Laboratory monitoring alarm system and method based on Internet of Things

Abstract

The invention discloses a laboratory monitoring and alarming system and method based on the Internet of Things, and relates to the technical field of laboratory monitoring and alarming, and the method comprises the steps: constructing a monitoring and alarming cloud platform, and analyzing the environment approximation degree between a laboratory and a historical laboratory; evaluating the standard data of the monitoring indexes of the characteristic history laboratory and the environment matching degree of the characteristic history laboratory to obtain abnormal monitoring data; the method comprises the following steps: acquiring abnormal monitoring data of a characteristic history laboratory of a laboratory, performing abnormality evaluation on monitoring indexes in the laboratory according to the abnormal monitoring data to obtain a target monitoring index, acquiring a historical monitoring record of the characteristic history laboratory, and performing optimization adjustment on standard data of the target monitoring index in the laboratory to obtain a target monitoring index; obtaining target standard data; and obtaining target standard data of the laboratory, carrying out safety monitoring on the laboratory in the current period, carrying out abnormity evaluation on the laboratory in combination with the target standard data, and carrying out intelligent alarm on the laboratory.

Description

Technical Field

[0001] The present invention relates to the technical field of laboratory monitoring and alarm technology, and in particular to a laboratory monitoring and alarm system and method based on the Internet of Things. Background Art

[0002] At present, the use of Internet of Things technology has become more and more common in the field of laboratory safety monitoring. The use of Internet of Things technology brings benefits to the laboratory's monitoring and alarm system, including but not limited to the following: 1. Real-time data monitoring. The application of Internet of Things technology enables monitoring equipment to collect and transmit data in a timely manner. The acquired data reflects the current status of the laboratory in real time, thereby achieving timely warning and reducing safety hazards; 2. Realize remote management. Through Internet of Things technology, laboratory managers can monitor the laboratory status anytime and anywhere; 3. Automation and intelligence of early warning. Internet of Things can realize automated control and management. The monitoring alarm system can automatically trigger alarms and execute preset response measures to reduce manual intervention.

[0003] At present, laboratory monitoring and alarm are mainly carried out by monitoring equipment to monitor the laboratory environment and conduct abnormal assessment of the laboratory based on the threshold of environmental data obtained by the monitoring equipment. However, in actual situations, the environment and layout of laboratories are different, which may cause the preset threshold of monitoring data to deviate from the actual situation of the laboratory, resulting in false alarms or untimely alarms, which will not only affect the normal work of the laboratory, but may also cause safety accidents in the laboratory, resulting in huge losses. Summary of the invention

[0004] The purpose of the present invention is to provide a laboratory monitoring alarm system and method based on the Internet of Things to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a laboratory monitoring and alarm method based on the Internet of Things, the method comprising: Step S100: construct a monitoring alarm cloud platform, obtain the experimental environment data of the laboratory, obtain the historical experimental environment data of the historical laboratory, analyze the degree of similarity between the environment of the laboratory and the historical laboratory, and obtain the characteristic historical laboratory; Step S200: Obtain historical abnormal alarm records and historical safety accident records of the feature history laboratory, obtain standard data of monitoring indicators of the feature history laboratory from the cloud platform, evaluate the standard data of monitoring indicators of the feature history laboratory, and the degree of fit with the environment of the feature history laboratory to obtain abnormal monitoring data; Step S300: Obtain the abnormal monitoring data of the characteristic history laboratory of the laboratory, and perform abnormality evaluation on the monitoring indicators in the laboratory according to the abnormal monitoring data to obtain the target monitoring indicators, obtain the historical monitoring records of the characteristic history laboratory, optimize and adjust the standard data of the target monitoring indicators in the laboratory, and obtain the target standard data; Step S400: Acquire the target standard data of the laboratory, conduct safety monitoring of the laboratory in the current cycle, and conduct abnormal evaluation of the laboratory in combination with the target standard data, and conduct intelligent alarm for the laboratory.

[0006] Furthermore, step S100 includes: Step S101: Acquire the experimental environment data of the laboratory, the experimental environment data of the laboratory includes data corresponding to various environmental parameters of the laboratory, and acquire historical experimental environment data of each historical laboratory from the cloud platform, the historical experimental environment data includes data corresponding to various environmental parameters of the historical laboratory; Step S102: Obtain the average value of each environmental parameter of the historical laboratory, and obtain the marked value of each environmental parameter in the historical laboratory, wherein the marked value B of the ath environmental parameter in the historical laboratory is a : , Among them, C a Expressed as the average value of the environmental parameter a in the historical laboratory; C´ a Expressed as the mean of the average values ​​of the ath environmental parameter in each historical laboratory and the laboratory; σ a It is expressed as the standard deviation of the mean value of the a-th environmental parameter in each historical laboratory and laboratory; Step S103: Based on the label values ​​of various environmental parameters in the historical laboratory, construct a feature vector X={X1, X2, X3, ..., X n}, where X1, X2, X3, ..., X n They are represented as, respectively, the labeled values ​​of the 1st, 2nd, 3rd, ..., nth environmental parameters in the historical laboratory; Step S104: analyzing the degree of similarity between the environment of the laboratory and each historical laboratory. The specific process of analyzing the degree of similarity between the environment of the laboratory and the dth historical laboratory is as follows: The environmental similarity score β between the computing laboratory and the dth historical laboratory d : , Among them, X d denoted as the characteristic vector of the d-th historical laboratory; Y denoted as the characteristic vector of the laboratory; Step S105: When the environment approximation score β d If the value is greater than the preset similarity score threshold, the environment between the d-th historical laboratory and the laboratory is determined to be similar, and the d-th historical laboratory is recorded as the characteristic historical laboratory of the laboratory.

