CN107833228B - Medical waste monitoring method, device and system and electronic equipment - Google Patents
Medical waste monitoring method, device and system and electronic equipment Download PDFInfo
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Abstract
The invention provides a medical waste monitoring method, a device and a system as well as electronic equipment, wherein the method comprises the steps of obtaining region coordinate information in medical waste data, and carrying out region division according to the region coordinate information to obtain a plurality of medical waste regions; acquiring the total amount of medical waste recovered in the same preset time in each medical waste area, and generating a data correlation degree sequence corresponding to each medical waste area according to the total amount of the medical waste; and respectively taking each data correlation degree sequence as an input feature, performing data anomaly analysis on each input feature by using a pre-established regional analysis model to obtain an analysis result corresponding to each medical waste region, and sending corresponding alarm prompt information to a monitoring terminal corresponding to a monitoring person according to the analysis result. Therefore, the medical waste data can be automatically analyzed, whether the medical waste data is abnormal or not can be judged, the alarm can be given in time, and the manpower and material resources can be saved while the automatic monitoring and the judgment accuracy are improved.
Description
Technical Field
The invention relates to the technical field of medical waste treatment, in particular to a medical waste monitoring method, a medical waste monitoring device, a medical waste monitoring system and electronic equipment.
Background
Medical waste refers to waste products generated by medical institutions in medical, preventive, health care and other related activities and having direct or indirect infectious, toxic and other hazardous properties, and specifically includes infectious, pathological, traumatic, medicinal and chemical waste products. The waste contains a large amount of bacterial viruses and has certain characteristics of space pollution, acute viral infection and latent infection, if the waste is not managed intensively and is discarded randomly, the waste is mixed with household garbage and is dispersed into the living environment of people, the atmosphere, water sources, land, animals and plants are polluted, the disease transmission is caused, and the physical and psychological health of people is seriously harmed. Today, the awareness of environmental protection is gradually increasing, and it is the responsibility and obligation of each citizen to protect the environment.
However, strict management of medical waste recycling is neglected in the medical waste treatment process due to careless understanding of the hazards of medical waste disposal or disposal. At present, in general, in the recovery management work of hospital medical waste, although electronic handover is performed, data loss, data missing or data counterfeiting still occurs. In view of the above situation, when monitoring medical waste, the health supervision department judges whether the data is abnormal or not by manually calling the medical waste data and performing comparative analysis, which consumes manpower and material resources and is difficult to realize real-time monitoring; and because the influence of artificial subjective factors is large, the phenomenon of misjudgment is easy to occur.
Disclosure of Invention
In view of the above, the present invention provides a method, an apparatus, a system and an electronic device for monitoring medical waste, so as to utilize a regional division and comparison method to automatically analyze medical waste data, determine whether medical waste data is abnormal and alarm in time, and save manpower and material resources while achieving automatic monitoring and improving accuracy of determination.
In a first aspect, an embodiment of the present invention provides a medical waste monitoring method, including:
obtaining region coordinate information in medical waste data, and performing region division according to the region coordinate information to obtain a plurality of medical waste regions;
acquiring the total amount of medical waste recovered in the same preset time in each medical waste area, and generating a data correlation degree sequence corresponding to each medical waste area according to the total amount of the medical waste;
respectively taking each data correlation degree sequence as an input feature, and performing data anomaly analysis on each input feature by using a pre-established regional analysis model to obtain an analysis result corresponding to each medical waste region;
and sending corresponding alarm prompt information to a monitoring terminal corresponding to a monitoring person according to the analysis result.
With reference to the first aspect, an embodiment of the present invention provides a first possible implementation manner of the first aspect, where the performing area division according to the region coordinate information to obtain a plurality of medical waste areas includes:
judging the county and city to which the medical waste data belongs according to the region coordinate information;
and dividing the range covered by all the region coordinate information according to the belonged county and city.
In combination with the first aspect, the present invention provides a second possible implementation manner of the first aspect, wherein the generating a data relevancy sequence corresponding to each medical waste area according to the total medical waste amount includes:
for each medical waste area, respectively comparing the total amount of medical waste in the medical waste area with the total amount of medical waste in each other medical waste area to calculate corresponding data relevancy;
and obtaining a data correlation degree sequence corresponding to each medical waste area according to the data correlation degrees.
