CN116072282B - Remote intelligent detection and analysis method and system for CT equipment - Google Patents
Remote intelligent detection and analysis method and system for CT equipment Download PDFInfo
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Abstract
The invention relates to the technical field of CT equipment detection, and particularly discloses a remote intelligent detection analysis method and a remote intelligent detection analysis system for CT equipment, wherein the method comprises the steps of determining installation parameters of an acquisition end according to a historical maintenance record; acquiring acquisition data of the acquisition end at regular time according to the detection frequency, identifying the acquisition data, and determining the working state of CT equipment; inputting a preset radiation prediction model according to the working state to obtain theoretical radiation quantity, and obtaining actual radiation quantity based on a preset detector; and comparing the theoretical radiation quantity with the actual radiation quantity, generating a maintenance plan according to the comparison result, and updating the historical maintenance record. According to the method, the installation parameters of the acquisition end are truly recorded according to the historical maintenance of the CT equipment, acquisition data acquired by the acquisition end are received, the acquisition data are analyzed, and a detection report is generated; the invention has high safety, strong comprehensiveness and higher efficiency.
Description
Technical Field
The invention relates to the technical field of CT equipment detection, in particular to a remote intelligent detection and analysis method and system for CT equipment.
Background
CT (Computed Tomography) it is an electronic computer tomography, it uses accurate collimated X-ray beam, gamma ray, supersonic wave, etc., and makes one-by-one section scan around a certain part of human body together with the very high-sensitivity detector, it has characteristics of fast scanning time, clear picture, etc., can be used for the inspection of various diseases; the rays used can be classified differently according to the type: x-ray CT (X-CT), gamma-ray CT (gamma-CT), and the like.
Along with the improvement of the technology level, the CT equipment has become the main stream equipment in the diagnosis process, and the cost and the importance are extremely high, so that the CT equipment needs to be detected and analyzed regularly, in particular to the detection and analysis in the use process; however, certain radiation exists in the using process of the CT equipment, the radiation is extremely harmful to human bodies, and detection and analysis are difficult to be carried out close to the equipment.
Therefore, how to design a remote detection analysis scheme for a CT device is a technical problem to be solved by the technical scheme of the invention.
Disclosure of Invention
The invention aims to provide a remote intelligent detection and analysis method and a system for CT equipment, which are used for solving the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a remote intelligent detection analysis method for a CT apparatus, the method comprising:
acquiring the equipment model of CT equipment, acquiring a historical maintenance record of equipment of the same type according to the equipment model, and determining the installation parameters of an acquisition end according to the historical maintenance record; the installation parameters comprise an installation position and a detection frequency;
acquiring acquisition data of the acquisition end at regular time according to the detection frequency, identifying the acquisition data, and determining the working state of CT equipment;
inputting a preset radiation prediction model according to the working state to obtain theoretical radiation quantity, and obtaining actual radiation quantity based on a preset detector;
and comparing the theoretical radiation quantity with the actual radiation quantity, generating a maintenance plan according to the comparison result, and updating the historical maintenance record.
As a further scheme of the invention: the step of acquiring the equipment model of the CT equipment, acquiring the history maintenance record of equipment of the same type according to the equipment model, and determining the installation parameters of the acquisition end according to the history maintenance record comprises the following steps:
receiving an equipment model of CT equipment input by a user, and sending a query request containing user information and the equipment model to a preset statistical end; when the statistic receiver receives a query request containing user information and equipment model, determining user permission according to the user information, extracting a history maintenance record corresponding to the equipment model based on the user permission, and sending the extracted history maintenance record to a user;
receiving a detection period input by a user, and intercepting the history maintenance record according to the detection period;
and establishing an equipment model according to the intercepted historical maintenance record, and determining the installation parameters of the acquisition end according to the equipment model.