[0007] Further, step S200 includes: Step S201: Acquire the characteristic history laboratory of the laboratory, acquire the historical abnormal alarm record of the characteristic history laboratory, and acquire the historical abnormal alarm data from the historical abnormal alarm record, where the historical abnormal alarm data is the maximum value of the monitoring indicator that triggers the alarm; Step S202: obtaining preset standard data of monitoring indicators of the characteristic history laboratory from the cloud platform, and marking the data as preset thresholds of the monitoring indicators; Step S203: Obtain each historical abnormal alarm record of the characteristic history laboratory. When the maximum value of a certain monitoring indicator of a certain historical abnormal alarm record in the characteristic history laboratory is greater than the threshold value of a certain monitoring indicator, the certain historical abnormal alarm record is recorded as a characteristic historical abnormal alarm record of a certain monitoring indicator. Step S204: Calculate the preset thresholds of each monitoring indicator of the characteristic history laboratory and the degree of fit with the first characteristic environment of the characteristic history laboratory, wherein the preset threshold of the dth monitoring indicator of the characteristic history laboratory and the degree of fit with the first characteristic environment of the characteristic history laboratory L d : , Among them, M d It is represented as the dth monitoring indicator, the total number of feature history abnormal alarm records in the feature history laboratory; M sum It is expressed as the total number of each historical abnormal alarm record in the feature history laboratory; Step S205: When the first characteristic environment compatibility L d is less than the preset first characteristic environment fit threshold, and determines that the preset threshold of the d-th monitoring indicator is not consistent with the characteristic history laboratory environment, and the preset threshold of the d-th monitoring indicator will trigger the characteristic history laboratory error alarm, and mark the d-th monitoring indicator as the first abnormal monitoring indicator of the characteristic history laboratory; Step S206: Obtain historical safety accident records of the characteristic history laboratory from the cloud platform, and extract historical safety accident data from the historical safety accident records. The historical safety accident data is data corresponding to various monitoring indicators in the characteristic history laboratory when a safety accident occurs in the characteristic history laboratory. The first abnormal monitoring indicator of the characteristic history laboratory is removed from the various monitoring indicators to obtain several monitoring indicators of the characteristic history laboratory; Step S207: Calculate the characteristic values ​​of several monitoring indicators in the historical safety accident records, wherein the characteristic ratio value F of the e-th monitoring indicator in the historical safety accident records is e =G e，max / Q e , where G e，max It is expressed as the maximum value of the e-th monitoring indicator of the characteristic history laboratory in the historical safety accident records, Q e It is represented as the threshold value of the monitoring indicator of item e; Step S208: When the characteristic ratio value of the e-th monitoring indicator is greater than a preset characteristic ratio threshold, the historical safety accident record is recorded as a characteristic historical safety accident record of the e-th monitoring indicator; Step S209: Calculate the preset threshold of the e-th monitoring indicator and the degree of fit H between it and the second characteristic environment of the characteristic history laboratory e =K e / K sum , where K sum It is represented by the total number of historical safety accident records of the characteristic history laboratory, K e It is expressed as the e-th monitoring indicator, the total number of characteristic historical safety accident records in the characteristic history laboratory; Step S210: When the second characteristic environment compatibility H e is greater than the preset second characteristic environment fit threshold, it is determined that the preset threshold of the e-th monitoring indicator is not consistent with the characteristic history laboratory environment, and the threshold of the e-th monitoring indicator cannot be used as a determination condition for the abnormal environment of the characteristic history laboratory, and the e-th monitoring indicator is recorded as the second abnormal monitoring indicator of the characteristic history laboratory; Step S211: Acquire the first abnormal monitoring indicator and the second abnormal monitoring indicator of the characteristic history laboratory, and aggregate them to obtain abnormal monitoring data of the characteristic history laboratory; The reason why the abnormal alarm in the feature history laboratory in the above steps is recorded is that the preset threshold does not conform to the actual situation of the feature history laboratory. The threshold is set too small, resulting in no abnormality in the feature history laboratory, but an alarm was still issued. The historical safety accident records show that there was no monitoring indicator in the feature history laboratory that was greater than the preset threshold, but a safety accident occurred in the feature history laboratory, indicating that the monitoring indicator was set too large, resulting in the inability to accurately judge the abnormality of the feature history laboratory. Therefore, obtaining abnormal monitoring indicators in the feature history laboratory from these two aspects not only makes the data acquisition more accurate, but also makes the data more comprehensive, which is helpful for subsequent abnormal judgment of the laboratory's monitoring indicators.

