Method, device and electronic equipment for generating motion assistance information

Abstract

This application discloses a method, apparatus, and electronic device for generating exercise assistance information. Relating to the field of sports and health, the method includes: acquiring first historical exercise information of a target user's target exercise and determining the target venue selected by the target user; determining an initial index value for the target user based on the first historical exercise information and venue information of the target venue; acquiring second historical exercise information of historical users who performed the target exercise at the target venue, and determining an index value compensation value based on the second historical exercise information and venue information; determining a target index value for the target user based on the index value compensation value and the initial index value; acquiring the target user's desired index value; and inputting the desired index value, target index value, venue information, and first historical exercise information into a large language model to obtain exercise assistance information. This application solves the problem of low accuracy in exercise assistance information generated by directly interacting with a large language model in related technologies.

Description

Technical Field

[0001] This application relates to the field of sports and health, and more specifically, to a method, apparatus, and electronic device for generating sports assistive information. Background Technology

[0002] As more and more users engage in running, especially long-distance running, they often interact with running assistance systems in sports and health apps before starting their runs to obtain relevant information, thereby reducing the risk of injury and improving performance.

[0003] However, current running assistance systems generally rely on users to obtain training suggestions from large language models through natural language interaction and make inferences based on general semantic models. This results in the output assistance information lacking personalization and dynamic adaptability, with low accuracy, making it difficult to meet the basic needs of scientific training and accurate decision-making.

[0004] There is currently no effective solution to the problem of low accuracy in motion-assisted information generated by directly interacting with large language models in related technologies. Summary of the Invention

[0005] The main objective of this application is to provide a method, apparatus, and electronic device for generating motion assistance information, in order to solve the problem of low accuracy of motion assistance information generated by directly interacting with a large language model in related technologies.

[0006] To achieve the above objectives, according to one aspect of this application, a method for generating motion assistance information is provided. The method includes: acquiring first historical motion information of a target user's target motion and determining a target venue selected by the target user, wherein the target venue is used to perform the target motion; determining an initial index value for the target user based on the first historical motion information and venue information of the target venue, wherein the initial index value is the index value of the target user performing the target motion at the target venue; acquiring second historical motion information of historical users performing the target motion at the target venue, and determining an index value compensation value for the target venue based on the second historical motion information and venue information; determining a target index value for the target user based on the index value compensation value and the initial index value; acquiring the target user's desired index value, and inputting the desired index value, target index value, venue information, and first historical motion information into a large language model to obtain motion assistance information.

[0007] Optionally, determining the initial index value of the target user based on the first historical motion information and the site information of the target site includes: obtaining the physiological data of the target user when performing the target motion from the first historical motion information; dividing the target site according to a preset site area range to obtain M site areas, and determining the regional feature information of each site area to obtain M regional feature information, where M is a positive integer; determining the motion index value of the target user in each site area based on the physiological data and the M regional feature information to obtain M sub-index values; and adding the M sub-index values ​​to obtain the initial index value.

[0008] Optionally, obtaining the second historical movement information of historical users who performed the target movement at the target site includes: obtaining the historical movement index value of each historical user in the target site, resulting in N historical movement index value information, wherein the historical movement index value information includes at least one of the following: the regional index value, environmental information, record-breaking status, replenishment status, and the total index value of the historical user in each area of ​​the target site, where N is a positive integer; obtaining the historical flat ground movement index value of each historical user, and determining the historical movement index value information and the historical flat ground movement index value of each historical user as the second historical movement information.

[0009] Optionally, determining the index compensation value of the target site based on the second historical motion information and site information includes: for each site area, calculating the difference between the regional index value and the historical flat ground motion index value to obtain an initial compensation value; determining the compensation parameters of the initial compensation value based on environmental information, record-breaking status, and replenishment status; processing the initial compensation value based on the compensation parameters to obtain the index compensation value of the site area; and adding the index compensation values ​​of the M site areas to obtain the index compensation value of the target site.

[0010] Optionally, determining the target indicator value for the target user based on the indicator value compensation value and the initial indicator value includes: determining a first weight value for the indicator value compensation value and a second weight value for the initial indicator value based on the target user's first historical motion information; and performing a weighted summation operation on the indicator value compensation value and the initial indicator value based on the first weight value and the second weight value to obtain the target indicator value.

[0011] Optionally, determining the first weight value of the indicator value compensation value and the second weight value of the initial indicator value based on the first historical movement information of the target user includes: determining whether the target user is performing the target movement for the first time at the target site based on the first historical movement information; if the target user is performing the target movement for the first time at the target site, determining the first weight value as the first value and the second weight value as the second value, wherein the first value is less than the second value; if the target user is not performing the target movement for the first time at the target site, determining the first weight value as the second value and the second weight value as the first value.

[0012] Optionally, the expected index value, target index value, venue information, and first historical motion information are input into the large language model to obtain motion assistance information, including: determining the size relationship between the expected index value and the target index value, and selecting initial prompt words based on the size relationship; adding the expected index value, target index value, venue information, and first historical motion information to the initial prompt words to obtain target prompt words; and inputting the target prompt words into the large language model to obtain motion assistance information.

[0013] To achieve the above objectives, according to another aspect of this application, a device for generating motion assistance information is provided. The device includes: a first acquisition unit, configured to acquire first historical motion information of a target user's target motion and determine a target venue selected by the target user, wherein the target venue is used to perform the target motion; a first determination unit, configured to determine an initial index value for the target user based on the first historical motion information and venue information of the target venue, wherein the initial index value is the index value of the target user performing the target motion at the target venue; a second acquisition unit, configured to acquire second historical motion information of historical users performing the target motion at the target venue and determine an index value compensation value for the target venue based on the second historical motion information and venue information; a second determination unit, configured to determine the target index value for the target user based on the index value compensation value and the initial index value; and a processing unit, configured to acquire the target user's desired index value and input the desired index value, target index value, venue information, and first historical motion information into a large language model to obtain motion assistance information.