With reference to the first aspect, an embodiment of the present invention provides a third possible implementation manner of the first aspect, where the establishing process of the localization analysis model includes:
determining initial values of model parameters of the regional analysis model;
training model parameters of the regional analysis model by adopting a deep learning model according to the initial value, the data correlation sequence and a preset analysis result;
and establishing the regional analysis model according to the model parameters obtained by training.
With reference to the third possible implementation manner of the first aspect, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect, where after the establishing the localization analysis model, the method further includes:
acquiring the data correlation degree sequence and a corresponding known analysis result;
inputting the data correlation degree sequence as an input feature into the regional analysis model for testing to obtain a test result;
and adjusting the model parameters of the regional analysis model according to the known analysis result and the test result.
With reference to the first aspect, an embodiment of the present invention provides a fifth possible implementation manner of the first aspect, where the method further includes:
receiving an inquiry request sent by a monitoring person through the monitoring terminal, wherein the inquiry request comprises a medical waste area identifier;
and calling and sending an analysis result corresponding to the medical waste area identifier to the monitoring terminal.
In a second aspect, embodiments of the present invention further provide a medical waste monitoring device, including:
the region division module is used for acquiring region coordinate information in the medical waste data, performing region division according to the region coordinate information and acquiring a plurality of medical waste regions;
the sequence generation module is used for acquiring the total amount of medical waste recovered in the same preset time of each medical waste area and generating a data correlation degree sequence corresponding to each medical waste area according to the total amount of the medical waste;
the result analysis module is used for respectively taking each data correlation degree sequence as an input feature, and performing data anomaly analysis on each input feature by using a pre-established regional analysis model to obtain an analysis result corresponding to each medical waste region;
and the alarm sending module is used for sending corresponding alarm prompt information to the monitoring terminal corresponding to the monitoring personnel according to the analysis result.
In a third aspect, an embodiment of the present invention further provides a medical waste monitoring system, including a monitoring terminal and a cloud server, where the monitoring terminal is connected to the cloud server, and the cloud server includes the medical waste monitoring device according to the second aspect;
the monitoring terminal is used for receiving and displaying the alarm prompt information sent by the cloud server.
In a fourth aspect, an embodiment of the present invention further provides a computer program that is executable on a processor, and the processor executes the computer program to implement the method according to the first aspect and any possible implementation manner thereof.
In a fifth aspect, the present invention further provides a computer-readable medium having non-volatile program code executable by a processor, where the program code causes the processor to execute the method described in the first aspect and any possible implementation manner thereof.
The embodiment of the invention has the following beneficial effects:
according to the technical scheme provided by the embodiment of the invention, the area where the medical waste is located is divided to obtain a plurality of medical waste areas, the data correlation degree sequence corresponding to each medical waste area is automatically analyzed by utilizing a preset regional analysis model, and the data correlation degree sequence is generated according to the total amount of the medical waste of each medical waste area, so that whether the total amount of the medical waste changes normally can be judged through analyzing the data correlation degree sequence, and whether the medical waste data is abnormal or not is judged, and then an alarm is given to a monitoring person. Therefore, by using the regional division and comparison method, the medical waste data is automatically analyzed, whether the medical waste data is abnormal or not is judged, and an alarm is given in time, so that the automatic monitoring is realized, the judgment accuracy is improved, and meanwhile, the manpower and the material resources are saved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic flow chart of a medical waste monitoring method provided by an embodiment of the invention;
FIG. 2 is a schematic view of a regionalization scheme provided by an embodiment of the present invention;
FIG. 3 is a flow chart illustrating a process of building a localization analysis model according to an embodiment of the present invention;
FIG. 4 is a schematic structural view of a medical waste monitoring device provided in accordance with an embodiment of the present invention;
FIG. 5 is a schematic diagram of a medical waste monitoring system provided by an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
When the medical waste is monitored by the prior health supervision department, the medical waste data is manually called and contrasted to analyze, so that whether the data is abnormal (data loss, data missing or data counterfeiting) is judged, manpower and material resources are consumed, and real-time monitoring is difficult to realize; and because the influence of artificial subjective factors is large, the phenomenon of misjudgment is easy to occur. Based on the above, the medical waste monitoring method, the medical waste monitoring device, the medical waste monitoring system and the electronic equipment provided by the embodiment of the invention can utilize a regional division and comparison method to realize automatic analysis of medical waste data, judge whether the medical waste data is abnormal or not and give an alarm in time, realize automatic monitoring, improve the accuracy of judgment and save manpower and material resources.
