Seat control method, system and apparatus

Abstract

This application discloses a seat control method, system, and device, relating to the field of device control technology. The method includes: acquiring user behavior and operation information on the seat, as well as sensor monitoring information of the seat; determining user state information based on the behavior and operation information and sensor monitoring information; generating a target control strategy for the seat based on the user state information; and controlling the seat to execute control actions corresponding to the target control strategy. Since the user state information is determined jointly based on both the user's operation behavior on the control device and the seat's sensor monitoring information, it accurately reflects the user's actual state during seat use. This allows the target control strategy generated based on the user state information to match the user's actual state, improving the accuracy of seat control.

Description

Technical Field

[0001] This application relates to the field of equipment control technology, and in particular to a seat control method, system and device. Background Technology

[0002] As people's work and life pace accelerates, the demand for comfortable and relaxing seating functions is constantly increasing. Smart chairs with functions such as massage, light adjustment, and music playback are widely used in offices, homes, and other settings. Smart chairs are usually equipped with corresponding control devices, allowing users to control various relaxation functions.

[0003] In related technologies, the massage mode, massage intensity, lighting effects, and music type of the chair are usually manually selected by the user through a control device. The chair operates according to the fixed mode selected by the user, and the control actions performed by the chair depend on the user's subjective choice, making it difficult to match the user's actual state during the use of the chair. Summary of the Invention

[0004] In view of this, one of the objectives of this application is to provide a seat control method, system and device that can match the actual state of the user during seat use, thereby improving the accuracy of seat control.

[0005] To achieve the above objectives, the technical solution of this application is implemented as follows: In a first aspect, embodiments of this application provide a seat control method, the method comprising: Acquire user behavior and operation information on the seat, as well as sensor monitoring information of the seat. The behavior and operation information is obtained based on the user's operation on the control device corresponding to the seat. Based on behavioral operation information and sensor monitoring information, determine the user's user status information; Generate a target control strategy for the seat based on the user's status information; Control the seat to perform control actions corresponding to the target control strategy.

[0006] In one possible implementation, user status information is determined based on behavioral operation information and sensor monitoring information, including: Obtain the user's historical baseline information; the historical baseline information is obtained based on the user's behavioral operation information and sensor monitoring information generated during the previous use of the seat, and is used to characterize the user's historical usage habits; Based on behavioral operation information and historical benchmark information, determine the first deviation information; The second deviation information is determined based on sensor monitoring information and historical benchmark information; The user status information is determined based on the first deviation information and the second deviation information.

[0007] In one possible implementation, the first deviation information is determined based on behavioral operation information and historical benchmark information, including: The operation events contained in the behavioral operation information are sequence encoded to obtain behavioral feature information; wherein, the behavioral feature information includes at least one of massage intensity adjustment information, music switching frequency and light adjustment frequency; The first deviation information is determined based on the behavioral characteristic information and the first benchmark information corresponding to the behavioral characteristic information in the historical benchmark information.

[0008] In one possible implementation, the second deviation information is determined based on sensor monitoring information and historical benchmark information, including: The sensor monitoring information is statistically processed according to the preset statistical window to obtain window statistical information; the window statistical information includes at least one of the following: continuous sitting time, pressure distribution offset, and number of sitting posture changes; The second deviation information is determined based on the window statistics and the second benchmark information corresponding to the window statistics in the historical benchmark information.

[0009] In one possible implementation, window statistics include continuous seating time and pressure distribution offset; Based on the window statistics and the second benchmark information corresponding to the window statistics in the historical benchmark information, the second deviation information is determined, including: If the continuous sitting time exceeds the baseline sitting time, a sedentary risk marker is generated; wherein, the second baseline information includes the baseline sitting time. If the pressure distribution offset is greater than the reference offset, a sitting posture offset marker is generated; wherein, the second reference information includes the reference offset. Secondary deviation information is determined based on sedentary risk markers and posture deviation markers.

[0010] In one possible implementation, a target control strategy for the seat is generated based on user state information, including: User state information is input into a pre-configured decision model, and the decision model outputs at least one candidate control strategy; wherein, the decision model is trained based on historical user state information and historical control strategies; Determine the target control strategy based on at least one candidate control strategy.

[0011] In one possible implementation, the decision model is also used to output the confidence level corresponding to the candidate control strategy; Determine the target control policy based on at least one candidate control policy, including: At least one candidate control strategy is verified using preset rules to obtain the verification results corresponding to each candidate control strategy. Based on the confidence level and verification results of each candidate control strategy, the target control strategy is determined from at least one candidate control strategy.

[0012] In one possible implementation, determining user status information based on first deviation information and second deviation information includes: Obtain the scene constraint information corresponding to the seat. The scene constraint information includes at least the usage period and the cumulative sitting time. Based on the scenario constraint information, determine the first weight of the first deviation information and the second weight of the second deviation information; The user status information is determined based on the first deviation information, the second deviation information, the first weight, and the second weight.

[0013] Secondly, embodiments of this application provide a seat control system, the system comprising: The acquisition module is used to acquire the user's behavior and operation information on the seat and the sensor monitoring information of the seat. The behavior and operation information is obtained based on the user's operation behavior on the control device corresponding to the seat. The determination module is used to determine the user's status information based on behavioral operation information and sensor monitoring information; The generation module is used to generate a target control strategy for the seat based on the user's status information. The control module is used to control the seat to perform control actions corresponding to the target control strategy.

[0014] Thirdly, embodiments of this application provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, it implements the method provided in the first aspect.

[0015] The seat control method provided in this application acquires user behavior information and seat sensor monitoring information. Based on these two information, it determines the user's state information, generates a target control strategy for the seat, and controls the seat to execute control actions corresponding to the target control strategy. Since the user state information is determined jointly by the user's operational behavior on the control device and the seat's sensor monitoring information, it accurately reflects the user's actual state during seat use. This allows the target control strategy generated based on the user state information to match the user's actual state, improving the accuracy of seat control. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. It should be understood that the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] The attached diagram is described below: Figure 1 This is a schematic diagram illustrating an application scenario of a seat control method provided in an embodiment of this application; Figure 2 A flowchart of a seat control method provided in an embodiment of this application; Figure 3 This is a schematic diagram of the functional modules of a seat control system provided in an embodiment of this application; Figure 4 This is a schematic diagram of an electronic hardware structure provided in an embodiment of this application.

[0018] Explanation of reference numerals in the attached figures: 10. Seats; 20. Control devices; 30. Electronic devices; 300. Seat control system; 310. Acquisition module; 320. Determination module; 330. Generation module; 340. Control module; 401. Processor; 402. Memory; 403. Communication interface; 410. Bus. Detailed Implementation

[0019] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0020] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0021] To facilitate a better understanding of the solutions in the embodiments of this application, the relevant technologies will be introduced first below.