[0014] To achieve the above objectives, according to another aspect of this application, an electronic device is provided, the electronic device including a memory storing an executable program; and a processor for running the program, wherein the program executes the above-described method for generating motion assistance information during runtime.

[0015] To achieve the above objectives, according to another aspect of this application, a computer program product is provided, including computer instructions that, when executed by a processor, implement the steps of the above-described method for generating motion assistance information.

[0016] In this embodiment, the following methods are employed: acquiring first historical motion information of the target user's target movement and determining the target venue selected by the target user, wherein the target venue is used to perform the target movement; determining the target user's initial index value based on the first historical motion information and the venue information of the target venue, wherein the initial index value is the index value of the target user performing the target movement at the target venue; acquiring second historical motion information of historical users performing the target movement at the target venue and determining the index value compensation value of the target venue based on the second historical motion information and the venue information; determining the target user's target index value based on the index value compensation value and the initial index value; acquiring the target user's expected index value and inputting the expected index value, target index value, venue information, and first historical motion information into a large language model to obtain motion assistance information, thereby determining the target user's target movement based on the user's intended movement goal. The system uses the site information and the user's first historical movement information to determine the initial index value for the user's movement in the target site. Based on the historical information of the target site, it determines the possible compensation value for the user's movement in the target site. Then, based on the compensation value and the initial index value, it determines the target index value for the user's movement in the target site. The target index value, the user's expected index value, the site information, and the user's first historical movement information are then input as parameters into the large language model. This allows the large language model to provide accurate movement guidance to the user based on the above information, achieving the goal of obtaining movement assistance information with a high degree of matching with the user. This improves the technical effect of generating movement assistance information and solves the technical problem of low accuracy of movement assistance information generated by directly interacting with the large language model in related technologies. Attached Figure Description

[0017] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 A hardware structure block diagram of a computer terminal for implementing a method for generating motion-assisted information is shown.

[0019] Figure 2 This is a flowchart of a method for generating motion assistance information according to Embodiment 1 of this application;

[0020] Figure 3 This is a schematic diagram of a motion assistance information generation device according to Embodiment 2 of this application;

[0021] Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0022] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] 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. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] It should be noted that the methods, devices, and electronic devices for generating exercise-assisted information as defined in this disclosure can be used in the field of sports and health, or in any field other than sports and health. The application fields of the methods, devices, and electronic devices for generating exercise-assisted information as defined in this disclosure are not limited.

[0026] It should be noted that all information, 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, stored data, and displayed data) used in this application are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with the relevant regulations and standards of the relevant regions, have taken necessary measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse use. If the user chooses to refuse, the process proceeds to the expert decision-making process. For example, this system has interfaces with relevant users or organizations. Before obtaining relevant information, a request to obtain the information needs to be sent to the aforementioned user or organization through the interface. After receiving consent from the aforementioned user or organization, the relevant information is obtained. Users can view the purpose of data use in real time through the authorization interface and have the right to withdraw authorization or delete data at any time. After authorization is withdrawn, the system will terminate the relevant data processing within 24 hours.

[0027] The embodiments or examples disclosed herein are not exhaustive, but merely illustrative of some embodiments or examples, and are not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment or example can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment or example can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment or example can be arbitrarily interchanged. Furthermore, optional methods or examples in a particular embodiment or example can be arbitrarily combined; moreover, embodiments or examples can be arbitrarily combined. For example, some or all steps of different embodiments or examples can be arbitrarily combined, and a particular embodiment or example can be arbitrarily combined with optional methods or examples of other embodiments or examples.

[0028] Example 1

[0029] According to an embodiment of this application, an embodiment of a method for generating motion assistance information is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a method for generating motion-assisted information is shown. Figure 1As shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, processing devices such as microprocessors or programmable logic devices), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface, a universal serial bus port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0031] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0032] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the motion assistance information generation method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the above-mentioned motion assistance information generation method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0033] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0034] The display may be, for example, a touchscreen LCD display that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0035] Under the aforementioned operating environment, this application provides the following: Figure 2 The method for generating motion assistance information is shown. Figure 2 This is a flowchart of a method for generating motion assistance information according to Embodiment 1 of this application, as follows: Figure 2 As shown, the method includes:

[0036] Step S201: Obtain the first historical motion information of the target user's target motion and determine the target venue selected by the target user, wherein the target venue is used to perform the target motion.

[0037] It should be noted that the execution entity in this embodiment can be a motion assistance information generation system. This system can be configured in a sports and health application and generate motion assistance information that matches the user when the user needs to generate motion assistance information.

[0038] It should be noted that the target sport refers to the sport in which the user requires exercise assistance, such as long-distance running or a marathon. First-hand historical exercise information refers to a set of structured exercise parameters directly related to the target sport, extracted from the target user's historical records. This may include, but is not limited to, exercise intensity, heart rate variability, cadence, average pace, longest single exercise distance, frequency of physical training, and duration of a single exercise session. The target venue is the specific location where the user plans to perform the target sport.

[0039] For example, when a user needs to generate motion assistance information, it is first necessary to determine that the user needs to perform a target motion, and obtain the first historical motion information of the target user when performing the target motion in a historical time, as well as the target venue for the target user to perform the target motion.

