CN118466378B - Intelligent comprehensive management and control system of electromechanical equipment based on intelligent hospital - Google Patents
Intelligent comprehensive management and control system of electromechanical equipment based on intelligent hospital Download PDFInfo
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Abstract
The intelligent comprehensive management and control system for the electromechanical equipment based on the intelligent hospital relates to the technical field of management and control of the electromechanical equipment and comprises a monitoring center, wherein the monitoring center is in communication connection with a data acquisition module, a data uplink module, a data analysis module and an early warning module: constructing an intelligent hospital comprehensive management platform based on a block chain technology, and collecting characteristic data sets of all electromechanical devices; performing security verification on the collected characteristic data set, and then encrypting and uploading the characteristic data set to a blockchain node; constructing a characteristic mark database, and constructing a coupling relation among all electromechanical devices; and constructing an electromechanical device characteristic data set prediction model, and performing early warning operation on the electromechanical device according to the real-time word face value sequences of various types of data acquired by the nodes of the Internet of things in real time, the real-time word face value sequences of various types of data output by the electromechanical device characteristic data set prediction model and the coupling relation among various electromechanical devices, so that the transparency and the efficiency of electromechanical device management are remarkably improved.
Description
Technical Field
The invention relates to the technical field of electromechanical equipment management and control, in particular to an electromechanical equipment intelligent comprehensive management and control system based on an intelligent hospital.
Background
Modern hospital electromechanical equipment relates to operations such as hospital water, electricity, heating, steam supply, equipment facility operation and maintenance management, material purchasing and distribution, bedding and clothing washing and downward receiving and delivering, dining halls, fire protection, vehicles, parking lots, elevators, air conditioners, oxygen supply and air supply, sewage treatment and the like, and the traditional electromechanical equipment facility management mode has various defects and disadvantages, particularly various information of the electromechanical equipment facilities are relatively closed and relatively poor in interaction, so that individual information islands are formed, the economy of the whole life cycle of the electromechanical equipment facilities is seriously influenced, and the requirements on aspects such as management, safe production, economic benefit, reliability and energy resource management of the electromechanical equipment facility are met.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide an electromechanical device intelligent comprehensive management and control system based on an intelligent hospital, which comprises a monitoring center, wherein the monitoring center is in communication connection with a data acquisition module, a data uplink module, a data analysis module and an early warning module:
The data acquisition module is used for constructing an intelligent hospital comprehensive management platform based on a block chain technology, setting an Internet of things node and acquiring a characteristic data set of each electromechanical device;
The data uplink module is used for carrying out security verification on the collected characteristic data set, and then encrypting and uploading the characteristic data set to a block chain node;
the data analysis module is used for constructing a characteristic mark database, storing characteristic mark data before each electromechanical device generates abnormal alarm information of a plurality of types of data into the characteristic mark database, and constructing a coupling relation among the electromechanical devices;
the early warning module is used for constructing an electromechanical device characteristic data set prediction model and carrying out early warning operation on electromechanical devices according to the real-time word face value sequences of various types of data acquired by the nodes of the Internet of things in real time, the real-time word face value sequences of various types of data output by the electromechanical device characteristic data set prediction model and the coupling relation among the electromechanical devices.
Further, the data acquisition module constructs an intelligent hospital integrated management platform based on a blockchain technology, sets up an internet of things node, and the process of acquiring the characteristic data set of each electromechanical device comprises the following steps:
The intelligent hospital comprehensive management platform is built based on a blockchain technology, a plurality of blockchain nodes are arranged in the intelligent hospital comprehensive management platform, the blockchain nodes are mutually linked to form a blockchain network, the blockchain nodes are used for being connected with Internet of things nodes which are deployed on electromechanical equipment in a preset range in a communication mode, the Internet of things nodes are used for collecting characteristic data sets of the electromechanical equipment and uploading the characteristic data sets to the blockchain nodes and marking collection time, and a collection period is set.
Further, the data uplink module performs security verification on the collected feature data set, and then the process of encrypting and uploading the feature data set to the blockchain node comprises the following steps:
presetting a mining node, a consensus mechanism, an intelligent contract and a blockchain communication protocol, when the node of the Internet of things acquires a characteristic data set of electromechanical equipment, carrying out security verification on the characteristic data set through the intelligent contract, then encrypting and uploading the characteristic data set to a blockchain node, after decrypting the received characteristic data set by a blockchain link point, packaging the decrypted characteristic data set into a new block through the mining node, broadcasting the new block to a blockchain network according to the blockchain communication protocol, verifying the new block by other blockchain link points in the blockchain network based on the consensus mechanism, adding the new block to the tail end of a blockchain after the verification of the new block is passed, and updating the characteristic data set in the new block into local blockchain copies of all blockchain nodes.
