threshold is transmitted and recorded as the additional transmission temperature. The system obtains consecutive initial transmission intervals during the temperature transmission process of the target workshop, forming continuous time periods. The continuous time period including the current moment is recorded as the current time period. Based on the fluctuation coefficient, deviation index, and number of additional transmission temperatures of each temperature in the current time period, it determines whether to update the temperature fluctuation threshold and the initial transmission interval. If an update is required, the temperature fluctuation threshold is updated to obtain the updated temperature fluctuation threshold. The initial transmission interval is updated based on the temperature fluctuation threshold and the updated temperature fluctuation threshold. Similarly, the humidity of the target workshop is transmitted according to the temperature transmission method.

[0005] Preferably, the temperature of the target workshop is divided into windows and the local fluctuation coefficient of each window is obtained, including: The local fluctuation coefficient of a window is obtained by averaging the absolute values ​​of the differences between each temperature within a window and the mean of all temperatures within that window, and then normalizing the average value.

[0006] Preferably, the fluctuation coefficient of each temperature is calculated separately by constructing a window centered on each temperature, including: The average of the absolute values ​​of the differences between each temperature within a window centered at a given temperature and the mean of all temperatures within that window is obtained and normalized to obtain the fluctuation coefficient of that temperature.

[0007] Preferably, cluster analysis is performed on the local fluctuation coefficients of each window corresponding to the historical processing of different batches of live sheep in the target workshop to obtain the temperature fluctuation threshold of the target workshop, including: Cluster the local fluctuation coefficients of different windows corresponding to the historical processing of different batches of live sheep in the target workshop to obtain different clusters; take the mean of all local fluctuation coefficients in the cluster with the most elements as the temperature fluctuation threshold of the target workshop.

[0008] Preferably, the initial transfer interval is obtained based on the temperature of different batches of live sheep processed in the target workshop in history, including: The temperature of a batch of live sheep processed in the target workshop is used to form the temperature sequence corresponding to that batch; the cumulative sum algorithm is used to obtain the stage point of the temperature sequence corresponding to each batch; the average of the time interval between any two adjacent stage points is used as the initial transmission interval.

[0009] Preferably, the deviation index of the temperature is obtained based on the deviation of a temperature in the target workshop from historical temperatures, including: The median of the normal temperature range of the target workshop is used as the standard reference value for the temperature of the target workshop; the absolute value of the difference between the mean temperature in the previous window when the current batch of live sheep is processed in the target workshop and the standard reference value is obtained and normalized to obtain the local temperature difference coefficient; a preset number of temperatures are taken sequentially forward from this temperature to form a temperature sequence to be analyzed; the sequence to be analyzed is divided into two parts, denoted as the front part and the back part, respectively; the similarity between the front part and the back part is obtained using the DTW algorithm, and the difference between the front and back parts is obtained by subtracting the similarity from the first preset value; the absolute value of the difference between this temperature and the mean temperature in the previous window when the current batch of live sheep is processed in the target workshop is normalized to obtain the independent temperature difference coefficient of this temperature; the difference between the front and back parts and the mean of the independent temperature difference coefficient of the temperature are multiplied by the local temperature difference coefficient to obtain the deviation index of this temperature.

[0010] Preferably, determining whether to update the temperature fluctuation threshold and initial transmission interval based on the fluctuation coefficient, deviation index, and number of additional transmission temperatures for each temperature in the current time period includes: The fluctuation coefficients of each temperature within the current time period are arranged into a fluctuation coefficient sequence according to time sequence; the deviation indices of each temperature within the current time period are arranged into a deviation index sequence according to time sequence; the mean of the time interval between every two adjacent fluctuation coefficients in the fluctuation coefficient sequence that are greater than the temperature fluctuation threshold is obtained to obtain the average distribution interval of the fluctuation coefficients; the mean of the time interval between every two adjacent deviation indices in the deviation index sequence that are greater than the deviation index threshold is obtained to obtain the average distribution interval of the deviation index; the trend performance coefficient of the current time period is obtained by subtracting the mean of the average distribution interval of the fluctuation coefficients and the average distribution interval of the deviation index from the first preset value. Obtain independent, complete initial transmission intervals with different values ​​of no additional transmission temperature or an additional transmission temperature less than twice the normal transmission temperature, and denote them as the initial transmission intervals to be analyzed. Obtain the trend performance coefficient of each initial transmission interval to be analyzed and perform clustering to obtain different clusters. Obtain the mean of all trend performance coefficients in the cluster containing the most trend performance coefficients as the trend performance coefficient threshold. If the number of additional transmission temperatures in the current period is greater than or equal to twice the number of normal transmission temperatures in the current period, and the trend performance coefficient in the current period is greater than or equal to the trend performance coefficient threshold, then update the temperature fluctuation threshold and the initial transmission interval.