[0040] For example, if the target sport is a marathon, it is necessary to obtain the sports information generated by the target user when he / she participated in a marathon in the past, as well as the sports venue where the target user is going to participate in the marathon.

[0041] Step S202: Determine the initial index value of the target user based on the first historical motion information and the site information of the target site, wherein the initial index value is the index value of the target user performing the target motion in the target site.

[0042] It should be noted that the venue information refers to all the physical parameters of the target venue that are digitally described, including the instantaneous slope, altitude, turning radius, turnaround point, route type, cumulative ascent and descent, and road surface drag coefficient of each segment after the entire course is divided into 100-meter-level micro-elements. These parameters constitute the static physical model of the target venue. The initial index value is the theoretical completion time derived from the first historical motion information and the venue physical model, through a nonlinear mapping function, simulating the user's energy distribution and speed maintenance capabilities throughout the entire course without exceeding the user's motion threshold. For example, the time required for a target user to complete a full marathon.

[0043] For example, after determining the first historical motion information and the target site, it is also necessary to determine the site information of the target site, and determine the theoretical ability of the target user to perform the target motion in the target site, i.e., the initial index value, based on the first historical motion information and the site information.

[0044] First, the parameters such as exercise intensity, heart rate trend, training frequency, and longest distance contained in the first historical exercise information are normalized and weighted to form a dynamic model of the target user's recent physical condition. This model reflects the user's maximum stable output capability under the premise of no external interference.

[0045] Similarly, it is also necessary to retrieve the site information of the target venue. This information can be a pre-built digital track model, which includes physical parameters such as instantaneous slope, altitude, turning radius, turnaround point, route type, total cumulative ascent and descent, and road surface material drag coefficient for each micro-element of the entire track divided into hundreds of meters. A nonlinear energy consumption mapping function is then constructed to transform the above physical parameters into a resistance compensation model for the motion process. This model considers the work done by gravity caused by slope, the centripetal force adjustment loss caused by turning, and the influence of road surface material on energy feedback. Combined with the target user's heart rate threshold and anaerobic tolerance, it simulates the maximum speed distribution and energy allocation strategy that can be maintained throughout the entire path. Finally, it outputs a theoretical completion time under ideal environmental conditions, determined only by individual ability and the physical characteristics of the site, i.e., the initial index value.

[0046] Step S203: Obtain the second historical motion information of historical users who have performed target motion in the target site, and determine the index value compensation value of the target site based on the second historical motion information and the site information.

[0047] It should be noted that the second historical exercise information refers to structured exercise data recorded by other historical users who completed the target exercise at the same target venue. This data may include, but is not limited to, completion time, pace profiles, heart rate fluctuations, training frequency, longest running distance, and ability level. The indicator compensation value is a correction amount calculated by cross-analyzing the second historical exercise information and the venue information to identify hidden difficulty features that are common in the target venue but not fully captured by the physical model.

[0048] For example, after obtaining the initial index values, it is also necessary to determine the possible index value compensation values ​​for the target venue. First, it is necessary to retrieve a large amount of historical exercise information of a large number of historical users who completed the target exercise at the same target venue. This information includes structured data such as completion time, segmented pace curves, heart rate fluctuations, training frequency, longest running distance, and ability level. Combined with the venue information of the target venue, multidimensional clustering and spatiotemporal mapping analysis are performed to construct a group performance profile of the target venue. This profile can include four dimensions, including: physical resistance distribution dimension, namely the spatiotemporal distribution characteristics of slope gradient field, vector turning density, and road surface resistance coefficient; group attenuation dimension, namely the speed attenuation slope of users of different ability levels in each kilometer segment and the common pace collapse points; environmental sensitivity factor, namely the heat load index and headwind probability distribution formed by the coupling of temperature, humidity, wind speed and track direction under historical meteorological conditions; and track supporting characteristics, namely the interference pattern of the spatial distribution of aid stations, medical points, and turnaround points on the exercise rhythm.

[0049] Through the above analysis, we can identify hidden difficulty nodes that are common in the target site but not fully captured by the physical model, such as continuous speed reduction caused by frequent turns, decreased energy feedback efficiency caused by road wear, and accumulation of headwind resistance caused by wind direction distribution. These phenomena are highly consistent in historical data, so a quantitative correction amount, namely the index value compensation value, can be calculated based on this.

[0050] Step S204: Determine the target indicator value for the target user based on the indicator value compensation value and the initial indicator value.

[0051] For example, after obtaining the indicator value compensation value and the initial indicator value, the indicator value compensation value and the initial indicator value can be weighted and summed through a dynamic weighted fusion mechanism to form the final predicted value for the target user, i.e., the target indicator value.

[0052] It should be noted that when determining the target indicator value based on the indicator compensation value and the initial indicator value, the weight ratio between the two can be dynamically adjusted based on the historical interaction records between the target user and the target venue: if the target user has not previously performed the target exercise at the target venue, the indicator compensation value is given a higher weight to compensate for the uncertainty caused by the lack of individual data with group experience; if the target user has completed the target exercise at the venue multiple times, the initial indicator value is given a higher weight to use the user's actual measurement data as the primary basis, thereby improving the accuracy of the calculated target indicator value.

[0053] Step S205: Obtain the expected index value of the target user, and input the expected index value, target index value, venue information and first historical motion information into the large language model to obtain motion assistance information.

[0054] It should be noted that the expected target value is the target completion time set by the user.

[0055] For example, after obtaining the target index value, the target user's expected index value, the target index value, the venue information of the target venue, and the first historical motion information can be used as a complete input set and transmitted to the large language model. Based on the logical relationship between the input parameters, the large language model generates motion assistance information with operational guidance significance. In this way, the motion assistance information assists the user in training, enabling the user to reach the expected index value from the current target index value, and provides the probability of reaching the expected index value from the target index value.