Further, the process of security verification of the feature data set by the smart contract includes:
Presetting a threshold interval corresponding to each type of data in the characteristic data set of the electromechanical device, acquiring the literal value corresponding to each type of data in the characteristic data set of the electromechanical device at the current acquisition time, judging whether the literal value corresponding to each type of data is positioned in the corresponding threshold interval, and marking the electromechanical device as a normal state if the literal value corresponding to each type of data is positioned in the corresponding threshold interval;
If the type data which is not located in the corresponding threshold value interval exists, acquiring scene information corresponding to the electromechanical device at the current acquisition time, acquiring a literal compensation value between the scene information and the type data, carrying out compensation adjustment on the type data according to the literal compensation value, judging whether the literal value corresponding to the type data after compensation adjustment is located in the corresponding threshold value interval, if the literal value is not located in the corresponding threshold value interval, generating abnormal alarm information of the type data, marking the electromechanical device as an abnormal state, and if the literal value is located in the abnormal state, marking the electromechanical device as a normal state.
Further, the process of obtaining the literal compensation value between the scene information and the type data includes:
and acquiring a characteristic data set and scene information of the electromechanical device in a plurality of historical acquisition periods, extracting a literal value sequence of each type of data in the characteristic data set under different scene information, acquiring the average literal value amplitude of each type of data under different scene information according to the literal value sequence, and taking the average literal value amplitude as the literal compensation value of each type of data among different scene information.
Further, the process of obtaining the feature flag data before each electromechanical device generates the abnormal alarm information of the plurality of types of data by the data analysis module includes:
Acquiring a characteristic data set before abnormal alarm information of certain type of data is generated by certain electromechanical equipment in a plurality of historical acquisition periods, acquiring average fluctuation coefficients of the various types of data and average pearson correlation coefficients between the certain type of data and other types of data according to a literal value sequence of the various types of data in the characteristic data set, taking the average fluctuation coefficients of the various types of data and the average pearson correlation coefficients between the certain type of data and other types of data as characteristic mark data before the abnormal alarm information of the certain type of data is generated, and so on, acquiring characteristic mark data before the abnormal alarm information of the various types of data is generated by each electromechanical equipment, and storing the characteristic mark data before the abnormal alarm information of the various types of data is generated by each electromechanical equipment into a characteristic mark database;
Simultaneously acquiring the equipment type and the position characteristic of each electromechanical equipment, setting an evaluation index weight matrix and a coupling coefficient grade according to the equipment type, the position characteristic and characteristic mark data before abnormal alarm information of a plurality of types of data of each electromechanical equipment as evaluation indexes, and acquiring a membership matrix of each electromechanical equipment to the coupling coefficient grade through fuzzy comprehensive evaluation;
And obtaining coupling coefficients among the electromechanical devices according to the membership degree matrix and the evaluation index weight matrix, presetting a coupling coefficient threshold, and if the coupling coefficients among the electromechanical devices are larger than the coupling coefficient threshold, constructing a coupling relation among the electromechanical devices.
Further, the process of constructing the electromechanical device characteristic data set prediction model by the early warning module comprises the following steps:
Constructing a prediction model of the characteristic data set of the electromechanical equipment based on deep learning, and taking the characteristic data set of each electromechanical equipment in a plurality of historical acquisition periods as a test set and a training set;
inputting the training set into the electromechanical device characteristic data set prediction model for training until the loss function training is stable, storing model parameters, testing the electromechanical device characteristic data set prediction model through a testing set until the electromechanical device characteristic data set prediction model meets the preset requirement, and outputting the electromechanical device characteristic data set prediction model;
and obtaining estimated literal value sequences of various types of data in the current acquisition period of each electromechanical device according to the electromechanical device characteristic data set prediction model.
Further, the process of performing the early warning operation of the electromechanical device by the early warning module according to the real-time word face value sequence of each type of data collected by the node of the internet of things in real time, the real-time word face value sequence of each type of data output by the electromechanical device feature data set prediction model and the coupling relation between the electromechanical devices comprises the following steps:
Acquiring real-time literal value sequences of various types of data of all electromechanical devices in a current acquisition period through an Internet of things node, comparing the real-time literal value sequences of the various types of data of all electromechanical devices with an estimated literal value sequence, acquiring average pearson correlation coefficients of the various types of data of all electromechanical devices, presetting an average pearson correlation coefficient threshold, marking the type data as suspicious type data if the average pearson correlation coefficients are smaller than the average pearson correlation coefficient threshold, marking the electromechanical device to which the type data belongs as suspicious electromechanical devices, acquiring other electromechanical devices with coupling relation with the suspicious electromechanical devices, judging whether the average pearson correlation coefficients of the type data consistent with the suspicious type data of the other electromechanical devices are smaller than the average pearson correlation coefficient threshold, marking the other electromechanical devices as other suspicious electromechanical devices if the average pearson correlation coefficients are smaller than the average pearson correlation coefficient threshold, and marking the other electromechanical devices as other normal devices if the average pearson correlation coefficients are not smaller than the average pearson correlation coefficient threshold;
If the number of other suspicious electromechanical devices is > (the number of other normal devices/2), extracting feature mark data of suspicious type data of suspicious electromechanical devices and other suspicious electromechanical devices in the current acquisition period, marking the feature mark data as first feature mark data, acquiring a plurality of electromechanical devices consistent with the suspicious electromechanical devices and other suspicious electromechanical devices in a feature mark database and type data consistent with the suspicious type data, extracting feature mark data before the plurality of electromechanical devices generate abnormal alarm information of the type data, marking the feature mark data as second feature mark data, carrying out consistency matching on the first feature mark data and the second feature mark data, if the first feature mark data is consistent with the second feature mark data, generating early warning signals of the suspicious type data, and if the first feature mark data is inconsistent with the second feature mark data, continuing the consistency matching operation of the first feature mark data and the second feature mark data until the first feature mark data is consistent with the second feature mark data or the number of other suspicious electromechanical devices (the number of other electromechanical devices/2) is less than or equal to or less than the normal number of other suspicious electromechanical devices;
if the number of other suspicious electromechanical devices is less than or equal to (the number of other normal devices/2), marking the Internet of things node of the suspicious electromechanical devices as an abnormal state, suspending the data acquisition process of the Internet of things node in the abnormal state, and carrying out rapid abnormality detection on the Internet of things node in the abnormal state.