[0008] Furthermore, step S300 includes: Step S301: Acquire the abnormal monitoring data of each characteristic history laboratory, and perform abnormality evaluation on each monitoring indicator of the laboratory. The process of performing abnormality evaluation on the βth monitoring indicator in the laboratory includes: Get the total number of characteristic historical laboratories whose β-th monitoring indicator is the first abnormal monitoring indicator U´ β , obtain the total number of characteristic historical laboratories whose β-th monitoring indicator is the second abnormal monitoring indicator U´´ β , calculate the abnormal score P of the βth monitoring indicator in the laboratory β : , Wherein, γ1 represents the preset first characteristic coefficient; γ2 represents the preset second characteristic coefficient, γ1+γ2=1; U sum It is expressed as the total number of laboratories with each characteristic history of the laboratory; Step S302: When the indicator abnormality score P β If the value is greater than the preset abnormal indicator score threshold, it is determined that the βth monitoring indicator in the laboratory is abnormal, and the βth monitoring indicator is recorded as the target monitoring indicator of the laboratory; Step S303: Obtain standard data of the target monitoring indicators of the laboratory from the cloud platform, and optimize and adjust the standard data of the target monitoring indicators in the laboratory. The specific optimization and adjustment process is: From each feature history laboratory, obtain a number of feature history laboratories having the same preset threshold value as the target monitoring indicator of the laboratory; Step S304: obtaining each historical normal operation record of several characteristic history laboratories, obtaining the average of the maximum values ​​of the target monitoring indicators in the several characteristic history laboratories from each historical normal operation record, and recording the average as the target threshold value of the target monitoring indicator in the laboratory; Step S305: Obtain target thresholds of several monitoring indicators of the laboratory, and use the target thresholds of several monitoring indicators of the laboratory to replace preset thresholds of several monitoring indicators of the laboratory in the standard data to obtain the target standard data of the laboratory.

[0009] Furthermore, step S400 includes: Step S401: Use a number of networked monitoring devices to monitor the laboratory in the current cycle and obtain data on various monitoring indicators in the laboratory; Step S402: Obtain the target standard data of the laboratory. When there is a monitoring indicator whose value is greater than the threshold in the target standard data among the various monitoring indicators monitored by the monitoring equipment of the laboratory in the current cycle, it is determined that there is an abnormality in the laboratory and an intelligent alarm is issued to the laboratory.

[0010] In order to better implement the above method, a laboratory monitoring and alarm system based on the Internet of Things is also proposed. The system includes an environmental approximate analysis module, a fit evaluation module, a target standard data module, and an intelligent monitoring and alarm module; The environment similarity analysis module is used to analyze the degree of environment similarity between the laboratory and the historical laboratory to obtain the characteristic historical laboratory; A fit evaluation module is used to evaluate the fit between the standard data of the monitoring indicators of the characteristic history laboratory and the environment of the characteristic history laboratory to obtain abnormal monitoring data; The target standard data module is used to evaluate the abnormality of the laboratory's monitoring indicators to obtain the target monitoring indicators, and optimize and adjust the standard data of the target monitoring indicators in the laboratory according to the historical monitoring records of the characteristic history laboratory to obtain the target standard data; The intelligent monitoring and alarm module is used to monitor the safety of the laboratory in the current cycle, conduct abnormal evaluation of the laboratory in combination with the target standard data, and issue intelligent alarms to the laboratory.

[0011] Further, the environment approximation analysis module includes an environment approximation scoring unit and an environment approximation analysis unit; An environmental similarity scoring unit is used to calculate the environmental similarity score between the laboratory and the historical laboratory; The environmental similarity analysis unit is used to analyze the degree of environmental similarity between the laboratory and each historical laboratory according to the environmental similarity score between the laboratory and each historical laboratory, and obtain a characteristic historical laboratory.

[0012] Further, the fit assessment module includes a historical data acquisition unit and a fit assessment unit; A historical data acquisition unit is used to record the historical abnormal alarms of the feature history laboratory, obtain the standard data of the monitoring indicators of the feature history laboratory from the cloud platform, and obtain the historical safety accident records of the feature history laboratory; The fit assessment unit is used to assess the standard data of the monitoring indicators of the characteristic history laboratory and the degree of fit with the environment of the characteristic history laboratory, and obtain the abnormal monitoring data of the characteristic history laboratory.

[0013] Further, the target standard data module includes an indicator abnormality scoring unit and a target standard data unit; An indicator abnormality scoring unit is used to calculate the indicator abnormality scores of various monitoring indicators in the laboratory; The target standard data unit is used to evaluate the abnormality of various monitoring indicators in the laboratory to obtain the target monitoring indicators of the laboratory, and to optimize and adjust the standard data of the target monitoring indicators in the laboratory to obtain the target standard data of the laboratory.

