Intelligent Monitoring Method for Environmental Temperature Data in Pediatric Departments

Abstract

The present invention relates to the technical field of hospital temperature monitoring, and specifically relates to an intelligent monitoring method for environmental temperature data in pediatric departments, including: dividing several different departments based on pediatrics, determining the theoretical temperature range of each department, and collecting the temperature data of each department; comparing the theoretical temperature range with the temperature data to construct an abnormal temperature set for each department, calculating the possibility of temperature abnormality, and determining the temperature fluctuation characteristics through the abnormal temperature set; correcting the possibility of temperature abnormality according to the temperature fluctuation characteristics, screening to obtain normal temperature departments and abnormal temperature departments; analyzing the temperature data of the abnormal temperature departments to obtain the possibility of temperature interference caused by human factors and the possibility of normal temperature regulation, and combining to calculate the abnormal credibility; when the abnormal credibility is greater than the judgment threshold, it is determined that a real temperature abnormality occurs in the currently analyzed department; so as to achieve precise temperature monitoring of different types of departments and improve the accuracy of temperature monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of hospital temperature monitoring, and particularly relates to an intelligent monitoring method for environmental temperature data in pediatric departments. Background Art

[0002] In the pediatric departments of hospitals, due to children being in different age stages, there are significant differences in their adaptability and requirements for environmental temperature. The age groups include infants, preschool children, and school-age children, and their immune capabilities against viruses and other pathogens also vary. Therefore, the control of environmental temperature data in pediatric departments requires greater precision and meticulousness than in other departments. Moreover, to ensure the comfort and health of children, it is necessary to continuously monitor the environmental temperature data in pediatric departments to promptly detect abnormal temperature conditions and adopt corresponding regulation strategies to maintain an appropriate environmental temperature.

[0003] Currently, the commonly used method for abnormal monitoring of temperature data in pediatric departments is to achieve it through an environmental temperature monitoring system. When an abnormal temperature is detected, the system will send a temperature regulation signal to the temperature regulation control system, i.e., the temperature control system, so as to achieve timely regulation of the temperature. However, this method mainly relies on the temperature change data within a single department and cannot achieve temperature regulation for different departments according to the regulation principle. Additionally, due to the movement of personnel between different departments, this monitoring method is easily interfered with, affecting the accuracy and reliability of temperature monitoring. Summary of the Invention

[0004] In order to solve the technical problems that the existing monitoring method has strong monitoring limitations and is easily interfered by human factors, affecting the monitoring accuracy, the purpose of the present invention is to provide an intelligent monitoring method for environmental temperature data in pediatric departments. The specific technical solutions adopted are as follows:

[0005] Based on the pediatric division, several different departments are determined, and the theoretical temperature range of each department is determined, and the temperature data of each department is collected;

[0006] The theoretical temperature range is compared with the temperature data to construct an abnormal temperature set for each department, the frequency of abnormal temperature occurrence is obtained to get the temperature abnormality possibility, and the temperature fluctuation characteristics are determined through the abnormal temperature set;

[0007] According to the temperature fluctuation characteristics, the temperature abnormality possibility is corrected and screened to respectively determine the departments with normal temperature and the departments with abnormal temperature;

[0008] Analyze the temperature data of the departments with abnormal temperature to obtain the possibility of temperature interference caused by human factors and the possibility of normal temperature regulation, and combine the two to calculate the abnormal credibility;

[0009] Set a judgment threshold. When the abnormal credibility is greater than the judgment threshold, it is determined that there is a real temperature anomaly in the current analysis department.

[0010] Preferably, compare the theoretical temperature range with the temperature data to construct the abnormal temperature set of each department, obtain the frequency of abnormal temperature occurrences to get the temperature anomaly possibility, and determine the temperature fluctuation characteristics through the abnormal temperature set, including:

[0011] When the temperature data is within the theoretical temperature range, it is considered normal and marked as 0; otherwise, it is considered abnormal. When the temperature data is higher than the theoretical temperature range, it is marked as positive, and when the temperature data is lower than the theoretical temperature range, it is marked as negative, generating a signed distance , and construct a positive abnormal temperature set and a negative abnormal temperature set respectively according to the positive and negative marks;

[0012] Obtain the frequency of abnormal temperature occurrences to get the temperature anomaly possibility;

[0013] Analyze the positive abnormal temperature set and the negative abnormal temperature set respectively to determine the temperature fluctuation characteristics in turn.

[0014] Preferably, obtain the frequency of abnormal temperature occurrences to get the temperature anomaly possibility, and the corresponding calculation formula is:

[0015]

[0016] Among them, represents the temperature anomaly possibility of the current analysis department; represents the frequency of abnormal temperature occurrences; represents the abnormal state mark of the th temperature data; represents the normalization function.

[0017] Preferably, determine the temperature fluctuation characteristics, and the corresponding calculation formula is:

[0018]

[0019] Among them, represents the temperature fluctuation characteristics of the abnormal temperature set ; represents the number of elements in the abnormal temperature set ; represents the temperature data within the corresponding time neighborhood; represents the standard deviation operation; represents the mean operation; represents the temperature anomaly possibility corresponding to the temperature data at the th acquisition moment within the corresponding time neighborhood; represents the Abnormal status marker for temperature data at a collection moment; Indicates a normalization function.

[0020] Preferably, correct the temperature anomaly possibility according to the temperature fluctuation characteristics, and perform screening to respectively determine the departments with normal temperature and the departments with abnormal temperature, including:

[0021] Select the maximum value in the positive and negative abnormal temperature sets based on the temperature fluctuation characteristics, and denote it as the temperature fluctuation value;

[0022] Correct the temperature anomaly possibility through the temperature fluctuation value;

[0023] Set a screening threshold. When the corrected temperature anomaly possibility is greater than the screening threshold, it indicates that the current analyzed department has abnormal temperature; when the corrected temperature anomaly possibility is less than the screening threshold, it indicates that the current analyzed department has normal temperature.