[0022] In general, the control of seats, such as smart seats with relaxation functions, often operates according to a fixed mode manually selected by the user through the control device. The control actions performed by the seat depend on the user's subjective choice and are difficult to match the user's actual state during the use of the seat.

[0023] Based on this, embodiments of this application provide a seat control method, a seat control system, an electronic device, a computer-readable storage medium, and a computer program product. The seat control method will be described in detail below, starting with an introduction to possible application scenarios of the embodiments of this application.

[0024] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating an application scenario of a seat control method provided in an embodiment of this application. For example... Figure 1 As shown, this application scenario includes a seat 10, a control device 20, and an electronic device 30. The seat 10 contains seat end hardware, as well as a sensor module, a massage execution module, a lighting control module, and a music playback module connected to the seat end hardware.

[0025] The aforementioned sensor module can be used to collect sensing and monitoring information from the seat 10. In some embodiments, the sensor module includes at least one of a seat cushion pressure sensor, a backrest pressure sensor, a lumbar support contact sensor, a backrest angle sensor, a posture sensor, and a temperature and humidity sensor. In some embodiments, the sensor module may also include a heart rate sensor, a respiration sensor, or a skin conductance sensor; alternatively, information collected by an external wearable device in the sensing and monitoring information can be obtained through the control device 20.

[0026] The massage execution module described above can be used to perform massage actions under the control of the chair end firmware. In some embodiments, the massage execution module includes at least one of a lumbar massage unit, a back massage unit, a seat cushion massage unit, a neck and shoulder massage unit, an airbag massage unit, or a vibration massage unit.

[0027] The aforementioned lighting control module can be used to perform lighting adjustment actions under the control of the chair end firmware. In some embodiments, the lighting control module includes at least one of the following: light strips, dot matrix lights, or light-emitting diode groups disposed in the backrest, bottom of the seat cushion, armrests, headrest, or lumbar support area, which can adjust the color, brightness, color temperature, frequency of change, and duration according to the lighting strategy.

[0028] The music playback module described above can be used to perform music playback actions. In some embodiments, the music playback module can be a Bluetooth speaker installed on the seat 10, or a player of the control device 20.

[0029] The aforementioned chair-end firmware can be used to collect sensing and monitoring information from the sensor module, receive control commands sent by the control device 20, and control the massage execution module, lighting control module, and music playback module according to the control commands. In some embodiments, the chair-end firmware can also control the heating module, ventilation module, or lumbar support adjustment module of the seat 10.

[0030] The aforementioned control device 20 is a mobile terminal with a mobile application corresponding to the seat 10 installed, such as a smartphone or tablet. The control device 20 can be used to collect: user operation behavior on the mobile application, receive sensor monitoring information uploaded by the seat 10, upload the operation behavior information and sensor monitoring information to the electronic device 30, receive the target control strategy returned by the electronic device 30, issue control commands to the seat 10, and control music playback.

[0031] Communication between the control device 20 and the seat 10, as well as between the control device 20 and the electronic device 30, is achieved via a wireless network. Specific wireless network communication methods can be selected as needed; this embodiment does not impose any specific limitations.

[0032] The aforementioned electronic device 30 can be a physical server or a cloud server.

[0033] Please see below Figure 2 , Figure 2 This application provides a flowchart of a seat control method, which can be applied to... Figure 1 The electronic device 30 shown can also be applied to the seat control system in the following embodiments. For the sake of consistency, the following method embodiments will be described using the seat control method applied to the electronic device 30 as an example. The seat control method includes, but is not limited to, steps 210 to 240.

[0034] Step 210: Obtain the user's behavior and operation information on the seat and the sensor monitoring information of the seat. The behavior and operation information is obtained based on the user's operation behavior on the control device corresponding to the seat.

[0035] Step 220: Determine the user's status information based on the behavioral operation information and sensor monitoring information.

[0036] Step 230: Generate a target control strategy for the seat based on the user status information.

[0037] Step 240: Control the seat to perform control actions corresponding to the target control strategy.

[0038] The seat control method provided in this application acquires user behavior information and seat sensor monitoring information. Based on these two information, it determines the user's state information, generates a target control strategy for the seat, and controls the seat to execute control actions corresponding to the target control strategy. Since the user state information is determined jointly by the user's operational behavior on the control device and the seat's sensor monitoring information, it accurately reflects the user's actual state during seat use. This allows the target control strategy generated based on the user state information to match the user's actual state, improving the accuracy of seat control.

[0039] The following will discuss how Figure 2 The steps of the Chinese method are explained in detail.

[0040] In step 210: The aforementioned behavioral operation information reflects the user's operational behavior on the control device 20 corresponding to the seat, and can be used to reflect the user's subjective preferences and potential emotional changes. For example, the user's operational behavior on the control device 20 includes clicking, swiping, page switching, and adjusting various functions of the seat 10.

[0041] In some embodiments, behavioral operation information may include at least one of the following: records of user selection of massage mode, records of adjustment of massage intensity, records of turning massage on or off, records of selection of light color, records of adjustment of light brightness, records of switching music, records of pausing music, records of accepting a recommended program, and records of rejecting a recommended program.

[0042] In some embodiments, behavioral operation information is recorded in the form of operation events. Each operation performed by the user on the control device 20 is recorded as an operation event. The operation event includes at least the operation type, operation time, pre-operation parameters, post-operation parameters, and operation result.

[0043] For example, operation event 001 is when the user adjusts the massage intensity from level 2 to level 4. The corresponding operation type is massage intensity adjustment, the parameter before operation is level 2, the parameter after operation is level 4, and the operation result is adjustment successful. Operation event 002 is when the user switches the music 5 times consecutively within 3 minutes. Operation event 003 is when the user turns off the system-recommended lighting mode. Operation event 004 is when the user accepts the system-recommended lower back relaxation massage mode.

[0044] The aforementioned sensing and monitoring information can be collected by a sensor module installed on the seat 10 and can be used to reflect the user's body posture, prolonged sitting, and the physical state of the seat 10. In some embodiments, the sensing and monitoring information may include at least one of the following: whether the user is seated, duration of continuous sitting, seat pressure distribution, backrest angle, seat height, lumbar support position, headrest position, armrest position, massage motor operating status, lighting status, and number of seat posture changes.

