Operating Room Garment Dispenser Replenishment Method, Device, Operating Room Garment Dispenser, and Storage Medium

Abstract

The present invention discloses a replenishment method and device for an operating room clothing dispenser, an operating room clothing dispenser, and a storage medium. The method includes: obtaining surgical scheduling data for a target time period and current laundry inventory data; wherein the surgical scheduling data includes surgical staff information and the number of operating tables; predicting the replenishment demand for surgical gowns during the target time period based on the surgical scheduling data, the current laundry inventory data, and a preset mapping relationship between hospital staff and clothing models; and replenishing according to the replenishment demand for surgical gowns. Through the present invention, intelligent replenishment of the operating room clothing dispenser can be achieved, effectively releasing human resources and improving the informatization management level of the operating room.

Description

Technical Field

[0001] The present invention relates to the technical field of clothing dispensers, and in particular, to a replenishment method and device for an operating room clothing dispenser, an operating room clothing dispenser, and a storage medium. Background Art

[0002] The surgical gowns in the surgical operating room can be dispensed through an automatic clothing dispenser.

[0003] However, the automatic clothing dispenser still requires manual monitoring of the clothing inventory in the dispenser and manual calculation of the replenishment quantity, which increases the workload of the staff. Summary of the Invention

[0004] Embodiments of the present invention solve the problem of large manual workload during the replenishment of the automatic clothing dispenser at the current stage by providing a replenishment method and device for an operating room clothing dispenser, an operating room clothing dispenser, and a storage medium.

[0005] According to a first aspect of the present invention, there is provided a replenishment method for an operating room clothing dispenser, including:

[0006] Obtaining surgical scheduling data and current laundry inventory data for a target time period; wherein, the surgical scheduling data includes surgical staff information and the number of operating tables;

[0007] Predicting the replenishment demand for surgical gowns in the target time period according to the surgical scheduling data, the current laundry inventory data, and a preset mapping relationship between hospital staff and clothing models;

[0008] Replenishing according to the replenishment demand for surgical gowns.

[0009] Optionally, the obtaining of the surgical scheduling data and the current laundry inventory data for the target time period includes:

[0010] Obtaining surgical scheduling data, current laundry inventory data, and historical dispensing record data of the clothing dispenser for the target time period;

[0011] The step of predicting the replenishment demand for surgical gowns in the target time period according to the surgical scheduling data, the current laundry inventory data, and a preset mapping relationship between hospital staff and clothing models specifically includes:

[0012] Predicting the replenishment demand for surgical gowns in the target time period according to the surgical scheduling data, the current laundry inventory data, a preset mapping relationship between hospital staff and clothing models, and the historical dispensing record data of the clothing dispenser.

[0013] Optionally, the obtaining of the surgical scheduling data and the current laundry inventory data for the target time period includes:

[0014] Obtain the surgical scheduling data for the target time period, the current laundry inventory data, and the current demand for surgical gowns;

[0015] The step of predicting the replenishment demand for surgical gowns in the target time period according to the surgical scheduling data, the current laundry inventory data, and the preset mapping relationship between hospital personnel and gown models specifically includes:

[0016] Predict the replenishment demand for surgical gowns in the target time period according to the surgical scheduling data, the current laundry inventory data, the preset mapping relationship between hospital personnel and gown models, the current demand for surgical gowns, and the historical distribution record data of the gown dispenser.

[0017] Optionally, the step of predicting the replenishment demand for surgical gowns in the target time period according to the surgical scheduling data, the current laundry inventory data, and the preset mapping relationship between hospital personnel and gown models includes:

[0018] Input the surgical scheduling data, the current laundry inventory data, and the preset mapping relationship between hospital personnel and gown models into the replenishment demand prediction neural network model to obtain the replenishment demand for surgical gowns in the target time period output by the replenishment demand prediction neural network model.

[0019] Optionally, after replenishing according to the replenishment demand for surgical gowns, the method further includes:

[0020] When an emergency occurs, obtain the type of the emergency;

[0021] According to the type of the event, match the target replenishment decision model from multiple preset replenishment decision models;

[0022] Perform replenishment according to the target replenishment decision model.

[0023] Optionally, the step of replenishing according to the replenishment demand for surgical gowns includes:

[0024] Control the robotic arm to grab the target surgical gown in the reserve warehouse;

[0025] Control the robotic arm to put the target surgical gown into the waiting-to-be-distributed compartment to complete the replenishment of a single surgical gown, update the actual replenishment quantity, and return to execute the control of the robotic arm to grab the target surgical gown in the reserve warehouse, and loop until the actual replenishment quantity reaches the replenishment demand for surgical gowns.

