Smart ward management method and system and electronic equipment

Abstract

The invention provides a smart ward management method and system and electronic equipment, and belongs to the technical field of smart wards. The method comprises the following steps: acquiring first environment data and first illumination data in a smart ward, and first physiological data and falling data of a patient; filtering and fusing the first environment data, the first illumination data and the first physiological data to obtain second environment data, second illumination data and second physiological data; determining a target treatment prescription according to the second physiological data; determining a target regulation quantity according to the target treatment prescription, the target setting information, the second environment data, the second illumination data, the second physiological data and the fall data; and controlling the lighting, sound and environment control unit based on the target regulation amount. The stability and reliability of the monitoring data are improved through data filtering and fusion, ward regulation and control are linked in time along with the state of the patient in combination with the target treatment prescription, and the real-time response ability and the individual adaptation ability of ward environment regulation and control are enhanced.

Description

Technical Field

[0001] This application relates to the field of smart ward technology, and in particular to a smart ward management method, system and electronic device. Background Technology

[0002] In healthcare settings, patient wards not only serve as the primary means of daily care and treatment, but also directly impact patients' rest quality and recovery process. With the development of smart healthcare and IoT technologies, the construction of smart wards typically requires continuous monitoring of patients' physiological states and the ward environment, along with corresponding adjustments to lighting, air conditioning, audio, and other facilities to improve nursing efficiency and patient comfort.

[0003] In related technologies, intelligent ward systems typically monitor ward environmental data and patient-related data through environmental detection equipment, bedside monitoring devices, and wearable devices, displaying, alerting, or recording this data on a platform. Meanwhile, lighting, temperature and humidity control, and audio playback are often controlled using timed strategies, preset scenarios, or manual intervention. However, in practical applications, monitoring data is easily affected by factors such as equipment accuracy, installation location, human movement, and external disturbances during collection, leading to data fluctuations or anomalies, thus affecting the reliability of alarms and status assessments. Furthermore, ward environment adjustments are usually based on fixed strategies, lacking the dynamic adjustment capability to match changes in patient conditions, making it difficult to adapt to the different needs of different recovery stages, thereby affecting the control effect and patient experience.

[0004] Therefore, in the intelligent monitoring and environmental control of smart wards, the limited stability and reliability of monitoring data, as well as the poor adaptability and real-time performance of environmental control, have become urgent problems to be solved. Summary of the Invention

[0005] This application provides a smart ward management method, system, and electronic device to solve the problems of limited stability and reliability of monitoring data and poor adaptability and real-time performance of environmental control in existing smart ward management processes.

[0006] Firstly, this application provides a smart ward management method, the method comprising: Acquire the first environmental data and first illuminance data within the smart ward, as well as the first physiological data and fall data of the patients within the smart ward; The first environmental data, the first illuminance data, and the first physiological data are filtered and fused respectively to obtain the second environmental data, the second illuminance data, and the second physiological data. Based on the second physiological data, a target treatment prescription is determined; wherein, the target treatment prescription is used to indicate a ward intervention plan for the patient, and the target treatment prescription includes a light therapy prescription and / or a music therapy prescription; Based on the target treatment prescription and the target setting information of the smart ward, as well as the second environmental data, the second illuminance data, the second physiological data and the fall data, the target control amount of the smart ward is determined; wherein, the target setting information is used to indicate the setting range of the environmental parameters and the setting range of the illuminance parameters of the smart ward; Based on the target control amount, the controlled units in the smart ward are controlled, and the controlled units include a lighting control unit, a sound control unit, and an environmental control unit.

[0007] In one possible design, the first environmental data includes the temperature, humidity, noise, oxygen concentration, carbon dioxide concentration, PM2.5 concentration, and atmospheric pressure within the smart ward. The first illuminance data includes the ambient illuminance, rhythmic illuminance, and perceived illuminance within the smart ward; The first physiological data includes the patient's heart rate data, respiratory rate data, blood pressure data, blood oxygen saturation data, and sleep-related data; The fall data includes alarm determination results, which are used to indicate whether the patient is in a normal state or a preset abnormal state, and the preset abnormal state includes a fall state.

[0008] In one possible design, the filtering and fusion processing of the first environmental data, the first illuminance data, and the first physiological data to obtain second environmental data, second illuminance data, and second physiological data includes: The first environmental data, the first illuminance data, and the first physiological data are filtered to obtain filtered environmental data, filtered illuminance data, and filtered physiological data. The filtered environmental data and the filtered illuminance data are respectively subjected to weighted fusion processing to obtain the second environmental data and the second illuminance data; For the filtered physiological data, a state model and an observation model are constructed, and the physiological data is subjected to state estimation processing based on Kalman filtering to obtain the second physiological data.

[0009] In one possible design, the filtered environmental data and the filtered illuminance data are respectively subjected to weighted fusion processing to obtain the second environmental data and the second illuminance data, including: Determine if the first parameter contains multi-channel acquired data; When there are multiple acquisition data for the first parameter, the multiple acquisition data for the first parameter are weighted and fused to obtain the second parameter corresponding to the first parameter; If there is one source of data for the first parameter, the first parameter is determined as the second parameter; wherein, the first parameter is any parameter in the filtered environmental data or the filtered illuminance data, and the second parameter is the parameter in the second environmental data or the second illuminance data that corresponds to the first parameter.

[0010] In one possible design, the light therapy prescription includes first treatment period information, first duration information, target illumination rhythm information, illumination color temperature setting information, and / or illumination change rate limitation information; The target illumination rhythm information is used to indicate the segmented illuminance target value during the treatment period; the illumination change rate limit information is used to indicate the allowable range of illuminance change rate per unit time.

[0011] In one possible design, the music therapy prescription includes second treatment period information, second duration information, target volume information, audio content identification information, audio playback mode information, and / or audio switching rule information; The audio content identification information is used to indicate the target audio content in the music category or audio resource library.

[0012] In one possible design, determining the target control level of the smart ward based on the target treatment prescription and the target setting information of the smart ward, as well as the second environmental data, the second illuminance data, the second physiological data, and the fall data, includes: Based on the target setting information and the second environmental data, the environmental deviation amount is determined; Based on the target setting information and the second illuminance data, the illuminance deviation is determined; Based on the target treatment prescription, determine the prescription control parameters; The environmental deviation, the illumination deviation, the second physiological data, the fall data, and the prescription control parameters are used as input variables, and the input variables are fuzzified to obtain the membership degree corresponding to the input variables. Fuzzy reasoning is performed on the membership degree based on a preset set of fuzzy rules to obtain an output fuzzy quantity; wherein, the set of fuzzy rules is related to the target treatment prescription; The output fuzzy quantity is defuzzified to obtain the target control quantity; wherein, the target control quantity includes lighting control quantity, audio control quantity and / or environmental control quantity, and the environmental control quantity includes temperature control quantity, humidity control quantity and / or air quality control quantity.

[0013] In one possible design, controlling the controlled unit within the smart ward based on the target control amount includes: Lighting control commands are generated based on the lighting control quantities, and the lighting control commands are sent to the lighting control unit to control the illuminance output and / or color temperature output of the lighting control unit; Based on the audio control quantity, an audio control command is generated and sent to the sound control unit to control the volume of the sound control unit and / or the playback status of the target audio content; An environmental control command is generated based on the environmental control quantity, and the environmental control command is sent to the environmental control unit to control the temperature, humidity and / or air quality of the smart ward.