As a further scheme of the invention: the step of establishing an equipment model according to the intercepted historical maintenance record and determining the installation parameters of the acquisition end according to the equipment model comprises the following steps:
reading a reference model from a preset model library according to the equipment model;
reading maintenance points in the history maintenance record and a maintenance mode thereof, and determining an abnormal level of the maintenance points according to the maintenance mode;
performing point location marking in the reference model according to the maintenance points and the abnormal levels thereof to obtain an actual model; the point position marking process comprises the steps of determining value scores of all the point positions, wherein the value scores are used for representing the detection efficiency of all the point positions;
and determining the installation position and the detection frequency of the acquisition end according to the value score.
As a further scheme of the invention: the step of acquiring the acquisition data of the acquisition end at fixed time according to the detection frequency, identifying the acquisition data, and determining the working state of the CT equipment comprises the following steps:
reading an actual model, and establishing a database taking each point as a label according to the point marking result;
acquiring acquisition data of the acquisition end at regular time according to the detection frequency, and inputting the acquisition data into a corresponding database;
sequentially reading the collected data in each database, carrying out relevance identification on the collected data, and clustering the collected data according to the relevance identification result;
and carrying out fluctuation recognition on the clustered acquired data, and determining the working state of the CT equipment.
As a further scheme of the invention: the step of sequentially reading the collected data in each database, carrying out relevance identification on the collected data, and clustering the collected data according to the relevance identification result comprises the following steps:
reading collected data in each database, and fitting the collected data into a fluctuation curve and a fluctuation function based on a list dotting method;
periodically identifying the fluctuation function, and determining the periodic characteristics of the fluctuation function;
clustering different databases according to the periodic characteristics;
and sequentially acquiring value scores of point positions corresponding to various acquired data, and selecting at least one group of fluctuation curves and fluctuation functions according to the value scores as a clustering result.
As a further scheme of the invention: the step of carrying out fluctuation recognition on the clustered acquired data to determine the working state of the CT equipment comprises the following steps:
reading a clustering result, and carrying out derivative of a preset order on the fluctuation function to obtain a function set taking the order as an index;
monotonicity analysis is carried out on the function group, and the function group is converted into a jump signal; the two values of the jump signal are used for representing an increasing function and a decreasing function respectively;
and inputting the jump signal into a trained identification model to obtain working parameters of the CT equipment.
The technical scheme of the invention also provides a remote intelligent detection and analysis system for the CT equipment, which comprises the following components:
the installation parameter determining module is used for obtaining the equipment model of the CT equipment, obtaining the historical maintenance record of equipment of the same type according to the equipment model, and determining the installation parameter of the acquisition end according to the historical maintenance record; the installation parameters comprise an installation position and a detection frequency;
the data identification module is used for acquiring the acquisition data of the acquisition end at regular time according to the detection frequency, identifying the acquisition data and determining the working state of the CT equipment;
the radiation acquisition module is used for inputting a preset radiation prediction model according to the working state to obtain theoretical radiation quantity, and acquiring actual radiation quantity based on a preset detector;
and the comparison updating module is used for comparing the theoretical radiation quantity with the actual radiation quantity, generating a maintenance plan according to the comparison result and updating the historical maintenance record.
As a further scheme of the invention: the installation parameter determining module comprises:
the query request sending unit is used for receiving the equipment model of the CT equipment input by a user and sending a query request containing user information and the equipment model to a preset statistical end; when the statistic receiver receives a query request containing user information and equipment model, determining user permission according to the user information, extracting a history maintenance record corresponding to the equipment model based on the user permission, and sending the extracted history maintenance record to a user;
the history record intercepting unit is used for receiving a detection period input by a user and intercepting the history maintenance record according to the detection period;
the model building unit is used for building an equipment model according to the intercepted historical maintenance record and determining the installation parameters of the acquisition end according to the equipment model.
As a further scheme of the invention: the model building unit includes:
the model reading subunit is used for reading a reference model according to the equipment model in a preset model library;
the abnormality determination subunit is used for reading the maintenance points and the maintenance modes thereof in the history maintenance record and determining the abnormality level of the maintenance points according to the maintenance modes;
the point position marking subunit is used for marking the point position in the reference model according to the maintenance points and the abnormal levels thereof to obtain an actual model; the point position marking process comprises the steps of determining value scores of all the point positions, wherein the value scores are used for representing the detection efficiency of all the point positions;
and the application subunit is used for determining the installation position and the detection frequency of the acquisition end according to the value score.