[0026] Optionally, after replenishing according to the replenishment demand for surgical gowns, the method further includes:

[0027] Output the replenishment demand for surgical gowns and the current laundry inventory data.

[0028] According to a second aspect of the present invention, there is provided an intelligent replenishment device for a clothing machine in an operating room, comprising:

[0029] The data acquisition module is used to obtain the surgery scheduling data and current laundry inventory data in the target time period;

[0030] A data processing module, configured to predict the demand for surgical gown replenishment in the target time period based on the surgical scheduling data, the current laundry inventory data, and a preset mapping relationship between hospital personnel and clothing models;

[0031] A control output module is used to replenish the surgical gowns according to the replenishment demand.

[0032] According to a third aspect of the present invention, there is provided an operating room clothing dispensing machine, comprising: a memory, a processor, and an operating room clothing dispensing machine replenishment program stored in the memory and executable on the processor, wherein the operating room clothing dispensing machine replenishment program, when executed by the processor, implements the various steps described in any possible implementation of the first aspect or the second aspect.

[0033] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium on which a replenishment program for an operating room clothing dispensing machine is stored, and when the operating room clothing dispensing machine replenishment program is executed by a processor, the various steps described in any possible implementation method of the first aspect or the second aspect are implemented.

[0034] The embodiments of the present invention propose a method and device for replenishing an operating room clothing machine, an operating room clothing machine, and a computer-readable storage medium, wherein the operating room clothing machine obtains surgical scheduling data and current laundry inventory data for a target time period; based on the surgical scheduling data, the current laundry inventory data, and a preset mapping relationship between hospital personnel and clothing models, the replenishment demand for surgical gowns in the target time period is predicted; and replenishment is performed based on the surgical gown replenishment demand.

[0035] The present invention obtains the surgical scheduling data and current laundry inventory data of the target time period, and processes these data information to deduce and predict the replenishment demand of large, medium and small surgical gowns, predicts the replenishment demand of surgical gowns in the target time period, and finally completes the replenishment according to the replenishment demand of surgical gowns. The present invention is different from the prior art that cannot achieve completely unmanned automatic clothing distribution machines and requires manual participation in replenishment. It can realize unmanned and intelligent replenishment of clothing distribution machines in operating rooms, effectively release human resources and improve the level of information management of operating rooms. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on the provided drawings.

[0037] Figure 1 It is a schematic structural diagram of an operating room clothing dispenser for the hardware operating environment involved in the solution of the embodiment of the present application;

[0038] Figure 2 It is a schematic flowchart of the first embodiment of the replenishment method for the operating room clothing dispenser of the present invention;

[0039] Figure 3 For Figure 2 It is a schematic flowchart of the specific implementation manners of the steps S201 and S202 in

[0040] Figure 4 For Figure 2 It is a schematic flowchart of the specific implementation manner of the step S202 in

[0041] Figure 5 For Figure 2 It is a schematic flowchart after the step S203 in

[0042] Figure 6 For Figure 2 It is a schematic flowchart of the specific implementation manner of the step S203 in

[0043] Figure 7 For Figure 2 It is a schematic flowchart after the step S203 in

[0044] Figure 8 It is a schematic overall operation flowchart involved in the solution of the embodiment of the present application;

[0045] Figure 9 It is a schematic functional module diagram of the replenishment device for the operating room clothing dispenser involved in the embodiment of the present invention.

[0046] The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the embodiments and the drawings. Specific Embodiments

[0047] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0048] The main solution of the embodiment of the present invention is: obtaining the surgical scheduling data for the target time period and the current laundry inventory data; predicting the replenishment demand for surgical gowns in the target time period according to the surgical scheduling data, the current laundry inventory data, and the preset mapping relationship between hospital personnel and clothing models; and finally replenishing according to the replenishment demand for surgical gowns.

[0049] In the prior art, the automatic gown dispenser only solves the problem of a large workload in the manual gown distribution process and cannot achieve full unattended operation; in the current workflow of the automatic gown dispenser, manual measurement of the replenishment quantity is still required, and the clothing inventory in the dispenser needs to be monitored, and then the clothes are manually stuffed into the gown slots, which is not intelligent enough.

[0050] The present invention provides a solution for the operating room gown dispenser. The surgical scheduling data for the target time period and the current laundry inventory data are obtained through the operating room gown dispenser; then, according to the surgical scheduling data, the current laundry inventory data, and the preset mapping relationship between hospital personnel and clothing models, the replenishment demand for surgical gowns in the target time period is predicted; and finally, replenishment is carried out according to the replenishment demand for surgical gowns. Different from the prior art where the automatic gown dispenser cannot achieve full unattended operation and requires manual participation in replenishment, the present invention can truly achieve unattended operation and intelligent replenishment of the operating room gown dispenser, effectively release human resources, and improve the informatization management level of the operating room.