[0014] Secondly, this application provides a smart ward management system, the system comprising: The acquisition module is used to acquire the first environmental data and the first illuminance data in the smart ward, as well as the first physiological data and fall data of the patients in the smart ward. The processing module is used to filter and fuse the first environmental data, the first illuminance data, and the first physiological data respectively to obtain the second environmental data, the second illuminance data, and the second physiological data. A first determining module is configured to determine a target treatment prescription based on the second physiological data; wherein the target treatment prescription is used to indicate a ward intervention plan for the patient, and the target treatment prescription includes a light therapy prescription and / or a music therapy prescription; The second determining module is used to determine the target control amount of the smart ward based on the target treatment prescription and the target setting information of the smart ward, as well as the second environmental data, the second illuminance data, the second physiological data and the fall data; wherein, the target setting information is used to indicate the setting range of the environmental parameters and the setting range of the illuminance parameters of the smart ward. The control module is used to control the controlled units in the smart ward based on the target control amount. The controlled units include a lighting control unit, a sound control unit, and an environmental control unit.

[0015] Thirdly, this application provides an electronic device, including: a memory and at least one processor; The memory stores computer-executed instructions; The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the method described in the first aspect or various possible designs of the first aspect.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed, implement the method described in the first aspect or various possible designs of the first aspect.

[0017] Fifthly, this application provides a computer program product, which includes computer program code that, when run on a computer, causes the computer to implement the method described in the first aspect or various possible designs of the first aspect.

[0018] In a sixth aspect, this application provides a chip, comprising: an interface circuit and a logic circuit, wherein the interface circuit is configured to receive signals from other chips outside the chip and transmit them to the logic circuit, or to send signals from the logic circuit to other chips outside the chip, and the logic circuit is configured to implement the method described in the first aspect or various possible designs of the first aspect.

[0019] This application provides a smart ward management method, system, and electronic device. In this smart ward management method, firstly, first environmental data and first illuminance data within the smart ward, as well as first physiological data and fall data of the patient, are acquired. Furthermore, the first environmental data, first illuminance data, and first physiological data are filtered and fused to obtain second environmental data, second illuminance data, and second physiological data. Compared to directly using the first environmental data, first illuminance data, and first physiological data for alarm or control decisions, the second environmental data, second illuminance data, and second physiological data can reduce the impact of acquisition noise, fluctuations, outliers, and multi-source differences on state representation at the data level. This makes the characterization of the ward environment, light environment, and patient physiological state more stable and consistent, thereby improving the stability of the monitoring data. Qualitative and reliable analysis is conducted. Based on this, the target treatment prescription for instructing ward intervention plans is further determined according to the second physiological data. The target control quantity is determined by combining the target setting information of the smart ward with the second environmental data, second illuminance data, second physiological data, and fall data. Finally, the lighting control unit, sound control unit, and environmental control unit are controlled according to the target control quantity. This allows the control of the smart ward to no longer rely solely on timed strategies, preset scenarios, or manual intervention. Instead, it can be guided by the treatment prescription corresponding to the patient's current physiological state and dynamically calculate and update the target control quantity based on the target setting information and real-time monitoring data. At the same time, the patient's fall-related status is included in the input constraints of the control decision, realizing the linkage adjustment and timely response of lighting, audio, and environmental parameters, thereby improving the adaptability and real-time performance of environmental control. Attached Figure Description

[0020] Figure 1 A flowchart illustrating a smart ward management method provided in an embodiment of this application; Figure 2 A flowchart illustrating another smart ward management method provided in this application embodiment; Figure 3 This is a schematic diagram of the structure of a smart ward management system provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims and drawings of this application are intended to cover non-exclusive inclusion.

[0023] The term "embodiment" as used herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of the phrase "embodiment" in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0024] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists, A and B can exist simultaneously, and B exists. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0025] Furthermore, the terms "first," "second," etc., in the specification and claims of this application or in the aforementioned drawings are used to distinguish different objects rather than to describe a specific order, and may explicitly or implicitly include one or more of the features.

[0026] In the description of this application, unless otherwise stated, "multiple" and "at least two" mean two or more (including two), and similarly, "multiple groups" and "at least two groups" mean two or more (including two groups).

[0027] In the description of this application, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, "connected" or "linked" can refer not only to a physical connection, but also to an electrical connection or a signal connection. For instance, it can be a direct connection, i.e., a physical connection, or an indirect connection through at least one intermediate component, as long as the circuit is connected. It can also refer to the internal connection between two components. A signal connection can refer not only to a signal connection through a circuit, but also to a signal connection through a medium, such as radio waves. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0028] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, different technical features in this application can be combined with each other.

[0029] Figure 1 This is a flowchart illustrating a smart ward management method provided in an embodiment of this application. Figure 1 As shown, the smart ward management method provided in this application embodiment specifically includes S101 to S105, and S101 to S105 will be described in detail below.

[0030] It should be noted that the implementing entity of the smart ward management method provided in this application embodiment can be a smart ward management system.

[0031] S101. Obtain the first environmental data and first illuminance data in the smart ward, as well as the first physiological data and fall data of the patients in the smart ward.

[0032] The first environmental data was collected by environmental monitoring equipment installed in the smart ward.

[0033] In one possible design, the first environmental data includes temperature, humidity, noise, oxygen concentration, carbon dioxide concentration, PM2.5 concentration, and atmospheric pressure within the smart ward.

[0034] In this embodiment, by collecting the first environmental data, basic monitoring information characterizing the environmental status of the smart ward can be obtained, and a data source can be provided for subsequent environmental data filtering and fusion processing, thereby forming a more stable and usable second environmental data. At the same time, the second environmental data can be compared with the environmental parameter setting range in the target setting information to determine the environmental deviation, and the environmental deviation can be used as the input basis for determining the target control quantity of the smart ward, thereby linking and controlling environmental parameters such as temperature, humidity, air quality and noise in the smart ward.

[0035] The first illuminance data was collected using illuminance detection equipment installed in the smart ward. The illuminance detection equipment can be a lux meter.

[0036] In one possible design, the first illuminance data includes the ambient illuminance, rhythmic illuminance, and perceived illuminance within the smart ward.

[0037] It should be noted that ambient illuminance refers to the objective light intensity formed by the lighting system and external light sources at a certain location or work surface in a smart ward. It is usually characterized by illuminance units and is used to reflect the physical brightness of the smart ward space.

[0038] Rhythmic illuminance refers to the intensity of effective light stimulation related to the human body's circadian rhythm regulation. It emphasizes the influence of the spectral composition and temporal characteristics of light on physiological mechanisms such as the human biological clock / melatonin inhibition, and is used to characterize the stimulation level of smart ward lighting at the level of rhythmic intervention.

[0039] Perceived illuminance is a measure of a patient's subjective perception of brightness in a light environment. It can be calculated or evaluated by illuminance detection equipment in combination with illuminance, color temperature, glare index, and / or a preset model. Perceived illuminance is a perceived brightness index that integrates objective illuminance with factors such as the light source spectrum, color temperature, spatial reflectance, glare level, and individual visual adaptation status.

[0040] In this embodiment, by collecting the first illuminance data, basic monitoring information characterizing the light environment status of the smart ward can be obtained, providing a data source for subsequent illuminance data filtering and fusion processing, thereby forming a more stable and usable second illuminance data. At the same time, the second illuminance data can be compared with the illuminance parameter setting range in the target setting information to determine the illuminance deviation, and the illuminance deviation can be used as the input basis for determining the target control amount of the smart ward, thereby adjusting the lighting output in the smart ward so that the light environment in the smart ward meets the needs of different treatment periods and nursing care.