[0024] Preferably, correct the temperature anomaly possibility through the temperature fluctuation value, and the corresponding calculation formula is:

[0025]

[0026] Among them, Indicates the corrected temperature anomaly possibility; Indicates the temperature anomaly possibility before correction; Indicates the temperature fluctuation value; Indicates the positive abnormal temperature set The temperature fluctuation characteristics of; Indicates the negative abnormal temperature set The temperature fluctuation characteristics of.

[0027] Preferably, analyze the temperature data of the departments with abnormal temperature to obtain the possibility of temperature interference caused by human factors and the possibility of normal temperature regulation, and combine the two to calculate the abnormal credibility, including:

[0028] The temperature data respectively include the temperature data inside and outside the departments with normal temperature and the departments with abnormal temperature;

[0029] Based on the temperature data inside the department with abnormal temperature and the temperature data outside the department with abnormal temperature, construct a temperature difference sequence, fit the temperature difference sequence to obtain the slope of the fitted straight line, obtain the temperature difference change amplitude of the department with abnormal temperature, and perform normalization processing. Combine the slope of the fitted straight line to obtain the possibility of temperature interference caused by human factors in the department with abnormal temperature;

[0030] The temperature data in the departments with normal temperature and the departments with abnormal temperature are respectively fitted to obtain fitting parameters. The temperature similarity between the departments with abnormal temperature and the departments with normal temperature is determined through the fitting parameters. The temperature data outside the departments with normal temperature and the departments with abnormal temperature are analyzed to obtain the possibility that the departments with abnormal temperature belong to normal temperature regulation;

[0031] The abnormal credibility degree of the departments with abnormal temperature is calculated by combining the possibility of temperature interference caused by human factors and the possibility of normal temperature regulation.

[0032] Preferably, a temperature difference sequence is constructed, and the corresponding calculation formula is:

[0033]

[0034] where, represents the temperature difference inside and outside the department with abnormal temperature; represents the temperature data outside the department with abnormal temperature; represents the temperature data inside the department with abnormal temperature;

[0035] The change amplitude of the temperature difference of the department with abnormal temperature is obtained and normalized, and the corresponding calculation formula is:

[0036]

[0037] where, represents the change amplitude of the temperature difference of the department with abnormal temperature; represents the temperature difference inside and outside the department at the end of the current analysis time neighborhood of the department with abnormal temperature; represents the temperature difference inside and outside the department at the beginning of the current analysis time neighborhood of the department with abnormal temperature;

[0038]

[0039] where, represents the change amplitude of the temperature difference after normalization; represents the normalization function;

[0040] The possibility that the temperature anomaly in the department is caused by human factors is obtained, and the corresponding calculation formula is:

[0041]

[0042] where, represents the possibility that the temperature anomaly in the department is caused by human factors; represents the slope of the fitting line; represents the change amplitude of the temperature difference after normalization; represents the normalization function.

[0043] Preferably, the fitting parameter is , where represents the slope of the temperature data in the normal temperature department or the abnormal temperature department changing with the ambient temperature, represents the theoretical equilibrium benchmark of the temperature in the normal temperature department or the abnormal temperature department;

[0044] The temperature similarity between the abnormal temperature department and the normal temperature department is determined by the fitting parameter, and the corresponding calculation formula is:

[0045]

[0046] where represents the temperature similarity between the currently analyzed abnormal temperature department and the th normal temperature department; represents the fitting parameter of the abnormal temperature department; represents the th fitting parameter of the normal temperature department; represents the regulated temperature of the abnormal temperature department; represents the th regulated temperature of the normal temperature department; represents the normalization function;

[0047] The possibility that the abnormal temperature department belongs to normal temperature regulation is obtained, and the corresponding calculation formula is:

[0048]

[0049] where represents the possibility that the currently analyzed abnormal temperature department belongs to normal temperature regulation; represents the correlation operation; represents the temperature similarity between the currently analyzed abnormal temperature department and the th normal temperature department; represents the temperature data outside the abnormal temperature department; represents the th temperature data outside the normal temperature department.

[0050] Preferably, the abnormal credibility of the abnormal temperature department is calculated by combining the possibility of temperature interference caused by human factors and the possibility of normal temperature regulation, and the corresponding calculation formula is:

[0051]

[0052] where represents the abnormal credibility; represents the possibility that the abnormal temperature department belongs to normal temperature regulation; Indicates the possibility of temperature interference caused by human factors in departments with abnormal temperatures; Represents the normalization function.

[0053] The present invention has the following beneficial effects:

[0054] By monitoring the internal temperature of departments in real time to evaluate whether the temperature changes in departments are within the normal fluctuation range, analyzing the collected temperature data, first classifying the departments, analyzing the temperature fluctuation characteristics in the time neighborhood, and performing correction, the departments are screened into departments with normal temperatures and departments with abnormal temperatures; then, combining the temperature change data inside and outside the departments and the change trend of the temperature data, analyze whether there is temperature fluctuation caused by human factors; then, by comparing and analyzing the departments with normal temperatures and the departments with abnormal temperatures, judge whether the temperature change is caused by normal environmental control measures or by some abnormal conditions during the control process, so as to determine the abnormal credibility of the temperature data, effectively monitor and manage the temperature conditions of the department environment, achieve accurate temperature monitoring of different types of departments, and improve the accuracy of temperature monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0056] Figure 1 It is a flowchart of the steps of an intelligent monitoring method for pediatric department environment temperature data provided by an embodiment of the present invention;