[0056] It should be noted that the exercise assistance information may include suggestions for segmented pacing, timing of replenishment, equipment selection recommendations, and energy allocation strategies. For example, when the target indicator value is significantly lower than the expected indicator value, suggestions for increasing training intensity can be generated; when the venue information shows high temperature and high humidity conditions, strategies for replenishing water and electrolytes can be output; when the first historical exercise information indicates that the user has strong lower limb strength, it is recommended to take advantage of this advantage to accelerate on a specific slope. The exercise assistance information is the final output.

[0057] The method for generating motion assistance information provided in this application embodiment involves: acquiring first historical motion information of a target user's target motion and determining the target venue selected by the target user, wherein the target venue is used to perform the target motion; determining the target user's initial index value based on the first historical motion information and the venue information of the target venue, wherein the initial index value is the index value of the target user performing the target motion at the target venue; acquiring second historical motion information of historical users performing the target motion at the target venue and determining the index value compensation value of the target venue based on the second historical motion information and the venue information; determining the target user's target index value based on the index value compensation value and the initial index value; acquiring the target user's expected index value; and inputting the expected index value, target index value, venue information, and first historical motion information into a large language model to obtain motion assistance information. This method is based on the user's upcoming... By combining the target venue information and the user's first historical exercise information, the initial index value for the user's exercise at the target venue is determined. Based on the historical information of the target venue, the possible compensation value for the user's exercise at the target venue is determined. Then, based on the compensation value and the initial index value, the target index value for the user's exercise at the target venue is determined. The target index value, the user's expected index value, the venue information, and the user's first historical exercise information are then input as parameters into the large language model. This allows the large language model to provide accurate exercise guidance to the user based on the above information, achieving the goal of obtaining exercise assistance information with a high degree of matching with the user. This achieves the technical effect of improving the accuracy of the generated exercise assistance information, and thus solves the technical problem of low accuracy of exercise assistance information generated by directly interacting with the large language model in related technologies.

[0058] Optionally, in the method for generating exercise assistance information provided in this application embodiment, determining the initial index value of the target user based on the first historical exercise information and the site information of the target site includes: obtaining the physiological data of the target user when performing the target exercise from the first historical exercise information; dividing the target site according to a preset site area range to obtain M site areas, and determining the regional feature information of each site area to obtain M regional feature information, where M is a positive integer; determining the exercise index value of the target user in each site area based on the physiological data and the M regional feature information to obtain M sub-index values; and adding the M sub-index values ​​to obtain the initial index value.

[0059] It should be noted that physiological data refers to quantitative parameters extracted from the first historical motion information, reflecting the target user's physiological state during exercise. These parameters may include, but are not limited to, maximum heart rate threshold, anaerobic threshold, and resting heart rate. The preset course area range refers to the track segmentation unit set for a unified analysis scale; its size can be set to the hundreds of meters level, ensuring spatially comparable physical consistency for each course area. A course area is M discrete segments of the target course divided according to the preset course area range, each area independently bearing specific terrain and path characteristics. Regional characteristic information refers to the set of physical parameters corresponding to each course area, which may include, but are not limited to, instantaneous slope, altitude, and turning radius. The exercise index value refers to the theoretical execution time calculated for the target user in a specific course area based on their physiological capabilities and the physical characteristics of that area. Sub-index values ​​are the exercise index values ​​corresponding to each course area.

[0060] For example, when determining the initial index values, it is first necessary to extract the target user's physiological data from the first historical exercise information, including parameters such as the maximum heart rate, anaerobic threshold range, pace stability, and fatigue recovery cycle that the target user has maintained stably in past training, and to construct a physiological limit model of the target user in endurance exercise.

[0061] Furthermore, it is necessary to retrieve the site information of the target site and divide the track into M continuous and equidistant site areas based on the preset site area range. Each area can be a 100-meter unit, for example, a 100-meter section is one area.

[0062] For each area of ​​the course, its corresponding regional feature information is extracted, including slope gradient, altitude change, turning radius, road surface material, and turnaround point, to form a physical resistance model for that area. Furthermore, the physiological data of the target user is nonlinearly mapped to the regional feature information of each area to establish a motion dynamics model. This model considers the work done by gravity caused by slope, the centripetal force adjustment loss caused by turning, and the impact of road surface material on energy feedback efficiency. Combined with the user's anaerobic threshold and heart rate tolerance, the maximum speed and energy allocation strategy that the user can maintain in that area are calculated, thus deriving the sub-index value for that area, i.e., the ideal completion time for the user in that 100-meter section.

[0063] Finally, the values ​​of the M sub-indicators are summed to obtain the initial index value, which is the theoretical total time for the target user to complete the entire target movement under ideal conditions.

[0064] This embodiment discretizes the target length into a hundred-meter-level field area and couples user physiological data with regional physical characteristics segment by segment to achieve accurate calculation of the initial index value, thus achieving the technical effect of accurately determining the initial index value of the target user's target movement in the target field.

[0065] Optionally, in the method for generating sports assistance information provided in this application embodiment, obtaining the second historical sports information of historical users who performed target sports in the target venue includes: obtaining the historical sports index values ​​of each historical user who performed target sports in the target venue, resulting in N historical sports index value information, wherein the historical sports index value information includes at least one of the following: the regional index value, environmental information, record-breaking status, replenishment status, and the total index value of the historical user who performed target sports in the target venue in each venue area of ​​the target venue, where N is a positive integer; obtaining the historical flat ground sports index value of each historical user, and determining the historical sports index value information and the historical flat ground sports index value of each historical user as the second historical sports information.