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0052] The "first" and "second" in the description and claims of the embodiments of the present invention are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order different from those illustrated or described herein.

[0053] Refer to Figure 1 , Figure 1 which is a schematic structural diagram of the operating room gown dispenser for the hardware operating environment involved in the solution of the embodiment of the present application.

[0054] As Figure 1As shown in the figure, the operating room clothing dispenser may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to implement the connection and communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a Wireless-Fidelity (WI-FI) interface). The memory 1005 may be a high-speed Random Access Memory (RAM) or a stable Non-Volatile Memory (NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0055] Those skilled in the art can understand that Figure 1 the structure shown in does not constitute a limitation on the operating room clothing dispenser, and it may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements.

[0056] As Figure 1 shown, the memory 1005, as a storage medium, may include an operating system, a data acquisition module, a data processing module, a control output module, and an operating room clothing dispenser replenishment program.

[0057] In Figure 1 the operating room clothing dispenser shown, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the operating room clothing dispenser of the present invention may be arranged in the operating room clothing dispenser. The operating room clothing dispenser calls the operating room clothing dispenser replenishment program stored in the memory 1005 through the processor 1001 and executes the operating room clothing dispenser replenishment method provided by the embodiments of the present application.

[0058] Based on the above hardware structure but not limited to the above hardware structure, the present invention provides a first embodiment of an operating room clothing dispenser replenishment method. Referring to Figure 2 , Figure 2 is a schematic flowchart of the first embodiment of the operating room clothing dispenser replenishment method of the present invention.

[0059] In this embodiment, the method includes:

[0060] Step S201: Obtain the surgical scheduling data for the target time period and the current laundry inventory data; wherein, the surgical scheduling data includes surgical staff information and the number of operating tables.

[0061] In this embodiment, the execution entity is the operating room clothing dispenser, which can be connected to the hospital's back-end server. The back-end server stores the hospital's surgical scheduling data and the current laundry inventory data. The operating room clothing dispenser can send a data call request to the back-end server to obtain this data, and then the data can be processed subsequently in the operating room clothing dispenser. Among them, the surgical scheduling data includes surgical staff information and the number of operating tables, and the surgical staff information is stored in the His (Hospital Information System), that is, the hospital management system; in addition, the current laundry inventory data includes the current inventory of surgical gowns.

[0062] In some embodiments, step S201 includes obtaining the surgical scheduling data for the target time period, the current laundry inventory data, and the historical dispensing record data of the clothing dispenser.

[0063] Among them, the historical dispensing record data of the clothing dispenser is automatically stored in the back-end server after each historical replenishment is completed. Similar to the aforementioned obtaining of the surgical scheduling data and the current laundry inventory data, the operating room clothing dispenser can send a data call request to the back-end server to obtain the historical dispensing record data of the clothing dispenser.

[0064] In some other embodiments, step S201 includes obtaining the surgical scheduling data for the target time period, the current laundry inventory data, and the current demand for surgical gowns.

[0065] Among them, the current demand for surgical gowns can be determined by the surgical gown demand orders for the target time period stored in the hospital's back-end server. Similarly, the operating room clothing dispenser can send a data call request to the back-end server to obtain the current demand for surgical gowns.

[0066] It can be understood that in some other embodiments, step S201 includes: obtaining the surgical scheduling data for the target time period, the current laundry inventory data, the historical dispensing record data of the clothing dispenser, and the current demand for surgical gowns.

[0067] In addition to obtaining the aforementioned surgical scheduling data for the target time period and the current laundry inventory data, in order to more reasonably predict the replenishment demand for surgical gowns in the target time period by using the method of big data analysis and prediction, it is also necessary to further obtain the historical dispensing record data of the clothing dispenser and / or the current demand for surgical gowns. After obtaining these data, the data can be processed subsequently in the operating room clothing dispenser to make the prediction result more accurate.