[0057] Figure 2 It is a schematic diagram of a department with normal temperature in the intelligent monitoring method for pediatric department environment temperature data provided by an embodiment of the present invention Figure 1 ;

[0058] Figure 3 It is a schematic diagram of a department with normal temperature in the intelligent monitoring method for pediatric department environment temperature data provided by an embodiment of the present invention Figure 2 ;

[0059] Figure 4 It is a schematic diagram of a department with abnormal temperature in the intelligent monitoring method for pediatric department environment temperature data provided by an embodiment of the present invention Figure 1 ;

[0060] Figure 5Schematic diagram of departments with abnormal temperature for the intelligent monitoring method of environmental temperature data in the pediatric department provided by an embodiment of the present invention Figure 2 ;

[0061] Figure 6 Schematic diagram of the possibility of temperature interference caused by human factors in departments with abnormal temperature for the intelligent monitoring method of environmental temperature data in the pediatric department provided by an embodiment of the present invention;

[0062] Figure 7 Schematic diagram of the possibility that departments with abnormal temperature for the intelligent monitoring method of environmental temperature data in the pediatric department provided by an embodiment of the present invention belong to normal temperature regulation. Detailed implementation manners

[0063] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes, in conjunction with the accompanying drawings and preferred embodiments, a method for intelligent monitoring of environmental temperature data in the pediatric department proposed according to the present invention, including its specific implementation manners, structures, features, and effects. In the following description, different "an embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0065] The following specifically describes the specific solution of a method for intelligent monitoring of environmental temperature data in the pediatric department provided by the present invention in conjunction with the accompanying drawings.

[0066] Please refer to Figure 1 , which shows a flowchart of the steps of a method for intelligent monitoring of environmental temperature data in the pediatric department provided by an embodiment of the present invention. The method includes:

[0067] Step S1: Divide several different departments based on pediatrics, determine the theoretical temperature range of each department, and collect the temperature data of each department;

[0068] Step S2: Compare the theoretical temperature range with the temperature data to construct an abnormal temperature set for each department, obtain the frequency of abnormal temperature occurrence to get the temperature abnormality possibility, and determine the temperature fluctuation characteristics through the abnormal temperature set;

[0069] Step S3: Correct the temperature abnormality possibility according to the temperature fluctuation characteristics, and perform screening to determine the departments with normal temperature and the departments with abnormal temperature respectively;

[0070] Step S4: Analyze the temperature data of the department with abnormal temperature to obtain the possibility of temperature interference caused by human factors and the possibility of normal temperature regulation, and combine the two to calculate the abnormal credibility level.

[0071] Step S5: Set a judgment threshold. When the abnormal credibility level is greater than the judgment threshold, it is determined that there is a real temperature abnormality in the currently analyzed department.

[0072] For better illustration, since the thermoregulatory center of children is not yet fully developed and is sensitive to changes in environmental temperature, a stable temperature in the pediatric department helps maintain normal body temperature. Moreover, children are weak in temperature during illness and have a higher requirement for environmental temperature comfort. A large temperature difference change will cause discomfort symptoms. Therefore, it is necessary to realize real-time monitoring and recording of the temperature changes in different functional departments of the pediatric department to continuously optimize the environmental conditions of the pediatric department and ensure that child patients of different ages receive treatment and care in a suitable environment.

[0073] Specifically, in step S1, several different departments are divided based on pediatrics. In the pediatric department, according to different populations or functions, the pediatric department is divided. For example, the pediatric department is divided into a children's room, an infant room, a neonatal room, etc. according to age to meet the different needs of children of different ages in terms of immune capacity; or the pediatric department is divided into a corridor, a ward, a diagnosis room, etc. according to function to ensure that each area can provide a suitable environment and conditions, that is, different departments maintain different temperatures to maintain the suitability of environmental factors such as temperature and humidity in each department; and no matter which way of division is adopted, each obtained area has the same theoretical temperature regulation method.

[0074] As an optional implementation method, in this embodiment, the pediatric department is divided into a children's room, an infant room, and a neonatal room according to the division method of different age groups.

[0075] Collect the temperature data of each department. Since each department is a relatively independent unit, the temperature regulation of each department is realized through its own temperature control system combined with actual analysis to optimize the existing temperature monitoring method and improve the applicable range; that is, collect the temperature data of each department through the temperature control system, that is, the supply air temperature and the return air temperature. Among them, the supply air temperature represents the adjusted temperature of the temperature control system itself, and the return air temperature represents the current environmental temperature of the department; in addition, although the corridor outside the department is also equipped with a temperature control system to collect temperature data, for the temperature control accuracy of the corridor, it does not need to be controlled with high precision like inside the department, so the temperature data outside the department can also be obtained through the temperature control system in the corridor outside the department.

[0076] Optionally, the temperature data includes, but is not limited to, any one of the data of the pediatric department category, the theoretical temperature range, the actual adjusted temperature, the indoor ambient temperature, and the outdoor ambient temperature, to ensure the temperature suitability of the pediatric department.

[0077] It can be explained that for pediatric departments with different temperature accuracy requirements, different data sampling frequencies are adopted, that is, the higher the accuracy requirement of the department, the greater the sampling frequency. Because different departments may have different sensitivities and requirements for temperature, it is necessary to adjust the sampling frequency according to the actual situation to ensure the accuracy and timeliness of the data. For example, the temperature accuracy in the operating room is usually higher, and the sampling frequency is set higher; while the temperature accuracy in the ward is lower, and the sampling frequency can be correspondingly reduced.

[0078] For better illustration, in this embodiment, the temperature data within the historical 60 minutes during the collection moment is selected as the time neighborhood for analysis.