[0041] The primary physiological data is collected through bedside monitoring devices, mattress-type monitoring devices, and / or wearable devices. Mattress-type monitoring devices can be sleep mattresses capable of monitoring patient physiological data, while wearable devices can be wristbands capable of monitoring patient physiological data.

[0042] In one possible design, the primary physiological data includes the patient's heart rate, respiratory rate, blood pressure, blood oxygen saturation, and sleep-related data.

[0043] It should be noted that sleep-related data includes data used to characterize a patient's sleep state and physiological workload during sleep. Sleep-related data can be collected by bedside sleep monitoring devices, wearable devices, and / or mattress-based monitoring devices.

[0044] The patient's sleep-related data includes the patient's sleep onset time, wakefulness time, bedtime, sleep stage distribution, sleep efficiency, deep sleep ratio, sleep stage statistics (including the time distribution of wakefulness, light sleep, deep sleep, out-of-bed and / or unmonitored periods), sleep continuity indicators (e.g., number of interruptions per hour), body movement indicators (e.g., number of body movements per hour), heart rate level indicators during sleep (e.g., mean resting heart rate, cumulative duration of tachycardia and / or cumulative duration of bradycardia), respiratory level indicators during sleep (e.g., mean respiratory rate, cumulative duration of tachycardia and / or cumulative duration of bradycardia), sleep breathing abnormality indicators (e.g., cumulative number of obstructive apnea / hypopnea episodes, cumulative number of central apnea / hypopnea episodes and / or cumulative apnea duration), and sleep-related risk information (e.g., apnea / hypopnea risk level and / or suspected atrial fibrillation risk level).

[0045] In this embodiment, by collecting sleep-related data, monitoring information characterizing the patient's nighttime rest and recovery status can be obtained, and this information can be used as a component of the first physiological data for subsequent filtering and fusion processing to form the second physiological data.

[0046] In this embodiment, by collecting the first physiological data, basic monitoring information characterizing the patient's physiological state can be obtained, and a data source can be provided for subsequent physiological data filtering and fusion processing, thereby forming a more stable and usable second physiological data. At the same time, the second physiological data can be used as the basis for determining the target treatment prescription, and further as the input basis for determining the target control quantity of the smart ward, so that the ward environment control can match the changes in the patient's state and meet the intervention needs of different rehabilitation stages.

[0047] The fall data is collected and output using millimeter-wave radar installed in the smart ward.

[0048] In one possible design, fall data includes alarm assessment results. These results indicate whether the patient is in a normal or pre-defined abnormal state.

[0049] Preset abnormal states are used to characterize a patient's state of fall or at-risk posture. Preset abnormal states include fall, abnormal posture, and prolonged stay in the bathroom. The types and / or judgment criteria of preset abnormal states can be pre-configured by the system.

[0050] It should be noted that the alarm judgment result is the state discrimination information output by the millimeter-wave radar based on continuous monitoring of the patient's activity status and according to the preset judgment logic. The state discrimination information can be represented in the form of integer state value, state code or flag field, and is used to distinguish the patient's normal state from at least one abnormal state.

[0051] For example, the alarm judgment result can be an integer status value, where a status value of "0" indicates normal; a status value of "1" indicates a fall, used to characterize a patient falling event; a status value of "2" indicates an abnormal patient posture, used to characterize a patient in a preset abnormal state; and a status value of "3" indicates prolonged stay in the bathroom, used to characterize a patient's stay in the bathroom area for a period of time that meets preset abnormal conditions.

[0052] It should be understood that the status values ​​and status meanings in this embodiment are only examples. In actual applications, the abnormal status types and their judgment conditions can be configured according to the needs of ward management.

[0053] In this embodiment, by collecting fall data, alarm judgment information representing the patient's safety status can be obtained. This alarm judgment information is used as the input basis for determining the target control quantity of the smart ward, enabling the smart ward to combine the patient's safety status with environmental control and lighting and audio intervention processes, and providing a reference for subsequent control decisions.

[0054] S102. The first environmental data, the first illuminance data, and the first physiological data are filtered and fused respectively to obtain the second environmental data, the second illuminance data, and the second physiological data.

[0055] It should be noted that the filtering process is used to preprocess the collected first environmental data, first illuminance data, and first physiological data to reduce acquisition noise, remove abnormal or redundant data, and handle abnormalities such as missing or abrupt changes, so that the data meets the input requirements for subsequent fusion calculations.

[0056] Fusion processing is used to comprehensively process multiple data points with similar parameters in the presence of multi-source or multi-channel acquired data, in order to obtain more stable and consistent results.

[0057] The second environmental data is obtained by filtering and fusing the first environmental data; the second illuminance data is obtained by filtering and fusing the first illuminance data; and the second physiological data is obtained by filtering and fusing the first physiological data. The second environmental data, second illuminance data, and second physiological data correspond to the first environmental data, first illuminance data, and first physiological data, respectively, in terms of data type and parameter dimensions.

[0058] In this embodiment, by filtering and fusing the first environmental data, the first illuminance data, and the first physiological data, the impact of acquisition noise, abnormal fluctuations, and differences in multi-source data on the monitoring results can be reduced, thereby improving the stability and consistency of the data. On the other hand, it can provide a more reliable data foundation for subsequently determining the target treatment prescription based on the second physiological data, and determining the deviation based on the second environmental data and the second illuminance data and further calculating the target control amount, thereby improving the accuracy and real-time performance of the smart ward's coordinated control decision-making.

[0059] S103. Based on the second physiological data, determine the target treatment prescription.

[0060] Among them, the target treatment prescription is used to indicate the ward intervention plan for the patient, and the target treatment prescription includes light therapy prescription and / or music therapy prescription.

[0061] It should be noted that the target treatment prescription is used to constrain and guide subsequent smart ward environment control strategies.

[0062] In one possible design, the light therapy prescription includes information on a first treatment period, information on a first duration, information on the target illumination rhythm, information on illumination color temperature settings, and / or information on illumination rate of change limits.

[0063] The first treatment period information is used to indicate the start and end time range of the light intervention, and the first duration information is used to indicate the cumulative intervention duration within the first treatment period. The target lighting rhythm information is used to indicate the segmented illuminance target value within the treatment period, so that the lighting control unit can adjust the lighting output according to the segmented target. The lighting color temperature setting information is used to indicate the target color temperature or segmented color temperature target within the treatment period. The lighting change rate limit information is used to indicate the allowable range of the illuminance change rate per unit time, so as to constrain the change process of the lighting output.

[0064] When a phototherapy prescription is determined based on second physiological data, since the second physiological data is used to characterize the patient's current physiological state and / or sleep state, first prescription determination features can be extracted from the second physiological data. The first prescription determination features may include sleep-related temporal features (e.g., time of falling asleep, time of waking up, time in bed, sleep stage distribution, sleep continuity indicators, body movement indicators, etc.), physiological load features (e.g., heart rate / respiratory level indicators and their fluctuations during sleep, etc.), and / or nursing scenario features (e.g., the patient's current rehabilitation stage and corresponding intervention type preference, etc.).

[0065] In one optional implementation, the system pre-establishes a phototherapy prescription library, which includes multiple phototherapy prescription templates. Each phototherapy prescription template includes its corresponding first treatment period information, first duration information, target illumination rhythm information, illumination color temperature setting information, and / or illumination change rate limit information. The system matches and selects a target phototherapy prescription template from the phototherapy prescription library based on the first prescription determination features corresponding to the second physiological data, and further instantiates the parameters in the target phototherapy prescription template to generate a phototherapy prescription.