[0045] In some embodiments, the sensing and monitoring information may further include temperature and humidity information of the environment in which the seat 10 is located. In other embodiments, the sensing and monitoring information may further include at least one of the user's heart rate information and respiratory information. The heart rate information and respiratory information may be collected by the heart rate sensor and respiratory sensor installed on the seat 10, or they may be collected by an external wearable device connected to the control device 20 and synchronized to the control device 20.

[0046] In one specific implementation of step 210, the seat end firmware of the seat 10 collects sensor monitoring information according to a preset collection cycle, and sends the sensor monitoring information to the control device 20 via Bluetooth; the control device 20 records the user's operation behavior on the mobile application terminal to obtain the behavior operation information, and uploads the behavior operation information and the received sensor monitoring information to the electronic device 30 via a wireless network, thereby obtaining the user's behavior operation information on the seat and the sensor monitoring information of the seat.

[0047] In some embodiments, the control device 20 can upload behavioral operation information and sensor monitoring information to the electronic device 30 in real time, or upload in batches according to a preset upload cycle, or upload only when the network status meets preset conditions. This embodiment does not make specific limitations.

[0048] In step 220: The aforementioned user status information can be understood as information characterizing the user's physical and mental state during the use of the seat 10. In some embodiments, the user status information may include at least one of fatigue state, sedentary state, irritability state, relaxation state, and focused state. In some embodiments, the user status information may also include a status score for characterizing the degree of different states in the above embodiments, such as at least one of fatigue score, sedentary score, irritability score, relaxation score, and focused score.

[0049] In some embodiments, when the sensor monitoring information indicates that the user's continuous sitting time is long and the number of sitting posture changes is reduced, and the behavior operation information indicates that the user frequently increases the massage intensity, the electronic device 30 can determine that the user's state information is fatigued. When the user's behavioral operation information indicates that the user frequently switches music, frequently adjusts the lights, and frequently rejects recommended options, the electronic device 30 can determine that the user's state information indicates an agitated state.

[0050] In step 230: The aforementioned target control strategy is a strategy used to instruct the seat 10 to perform corresponding control actions. In some embodiments, the target control strategy may include at least one of a massage strategy, a lighting strategy, a music strategy, and a health reminder strategy.

[0051] In some embodiments, the massage strategy may include at least one of massage mode, massage intensity, and massage duration. The massage mode may include, for example, lower back relaxation, shoulder and neck relaxation, or full-body relaxation; the massage intensity may be, for example, level 1 to level 5.

[0052] In some embodiments, a lighting strategy may include at least one of light color, light brightness, light color temperature, lighting effects, and duration. The light color may be, for example, a warm color, and the lighting effects may be, for example, constant light, breathing light, or gradient light.

[0053] In some embodiments, a music strategy may include at least one of music genre, volume, and playback duration. Music genre may include, for example, soothing light music.

[0054] In some embodiments, health reminder strategies may include reminder content, such as suggesting relaxation of the lower back or recommending short periods of activity after a massage.

[0055] In some embodiments, the target control strategy is used to instruct at least two of the following to be performed in conjunction: massage, lighting adjustment, and music playback. For example, the target control strategy may instruct that while performing a back relaxation massage, the lighting is adjusted to a warm-colored, low-brightness breathing light, and soothing music is played, thereby achieving multimodal coordinated relaxation control.

[0056] In step 240, after generating the target control strategy, the electronic device 30 can return the target control strategy to the control device 20, and use the control device 20 to convert the target control strategy into a control command that the seat 10 can recognize, and continue to use the control device 20 to send the control command to the seat 10 via Bluetooth or other forms of wireless network.

[0057] The seat end firmware of seat 10 can control the massage execution module to perform corresponding massage actions and control the light control module to perform corresponding light adjustment actions according to control commands. In some embodiments, music playback can be performed by the player of control device 20 or by the Bluetooth speaker of seat 10. Health prompts can be displayed through the display interface of control device 20 or broadcast via voice.

[0058] For example, the control actions corresponding to the target control strategy may include: starting a lumbar and back relaxation massage, setting the massage intensity to level 3, setting the massage duration to 15 minutes, setting the light to a warm color, setting the light brightness to 40%, setting the light effect to a breathing light, playing soothing light music, and displaying at least one of the following: a sedentary reminder or a relaxation prompt.

[0059] Through steps 210 to 240 above, the control actions performed by the seat 10 no longer depend solely on the fixed mode manually selected by the user, but are executed according to the target control strategy generated based on the user state information determined by combining behavioral operation information and sensor monitoring information. This enables the control actions of the seat 10 to match the actual state of the user during the use of the seat 10, thereby improving the accuracy of controlling the seat 10.

[0060] In one possible implementation, user status information is determined based on behavioral operation information and sensor monitoring information, including: Obtain the user's historical baseline information; the historical baseline information is obtained based on the user's behavioral operation information and sensor monitoring information generated during the previous use of the seat, and is used to characterize the user's historical usage habits; Based on behavioral operation information and historical benchmark information, determine the first deviation information; The second deviation information is determined based on sensor monitoring information and historical benchmark information; The user status information is determined based on the first deviation information and the second deviation information.

[0061] This embodiment introduces historical benchmark information to determine the first deviation information of behavioral operation information relative to the historical benchmark information, and the second deviation information of sensor monitoring information relative to the historical benchmark information. Then, based on the first deviation information and the second deviation information, the user status information is determined. This can identify whether the user's real-time performance deviates from their personal historical usage habits, avoid misjudgment caused by using a uniform fixed threshold to judge the user status, and thus improve the accuracy of determining the user status information.

[0062] Specifically, the electronic device 30 can perform statistical learning based on the behavioral operation information and sensor monitoring information reported by the user during the use of the seat 10 within a preset period of time in the past, and generate historical benchmark information corresponding to the user, such as the preset period of time in the past, like 30 days or the past 90 days.

[0063] In some embodiments, historical baseline information may include at least one of the following: user's commonly used massage modes, average massage intensity, commonly used light colors, commonly used light brightness, commonly used music types, average continuous sitting time, average number of sitting posture changes, average music switching frequency, historical recommendation acceptance rate, and historical fatigue state judgment results at different time periods.

[0064] The aforementioned historical baseline information can be stored in electronic device 30. After receiving behavioral operation information and sensor monitoring information, electronic device 30 reads the historical baseline information of the corresponding user. In some embodiments, the historical baseline information can also be synchronized by electronic device 30 to control device 20 for storage, so that control device 20 can use it locally.

[0065] In some embodiments, when the user is a new user and the data generated during the previous use of seat 10 is insufficient, the electronic device 30 may use the default baseline information as the historical baseline information corresponding to the user, and gradually update the historical baseline information during the user's subsequent use of seat 10.