[0014] Further, the intelligent monitoring alarm module includes an intelligent monitoring alarm unit; The intelligent monitoring and alarm unit is used to monitor the laboratory in the current cycle, obtain data on various monitoring indicators in the laboratory, and issue intelligent alarms to the laboratory.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention realizes intelligent monitoring and alarm of the laboratory, obtains a characteristic historical laboratory with a similar environment by analyzing the environmental similarity between the laboratory and the historical laboratory, and evaluates the thresholds of various monitoring indicators in the characteristic historical laboratory to determine whether the characteristic historical laboratory can be accurately judged as abnormal, and obtains abnormal monitoring data in the characteristic historical laboratory. With the help of the abnormal monitoring data in the characteristic historical laboratory, it is evaluated whether there are problems with the preset thresholds of various monitoring indicators in the laboratory, and combined with the historical data of the characteristic historical laboratory, the standard data of the target monitoring indicators in the laboratory are optimized and adjusted, so that the preset monitoring indicators are highly consistent with the actual situation of the laboratory, greatly reducing the probability of false alarms and safety accidents in the laboratory, and ensuring the safety of the laboratory. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a method flow chart of the laboratory monitoring alarm system and method based on the Internet of Things of the present invention; Figure 2 It is a module schematic diagram of the laboratory monitoring alarm system and method based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution, a laboratory monitoring alarm method based on the Internet of Things, the method comprising: Step S100: construct a monitoring alarm cloud platform, obtain the experimental environment data of the laboratory, obtain the historical experimental environment data of the historical laboratory, analyze the degree of similarity between the environment of the laboratory and the historical laboratory, and obtain the characteristic historical laboratory; Wherein, step S100 includes: Step S101: Acquire the experimental environment data of the laboratory, the experimental environment data of the laboratory includes data corresponding to various environmental parameters of the laboratory, and acquire historical experimental environment data of each historical laboratory from the cloud platform, the historical experimental environment data includes data corresponding to various environmental parameters of the historical laboratory; For example, various environmental parameters include laboratory temperature, laboratory humidity, etc.; Step S102: Obtain the average value of each environmental parameter of the historical laboratory, and obtain the marked value of each environmental parameter in the historical laboratory, wherein the marked value B of the ath environmental parameter in the historical laboratory is a : , Among them, C a Expressed as the average value of the environmental parameter a in the historical laboratory; C´ a Expressed as the mean of the average values ​​of the ath environmental parameter in each historical laboratory and the laboratory; σ a It is expressed as the standard deviation of the mean value of the a-th environmental parameter in each historical laboratory and laboratory; Step S103: Based on the label values ​​of various environmental parameters in the historical laboratory, construct a feature vector X={X1, X2, X3, ..., X n}, where X1, X2, X3, ..., X n They are represented as, respectively, the labeled values ​​of the 1st, 2nd, 3rd, ..., nth environmental parameters in the historical laboratory; Step S104: analyzing the degree of similarity between the environment of the laboratory and each historical laboratory. The specific process of analyzing the degree of similarity between the environment of the laboratory and the dth historical laboratory is as follows: The environmental similarity score β between the computing laboratory and the dth historical laboratory d : , Among them, X d denoted as the characteristic vector of the d-th historical laboratory; Y denoted as the characteristic vector of the laboratory; Step S105: When the environment approximation score β d If the value is greater than the preset similarity score threshold, the environment between the d-th historical laboratory and the laboratory is determined to be similar, and the d-th historical laboratory is recorded as the characteristic historical laboratory of the laboratory; Step S200: Obtain historical abnormal alarm records and historical safety accident records of the feature history laboratory, obtain standard data of monitoring indicators of the feature history laboratory from the cloud platform, evaluate the standard data of monitoring indicators of the feature history laboratory, and the degree of fit with the environment of the feature history laboratory to obtain abnormal monitoring data; Wherein, step S200 includes: Step S201: Acquire the characteristic history laboratory of the laboratory, acquire the historical abnormal alarm record of the characteristic history laboratory, and acquire the historical abnormal alarm data from the historical abnormal alarm record, where the historical abnormal alarm data is the maximum value of the monitoring indicator that triggers the alarm; Step S202: obtaining preset standard data of monitoring indicators of the characteristic history laboratory from the cloud platform, and marking the data as preset thresholds of the monitoring indicators; Step S203: Obtain each historical abnormal alarm record of the characteristic history laboratory. When the maximum value of a certain monitoring indicator of a certain historical abnormal alarm record in the characteristic history laboratory is greater than the threshold value of a certain monitoring indicator, the certain historical abnormal alarm record is recorded as a characteristic historical abnormal alarm record of a certain monitoring indicator. Step S204: Calculate the preset thresholds of each monitoring indicator of the characteristic history laboratory and the degree of fit with the first characteristic environment of the characteristic history laboratory, wherein the preset threshold of the dth monitoring indicator of the characteristic history laboratory and the degree of fit with the first characteristic environment of the characteristic history laboratory L d : , Among them, M d It is represented as the dth monitoring indicator, the total number of feature history abnormal alarm records in the feature history laboratory; M sum It is expressed as the total number of each historical abnormal alarm record in the feature history laboratory; For example, various monitoring indicators include laboratory temperature, laboratory humidity, etc.; Step S205: When the first characteristic environment compatibility L d is less than the preset first characteristic environment fit threshold, and determines that the preset threshold of the d-th monitoring indicator is not consistent with the characteristic history laboratory environment, and the preset threshold of the d-th monitoring indicator will trigger the characteristic history laboratory error alarm, and mark the d-th monitoring indicator as the first abnormal monitoring indicator of the characteristic history laboratory; Step S206: Obtain historical safety accident records of the characteristic history laboratory from the cloud platform, and extract historical safety accident data from the historical safety accident records. The historical safety accident data is data corresponding to various monitoring indicators in the characteristic history laboratory when a safety accident occurs in the characteristic history laboratory. The first abnormal monitoring indicator of the characteristic history laboratory is removed from the various monitoring indicators to obtain several monitoring indicators of the characteristic history laboratory; Step S207: Calculate the characteristic values ​​of several monitoring indicators in the historical safety accident records, wherein the characteristic ratio value F of the e-th monitoring indicator in the historical safety accident records is e =G e，max / Qe , where G e，max It is expressed as the maximum value of the e-th monitoring indicator of the characteristic history laboratory in the historical safety accident records, Q e It is represented as the threshold value of the monitoring indicator of item e; Step S208: When the characteristic ratio value of the e-th monitoring indicator is greater than a preset characteristic ratio threshold, the historical safety accident record is recorded as a characteristic historical safety accident record of the e-th monitoring indicator; Step S209: Calculate the preset threshold of the e-th monitoring indicator and the degree of fit H between it and the second characteristic environment of the characteristic history laboratory e =K e / K sum , where K sum It is represented by the total number of historical safety accident records of the characteristic history laboratory, K e It is expressed as the e-th monitoring