Waste medical data processing method and device and computing equipment

Abstract

The invention provides a medical waste data processing method and apparatus, and a computing device. The medical waste data processing method comprises the steps of obtaining at least one kind of original processing data in a medical waste processing process; preprocessing the at least one kind of original processing data to obtain preprocessed target data; inputting the target data into a medical waste output prediction model for prediction processing to obtain a prediction result of the medical waste output; the medical waste output prediction model is determined by training a preset network model according to a preset medical waste data training set; the preset medical waste data training set comprises medical waste historical processing data; and sending the prediction result to a far-end monitoring platform. According to the method, the medical waste output can be predicted, so that the information utilization rate and the resource allocation efficiency in medical waste treatment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical waste treatment, and in particular to a method, device and computing equipment for processing medical waste data. Background Art

[0002] With the rapid development of the medical industry, the amount of medical waste is increasing. How to efficiently and safely handle medical waste has become an urgent problem to be solved. The existing medical waste treatment system cannot effectively process data, resulting in low information utilization, insufficient expected response measures, and low management efficiency. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method, device and computing equipment for processing medical waste data, which can predict the amount of medical waste generated, so as to improve the information utilization rate and resource allocation efficiency in medical waste treatment.

[0004] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0005] A method for processing medical waste data, comprising:

[0006] Obtaining at least one type of original processing data in the medical waste treatment process;

[0007] Preprocessing the at least one original processed data to obtain preprocessed target data;

[0008] The target data is input into a medical waste generation prediction model for prediction processing to obtain a prediction result of the medical waste generation; the medical waste generation prediction model is determined by training a preset network model based on a preset medical waste data training set; the preset medical waste data training set includes: medical waste historical processing data;

[0009] The prediction results are sent to a remote monitoring platform.

[0010] Optionally, preprocessing the at least one original processed data to obtain preprocessed target data includes:

[0011] Performing data cleaning on the original processed data to obtain first data;

[0012] Performing data format conversion on the first data to obtain second data;

[0013] The second data is subjected to data encoding processing to obtain target data.

[0014] Optionally, the training process of the medical waste generation prediction model includes:

[0015] Obtain historical medical waste treatment data;

[0016] Inputting the historical processing data into the first processing layer of the preset network model for feature extraction processing to obtain feature variables;

[0017] The characteristic variables are input into the second processing layer of the preset network model for parameter calculation processing to obtain a medical waste generation prediction model.

[0018] Optionally, the historical processing data is input into the first processing layer of the preset network model for feature extraction processing to obtain feature variables, including:

[0019] Visually displaying the historical processing data through the first processing layer of the preset network model to obtain a data relationship diagram;

[0020] Relation feature processing is performed on the data relationship graph to obtain feature variables.

[0021] Optionally, the characteristic variables are input into the second processing layer of the preset network model for parameter calculation processing to obtain a medical waste generation prediction model, including:

[0022] Calculating the parameters of the characteristic variables through the second processing layer of the preset network model to obtain an initial medical waste generation prediction model;

[0023] Parameters of the initial medical waste generation prediction model are optimized to obtain a medical waste generation prediction model.

[0024] Optionally, the initial medical waste generation prediction model is optimized in parameters to obtain a medical waste generation prediction model, including:

[0025] According to the prediction loss function of the initial medical waste generation prediction model, the parameters are optimized to obtain the medical waste generation prediction model.

[0026] Optionally, the target data is input into a medical waste generation prediction model for prediction processing to obtain a prediction result of the medical waste generation, including:

[0027] Inputting the target data into the first processing layer of the medical waste generation prediction model for processing to obtain an input set;

[0028] The input set is input into the second processing layer of the medical waste generation prediction model for processing to obtain a prediction result of the medical waste generation.