[0066] The aforementioned first deviation information can be used to characterize the degree to which the behavioral operation information deviates from the user's historical usage habits. For example, the electronic device 30 can determine massage intensity deviation as the first deviation information, where the massage intensity deviation is the difference between the massage intensity in the behavioral operation information and the commonly used massage intensity in the historical reference information. The electronic device 30 can also determine music switching frequency deviation as the first deviation information, where the music switching frequency deviation is the difference between the music switching frequency in the behavioral operation information and the average music switching frequency in the historical reference information. The electronic device 30 can also determine light brightness deviation as the first deviation information, where the light brightness deviation is the difference between the light brightness in the behavioral operation information and the commonly used light brightness in the historical reference information.

[0067] The aforementioned second deviation information can be used to characterize the degree to which the sensor monitoring information deviates from the user's historical usage habits. For example, the electronic device 30 can determine the continuous seating duration deviation as the second deviation information, where the continuous seating duration deviation is the difference between the continuous seating duration in the sensor monitoring information and the average continuous seating duration in the historical benchmark information.

[0068] In some embodiments, the electronic device 30 may perform rule-based judgment, weighted scoring, or model-based judgment on the first deviation information and the second deviation information to determine the user status information.

[0069] For example, if the first deviation information indicates a positive and significant deviation in massage intensity (e.g., the user adjusts the massage intensity from the commonly used level 2 to level 5), and the second deviation information indicates a significantly longer continuous sitting time than the historical average continuous sitting time, the electronic device 30 can determine that the user's state information indicates fatigue. It should be noted that if another user's historically commonly used massage intensity is level 5, then a massage intensity of level 5 for that user does not necessarily indicate that the user is fatigued. In this embodiment, by comparing deviations based on historical benchmark information, misjudgments caused by using a uniform fixed threshold for different users can be avoided.

[0070] Considering that behavioral operation information and sensor monitoring information are generated at different frequencies, in some embodiments, the electronic device 30 may perform time alignment processing on the behavioral operation information and sensor monitoring information before determining the user status information.

[0071] In one possible implementation, the first deviation information is determined based on behavioral operation information and historical benchmark information, including: The operation events contained in the behavioral operation information are sequence encoded to obtain behavioral feature information; wherein, the behavioral feature information includes at least one of massage intensity adjustment information, music switching frequency and light adjustment frequency; The first deviation information is determined based on the behavioral characteristic information and the first benchmark information corresponding to the behavioral characteristic information in the historical benchmark information.

[0072] This embodiment uses sequence encoding to transform discrete operation events into behavioral feature information, enabling electronic devices to recognize the state changes implied in the user's subjective adjustment behavior. By comparing the information with the first reference information, the first deviation information is determined, thereby improving the accuracy and reliability of the determined first deviation information.

[0073] In some embodiments, the behavioral characteristic information includes at least one of massage intensity adjustment information, music switching frequency, and light adjustment frequency. The massage intensity adjustment information may include at least one of the number of times the massage intensity was adjusted, the adjustment direction, and the adjustment amplitude within the statistical period; the music switching frequency refers to the number of times the user switched music within the statistical period; and the light adjustment frequency refers to the number of times the user adjusted the brightness or color of the lights within the statistical period.

[0074] For example, after sequence encoding, a user's frequent music switching events within a short period of time can be transformed into behavioral characteristic information representing low music matching degree or increased user irritability.

[0075] For example, a user's action of continuously increasing the massage intensity can be converted into behavioral characteristic information representing muscle fatigue or increased massage demand.

[0076] For example, a user's action of turning off the recommended lighting mode can be transformed into behavioral characteristic information that indicates that the lighting strategy does not conform to the user's preferences.

[0077] The aforementioned first reference information is a portion of the historical reference information corresponding to the behavioral characteristic information. In some embodiments, the first reference information may include at least one of historically commonly used massage intensity, historically average music switching frequency, and historically average light adjustment frequency.

[0078] In some embodiments, the electronic device 30 may determine first deviation information based on the comparison result between behavioral feature information and first reference information.

[0079] For example, if a user increases the massage intensity more than twice within 10 minutes, or the massage intensity is more than two levels higher than the historically commonly used massage intensity, the electronic device 30 generates a fatigue enhancement flag. If a user switches music more than five times within 15 minutes, and this number is more than twice the historical average number of music switches, the electronic device 30 generates a music mismatch flag. If a user adjusts the brightness or color of the lights more than three times within 10 minutes, or the user turns off the recommended lights more than twice consecutively, the electronic device 30 generates a light mismatch flag. The first deviation information may include at least one of the fatigue enhancement flag, the music mismatch flag, and the light mismatch flag.

[0080] In one possible implementation, the second deviation information is determined based on sensor monitoring information and historical benchmark information, including: The sensor monitoring information is statistically processed according to the preset statistical window to obtain window statistical information; the window statistical information includes at least one of the following: continuous sitting time, pressure distribution offset, and number of sitting posture changes; The second deviation information is determined based on the window statistics and the second benchmark information corresponding to the window statistics in the historical benchmark information.

[0081] In this embodiment, the preset statistical window is a pre-set time statistical unit, such as 5 minutes, 15 minutes, or 30 minutes, which can be configured according to the model of the seat 10, the user's age, or the usage scenario. Since the sensor monitoring information is usually continuously sampled or periodically reported, using the preset statistical window for statistical processing can reduce the interference of sensor sampling noise on the judgment of user status information, thereby improving the stability and reliability of the determined second deviation information.

[0082] In some embodiments, the window statistics include at least one of continuous sitting time, pressure distribution offset, and number of posture changes. Continuous sitting time refers to the duration during which the user remains seated after sitting down; pressure distribution offset characterizes the degree to which the seat pressure distribution deviates from an equilibrium state; and the number of posture changes refers to the number of times the user adjusts their posture within a preset statistical window.

[0083] In some embodiments, the window statistics may also include at least one of the following: average pressure distribution, backrest angle variation, and sitting posture variation frequency.

[0084] The aforementioned pressure distribution offset can be calculated using the following formula: P_offset = |P_left - P_right| / P_total; in: P_offset represents the pressure distribution offset; P_left represents the total pressure value on the left side; P_right represents the total pressure value on the right side; P_total represents the total seat pressure.

[0085] When the pressure distribution offset continuously exceeds the preset threshold, the electronic device 30 can determine that the user has a risk of posture deviation; when the frequency of posture changes decreases and the continuous sitting time increases, the electronic device 30 can determine that the user is fatigued or in a prolonged sitting state.