[0068] Among them, the aforementioned operation scheduling data, the current laundry inventory data, the historical distribution record data of the clothing dispenser, and the current demand for surgical gowns can be obtained through methods such as Http requests, HttpService, database views, and table imports. An Http (Hypertext Transfer Protocol) request, that is, a hypertext transfer protocol request, can use a variety of request methods. Among them, Http1.0 defines three request methods: GET, POST, and HEAD methods; while Http1.1 adds six new request methods: OPTIONS, PUT, PATCH, DELETE, TRACE, and CONNECT methods. A complete Http request includes: establishing a TCP connection; the web browser sending a request command to the web server; the web browser sending request header information; the web server replying; the web server sending reply header information; the web server sending data to the browser; the web server closing the TCP connection. Among them, the TCP (Transmission Control Protocol) protocol model contains a series of network protocols that form the basis of the Internet and is the core protocol of the Internet, that is, the Internet.

[0069] In addition, the aforementioned data can also be imported using the HttpService interface. Among them, the HttpService interface transmits string information.

[0070] Step S303, based on the operation scheduling data, the current laundry inventory data, the preset mapping relationship between hospital personnel and clothing models, the current demand for surgical gowns, and the historical distribution record data of the clothing dispenser, predict the replenishment demand for surgical gowns during the target time period.

[0071] After obtaining the above-mentioned various data, the operating room clothing dispenser operation data processing program uses the method of big data analysis and prediction to process these data information to predict the replenishment demand for surgical gowns during the target time period.

[0072] Step S202, based on the operation scheduling data, the current laundry inventory data, and the preset mapping relationship between hospital personnel and clothing models, predict the replenishment demand for surgical gowns during the target time period;

[0073] The hospital management system also stores a preset mapping relationship between hospital personnel and clothing models, that is, medical staff A corresponds to clothing model a; medical staff B corresponds to clothing model b. Among them, clothing models a and b may be the same or different. After obtaining this mapping relationship, the operating room clothing dispenser combines the aforementioned operating room scheduling data and the current laundry inventory data, runs a data processing program, and uses the method of big data analysis and prediction to process these data and information to predict the replenishment demand for surgical gowns during the target time period.

[0074] Step S203: replenish goods according to the replenishment demand of the surgical gowns.

[0075] The operating room clothing dispenser replenishes goods according to the predicted replenishment demand for surgical gowns, so as to realize intelligent replenishment of the clothing dispenser during the target time period without unattended operation, which can effectively release human resources.

[0076] In some embodiments, a decision-making center and an automatic replenishment control mechanism are set in the operating room clothing dispenser. There are various pre-set replenishment decision-making models in the decision-making center. After receiving the result of the replenishment quantity of surgical gowns during the target time period predicted above, the optimal replenishment decision-making model is matched according to the above result, and then a replenishment control instruction is generated. Finally, the instruction is sent to the automatic replenishment control mechanism to make the automatic replenishment control mechanism perform the replenishment operation.

[0077] Among them, the replenishment decision-making model is established by mathematical abstraction based on the experience of daily manual replenishment: factors such as the replenishment time point, the replenishment quantity of each size and model of clothing, and whether the unissued clothing in the current inventory bin is taken off the shelf are used as controllable factors of the replenishment decision-making model; factors such as unexpected additional surgeries and interns' visits are used as uncontrollable factors. The replenishment decision-making model is established by fully considering various controllable and uncontrollable factors and performing mathematical abstraction.

[0078] The following is an example to illustrate a replenishment decision-making model: Under normal circumstances, the operating room clothing dispenser controls the automatic replenishment control mechanism to replenish goods at normal times and in normal quantities and then clean up. In case of emergencies, the decision-making center can also make timely adjustments. For example, when there are temporary arrangements such as intern visits, the decision-making center will issue a replenishment control instruction to arrange for early replenishment; another example is that when there is an unexpected additional surgery, the decision-making center will issue a replenishment control instruction to arrange for an increase in the replenishment quantity; in addition, when the demand for surgical gowns increases due to emergencies, the decision-making center will issue a replenishment control instruction to make the automatic replenishment control mechanism not clean up the bin temporarily. In this way, various controllable factors and some uncontrollable factors are comprehensively considered. When the above result matches this replenishment decision-making model, a replenishment control instruction is generated according to this model for replenishment. And there are various pre-set replenishment decision-making models in the decision-making center, which can cover various uncontrollable factors.

[0079] After fully considering various conventional controllable factors and unexpected uncontrollable factors and obtaining the aforesaid replenishment control instruction, the instruction is sent to the automatic replenishment control mechanism. After receiving the instruction, the automatic replenishment control mechanism completes the replenishment operation according to the requirements.