To facilitate understanding of the present embodiment, a medical waste monitoring method disclosed in the present embodiment will be described in detail.
The first embodiment is as follows:
the medical waste monitoring method provided by the embodiment of the invention can be suitable for medical waste recovery, garbage recovery or other similar data analysis scenes. Fig. 1 shows a flow chart of a medical waste monitoring method provided by an embodiment of the invention. As shown in fig. 1, the medical waste monitoring method includes:
step S101, obtaining region coordinate information in the medical waste data, and performing region division according to the region coordinate information to obtain a plurality of medical waste regions.
Specifically, the medical waste data includes the type of the medical waste, the weight of the medical waste, and the recycling address or the region coordinate information where the recycling address is located. The region coordinate information can be determined according to the recycling address, and the region coordinate information can be directly input when medical waste is recycled. The partition manner in step S101 may be self-partitioned according to a rule set by a user, which is not limited herein. The following illustrates the manner of region division:
in one embodiment, in step S101, performing area division according to the region coordinate information, and obtaining the plurality of medical waste areas includes: judging the county and the city to which the medical waste data belongs according to the region coordinate information; and dividing the range covered by all the region coordinate information according to the belonged county and city.
Wherein, the range covered by all the regional coordinate information is the recovery range of all the medical wastes. Specifically, the coverage area of each county and city is known, and the county and city to which the region coordinate information corresponding to the medical waste information belongs can be determined according to the region coordinate information corresponding to each medical waste information. The entire collection range may be divided into regions according to the county and city to which the collection range belongs. If the range covered by all the region coordinate information includes five prefectures, the range covered by all the region coordinate information is divided into five medical waste regions according to the five prefectures.
In another embodiment, the division may be performed in a rectangular ring manner, as shown in fig. 2, taking three rectangles as an example, from inside to outside, the three rectangles are respectively T1、T2、T3. Rectangle T3Including the range covered by all the regional coordinate information, namely the whole recovery range, namely, determining a rectangle T containing all the regional coordinate information according to related software3After determining the rectangle T3Then, the rectangle T can be set according to the side length ratio1、T2、T3The center point of each rectangle can be specified according to the recovery range by the user. As in one embodiment, the rectangle is a square, rectangle T1、T2、T3Has a side length ratio of 1:2:3, rectangular T1、T2、T3Is the same, and the rectangle T is determined by the relevant software3After, i.e. after confirmation of its center point, the rectangle T1、T2Is also determined. The specific division result is a rectangle T1The coverage area is a medical waste area S1Rectangular T1And the rectangle T2The rectangular ring in between covers the medical waste area S2Rectangular T2And the rectangle T3The rectangular ring in between covers the medical waste area S3. The vertex position coordinates of each rectangle are known, and then the medical waste area to which each medical waste data belongs is determined according to the region standard coordinate information corresponding to each medical waste data. In the classification mode, each recovery address is random, and compared with classification in county and city, data counterfeiting behavior can be effectively avoided.
Step S102, acquiring the total amount of medical waste recovered in the same preset time of each medical waste area, and generating a data correlation degree sequence corresponding to each medical waste area according to the total amount of the medical waste.
Specifically, the total amount of medical waste in the medical waste area is obtained by adding the weights of the medical waste in all the medical waste data recovered at the preset time in the medical waste area. The preset time may be set by the user according to specific situations, such as a day, a week or two weeks, and the specific time is not limited herein.
In an optional embodiment, in step S102, generating the data relevancy sequence corresponding to each medical waste area according to the total amount of the medical waste includes:
for each medical waste area, respectively comparing the total amount of medical waste in the medical waste area with the total amount of medical waste in each other medical waste area to obtain corresponding data relevancy; and obtaining a data correlation degree sequence corresponding to each medical waste area according to the data correlation degrees.
Considering that the total amount of medical waste in each medical waste area may vary depending on seasons or other factors, the data correlation is used to determine whether the data is abnormal, and the data correlation may represent a contrast between any two total amounts of medical waste.