[0011] Preferably, updating the initial transmission interval based on a temperature fluctuation threshold and an updated temperature fluctuation threshold includes: If the temperature fluctuation threshold is less than the updated temperature fluctuation threshold, an adjustment coefficient is obtained by subtracting the absolute value of the difference between the temperature fluctuation threshold and the updated temperature fluctuation threshold from the first preset value. The initial transmission interval is then multiplied by the adjustment coefficient to obtain the updated transmission interval. If the temperature fluctuation threshold is equal to the updated temperature fluctuation threshold, the initial transmission interval is used as the updated transmission interval. If the temperature fluctuation threshold is greater than the updated temperature fluctuation threshold, an adjustment coefficient is obtained by adding the absolute value of the difference between the temperature fluctuation threshold and the updated temperature fluctuation threshold to the first preset value. The initial transmission interval is then multiplied by the adjustment coefficient to obtain the updated transmission interval.

[0012] The embodiments of this invention have at least the following beneficial effects: This application collects the temperature and humidity of the target workshop when processing the current batch of live sheep, then divides the temperature of the target workshop into windows and obtains the local fluctuation coefficient of each window, thereby calculating the temperature fluctuation threshold and obtaining the temperature fluctuation coefficient, and then obtaining the temperature of the target workshop when processing different batches of live sheep in history to obtain the initial transmission interval; obtains the deviation index of the temperature based on the deviation of a temperature of the target workshop from the historical temperature; performs cluster analysis on the deviation indices of each temperature in the target workshop in history to obtain the deviation index threshold, and records the temperature of the target workshop transmitted based on the initial transmission interval as the normal transmission temperature; and identifies the temperatures of the target workshop whose fluctuation coefficient or deviation index is greater than the temperature fluctuation threshold or deviation index threshold. The temperature is transmitted and recorded as an additional transmission temperature. This allows for the inclusion of situations where the data may continuously rise or fall but with gradual fluctuations during data transmission, thus improving the accuracy of the collected temperature data. Furthermore, the continuous initial transmission intervals where additional transmission temperatures occur during the temperature transmission process in the target workshop are obtained to form continuous time periods. The continuous time period including the current moment is recorded as the current time period. Based on the fluctuation coefficient, deviation index, and the number of additional transmission temperatures in each temperature within the current time period, it is determined whether to update the temperature fluctuation threshold and initial transmission interval. This updated temperature fluctuation threshold and initial transmission interval are more suitable for subsequent workshop temperature data collection, improving the accuracy of transmission. Similarly, the humidity data of the workshop is transmitted in the same way to improve the accuracy of temperature and humidity transmission. Attached Figure Description

[0013] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This application provides a flowchart of a method for real-time monitoring, acquisition, and processing of workshop temperature and humidity data. Detailed Implementation

[0015] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a real-time monitoring, acquisition, and processing method for workshop temperature and humidity data proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0017] The following description, in conjunction with the accompanying drawings, details the specific scheme of the workshop temperature and humidity data real-time monitoring, acquisition, and processing method provided by the present invention.

[0018] Example: The main application scenario of this invention is as follows: This application analyzes the temperature and humidity data collected from different workshops to determine the temperature and humidity data that need to be transmitted to each workshop, then transmits the data, and monitors the temperature and humidity of the workshop based on the transmitted data.

[0019] Please see Figure 1 The diagram illustrates a flowchart of a method for real-time monitoring, acquisition, and processing of workshop temperature and humidity data according to an embodiment of the present invention. The method includes the following steps: Step S1: Collect the temperature and humidity of the target workshop when processing the current batch of live sheep; divide the temperature of the target workshop into windows and obtain the local fluctuation coefficient of each window; construct windows with each temperature as the center and calculate the fluctuation coefficient of each temperature; perform cluster analysis on the local fluctuation coefficients of each window corresponding to different batches of live sheep in the history of the target workshop to obtain the temperature fluctuation threshold of the target workshop.