[0080] Further, referring to Figure 4 , Figure 4 For Figure 2 the flowchart of the specific implementation manner of step S202 in

[0081] Step S401, inputting the surgical scheduling data, the current laundry inventory data, and the preset mapping relationship between hospital personnel and clothing models into the replenishment demand prediction neural network model;

[0082] Although the aforesaid surgical scheduling data, the current laundry inventory data, and the preset mapping relationship between hospital personnel and clothing models are closely related to the surgical gown replenishment demand in the target time period and are significant factors directly affecting the replenishment demand, the future growth trend of surgical gown demand cannot be simply deduced therefrom. Machine learning must be carried out on a certain amount of training sets. Therefore, in order to reasonably deduce and predict the demand for surgical gowns of various models, the historical distribution record data of the clothing dispenser is used as the training set, and the replenishment demand prediction neural network model is used for deduction. In this embodiment, a BP (BackPropagation) network, that is, an error backpropagation neural network, can be selected. The BP network includes an input layer, a hidden layer, and an output layer. Among them, the input layer is the historical distribution data of the clothing dispenser, the output layer is the demand for various sizes and models of clothing, and the hidden layer includes 4 neurons such as the surgical scheduling volume, the number of medical staff, the laundry output volume, and the target time period. Among them, the historical distribution data of the clothing dispenser in a certain time period and the demand corresponding to the historical distribution data of the clothing dispenser in this certain time period are used as a training sample, and a training set is constructed through at least one training sample to determine parameters such as the weights and thresholds of the model.

[0083] The training process of the BP network is as follows: The input layer receives the input data, uses the weights to model the input, the hidden layer performs relevant calculations and outputs at the output layer. The difference between the actual output and the expected output obtained is the error, and then the error is returned to the hidden layer and the weights are adjusted to reduce the error in the subsequent process. This process is repeated until the required output is obtained, thereby obtaining the mapping relationship from input to output. It should be noted that the richer the historical record data, the better the effect of machine learning, and the closer the finally predicted result is to the real situation.

[0084] The BP network selects the S-type transfer function Among them, e is the base of the natural logarithm, x is the input, f(x) is the output, and -x is the power. The S-shaped propagation function is a commonly used non-linear activation function in the BP neural network, namely the Sigmoid function, which is generally used for the output of hidden layer neurons and can be divided into the Log-Sigmoid function (the so-called S-shaped function is generally the abbreviation of this) and the Tan-Sigmoid function (also known as the hyperbolic tangent S-shaped function). In the present invention, the transfer function selected for the BP network is the Log-Sigmoid function.

[0085] In addition, the BP network continuously adjusts the network weights and thresholds through the backpropagation error function to minimize the error function. The backpropagation error function Among them, E is the error function, which is equal to the sum of the errors of each output unit, T i is the expected output, and O i is the calculated output of the BP network. Among them, error backpropagation is to backpropagate the output error layer by layer and distribute the error to all units of each layer, so as to obtain the error signals of each layer of units. This error signal is used as the basis for correcting the weights of each unit. This process of adjusting the weights of each layer in the forward propagation of the signal and the backward propagation of the error is carried out cyclically. The process of continuously adjusting the weights is also the learning and training process of the BP network. This process is known to continue until the error of the network output is reduced to an acceptable level or until a pre-set number of learning times is reached.

[0086] Step S402: Obtain the replenishment demand quantity of surgical gowns for the target time period output by the replenishment demand quantity prediction neural network model.

[0087] After obtaining the mapping relationship between the input and output of the aforementioned BP network and minimizing the error through repeated parameter adjustment, it can be considered that this mapping relationship also holds for any unknown prediction set. Thus, when new data is acquired by the operating room gown dispenser, the operating room gown dispenser inputs this new data as a prediction set into the BP network, and the replenishment demand quantities of surgical gowns of each model size in the current state can be deduced and predicted using this mapping relationship.

[0088] Further, referring to Figure 5 , Figure 5 is Figure 2 a schematic flow diagram after step S203 in

[0089] Step S501: When an emergency occurs, obtain the type of the emergency.

[0090] Under normal circumstances, a complete replenishment process replenishes goods according to normal time and normal replenishment quantity, and the grid can be cleared and removed from the shelves after replenishment is completed. However, in actual situations, in addition to the normal work process, there will be many unexpected events. In order to more reasonably and accurately predict the replenishment demand of surgical gowns, these unexpected events, as uncontrollable factors, must also be fully considered. And unexpected events should be distinguished according to their types: for example, common unexpected events include intern visits, sudden additional surgeries, changes in medical staff, etc. After distinguishing different types of unexpected events in this way, it is convenient to respond to unexpected events reasonably and effectively, thus ensuring the smooth completion of subsequent replenishment operations.