In the following, the medical waste region S is assumed by taking the rectangular ring division method in step S101 as an example1The total amount of medical waste generated in the process is theta18 tons, medical waste area S2The total amount of medical waste generated in the process is theta210 tons, medical waste area S3The total amount of medical waste generated in the process is theta320 tons. For medical waste area S1Medical waste area S1The total amount of medical waste and the medical waste area S2Medical waste area S3Respectively comparing and calculating the total amount of the medical waste in each of other medical waste areas to obtain corresponding data correlation degrees, wherein if a proportional algorithm is adopted during the comparison calculation, the data correlation degrees obtained after the respective comparison calculation are respectively delta1＝Θ1/Θ2＝8/10＝0.8，δ2＝Θ1/Θ38/20-0.4. The medical waste region S may then be obtained1Corresponding data correlation sequence δ ═ { δ ═ δ1,δ20.8, 0.4. Similarly, the medical waste region S can be obtained2Corresponding data phaseSequence of correlations 1.25,0.5, medical waste region S3Corresponding data correlation series 2.5, 2. It should be noted that the numerical values set forth in the above examples do not limit the scope of the present invention.
And step S103, respectively taking each data correlation degree sequence as an input feature, and performing data anomaly analysis on each input feature by using a pre-established regional analysis model to obtain an analysis result corresponding to each medical waste region.
In order to realize automatic data analysis, a regional analysis model for data analysis is established in advance. The regional analysis model is obtained by training samples of a plurality of data correlation sequences, and the detection effect of the regional analysis model obtained by corresponding training is better when the number of the samples is larger. In one embodiment, the training may be performed by comparing positive and negative samples.
In one embodiment, as shown in fig. 3, the process of building the localization analysis model includes:
step S301, determining the initial value of the model parameter of the regional analysis model.
In this embodiment, the training of the regional analysis model is performed by applying a deep learning method, and it is first necessary to determine an initial value of a model parameter of the regional analysis model, and specifically, the parameter may be set empirically.
And step S302, training model parameters of the regional analysis model by adopting a deep learning model according to the initial value, the data correlation sequence and a preset analysis result.
In one embodiment, the analysis result may be set according to a user's definition, such as the analysis result R ═ { R1, R2} ═ normal, abnormal } or R ═ R1, R2, R3, R4} { normal, low recovery amount, high recovery amount, abnormal }. Specifically, a data relevancy sequence as a sample is used as an input feature, an analysis result is used as a conclusion set, and meanwhile, the conclusion set is input into a deep learning model, and model parameters of the regional analysis model are trained. Wherein the initial parameters in the deep learning model are the initial values of the model parameters of the regional analysis model determined in step S301.
Step S303, establishing a regional analysis model according to the model parameters obtained by training.
After the specific values of the model parameters are obtained through training, a regional analysis model is established by combining the existing deep learning model.
Further, in order to improve the accuracy of the regional analysis model, after the regional analysis model is established, the method further includes: acquiring a data correlation sequence and a corresponding known analysis result; inputting the data correlation degree sequence as an input feature into a regional analysis model for testing to obtain a test result; and adjusting the model parameters of the regional analysis model according to the known analysis result and the test result.
Specifically, the regional analysis model is tested by using a data correlation sequence of a known analysis result, and if the test result is different from the known analysis result, the model parameters of the regional analysis model are finely adjusted until the test result is close to the known analysis result, namely the error rate is within a set range.
Further, after the model parameters of the regional analysis model are adjusted until the test result is close to the known analysis result, the regional analysis model is reestablished according to the adjusted model parameters of the regional analysis model.
And step S104, sending corresponding alarm prompt information to a monitoring terminal corresponding to the monitoring personnel according to the analysis result.
In an optional embodiment, if the analysis result is abnormal or other identifiers indicating the abnormality, sending alarm indication information indicating data abnormality to the monitoring terminal; if the analysis result is that the recovery amount is lower or other marks representing lower recovery amount, sending alarm indication information indicating lower recovery amount to the monitoring terminal; and if the analysis result is 'higher recovery amount' or other marks indicating higher recovery amount, sending alarm indication information indicating higher recovery amount to the monitoring terminal.