[0066] For example, the system determines the patient's sleep-wake rhythm characteristics based on the second physiological data, and determines the first treatment period information accordingly; it determines the first duration information based on the state change trend represented by the second physiological data; it generates or selects segmented illuminance target values ​​based on the intervention needs of different stages within the first treatment period, thereby determining the target lighting rhythm information; it determines the corresponding color temperature setting based on the segmented configuration of the first treatment period, thereby determining the lighting color temperature setting information; and it determines the allowable range of illuminance change rate based on preset comfort constraints or control strategy constraints, thereby determining the lighting change rate limit information. Through the above methods, the light therapy prescription can be adapted to the state reflected by the patient's second physiological data, and provides prescription parameter basis for determining the target regulation amount and controlling the lighting control unit in subsequent steps.

[0067] In one possible design, the music therapy prescription includes second treatment period information, second duration information, target volume information, audio content identification information, audio playback mode information, and / or audio switching rule information.

[0068] The second treatment period information indicates the start and end time range of the music intervention, and the second duration information indicates the cumulative intervention duration within the second treatment period. The target volume information indicates the target volume level or segmented volume target of the sound control unit during the music intervention, so that the sound control unit can adjust the audio output according to the target volume. The audio content identification information indicates the target audio content in the music category or audio resource library, used to determine the audio resources to be played. The audio playback mode information indicates the playback method of the target audio content, which may include one or more of single loop, playlist loop, sequential playback, random playback, segmented playback, and / or timed playback. The audio switching rule information indicates the switching strategy of the audio content during playback, which may include one or more of switching based on playback duration, switching based on playback stage, switching based on changes in patient status, and / or event-triggered switching, to constrain the timing and method of switching the audio content.

[0069] When music therapy prescriptions are determined based on second physiological data, since the second physiological data is used to characterize the patient's current physiological state and / or sleep state, second prescription determination features can be extracted from the second physiological data. The second prescription determination features may include sleep-related temporal features (e.g., time of falling asleep, time of awakening, time in bed, sleep stage distribution, sleep continuity indicators, body movement indicators, etc.), physiological load features (e.g., heart rate / respiratory level indicators and their fluctuations during sleep, etc.), emotion or relaxation-related representation features (e.g., heart rate variability related indicators, respiratory rhythm stability related indicators, etc.), and / or nursing scenario features (scenario types such as rest / rehabilitation training / nighttime sleep, etc.).

[0070] In one optional implementation, the system pre-establishes a music therapy prescription library, which includes multiple music prescription templates. Each music prescription template includes its corresponding second treatment period information, second duration information, target volume information, audio content identification information, audio playback mode information, and / or audio switching rule information. The system matches and selects a target music therapy prescription template from the music therapy prescription library based on the second prescription determination features corresponding to the second physiological data, and further instantiates the parameters in the target music therapy prescription template to generate a music therapy prescription.

[0071] For example, the system determines the patient's sleep-wake rhythm characteristics based on second physiological data, and determines the second treatment period information accordingly; it determines the second duration information based on the state change trend represented by the second physiological data; it determines the target volume information based on the intervention needs of different stages within the second treatment period, and can further generate segmented volume targets to adapt to the intervention intensity of different stages; it determines the music category preference or target audio content identifier in the audio resource library based on the second prescription determination characteristics, thereby determining the audio content identifier information; it determines the corresponding audio playback mode based on the segmented configuration of the second treatment period or the nursing scenario type, thereby determining the audio playback mode information; and it determines the triggering conditions and switching methods for audio content switching based on preset nursing strategies or control strategies, thereby determining the audio switching rule information. Through the above methods, the music therapy prescription can be adapted to the state reflected by the patient's second physiological data, and provides prescription parameter basis for determining the target regulation amount and controlling the sound control unit in subsequent steps.

[0072] S104. Based on the target treatment prescription and the target setting information of the smart ward, as well as the second environmental data, second illuminance data, second physiological data and fall data, determine the target control quantity of the smart ward.

[0073] Among them, the target setting information is used to indicate the setting range of environmental parameters and illuminance parameters in the smart ward.

[0074] It should be noted that the environmental parameter setting range includes the temperature setting range, humidity setting range, noise setting range, oxygen concentration setting range, carbon dioxide concentration setting range, PM2.5 concentration setting range, and atmospheric pressure setting range of the smart ward.

[0075] The illuminance parameter setting range includes the ambient illuminance setting range, the rhythmic illuminance setting range, and the perceived illuminance setting range.

[0076] Specifically, the target control quantity is used to characterize the control target of the lighting control unit, sound control unit and environmental control unit in the smart ward, and may include lighting control quantity, audio control quantity and / or environmental control quantity.

[0077] Lighting control parameters are used to indicate the target illuminance, color temperature, and their variation constraints for lighting output; audio control parameters are used to indicate the target volume, target audio content, and its playback strategy for audio output; environmental control parameters are used to indicate the adjustment range of environmental parameters such as temperature, humidity, and air quality.

[0078] S105. Based on the target control quantity, control the controlled units in the smart ward. The controlled units include a lighting control unit, a sound control unit, and an environmental control unit.

[0079] The system generates control commands corresponding to the lighting control unit, sound control unit, and environmental control unit based on the target control amount, and sends the control commands to the corresponding lighting control unit, sound control unit, and environmental control unit to drive them to perform corresponding adjustment actions according to the target control amount.

[0080] The lighting control unit is used to adjust the lighting output of the smart ward, the sound control unit is used to adjust the audio output of the smart ward, and the environmental control unit is used to adjust the environmental parameters of the smart ward. By controlling the lighting control unit, sound control unit, and environmental control unit, the lighting, audio, and environmental adjustments of the smart ward can be matched with the target treatment prescription and target setting information, and provide a basis for subsequent smart ward management and nursing interventions.

[0081] This application provides a smart ward management method. First, it acquires first environmental data and first illuminance data within the smart ward, as well as first physiological data and fall data of the patient. Then, it further filters and fuses the first environmental data, first illuminance data, and first physiological data to obtain second environmental data, second illuminance data, and second physiological data. Compared to directly using the first environmental data, first illuminance data, and first physiological data for alarm or control decisions, the second environmental data, second illuminance data, and second physiological data can reduce the impact of acquisition noise, fluctuations, outliers, and multi-source differences on state representation at the data level, making the characterization of the ward environment, light environment, and patient physiological state more stable and consistent, thereby improving the stability and reliability of the monitoring data. Based on this foundation, the target treatment prescription for instructing ward intervention plans is further determined according to the second physiological data. Combined with the target setting information of the smart ward, as well as the second environmental data, second illuminance data, second physiological data, and fall data, the target control quantity is determined. Finally, the lighting control unit, sound control unit, and environmental control unit are controlled according to the target control quantity. This enables the control of the smart ward to no longer rely solely on timed strategies, preset scenarios, or manual intervention, but can be guided by the treatment prescription corresponding to the patient's current physiological state. The target control quantity is dynamically calculated and updated based on the target setting information and real-time monitoring data. At the same time, the patient's fall-related status is included in the input constraints of the control decision, realizing the linkage adjustment and timely response of lighting, audio, and environmental parameters, thereby improving the adaptability and real-time performance of environmental control.

[0082] In the above embodiments, it should be noted that the smart ward management system includes a server. Data collected by environmental monitoring devices, illuminance monitoring devices, mattress-type monitoring devices, and millimeter-wave radar are sent to the server via the standard Message Queuing Telemetry Transport (MQTT) protocol. Data collected by the wristband is sent to the server via the Transmission Control Protocol (TCP). After receiving the data sent by the environmental monitoring devices, illuminance monitoring devices, mattress-type monitoring devices, millimeter-wave radar, and wristband, the server executes the method steps shown in S101 to S105 above to realize the management of the smart ward.