[0079] It can be understood that based on the analysis of any one of the pediatric departments, if there is a significant deviation between the temperature data collected during the monitoring process of the department and the theoretical temperature range, it indicates that there is a problem with the temperature of the department; in addition, if within a period of time, the temperature data of the department shows that abnormal conditions persist, and the gap between the temperature data and the theoretical temperature range is large, it indicates a temperature anomaly; on the contrary, if the temperature data of the department is within the theoretical temperature range, or even if there is an anomaly, it only fluctuates slightly at the edge of the theoretical temperature range, it indicates that the actual temperature data is within the normal range and there is no abnormal condition that requires special attention.

[0080] Further, in step S2, it includes:

[0081] Step S21: When the temperature data is within the theoretical temperature range, it indicates normal and is marked as 0; otherwise, it indicates abnormal. When the temperature data is higher than the theoretical temperature range, it is marked as positive, and when the temperature data is lower than the theoretical temperature range, it is marked as negative, generating a phase distance , and respectively constructing a positive abnormal temperature set and a negative abnormal temperature set according to the positive mark and the negative mark.

[0082] It is explained that the value of the phase distance represents the minimum value of the difference between the corresponding temperature data within the time neighborhood and the theoretical temperature range. When the temperature data is higher than and / or lower than the theoretical temperature range, the status is marked as +1 or -1 respectively. For example, the temperature data collected at the current collection moment in the children's room is 15°C, indicating abnormal, and the direction is marked as negative, and the phase distance value is determined to be , that is reflects the temperature anomaly status at the corresponding collection moment; then respectively constructing a positive abnormal temperature set and a negative abnormal temperature set according to the positive mark and the negative mark, and respectively denoted as and .

[0083] Step S22: Obtain the frequency of abnormal temperature occurrence to obtain the possibility of temperature abnormality.

[0084] It is explained that both excessively high and excessively low temperatures indicate abnormal temperatures in the department, and if abnormal temperature fluctuations occur only part of the time within the time range, even though the temperature data in the department is within a smaller range than the theoretical temperature, it indicates that the department still has large temperature anomalies.

[0085] Further, in step S22, the frequency of abnormal temperature occurrence is obtained to obtain the possibility of temperature abnormality, and the corresponding calculation formula is:

[0086]

[0087] in, Indicates the possibility of abnormal temperature in the current analysis department; Indicates the frequency of abnormal temperature occurrence; Indicates Abnormal status mark of temperature data; Represents the normalization function.

[0088] To explain, It indicates the frequency of abnormal temperature occurrence, that is, it reflects the duration of abnormal temperature in this time neighborhood.

[0089] Step S23: Analyze the positive abnormal temperature set and the negative abnormal temperature set respectively, and determine the temperature fluctuation characteristics in turn.

[0090] It can be understood that if the similarity of abnormal temperature data in any of the positive abnormal temperature set and the negative abnormal temperature set is higher, it means that the fluctuation amplitude that causes the abnormal temperature data in the time neighborhood remains basically consistent. At this time, the smaller the difference between the corresponding abnormal temperature data and the theoretical temperature range, the fewer times the temperature data is abnormal, which means that the abnormal temperature data monitored within the corresponding time is normal temperature fluctuation. Therefore, the positive abnormal temperature set and the negative abnormal temperature set are analyzed separately to determine whether the temperature data in any of the sets belongs to normal temperature fluctuation to avoid incorrect screening.

[0091] Furthermore, in step S23, the temperature fluctuation characteristics are determined, and the corresponding calculation formula is:

[0092]

[0093] in, Represents abnormal temperature set Temperature fluctuation characteristics; Indicates the number of elements in the abnormal temperature set ; Represents the temperature data within the corresponding time neighborhood; Indicates the standard deviation operation; Indicates the mean operation; Indicates the th temperature anomaly probability corresponding to the temperature data at the collection time within the corresponding time neighborhood; Indicates the th abnormal status flag of the temperature data at the collection time within the corresponding time neighborhood; Indicates the normalization function.

[0094] Make an explanation, Indicates the temperature fluctuation characteristics of the abnormal temperature set , that is, the temperature fluctuation characteristics of the positive abnormal temperature set are , and the temperature fluctuation characteristics of the negative abnormal temperature set are ; when the temperature fluctuation characteristic is larger, it indicates that the temperature data in the corresponding abnormal temperature set shows an obvious deviation from the theoretical temperature range, indicating that the temperature fluctuation within this time neighborhood exceeds the normal temperature fluctuation range and belongs to abnormal fluctuation. Therefore, further analysis is required to timely discover potential problems and take corresponding preventive measures to prevent problems in temperature regulation.

[0095] Furthermore, in step S3, it includes:

[0096] Step S31: Select the maximum value from the positive abnormal temperature set and the negative abnormal temperature set based on the temperature fluctuation characteristic, and record it as the temperature fluctuation value.

[0097] Make an explanation. Based on the previously obtained positive abnormal temperature set and negative abnormal temperature set temperature fluctuation characteristics and , select the maximum value of the temperature fluctuation condition as the temperature fluctuation value within the time neighborhood for the current analysis , and the corresponding calculation formula is:

[0098]

[0099] That is, by restricting the temperature fluctuation, the monitoring accuracy of the department temperature data is improved.

[0100] Step S32: Correct the temperature anomaly probability through the temperature fluctuation value.

[0101] Furthermore, in step S32, correcting the temperature anomaly probability through the temperature fluctuation value, the corresponding calculation formula is:

[0102]

[0103] Among them, represents the corrected temperature anomaly probability; represents the temperature anomaly probability before correction; represents the temperature fluctuation value; represents the temperature fluctuation characteristics of the set of positive anomaly temperatures ; represents the temperature fluctuation characteristics of the set of negative anomaly temperatures .