Further, in order to facilitate the monitoring personnel to view the analysis result and further improve the user experience, the medical waste monitoring method further comprises the following steps: receiving an inquiry request sent by a monitoring person through a monitoring terminal, wherein the inquiry request comprises a medical waste area identifier; and calling and sending an analysis result corresponding to the medical waste area identifier to the monitoring terminal.
Further, after obtaining the analysis result corresponding to each medical waste region in step S103, the method further includes: and sending the analysis result obtained in the step 103 to a monitoring terminal corresponding to a monitoring person, so that the monitoring terminal displays the analysis result. Wherein the expression form of the analysis result can be one or more of a chart, characters or voice.
According to the technical scheme provided by the embodiment of the invention, the area where the medical waste is located is divided to obtain a plurality of medical waste areas, the data correlation degree sequence corresponding to each medical waste area is automatically analyzed by utilizing a preset regional analysis model, and the data correlation degree sequence is generated according to the total amount of the medical waste of each medical waste area, so that whether the total amount of the medical waste changes normally can be judged through analyzing the data correlation degree sequence, and whether the medical waste data is abnormal or not is judged, and then an alarm is given to a monitoring person. Therefore, by using the regional division and comparison method, the medical waste data can be automatically analyzed, whether the medical waste data is abnormal or not can be judged, the alarm can be given in time, the method is more visual and convenient, and the manpower and material resources can be saved while the automatic monitoring and the judgment accuracy are improved.
Example two:
with respect to the medical waste monitoring method in the first embodiment, fig. 4 shows a schematic structural diagram of the medical waste monitoring device provided by the embodiment of the invention. As shown in fig. 4, the medical waste monitoring apparatus includes:
the region division module 11 is used for acquiring region coordinate information in the medical waste data, performing region division according to the region coordinate information, and acquiring a plurality of medical waste regions;
the sequence generation module 12 is configured to obtain the total amount of medical waste recovered in the same preset time in each medical waste area, and generate a data correlation sequence corresponding to each medical waste area according to the total amount of medical waste;
the result analysis module 13 is configured to use each data correlation degree sequence as an input feature, perform data anomaly analysis on each input feature by using a pre-established regional analysis model, and obtain an analysis result corresponding to each medical waste region;
and the alarm sending module 14 is configured to send corresponding alarm prompt information to the monitoring terminal corresponding to the monitoring person according to the analysis result.
Further, the sequence generating module 12 includes:
a calculating unit 121, configured to calculate, for each medical waste area, a corresponding data correlation by comparing the total amount of medical waste in the medical waste area with the total amount of medical waste in each of the other medical waste areas;
the generating unit 122 is configured to obtain a data correlation degree sequence corresponding to each medical waste region according to each data correlation degree.
Further, the medical waste monitoring device further comprises a model establishing module, wherein the model establishing module is used for determining the initial value of the model parameter of the regional analysis model; according to the initial value, the data correlation sequence and a preset analysis result, training model parameters of a regional analysis model by adopting a deep learning model; and establishing a regional analysis model according to the model parameters obtained by training.
According to the technical scheme provided by the embodiment of the invention, the area where the medical waste is located is divided to obtain a plurality of medical waste areas, the data correlation degree sequence corresponding to each medical waste area is automatically analyzed by utilizing a preset regional analysis model, and the data correlation degree sequence is generated according to the total amount of the medical waste of each medical waste area, so that whether the total amount of the medical waste changes normally can be judged through analyzing the data correlation degree sequence, and whether the medical waste data is abnormal or not is judged, and then an alarm is given to a monitoring person. Therefore, by using the regional division and comparison method, the medical waste data is automatically analyzed, whether the medical waste data is abnormal or not is judged, and an alarm is given in time, so that the automatic monitoring is realized, the judgment accuracy is improved, and meanwhile, the manpower and the material resources are saved.
Example three:
fig. 5 shows a schematic structural diagram of a medical waste monitoring system provided by an embodiment of the invention. As shown in fig. 5, the medical waste monitoring system includes a monitoring terminal 500 and a cloud server 600, the monitoring terminal is connected to the cloud server, and the cloud server includes the medical waste monitoring apparatus according to the second embodiment.