indicator, the total number of characteristic historical safety accident records in the characteristic history laboratory; For example, the total number of historical safety accident records in the feature history laboratory is K sum is 50, the second monitoring indicator, the total number of characteristic historical safety accident records K2 in the characteristic history laboratory is 20; Calculate the preset threshold of the second monitoring indicator, and the degree of fit with the second characteristic environment of the characteristic history laboratory H2=20 / 50=0.4; Step S210: When the second characteristic environment compatibility H e is greater than the preset second characteristic environment fit threshold, it is determined that the preset threshold of the e-th monitoring indicator is not consistent with the characteristic history laboratory environment, and the threshold of the e-th monitoring indicator cannot be used as a determination condition for the abnormal environment of the characteristic history laboratory, and the e-th monitoring indicator is recorded as the second abnormal monitoring indicator of the characteristic history laboratory; Step S211: Acquire the first abnormal monitoring indicator and the second abnormal monitoring indicator of the characteristic history laboratory, and aggregate them to obtain abnormal monitoring data of the characteristic history laboratory; Step S300: Obtain the abnormal monitoring data of the characteristic history laboratory of the laboratory, and perform abnormality evaluation on the monitoring indicators in the laboratory according to the abnormal monitoring data to obtain the target monitoring indicators, obtain the historical monitoring records of the characteristic history laboratory, optimize and adjust the standard data of the target monitoring indicators in the laboratory, and obtain the target standard data; Wherein, step S300 includes: Step S301: Acquire the abnormal monitoring data of each characteristic history laboratory, and perform abnormality evaluation on each monitoring indicator of the laboratory. The process of performing abnormality evaluation on the βth monitoring indicator in the laboratory includes: Get the total number of characteristic historical laboratories whose β-th monitoring indicator is the first abnormal monitoring indicator U´ β , obtain the total number of characteristic historical laboratories whose β-th monitoring indicator is the second abnormal monitoring indicator U´´ β , calculate the abnormal score P of the βth monitoring indicator in the laboratory β : , Wherein, γ1 represents the preset first characteristic coefficient; γ2 represents the preset second characteristic coefficient, γ1+γ2=1; U sum It is expressed as the total number of laboratories with each characteristic history of the laboratory; For example, the total number of characteristic history laboratories whose first monitoring indicator is the first abnormal monitoring indicator is U'1, which is 20; the total number of characteristic history laboratories whose first monitoring indicator is the second abnormal monitoring indicator is U'1, which is 30; the total number of characteristic history laboratories U sum is 100; the first characteristic coefficient γ1 is 0.4; the second characteristic coefficient γ2 is 0.6; Calculate the indicator anomaly score P1 for the first monitoring indicator in the laboratory: , Step S302: When the indicator abnormality score P β If the value is greater than the preset abnormal indicator score threshold, it is determined that the βth monitoring indicator in the laboratory is abnormal, and the βth monitoring indicator is recorded as the target monitoring indicator of the laboratory; Step S303: Obtain standard data of the target monitoring indicators of the laboratory from the cloud platform, and optimize and adjust the standard data of the target monitoring indicators in the laboratory. The specific optimization and adjustment process is: From each feature history laboratory, obtain a number of feature history laboratories having the same preset threshold value as the target monitoring indicator of the laboratory; Step S304: obtaining each historical normal operation record of several characteristic history laboratories, obtaining the average of the maximum values ​​of the target monitoring indicators in the several characteristic history laboratories from each historical normal operation record, and recording the average as the target threshold value of the target monitoring indicator in the laboratory; Step S305: Obtain target thresholds of several monitoring indicators of the laboratory, and use the target thresholds of several monitoring indicators of the laboratory to replace preset thresholds of several monitoring indicators of the laboratory in the standard data, so as to obtain target standard data of the laboratory; Step S400: Obtain the target standard data of the laboratory, conduct safety monitoring on the laboratory in the current cycle, and conduct abnormal evaluation on the laboratory in combination with the target standard data, and conduct intelligent alarm on the laboratory; Wherein, step S400 includes: Step S401: Use a number of networked monitoring devices to monitor the laboratory in the current cycle and obtain data on various monitoring indicators in the laboratory; Step S402: Obtain the target standard data of the laboratory. When there is a monitoring indicator whose value is greater than the threshold value in the target standard data among the various monitoring indicators monitored by the monitoring equipment of the laboratory in the current cycle, it is determined that there is an abnormality in the laboratory, and an intelligent alarm is issued to the laboratory; In order to better implement the above method, a laboratory monitoring and alarm system based on the Internet of Things is also proposed. The system includes an environmental approximate analysis module, a fit evaluation module, a target standard data module, and an intelligent monitoring and alarm module; The environment similarity analysis module is used to analyze the degree of environment similarity between the laboratory and the historical laboratory to obtain the characteristic historical laboratory; A fit evaluation module is used to evaluate the fit between the standard data of the monitoring indicators of the characteristic history laboratory and the environment of the characteristic history laboratory to obtain abnormal monitoring data; The target standard data module is used to evaluate the abnormality of the laboratory's monitoring indicators to obtain the target monitoring indicators, and optimize and adjust the standard data of the target monitoring indicators in the laboratory according to the historical monitoring records of the characteristic history laboratory to obtain the target standard data; The intelligent monitoring and alarm module is used to monitor the safety of the laboratory in the current cycle, conduct abnormal evaluation of the laboratory in combination with the target standard data, and issue intelligent alarms to the laboratory; Among them, the environment approximation analysis module includes an environment approximation scoring unit and an environment approximation analysis unit; An environmental similarity scoring unit is used to calculate the environmental similarity score between the laboratory and the historical laboratory; An environmental approximation analysis unit is used to analyze the degree of environmental approximation between the laboratory and each historical laboratory according to the environmental approximation scores between the laboratory and each historical laboratory, and obtain a characteristic historical laboratory; Among them, the fit assessment module includes a historical data acquisition unit and a fit assessment unit; A historical data acquisition unit is used to record the historical abnormal alarms of the feature history laboratory, obtain the standard data of the monitoring indicators of the feature history laboratory from the cloud platform, and obtain the historical safety accident records of the feature history laboratory; A fit evaluation unit is used to evaluate the standard data of the monitoring indicators of the characteristic history laboratory and the degree of fit with the environment of the characteristic history laboratory, and obtain abnormal monitoring data of the characteristic history laboratory; Among them, the target standard data module includes an indicator abnormality scoring unit and a target standard data unit; An indicator abnormality scoring unit is used to calculate the indicator abnormality scores of various monitoring indicators in the laboratory; The target standard data unit is used to evaluate the abnormality of various monitoring indicators in the laboratory, obtain the target monitoring indicators of the laboratory, and optimize and adjust the standard data of the target monitoring indicators in the laboratory to obtain the target standard data of the laboratory; Wherein, the intelligent monitoring and alarm module includes an intelligent monitoring and alarm unit; The intelligent monitoring and alarm unit is used to monitor the laboratory in the current cycle, obtain data on various monitoring indicators in the laboratory, and issue intelligent alarms to the laboratory.