[0029] An embodiment of the present invention further provides a device for processing medical waste data, comprising:

[0030] An acquisition module, used to acquire at least one kind of original processing data in the medical waste treatment process;

[0031] The processing module is used to pre-process the at least one raw processing data to obtain pre-processed target data; input the target data into a medical waste generation prediction model for prediction processing to obtain a prediction result of the medical waste generation; the medical waste generation prediction model is determined by training a preset network model based on a preset medical waste data training set; the preset medical waste data training set includes: medical waste historical processing data; and the prediction result is sent to a remote monitoring platform.

[0032] An embodiment of the present invention further provides a computing device, comprising:

[0033] one or more processors;

[0034] A storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the medical waste data processing method described in the present invention.

[0035] An embodiment of the present invention further provides a computing device readable storage medium, wherein the computing device readable storage medium stores a program, and when the program is executed by a processor, the medical waste data processing method of the present invention is implemented.

[0036] The above technical solution of the present invention has at least the following technical effects:

[0037] The above-mentioned medical waste data processing method of the present invention obtains at least one original processing data in the medical waste treatment process; pre-processes the at least one original processing data to obtain the pre-processed target data; inputs the target data into the medical waste generation prediction model for prediction processing to obtain the prediction result of the medical waste generation; the medical waste generation prediction model is determined by training a preset network model based on a preset medical waste data training set; the preset medical waste data training set includes: medical waste historical processing data; and sends the prediction result to a remote monitoring platform. It is possible to predict the amount of medical waste generated to improve the information utilization rate and resource allocation efficiency in medical waste treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of a method for processing medical waste data of the present invention;

[0039] Figure 2 It is a schematic diagram of a device for processing medical waste data of the present invention. DETAILED DESCRIPTION

[0040] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0041] like Figure 1 As shown, an embodiment of the present invention provides a method for processing medical waste data, including:

[0042] Step S1, obtaining at least one original processing data in the medical waste treatment process;

[0043] Step S2, preprocessing the at least one original processed data to obtain preprocessed target data;

[0044] Step S3, inputting the target data into a medical waste generation prediction model for prediction processing to obtain a prediction result of the medical waste generation; the medical waste generation prediction model is determined by training a preset network model based on a preset medical waste data training set; the preset medical waste data training set includes: medical waste historical processing data;

[0045] Step S4, sending the prediction result to a remote monitoring platform.

[0046] In this embodiment, at least one kind of original processing data in the medical waste treatment process is first obtained, such as the type, quantity, weight, volume and other data of the collected medical waste; then the original processing data is pre-processed, and the original processing data is screened, eliminated, integrated, and unified in format, etc., to obtain target data that is easy to process and apply; thirdly, the target data is input into a medical waste generation prediction model for prediction processing, and the medical waste generation is predicted using the prediction model to obtain a prediction result; finally, the prediction result is sent to a remote monitoring platform; wherein the prediction model is trained based on the historical medical waste processing data; the medical waste data processing method of the present invention can conduct in-depth data mining and analysis on the collected data, can discover the laws of waste generation and treatment, predict future development trends, and provide a scientific basis for policy formulation and resource allocation.

[0047] In an optional embodiment of the present invention, in step S2, preprocessing the at least one original processed data to obtain preprocessed target data includes:

[0048] Performing data cleaning on the original processed data to obtain first data;

[0049] Performing data format conversion on the first data to obtain second data;

[0050] The second data is subjected to data encoding processing to obtain target data.

[0051] In this embodiment, the original processing data of medical waste treatment is preprocessed. First, the original processing data is cleaned to remove abnormal data and error data that are obviously beyond the normal range in the original processing data to obtain first data; then, the first data is converted into a data format to uniformly convert the data collected by different sensors into a standard format, for example, the weight data is converted into kg, and the time data is converted into a YYYY-MM-DD HH:MM:SS format to obtain second data; finally, the second data is encoded to integrate and encode the data collected by different sensors to form a complete medical waste data record to obtain target data.