As a further scheme of the invention: the data identification module comprises:
the database establishing unit is used for reading the actual model and establishing a database taking each point as a label according to the point marking result;
the data input unit is used for acquiring the acquisition data of the acquisition end at fixed time according to the detection frequency and inputting the acquisition data into a corresponding database;
the association recognition unit is used for sequentially reading the collected data in each database, carrying out association recognition on the collected data and clustering the collected data according to an association recognition result;
and the fluctuation identification unit is used for carrying out fluctuation identification on the clustered acquired data and determining the working state of the CT equipment.
Compared with the prior art, the invention has the beneficial effects that: according to the method, the installation parameters of the acquisition end are truly recorded according to the historical maintenance of the CT equipment, the acquired data acquired by the acquisition end are received, the acquired data are subjected to relevance analysis, the data processing amount of the acquired data is reduced according to the relevance analysis result, and the target acquired data are obtained; carrying out fluctuation analysis on the target acquisition data to generate a detection report; the invention has high safety, strong comprehensiveness and higher efficiency.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
Fig. 1 is a flow chart of a remote intelligent detection analysis method for a CT apparatus.
Fig. 2 is a first sub-flowchart of a remote intelligent detection analysis method for a CT apparatus.
Fig. 3 is a second sub-flowchart block diagram of a remote intelligent detection analysis method for a CT apparatus.
Fig. 4 is a block diagram showing the construction of a remote intelligent detection and analysis system for a CT apparatus.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
Fig. 1 is a flow chart of a remote intelligent detection and analysis method for a CT apparatus, and in an embodiment of the present invention, a remote intelligent detection and analysis method for a CT apparatus includes:
step S100: acquiring the equipment model of CT equipment, acquiring a historical maintenance record of equipment of the same type according to the equipment model, and determining the installation parameters of an acquisition end according to the historical maintenance record; the installation parameters comprise an installation position and a detection frequency;
each CT device has a device model, the device model can inquire the history maintenance record of the similar device, and the installation parameters of the acquisition end can be determined according to the history maintenance record; the acquisition end can be various sensors with data transmission functions.
Step S200: acquiring acquisition data of the acquisition end at regular time according to the detection frequency, identifying the acquisition data, and determining the working state of CT equipment;
acquiring acquisition data by an acquisition end, and analyzing the acquisition data to determine the working state of the CT equipment.
Step S300: inputting a preset radiation prediction model according to the working state to obtain theoretical radiation quantity, and obtaining actual radiation quantity based on a preset detector;
each working state corresponds to different radiation parameters, theoretical radiation parameters (theoretical data) are determined by the working states, and actual radiation parameters (actual data) can be obtained by the detector.
Step S400: comparing the theoretical radiation quantity with the actual radiation quantity, generating a maintenance plan according to the comparison result, and updating the historical maintenance record;
by comparing the theoretical data with the actual data, it can be determined whether the device is in a predictable state, and if the device is in an unpredictable state, a maintenance plan for the device needs to be generated, and in the maintenance process, the historical maintenance record needs to be updated.
Fig. 2 is a first sub-flowchart of a remote intelligent detection and analysis method for a CT device, where the step of obtaining a device model of the CT device, obtaining a history maintenance record of devices of the same model according to the device model, and determining an installation parameter of an acquisition end according to the history maintenance record includes:
step S101: receiving an equipment model of CT equipment input by a user, and sending a query request containing user information and the equipment model to a preset statistical end;
the equipment model of the CT equipment is input by a user, and when the user sends a query request containing the equipment model to the statistics end, the user information is synchronously sent; when the statistic receiver receives a query request containing user information and equipment model, determining user permission according to the user information, extracting a history maintenance record corresponding to the equipment model based on the user permission, and sending the extracted history maintenance record to a user; the statistical end is a preset port and is used for acquiring the history maintenance record of each device.
Step S102: receiving a detection period input by a user, and intercepting the history maintenance record according to the detection period;
when a user wants to perform detection analysis on the CT apparatus, a detection period is also required to be input, and detection analysis is performed on the CT apparatus within a certain period.