[0086] The aforementioned second baseline information is a portion of the historical baseline information corresponding to the window statistics. In some embodiments, the second baseline information may include at least one of the following: historical average continuous sitting time, historical average number of sitting posture changes, and historical pressure distribution baseline range.

[0087] The electronic device 30 can determine the second deviation information based on a comparison between window statistics and second reference information. For example, the second deviation information may include at least one of continuous sitting time deviation, number of sitting posture changes deviation, and pressure distribution offset deviation.

[0088] In one possible implementation, window statistics include continuous seating time and pressure distribution offset; Based on the window statistics and the second benchmark information corresponding to the window statistics in the historical benchmark information, the second deviation information is determined, including: If the continuous sitting time exceeds the baseline sitting time, a sedentary risk marker is generated; wherein, the second baseline information includes the baseline sitting time. If the pressure distribution offset is greater than the reference offset, a sitting posture offset marker is generated; wherein, the second reference information includes the reference offset. Secondary deviation information is determined based on sedentary risk markers and posture deviation markers.

[0089] The aforementioned second benchmark information includes benchmark sitting time and benchmark offset. Benchmark sitting time is a benchmark for the sitting time used to determine the risk of prolonged sitting. Benchmark sitting time can be the historical average continuous sitting time or a preset fixed time.

[0090] In some embodiments, the baseline seating duration includes a first baseline seating duration T_1 and a second baseline seating duration T_2. When the continuous seating duration is greater than T_1, the electronic device 30 generates a Level 1 sedentary risk marker; when the continuous seating duration is greater than T_2, the electronic device 30 generates a Level 2 sedentary risk marker. For example, T_1 is 60 minutes and T_2 is 90 minutes.

[0091] In some embodiments, the reference offset includes a first reference offset delta_P1 and a second reference offset delta_P2. When the pressure distribution offset is greater than delta_P1 and continues to exceed t1, the electronic device 30 generates a sitting posture offset marker; when the pressure distribution offset is greater than delta_P2 and continues to exceed t2, the electronic device 30 generates a significant sitting posture offset marker. For example, delta_P1 is set to 20%, t1 to 3 minutes, delta_P2 to 35%, and t2 to 1 minute.

[0092] In some embodiments, the electronic device 30 determines second deviation information based on sedentary risk markers and posture deviation markers, which may include: directly determining the sedentary risk markers and posture deviation markers as the second deviation information. It may also include: quantifying the sedentary risk markers and posture deviation markers, for example, a first-level sedentary risk marker corresponding to a first deviation score S1 and a second-level sedentary risk marker corresponding to a second deviation score S2, and determining the quantized deviation score as the second deviation information.

[0093] In one possible implementation, a target control strategy for the seat is generated based on user state information, including: User state information is input into a pre-configured decision model, and the decision model outputs at least one candidate control strategy; wherein, the decision model is trained based on historical user state information and historical control strategies; Determine the target control strategy based on at least one candidate control strategy.

[0094] This embodiment inputs preprocessed structured user state information into a decision model, uses the decision model to generate candidate control strategies through comprehensive analysis of multi-dimensional information, and then determines the target control strategy accordingly, which can further improve the matching degree between the target control strategy and the user state.

[0095] The aforementioned decision-making model can be a pre-configured model on the electronic device 30, such as a large language model. The decision-making model is trained based on historical user state information and historical control strategies. The historical user state information includes the state information of multiple previously recorded user samples, and the historical control strategies include the control strategies executed corresponding to the state information of each user sample. The electronic device 30 trains the initial model using the historical user state information as training input and the historical control strategies corresponding to the historical user state information as supervision information to obtain the decision-making model.

[0096] It should be noted that the control device 20 does not directly run the decision model. The control device 20 is only responsible for collecting information, uploading information, displaying recommendation results, and issuing control commands. The analysis and judgment of the decision model are all completed by the electronic device 30, thereby avoiding the consumption of computing power caused by running the decision model on the control device 20.

[0097] The input to the decision model is not unprocessed raw information, but structured data that has undergone preprocessing, time alignment, bias calculation, weighted scoring, and historical comparison. Electronic device 30 combines user status information, historical bias characteristics, status scores, and scenario constraints into structured data in a fixed format and inputs it into the decision model. This avoids the decision model directly dealing with messy data and improves the stability and interpretability of the output results.

[0098] Electronic device 30 can utilize a decision model to first perform state semantic judgment based on the input structured data. The judgment content includes at least: whether the user has a tendency to fatigue, whether the user is at risk of prolonged sitting, whether the user has a tendency to be irritable or uncomfortable, which control strategy should lean towards—relaxation, alertness, rest, or reminder—and the main basis for the state judgment. Then, the decision model generates candidate control strategies. Candidate control strategies include at least one of the following: candidate massage mode, candidate massage intensity, candidate massage duration, candidate light color, candidate light brightness, candidate light effect, candidate music type, candidate health prompt content, and recommendation reason.

[0099] For example, the output of the decision model could be: the user's state is fatigued and accompanied by mild irritability, judged by the following criteria: the user's continuous sitting time is significantly higher than the historical average, the number of times the sitting posture changes is reduced, the number of times the massage intensity is adjusted is increased, and the number of times the music is switched is higher than the historical level; the massage strategy is a back relaxation mode, intensity level 3, duration 15 minutes; the lighting strategy is warm color, brightness 40%, breathing effect; the music strategy is soothing light music, volume 35, playback duration 15 minutes; the health suggestion is to relax the back and waist and to get up and move around briefly after the massage.

[0100] When the decision model outputs a candidate control strategy, the electronic device 30 can determine the candidate control strategy as the target control strategy; when the decision model outputs multiple candidate control strategies, the electronic device 30 can determine the target control strategy from the multiple candidate control strategies according to preset selection conditions.

[0101] In one possible implementation, the decision model is also used to output the confidence level corresponding to the candidate control strategy; Determine the target control policy based on at least one candidate control policy, including: At least one candidate control strategy is verified using preset rules to obtain the verification results corresponding to each candidate control strategy. Based on the confidence level and verification results of each candidate control strategy, the target control strategy is determined from at least one candidate control strategy.

[0102] In this embodiment, the output of the decision model is not directly used to control the seat 10. Instead, it is first verified by preset rules and then the target control strategy is determined by combining the confidence level. This avoids the safety risks caused by the model output directly controlling the seat 10, and takes into account both the intelligence of strategy generation and the safety and reliability of control execution.

[0103] The confidence level mentioned above can be used to characterize the degree of trustworthiness of the decision model in its output candidate control strategies. The confidence level ranges from 0 to 1, for example.