[0066] It should be noted that "historical users" refers to other individuals who have completed the target exercise at the target venue. Historical performance metrics are the quantitative performance values ​​recorded by historical users when performing the target exercise at the target venue. Regional performance metrics refer to the local performance parameters achieved by historical users in each micro-region of the target venue, divided into 100-meter segments, including average pace, heart rate changes, and cadence fluctuations in that segment, reflecting their dynamic response capabilities under local terrain and environmental conditions. Environmental information refers to the real-time weather and climate conditions encountered by historical users when completing the target exercise, including parameters such as temperature, humidity, wind speed, and air pressure, used to correlate group performance with environmental disturbances. Breakthrough record status is a binary indicator of whether a historical user has broken their personal best at the target venue, used to identify the incentive or inhibitory effect of the venue on an individual's breakthrough ability. Supplementation status refers to the timing, type, and quantity of supplementation ingested by historical users during the target exercise, such as the number of energy gel intakes and the frequency of water replenishment, reflecting their energy management strategies. Historical flat terrain athletic performance index values ​​are the benchmark athletic performance values ​​achieved by historical users in an ideal flat terrain environment with no significant slope, no backtracking, and no environmental interference. They represent their ideal athletic performance unaffected by the terrain.

[0067] For example, when determining the second historical movement information of a historical user, for any historical user, it is first necessary to retrieve the movement records of all historical users who have completed the target movement at the target site from the database, and extract the total index value of each historical user, that is, the total time spent by them to finally complete the target movement at the site.

[0068] Subsequently, the target area was divided into several micro-areas at the 100-meter level. For each historical user, the regional index value in each area was extracted. This index value includes parameters such as average pace, instantaneous heart rate, cadence change, and energy consumption estimation for that section of the path. It is used to characterize their local performance under different slopes, turns, and road resistance. At the same time, it is also necessary to obtain environmental information synchronized with the time when the historical user completed the exercise, including measured meteorological parameters such as temperature, humidity, wind speed, and air pressure, to establish the correlation between group performance and environmental disturbances.

[0069] Furthermore, it is possible to extract whether the historical user achieved a personal best during the exercise to identify whether the venue has the potential to promote personal breakthroughs.

[0070] In addition, the complete trajectory of the user's replenishment intake during exercise can be obtained, including the time and amount of the first energy gel intake, the frequency and location of water replenishment, which can be used to analyze the impact of replenishment strategies on endurance maintenance.

[0071] Finally, all the above information, including regional indicator values, environmental information, breakthrough record status, replenishment status, and total indicator value, is integrated into the user's complete historical sports indicator value information.

[0072] It should be noted that the historical flat ground movement index value achieved by the historical user in a flat environment without terrain interference is also obtained. This value is determined by the user's best performance record in completing the same distance target movement on a flat surface in the past, and serves as an independent reference for the user's basic physical fitness level. The historical movement index value information of each historical user is bound to its corresponding historical flat ground movement index value to form a complete and comparable second historical movement information.

[0073] This embodiment systematically collects multi-dimensional behavioral data of historical users at the target site and binds it to their flat ground benchmark capabilities to construct complete second historical motion information that includes regional performance, environmental response, breakthrough behavior and energy management strategies. This achieves the technical effect of accurately obtaining the second historical motion information of each historical user.

[0074] Optionally, in the method for generating sports assistance information provided in this application embodiment, determining the index value compensation value of the target site based on the second historical sports information and site information includes: for each site area, calculating the difference between the area index value and the historical flat ground sports index value to obtain an initial compensation value; determining the compensation parameter of the initial compensation value based on environmental information, record-breaking status, and replenishment status; processing the initial compensation value based on the compensation parameter to obtain the index value compensation value of the site area; and adding the index value compensation values ​​of M site areas to obtain the index value compensation value of the target site.

[0075] For example, when calculating the compensation value of the index value, it is first necessary to calculate the compensation value of each site area separately. First, it is necessary to obtain the difference between the regional index value of historical users and the historical flat ground movement index value. This difference is the initial compensation value, which represents the loss of movement efficiency in the area due to physical factors such as terrain, slope, and resistance.

[0076] Furthermore, it is necessary to obtain the environmental information, record-breaking status, and replenishment status of the historical user in that area. Based on the coupling degree of temperature and humidity in the environmental information, the degree of their impact on heat load is judged. If the temperature and humidity index is significantly higher than the historical average, the weight of the initial compensation value is increased, indicating that the environment exacerbates physical exertion. If the record-breaking status is "refreshed," it indicates that the area may have a positive incentive effect. In this case, the initial compensation value can be reduced to eliminate the interference of psychological advantage on performance. If the replenishment status shows that there was energy intake or water replenishment before or after the area, it is judged that the area is near a replenishment point. Pace fluctuations may be caused by deceleration or pauses. Therefore, the initial compensation value is negatively adjusted to eliminate non-exercise time loss.

[0077] Furthermore, based on the above three factors, the compensation parameters for the compensation value of the site area are determined comprehensively, and the compensation value and the compensation parameters are multiplied together to obtain the index compensation value of the site area.

[0078] Finally, the above processing is performed on all M site areas in the target site to obtain M independent index compensation values. The index compensation values ​​of all site areas are then summed to obtain the overall index compensation value of the target site.

[0079] This embodiment uses the difference between historical users' regional index values ​​and historical flatland movement index values ​​as the initial compensation value, and introduces environmental information, record-breaking status, and replenishment status as dynamic compensation parameters to achieve the technical effect of accurately determining the index value compensation value of the target site.