[0052] In an optional embodiment of the present invention, the training process of the medical waste generation prediction model includes:

[0053] Step S41, obtaining historical medical waste processing data;

[0054] Step S42, inputting the historical processing data into the first processing layer of the preset network model for feature extraction processing to obtain feature variables;

[0055] Step S43, inputting the characteristic variables into the second processing layer of the preset network model for parameter calculation processing to obtain a medical waste generation prediction model.

[0056] In this embodiment, the historical processing data of medical waste is first obtained. For example, the historical processing data obtained is as follows:

[0057] Medical waste weight Medical waste volume Amount of recyclable plastics 67kg <![CDATA[1.8m 3 ]]> 17.8kg 135kg <![CDATA[4.1m 3 ]]> 41.9kg 103kg <![CDATA[3.3m 3 ]]> 32.5kg

[0058] The medical waste generation prediction model is obtained through training based on the historical medical waste treatment data. First, the acquired historical treatment data is input into the first processing layer of the preset network model. The first processing layer is used to mine the relationship between the medical waste treatment results and the initial information of the medical waste treatment in the historical data. By mining the relationship between the initial information and the treatment results, the data relationship characteristics between the treatment results and the initial information are found, that is, the descriptive analysis of the historical medical waste treatment data is performed to obtain the change trend data, and the characteristic variables related to the medical waste treatment results are obtained;

[0059] The characteristic variables are then input into the second processing layer of the preset network model. The second processing layer is used to calculate the model parameters. The initial prediction model establishes a parameterized relationship representation. The parameters need to be iteratively optimized to improve the availability and accuracy of the prediction model. After the model parameters are calculated and determined by the second processing layer, a medical waste generation prediction model is obtained.

[0060] In an optional embodiment of the present invention, in step S42, the historical processing data is input into the first processing layer of the preset network model for feature extraction processing to obtain feature variables, including:

[0061] Step S421, visually displaying the historical processing data through the first processing layer of the preset network model to obtain a data relationship diagram;

[0062] Step S422, performing relationship feature processing on the data relationship graph to obtain feature variables.

[0063] In this embodiment, the historical processing data is input into the first processing layer of the preset layer network model. The first processing layer first establishes a historical processing data coordinate system based on the historical processing data, wherein the horizontal axis is the initial information independent variable data and the vertical axis is the processing result dependent variable data. Then, the data point distribution of the independent variable data and the dependent variable data is plotted in the coordinate system to obtain a data relationship diagram.

[0064] Then, data variables strongly correlated with the processing results are obtained according to the data relationship diagram, and the relationship characteristics between the data variables and the processing results are extracted to obtain characteristic variables; specifically, for example, recyclable plastic products in the medical waste treatment process, such as disposable infusion sets, blood transfusion sets, plastic bottles, etc., can be made into recycled plastic particles after crushing and used to produce plastic products or other materials. According to the historical processing data of the relationship between recyclable plastic products and plastic medical waste, a data relationship diagram is drawn. According to the data distribution of the data relationship diagram, it is found that the number of recyclable plastic products has a strong correlation with the weight and volume of plastic medical waste, and the relationship characteristics between the weight and volume factors of plastic medical waste and the number of recyclable plastic products are extracted as characteristic variables.

[0065] In an optional embodiment of the present invention, in step S43, the characteristic variable is input into the second processing layer of the preset network model for parameter calculation processing to obtain a medical waste generation prediction model, including:

[0066] Step S431, calculating the parameters of the characteristic variables through the second processing layer of the preset network model to obtain an initial medical waste generation prediction model;

[0067] Step S432, optimizing the parameters of the initial medical waste generation prediction model to obtain a medical waste generation prediction model.