Further, the process of performing rapid anomaly detection on the internet of things node in the anomaly state comprises the following steps:
Acquiring other normal devices with coupling relation with suspicious electromechanical devices of the internet of things node in an abnormal state, acquiring other normal devices closest to the suspicious electromechanical devices from the other normal devices according to position characteristics, marking the other normal devices as temporary detection devices, constructing a wireless communication link between the internet of things node of the temporary detection devices and the electromechanical devices, acquiring a real-time word face value sequence of suspicious type data of the suspicious electromechanical devices through the wireless communication link by the internet of things node of the temporary detection devices, acquiring an average pearson correlation coefficient between the real-time word face value sequence and an estimated face value sequence corresponding to the suspicious type data, judging whether the average pearson correlation coefficient is smaller than the average pearson correlation coefficient, if so, generating abnormal alarm information of the suspicious type data, marking the suspicious electromechanical devices as abnormal states, if not smaller, marking the internet of things node of the abnormal states as fault states, and generating an alarm signal of the internet of things node of the fault states.
Compared with the prior art, the invention has the beneficial effects that:
1. Constructing an intelligent hospital comprehensive management platform based on a block chain technology, setting nodes of an Internet of things, and collecting characteristic data sets of all electromechanical devices; the acquired characteristic data set is subjected to security verification, then the characteristic data set is encrypted and uploaded to a blockchain node, the security and the integrity of hospital equipment data can be ensured by utilizing the characteristics of a blockchain through the decentralization and the distributed account book, the characteristic data set acquired by the nodes of the Internet of things can be safely stored and transmitted, the data is prevented from being tampered or lost, meanwhile, all equipment data changes can be traced and audited because the data change recorded by the blockchain is non-tamperable, the transparency is very useful for the history record, maintenance history and fault diagnosis of the operation of the electromechanical equipment, the transparency and the efficiency of the management of the electromechanical equipment are remarkably improved, meanwhile, an intelligent contract is set, the security verification can be performed by utilizing an automatic execution code based on the blockchain, the processes in aspects of automatic equipment maintenance, energy management, security monitoring and the like of the intelligent contract are utilized, and the possibility of human intervention and errors is reduced;
2. Constructing a characteristic mark database, storing characteristic mark data before each electromechanical device generates abnormal alarm information of a plurality of types of data into the characteristic mark database, and constructing a coupling relation among the electromechanical devices; constructing an electromechanical device characteristic data set prediction model, performing early warning operation of electromechanical devices according to real-time word face value sequences of various types of data acquired by nodes of the Internet of things in real time, the real-time word face value sequences of various types of data output by the electromechanical device characteristic data set prediction model and coupling relations among all electromechanical devices, and performing early warning operation of the electromechanical devices according to device types, position characteristics and characteristic mark data before abnormal alarm information of various types of data of all electromechanical devices, wherein the meaning of the coupling coefficients among the electromechanical devices is that the coupling relations are constructed among the electromechanical devices with similar external environments and internal operation states, if the pearson correlation coefficient between the real-time word face value sequences of a certain electromechanical device and the estimated word face value sequences is smaller than a pearson correlation coefficient threshold value, the electromechanical devices are not abnormal, and the fact that the electromechanical devices are not abnormal is possibly only because the data processing pressure ratio of the electromechanical devices of the nodes is larger or the block chain network is jammed, the nodes of the electromechanical devices are not transmitting real-time data of the electromechanical devices or error data of the electromechanical devices within a set time is not required to be caused, in order to reduce misjudgment conditions, the coupling coefficients between the electromechanical devices with similar external environments and the internal operation states are constructed, if the pearson correlation coefficients between the real-time word face values of the electromechanical devices and the estimated electromechanical devices are continuously influenced by the pearson correlation coefficients between the electromechanical devices and the estimated values, and the pearson correlation coefficients can be prevented from being continuously influenced by the fact that the electromechanical devices are normal operation, the continuity of medical service is ensured.