[0080] Optionally, in the method for generating motion assistance information provided in this application embodiment, determining the target indicator value of the target user based on the indicator value compensation value and the initial indicator value includes: determining a first weight value of the indicator value compensation value and a second weight value of the initial indicator value based on the first historical motion information of the target user; and performing a weighted summation operation on the indicator value compensation value and the initial indicator value based on the first weight value and the second weight value to obtain the target indicator value.

[0081] For example, the following formula can be used to calculate the target indicator value:

[0082] T final =w1·T ind +w2·T cround ;

[0083] Among them, T final T is the target indicator value. ind The indicator value is the compensation value, w1 is the first weight value, and T is the compensation value. cround w1 is the initial index value, and w2 is the second weight value.

[0084] This embodiment achieves a precise fusion of individual predictions and group experience by setting different weights for the indicator value compensation value and the initial indicator value, thereby improving the technical effect of determining the target indicator value.

[0085] Optionally, in the method for generating exercise assistance information provided in this application embodiment, determining the first weight value of the indicator value compensation value and the second weight value of the initial indicator value based on the first historical exercise information of the target user includes: determining whether the target user is performing the target exercise for the first time at the target venue based on the first historical exercise information; if the target user is performing the target exercise for the first time at the target venue, determining the first weight value as a first value and the second weight value as a second value, wherein the first value is less than the second value; if the target user is not performing the target exercise for the first time at the target venue, determining the first weight value as the second value and the second weight value as the first value.

[0086] For example, when determining the weight values ​​of the indicator value compensation value and the initial indicator value, it can be determined whether the target user is performing the target exercise for the first time at the target site based on the first historical exercise information. If the user has never recorded any results of performing the target exercise at the target site in the historical data, it is determined that the user is performing the target exercise for the first time at the target site. In this case, the first weight value is set to a first value, which is a preset low value, such as 0.3, indicating a low degree of dependence on group experience, so as to avoid the bias caused by over-reliance on the statistical average when individual data is lacking. At the same time, the second weight value is set to a second value, which is a preset high value, such as 0.7, so that when the user has no site experience, the physiological ability and site physical model are trusted first to determine the target indicator value.

[0087] If it is determined that the target user has performed the target movement at the target site before, that is, there is at least one record of completing the target movement at the target site in the first historical movement information, then the weight configuration is reversed, and the first weight value is adjusted to the second value. That is, the weight of group experience in the prediction is increased, so as to utilize the systematic patterns revealed by the user's actual performance data at the site, such as the general speed reduction of specific road sections, the impact of supply strategies, environmental sensitivity, etc. At the same time, the second weight value is adjusted to the first value to reduce the dominance of the individual model.

[0088] This embodiment performs a binary judgment operation on the user by determining whether it is the first time the target motion is executed, thereby achieving the technical effect of accurately determining the weight values ​​of the indicator value compensation value and the initial indicator value.

[0089] Optionally, in the method for generating sports assistance information provided in this application embodiment, inputting the expected index value, target index value, venue information, and first historical sports information into a large language model to obtain sports assistance information includes: determining the size relationship between the expected index value and the target index value, and selecting an initial prompt word based on the size relationship; adding the expected index value, target index value, venue information, and first historical sports information to the initial prompt word to obtain a target prompt word; and inputting the target prompt word into the large language model to obtain sports assistance information.

[0090] For example, when generating exercise assistance information, the expected indicator value and the target indicator value of the target user are first obtained, and the relationship between the two is determined: if the expected indicator value is greater than or equal to the target indicator value, for example, the expected indicator value is 2h10min and the target indicator value is 2h, then it indicates that the target user's ability meets the requirements of the expected indicator value, and the first type of prompt word can be selected, so that the generated exercise assistance information is biased towards running planning, consolidating physical fitness, etc.

[0091] If the expected indicator value is less than the target indicator value, for example, the expected indicator value is 2h and the target indicator value is 2h10min, it indicates that the user's current state cannot reach the expected indicator value. In this case, the second prompt word can be selected to guide the model to emphasize the training improvement path and risk warning, and to provide the user with improvement suggestions and risk information.

[0092] After determining the prompt words, the expected index value, target index value, venue information, and first historical exercise information can be used as structured parameters to fill the corresponding initial prompt word template to form the target prompt word. For example, "Based on the user's goal {expected index value}, the system's prediction {target index value}, the venue characteristics {venue information}, and the user's physical condition {first historical exercise information}, generate scientific and feasible exercise assistance suggestions", thereby completing the operation of generating exercise assistance information.

[0093] It should be noted that after obtaining the target index value, multiple sets of virtual sample data can be generated in parallel within the preset confidence intervals of physiological fluctuations, environmental noise, and random resistance of the track, using the target index value as the center. By statistically analyzing the frequency of achieving the expected index value in this sample set, the probability of the target user achieving the expected index value can be determined, and the probability value can be used as a feedback parameter in the sports assistance information.

[0094] This embodiment determines prompt words based on the user's expected and target indicator values, and generates motion assistance information based on the prompt words and multidimensional information, thereby improving the accuracy of motion assistance information and its matching degree with the user.

[0095] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0096] Example 2

[0097] This application also provides a motion assistance information generation apparatus. It should be noted that the motion assistance information generation apparatus of this application can be used to execute the motion assistance information generation method provided in the above embodiments. The motion assistance information generation apparatus provided in this application will be described below.