[0020] The processing of live sheep is divided into several different workshops, such as slaughtering workshop, aging workshop, quick-freezing workshop and cold storage workshop. Furthermore, temperature sensors and humidity sensors need to be installed in different workshops. The installation location of the sensors needs to avoid interference sources such as heat sources, cold sources and ventilation openings. In this application, the data acquisition frequency is set to once per second, which can be adjusted by the implementer according to the actual situation.

[0021] Therefore, any workshop can be taken as the target workshop. The temperature in the target workshop will be used as an example for analysis. The humidity will be processed in the same way as the temperature. In this way, the temperature and humidity of the target workshop when processing the current batch of live sheep can be collected. At the same time, the temperature and humidity data of different batches of live sheep in history can be obtained.

[0022] The primary scenario requiring temperature and humidity monitoring in the fresh meat quick-freezing workshop after live sheep slaughter is the fresh meat processing and preservation scenario. The main workshops are the aging workshop and the quick-freezing workshop. The aging workshop is mainly used to hang the slaughtered sheep carcasses on aging racks, maintaining a low temperature and high humidity environment. The main requirements are temperature control at 0-4℃ for 12-24 hours to ensure a uniform temperature drop and complete the aging process, with a relative humidity of 85%-90% to prevent excessive evaporation of surface moisture and subsequent dehydration. The quick-freezing workshop is used to rapidly cool the aging sheep meat to below -18℃, ensuring the core temperature drops to below -28℃, with an airflow velocity of 3-4 m / s, ensuring freezing is completed within 48 hours, and a relative humidity of 90%-95% to reduce ice crystal formation and its damaging effect on meat quality. The meat is then cut and packaged at 10-12℃ to prevent the meat temperature from rising and causing microbial growth. Under normal circumstances, the workshop temperature and humidity are relatively stable with minimal fluctuations. However, the following situations may cause changes or abnormalities in temperature and humidity data: long-term operation or insufficient maintenance of the compressor may lead to a reduction in cooling capacity, which may cause the workshop temperature to rise slowly; frost may form on the evaporator, and excessive frost may hinder heat exchange, resulting in a decrease in cooling efficiency and temperature fluctuations; excessively high air velocity during the acid removal stage may accelerate the evaporation of moisture on the surface of the meat, resulting in a decrease in local humidity; when cleaning the floor or equipment, moisture evaporation may temporarily increase humidity, etc.

[0023] Therefore, the temperature and humidity requirements of the workshop can be used as a reference value. Based on a large amount of historical temperature and humidity data under different scenarios, the data change and fluctuation situation can be obtained to obtain the data fluctuation threshold, including the temperature fluctuation threshold and the humidity fluctuation threshold.

[0024] In all workshop activities, there are generally standard procedures, such as live sheep inspection, pre-slaughter showering and anesthesia, stabbing and bleeding, decapitation and skinning, aging treatment, and packaging. Following these procedures, a large amount of historical temperature and humidity data is acquired. Taking temperature data as an example, the temperature during the processing of the current batch of live sheep in the target workshop is divided into windows. The average of the absolute values ​​of the differences between each temperature within a window and the mean of all temperatures within that window is obtained and normalized to obtain the local fluctuation coefficient of that window.

[0025] The specific calculation model for the local fluctuation coefficient is as follows: , in, This represents the local fluctuation coefficient of the temperature of the a-th window when the target workshop processes the current batch of live sheep; norm represents the normalization operation, and n represents the window size. If the collection frequency is high and the data volume is large, a larger window size, such as 9, can be set to divide the temperature data into windows of size 9. The implementer can adjust the window size according to the actual situation. This represents the i-th temperature in the window. This represents the average temperature across all temperatures within the window. Additionally, when selecting windows sequentially, if the selection is insufficient, the preceding data can be used to fill the gaps. Similarly, by analyzing the historical temperatures of different batches of live sheep processed in the target workshop, the local fluctuation coefficients for each window corresponding to those historical batches can be obtained.

[0026] Furthermore, it is necessary to obtain the temperature fluctuation coefficients when processing the current batch of live sheep. Specifically, the average of the absolute values ​​of the differences between each temperature within a window centered on a certain temperature and the mean of all temperatures within that window is obtained and normalized to obtain the temperature fluctuation coefficient.