Step S103: establishing an equipment model according to the intercepted historical maintenance record, and determining installation parameters of an acquisition end according to the equipment model;
and establishing an equipment model by using the historical maintenance record, and determining the installation positions of some acquisition ends and the working parameters of the acquisition ends in the established equipment model, wherein the working parameters at least comprise detection frequency.
As a preferred embodiment of the technical scheme of the present invention, the step of establishing an equipment model according to the intercepted historical maintenance record and determining the installation parameters of the acquisition end according to the equipment model comprises:
reading a reference model from a preset model library according to the equipment model;
in the design and development stage, there are some design drawings for each device, and under the current state of the art, a developer will build a three-dimensional model, where the three-dimensional model is the reference model that is desired to be read in the above description.
Reading maintenance points in the history maintenance record and a maintenance mode thereof, and determining an abnormal level of the maintenance points according to the maintenance mode;
reading a maintenance position and a maintenance mode of the CT equipment in a history maintenance record, wherein the maintenance mode represents a problem occurring in a corresponding maintenance position; depending on what kind of problem has occurred, an abnormality level for the corresponding maintenance position can be determined.
Performing point location marking in the reference model according to the maintenance points and the abnormal levels thereof to obtain an actual model; the point position marking process comprises the steps of determining value scores of all the point positions, wherein the value scores are used for representing the detection efficiency of all the point positions;
determining different marking modes according to different abnormal levels, wherein the marking modes are represented by a numerical value named value score; the anomaly probabilities of different value points are different; the points where no problem occurs are marked by default abnormal levels.
Determining the installation position and detection frequency of the acquisition end according to the value score;
the value score represents the abnormal probability, and the higher the abnormal probability is, the higher the acquisition value of the data is, so that the installation position of the acquisition end can be determined according to the value score, and the detection frequency is further determined; wherein, the higher the anomaly probability, the higher the detection frequency.
Fig. 3 is a second sub-flowchart of a remote intelligent detection and analysis method for a CT apparatus, where the steps of periodically acquiring the acquired data of the acquisition end according to the detection frequency, identifying the acquired data, and determining the working state of the CT apparatus include:
step S201: reading an actual model, and establishing a database taking each point as a label according to the point marking result;
step S202: acquiring acquisition data of the acquisition end at regular time according to the detection frequency, and inputting the acquisition data into a corresponding database;
step S203: sequentially reading the collected data in each database, carrying out relevance identification on the collected data, and clustering the collected data according to the relevance identification result;
step S204: and carrying out fluctuation recognition on the clustered acquired data, and determining the working state of the CT equipment.
Reading the generated actual model, then regularly acquiring data acquired by different acquisition ends, and then storing the data into a corresponding database; one point location corresponds to one database; and the data in each database is acquired in sequence, and the data are subjected to relevance identification, so that the relevant data can be counted and classified.
Partial data is selected from the similar data for fluctuation identification, so that the data volume of the data to be analyzed can be greatly reduced, and the working state of CT equipment can be further determined; the evaluation criteria of the working state are optionally defined by the staff, and most simply are represented by a numerical value for reflecting whether the working state is normal or not.
As a preferred embodiment of the present invention, the step of sequentially reading collected data in each database, performing relevance recognition on the collected data, and clustering the collected data according to a relevance recognition result includes:
reading collected data in each database, and fitting the collected data into a fluctuation curve and a fluctuation function based on a list dotting method;
the acquired data contains time information, statistics is carried out on the acquired data according to the time information, a data table can be obtained, coordinate points are determined in a preset coordinate axis by the data table, and a fluctuation function and a fluctuation curve can be obtained by fitting the coordinate points.
Periodically identifying the fluctuation function, and determining the periodic characteristics of the fluctuation function;
judging the periodicity of the fluctuation function to obtain periodic characteristics; the process is carried out by adopting the existing period analysis technology.
Clustering different databases according to the periodic characteristics;
by comparing different periodic characteristics, it can be easily judged which fluctuation functions are the same increase and decrease, and the same increase and decrease represents that the two fluctuation functions are associated; and classifying the databases corresponding to the fluctuation functions if the association exists.