[0083] In the above embodiments, the system needs to filter and fuse the first environmental data, the first illuminance data, and the first physiological data respectively to obtain the second environmental data, the second illuminance data, and the second physiological data. Next, the specific process by which the system filters and fuses the first environmental data, the first illuminance data, and the first physiological data to obtain the second environmental data, the second illuminance data, and the second physiological data will be described in detail.

[0084] Figure 2 This is a flowchart illustrating another smart ward management method provided in an embodiment of this application. Figure 2 As shown, in one possible embodiment, the method steps shown in S102 can be implemented by S1021 to S1023, which will be described in detail below.

[0085] S1021. The first environmental data, the first illuminance data, and the first physiological data are filtered respectively to obtain the filtered environmental data, the filtered illuminance data, and the filtered physiological data.

[0086] It should be noted that the filtering process includes, but is not limited to: validating the sampled values ​​to remove obviously invalid data; identifying and deleting, replacing or correcting mutation points and outliers; completing or interpolating missing data; smoothing high-frequency jitter data; and deduplicating duplicate reported data.

[0087] In one alternative implementation, different filtering strategies can be set for different types of parameters. For example, for environmental parameters in the first environmental data and illuminance parameters in the first illuminance data, fluctuations can be suppressed by using methods such as sliding window smoothing, median processing, and exponential smoothing, and outliers can be processed in combination with preset reasonable ranges or rate of change constraints. For physiological parameters in the first physiological data, anomalies can be identified and corrected by combining reasonable ranges, trend constraints, and continuity constraints of physiological parameters.

[0088] In this embodiment, each environmental parameter in the first environmental data is filtered to obtain the corresponding filtered environmental parameters, and filtered environmental data is formed based on the filtered environmental parameters; each illuminance parameter in the first illuminance data is filtered to obtain the corresponding filtered illuminance parameters, and filtered illuminance data is formed based on the filtered illuminance parameters; each physiological parameter in the first physiological data is filtered to obtain the corresponding filtered physiological parameters, and filtered physiological data is formed based on the filtered physiological parameters.

[0089] S1022. Perform weighted fusion processing on the filtered environmental data and the filtered illuminance data respectively to obtain the second environmental data and the second illuminance data.

[0090] In one possible embodiment, the method steps shown in S1022 can be implemented by Sa1 to Sa3, which are described in detail below.

[0091] Sa1. Determine whether the first parameter contains multi-channel acquired data.

[0092] The first parameter is any parameter in either the filtered environmental data or the filtered illuminance data.

[0093] It should be noted that the system can establish data source identification information for the first parameter, which is used to characterize the acquisition device identifier, acquisition channel identifier and / or data reporting source identifier corresponding to the first parameter.

[0094] After obtaining the filtered environmental data or filtered illuminance data, the system counts the number of valid data streams for the first parameter based on the data source identification information to determine whether the first parameter has multiple data streams. If the number of valid data streams corresponding to the first parameter is greater than one, it is determined that the first parameter has multiple data streams, and the method steps shown in Sa2 are executed subsequently; if the number of valid data streams corresponding to the first parameter is one, it is determined that the first parameter has one data stream, and the method steps shown in Sa3 are executed subsequently.

[0095] Sa2. In the case that there are multiple acquisition data for the first parameter, the multiple acquisition data for the first parameter are weighted and fused to obtain the second parameter corresponding to the first parameter.

[0096] The second parameter is the parameter in the second environmental data or the second illuminance data that corresponds to the first parameter.

[0097] When the system determines that the first parameter corresponds to at least two valid data acquisition channels, it can acquire the acquisition values ​​of each channel for the first parameter and configure a corresponding weighting coefficient for each acquisition value. The weighting coefficient can be a preset value or dynamically determined based on the data quality assessment results of the acquisition sources. Subsequently, the system performs a weighted calculation on each acquisition value based on the weighting coefficient to obtain the fusion result of the first parameter, and writes the fusion result as the second parameter into the second environmental data or the second illuminance data.

[0098] It should be noted that the weighting coefficients can be set in relation to factors such as the accuracy level of the acquisition equipment, differences in installation location, historical stability, data integrity, and / or real-time validity, so that the fusion results can more stably represent the actual state of the first parameter and provide reliable input for the determination of subsequent target control quantities.

[0099] Sa3. If there is one channel of data collected for the first parameter, the first parameter is determined as the second parameter.

[0100] When it is determined that only one source of data exists for the first parameter, it means that there are no multiple inputs requiring multi-source weighted fusion for the first parameter. In this case, the current value of the first parameter can be directly output as the fusion result, and this fusion result can be written into the second environmental data or the second illuminance data as the second parameter corresponding to the first parameter in the same dimension. This method avoids unnecessary fusion calculations on single-source data while ensuring data consistency and parameter correspondence, thereby reducing processing overhead and improving the real-time performance of data processing.

[0101] For example, a smart ward may have N environmental sensors installed. Due to differences in the installation positions and angles of these N sensors, the environmental data measured by them will vary. Let's assume that the CO2 (carbon dioxide) concentrations measured by the N environmental sensors in the smart ward are as follows: , , ..., And assign weight values ​​to each of the N CO2 concentration data points. The weight values ​​for the N CO2 concentration data points are as follows: , , ..., , for The corresponding weights for The corresponding weights for The corresponding weights, and Finally, based on N CO2 concentration data points and their corresponding weight values, the final CO2 concentration value in the smart ward is calculated. (Second parameter) , It is a positive integer, and .

[0102] S1023. For the filtered physiological data, construct a state model and an observation model, and perform state estimation processing on the physiological data based on Kalman filtering to obtain the second physiological data.

[0103] The system employs a state model to describe the evolution of the patient's physiological state over time, and an observation model to describe the correspondence between the patient's physiological state and the filtered physiological data. The system can use the physiological state quantities to be estimated (such as the state quantities corresponding to heart rate, respiratory rate, blood pressure, blood oxygen saturation, etc., or their combined state vectors) as the filtered state, and the filtered physiological data as the observation input. At each sampling time, the system predicts the physiological state based on the state model, and corrects and updates the prediction results with the observation input based on the observation model, thereby recursively obtaining the estimated result of the physiological state.

[0104] For example, for the blood pressure parameter in the filtered physiological data, firstly, the system state is defined. , Represents the set of system states. This represents the blood pressure parameter in the filtered physiological data. This represents the rate of change in the patient's blood pressure; secondly, a state observation equation is established: since vital signs usually do not undergo drastic, instantaneous changes, the most commonly used model is that the state at the next moment equals the state at the current moment, but the influence of process noise needs to be considered, i.e. , express The system state at any given moment. express The system state at any given moment. Let be the state transition matrix; then, establish the covariance prediction equation to predict the estimation uncertainty, and consider process noise (caused by the uncertainty of the system model). The covariance prediction equation is: , Represents the process noise matrix. express The system covariance matrix at time t, express The system covariance matrix at time t, This represents the transpose of the state transition matrix; finally, the Kalman gain is calculated. Essentially, it's a weighting factor used to balance the confidence levels of predicted and measured values. , The measurement matrix describes how the system state is mapped to the data acquired by the sensors. This represents the transpose of the measurement matrix. This represents measurement noise (i.e., errors present during sensor measurement, reflecting the degree of distrust in the sensor's measurements); then, a weighted average is calculated between the predicted state value and the measured value to obtain the optimal state estimate. It incorporates new measurement information to update the covariance of the estimation error. ; , This represents the sensor measurement value, i.e., the blood pressure parameter in the filtered physiological data; , Represents the identity matrix.