[0027] The above calculations established the local fluctuation coefficients for temperature at each window during the processing of the current batch of live sheep. Further, the local fluctuation coefficients for each window during the processing of different batches of live sheep in the target workshop's history were clustered to obtain different clusters. The mean of all local fluctuation coefficients in the cluster with the most elements was taken as the temperature fluctuation threshold for the target workshop. The DBSCAN clustering algorithm was used, which obtains the temperature fluctuation threshold based on the principle of majority rule. Similarly, the humidity fluctuation threshold for the target workshop can be obtained.

[0028] Step S2: Obtain the initial transmission interval based on the temperature of different batches of live sheep processed in the target workshop in history; obtain the deviation index of a temperature in the target workshop based on the deviation of a temperature from the historical temperature; and obtain the deviation index threshold by performing cluster analysis on the deviation indices of each temperature in the target workshop in history.

[0029] Dead zone filtering is mainly determined based on the data fluctuation threshold (the obtained temperature fluctuation threshold and humidity fluctuation threshold). The traditional method is to transmit only when the data fluctuation exceeds the fluctuation threshold, and not transmit otherwise. Therefore, according to the above steps, the preliminary temperature fluctuation threshold and humidity fluctuation threshold of each workshop are obtained. As needed, it is possible to choose to transmit only abnormal data or transmit both normal and abnormal data. If both normal and abnormal data are transmitted, an initial transmission interval can be set to transmit a fixed portion of normal data when the data change is within the fluctuation range.

[0030] The initial transmission interval can be set according to the average time interval of the temperature data of different batches of processing in the target workshop. The initial transmission interval is obtained based on the temperature of different batches of live sheep processed in the target workshop in history. This ensures that the data at the time point of relatively obvious data change can be transmitted to the cloud. The data at other times is compared with the data fluctuation coefficient and the temperature fluctuation threshold. If the fluctuation is greater than the temperature fluctuation threshold, it is transmitted; otherwise, it is not transmitted.

[0031] Specifically, the temperature of a batch of live sheep processed in the target workshop is used to form a temperature sequence corresponding to that batch; the cumulative sum algorithm is used to obtain the stage points of the temperature sequence corresponding to each batch; and the average of the time interval between any two adjacent stage points is used as the initial transmission interval.

[0032] However, the above method judges based on the fluctuation of temperature data. In reality, data may change slowly, meaning local fluctuations are small, but the overall data deviates from the normal value. Therefore, data with a fluctuation coefficient less than or equal to the temperature fluctuation threshold needs to be judged a second time. The deviation index of the current data is calculated by judging whether the data change has deviated based on the trend of the data value and historical statistical values.

[0033] Specifically, the median of the normal temperature range of the target workshop is used as the standard reference value for the temperature of the target workshop; the absolute value of the difference between the mean temperature in the previous window when the current batch of live sheep is processed in the target workshop and the standard reference value is obtained and normalized to obtain the local temperature difference coefficient; a preset number of temperatures are taken sequentially forward from this temperature to form a temperature sequence to be analyzed; the sequence to be analyzed is divided into two parts, denoted as the front part and the back part, respectively; the similarity between the front part and the back part is obtained using the DTW algorithm, and the difference between the front and back parts is obtained by subtracting the similarity from the first preset value; the absolute value of the difference between this temperature and the mean temperature in the previous window when the current batch of live sheep is processed in the target workshop is normalized to obtain the independent temperature difference coefficient of this temperature; the difference between the front and back parts and the mean of the independent temperature difference coefficients are multiplied by the local temperature difference coefficient to obtain the deviation index of this temperature.

[0034] The specific calculation model for the deviation index is as follows: , in, This represents the deviation index of the j-th temperature in the target workshop; This represents the mean temperature within the previous window when the j-th temperature of the current batch of live sheep is processed in the target workshop, where norm represents the normalization operation. This indicates the standard reference value for the temperature of the target workshop. Indicates the local temperature difference coefficient. This indicates the normalized similarity between the first and second parts of the sequence to be analyzed. The preset number is 6. Based on good time decay and the minimum number of data references, m is set to 3. The first part of the sequence to be analyzed consists of 3 temperatures, and the second part consists of 3 temperatures. If the similarity is high, it means that the temperature change trend is more consistent and the possibility of deviation is small. This represents the j-th temperature in the target workshop. If the difference between historical data and the reference value is small, and the difference between the j-th temperature data and the average value of the historical data window is small, and the data change trend is relatively consistent, then the current data deviation index is small, and the probability of the current data deviation is low. In other calculation cases, the probability of the deviation index is high. The first preset value is 1.