Sequentially acquiring value scores of point positions corresponding to various acquired data, and selecting at least one group of fluctuation curves and fluctuation functions according to the value scores as clustering results;
and selecting a plurality of data with higher value scores (with great analysis significance) from each class of databases, namely, taking the data as a clustering result.
Specifically, the step of performing fluctuation recognition on the clustered acquired data to determine the working state of the CT apparatus includes:
reading a clustering result, and carrying out derivative of a preset order on the fluctuation function to obtain a function set taking the order as an index;
monotonicity analysis is carried out on the function group, and the function group is converted into a jump signal; the two values of the jump signal are used for representing an increasing function and a decreasing function respectively;
and inputting the jump signal into a trained identification model to obtain working parameters of the CT equipment.
The above specifically describes the fluctuation analysis process, and firstly, the clustering result (the set of fluctuation functions) is derived, and the number of times of the derivation is preset by the user; then, judging the monotonicity condition of each function according to the derivative result, wherein the monotonicity condition reflects the fluctuation condition of each point position in the CT equipment; the determining process of the fluctuation condition is completed by a preset identification model, the identification model is input as jump information reflecting the monotonicity condition, and the jump information is output as working parameters of the CT equipment. Wherein, each index or expression mode of the working parameter is determined by staff according to the situation.
Example 2
Fig. 4 is a block diagram of a remote intelligent detection and analysis system for a CT apparatus, in which the system 10 includes:
the installation parameter determining module 11 is used for obtaining the equipment model of the CT equipment, obtaining the historical maintenance record of equipment of the same model according to the equipment model, and determining the installation parameter of the acquisition end according to the historical maintenance record; the installation parameters comprise an installation position and a detection frequency;
the data identification module 12 is configured to acquire the acquired data of the acquisition end at regular time according to the detection frequency, identify the acquired data, and determine the working state of the CT apparatus;
the radiation obtaining module 13 is configured to input a preset radiation prediction model according to the working state, obtain a theoretical radiation amount, and obtain an actual radiation amount based on a preset detector;
and the comparison and update module 14 is used for comparing the theoretical radiation quantity and the actual radiation quantity, generating a maintenance plan according to the comparison result and updating the historical maintenance record.
The installation parameter determination module 11 includes:
the query request sending unit is used for receiving the equipment model of the CT equipment input by a user and sending a query request containing user information and the equipment model to a preset statistical end; when the statistic receiver receives a query request containing user information and equipment model, determining user permission according to the user information, extracting a history maintenance record corresponding to the equipment model based on the user permission, and sending the extracted history maintenance record to a user;
the history record intercepting unit is used for receiving a detection period input by a user and intercepting the history maintenance record according to the detection period;
the model building unit is used for building an equipment model according to the intercepted historical maintenance record and determining the installation parameters of the acquisition end according to the equipment model.
The model building unit includes:
the model reading subunit is used for reading a reference model according to the equipment model in a preset model library;
the abnormality determination subunit is used for reading the maintenance points and the maintenance modes thereof in the history maintenance record and determining the abnormality level of the maintenance points according to the maintenance modes;
the point position marking subunit is used for marking the point position in the reference model according to the maintenance points and the abnormal levels thereof to obtain an actual model; the point position marking process comprises the steps of determining value scores of all the point positions, wherein the value scores are used for representing the detection efficiency of all the point positions;
and the application subunit is used for determining the installation position and the detection frequency of the acquisition end according to the value score.
The data identification module 12 includes:
the database establishing unit is used for reading the actual model and establishing a database taking each point as a label according to the point marking result;
the data input unit is used for acquiring the acquisition data of the acquisition end at fixed time according to the detection frequency and inputting the acquisition data into a corresponding database;
the association recognition unit is used for sequentially reading the collected data in each database, carrying out association recognition on the collected data and clustering the collected data according to an association recognition result;
and the fluctuation identification unit is used for carrying out fluctuation identification on the clustered acquired data and determining the working state of the CT equipment.