[0105] In this embodiment, the first environmental data, first illuminance data, and first physiological data are filtered to remove anomalies, suppress noise, and improve data validity. Furthermore, the filtered environmental and illuminance data are weighted and fused to reduce the impact of differences in multi-source acquisition on the results. Simultaneously, the filtered physiological data are subjected to Kalman filtering based on a state model and an observation model for state estimation to enhance the continuity and stability of the physiological data. Therefore, at the data processing level, environmental, illuminance, and physiological monitoring data can be denoised, stabilized, and their consistency enhanced, providing a more reliable data foundation for subsequent target control calculations, thereby improving the stability and accuracy of smart ward monitoring and control decisions.

[0106] In the above embodiments, the system needs to determine the target control level of the smart ward based on the target treatment prescription, the target setting information of the smart ward, and the second environmental data, second illuminance data, second physiological data, and fall data. Next, the specific process by which the system determines the target control level of the smart ward based on the target treatment prescription, the target setting information of the smart ward, and the second environmental data, second illuminance data, second physiological data, and fall data will be described in detail.

[0107] In one possible embodiment, the method steps shown in S104 can be implemented by S1041 to S1046, which are described in detail below.

[0108] S1041. Determine the environmental deviation amount based on the target setting information and the second environmental data.

[0109] For any environmental parameter in the second environmental data, the environmental parameter is compared with the setting range corresponding to the environmental parameter in the target setting information to determine the deviation component of the environmental parameter. The environmental deviation quantity is formed by combining the deviation components of each environmental parameter.

[0110] The environmental deviation can be used to characterize the degree of deviation of the current environmental state of the smart ward from the target setting information, and serve as one of the input variables for subsequent fuzzification processing and fuzzy inference.

[0111] S1042. Determine the illuminance deviation based on the target setting information and the second illuminance data.

[0112] For any illuminance parameter in the second illuminance data, the illuminance parameter is compared with the setting range corresponding to the illuminance parameter in the target setting information to determine the deviation component of the illuminance parameter. The illuminance deviation amount can be formed by combining the deviation components of each illuminance parameter.

[0113] Illuminance deviation can be used to characterize the degree of deviation of the current light environment state of the smart ward from the target setting information, and serve as one of the input variables for subsequent fuzzification processing and fuzzy inference.

[0114] S1043. Determine prescription control parameters based on the target treatment prescription.

[0115] The system can extract prescription information related to regulatory decisions from the target treatment prescription and determine the prescription information as prescription regulation parameters to constrain and guide the calculation of subsequent target regulation amounts.

[0116] In one possible implementation, when the target treatment prescription includes a light therapy prescription, the prescription control parameters may include one or more of the following: first treatment period information, first duration information, target illumination rhythm information, illumination color temperature setting information, and illumination change rate limit information, to characterize the execution period, intensity, and variation constraints of the light intervention; when the target treatment prescription includes a music therapy prescription, the prescription control parameters may include one or more of the following: second treatment period information, second duration information, target volume information, audio content identification information, audio playback mode information, and audio switching rule information, to characterize the execution period, intensity, and playback strategy of the audio intervention. The resulting prescription control parameters can be used as one of the input variables for subsequent fuzzification processing and fuzzy inference to match the determination process of the target control quantity with the target treatment prescription.

[0117] S1044. Using environmental deviation, illuminance deviation, second physiological data, fall data, and prescription control parameters as input variables, and performing fuzzification on the input variables, the membership degree corresponding to the input variables is obtained.

[0118] Fuzzification is used to convert the numerical representation of input variables into membership degree representations on fuzzy sets, facilitating subsequent inference calculations based on fuzzy rules. The system can set corresponding fuzzy linguistic variables and their membership functions for each input variable, and map the current value of the input variable to the membership degree value of one or more fuzzy linguistic variables.

[0119] Fuzzy linguistic variables can be used to characterize the state levels of input variables. For example, environmental deviations and illumination deviations can be divided into three levels: low, moderate, and high; the physiological states corresponding to the second physiological data can be divided into three levels: stable, abnormal, and requiring intervention; the safety states corresponding to fall data can be divided into two levels: normal and abnormal; and the prescription constraints corresponding to prescription control parameters can be divided into two levels: on and off, or three levels: low, medium, and high. Through the above fuzzification process, the membership degrees of each input variable on different fuzzy sets can be obtained, providing an input basis for subsequent fuzzy inference.

[0120] S1045. Perform fuzzy reasoning on the membership degree based on the preset fuzzy rule set to obtain the output fuzzy quantity.

[0121] Among them, the fuzzy rule set is related to the target treatment prescription.

[0122] It should be noted that the fuzzy rule set is used to describe the mapping relationship between input variables and control decisions. The system can trigger and calculate the matching degree of each rule according to the membership degree corresponding to the environmental deviation, illumination deviation, second physiological data, fall data and prescription control parameters, and synthesize the reasoning results of each rule to obtain the output fuzzy quantity.

[0123] The output fuzzy quantity is used to characterize the value distribution or membership distribution of the target control quantity in the fuzzy domain, such as characterizing the membership of lighting control quantity, audio control quantity and / or environmental control quantity under different control levels.

[0124] Specifically, the fuzzy rule set can be selected, switched, or configured and updated based on the prescription type and / or prescription parameters of the target treatment prescription, so that the fuzzy inference process can follow prescription constraints and match prescription intervention needs.

[0125] For example, if the target treatment prescription includes a light therapy prescription, the system can select a subset of rules related to light intervention to output ambiguity related to the lighting control quantity; if the target treatment prescription includes a music therapy prescription, the system can select a subset of rules related to audio intervention to output ambiguity related to the audio control quantity; and, in the event of a patient safety abnormality, the output ambiguity can be adjusted based on rules related to the safety status.

[0126] S1046. Defuzzify the output fuzzy quantity to obtain the target control quantity.

[0127] The target control quantities include lighting control quantities, audio control quantities and / or environmental control quantities, and the environmental control quantities include temperature control quantities, humidity control quantities and / or air quality control quantities.

[0128] Output fuzzy quantities are typically represented by the membership distribution of target control variables at different control levels; defuzzification processing is used to convert the fuzzy domain results into deterministic control quantities that can be directly used for equipment control. The system can perform defuzzification operations on the output fuzzy quantities to obtain the target values ​​or target adjustment ranges of each control variable, thereby obtaining the target control quantity.

[0129] In one alternative implementation, the defuzzification process can be implemented using any of the centroid method, the maximum membership method, or the weighted average method to output the corresponding lighting control quantity, audio control quantity, and / or environmental control quantity.

[0130] Lighting control parameters are used to characterize the target illuminance output, target color temperature output, and / or adjustment range under illuminance variation constraints of the lighting control unit; audio control parameters are used to characterize the control parameters of the target volume, target audio content, and / or playback status of the sound control unit; environmental control parameters are used to characterize the adjustment control parameters of the environmental control unit for temperature, humidity, and / or air quality.

[0131] Among them, air quality control quantities include fresh air volume control quantities, air purification control quantities, and / or exhaust volume control quantities.