[0104] The aforementioned preset rules can be used to verify the safety, scene adaptability, and device support capabilities of candidate control strategies. In some embodiments, the preset rules may include at least one of the following: whether the massage intensity exceeds a safety limit, whether the massage duration exceeds a preset limit, whether the light brightness meets the time limit for use, whether the music volume exceeds the user's historical acceptable range, whether the candidate massage mode is supported by the seat 10, whether the candidate lighting effect is supported by the seat 10, and whether the user has recently rejected similar recommendations multiple times.

[0105] The verification results include verification passed and verification failed. In some embodiments, for candidate control strategies that fail verification, the electronic device 30 can correct them and then re-verify them. For example, when the light brightness in the candidate control strategy is 70%, while the upper limit of light brightness at night is 50%, the electronic device 30 corrects the light brightness to below 50%; when the candidate massage mode is not supported by the seat 10, the electronic device 30 replaces the candidate massage mode with a similar massage mode supported by the seat 10; when the candidate lighting effect is not supported by the seat 10, the electronic device 30 replaces the candidate lighting effect with a similar lighting effect.

[0106] In some embodiments, the electronic device 30 may determine the candidate control strategy that has passed verification and has the highest confidence level as the target control strategy.

[0107] In some embodiments, if the confidence level of a candidate control strategy is greater than or equal to a preset confidence threshold, the electronic device 30 adopts the candidate control strategy; if the confidence level of a candidate control strategy is less than the preset confidence threshold, the electronic device 30 does not adopt the candidate control strategy output by the decision model, but can determine the default control strategy or conservative recommended strategy in the preset rule base as the target control strategy.

[0108] In one possible implementation, determining user status information based on first deviation information and second deviation information includes: Obtain the scene constraint information corresponding to the seat. The scene constraint information includes at least the usage period and the cumulative sitting time. Based on the scenario constraint information, determine the first weight of the first deviation information and the second weight of the second deviation information; The user status information is determined based on the first deviation information, the second deviation information, the first weight, and the second weight.

[0109] This embodiment dynamically adjusts the weights of the first deviation information and the second deviation information based on the scenario constraint information, and then performs weighted fusion to determine the user status information. This allows the determination of user status information to be adapted to different usage scenarios and avoids overall judgment errors caused by the absence or abnormality of a certain type of information, thereby improving the reliability of determining user status information.

[0110] The aforementioned scenario constraint information can be used to characterize the usage scenarios of the seat 10. In some embodiments, the scenario constraint information includes at least the usage period and the cumulative seating duration, and may also include at least one of the following: cumulative seating duration for the day, whether it is used at night, whether it is during lunch break, device operating status, network status, and available seat functions. The usage period includes, for example, morning, afternoon, night, and lunch break; the cumulative seating duration is a different parameter from the continuous seating duration in the aforementioned window statistics information. The cumulative seating duration is used for the total seating duration for the entire day, while the continuous seating duration is used for the duration of a single seating session.

[0111] The electronic device 30 determines the first weight w1 of the first deviation information and the second weight w2 of the second deviation information based on the scene constraint information.

[0112] For example, in the initial state, the first weight w1 and the second weight w2 can each be a preset initial value; When the cumulative sitting time exceeds the preset time, the electronic device 30 increases the second weight w2 to strengthen the influence of sedentary-related sensor monitoring information on the state judgment. When the usage period is during intense scenarios such as nighttime or lunch break, the electronic device 30 increases the influence of scenario constraint information on state judgment and adjusts the first weight w1 and the second weight w2 accordingly. When sensor monitoring information is abnormal or missing, electronic device 30 reduces the second weight w2 and correspondingly increases the first weight w1. When a user has few recent actions resulting in insufficient action information, the electronic device 30 reduces the first weight w1 and correspondingly increases the second weight w2. When the user is a new user and historical benchmark information is insufficient, the electronic device 30 reduces the impact of the portion of the first deviation information and the second deviation information compared with the historical benchmark information on the state judgment.

[0113] In some embodiments, the electronic device 30 performs weighted fusion of the first deviation information and the second deviation information to obtain a status score, and determines the user's status information based on the status score. The status score may include at least one of fatigue score, sedentary score, irritability score, relaxation score, focus score, and recommendation matching score.

[0114] For example, the fatigue score S_fatigue can be calculated using the following formula: S_fatigue = S_duration × α1 + S_posture × α2 + S_massage × α3 + S_deviation × α4 + S_scene × α5; in: S_fatigue represents the fatigue score; S_duration represents the rating for continuous sitting time; S_posture represents the score for the decrease in sitting posture changes; S_massage represents the increased massage intensity rating; S_deviation represents the historical deviation score; S_scene represents the scene correction score; α1, α2, α3, α4 and α5 represent the weight coefficients corresponding to each score.

[0115] For example, α1 is 0.25, α2 is 0.20, α3 is 0.25, α4 is 0.20, and α5 is 0.10.

[0116] For example, the irritability score S_irritation can be calculated using the following formula: S_irritation = S_music × β1 + S_reject × β2 + S_light × β3 + S_habit × β4 + S_scene' × β5; in: S_irritation represents the irritability rating; S_music indicates a rating for frequent music switching; S_reject indicates that the recommended solution is rejected. S_light represents the score for frequent lighting adjustments; S_habit represents the score for behavioral deviation from historical habits; S_scene' represents the scene correction score; β1, β2, β3, β4 and β5 represent the weight coefficients corresponding to each score.

[0117] For example, β1 is 0.30, β2 is 0.25, β3 is 0.20, β4 is 0.15, and β5 is 0.10.

[0118] The scores for each item can be obtained by mapping the labels or deviation scores contained in the first deviation information and the second deviation information.

[0119] In some embodiments, the electronic device 30 also constrains the target control strategy based on scene constraint information. For example, in a nighttime scene, the upper limit of light brightness is limited; in a lunch break scene, low-intensity massage and soothing music are given priority; when the continuous sitting time exceeds a preset time, the weight of the sedentary reminder is increased; when the seat 10 does not support a certain lighting effect, it is automatically replaced with a similar lighting effect.

[0120] It should be noted that the various embodiments described in this application can be combined with each other or implemented individually without conflict, and this application does not limit this.

[0121] To facilitate a better understanding of the solutions in the embodiments of this application, the seat control methods provided in the above embodiments will be described in general with reference to a specific application example below.

[0122] User A sits on seat 10 at 20:30, and control device 20 establishes a Bluetooth connection with seat 10. Seat 10 begins to periodically transmit sensor monitoring information such as seat status, pressure distribution, backrest angle, massage status, and lighting status to control device 20. Control device 20 simultaneously records user A's operation behavior in the mobile application and uploads the operation information and sensor monitoring information to electronic device 30.