[0019] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A laboratory monitoring and alarm method based on the Internet of Things, characterized in that: The method comprises: Step S100: construct a monitoring alarm cloud platform, obtain the experimental environment data of the laboratory, obtain the historical experimental environment data of the historical laboratory, analyze the degree of similarity between the environment of the laboratory and the historical laboratory, and obtain a characteristic historical laboratory; Step S200: Obtain historical abnormal alarm records and historical safety accident records of the characteristic history laboratory, obtain standard data of monitoring indicators of the characteristic history laboratory from the cloud platform, evaluate the standard data of monitoring indicators of the characteristic history laboratory, and the degree of fit with the environment of the characteristic history laboratory to obtain abnormal monitoring data; Step S300: Acquire abnormal monitoring data of the characteristic history laboratory of the laboratory, and perform abnormality evaluation on the monitoring indicators in the laboratory according to the abnormal monitoring data to obtain target monitoring indicators, acquire historical monitoring records of the characteristic history laboratory, optimize and adjust standard data of target monitoring indicators in the laboratory to obtain target standard data; Step S400: Acquire the target standard data of the laboratory, perform safety monitoring on the laboratory in the current cycle, and perform abnormal evaluation on the laboratory in combination with the target standard data, and perform intelligent alarm on the laboratory.

2. The laboratory monitoring and alarm method based on the Internet of Things according to claim 1 is characterized in that: The step S100 includes: Step S101: Acquire the experimental environment data of the laboratory, wherein the experimental environment data of the laboratory includes data corresponding to various environmental parameters of the laboratory, and acquire historical experimental environment data of each historical laboratory from the cloud platform, wherein the historical experimental environment data includes data corresponding to various environmental parameters of the historical laboratory; Step S102: Obtain the average value of each environmental parameter of the historical laboratory, and obtain the mark value of each environmental parameter in the historical laboratory, wherein the mark value B of the ath environmental parameter in the historical laboratory is a : ， Among them, C a Expressed as the average value of the environmental parameter item a in the historical laboratory; C´ a It is expressed as the mean of the average values ​​of the a-th environmental parameter in each historical laboratory and the laboratory; σ a Expressed as the standard deviation of the mean value of the a-th environmental parameter in each of the historical laboratories and the laboratory; Step S103: Based on the label values ​​of various environmental parameters in the historical laboratory, construct a feature vector X={X1, X2, X3, ..., X n }, where X1, X2, X3, ..., X n Respectively represented as the marked values ​​of the 1st, 2nd, 3rd, ..., nth environmental parameters in the historical laboratory; Step S104: analyzing the degree of similarity between the environment of the laboratory and each of the historical laboratories, wherein the specific process of analyzing the degree of similarity between the environment of the laboratory and the dth historical laboratory is as follows: Calculate the environmental similarity score β between the laboratory and the d-th historical laboratory d : ， Among them, X d is represented as the characteristic vector of the d-th historical laboratory; Y is represented as the characteristic vector of the laboratory; Step S105: When the environment approximation score β d If the value is greater than a preset similarity score threshold, it is determined that the environment between the d-th historical laboratory and the laboratory is similar, and the d-th historical laboratory is recorded as the characteristic historical laboratory of the laboratory.