[0132] In this embodiment, the second environmental data and second illuminance data are first converted into environmental deviation and illuminance deviation quantities, respectively, based on the target setting information, to characterize the degree of deviation of the current state from the target setting information. Then, prescription control parameters are extracted based on the target treatment prescription, thus simultaneously introducing ward target setting constraints and prescription intervention constraints into the input side of the control decision. On this basis, the environmental deviation, illuminance deviation, second physiological data, fall data, and prescription control parameters are uniformly used as input variables for fuzzification processing, mapping various heterogeneous inputs from deterministic values ​​to comparable membership representations. Then, a preset set of fuzzy rules is used to perform fuzzy reasoning on the membership to obtain output fuzzy quantities. Through defuzzification, the output fuzzy quantities are converted into target control quantities that can be directly used for control execution. Therefore, it is possible to comprehensively integrate and link multi-source monitoring data, patient status, and prescription constraints within the same decision framework. This allows the generation of control quantities to reflect the degree of deviation of the environment and illuminance from the target setting, match changes in the patient's physiological state, and dynamically adjust in conjunction with safety status inputs, thereby improving the adaptability, real-time performance, and overall consistency of the smart ward control strategy.

[0133] In the above embodiments, the system needs to control the controlled units within the smart ward based on the target control amount. The following will describe in detail the specific process by which the system controls the controlled units within the smart ward based on the target control amount.

[0134] In one possible embodiment, the method steps shown in S105 can be implemented by S1051 to S1053, which are described in detail below.

[0135] S1051. Generate lighting control commands based on lighting control quantities, and send the lighting control commands to the lighting control unit to control the illuminance output and / or color temperature output of the lighting control unit.

[0136] Specifically, the system can determine the target illuminance, target color temperature, and / or corresponding adjustment range that the lighting control unit needs to achieve based on the lighting control parameters, and generate lighting control commands that conform to the communication protocol or control interface requirements of the lighting control unit. These lighting control commands include, but are not limited to: target illuminance parameters, target color temperature parameters, dimming / color adjustment control parameters, execution time or duration parameters, and device identification information for identifying the target lighting equipment or control channel. Subsequently, the system sends the lighting control commands to the lighting control unit via wired or wireless communication, enabling the lighting control unit to adjust the illuminance output and / or color temperature output according to the lighting control commands, thereby achieving control of the smart ward lighting environment.

[0137] S1052. Generate audio control commands based on audio control quantities, and send the audio control commands to the sound control unit to control the volume of the sound control unit and / or the playback status of the target audio content.

[0138] Specifically, the system can determine the target volume level, target audio content, and corresponding playback control strategy required by the sound control unit based on the audio control parameters, and generate audio control instructions that conform to the communication protocol or control interface requirements of the sound control unit. These audio control instructions include, but are not limited to: target volume parameters, audio content identification parameters (used to indicate the target audio content in the audio resource library), playback control parameters (e.g., play / pause / stop, loop / sequential / random playback mode control), and device identification information for identifying the target audio device or control channel. Subsequently, the system sends the audio control instructions to the sound control unit via wired or wireless communication, enabling the sound control unit to adjust the volume and control the playback status of the target audio content according to the audio control instructions, thereby realizing the execution of audio intervention in the smart ward.

[0139] S1053. Generate environmental control commands based on environmental control quantities and send the environmental control commands to the environmental control unit to control the temperature, humidity and / or air quality of the smart ward.

[0140] The environmental control unit includes a temperature and humidity control subunit and an air quality control subunit.

[0141] It should be noted that the temperature and humidity control subunit can be an air conditioner, and the air quality control subunit can be a fresh air system.

[0142] Specifically, the system determines the environmental adjustment target or adjustment range of the smart ward within the target control cycle based on environmental control quantities, and generates environmental control commands that conform to the communication protocol or control interface requirements of the environmental control unit. Environmental control commands may include, but are not limited to: temperature adjustment parameters, humidity adjustment parameters, air quality adjustment parameters (e.g., fresh air volume adjustment parameters, air purification adjustment parameters, and / or exhaust air adjustment parameters), adjustment level or mode parameters, execution time or duration parameters, and device identification information for identifying target environmental equipment or control channels. Subsequently, the system sends the environmental control commands to the environmental control unit via wired or wireless communication, enabling the temperature and / or humidity control subunit to adjust the temperature and / or humidity of the smart ward according to the environmental control commands, and enabling the air quality control subunit to adjust the air quality of the smart ward according to the environmental control commands, thereby achieving coordinated control of the environmental parameters of the smart ward.

[0143] In this embodiment, control commands matching the lighting control unit, sound control unit, and environmental control unit are generated based on the target control amount, and the control commands are sent to the corresponding controlled units to drive them to perform illuminance / color temperature adjustment, volume / audio content playback control, and temperature, humidity, and / or air quality adjustment. Therefore, the upstream control decision results can be transformed into executable device-level control actions, enabling the smart ward to form a closed loop for the linkage control of lighting, audio, and environmental parameters. This ensures the feasibility and timeliness of the control strategy at the execution level and supports real-time adjustment based on patient status and ward set goals.

[0144] In one possible embodiment, the smart ward management method may further include terminal data display and interaction, used to realize the visualization of ward management information and the prompting of abnormal events.

[0145] Specifically, a data display interface can be designed on the terminal side. After determining the first environmental data, the first illuminance data, and the first physiological data, the first environmental data, the first illuminance data, and the first physiological data can be displayed on the data display interface.

[0146] Furthermore, the system can record and archive the patient's primary physiological data according to a preset time dimension to form a daily statistical record of physiological data, and support the query and retrospection of physiological data records; at the same time, it can display the real-time values ​​or changing trends of the primary environmental data and primary illuminance data environmental parameters of the smart ward.

[0147] In this embodiment, when the first environmental data, the first illuminance data, and / or the first physiological data meet the preset abnormal conditions, a message prompt and / or an alarm pop-up can be output on the terminal side to prompt nursing staff to confirm and handle the abnormal situation, thereby realizing the visualized management of the smart ward and timely response to abnormal events.

[0148] Figure 3 This is a schematic diagram of the structure of a smart ward management system provided in an embodiment of this application. Figure 3 As shown, the smart ward management system 300 provided in this embodiment includes an acquisition module 301, a processing module 302, a first determination module 303, a second determination module 304, and a control module 305.

[0149] The acquisition module 301 is used to acquire the first environmental data and the first illuminance data in the smart ward, as well as the first physiological data and fall data of the patients in the smart ward.

[0150] The processing module 302 is used to filter and fuse the first environmental data, the first illuminance data, and the first physiological data respectively to obtain the second environmental data, the second illuminance data, and the second physiological data.

[0151] The first determining module 303 is used to determine a target treatment prescription based on the second physiological data; wherein the target treatment prescription is used to indicate the ward intervention plan for the patient, and the target treatment prescription includes a light therapy prescription and / or a music therapy prescription.

[0152] The second determining module 304 is used to determine the target control amount of the smart ward based on the target treatment prescription and the target setting information of the smart ward, as well as the second environmental data, the second illuminance data, the second physiological data and the fall data; wherein, the target setting information is used to indicate the setting range of the environmental parameters and the setting range of the illuminance parameters of the smart ward.

[0153] The control module 305 is used to control the controlled units in the smart ward based on the target adjustment amount. The controlled units include a lighting control unit, a sound control unit, and an environmental control unit.

[0154] It should be understood that the corresponding processes performed by each module have been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0155] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 400 provided in this embodiment includes a memory 401 and a processor 402.

[0156] The memory 401 can be a separate physical unit, connected to the processor 402 via a bus 403. Alternatively, the memory 401 and processor 402 can be integrated and implemented in hardware. The memory 401 stores program instructions, which the processor 402 calls to execute the operations performed by the smart ward management system in any of the above method embodiments.