[0068] In this embodiment, the characteristic variables are input into the second processing layer of the preset network model. The second processing layer combines the historical medical waste processing data to perform parameter calculation on the characteristic variables, determine the initial parameters of the model, and obtain the initial medical waste generation prediction model; then, the initial medical waste generation prediction model is subjected to parameter optimization processing to determine the optimized parameters, thereby obtaining the medical waste generation prediction model;

[0069] Specifically, a network model is preset, such as z=β0+β1p1+β2p2+...+β i p i , where z is the prediction result of each medical waste treatment data, β0, β1, β2...β i is the model parameter, p i The second processing layer combines the historical medical waste processing data to calculate the initial values ​​of the characteristic variable parameters β0, β1, β2...βi, and obtains the initial medical waste generation prediction model; then the optimization method is used to optimize β0, β1, β2...βi. i The initial values ​​of are optimized to obtain the optimized parameters β0′, β1′, β2′...β i ′, and obtain the prediction model of medical waste generation.

[0070] In an optional embodiment of the present invention, in step S432, the parameters of the initial medical waste generation prediction model are optimized to obtain the medical waste generation prediction model, including:

[0071] Step S4321, optimizing the parameters according to the loss function of the initial medical waste generation prediction model to obtain a medical waste generation prediction model.

[0072] In this embodiment, the parameters of the initial medical waste generation prediction model are optimized. First, based on the initial medical waste generation prediction model and in combination with the historical medical waste processing data, a prediction loss function is constructed using the prediction results of the medical waste generation prediction model and the actual values ​​in the historical medical waste processing data. Then, the parameters are optimized by calculating the parameter values ​​that minimize the loss function to obtain the medical waste generation prediction model.

[0073] Specifically, according to the initial medical waste generation prediction model z = β0 + β1p1 + β2p2 + ... + β i p i , establish the multivariate linear regression equation: f(β)=β0β0+β1p1+β2p2...+β i p i

[0074] Through this equation, the optimal β set is selected to make the fitting function f(β) as close to the actual value as possible. The loss function is used to calculate the gap between the predicted value and the actual value. According to the multivariate linear regression equation, the loss function is constructed as follows:

[0075]

[0076] Where s(β) is the loss function, m represents the number of samples selected for iteration each time, p represents the independent variable of the feature parameter, q represents the actual measured result in the data sample, and f β (p j ) represents the prediction result.

[0077] Calculate the gradient of the loss function s(β). The gradient is a vector field composed of vectors of each dimension and the partial derivative of a given point on a function. The gradient represents the direction of the function's maximum rate of change and the fastest growth direction. In the process of solving the minimum value of the loss function, you only need to make the loss function move in the direction of the negative gradient to reduce the loss function faster, ensuring that each iteration moves in the direction of smaller error. The iteration formula of β is: in Represents derivative calculation; using this formula, after adding new sample data in each round of iteration, the value of each parameter is updated according to the calculated gradient, and the loss function is continuously reduced. After multiple rounds of iteration, relatively good estimates of each parameter can be obtained, and the parameters are brought into the initial medical waste generation prediction model to obtain the medical waste generation prediction model.

[0078] In an optional embodiment of the present invention, in step S3, the target data is input into a medical waste generation prediction model for prediction processing to obtain a prediction result of the medical waste generation, including:

[0079] Step S31, inputting the target data into the first processing layer of the medical waste generation prediction model for processing to obtain an input set;

[0080] Step S32: input the input set into the second processing layer of the medical waste generation prediction model for processing to obtain a prediction result of the medical waste generation.

[0081] In this embodiment, the target data of multiple medical waste data are input into the first layer of the medical waste generation prediction model for processing, and converted into multiple data satisfying p1, p2, p3, ... i format data, get the input set, and set the set that satisfies p1, p2, p3.....p i The input set of the format is input to complete the training of the medical waste generation prediction model z=β0+β1p1+β2p2+...+β i p i Processing is carried out to obtain the predicted results of medical waste generation.