[0098] According to an embodiment of this application, an apparatus for implementing the above-described method for generating motion assistance information is also provided. Figure 3 This is a schematic diagram of a motion assistance information generation device according to Embodiment 2 of this application, as shown below. Figure 3 As shown, the device includes:

[0099] The first acquisition unit 31 is used to acquire the first historical motion information of the target user's target motion and determine the target site selected by the target user, wherein the target site is used to execute the target motion.

[0100] The first determining unit 32 is used to determine the initial index value of the target user based on the first historical motion information and the site information of the target site, wherein the initial index value is the index value of the target user performing the target motion in the target site.

[0101] The second acquisition unit 33 is used to acquire the second historical movement information of historical users who have performed target movements in the target site, and to determine the index value compensation value of the target site based on the second historical movement information and the site information.

[0102] The second determining unit 34 is used to determine the target indicator value of the target user based on the indicator value compensation value and the initial indicator value.

[0103] Processing unit 35 is used to obtain the expected index value of the target user, and input the expected index value, target index value, venue information and first historical motion information into the large language model to obtain motion assistance information.

[0104] The motion assistance information generation device provided in this application embodiment acquires first historical motion information of a target user's target motion through a first acquisition unit 31 and determines the target venue selected by the target user, wherein the target venue is used to perform the target motion; a first determination unit 32 determines the initial index value of the target user based on the first historical motion information and the venue information of the target venue, wherein the initial index value is the index value of the target user performing the target motion at the target venue; a second acquisition unit 33 acquires second historical motion information of historical users performing the target motion at the target venue and determines the index value compensation value of the target venue based on the second historical motion information and the venue information; a second determination unit 34 determines the target index value of the target user based on the index value compensation value and the initial index value; and a processing unit 35 acquires the target user's expected index value and inputs the expected index value, the target index value, the venue information, and the first historical motion information into a large language model to obtain motion assistance information. By using the venue information of the target venue where the user is about to exercise and the user's first historical exercise information, the initial index value of the user's exercise at the target venue is determined. Then, based on the historical information of the target venue, the possible compensation value of the user's exercise at the target venue is determined. Finally, based on the compensation value and the initial index value, the target index value of the user's exercise at the target venue is determined. The target index value, the user's expected index value, the venue information, and the user's first historical exercise information are all input as parameters into the large language model. This allows the large language model to provide accurate exercise guidance to the user based on the above information, achieving the goal of obtaining exercise assistance information with a high degree of matching with the user. This achieves the technical effect of improving the accuracy of the generated exercise assistance information, and solves the technical problem of low accuracy of exercise assistance information generated by directly interacting with the large language model in related technologies.

[0105] Optionally, in the motion assistance information generation device provided in this application embodiment, the first determining unit 32 includes: a first acquisition module, used to acquire physiological data of the target user when performing the target motion from the first historical motion information; a segmentation module, used to segment the target venue according to a preset venue area range to obtain M venue areas, and determine the regional feature information of each venue area to obtain M regional feature information, where M is a positive integer; a first determining module, used to determine the motion index value of the target user in each venue area according to the physiological data and the M regional feature information to obtain M sub-index values; and a first calculation module, used to add the M sub-index values ​​to obtain an initial index value.

[0106] Optionally, in the motion assist information generation device provided in this application embodiment, the second acquisition unit 33 includes: a second acquisition module, used to acquire the historical motion index values ​​of each historical user performing target motion in the target field, obtaining N historical motion index value information, wherein the historical motion index value information includes at least one of the following: the regional index value, environmental information, record-breaking status, replenishment status, and the total index value of the historical user performing target motion in the target field in each field area of ​​the target field, where N is a positive integer; and a third acquisition module, used to acquire the historical flat ground motion index value of each historical user, and determine the historical motion index value information and the historical flat ground motion index value of each historical user as the second historical motion information.

[0107] Optionally, in the sports assist information generation device provided in this application embodiment, the second acquisition unit 33 includes: a second calculation module, used to calculate the difference between the regional index value and the historical flat ground sports index value for each field area to obtain an initial compensation value; a second determination module, used to determine the compensation parameters of the initial compensation value based on environmental information, record-breaking status and replenishment status; a first processing module, used to process the initial compensation value according to the compensation parameters to obtain the index value compensation value of the field area; and a third calculation module, used to add the index value compensation values ​​of M field areas to obtain the index value compensation value of the target field.

[0108] Optionally, in the motion assistance information generation device provided in this application embodiment, the second determining unit 34 includes: a third determining module, used to determine a first weight value of the indicator value compensation value and a second weight value of the initial indicator value based on the first historical motion information of the target user; and a fourth calculating module, used to perform a weighted summation operation on the indicator value compensation value and the initial indicator value based on the first weight value and the second weight value to obtain the target indicator value.

[0109] Optionally, in the motion assistance information generation device provided in this application embodiment, the third determining module includes: a judging submodule, used to determine whether the target user is performing the target motion for the first time in the target venue based on the first historical motion information; a first determining submodule, used to determine a first weight value as a first value and a second weight value as a second value when the target user is performing the target motion for the first time in the target venue, wherein the first value is less than the second value; and a second determining submodule, used to determine the first weight value as the second value and the second weight value as the first value when the target user is not performing the target motion for the first time in the target venue.

[0110] Optionally, in the motion assistance information generation device provided in this application embodiment, the processing unit 35 includes: a judgment module, used to judge the size relationship between the expected index value and the target index value, and select an initial prompt word according to the size relationship; an adding module, used to add the expected index value, the target index value, the venue information, and the first historical motion information to the initial prompt word to obtain the target prompt word; and a second processing module, used to input the target prompt word into a large language model to obtain motion assistance information.