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Fig. 1 is a schematic diagram of an intelligent comprehensive management and control system for electromechanical devices based on intelligent hospitals according to an embodiment of the application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
As shown in fig. 1, the intelligent comprehensive management and control system of the electromechanical equipment based on the intelligent hospital comprises a monitoring center, wherein the monitoring center is in communication connection with a data acquisition module, a data uplink module, a data analysis module and an early warning module:
The data acquisition module is used for constructing an intelligent hospital comprehensive management platform based on a block chain technology, setting an Internet of things node and acquiring a characteristic data set of each electromechanical device;
The data uplink module is used for carrying out security verification on the collected characteristic data set, and then encrypting and uploading the characteristic data set to a block chain node;
the data analysis module is used for constructing a characteristic mark database, storing characteristic mark data before each electromechanical device generates abnormal alarm information of a plurality of types of data into the characteristic mark database, and constructing a coupling relation among the electromechanical devices;
the early warning module is used for constructing an electromechanical device characteristic data set prediction model and carrying out early warning operation on electromechanical devices according to the real-time word face value sequences of various types of data acquired by the nodes of the Internet of things in real time, the real-time word face value sequences of various types of data output by the electromechanical device characteristic data set prediction model and the coupling relation among the electromechanical devices.
It should be further described that, in the specific implementation process, the process of constructing the intelligent hospital integrated management platform based on the blockchain technology, setting the nodes of the internet of things, and collecting the characteristic data set of each electromechanical device includes:
The intelligent hospital comprehensive management platform is built based on a blockchain technology, a plurality of blockchain nodes are arranged in the intelligent hospital comprehensive management platform, the blockchain nodes are mutually linked to form a blockchain network, the blockchain nodes are used for being connected with Internet of things nodes which are deployed on electromechanical equipment in a preset range in a communication mode, the Internet of things nodes are used for collecting characteristic data sets of the electromechanical equipment and uploading the characteristic data sets to the blockchain nodes and marking collection time, and a collection period is set.
It should be further noted that, in the implementation process, in the electromechanical device intelligent comprehensive management and control system of the intelligent hospital, the types of the covered electromechanical devices are very wide, each device has specific functions and management requirements, and the following are the types of electromechanical devices included in the invention:
An air conditioning system: the air conditioning equipment is responsible for controlling the air quality and temperature in the hospital, and comprises air conditioning equipment in various areas such as ward, operating room, diagnosis and treatment area and the like;
An electric power supply system: the system comprises a generator and substation equipment, and is responsible for providing stable power supply for hospitals;
And (3) a water supply and drainage system: including water pumps, plumbing, etc., for water supply, circulation, and drainage;
an illumination system: comprising an LED lighting device for providing illumination of various areas inside the hospital;
Fire safety system: comprises a fire alarm system, a fire water system and the like, the fire safety in the hospital is ensured;
Security monitoring system: the system comprises a closed-circuit television monitoring system, an access control system and the like, and is used for monitoring and managing the internal safety of a hospital;
Elevator and escalator systems: the device is used for vertical transportation of personnel and articles in a hospital, and ensures quick communication of all floors of the hospital;
medical gas system: such as various medical gas supply systems (such as oxygen, nitrogen and the like), and provide necessary gas support for various medical equipment in hospitals;
Medical device and monitoring system: including various medical diagnostic devices, surgical devices, monitors, etc., for the provision and monitoring of medical services.
It should be further described that, in the implementation process, the feature data set of the electromechanical device to be collected based on the intelligent comprehensive management and control system of the electromechanical device in the intelligent hospital includes:
Operation data: the method comprises the steps of real-time operation state, working parameters, operation time and the like of the equipment, and is used for monitoring the operation state of the equipment in real time;
Energy consumption data: recording the energy consumption conditions of the equipment, including information such as electricity consumption, energy consumption and the like;
environmental data: such as environmental data like temperature, humidity, air quality, number of people, etc.
It should be further noted that, in the implementation process, the process of performing security verification on the collected feature data set and then encrypting and uploading the feature data set to the blockchain node includes:
presetting a mining node, a consensus mechanism, an intelligent contract and a blockchain communication protocol, when the node of the Internet of things acquires a characteristic data set of electromechanical equipment, carrying out security verification on the characteristic data set through the intelligent contract, then encrypting and uploading the characteristic data set to a blockchain node, after decrypting the received characteristic data set by a blockchain link point, packaging the decrypted characteristic data set into a new block through the mining node, broadcasting the new block to a blockchain network according to the blockchain communication protocol, verifying the new block by other blockchain link points in the blockchain network based on the consensus mechanism, adding the new block to the tail end of a blockchain after the verification of the new block is passed, and updating the characteristic data set in the new block into local blockchain copies of all blockchain nodes.
By utilizing the characteristics of the block chain, such as decentralization and distributed account book, the safety and the integrity of hospital equipment data can be ensured, the characteristic data set collected by the nodes of the Internet of things can be safely stored and transmitted, the data is prevented from being tampered or lost, meanwhile, as the data change recorded by the block chain is not tamperable, all equipment data changes can be traced and audited, the transparency is very useful for the history record, maintenance history and fault diagnosis of the operation of the electromechanical equipment, the transparency and the efficiency of the management of the electromechanical equipment are obviously improved, meanwhile, an intelligent contract is set, the intelligent contract is based on the automatic execution code of the block chain, the safety verification can be carried out according to the preset condition characteristic data set, and the artificial intervention and the possibility of errors are reduced by utilizing the flows of the aspects of intelligent contract automation equipment maintenance, energy management, safety monitoring and the like.