[0157] Optionally, when some or all of the methods in the above embodiments are implemented by software, the electronic device 400 may also include only the processor 402. A memory 401 for storing programs is located outside the electronic device 400, and the processor 402 is connected to the memory via circuits / wires to read and execute the programs stored in the memory. The processor 402 may be a central processing unit (CPU), a network processor (NP), or a combination of a CPU and an NP. The processor 402 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0158] Memory 401 may include volatile memory, such as random-access memory (RAM); memory may also include non-volatile memory, such as flash memory, hard disk drive (HDD) or solid-state drive (SSD); memory may also include combinations of the above types of memory.

[0159] For example, this application provides a chip including: an interface circuit and a logic circuit. The interface circuit is used to receive signals from other chips outside the chip and transmit them to the logic circuit, or to send signals from the logic circuit to other chips outside the chip. The logic circuit is used to perform the operations performed by the smart ward management system in the above method embodiments.

[0160] For example, this application provides a computer-readable storage medium storing computer program instructions thereon, which are executed by the processor of an electronic device to cause the electronic device to perform the operations performed by the smart ward management system in the above method embodiments.

[0161] For example, this application provides a computer program product that, when run on an electronic device, causes the electronic device to perform the operations performed by the smart ward management system in the above method embodiments.

[0162] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart ward management method, characterized in that, The method includes: Acquire the first environmental data and first illuminance data within the smart ward, as well as the first physiological data and fall data of the patients within the smart ward; The first environmental data, the first illuminance data, and the first physiological data are filtered and fused respectively to obtain the second environmental data, the second illuminance data, and the second physiological data. Based on the second physiological data, a target treatment prescription is determined; wherein, the target treatment prescription is used to indicate a ward intervention plan for the patient, and the target treatment prescription includes a light therapy prescription and / or a music therapy prescription; Based on the target treatment prescription and the target setting information of the smart ward, as well as the second environmental data, the second illuminance data, the second physiological data and the fall data, the target control amount of the smart ward is determined; wherein, the target setting information is used to indicate the setting range of the environmental parameters and the setting range of the illuminance parameters of the smart ward; Based on the target control amount, the controlled units in the smart ward are controlled, and the controlled units include a lighting control unit, a sound control unit, and an environmental control unit.

2. The method according to claim 1, characterized in that, The first environmental data includes the temperature, humidity, noise, oxygen concentration, carbon dioxide concentration, PM2.5 concentration, and atmospheric pressure within the smart ward; The first illuminance data includes the ambient illuminance, rhythmic illuminance, and perceived illuminance within the smart ward; The first physiological data includes the patient's heart rate data, respiratory rate data, blood pressure data, blood oxygen saturation data, and sleep-related data; The fall data includes alarm determination results, which are used to indicate whether the patient is in a normal state or a preset abnormal state, and the preset abnormal state includes a fall state.

3. The method according to claim 1, characterized in that, The step of filtering and fusing the first environmental data, the first illuminance data, and the first physiological data to obtain second environmental data, second illuminance data, and second physiological data includes: The first environmental data, the first illuminance data, and the first physiological data are filtered to obtain filtered environmental data, filtered illuminance data, and filtered physiological data. The filtered environmental data and the filtered illuminance data are respectively subjected to weighted fusion processing to obtain the second environmental data and the second illuminance data; For the filtered physiological data, a state model and an observation model are constructed, and the physiological data is subjected to state estimation processing based on Kalman filtering to obtain the second physiological data.

4. The method according to claim 3, characterized in that, The filtered environmental data and the filtered illuminance data are respectively subjected to weighted fusion processing to obtain the second environmental data and the second illuminance data, including: Determine if the first parameter contains multi-channel acquired data; When there are multiple acquisition data for the first parameter, the multiple acquisition data for the first parameter are weighted and fused to obtain the second parameter corresponding to the first parameter; If there is one source of data for the first parameter, the first parameter is determined as the second parameter; wherein, the first parameter is any parameter in the filtered environmental data or the filtered illuminance data, and the second parameter is the parameter in the second environmental data or the second illuminance data that corresponds to the first parameter.

5. The method according to claim 1, characterized in that, The light therapy prescription includes information on the first treatment period, information on the first duration, information on the target illumination rhythm, information on the illumination color temperature setting, and / or information on the illumination change rate limitation. The target illumination rhythm information is used to indicate the segmented illuminance target value during the treatment period; the illumination change rate limit information is used to indicate the allowable range of illuminance change rate per unit time.

6. The method according to claim 1, characterized in that, The music therapy prescription includes second treatment period information, second duration information, target volume information, audio content identification information, audio playback mode information, and / or audio switching rule information; The audio content identification information is used to indicate the target audio content in the music category or audio resource library.

7. The method according to claim 1, characterized in that, The step of determining the target control level of the smart ward based on the target treatment prescription and the target setting information of the smart ward, as well as the second environmental data, the second illuminance data, the second physiological data, and the fall data, includes: Based on the target setting information and the second environmental data, the environmental deviation amount is determined; Based on the target setting information and the second illuminance data, the illuminance deviation is determined; Based on the target treatment prescription, determine the prescription control parameters; The environmental deviation, the illumination deviation, the second physiological data, the fall data, and the prescription control parameters are used as input variables, and the input variables are fuzzified to obtain the membership degree corresponding to the input variables. Fuzzy reasoning is performed on the membership degree based on a preset set of fuzzy rules to obtain an output fuzzy quantity; wherein, the set of fuzzy rules is related to the target treatment prescription; The output fuzzy quantity is defuzzified to obtain the target control quantity; wherein, the target control quantity includes lighting control quantity, audio control quantity and / or environmental control quantity, and the environmental control quantity includes temperature control quantity, humidity control quantity and / or air quality control quantity.

8. The method according to claim 7, characterized in that, The control of the controlled unit in the smart ward based on the target control amount includes: Lighting control commands are generated based on the lighting control quantities, and the lighting control commands are sent to the lighting control unit to control the illuminance output and / or color temperature output of the lighting control unit; Based on the audio control quantity, an audio control command is generated and sent to the sound control unit to control the volume of the sound control unit and / or the playback status of the target audio content; An environmental control command is generated based on the environmental control quantity, and the environmental control command is sent to the environmental control unit to control the temperature, humidity and / or air quality of the smart ward.

9. A smart ward management system, characterized in that, The system includes: The acquisition module is used to acquire the first environmental data and the first illuminance data in the smart ward, as well as the first physiological data and fall data of the patients in the smart ward. The processing module is used to filter and fuse the first environmental data, the first illuminance data, and the first physiological data respectively to obtain the second environmental data, the second illuminance data, and the second physiological data. A first determining module is configured to determine a target treatment prescription based on the second physiological data; wherein the target treatment prescription is used to indicate a ward intervention plan for the patient, and the target treatment prescription includes a light therapy prescription and / or a music therapy prescription; The second determining module is used to determine the target control amount of the smart ward based on the target treatment prescription and the target setting information of the smart ward, as well as the second environmental data, the second illuminance data, the second physiological data and the fall data; wherein, the target setting information is used to indicate the setting range of the environmental parameters and the setting range of the illuminance parameters of the smart ward. The control module is used to control the controlled units in the smart ward based on the target control amount. The controlled units include a lighting control unit, a sound control unit, and an environmental control unit.

10. An electronic device, characterized in that, include: Memory and at least one processor; The memory stores computer-executed instructions; The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the method as described in any one of claims 1 to 8.

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