The functions which can be realized by the remote intelligent detection and analysis method for the CT equipment are all completed by computer equipment, the computer equipment comprises one or more processors and one or more memories, at least one program code is stored in the one or more memories, and the program code is loaded and executed by the one or more processors to realize the functions of the remote intelligent detection and analysis method for the CT equipment.
The processor takes out instructions from the memory one by one, analyzes the instructions, then completes corresponding operation according to the instruction requirement, generates a series of control commands, enables all parts of the computer to automatically, continuously and cooperatively act to form an organic whole, realizes the input of programs, the input of data, the operation and the output of results, and the arithmetic operation or the logic operation generated in the process is completed by the arithmetic unit; the Memory comprises a Read-Only Memory (ROM) for storing a computer program, and a protection device is arranged outside the Memory.
For example, a computer program may be split into one or more modules, one or more modules stored in memory and executed by a processor to perform the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program in the terminal device.
It will be appreciated by those skilled in the art that the foregoing description of the service device is merely an example and is not meant to be limiting, and may include more or fewer components than the foregoing description, or may combine certain components, or different components, such as may include input-output devices, network access devices, buses, etc.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal device described above, and which connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used for storing computer programs and/or modules, and the processor may implement various functions of the terminal device by running or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as an information acquisition template display function, a product information release function, etc.), and the like; the storage data area may store data created according to the use of the berth status display system (e.g., product information acquisition templates corresponding to different product types, product information required to be released by different product providers, etc.), and so on. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
The modules/units integrated in the terminal device may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on this understanding, the present invention may implement all or part of the modules/units in the system of the above-described embodiments, or may be implemented by instructing the relevant hardware by a computer program, which may be stored in a computer-readable storage medium, and which, when executed by a processor, may implement the functions of the respective system embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (4)
1. A remote intelligent detection and analysis method for a CT apparatus, the method comprising:
acquiring the equipment model of CT equipment, acquiring a historical maintenance record of equipment of the same type according to the equipment model, and determining the installation parameters of an acquisition end according to the historical maintenance record; the installation parameters comprise an installation position and a detection frequency;
acquiring acquisition data of the acquisition end at regular time according to the detection frequency, identifying the acquisition data, and determining the working state of CT equipment;
inputting a preset radiation prediction model according to the working state to obtain theoretical radiation quantity, and obtaining actual radiation quantity based on a preset detector;
comparing the theoretical radiation quantity with the actual radiation quantity, generating a maintenance plan according to the comparison result, and updating the historical maintenance record;
the step of acquiring the equipment model of the CT equipment, acquiring the history maintenance record of equipment of the same type according to the equipment model, and determining the installation parameters of the acquisition end according to the history maintenance record comprises the following steps:
receiving an equipment model of CT equipment input by a user, and sending a query request containing user information and the equipment model to a preset statistical end; when the statistic receiver receives a query request containing user information and equipment model, determining user permission according to the user information, extracting a history maintenance record corresponding to the equipment model based on the user permission, and sending the extracted history maintenance record to a user;
receiving a detection period input by a user, and intercepting the history maintenance record according to the detection period;
establishing an equipment model according to the intercepted historical maintenance record, and determining installation parameters of an acquisition end according to the equipment model;
the step of establishing an equipment model according to the intercepted historical maintenance record and determining the installation parameters of the acquisition end according to the equipment model comprises the following steps:
reading a reference model from a preset model library according to the equipment model;
reading maintenance points in the history maintenance record and a maintenance mode thereof, and determining an abnormal level of the maintenance points according to the maintenance mode;
performing point location marking in the reference model according to the maintenance points and the abnormal levels thereof to obtain an actual model; the point position marking process comprises the steps of determining value scores of all the point positions, wherein the value scores are used for representing the detection efficiency of all the point positions;
determining the installation position and detection frequency of the acquisition end according to the value score;
the step of acquiring the acquisition data of the acquisition end at fixed time according to the detection frequency, identifying the acquisition data, and determining the working state of the CT equipment comprises the following steps:
reading an actual model, and establishing a database taking each point as a label according to the point marking result;
acquiring acquisition data of the acquisition end at regular time according to the detection frequency, and inputting the acquisition data into a corresponding database;
sequentially reading the collected data in each database, carrying out relevance identification on the collected data, and clustering the collected data according to the relevance identification result;
and carrying out fluctuation recognition on the clustered acquired data, and determining the working state of the CT equipment.