It should be further noted that, in the implementation process, the process of performing security verification on the feature data set through the smart contract includes:
Presetting a threshold interval corresponding to each type of data in the characteristic data set of the electromechanical device, acquiring the literal value corresponding to each type of data in the characteristic data set of the electromechanical device at the current acquisition time, judging whether the literal value corresponding to each type of data is positioned in the corresponding threshold interval, and marking the electromechanical device as a normal state if the literal value corresponding to each type of data is positioned in the corresponding threshold interval;
If the type data which is not located in the corresponding threshold value interval exists, acquiring scene information corresponding to the electromechanical device at the current acquisition time, acquiring a literal compensation value between the scene information and the type data, carrying out compensation adjustment on the type data according to the literal compensation value, judging whether the literal value corresponding to the type data after compensation adjustment is located in the corresponding threshold value interval, if the literal value is not located in the corresponding threshold value interval, generating abnormal alarm information of the type data, marking the electromechanical device as an abnormal state, and if the literal value is located in the abnormal state, marking the electromechanical device as a normal state.
It should be further noted that, in the implementation process, the process of obtaining the literal compensation value between the scene information and the type data includes:
and acquiring a characteristic data set and scene information of the electromechanical device in a plurality of historical acquisition periods, extracting a literal value sequence of each type of data in the characteristic data set under different scene information, acquiring the average literal value amplitude of each type of data under different scene information according to the literal value sequence, and taking the average literal value amplitude as the literal compensation value of each type of data among different scene information.
It should be further noted that, in the implementation process, the calculation formula for obtaining the average amplitude of the word face value is:
；
where s represents the average amplitude of the word denomination, A literal value representing the time t in the literal value sequence,The average value of the literal values representing the literal value sequence and n represents the total number of times of the literal value sequence.
It should be further noted that the scenario information includes diagnosis areas such as an outpatient department, an emergency department, an internal department, a surgical department, a gynecological department, a pediatric department, an inspection department, an image department, a plurality of treatment areas, a monitoring area, and the like.
It should be further noted that, in the implementation process, the process of obtaining the feature flag data before each electromechanical device generates the abnormality alarm information of several types of data includes:
Acquiring a characteristic data set before abnormal alarm information of certain type of data is generated by certain electromechanical equipment in a plurality of historical acquisition periods, acquiring average fluctuation coefficients of the various types of data and average pearson correlation coefficients between the certain type of data and other types of data according to a literal value sequence of the various types of data in the characteristic data set, taking the average fluctuation coefficients of the various types of data and the average pearson correlation coefficients between the certain type of data and other types of data as characteristic mark data before the abnormal alarm information of the certain type of data is generated, and so on, acquiring characteristic mark data before the abnormal alarm information of the various types of data is generated by each electromechanical equipment, and storing the characteristic mark data before the abnormal alarm information of the various types of data is generated by each electromechanical equipment into a characteristic mark database;
Simultaneously acquiring the equipment type and the position characteristic of each electromechanical equipment, setting an evaluation index weight matrix and a coupling coefficient grade according to the equipment type, the position characteristic and characteristic mark data before abnormal alarm information of a plurality of types of data of each electromechanical equipment as evaluation indexes, and acquiring a membership matrix of each electromechanical equipment to the coupling coefficient grade through fuzzy comprehensive evaluation;
And obtaining coupling coefficients among the electromechanical devices according to the membership degree matrix and the evaluation index weight matrix, presetting a coupling coefficient threshold, and if the coupling coefficients among the electromechanical devices are larger than the coupling coefficient threshold, constructing a coupling relation among the electromechanical devices.
It should be further noted that, in the implementation process, the calculation formula for obtaining the average fluctuation coefficient of each type of data and the average pearson correlation coefficient between a certain type of data and other types of data is as follows:
；；
Wherein, Representing the average fluctuation coefficient of the data type i,A literal value representing the data type i at time t, N represents the total number of times,Representing the average pearson correlation coefficient between data type i and data type j,Representing the average word denomination of data type i,The literal value representing the data type j at time t,Representing the average word denomination of data type j.
It should be further noted that, in the implementation process, the process of obtaining the coupling coefficient between the electromechanical devices according to the membership matrix and the evaluation index weight matrix includes:
the method comprises the steps of obtaining a fuzzy comprehensive evaluation matrix of an evaluation index by fusing an index weight matrix and a membership matrix of the evaluation index through a formula, obtaining membership of each electromechanical device to different coupling coefficients according to the fuzzy comprehensive evaluation matrix, screening out the coupling coefficient with the highest membership corresponding to each electromechanical device, and taking the coupling coefficient with the highest membership corresponding to each electromechanical device as the coupling coefficient of each electromechanical device;
Wherein, the formula is:
；
Wherein, A fuzzy comprehensive evaluation matrix for the evaluation index,An index weight matrix for the evaluation index,For the membership matrix of the group,The weight matrix representing the evaluation index is multiplied by an element at a position corresponding to the membership matrix,And the weighting parameters are used for controlling the balance between the weight matrix and the membership matrix in the fuzzy comprehensive evaluation matrix of the evaluation index.