3. The laboratory monitoring and alarm method based on the Internet of Things according to claim 2 is characterized in that: The step S200 includes: Step S201: Acquire the characteristic history laboratory of the laboratory, acquire the historical abnormal alarm record of the characteristic history laboratory, and acquire the historical abnormal alarm data from the historical abnormal alarm record, wherein the historical abnormal alarm data is the maximum value of the monitoring indicator that triggers the alarm; Step S202: obtaining preset standard data of the monitoring indicators of the characteristic history laboratory from the cloud platform, wherein the marking data is a preset threshold value of the monitoring indicator; Step S203: Acquire each historical abnormal alarm record of the characteristic history laboratory. When the maximum value of a certain monitoring indicator of a certain historical abnormal alarm record in the characteristic history laboratory is greater than the threshold value of the certain monitoring indicator, record the certain historical abnormal alarm record as the characteristic historical abnormal alarm record of the certain monitoring indicator. Step S204: Calculate the preset thresholds of the monitoring indicators of the feature history laboratory and the degree of fit with the first feature environment of the feature history laboratory, wherein the preset threshold of the dth monitoring indicator of the feature history laboratory and the degree of fit with the first feature environment of the feature history laboratory L d : ， Among them, M d M is represented by the dth monitoring indicator, the total number of feature history abnormal alarm records in the feature history laboratory; sum It is represented by the total number of each historical abnormal alarm record in the characteristic history laboratory; Step S205: When the first characteristic environment compatibility L d is less than the preset first characteristic environment fit threshold, it is determined that the preset threshold of the d-th monitoring indicator is not consistent with the characteristic history laboratory environment, and the preset threshold of the d-th monitoring indicator will trigger the characteristic history laboratory error alarm, and the d-th monitoring indicator is marked as the first abnormal monitoring indicator of the characteristic history laboratory; Step S206: Obtain historical safety accident records of the characteristic history laboratory from the cloud platform, and extract historical safety accident data from the historical safety accident records, wherein the historical safety accident data is data corresponding to various monitoring indicators in the characteristic history laboratory when a safety accident occurs in the characteristic history laboratory, and remove the first abnormal monitoring indicator of the characteristic history laboratory from the various monitoring indicators to obtain several monitoring indicators of the characteristic history laboratory; Step S207: Calculate the characteristic values ​​of the several monitoring indicators in the historical safety accident records, wherein the characteristic ratio value F of the e-th monitoring indicator in the historical safety accident records is e =G e，max / Q e , where G e，max It is represented by the maximum value of the monitoring index of the eth item of the characteristic history laboratory in the historical safety accident record, Q e It is represented as the threshold value of the monitoring indicator of item e; Step S208: When the characteristic ratio value of the e-th monitoring indicator is greater than a preset characteristic ratio threshold, the historical safety accident record is recorded as a characteristic historical safety accident record of the e-th monitoring indicator; Step S209: Calculate the preset threshold of the e-th monitoring indicator and the degree of compatibility H with the second characteristic environment of the characteristic history laboratory e =K e / K sum , where K sum It is represented by the total number of historical safety accident records of the characteristic history laboratory, K e Expressed as the e-th monitoring indicator, the total number of characteristic historical safety accident records in the characteristic historical laboratory; Step S210: When the second characteristic environment compatibility H e is greater than the preset second characteristic environment fit threshold, it is determined that the preset threshold of the e-th monitoring indicator does not match the characteristic history laboratory environment, and the threshold of the e-th monitoring indicator cannot be used as a determination condition for the environmental abnormality of the characteristic history laboratory, and the e-th monitoring indicator is recorded as the second abnormal monitoring indicator of the characteristic history laboratory; Step S211: Acquire the first abnormal monitoring indicator and the second abnormal monitoring indicator of the characteristic history laboratory, and aggregate them to obtain the abnormal monitoring data of the characteristic history laboratory.