[0091] Step S502: According to the event type, match the target replenishment decision model from multiple preset replenishment decision models;

[0092] There is a decision center set in the surgical gown dispenser in the operating room. Multiple replenishment decision models are preset in the decision center. After obtaining the event type of the aforementioned unexpected event, the decision center matches the result with the preset replenishment decision models. When the optimal replenishment decision model is matched, a replenishment control instruction is generated according to this model for replenishment. It should be emphasized that multiple replenishment decision models have been established in advance in the decision center to meet the needs of different situations.

[0093] Among them, the replenishment decision model is established through mathematical abstraction based on the experience of daily manual replenishment: factors such as the replenishment time point, the replenishment quantity of each size and model of clothing, and whether the unissued clothing in the current inventory grid is removed from the shelves are used as controllable factors of the replenishment decision model; factors such as sudden additional surgeries and intern visits are used as uncontrollable factors. Various controllable and uncontrollable factors are fully considered and mathematical abstraction is carried out to establish the replenishment decision model.

[0094] Step S503: Carry out replenishment according to the target replenishment decision model.

[0095] There is also an automatic replenishment control mechanism set in the surgical gown dispenser in the operating room. After fully considering various conventional controllable factors and sudden uncontrollable factors and matching the optimal replenishment decision model, the decision center will generate a replenishment control instruction according to this replenishment decision model and send this instruction to the automatic replenishment control mechanism. After receiving the instruction, the automatic replenishment control mechanism completes the replenishment operation according to the requirements.

[0096] Among them, according to different emergencies and corresponding replenishment decision-making models, the decision-making center will generate different replenishment control instructions: for example, when there are temporary arrangements such as internship visits, the decision-making center will issue a replenishment control instruction to arrange for early replenishment; another example is that when there is an emergency additional surgery, the decision-making center will issue a replenishment control instruction to arrange for an increase in the replenishment quantity; in addition, when there is an emergency that causes an increase in the demand for surgical gowns, the decision-making center will issue a replenishment control instruction to make the automatic replenishment control mechanism temporarily not clean the compartments.

[0097] Further, referring to Figure 6 , Figure 6 For Figure 2 the flow schematic diagram of the specific implementation manner of step S203 in

[0098] Step S601, controlling the robotic arm to grab the target surgical gown in the reserve warehouse;

[0099] The automatic replenishment control mechanism controls the automatic robotic arm installed on it to perform the replenishment operation. After receiving the aforementioned replenishment control instruction, the automatic replenishment control mechanism controls the automatic robotic arm to grab the clothing to complete the replenishment according to the instruction. Under normal circumstances, the automatic robotic arm grabs the clothing in the reserve warehouse according to the control instruction and puts the clothing into the to-be-issued compartment; but when the inventory of the clothing in the reserve warehouse is insufficient or the compartment is locked and not released, the replenishment will fail. At this time, the replenishment process is interrupted, the automatic robotic arm stops operating, and the automatic replenishment control mechanism feeds back the replenishment failure information to the user device and displays it on the display screen of the user device to remind the administrator to check the corresponding situation. When the administrator checks and solves the corresponding problem, then issue the instruction again to complete the replenishment. Among them, the user device includes a computer, mobile phone or tablet used by the administrator, etc.

[0100] Step S602, controlling the robotic arm to put the target surgical gown into the to-be-issued compartment to complete the replenishment of a single surgical gown, updating the actual replenishment quantity, and returning to execute the control of the robotic arm to grab the target surgical gown in the reserve warehouse, and looping until the actual replenishment quantity reaches the surgical gown replenishment demand quantity.

[0101] Under normal replenishment circumstances, the automatic robotic arm continuously grabs surgical gowns one by one from the inventory and puts them into the to-be-issued compartment, and counts and updates the actual replenishment quantity in real time until the actual replenishment quantity reaches the previously obtained surgical gown replenishment demand quantity and then stops operating.

[0102] Further, referring to Figure 7 , Figure 7 For Figure 2 the implementation steps after step S203 in

[0103] Step S701: Output the replenishment demand quantity of the surgical gowns and the current laundry inventory data.

[0104] After the automatic replenishment control mechanism completes the foregoing replenishment operation, it will update in real time data information such as the replenishment demand quantity of surgical gowns in the next time period, the current inventory in the laundry, and the cumulative quantity of gowns issued by the gown dispenser on the same day, and feedback this information to the user device through the foregoing HttpService publishing interface and display it on the display screen of the user device. The administrator can monitor these data changes in real time and perform operations such as increasing replenishment and troubleshooting when necessary.

[0105] Refer to Figure 8 , Figure 8 which is a schematic diagram of the overall operation process involved in the solution of the embodiment of the present application.