[0035] Since the deviation trend has a periodic occurrence, the temperatures collected in the early stages of processing of live sheep from different batches in the same workshop can be obtained. Since the window length is 9, the deviation index of each temperature collected for each batch is calculated starting from the 10th temperature. The obtained deviation indices are clustered, and cluster analysis is performed according to the above method for obtaining the temperature fluctuation threshold. The mean of the deviation index in the cluster containing the most deviation indices is taken as the deviation index threshold.

[0036] Similarly, the temperature fluctuation threshold, humidity fluctuation threshold, temperature deviation index threshold, and humidity deviation index threshold for each workshop can be obtained. Step S3: Record the temperature of the target workshop transmitted based on the initial transmission interval as the normal transmission temperature; transmit the temperatures in the target workshop whose fluctuation coefficient or deviation index is greater than the temperature fluctuation threshold or deviation index threshold, and record them as the additional transmission temperatures.

[0037] The initial transmission interval, temperature fluctuation threshold, and deviation index threshold of the target workshop were obtained. Based on these, the temperature of the current batch of live sheep processed in the target workshop can be transmitted. Specifically, the temperature is transmitted according to the initial transmission interval. For example, if the initial transmission interval is 10 minutes, the temperature is transmitted once every 10 minutes. For other temperatures, if the temperature fluctuation coefficient is greater than the temperature fluctuation threshold, or the temperature deviation index is greater than the deviation index threshold, the temperature is transmitted.

[0038] The temperature of the target workshop transmitted based on the initial transmission interval is recorded as the normal transmission temperature; at the same time, the temperatures in the target workshop whose fluctuation coefficient or deviation index is greater than the temperature fluctuation threshold or deviation index threshold are transmitted and recorded as the additional transmission temperatures.

[0039] It should be noted that when transmitting the humidity of the target workshop, as well as the temperature and humidity of other workshops, the transmission method is the same as that for transmitting the temperature of the target workshop.

[0040] Step S4: Obtain consecutive initial transmission intervals that result in additional transmission temperatures during the temperature transmission process in the target workshop, forming consecutive time periods; denote the consecutive time periods including the current time as the current time period; determine whether to update the temperature fluctuation threshold and initial transmission interval based on the temperature fluctuation coefficient, deviation index, and the number of additional transmission temperatures in the current time period; if updating is required, update the temperature fluctuation threshold to obtain the updated temperature fluctuation threshold, and update the initial transmission interval based on the temperature fluctuation threshold and the updated temperature fluctuation threshold. Similarly, transmit the humidity of the target workshop according to the temperature transmission method.

[0041] The above steps transmit the temperature of the target workshop. If, within a time period containing several complete initial transmission intervals (each of these complete initial transmission intervals includes additional transmission temperatures), the number of additional transmission temperatures is greater than twice the number of normal transmission temperatures (for example, if the initial transmission interval is 10 minutes, then within 20 minutes there are 2 normal transmission temperatures, 7 actual transmission temperatures, and 5 additional transmission temperatures, which is greater than or equal to twice the number of normal transmission temperatures (4), then the temperatures including the additional transmission temperatures need to be analyzed to determine if the temperature fluctuation is normal. If it is normal, the temperature fluctuation threshold may not be suitable for the current data dead zone filtering and needs to be adjusted.

[0042] The consecutive initial transmission intervals in which additional transmission temperature occurs during the temperature transmission process of the target workshop are used to form consecutive time periods. For example, if the initial transmission intervals are numbered 1, 2, 3, 4, 5, 6, 7, 8 according to time, then additional transmission temperature occurs in the 1st, 2nd, and 3rd consecutive initial transmission intervals, and additional transmission temperature occurs in the 5th, 6th, and 7th consecutive initial transmission intervals. Therefore, the 1st, 2nd, and 3rd consecutive initial transmission intervals are combined into one consecutive time period, and the 5th, 6th, and 7th consecutive initial transmission intervals are combined into another consecutive time period.

[0043] Since it is necessary to determine in real time whether the initial transmission interval and the corresponding temperature fluctuation threshold need to be updated or adjusted, it is necessary to obtain a continuous time period that includes the current time and record it as the current time period. If there is no current time period that includes the current time, it means that the initial transmission interval and the corresponding temperature fluctuation threshold do not need to be updated or adjusted at this time.