[0104] It is explained that the temperature fluctuation value is used to determine the abnormal fluctuation of the temperature, and is corrected in combination with the temperature anomaly probability to realize continuous monitoring of the temperature fluctuation, so as to determine the abnormal situation in the time neighborhood and avoid potential damage.

[0105] Please combine Figures 2 - 5 , which respectively show the schematic diagrams of normal temperature departments, Figure 1 schematic diagrams of normal temperature departments, Figure 2 schematic diagrams of abnormal temperature departments Figure 1 and schematic diagrams of abnormal temperature departments Figure 2 for the intelligent monitoring method of environmental temperature data in the pediatric department. Among them, in the schematic diagrams of normal temperature departments Figure 1 and schematic diagrams of normal temperature departments Figure 2 , different line segments are used to respectively determine the indoor temperature, outdoor temperature, set temperature of the department and theoretical temperature range of the department. In addition, the shaded part is used to represent the suitable temperature interval of the corresponding department; while in the schematic diagrams of abnormal temperature departments Figure 1 and schematic diagrams of abnormal temperature departments Figure 2 , the shaded part is used to represent the acquisition interval of abnormal temperatures.

[0106] Step S33: Set a screening threshold. When the corrected temperature anomaly probability is greater than the screening threshold, it indicates that the currently analyzed department is an abnormal temperature department; when the corrected temperature anomaly probability is less than the screening threshold, it indicates that the currently analyzed department is a normal temperature department.

[0107] As an alternative implementation, in this embodiment, the screening threshold is .

[0108] Specifically, the corrected temperature anomaly probability of each department to be analyzed is obtained in sequence. If the corrected temperature anomaly probability is greater than 0.2, it indicates that the temperature of the current department is abnormal. Otherwise, it indicates that the temperature of the current department is normal. Based on this, all departments are classified to respectively determine normal temperature departments and abnormal temperature departments.

[0109] Understandably, since the children's department is populated by children with low living capabilities, there is usually a need for more frequent personnel access, including medical staff, patient families, and other relevant personnel. The frequent personnel flow causes the temperature data within the department to become highly unstable. That is, every time someone enters or exits, the air exchange inside and outside the department affects the change in temperature data within the department, making the temperature data inside and outside the department tend to be the same. Therefore, this frequent personnel flow causes the temperature within the department to become very unstable. So, if only relying on temperature monitoring equipment to determine whether the temperature data is abnormal, normal temperature fluctuations in this situation may be wrongly regarded as abnormal temperatures rather than real temperature abnormalities, resulting in inaccurate temperature monitoring. Therefore, although the temperature monitoring in some departments shows abnormalities, in fact, the temperatures in these departments may be in a normal adjustment process, leading to misjudgment of the temperature and further reducing the credibility of the monitored temperature data.

[0110] Furthermore, in step S4, it includes:

[0111] The temperature data respectively includes the temperature data inside and outside the departments with normal temperature and the departments with abnormal temperature.

[0112] Please refer to Figure 6 , which shows a schematic diagram of the possibility of temperature interference caused by human factors in the temperature-abnormal departments in the intelligent monitoring method for the environmental temperature data of the pediatric department provided by an embodiment of the present invention. Among them, the horizontal axis represents the department number of the temperature-abnormal department; the vertical axis represents the possibility of temperature interference caused by human factors in the temperature-abnormal department.

[0113] Step S41: Based on the temperature data inside the temperature-abnormal department and the temperature data outside the temperature-abnormal department, construct a temperature difference sequence, fit the temperature difference sequence to obtain the slope of the fitted straight line, obtain the change amplitude of the temperature difference in the temperature-abnormal department, and perform normalization processing. Combine with the slope of the fitted straight line to obtain the possibility of temperature interference caused by human factors in the temperature-abnormal department.

[0114] Make an explanation. Arbitrarily select a temperature-abnormal department for analysis. Based on the time neighborhood corresponding to the current analysis, subtract the temperature data outside the temperature-abnormal department from the temperature data inside the temperature-abnormal department to obtain the corresponding temperature difference sequence.

[0115] Furthermore, in step S41, when constructing the temperature difference sequence, the corresponding calculation formula is:

[0116]

[0117] Among them, represents the temperature difference inside and outside the temperature-abnormal department; Represents the temperature data outside the department with abnormal temperature; Represents the temperature data inside the department with abnormal temperature.

[0118] Then, perform a least squares fit on the temperature difference sequence to obtain the slope of the fitted line. Among them, the least squares fit is to find the best function match for the data by minimizing the sum of the squares of the errors to obtain the fitted line, and then through Calculate to obtain the slope, where Represents the slope, Represents the intercept, and through the slope Illustrates the change rate of the fitted line variables and ; If the slope is less than 0 and close to 0, it indicates that the temperature difference between inside and outside the department with abnormal temperature decreases over time, and at this time, the possibility of human factors causing air flow to affect temperature change is greater.

[0119] Obtain the temperature difference change amplitude of the department with abnormal temperature, and the corresponding calculation formula is:

[0120]

[0121] Among them, Represents the temperature difference change amplitude of the department with abnormal temperature; Represents the temperature difference between inside and outside the department at the end within the time neighborhood currently analyzed for the department with abnormal temperature; Represents the temperature difference between inside and outside the department at the beginning within the time neighborhood currently analyzed for the department with abnormal temperature.