[0123] Within the preset statistics window of the most recent 15 minutes, the electronic device 30 obtained the following: continuous sitting time of 92 minutes, cumulative sitting time of 410 minutes on the same day, number of sitting posture changes 2 times, pressure distribution offset of 24%, number of massage intensity adjustments 4 times, massage intensity level 4, number of music switches 7 times, number of light brightness adjustments 3 times, and the usage period was at night.

[0124] Electronic device 30 queries the historical baseline information corresponding to user A and obtains: historical average continuous sitting time of 55 minutes, historical average number of sitting posture changes of 6 times / 15 minutes, historical commonly used massage intensity of 2 levels, historical average number of music changes of 2 times / 15 minutes, historical commonly used massage mode of waist and back relaxation, historical commonly used lighting mode of warm color and low brightness, and historical commonly used music type of light music.

[0125] The electronic device 30 first performs window statistical processing on the sensor monitoring information. Because the continuous sitting time of 92 minutes exceeds 90 minutes, a secondary sedentary risk marker is generated; because the pressure distribution offset of 24% is greater than 20% and lasts for more than 3 minutes, a posture offset marker is generated; because the number of posture changes is lower than the historical average, a posture change reduction marker is generated. Then, the electronic device 30 performs sequence encoding processing on the behavioral operation information. User A changed music 7 times within 15 minutes, more than 5 times and more than twice the historical average number of music changes, generating a music mismatch marker; User A adjusted the massage intensity from the historically used level 2 to level 4, generating a fatigue enhancement marker.

[0126] Subsequently, the electronic device 30 determines the weights based on the scenario constraint information and performs fusion calculations, obtaining a fatigue score of 82, a sedentary score of 78, an irritability score of 64, a relaxation score of 35, and a recommendation matching score of 58. The electronic device 30 inputs the above structured data into the decision model, and the decision model outputs that the user's state is fatigued with mild irritability, with a confidence level of 0.84, and generates candidate control strategies: lower back relaxation massage, massage intensity level 3, massage duration 15 minutes, warm-colored breathing light, light brightness 40%, and soothing soft music.

[0127] Electronic device 30 verifies candidate control strategies using preset rules. The light brightness of 40% does not exceed the nighttime light brightness limit of 50%, so the verification passes; the massage intensity level 3 and massage duration of 15 minutes are both within safe ranges, so the verification passes; the seat 10 supports lumbar and back relaxation massage and a breathing light effect, so the verification passes; the confidence level of 0.84 is greater than the preset confidence threshold of 0.75. Electronic device 30 determines this candidate control strategy as the target control strategy and returns it to control device 20. The target control strategy includes: massage mode is lumbar and back relaxation, massage intensity is level 3, massage duration is 15 minutes, light color is warm, light brightness is 40%, lighting effect is breathing, music type is soothing light music, and the health reminder is to suggest briefly getting up and moving around after the massage.

[0128] The control device 20 converts the target control strategy into control commands that the seat 10 can recognize, and sends them to the seat 10 via BLE Bluetooth. The seat end firmware of the seat 10 executes the corresponding massage and lighting controls according to the control commands, while the control device 20 plays soothing music and displays health tips.

[0129] If user A accepts the recommended plan and does not manually turn off the massage or reduce the massage intensity after the massage ends, electronic device 30 increases the recommendation weight of back relaxation massage, warm-colored low-brightness lighting, and soothing light music in nighttime fatigue scenarios; if user A manually turns off the lights or changes the music, electronic device 30 records the feedback and reduces the recommendation priority of the corresponding lighting strategy or music strategy in subsequent similar scenarios.

[0130] In some embodiments, after the control seat 10 executes a control action corresponding to the target control strategy, the electronic device 30 also acquires user feedback data on the control action and updates historical baseline information based on the feedback data for subsequent use in determining user status information and generating the target control strategy. The feedback data is obtained based on the user's actions after the control action is executed, and may include whether the user accepts the recommended plan, modifies the massage intensity, turns off the massage prematurely, modifies the light color or brightness, switches music, turns off health prompts, or repeatedly selects at least one of a certain type of plan.

[0131] For example, if a user repeatedly accepts warm-colored, low-brightness lighting at night, the electronic device 30 increases the recommendation weight of warm-colored lighting when the user uses the seat 10 at night; if the user repeatedly lowers the massage intensity from level 3 to level 2, the electronic device 30 decreases the user's default massage intensity; if the user repeatedly rejects a certain type of music, the electronic device 30 decreases the recommendation priority of that music type; if the user gets up after a sedentary reminder, the electronic device 30 increases the effectiveness score of the sedentary reminder strategy. In some embodiments, when a user accepts a certain type of recommendation scheme N times consecutively, the electronic device 30 increases the recommendation weight of that type of recommendation scheme, N for example, 3; when the user rejects a certain type of recommendation scheme N times consecutively, the electronic device 30 decreases the recommendation weight of that type of recommendation scheme, N for example, 2.

[0132] Through the aforementioned feedback loop, historical benchmark information and target control strategies can be dynamically updated as user habits change, making the subsequently generated target control strategies more in line with the user's actual habits.

[0133] In some embodiments, the seat control method provided in this application is applied to the control device 20. In the event of network anomalies, unavailability of the electronic device 30, or unauthorized data upload by the user, the control device 20 can use the most recently synchronized historical reference information and simplified model from the electronic device 30 to determine the state, generating only low-risk control strategies, such as light massage, low-brightness lighting, or local music playback, without performing strong stimulation massage or high-frequency light changes. After the network is restored, the control device 20 synchronizes the local execution records and feedback data to the electronic device 30, which then performs supplementary analysis and model updates. Therefore, even when the electronic device 30 is unavailable, the normal operation of basic relaxation functions can still be guaranteed, and data consistency can be ensured.

[0134] The seat control method provided in this application embodiment can be applied to various smart seat products such as office chairs, gaming chairs, massage chairs, study chairs, and health care chairs, and is not limited here.

[0135] Corresponding to the above method embodiments, this application also provides a seat control system, please refer to... Figure 3 , Figure 3 This is a functional module diagram of a seat control system 300 provided in an embodiment of this application. The seat control system 300 includes: The acquisition module 310 is used to acquire the user's behavior and operation information on the seat and the sensor monitoring information of the seat. The behavior and operation information is obtained based on the user's operation behavior on the control device corresponding to the seat. The determination module 320 is used to determine the user's status information based on behavioral operation information and sensor monitoring information; The generation module 330 is used to generate a target control strategy for the seat based on the user status information; The control module 340 is used to control the seat to perform control actions corresponding to the target control strategy.