[0082] The specific implementation process of the above method of the present invention is as follows:

[0083] Acquire at least one type of raw processing data in the medical waste treatment process through sensors and transmission networks;

[0084] Performing data cleaning on the original processed data to obtain first data;

[0085] Performing data format conversion on the first data to obtain second data;

[0086] Performing data encoding processing on the second data to obtain target data;

[0087] Inputting the target data into the first processing layer of the trained medical waste generation prediction model for processing to obtain an input set;

[0088] The input set is input into the second processing layer of the medical waste generation prediction model for processing to obtain a prediction result of the medical waste generation.

[0089] This embodiment of the present invention mines the data relationship characteristics between the medical waste treatment results and the initial information of the medical waste treatment in the historical data based on the historical medical waste treatment data, establishes a mathematical model between the initial information and the treatment results, and then calculates and optimizes the parameters of the preset network model to obtain a medical waste generation prediction model; the original treatment data is pre-processed to obtain target data, and the target data is input into the medical waste generation prediction model for predictive processing to obtain the predicted results of the medical waste generation; the medical waste data processing method of the present invention can conduct in-depth mining and analysis on the collected data, can discover the laws of waste generation and treatment, predict future development trends, and improve the information utilization rate and resource allocation efficiency in medical waste treatment.

[0090] like Figure 2 As shown, in an embodiment of the present invention, a medical waste data processing device 20 is also provided, comprising:

[0091] An acquisition module 21, used to acquire at least one kind of original processing data in the medical waste treatment process;

[0092] The processing module 22 is used to pre-process the at least one raw processing data to obtain pre-processed target data; input the target data into the medical waste generation prediction model for prediction processing to obtain the prediction result of the medical waste generation; the medical waste generation prediction model is determined by training a preset network model based on a preset medical waste data training set; the preset medical waste data training set includes: medical waste historical processing data; and send the prediction result to a remote monitoring platform.

[0093] Optionally, preprocessing the at least one original processed data to obtain preprocessed target data includes:

[0094] Performing data cleaning on the original processed data to obtain first data;

[0095] Performing data format conversion on the first data to obtain second data;

[0096] The second data is subjected to data encoding processing to obtain target data.

[0097] Optionally, the training process of the medical waste generation prediction model includes:

[0098] Obtain historical medical waste treatment data;

[0099] Inputting the historical processing data into the first processing layer of the preset network model for feature extraction processing to obtain feature variables;

[0100] The characteristic variables are input into the second processing layer of the preset network model for parameter calculation processing to obtain a medical waste generation prediction model.

[0101] Optionally, the historical processing data is input into the first processing layer of the preset network model for feature extraction processing to obtain feature variables, including:

[0102] Visually displaying the historical processing data through the first processing layer of the preset network model to obtain a data relationship diagram;

[0103] Relation feature processing is performed on the data relationship graph to obtain feature variables.

[0104] Optionally, the characteristic variables are input into the second processing layer of the preset network model for parameter calculation processing to obtain a medical waste generation prediction model, including:

[0105] Calculating the parameters of the characteristic variables through the second processing layer of the preset network model to obtain an initial medical waste generation prediction model;

[0106] Parameters of the initial medical waste generation prediction model are optimized to obtain a medical waste generation prediction model.

[0107] Optionally, the initial medical waste generation prediction model is optimized in parameters to obtain a medical waste generation prediction model, including:

[0108] According to the prediction loss function of the initial medical waste generation prediction model, the parameters are optimized to obtain the medical waste generation prediction model.

[0109] Optionally, the target data is input into a medical waste generation prediction model for prediction processing to obtain a prediction result of the medical waste generation, including:

[0110] Inputting the target data into the first processing layer of the medical waste generation prediction model for processing to obtain an input set;

[0111] The input set is input into the second processing layer of the medical waste generation prediction model for processing to obtain a prediction result of the medical waste generation.

[0112] It should be noted that the device is a device corresponding to the above method, and all implementation methods in the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects.