[0122] It can be explained that if the temperature difference change amplitude , it indicates that the current temperature difference has decreased within this time neighborhood, that is, the temperature difference between inside and outside the department with abnormal temperature is similar; conversely, if , it indicates that the temperature difference between inside and outside the department with abnormal temperature is larger, indicating that the temperature data between inside and outside the department with abnormal temperature shows obvious regional similarity characteristics within this time neighborhood, and at this time, it reflects that the possibility of human factors causing temperature change in the department with abnormal temperature is greater; if the temperature difference does not show a decreasing characteristic or the temperature inside and outside the department with abnormal temperature tends to be consistent, at this time, the possibility of abnormal monitored temperature caused by human factors is smaller.

[0123] Then perform normalization processing, and the corresponding calculation formula is:

[0124]

[0125] Among them, Represents the temperature difference change amplitude after normalization processing; Represents the normalization function.

[0126] Obtain the possibility that the temperature interference in the department with abnormal temperature is caused by human factors. The corresponding calculation formula is:

[0127]

[0128] Among them, represents the possibility that the temperature interference in the department with abnormal temperature is caused by human factors; represents the slope of the fitting line; represents the amplitude change of the temperature difference after normalization; represents the normalization function.

[0129] It can be understood that, based on the obtained possibility that the temperature interference in the department with abnormal temperature is caused by human factors, judgments are made on the direction and magnitude of the temperature amplitude change. However, this will cause certain misjudgments. That is, due to the environmental temperature change outside the department with abnormal temperature, or a large amount of heat generated by the relevant medical equipment operating in the pediatric department, the temperature in the department is severely high. At this time, the temperature control system in the department adjusts the temperature, causing the temperature difference between the inside and outside of the department with abnormal temperature to show a decreasing trend in the regulation of temperature data. And these temperature changes are not normal changes caused by human factors, and there may also be abnormal temperature regulation, which further leads to inaccurate and untimely monitoring and regulation of the temperature data in the pediatric department.

[0130] Step S42: Respectively perform fitting on the temperature data in the departments with normal temperature and the departments with abnormal temperature to obtain fitting parameters. Determine the temperature similarity between the departments with abnormal temperature and the departments with normal temperature through the fitting parameters, analyze the temperature data outside the departments with normal temperature and the departments with abnormal temperature, and obtain the possibility that the departments with abnormal temperature belong to normal temperature regulation.

[0131] It can be explained that any department in pediatrics has the same temperature regulation standard in the same type. Therefore, the temperature of these departments is adjusted through their respective temperature control systems to ensure that the indoor temperature can reach a relatively consistent level. However, based on this situation for temperature regulation, the indoor temperatures of the departments are similar, and the temperature difference outside the departments may be caused by environmental temperature changes. If a certain department has abnormal temperature, but this abnormality is within the normal temperature fluctuation range, then the temperature regulation of the department with abnormal temperature should be similar to that of the department with normal temperature. And after the temperature is regulated, it still takes a certain amount of time for the temperature data inside and outside the department to reach a balanced state and reach a similar temperature situation. Therefore, for the departments in the process of temperature regulation, a certain period of time is also needed to observe whether their temperatures can finally reach the expected balanced state.

[0132] Therefore, when the temperature control parameters of departments are similar, the changing trend of temperature data within the departments will still show a certain regularity. If there are significant differences between the temperature trends of a department with abnormal temperature and those of departments with normal temperature, it indicates that such temperature differences are highly correlated with the changing ambient temperature outside the departments. Specifically, the greater the temperature difference between a department with abnormal temperature and a department with normal temperature of the same type, the greater the ambient temperature difference outside the department, that is, there is a positive correlation between the temperature differences inside and outside the department. When the temperature data inside the department is abnormal and the ambient temperature outside the department causes the temperature data inside the department to fail to reach equilibrium through the normal control mechanism, the abnormality of the temperature data in the department with abnormal temperature is more likely to be a temporary phenomenon during the temperature control process of the department.

[0133] Further, in step S42, the fitting parameter is , where represents the slope of the temperature data in a department with normal temperature or a department with abnormal temperature changing with the ambient temperature, and represents the theoretical temperature equilibrium benchmark of a department with normal temperature or a department with abnormal temperature.

[0134] An explanation is made. The least squares fitting is performed on the temperature data in the department with abnormal temperature to obtain the fitting parameter, which reflects the trend characteristics of the temperature data change in the corresponding department.

[0135] The temperature similarity between the department with abnormal temperature and the department with normal temperature is determined through the fitting parameter. The corresponding calculation formula is:

[0136]

[0137] where represents the temperature similarity between the currently analyzed department with abnormal temperature and the th department with normal temperature; represents the fitting parameter of the department with abnormal temperature; represents the th fitting parameter of the department with normal temperature; represents the controlled temperature of the department with abnormal temperature; represents the th controlled temperature of the department with normal temperature; represents the normalization function.

[0138] The probability that the department with abnormal temperature belongs to normal temperature control is obtained. The corresponding calculation formula is:

[0139]

[0140] where represents the probability that the currently analyzed department with abnormal temperature belongs to normal temperature control; Indicates a correlation operation; Indicates the temperature similarity between the currently analyzed temperature-abnormal department and the th temperature-normal department; Indicates the temperature data outside the temperature-abnormal department; Indicates the th temperature data outside the temperature-normal department.

[0141] Make an explanation, Indicates a correlation operation, which is used to calculate the correlation of the difference sequence between the temperature similarity sequence and the temperature data to measure the linear relationship between the two sequences. The result range of this value is , where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no linear correlation.

[0142] Step S43: Calculate the abnormal credibility degree of the temperature-abnormal department by combining the possibility of temperature interference caused by human factors and the possibility of normal temperature regulation.