It should be further noted that, in the specific implementation process, according to the device type, the position characteristic and the characteristic mark data before the abnormal alarm information of the plurality of types of data of each electromechanical device as evaluation indexes, the meaning of obtaining the coupling coefficient between the electromechanical devices is to construct a coupling relationship between the electromechanical devices with approximate external environment and internal operation state, if the pearson correlation coefficient between the real-time literal sequence and the estimated literal sequence of a certain electromechanical device is smaller than the pearson correlation coefficient threshold, the electromechanical devices may not be abnormal, and may only be because the data processing pressure ratio of the electromechanical device internet of things node is larger or the block chain network is congested or the fault occurs at the electromechanical device internet of things node, so that the real-time data of the electromechanical devices or the fault data of the electromechanical devices are not transmitted in the specified time, in order to reduce the misjudgment, the coupling relationship is constructed between the electromechanical devices with approximate external environment and internal operation state, and if the pearson correlation coefficient between the real-time literal sequence and the estimated literal sequence of other electromechanical devices with the coupling relationship is used to judge whether the suspicious electromechanical devices has multiple operation effects, which can continuously influence the health of the medical diagnosis and avoid the normal operation of the medical devices.
It should be further noted that, in the implementation process, the process of constructing the electromechanical device characteristic data set prediction model includes:
Constructing a prediction model of the characteristic data set of the electromechanical equipment based on deep learning, and taking the characteristic data set of each electromechanical equipment in a plurality of historical acquisition periods as a test set and a training set;
inputting the training set into the electromechanical device characteristic data set prediction model for training until the loss function training is stable, storing model parameters, testing the electromechanical device characteristic data set prediction model through a testing set until the electromechanical device characteristic data set prediction model meets the preset requirement, and outputting the electromechanical device characteristic data set prediction model;
and obtaining estimated literal value sequences of various types of data in the current acquisition period of each electromechanical device according to the electromechanical device characteristic data set prediction model.
It should be further described that, in the specific implementation process, the process of performing early warning operation of the electromechanical device according to the real-time word face value sequence of each type of data collected by the node of the internet of things in real time, the real-time word face value sequence of each type of data output by the electromechanical device feature data set prediction model, and the coupling relationship between each electromechanical device includes:
Acquiring real-time literal value sequences of various types of data of all electromechanical devices in a current acquisition period through an Internet of things node, comparing the real-time literal value sequences of the various types of data of all electromechanical devices with an estimated literal value sequence, acquiring average pearson correlation coefficients of the various types of data of all electromechanical devices, presetting an average pearson correlation coefficient threshold, marking the type data as suspicious type data if the average pearson correlation coefficients are smaller than the average pearson correlation coefficient threshold, marking the electromechanical device to which the type data belongs as suspicious electromechanical devices, acquiring other electromechanical devices with coupling relation with the suspicious electromechanical devices, judging whether the average pearson correlation coefficients of the type data consistent with the suspicious type data of the other electromechanical devices are smaller than the average pearson correlation coefficient threshold, marking the other electromechanical devices as other suspicious electromechanical devices if the average pearson correlation coefficients are smaller than the average pearson correlation coefficient threshold, and marking the other electromechanical devices as other normal devices if the average pearson correlation coefficients are not smaller than the average pearson correlation coefficient threshold;
If the number of other suspicious electromechanical devices is > (the number of other normal devices/2), extracting feature mark data of suspicious type data of suspicious electromechanical devices and other suspicious electromechanical devices in the current acquisition period, marking the feature mark data as first feature mark data, acquiring a plurality of electromechanical devices consistent with the suspicious electromechanical devices and other suspicious electromechanical devices in a feature mark database and type data consistent with the suspicious type data, extracting feature mark data before the plurality of electromechanical devices generate abnormal alarm information of the type data, marking the feature mark data as second feature mark data, carrying out consistency matching on the first feature mark data and the second feature mark data, if the first feature mark data is consistent with the second feature mark data, generating early warning signals of the suspicious type data, and if the first feature mark data is inconsistent with the second feature mark data, continuing the consistency matching operation of the first feature mark data and the second feature mark data until the first feature mark data is consistent with the second feature mark data or the number of other suspicious electromechanical devices (the number of other electromechanical devices/2) is less than or equal to or less than the normal number of other suspicious electromechanical devices;
if the number of other suspicious electromechanical devices is less than or equal to (the number of other normal devices/2), marking the Internet of things node of the suspicious electromechanical devices as an abnormal state, suspending the data acquisition process of the Internet of things node in the abnormal state, and carrying out rapid abnormality detection on the Internet of things node in the abnormal state.