[0044] If there exists a continuous time period (current time period) that includes the current moment, it is necessary to determine whether to update and adjust the initial transmission interval and the corresponding temperature fluctuation threshold.

[0045] If the temperature fluctuations during the current period are normal fluctuations, such as during the pre-slaughter processing stage, the body temperature of a live sheep is about 38-39℃. After slaughter, muscle metabolism has not completely stopped, and the internal temperature may rise briefly. Rapid cooling is achieved through operations such as rinsing and cooling. However, the difference in cooling rate between different parts (such as thick muscle groups and thin muscle groups) may lead to uneven local temperatures, with a fluctuation range of about ±2℃. During the quick-freezing stage, fresh meat needs to pass through the ice crystal formation zone (-1℃ to -5℃) within 2-4 hours. The refrigeration system needs to operate at high frequency, resulting in an overall temperature fluctuation of ±1-3℃ in the workshop. That is, the fluctuation range changes. This is a normal fluctuation. Therefore, when calculating the deviation index at the beginning, the deviation will be more obvious due to the change in data fluctuation. This may lead to an increase in the deviation index or fluctuations exceeding the temperature fluctuation threshold, resulting in more additional temperature transmission. However, in the data collection sequence, after such changes occur, data with increased deviation index or fluctuations exceeding the original temperature fluctuation threshold often appear continuously or in clusters. Therefore, if the collected data shows such a trend, it indicates that normal fluctuations may have occurred. Calculate its trend and then determine whether to update.

[0046] Therefore, based on the fluctuation coefficient, deviation index, and number of additional transmitted temperatures in the current time period, it is determined whether to update the temperature fluctuation threshold and the initial transmission interval.

[0047] Specifically, the fluctuation coefficients of each temperature within the current time period are arranged into a fluctuation coefficient sequence according to time; the deviation indices of each temperature within the current time period are arranged into a deviation index sequence according to time; the average distribution interval of the fluctuation coefficients is obtained by taking the mean of the time interval between every two adjacent fluctuation coefficients in the fluctuation coefficient sequence that are greater than the temperature fluctuation threshold; the average distribution interval of the deviation index is obtained by taking the mean of the time interval between every two adjacent deviation indices in the deviation index sequence that are greater than the deviation index threshold; and the trend performance coefficient of the current time period is obtained by subtracting the mean of the average distribution interval of the fluctuation coefficients and the average distribution interval of the deviation index from a first preset value. Obtain independent, complete initial transmission intervals with different values ​​of no additional transmission temperature or an additional transmission temperature less than twice the normal transmission temperature, and denote them as the initial transmission intervals to be analyzed. Obtain the trend performance coefficient of each initial transmission interval to be analyzed and perform clustering to obtain different clusters. Obtain the mean of all trend performance coefficients in the cluster containing the most trend performance coefficients as the trend performance coefficient threshold. If the number of additional transmission temperatures in the current period is greater than or equal to twice the number of normal transmission temperatures in the current period, and the trend performance coefficient in the current period is greater than or equal to the trend performance coefficient threshold, then update the temperature fluctuation threshold and the initial transmission interval.

[0048] The formula for calculating the trend performance coefficient is as follows: , Indicates the trend performance coefficient. It represents the average time interval between any two adjacent deviation indices in the deviation index sequence that are greater than the deviation index threshold, which is also the average distribution interval of the deviation index. This represents the average time interval between any two adjacent fluctuation coefficients in the fluctuation coefficient sequence that are greater than the temperature fluctuation threshold. It is also the average distribution interval of the fluctuation coefficients. The first preset value is 1, and norm represents the normalization operation. If the average distribution interval is smaller, it indicates that there are abnormal deviations in the data. Data with exponentially increasing fluctuations or fluctuations exceeding the original threshold will often appear continuously or in clusters. Such trends are obvious, and the trend performance coefficient is larger.

[0049] Furthermore, the temperature fluctuation threshold needs to be updated to obtain the updated temperature fluctuation threshold. The update method is the same as the calculation method of the temperature fluctuation threshold mentioned above, using the temperatures of each batch of live sheep in history and the current batch of live sheep for updating.