[0143] It can be understood that based on the foregoing analysis of the temperature data, if the currently analyzed temperature-abnormal department belongs to the possibility of normal temperature regulation and the corresponding possibility of temperature interference caused by human factors is relatively high, it indicates that the temperature abnormality phenomenon of the temperature-abnormal department belongs to the normal situation, that is, the abnormal credibility degree of the corresponding temperature data is relatively low, indicating that the temperature-abnormal department is not a real abnormality; on the contrary, when the abnormal credibility degree of the temperature data is high, it indicates that the temperature data of the temperature-abnormal department has an abnormal real situation.

[0144] Furthermore, in step S43, the abnormal credibility degree of the temperature-abnormal department is calculated by combining the possibility of temperature interference caused by human factors and the possibility of normal temperature regulation. The corresponding calculation formula is:

[0145]

[0146] Among them, Indicates the abnormal credibility degree; Indicates the possibility that the temperature-abnormal department belongs to normal temperature regulation; Indicates the possibility that the temperature-abnormal department is caused by temperature interference of human factors; Indicates a normalization function.

[0147] Please refer to Figure 7, which shows a schematic diagram of the possibility of normal temperature regulation in the temperature-abnormal departments for the intelligent monitoring method of pediatric department environmental temperature data provided by an embodiment of the present invention. Among them, the horizontal axis represents the department number corresponding to the temperature-abnormal department; the vertical axis represents the abnormal credibility corresponding to the temperature-abnormal department; the horizontal line in the figure represents the judgment threshold; it can be seen from the figure that the temperature-abnormal departments 1, 4, and 6 reflect real temperature regulation abnormal situations, while the temperature-abnormal departments 2, 3, and 5 belong to the temperature abnormalities caused by normal human factors or normal temperature regulation situations.

[0148] It is explained that in step S5, a judgment threshold is set. When the abnormal credibility is greater than the judgment threshold, it is determined that a real temperature abnormality occurs in the currently analyzed department.

[0149] As an optional implementation manner, in this embodiment, the judgment threshold is .

[0150] It can be explained that when the abnormal credibility is greater than the judgment threshold, that is, , it indicates that a real temperature abnormality has occurred in the currently analyzed temperature-abnormal department, and corresponding temperature abnormality prompts need to be given to the medical staff. Then, according to the foregoing temperature abnormality judgment, each temperature-abnormal department is judged to obtain all departments belonging to real temperature abnormalities, and corresponding treatments are made to timely solve potential problems.

[0151] It can be understood that by real-time monitoring of the internal temperature of the department to evaluate whether the temperature change of the department is within the normal fluctuation range, analyzing the collected temperature data, first defining the category of the department, analyzing the temperature fluctuation characteristics in the time neighborhood, and performing correction, the departments are screened into normal-temperature departments and temperature-abnormal departments; then, combining the temperature change data inside and outside the department and the change trend of the temperature data, analyzing whether there are temperature fluctuations caused by human factors; then, by comparing and analyzing the normal-temperature departments and the temperature-abnormal departments, judging whether the temperature change is caused by normal environmental regulation measures or temperature abnormalities caused by certain abnormal situations in the regulation process, and then determining the abnormal credibility of the temperature data, so as to effectively monitor and manage the temperature status of the department environment, realize accurate temperature monitoring of different types of departments, and improve the accuracy of temperature monitoring.

[0152] It should be noted that: the above-mentioned sequence of embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0153] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized respectively.

Claims

1. An intelligent monitoring method for environmental temperature data in the pediatric department, characterized in that The method includes: Dividing several different departments based on pediatrics, determining the theoretical temperature range of each department, and collecting the temperature data of each department; Comparing the theoretical temperature range with the temperature data to construct the abnormal temperature set of each department, obtaining the frequency of abnormal temperature occurrences to get the temperature abnormality possibility, and determining the temperature fluctuation characteristics through the abnormal temperature set; Correcting the temperature abnormality possibility according to the temperature fluctuation characteristics, and performing screening to respectively determine the departments with normal temperature and the departments with abnormal temperature; Analyzing the temperature data of the departments with abnormal temperature to obtain the possibility of temperature interference caused by human factors and the possibility of normal temperature regulation, and combining the two to calculate the abnormal credibility; Setting a judgment threshold, and when the abnormal credibility is greater than the judgment threshold, determining that there is a real temperature abnormality in the currently analyzed department; Analyzing the temperature data of the departments with abnormal temperature to obtain the possibility of temperature interference caused by human factors and the possibility of normal temperature regulation, and combining the two to calculate the abnormal credibility, including: The temperature data respectively includes the temperature data inside and outside the departments with normal temperature and the departments with abnormal temperature; Based on the temperature data inside the department with abnormal temperature and the temperature data outside the department with abnormal temperature, constructing a temperature difference sequence, fitting the temperature difference sequence to obtain the slope of the fitting line, obtaining the temperature difference change range of the department with abnormal temperature, and performing normalization processing, and combining the slope of the fitting line to obtain the possibility of temperature interference caused by human factors in the department with abnormal temperature; Respectively fitting the temperature data inside the departments with normal temperature and the departments with abnormal temperature to obtain fitting parameters, determining the temperature similarity between the department with abnormal temperature and the departments with normal temperature through the fitting parameters, analyzing the temperature data outside the departments with normal temperature and the departments with abnormal temperature, and obtaining the possibility that the department with abnormal temperature belongs to normal temperature regulation; Combining the possibility of temperature interference caused by human factors and the possibility of normal temperature regulation to calculate the abnormal credibility of the department with abnormal temperature.