4. The laboratory monitoring and alarm method based on the Internet of Things according to claim 3 is characterized in that: The step S300 includes: Step S301: Acquire abnormal monitoring data of each characteristic history laboratory of the laboratory, and perform abnormality evaluation on each monitoring indicator of the laboratory, wherein the process of performing abnormality evaluation on the βth monitoring indicator in the laboratory includes: Get the total number U of characteristic historical laboratories whose β-th monitoring indicator is the first abnormal monitoring indicator β , obtain the total number U´´ of characteristic historical laboratories whose β-th monitoring indicator is the second abnormal monitoring indicator β , calculate the abnormal score P of the βth monitoring indicator in the laboratory β : ， Wherein, γ1 represents the preset first characteristic coefficient; γ2 represents the preset second characteristic coefficient, γ1+γ2=1; U sum The total number of each characteristic history laboratory represented by the laboratory; Step S302: When the indicator abnormality score P β If the value is greater than a preset indicator abnormality score threshold, it is determined that the βth monitoring indicator in the laboratory is abnormal, and the βth monitoring indicator is recorded as the target monitoring indicator of the laboratory; Step S303: Obtain the standard data of the target monitoring indicators of the laboratory from the cloud platform, and optimize and adjust the standard data of the target monitoring indicators in the laboratory. The specific optimization and adjustment process is: From each of the feature history laboratories, obtain a number of feature history laboratories having the same preset threshold value as the target monitoring indicator of the laboratory; Step S304: Obtain each historical normal operation record of the several characteristic history laboratories, obtain the average of the maximum values ​​of the target monitoring indicators in the several characteristic history laboratories from the various historical normal operation records, and record the average as the target threshold value of the target monitoring indicator in the laboratories; Step S305: Obtain target thresholds of several monitoring indicators of the laboratory, and use the target thresholds of several monitoring indicators of the laboratory to replace preset thresholds of several monitoring indicators of the laboratory in the standard data to obtain target standard data of the laboratory.

5. The laboratory monitoring and alarm method based on the Internet of Things according to claim 4 is characterized in that: The step S400 includes: Step S401: using a plurality of networked monitoring devices to monitor the laboratory in the current cycle and obtain data of various monitoring indicators in the laboratory; Step S402: Obtain the target standard data of the laboratory. When there is a monitoring indicator whose value is greater than the threshold in the target standard data among the various monitoring indicators monitored by the monitoring equipment of the laboratory in the current cycle, it is determined that there is an abnormality in the laboratory and an intelligent alarm is issued to the laboratory.

6. A laboratory monitoring and alarm system based on the Internet of Things, used to execute the laboratory monitoring and alarm method based on the Internet of Things according to any one of claims 1 to 5, characterized in that: The system includes an environmental proximity analysis module, a fit assessment module, a target standard data module, and an intelligent monitoring and alarm module; The environment similarity analysis module is used to analyze the degree of environment similarity between the laboratory and the historical laboratory to obtain a characteristic historical laboratory; The fit evaluation module is used to evaluate the fit between the standard data of the monitoring indicators of the characteristic history laboratory and the environment of the characteristic history laboratory to obtain abnormal monitoring data; The target standard data module is used to evaluate the abnormality of the monitoring indicators of the laboratory to obtain the target monitoring indicators, and optimize and adjust the standard data of the target monitoring indicators in the laboratory according to the historical monitoring records of the characteristic history laboratory to obtain the target standard data; The intelligent monitoring and alarm module is used to perform safety monitoring on the laboratory in the current cycle, conduct abnormal evaluation on the laboratory in combination with the target standard data, and perform intelligent alarm on the laboratory.

7. The laboratory monitoring and alarm system based on the Internet of Things according to claim 6, characterized in that: The environment approximation analysis module includes an environment approximation scoring unit and an environment approximation analysis unit; The environment similarity scoring unit is used to calculate the environment similarity score between the laboratory and the historical laboratory; The environmental similarity analysis unit is used to analyze the degree of environmental similarity between the laboratory and each historical laboratory according to the environmental similarity scores between the laboratory and each historical laboratory, and obtain a characteristic historical laboratory.

8. The laboratory monitoring and alarm system based on the Internet of Things according to claim 6, characterized in that: The fit assessment module includes a historical data acquisition unit and a fit assessment unit; The historical data acquisition unit is used to obtain the historical abnormal alarm records of the characteristic history laboratory, obtain the standard data of the monitoring indicators of the characteristic history laboratory from the cloud platform, and obtain the historical safety accident records of the characteristic history laboratory; The compatibility evaluation unit is used to evaluate the degree of compatibility between the standard data of the monitoring indicators of the characteristic history laboratory and the environment of the characteristic history laboratory, and obtain abnormal monitoring data of the characteristic history laboratory.

9. The laboratory monitoring and alarm system based on the Internet of Things according to claim 6, characterized in that: The target standard data module includes an indicator abnormality scoring unit and a target standard data unit; The indicator abnormality scoring unit is used to calculate the indicator abnormality score of each monitoring indicator in the laboratory; The target standard data unit is used to evaluate the abnormality of various monitoring indicators in the laboratory to obtain the target monitoring indicators of the laboratory, and to optimize and adjust the standard data of the target monitoring indicators in the laboratory to obtain the target standard data of the laboratory.

10. The laboratory monitoring and alarm system based on the Internet of Things according to claim 6, characterized in that: The intelligent monitoring and alarm module includes an intelligent monitoring and alarm unit; The intelligent monitoring and alarm unit is used to monitor the laboratory in the current cycle, obtain data of various monitoring indicators in the laboratory, and perform intelligent alarm on the laboratory.

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