[0113] The embodiment of the present invention further provides a computing device, including: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the medical waste data processing method of the present invention. All implementation methods in the above method embodiment are applicable to the embodiment of the device, and can also achieve the same technical effect.

[0114] The embodiment of the present invention further provides a computing device readable storage medium, including: the computing device readable storage medium stores a program, and when the program is executed by a processor, the medical waste data processing method of the present invention is implemented. All implementations in the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects.

[0115] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0117] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0118] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0119] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0120] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0121] In addition, it should be noted that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it is understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0122] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the purpose of the present invention can also be achieved by simply providing a program product containing a program code for implementing a method or device. That is to say, such a program product also constitutes the present invention, and a storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order. Some steps can be performed in parallel or independently of each other.

[0123] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for processing medical waste data, characterized in that: include: Obtaining at least one type of original processing data in the medical waste treatment process; Preprocessing the at least one original processed data to obtain preprocessed target data; Inputting the target data into a medical waste generation prediction model for prediction processing to obtain a prediction result of medical waste generation; The medical waste generation prediction model is determined by training a preset network model based on a preset medical waste data training set; The preset medical waste data training set includes: medical waste historical processing data; The prediction results are sent to a remote monitoring platform.

2. The method for processing medical waste data according to claim 1, characterized in that: Preprocessing the at least one original processed data to obtain preprocessed target data includes: Performing data cleaning on the original processed data to obtain first data; Performing data format conversion on the first data to obtain second data; The second data is subjected to data encoding processing to obtain target data.

3. The method for processing medical waste data according to claim 1, characterized in that: The training process of the medical waste generation prediction model includes: Obtain historical medical waste treatment data; Inputting the historical processing data into the first processing layer of the preset network model for feature extraction processing to obtain feature variables; The characteristic variables are input into the second processing layer of the preset network model for parameter calculation processing to obtain a medical waste generation prediction model.

4. The method for processing medical waste data according to claim 3, characterized in that: The historical processing data is input into the first processing layer of the preset network model for feature extraction processing, Get characteristic variables, including: Visually displaying the historical processing data through the first processing layer of the preset network model to obtain a data relationship diagram; Relation feature processing is performed on the data relationship graph to obtain feature variables.

5. The method for processing medical waste data according to claim 3, characterized in that: The characteristic variables are input into the second processing layer of the preset network model for parameter calculation processing to obtain a medical waste generation prediction model, including: Calculating the parameters of the characteristic variables through the second processing layer of the preset network model to obtain an initial medical waste generation prediction model; Parameters of the initial medical waste generation prediction model are optimized to obtain a medical waste generation prediction model.

6. The method for processing medical waste data according to claim 5, characterized in that: Optimizing the parameters of the initial medical waste generation prediction model to obtain a medical waste generation prediction model, including: According to the prediction loss function of the initial medical waste generation prediction model, the parameters are optimized to obtain the medical waste generation prediction model.

7. The method for processing medical waste data according to claim 1, characterized in that: The target data is input into the medical waste generation prediction model for prediction processing to obtain the prediction result of the medical waste generation, including: Inputting the target data into the first processing layer of the medical waste generation prediction model for processing to obtain an input set; The input set is input into the second processing layer of the medical waste generation prediction model for processing to obtain a prediction result of the medical waste generation.

8. A device for processing medical waste data, characterized in that: include: An acquisition module, used to acquire at least one kind of original processing data in the medical waste treatment process; A processing module, used for preprocessing the at least one original processed data to obtain preprocessed target data; Inputting the target data into a medical waste generation prediction model for prediction processing to obtain a prediction result of medical waste generation; The medical waste generation prediction model is determined by training a preset network model based on a preset medical waste data training set; The preset medical waste data training set includes: medical waste historical processing data; The prediction results are sent to a remote monitoring platform.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 7.

10. A computing device readable storage medium, characterized in that: The computing device readable storage medium stores a program, which implements the method according to any one of claims 1 to 7 when executed by a processor.

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