It should be further described that, in the specific implementation process, the process of performing rapid anomaly detection on the internet of things node in the anomaly state includes:
Acquiring other normal devices with coupling relation with suspicious electromechanical devices of the internet of things node in an abnormal state, acquiring other normal devices closest to the suspicious electromechanical devices from the other normal devices according to position characteristics, marking the other normal devices as temporary detection devices, constructing a wireless communication link between the internet of things node of the temporary detection devices and the electromechanical devices, acquiring a real-time word face value sequence of suspicious type data of the suspicious electromechanical devices through the wireless communication link by the internet of things node of the temporary detection devices, acquiring an average pearson correlation coefficient between the real-time word face value sequence and an estimated face value sequence corresponding to the suspicious type data, judging whether the average pearson correlation coefficient is smaller than the average pearson correlation coefficient, if so, generating abnormal alarm information of the suspicious type data, marking the suspicious electromechanical devices as abnormal states, if not smaller, marking the internet of things node of the abnormal states as fault states, and generating an alarm signal of the internet of things node of the fault states.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (1)
1. Electromechanical device intelligent comprehensive management and control system based on wisdom hospital, its characterized in that includes the monitoring center, monitoring center communication connection has data acquisition module, data linking module, data analysis module and early warning module in advance:
The data acquisition module is used for constructing an intelligent hospital comprehensive management platform based on a block chain technology, setting an Internet of things node and acquiring a characteristic data set of each electromechanical device;
The data acquisition module constructs an intelligent hospital integrated management platform based on a block chain technology, sets up an Internet of things node, and the process of acquiring the characteristic data set of each electromechanical device comprises the following steps:
An intelligent hospital comprehensive management platform is built based on a blockchain technology, a plurality of blockchain nodes are arranged in the intelligent hospital comprehensive management platform, the blockchain nodes are mutually linked to form a blockchain network, the blockchain nodes are used for being in communication connection with the nodes of the Internet of things which are deployed on the electromechanical equipment within a preset range, the nodes of the Internet of things are used for acquiring characteristic data sets of the electromechanical equipment and uploading the characteristic data sets to the blockchain nodes and marking acquisition time, and an acquisition period is set;
The data uplink module is used for carrying out security verification on the collected characteristic data set, and then encrypting and uploading the characteristic data set to a block chain node;
The data uplink module performs security verification on the collected characteristic data set, and then the process of encrypting and uploading the characteristic data set to the blockchain node comprises the following steps:
Presetting a mining node, a consensus mechanism, an intelligent contract and a blockchain communication protocol, when the node of the Internet of things acquires a characteristic data set of electromechanical equipment, carrying out security verification on the characteristic data set through the intelligent contract, then encrypting and uploading the characteristic data set to a blockchain node, after decrypting the received characteristic data set by a blockchain link point, packaging the decrypted characteristic data set into a new block through the mining node, broadcasting the new block to a blockchain network according to the blockchain communication protocol, verifying the new block by other blockchain link points in the blockchain network based on the consensus mechanism, adding the new block to the tail end of a blockchain after the verification of the new block is passed, and updating the characteristic data set in the new block into local blockchain copies of all blockchain nodes;
the process of security verification of a feature data set by an intelligent contract includes:
Presetting a threshold interval corresponding to each type of data in the characteristic data set of the electromechanical device, acquiring the literal value corresponding to each type of data in the characteristic data set of the electromechanical device at the current acquisition time, judging whether the literal value corresponding to each type of data is positioned in the corresponding threshold interval, and marking the electromechanical device as a normal state if the literal value corresponding to each type of data is positioned in the corresponding threshold interval;
Acquiring scene information corresponding to the electromechanical equipment at the current acquisition time if the type data which is not located in the corresponding threshold interval exists, acquiring a literal compensation value between the scene information and the type data, performing compensation adjustment on the type data according to the literal compensation value, judging whether the literal value corresponding to the type data after compensation adjustment is located in the corresponding threshold interval, if the literal value is not located in the corresponding threshold interval, generating abnormal alarm information of the type data, marking the electromechanical equipment as an abnormal state, and if the literal value is located in the abnormal state, marking the electromechanical equipment as a normal state;
the process of obtaining the literal compensation value between the scene information and the type data includes:
Acquiring a characteristic data set and scene information of the electromechanical device in a plurality of historical acquisition periods, extracting a literal value sequence of each type of data in the characteristic data set under different scene information, acquiring the average literal value amplitude of each type of data under different scene information according to the literal value sequence, and taking the average literal value amplitude as the literal compensation value of each type of data among different scene information;
the data analysis module is used for constructing a characteristic mark database, storing characteristic mark data before each electromechanical device generates abnormal alarm information of a plurality of types of data into the characteristic mark database, and constructing a coupling relation among the electromechanical devices;
the process of acquiring the characteristic mark data before the abnormal alarm information of a plurality of types of data is generated by each electromechanical device by the data analysis module comprises the following steps:
Acquiring a characteristic data set before abnormal alarm information of certain type of data is generated by certain electromechanical equipment in a plurality of historical acquisition periods, acquiring average fluctuation coefficients of the various types of data and average pearson correlation coefficients between the certain type of data and other types of data according to a literal value sequence of the various types of data in the characteristic data set, taking the average fluctuation coefficients of the various types of data and the average pearson correlation coefficients between the certain type of data and other types of data as characteristic mark data before the abnormal alarm information of the certain type of data is generated, and so on, acquiring characteristic mark data before the abnormal alarm information of the various types of data is generated by each electromechanical equipment, and storing the characteristic mark data before the abnormal alarm information of the various types of data is generated by each electromechanical equipment into a characteristic mark database;