The monitoring terminal may be one or more of a desktop computer, a notebook computer, a tablet computer, a mobile phone, or a PDA (personal digital Assistant). The monitoring terminal is used for receiving alarm prompt information sent by the cloud server. In addition, the monitoring terminal can also receive and display the analysis result sent by the cloud server. The display mode can be one or more of icons, characters and voice.
In an optional embodiment, the monitoring terminal is further configured to send a query request to a cloud server, and receive a corresponding analysis result returned by the cloud server. Wherein the query request includes a medical waste region identification. Thus, the monitoring person can investigate the analysis result of the specified medical waste region at any time.
Furthermore, monitoring personnel can set a timing query function through the monitoring terminal, and the cloud server periodically sends an analysis result of the corresponding medical waste area to the monitoring terminal. Therefore, monitoring personnel can conveniently check the regions which are mainly monitored by the monitoring personnel, and the user experience is improved.
According to the technical scheme provided by the embodiment of the invention, the area where the medical waste is located is divided to obtain a plurality of medical waste areas, the data correlation degree sequence corresponding to each medical waste area is automatically analyzed by utilizing a preset regional analysis model, and the data correlation degree sequence is generated according to the total amount of the medical waste of each medical waste area, so that whether the total amount of the medical waste changes normally can be judged through analyzing the data correlation degree sequence, and whether the medical waste data is abnormal or not is judged, and then an alarm is given to a monitoring person. Therefore, by using the regional division and comparison method, the medical waste data is automatically analyzed, whether the medical waste data is abnormal or not is judged, and an alarm is given in time, so that the automatic monitoring is realized, the judgment accuracy is improved, and meanwhile, the manpower and the material resources are saved.
Example four:
referring to fig. 6, an embodiment of the present invention further provides an electronic device 100, including: a processor 40, a memory 41, a bus 42 and a communication interface 43, wherein the processor 40, the communication interface 43 and the memory 41 are connected through the bus 42; the processor 40 is arranged to execute executable modules, such as computer programs, stored in the memory 41.
The Memory 41 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 43 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, etc. may be used.
The bus 42 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 6, but that does not indicate only one bus or one type of bus.
The memory 41 is used for storing a program, the processor 40 executes the program after receiving an execution instruction, and the method executed by the apparatus defined by the flow process disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 40, or implemented by the processor 40.
The processor 40 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 40. The Processor 40 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory 41, and the processor 40 reads the information in the memory 41 and completes the steps of the method in combination with the hardware thereof.
The medical waste monitoring device, the medical waste monitoring system and the electronic equipment provided by the embodiment of the invention have the same technical characteristics as the medical waste monitoring method provided by the embodiment, so that the same technical problems can be solved, and the same technical effects can be achieved.
The computer program product for performing the medical waste monitoring method provided by the embodiment of the present invention includes a computer readable storage medium storing a processor executable nonvolatile program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment, and will not be described herein again.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the apparatus, the system and the electronic device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A medical waste monitoring method, comprising:
obtaining region coordinate information in medical waste data, and performing region division according to the region coordinate information to obtain a plurality of medical waste regions;
acquiring the total amount of medical waste recovered in the same preset time in each medical waste area, and generating a data correlation degree sequence corresponding to each medical waste area according to the total amount of the medical waste;
respectively taking each data correlation degree sequence as an input feature, and performing data anomaly analysis on each input feature by using a pre-established regional analysis model to obtain an analysis result corresponding to each medical waste region;
sending corresponding alarm prompt information to a monitoring terminal corresponding to a monitoring person according to the analysis result;
the establishing process of the regional analysis model comprises the following steps:
determining initial values of model parameters of the regional analysis model;
training model parameters of the regional analysis model by adopting a deep learning model according to the initial value, the data correlation sequence and a preset analysis result;
and establishing the regional analysis model according to the model parameters obtained by training.
2. The method of claim 1, wherein the performing the region division according to the regional coordinate information to obtain a plurality of medical waste regions comprises:
judging the county and city to which the medical waste data belongs according to the region coordinate information;
and dividing the range covered by all the region coordinate information according to the belonged county and city.
3. The method of claim 1, wherein generating the sequence of data correlations for each of the medical waste regions based on the total amount of medical waste comprises:
for each medical waste area, respectively comparing the total amount of medical waste in the medical waste area with the total amount of medical waste in each other medical waste area to calculate corresponding data relevancy;
and obtaining a data correlation degree sequence corresponding to each medical waste area according to the data correlation degrees.