[0050] After the update is completed, the initial transmission interval is updated based on the temperature fluctuation threshold and the updated temperature fluctuation threshold. Specifically, if the temperature fluctuation threshold is less than the updated temperature fluctuation threshold, an adjustment coefficient is obtained by subtracting the absolute value of the difference between the temperature fluctuation threshold and the updated temperature fluctuation threshold from the first preset value. The initial transmission interval is then multiplied by the adjustment coefficient to obtain the updated transmission interval. If the temperature fluctuation threshold is equal to the updated temperature fluctuation threshold, the initial transmission interval is used as the updated transmission interval. If the temperature fluctuation threshold is greater than the updated temperature fluctuation threshold, an adjustment coefficient is obtained by adding the absolute value of the difference between the temperature fluctuation threshold and the updated temperature fluctuation threshold to the first preset value. The initial transmission interval is then multiplied by the adjustment coefficient to obtain the updated transmission interval.

[0051] The updated calculation model for the transmission interval is as follows: , in, Indicates the updated transmission interval. Indicates the initial transmission interval. Indicates the temperature fluctuation threshold. This indicates the updated temperature fluctuation threshold.

[0052] Similarly, the temperature and humidity of other workshops and the humidity of the target workshop are transmitted in the same way as described above, and the update methods for the temperature fluctuation threshold and transmission interval are also the same.

[0053] In summary, the fluctuation coefficient of each collected data point is calculated to determine if it exceeds the fluctuation threshold (temperature or humidity). If not, it is calculated to determine if it exceeds the deviation index threshold. If neither is exceeded, the data is transmitted according to the acquired transmission interval. If the fluctuation coefficient of the collected data exceeds the fluctuation threshold, it is transmitted directly. Alternatively, if the fluctuation coefficient is less than or equal to the fluctuation threshold, but the deviation index exceeds the deviation index threshold, it is transmitted directly. The data transmitted within the current time period is evaluated to determine if the additional transmitted data is more than twice the normal transmitted data. A trend performance coefficient is then calculated to determine if the fluctuation threshold and transmission interval need updating. If updating is required, it is performed using the methods described above. Upon receiving the transmitted data, the temperature and humidity environment of the workshop is monitored based on the normal temperature and humidity range.

[0054] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0055] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0056] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for real-time monitoring, acquisition, and processing of workshop temperature and humidity data, characterized in that, The method includes: The process involves collecting temperature and humidity data from the target workshop during the processing of the current batch of live sheep; dividing the temperature in the target workshop into windows and obtaining the local fluctuation coefficient for each window; constructing windows centered on each temperature and calculating the fluctuation coefficient for each temperature; and performing cluster analysis on the local fluctuation coefficients of each window corresponding to different batches of live sheep processed in the target workshop's history to obtain the temperature fluctuation threshold for the target workshop. This includes: clustering the local fluctuation coefficients of each window corresponding to different batches of live sheep processed in the target workshop's history to obtain different clusters; and taking the mean of all local fluctuation coefficients in the cluster containing the most elements as the temperature fluctuation threshold for the target workshop. The initial transmission interval is obtained based on the temperature at which different batches of live sheep were processed in the target workshop in the past; the deviation index of a temperature in the target workshop is obtained based on the deviation of a temperature from the historical temperature; and the deviation index threshold is obtained by performing cluster analysis on the deviation indices of each temperature in the target workshop in the past. The temperature of the target workshop transmitted based on the initial transmission interval is recorded as the normal transmission temperature; the temperature of the target workshop whose fluctuation coefficient or deviation index is greater than the temperature fluctuation threshold or deviation index threshold is transmitted and recorded as the additional transmission temperature. The continuous initial transmission intervals during the temperature transmission process of the target workshop where additional transmission temperature occurs are formed into continuous time periods; the continuous time period including the current time is recorded as the current time period; The decision to update the temperature fluctuation threshold and initial transmission interval is based on the fluctuation coefficient, deviation index, and number of additional transmitted temperatures for each temperature during the current time period, including: The fluctuation coefficients of each temperature within the current time period are arranged into a fluctuation coefficient sequence according to time; the deviation indices of each temperature within the current time period are arranged into a deviation index sequence according to time; the mean of the time interval between every two adjacent fluctuation coefficients greater than the temperature fluctuation threshold within the fluctuation coefficient sequence is obtained to obtain the average distribution interval of the fluctuation coefficients; the mean of the time interval between every two adjacent deviation indices greater than the deviation index threshold within the deviation index sequence is obtained to obtain the average distribution interval of the deviation indexes; the normalized value obtained by subtracting the mean of the average distribution interval of the fluctuation coefficients and the average distribution interval of the deviation indexes from a first preset value is used to obtain the value of the current time period. Trend performance coefficient; Obtain independent and complete initial transmission intervals with different no-extra-transmission temperatures or extra-transmission temperatures less than twice the normal transmission temperature, and denote them as the initial transmission intervals to be analyzed; Obtain the trend performance coefficient of each initial transmission interval to be analyzed and cluster them to obtain different clusters; Obtain the mean of all trend performance coefficients in the cluster containing the most trend performance coefficients as the trend performance coefficient threshold; If the number of extra-transmission temperatures in the current period is greater than or equal to twice the number of normal transmission temperatures in the current period, and the trend performance coefficient of the current period is greater than or equal to the trend performance coefficient threshold, then update the temperature fluctuation threshold and the initial transmission interval; If an update is required, the temperature fluctuation threshold is updated to obtain the updated temperature fluctuation threshold. The initial transmission interval is then updated based on the temperature fluctuation threshold and the updated temperature fluctuation threshold. Similarly, the humidity of the target workshop is transmitted according to the temperature transmission method. The step of updating the initial transmission interval based on the temperature fluctuation threshold and the updated temperature fluctuation threshold includes: If the temperature fluctuation threshold is less than the updated temperature fluctuation threshold, an adjustment coefficient is obtained by subtracting the absolute value of the difference between the temperature fluctuation threshold and the updated temperature fluctuation threshold from the first preset value. The initial transmission interval is then multiplied by the adjustment coefficient to obtain the updated transmission interval. If the temperature fluctuation threshold is equal to the updated temperature fluctuation threshold, the initial transmission interval is used as the updated transmission interval. If the temperature fluctuation threshold is greater than the updated temperature fluctuation threshold, an adjustment coefficient is obtained by adding the absolute value of the difference between the temperature fluctuation threshold and the updated temperature fluctuation threshold to the first preset value. The initial transmission interval is then multiplied by the adjustment coefficient to obtain the updated transmission interval.