[0106] As Figure 8 shown, this solution is mainly used for the gown dispenser in the operating room. The gown dispenser in the operating room first obtains the surgical scheduling data for the target time period, the current laundry inventory data, the mapping relationship between the hospital personnel and gown models preset, and the historical gown issuance record data of the gown dispenser, and then processes these data information by combining the method of big data prediction analysis to deduce and predict the upper and lower limits of the demand quantity range of large, medium, and small sized gowns. Then, the obtained results are sent to the decision center so that the decision center matches the results into the preset replenishment decision model, and makes an intelligent replenishment decision based on the matching results, that is, generates a replenishment control instruction and transmits it to the automatic replenishment control mechanism. Finally, the automatic replenishment control mechanism completes the replenishment according to the replenishment control instruction.

[0107] In addition, after completing this series of replenishment operations, the intelligent replenishment system of the gown dispenser in the operating room also needs to obtain data field information such as the replenishment demand quantity of surgical gowns in the next time period of the gown dispenser, the current inventory in the compartments, the remaining quantity of gowns in the reserve warehouse, and the cumulative quantity of gowns issued by the gown dispenser on the same day, and push this information to the administrator and display it on the large screen through the publishing interface HttpService for the convenience of the administrator to monitor and manage at any time.

[0108] Based on the same inventive concept, the embodiment of the present invention also provides a replenishment device for a gown dispenser in the operating room. Refer to Figure 9 shown, it includes the following program modules:

[0109] A data acquisition module 910, configured to acquire the surgical scheduling data for the target time period and the current laundry inventory data;

[0110] A data processing module 920, configured to predict the replenishment demand quantity of surgical gowns for the target time period according to the surgical scheduling data, the current laundry inventory data, and the mapping relationship between the hospital personnel and gown models preset;

[0111] A control output module 930 for replenishing goods according to the replenishment demand quantity of the surgical gowns.

[0112] As an optional embodiment, the replenishment device for the operating room gown dispenser may further include:

[0113] A data input module for inputting the surgical scheduling data, the current laundry inventory data, and the preset mapping relationship between hospital personnel and clothing models into the replenishment demand prediction neural network model.

[0114] As an optional embodiment, the replenishment system for the operating room gown dispenser may further include:

[0115] A data output module for outputting the replenishment demand quantity of the surgical gowns and the current laundry inventory data.

[0116] As an optional embodiment, the replenishment device for the operating room gown dispenser may further include:

[0117] A model matching module for matching a target replenishment decision model from multiple preset replenishment decision models according to the event type.

[0118] As an optional embodiment, the replenishment device for the operating room gown dispenser may further include:

[0119] A replenishment control module for controlling the robotic arm to place the target surgical gown into the waiting-to-be-issued compartment to complete the replenishment of a single surgical gown, updating the actual replenishment quantity, and returning to execute the control of the robotic arm to grab the target surgical gown in the reserve warehouse, and looping until the actual replenishment quantity reaches the replenishment demand quantity of the surgical gowns.

[0120] In addition, in one embodiment, the present application further provides a computer storage medium, on which a computer program is stored, and when the computer program is run by a processor, the steps of the method in the foregoing first embodiment are implemented.

[0121] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disc, or CD-ROM; or it may be various devices including one or any combination of the foregoing memories. The computer may be various computing devices including smart terminals and servers.

[0122] In some embodiments, the executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted language, or declarative or procedural language), and may be deployed in any form, including being deployed as an independent program or being deployed as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0123] As an example, executable instructions may or may not correspond to files in a file system and may be stored as part of a file that holds other programs or data. For example, they may be stored in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program being discussed, or in multiple cooperating files (such as files that hold one or more modules, subroutines, or portions of code).

[0124] As an example, executable instructions may be deployed to execute on one computing device, or on multiple computing devices located at one site, or on multiple computing devices distributed across multiple sites and interconnected by a communication network.

[0125] The above are only the preferred embodiments of the present invention and do not limit the patent scope of the present invention. All equivalent structural or equivalent process transformations made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, are equally included in the patent protection scope of the present invention.