4. The method of claim 1, wherein after the building the localization analysis model, further comprising:
acquiring the data correlation degree sequence and a corresponding known analysis result;
inputting the data correlation degree sequence as an input feature into the regional analysis model for testing to obtain a test result;
and adjusting the model parameters of the regional analysis model according to the known analysis result and the test result.
5. The method of claim 1, further comprising:
receiving an inquiry request sent by a monitoring person through the monitoring terminal, wherein the inquiry request comprises a medical waste area identifier;
and calling and sending an analysis result corresponding to the medical waste area identifier to the monitoring terminal.
6. A medical waste monitoring device, comprising:
the region division module is used for acquiring region coordinate information in the medical waste data, performing region division according to the region coordinate information and acquiring a plurality of medical waste regions;
the sequence generation module is used for acquiring the total amount of medical waste recovered in the same preset time of each medical waste area and generating a data correlation degree sequence corresponding to each medical waste area according to the total amount of the medical waste;
the result analysis module is used for respectively taking each data correlation degree sequence as an input feature, and performing data anomaly analysis on each input feature by using a pre-established regional analysis model to obtain an analysis result corresponding to each medical waste region;
the alarm sending module is used for sending corresponding alarm prompt information to a monitoring terminal corresponding to a monitoring person according to the analysis result;
the establishing process of the regional analysis model comprises the following steps:
determining initial values of model parameters of the regional analysis model;
training model parameters of the regional analysis model by adopting a deep learning model according to the initial value, the data correlation sequence and a preset analysis result;
and establishing the regional analysis model according to the model parameters obtained by training.
7. A medical waste monitoring system, comprising a monitoring terminal and a cloud server, wherein the monitoring terminal is connected with the cloud server, and the cloud server comprises the medical waste monitoring device according to claim 6;
the monitoring terminal is used for receiving and displaying the alarm prompt information sent by the cloud server.
8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 5 when executing the computer program.
9. A computer-readable medium having non-volatile program code executable by a processor, wherein the program code causes the processor to perform the method of any of claims 1 to 5.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8542121B2 (en) * | 2006-09-06 | 2013-09-24 | Casella Waste Systems, Inc. | Systems and methods for identifying and collecting banned waste |
| CN105414145A (en) * | 2015-10-30 | 2016-03-23 | 青岛光电医疗科技有限公司 | Medical waste environment-friendly treatment equipment and method special for medical institution |
| CN105654409A (en) * | 2016-01-06 | 2016-06-08 | 刘志钢 | Medical waste intelligent transshipment system |
| CN105730935A (en) * | 2016-03-11 | 2016-07-06 | 河南博苑环保科技有限公司 | System and method for managing medical waste internet of things |
| CN105894422A (en) * | 2016-05-12 | 2016-08-24 | 刘海 | Network management closed medical waste transfer system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7848905B2 (en) * | 2000-12-26 | 2010-12-07 | Troxler Electronic Laboratories, Inc. | Methods, systems, and computer program products for locating and tracking objects |
-
2017
- 2017-10-31 CN CN201711051682.9A patent/CN107833228B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8542121B2 (en) * | 2006-09-06 | 2013-09-24 | Casella Waste Systems, Inc. | Systems and methods for identifying and collecting banned waste |
| CN105414145A (en) * | 2015-10-30 | 2016-03-23 | 青岛光电医疗科技有限公司 | Medical waste environment-friendly treatment equipment and method special for medical institution |
| CN105654409A (en) * | 2016-01-06 | 2016-06-08 | 刘志钢 | Medical waste intelligent transshipment system |
| CN105730935A (en) * | 2016-03-11 | 2016-07-06 | 河南博苑环保科技有限公司 | System and method for managing medical waste internet of things |
| CN105894422A (en) * | 2016-05-12 | 2016-08-24 | 刘海 | Network management closed medical waste transfer system |
Non-Patent Citations (1)
| Title |
|---|
| 一种医疗废物集中处置中心营运信息管理系统;廖晨达等;《计算机应用》;20061031;第26卷(第10期);第2519-2521页 * |
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