Simultaneously acquiring the equipment type and the position characteristic of each electromechanical equipment, setting an evaluation index weight matrix and a coupling coefficient grade according to the equipment type, the position characteristic and characteristic mark data before abnormal alarm information of a plurality of types of data of each electromechanical equipment as evaluation indexes, and acquiring a membership matrix of each electromechanical equipment to the coupling coefficient grade through fuzzy comprehensive evaluation;
Acquiring coupling coefficients among the electromechanical devices according to the membership matrix and the evaluation index weight matrix, presetting a coupling coefficient threshold value, and if the coupling coefficients among the electromechanical devices are larger than the coupling coefficient threshold value, constructing a coupling relation among the electromechanical devices;
The early warning module is used for constructing an electromechanical device characteristic data set prediction model and carrying out early warning operation on electromechanical devices according to real-time word face value sequences of various types of data acquired by the nodes of the Internet of things in real time, the real-time word face value sequences of various types of data output by the electromechanical device characteristic data set prediction model and coupling relations among various electromechanical devices;
the process of constructing the electromechanical device characteristic data set prediction model by the early warning module comprises the following steps:
Constructing a prediction model of the characteristic data set of the electromechanical equipment based on deep learning, and taking the characteristic data set of each electromechanical equipment in a plurality of historical acquisition periods as a test set and a training set;
inputting the training set into the electromechanical device characteristic data set prediction model for training until the loss function training is stable, storing model parameters, testing the electromechanical device characteristic data set prediction model through a testing set until the electromechanical device characteristic data set prediction model meets the preset requirement, and outputting the electromechanical device characteristic data set prediction model;
obtaining estimated literal value sequences of various types of data in the current acquisition period of each electromechanical device according to the electromechanical device characteristic data set prediction model;
The early warning module carries out the early warning operation of the electromechanical equipment according to the real-time word face value sequence of various types of data acquired by the nodes of the Internet of things in real time, the real-time word face value sequence of various types of data output by the electromechanical equipment characteristic data set prediction model and the coupling relation among all electromechanical equipment, wherein the process of carrying out the early warning operation of the electromechanical equipment comprises the following steps:
Acquiring a real-time word face value sequence of each type of data of each electromechanical device in a current acquisition period, comparing the real-time word face value sequence of each type of data with an estimated word face value sequence, acquiring an average pearson correlation coefficient of each type of data, presetting an average pearson correlation coefficient threshold, marking the type data as suspicious type data if the type data with the average pearson correlation coefficient smaller than the average pearson correlation coefficient threshold exist, marking electromechanical devices to which the type data belong as suspicious electromechanical devices, acquiring other electromechanical devices with coupling relation with the suspicious electromechanical devices, judging whether the average pearson correlation coefficient of the type data consistent with the suspicious type data of the other electromechanical devices is smaller than the average pearson correlation coefficient threshold, marking the other electromechanical devices as other suspicious electromechanical devices if the average pearson correlation coefficient is smaller than the average pearson correlation coefficient threshold, marking the type data consistent with the suspicious type data of the other suspicious electromechanical devices as other suspicious electromechanical devices if the type data is not smaller than the normal electromechanical devices;
if the number of other suspicious electromechanical devices is greater than half of the number of other normal devices, performing early warning operation of the suspicious electromechanical devices in the current acquisition period;
If the number of other suspicious electromechanical devices is less than or equal to half of the number of other normal devices, marking the Internet of things nodes of the suspicious electromechanical devices as abnormal states, suspending the data acquisition process of the Internet of things nodes in the abnormal states, and carrying out rapid abnormality detection on the Internet of things nodes in the abnormal states;
the process for performing early warning operation of suspicious electromechanical equipment in the current acquisition period comprises the following steps:
extracting feature mark data of suspicious type data of suspicious electromechanical equipment and other suspicious electromechanical equipment in a current acquisition period, marking the feature mark data as first feature mark data, acquiring a plurality of electromechanical equipment consistent with the suspicious electromechanical equipment and other suspicious electromechanical equipment and type data consistent with the suspicious type data in a feature mark database, extracting feature mark data before abnormal alarm information of the type data is generated by the plurality of electromechanical equipment, marking the feature mark data as second feature mark data, performing consistent matching on the first feature mark data and the second feature mark data, generating early warning signals of the suspicious type data if the first feature mark data is consistent with the second feature mark data, and continuing consistent matching operation on the first feature mark data and the second feature mark data if the first feature mark data is inconsistent with the second feature mark data until the first feature mark data is consistent with the second feature mark data or the number of other suspicious electromechanical equipment is less than or equal to half of the number of other normal equipment;
the process for carrying out rapid anomaly detection on the nodes of the Internet of things in the anomaly state comprises the following steps:
Acquiring other normal devices with coupling relation with suspicious electromechanical devices of the internet of things node in an abnormal state, acquiring other normal devices closest to the suspicious electromechanical devices from the other normal devices according to position characteristics, marking the other normal devices as temporary detection devices, constructing a wireless communication link between the internet of things node of the temporary detection devices and the electromechanical devices, acquiring a real-time word face value sequence of suspicious type data of the suspicious electromechanical devices through the wireless communication link by the internet of things node of the temporary detection devices, acquiring an average pearson correlation coefficient between the real-time word face value sequence and an estimated face value sequence corresponding to the suspicious type data, judging whether the average pearson correlation coefficient is smaller than the average pearson correlation coefficient, if so, generating abnormal alarm information of the suspicious type data, marking the suspicious electromechanical devices as abnormal states, if not smaller, marking the internet of things node of the abnormal states as fault states, and generating an alarm signal of the internet of things node of the fault states.
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