[0111] It should be noted that the first acquisition unit 31, the first determination unit 32, the second acquisition unit 33, the second determination unit 34, and the processing unit 35 mentioned above correspond to steps S201 to S205 in Embodiment 1. The instances and application scenarios implemented by each of the above units and the corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware components or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above modules can also be part of a device and can run in the computer terminal 10 provided in Embodiment 1.

[0112] Example 3

[0113] Embodiments of this application may provide an electronic device. Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 4 As shown, the electronic device may include: one or more ( Figure 4 (Only one is shown) processor 1002, memory 1004, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0114] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-described methods. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0115] Those skilled in the art will understand that Figure 4The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 4 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 4 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 4 The different configurations shown.

[0116] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0117] Example 4

[0118] Embodiments of this application also provide a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the motion assistance information generation method provided in Embodiment 1.

[0119] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0120] Embodiments of this application also provide a computer program product, which, when executed on a data processing device, is adapted to perform the steps of a method for generating motion assist information.

[0121] Embodiments of this application also provide a computer-readable storage medium, which includes a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to execute the above-described method for generating motion assistance information.

[0122] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0123] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0124] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0125] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0126] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0127] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0128] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for generating motion-assisted information, characterized in that, include: Obtain first historical motion information of the target user's target motion, and determine the target venue selected by the target user, wherein the target venue is used to perform the target motion; The initial index value of the target user is determined based on the first historical movement information and the site information of the target site, wherein the initial index value is the index value of the target user performing the target movement at the target site; Obtain second historical movement information of historical users who performed the target movement at the target site, and determine the index value compensation value of the target site based on the second historical movement information and the site information; The target indicator value for the target user is determined based on the indicator value compensation value and the initial indicator value. Obtain the expected index value of the target user, and input the expected index value, the target index value, the venue information and the first historical motion information into the large language model to obtain motion assistance information.

2. The method according to claim 1, characterized in that, Determining the initial index value of the target user based on the first historical motion information and the venue information of the target venue includes: Obtain physiological data of the target user when performing the target movement from the first historical motion information; The target site is divided into M site areas according to a preset site area range, and the regional feature information of each site area is determined to obtain M regional feature information, where M is a positive integer; Based on the physiological data and the M regional feature information, the exercise index values ​​of the target user in each venue area are determined, resulting in M ​​sub-index values; The initial index value is obtained by adding the M sub-index values ​​together.

3. The method according to claim 2, characterized in that, Obtaining the second historical movement information of historical users who performed the target movement at the target site includes: Obtain the historical motion index values ​​of each historical user performing the target motion at the target venue, and obtain N historical motion index value information, wherein the historical motion index value information includes at least one of the following: the regional index value, environmental information, record-breaking status, replenishment status of the historical user in each venue area of ​​the target venue, and the total index value of the historical user performing the target motion at the target venue, where N is a positive integer; Obtain the historical flat ground movement index value for each historical user, and determine the historical movement index value information and the historical flat ground movement index value for each historical user as the second historical movement information.

4. The method according to claim 3, characterized in that, The index compensation value for the target site is determined based on the second historical motion information and the site information, including: For each site area, the difference between the area index value and the historical flat ground movement index value is calculated to obtain the initial compensation value; The compensation parameters for the initial compensation value are determined based on the environmental information, the breakthrough record status, and the supply status. The initial compensation value is processed according to the compensation parameters to obtain the index compensation value of the site area; The index compensation values ​​of the M site areas are added together to obtain the index compensation value of the target site.

5. The method according to claim 1, characterized in that, Determining the target indicator value for the target user based on the indicator value compensation value and the initial indicator value includes: Based on the first historical motion information of the target user, determine the first weight value of the indicator value compensation value and the second weight value of the initial indicator value; The target index value is obtained by performing a weighted summation operation on the index value compensation value and the initial index value based on the first weight value and the second weight value.

6. The method according to claim 5, characterized in that, Determining the first weight value of the indicator value compensation value and the second weight value of the initial indicator value based on the first historical motion information of the target user includes: Based on the first historical motion information, determine whether the target user is performing the target motion for the first time at the target venue; When the target user is performing the target movement for the first time at the target site, the first weight value is determined as a first value, and the second weight value is determined as a second value, wherein the first value is less than the second value; If the target user is not performing the target movement for the first time at the target site, the first weight value is determined as the second value, and the second weight value is determined as the first value.

7. The method according to claim 1, characterized in that, The expected index value, the target index value, the venue information, and the first historical motion information are input into the large language model to obtain motion assistance information including: Determine the relationship between the expected indicator value and the target indicator value, and select initial prompt words based on the relationship; The expected index value, the target index value, the site information, and the first historical movement information are added to the initial prompt word to obtain the target prompt word; The target prompt word is input into the large language model to obtain the motion assistance information.

8. A device for generating motion-assisted information, characterized in that, include: The first acquisition unit is used to acquire first historical motion information of the target user's target motion and determine the target venue selected by the target user, wherein the target venue is used to perform the target motion; The first determining unit is configured to determine the initial index value of the target user based on the first historical movement information and the site information of the target site, wherein the initial index value is the index value of the target user performing the target movement at the target site; The second acquisition unit is used to acquire the second historical movement information of historical users who have performed the target movement in the target site, and to determine the index value compensation value of the target site based on the second historical movement information and the site information. The second determining unit is used to determine the target indicator value of the target user based on the indicator value compensation value and the initial indicator value. The processing unit is used to obtain the expected index value of the target user, and input the expected index value, the target index value, the venue information and the first historical motion information into the large language model to obtain motion assistance information.

9. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the method for generating motion-assisted information according to any one of claims 1 to 7.

10. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, executes the method for generating motion-assisted information according to any one of claims 1 to 7.

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