[0136] The seat control system provided in this application embodiment can achieve the following: Figure 1 The various processes implemented in the Chinese method embodiments can achieve similar or the same technical effects, and will not be described again here to avoid repetition.

[0137] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant regulations. The acquisition, storage, use and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations.

[0138] In addition to the methods and systems provided in the above embodiments, this application also provides an electronic device. Please see [link to documentation]. Figure 4 , Figure 4 This is a schematic diagram of an electronic hardware structure provided in an embodiment of this application.

[0139] like Figure 4 The electronic device 30 shown may include a processor 401 and a memory 402 storing computer program instructions.

[0140] Specifically, the processor 401 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0141] Memory 402 may include mass storage for data or instructions. For example, and not limitingly, memory 402 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 402 may include removable or non-removable (or fixed) media. Where appropriate, memory 402 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 402 is non-volatile solid-state memory.

[0142] In some embodiments, memory 402 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, generally, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described in the methods provided according to embodiments of this application.

[0143] The processor 401 implements the method provided in the above embodiments by reading and executing computer program instructions stored in the memory 402.

[0144] In one example, the electronic device 30 may also include a communication interface 403 and a bus 410. The processor 401, memory 402, and communication interface 403 are connected via the bus 410 and communicate with each other.

[0145] The communication interface 403 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0146] Bus 410 includes hardware, software, or both, that couples components of electronic device 30 together. For example, and not limitingly, the bus may include Accelerated Graphics Port (AGP) or other graphics buses, Enhanced Industry Standard Architecture (EISA) buses, Front Side Bus (FSB), HyperTransport (HT) interconnects, Industry Standard Architecture (ISA) buses, Infinite Bandwidth Interconnects, Low Pin Count (LPC) buses, memory buses, Microchannel Architecture (MCA) buses, Peripheral Component Interconnect (PCI) buses, PCI-Express (PCI-X) buses, Serial Advanced Technology Attachment (SATA) buses, Video Electronics Standards Association Local (VLB) buses, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 410 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0147] Furthermore, in conjunction with the methods provided in the above embodiments, this application embodiment can be implemented using a computer-readable storage medium. This computer-readable storage medium stores computer program instructions; when executed by a processor, these computer program instructions implement any of the methods in the above embodiments.

[0148] Furthermore, in conjunction with the methods provided in the above embodiments, this application embodiment can provide a computer program product for implementation. This program product is stored in a storage medium and executed by at least one processor to implement the various processes of the embodiments of the methods provided in the above embodiments, achieving similar or identical technical effects. To avoid repetition, further details are omitted here.

[0149] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0150] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0151] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0152] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0153] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A seat control method, characterized in that, include: The system acquires user behavior information on the seat and sensor monitoring information of the seat, wherein the behavior information is obtained based on the user's operation behavior on the control device corresponding to the seat. Based on the behavioral operation information and the sensor monitoring information, the user's user status information is determined; Based on the user status information, a target control strategy for the seat is generated; Control the seat to perform control actions corresponding to the target control strategy.

2. The method according to claim 1, characterized in that, The step of determining the user's status information based on the behavioral operation information and the sensor monitoring information includes: Obtain the historical baseline information corresponding to the user; wherein, the historical baseline information is obtained based on the user's behavioral operation information and sensor monitoring information generated during the user's previous use of the seat, and is used to characterize the user's historical usage habits; Based on the behavioral operation information and the historical benchmark information, the first deviation information is determined; The second deviation information is determined based on the sensor monitoring information and the historical benchmark information; The user status information is determined based on the first deviation information and the second deviation information.

3. The method as described in claim 2, characterized in that, The step of determining the first deviation information based on the behavioral operation information and the historical benchmark information includes: The operation events contained in the behavioral operation information are sequence encoded to obtain behavioral feature information; wherein, the behavioral feature information includes at least one of massage intensity adjustment information, music switching frequency, and light adjustment frequency; The first deviation information is determined based on the behavioral feature information and the first benchmark information corresponding to the behavioral feature information in the historical benchmark information.

4. The method as described in claim 2, characterized in that, The step of determining the second deviation information based on the sensor monitoring information and the historical benchmark information includes: The sensor monitoring information is statistically processed according to a preset statistical window to obtain window statistical information; wherein, the window statistical information includes at least one of continuous sitting time, pressure distribution offset, and number of sitting posture changes; The second deviation information is determined based on the window statistics and the second benchmark information corresponding to the window statistics in the historical benchmark information.

5. The method as described in claim 4, characterized in that, The window statistics include the continuous seating duration and the pressure distribution offset. The step of determining the second deviation information based on the window statistics and the second benchmark information corresponding to the window statistics in the historical benchmark information includes: If the continuous sitting time exceeds the baseline sitting time, a sedentary risk marker is generated; wherein, the second baseline information includes the baseline sitting time; If the pressure distribution offset is greater than the reference offset, a sitting posture offset marker is generated; wherein, the second reference information includes the reference offset; The second deviation information is determined based on the sedentary risk marker and the posture deviation marker.

6. The method as described in claim 1, characterized in that, The step of generating a target control strategy for the seat based on the user status information includes: The user state information is input into a pre-configured decision model, and the decision model is used to output at least one candidate control strategy; wherein, the decision model is trained based on historical user state information and historical control strategies; The target control strategy is determined based on the at least one candidate control strategy.

7. The method as described in claim 6, characterized in that, The decision model is also used to output the confidence level corresponding to the candidate control strategy; Determining the target control strategy based on the at least one candidate control strategy includes: At least one candidate control strategy is verified using preset rules to obtain the verification results corresponding to each candidate control strategy. The target control strategy is determined from the at least one candidate control strategy based on the confidence level and the verification result of each candidate control strategy.

8. The method as described in claim 2, characterized in that, Determining the user status information based on the first deviation information and the second deviation information includes: Obtain the scene constraint information corresponding to the seat, wherein the scene constraint information includes at least the usage period and the cumulative sitting time; Based on the scenario constraint information, determine the first weight of the first deviation information and the second weight of the second deviation information; The user status information is determined based on the first deviation information, the second deviation information, the first weight, and the second weight.

9. A seat control system, characterized in that, The system includes: The acquisition module is used to acquire the user's behavior and operation information on the seat and the sensor monitoring information of the seat. The behavior and operation information is obtained based on the user's operation behavior on the control device corresponding to the seat. The determination module is used to determine the user's user status information based on the behavioral operation information and the sensor monitoring information; The generation module is used to generate a target control strategy for the seat based on the user status information; The control module is used to control the seat to perform control actions corresponding to the target control strategy.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the method of any one of claims 1 to 8.

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