Claims

1. A replenishment method for an operating room clothing dispenser, characterized in that, The method includes the following steps: Obtain the surgical scheduling data for the target time period and the current laundry inventory data; wherein, the surgical scheduling data includes surgical staff information and the number of operating tables; Predict the replenishment demand quantity of surgical gowns for the target time period based on the surgical scheduling data, the current laundry inventory data, and the preset mapping relationship between hospital staff and clothing models; Replenish goods according to the replenishment demand quantity of surgical gowns; When an emergency occurs, obtain the type of the emergency; Match a target replenishment decision model from multiple preset replenishment decision models according to the type of the emergency; the type of the emergency includes intern visits, sudden additional surgeries, or changes in medical staff; Replenish goods according to the target replenishment decision model; The obtaining of the surgical scheduling data for the target time period and the current laundry inventory data includes: Obtain the surgical scheduling data for the target time period, the current laundry inventory data, and the historical dispensing record data of the gown dispenser; The step of predicting the replenishment demand quantity of surgical gowns for the target time period according to the surgical scheduling data, the current laundry inventory data, and the preset mapping relationship between hospital staff and clothing models specifically includes: Predict the replenishment demand quantity of surgical gowns for the target time period based on the surgical scheduling data, the current laundry inventory data, the preset mapping relationship between hospital staff and clothing models, and the historical dispensing record data of the gown dispenser; the historical dispensing record data of the gown dispenser is obtained after each replenishment is completed in history.

2. The method according to claim 1, characterized in that, The obtaining of the surgical scheduling data for the target time period, the current laundry inventory data, and the historical dispensing record data of the gown dispenser includes: Obtain the surgical scheduling data for the target time period, the current laundry inventory data, the historical dispensing record data of the gown dispenser, and the current demand quantity of surgical gowns; The step of predicting the replenishment demand quantity of surgical gowns for the target time period according to the surgical scheduling data, the current laundry inventory data, and the preset mapping relationship between hospital staff and clothing models specifically includes: Predict the replenishment demand quantity of surgical gowns for the target time period based on the surgical scheduling data, the current laundry inventory data, the preset mapping relationship between hospital staff and clothing models, the current demand quantity of surgical gowns, and the historical dispensing record data of the gown dispenser.

3. The method according to claim 1, wherein The predicting of the replenishment demand quantity of surgical gowns for the target time period according to the surgical scheduling data, the current laundry inventory data, and the preset mapping relationship between hospital staff and clothing models includes: Input the surgical scheduling data, the current laundry inventory data, the preset mapping relationship between hospital staff and clothing models, and the historical dispensing record data of the gown dispenser into a replenishment demand quantity prediction neural network model to obtain the replenishment demand quantity of surgical gowns for the target time period output by the replenishment demand quantity prediction neural network model.

4. The method according to claim 1, wherein The replenishing of goods according to the replenishment demand quantity of surgical gowns includes: Control the robotic arm to grasp the target surgical gowns in the reserve warehouse; Control the robotic arm to place the target surgical gown into the dispensing slot to complete the replenishment of a single surgical gown, update the actual replenishment quantity, and then return to control the robotic arm to grasp the target surgical gown in the reserve warehouse, and loop until the actual replenishment quantity reaches the surgical gown replenishment demand.

5. The method according to any one of claims 1 to 4, characterized in that After replenishing according to the surgical gown replenishment demand, the method further includes: Output the surgical gown replenishment demand and the current laundry inventory data.

6. A replenishment device for an operating room gown dispenser, characterized in that, The device includes: A data acquisition module, configured to acquire the surgical scheduling data and the current laundry inventory data for a target time period; wherein, the surgical scheduling data includes surgical staff information and the number of operating tables; A data processing module, configured to predict the surgical gown replenishment demand for the target time period according to the surgical scheduling data, the current laundry inventory data, and the preset mapping relationship between hospital staff and clothing models; A control output module, configured to replenish according to the surgical gown replenishment demand; A model matching module, configured to, when an emergency occurs, acquire the event type of the emergency; according to the event type, match a target replenishment decision model from multiple preset replenishment decision models; the event types include intern visits, sudden additional surgeries, or changes in medical staff; and replenish according to the target replenishment decision model; The data acquisition module is specifically configured to acquire the surgical scheduling data, the current laundry inventory data, and the historical dispensing record data of the gown dispenser for a target time period; The data processing module is specifically configured to predict the surgical gown replenishment demand for the target time period according to the surgical scheduling data, the current laundry inventory data, the preset mapping relationship between hospital staff and clothing models, and the historical dispensing record data of the gown dispenser; the historical dispensing record data of the gown dispenser is obtained after each historical replenishment is completed.

7. An operating room clothing dispenser, characterized in that, It includes a memory, a processor, and a surgical gown dispenser replenishment program stored on the memory and executable on the processor. When the surgical gown dispenser replenishment program is executed by the processor, the steps of the surgical gown dispenser replenishment method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium, characterized in that, A surgical gown dispenser replenishment program is stored on the computer-readable storage medium. When the surgical gown dispenser replenishment program is executed by the processor, the steps of the surgical gown dispenser replenishment method according to any one of claims 1 to 5 are implemented.

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