2. The intelligent monitoring method for environmental temperature data in the pediatric department according to claim 1, wherein Comparing the theoretical temperature range with the temperature data to construct the abnormal temperature set of each department, obtaining the frequency of abnormal temperature occurrences to get the temperature abnormality possibility, and determining the temperature fluctuation characteristics through the abnormal temperature set, including: When the temperature data is within the theoretical temperature range, it indicates normal and is marked as 0; otherwise, it indicates abnormality. When the temperature data is higher than the theoretical temperature range, it is marked as positive, and when the temperature data is lower than the theoretical temperature range, it is marked as negative, generating a phase distance , and a positive abnormal temperature set and a negative abnormal temperature set are respectively constructed according to the positive mark and the negative mark; Obtaining the frequency of abnormal temperature occurrences to get the temperature abnormality possibility; Analyzing the positive and negative abnormal temperature sets respectively to sequentially determine the temperature fluctuation characteristics.

3. The intelligent monitoring method for environmental temperature data in the pediatric department according to claim 2, characterized in that, Obtaining the frequency of abnormal temperature occurrences to get the temperature abnormality possibility, and the corresponding calculation formula is: Among them, represents the possibility of abnormal temperature in the current analysis department; represents the frequency of abnormal temperature occurrences; represents the abnormal status flag of the nth temperature data; represents the normalization function.

4. The intelligent monitoring method for environmental temperature data in the pediatric department according to claim 2, characterized in that, Determining the temperature fluctuation characteristics, and the corresponding calculation formula is: Among them, represents the temperature fluctuation characteristics of the abnormal temperature set ; represents the number of elements in the abnormal temperature set ; represents the temperature data within the corresponding time neighborhood represents the standard deviation operation; represents the mean operation; represents the temperature anomaly possibility corresponding to the temperature data at the th acquisition moment within the corresponding time neighborhood; represents the anomaly status flag of the temperature data at the th acquisition moment within the corresponding time neighborhood; represents the normalization function.

5. The intelligent monitoring method for environmental temperature data in the pediatric department according to claim 2, characterized in that, Correcting the temperature abnormality possibility according to the temperature fluctuation characteristics, and performing screening to respectively determine the departments with normal temperature and the departments with abnormal temperature, including: Selecting the maximum value in the positive and negative abnormal temperature sets based on the temperature fluctuation characteristics, and recording it as the temperature fluctuation value; Correcting the temperature abnormality possibility through the temperature fluctuation value; Setting a screening threshold, when the corrected temperature abnormality possibility is greater than the screening threshold, it indicates that the currently analyzed department is a department with abnormal temperature; when the corrected temperature abnormality possibility is less than the screening threshold, it indicates that the currently analyzed department is a department with normal temperature.

6. The intelligent monitoring method for environmental temperature data in the pediatric department according to claim 5, characterized in that, Correct the possibility of temperature anomaly through the temperature fluctuation value, and the corresponding calculation formula is: Among them, represents the corrected temperature anomaly possibility; represents the temperature anomaly possibility before correction; represents the temperature fluctuation value; represents the temperature fluctuation characteristics of the set of positive anomaly temperatures; represents the temperature fluctuation characteristics of the set of negative anomaly temperatures.

7. The intelligent monitoring method for environmental temperature data in the pediatric department according to claim 1, characterized in that, Construct a temperature difference sequence, and the corresponding calculation formula is: Among them, represents the temperature difference inside and outside the department with abnormal temperature; represents the temperature data outside the department with abnormal temperature; represents the temperature data inside the department with abnormal temperature; Obtain the temperature difference change range of the temperature anomaly department and perform normalization processing, and the corresponding calculation formula is: Among them, represents the temperature difference change range of the department with abnormal temperature; represents the temperature difference inside and outside the department at the end within the currently analyzed time neighborhood of the department with abnormal temperature; represents the temperature difference inside and outside the department at the beginning within the currently analyzed time neighborhood of the department with abnormal temperature; Among them, represents the amplitude of the temperature difference change after normalization processing; represents the normalization function; Get the possibility of temperature interference caused by human factors in the temperature anomaly department, and the corresponding calculation formula is: Among them, indicates the possibility of temperature interference caused by human factors in the department with abnormal temperature; represents the slope of the fitted straight line; represents the change range of the temperature difference after normalization processing; represents the normalization function.

8. The intelligent monitoring method for environmental temperature data in the pediatric department according to claim 1, characterized in that, The fitting parameter is , where represents the slope of the temperature data in the normal temperature department or the abnormal temperature department changing with the ambient temperature, represents the theoretical equilibrium benchmark of the temperature in the normal temperature department or the abnormal temperature department; Determine the temperature similarity between the temperature anomaly department and the normal temperature department through the fitting parameter, and the corresponding calculation formula is: Among them, represents the temperature similarity between the currently analyzed temperature-abnormal department and the th temperature-normal department; represents the fitting parameter of the temperature-abnormal department; represents the th fitting parameter of the temperature-normal department; represents the regulated temperature of the temperature-abnormal department; represents the th regulated temperature of the temperature-normal department; represents the normalization function; Get the possibility that the temperature anomaly department belongs to normal temperature regulation, and the corresponding calculation formula is: Among them, represents the possibility that the department with abnormal temperature currently analyzed belongs to normal temperature regulation; represents the correlation operation; represents the temperature similarity between the department with abnormal temperature currently analyzed and the th department with normal temperature; represents the temperature data outside the department with abnormal temperature; represents the th temperature data outside the department with normal temperature.

9. The intelligent monitoring method for environmental temperature data in the pediatric department according to claim 1, characterized in that, Calculate the anomaly credibility degree of the temperature anomaly department by combining the possibility of temperature interference caused by human factors and the possibility of normal temperature regulation, and the corresponding calculation formula is: Among them, represents the abnormal credibility; represents the possibility that the department with abnormal temperature belongs to normal temperature regulation; represents the possibility of temperature interference caused by human factors in the department with abnormal temperature; represents the normalization function.

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