2. The remote intelligent detection and analysis method for CT equipment according to claim 1, wherein the step of sequentially reading the collected data in each database, performing correlation recognition on the collected data, and clustering the collected data according to the correlation recognition result comprises:
reading collected data in each database, and fitting the collected data into a fluctuation curve and a fluctuation function based on a list dotting method;
periodically identifying the fluctuation function, and determining the periodic characteristics of the fluctuation function;
clustering different databases according to the periodic characteristics;
and sequentially acquiring value scores of point positions corresponding to various acquired data, and selecting at least one group of fluctuation curves and fluctuation functions according to the value scores as a clustering result.
3. The remote intelligent detection and analysis method for CT equipment according to claim 2, wherein the step of performing fluctuation recognition on the clustered acquired data to determine the operation state of the CT equipment comprises:
reading a clustering result, and carrying out derivative of a preset order on the fluctuation function to obtain a function set taking the order as an index;
monotonicity analysis is carried out on the function group, and the function group is converted into a jump signal; the two values of the jump signal are used for representing an increasing function and a decreasing function respectively;
and inputting the jump signal into a trained identification model to obtain working parameters of the CT equipment.
4. A remote intelligent detection and analysis system for a CT apparatus, the system comprising:
the installation parameter determining module is used for obtaining the equipment model of the CT equipment, obtaining the historical maintenance record of equipment of the same type according to the equipment model, and determining the installation parameter of the acquisition end according to the historical maintenance record; the installation parameters comprise an installation position and a detection frequency;
the data identification module is used for acquiring the acquisition data of the acquisition end at regular time according to the detection frequency, identifying the acquisition data and determining the working state of the CT equipment;
the radiation acquisition module is used for inputting a preset radiation prediction model according to the working state to obtain theoretical radiation quantity, and acquiring actual radiation quantity based on a preset detector;
the comparison updating module is used for comparing the theoretical radiation quantity with the actual radiation quantity, generating a maintenance plan according to a comparison result and updating the historical maintenance record;
the installation parameter determining module comprises:
the query request sending unit is used for receiving the equipment model of the CT equipment input by a user and sending a query request containing user information and the equipment model to a preset statistical end; when the statistic receiver receives a query request containing user information and equipment model, determining user permission according to the user information, extracting a history maintenance record corresponding to the equipment model based on the user permission, and sending the extracted history maintenance record to a user;
the history record intercepting unit is used for receiving a detection period input by a user and intercepting the history maintenance record according to the detection period;
the model building unit is used for building an equipment model according to the intercepted historical maintenance record and determining the installation parameters of the acquisition end according to the equipment model;
the model building unit includes:
the model reading subunit is used for reading a reference model according to the equipment model in a preset model library;
the abnormality determination subunit is used for reading the maintenance points and the maintenance modes thereof in the history maintenance record and determining the abnormality level of the maintenance points according to the maintenance modes;
the point position marking subunit is used for marking the point position in the reference model according to the maintenance points and the abnormal levels thereof to obtain an actual model; the point position marking process comprises the steps of determining value scores of all the point positions, wherein the value scores are used for representing the detection efficiency of all the point positions;
an application subunit, configured to determine an installation position and a detection frequency of the acquisition end according to the value score;
the data identification module comprises:
the database establishing unit is used for reading the actual model and establishing a database taking each point as a label according to the point marking result;
the data input unit is used for acquiring the acquisition data of the acquisition end at fixed time according to the detection frequency and inputting the acquisition data into a corresponding database;
the association recognition unit is used for sequentially reading the collected data in each database, carrying out association recognition on the collected data and clustering the collected data according to an association recognition result;
and the fluctuation identification unit is used for carrying out fluctuation identification on the clustered acquired data and determining the working state of the CT equipment.
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