2. The method for real-time monitoring, acquisition, and processing of workshop temperature and humidity data according to claim 1, characterized in that, The step of dividing the temperature of the target workshop into windows and obtaining the local fluctuation coefficient of each window includes: The local fluctuation coefficient of a window is obtained by averaging the absolute values ​​of the differences between each temperature within a window and the mean of all temperatures within that window, and then normalizing the average value.

3. The method for real-time monitoring, acquisition, and processing of workshop temperature and humidity data according to claim 1, characterized in that, The step of constructing a window centered on each temperature and calculating the fluctuation coefficient for each temperature separately includes: The average of the absolute values ​​of the differences between each temperature within a window centered at a given temperature and the mean of all temperatures within that window is obtained and normalized to obtain the fluctuation coefficient of that temperature.

4. The method for real-time monitoring, acquisition, and processing of workshop temperature and humidity data according to claim 1, characterized in that, The process of obtaining the initial transmission interval based on the temperature of different batches of live sheep processed in the target workshop in history includes: The temperature of a batch of live sheep processed in the target workshop is used to form the temperature sequence corresponding to that batch; the cumulative sum algorithm is used to obtain the stage point of the temperature sequence corresponding to each batch; the average of the time interval between any two adjacent stage points is used as the initial transmission interval.

5. The method for real-time monitoring, acquisition, and processing of workshop temperature and humidity data according to claim 1, characterized in that, The step of obtaining the temperature deviation index based on the deviation of a temperature in the target workshop from historical temperatures includes: The median of the normal temperature range of the target workshop is used as the standard reference value for the temperature of the target workshop; the absolute value of the difference between the mean temperature in the previous window when the current batch of live sheep is processed in the target workshop and the standard reference value is obtained and normalized to obtain the local temperature difference coefficient; a preset number of temperatures are taken sequentially forward from this temperature to form a temperature sequence to be analyzed; the sequence to be analyzed is divided into two parts, denoted as the front part and the back part, respectively; the similarity between the front part and the back part is obtained using the DTW algorithm, and the difference between the front and back parts is obtained by subtracting the similarity from the first preset value; the absolute value of the difference between this temperature and the mean temperature in the previous window when the current batch of live sheep is processed in the target workshop is normalized to obtain the independent temperature difference coefficient of this temperature; the difference between the front and back parts and the mean of the independent temperature difference coefficient of the temperature are multiplied by the local temperature difference coefficient to obtain the deviation index of this temperature.

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